Characterization of Chronic Lymphocytic Leukemia by Acgh/MLPA

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE BIOLOGIA VEGETAL

Characterization of Chronic Lymphocytic Leukemia by aCGH/MLPA

Diana Cristina Antunes Candeias Adão

Mestrado em Biologia Molecular e Genética

Dissertação orientada por: Professora Doutora Isabel Maria Marques Carreira Professor Doutor Manuel Carmo Gomes

2018

Agradecimentos

Começo por agradecer ao CIMAGO e à ACIMAGO por todo o apoio prestado no âmbito do desenvolvimento deste trabalho, tanto a nível logístico como financeiro. Agradeço também ao Laboratório de Citogenética e Genómica e ao Laboratório de Oncologia e Hematologia da FMUC, pelo fornecimento dos equipamentos e bens necessários à realização deste projeto.

À Professora Doutora Isabel Marques Carreira, muito obrigada por me permitir desenvolver o meu projeto de tese de mestrado no Laboratório de Citogenética e Genómica da FMUC e por orientar este trabalho. Agradeço tudo o que me ensinou, o apoio e, acima de tudo, o exemplo que é como profissional na área da Genética Humana, que em muito contribuiu para o meu desenvolvimento e crescimento a nível académico e profissional.

Ao Professor Doutor Manuel Carmo Gomes, pela sempre célere e incondicional orientação durante este ano. Mostrou-se sempre disponível para responder às minhas dúvidas e questões, e por isso demonstro o meu agradecimento.

À Professora Doutora Ana Bela Sarmento Ribeiro, pela proposta que me apresentou para trabalhar no âmbito da Leucemia Linfocítica Crónica, pelo apoio nas correções necessárias, e pela possibilidade que me deu de realizar alguns passos do meu trabalho nas instalações do Laboratório de Oncologia e Hematologia da FMUC.

Ao Miguel Pires, por todos os ensinamentos ao nível do trabalho laboratorial, pela enorme disponibilidade e apoio na realização desta tese, bem como pela amizade e conselhos que me deu.

À Doutora Ana Cristina Gonçalves, que sempre me ajudou na correção de conteúdos da tese e nas questões relacionadas com as amostras a estudar. Muito obrigada pelo apoio.

À Susana Ferreira, Ilda Ribeiro, Mariana Val, Joana Jorge e Raquel Alves, pelo apoio ao nível de aplicação de técnicas e interpretação de resultados, bem como pela amizade.

À Drª Amélia Pereira e ao Dr. José Pedro Carda, do Serviço de Medicina do Hospital Distrital da Figueira da Foz e Departamento de Hematologia Clínica dos CHUC, respetivamente, pela disponibilização de amostras para este estudo.

Um especial agradecimento a todos os doentes a serem seguidos nos serviços referidos, que aceitaram fornecer as suas amostras e contribuir para o desenvolvimento deste estudo.

À equipa técnica da Unidade de Gestão Operacional Citometria do Serviço de Patologia Clínica dos CHUC, que se mostrou sempre prestável na preparação das amostras disponíveis. Apresento um especial agradecimento ao coordenador deste serviço, Professor Doutor Artur Paiva.

À restante equipa do Laboratório de Citogenética e Genómica da FMUC, Professora Doutora Joana Barbosa de Melo, Alexandra Marques, Ana Jardim, Cláudia Pais, Lúcia Simões, Patrícia Paiva, Nuno Lavoura, Marta Pinto, Carla Henriques e Sónia Pereira, pela forma como me receberam, por tudo o que me ensinaram ao nível do trabalho deste laboratório e pela amizade.

Às minhas colegas Alexandra Oliveira, Inês Gonçalves, Inês Tavares, Laura Silvério, Luísa Esteves e Mariana Tomás que sempre estiveram disponíveis para discutir ideias, pontos de vista e dar conselhos. Obrigada pelo companheirismo e pelos bons momentos que passámos.

À minha colega de Licenciatura Ana Gaspar, pela companhia nas sessões de escrita da tese, pelo apoio e motivação, pela partilha das preocupações e conquistas deste ano e, principalmente, pela amizade que mantemos.

Ao Zé Miguel, por me ter ajudado a manter a motivação para a tese até ao fim, e por ouvir sempre os meus relatos sobre o dia-a-dia do laboratório!

Por último, quero agradecer a toda a minha família pelo apoio que me deram, especialmente aos meus pais e à minha irmã, pois sempre me motivaram e acreditaram que eu seria capaz de chegar onde cheguei. Para vocês, um beijinho grande!

Resumo

Palavras-chave: Leucemia Linfocítica Crónica; CNVs; iFISH; aCGH; MLPA

A Leucemia Linfocítica Crónica (LLC) é o tipo de leucemia mais frequente na população adulta de países ocidentais, cuja idade média ao diagnóstico varia entre os 67 e os 72 anos. É caracterizada pela expansão clonal e acumulação de linfócitos B neoplásicos no sangue periférico e na medula óssea, resistentes à apoptose e imunologicamente incompetentes.. Estas células têm também a capacidade de infiltrar outros órgãos como o baço ou o fígado. Os indivíduos com LLC apresentam diversas alterações genómicas características, que estão associadas a diferentes subtipos da doença com diferentes comportamentos biológicos e clínicos. Os dois principais subtipos de LLC são caracterizados pela ausência ou presença de genes mutados na região codificante da cadeia pesada da região variável das imunoglobulinas (IGHV), sendo que o estado não mutado destes genes é o que constitui a situação de doença mais agressiva. A sobre- expressão dos genes CD38 e ZAP70 é um parâmetro que auxilia na estratificação de doentes em grupos com diferentes prognósticos que, possivelmente, necessitam de diferentes estratégias terapêuticas. Para além destes marcadores, podem ocorrer alterações epigenéticas; mutações somáticas nos genes MYD88, NOTCH1, SF3B1 e TP53; variações no número de cópias (CNVs) de segmentos de DNA; trissomias 9, 12, 18 e 19, e translocações que envolvam o locus IGH. Uma CNV é um segmento de DNA com pelo menos 1 kilobase (Kb) que apresenta variação no número das suas cópias (perda ou ganho) relativamente a um DNA de referência de um indivíduo saudável. Uma translocação recíproca é uma troca de posições entre dois segmentos de DNA, entre cromossomas não homólogos. Ao longo das últimas décadas estas alterações têm vindo a ser detetadas, na prática clínica, pela técnica de Hibridização Fluorescente In Situ aplicada a células em interfase (iFISH). A iFISH permitiu realizar uma melhor estratificação destes doentes em diferentes grupos, aos quais são associados diferentes prognósticos. Estes grupos baseiam-se na presença de: (i) deleção em 13q como única alteração; (ii) nenhuma alteração; (iii) trissomia 12; (iv) deleção em 11q e (v) deleção em 17p (grupos apresentados por ordem crescente de pior prognóstico previsto), sendo (i) o grupo de melhor prognóstico e (v) o de pior prognóstico. Posteriormente, foram adicionadas a este painel sondas para a deteção da deleção em 6q e translocações do gene IGH. No entanto, devido à heterogeneidade biológica e clínica desta doença, existe consciência da importância de procurar novas CNVs com possível valor como marcadores de prognóstico. De facto, nem todas as terapias desenvolvidas são aplicáveis a doentes com diferentes tipos de alterações. Um exemplo disso é o caso dos doentes com a del(17p) e/ou mutação do gene TP53. Neste grupo de doentes, a quimioimunoterapia é altamente desaconselhada devido ao facto de esta recorrer a agentes dependentes da atividade da proteína p53 que, por estar ausente e/ou mutada nos doentes com del(17p) e/ou mutação do gene TP53, impede que esta terapêutica conduza a resultados favoráveis. Assim sendo, os doentes com estas alterações são candidatos a terapêutica dirigida recorrendo a inibidores da via BTK, como o ibrutinib. Com este trabalho piloto, o nosso grupo realizou a caracterização de uma coorte de 20 doentes com LLC, ao nível do seu conteúdo em CNVs. Para tal, aplicámos as técnicas de Amplificação Multiplex de Sondas Dependente de Ligação (MLPA) e arrays de Hibridização Genómica Comparativa (aCGH), após extração de DNA a partir de células mononucleares do sangue periférico (PBMCs), e tendo em conta o genoma de referência GRCh37/hg9 (Genome Reference Consortium Human Build 37/human genome 19). Após a obtenção de resultados, fizemos um estudo de sobrevivência global ao longo de um período médio de follow-up de 114 meses, aplicando o método estatístico de Kaplan-Meier. Com base nos resultados de MLPA, dividimos as amostras em duas categorias: um grupo com doentes que tinham ≥2 alterações e outro com doentes que tinham <2 alterações. Interpretámos, também, a distribuição de algumas das alterações que detetámos em maior

III

frequência: del(11q), del(14q), e mutação no gene NOTCH1, em doentes agrupados de acordo com o seu estadiamento pelos sistemas Binet e Rai (sistemas de classificação de estadiamento específicos para LLC). Para tal, aplicámos o teste qui-quadrado de Pearson. Relativamente à análise de sobrevivência, apesar de os nossos resultados não terem apresentado significância estatística, estes sugerem que existe uma associação entre o aumento no número de alterações genómicas que um doente tem e a diminuição na taxa de sobrevivência. Quanto às prevalências das alterações del(11q), del(14q) e mutação do gene NOTCH1, verificámos que estas são significativamente mais altas em doentes que se encontram num estadiamento intermédio/alto de risco de doença do que em doentes com estadiamento de baixo risco, o que sugere a sua importância como marcadores de mau prognóstico. Isto é algo que já foi previamente descrito para a del(11q) e para a mutação em NOTCH1 mas, tanto quanto é do nosso conhecimento, é algo que não foi ainda visto, por outros grupos, para doentes com a alteração del(14q). Para além das conclusões tiradas relativamente às alterações genómicas em doentes com esta neoplasia, foi-nos possível fazer algumas comparações entre as técnicas aplicadas. Apresentamos, também, uma comparação crítica entre a aplicação destas tecnologias e a utilização da técnica de diagnóstico iFISH, tanto ao nível da prática clínica como no âmbito da investigação científica. O MLPA e o aCGH demonstraram ser técnicas apropriadas para realizar a caracterização genómica da LLC, uma vez que estas foram capazes de detetar CNVs. A técnica de aCGH permitiu- nos detetar um total de 134 alterações, das quais a maior parte foi vista em regiões que não são estudadas pelo iFISH. Isto provou o seu valor do aCGH como técnica aplicável na investigação de alterações genómicas na LLC. Realizámos, de seguida, a validação dos resultados de aCGH pela técnica de MLPA, o que nos permitiu chegar à conclusão de que todas as alterações detetadas por ambas as técnicas são reais. Concluímos também que o MLPA é uma técnica candidata a substituir a aCGH no âmbito da investigação, principalmente por ser menos dispendiosa e menos morosa. Num contexto de prática clínica, o MLPA poderia eventualmente substituir o iFISH pois também fornece informação sobre a presença das alterações mais comuns: del(11q), trissomia 12, del(13q) e del(17p), bem como sobre a presença de outras alterações características da LLC, mas menos prevalentes: ganho em 2p, del(6q), perda em 8p, ganho em 8q, del(9p21) e trissomia 19. Para além disso, deteta também possíveis mutações somáticas nos genes NOTCH1, MYD88 e SF3B1, que podem ocorrer nesta doença. Contudo, o iFISH é a única destas três técnicas que é capaz de detetar mosaicismos de baixa expressão e a ocorrência de translocações na LLC, tais como: t(14,18)(q32,q24), t(14,18)(q32,q21) e t(14,19)(q32,q13). Estas translocações ocorrem em até 5% dos casos diagnosticados. As nossas perspetivas para trabalhos futuros consistem em aumentar o número de amostras estudadas bem como o tempo de seguimento destas com o objetivo de, numa próxima análise de sobrevivência, alcançar uma maior confiança nos resultados obtidos. Para além disso, temos também o objetivo de reavaliar a importância da del(14q) como possível marcador de mau prognóstico, num grupo maior de doentes. Finalmente, esperamos ser capazes de conhecer melhor a distribuição das alterações que encontrámos com elevada prevalência (del(5q13.2), del(8q24.23), del(22q11.22), del(Xq21.1), dup(4p16.3), dup(6p25.3), dup(8p11.1), and dup(10q11.22)) numa coorte de maior de doentes. Esperamos conseguir identificar, de entre estas alterações, possíveis marcadores de doença, algo que será apenas possível por comparação com uma população controlo. Com o nosso trabalho, esperamos incentivar não só a procura de novos alvos terapêuticos como também a descoberta e avaliação de novas terapias. Por último, propomos a continuação de estudos de caracterização genómica da leucemia linfocítica crónica, destacando o aCGH e MLPA como técnicas preferenciais para esse fim.

Abstract

Keywords: Chronic Lymphocytic Leukemia; Copy Number Variation; aCGH; iFISH; MLPA

Chronic lymphocytic leukemia (CLL) is one of the most common hematological malignancies, being the most recurrent type of leukemia in the adult population in western countries. It is characterized by a clonal expansion and accumulation of neoplastic B lymphocytes on peripheral blood and in the bone marrow. There are some characteristic genomic alterations in these individuals, linked to different subtypes of CLL with different biological and clinical behaviors. The two main types of CLL are characterized by either the lack or the presence of mutated genes at the coding region of the immunoglobulins heavy chain’s variable region (IGHV), being the unmutated state the one leading to a more aggressive disease course. Overexpression of the molecular markers CD38 and ZAP70 are also parameters that determine disease stage and help to stratify patients into groups with different prognosis and possible different therapy strategies. Furthermore, there are some CLL characteristic genomic alterations: epigenetic alterations; somatic mutations in MYD88, NOTCH1, SF3B1, and TP53; copy number variations (CNVs) of some DNA regions; trisomies 9, 12, 18, or 19; and some translocations involving the IGH locus. Over the last decades, CLL reported CNVs have been evaluated with the standard technique of interphase Fluorescent In Situ Hybridization (iFISH), in order to stratify patients into different groups of predicted outcomes. Those groups are based on the presence of: (i) 13q deletion as sole alteration; (ii) no alterations; (iii) Trisomy 12; (iv) 11q deletion; and (v) 17p deletion. These groups are presented in order of best (i) to worse disease outcome (v). Subsequently, probes for the detection of 6q deletion and IGH translocations have been added. However, due to the clinical and biological heterogeneity of this disease, it is currently acknowledged that it is important to search for other CNVs that may become new disease markers. Our team has characterized, in this pilot study, a cohort of 20 CLL patients, regarding their CNVs content, in agreement with the Genome Reference Consortium Human Build 37/human genome 19 (GRCh37/hg9). We have applied Multiplex Ligation-dependent Probe Amplification (MLPA) and array Comparative Genomic Hybridization (aCGH) technologies. We conducted an overall survival analysis of patients presenting either ≥2 alterations or <2 alterations detected by MLPA, based on a median follow-up time of 114 months. Moreover, we have interpreted the distribution of some of the common alterations found: del(11q), del(14q), and NOTCH1 mutation, in patients grouped according to their disease staging by Binet and Rai CLL staging systems. As for the survival analysis, despite the lack of statistical significance, our results show an association between the increasing number of alterations in a patient and a shorter overall survival. Finally, the prevalence of del(11q), del(14q), and NOTCH1 mutation in our samples are significantly higher in patients with intermediate/high risk stages than in low risk disease stage patients, suggesting their value as bad prognosis markers. That is something already known for del(11q) and NOTCH1 mutation but appears to be a new information for patients with del(14q). We conclude that MLPA and aCGH are competent techniques for the genomic characterization of CLL. The aCGH technique has detected additional alterations in regions other than the ones evaluated by the standard diagnosis method iFISH, proving its value as a research tool in CLL. The aCGH results regarding reported CLL CNVs were positively validated by MLPA, showing that MLPA is a good substitute of the aCGH technique on day-to-day research. Moreover, MLPA has the advantages of being a less expensive and less time-consuming technique. In terms of clinical practice, MLPA could substitute iFISH for delivering the same results on del(11q), trisomy 12, del(13q), and del(17p) assessment, and the additional information on the other CNVs it also evaluates (2p gain, del(6q), 8p loss, 8q gain, del(9p21), and trisomy 19), as well as the detection of NOTCH1, MYD88, and SF3B1

somatic mutations. However, iFISH is the only one of these three techniques capable of detecting low expression mosaicism and the presence of translocations, a type of alteration that occurs in up to 5% of patients, making it still irreplaceable. In the future, all our results should be re-evaluated using a larger cohort of patients. Additionally, the survival analysis must be repeated when a larger follow-up time is reached, and the number of samples analyzed is greater. It is also important to re-assess del(14q) value as a possible bad prognosis marker. Additionally, we intend to better characterize the prevalence of del(5q13.2), del(8q24.23), del(22q11.22), del(Xq21.1), dup(4p16.3), dup(6p25.3), dup(8p11.1), and dup(10q11.22) in a larger cohort of CLL patients. Among these alterations, it would be interesting if we could identify possible new markers of diseases something only possible after comparison with a control groups. With our work, we expect to stimulate further research on new therapeutic targets and new treatment strategies. At last, we encourage the continuation of CLL genomic characterization, with the use of both aCGH and MLPA.

Index

Agradecimentos ...... II Resumo ...... IV Abstract ...... VI Index ...... VIII List of figures and tables ...... X Abbreviations ...... XI Chapter 1. Introduction...... 1 1.1 Chronic Lymphocytic Leukemia: General characterization ...... 1 1.1.1 CLL diagnosis, staging, and prognostic assessment ...... 1 1.1.2 Prognostic markers in CLL ...... 2 1.1.3 Monoclonal B cell lymphocytosis: a previous state of CLL ...... 3 1.1.4 CLL treatment and the advantages of targeted therapy ...... 3 1.1.5 Possibly altered pathways in CLL ...... 4 1.1.6 CLL genomic alterations ...... 4 1.1.6.1 Chromosomal abnormalities: reported CNVs and translocations in CLL ...... 4 1.1.6.1.1 Genomic alterations as prognostic markers in CLL ...... 5 1.1.6.1.2 Deletions at 13q ...... 5 1.1.6.1.3 Trisomy 12 ...... 5 1.1.6.1.4 Deletions at 11q ...... 6 1.1.6.1.5 Deletions at 17p and TP53 mutations ...... 6 1.1.6.1.6 Deletions at 6q, 14q, 8p losses, and 2p and 8q gains ...... 7 1.1.6.2 Somatic mutations and SNPs in CLL ...... 7 1.1.6.3 Epigenetic alterations in CLL: altered methylation profiles ...... 8 1.1.6.4 Clonal evolution in CLL ...... 8 1.2 CLL genomic characterization: comparing the effectiveness of conventional and molecular cytogenetic techniques ...... 9 1.2.1 Applying aCGH for copy number analysis ...... 11 1.2.2 Applying MLPA for copy number analysis ...... 12 1.3 Objectives ...... 13

Chapter 2. Materials and methods ...... 14 2.1 Selection and characterization of the study population ...... 14

2.2 Samples collection and preparation ...... 15

2.2.1 PBMCs separation ...... 15

VII

2.2.2 DNA extraction and purity determination ...... 15

2.3 Genomic analysis by aCGH and MLPA ...... 16

2.4 Interpretation of aCGH results ...... 16

2.5 Statistical analysis ...... 17

Chapter 3. Results and discussion ...... 18 3.1 General findings by aCGH and MLPA ...... 18

3.2 aCGH results ...... 19

3.2.1 Chromosome 13q deletions ...... 21

3.2.2 Trisomy 12 ...... 21

3.2.3 Chromosome 11q deletions ...... 22

3.2.4 Deletions at 6q, 9p, 14q, losses at 8p, and 2p and 8q gains ...... 22

3.2.5 other common CNVs found by aCGH ...... 23

3.2.5.1 dup(4p16.3) ...... 24 3.2.5.2 del(5q13.2) ...... 24 3.2.5.3 dup(6p25.3) ...... 25 3.2.5.4 del(22q11.22) ...... 25 3.2.5.5 dup(8p11.1), del(8q24.23), dup(10q11.22), and del(Xq21.1) ...... 26 3.3 MLPA results ...... 26

3.3.1 Chromosomes 11q and 13q deletions and trisomy 12 ...... 26

3.3.2 Losses at 6q, 9p, 14q, losses at 8p, gains at 8q, and NOTCH1 mutation ...... 26

3.4 Comparison between aCGH and MLPA results ...... 27

3.5 Survival analysis for patients with two or more CNVs ...... 28

Chapter 4. Conclusions and future perspectives ...... 29 Chapter 5. References...... 30 Chapter 6. Annexes ...... 43

VIII

List of figures and tables

Figure 1.1 – Origins of B-CLL cells...... 2 Figure 1.2 (a-c) – Schematic representation of aCGH labelling...... 12 Figure 1.3 (a-e) – Schematic representation of the preparation of a MLPA reaction...... 13 Table 2.1 – Clinical data obtained for the 20 CLL samples studied...... 14 Figure 2.1 (A and B) – Result of a density gradient centrifugation, using Ficoll-Paque...... 15 Figure 3.1 – Prevalence of reported CLL alterations found by aCGH...... 18 Figure 3.2 – Prevalence of reported CLL alterations found by MLPA...... 18 Table 3.1 – Alterations found by MLPA in CLL samples studied...... 19 Figure 3.3 – 850 bands-resolution ideogram of the human karyotype with the alterations found by aCGH...... 20 Figure 3.4 (A-F) – Chromosome view of characteristic CLL genomic alterations we found in our samples, using aCGH...... 21 Table 3.2 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of del(11q)...... 22 Table 3.3 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of del(14q)...... 23 Figure 3.5 Deletions detected at 5q, using aCGH...... 24 Figure 3.6 Deletions detected at 22q, using aCGH...... 25 Table 3.4 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of NOTCH1 mutation...... 27 Figure 3.7 – Electropherogram for sample CLL11...... 28 Figure 3.8 – Survival curve obtained by the Kaplan-Meier method for OS analysis...... 28 Table 6.1 in annex – Alterations found by aCGH in CLL samples studied...... 43

Abbreviations

ACAT – Acetyl-CoA Acetyltransferase 1 aCGH – Array Comparative Genomic Hybridization amp – Amplification ARID1A – AT-rich interactive domain-containing protein 1A ATM – ATM serine/threonine kinase ATP1B2 – ATPase Na+/K+ Transporting Subunit Beta 2 BAC – Bacterial artificial chromosome BCL2 – B-cell CLL/lymphoma 2 BCL2L1 – BCL2-like 1 BCL3 – B-cell CLL/lymphoma 3 BCR – B-cell receptor BIRC2 – Baculoviral IAP Repeat Containing 2 BIRC3 – Baculoviral IAP Repeat Containing 3 bp – Base pair BTK – Bruton’s tyrosine kinase C11orf53 – 11 Open Reading Frame 53 CASP1 – Caspase 1 CASP4 – Caspase 4 CASP5 – Caspase 5 CASP12 – Caspase 12 CCND1 – Cyclin D1 CCND3 – Cyclin D3 CD5 – T-cell surface glycoprotein CD5 CD10 – Neprilysin CD19 – B-lymphocyte antigen CD19 CD20 – B-lymphocyte antigen CD20 CD22 – B-cell receptor CD20 CD27 – CD27 Antigen CD38 – ADP-ribosyl cyclase/cyclic ADP ribose hydrolase 1 CD79b – B-cell antigen receptor complex-associated protein beta chain CD200 – Cell surface glycoprotein CD200 receptor 1 CDK4 – Cyclin-dependent kinase 4 CDKN1B – Cyclin-dependent kinase inhibitor 1B CDKN2A – Cyclin-dependent kinase inhibitor 2A CDKN2B – Cyclin-dependent kinase inhibitor 2A CGH – Comparative Genomic Hybridization CHD2 – Chromodomain Helicase DNA Binding Protein 2 Chr – Chromosome CHUC – Centro Hospitalar e Universitário de Coimbra CK – Complex Karyotype ClinGen – Clinical Genome Resource CLL – Chronic Lymphocytic Leukemia CLLU1 – Chronic Lymphocytic Leukemia up-regulated protein 1 CNV – Copy Number Variation CUL5 – Cullin-5 Cy3 – Cyanine 3

Cy5 – Cyanine 5 del – Deletion DGV – Database of Genomic Variants DLC1 – Deleted in Liver Cancer 1 DLEU2 – Deleted in Lymphocytic Leukemia 2 DLEU7 – Deleted in Lymphocytic Leukemia 7 DMSO – Dimethyl Sulfoxide DNA – Deoxyribonucleic Acid DSB – Double-Strand Break dup – Duplication DUSP22 – Dual Specificity Phosphatase 22 E2F1 – E2F Transcription Factor 1 EDTA – Ethylenediaminetetraacetic acid FAM110C – Family with Sequence Similarity 110 Member C FISH – Fluorescence in situ Hybridization GATA2 – GATA Binding Protein 2 GRCh37/hg9 – Genome Reference Consortium Human Build 37/human genome 19 GTF2H2 – General Transcription Factor IIH Subunit 2 HIP1R – Huntingtin Interacting Protein 1 Related iFISH – Interphase Fluorescence in situ Hybridization Ig – Immunoglobulin IgA – Immunoglobulin A IgD – Immunoglobulin D IgG – Immunoglobulin G IGH – Immunoglobulin Heavy Chain IGHV – Immunoglobulin Heavy Chain Variable Region IgM – Immunoglobulin M IKZF3 – IKAROS Family Zinc Finger 3 IRF4 – Interferon Regulatory Factor 4 KDELC2 – KDEL Motif Containing 2 KRAS – KRAS Proto-Oncogene, GTPase LCR – Low-Copy Repeats LDH – Lactate Dehydrogenase LEF1 – Lymphoid Enhancer Binding Factor 1 LPO – Left Probe Oligonucleotide mir15a/16-1 locus – MicroRNA 15a and MicroRNA 16-1 locus miRNA – MicroRNA miRNA-34a – MicroRNA 34a MBL – Monoclonal B lymphocytosis MDM2 – MDM2 Proto-Oncogene MDR – Minimal Deleted Region MGA – MGA, MAX Dimerization Protein MI – Methylation Index MIR650 – MicroRNA 650 MLPA – Multiplex Ligation-Dependent Probe Amplification MRE11 – MRE11 Homolog, Double Strand Break Repair Nuclease mRNA – Messenger RNA MTAP – Methylthioadenosine Phosphorylase

MYC – MYC Proto-Oncogene, BHLH Transcription Factor MYCN – MYCN Proto-Oncogene, BHLH Transcription Factor MYD88 – Myeloid Differentiation Primary Response 88 MYF6 – Myogenic Factor 6 NAHR – Nonallelic Homologous Recombination NAIP – NLR Family Apoptosis Inhibitory Protein NF-Kb – Nuclear factor κ-light-chain-enhancer of activated B cells pathway NFKBIE – NFKB Inhibitor Epsilon NFKB2 – Nuclear Factor Kappa B Subunit 2 NGS – Next-Generation Sequencing NOTCH1 – Translocation-Associated Notch Protein TAN-1 NPAT – Nuclear Protein, Coactivator of Histone Transcription NRAS – NRAS Proto-Oncogene, GTPase OMIM – Online Mendelian Inheritance in Man OS – Overall Survival p53 – Tumor Protein 53 PAX5 – Paired Box 5 PBMC – Peripheral Blood Mononuclear B Cell PCR – Polymerase Chain Reaction PDGFRL – Platelet Derived Growth Factor Receptor Like PFS – Progression-Free Survival PRAME – Preferentially Expressed Antigen in Melanoma PTEN – Phosphatase and Tensin Homolog PTPN11 – Protein Tyrosine Phosphatase, Non-Receptor Type 11 qPCR – Quantitative Polymerase Chain Reaction RB1 - RB Transcriptional Corepressor 1 RCN – Relative Copy Number RDX – Radixin RFU – Relative Fluorescent Units RPO – Right Probe Oligonucleotide RUNX1 – Runt Related Transcription Factor 1 SERF1A – Small EDRK-Rich Factor 1A SF3B1 – Splicing Factor 3b Subunit 1 SHM – Somatic Hypermutation SLL – Small Lymphocytic Lymphoma SMARCA2 – SWI/SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin, Subfamily A, Member 2 SMN1 – Survival of Motor Neuron 1, Telomeric SMN2 – Survival of Motor Neuron 2, Centromeric SNP – Single-Nucleotide Polymorphism ssDNA – Single-Strand DNA SYK – Spleen Associated Tyrosine Kinase TAL1 – TAL BHLH Transcription Factor 1, Erythroid Differentiation Factor TERT – Telomerase Reverse Transcriptase TP53 – Tumor Protein P53 trp – Triplication TTFT – Time to First Treatment UCSC – University of California Santa Cruz

XII

WGS – Whole Genome Sequencing WHO – World Health Organization XPO1 – Exportin 1 ZAP70 – Zeta Chain of T Cell Receptor Associated Protein Kinase 70 ZMYM3 – Zinc Finger MYM-Type Containing 3 ZNF280A – Zinc Finger Protein 280A ZNF280B – Zinc Finger Protein 280B ZNF292 – Zinc Finger Protein 292 ZNF595 – Zinc Finger Protein 595 ZNF718 – Zinc Finger Protein 718

XIII

Chapter 1. Introduction

1.1 Chronic Lymphocytic Leukemia: General characterization

Chronic lymphocytic leukemia (CLL) is the most common type of leukemia and one of the most common lymphoid malignancies, being biologically and clinically very heterogeneous [1]. It affects mainly adults and western countries’ populations, with a median age at diagnose between 67 and 72 years old. Life expectancy for these patients ranges from months to decades [2]. There is a twofold increased risk for men to develop this malignancy compared to women, and increased age also seems to be a risk factor for CLL development [1]. CLL patients’ first-degree relatives have an 8.5 times increased risk of developing the disease, in comparison to the general population [1, 3], suggesting that family history is a risk factor for this condition [4, 5]. Also monoclonal B cell lymphocytosis (MBL) and small lymphocytic lymphoma (SLL) constitute risk factors for the development of this disease [1]. Although the driving causes for CLL are unknown, it is possible to characterize CLL as a hematological malignancy presenting clonal expansion of neoplastic B cells that accumulate in peripheral blood and infiltrate the bone marrow and lymphoid tissues such as lymph nodes, the liver, and the spleen [6, 7]. This accumulation is due to these cells’ inability to suffer apoptosis, sometimes because of the overexpression of BCL2 gene, an anti-apoptotic gene located at 18q21.33 [8, 9]. B-CLL cells differentiation might be blocked, also due to genetic alterations such as the mutation of an enhancer of PAX5 gene, located at 9p13. The PAX5 expression product is a transcription factor involved in B cell differentiation that suffers under-expression when the referred enhancer is mutated [1]. This suggests the need for the assessment of CLL driver alterations also present at the non-coding genome [7]. Although it was thought cell cycle arrest of neoplastic B lymphocytes to be the main cause for these B cell’s accumulation in CLL, it has been unveiled that this it occurs also due to the proliferation of some neoplastic cells that are able to multiply: between 0.1-1% neoplastic CLL lymphocytes duplicate, per day [2]. In fact, the disease course depends on the balance between these two types of cells[10-12].

1.1.1 CLL diagnosis, staging, and prognosis assessment

CLL is diagnosed through blood smears, cell counting, and immunophenotyping. An initial counting of >5,000 clonal lymphocytes/µl of peripheral blood, that remains for at least three months, and small mature lymphocytes with small cytoplasm and dense nucleus are CLL features detected at diagnosis. In advanced stages, cell counting can reach up to ≥10,000 lymphocytes/µl [6]. CLL patients may be stratified according to Rai and Binet staging systems, that acknowledge three prognostic types: poor, intermediate, and good prognosis. They are suitable helpers for disease risk determination and in the choice of patients who do or do not need immediate treatment at the time of diagnosis. Rai staging system has five risk stages, ranging from 0-IV: stage 0 and I – low risk, II – intermediate risk, and III and IV – high risk [13, 14]. The Binet staging system has three risk stages: A – low risk, B – intermediate risk, and C – high risk [15]. The Rai system defines low risk disease as the presence of leukemic cells and lymphocytosis; intermediate-risk disease by the presence of lymphocytosis and splenomegaly and/or enlarged lymph nodes; and high-risk disease as having the conditions from the first two stages, and also disease-related anemia and/or thrombocytopenia [6]. As for the Binet staging system, it stratifies patients based on the number of anatomical affected regions, like the lymph nodes from the neck, groins, axillae, spleen, and liver [6]. However, these staging systems do not take into account the molecular alterations and cannot predict the evolution course of the disease and so it is important to consider informative clinical markers for disease progression, enabling outcome prediction [16].

1.1.2 Prognostic markers in CLL

B-CLL cells immunophenotypic analysis shows cell-surface expression of CD5, CD19, CD200 [16], generally reduced expression of CD20 [17, 18], CD22, CD79b, and lack of CD10, compared to healthy B cells [2, 19]. The CD20 level of expression is variable amongst CLL patients, depending on the cytogenetical abnormalities present. Supporting this is the finding of Tam et al. [17], that detected a significant increase on CD20 expression in patients with trisomy 12 and a significant decreased expression in patients with del(11q). The expression levels of CD38 and ZAP70 are informative about CLL subtype. ZAP70 overexpression indicates a more aggressive disease [8, 20], as well as CD38 overexpression [21-23]. ZAP70 overexpression is known to be an aggressive disease marker once it is linked to increased B Cell receptor (BCR) signaling, a feature that leads to an aggressive phenotype as well as to the modulation of other signaling pathways [8, 24]. As a matter of fact, the discovery that these two genes’ overexpression coexist with unmutated immunoglobulin heavy chain variable region (IGHV), a bad prognosis marker, has simplified patients’ prognosis assessment since IGHV state evaluation is a complex and dispendious process [10, 25]. However, after that discovery, CD38 overexpression has been determined to be an independent prognostic factor from IGHV mutational state because its expression levels may vary during the course of disease [10, 26]. ZAP70 consists, then, in a more secure source for the assessment of IGHV mutational state once it maintains a stable expression level during the course of disease [10, 27, 28]. Still, its evaluation is not yet applied for clinical practice purposes. CLL has a heterogeneous clinical course, being divided in two subtypes characterized by the lack or presence of somatic hypermutation (SHM) of the IGHV of B cell receptors. B lymphocytes with mutated IGHV result from B cells that went to lymph node’s germinal center and differentiated, whereas B lymphocytes with unmutated IGHV result from naive B cells that did not suffer SHM (Figure 1.1). Thus, B cell differentiation determines the behavior of the disease. It is more aggressive when B lymphocytes are less mature, which means that patients with unmutated IGHV present a more preoccupying situation [1]. Knowing cell-surface immunoglobulins (Ig) is also important to characterize CLL because it gives information about the origin of anomalous B cells [8, 29]. IgM and IgD are mainly present in IGHV-unmutated B cells, whilst IgA and IgG are characteristic of more mature cells that underwent class-switch recombination, the IGHV-mutated B cells (Figure 1.1) [8].

Figure 1.1 – Origins of B-CLL cells. Cancer cells in CLL are CD5+ B cells that either remain in the blood stream or enter lymph nodes, experiencing SHM and, in both cases, presenting surface expression of IgM and IgD. Inside lymph nodes and after SHM, B cells may suffer apoptosis after the selection of low affinity cells or may be presented to antigens. That leads to the exit, to the blood stream, of IgM+IgD+ IGHV-mutated cells or of IGHV-mutated cells that underwent class-switch recombination. (Adapted from Kipps et al. 2017).

1.1.3 Monoclonal B cell lymphocytosis: a previous state of CLL

Monoclonal B cell lymphocytosis is characterized by the presence of fewer than 5,000 monoclonal B cells/µl, a subset of B cells of which the clinical significance is unknown. In 75% of MBL cases, these cells present the B-CLL cells phenotype [4], leading to the so-called CLL-phenotype MBL, but are characterized by the absence of any other CLL symptoms [30]. MBL might present either a low or high count of monoclonal B cells: <500 cells/µl and >500 cells/µl, respectively [1]. The fact that this phenotype is detected in about 3% of the healthy adult population suggests the possibility of this being the previous stage of CLL [4, 8]. A study from Goldin et al. [5] reported the development of MBL in first-degree relatives of CLL individuals in a higher frequency than in the general population, suggesting that MBL is a candidate marker for CLL development [4, 5]. Hence, detecting this condition is a valuable step to identify patients for regular follow-ups and treat possible future CLL situations from an early stage. A follow-up study was conducted by Rawstron et al. [30] with the intent of determining if CLL chromosomal abnormalities also occur in CLL-phenotype MBL. This study also evaluated if this phenotype may lead to a CLL situation requiring chemotherapy. The main results were that individuals with the CLL-phenotype MBL presented del(13q14) and trisomy 12 in the same frequencies as described for CLL (48% and 20%, respectively) and an IGHV mutation frequency of 87% (higher than the frequency found in the literature for IGHV-mutation frequency in CLL, that ranges from 60-65%). They also found that 15% of CLL-phenotype MBL individuals evolved to CLL, suggesting that, in fact, exists a relation between CLL and CLL-phenotype MBL. Some of these patients ended up needing chemotherapy but, having all the results in consideration, it was possible to conclude that to predict the outcome of CLL-phenotype MBL individuals is a difficult task [30]. More recently, genetically modified mice with both mir15a/16-1 locus and DLEU2 gene deleted (both located at 13q), ended up developing monoclonal B-cell lymphocytosis-like disorder and, ultimately, CLL. This supports the hypothesis that both deleted elements influence CLL leukemogenesis, possibly enhancing the appearance of a pre-CLL monoclonal B-cell lymphocytosis that will result in CLL development [6, 31].

1.1.4 CLL treatment and the advantages of targeted therapy

To decide on the course of CLL treatment, some clinical aspects should be considered: age, staging of the disease, and the presence or absence of del(17p), and of TP53 mutations. The main therapies are based on drugs that lead to different outcomes depending on patient’s phenotype. Fludarabine, cyclophosphamide, and rituximab are the first-line strategies for CLL patients, contributing to an increased progression-free survival (PFS) and overall survival (OS), and often results in a remission period of more than 10 years. However, these strategies are not advised for patients that have del(17p) and/or BIRC3 and TP53 mutation [32-34]. When present, these alterations lead to a decreased response to chemoimmunotherapy. Thus, therapeutic alternatives based on drugs which action is independent of p53 (the expression product of TP53) such as ibrutinib, were developed [35, 36]. It is an irreversible inhibitor of bruton’s tyrosine kinase (BTK) and is recommended for patients with relapsed disease [32, 37, 38] and for disease control in patients with high-risk characteristics such as del(11q) and unmutated IGHV [32]. Targeted therapy has been extensively studied and developed over the last years, regarding CLL. The main advantage is that it is designed to act upon a specific target, which therefore guarantees increased security in the resulting effect. The Chronic Lymphocytic Leukemia Treatment publication by the National Cancer Institute mentions one of the possible targeted therapies as being monoclonal antibody therapy [39]. New anti-CD20 monoclonal antibodies are an example of antibodies used for

this purpose, which are used in obinutuzumab, ofatumumab, and rituximab therapies [7, 37], three strategies that target B-CLL cells’ CD20 [6, 40]. Despite the variety of treatments available, in some cases there is no need for them, as about one third of CLL patients can survive for twenty or more years without receiving any treatment [10, 41].

1.1.5 Possibly altered pathways in CLL

Several genetic and epigenetic alterations culminate in dysfunctions of lymphoid cell’s signaling pathways such as Notch, NF-kB, BCR, DNA damage repair, cell cycle regulation, apoptosis, chromatin modification, and RNA metabolism [42]. This results in CLL development and progression, being some genetic variants associated with familial CLL [1]. For instance, the NF-kB and BCR signaling pathways are commonly altered, being constitutively activated, resulting in an increased expression of anti-apoptotic genes such as BCL2 and BCL2L1 [2, 7]. The activation of the NF-kB signaling pathway is thought to be a mechanism of resistance to disease treatment, in CLL [43, 44], and may be abnormally activated due to the mutation or deletion of the BIRC3 gene, a negative regulator of this pathway [44, 45].

1.1.6 CLL genomic alterations

1.1.6.1 Chromosomal abnormalities: reported CNVs and translocations in CLL

Chromosomal abnormalities such as copy number variations (CNVs), trisomies, and translocations are detected, by Interphase Fluorescent In Situ Hybridization (iFISH), in about 80% of diagnosed CLL cases [2]. A CNV is a segment of DNA with 1Kb or more, that presents a variation (deletion or amplification) in the number of its copies, in comparison to a reference healthy DNA [46]. Some CNVs are harmful for the individual, and others are not, and that is why it is possible to stratify CNVs as: benign, likely to be benign, likely to be pathogenic, pathogenic (due to overlapping with microdeletions or microduplications related to known syndromes), or with unknown clinical significance [47, 48]. Considering CLL CNVs, the common known alterations are: the gains at 2p and 8q; deletions at 6q, 11q, 13q, 14q, 17p, and losses at 8p. Moreover, trisomy 12 is also commonly found in CLL patients [35]. The 2p and 8q gains, and 8p loss appear in 2-5% of CLL cases, del(6q) in ~6%, del(17p) in 3-8%, del(14q) in ~8%, trisomy 12 and del(11q) appear in 10-20% of cases, and del(13q) in 40-60% of cases, representing the most common CNV in CLL [49]. Chromosomal translocations might be found in 32-42% of patients [35, 50]. As an example of CLL translocations, and showing up in less than 5% of patients, are the translocations between chromosomes 14 and 18: t(14,18)(q32,q24); t(14,18)(q32,q21); or between chromosomes 14 and 19: t(14,19)(q32,q13) [35, 51]. These translocations lead to the relocation of the IGH locus, located at 14q32.33 [10, 52]. Although some studies have shown that the prognostic significance of translocations involving this locus depends on the other chromosome involved, their prognostic interpretation is controversial. In t(14,19)(q32,q13), the prognostic seems to be poor once it involves the BCL3 locus, located at 19q13.32, which regulates the expression of NF-kB target genes [53], and also for being associated to trisomy 12 and unmutated IGHV [35, 54]. As for t(14,18)(q32,q21), the resulting fusion gene is IGH-BCL2, and it has also been related to poor outcome [51, 55]. It is possible to find complex karyotypes (CK) in approximately 16% of CLL cases [35, 50]. CK usually involves three or more chromosomal abnormalities at the same time, causing an aggressive course of disease with short patient survival [10, 56]. It is known to be caused by impaired DNA double-strand break response, due to altered function of ATM, TP53, and RB1 genes, since they have a role in cell cycle checkpoint and apoptosis regulation [35, 57]. In CLL, this complexity seems to be

related to worse outcomes since it shortens the time to first treatment (TTFT), OS, and leads to worse response to therapy [21, 35, 56]. Besides CNVs’ number, also their size is a parameter for genomic complexity evaluation. CLL patients with poor prognosis abnormalities have shown to be the main group of patients with the largest CNVs, suggesting that genomic complexity might be itself a disease progression marker [21].

1.1.6.1.1 Genomic alterations as prognostic markers in CLL

The 13q deletion, when found as a sole alteration, is associated with favorable prognosis. Trisomy 12 is considered to be an intermediate risk alteration, and del(11q) and del(17p) are associated with a poor prognosis [42, 58]. Despite the fact that all of these alterations contribute to CLL pathogenicity, it is common to consider gains as being less harmful than losses [59]. In this disease, it is more common to find deletions than amplifications [48, 60].

1.1.6.1.2 Deletions at 13q

Deletions in the 13q region might involve RB1, a tumor suppressor gene that is also involved in cell cycle regulation, promoting G0/G1 transition [61]. RB1 gene deletion is associated with short TTFT and short OS [35, 62], contributing to accelerated disease progression [16, 63]. Single- Nucleotide Polymorphisms (SNP) microarray analysis of del(13q) cases have shown deletions of RB1 in only some cases, while the miRNA15a/16-1 locus was always deleted [49], which seems to support the idea that the absence of this last locus is a cause for CLL pathogenicity. It is located at 13q14.3 and downregulates anti-apoptotic genes such as BCL2 by targeting their mRNA [9, 10, 31, 64]. With this said, in case of del(13q), the absence of this locus leads to the upregulation of BCL2, increasing its expression, resulting in an enhanced anti-apoptotic function of the BCL2 gene product [9, 10]. Despite that, deletions involving 13q are considered low-risk abnormalities when detected as the only abnormality in the patient but lead to increased poor outcome when coexisting with other alterations [21, 58]. Bigger 13q deletions also lead to a worse outcome [21, 65]. In fact, del(13q) may occur with variable breakpoints, with a minimum size of 300Kb and a maximum of 70 megabases (Mb) [62]. The minimal deleted region (MDR) in 13q has been demarcated to begin in a distal position relatively to RB1, encompassing DLEU2 gene and the miRNA15a/16-1 locus [66, 67]. Ouillette et al. [62] classified the 13q deletion as type I del(13q), also known as “short” del(13q), or as type II del(13q). Whereas type I 13q deletions have <2Mb, type II del(13q) or “large” del(13q), often includes the RB1 gene, in contrast to type I [68, 69]. It is known that this alteration can occur in heterozygosity in about 76% of cases and in homozygosity in 24% of patients, being the homozygous state a characteristic later event in CLL development [10, 22, 23]. Moreover, Hernandez et al. [70] have seen that the increased burden of cancer cells with del(13q) is positively correlated with worse outcome, a discovery that made possible the stratification in two prognostic groups: one group with worse prognosis and short TTFT due to high percentage of del(13q) cells, and a group of better outcome with low percentage proportion of del(13q) cells [10].

1.1.6.1.3 Trisomy 12

Trisomy 12, related to an intermediate risk prognosis, frequently appears associated with other chromosomal abnormalities such as deletions on 11q, 13q, 14q, 17p, and trisomies 18 and 19 [35, 58]. Some trisomy 12 cases specifically with mutated-IGHV have already shown the tendency to acquire trisomy of chromosome 19 [35, 71]. This alteration occurs mostly in early stages of CLL and it seems

to be a driver for gene mutations in the transcription factor NOTCH1 and tumor suppressor gene TP53, located at 9q34.3 and 17p13.1, respectively [35, 72, 73]. From this aneuploidy results the upregulation of genes such as CDKN1B, CDK4, HIP1R, MYF6, MDM2 [35, 74] and CLLU1 [10, 75]. CLLU1, located at 12q22, has been proposed as a prognosis marker once its elevated burden of expression has been connected to shorter OS [49, 75]; the MDM2 gene is located at 12q15 and its upregulation leads to cell cycle deregulation [35, 49, 76]; and CDK4 expression product is a kinase that regulates the activity of the transcription factor E2F1, involved in cell proliferation, meaning that CDK4 upregulation leads to E2F1 overexpression. Hence, CDK4 upregulation might cause the over- proliferation of B cells with trisomy 12. This examples help explaining the pathogenesis of trisomy 12 [35, 77].

1.1.6.1.4 Deletions at 11q

Deletions at 11q can be variable in sizes, most of the times being larger than 20Mb [35, 78, 79]. The minimal deleted region spans from 11q22.3-q23.1, including the ATM gene (11q22.3) in about 30% of patients [10, 80] [81]. The ATM gene is a tumor suppressor gene also involved in the DNA damage repair pathway, being responsible for the induction of apoptosis when a damage is not repairable, or in the restoration of double-strand breaks (DSB) that might occur in the DNA [10, 82]. Other possibly deleted genes in this region are: RDX, CUL5, ACAT, NPAT, KDELC2, MRE11, and BIRC3 [35]. Marasca et al. [83] have detected an association between del(11q), genomic instability, and increased complexity once it is regularly accompanied by other CNVs, such as del(17p). In conclusion, the prognostic information given by this CNV is of poor outcome once it leads, almost in every case, to disease progression whit short TTFT, OS, and remission duration [35, 84].

1.1.6.1.5 Deletions at 17p and TP53 mutations

Deletions in 17p region are less prevalent than the anomalies described on the last sections, but may rise up to 40% in patients undergoing chemotherapy, suggesting that these deletions may be triggered after treatment [44, 85]. It is already known that the standard chemoimmunotherapy treatments based on fludarabine, cyclophosphamide, and rituximab are not effective on del(17p) and/or TP53-mutated patients, or even in BIRC3 and IKZF3-mutated patients, since these alterations confer an advantage to tumor cells [7, 35, 86]. Therefore, del(17p) is usually classified as a bad prognosis marker and the treatment of patients with this genomic alteration should not include chemoimmunotherapy. This deletion can appear as an early event or a secondary alteration acquired during clonal evolution, and that difference determines patient’s median OS: 4 to 5 years and 1 to 1.5 years, respectively [35, 87]. It is common to detect TP53 mutation in ~75% of del(17p) cases, on the remaining allele [35]. Even if del(17p) exists without this mutation on the other allele, or if TP53 mutation occurs in absence of del(17p), the disease prognosis is still bad [35, 88]. In fact, del(17p) is always associated with a very aggressive disease behavior, leading to short OS, due to unresponsiveness to therapy [10, 58, 89, 90]. TP53 expression might also be dysfunctional due to MDM2 overexpression caused by trisomy 12 [35, 76]. MDM2 overexpression leads to cell cycle deregulation since, in healthy situations, its product regulates p53 degradation. Enhanced degradation of p53 will affect p53 dependent genes and miRNAs. For example: miRNA-34a, a miRNA involved in the regulation of cell cycle arrest and apoptosis, is underexpressed in case of abnormal p53 degradation, leading to a more aggressive disease and therapy resistance [35, 91]. It is then easy to understand why fludarabine based therapy has no effect on these individuals: it is a p53 dependent

drug, and the absence of a functional TP53 gene incapacitates fludarabine’s required conditions to function properly [10, 22]. Concerning the size of this alteration, it is usual to find the short arm almost entirely deleted [10]. The minimal deleted region has 34Kb and encompasses ATP1B2 and TP53 genes, both located at 17p13.1 [92]. With the loss of this segment, genes such as TP53, CCND1, CCND3, BCL2, ATM, and SYK become downregulated, being TP53 the most significantly underexpressed, whereas other genes suffer upregulation (e.g. MYC, located at 8q). Altogether, these altered genes lead to increased genetic instability of del(17p) patients [77, 93]. Other alterations that, in some studies, have been associated with del(17p) are: del(4p), 8p loss, del(18p), del(20p), and 8q gain [21, 35, 94]. For instance, 8p loss and 8q gain have been identified in 80% and 44% of del(17p) cases, respectively [35, 95, 96]. Patients with del(17p) are also immunophenotypically atypical, presenting overexpression of CD79b, CD38, ZAP70, and unmutated IGHV [35, 97].

1.1.6.1.6 Deletions at 6q, 14q, 8p losses, and 2p and 8q gains

Concerning 2p gains, they occur only in ~5% of CLL cases, and may encompass the MYCN gene, located at 2p24.3 [98]. With that alteration, MYCN undergoes overexpression, suggesting its pathogenicity in CLL because of its known involvement in transcriptional misregulation in cancer and apoptosis [35, 99]. In fact, this gain appears as a secondary alteration in about 28% of untreated CLL patients, indicating a poor outcome prognosis [100]. The association between this alteration and del(11q) encompassing ATM gene is indicative of an even worse outcome since ATM absence is related to rapid disease progression [16, 101]. The deletions found at 6q occur in 3 to 7% of cases [35]. They show high heterogeneity, which difficult the determination of the minimal deleted region and consequently restrain knowledge of the genes which are responsible for the pathogenicity associated with this alteration [35, 102]. However, it is commonly considered as an intermediate-risk alteration [10, 103]. As for the gains and losses usually found in chromosome 8 (8p loss and 8q gain), their prevalence in CLL ranges from 2 to 5% of patients, and are known as valuable prognosis markers [35, 95]. The gains in 8q, when encompassing the MYC gene, are indicative of an aggressive phenotype since this gene’s overexpression is associated with CLL progression and high risk clinical stage [104]. Deletions at 14q occur with a prevalence of ~8% of CLL cases [49]. Their physiological consequences are not well known yet, making it difficult to determine their prognostic value. Breakpoints may occur in centromeric or telomeric regions [105].

1.1.6.2 Somatic mutations and SNPs in CLL

Among the most frequent somatic mutations in CLL are: IGHV mutations (60-65% of cases); TP53 gene mutation (4-12% of untreated patients); ATM gene mutation (12% of cases, and in 30% of patients with del(11q)) [10, 106, 107]; NOTCH1 gene mutations (10% of patients); SF3B1 gene mutation (5-10% of cases); BIRC3 gene mutation (4% of cases), and MYD88 gene mutation (3-5% of cases) [49]. Some patients may even show germline ATM mutations, which is a risk factor for the development of CLL [10, 108]. NOTCH1 is involved in cell growth, differentiation, and self-renewal through the regulation of several genes such as MYC, TAL1, GATA2, RUNX1. NOTCH1 mutations are linked to short survival, short treatment resistance, and disease progression [49], frequently appearing in trisomy 12 cases that have a poor outcome prognosis [35]. The mutations in this gene lead to the constitutive activation of the Notch signaling pathway, enabling cell’s resistance to apoptosis and steering altered expression of the genes that depend on this pathway [10, 109]. The majority of NOTCH1 mutation cases in CLL occur simultaneously with unmutated IGHV, indicating a poor disease outcome [110]. SF3B1 mutations are considered to be bad prognosis markers, frequently

occurring in del(11q) cases, and also both in IGHV-mutated and IGHV-unmutated patients [10, 111]. Other possibly mutated genes are: BIRC3, NFKBIE, NFKB2 (involved in NF-kB signaling pathway); MYD88 and IKZF3 (involved in BCR signaling); CDKN1B and CDKN2A (involved in cell cycle regulation); CHD2, ARID1A and ZMYM3 (involved in chromatin modification); SF3B1, MGA and XPO1 (involved in RNA metabolism); and other genes such as KRAS, NRAS, PTPN11, ZNF292, and SMARCA2 [42, 49]. BIRC3 and SF3B1 gene mutations have been found to occur in patients that underwent fludarabine treatment, suggesting the refractory effect of this drug in those patients. In fact, previous reports have found that BIRC3 mutations are detected in 24% of fludarabine-refractory CLL patients [44, 85]. These mutations might therefore account for the ineffectiveness of fludarabine when used in patients with BIRC3 and SF3B1 mutations, suggesting that some therapeutic adjustments have to be made [10, 112]. Finally, genetic variants resulting from SNPs in genes such as IRF4, LEF1, BCL2, TERT or, for example, SNPs on the miRNA15a/16-1 locus might be found, being related with familial CLL [1].

1.1.6.3 Epigenetic alterations in CLL: altered methylation profiles

Epigenetic alterations such as hypomethylation of BCL2 gene [113] and/or hypermethylation of CD38 gene may also occur in CLL [10], resulting in a heterogeneous methylation profile [1]. The CD38 gene, located at 4p15.32, is involved in apoptosis induction [114, 115]. If hypermethylated, CD38 is underexpressed, consequently resulting in reduced apoptosis. As for the BCL2 gene, as previously mentioned in section 1.1, it is an anti-apoptotic gene. This means that, when hypomethylated, BCL2 is overexpressed. CLL patient’s genome is usually globally hypomethylated [10, 116-118], but can also present some tumor suppressor gene’s promoters hypermethylated [10, 114, 119]. Yu et al.[120] quantified methylated DNA through methylation index (MI) analysis in CLL patients with different Rai stages, and it has shown to be useful and informative on CLL progression: most Rai 0-I patients have shown low MI; clinically stable disease patients had an even lower MI; and Rai IV patients presented the highest MI values. Elevated MI values suggest the hypermethylation of tumor suppressor gene’s promoters, which relate to a poor outcome [10, 120].

1.1.6.4 Clonal evolution in CLL

Clonal evolution should be taken into account since it determines the development of this disease, both in treated and untreated patients. According to some follow-up studies using Fluorescence In Situ Hybridization (FISH), clonal evolution occurs in up to 43% of CLL patients [21, 23, 121] and most of the times it arises as a late event [16]. However, each patient has a different probability of developing clonal evolution, based on age and CLL baseline features such as IGHV mutational state and lactate dehydrogenase (LDH) level [122]. Clonal evolution is characterized by the appearance of sub-clones with newly acquired genetic abnormalities [21], a phenomenon that depends on: 1) selective pressures from the microenvironment in which CLL B cells are located [35, 73]; 2) genetic instability driven by genetic and epigenetic alterations [16, 123]; 3) and by the interacting immune system cells present, such as T cells [8, 124]. The study of clonal evolution has been proven to be important for prognosis determination at the time of diagnosis. For example, the presence of various clones in early stages is indicative of a potentially more aggressive disease in the future [16, 23]. Hence, an initial assessment on clonal diversity has the potential to help predicting the disease course and consequently to help stratify patients according to their risk level. Clonal evolution has been linked to disease progression, and most of the times to a poor outcome due to the presence of unmutated IGHV and ZAP70 and CD38 overexpression [21-23, 121]. In fact, Berkova et al. [121] succeeded to positively correlate the

presence of these three markers with the development of clonal evolution. Also in 2011, and using genomic SNP microarray analysis in a follow-up study, Gunnarsson et al.[21] found an increased occurrence of clonal evolution in IGHV-unmutated samples relatively to IGHV-mutated samples. Even among IGHV-mutated samples, it was possible to detect a different incidence of clonal evolution, being that untreated IGHV-mutated individuals did not shown any novel genomic alteration in the follow-up study, whereas treated IGHV-mutated individuals did show some novel alterations. In conclusion, more aggressive B cell phenotypes seem to be a risk factor for CLL clonal evolution and disease progression. CLL genomic alterations such as del(13q), trisomy 12, NOTCH1 mutation, or MYD88 mutation usually occur as early events [35, 73]. It has been reported that this disease might be originated by large genomic losses or gains [6, 86]. It has also been found that del(13q) can result from clonal evolution since it was found to be the most prevalent newly-acquired abnormality in the CLL follow- up study previously referenced [22]. Loss of the miRNA15a/16-1 locus (located at 13q) seems to be one of the triggering events of CLL [8, 66]. There is also the possibility that CLL is driven by the recognition of autoantigens or bacterial compounds such as lipid A [8, 125]. On the other hand, abnormalities such as ATM, TP53, or SF3B1 mutations occur in more advanced stages, clearly resulting from clonal evolution [35, 73]. Alterations as del(11q) and del(17p) might occur both as early or late events [35, 87, 126], but appear mostly in later stages of disease [12, 58]. Regarding patients undergoing chemotherapy, the events of clonal evolution are triggered by the selection of specific sub-clones, mainly with TP53 and/or SF3B1 mutations, that represent driver mutations, and that proliferate and accumulate until they substitute the other existing sub-clones [35, 73].

1.2 CLL genomic characterization: comparing the effectiveness of conventional and molecular cytogenetic techniques

Over the last decades, several techniques such as conventional cytogenetics through chromosome banding; iFISH; Multiplex Ligation-dependent Probe Amplification (MLPA); Comparative Genomic Hybridization (CGH); and microarray comparative genomic hybridization technologies like bacterial artificial chromosomes (BAC) arrays, SNP microarrays, and oligonucleotide microarrays, have enabled clinicians and researchers to characterize CLL genomic alterations. All these techniques have contributed to the study of CLL patients since they detect the unbalanced chromosomic alterations that recurrently occur in this pathology. However, it is important to bear in mind the pros and cons of the different techniques mentioned. Therefore, on the next paragraphs, they will be briefly discussed and compared. Cytogenetic abnormalities are important prognostic markers in CLL, making the genetic characterization of CLL patients extremely important in clinical practice since the treatment of each patient depends on detailed knowledge of his/hers genomic abnormalities [1]. The detection of chromosomal alterations by conventional cytogenetics using G-banding cytogenetics (GTG) enables the study of chromosome number and structure but it is conditioned by the characteristic cell cycle arrest between phases G0/G1 that occurs on this disease, making it difficult to obtain an acceptable mitotic index needed for analysis [10, 127]. The alterations described in section 1.1.6 are considered valuable prognostic markers since their detection and the determination of the affected genes enables the development of specific personalized therapies [128-130]. For example, patients with del(13q) usually have a good prognosis whereas patients with del(17p) have poor prognosis and short survival. This knowledge supports the importance of a more precise genomic characterization of CLL diagnosed patients, in order to apply personalized therapeutic strategies and even to identify new risk patient groups and therapeutic targets. Some improvements have been achieved concerning medium

cultures, due to the addition of stimuli that lead to a higher mitotic index, allowing the detection of up to 40% of total alterations [10]. Yet, this remains far from satisfactory. Therefore, interphase nuclei are commonly studied by iFISH, enabling the detection of up to 80% of total alterations [49]. The iFISH panel for CLL nowadays used in clinical practice is based on the study from Döhner et al. [58]. These authors determined the main groups of alterations as independent factors for progression and survival assessment as being: normal karyotype, del(13q), trisomy 12, del(11q), and del(17p) [58]. More recently, additional probes were added to the panel, for both del(6q) and IGH translocations detection. This technique provides information concerning the percentage of nuclei with known chromosomal alterations that occur in a lower or higher percentage of B cells, also being informative regarding a better or worse outcome [35, 70, 83] and thus making iFISH the main form of risk stratification of CLL patients [35, 58]. Nevertheless, this approach is based on specific probes for the most common chromosomal alterations, which do not allow the detection of less frequent CLL genomic alterations [127]. Consequently, it underestimates the complexity of patients’ genomes [10]. Another limitation of the analyses using iFISH is its resolution, which is limited to the 150 to 900Kb range [131]. In the last decades, CGH has appeared and contributed to the characterization of the disease, based on copy number analysis. More recently, technologies based on genomic microarrays such as oligonucleotides and SNP microarrays have been developed, showing a higher-resolution on the detection of genomic losses and gains as well as in the determination of alterations’ size, in comparison to iFISH [35]. Both CGH and array Comparative Genomic Hybridization (aCGH) enable a genome wide analysis through whole genome targeting, differing from each other regarding the type of arrays used. In CGH, a reference sample and a test sample are marked with different fluorescent dyes and are subsequently hybridized to metaphase chromosomes from an healthy individual. By the measurement of the resulting fluorescence signal, it is possible to detect if CNVs are present and if they consist in gains or losses. However, this technique is limited by the resolution of metaphase chromosomes, that do not allow to detect alterations smaller than 3-10Mb [132, 133]. In order to overcome this limitation, the allying between CGH and the advances in microarrays technologies enabled the development of aCGH, which may detect genomic alterations with ~10 Kb or more [134]. DNA probes for microarray analysis can vary between cloned or synthetic DNA fragments: bacterial artificial chromosomes (BAC) clones or artificial DNA in form of oligonucleotides, or even genomic Polymerase Chain Reaction (PCR) products [135-137]. Comparisons between iFISH and microarray results have shown a very high concordance in various works (79-98%) [35, 79, 138]. In a comparison between iFISH and SNP microarray results, Zhang et al. [16] reached a concordance level of 98%. The 100% agreement is usually not reached since microarrays detect submicroscopic CNVs which are out of reach for iFISH because its probes are designed to target specific regions that may not be the exact CNV's locations. An example of this is given by Gunn et al. [79] that, using array analysis, detected 11q deletions that did not include the ATM gene, something that until then had not been noticed by the standard iFISH analysis. In this particular case, this deletion was found to be in a distal position relatively to the locus in which the standard iFISH probe hybridizes, a probe that was designed to hybridize in a location encompassing the ATM locus [79]. Also, the detection frequency of 6q deletions in O’Malley et al. [139] is another example: its detection rates rose when using array analysis rather than iFISH, also because iFISH probes did not target all the 6q altered region that, per se, is highly variable since a minimal deleted region has not yet been determined for this alteration [139]. It is expected that techniques such as New Generation Sequencing (NGS), SNP microarrays, Whole Genome Sequencing (WGS), MLPA, and aCGH become more frequently applied for a more detailed characterization and, in the future, as techniques for an efficient stratification of CLL patients [127]. An advantage of all these techniques in comparison to conventional cytogenetics is that there is no need for dividing cells for analysis [49]. Bearing in mind that iFISH is the technology applied for

genomic alterations’ assessment in clinical practice, it is important to remember that it has some difficulties in the detection of small CNVs. It also only detects common alterations for which there are targeting probes, whereas whole genome analysis for a large variety of possible altered targets gives information on a vast number of regions, as well as the location of breakpoints [16, 140]. The aCGH technology has led to the knowledge that humans, and other mammals, have a wider genetical complexity than previously thought since it enabled the identification of new benign heteromorphisms, mainly small CNVs such as deletions and amplifications [47, 141, 142]. In fact, the human healthy genome is abundantly composed by CNVs (approximately 4.8% of its totality) [59] and in several cases those CNVs are linked to disease developing events. The Database of Genomic Variants (DGV) most recent statistics refer that 95.33% of OMIM Morbid Map genes have been found overlapped by CNVs [143]. CNVs may either encompass protein coding genes or not. Either way, they may affect phenotypes by altering the expression levels of nearby genes, not integrated in the CNV [48]. Array CGH consists, undoubtedly, in a source of detailed information on CNVs regarding their size, genomic content, and breakpoints determination [128, 144-147]. With this said, the detection of new common alterations is beyond question a valuable discovery, and not knowing their role in CLL outcome makes them worthy to investigate as they might turn out to be suitable markers of disease [16].

1.2.1 Applying aCGH for copy number analysis

Array CGH is a powerful tool for the detection of structural imbalances such as deletions and duplications in a whole genome manner, with a high resolution performance [148]. In the study of genetic alterations in leukemia, this technology has already proven its value in the detection of CNVs [44]. To analyze a patient’s DNA sample using aCGH it is necessary to use a reference DNA sample in the same amount as the sample to be analyzed (if using a 180K microarray, no less than 500 ng). It is possible to use pooled male or female DNA from different individuals as a control, or even DNA from single healthy male or female individuals. The patient’s and control samples are differentially stained, in separate tubes, with two dyes: green cyanine 3 (Cy3), for the control, and red cyanine 5 (Cy5) for the patient (Figure 1.2a). Next, they are mixed and added to a glass slide containing an array with oligonucleotide probes with a size between 25-85mer, in which the sample and reference DNA competitively co-hybridize (Figure 1.2b) [149]. Subsequently, the slide is scanned by a microarray scanner, resulting in an imagine file in which are present the resulting fluorescence intensities from hybridized samples. Copy number analysis is conducted by means of a software used to detect and interpret signal intensities. The software measures the ratio between the intensities of both dyes, which gives the proportion of DNA copy numbers hybridized, for both samples. Target spots showing a green signal, corresponding to a lower Cy5:Cy3 ratio, indicate that the sample hybridized in a lower quantity than the reference sample, representing the presence of a deletion at that genomic region of the sample. A red signal indicates increased hybridization of the patient’s sample, linked to a higher Cy5:Cy3 ratio, thus indicating the presence of an amplification on the patient’s genome, in that region. If the signal is yellow, it is considered normal, meaning that both samples co-hybridized in the same amount, at those array spots, since they have the same DNA quantity (Fig. 1.2c) [149-151].

a) Control b) Hybridization with Cy3 slide probes

. Mix

c) Microarray Scan DNA sample Cy5

Deletion Amplification Normal Figure 1.2 (a-c) – Schematic representation of aCGH labelling: a) Staining of the control sample with fluorescent dye cyanine 3 (Cy3) and the DNA sample with cyanine 5 (Cy5), in separate tubes. Labelled control and DNA sample are mixed. b) The mix is added to the slide for competitive hybridization with complementary oligonucleotide probes. c) The scanning of the slide detects the intensity of the fluorescent signals, and the ratio Cy5:Cy3 gives the proportion of copy numbers of DNA samples in both samples. An imagine file shows target spots that may present to be: green, corresponding to a lower Cy5:Cy3 ratio, indicating that the sample hybridized in a lower quantity than the control sample (deletion at that genomic region of the sample); red, indicating the increased hybridization of the patient’s sample, linked to a higher Cy5:Cy3 ratio (amplification at that genomic region, on the patient’s sample); yellow, being considered normal, which means that both samples co-hybridized in the same amount, at those array spots, once they have the same DNA quantity [154-156].

1.2.2 Applying MLPA for copy number analysis

Another useful technology to study CNVs is MLPA, which is a semi-quantitative technique for copy number variation analysis, enabling the detection of chromosomal losses and gains. The main advantages of this technique are: 1) the need for a reduced minimal input of DNA for analysis (20 ng); 2) being low-priced, in comparison to techniques such as iFISH and aCGH; 3) High sensitivity and reproducibility; 4) the possibility of analyzing multiple samples and to quantify up to 60 different genomic targets simultaneously; 5) It is a fast technique, thus enabling rapid obtention of results [152, 153]. It uses probes to detect sequences with approximately sixty nucleotides, often enabling the detection of single-exon alterations [152]. Each probe is composed by two adjacent oligonucleotides (two hemi-probes), with 130-500nt of length each. After DNA sample’s denaturation, these hemi- probes hybridize with adjacent sequences in a ssDNA, being subsequently fused by a ligase (Figure 1.3a and b). After that, the amplification of ligated probes is done by PCR (Figure 1.3c). The reverse right probe oligonucleotide (RPO) has a stuffer sequence that gives a certain size to the probe (can be variable), but that does not hybridize [154]. Both hemi-probes have a primer, but only one of them is associated to a fluorochrome. The measurement of the fluorescence signal that it emits, when amplification occurs, is what allows the detection of the amplification products, during their separation, and consequently allows the detection of altered regions with a CNV, mutation, or SNP. This separation is done by capillarity electrophoresis and, after that, a proper software for data treatment constructs an electropherogram showing the height of the samples’ fluorescent peaks in comparison to the height of reference peaks (Figure 1.3d and e). The comparison between sample’s and reference’s peaks is informative of the number of copies of each target: a peak higher than normal relates to an amplification, and shorter than that is indicative of a deletion. Associated with these peaks, are the relative copy number (RCN) values. In case of no alterations being detected, a theoretical RCN value of 1.0 is expected, for a deletion a value of 0.5, and for a duplication a value of 1.5. However, these threshold values may vary. RCN values <0.5 are interpreted as referring to a biallelic deletion [153, 155]. This values result from the reason between 12

the areas of test sample’s peak and of the reference sample [68]. This is done with multiple reference samples obtained from healthy individuals that ought to be as much similar to the tested sample as possible, or also with a commercial DNA [156]. Also, the interpretations of reference probes for the evaluation of denaturation effectiveness (D fragments) and for the evaluation of sufficient DNA content (Q fragments) is done during result’s interpretation, for quality control purposes [156].

a) Probes hybridization b) Probes ligation c) Polymerase chain reaction

LPO RPO Ligase DNA Polymerase

Forward and reverse primers

ssDNA ssDNA Stuffer sequence Fluorescent dye

d) Capillarity electrophoresis system e) Electropherogram

Figure 1.3 (a-e) – Schematic representation of the preparation of a MLPA reaction: a) Two hemi-probes: LPO (left probe oligonucleotide) and RPO (right probe oligonucleotide) adjacently hybridized to a ssDNA. Both have a primer ligated, but only one has a fluorescent dye; b) Hemi-probes are ligated by a ligase, forming a complete probe; c) Polymerase chain reaction (PCR) of ligated probes occurs, leading to the amplification of DNA regions ligated to a MLPA probe; d) Amplified fragments are separated by a capillarity electrophoresis system; e) Fluorescence signals for each fragment are read and used to construct a electropherogram for result analysis [158-161].

In previous works, this technique’s results have been compared to iFISH results in order to prove its accuracy and importance as an alternative for CLL characterization [153, 157, 158]. These works have shown that MLPA is a valid and useful form of evaluating CLL sample’s genomic alterations since it identified almost every alteration found by iFISH. Apparently, its only inconvenient is the need for a minimum threshold of 20-30% altered cells of a certain clone to be able to detect that alteration, whilst iFISH detects alterations at a minimum of 5-10% of cells (low level mosaicism) [68, 159]. Compared to iFISH, MLPA has three additional advantages: there are several MLPA kits that evaluate more regions than the standard iFISH panel of probes used [160, 161], it is less cost-effective, and less time-consuming[44].

1.3. Objectives

The fact that CLL genomic lesions with clinical relevance are mainly chromosomal gains and losses makes CLL samples suitable for whole genome analysis by aCGH and MLPA [128]. In this context, the main goal of this work was to characterize the genome of CLL patients using aCGH and MLPA regarding their genomic alterations, and also to compare these techniques’ results, enabling their validation. Furthermore, and using aCGH, we also intended to search for additional prevalent alterations with possible value as biomarkers, in this disease. Moreover, we intend to show the value of aCGH and MLPA as alternative genomic characterization techniques to the conventional technique iFISH. It is expected that our findings stimulate future investigation and work on the characterization of CLL patients’ genome and novel disease biomarkers determination.

Chapter 2. Materials and methods

2.1 Selection and characterization of the study population

We have studied 20 CLL samples from two different services: CLL1 to CLL7 came from the Hospital Distrital da Figueira da Foz, and CLL8 to CLL20 samples came from the Departamento de Hematologia Clínica do Centro Hospitalar e Universitário de Coimbra (CHUC). Patients were diagnosed and classified according to the World Health Organization (WHO) 2016 classification and stratified according to both Rai and Binet staging systems. Patients’ samples were collected according to the Helsinki II Declaration and written informed consent were obtained from each participant prior to entering the study. The patients’ clinical data is presented in Table 2.1.

Table 2.1 – Clinical data obtained for the 20 CLL samples studied. Some samples did not have the information on these clinical parameters. (ND – Not Determined)

Age at Lymphocyte Age Binet B Sample Gender diagnosis Rai stage count (years) stage symptoms (years) (x106/ml) CLL1 M 59 58 C IV Yes 60.61 CLL2 F 83 81 A II No 7.25 CLL3 F 85 75 A 0 No 22.02 CLL4 M 83 76 A 0 No 13.59 CLL5 M 75 58 A 0 No 5.95 CLL6 M 78 70 A 0 No 67.3 CLL7 M 76 64 A 0 No 11.3 CLL8 F 74 74 A 0 No 31 CLL9 F 59 59 A ND No 7.92 CLL10 M 84 84 A 0 No 11.77 CLL11 M 83 83 C III No 45 CLL12 F 94 90 A 0 No 15.82 CLL13 M ------CLL14 F 75 72 A 0 No 65.73 CLL15 F 45 44 A ND No 4.3 CLL16 M ------CLL17 F 78 78 A II No 6.7 CLL18 F 68 50 C III No 68.1 CLL19 M 80 68 A 0 No 9.72 CLL20 F 75 74 A 0 No 19.6

For all 20 patients studied, we did not manage to get all the clinical information needed for a complete genotype/phenotype correlation. We were only able to obtain the full information on the sex and B symptoms. The data on the remaining characteristics presented in Table 2.1 were based upon a smaller number of patients: age, age at diagnosis, Binet stage, and lymphocyte count were not available for two samples (CLL13 and CLL16) and Rai stage was not available for four samples (CLL9, CLL13, CLL15, and CLL16). Among the 18 patients whose follow-up time is known, 12 have been followed for less than 41 months (approximately 3.5 years) and the rest has been followed between 87 and 197 months. The maximum follow-up time of a patient in this cohort (sample CLL5)

was of 197 months (approximately 16 years), but the total follow-up time of the study was of 209 months. Until now, most patients have only been followed, at the clinic, for a reduced period. Samples from the Medicine Service of Hospital Distrital da Figueira da Foz were collected, at the time of diagnosis, from patients diagnosed long before the others, being that we have used their cells that were being kept frozen in DMSO. Due to the reduced time of study of most samples, the information on survival is scarce. Until this date we are aware of only 2 patients (10%) deceased.

2.2 Samples’ collection and preparation

2.2.1 PBMCs separation

Peripheral blood samples were collected into EDTA tubes. To obtain lymphocytes B for analysis, PBMCs were separated by ficoll density gradient centrifugation using Ficoll-Paque Plus GE Healthcare, 17-1440-02 (GE Healthcare Bio-Sciences AB), according to the manufacturer’s instructions [162]. The resulting layers of this centrifugation are represented on the image bellow (Figure 2.1).

PBMCs layer

A B

Figure 2.1 (A and B) - Result of a density gradient centrifugation, using Ficoll-Paque. A - Photography of the density gradient centrifugation of one of our samples, using Ficoll-Paque. B - Representation of the states before and after a density gradient centrifugation with Ficoll-Paque. At the endpoint, different layers with different content are organized according to their density. The PBMCs layer is located between the ficoll layer and the plasma layer. (Adapted from “Isolation of mononuclear cells from human peripheral blood by density gradient centrifugation” (Miltenyi Biotec GmbH Germany) [163]).

Initial whole-blood cell counts of our samples were determined using the hematology analyzer Hema Screen 18 LIH D170K (HOSPITEX DIAGNOSTICS, Italy). Sample were diluted, according to their final whole-blood cell counts. Knowing final cell density was important for the determination of the approximate number of cells needed for DNA extraction.

2.2.2 DNA extraction and purity determination

DNA was extracted from PBMCs using High Pure PCR Template Purification Kit (Roche Diagnostics GmbH, Mannheim, Germany), adapted from manufacturer’s instructions [164]. It was necessary to optimize the protocol, due to the high number of sample’s PBMCs. The optimization consisted on increasing the incubation time from 10 to 15 minutes and increasing the volume of Proteinase K added from 40µl to 50 µl, to ensure optimal cell lysis. We have also decided not to use the kit’s elution buffer, and instead use elution buffer JETquick Blood DNA 10mM Tris-GI

(GENOMED). Since this kit has an upper limit for extraction, we used no more than around 10 million PBMCs for DNA extraction. DNA concentration and purity (260/280 and 260/230 ratios) were quantified by UV spectrophotometric analysis using a NanoDrop 1000 spectrophotometer and software (Thermo Scientific, Wilmington, USA). After extraction and quantification, DNA samples were kept at 4⁰C.

2.3 Genomic analysis by aCGH and MLPA

The genomic characterization was done by aCGH and MLPA. Both technologies allowed us to search for genomic imbalances in a whole-genome manner and targeted approach, respectively. For aCGH, an oligoarray comparative genomic hybridization (oligoarray-CGH) analysis was performed using Agilent SurePrint G3 Human Genome microarray 180K (Agilent Technologies, Santa Clara, CA), according to the manufacturer’s instructions [165]. This is an oligonucleotide microarray containing approximately 180,000 sixty-mer probes with a 17Kb average probe spacing. Reference male and female DNA were also obtained from Agilent. Sample labelling was performed using Agilent Genomic DNA enzymatic labelling kit (Agilent), with the patient’s DNA labelled in red cyanine 5 (Cy5) and the controls DNA labelled in green cyanine 3 (Cy3). After hybridization, the oligoarray-CGH slide was scanned on an Agilent scanner, processed with Feature Extraction (v10.7.3.1) software (Agilent Technologies) [166]. Our results were analysed using CytoGenomics (v2.9.2.4) software (Agilent Technologies) and by Agilent Genomic Workbench (v6.5) software (Agilent Technologies). Agilent Genomic Workbench had the following settings: ADM2 as aberration algorithm, threshold of 6.0, moving average 2 Mb [167]. For MLPA analysis, the CLL specific sets of probes SALSA MLPA probemix-B1 P037 / probemix-B1 P038 (MRC Holland Amsterdam, The Netherlands) were used, and MLPA was done according to manufacturer’s instructions [156]. With 54 and 51 target probes respectively, probemix- B1 P037 and probemix-B1 P038 target common alterations in CLL, including: 2p gains, 6q deletions, 8p loss and 8q gain, 9p21 deletion, PTEN deletion at 10q23.31, 11q deletion, trisomy 12, 13q14 deletion, 14q deletion, 17p deletion, and trisomy 19. Moreover, probemix-B-1 P038 contains probes for three common somatic mutations in CLL: NOTCH1 mutation c.7541-7542delCT, MYD88 mutation c.794T>C, and SF3B1 mutation c.2098A>G. Also, 9 control probes (Q-fragments, D- fragments, and X and Y fragments) are included in these kits, to assess sample’s DNA quantity, DNA correct denaturation, and sample gender [160]. Reactions were carried out in a thermal cycler equipped with a heat lid (ABI 2720, Applied Biosystems, Foster City, CA, USA). Amplification products analysis was done by capillarity electrophoresis system using a 3500 Series Genetic Analyser (Applied Biosystems, Foster City, CA, USA). Results analysis was carried out using the Coffalyser software v.140721.1958 (MRC Holland Amsterdam, The Netherlands). We considered RCN values <0.8 as deletion events, and RCN values >1.2 as duplication events. All samples were submitted to aCGH analysis, of which all but five (CLL5, CLL6, CLL8, CLL10, and CLL14) were also analysed by MLPA. These two techniques were used to study the same samples to validate results and compare possible differences, enabling us to make a comparison between their sensitivity and resolution.

2.4 Interpretation of aCGH results

Genomic alterations found by aCGH were pinpointed by means of a schematic image of the human genome, an ideogram with every chromosome having a resolution of 850 bands, and according to the GRCh37/human genome 19. Every sample was given a certain colour, and all the corresponding

alterations were represented by that colour. This enabled us to rapidly point out common alterations between samples. Among common alterations between a group of samples, we identified the minimal common region and searched for their genomic content with the UCSC Genome Browser (University of California Santa Cruz) [168]. In this online platform, we searched for genomic regions according to GRCh37/human genome 19. For every minimal common region, we searched for: 1) gene content; 2) ClinGen information on benign and pathological losses and gains [169]; 3) chromosomal imbalances and phenotypes from DECIPHER platform [170]; 4) OMIM genes; and 5) OMIM phenotypes. We have then selected some of the genes located at the altered regions for further discussion about their influence on CLL phenotype, bearing in mind their biological roles.

2.5 Statistical analysis

For assessment of the prognostic value of alterations found in our samples, we used Pearson’s chi- squared (χ2) test. For this, patients were grouped according to their risk stratification given by Binet and Rai staging systems information (Table 2.1). We have created the groups “Low risk” and “High risk” for patients with Binet stage A and Binet stage C, respectively, and the groups “Low risk” and “Intermediate/High risk” for patients with Rai stage 0 and I and Rai stages II to IV, respectively. The null hypothesis is that there is a non-association between having an alteration and the risk category where a patient was placed in the staging system (Binet or Rai). The null hypothesis was rejected whenever p<0.05, which was considered indicative of the predictive value of the alteration. SPSS software was used to conduct survival analysis of patients with <2 alterations or ≥2 alterations detected by MLPA, using the Kaplan-Meier statistical method and log-rank test to determine results’ significance. We have tested the null hypothesis that survival curves of patients who have different numbers of alterations are similar. Results with a value of p<0.05 were considered statistically significant.

Chapter 3. Results and discussion

3.1. General findings by aCGH and MLPA

Using the aCGH for the analysis of 20 CLL samples, we have found the following reported alterations in CLL: del(14q) (35.0%, N=7); del(13q) (35.0%, N=7), trisomy 12 (30.0%, N=6), del(6q) (25.0%, N=5), del(8p) (15.0%, N=3), del(11q) (10.0%, N=2), dup(8q) (10.0%, N=2), and dup(2p) (5.0%, N=1), all of them already described in the literature as common CLL alterations (Figure 3.1) [58, 138, 171]. However, the prevalence in which some of these alterations occur in our samples is not the same as the prevalence reported in the literature: del(13q) appears in less cases than it was expected and trisomy 12, del(6q), del(8p), dup(8q), and del(14q) appear in more cases than expected [49]. Moreover, we have found an alteration considered very infrequent in CLL: del(9p21.3) (10.0%, N=2) [172].

Prevalence of reported CLL alterations found by aCGH 50% 45% 40% 35% 30% 25%

20%

15% 10% 5% 0% del(14q) del(13q) Trisomy 12 del(6q) del(8p) del(11q) del(9p) dup(8q) dup(2p) Figure 3.1 – Prevalence of reported CLL alterations found by aCGH. These values refer to the 20 samples studied by aCGH, being del(14q) and del(13q) the most common alterations, appearing in 35.0% of cases (N=7), and dup(2p) the least frequent, appearing in 2% of samples (N=1).

Fifteen samples were analysed by MLPA: CLL1 to CLL4, CLL7, CLL9, CLL11 to CLL13, and CLL15 to CLL20. A total of 17 different alterations were detected in these samples. Of the reported CLL alterations, we have found: del(13q) (46.6%, N=7), trisomy 12 (33.3%, N=5), del(14q) (13.3%, N=2), del(11q) (13.3%, N=2), del(6q) (13.3%, N=2), del(8p) (6.6%, N=1), dup(8q) (6.6%, N=1), and homozygous del(9p) (6.6%, N=1). Furthermore, we have identified cases with the mutation c.7541- 7542delCT on NOTCH1 gene (20.0% N=3) (Figure 3.2).

Prevalence of reported CLL alterations detected by MLPA 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% del(13q) Trisomy 12 NOTCH1NOTCH1 del(14q) del(11q) del(6q) del(8p) dup(8q) del(9p) mutationmuta tion Figure 3.2 - Prevalence of reported CLL alterations found by MLPA. These values refer to the 15 samples studied by MLPA, being del(13q) the most common alteration, appearing in 46.6% of cases (N=7), and del(8p), dup(8q), and del(9p) the least frequent, all three appearing in 6.6% of samples (N=1). 18

The prevalence obtained for del(13q) (46.6%) and for del(11q) (13.3%) are close to the values described in the literature, as well as the prevalence values of the other alterations found, except for del(2p24.3) and dup(11q22.3), two alterations that are not characteristic of CLL [49]. Alterations found by MLPA are listed in Table 3.1.

Table 3.1 - Alterations found by MLPA in CLL samples studied. (*Alterations appearing in mosaicism / ** Homozygous alterations bp – Base pairs / del – Deletion / dup – Duplication). Samples Alterations Start End Size (bp)

CLL1 del(11q22.3-q23.3) 108093834 115375205 7,281,372 del(2p24.3) 16082198 16082260 63 del(8p21.3) 22886035 23082538 196,504 dup(8q24.21) 128752966 128753189 224 CLL2 del(9p21.3) ** 21971213 21971280 68 del(11q22.3) 108093834 110108333 2,014,500 del(13q14.2) 49705394 49705460 67 del(6q21)* 106968853 108214881 1,246,029 CLL3 Trisomy 12 - - - CLL4 Trisomy 12 - - - Trisomy 12 - - - CLL7 del(13q14.2-q14.3)* 50589590 51417417 827,828 del(14q32.33) 106311852 106312206 355 CLL9 del(13q14.2-q14.3) 50656226 50884429 228,204 NOTCH1 c.7541-7542delCT * - - - CLL11 Trisomy 12 - - - del(14q32.33) 106311852 106312206 355 CLL12 Trisomy 12 - - - dup(11q22.3) 108093834 108093897 74 CLL13 del(13q14.2-q14.3)* 49037660 51417417 2,379,758 CLL15 NOTCH1 c.7541-7542delCT * - - - CLL16 - - - - CLL17 NOTCH1 c.7541-7542delCT * - - - CLL18 del(13q14.2-q14.3) 50556747 51287084 730,338 del(6q21)* 106968853 108214881 1,246,029 CLL19 del(13q14.2-q14.3) 50556747 51417417 860,671 CLL20 del(13q14.2-q14.3) ** 50589590 51417417 827, 828

3.2 aCGH results

We were able to detect a total of 134 copy number alterations, with an average of 6.7 CNVs/sample (Figure 3.3). Similarly to previous CLL studies using aCGH, the percentage of losses was higher than the percentage of gains: 65.0% of losses (N=87) and 35.0% of gains (N=47), resulting in an average of 4.35 losses/sample and of 2.35 gains/sample [67, 172-174].

left side of

Red boxes highlight the eight most frequent

alterations found by accordingaCGH to the GRCh37/human genome 19. are Losses positioned on the the

s s alterations have the same colour, according to the colour code presented above.

CLL: dup(4p16.3), del(5q13.2), dup(6p25.3), dup(8p11.1), del(8q24.23), dup(10q11.22), del(22q11.22), and del(Xq21.1). dup(10q11.22), del(22q11.22), and del(8q24.23), dup(8p11.1), del(5q13.2), dup(6p25.3), dup(4p16.3), CLL:

’

with unknown prognostic value in prognostic unknown with

ideogram resolution of the human karyotype with -

850 bands

have we found,

each each chromosome and gains on the right side. Each sample alterations Figure 3.

3.2.1 Chromosome 13q deletions

Using aCGH, we have found 13q deletions in seven samples (35.0%), representing one of the two most common alterations, as expected [49]. Six of these seven samples presented a heterozygous deletion, which is the most common form described [10]. These alteration presented variable breakpoints, something already reported in previous works [62, 68]. The sample CLL20 presented a homozygous 13q deletion of 912,811 bp. As presented at Table 6.1 (in annex), we can conclude that two samples (CLL13, CLL14) have a type II del(13q) since they are >2Mb: 2.7Mb and 18.2Mb, respectively. Only in CLL14 occurred the complete deletion of the RB1 gene, being only partially deleted in CLL13. Figure 3.2A shows the chromosome view obtained for the del(13q) of CLL14, extracted by the aCGH results’ analysis software used.

del(13q) A Trisomy 12 B del(11q) C CLL14 CLL12 CLL1

D E 1 F

del(8p) dup(2p) del(6q) dup(8q) 2 CLL12 CLL3 CLL2 Figure 3.4 (A(A--F) – Chromosome view of characteristic CLL genomic alterations we have found in samples, using aCGH. ImagesImages extractedextracted byby CCytoGenomicsytoGenomics software. A - 13q deletion found in sample CLL14 [del(13q14.13q21.31)]; B - Trisomy 12 of sample CLL12; C - 11q deletion of sample CLL1 [del(11q21q23.3)]; D – 2p duplication found in CLL12 [2p25.3]; E – 6q deletiondeletion inin sample sample CLL3 CLL3 [del(6q16.1q22.31)]; [del(6q16.1q22.31)]; F1 F1 – 8p– 8pdeletion deletion (given (given by the by topthe red top dots) red dots)and F2 and - 8q F2 duplication - 8q duplication (given (givenby the bottomby the bottom blue dots) blue in dots) sample in sample CLL2 [del(8p23.3p2CLL2 [del(8p23.3p21.3)1.3) and dup(8q24 and dup(8q24.13q24.3)]..13q24.3)].

As for the miRNA15a/miRNA16-1 locus, it is completely lost in every sample except for CLL9. The minimal common region deleted was determined to be between nucleotide positions 50,640,433 and 51,337,792, with a total size of ~700Kb, which is contained in the minimal deleted region reported in the literature for this alteration [10].

3.2.2 Trisomy 12

Trisomy 12 was detected in six samples (30.0%), consisting in the third most common alteration. Sample CLL3 has three other common alterations in CLL: dup(8q11.23), del(6q16.1q22.31), and del(14q32.33); CLL4 presents two deletions in 6q: del(6q12) and del(6q16.1); CLL7 has a

del(13q14.2q14.3) (an alteration that has previously been seen simultaneously with trisomy 12) [35, 58]; CLL11 has a del(14q32.33); and CLL12 has a dup(2p25.3). Of note, 14q deletions are, alike del(13q), commonly found in trisomy 12 cases [35, 58]. Figure 3.4B shows the chromosome view obtained for trisomy 12 in CLL12, being representative of all other cases of trisomy 12 mentioned.

3.2.3 Chromosome 11q deletions

Of all the common alterations found in our samples, del(11q) is the one usually linked to the worst prognosis. We were able to find this deletion in two samples (CLL1 and CLL2), showing a prevalence of 10.0%. The minimal common region deleted is located between chr11:102,239,689-111,137,249, with ~8.8Mb. This alteration usually has more than 20 Mb [10, 78]. In fact, we have found a del(11q) with ~19 Mb in CLL1 (Figure 3.4C). However, the minimal common region is located at 11q22.2q23.1, and is very similar to the literature’s described minimal deleted region for this alteration (11q22.3q23.1) [10, 78]. In both samples, the alteration is very large, encompassing a series of genes with important roles in cell’s homeostasis. Among them, we highlight the following genes from the minimal common region: the anti-apoptotic gene BIRC2 (involved in the NF-kB signaling pathway) [175], the tumor suppressor gene ATM [81], and CASP1, CASP4, CASP5, and CASP12, for their known role in the apoptotic pathway [176]. Both breakpoints from the minimal common region are in an intragenic region: the 102,239,689 breakpoint is on the BIRC2 gene, and 111,137,249 breakpoint on the C11orf53 gene. The fact that the ATM gene is deleted in both CLL1 and CLL2 suggests that these patients are prone to accumulate more alterations over time, due to the impairment of this gene’s DNA damage repair and double-strand breaks restoration functions. We could in fact see that these two samples had an amount of alterations above the average, per sample (11 in CLL1 and 9 in CLL2, in comparison to an average of 6.7 CNVs/sample) (Table 6.1 in annex). This is in agreement with what Marasca et al. [83] described concerning the existence of an association between del(11q) occurrence and genomic instability. Qui-squared test for the evaluation of del(11q) prognosis value was conducted based on the information presented on Table 3.2.

Table 3.2 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of del(11q).

Rai stage del(11q) Low Risk Intermediate/High Risk Total Present 0 2 2 Absent 11 3 14 Total 11 5 16

In terms of prognostic value of this alteration, we have obtained a significant result suggesting its value as a marker for bad prognosis (p=0.025), as it only occurs in Rai intermediate/high risk patients. This result is in agreement with the prognostic value linked to this deletion [58].

3.2.4 Deletions at 6q, 9p, 14q, losses at 8p, and 2p and 8q gains

Only sample CLL12 had a 2p gain (Figure 3.4D). This alteration is located at the 2p25.3 region, between nucleotide positions 17,019 and 69,989 and with a size of ~53Kb. This region contains FAM110C, a gene that encodes for a protein expressed in interphase microtubules, centrosomes, and the mitotic spindle. A previous study by Hauge et al. [177] has shown that overexpression of this gene leads to aberrant microtubules and to G1 cell cycle arrest [177].

Five different deletions at 6q were found: del(6q16.1q22.31), del(6q12), del(6q16.1), del(6q13.3q16.3), and del(6q16.3q22.31) (Table 6.1 in annex). There is no minimal common region between these alterations. This was expected since other authors have referred the difficulty in determining a minimal deleted region for del(6q) [35, 96]. Edelmann et al. [102] identified a del(6q21) with 2.5Mb. The deletions del(6q16.1q22.31) in CLL3, and del(6q16.3q22.31) in CLL19, are two alterations that encompass the deleted region found by Edelmann et al. [102] (Figure 3.4E). In chromosome 8, we have identified three 8p losses. It is common to find the coexistence of del(8p) and dup(8q) and, in fact, we were able to identify a sample (CLL2) with that feature (Figure 3.4F1 and F2) [96]. The alterations del(8p23.3p21.3) and dup(8q24.13q24.3) are both >20Mb. Among the genes deleted in del(8p23.3p21.3), DLC1 and PDGFRL have already been seen involved in some type of cancer [178, 179]. As for this sample’s duplication at 8q, it contains the MYC gene, known for its involvement in many cancers as a proto-oncogene, and involved in CLL progression [180]. The deletions found in 9p21.3 were identified in samples CLL2 and CLL5. However, in no case the CDKN2A and CDKN2B genes, both located at 9p21.3 and commonly found deleted in CLL and other types of cancer, were found deleted [172]. In CLL2, del(9p21.3) occurs in homozygosity, leading to partial deletion of the housekeeping gene MTAP [181]. Homozygous deletions of this gene have already been described in gastrointestinal stromal tumor cases as an independent predictor of worse outcome and as a marker of disease progression by Huang et al. [182]. Five different 14q deletions were detected in our samples, in a total of seven samples (35.0%) presenting different breakpoints from each other, except for del(14q21.1) found in CLL5 and CLL7 (Table 6.1 in annex). We could not determine a minimal common region since there was no overlapping between them. CLL3, CLL11, and CLL18 had 14q32.33 deletion, a region found deleted with high prevalence in Gunn et al. [138]. These authors found del(14q32.33) in 54% of cases. However, they have suggested this high prevalence to be due not only to some CLL clones, but also to the occurrence of translocations that may have led to the loss of genomic material at the region of the break [138]. Also, it is a known polymorphic region, possibly presenting CNVs even in phenotypically normal individual [138, 183]. Qui-squared test for the evaluation of del(14q) prognosis value was conducted based on the information presented on Table 3.3.

Table 3.3 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of del(14q).

Rai stage risk del(14q) Low Intermediate/High Total Present 3 4 7 Absent 8 1 9 Total 11 5 16

Until now, the prognostic value of this alteration is controversial. We suggest that this alteration might be a marker of bad prognosis due to the result obtained after applying the qui-squared statistical test (p=0.049).

3.2.5 Other common CNVs found by aCGH

In addition to the alterations with known prognostic value that we have described in the previous sections, we have found eight other common alterations in our samples: del(5q13.2), del(8q24.23), del(22q11.22), del(Xq21.1), dup(4p16.3), dup(6p25.3), dup(8p11.1), and dup(10q11.22). The mechanism by which these alterations seem to appear is nonallelic homologous recombination

(NAHR) that leads to an unequal homologous recombination between chromatids. It consists in a cross-over between paralogous chromatids, in regions that, are characterized by the presence of variable low-copy repeats (LCR) sequences that present high homology [184]. Further details are provided next, regarding the eight additional alterations we have found.

3.2.5.1 dup(4p16.3)

The duplication in 4p16.3 was found in 25.0% of samples (N=5). In every case, the start and end breakpoints are the same, and the region duplicated has a size of ~32Kb (Table 6.1 in annex). We could not find any known relation between 4p16.3 and cancerous events. This region contains genes ZNF595 and ZNF718, both being only partially duplicated. ZNF595 is known to be involved in transcription regulation events, and it is thought that ZNF718 is also involved in such events [185, 186]. Hence, the fact that only a part of these two genes is duplicated, suggests that their transcription leads to the formation of deficient zinc-finger proteins, consequently leading to their altered function as transcription factors.

3.2.5.2 del(5q13.2)

The del(5q13.2) was found with a prevalence of 45.0% (N=9). The minimal deleted region is located between positions 69,925,286 and 70,388,844, with ~463 Kb, encompassing five protein coding genes: SMN1, SMN2, GTF2H2, SERF1A, and NAIP (Figure 3.5). To the exception of SERF1A, the other four genes encode for proteins whose function in cells is known [187]. We highlight SMN1, SMN2, and GTF2H2 for being involved in DNA transcription regulation. SMN1 and SMN2 also have an important role in the termination of transcription events, more exactly in the separation of the hybrid DNA-RNA [188].

CLL1 CLL2 CLL3 CLL4 CLL5 CLL9 CLL11 CLL13 CLL16

69,925,286 70,388,844 Figure 3.5 – Deletions detected at 5q, using aCGH. In this cohort, 9 samples presented a del(5q) (45.0%). The minimal deleted region, corresponding to the region between CLL11 start breakpoint and CLL9 end breakpoint, is demarcated by red dashed lines.

Previous studies have also found deletions in the long arm of chromosome 5 and have subsequently proposed this as a novel recurrent deletion in CLL. Rodríguez et al. [174] found a significant association between the presence of del(13q) and both del(5q13.3q14.1) and del(5q31.1). Gunn et al. [138] found three cases with del(5q14.1q21.3), all of them occurring simultaneously with del(13q), and one of them also with del(6q) and del(11q). On the other hand, Karakosta et al. [189] have studied two patients with interstitial deletions in 5q, (del(5q15q31) and del(5q33q35)), as the only CNVs present, and have suggested their role in CLL pathogenicity. A follow-up study of those patients was

conducted, and no new significant clinical alterations have been noticed, suggesting that del(5q) is associated with a good prognosis [189]. Altogether, the previous findings and our results suggest this alteration may be a novel disease marker to have in consideration in the future.

3.2.5.3 dup(6p25.3)

In the case of dup(6p25.3), the prevalence was of 35.0%2 (N=7). However, two of these samples (CLL15 and CLL19) had duplications covering regions in a proximal position to the duplicated region of the other five samples (Table 6.1 in annex). We have identified the minimal common region to those five samples to be chr6:274,653-293,493, a region in which the DUSP22 gene is partially included. Amongst its functions are: activation of the JNK signaling pathway and regulation of cell proliferation and apoptosis. It is also known to be involved in the development of lymphatic system cancer [190, 191]. With this alteration, and in every case, only a part of the DUSP22 gene is being duplicated, possibly leading to a malformation of the resulting protein and to the impairment of both cell proliferation and apoptosis functions, as well as of the JNK signaling pathway.

3.2.5.4 del(22q11.22)

We have found this alteration in 30.0% of our samples (N=6) (Table 6.1 in annex). The minimal common region deleted is located between positions 23,056,562 and 23,056,621, presenting a total size of 60 bp (Figure 3.6). This region was determined bearing in mind the six heterozygous deletions found, each present in a different sample. However, we also found an additional homozygous del(22q11.22) in sample CLL16 (Table 6.1 in annex). This deletion encompasses a region that is common to the 22q deletions found in four other samples (CLL1, CLL4, CLL5, CLL13): chr22:23,158,685-23,228,869. In fact, they even share the exact same end breakpoint: nucleotide position 23,228,869. In this interval is located the MIR650 gene, known to have a significant influence in CLL phenotype due to its role in B cell proliferation, by regulating the expression of genes such as EBF3, ING4, and CDK1 [192]. Considering that it is deleted in these samples, and also bearing in mind a previous study by Mraz et al. [192] showing a favourable prognosis for patients with elevated MIR650 expression, we suggest this deletion’s value as a possible CLL marker. Further studies are needed to confirm this.

CLL1 CLL4 CLL5 CLL13

CLL16 ** CLL19

23,056,562 23,056,621

Figure 3.6 – Deletions detected at 22q, using aCGH. In this cohort, 6 samples presented a del(22q) (30.0%). The minimal deleted region, corresponding to the region between CLL5 start breakpoint and CLL19 end breakpoint, is demarcated by red dashed lines. (** Homozygous deletion).

Four of these six samples: CLL1, CLL4, CLL 16 and CLL19, have some protein coding genes deleted. Of those, we highlight the PRAME, ZNF280A, ZNF280B, and GGTLC2 genes. Gunn et al.

[193] have also found this deletion, but in their case with a prevalence of 15%. In their minimal common region, they found these four genes deleted, and subsequent quantitative real-time polymerase chain reaction (qPCR) studies for gene expression have shown that PRAME, ZNF280A, and ZNF280B suffer significant alterations in their expression pattern in CLL patients with del(22q11.22) [194, 195]. More recently, a work on hepatocellular carcinoma using a mouse model has shown that PRAME’s downregulation induces the p53 pathway to promote apoptosis [196]. If it ought to be seen in CLL, it would suggest that PRAME’s deletion may be beneficial for CLL patients, due to the possible induction of clonal cells’ apoptosis. As for ZNF280A and ZNF280B, these are genes possibly involved in transcriptional regulation, and their absence suggests defaults in that function [193].

3.2.5.5 dup(8p11.1), del(8q24.23), dup(10q11.22), and del(Xq21.1)

The del(8q24.23) always appeared with the same exact breakpoints, occurring in 20.0% of samples (N=4), and with a deleted segment of ~157Kb; dup(8p11.1) occurred in 15.0% of samples (N=3), with a minimal common region of ~10Kb, and dup(10q11.22), also with a prevalence of 15.0% (N=3) had a minimal common region of ~200Kb. At last, del(Xq21.1), also appearing in 15.0% of samples (N=3), presented a size of ~1Kb (Table 6.1 in annex). We have decided to group these alterations because, although occurring in a relatively high prevalence, they are small and do not cover any intragenic or intergenic region with known involvement in neoplastic events. Moreover, DECIPHER entries for patients with these alterations were all classified as “likely benign” or with “unknown clinical significance”, according to the DGV CNVs classification [197]. This suggests that these alterations might either consist in common and benign polymorphisms or in susceptibility CNVs for CLL. However, at this point, no further conclusions can be drawn due to our limited sample size.

3.3 MLPA results

3.3.1 Chromosomes 11q and 13q deletions, and Trisomy 12

Deletions at 11q were detected in two samples (CLL1 and CLL2) (13.3%). In both, the deleted regions were larger than >2 Mb. In the seven samples with del(13q) (46.6%), detected by MLPA, six had a del(13q14.2-q14.3) and one a del(13q14.2). Two samples (CLL7 and CLL20) presented equal breakpoints (Table 3.2). In six cases, these deletions are <2Mb and do not encompass the RB1 gene, leading to their classification as type I del(13q). CLL13 has been classified as type II del(13q) once it is >2MB and also encompasses a part of the RB1 gene. Also, to the exception of samples CLL2 and CLL9, samples with this alteration have the miRNA15a/16-1 locus deleted. We have identified trisomy 12 in 5 out of 15 samples analysed by MLPA (33.3%) (Table 3.2). Those 5 samples were also analysed by aCGH, with which we have also detected these trisomy 12 cases. This shows that both techniques are equally suitable for detecting this alteration.

3.3.2 Losses at 6q, 9p, 14q, losses at 8p, gains at 8q, and NOTCH1 mutation

In addition to the above referred three common alterations found by MLPA, we have also found other CLL typical alterations in our samples: del(8p21.3), dup(8q24.21), and del(9p21.3) in CLL2, del(6q21) in CLL3 and CLL19, del(14q32.33) in CLL7 and CLL11, and the mutation c.7541-

7542delCT, in NOTCH1, in CLL11, CLL15, and CLL17. Qui-squared test for the evaluation of NOTCH1 mutation prognosis value was conducted based on the information presented on Table 3.4.

Table 3.4 – Contingency table in which the qui-squared test was based on, for the evaluation of the prognosis value of NOTCH1 mutation.

Rai stage NOTCH1 mutation Low Risk Intermediate/High Risk Total Present 0 2 2 Absent 11 3 14 Total 11 5 16

This mutation occurred only in Rai intermediate/high risk patients. This result suggests that this mutation is a marker of poor outcome in CLL, something already known about its prognostic value (p=0.025) [35].

3.4 Comparison between aCGH and MLPA results

The del(13q14.2) found by MLPA in CLL2, and del(14q32.33) in CLL7, were not detected by aCGH. The MLPA probes giving these results hybridized in regions for which there were no aCGH probes available. This is an understandable result as it is already known that the aCGH technology, despite of being a whole-genome technique, does not cover every single region of the genome due to the space in between probes, which is of approximately 17Kb [166]. Every alteration detected by both techniques presented a smaller size when identified by MLPA than when identified by aCGH. In fact, previous works (e.g. Stevens-Kroef et al. [68]) have shown different breakpoints for the same alterations after analysing the same samples with both MLPA and aCGH. These differences were due to the inexistence of MLPA probes for the total spanning of the deleted/amplified regions. It does not seem to be problematic since MLPA probes used enable the detection of the most commonly altered positions contained in the common altered regions in CLL. The advantage of using aCGH, in those cases, was that it allowed to redefine the size of alterations, being more precise in breakpoint’s determination, thus being more sensitive than MLPA. We can also conclude that MLPA enabled the validation of aCGH results. One advantage we had in applying these commercially available MLPA probe kits was the possibility to search for mutations in three recurrently mutated genes in CLL: NOTCH1, SF3B1, and MYD88. This enabled us to detect the NOTCH1 c.7541-7542delCT mutation in samples CLL11, CLL15, and CLL17, even though only in a small quantity of clones: 25%, 8%, and 13%, respectively. Figure 3.7 presents the electropherogram obtained for CLL15, showing the existence of this mutation. The prevalence of this alteration in our samples, was 20% being almost two times the 10.0% value reported in the literature [49].

CLL15 Electropherogram

Figure 3.7 - Electropherogram for sample CLL15. This figure shows the height of the resulting fluorescent peaks for every probe tested with the MLPA kit P038, in relative fluorescent units (RFU). NOTCH1 gene was found mutated in 8% of cells (red arrow).

3.5 Survival analysis for patients with two or more alterations

The results obtained for this analysis seem to suggest that having 2≤ alterations leads to a reduced OS. The survival curve obtained is presented in Figure 3.8. Presenting karyotypic complexity (three or more alterations in the same sample) could be linked to an aggressive course of disease and reduced survival [56]. However, this result is not statistically supported, as the null hypothesis was not rejected (p=0.106). This is probably caused by the small sample size and the short and heterogeneous time of follow-up, that had a median duration of 114 months (most patients were only followed for a short period of time). It is therefore necessary to study more patients and to extend the time of follow-up, and then it will eventually be possible to state convincingly that the increasing number of alterations appears as a marker of bad prognosis and reduced OS in CLL.

MLPA  2 alterations

MLPAMLPA  22≤ alterations alterations

Cumulative Survival

p = 0.106

Figure 3.8 – Survival curve obtained by the Kaplan-Meier method for OS analysis. This curve suggests an association between the presence of 2≤ alterations and a decrease on the OS of patient. The group with <2 alterations has 11 patients of which none have died. The group with ≥2 alterations has 4 patients, of which 2 are deceased.

Chapter 4. Conclusions and future perspectives

Both techniques applied proved to be suitable for CLL genomic characterization of CNVs content since they have identified several reported CLL alterations. The aCGH has detected several other CNVs that complement the characterization of the samples we have studied. MLPA had the additional advantage of detecting some reported CLL somatic mutations. Applying aCGH, we have proved that these patients have a lot of additional CNVs on their B lymphocytes, that are not detected by the standard diagnosis technique iFISH. Some of the additional alterations were more frequent than others. However, they are classified by DGV as having “unknown clinical significance”, which means that they might either be polymorphic variants with no influence in this malignancy or susceptibility variants. To assess these alterations’ linkage to CLL as possible disease biomarkers, in the future, an extensive analysis of a greater number of patients and also of a group of control patients is required. The validation of aCGH results by MLPA targeted probes confirmed that all CLL characteristic alterations found by aCGH were real, proving that both aCGH and MLPA could be used for additional samples’ characterization. Since aCGH is a more expensive and time-consuming technique than MLPA, the later has obvious advantages. MLPA also has the advantage of detecting eight CLL alterations other than the four most common ones evaluated by iFISH: 2p gain; del(6q); 8p loss; 8q gain; del(9p21); PTEN deletion at 10q23.31; del(14q); and trisomy 19; as well as mutations on NOTCH1, SF3B1, and MYD88. Despite that, iFISH continues to be the best CLL genomic characterization technique because: it detects the most common CNVs with known prognosis value that aid to the risk stratification of patients; it does a single-cell analysis that enables the detection of low expression mosaicism; and it evaluates the presence of some possible translocations. Neither aCGH or MLPA can detect low expression mosaicism and translocations because these are semi- quantitative techniques and, therefore, only detect unbalanced alterations. The detection of a reduced number of clones with CLL alterations (5-10% of cells) is something very important to keep in mind since those populations can, eventually, substitute the remaining clones, consisting of a clonal fluctuation event. This could occur after a therapy that only ablates certain clones, enabling the expansion of other clonal populations. However, since CLL patients usually present a high percentage of leukemic cells (>20%), in most of the cases MLPA could detect the mosaicism. With this said, we conclude that aCGH and MLPA should not, at this stage, substitute iFISH in clinical practice, but MLPA could add value to the stratification of patients by detecting somatic mutations, complementing iFISH information. In terms of research purposes, aCGH represents the most interesting technology since it detects additional alterations that might constitute possible markers of disease or disease progression, giving information on patients’ overall survival, response to treatment, and free-survival. Hence, it is crucial that further studies like ours are conducted, with the goal of obtaining a more detailed characterization of this malignancy. MLPA also appears to be a good candidate for research purposes, as all CNVs detected by this alteration, in our cohort, were also identified by aCGH. Concerning our findings on CLL prognosis markers, we were able to see a significant correlation between high risk stage and the presence of del(11q) or NOTCH1 c.7541-7542delCT mutation, confirming their value as markers of bad prognosis, as already described on the literature. Additionally, we could reach the same conclusion for the del(14q). This last result is an interesting finding since the value of this alteration’s prognosis in CLL is not yet fully understood. Although being a statistically significant result, we intend to continue assessing the presence of this alteration on a larger cohort of CLL patients. It is essential that new prognosis markers are found, in order to discover possible new therapy targets that contribute to a broader variety of therapeutic choices, leading to a positive influence on patient’s overall survival.

As for the survival analysis based on patients with 2≤ alterations, we could not statistically confirm the influence of the increasing number of alterations on overall survival, although the result obtained shows a conspicuous decrease in the overall survival of these patients during the follow-up period. The lack of statistical support is most certainly due to the reduced size of our cohort of patients and to the short and heterogeneous time of follow-up. In the future, we expect to increase our cohort and, evidently, we will have a larger time of follow-up. Hopefully, after that, we will review the importance of these findings and draw better fundamented conclusions. In sum, aCGH and MLPA are advantageous techniques for the genomic characterization of CLL, due to this disease high content in CNVs. In a follow-up to this work, we intend to investigate more patients and evaluate if some of the alterations we have found with high prevalence, but with no known diagnosis or prognostic value, continue to appear in high prevalence: del(5q13.2); del(8q24.23); del(22q11.22); del(Xq21.); dup(4p16.3); dup(6p25.3); dup(8p11.1); and dup(10q11.22). By the comparison with adequate control groups, we intend to evaluate whether they can be proposed as new CLL biomarkers, or not. Further studies will then be necessary to understand the biological and clinical impact of those new markers. Until now, our major interest is centred on the occurrence of del(5q13.2) and del(22q11.22). That is for these being two of the most prevalent additional alterations we have found with this work and also due to previous works that acknowledged these two alterations as possible disease markers, as previously discussed in sections 3.2.5.2 and 3.2.5.4. We are also interested on assessing the association between the number of alterations on overall survival and in the re-evaluation of del(14q) as a bad prognosis marker. For these purposes, more samples must be analysed.

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6. Annexes

Table 6.1 – Alterations found by aCGH in CLL samples studied: common CNVs in CLL are on the left column and the additional alterations found are on the right column. (* Alterations appearing in mosaicism / ** Homozygous alterations / amp - Amplification / bp – Base pairs / del - Deletion / dup - Duplication / trp – Triplication. Recurrent additional alterations are highlighted in blue boxes ).

Characteristic Additional Samples Start End Size (bp) Start End Size (bp) CNVs alterations del(1q23.2q23.3)* 159970948 162037172 2,066,225 del(1q32.1q42.12)* 205271379 226565823 21,294,445 del(1q42.2q42.3)* 234321614 234893537 571,924

del(4p16.3p15.33)* 72447 12665223 12,592,777 del(11q21q23.3)* 96688835 115731710 19,042,876 CLL1 del(4p15.2) 24907804 27504505 2,596,702 del(14q24.3) 74001651 74042335 40,685 del(5q13.2)* 69426248 70613301 1,187,054

del(6p22.2) 26703037 26745250 42,214 del(22q11.22) 22728411 23228869 500,459

43 amp(Xq21.1) 77121844 77123829 1,986

dup(2q11.2) 100696930 101038857 341,928 del(8p23.3p21.3) 176452 23245561 23,069,110 del(5q13.2) 69705562 70404381 698,820 dup(8q24.13q24.3) 125762045 146294098 20,532,054 CLL2 del(7q31.1) 111209626 111274663 65,038 del(9p21.3)** 21905380 21957794 52,415 del(7q31.33q32.3) 123990726 130842668 6,851,943 del(11q22.2q23.1) 102239689 111137249 8,897,561 dup(20p13) 60747 107444 47,698 dup(4p16.3) 36424 68211 31788 del(6q16.1q22.31)* 98638966 126034453 27,395,488 del(5q13.2) 69705562 70388844 683,283 dup(8q11.23) 53332060 53467396 135,337 del(6p25.3)* 259318 366807 107,490 CLL3 Trisomy 12 163593 133779076 129,933,944 del(8q24.23) 137693236 137850185 156,950 del(14q32.33) 107145681 107169357 23,677 dup(10q11.22) 46951237 47148554 197,318 del(Xq21.1)** 77121844 77123829 1,986 del(5q13.2)* 69426248 70613301 1,187,054 del(6q12) 65662415 65726670 64,256 trp(16p11.2p11.1) 34482042 34727349 245,308 CLL4 del(6q16.1) 95368644 95456892 88,249 del(22q11.22) 22531755 23228869 697,115 Trisomy 12 163593 133779076 129,933,944 dup(Yq11.221) 16174765 16287170 112,406

Table 6.1 – (Continuation). dup(1p36.13) 16360896 16387805 26,910 del(1q44) 248727929 248785562 57,634 del(3p14.2) 60445746 60487020 41,275 del(9p21.3) 25259013 25346971 87,959 del(5q13.2) 68849594 70613301 1,763,708 CLL5 del(14q21.1) 41621369 41657239 35,871 del(6p22.2) 26703037 26757698 54,662

del(15q13.3) 32509670 32514980 5,311 del(22q11.22) 23056562 23228869 172,308 dup(22q13.2) 42792565 42946279 153,715 amp(3q26.1) 162540808 162619141 78,334 del(8p11.22) 39237438 39362887 125,450 dup(14q24.3) 74001651 74042335 40,685 CLL6 del(14q24.2) 70674062 70939109 265,048 amp(14q32.33) 106405703 106762386 356,684

dup(Yq11.221) 19396209 19566674 170,466 dup(1p21.1) 104107530 104161031 53,502 Trisomy 12 163593 133779076 129,933,944 dup(15q11.2) 22765628 23217514 451,887 CLL7 del(13q14.2q14.3)* 50566628 51541846 975,219 del(18q12.1)* 27007249 27271804 264,556 del(14q21.1) 41621369 41657239 35,871

44 dup(Xq26.3) 134973695 135076043 102,349

trp(1p21.1) 104107530 104163810 56,281 dup(4p16.3) 36424 68211 31,788 del(6p21.32) 32497303 32536982 39,680 dup(6q27) 168557924 168764556 34,347 CLL8 - - - - dup(10q11.22) 46951237 47148554 197,318 del(15q13.2) 30921317 30971589 49,673 dup(15q13.3) 32065000 32514980 449,981 del(20q13.32) 56859986 56894332 34,347 del(5q13.2) 69705562 70388844 683,283 del(8p21.2) 24424799 24634037 209,239 dup(6p25.3) 259528 293615 34,088 CLL9 del(13q14.2q14.3) 50640433 51337792 697,360 del(17q21.31) 43593476 43675467 81,992

del(18q21.1) 46998450 47001095 2,646 dup(15q11.2) 24533217 24800370 267,154 del(18q21.1) 46999262 47001095 1,834 Trisomy 12 163593 133779076 129,933,944 CLL10 del(19q12) 29008203 29214891 206,689

del(Yp11.31p11.2)* 2650313 9901314 7,251,002 del(Yq11.21q11.23)* 13976140 28461032 14,484,893

Table 6.1 – (Continuation). del(1q21.2) 149041013 149209289 168,277 del(1q44) 245647384 245706269 58,886 del(1q44) 248686495 248785562 99,068 dup(4p16.1) 9371016 9558142 187,127 Trisomy 12 163593 133779076 129,933,944 CLL11 del(5q13.2)* 69925286 70657747 732,462 del(14q32.33) 106168379 106327993 159,615 dup(8p11.1) 43371449 43383206 11,758 del(8q24.23) 137693236 137850185 156,950 del(10p12.1) 27613431 27694710 81,280 dup(20q13.33) 61540243 62315381 775,139 del(2p11.2)* 87374004 88005418 631,415 dup(4p16.3) 36424 68211 31,788 dup(2p25.3) 17019 69989 52,971 CLL12 dup(6p25.3) 259318 293615 34,298 Trisomy 12 163593 133779076 129,933,944 dup(7q36.3) 159049815 159128556 78,742 del(Xp22.33) 536049 543388 7,340 del(2p13.1)* 74320231 74592003 271,773

45 del(4q24)* 105733846 106260307 526,462

del(5q13.2)* 68849594 70657747 1,808,154 CLL13 del(13q14.2q14.3)* 49027199 51729918 2,702,720 del(10p15.3p14)* 162490 8907926 8,745,437 del(12p13.2)* 11803703 12135000 331,298 dup(15q11.2) 24583967 24777982 194,016 del(22q11.22)* 22806884 23228869 421,986 dup(1p31.3) 68516108 68517713 1,606 del(5p13.3) 28935840 29130680 194,841 dup(6p25.3) 259318 389482 130,165 CLL14 del(13q14.13q21.31)* 45949090 64248924 18,299,835 dup(8p11.1) 43365995 43383206 17,212 del(11p15.4)* 2904944 2905045 102 del(15q11.2)* 23930537 23930860 324 del(Xq27.3)* 146992514 146993413 900

Table 6.1 – (Continuation). del(2q31.1) 171071681 171477928 406,248 dup(4p16.3) 36424 85099 48,676 dup(6p25.3) 274653 293493 18,841 dup(6p25.3) 1611440 1611489 50 CLL15 del(8p23.1) 7169490 7752586 583,097 del(8q24.23) 137693236 137850185 156,950 dup(10q26.3) 133501509 133623706 122,198 dup(11q25) 134353814 134728479 374,666 dup(14q21.2) 44836428 44895653 59,226 del(Xq21.1)** 77121844 77123829 1,986 del(1q44) 248727929 248785562 57,634 del(2q37.3) 242953547 243028452 74,906 del(5q13.2)* 69705562 70613301 907,740 del(5q15) 97817226 97898577 81,352 CLL16 - - - - amp(6p25.3) 1611440 1611489 50 dup(8p11.1) 43365995 43381512 15,518

46 del(16p13.11p12.3) 16544699 16835162 290,464

del(22q11.22) 22780968 23156685 447,902 del(22q11.22)** 23158685 23228869 70,185 dup(4p16.3) 36424 68211 31,788 CLL17 del(14q32.2)* 101291239 101293194 1,956 del(8q24.23) 137693236 137850185 156,950 amp(Xq21.1) 77121844 77123829 1,986 del(5p14.1) 25619538 26132350 512,813 del(13q14.2q14.3) 50544724 51363599 818,876 CLL18 dup(8p23.1) 7169490 7334684 165,195 del(14q32.33)* 107124520 107182658 58,139 dup(10q11.22) 46951237 47897498 946,262 del(1q31.3) 196837584 196891668 54,085 del(6q14.3q16.3) 87804545 10207955 14,213,411 dup(6p25.3) 1611194 1611489 296 CLL19 del(6q16.3q22.31)* 102474505 123325628 20,851,124 del(22q11.22) 22780968 23056621 275,654 del(13q14.2q14.3) 49928885 51462302 1,533,418 dup(Xp22.2) 10401388 10470366 68,979 dup(Xq26) 148886475 149084320 197,846 del(2q37.3) 240469961 240541160 71,200 CLL20 del(13q14.2q14.3)** 50588799 51501609 912,811 del(Xq21.1)** 77121844 77123829 1,986