WES) and Bioinformatics Analysis
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Vaisitti T et al. Supplementary Materials and Methods Whole Exome Sequencing (WES) and Bioinformatics Analysis Genomic DNA from each sample was sheared and used for the construction of paired end sequencing library as described in the protocol provided by Illumina. The exome was captured using the SureSelect Human All Exon V6 (Agilent) following the manufacturer’s instructions. Samples were sequenced using Illumina Hiseq4000. We used Genome_GPS v3.0.2 (formerly named as TREAT)(1) as a comprehensive secondary analysis pipeline for exome sequencing data at Mayo Clinic. FASTQ files were aligned to the hg19 reference genome using Novoalign (VN:V2.08.01) with the following options: --hdrhd off -v 120 -c 4 -i PE 425,80 -x 5 -r Random. Realignment and recalibration was performed using GATK (VN:3.3-0) (2) best practices version 3 for each family separately. Multi-sample Variant calling was performed using the GATK (VN: 3.3-0) Haplotype Caller and variants are filtered using variant recalibration Variant Quality Score Recalibrator (VQSR) for both SNVs and INDELs. Somatic Variants were called using Mutect2 from GATK(VN:3.6) using default parameters. Identified variants are annotated using BioR (3) framework with functional features, impact prediction, and clinical significance using CAVA, ClinVar, HGMD, and ExAC population frequencies. BioR includes gene annotation from NCBI/Ensembl and UCSC, Gene annotated pathways (KEGG), tissue specificity, GeneCards and Gene Ontology (GO), dbSNP, GWAS catalog, HapMap, and 1000 Genomes. Furthermore, it provides annotation from the Catalogue of Somatic Mutations in Cancer (COSMIC, Wellcome Trust Sanger Institute), SIFT, and PolyPhen-2. We used the Exome Aggregation Consortium (ExAC) data to filter out common SNPs (http://exac.broadinstitute.org/). Alternate alleles with frequency > 0.0001 in ExAC database were filtered out. Additionally, somatic variant calls with total read depth less than 30X were excluded from further analysis. Finally, non-synonymous variants of significant interest were visually inspected using IGV (4). Copy number variations (CNVs) were calculated using 1 Vaisitti T et al. PatternCNV (5). Targeted deep sequencing Mutations were validated and followed up overtime using semiconductor-sequencing technology (IonTorrent PGM) as per manufacturer’s protocol. Twenty-six genes, previously found to be significantly mutated in CLL and indicated as putative CLL driver genes (6), and two genes with mutations associated with Ibrutinib resistance (BTK and PLCG2) were selected for targeted deep sequencing (Table S3). All coding regions were amplified in 200bp amplicons by multiplex PCR using customized oligos (Ion Ampliseq designer). Libraries were templated and enriched using IonOneTouch2 and IonOneTouch ES automated systems, respectively. Samples were sequenced using the 318TM chip (ThermoFisher). Raw data was aligned and indexed in BAM and BAI files using the IonTorrent suite. Variants were called using IonTorrent Somatic VariantCaller version 4.6.0.7 and low stringency settings (ThermoFisher). VCF files were annotated using BioR. Somatic variants with a Mapping Quality <20 or read depth <10X were removed. Finally, variants of significant interest were visually inspected using Integrative Genomics Viewer (IGV). RNA sequencing data analyses Venn diagrams were obtained with Venny2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html). To highlight cell processes that were conserved between PDXs and primary sample, enrichment analysis was performed on sequences with |logFc|<0.5 to exclude minor modulations. To do so, genes were classified according to their GO annotations and cellular pathway association according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, using online tools: Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources (http://david.abcc.ncifcrf.gov/), and GeneCodis (http://genecodis.cnb.csic.es/), and applying cumulative hypergeometric distribution and a cut-off on Bonferroni-corrected P-value (p=0.05). GO were clustered by semantic similarity 2 Vaisitti T et al. using the online tool REVIGO (http://revigo.irb.hr/). To analyze genes that were consistently differentially expressed in PDX samples compared to primary, enrichment analyses were performed on sequences with |logFc|>1 and genes classified as described above. Pathway analysis by qRT-PCR and western blotting RNA was extracted using RNeasy Plus Mini kit (Qiagen, Milan, Italy) and converted to cDNA using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems). qRT-PCR was performed using the 7900 HT Fast Real Time PCR System (SDS 2.3 software). Primers for NOTCH1 (Hs01062014_m1), HES1 (Hs00172878_m1), DTX1 (Hs00269995_m1), HES5 (Hs01387463_g1), HEY1 (Hs01114113_m1), HEY2 (Hs00232622_m1), NOTCH2 (Hs01050702_m1), CCL3 (Hs00234142_m1), MYC (Hs00905030_m1), NFKB1 (Hs00765730_m1), RELA (Hs00153294_m1) and B2M (Hs00984230_m1) were from Life Technologies. Reactions were done in triplicate from the same cDNA reaction (technical replicates). For each gene, expression levels were computed as the difference (ΔCT) between the target gene CT and b-2-microglobulin (B2M) CT. Total lysates were resolved by Bolt SDS‐PAGE gels and transferred to nitrocellulose membranes (ThermoFisher Scientific). The following antibodies were used: anti-NF-kB1/p105-p50 (#3035), - p65 (#8242), cleaved Notch1 (#4147), total Notch1 (#4380), P-Btk (#5082), Btk (#8547), c-Myc (#13987), P-Syk (#2710), P-Jnk (#4671), P-p38 (#9211), P-Lyn (#2731), P-PLCg2 (#3871/3874) (all from Cell Signaling Technologies, Milan, Italy). Bands were detected with an HRP-conjugated goat anti‐mouse IgG antibody (PerkinElmer) or anti-rabbit antibody (Santa Cruz Biotechnology). An anti- actin HRP-conjugated antibody was used as a loading control (sc-1616, Santa Cruz Biotechnology). Blots were developed using enhanced chemiluminescence and images acquired with the ImageQuant LAS 4000 ChemiDoc (GE Healthcare). 3 Vaisitti T et al. References 1. Asmann YW, Middha S, Hossain A, Baheti S, Li Y, Chai HS, et al. TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data. Bioinformatics 2012;28:277-8 2. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303 3. Kocher JP, Quest DJ, Duffy P, Meiners MA, Moore RM, Rider D, et al. The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation. Bioinformatics 2014;30:1920-2 4. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol 2011;29:24-6 5. Wang C, Evans JM, Bhagwate AV, Prodduturi N, Sarangi V, Middha M, et al. PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data. Bioinformatics 2014;30:2678-80 6. Barrio S, Shanafelt TD, Ojha J, Chaffee KG, Secreto C, Kortum KM, et al. Genomic characterization of high-count MBL cases indicates that early detection of driver mutations and subclonal expansion are predictors of adverse clinical outcome. Leukemia 2017;31:170-6 4 Vaisitti T et al. Supplementary Figure Legends Supplementary Figure S2. Schematic representation of the RS-PDX models. Primary leukemic cells were sub-cutaneously (s.c.) injected in NSG mice and left to engraft until tumor masses were palpable. Mice were euthanized and a suspension of leukemic single cells was obtained from the tumor mass, re-implanted and propagated in other mice (continuous line of the box). This step was repeated several times to obtain stable PDXs. After several passages (indicated in the left), both models were implemented by intra-venous (i.v.) injection of RS cells in the tail vein of NSG mice (dotted line of the box). d: days. Supplementary Figure S6. Differentially expressed genes in primary and RS-PDX. (A-B) Graphs showing GO enrichment of genes consistently up- or down-regulated in PDX models compared to the relative primary sample. (C-D) Expression levels of genes enriched in the above mentioned gene ontologies plotted as Log10 RPKM. For each category, genes belonging to primary sample and sequential PDXs are shown. Number in brackets indicates the number of genes to that GO. 5 A Passage Interval of Engrafted; s.c. number Engraftment Engrafted; i.v.. P0 RS9737 P1 45 d RS9737_1A RS9737_1B P2 25 d RS9737_2aA P3 25 d RS9737_3aA RS9737_3aB P4 25 d RS9737_4aA RS9737_4aB P5 30 d RS9737_5aA RS9737_5aB P6 25 d RS9737_6aA RS9737_6aC RS9737_6aD RS9737_6bA RS9737_6bB P7 25 d RS9737_7aA RS9737_7aA RS9737_7aB P8 21 d RS9737_8aA RS9737_8aB RS9737_8aA RS9737_8aB P9 21 d RS9737_9aC RS9737_9aD RS9737_9bA RS9737_9bB P10 21 d RS9737_10cA RS9737_10dB B Passage Interval of Not engrafted; s.c. number Engraftment Engrafted; s.c. P0 Engrafted; i.v.. RS1316 P1 180 d RS1316_1A RS1316_1B P2 60 d RS1316_2aA RS1316_2aB RS1316_2aC RS1316_2aD P3 60 d RS1316_3aA RS1316_3aB RS1316_3aC P4 60 d RS1316_4aA RS1316_4aB RS1316_4cA P5 60 d RS1316_5aA P6 60 d RS1316_6aA P7 40 d RS1316_7aA P8 40 d RS1316_8aA RS1316_8aB P9 40 d RS1316_9aA RS1316_9aB RS1316_9bA P10 42 d RS1316_10aA RS1316_10bB RS1316_10bC Supplementary Figure 2 G889V H384N D411Y SETD2 SET LCR 2564 aa EGR2 DUF3446 ZN ZN ZN 467 aa AWS Post-SET WW G44R N1516S P2514fs4 NOTCH1 2555 aa MED12 LCEWAV PQL 2177 aa EGF-like LNR HD ANK TAD PEST NOTCH2 2471 aa MED E88K E96G C481S G13C L19F T58I BTK PH TH SH3 SH2 TK 659 aa KRAS G-domain