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A Four-Gene Decision Tree Signature Published OnlineFirst October 10, 2018; DOI: 10.1158/1535-7163.MCT-18-0243 Companion Diagnostic, Pharmacogenomic, and Cancer Biomarkers Molecular Cancer Therapeutics A Four-gene Decision Tree Signature Classification of Triple-negative Breast Cancer: Implications for Targeted Therapeutics Jelmar Quist1,2,3, Hasan Mirza1,2,3, Maggie C.U. Cheang4,5, Melinda L. Telli6, Joyce A. O'Shaughnessy7, Christopher J. Lord5,8, Andrew N.J. Tutt2,3,5, and Anita Grigoriadis1,2,3 Abstract The molecular complexity of triple-negative breast cancers in early diagnosed TNBCs. In TNBC patients with metastatic (TNBCs) provides a challenge for patient management. We set disease, the MC6 proportion was reduced to 25%, and was out to characterize this heterogeneous disease by combining independently associated with a higher response rate to transcriptomics and genomics data, with the aim of revealing platinum-based chemotherapy. In TNBC cell line data, convergent pathway dependencies with the potential for treat- platinum sensitivity was recapitulated, and a sensitivity ment intervention. A Bayesian algorithm was used to integrate to the inhibition of the phosphatase PPM1D was revealed. molecular profiles in two TNBC cohorts, followed by valida- Molecularly, MC6-TNBCs displayed high levels of telomeric þ þ tion using five independent cohorts (n ¼ 1,168), including allelic imbalances, enrichment of CD4 and CD8 immune three clinical trials. A four-gene decision tree signature was signatures, and reduced expression of genes negatively reg- identified, which robustly classified TNBCs into six subtypes. ulating the MAPK signaling pathway. These observations All four genes in the signature (EXO1, TP53BP2, FOXM1, and suggest that our integrative classification approach may RSU1) are associated with either genomic instability, malig- identify TNBC patients with discernible and theoretically nant growth, or treatment response. One of the six subtypes, pharmacologically tractable features that merit further MC6, encompassed the largest proportion of tumors (50%) studies in prospective trials. Introduction responsive to chemotherapy, such as those with BRCA1 muta- tions, its effectiveness remains limited in an unselected TNBC Triple-negative breast cancers (TNBCs), defined by the lack of population. Many agents are presently in clinical development, estrogen receptor (ER), progesterone receptor (PgR), and human including PARP inhibitors and platinum salts (3–5), MEK inhi- epidermal growth factor receptor 2 (HER2) expression, display bitors (6), and immunological agents (7). For some, predictive remarkable molecular complexity and heterogeneous clinical biomarkers have been recognized, while for others appropriate behavior (1). The overall prognosis of women with TNBC after molecular features enabling optimal patient selection are still metastatic relapse is significantly poorer when compared with that lacking (8, 9). of women with other breast cancer subtypes (2). Chemotherapy Several studies have sought to take advantage of molecular remains the only systemic therapeutic approach for patients with profiling to classify TNBCs and subsequently provide clinically TNBC. Although subpopulations can be identified that are more relevant information (10–13). One of the most recent classifica- tions, TNBCtype-4, is based on gene expression and classifies TNBC into four subtypes. Among those, basal-like 1 (BL1) TNBCs 1Cancer Bioinformatics, Cancer Centre at Guy's Hospital, King's College London, were shown to achieve significantly higher pathological complete 2 London, United Kingdom. Breast Cancer Now Research Unit, Cancer Centre at response (pCR) rates (49%) compared with all other subtypes 3 Guy's Hospital, King's College London, London, United Kingdom. School of (31%) when treated with neoadjuvant chemotherapy. However, Cancer and Pharmaceutical Sciences, Cancer Research UK King's Health Part- fi ners Centre, King's College London, London, United Kingdom. 4Clinical Trials and classi cation approaches making use of multi-omics data are fi Statistics Unit (ICR-CTSU), The Institute of Cancer Research, Surrey, United warranted to identify patients that may bene t from treatment Kingdom. 5Breast Cancer Now Toby Robins Research Centre, The Institute of beyond the standard-of-care (12). Cancer Research, London, United Kingdom. 6Department of Medicine, Stanford TNBCs are characterized by extensive genomic instability (14, 7 University School of Medicine, Stanford, California. Baylor University Medical 15). Measures capturing overall levels of genomic instability, like 8 Center, Texas Oncology, US Oncology, Dallas, Texas. The CRUK Gene Function a chromosomal instability gene signature (CIN70), are informa- Laboratory, The Institute of Cancer Research, London, United Kingdom. À tive for outcome prediction in patients with ER breast cancer Note: Supplementary data for this article are available at Molecular Cancer (16). Similarly, genomic signatures, including Scars of Chromo- Therapeutics Online (http://mct.aacrjournals.org/). somal Instability measures (SCINS; ref. 17), Number of Telomeric Corresponding Author: Anita Grigoriadis, King's College London, Innovation Allelic Imbalance (NtAI; ref. 18), the Homologous Recombina- Hub, Cancer Centre at Guy's Hospital, London SE1 9RT, United Kingdom. Phone: tion Deficiency (HRD) score (19), and HRDetect (20), are indic- 4420-7188-2360; E-mail: [email protected] ative of BRCAness and predictive of response to chemotherapy, doi: 10.1158/1535-7163.MCT-18-0243 particularly in the neoadjuvant setting (21). In metastatic TNBC Ó2018 American Association for Cancer Research. trials [TNT (22) and TBCRC009 (23)], these methods were not 204 Mol Cancer Ther; 18(1) January 2019 Downloaded from mct.aacrjournals.org on September 26, 2021. © 2019 American Association for Cancer Research. Published OnlineFirst October 10, 2018; DOI: 10.1158/1535-7163.MCT-18-0243 Integrative Analysis of Triple-negative Breast Cancers able to identify patients specifically responding to platinum- Affymetrix SNP 6.0 copy number data based chemotherapy, suggesting some refinement of such Raw Affymetrix SNP 6.0 data were normalized, allele-specific approaches is required. signal intensity measures were generated, and log R ratios and B- Here, we developed a four-gene decision tree signature based allele frequencies were obtained using PennCNV-Affy (30, 31). on integrated transcriptomics and genomics data that robustly Allele specific copy number data were obtained using the ASCAT classifies TNBC into six subtypes across 1,168 TNBCs. In TNBC algorithm (32). patients with metastatic disease, our classification identified a subgroup of tumors sensitive to platinum-based chemotherapy. Cancer cell line data Molecular characteristics of this subgroup included increased Transcriptomics and drug response data for TNBC cell lines number of allelic imbalanced aberrations in their telomeres, (n ¼ 16) were acquired from the Genomics of Drug Sensitivity in decreased inactivation of MAPK signaling, and enrichment of Cancer (GDSC; ref. 33). The area under the dose–response curve þ þ CD4 and CD8 immune signatures. (AUC) values represent the relative sensitivity (low AUC) and resistance (high AUC) for each drug within each cell line. Associa- tions between drug response and MC6 were assessed using Mann– Materials and Methods Whitney U tests. Clinical sample data TNBCs from the previously described Guy's TNBC (E-MTAB- Statistical methods 5270 and E-MTAB-2626; ref. 24) were selected on the basis of IHC Detailed information on the integrative analysis and statistical status of HER2, ER, and PgR. For 88 TNBCs, patient matched methods are described in the online Supplementary Information. Affymetrix GeneChip Human Exon 1.0ST gene expression and In brief, genomics and transcriptomics data were integrated Affymetrix SNP 6.0 copy number data were available. independently in Guy's TNBC and METABRIC TNBC using the METABRIC TNBC is a subset of the METABRIC study COpy Number and EXpression In Cancer (CONEXIC) algorithm (EGAD00010000164; ref. 10). Triple-negative status was based (34). The identified four-gene signature required for TNBC sub- on IHC-assessed ER and HER2 status. Patient matched Illumina typing was normalized before being applied to additional Human HT-12 v3 gene expression and Affymetrix SNP 6.0 copy cohorts. number data were available for 112 TNBCs and processed, as reported previously (24). Results A triple-negative subset of TCGA BRCA (25) was used as the TCGA TNBC cohort. For 95 TNBCs, defined by ER and HER2 Integrative analysis of transcriptomics and genomics data fi status, gene expression was available on the Agilent 244K Custom identi es a four-gene decision tree signature Gene Expression array. To determine whether combined transcriptomics and geno- fi The TNBC616 cohort is a compilation of 24 different breast mics data could lead to a robust TNBC classi cation, we applied cancer cohorts (Supplementary Table S1). A total of 3,495 breast the previously published CONEXIC algorithm (34) to two inde- ¼ cancers were obtained, of which 616 were defined as triple pendent TNBC cohorts; Guy's TNBC (n 88; ref. 24) and TNBCs ¼ negative based on HER2, ER, and PgR status. Details are provided from the METABRIC study (METABRIC TNBC; n 112; in the online Supplementary Information. ref. 10; Fig. 1A; Supplementary Table S2). CONEXIC constructs gene sets, defined as groups of genes, which share similar expres- Clinical trial data sion patterns across all samples. As a next step, CONEXIC builds The PrECOG
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