Biology subjects

Winer, E. P.

Publications and source records attributed to Winer, E. P..

3 recordsLinked to original sources

bioRxiv2019

Prevalence and mutational determinants of high tumor mutation burden in breast cancer

BackgroundHigh tumor mutation burden (TMB) has been associated with benefit to immunotherapy in multiple tumor types. However, the prevalence of hypermutated breast cancer is not well described. The aim of this study is to evaluate frequency, mutational patterns, and genomic profile of hypermutated breast cancer.\n\nPatients and MethodsWe used de-identified data from individuals with primary or metastatic breast cancer from six different publicly available genomic studies. The prevalence of hypermutated breast cancer was determined among 3969 patients samples that underwent whole exome sequencing or gene panel sequencing. Samples were classified as having high TMB if they had [≥]10 mutations per megabase (mut/Mb). An additional 8 patients were identified from a Dana-Farber Cancer Institute cohort for inclusion in the hypermutated cohort. Among patients with high TMB, the mutational patterns, and genomic profile were determined. A subset of patients was treated with regimens containing PD-1 inhibitors.\n\nResultsThe median TMB was 2.63 mut/Mb. Median TMB significantly varied according to tumor subtype (HR-/HER2-> HER2+ > HR+/HER2-, p < 0.05) and sample type (metastatic > primary, p 2.2x10-16). Hypermutated tumors were found in 198 patients (5%), with an enrichment in metastatic versus primary tumors (8.4% versus 2.9%, p = 6.5 x 10-14). APOBEC activity (59.2%), followed by mismatch repair deficiency (MMRd; 36.4%), were the most common mutational processes among hypermutated tumors. Three patients with hypermutated breast cancer--including two with a dominant APOBEC activity signature and one with a dominant MMRd signature--treated with pembrolizumab-based therapies derived an objective and durable response to therapy.\n\nConclusionHypermutation occurs in 5% of all breast cancers, with an enrichment in metastatic tumors. Different mutational signatures are present in this population, with APOBEC activity being the most common dominant process. Preliminary data suggest that hypermutated breast cancers are more likely to benefit from PD-1 inhibitors.\n\nKey MessageHigh tumor mutation burden is found in 5% of all breast cancers and is more common in metastatic tumors. While different mutational signatures are present in hypermutated tumors, APOBEC activity is the most common dominant process. Preliminary data suggest that those tumors are more likely to benefit from PD-1 inhibitors.

genomics↗

bioRxiv2019

Machine learning predicts rapid relapse of triple negative breast cancer

PurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years).\n\nPatients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or no relapse (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.\n\nResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.\n\nConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

cancer biology↗

bioRxiv2019

Acquired FGFR and FGF alterations confer resistance to estrogen receptor (ER) targeted therapy in ER+ metastatic breast cancer

Beyond acquired mutations in the estrogen receptor (ER), mechanisms of resistance to ER-directed therapies in ER+ breast cancer have not been clearly defined. We conducted a genome-scale functional screen spanning 10,135 genes to investigate genes whose overexpression confer resistance to selective estrogen receptor degraders. Pathway analysis of candidate resistance genes demonstrated that the FGFR, ERBB, insulin receptor, and MAPK pathways represented key modalities of resistance. In parallel, we performed whole exome sequencing in paired pre-treatment and post-resistance biopsies from 60 patients with ER+ metastatic breast cancer who had developed resistance to ER-targeted therapy. The FGFR pathway was altered via FGFR1, FGFR2, or FGF3 amplifications or FGFR2 mutations in 24 (40%) of the post-resistance biopsies. In 12 of the 24 post-resistance tumors exhibiting FGFR/FGF alterations, these alterations were not detected in the corresponding pre-treatment tumors, suggesting that they were acquired or enriched under the selective pressure of ER-directed therapy. In vitro experiments in ER+ breast cancer cells confirmed that FGFR/FGF alterations led to fulvestrant resistance as well as cross-resistance to the CDK4/6 inhibitor palbociclib. RNA sequencing of resistant cell lines treated with different drug combinations demonstrated that FGFR/FGF induced resistance through ER reprogramming and activation of the MAPK pathway. The resistance phenotypes were reversed by FGFR inhibitors, a MEK inhibitor, and/or a SHP2 inhibitor, suggesting potential treatment strategies. The detection of targetable, clonally acquired genetic alterations in the FGFR pathway in metastatic tumor biopsies highlights the value of serial tumor testing to dissect mechanisms of resistance in human breast cancer and its potential application in directing clinical management.

cancer biology↗