Search

Supplementary Figure 1. Flow diagram of patients and samples used in analysis. Blue boxes represent sub-cohorts used for various parts of the study.

Purpose:We performed whole-exome sequencing (WES) of pre- and posttreatment cancer tissues to assess the somatic mutation landscape of tumors before and after neoadjuvant taxane and anthracycline chemotherapy with or without bevacizumab.Experimental Design:Twenty-nine pretreatment biopsies from the SWOG S0800 trial were subjected to WES to identify mutational patterns associated with response to neoadjuvant chemotherapy. Nine matching samples with residual cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy.Results:In pretreatment samples, a higher proportion of mutation signature 3, a BRCA-mediated DNA repair deficiency mutational signature, was associated with higher rate of pathologic complete response (pCR; median signature weight 24%, range 0%–38% in pCR vs. median weight 0%, range 0%–19% in residual disease, Wilcoxon rank sum, Bonferroni P = 0.007). We found no biological pathway level mutations associated with pCR or enriched in posttreatment samples. We observed statistically significant enrichment of high functional impact mutations in the “E2F targets” and “G2–M checkpoint” pathways in residual cancer samples implicating these pathways in resistance to therapy and a significant depletion of mutations in the “myogenesis pathway” suggesting the cells harboring these variants were effectively eradicated by therapy.Conclusions:These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy frequently have alterations in cell-cycle–regulating genes but different genes of the same pathways are affected in different patients.

SUMMARY Although it is established that fatty acid (FA) synthesis supports anabolic growth in cancer, the role of exogenous FA uptake remains elusive. Here we show that, during acquisition of resistance to HER2 inhibition, metabolic rewiring of breast cancer cells favors reliance on exogenous FA uptake over de novo FA synthesis. Through cDNA microarray analysis, we identify the FA transporter CD36 as a critical gene upregulated in cells with acquired resistance to the HER2 inhibitor lapatinib. Accordingly, resistant cells exhibit increased exogenous FA uptake and metabolic plasticity. Genetic or pharmacological inhibition of CD36 suppresses the growth of lapatinib-resistant but not lapatinib-sensitive cells in vitro and in vivo. Deletion of Cd36 in mammary tissues of MMTV-neu mice significantly attenuates tumorigenesis. In breast cancer patients, CD36 expression increases following anti-HER2 therapy, which correlates with a poor prognosis. Our results define CD36-mediated metabolic rewiring as an essential survival mechanism in HER2-positive breast cancer., In Brief The functional significance of lipid metabolism in cancer cells is not fully understood. Feng et al. show that the fatty acid transporter CD36 is essential for survival of breast cancer cells during anti-HER2 therapy, highlighting the role of lipid metabolism in acquired resistance to targeted therapy., Graphical Abstract

Background: Dual anti-HER2 blockade resulted in increased pathologic complete response rate (pCR) in the 3 arm NeoALTTO trial. High immune gene expression and the absence of PIK3CA pathway mutations are predictive of pCR in all treatment arms but no markers were identified that could predict which patients require dual HER2 targeted therapy. The goal of this analysis was to examine if TRBV expression could add to the predictive function of previously identified immune markers. Patients and Methods: We analyzed RNA and Whole Exome sequencing data from 245 cancers (54% of all patients) included in the trial. The TRBV reference sequences were obtained from the International ImMunoGeneTics information system. Reads were aligned using a custom BLAST mapping pipeline and normalized by the total number of aligned reads in each sample. We calculated 3 T cell receptor metrics for each tumor including (i) total TRBV chain expression level, (ii) Shannon entropy of the normalized unique TRBV-expression frequencies which reflect TCR diversity and (iii) we also used non-negative matrix factorization (NMF) to define TRBV co-expression metagenes (TRBVMG). We evaluated correlation between these metrics and immune and proliferation gene expression signatures and genomic features of the cancer including clonal heterogeneity and mutation load. We assessed association between TRBV and pCR using multivariate logistic regression. Results: 65 distinct TRBV variants showed heterogeneous expression levels across cancers with strong co-expression patterns. Total TRBV expression correlated strongly with immune metagene expression (Spearman's ρ=0.93, P Conclusions: TRBV expression pattern can provide predictive information beyond known immune gene expression signatures. High expression of TRBV4.3, TRBV6.3 and TRBV7.2 variants is associated with higher pCR rate with dual HER2 targeted and paclitaxel neoadjuvant therapy. Citation Format: Powles R, Redmond D, Sotiriou C, Loi S, Fumagalli D, Nuciforo P, Harbeck N, de Azambuja E, Sarp S, Di Cosimo S, Huober J, Baselga J, Piccart-Gebhart M, Elemento O, Hatzis C, Pusztai L. T-cell receptor beta chain variable region (TRBV) expression patterns predict response to combined trastuzumab/lapatinib treatment in the NeoALTTO/BIG-1-06 trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-01.

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits’ genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD’s correlation with cognitive traits and hints at an autoimmune component for ALS.

Purpose: We performed whole-exome sequencing (WES) of pre- and posttreatment cancer tissues to assess the somatic mutation landscape of tumors before and after neoadjuvant taxane and anthracycline chemotherapy with or without bevacizumab. Experimental Design: Twenty-nine pretreatment biopsies from the SWOG S0800 trial were subjected to WES to identify mutational patterns associated with response to neoadjuvant chemotherapy. Nine matching samples with residual cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy. Results: In pretreatment samples, a higher proportion of mutation signature 3, a BRCA-mediated DNA repair deficiency mutational signature, was associated with higher rate of pathologic complete response (pCR; median signature weight 24%, range 0%–38% in pCR vs. median weight 0%, range 0%–19% in residual disease, Wilcoxon rank sum, Bonferroni P = 0.007). We found no biological pathway level mutations associated with pCR or enriched in posttreatment samples. We observed statistically significant enrichment of high functional impact mutations in the “E2F targets” and “G2–M checkpoint” pathways in residual cancer samples implicating these pathways in resistance to therapy and a significant depletion of mutations in the “myogenesis pathway” suggesting the cells harboring these variants were effectively eradicated by therapy. Conclusions: These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy frequently have alterations in cell-cycle–regulating genes but different genes of the same pathways are affected in different patients.

IMPORTANCE: Dual anti-HER2 blockade increased the rate of pathologic complete response (pCR) in the Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimisation (NeoALTTO) trial, and high immune gene expression was associated with pCR in all treatment arms. So far, no marker has been identified that is specifically associated with the benefit from dual HER2 blockade. OBJECTIVE: To examine if use of the T-cell β chain variable genes adds to the potential association of immune gene signatures with response to dual HER2 blockade. DESIGN, SETTING, AND PARTICIPANTS: In the NeoALTTO trial, HER2-positive patients recruited between January 5, 2008, and May 27, 2010, were treated with paclitaxel plus either lapatinib or trastuzumab or both as neoadjuvant therapy. In this study, RNA sequencing data from baseline tumor specimens of 245 patients in the NeoALTTO trial were analyzed and reads were aligned to TRBV gene reference sequences using a previously published Basic Local Alignment Search Tool T-cell receptor mapping pipeline. Total TRBV gene use, Shannon entropy, and gene richness were calculated for each tumor, and nonnegative matrix factorization was used to define TRBV co-use metagenes (TMGs). The association between TRBV metrics, tumor genomic metrics, and response was assessed with multivariable logistic regression. Statistical analysis was performed from January 23 to December 2, 2017. MAIN OUTCOMES AND MEASURES: The association between TRBV use metrics and pCR. RESULTS: Among the 245 women with available data (mean [SD] age, 49 [11] years), total TRBV use correlated positively with a gene expression signature for immune activity (Spearman ρ = 0.93; P

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits’ genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer’s disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD’s correlation with cognitive traits and hints at an autoimmune component for ALS.

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline., Author Summary Genome-wide association study has been a productive approach to studying human complex diseases in the past decade, yet challenges still remain in both identifying and interpreting disease-associated loci. In our previous work, we showed that integrated analysis of GWAS summary statistics and genomic functional annotations could enhance the performance of signal prioritization. In this paper, we further improve our annotation through integrating a rich collection of epigenomic data from the Roadmap Epigenomics Project. We introduce GenoSkyline, a statistical framework to predict tissue-specific functional regions in the human genome, and demonstrate its ability to capture tissue-specific functionality through extensive case studies. We then illustrate a variety of ways that GenoSkyline could benefit post-GWAS analysis. The performance of signal prioritization is further improved using annotations of disease-related tissue types. Furthermore, combining GenoSkyline annotations with GWAS results allow us to partition heritability by tissue types and generate new hypotheses regarding the disease etiology behind each risk locus, thereby providing novel biological insights to many human complex diseases. GenoSkyline is powerful, robust, and customizable. We believe that GenoSkyline and its applications can guide genetics research at multiple resolutions and greatly benefit the broader scientific community.

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data., Author summary Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and early treatment strategies. For example, mutations in BRCA1 and BRCA2 have been used to evaluate women’s breast cancer risk and as a guideline for early screening. However, genetic risk prediction models also present important challenges, including extreme high-dimensionality, limited access to and efficient computational methods for individual-level genotype data. To make use of rich GWAS summary statistics, we propose a novel method to address these challenges by integrating genomic functional annotations, which have been successfully applied in GWAS to generate biological insights. We demonstrate the improvement in accuracy in both extensive simulation studies and real data analysis of breast cancer, Crohn’s disease, celiac disease, rheumatoid arthritis and type-II diabetes.

Background While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars. Results We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Conclusions Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0134-9) contains supplementary material, which is available to authorized users.