Analysis of Pre- and Posttreatment Tissues from the SWOG S0800 Trial Reveals an Effect of Neoadjuvant Chemotherapy on the Breast Cancer Genome.

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.

CD36-Mediated Metabolic Rewiring of Breast Cancer Cells Promotes Resistance to HER2-Targeted Therapies.

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.

Association of T-Cell Receptor Repertoire Use With Response to Combined Trastuzumab-Lapatinib Treatment of HER2-Positive Breast Cancer: Secondary Analysis of the NeoALTTO Randomized Clinical Trial.

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.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

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.

Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations.

Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.

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.