High-resolution expression profiling to support a new immune classification scheme for triple-negative breast cancer

Routine collection in the clinic for instance already allows high-throughput genotyping and genome-scale studies of copy-number variation and DNA methylation, in addition to more traditional assays like histological tests. The latest advances now allow higher resolution profiling, for instance, the discriminating alternative gene transcripts. With transcripts rather than genes being long recognized as the functionally relevant units, this is a game-changer for the field. The interpretation of these high-resolution data, however, remains non-trivial and constitutes a key challenge for years to come. We have assembled an interdisciplinary group of experts with unique access to both the latest computational advances and state-of-the-art genomics data collection, in a carefully chosen large clinical cohort. Our focus will be on triple-negative breast cancer (TNBC), which does not express the estrogen receptor (ER), progesterone receptor (PR), and Her2/neu genes. As a result, it is more difficult to treat because most chemotherapies target one of these three receptors. While combination therapies can help some patients, even therapies that target multiple or other receptors often cannot provide successful treatment, and this remains hard to predict. In collaboration between Professor Shi of Fudan University and Dr. Lajos Pusztai of Yale University, we collect whole exome/genome sequencing (WES/WGS) data, matching RNA-seq gene expression data, Affymetrix OncoScan CNV data, and histological samples of 500 triple negative breast cancers, in order to identify molecular events and markers that cause different levels of immune infiltration in TNBC. The hypothesis is that germline or somatic DNA sequence alterations and their effects on gene expression determine why some breast cancers attract a large number of lymphocytes while others remain lymphocyte-poor. Tumors with intermediate or strong lymphocytic infiltration may be the most responsive to immune checkpoint therapy. We will explore high-resolution expression profiling for an integrated analysis of genomic, methylation, gene, and transcript level variation to support a new immune classification scheme for triple-negative breast cancer. This approach will yield higher power and performance: We can draw strength from multiple complementary genome scale sources, and we exploit transcript level signals, improving functional relevance. We will advance computational methods for high-resolution signal estimation, data integration, and patient sample classification, working close to the clinical source of the data. Developments will be cross-validated using the unique histological samples available in this collaboration.