Extraordinary responder cell lines reveal unique targetable genetic dependencies

点击翻译按钮获取中文摘要

 DESCRIPTION (provided by applicant): Members of our laboratory, the Broad Institute and the Dana-Farber Cancer Institute have generated large-scale datasets in hundreds of cancer cell lines and tumor samples, which include genetic dependency screens and gene expression profiles. Our group analyzed existing data from large-scale RNAi experiments and found multiple genes that are required for luminal breast cancer cell survival. These include the transcription factor SPDEF, the pioneer factor FOXA1, and the histone modifying enzymes KDM1A and MLL2. Because knockdown of these genes results in a robust decrease in survival specifically in luminal breast cancer cells, inhibiting these essential proteins with small molecules would likely result in a large therapeutic window where cancer cells would be targeted and normal cells would be spared. These genes have all been associated with estrogen receptor (ER) signaling in previous studies; however, our data suggest that these genes are also necessary in ER-negative luminal breast cancer cell growth. This proposal seeks to determine the role of these genes in luminal breast cancer cell signaling and survival so that potential therapeutic strategies can be designed and tested. Our laboratory's expertise in breast cancer genomics and access to large genomic datasets via the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) presents unique opportunities to study these genes. We will utilize these data to determine if ER-independent SPDEF expression is driven by genomic alterations. We will also assess the efficacy of inhibiting the chromatin modifiers KDM1A and MLL2 as a potential strategy to treat this subset of breast tumors. While treatment options exist, a large number of luminal breast cancer patients do not respond to the latest targeted therapies such as anti-estrogen hormone treatment or anti-ERBB2 antibodies. Thoroughly studying the role of these additional dependent genes may open new therapeutic avenues or enhance existing treatments for those patients with unmet clinical needs. Validating these dependencies would increase the number of therapeutic targets and further stratify luminal breast cancer patients so that tumor genetic contexts and targeted therapies are optimally matched.