Triple-negative breast cancer is the most aggressive form of breast cancer. Despite progress in new treatments, approximately one third of patients relapse or have no effect from treatment. One possible explanation is the presence of so-called "cancer stem cells", which are the cells thought to give rise to the tumor. Indeed, previous studies have shown that cancer stem cells contribute to treatment resistance, as well as the development and spread of cancer.
Cancer cells, including cancer stem cells, rarely survive alone, and depend on an environment (or "niche") of other cells to survive. Unfortunately, we lack reliable methods to identify cancer stem cells and their surrounding niche, so we know little about them. In this project, we will therefore develop new methods using computer algorithms to identify cancer stem cells and their niche in triple-negative breast cancer. We will then validate the results from the algorithms in the laboratory using cultured tissues that mimic the original cancerous tissue.
If successful, the results from this project could provide opportunities for the development of new treatments, to help improve patient outcome and quality-of-life.
Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer. Although promising new drugs based on PARP inhibition and immunotherapy can extend survival in selected patients, 30-40% of patients relapse or fail therapy. Increasing evidence suggest that TNBC tumors harbor cancer stem cells, which have been implicated in cancer development, maintenance, metastasis, and therapy resistance. However, how the microenvironment in TNBC tumors shapes the development of cancer stem cells and their niche is unknown. Importantly, understanding the interplay between the tumor microenvironment (TME) and the cancer stem cells could reveal novel therapeutic targets and improve patient outcome. Current methods to study cancer stem cells and their surrounding niche rely on the use of protein labels called antibodies, but are limited to a small number of known markers, and may miss rare cell states. Commercial assays for single cell and spatial transcriptomics have provided novel opportunities to study over 20,000 markers simultaneously, but either lack spatial information or single cell resolution. We hypothesize that in silico decoding of the TNBC cancer stem cell niche will uncover novel candidates for targeted therapies that could transform TNBC treatment. To address this hypothesis, we propose to (i) develop CytoNICHE, an integrative computational framework to resolve the developmental hierarchy of the cancer stem cell niche; (ii) apply it to TNBC tumors profiled by scRNA-seq and spatial transcriptomics to generate an atlas of the TNBC cancer stem cell niche in relation to therapy; and (iii) prioritize factors from the TME that regulate the development and maintenance of cancer stem cells and test them in a human TNBC organoid system. If successful, this proposal will reveal novel candidates for drug targeting that could vastly improve TNBC outcomes, and the resulting framework will facilitate the investigation of the cancer stem cell niche in other cancers.