AACR-Triple Negative Breast Cancer Foundation-Carol's Crusade for a Cure Foundation Career Development Award for Metastatic Triple-negative Breast Cancer Research
The AACR-Triple Negative Breast Cancer Foundation-Carol’s Crusade for a Cure Foundation Career Development Award for Metastatic Triple-negative Breast Cancer Research represents a joint effort to encourage and support junior faculty, who have completed their most recent doctoral degree or medical residency within the past 11 years, to conduct metastatic triple-negative breast cancer research and establish a successful career path in this field. The research proposed for funding may be basic, translational, or clinical in nature and must have direct applicability and relevance to metastatic triple-negative breast cancer.
dynamics of triple-negative breast cancer metastasis
Metastasis is the most insidious and lethal aspect of breast cancer. Triple-negative breast cancer (TNBC), which lacks the expression of the estrogen, progesterone, and HER2 receptors accounts for approximately 15 percent of all breast tumors and represents a particularly aggressive and heterogeneous subtype. TNBC portends overall worse survival with a particularly high risk of recurrence in the first five years after diagnosis. Progress in understanding the molecular determinants and dynamics of TNBC metastasis has been hindered by the challenge in obtaining clinically annotated paired primary tumors and metastases and the need for a quantitative evolutionary framework to interpret these processes. Hence, critical questions concerning when and how metastatic potential is specified in TNBC, remain unanswered. Here we propose to leverage a clinically annotated retrospective cohort of paired primary TNBCs and distant metastases to characterize patterns of spatio-temporal genomic heterogeneity and to enable the unbiased discovery of candidate metastasis drivers. In parallel, we will exploit the fact that somatically acquired mutations report on the past proliferative history of cancer cells and can be used to infer tumor subclonal architecture and evolutionary trajectories. By analyzing the genomic data within a novel spatial computational framework, we will infer clinically relevant patient-specific parameters and the dynamics of metastatic progression. This approach will lead to a better understanding of the metastatic process and has the potential to inform earlier intervention and treatment strategies, thereby reducing the mortality associated with TNBC.
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