Molecular Basis of Breast Tumor Evolution
Research in my laboratory is dedicated to the molecular analysis of human breast cancer. Our goal is to identify differences between normal and cancerous breast tissue, determine their consequences, and use this information to improve the clinical management of breast cancer patients. The three main areas of our interests are: (1) how to accurately predict breast cancer risk and prevent breast cancer initiation or progression from in situ to invasive disease, (2) better understand drivers of tumor evolution with special emphasis on metastatic progression and therapeutic resistance, and (3) novel therapeutic targets in breast cancer with particular focus on “bad” cancers such as triple-negative breast cancer and inflammatory breast cancer. All of our studies start with analyzing samples from breast cancer patients (or normal healthy women for the risk studies), formulate hypotheses based on our observations, use experimental models to test these, and then translate back our findings into clinical care.
Highlights from our breast cancer risk and prevention study: The highest impact on breast cancer-associated morbidity and mortality will be achieved with two tools. The first tool is a test that accurately predicts an individual’s risk of developing breast cancer. This will allow us to identify who needs preventive action and who does not. Second, is to discover the best agent for prevention that will be universally effective. We know that inheriting mutated BRCA1 and BRCA2 genes confer a high risk of breast cancer, and the most effective prevention strategy currently available is prophylactic oophorectomy and mastectomy. Other significant determinants of breast cancer risk are reproductive history and mammographic density. Epidemiological data suggest that pregnancy induces long-lasting effects in the normal breast, except in BRCA1 and BRCA2 mutation carriers, where pregnancy does not decrease breast cancer risk.
What cells need to be eliminated in the breast to reduce risk? A number of studies have shown that breast epithelial progenitor cells are likely the “cell-of-origin” of breast cancer. It stands to reason then, that eliminating them will abolish tumor development. In recent work we analyzed and characterized multiple cell types from normal breast tissues of nulliparous and parous women, including BRCA1 and BRCA2 mutation carriers. We detected the most significant differences in breast epithelial progenitors and found that the frequency of these cells is higher in women with higher risk of breast cancer. We have also identified key signaling pathways important for their proliferation and showed that by modulating the activity of these pathways we can decrease the frequency of the progenitor cells, thus, potentially reducing breast cancer risk. We propose that the progenitor markers identified can be used for breast cancer risk prediction and that depleting these progenitors will decrease the risk of breast cancer. We are pursuing these studies in large cohorts in women and in rodent models of breast cancer (prevention) with immediate plans to translate our findings to high risk women as the drugs used to deplete these progenitors are already in clinical trials for cancer treatment.
Highlights from our cancer heterogeneity studies: With rare exceptions tumors are thought to originate from a single cell. Yet, at the time of diagnosis the majority of human tumors display startling heterogeneity in many structural and physiological features, such as cell size, shape, metastatic proclivity, and sensitivity to therapy. This diversity within tumors (intratumor) complicates the study and treatment of cancer because small tumor samples may not be representative of the whole tumor and because a treatment that targets one tumor cell population, may not affect another, leading to a poor clinical response. On the positive side, intratumor diversity is a type of “looking glass” for a particular cancer from which we can both learn its past and predict its future.
Until recently, mainstream cancer research has been focusing on the identification and therapeutic targeting of “cancer-driving” genetic alterations. However, recent large-scale sequencing of breast cancer genomes has been disappointing and identified relatively few recurrent mutations that could be explored for therapy. In addition, most of the mutations were detected only in a subset of tumors and at a low frequency, making it difficult to determine their relevance in tumorigenesis. The outcome of these sequencing studies reinforced the already high interest in intratumor heterogeneity. Intratumor heterogeneity for heritable traits is a fundamental challenge in breast cancer, underlying disease progression and treatment resistance. Yet our understanding of its mechanisms, and as a consequence, our ability to control it remains limited. This is largely due to the cancer-gene and cancer cell-focus of mainstream cancer research and the reliance on experimental models that poorly reproduce this key aspect of the human disease.
We have developed a model of intratumor clonal (i.e., group of cells with common ancestry) heterogeneity in breast cancer and utilized this to assess the functional relevance of clonal interactions in metastatic progression. We found that polyclonal tumors were commonly metastatic, even though none of the individual clones present in them showed this behavior in monoclonal tumors. We have also analyzed breast tumor samples before and after pre-operative chemotherapy, or at different stages of disease progression (i.e., primary and metastatic lesions) for the degree of intratumor genetic and phenotypic heterogeneity at the single cell level. We found that tumors with the lowest pretreatment genetic diversity responded the best to treatment and that distant metastatic lesions had higher genetic diversity compared to primary tumors and lymph node metastases. Lastly, we have developed mathematical models based on these experimental data that can infer the evolution of tumors during treatment. Based on these data, we hypothesize that intratumor heterogeneity per se is a driver of metastatic spread and therapeutic resistance. Thus, measures of intratumor heterogeneity can be used to predict the risk of metastasis and to personalize therapy based on this. At the same time, understanding of how heterogeneity within tumors promotes disease progression may reveal new therapeutic targets and would allow us to design more effective and individualized treatment strategies.