Dynamik der Zellschicksalsentscheidungen nach Brustkrebs-Strahlentherapie

Most breast cancer patients receive adjuvant radiation therapy. Despite its success, still many patients gain little or no benefit from this treatment, as evidenced from the elevated rates of recurrence, distant metastatic spread, and breast cancer deaths. Currently there are no robust biomarkers to predict the outcome of radiotherapy. Variability in response to radiation might be due to the heterogeneity of breast cancers cells; cells show wide ranges of radioresistance and capability to adapt to changing environments. Our ultimate aim is to develop tumor subtype-specific radiation strategies that maximize the damage in cancerous cells while minimizing the damage to normal cells. Recent studies have shown that radiotherapy is particularly effective for classical breast cancers subtypes luminal A and HER2. This suggests a potential connection between breast cancer subtype and radio-sensitivity. We will study how single genetic modifications frequently found in breast cancer subtypes determine changes in the cellular fate. Current therapies only use three classical markers to distinguish tumor subtypes, and follow a standardized protocol of daily radiation doses, given over 5 to 6 weeks. Upon DNA damage cells initiate diverse cellular programs ranging from DNA repair and transient cell cycle arrest to terminal fates such as cell death and senescence. The connection between breast cancer subtypes, with their associated genetic portraits, to each of these alternative cellular outcomes remains largely unknown. In addition, the optimal radiation frequency for destroying various breast cancer subtypes has never been explored experimentally. In Aim 1 we will combine experimental approach at the single-cell level together with computational tools to quantitatively investigate the temporal response and fate of a collection of cells upon different frequencies of radiation. We will study the response of a collection of cells carrying single somatic modifications frequently found in breast cancer. In our second aim we will determine how cycling cell-intrinsic factors affect cellular outcomes. We will track internal cellular states of a subset of lines selected from Aim 1, and follow their dynamics in response to radiation with the goal of determining why different cells carrying different modifications show different outcomes in response to a similar treatment. This will help to identify the optimal cellular state for treatment. In Aim 3 we will use a mouse xenograft model to validate the optimal frequency and initial cellular state for destroying breast tumors in-vivo. We will implement computational tools for automatized single-cell tracking, statistical analysis of the single-cell data, and develop simple mathematical models to correlate cellular states with cell-fate in response to various radiation frequencies. This interdisciplinary study will identify optimal timings for radiation tailored to specific mutations frequently found in breast cancer.