Disease Progression Modeling of Bladder Cancer

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT Carcinogenesis may be viewed as a multistep evolutionary process characterized by accumulation of genetic and epigenetic alterations, driven by selective pressures imposed by the microenvironment. The delineation of tumor evolution would provide invaluable insights into tumor biology and lay a foundation for the development of improved diagnostics, prognostics and targeted therapeutics. Time-series data are ideal for deriving models of dynamic progression, but this is impossible to collect in human cancer because of the need for timely surgical intervention and systemic therapy, which alter the natural history of the disease and exert selection pressures that affect tumor evolution. To overcome the human serial sampling issue, we have devised a computational strategy to understand cancer evolution by deriving pseudo time-series data from ‘static’ samples (excised tissue specimens). The design is based on the rationale that each sample can provide a snapshot of the disease process, and if the number of samples is sufficiently large we can recover a visualization of disease progression. We demonstrated the utility of the developed pipeline - referred to as CancerMapp - by applying it to the analysis of gene expression data from over 9,000 breast tissue samples. Breast cancer progression modeling identified 2 major trajectories to malignancy – an early split to basal tumors, and a continuum through luminal tumors. The computational approach and the breast cancer model concept have since been validated in independent studies, and our findings have provided the impetus for a number of investigations at our institute and by colleagues in the field. Built logically on our previous work, we now propose a large-scale interdisciplinary research plan to derive a progression model for bladder cancer (BLCA). BLCA is among the five most common malignancies worldwide. In the US alone, new cases for 2018 are estimated at 72,500 with estimated deaths at over 15,000, figures that are anticipated to increase in the near future. Classification of BLCA into multiple molecular subtypes has recently been proposed and has the potential to impact clinical management. Nonetheless, significant biologic subgroup heterogeneity remains, and more work is needed before a unified classification system can gain wide acceptance. More importantly, there is, as yet, no understanding of the inter-relationships between subtypes. Insights into how subtypes are related and how cancer evolution influences the observed changes in molecular pathologic phenotype is the next level of analysis required and is the focus of this proposal. The proposed work will inform a range of research directions that were previously unattainable. The derivation of a BLCA roadmap and the identification of pivotal molecular events that drive stepwise cancer progression will provide new insights into tumor biology and guide the development of improved cancer diagnostics, prognostics and targeted therapeutics. Annotated progression maps can also guide the design of clinical trials and animal studies to focus on pivotal points of cancer development, which may yield the best return with limited resources.