Christina Curtis, Ph.D. - US grants

DESCRIPTION (provided by applicant): Therapeutic resistance is a major cause of patient mortality, and is nearly universal in solid tumors, including breast cancer. For example, trastuzumab (herceptin) is the archetype targeted therapy for the 20% of human epidermal growth factor receptor 2 (HER2)-positive (HER2+) breast cancer patients, but treatment only partially lowers the risk of recurrence in early stage disease, and is not curative in the advanced setting. While both cancer stem cells (CSCs) and intra-tumor heterogeneity (ITH) are thought to contribute to tumor progression and resistance, mechanisms of resistance remain poorly characterized in the human system, and will only be addressed when resistant subclones are identified and successfully targeted. Although the apparent chaos that characterizes cancer genomes is daunting, tumors are governed by evolutionary principles that can be measured and exploited. However, quantitative approaches that account for clonal evolution, ITH, and CSCs are needed. To this end, we have developed an innovative experimental and computational framework that exploits the fact that somatically acquired report on the past proliferative history of cancer cells and can be used to infer their subclonal architecture and evolutionary trajectories. By integrating genomic profiles from patient samples in a multi-scale model of tumor growth and statistical inference framework, this approach enables measurement of the dynamics of clonal expansions and patient-specific parameters. We hypothesize that a detailed characterization of tumor evolutionary dynamics and molecular changes in clinical samples during treatment will enable the unbiased identification of novel biomarkers and mechanisms of resistance. Given that HER2 is a validated therapeutic target for which several effective, but imperfect treatments exist, this is an excellent model in which to understand mechanisms of resistance. We propose an integrated molecular analysis of serial tissue specimens from HER2+ breast cancer patients treated in clinical trials with neoadjuvant single and dual agent HER2-targeted therapies to identify biomarkers of resistance (Aim 1). The genomic data will be analyzed in our computational framework to quantify CSC dynamics and temporal patterns of clonal evolution under treatment selective pressure (Aim 2). We will further characterize mechanisms of resistance, treatment-associated temporal molecular changes, and resistant subpopulations using patient- derived xenograft models and short-term primary patient cultures (Aim 3). By interrogating clonal evolution during therapy, our innovative approach will identify mechanisms of resistance and tumor dynamics that inform biomarker-driven treatment strategies. This strategy represents a new paradigm for treatment stratification with broad utility for other cancers.

PROJECT SUMMARY/ABSTRACT The deluge of multi-scale ?omics? data from The Cancer Genome Atlas Project (TCGA) and other cancer profiling projects has revealed remarkable genetic and epigenetic complexity and tremendous intrapatient variation. Accordingly, a particularly acute need exists for accurate, scalable human cancer models that can functionally interrogate these extensive datasets, identify driver oncogenic events from benign passengers and characterize their relevance to treatment response. For the last four years the Stanford Cancer Target Discovery and Development (CTD2) Center has pursued human ?organoid? culture methods for cancer modeling and driver oncogene discovery. Primary 3D organoid cultures afford the unusual opportunity to initiate cancer de novo within the epi/genetic ?tabula rasa? of cultured primary human wild-type tissue, versus the corresponding and often poorly-defined complexity of long- passaged 2D cancer cell lines. This creates a highly defined baseline for cancer modeling and functional driver oncogene validation that is leveraged throughout. Our overall approach applies state-of-the-art systems biology and robust computational resources to large-scale cancer profiling datasets, thus nominating candidate drivers that undergo direct functional evaluation in human organoid culture. This experimental scope leverages a highly synergistic team of Calvin Kuo (reporting PI, organoids), Hanlee Ji (multi-PI, cancer ITH, genomics), Christina Curtis (multi-PI, tumor evolution, cancer systems biology), Olivier Gevaert (cancer systems biology, epigenetics) and Michael Bassik (high-throughput functional genomics). Accordingly, Aims 1 and 2 couple bioinformatic prioritization of TCGA copy number alteration (CNA) and methylation data for driver discovery via organoid-based barcoded lentiviral screens and orthogonal cDNA, shRNA and CRISPR approaches. Aim 3 exploits the ability to longitudinally observe de novo genomic and epigenomic evolution in oncogene- engineered wild-type organoids to nominate networks of cooperating oncogenes that undergo iterative organoid functional validation. Lastly, Aim 4 explores the utility of organoids to model de novo treatment resistance, using archetypal targeted and chemotherapy perturbagens as proof-of-principle and employing single cell RNA- seq/intratumoral heterogeneity and exome sequencing endpoints.

Summary Clonal evolution is the driving force behind many current public health issues such as cancer and infectious disease. However, limited efforts have been invested in treating and preventing these conditions from an evolutionary perspective. Critically, the ability to forecast tumor evolution depends on the relative contribution of deterministic and stochastic processes. Although direct observations of human tumor evolution are impractical, patterns of somatic alterations amongst cells within a tumor faithfully report on their past proliferative history. Unexpectedly, we recently found that after transformation, some tumors grow in the absence of stringent selection, compatible with effectively neutral evolution. This led to our description of a novel Big Bang model of tumor growth where the tumor grows as a single terminal expansion populated by numerous heterogeneous?and effectively equally fit subclones. This new model contrasts with the de facto sequential clonal expansion model, and suggests that tumor-initiating events are both necessary and sufficient to propagate subsequent growth. Moreover, these findings raise the tantalizing possibility that the earliest events during tumor growth shape its subsequent evolutionary trajectory. Here we rigorously test the novel hypothesis that early tumor evolution is deterministic and seek to define its contingencies. We thus perform oncogene-engineering and cellular barcoding of wild-type human organoids to characterize clonal dynamics and the functional determinants of increased fitness during in vitro tumor evolution. This innovative lineage tracing strategy enables the direct measurement of evolutionary parameters in human cells, while rendering a comprehensive genotype to phenotype map during tumor progression. In parallel, we will infer the timing of metastatic dissemination and evaluate whether the metastatic phenotype is specified early through computational and mathematical modeling of patient genomic data. This systems biology approach will evaluate the predictability of tumor evolution towards the development of models to forecast disease progression and guide earlier detection, thereby reducing cancer related mortality. ! !