Abstract SY22-01: Multiscale perspective of combination therapy for human cancer

Until we can fully resolve the cellular and molecular heterogeneity of human cancer and explain the intra- and inter-cellular coregulation that drives cancer progression, we will continue designing combination therapies by working from clues at lower scales of resolution. One approach to extract more comprehensive knowledge of human cancer from these lower resolved scales is to generate an integrated view of the disease that is consistent across these scales. This presentation analyzes combination cancer therapy at three scales that are rarely studied together: (1) protein measurements at the scale of single cells, (2) gene regulatory network reconstructions at the tissue scale, and (3) survival outcomes at the population scale. At each scale, we describe novel biocomputational efforts aimed at developing a rationale for targeted combination therapy, identifying a design for combination therapy, or estimating the effect of combination therapy. Cellular Scale: Rationalizing Targeted Combination Therapy Perhaps the most pressing rationale for combination therapy for cancer is based on its cellular heterogeneity. Cancer is known to acquire mutations as it progresses but increasingly it is being recognized as also having a hierarchical structure composed of stem cells, progenitor and further differentiated cell types. Targeting the tumorigenic component of cancer has become a pursuit that not only aims to identify the cancer stem cell, but also identify its cell of origin and explain its coregulation within its lineage and microenvironment. We developed a class of biocomputational algorithms to identify a hierarchical cellular structure underlying the heterogeneity of cancer and suggest combined therapeutic strategies that target multiple malignant and pro-malignant cell types. In particular, SPADE is an algorithm that is designed to infer a cellular hierarchy from flow and mass cytometry datasets (Qui et al., Nature Biotech 2011) and recently was applied to analyze normal human bone marrow and hematological malignancies (Bendall et al., Science 2011). We will demonstrate the application of SPADE in AML and its implications for targeted combination therapy. Tissue Scale: Designing Combination Therapy To identify molecular drug targets for cancer, numerous biocomputational approaches have been proposed to identify key genes that drive cancer progression using the high-throughput genomic data, often derived from human tumors composed of malignant and non-malignant cells. Many of these approaches rely on gene expression microarray data to reverse-engineer gene regulatory networks that are then used to infer likely cancer drivers and their targets. We propose a biocomputational network-based approach to identify such relationships by integrating a variety of genomic data. In our approach, for a given gene to be considered a candidate driver, we require that its gene expression be influenced by its own genomic alternations, measured by its DNA copy number, methylation and/or mutation variation. Our rationale is that genes that are influenced by multiple genomic events in a significant subset of samples are unlikely to be randomly deregulated. We then apply a variant of module network analysis (Segal et al., Bioinformatics 1993) to determine the downstream targets of the potential driver genes. This approach essentially clusters the gene expression data into gene modules of co-expressed genes and assigns a regulatory program to each module. The regulatory program of each module is defined by a sparse linear combination of driver genes that predict the module9s expression. Gene modules that are correlated with a drug response are selected and the genes comprising their regulatory program are identified as the potential drivers related to drug response. Enrichment analysis of these gene modules allows us to identify the key biological processes that are driven by the regulators/drivers. We applied this strategy to identify candidate drivers of drug response for ovarian cancer using data from The Cancer Genome Atlas (TCGA). Several gene modules are correlated to the response of several ovarian cancer drugs including gemcitabine, doxorubicin and topotecan, suggestive of a role for combinational therapy. Population Scale: Estimating the Efficacy of Combination Therapy Often the survival efficacy of cancer therapy is reported using a proportional hazards model, which assumes a constant treatment effect over time relative to a baseline survival. We propose an alternative hypothesis wherein some treatments have early survival effects, that is, they are effective only shortly after use, whereas other treatments have late survival effects, and combination treatments have mixed time-dependent effects that are not necessarily additive. Understanding the time-dependent nature of adjuvant therapies can provide biological insights about the effect of the drug on the disease, including whether the drug is curative or delays progression. To find the evidence to support the hypothesis of time-dependent effects of adjuvant therapy, we analyze breast cancer survival trends from the National Cancer Institute9s tumor registry on Surveillance, Epidemiology and End Results (SEER). Our analysis relies on first back-calculating biomarker-specific breast cancer survival curves in the absence of screening and all adjuvant therapies. This back-calculation allows us to then analyze time-dependent effects on annual hazards of breast cancer death as new interventions were introduced in the US. It also enables us to compare the observed survival trends to counterfactual possibilities. We will illustrate examples in the case of breast cancer with ER- and her2-targeted combination therapies. Summary Undoubtedly the rationale, design and efficacy of combination therapy can be derived from a variety of perspectives. In this presentation, we motivate an alternative view of combination therapies by proposing a multiscale perspective. We focus on the rationale for combination therapy at the cellular scale, the design of combination therapy at the tissue scale, and the efficacy of combination therapy at the population scale, for varying malignancies. The obvious next step is to pursue the rationale, design and efficacy of combination therapy by considering all three (and more) scales simultaneously in one malignancy. We conclude with a perspective on biocomputation as a means to achieve multiscale integration that offers a more comprehensive understanding of combination therapy for human cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY22-01. doi:1538-7445.AM2012-SY22-01