Search

What happens in the early and still undetectable human malignancy is unknown because direct observations are impractical. Here we present a “Big Bang” model, whereby a tumor grows predominantly as a single expansion producing numerous intermixed sub-clones, which are not subject to stringent clonal selection. In this model, both public and most detectable private mutations arise during the earliest phase of tumor growth. Multi-scale genomic profiling of 349 individual glands sampled from 15 colorectal tumors revealed the absence of selective sweeps, uniformly high intra-tumor heterogeneity, and sub-clone mixing in distant tumor regions, as postulated by the Big Bang. By integrating the data in a spatial model of tumor growth and statistical inference framework we also verified the most striking prediction of our model, namely that most detectable intra-tumor heterogeneity originates from private alterations acquired early during growth, and not from the later expansion of selected sub-clones. Hence, early sub-clones define the genomic profile of colorectal carcinomas and advanced adenomas, whereas potentially dangerous late-arising sub-clones will go undetected. Moreover, our results suggest that sub-clone mixing may be a biomarker of malignant potential. This new model provides a quantitative framework that explains the origins of intra-tumor heterogeneity and tumor growth dynamics with significant clinical implications. Citation Format: Andrea Sottoriva, Haeyoun Kang, Zhicheng Ma, Trevor A. Graham, Matthew Salomon, Junsong Zhao, Paul Marjoram, Kim Siegmund, Michael F. Press, Darryl Shibata, Christina Curtis. A Big Bang model of human colorectal tumor growth. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-23.

Purpose: Although most stage II colon cancers are potentially curable by surgery alone, ∼20% of patients relapse, suggesting a need for establishing prognostic markers that can identify patients who may benefit from adjuvant chemotherapy. We tested the hypothesis that differences in expression of apoptosis-regulating proteins account for differences in clinical outcome among patients with early-stage colorectal cancer. Experimental Design: Tissue microarray technology was employed to assay the expression of apoptosis-regulating proteins by immunohistochemistry in 106 archival stage II colorectal cancers, making correlations with disease-specific survival. The influence of microsatellite instability (MSI), tumor location (left versus right side), patient age, and gender was also examined. Results: Elevated expression of several apoptosis regulators significantly correlated with either shorter (cIAP2; TUCAN) or longer (Apaf1; Bcl-2) overall survival in univariate and multivariate analyses. These biomarkers retained prognostic significance when adjusting for MSI, tumor location, patient age, and gender. Moreover, certain combinations of apoptosis biomarkers were highly predictive of death risk from cancer. For example, 97% of patients with favorable tumor phenotype of cIAP2low plus TUCANlow were alive at 5 years compared with 60% of other patients (P = 0.00003). In contrast, only 37% of patients with adverse biomarkers (Apaf1low plus TUCANhigh) survived compared with 83% of others at 5 years after diagnosis (P< 0.0001). Conclusions: Immunohistochemical assays directed at detection of certain combinations of apoptosis proteins may provide prognostic information for patients with early-stage colorectal cancer, and therefore could help to identify patients who might benefit from adjuvant chemotherapy or who should be spared it.

In recent years, intra-tumor heterogeneity (ITH) has emerged as a fundamental property of many solid tumors. This feature is the product of cancer evolution and may be implicated in malignant progression and clinical outcome. ITH not only reflects the sequence of malignant events that have occurred in the neoplasm, but it also embeds a signature of the past proliferative history of tumor cells. This signature can be exploited to infer tumor dynamics and to measure properties of the cellular organization that render each cancer distinct. In particular, by integrating multiple types of genomic data derived from different regions of an individual tumor, with a 3-dimensional agent-based model of tumor growth, we can infer clinically relevant cancer parameters directly in human samples. This approach is highly flexible, enabling several malignant processes to be considered, and provides a natural framework with which to interpret the apparent complexity of cancer genomic data, with implications for understanding mechanisms of tumor progression. We demonstrate the utility of this approach on a cohort of breast and colorectal cancers. Citation Format: Andrea Sottoriva, Zhicheng Ma, Haeyoun Kang, Christina Curtis. Exploiting intra-tumor heterogeneity through agent-based models of tumor growth to infer properties of human malignancies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY36-01. doi:10.1158/1538-7445.AM2013-SY36-01