Showing papers by "Andrea Sottoriva published in 2016"

Identification of neutral tumor evolution across cancer types.

Abstract: Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine

Abstract: The ability to predict the future behavior of an individual cancer is crucial for precision cancer medicine. The discovery of extensive intratumor heterogeneity and ongoing clonal adaptation in human tumors substantiated the notion of cancer as an evolutionary process. Random events are inherent in evolution and tumor spatial structures hinder the efficacy of selection, which is the only deterministic evolutionary force. This review outlines how the interaction of these stochastic and deterministic processes, which have been extensively studied in evolutionary biology, limits cancer predictability and develops evolutionary strategies to improve predictions. Understanding and advancing the cancer predictability horizon is crucial to improve precision medicine outcomes.

EGFRvIII mutations can emerge as late and heterogenous events in glioblastoma development and promote angiogenesis through Src activation

Abstract: Background Amplification of the epidermal growth factor receptor (EGFR) and its mutant EGFRvIII are among the most common genetic alterations in glioblastoma (GBM), the most frequent and most aggressive primary brain tumor. Methods In the present work, we analyzed the clonal evolution of these major EGFR aberrations in a small cohort of GBM patients using a unique surgical multisampling technique. Furthermore, we overexpressed both receptors separately and together in 2 patient-derived GBM stem cell lines (GSCs) to analyze their functions in vivo in orthotopic xenograft models. Results In human GBM biopsies, we identified EGFR amplification as an early event because EGFRvIII mutations emerge from intratumoral heterogeneity later in tumor development. To investigate the biological relevance of this distinct developmental pattern, we established experimental model systems. In these models, EGFR+ tumor cells showed activation of classical downstream signaling pathways upon EGF stimulation and displayed enhanced invasive growth without evidence of angiogenesis in vivo. In contrast, EGFRvIII+ tumors were driven by activation of the prototypical Src family kinase c-Src that promoted VEGF secretion leading to angiogenic tumor growth. Conclusions The presented work shows that sequential EGFR amplification and EGFRvIII mutations might represent concerted evolutionary events that drive the aggressive nature of GBM by promoting invasion and angiogenesis via distinct signaling pathways. In particular, c-SRC may be an attractive therapeutic target for tumors harboring EGFRvIII as we identified this protein specifically mediating angiogenic tumor growth downstream of EGFRvIII.

The cancer stem cell fraction in hierarchically organized tumors can be estimated using mathematical modeling and patient-specific treatment trajectories

Abstract: Many tumors are hierarchically organized and driven by a subpopulation of tumor-initiating cells (TIC), or cancer stem cells. TICs are uniquely capable of recapitulating the tumor and are thought to be highly resistant to radio- and chemotherapy. Macroscopic patterns of tumor expansion before treatment and tumor regression during treatment are tied to the dynamics of TICs. Until now, the quantitative information about the fraction of TICs from macroscopic tumor burden trajectories could not be inferred. In this study, we generated a quantitative method based on a mathematical model that describes hierarchically organized tumor dynamics and patient-derived tumor burden information. The method identifies two characteristic equilibrium TIC regimes during expansion and regression. We show that tumor expansion and regression curves can be leveraged to infer estimates of the TIC fraction in individual patients at detection and after continued therapy. Furthermore, our method is parameter-free; it solely requires the knowledge of a patient's tumor burden over multiple time points to reveal microscopic properties of the malignancy. We demonstrate proof of concept in the case of chronic myeloid leukemia (CML), wherein our model recapitulated the clinical history of the disease in two independent patient cohorts. On the basis of patient-specific treatment responses in CML, we predict that after one year of targeted treatment, the fraction of TICs increases 100-fold and continues to increase up to 1,000-fold after 5 years of treatment. Our novel framework may significantly influence the implementation of personalized treatment strategies and has the potential for rapid translation into the clinic. Cancer Res; 76(7); 1705-13. ©2016 AACR.

Quantification of subclonal selection in cancer from bulk sequencing data

Abstract: Recent studies have identified prevalent subclonal architectures within many cancer types. However, the temporal evolutionary dynamics that produce these subclonal architectures remain unknown. Here we measure evolutionary dynamics in primary human cancers using computational modelling of clonal selection applied to high throughput sequencing data. Our approach simultaneously determines the subclonal architecture of a tumour sample, and measures the mutation rate, the selective advantage, and the time of appearance of subclones. Simulations demonstrate the accuracy of the method, and revealed the degree to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from gastric and lung cancers revealed that detectable subclones consistently emerged early during tumour growth and had considerably large fitness advantages (>20% growth advantage). Our quantitative platform provides new insight into the evolutionary history of cancers by facilitating the measurement of fundamental evolutionary parameters in individual patients.

Functional versus non-functional intratumor heterogeneity in cancer.

Abstract: Next-generation sequencing data from human cancers are often difficult to interpret within the context of tumor evolution. We developed a mathematical model describing the accumulation of mutations under neutral evolutionary dynamics and showed that 323/904 cancers (∼30%) from multiple types were consistent with the neutral model of tumor evolution.

Population Genetics of Neoplasms

Abstract: Cancer is a complex disease of the genome that arises from the interplay of numerous underlying biological processes occurring within and between cells. Cancer population genetics aims at investigating malignant dynamics by studying the distribution of somatic alterations in cancer cell populations. Such aberrant DNA modifications lead to the development of cellular malignant traits like cancer invasion, metastasis and therapy resistance. The study of population genetics in neoplasms integrates mathematical modeling of evolving populations with molecular and epidemiological cancer data. The goal is to infer fundamental properties of tumors and predict the progression of the disease. With the ever-growing amount of data produced by genomic techniques, cancer population genetics represents a quantitative tool to begin making sense to this massive amount of information.

TITLE: The cancer stem cell fraction in hierarchically

Abstract: 4 RUNNING TITLE: Estimating the fraction of cancer stem cells in tumors 5 6 Benjamin Werner, Jacob G. Scott, Andrea Sottoriva, Alexander R. A. Anderson, 7 Arne Traulsen, Philipp M. Altrock* 8 Centre for Evolution and Cancer, The Institute of Cancer Research, Sutton, London SM2 5NG, UK 9 Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Ploen, GER 10 Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, 11 Tampa, FL 33612, USA 12 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 13 02215, USA 14 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA 15 Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA 16