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Gerold, Jeff

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Gerold

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Gerold, Jeff
Author's Publications: search.filters.isAuthorOfPublication.1bb0805c-800d-48c6-ad7e-0668f828a932

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    Quantifying Clonal and Subclonal Passenger Mutations in Cancer Evolution
    (Public Library of Science, 2016) Bozic, Ivana; Gerold, Jeff; Nowak, Martin
    The vast majority of mutations in the exome of cancer cells are passengers, which do not affect the reproductive rate of the cell. Passengers can provide important information about the evolutionary history of an individual cancer, and serve as a molecular clock. Passengers can also become targets for immunotherapy or confer resistance to treatment. We study the stochastic expansion of a population of cancer cells describing the growth of primary tumors or metastatic lesions. We first analyze the process by looking forward in time and calculate the fixation probabilities and frequencies of successive passenger mutations ordered by their time of appearance. We compute the likelihood of specific evolutionary trees, thereby informing the phylogenetic reconstruction of cancer evolution in individual patients. Next, we derive results looking backward in time: for a given subclonal mutation we estimate the number of cancer cells that were present at the time when that mutation arose. We derive exact formulas for the expected numbers of subclonal mutations of any frequency. Fitting this formula to cancer sequencing data leads to an estimate for the ratio of birth and death rates of cancer cells during the early stages of clonal expansion.
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    Abstract 2374: Reconstructing the evolutionary history of metastatic cancers
    (American Association for Cancer Research (AACR), 2016) Reiter, Johannes; Makohon-Moore, Alvin P.; Gerold, Jeff; Bozic, Ivana; Chatterjee, Krishnendu; Iacobuzio-Donahue, Christine A.; Vogelstein, Bert; Nowak, Martin
    Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.
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    Publication
    Reconstructing metastatic seeding patterns of human cancers
    (Springer Nature, 2017) Reiter, Johannes; Makohon-Moore, Alvin P.; Gerold, Jeff; Bozic, Ivana; Chatterjee, Krishnendu; Iacobuzio-Donahue, Christine A.; Vogelstein, Bert; Nowak, Martin
    Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. In silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (15-95%) and sequencing depths (25-800x) demonstrates the accuracy of Treeomics compared to existing methods.