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Reiter, Johannes

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Reiter

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Johannes

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Reiter, Johannes

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2016 - 20173

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Author's Publications: search.filters.isAuthorOfPublication.355bc8f2-1a72-43b1-ab5f-adb88ccdc596

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    Origins of lymphatic and distant metastases in human colorectal cancer
    (American Association for the Advancement of Science (AAAS), 2017) Nahrendorf, Kamila; Reiter, Johannes; Brachtel, Elena; Lennerz, Jochen; van de Wetering, Marc; Rowan, Andrew; Cai, Tianxi; Clevers, Hans; Swanton, Charles; Nowak, Martin; Elledge, Stephen; Jain, Rakesh
    The spread of cancer cells from primary tumors to regional lymph nodes is often associated with reduced survival. One prevailing model to explain this association posits that fatal, distant metastases are seeded by lymph node metastases. This view provides a mechanistic basis for the TNM staging system and is the rationale for surgical resection of tumor-draining lymph nodes. Here we examine the evolutionary relationship between primary tumor, lymph node, and distant metastases in human colorectal cancer. Studying 213 archival biopsy samples from 17 patients, we used somatic variants in hypermutable DNA regions to reconstruct high-confidence phylogenetic trees. We found that in 65% of cases, lymphatic and distant metastases arose from independent subclones in the primary tumor, whereas in 35% of cases they shared common subclonal origin. Therefore, two different lineage relationships between lymphatic and distant metastases exist in colorectal cancer.
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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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    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.