Dataset Information

Integrated Genomics for Pinpointing Survival Loci within Arm-Level Somatic Copy Number Alterations.

ABSTRACT: The identification of driver loci underlying arm-level somatic copy number alterations (SCNAs) in cancer has remained challenging and incomplete. Here, we assess the relative impact and present a detailed landscape of arm-level SCNAs in 10,985 patient samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Furthermore, using chromosome 9p loss in lower grade glioma (LGG) as a model, we employ a unique multi-tiered genomic dissection strategy using 540 patients from three independent LGG datasets to identify genetic loci that govern tumor aggressiveness and poor survival. This comprehensive approach uncovered several 9p loss-specific prognostic markers, validated existing ones, and redefined the impact of CDKN2A loss in LGG.

SUBMITTER: Roy DM

PROVIDER: S-EPMC4864611 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Integrated Genomics for Pinpointing Survival Loci within Arm-Level Somatic Copy Number Alterations.

Roy David M DM Walsh Logan A LA Desrichard Alexis A Huse Jason T JT Wu Wei W Gao JianJiong J Bose Promita P Lee William W Chan Timothy A TA

Cancer cell 20160501 5

The identification of driver loci underlying arm-level somatic copy number alterations (SCNAs) in cancer has remained challenging and incomplete. Here, we assess the relative impact and present a detailed landscape of arm-level SCNAs in 10,985 patient samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Furthermore, using chromosome 9p loss in lower grade glioma (LGG) as a model, we employ a unique multi-tiered genomic dissection strategy using 540 patients from three independent ...[more]

PMID: 27165745

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Project description:Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease.SignificanceThe chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

Dataset Information

Integrated Genomics for Pinpointing Survival Loci within Arm-Level Somatic Copy Number Alterations.

Publications

Integrated Genomics for Pinpointing Survival Loci within Arm-Level Somatic Copy Number Alterations.

Similar Datasets

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