Dataset Information

New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach.

ABSTRACT: Next-generation sequencing (NGS) technology has advanced knowledge of the genomic landscape of ovarian cancer, leading to an innovative molecular classification of the disease. However, patient survival and response to platinum-based treatments are still not predictable based on the tumor genetic profile. This retrospective study characterized the repertoire of somatic mutations in advanced ovarian cancer to identify tumor genetic markers predictive of platinum chemo-resistance and prognosis. Using targeted NGS, 79 primary advanced (III-IV stage, tumor grade G2-3) ovarian cancer tumors, including 64 high-grade serous ovarian cancers (HGSOCs), were screened with a 26 cancer-genes panel. Patients, enrolled between 1995 and 2011, underwent primary debulking surgery (PDS) with optimal residual disease (RD < 1 cm) and platinum-based chemotherapy as first-line treatment. We found a heterogeneous mutational landscape in some uncommon ovarian histotypes and in HGSOC tumor samples with relevance in predicting platinum sensitivity. In particular, we identified a poor prognostic signature in patients with HGSOC harboring concurrent mutations in two driver actionable genes of the panel. The tumor heterogeneity described, sheds light on the translational potential of targeted NGS approach for the identification of subgroups of patients with distinct therapeutic vulnerabilities, that are modulated by the specific mutational profile expressed by the ovarian tumor.

SUBMITTER: Garziera M

PROVIDER: S-EPMC6627128 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach.

Garziera Marica M Roncato Rossana R Montico Marcella M De Mattia Elena E Gagno Sara S Poletto Elena E Scalone Simona S Canzonieri Vincenzo V Giorda Giorgio G Sorio Roberto R Cecchin Erika E Toffoli Giuseppe G

Cells 20190614 6

Next-generation sequencing (NGS) technology has advanced knowledge of the genomic landscape of ovarian cancer, leading to an innovative molecular classification of the disease. However, patient survival and response to platinum-based treatments are still not predictable based on the tumor genetic profile. This retrospective study characterized the repertoire of somatic mutations in advanced ovarian cancer to identify tumor genetic markers predictive of platinum chemo-resistance and prognosis. Us ...[more]

PMID: 31197119

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Project description:BackgroundIn recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.ResultsGAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.ConclusionsThe difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.

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Dataset Information

New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach.

Publications

New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach.

Similar Datasets

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