Dataset Information

Molecular Landscape and Treatment Options for Patients with Metastatic Colorectal Cancer.

ABSTRACT: Over the last 20 years, median survival for patients with metastatic colorectal cancer (CRC) has remarkably improved from about 12 to over 30 months, mainly because of the development of new agents and patient selection using predictive biomarkers. However, the identification of the most effective treatment for an individual patient is still a challenge. Molecular profiling of CRC has made great progress, but it is limited by tumor heterogeneity and absence of driver mutation. However, RAS, BRAF, and microsatellite instability are validated biomarker recommended by NCCN and ESMO. In this review, we discuss recent advances and future developments.

SUBMITTER: Miyamoto Y

PROVIDER: S-EPMC5705494 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Molecular Landscape and Treatment Options for Patients with Metastatic Colorectal Cancer.

Miyamoto Yuji Y Zhang Wu W Lenz Heinz-Josef HJ

Indian journal of surgical oncology 20160727 4

Over the last 20 years, median survival for patients with metastatic colorectal cancer (CRC) has remarkably improved from about 12 to over 30 months, mainly because of the development of new agents and patient selection using predictive biomarkers. However, the identification of the most effective treatment for an individual patient is still a challenge. Molecular profiling of CRC has made great progress, but it is limited by tumor heterogeneity and absence of driver mutation. However, RAS, BRAF ...[more]

PMID: 29203992

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Project description:The approval of targeted therapies for metastatic colorectal cancer (mCRC) has led to important improvements in patient outcomes. However, it is still necessary to increase individualisation of treatments based on tumour genetic profiles to optimise efficacy, while minimising toxicity. As such, there is currently great focus on the discovery and validation of further biomarkers in mCRC, with many new potential prognostic and predictive markers being identified alongside developments in patient molecular profiling technologies. Here, we review data for validated and emerging biomarkers impacting treatment strategies in mCRC. We completed a structured literature search of the PubMed database to identify relevant publications, limiting for English-language publications published between 1 January 2014 and 11 July 2018. In addition, we performed a manual search of the key general oncology and CRC-focused congresses to identify abstracts reporting emerging mCRC biomarker data, and of ClinicalTrials.gov to identify ongoing clinical trials investigating emerging biomarkers in mCRC and/or molecular-guided clinical trials. There is solid evidence supporting the use of BRAF status as a prognostic biomarker and DYPD, UGT1A1, RAS, and microsatellite instability as predictive biomarkers in mCRC. There are a number of emerging biomarkers that may prove to be clinically relevant in the future to have prognostic (HPP1 methylation), predictive (HER3, microRNAs, anti-angiogenic markers, and CRC intrinsic subtypes), or both prognostic and predictive values (HER2, CpG island methylator phenotype, tumour mutational load, gene fusions, and consensus molecular subtypes). As such, new biomarker-led treatment strategies in addition to anti-epidermal growth factor receptor and anti-angiogenetic treatments are being explored. Biomarkers that are not recommended to be tested in clinical practice or are unlikely to be imminently clinically relevant for mCRC include thymidylate transferase, ERCC1, PIK3CA, and PTEN. We highlight the clinical utility of existing and emerging biomarkers in mCRC and provide recommended treatment strategies according to the biomarker status. An update on ongoing molecular-guided clinical trials is also provided.

Dataset Information

Molecular Landscape and Treatment Options for Patients with Metastatic Colorectal Cancer.

Publications

Molecular Landscape and Treatment Options for Patients with Metastatic Colorectal Cancer.

Similar Datasets

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