Understanding the somatic mutations that drive cancer progression

Tumor profiling with next-generation sequencing enables accurate variant identification among many cancer types

Somatic Mutations

Molecular profiling is critical for identifying and characterizing the unique somatic mutations that accrue in cancer cells. Comprehensive tumor and blood profiles can help to identify biomarkers that are prognostic or predictive, relevant in clinical trials, or cited in recent clinical studies.

Next-generation sequencing (NGS) is becoming more widely adopted as a valuable method for somatic mutation analysis in cancer. NGS offers high sensitivity, ease of use, and accurate data quality for identifying even rare mutations successfully. These advantages are driving increased adoption of NGS in clinical cancer research.

Pan-Cancer Profiling

Although all cancers are unique, many share common driver mutations. Pan-cancer profiling enables researchers to detect relevant somatic mutations and other variants across multiple cancer types, regardless of tumor origin.

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Hematological Cancer Profiling

Liquid tumors, such as myeloid and lymphoid malignancies, often carry somatic mutations that are distinct from solid tumors. Molecular profiling of hematological cancers assesses mutiple relevant genes and classes of genetic mutations at one time.

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Solid Tumor Profiling

Targeted sequencing of solid tumors can identify somatic mutations associated with cancer, even among highly heterogeneous tissues. Visit our Targeted Cancer Sequencing page and view the "Solid Tumor" tab to find solutions for studying solid tumors.

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All Cancer Research Products

View a comprehensive list of Illumina cancer research products; filter to narrow your results. To find products for somatic mutation detection, filter by "Somatic Variants" (under "Variant Class").

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Tumor-Normal Comparisons

Through tumor-normal whole-genome sequencing, researchers can comprehensively compare tumor and normal samples to identify somatic mutations and other alterations in both coding and noncoding regions of the genome. This hypothesis-free approach highlights the genetic changes specific to a tumor sample relative to a matched normal sample in a genome-wide manner.

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Deep Sequencing

Because of tumor heterogeneity and normal cell contamination, sequencing of a tumor sample often identifies a mix of DNA signatures representing the constituent cell types of that particular tumor sample. Thus, detection of true somatic mutations requires deep sequencing to detect driver mutations at low allelic fractions within the sample.

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Featured Somatic Mutation Articles

Glioma sample
NGS for Brain Tumor Studies

Se Hoon Kim, MD, PhD discusses studies of glioma genetic markers and explains why he believes somatic mutation profiling with NGS will become routine in clinical practice.

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NGS Implementation in Diagnostic Labs
NGS Implementation in Oncology

Key opinion leaders discuss the ongoing challenges and future potential of NGS in clinical oncology, as well as considerations for somatic and germline mutation detection.

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Tobacco leaves
The Genetic Basis of Oral Cancer

Researchers identify a broad range of genomic alterations associated with a deadly oral cancer, including somatic mutations.

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Somatic Variant Detection with the DRAGEN Bio-IT Platform

The Illumina DRAGEN (Dynamic Read Analysis for GENomics) Bio-IT Platform provides accurate, ultra-rapid secondary analysis of NGS data. A variety of analysis apps are available, including the DRAGEN Somatic Pipeline, which includes tumor-only and tumor–normal modes for detecting somatic variants in whole-genome and whole-exome sequencing samples.

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Cancer Whole-Genome Sequencing

Get  a genome-wide view of somatic mutations and other genomic alterations present in cancer tissue, and discover novel cancer-associated variants.

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Cancer Exome Sequencing

Focus on sequencing coding regions, which frequently contain mutations that affect tumor progression, for a cost-effective approach.

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Cancer RNA Sequencing

Understand the functional effects of somatic mutations by detecting gene fusions, novel transcript isoforms, and changes in transcript abundance.

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