Cancer is a disease of the genome, and is no longer seen as just one homogenous disease, but a collection of hundreds of diseases, each driven by unique genomic characteristics.¹ Mutation classes include single nucleotide variants (SNVs), copy number alterations (CNAs), rearrangements and insertions and deletions (indels), and a single tumor could have multiple types of mutations in more than one gene.

Certain mutations are actionable and can be targeted by therapeutic intervention. Additionally, some mutations are driver mutations, occurring in tumor suppressor genes or oncogenes and contribute to cancer progression. Others are passenger mutations, which are acquired during the evolution of cancer but do not contribute toward disease progression.

Several molecular testing strategies have been developed to detect these genetic alterations.

Single-gene testing

First-generation sequencing has been used for the identification of single genes, such as EGFR or KRAS for lung cancer testing. PCR has been used for testing EGFR, while fluorescence in situ hybridization (FISH) is utilized for detecting ALK and ROS1 rearrangements in NSCLC.

In many cases, single-gene testing has historically been viewed as a cost-effective approach, typically due to simpler workflows and producing rapid results. However, recent studies show advantages and cost-savings in employing a multi-gene testing approach.^2-4

Since multiple gene alterations are implicated in cancers, testing for a single gene results in the potential loss of information from patient samples. In general, acquiring biopsies is challenging, particular for certain cancer types like NSCLC. Reports demonstrate that approximately 30% of single-gene tests fail because of inadequate biopsy samples or DNA for testing.⁵ Such inadequacies and failure to yield enough information about a patient’s cancer type limits a clinician’s ability to make accurate and informed decisions about care.

Multiplex hot-spot testing

Advances in molecular techniques have led to the use of multiplex panels with mutational hot-spot testing. These assays are either PCR, FISH or NGS-based, targeting several actionable mutations.

While able to detect more mutations than single-gene testing, hot-spot tests cannot detect all four major classes of mutations in a single workflow. For example, some ALK rearrangements in NSCLC were found to be negative with FISH but were later detected by comprehensive genomic profiling (CGP).⁶ Furthermore, while these tests can identify SNVs and some indels, these assays can miss CNVs and chromosomal rearrangements.⁷ Focusing on a limited number of genes may also increase the risk of missing new mutations associated with disease progression or response to treatment.⁸

To that end, CGP is an NGS approach that can detect hundreds of genetic mutations within a single workflow, enabling a more complete evaluation of cancer mutations and being able to address challenges associated with other techniques.

Comprehensive genomic profiling (CGP)

CGP uses the power of massively parallel NGS to sequence genes with somatic alterations that have been implicated in cancer, identifying all four major types of mutations and clinically relevant and actionable mutations. CGP also enables the detection of the genomic signatures of tumor mutational burden (TMB), microsatellite instability (MSI) and loss of heterozygosity (LOH).

For example, studies in lung cancer demonstrate that CGP is an effective method to test a multitude of alterations in one test.⁶ Additionally, a major advantage of CGP is its scalability and the ability to identify mutations across the genome with high sensitivity and specificity.⁹ Given the difficulties in obtaining patient samples, CGP is an ideal testing solution to analyze hundreds of mutations from a single sample.

CGP also affords a pan-cancer approach to tumor profiling. Whereas many tumors can share common mutations, an individual tumor can harbor different mutations. Therefore, a comprehensive approach of screening a wide array of mutations that is tissue agnostic could be helpful in gaining a broader understanding of disease progression and inform more precise therapeutic intervention.

CGP can provide more valuable and accurate information than single-gene or multiplex hot-spot testing, and is now emerging as an approach that should be integrated into regular molecular analysis of cancer samples from patients. With its ability to detect hundreds of different mutations and identify multiple chromosomal alterations that can be missed by other techniques, CGP has the potential to become widely incorporated into cancer care and help ensure patients receive better targeted personalized treatment.