Oncology has taken a decisive genomic turn in the last couple of decades, accelerating the move to personalized, individualized, precision medicine. Personalized medicine entails the application of the right therapy to the right patient at the right time. The selection of the most appropriate therapy, and the timing of treatment, should include the entirety of genetic information from a tumor; simply knowing where a tumor originated, or the existence of a specific driver mutation, may be insufficient to fully exploit the available approved and investigative options.
Comprehensive genomic profiling (CGP) uses next-generation sequencing (NGS)-based methods to describe the genomic makeup of the tumor. CGP finds both known and novel alterations, as well as genomic signatures, to identify variations that may be of prognostic, diagnostic, and predictive value, thus informing the choice of available therapies.
As a research tool, CGP can assess and characterize not just a single biomarker, but a wide variety of pan-cancer indications, identifying patterns and relationships among known and novel biomarkers, which may have heretofore gone unobserved. In addition, broadly capturing these across diverse population furthers our understanding of cancer, and can ultimately result in development of new therapies. A PubMed search in early 2022 found more than 500 publications referencing “comprehensive genomic profiling” for both specific and pan-cancer indications.
A cancer patient’s tumor used to be stratified by the origin of their disease—lung cancer, for example, or breast cancer. These broad categories allowed only for broad treatments such as chemotherapy or radiation therapy, rather than targeting the molecular underpinnings of each patient’s unique disease.
The 2010s saw a shift to patients being stratified by biomarker, with the increasing availability of testing for mutations in single genes, or in multiple loci queried by targeted tumor panels (hotspots). These may include select insertions and deletions, rearrangements and fusions, and copy number variations, in addition to single nucleotide variations.
Single-gene testing will find mutations in the gene being tested, but will miss mutations in other loci that may be important for diagnosis and treatment. Sequential single gene testing and hotspot panels may pick up additional variants, but will still miss many actionable mutations as well as related mutations across related genes.
In a single test, CGP can accurately identify genomic signatures such as tumor mutational burden (TMB) and microsatellite instability (MSI), in addition to identifying the mutation classes mentioned above. Importantly, CGP more readily identifies rare mutations that may be important for treatment decisions, such as NTRK fusions and biomarkers that may indicate a lack of responsiveness to treatment, such as some mutations in STK11 and PTEN.
For perspective, consider that in 2004, the driver mutations in most NSCLC cases were unknown; exceptions were typically KRAS and EGFR variants. By 2014, drivers were detected in about a 30% of NSCLC cases, leaving about 60%–70% of cases with no detected driver. However, by 2020, due to the discovery of many more biomarkers and the implementation of CGP, only ~3% of NSCLC cases are driven by mutations not included in the actionable variants covered by CGP panels.
In addition to guiding treatment decisions that improve patient outcomes, CGP panels are also very valuable to bench researchers who are trying to better understand disease and guide the development of treatments. Many of the insights gained by these researchers will, in the long term, also benefit patients.
CGP enables more efficient use of limited patient-derived tissue. A single test will provide genomic insights to the genomic questions previously requiring a multitude of tests. This approach will save on time-to-results, especially if a second test is ordered only after the results of the first test are available, and thus speed time-to-treatment. This approach will also minimize the depletion of precious samples and obviate, for example, the possible need for re-biopsy or loss of clinical study samples.
CGP panels query a wide range of genes and regions, allowing them to easily identify both known and unknown variants within. This affords not only a comprehensive insight into many biomarkers, but allows for exploration of the relationship among them as well.
The majority of cancer care now involves some aspect of targeted therapy, and the National Comprehensive Cancer Network (NCCN) now recommends NGS-based analysis to inform the treatment of a wide range of cancer types. As such, CGP is becoming the preferred method for determining genetic and genomic signatures that can guide treatment decisions.
Pao W, Girard N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 2011 Feb;12(2):175-80. doi: 10.1016/S1470-2045(10)70087-5. PMID: 21277552.
Johnson, B.E., et al (2013) ASCO Annual Meeting: abstract 8019
Global Oncology Trends 2021: Outlook to 2025. IQVIA Report