Fueled by advances in next-generation sequencing and consortium-scale efforts, the field of cancer genomics is maturing at a rapid pace. As the catalog of genetic lesions in cancer expands across samples and tumor types, we are learning more and more about the DNA sequence changes underlying tumor development, growth, progression, and response to treatment. One would think that these advances would quickly translate into better diagnosis and treatment of the disease. If only it were so. Despite their potential to improve patient management and care, the findings of cancer genomics efforts have been slow to reach the clinic.
One Step Forward: Limited Genetic Testing
There has been some progress. Major cancer centers like Johns Hopkins University announced that they’ll begin applying standard genetic tests to every cancer patient that comes in the door. While limited to a handful of common, clinically-actionable mutations, the test provides some genetic information that could guide the prognosis and treatment.
Genes Currently Tested | |||
ALK BRAF CHIC2 CSF1R CTNNB1 DNMT3A EGFR |
FLT3 IDH1 IDH2 JAK2 KIT KRAS MET |
MAPK1 (ERK) MAPK2 (MEK) MLL NPM1 NRAS PDGFRA PIK3CA |
PTEN PTPN11 RET RUNX1 TP53 WT1 |
It is good to see a general acknowledgement that genomic information is relevant for cancer patient management. And these clinical testing panels do offer some important advantages. First and foremost, these are well-established cancer genes which offer relevant diagnostic/prognostic/treatment-related information. Second, the limited scope allows for a perfection of technical assays, assurance of completeness, and a reasonable scope for interpretation of the findings. Third, mutations in these genes are recurrent across a number of tumor types, which means that this standard test can be given to any cancer patient, with a good chance of finding something actionable. Finally, the use of sequencing instead of a genotyping platform makes it feasible to detect rare, occult mutations without knowing the position and variant allele beforehand.
Limitations of Focused Testing Panels
Of course, for those of us in the sequencing world, it’s hard not to see the disadvantage. The use of sequencing and FISH will improve the sensitivity of the assay, but some types of alterations (such as SVs) will be missed. Case in point: in a study published in JAMA this year, Welch and colleagues used whole-genome sequencing to identify a cryptic fusion oncogene (bcr3 PML-RARA) in a patient with acute promyelocytic leukemia (APL). This discovery qualified the patient for treatment with all-trans retinoic acid (ATRA), which induced cancer remission and saved her life.
There are currently 468 known, curated cancer genes according to the Cancer Gene Consensus, and somatic mutations have been reported in hundreds (if not thousands) of others. Large-scale sequencing efforts are revealing that a single tumor may harbor anywhere from ten to 1,000 mutations in coding genes. Yes, only a fraction of these are likely to be driver events, and some of those will occur in genes currently tested for in these panels. Even so, we know other important cancer genes are out there. Once they’re discovered and validated, they may have clinical relevance. Sure, they can be added to the panel, but that doesn’t help any patients that were already tested.
Why Not Exome or Whole-genome Sequencing?
Some have argued that whole-genome sequencing is too expensive for use as a diagnostic tool. This is no longer a valid excuse; due to the plummeting cost of sequencing, the cost per sequenced genome is less than $10,000. That seems like a lot until you think about what surgery, radiation, chemotherapy, and other state-of-the-art treatments cost. Why not apply whole-genome, or at least whole-exome sequencing to every tumor that comes in the door? Doing so would offer a number of advantages:
- For the patient, it would provide a catalogue of their tumor’s somatic mutations that could be stored and referred to as new relevant cancer genes are discovered.
- For the clinician, it would provide a new avenue of investigation to be taken when all other treatment strategies have failed. A guided shot in the dark is better than no shot in the dark at all.
- For other patients, this information might be valuable. Here’s the list of mutations your tumor harbors. Here are ones we’ve seen before, and here’s how those patients responded to the treatment you’re about to receive.
- For researchers, standard-of-care clinical tumor sequencing could contribute substantially to our catalogue of somatic mutations, enabling new recurrent genes to be found, and new clinical correlations identified.
I applaud the efforts of Johns Hopkins, Washington University, and other major centers to incorporate genetic testing into cancer care. This is an important practical step as well as a symbolic one: it acknowledges that genomic information has clinical consequences that should be used in patient care. At the same time, I say it’s not enough. We should continue to push until more comprehensive genome sequencing is the standard of care in cancer diagnosis and treatment.
Focused panels offer the potential for high depth sequencing that can address cancer heterogeneity in a way that conventional WGS cannot. Although it is true that the overall sensitivity of detection of a possible aberration relevant to cancer will be reduced by the breadth of a targeted panel of genes, it is also true that 30-50x sequencing can’t achieve the detection of low abundance mutations that may be clinically relevant. The Pareto principle can drive “smart” gene panel selection and provide depth. Without it, you may leave information that is highly actionable buried as statistical noise in low read depth sequencing. Given the economics of the process, the ability of a panel of well established clinically actionable genes to represent a significant fraction of many cancers (and thus eliminate guesswork with other possible changes that may be detected that have not been properly vetted), and the bandwidth reserved by such content selection to enable high depth, I would vote for this approach over WGS.