Despite Advances, Interpreting Data From Genomics and Precision Medicine Lags

Arjun V. Balar, MD

I think one of the most important advancements in biomedical technology that has improved our understanding of the complexities of cancer is the ability to sequence the cancer genome for any individual patient, in a rapid and cost-effective manner, to help us make treatment decisions in the clinic. The idea seems simple enough: Identify all mutations in the tumor DNA and find a drug that might target one of them. The concept of targeted therapy in cancer is dependent on having effective drugs to target mutations and, most importantly and not trivially, having comprehensive, reliable, and reproducible genetic information that is biologically relevant and useful in the clinic.

There are numerous companies providing next-generation sequencing (NGS) platforms, utilizing a variety of technologies. Each new method promises to provide more clinically useful information while also requiring that less genetic material be taken from previously unusable sources such as old paraffin blocks, fine-needle aspirates, and peripheral blood. The reports generated can be dizzying for a clinician, especially community oncologists who must keep up with the relentless pace of change in cancer with new treatments and standards of care for a broad range of cancer diagnoses. It’s hard enough keeping up with the new treatments—it can often feel like we have a new FDA approval each week—but what’s been seemingly neglected is informing and educating the oncology community about how to interpret and use genomic information from these NGS technologies that is meant to help inform these new treatments. Not all NGS technologies are created equal, and there are certainly significant differences between sequencing platforms with regard to the genes and the mutations or alterations covered (and not covered) and whether matched normal is used to filter out single nucleotide polymorphisms or other variants of unknown significance.