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Genomic testing has ‘transformed’ lung cancer care
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A majority of patients with lung cancer who underwent full genotyping expressed an oncogenic driver and were subsequently matched with a targeted therapy, according to study results.
Genotype-direct therapy also was associated with improved survival, Mark G. Kris, MD, the Ruane chair in thoracic oncology at Memorial Sloan Kettering Cancer Center, and colleagues found.
Mark G. Kris
“This has transformed the way we treat people with lung cancers,” Kris said in a press release. “When we find these specific genetic changes, the doctor can choose drugs and clinical trials specifically targeting those oncogenic drivers. When that happens, the chance of shrinkage is much higher than with standard chemotherapies. The side effects are much less because the cancer cells are much more dependent on these oncogenes than normal cells.”
Kris and colleagues evaluated data from 1,007 patients with metastatic lung adenocarcinomas who were tested for at least one gene between 2009 and 2012.
Of the 733 patients who underwent full genotype testing for 10 genes, a majority (n=466; 64%) expressed an oncogenic driver.
The most frequently observed oncogenic driver was KRAS (25%), followed by sensitizing EGFR (17%), ALK rearrangements (8%), other EGFR (4%), ERBB2 (3%), BRAF (2%), PIK3CA (˂1%), MET amplification (˂1%), NRAS (˂1%) and MEK1 (˂1%). Twenty-four patients (3%) expressed two or more genes. No patients expressed AKT1.
Of the patients who harbored oncogenic drivers, 275 were matched with a targeted therapy or participated in a trial.
Among patients with an oncogenic driver, median survival was longer for those who received targeted treatment than those who did not (3.5 years vs. 2.4 years; propensity score-adjusted HR=0.69; 95% CI, 0.53-0.9). Patients without a detectable driver survived for a median 2.1 years (P˂.001).
Forty-nine patients who harbored a mutation other than EGFR and ALK received targeted treatment. Median survival for that group was 4.9 years.
“This study showed that we can routinely and simultaneously test for the most important genetic changes in the tumors of patients with lung cancers,” researcher Marc Ladanyi, MD, Memorial Sloan Kettering’s chief of molecular diagnostics, said in a press release. “This information can guide the selection of targeted therapies to treat lung cancer patients because a key finding of the study suggested better outcomes for patients whose tumors had oncogenic drivers and who were given the appropriate targeted treatment.”
The study reflects the need for larger, multi-institutional genomic studies, Boris Pasche, MD, PhD of the Comprehensive Cancer Center of Wake Forest University, and Stefan C. Grant, MD, JD, MBA, of the department of hematology/oncology at the University of Alabama at Birmingham, wrote in an accompanying editorial.
“With the identification of a continually expanding list of targetable driver mutations that occur in a very small fraction of patients and with a large number of therapeutic agents to be tested, it is no longer feasible or an effective use of resources to undertake these studies within a single institution,” Pasche and Grant wrote. “This will require a level of collaboration among investigators, institutions, funding agencies and the pharmaceutical industry that presently does not exist. Such an approach, however, represents the most efficient way to develop novel treatments for rare but targetable oncogenic drivers in a timely fashion.”
For more information:
- Kris MG. JAMA. 2014;doi:10.1001/jama.2014.3741.
- Pasche B. JAMA. 104;doi:10.1001/jama.2014.3742.
Disclosure: The study was funded by the NCI. See the study for a full list of the researchers’ relevant financial disclosures.
Perspective
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Derek Raghavan, MD, PhD, FACP, FRACP, FASCO
Although it is clear that we are making steady progress in the application of targeted therapies based on gene expression studies, we still have to apply basic clinical trials methodology — including robust statistical analysis — to the design of our trials and the interpretation of results. When considering the impact of new treatments on those patients who received them, we should not forget the impact of potential case selection bias.
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