For some patients with non-small cell lung cancer (NSCLC) or other solid tumors, such as melanoma or kidney cancer, immune checkpoint inhibition can offer dramatic, life-prolonging benefits. But many patients’ tumors do not respond. And because these therapies come with the risk of life-threatening immune-related adverse events (irAEs), some patients may become exposed to potential harm.

Predictive biomarkers are therefore urgently needed to help identify which patients are most likely to benefit from immunotherapy, and which patients should skip drugs from this class. Although strategies for patient stratification using biomarkers have been under investigation, few currently have real clinical utility.

Tumor expression of programmed death ligand 1 (PD-L1) is currently the only predictive biomarker for immune checkpoint inhibition that has been “somewhat validated,” said Saman Maleki, PhD, adjunct research professor at the department of oncology, Western University, who is also part of the London Regional Cancer Program, Lawson Health Research Institute,  London, Ontario, Canada.

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“It’s not perfect but it’s what we’ve got,” Dr Maleki told Cancer Therapy Advisor. “Developing a predictive biomarker for immunotherapy is very different from predictive biomarkers for targeted tumor therapies.”

Most candidate immunotherapy biomarkers are more prognostic than treatment-predictive. They are associated with patients’ general prognosis in lung cancer, for example, Dr Maleki said — they identify patients who may be good candidates for treatment in general, rather than predict who may respond to immunotherapy, specifically.

“For a predictive biomarker, you want something that you can say with some certainty: changes in this marker are associated with response to this particular therapy,” he explained.

One challenge with optimizing PD-L1’s predictive power has been wide variation in how expression is measured and defined. Different biopsy-sampling strategies, tissue-processing approaches, reagents, and expression-quantification methodologies can all affect test specificities and sensitivities (true-negative and true-positive rates).1-3 Variations in the definitions of and cut-off thresholds for PD-L1 positivity can also confound conclusions.

Tumor heterogeneity has been another concern: tumors represent populations of competing and genetically different cell lines, so PD-L1 expression in one biopsied region of a tumor might not represent the biology of other regions.

But a recent study by researchers in Japan attempts to address that concern and clarify how best to identify tumor PD-L1 expression status.4 Among patients with non-squamous NSCLC, a single tumor biopsy sample containing at least 100 cells is needed to accurately evaluate PD-L1 expression for predicting responses to immunotherapy with nivolumab, they reported in the Journal of Thoracic Oncology.4

“It’s an important paper,” Dr Maleki said. “We know now that the number of cells actually matters for establishing PD-L1 levels and that it [may] correlate with efficacy of treatment later on.”

“When you’re doing a biopsy, you are only looking at 1 region of the tumor and it might not be representative of the entire tumor,” he said. “This is an important study that shows that if you have the right number of cells, you don’t need to do multiple biopsies to determine PD-L1 expression.”

For oncologists practicing in community settings where multiple biopsies might be impractical, that’s important information, Dr Maleki said. “Done right, 1 biopsy can give you the needed information.”