“Predictive biomarkers are critical for identifying patients who could benefit the most from treatment. Perhaps nowhere else in oncology today are these markers more urgently needed than in the growing field of immunotherapy.”
- Catherine S. M. Diefenbach, MD, ASCO Expert
“Most predictive markers for immunotherapies are still investigational, and researchers are understandably looking for those biomarkers within the tumor itself, focusing on either the immune cells in the tumor, or the tumor cells, or both. However, there is substantial interest in developing predictive biomarkers from blood to avoid biopsies. Developing predictive biomarkers for some types of immune therapies, or for combinations of immune therapies, remains a major challenge for the field.”
- Mario Sznol, MD, ASCO Expert
Developing and validating predictive markers for treatment outcomes and then confirming how those markers can be used to help patients and clinicians choose appropriate therapies is often a long step-wise process. New research presented at the 2019 ASCO-SITC Clinical Immuno-Oncology Symposium (#ImmnoOnc19) points to multiple potential approaches for determining which patients are most likely to benefit from immunotherapy.
Chemotherapy has been used for decades without any biomarker guidance to aid in predicting the likelihood for treatment success. In 2004, a 21-gene predictive marker assay for breast cancer treatment, OncotypeDx1, became available. Others followed shortly thereafter. Subsequently, it took over a decade to design and conduct the TAILORx2 clinical trial; the results showed that the OncotypeDx test could reliably predict who would benefit from adjuvant chemotherapy in addition to adjuvant endocrine therapy.
The activity of immunotherapy is dependent on interactions between a person’s immune system and the tumor, both of which could impact the outcome of any intervention. Today, there is only one predictive biomarker approved to guide decision-making about offering certain immunotherapy medicines called checkpoint inhibitors – expression of the programmed death-ligand 1 (PD-L1). This ligand binds to a checkpoint protein called PD-1 found on activated T cells and inhibits the T cells from attacking tumor cells. If PD-L1 or PD-1 are blocked, then the immune system can be unleashed to attack cancer cells.
In some settings, PD-L1 expression within the tumor microenvironment is an indicator that an immune response has already been activated against the cancer. How much PD-L1 expression is required for response to PD-1 or PD-L1 blockade has not been well quantified, varies between different types of cancer, and sometimes varies with different checkpoint inhibitor therapies.
No other validated, predictive biomarkers have been found to definitively guide clinicians and patients in determining whether a checkpoint inhibitor may be helpful or harmful to a patient. Several biomarkers under investigation, however, appear to complement or improve upon PD-L1 assays.
“It is clear that while checkpoint inhibitors produce remarkable responses in some patients, others yield no benefit at all,” said Dr. Diefenbach, ASCO. “Similarly, we lack tools to evaluate biomarkers that could identify which patients will tolerate these therapies well and which patients are at risk of substantial immune toxicity.”
Newer and more precise markers have been in development for several years. The 2019 ASCO-SITC Clinical Immuno-Oncology Symposium highlighted a half-dozen or so of the latest studies that show progress in identifying predictive markers, including:
- The use of germline, or inherited, mutations to help predict the risk of immune-related adverse events that are grade 2 or higher. Researchers found, with about 80% accuracy, potential biomarkers of toxicity in microRNA binding sites (RAC1, CD274, KRAS), immune-related genes (IL2RA, FCGR2A) and a DNA repair gene (ATM). The findings were consistent across cancer types examined.3
- Tumor mutational burden (TMB) measures the quantity of mutations in a tumor. However, which tissues are biopsied can affect the reliability of TMB as a predictive marker. According to one finding, TMB varied within tumors and between tumors in 17% of patient samples.4
- Genetic alterations in plasma cell-free DNA, typically referred to as circulating tumor DNA, or ctDNA, showed that mutations to the EGFR, 1ALK, PTEN, and STK11 genes conferred poor outcomes with immunotherapy, while mutations to the KRAS or TP53 genes predicted good outcomes to immunotherapy.5
- Liquid biopsies were used to explore whether protein profiles could be predictive for response to immunotherapies. Researchers determined that differentially-expressed proteins, or those expressed at different quantities and levels in a cell, could help distinguish between patients who did or did not respond to checkpoint inhibitors.6
- Specific molecules found in non-small cell lung cancer (NSCLC), called Galectin 1 and 3, regulate cell death. Investigators found that low to intermediate expression of Galectin-1 in lung tissue and low to intermediate expression of Galectin-3 within a tumor itself predicted a favorable response to immunotherapy.7
- Researchers found that two antibodies, NY-ESO-1 and XAGE, identified via blood tests, helped predict tumor shrinkage and reduced tumor burden in NSCLCs treated with certain immunotherapies.8 These findings build on past research suggesting that certain cancers express specific antigens that can trigger a spontaneous immune response.
Meanwhile, researchers are working to develop new checkpoint inhibitors and simultaneously identify predictive biomarkers to be used in conjunction with the new medicines. Development of predictive biomarkers for other types of medicines that modulate the immune system, but do not target specific immune-inhibitory molecules within the tumor, will be more challenging.
“Many of the predictive markers currently being explored in laboratories worldwide will require extensive validation,” said Dr. Diefenbach. “New checkpoint inhibitors continue to be developed, so a synergistic approach between therapeutic development and predictive marker validation should be the goal.”
- Paik S, Shak S, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer New Engl J Med. 351(27):2817–2826, 2004
- Sparano JA, Gray, RJ et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. New Engl J Med. 379:111-121, 2018
- Weidhaas JB, Scheffler AW, et al. A germline microRNA-based biomarker signature of immune-associated toxicity to anti-PD1/PDL1 therapy. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245543
- Conroy JM, Pabla S, et al. Tumor Mutational Burden (TMB): Assessment of inter- and intra-tumor heterogeneity. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245303
- Guibert N, Jones G, et al. Targeted sequencing of plasma cell-free DNA to predict response to PD1 inhibitors in advanced non-small cell lung cancer. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245297
- Mehta A, Kasumova G, et al. Liquid biopsy using plasma proteomic profiling to reveal predictors of immunotherapy response. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245457
- Capalbo C, Filetti M, et al. Galectins role as predictive markers for anti-PD-1-based immunotherapy in non-small cell lung cancer. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245545
- Oka M, Ohue Y, et al. Serum NY-ESO-1 and XAGE1 antibodies as predictive biomarkers in anti-PD-1 therapy for non-small-cell lung cancer. 2019 ASCO-SITC Clinical Immuno-Oncology Symposium. Abstract ID: 245253