Imperfect Predictors for Lung Cancer Immunotherapy—A Field for Further Research

The armamentarium for lung cancer immunotherapy has been strengthened using two groups of monoclonal antibodies: 1) anti-PD-1 antibodies, including pembrolizumab and nivolumab, which block the programmed death 1 receptor on the lymphocyte surface, resulting in increasing activity of these cells, and 2) anti-PD-L1 antibodies, including atezolizumab, durvalumab, and avelumab, which block the ligand for the PD-1 molecule on tumor cells and on tumor-infiltrating immune cells. The effectiveness of both groups of antibodies has been proven in many clinical trials, which translates into positive immunotherapeutic registrations for cancer patients. Regarding the predictive factor, PD-L1 expression on cancer cells is the only biomarker validated in prospective clinical trials used for qualification to immunotherapy in advanced non-small cell lung cancer (NSCLC) patients. However, it is not an ideal one. Unfortunately, no clinical benefits could be noted in patients with high PD-L1 expression on tumor cells against the effectiveness of immunotherapy that may be observed in patients without PD-L1 expression. Furthermore, the mechanism of antitumor immune response is extremely complex, multistage, and depends on many factors. Cancer cells could be recognized by the immune system, provided tumor-specific antigen presentation, and these arise as a result of somatic mutations in tumor cells. Based on novel immunotherapy registration, high tumor mutation burden (TMB) has become an important predictive factor. The intensity of lymphocyte infiltration in tumor tissue may be another predictive factor. The effectiveness of anti-PD-L1 immunotherapy is observed in patients with high expression of genes associated with the effector function of T lymphocytes (i.e., their ability to produce IFN-gamma). This does not end the list of potential factors that become useful in qualification of cancer patients for immunotherapy. There remains a need to search for new and perfect predictive factors for immunotherapy.