ABSTRACT
Head and neck cancers are still one of the most common types of cancer in the world. They rank in the leading sixth place in terms of incidence globally, and the incidence continues to rise. The mortality rates remain at high levels. Pathological subclassification places squamous cell carcinoma of the head and neck (HNSCC) in the first place concerning the histological forms of head and neck cancers; a tumor with extremely aggressive behavior and high mortality rates. The tumor microenvironment is a very complex ecosystem of cellular and non-cellular components, characterized by unique features, that contribute to the appearance of immunosuppression and diminished anticancer immunity, impacting patient prognosis and treatment outcome. Despite many important advances in therapy, resistance to therapy represents a difficult challenge in HNSCC patients. Tumor progression, metastasis, and response to therapy are all influenced by the complex ecosystem represented by the tumor microenvironment and by the interactions between cellular and non-cellular components of this system. Therefore, the tumor microenvironment, in the light of recent data, is not an innocent bystander. In the last few years, there has been a sustained effort to characterize the tumor microenvironment, to identify targets of response and identify other mechanisms of tumor-specific immune responses, or to discover other biomarkers of response. There is an urgent need to understand how to properly select patients, the therapy sequence, and how to use feasible biomarkers that can help to identify the patient who may obtain the most benefit from available therapies.
ABSTRACT
BACKGROUND: Literature suggests that high body mass index can be correlated with better response to immune checkpoint inhibitors. On the other hand, sarcopenia seems to be a negative predictive marker. Materials and methods: The present analysis is a retrospective, multicenter trial that included patients with metastatic melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma treated with nivolumab between 2018 and 2020. Patients were stratified by creatinine levels both at treatment initiation and at first follow-up (at three months) and by BMI for the same intervals, as recorded in the patients' charts. Creatinine was considered a surrogate marker for sarcopenia. IBM SPSS version 20 was used for statistical analysis. Results: A total of 57 (n = 57) patients were included in the trial. Overall response rate (ORR) for the entire population was 38.59% (p = 0.02). Patients with BMI lower than 25 had an ORR of 28.5% (p = 0.003), whereas patients with BMI higher than 25 had an ORR of 42.3% (p = 0.002). Patients who gained weight during treatment had a lower probability of having progressive disease (OR = 0.4 [95% CI; 0.4-1.2]), as did patients with creatinine higher than 0.9 (OR = 0.39 [95% CI: 0.13-1.14]). No superiority was found in progression-free survival (PFS) when patients were dichotomized for BMI = 25 or BMI = 18.5. Mean PFS in the BMI under 18.5 group was 10.2 months [95% CI: 5.8-23.1], versus 11.2 for BMI over 18.5 [95% CI: 5.3-25.3], p < 0.03. Mean PFS for the BMI under 25 was 11.2 months [95% CI: 7.2-20.1], vs. 13.3 months [95% CI: 6.4-22] for the BMI over 25, p < 0.001. There were also differences in PFS in the patients with baseline creatinine over 0.9 when compared with under 0.9 values. Mean PFS in the first group was 19.78 months [95% CI: 16.23-22.9] vs. 16.1 [95% CI: 12.2-20.3], p < 0.001. Conclusion: Patients treated with nivolumab who have weight gain during treatment have a better PFS than the ones who do not. Creatinine levels of over 0.9 at treatment initiation also have positive predictive value.
