Emerging Biomarkers in Immune Oncology to Guide Lung Cancer Management

Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer

Background

Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of small cell lung cancer (SCLC), but only a minority of patients benefit from this therapy. Therefore, it is critical to identify potential risk factors that could predict the efficacy of ICI treatment in SCLC patients and identify patient subgroups who may benefit the most from ICI therapy.

Methods

Our study included a total of 183 SCLC patients who had received at least one dose of ICI treatment. We utilized both logistic regression and Cox proportional hazard regression to evaluate whether various patient clinical factors and serum biomarkers could serve as predictors of patient response to treatment and overall survival (OS) during ICI therapy.

Results

Logistic regression showed that patients with a history of surgery (p=0.003, OR 9.06, 95% CI: (2.17, 37.9)) and no metastasis (p=0.008, OR 7.82, 95% CI: (1.73, 35.4)) exhibited a higher odds of response to ICI treatment. Cox regression analyses demonstrated that pretreatment blood albumin (p=0.003, HR 1.72, 95% CI: (1.21, 2.45)) and derived neutrophil to lymphocyte ratio (dNLR) (p=0.003, HR 1.71, 95% CI: (1.20-2.44)) were independent predictors for OS in SCLC patients. By establishing a pre-treatment prognostic scoring system based on baseline albumin and dNLR, we found that patients with high albumin and low dNLR exhibited a significantly better prognosis than those with low albumin and high dNLR in both the full (P<.0001, HR 0.33, 95% CI: 0.20-0.55) and the metastatic cohort (P<.0001, HR 0.28, 95% CI: 0.15-0.51). The better prognostic group also had younger age, higher BMI and lower systemic inflammatory biomarker values than the unfavorable group (P<.0001).

Conclusion

Our data reveals the significant role of metastasis status and treatment history in predicting the initial response of SCLC patients to ICI treatment. However, baseline serum albumin and dNLR provide a more precise prognostic prediction for patient OS. The scoring system based on albumin and dNLR enhances the ability to stratify patient prognosis and holds the potential to guide clinical decision-making for SCLC patients undergoing ICI therapy.

Insights into the management of penile squamous cell carcinoma: from conventional approaches to emerging novel therapies

INTRODUCTION

Penile squamous cell carcinoma (PSCC), a rare genitourinary cancer, is associated with poor outcomes due to limited treatment effectiveness, especially in advanced stages.

AREAS COVERED

While chemotherapy and/or surgery remain the standard of care, emerging therapies like immunotherapy, targeted therapy, and human papillomavirus (HPV) directed therapies show promise. Key to advancing treatment is understanding the immune microenvironment to gain insights into tumor resistance mechanisms and potential therapeutic targets. The scarcity of data on PSCC is a major obstacle in advancing research for this rare cancer.

EXPERT OPINION

Future research should prioritize collaborative efforts across various research centers and countries. Enhancing data sharing and pooling resources can lead to a more comprehensive understanding of PSCC, ultimately supporting the development of precision medicine strategies tailored to this specific cancer type. This collaborative approach is essential for making significant strides in PSCC treatment and care.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside

Background

Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients.

Methods

Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab.

Results

Models incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank).

Discussion

This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.