A Composite Biomarker of Derived Neutrophil–Lymphocyte Ratio and Platelet–Lymphocyte Ratio Correlates With Outcomes in Advanced Gastric Cancer Patients Treated With Anti-PD-1 Antibodies

Prognostic relevance of platelet lymphocyte ratio (PLR) in gastric cancer patients receiving immune checkpoint inhibitors: a systematic review and meta-analysis

Objectives

The prognostic relevance of the platelet-to-lymphocyte ratio (PLR) in gastric cancer (GC) patients undergoing immune checkpoint inhibitor (ICI) treatment remains unclear. This meta-analysis aimed to determine the prognostic impact of PLR in this specific patient cohort.

Methods

We searched the PubMed, Cochrane Library, CNKI, and EMBASE databases, including literature published up to September 2023, to investigate the prognostic implications of PLR in patients with gastric cancer undergoing immune checkpoint inhibitor therapy. Outcome measures encompassed overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rates (DCR).

Results

Nine studies from seven articles comprising 948 eligible patients were selected. The results revealed a significant correlation between elevated PLR and poorer OS and progression-free survival (PFS) (OS: HR 1.67, 95% CI 1.39-2.00, p < 0.001; PFS: HR 1.51, 95% CI 1.29-1.76, p < 0.001). Subgroup analyses were performed to validate the robustness of the results. Moreover, a meta-analysis of four studies investigating the correlation between the PLR in gastric cancer (GC) patients and the objective response rate/disease control rate (ORR/DCR), showed no significant association between the PLR and ORR/DCR (ORR: RR = 1.01, p = 0.960; DCR: RR = 0.96, p = 0.319).

Conclusions

This meta-analysis indicates that elevated PLR in GC patients undergoing ICI treatment is significantly linked to worse OS and PFS. Therefore, PLR can serve as a prognostic indicator of post-treatment outcomes in patients with GC receiving ICIs. Further prospective studies are required to assess the reliability of these findings.

Systematic review registration

https://inplasy.com/, identifier INPLASY2023120103.

Impact of PIK3CA and cell cycle pathway genetic alterations on durvalumab efficacy in patients with head and neck squamous cell carcinoma: Post hoc analysis of TRIUMPH study

OBJECTIVES

This study aimed to investigate whether genetic alterations in PI3KCA and the cell cycle pathways influence the efficacy of durvalumab, an immune checkpoint inhibitor, in patients with head and neck squamous cell carcinoma (HNSCC) who had previously failed platinum-based treatment.

MATERIALS AND METHODS

We obtained data from a phase II umbrella trial of patients with HNSCC who failed platinum-based treatment (TRIUMPH, NCT03292250). Patients receiving durvalumab treatment comprised those with PIK3CA alterations (Group A), those with cell cycle pathway alterations such as CDKN2A (Group B), and those with no druggable genetic alterations (Group C). We analyzed the overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in each group and evaluated the potential predictive factors for durvalumab.

RESULTS

We analyzed the data of 87 patients: 18, 12, and 57 in groups A, B, and C, respectively. The ORRs were 27.8 %, 8.3 %, and 15.8 % in Groups A, B, and C, respectively (P = 0.329), and the median PFS for each group was 2.3, 1.6, and 1.7 months, respectively, with no significant differences between the groups (P = 0.24). Notably, patients with lower neutrophil-lymphocyte ratio (NLR) (≤5.8) had longer PFS (median, 2.8 vs 1.6 months, P < 0.001), while those with lower platelet-lymphocyte ratio (PLR) (≤491.2) exhibited longer PFS (median, 1.8 vs 1.2 months, P < 0.001).

CONCLUSION

Durvalumab's efficacy was similar, irrespective of the presence of PIK3CA or cell cycle pathway genetic alterations in patients with platinum-resistant HNSCC. The NLR and PLR may be promising predictive biomarkers.

A practical prognostic peripheral blood-based risk model for the evaluation of the likelihood of a response and survival of metastatic cancer patients treated with immune checkpoint inhibitors

BACKGROUND

Less than half of unselected metastatic cancer patients benefit from the immune checkpoint inhibitor (ICI) therapy. Systemic cancer-related inflammation may influence the efficacy of ICIs and thus, systemic inflammatory markers could have prognostic and/or predictive potential in ICI therapy. Here, we aimed to identify a combination of inflammation-related laboratory parameters to establish a practical prognostic risk model for the pretreatment evaluation of a response and survival of ICI-treated patients with different types of metastatic cancers.

METHODS

The study-cohort consisted of a real-world patient population receiving ICIs for metastatic cancers of different origins (n = 158). Laboratory parameters determined before the initiation of the ICI treatment were retrospectively collected. Six inflammation-related parameters i.e., elevated values of neutrophils, platelets, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and lactate dehydrogenase (LDH), and the presence of anemia, were each scored with one point, giving 0-6 risk points for each patient. The patients with information of all these six parameters (n = 109) were then stratified into low-risk (0-3 points) and high-risk (4-6 points) groups. The overall response rate (ORR), overall survival (OS), and progression-free survival (PFS) according to the risk scores were determined.

RESULTS

The risk model was strongly associated with the outcome of the patients. The ORR to ICI treatment in the high-risk group was 30.3% in comparison to 53.9% in the low-risk group (p = 0.023). The medians for OS were 10.0 months and 27.3 months, respectively (p < 0.001), and the corresponding medians for PFS were 3.9 months and 6.3 months (p = 0.002). The risk group remained as a significant prognostic factor for both OS (HR 3.04, 95% CI 1.64-5.64, p < 0.001) and PFS (HR 1.79, 95% CI 1.04-3.06, p = 0.035) in the Cox multivariate analyses.

CONCLUSIONS

We propose a readily feasible, practical risk model consisted of six inflammation-related laboratory parameters as a tool for outcome prediction in metastatic cancer patients treated with ICIs. The risk model was strongly associated with the outcome of the patients in terms of all the evaluated indicators i.e., ORR, OS and PFS. Yet, further studies are needed to validate the risk model.

Tumor educated platelet: the novel BioSource for cancer detection

Platelets, involved in the whole process of tumorigenesis and development, constantly absorb and enrich tumor-specific substances in the circulation during their life span, thus called "Tumor Educated Platelets" (TEPs). The alterations of platelet mRNA profiles have been identified as tumor markers due to the regulatory mechanism of post-transcriptional splicing. Small nuclear RNAs (SnRNAs), the important spliceosome components in platelets, dominate platelet RNA splicing and regulate the splicing intensity of pre-mRNA. Endogenous variation at the snRNA levels leads to widespread differences in alternative splicing, thereby driving the development and progression of neoplastic diseases. This review systematically expounds the bidirectional tumor-platelets interactions, especially the tumor induced alternative splicing in TEP, and further explores whether molecules related to alternative splicing such as snRNAs can serve as novel biomarkers for cancer diagnostics.

A composite indicator of derived neutrophil–lymphocyte ratio and lactate dehydrogenase correlates with outcomes in pancreatic carcinoma patients treated with PD-1 inhibitors

Background

There are currently no established biomarkers that can predict whether advanced pancreatic carcinoma (PC) patients would benefit from immune checkpoint inhibitors (ICIs). Our study investigated whether the pretreatment composite biomarker of derived neutrophil–lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH) can be used as a reliable prognostic factor for the survival of PC patients receiving PD-1 inhibitor therapy.

Methods

Patients with advanced PC treated with PD-1 inhibitors at a single center from September 2015 to September 2020 were included. The high levels of dNLR (≥3) and LDH (≥250 U/L) were considered to be risk factors. Based on these two risk factors, patients in this study were categorized into two risk groups: the good dNLR-LDH group, without risk factors, and the intermediate/poor dNLR-LDH group, with one to two risk factors. Overall survival (OS) and progression-free survival (PFS) served as this study’s primary and secondary endpoints. Cox regression models were used to identify independent prognostic factors for survival benefit.

Results

There were 98 patients in our study. The good group included 61 (62.2%) patients and the intermediate/poor group included 37 (37.8%). The overall patients with PC who received immunotherapy had a median OS of 12.1 months, and the good dNLR-LDH group had a significantly longer OS compared with the intermediate/poor dNLR-LDH group (44.2 vs. 6.4 months; p &lt; 0.010); median PFS was 3.7 and 2.5 months (p = 0.010). The number of metastatic sites &gt;2 and immunotherapy as third-line or later was associated with worse PFS, and the line of immunotherapy and the dNLR-LDH indicator were independent prognostic factors for OS, according to multivariate analysis.

Conclusion

The pretreatment composite biomarker of dNLR and LDH can be used as a prognostic biomarker in patients with advanced PC treated with PD-1 inhibitors.

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.