Prognostic value of inflammatory and nutritional indexes among advanced NSCLC patients receiving PD-1 inhibitor therapy

Relationship between the combination of platelet count and neutrophil-lymphocyte ratio and prognosis of patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors plus chemotherapy: A retrospective cohort study

BACKGROUND

The relationship between the combination of platelet count and neutrophil-lymphocyte ratio (COP-NLR) and prognosis in patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) combination therapy with chemotherapy remains unclear. Thus, we investigated prognostic factors, including the COP-NLR, to identify patients who could benefit from the therapeutic efficacy of ICI combination therapy for advanced NSCLC. Furthermore, we evaluated the relationship between the COP-NLR score during ICI combination therapy and treatment response.

METHODS

We conducted a retrospective cohort study of 88 patients with NSCLC who initially received ICI combination therapy. The primary outcome was overall survival (OS). The prognostic factors were extracted using the Cox proportional hazards model. The relationship between COP-NLR score at 3 weeks after starting ICI combination therapy and a good response (complete response [CR] and partial response [PR]) to treatment was analyzed using the chi-square test.

RESULTS

The median OS was 15.7 months. In the multivariable analysis, Eastern Cooperative Oncology Group Performance Status (ECOG PS) 2, distant metastatic sites ≥2, and baseline COP-NLR scores of 1, 2 were extracted as significant poor prognostic factors. The proportion of patients with CR and PR in the 3-week COP-NLR score of 0 group was significantly higher than that in scores of 1, 2 group.

CONCLUSIONS

Baseline COP-NLR, ECOG PS, and number of distant metastatic sites were prognostic factors in patients with NSCLC with ICI combination therapy. A lower 3-week COP-NLR was associated with a good response to treatment.

Clinical predictive factors of the efficacy of immune checkpoint inhibitors and kinase inhibitors in advanced hepatocellular cancer

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly aggressive tumor associated with significant morbidity and mortality rates. Combination therapy with immune checkpoint inhibitors (ICIs) and kinase inhibitors has emerged as a promising strategy for liver cancer treatment in recent years. However, the clinical factors predicting the outcomes of combination therapy in patients with advanced liver cancer remain uncertain. Therefore, this study investigated the relationships between clinical predictors and the efficacy of ICI plus kinase inhibitor therapy to personalize treatment plans.

METHODS

We retrospectively enrolled 98 patients who received combination treatment with ICIs and kinase inhibitors for advanced HCC. Based on blood lipid levels and other clinical factors prior to treatment, we investigated potential biomarkers that could predict treatment responses in this patient population.

RESULTS

Mean progression-free survival (PFS) and overall survival (OS) in this cohort were 10.1 and 17.2 months, respectively. Via multivariate analysis, the absence of extrahepatic metastasis, the absence of portal vein thrombosis (PVT), neutrophil-to-lymphocyte ratio (NLR) < 3.225, platelet-to-lymphocyte ratio (PLR) < 140.75, and prognostic nutritional index (PNI) ≥ 37.25 were identified as independent predictors of improved PFS. Factors associated with better OS included PLR < 140.75 and total cholesterol (TC) < 3.46 mmol/L. Univariate analysis identified significant associations of Eastern Cooperative Oncology Group performance status (ECOG PS), hepatitis B virus (HBV) DNA levels, Child-Pugh classification, alpha-fetoprotein (AFP), TC, and the receipt of regorafenib with PFS. Additionally, ECOG PS, Child-Pugh classification, AFP, PVT, NLR, PNI, and the receipt of regorafenib were significantly associated with OS.

CONCLUSIONS

PLR and TC were potential clinical predictive factors for survival outcomes in patients with advanced HCC who received ICI/kinase inhibitor combination therapy. It is important to know the clinical characteristics of patients prior to treatment initiation to optimize outcomes.

Value of computed tomography radiomics combined with inflammation indices in predicting the efficacy of immunotherapy in patients with locally advanced and metastatic non-small cell lung cancer

Background

Although immunotherapy has revolutionized the treatment landscape of lung cancer and improved the prognosis of this malignancy, many patients with lung cancer still are not able to benefit from it because of many different reasons. The expression of programmed death ligand-1 (PD-L1) in tumor cells has been approved for the prediction of immunotherapy efficacy; however, its clinical application has been limited by the invasiveness of PD-L1 determination and the heterogeneity of tumor cells. As a promising technology, radiomics has made significant progress in the diagnosis and treatment of lung cancer. Thus, we constructed a noninvasive predictive model which based on radiomics to predict the immunotherapy efficacy of lung caner patients.

Methods

Data of 82 patients with stage IIIa/IVb NSCLC who received immunotherapy at the First Affiliated Hospital of Soochow University from December 2019 to January 2023 were retrospectively collected. These patients were followed up for durable clinical benefit (DCB), as defined by whether progression-free survival (PFS) reached 12 months. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen for the radiomic features in the training set, and a radiomics score (Rad-score) was calculated. The clinical baseline data were analyzed, and the peripheral blood inflammation indices were calculated. Univariate and multivariate analyses were performed to identify the applicable indices, which were combined with the Rad-score to create a comprehensive forecasting model (CFM) and nomograms. Internal validation was performed in the validation set.

Results

Up to the last follow-up time, 48 of 82 patients had a PFS of more than 12 months. The area under the receiver operating characteristic (ROC) curve (AUC) of the Rad-score was 0.858 and 0.812, respectively, in the training set and validation set. A systemic immune-inflammation index (SII) score of <500.88 after two cycles of immunotherapy was a protective factor for PFS >12 months [odds ratio (OR) 0.054; P=0.003]. The CFM had an AUC of 0.930 and 0.922, respectively, in the training and validation sets. The calibration curves and decision curve analysis (DCA) demonstrated the reliability and clinical applicability of the model, respectively.

Conclusions

The radiomics model performed well in predicting whether patients with locally advanced or metastatic NSCLC can achieve DCB after receiving immunotherapy. The CFM had good predictive performance and reliability.

Identification of C-PLAN index as a novel prognostic predictor for advanced lung cancer patients receiving immune checkpoint inhibitors

Objective

Increasing studies have highlighted the potential utility of non-invasive prognostic biomarkers in advanced lung cancer patients receiving immune checkpoint inhibitor (ICI) based anti-cancer therapies. Here, a novel prognostic predictor named as C-PLAN integrating C-reactive protein (CRP), Performance status (PS), Lactate dehydrogenase (LDH), Albumin (ALB), and derived Neutrophil-to-lymphocyte ratio (dNLR) was identified and validated in a single-center retrospective cohort.

Methods

The clinical data of 192 ICI-treated lung cancer patients was retrospectively analyzed. The pretreatment levels of CRP, PS, LDH, ALB and dNLR were scored respectively and then their scores were added up to form C-PLAN index. The correlation of C-PLAN index with the progression-free survival (PFS) or overall survival (OS) was analyzed by a Kaplan-Meier model. The multivariate analysis was used to identify whether C-PLAN index was an independent prognostic predictor.

Results

A total of 88 and 104 patients were included in the low and high C-PLAN index group respectively. High C-PLAN index was significantly correlated with worse PFS and OS in ICI-treated lung cancer patients (both p<0.001). The multivariate analysis revealed high C-PLAN index was an independent unfavorable factor affecting PFS (hazard ratio (HR)=1.821; 95%confidence interval (CI)=1.291-2.568) and OS (HR=2.058, 95%CI=1.431-2.959). The high C-PLAN index group had a significantly lower disease control rate than the low C-PLAN index group (p=0.024), while no significant difference was found for objective response rate (p=0.172). The subgroup analysis based on clinical features (pathological type, therapy strategy, TNM stage and age) confirmed the prognostic value of C-PLAN index, except for patients receiving ICI monotherapy or with age ranging from 18 to 65 years old. Finally, a nomogram was constructed based on C-PLAN index, age, gender, TNM stage and smoking status, which could predict well the 1-, 2- and 3-year survival of ICI-treated lung cancer patients.

Conclusion

The C-PLAN index has great potential to be utilized as a non-invasive, inexpensive and reliable prognostic predictor for advanced lung cancer patients receiving ICI-based anti-cancer therapies.

Tertiary lymphoid structures combined with biomarkers of inflammation are associated with the efficacy of neoadjuvant immunochemotherapy in resectable non-small cell lung cancer: A retrospective study

BACKGROUND

Neoadjuvant immunochemotherapy can effectively downstage tumors and reduce the risk of postoperative recurrence and distant metastasis in patients with non-small cell lung cancer (NSCLC). In this study, we investigated the correlation between inflammatory biomarkers and tertiary lymphoid structure (TLS) expression. We also compared the predictive values of these inflammatory parameters, TLSs, and a combination of inflammatory parameters and TLSs for neoadjuvant efficacy in patients with NSCLC.

METHODS

We retrospectively analyzed the clinical information of 106 patients with NSCLC who underwent neoadjuvant immunochemotherapy and radical surgery at Shandong Cancer Hospital between June 2020 and June 2022.

RESULTS

TLS was evaluated using hematoxylin-eosin staining and immunohistochemically-stained tissue sections. Logistic analysis was performed to determine the correlation between inflammatory parameters, TLSs, and the factors affecting major pathological response (MPR). Receiver operating characteristic curves and the C-index were used to evaluate the predictive value of the nomogram models for MPR. The systemic immune-inflammatory index (SII) was an independent predictor of high TLS abundance and maturity. Platelet-to-lymphocyte ratio (PLR) ≤201.8, TLS abundance, and TLS maturity were independent predictors of MPR. The PLR-TLS combined model performed better in assessing the MPR in patients with NSCLC than models using single indicators.

CONCLUSION

Our study demonstrated that the SII is an independent predictor of both TLS abundance and maturity. Both TLSs and PLR can predict MPR rates in patients with NSCLC receiving neoadjuvant immunochemotherapy. However, assessing the MPR in patients with NSCLC using a combination of PLR and TLSs is more accurate than using either indicator alone.

Prognostic Role of Pretreatment Prognostic Nutritional Index in Advanced Lung Cancer Patients Receiving First-Line Immunotherapy: A Meta-Analysis

The aim of this study was to further explore the association between pretreatment prognostic nutritional index (PNI) and survival among advanced lung cancer patients who received the first-line immunotherapy based on current relevant studies. Several databases were searched up to July 17, 2023. Progression-free survival (PFS) and overall survival (OS) were primary outcomes and the hazard ratios (HRs) with 95% confidence intervals (CIs) were combined. Subgroup analysis based on the pathological type [non-small cell lung cancer (NSCLC) vs small cell lung cancer (SCLC)] and combination of other therapies (yes vs no) were performed. Ten studies with 1291 patients were included eventually. The pooled results demonstrated that higher pretreatment PNI was significantly related to improved PFS (HR=0.62, 95% CI: 0.48-0.80, P＜0.001) and OS (HR=0.52, 95% CI: 0.37-0.73, P＜0.001). Subgroup analysis revealed that the predictive role of pretreatment PNI for PFS (HR=0.61, 95% CI: 0.45-0.81, P=0.001) and OS (HR=0.52, 95% CI: 0.35-0.77, P=0.001) was only observed among NSCLC patients and the combination of other therapies did not cause an impact on the prognostic role of PNI in lung cancer. Pretreatment PNI was significantly associated with prognosis in advanced NSCLC receiving first-line immunotherapy and patients with a lower pretreatment PNI had poorer survival.

Prognostic Potential of the Prognostic Nutritional Index in Non-Small Cell Lung Cancer Patients Receiving Pembrolizumab Combination Therapy with Carboplatin and Paclitaxel/Nab-Paclitaxel

INTRODUCTION

Pembrolizumab (Pemb) therapy in conjunction with carboplatin and paclitaxel (PTX)/nab-PTX has been efficacious in treating non-small cell lung cancer (NSCLC). However, the response predictors of this combination therapy (Pemb-combination) remain undetermined. We aimed to evaluate whether Glasgow prognostic score (GPS), neutrophil-to-lymphocyte ratio (NLR), body mass index (BMI), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) are potential factors in prognosticating the response to Pemb-combination therapy in advanced NSCLC patients.

METHODS

We retrospectively recruited 144 NSCLC patients receiving first-line treatment with Pemb-combination therapy from 13 institutions between December 1, 2018, and December 31, 2020. GPS, NLR, BMI, PLR, and PNI were assessed for their efficacy as prognostic indicators. Cox proportional hazard models and the Kaplan-Meier method were used to compare the progression-free survival (PFS) and overall survival (OS) of the patients.

RESULTS

The treatment exhibited a response rate of 63.1% (95% confidence interval [CI]: 55.0-70.6%). Following Pemb-combination administration, the median PFS and OS were 7.3 (95% CI: 5.3-9.4) and 16.5 (95% CI: 13.9-22.1) months, respectively. Contrary to PNI, NLR, GPS, BMI, and PLR did not display substantially different PFS in univariate analysis. However, multivariate analysis did not identify PNI as an independent prognostic factor for PFS. Furthermore, univariate analysis revealed that GPS, BMI, and PLR exhibited similar values for OS but not NLR and PNI. Patients with PNI ≥45 were predicted to have better OS than those with PNI <45 (OS: 23.4 and 13.9 months, respectively, p = 0.0028). Multivariate analysis did not establish NLR as an independent prognostic factor for OS.

CONCLUSION

The PNI evidently predicted OS in NSCLC patients treated with Pemb-combination as first-line therapy, thereby validating its efficiency as a prognostic indicator of NSCLC.

Identification of prognostic nutritional index as a reliable prognostic indicator for advanced lung cancer patients receiving immune checkpoint inhibitors

Background

Prognostic nutritional index (PNI) has been identified as a reliable prognostic factor for cancer adjuvant therapy. However, its prognostic value in lung cancer patients receiving immune checkpoint inhibitors (ICIs) remains inconclusive.

Method

A systematic literature review and meta-analysis was performed based on online databases before March 1th 2023. The correlation of PNI with overall survival (OS) or progression-free survival (PFS) was determined using the hazard ratios (HRs) coupled with 95% confidence intervals (CIs). Then, a retrospective cohort enrolling 123 ICI-treated lung cancer patients from two hospitals was utilized for validation and further investigation.

Results

A total of 14 studies enrolling 1,260 lung cancer patients were included in the meta-analysis. The high PNI level was significantly correlated with better OS (HR = 2.56, 95% CI = 1.86-3.54) and PFS (HR = 1.91, 95% CI = 1.53-2.40) of the lung cancer patients. The subgroup analysis confirmed the results except for the PFS in patients receiving anti-PD-1 therapy (HR = 1.51, 95% CI = 0.86-2.65). In the retrospective study, the high PNI level was identified as a favorable factor for OS and PFS not only in the whole cohort but also in the subgroups stratified by non-small cell lung cancer and small cell lung cancer. The high PNI was also correlated with better anti-cancer therapy response and performed better than body mass index and serum albumin level in OS prediction. Finally, we established a novel prognostic nomogram based on PNI and other clinical parameters. The nomogram was found to perform well in predicting the 1-year OS of ICI-treated lung cancer patients.

Conclusion

Both the meta-analysis and retrospective work demonstrate the PNI is a reliable prognostic factor for advanced lung cancer patients receiving ICI-based therapies. Our study further highlights the crucial role of nutrition assessment and intervention in cancer immunotherapy.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42023424146.

Soluble biomarkers to predict clinical outcomes in non-small cell lung cancer treated by immune checkpoints inhibitors

Lung cancer remains the first cause of cancer-related death despite many therapeutic innovations, including immune checkpoint inhibitors (ICI). ICI are now well used in daily practice at late metastatic stages and locally advanced stages after a chemo-radiation. ICI are also emerging in the peri-operative context. However, all patients do not benefit from ICI and even suffer from additional immune side effects. A current challenge remains to identify patients eligible for ICI and benefiting from these drugs. Currently, the prediction of ICI response is only supported by Programmed death-ligand 1 (PD-L1) tumor expression with perfectible results and limitations inherent to tumor-biopsy specimen analysis. Here, we reviewed alternative markers based on liquid biopsy and focused on the most promising biomarkers to modify clinical practice, including non-tumoral blood cell count such as absolute neutrophil counts, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and derived neutrophil to lymphocyte ratio. We also discussed soluble-derived immune checkpoint-related products such as sPD-L1, circulating tumor cells (detection, count, and marker expression), and circulating tumor DNA-related products. Finally, we explored perspectives for liquid biopsies in the immune landscape and discussed how they could be implemented into lung cancer management with a potential biological-driven decision.

Effect of Cancer-Related Cachexia and Associated Changes in Nutritional Status, Inflammatory Status, and Muscle Mass on Immunotherapy Efficacy and Survival in Patients with Advanced Non-Small Cell Lung Cancer

Immune checkpoint inhibitor (ICI)-based immunotherapy has significantly improved the survival of patients with advanced non-small cell lung cancer (NSCLC); however, a significant percentage of patients do not benefit from this approach, and predictive biomarkers are needed. Increasing evidence demonstrates that cachexia, a complex syndrome driven by cancer-related chronic inflammation often encountered in patients with NSCLC, may impair the immune response and ICI efficacy. Herein, we carried out a prospective study aimed at evaluating the prognostic and predictive role of cachexia with the related changes in nutritional, metabolic, and inflammatory parameters (assessed by the multidimensional miniCASCO tool) on the survival and clinical response (i.e., disease control rate) to ICI-based immunotherapy in patients with advanced NSCLC. We included 74 consecutive patients. Upon multivariate regression analysis, we found a negative association between IL-6 levels (odds ratio (OR) = 0.9036; 95%CI = 0.8408-0.9711; p = 0.0025) and the miniCASCO score (OR = 0.9768; 95%CI = 0.9102-0.9999; p = 0.0310) with the clinical response. As for survival outcomes, multivariate COX regression analysis found that IL-6 levels and miniCASCO-based cachexia severity significantly affected PFS (hazard ratio (HR) = 1.0388; 95%CI = 1.0230-1.0548; p < 0.001 and HR = 1.2587; 95%CI = 1.0850-1.4602; p = 0.0024, respectively) and OS (HR = 1.0404; 95%CI = 1.0221-1.0589; p < 0.0001 and HR = 2.3834; 95%CI = 1.1504-4.9378; p = 0.0194, respectively). A comparison of the survival curves by Kaplan-Meier analysis showed a significantly lower OS in patients with cachexia versus those without cachexia (p = 0.0323), as well as higher miniCASCO-based cachexia severity (p = 0.0428), an mGPS of 2 versus those with a lower mGPS (p = 0.0074), and higher IL-6 levels (>6 ng/mL) versus those with lower IL-6 levels (≤6 ng/mL) (p = 0.0120). In conclusion, our study supports the evidence that cachexia, with its related changes in inflammatory, body composition, and nutritional parameters, is a key prognostic and predictive factor for ICIs. Further larger studies are needed to confirm these findings and to explore the potential benefit of counteracting cachexia to improve immunotherapy efficacy.