The predictive value of inflammatory biomarkers for major pathological response in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy and its association with the immune-related tumor microenvironment: a multi-center study

Biomarkers of response to camrelizumab combined with apatinib: an analysis from a phase II trial in recurrent/metastatic nasopharyngeal carcinoma

BACKGROUND

This study aims to develop a peripheral blood-based model that can predict the response to the combination therapy of camrelizumab and apatinib as a second-line or later-line treatment regimen in patients with recurrent/metastatic nasopharyngeal carcinoma (R/M-NPC).

METHODS

We collected peripheral blood routine data from 72 patients with R/M-NPC from two clinical trial studies (NCT04547088, NCT04548271). Utilising the least absolute shrinkage and selection operator Cox regression model, we built a peripheral blood signature and developed a prognostic nomogram through multivariable analysis. Spectral flow cytometry analysed peripheral blood mononuclear cell immunophenotyping.

RESULTS

Six indicators (WBC, MCV, HCT, MCHC, P-LCR, MLR) were included to construct the peripheral blood signature. By combining this signature with Epstein-Barr virus DNA, distant lymph node metastasis and previous PD-1 inhibitor treatment, we constructed a peripheral blood-based nomogram that showed favourable performance. High-risk individuals had lower overall survival than low-risk individuals (P < 0.05). Immunophenotyping revealed that the high-risk individuals had increased monocytic myeloid-derived suppressor cells, Tregs and decreased CD8 effector memory cells (P < 0.05).

CONCLUSIONS

We established a model that could predict the prognosis of combined therapy. The model could predict outcomes and reflect the systemic immune and inflammatory status, which is beneficial for risk stratification and therapeutic modification.

Chemoimmunotherapy synergism: mechanisms and clinical applications

Chemoimmunotherapy, combining chemotherapy and immunotherapy, has emerged as a promising strategy for treating various cancers. This approach leverages the complementary mechanisms of both modalities to enhance tumor eradication. Recent advances have shed new light on the synergistic interactions between chemotherapy and immunotherapy, revealing complex mechanisms that contribute to improved clinical outcomes. Chemotherapy induces immunogenic cell death, releasing tumor antigens and damage-associated molecular patterns (DAMPs) that stimulate immune responses. It also modulates the tumor microenvironment, enhancing immune cell infiltration and reducing immunosuppressive elements. Concurrently, immunotherapy, particularly immune checkpoint inhibitors, activates the immune system to more effectively target and destroy cancer cells. Clinical evidence demonstrates significant benefits of chemoimmunotherapy in various cancers, including non-small-cell lung cancer, triple-negative breast cancer, and melanoma. Recent trials, such as KEYNOTE- 189 and IMpassion130, have shown improved overall survival and progression-free survival compared to chemotherapy alone. Emerging biomarkers, including tumor mutational burden, Programmed Death Ligand- 1 (PD-L1) expression, and immune cell infiltration patterns, are refining patient selection and response prediction. Novel strategies, such as nanoparticle-based drug delivery systems and personalized medicine approaches, are being explored to optimize chemoimmunotherapy combinations. However, challenges remain, including managing treatment-related toxicities, determining optimal dosing and sequencing, and addressing potential resistance mechanisms. Ongoing research focuses on elucidating the complex interplay between chemotherapy-induced immunomodulation and immune checkpoint inhibition to further improve treatment efficacy and patient outcomes. This review provides a comprehensive update on the mechanisms, clinical applications, and future directions of chemoimmunotherapy, highlighting its potential to revolutionize cancer treatment strategies. Clinical trial number: not applicable.

Peripheral inflammatory factors as prognostic predictors for first-line PD-1/PD-L1 inhibitors in advanced non-small cell lung cancer

Immune checkpoint inhibitors (ICIs) have significantly improved the efficacy and prognosis of patients with non-small cell lung cancer (NSCLC). However, there remains a lack of optimal predictive biomarkers for assessing the response of ICIs. This study aimed to evaluate peripheral inflammatory factors as potential predictive biomarkers for NSCLC patients treated with ICIs. We retrospectively analyzed the correlation between peripheral inflammatory factors and the efficacy and prognosis of 124 patients with driver gene-negative advanced NSCLC who received first-line ICIs at our center from September 2018 to June 2022. Progression-free survival (PFS) was estimated using the Kaplan-Meier method. The association between the factors and multiple endpoints were investigated using univariate and multivariate analyses. A total of 124 patients were enrolled in this study. The objective response rate (ORR) was 49.2% and the disease control rate (DCR) was 97.6%, respectively. The median PFS was 12.7 months. The ORR differed statistically between groups based on the NLR, SII, with higher ORR observed in patients with an NLR ratio < 0.68, SII at 6 weeks < 531.26, and SII ratio < 0.74 (p < 0.05). The univariate analysis indicated that ECOG 0-1, smoking, NLR at 6 weeks < 2.72, NLR ratio < 0.68, LMR < 1.34, LMR ratio ≥ 1.38, and SII at 6 weeks < 531.26 were associated with longer PFS (p < 0.05). The multivariate analysis revealed that smoking (p = 0.013), baseline LMR (p = 0.015), and SII at 6 weeks (p = 0.010) were independent predictors of PFS. NLR, LMR, and SII maybe biomarkers for predicting the efficacy and prognosis of first-line ICIs therapy in driver gene-negative advanced NSCLC.

Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data

The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation of response patterns in a real-world setting. We propose a multivariate Bayesian joint model using generalized linear mixed effects, trained and validated on RWD from 424 advanced NSCLC patients retrospectively collected from three clinical centers. Center1 was used as training ( N = 212 ), while Center2 and Center3 were used as independent testing sets ( N = 137 and N = 75 , respectively). Peripheral blood data (PBD) were collected at baseline and at three follow-up time points, alongside demographic and epidemiologic features. Six models were trained to predict progression-free survival at 6 months, PFS(6), using different number of longitudinal samples (baseline, two, or four time points) of the neutrophil-to-lymphocyte ratio (NLR) or a multivariate feature selection. Long-term predictions at 12 and 24 months were also evaluated. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUC). The proposed model significantly improved prediction performance, achieving AUCs of 0.870, 0.804 and 0.827 at 6, 12 and 24 months for Center2, and 0.824, 0.822 and 0.667 for Center3. There was also a significant difference in PFS and overall survival (OS) between predicted response groups, defined by a 6-month PFS cutoff (log-rank test p < 0.001 ). Our study suggests that the integration of multiple biomarkers and monitored PBD in an RWD-based Bayesian joint model framework significantly improves immunotherapy response prediction in advanced NSCLC compared to conventional approaches involving biomarker data at baseline only.

Monocyte-related markers as predictors of immune checkpoint inhibitor efficacy and immune-related adverse events: a systematic review and meta-analysis

The efficacy and off-target effects of immune checkpoint inhibitors (ICI) in cancer treatment vary among patients. Monocytes likely contribute to this heterogeneous response due to their crucial role in immune homeostasis. We conducted a systematic review and meta-analysis to evaluate the impact of monocytes on ICI efficacy and immune-related adverse events (irAEs) in patients with cancer. We systematically searched PubMed, Web of Science, and Embase for clinical studies from January 2000 to December 2023. Articles were included if they mentioned cancer, ICI, monocytes, or any monocyte-related terminology. Animal studies and studies where ICIs were combined with other biologics were excluded, except for studies where two ICIs were used. This systematic review was registered with PROSPERO (CRD42023396297) prior to data extraction and analysis. Monocyte-related markers, such as absolute monocyte count (AMC), monocyte/lymphocyte ratio (MLR), specific monocyte subpopulations, and m-MDSCs were assessed in relation to ICI efficacy and safety. Bayesian meta-analysis was conducted for AMC and MLR. The risk of bias assessment was done using the Cochrane-ROBINS-I tool. Out of 5787 studies identified in our search, 155 eligible studies report peripheral blood monocyte-related markers as predictors of response to ICI, and 32 of these studies describe irAEs. Overall, based on 63 studies, a high MLR was a prognostic biomarker for short progression-free survival (PFS) and overall survival (OS) hazard ratio (HR): 1.5 (95% CI: 1.21-1.88) and 1.52 (95% CI:1.13-2.08), respectively. The increased percentage of classical monocytes was an unfavorable predictor of survival, while low baseline rates of monocytic myeloid-derived suppressor cells (m-MDSCs) were favorable. Elevated intermediate monocyte frequencies were associated but not significantly correlated with the development of irAEs. Baseline monocyte phenotyping may serve as a composite biomarker of response to ICI; however, more data is needed regarding irAEs. Monocyte-related variables may aid in risk assessment and treatment decision strategies for patients receiving ICI in terms of both efficacy and safety.

Combined impact of prognostic nutritional index, fibrinogen-to-albumin ratio, and neutrophil-to-lymphocyte ratio on surgical outcomes and prognosis in hepatocellular carcinoma

This study evaluated the predictive value of the prognostic nutritional index (PNI), fibrinogen-to-albumin ratio (FAR), and neutrophil-to-lymphocyte ratio (NLR) for overall survival in hepatocellular carcinoma (HCC) patients. A total of 283 HCC cases from Hunan Provincial People's Hospital were included in the analysis, with 45 additional patients as external validation. The relationship between these indices and patient prognosis was further evaluated using the Kaplan-Meier method and Cox regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive performance of these indices for overall survival (OS) and to determine the optimal cutoff values. ROC curve analysis revealed that the area under the curve (AUC) for PNI, FAR, and NLR was 0.723, 0.857, and 0.872, respectively. Multivariate analysis identified hepatitis history, intraoperative blood transfusion, FAR, NLR, and PNI as independent prognostic factors (all P<0.05). The resulting prediction model demonstrated strong performance in both the training (C-index =0.917) and external validation (C-index =0.853) cohorts, with AUCs of 0.889 and 0.931 for 6-month and 1-year prediction in the validation set, respectively. These findings suggest that preoperative levels of peripheral blood PNI, FAR, and NLR are closely associated with the surgical prognosis of HCC patients. The prognostic prediction model developed based on these indices exhibits good predictive efficacy.

Nutritional and inflammatory status dynamics reflect preoperative treatment response and predict prognosis in locally advanced rectal cancer: A retrospective multi-institutional analysis

BACKGROUND

Systemic inflammation, as an important host property, is the most representative tumor-host interactions in cancer, and the development of malignant neoplasms may contribute to impairment on nutritional status. This study aimed to investigate the potential ability of nutritional and inflammatory index in predicting neoadjuvant chemoradiotherapy efficacy and prognosis in locally advanced rectal cancer (LARC).

METHODS

This study was conducted using multi-institutional data. A total of 507 patients (262 in the training and 245 in the validation cohort) with stage IIA-IIIC LARC fit for neoadjuvant chemoradiotherapy were recruited from 2012 to 2014 were included in this study. Advanced lung cancer inflammation index (ALI) reflected nutritional and inflammatory status. The ALI was calculated as body mass index (BMI) × albumin × neutrophil/lymphocyte. Logistic regression model was used to identify predictive factors for preoperative treatment response. Cox multivariate regression models were used to analyze the factors affecting disease-free survival (DFS) and overall survival (OS).

RESULTS

In the training cohort, patients with high pretreatment ALI were observed to be associated with young patients, never smoked, relatively high BMI, and early-stage pathologic TNM staging. The receiver operating characteristic curve indicated that pretreatment ALI and its changing was the single most important factor determining outcomes than other inflammatory indicators. The 10-year DFS and OS rates of the whole group were 63.6% and 74.1% respectively. Patients with low pretreatment ALI and ALI change had significantly poorer 10-year DFS (P < .001 and P = .001) and 10-year OS (P = .002 and P = .025) rates than those with high ALI and ALI change. Similar findings were observed in the validation cohort. Multivariate analysis revealed that pretreatment ALI (P = .047 and P = .006) and ALI change (P = .027 and P = .041) were identified as independent prognostic factors for DFS. Meanwhile, high pretreatment ALI (P = .020 and P = .010), high systemic immune-inflammation index (SII) change (P = .040 and P = .012) and clinical stage T2-T3 were independent protective factors for OS. Furthermore, multivariate logistic regression analyses revealed that pretreatment ALI, ALI change, and SII change could independently predict efficacy of neoadjuvant chemoradiotherapy.

CONCLUSION

Our results suggest that as a feasible indicator of nutritional and inflammatory status, the ALI shows better efficiency than other inflammatory indicators in predicting efficacy of neoadjuvant chemoradiotherapy and prognosis.

The association of systemic immune-inflammation index with incident breast cancer and all-cause mortality: evidence from a large population-based study

BACKGROUND

Chronic low-grade inflammation is recognized as a significant factor in various health outcomes, including the development and progression of breast cancer. The Systemic Immune-Inflammation Index (SII), a novel marker derived from routine blood counts, has been suggested as a predictor of all-cause mortality and cardiovascular mortality. However, its predictive value in a nationwide representative population, particularly for breast cancer incidence and mortality, is not well-established.

METHODS

This study aimed to assess the association of SII and the risk of breast cancer incidence and all-cause mortality in breast cancer patients within the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. SII was calculated from complete blood count parameters. We used multifactor regression models to examine the associations between SII and the outcomes of interest.

RESULTS

A total of 21,058 female participants were included in the study, of which 557 (2.7%) were identified as having breast cancer. After adjusting for multiple potential confounders, the relationship between SII and the incidence of breast cancer revealed an inverse L-shaped association. The optimal inflection point for SII/100 was determined to be 5.09. Below this threshold, there was a significant increase in the risk of breast cancer (OR=1.05, 95% CI: 1.02-1.09). Within the breast cancer population, SII exhibited a J-shaped relationship with all-cause mortality. The optimal inflection point for SII/100 in this context was 5.22, and above this threshold, there was a marked escalation in all-cause mortality (HR=1.09, 95% CI: 1.04-1.14).

CONCLUSION

The SII, as a novel inflammatory composite index, is significantly associated with the risk of breast cancer incidence and all-cause mortality in breast cancer patients. These findings highlight the importance of monitoring systemic inflammation and suggest that SII could serve as a valuable prognostic tool.

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer

BACKGROUND

The anti-angiogenic drugs showed remarkable efficacy in the treatment of lung cancer. Nonetheless, the potential roles of the intra-tumoral immune cell abundances and peripheral blood immunological features in prognosis prediction of patients with advanced lung cancer receiving anti-angiogenesis-based therapies remain unknown. In this study, we aimed to develop an immune-based model for early identification of patients with advanced lung cancer who would benefit from anti-angiogenesis-based therapies.

METHODS

We assembled the real-world cohort of 1058 stage III-IV lung cancer patients receiving the anti-angiogenesis-based therapies. We comprehensively evaluated the tumor immune microenvironment characterizations (CD4, CD8, CD68, FOXP3, and PD-L1) by multiplex immunofluorescence (mIF), as well as calculated the systemic inflammatory index by flow cytometry and medical record review. Based on the light gradient boosting machine (LightGBM) algorithm, a machine-learning model with meaningful parameters was developed and validated in real-world populations.

RESULTS

In the first-line anti-angiogenic therapy plus chemotherapy cohort (n = 385), the intra-tumoral proportion of CD68 + Macrophages and several circulating inflammatory indexes were significantly related to drug response (p < 0.05). Further, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), the systemic inflammation response index (SIRI), and myeloid to lymphoid ratio (M:L) were identified to construct the non-invasive prediction model with high predictive performance (AUC: 0.799 for treatment response and 0.7006-0.915 for progression-free survival (PFS)). Additionally, based on the unsupervised hierarchical clustering results, the circulating cluster 3 with the highest levels of NLR, MLR, SIRI, and M: L had the worst PFS with the first-line anti-angiogenic therapy plus chemotherapy compared to other circulating clusters (2.5 months, 95 % confidence interval 2.3-2.7 vs. 6.0-9.7 months, 95 % confidence interval 4.9-11.1, p < 0.01). The predictive power of the machine-learning model in PFS was also validated in the anti-angiogenic therapy plus immunotherapy cohort (n = 103), the anti-angiogenic monotherapy cohort (n = 284), and the second-line anti-angiogenic therapy plus chemotherapy cohort (n = 286).

CONCLUSIONS

Integrating pre-treatment circulating inflammatory biomarkers could non-invasively and early forecast clinical outcomes for anti-angiogenic response in lung cancer. The immune-based prognostic model is a promising tool to reflect systemic inflammatory status and predict clinical prognosis for anti-angiogenic treatment in patients with stage III-IV lung cancer.

Prognostic value of immuno-inflammatory biomarkers in esophageal squamous cell carcinoma patients receiving immunotherapy combined with chemoradiotherapy and its association with immuno-genomic landscape

BACKGROUND

The clinical significance of immuno-inflammatory indicators and the underlying biological basis in patients with esophageal squamous cell carcinoma (ESCC) who receive chemoradiotherapy (CRT) combined with immunotherapy remains unclear. This study aims to evaluate the prognostic value of immuno-inflammatory biomarkers, develop a prognostic model, and explore the underlying mechanisms.

METHODS

This study included 212 ESCC patients who received CRT and anti-PD-1 immunotherapy. Association between progression-free survival (PFS) and immuno-inflammatory biomarkers, including absolute lymphocyte count (ALC), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio was analyzed. A nomogram was built based on the independent prognostic factors identified using multivariable Cox regression model. Pre-treatment tumor samples from 47 patients were collected for RNA sequencing to investigate the immune-related tumor microenvironment.

RESULTS

Patients experienced significant changes in immuno-inflammatory biomarkers during CRT, which gradually recovered after radiotherapy. Body mass index < 18.5 (HR, 1.85; P = 0.032), N3 stage (HR, 2.41; P = 0.002), high pre-CRT PLR (HR, 1.53; P = 0.037), low ALC nadir (HR, 1.84; P = 0.006), and high post-CRT NLR (HR, 2.12; P = 0.002) were independent prognostic factors for unfavorable PFS, which were incorporated into a nomogram with a concordance index of 0.70 (95% CI, 0.67-0.72). High-risk patients stratified by the nomogram had worse survival and were associated with lower levels of leukocyte and T cell activation, proliferation, and migration and less intratumoral immune cell infiltration.

CONCLUSIONS

Pre-CRT PLR, ALC nadir during CRT, and post-CRT NLR were significantly associated with PFS in patients with ESCC receiving CRT and immunotherapy. A nomogram model with good prognostic ability was developed.

New prognostic index for neoadjuvant chemotherapy outcome in patients with advanced high-grade serous ovarian cancer

BACKGROUND

A validated prognostic index for the outcome of patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy (NACT) remains elusive. To address this need, we developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) to improve predictive accuracy.

METHODS

We encompassed an analysis of the clinicopathological characteristics of patients with advanced HGSOC who were administered platinum-based NACT. Blood inflammatory composite markers were calculated and converted into binary values using optimal cutoffs. Omental hematoxylin and eosin (H&E) stained slides were selected for the assessment of chemotherapy response score (CRS), which served as a measure of NACT efficacy. Logistic regression analysis and Cox proportional hazards regression model were utilized to construct a prognostic index.

RESULTS

Multivariate logistic analysis showed that both CRS and neutrophil-to-lymphocyte ratio (NLR) independently influenced the response to platinum-based chemotherapy. Meanwhile, Kaplan-Meier and Cox regression analysis revealed that CRS score was significantly correlated with progression-free survival (PFS) and overall survival (OS), and patients with high NLR showed poor OS. We further developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) based on the CRS and NLR. The area under the curve (AUC) value of ONCPI was 0.771 (P < 0.001, 95% CI: 0.656-0.887) for the prediction of platinum resistance. This AUC value surpasses that of the individual NLR and CRS, which were 0.670 (P = 0.018, 95% CI: 0.547-0.793) and 0.714 (P = 0.003, 95% CI: 0.590-0.839), respectively. Moreover, survival analysis suggested that patients with ONCPI of 0 and 1 were significantly associated with improved PFS and OS.

CONCLUSIONS

The ONCPI emerges as a significant prognostic marker for predicting NACT outcome in advanced HGSOC patients and holds promise for integration into clinical practice and risk-stratified trial design.

Combining serum inflammatory markers and clinical factors to predict survival in metastatic urothelial carcinoma patients treated with immune checkpoint inhibitors

Background

Despite the revolutionary impact of immune checkpoint inhibitors (ICIs) on the treatment of metastatic urothelial carcinoma (mUC), the clinical utility of reliable prognostic biomarkers to foresee survival outcomes remains underexplored.

Objectives

The purpose of this study was to ascertain the prognostic significance of serum inflammatory markers in mUC patients undergoing ICI therapy.

Design

This is a retrospective, multicenter study.

Methods

Data were collected from two independent medical centers in Taiwan, encompassing a validation and a training cohort (TC). Patients with histopathologically confirmed urothelial carcinoma who received at least one cycle of ICI monotherapy were included. Serum inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were calculated prior to ICI therapy. Statistical analyses involved the use of receiver operating characteristic (ROC) curves to determine optimal biomarker cutoffs and Cox proportional hazards models to evaluate the independent predictive capability of these markers.

Results

A total of 192 patients were enrolled. In the univariate analysis, serum markers such as NLR, PLR, SII, and Hb were significantly associated with overall survival (OS) in both the training and validation cohorts (VC). White blood cells, NLR, and SII demonstrated a robust correlation with progression-free survival across both cohorts. Multivariate analysis revealed that Eastern Cooperative Oncology Group performance status ⩾2 (p < 0.001), visceral metastasis (p < 0.001), leukocytosis (p < 0.001), Hb levels ⩾10 mg/dL (p = 0.008), and NLR ⩾5 (p = 0.032) as independent predictors of OS. A prognostic nomogram integrating these independent factors yielded a C-index for a 3-year OS of 0.769 in the TC and 0.657 in the VC.

Conclusion

Serum inflammatory markers, combined with clinicopathologic factors, provide a practical prognostic tool in mUC treatment with ICIs.

Spatial effects of infiltrating T cells on neighbouring cancer cells and prognosis in stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Complete Blood Count-Based Biomarkers as Predictors of Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients with PD-L1 < 50% Treated with First-Line Chemoimmunotherapy

Background: The aim of the study was to investigate a series of complete blood cell count-based biomarkers of systemic inflammation as predictors of clinical outcomes in patients who underwent first-line chemoimmunotherapy for advanced NSCLC. Methods: Consecutive patients with pathologically diagnosed stage III/IV NSCLC and PD-L1 < 50% who underwent first-line chemoimmunotherapy were retrospectively enrolled. The clinical outcomes used for biomarker evaluation were Objective Response Rate (ORR) and Overall Survival (OS). Results: Non-responders had significantly higher values of neutrophil to lymphocyte ratio (NLR, median: 5.36; IQR: 2.78-10.82 vs. 3.31; IQR: 2.15-4.12, p = 0.019), neutrophil to monocyte ratio (NMR, median: 14.00; IQR: 8.82-21.20 vs. 9.20; IQR: 7.45-11.20, p = 0.013), and systemic inflammation index (SII, median: 1395; IQR: 929-3334 vs. 945; IQR: 552-1373, p = 0.025), but only NLR and NMR remained independently associated with clinical response in multivariate logistic regression. In the univariate analysis, white blood cells (OR:1.2202; 95% CI: 1.0339-1.4400, p = 0.019), neutrophils (OR:1.2916; 95% CI: 1.0692-1.5604, p = 0.008), NLR (OR:1.3601: 95% CI: 1.0949-1.6896, p = 0.005) and NMR (OR:1.2159; 95% CI: 1.00396-1.4221, p = 0.015) were significantly associated with survival; Cox regression models confirmed that neutrophils, NLR, and MLR were independently associated with survival; NLR, at a cut-off value of 4.0, showed the better AUC (0.749) in predicting OS. Conclusions: Baseline complete blood cell count biomarkers, especially the NLR, can predict clinical outcomes in patients with advanced NSCLC treated with first-line chemoimmunotherapy.

Peripheral CD8+PD-1+ T cells as novel biomarker for neoadjuvant chemoimmunotherapy in humanized mice of non-small cell lung cancer

Neoadjuvant immunotherapy has shown promising clinical activity in the treatment of early non-small cell lung cancer (NSCLC); however, further clarification of the specific mechanism and identification of biomarkers are imperative prior to implementing it as a daily practice. The study investigated the reprogramming of T cells in both tumor and peripheral blood following neoadjuvant chemoimmunotherapy in a preclinical NSCLC mouse model engrafted with a human immune system. Samples were also collected from 21 NSCLC patients (Stage IA-IIIB) who received neoadjuvant chemoimmunotherapy, and the dynamics of potential biomarkers within these samples were measured and further subjected to correlation analysis with prognosis. Further, we initially investigated the sources of the potential biomarkers. We observed in the humanized mouse model, neoadjuvant chemoimmunotherapy could prevent postoperative recurrence and metastasis by increasing the frequency and cytotoxicity of CD8+ T cells in both peripheral blood (p < 0.001) and tumor immune microenvironment (TIME) (p < 0.001). The kinetics of peripheral CD8+PD-1+ T cells reflected the changes in the TIME and pathological responses, ultimately predicting survival outcome of mice. In the clinical cohort, patients exhibiting an increase in these T cells post-treatment had a higher rate of complete or major pathological response (p < 0.05) and increased immune infiltration (p = 0.0012, r = 0.792). We identified these T cells originating from tumor draining lymph nodes and subsequently entering the TIME. In conclusion, the kinetics of peripheral CD8+PD-1+ T cells can serve as a predictor for changes in TIME and optimal timing for surgery, ultimately reflecting the outcomes of neoadjuvant chemoimmunotherapy in both preclinical and clinical setting.

Variation of peripheral blood-based biomarkers for response of anti-PD-1 immunotherapy in non-small-cell lung cancer

PURPOSE

Immune checkpoint inhibitors (ICIs) for non-small-cell lung cancer (NSCLC) are on the rise, but unfortunately, only a small percentage of patients benefit from them in the long term. Thus, it is crucial to identify biomarkers that can forecast the efficacy of immunotherapy.

METHODS

We retrospectively studied 224 patients with NSCLC who underwent anti-PD-1 therapy. The role of biomarkers and clinical characteristics were assessed in a prognostic model.

RESULTS

Only 14.3% of patients had both programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) outcomes, highlighting the need to investigate more available biomarkers. Our analysis found a correlation between histological PD-L1 TPS and hematological PD-1 expression. Analysis of hematological biomarkers revealed that elevated expression of CD4/CD8 and LYM% are positively associated with effective immunotherapy, while PD-1+ on T cells, NLR, and MLR have a negative impact. Moreover, high level of ΔCEA%, CYFRA21-1 and LDH may suggest ineffective ICIs. We also observed that disparate immunotherapy drugs didn't significantly impact prognosis. Lastly, by comparing squamous carcinoma and adenocarcinoma cohorts, ΔCEA%, CD3+PD-1+, CD4+PD-1+, and CD4/CD8 are more important in predicting the prognosis of adenocarcinoma patients, while age is more significant for squamous carcinoma patients.

CONCLUSION

Our research has yielded encouraging results in identifying a correlation between immunotherapy's response and clinical characteristics, peripheral immune cell subsets, and biochemical and immunological biomarkers. The screened hematological detection panel could be used to forecast an NSCLC patient's response to anti-PD-1 immunotherapy with an accuracy rate of 76.3%, which could help customize suitable therapeutic decision-making.

Derived Neutrophils to Lymphocyte Ratio Predicts Survival Benefit from TPF Induction Chemotherapy in Local Advanced Oral Squamous Cellular Carcinoma

BACKGROUND

This study aimed to evaluate the derived neutrophil to lymphocyte ratio (dNLR) in predicting the prognosis of patients with locally advanced oral squamous cell carcinoma (LAOSCC) and to assess the survival benefits from docetaxel, cisplatin, and 5-fluorouracil (5-FU) (TPF) induction chemotherapy (IC).

METHODS

Patients from a phase III trial involving TPF IC in stage III/IVA OSCC patients (NCT01542931) were enrolled. Receiver operating characteristic curves were constructed, and the area under the curve was computed to determine dNLR cutoff points. Kaplan-Meier survival estimates and Cox proportional hazards models were used for longitudinal analysis.

RESULTS

A total of 224 patients were identified (median age: 55.4 years; range: 26 to 75 years; median follow-up: 90 months; range: 3.2 to 93 months). The cutoff point for the dNLR was 1.555. Multivariate analysis showed that the dNLR was an independent negative predictive factor for survival (overall survival (OS): hazard ratio (HR) = 1.154, 95% confidence interval (CI): 1.018-1.309, p = 0.025; disease-free survival (DFS): HR = 1.123, 95% CI: 1.000-1.260, p = 0.050; local recurrence-free survival (LRFS): HR = 1.134, 95% CI: 1.002-1.283, p = 0.047; distant metastasis-free survival (DMFS): HR = 1.146, 95% CI: 1.010-1.300, p = 0.035). A low dNLR combined with cTNM stage III disease predicted benefit from TPF IC for the patients [OS (χ2 = 4.674, p = 0.031), DFS (χ2 = 7.134, p = 0.008), LRFS (χ2 = 5.937, p = 0.015), and DMFS (χ2 = 4.832, p = 0.028)].

CONCLUSIONS

The dNLR is an independent negative predictive factor in LAOSCC patients. Patients with cTNM stage III disease and a low dNLR can benefit from TPF IC.

Pulmonary function test-related prognostic models in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy

Background

This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy.

Methods

Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.

Results

Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.

Conclusion

The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.

Predictive value of dynamic changes in peripheral blood inflammation and blood lipid-related indices for the lung cancer treatment efficacy

To investigate the dynamics of inflammation and lipid-related indicators in lung cancer patients and their impact on treatment efficacy. A retrospective analysis was conducted on 133 lung cancer patients who seek for primary treatment at Wujin Hospital Affiliated to Jiangsu University from January 2019 to August 2022. The inflammation and blood lipid-related indicators were collected 1 week before treatment and after 2 cycles of treatment. We compared the changes in these indicators among patients with different treatment methods and outcomes. The diagnostic value of the dynamic changes in each index for disease progression was calculated using the ROC curve. The risk factors influencing disease development were identified using multifactorial logistic regression analysis. After 2 cycles of treatment, the white blood cell count (WBC, P<0.001), neutrophil count (NC, P<0.001), neutrophil-to-lymphocyte ratio (NLR, P<0.001) in the disease progression (PD) group were significantly increased, triglyceride (TG, P=0.023), apolipoprotein A1 (APO-A1, P=0.009) was significantly decreased. The results showed that ∆NC had the highest sensitivity (88.24%) in predicting disease progression, and ∆WBC had the best specificity (77.78%). Multivariate regression analysis showed that ΔWBC (P<0.001), ΔTG (P=0.041), and treatment method (P=0.010) were independent risk factors for disease progression (PD). The changes of WBC and TG before and after treatment are promising indicators for predicting the progression of lung cancer and may offer a new direction for lung cancer treatment.

Potential predictors of the pathologic response after neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer: a narrative review

Background and Objective

Neoadjuvant chemoimmunotherapy (NACI) is the standard of care for patients with resectable non-small cell lung cancer (NSCLC). Although the pathological complete response (pCR) after NACI reportedly exceeds 20%, an optimal predictor of pCR is yet to be established. This review aims to examine the possible predictors of pCR after NACI.

Methods

We identified research article published between 2018 and 2022 in English by the PubMed database. Fifty research studies were considered as relevant article, and were examined to edit information for this narrative review.

Key Content and Findings

Recently, several studies have explored potential biomarkers for the pathological response after NACI. For example, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) imaging, tumor microenvironment (TME), genetic alternation such as circulating tumor DNA (ctDNA), and clinical markers such as neutrophil-to-lymphocyte ratio (NLR) and smoking signature were assessed in patients with resectable NSCLC to predict the pathological response after NACI. Based on the PET response criteria, the complete metabolic response (CMR) achieved a positive predictive value (PPV) of 71.4% for predicting pCR, and the decreasing rate of post-therapy maximum standardized uptake value (SUVmax) after NACI substantially correlated with the major pathological response (MPR). TME, as a significant marker for MPR in tumor specimens, was identified as an increase in CD8+ T cells and decrease in CD3+ T cells or Foxp3 T cells. Considering blood samples, TME comprised an increase in CD4+PD-1+ cells or natural killer cells and a decrease in CD3+CD56+CTLA4+ cells, total T cells, Th cells, myeloid-derived suppressor cells (MDSCs), or regulatory T cells. Although low pretreatment levels of ctDNA and undetectable ctDNA levels after NACI were markedly associated with survival, the relationship between ctDNA levels and pCR remains elusive. Moreover, the patients with a high baseline NLR had a low incidence of pCR. Heavy smoking (>40 pack-years) was favorable for predicting pathological response.

Conclusions

A reduced rate of 18F-FDG uptake post-NACI and TME-related surface markers on lymphocytes could be optimal predictors for pCR. However, the role of these pCR predictors for NACI remains poorly validated, warranting further investigations. This review focuses on predictive biomarkers for pathological response after NACI in patients with resectable NSCLC.

The fibrosis-4 index is a prognostic factor for cholangiocarcinoma patients who received immunotherapy

Background

Research of immunotherapy for cholangiocarcinoma has yielded some results, but more clinical data are needed to prove its efficacy and safety. Moreover, there is a need to identify accessible indexes for selecting patients who may benefit from such treatments.

Methods

The medical records of 66 cholangiocarcinoma patients who underwent immunotherapy were retrospectively collected. The effectiveness of immunotherapy was assessed by tumor response, progression-free survival (PFS), and overall survival (OS), while safety was evaluated by adverse events during treatment. Univariate and multivariate Cox regression analyses were performed to identify prognostic risk factors for PFS and OS, and Kaplan-Meier curves of potential prognostic factors were drawn.

Results

Overall, in this study, immunotherapy achieved an objective response rate of 24.2% and a disease control rate of 89.4% for the included patients. The median PFS was 445 days, and the median OS was 772.5 days. Of the 66 patients, 65 experienced adverse events during treatment, but none had severe consequences. Multivariate Cox analysis indicated that tumor number is a prognostic risk factor for disease progression following immunotherapy in cholangiocarcinoma patients, while tumor differentiation and the fibrosis-4 (FIB-4) index are independent risk factors for OS.

Conclusion

In general, immunotherapy for cholangiocarcinoma is safe, with adverse events remaining within manageable limits, and it can effectively control disease progression in most patients. The FIB-4 index may reflect the potential benefit of immunotherapy for patients with cholangiocarcinoma.

Exploring the prognostic impact of absolute lymphocyte count in patients treated with immune-checkpoint inhibitors

BACKGROUND

The role of immune checkpoint inhibitors (ICI) expands but affordable and reproducible prognostic biomarkers are needed. We investigated the association between baseline and 3-month absolute lymphocyte count (ALC) and survival for patients on ICI.

METHODS

A retrospective study investigated patients who received ICI July 2014-August 2019. Survival probabilities were calculated for lymphocyte subsets. Univariate and multivariate analyses were performed to investigate risk factors for lymphopenia.

RESULTS

Among 179 patients, median age was 62 and 41% were female. The most common diagnoses were melanoma (41%) and lung cancer (40%). Median PFS was 6.5 months. 27% had baseline lymphopenia (ALC < 1 × 109cells/L) and no significant difference in PFS or OS to those with normal ALC. However, 31% had lymphopenia at 3 months and significantly shorter OS than those without (9.8 vs 18.3 months, p < 0.001). Those with baseline lymphopenia who recovered counts at 3 months had no difference in PFS (median NR vs 13.0 months, p = 0.48) or OS (22 vs 18.3 months, p = 0.548) to those never lymphopenic. The strongest risk factor for lymphopenia on multivariable analysis was previous radiation therapy (RT).

CONCLUSIONS

3-month lymphopenia is a negative prognostic marker in cancer patients on ICI. Previous RT is significantly associated with lymphopenia.

Predictive value of peripheral blood biomarkers in patients with non-small-cell lung cancer responding to anti-PD-1-based treatment

BACKGROUND

The introduction of the anti-PD-1 antibody has greatly improved the clinical outcomes of patients with non-small cell lung cancer (NSCLC). In this study, we retrospectively analyzed the efficacy of PD-1 antibody-based therapy in patients with locally advanced inoperable or metastatic NSCLC and reported an association between peripheral blood biomarkers and clinical response in these patients.

METHODS

This single-center study included medical record data of patients with NSCLC treated with the PD-1 antibody as a first-line or subsequent line of treatment, either as monotherapy or in combination with chemotherapy. The patients were enrolled from 2020 to 2022. We dynamically evaluated multiple Th1 and Th2 cytokines in the blood serum and analyzed the phenotype of T cells from the peripheral blood to explore the correlation between cytokine levels, T cell phenotypes, and clinical response.

RESULTS

A total of 88 patients with stage IIIA-IV NSCLC were enrolled, out of which 60 (68.18%) achieved a partial response (PR), 13 (14.77%) had stable disease (SD), and 15 (17.05%) experienced disease progression (PD). The disease control rate was 82.95%. Our results suggested a significant reduction (P = 0.002, P < 0.005) in lymphocyte absolute counts after treatment in patients with PD. Higher levels of IFN-γ (P = 0.023, P < 0.05), TNF-α (P = 0.00098, P < 0.005), IL-4 (P = 0.0031, P < 0.005), IL-5 (P = 0.0015, P < 0.005), and IL-10 (P = 0.036, P < 0.05) were detected in the peripheral blood before treatment in the PR group compared to the PD group. Moreover, patients with high levels of IL-5, IL-13, IL-4, IL-6, IFN-γ, and TNF-α (> 10 ng/mL) had superior progression-free survival compared to those with low levels (< 10 ng/mL). Furthermore, PD-1 expression on CD8+ T cells was higher in patients who showed a PR than in those who did not show a response (SD + PD; P = 0.042, P < 0.05).

CONCLUSIONS

The findings of this study imply that the decrease in absolute blood lymphocyte counts after treatment is correlated with disease progression. Serum cytokine levels may predict the effectiveness and survival rates of anti-PD-1 blockade therapy in patients with NSCLC. In addition, PD-1 expression on CD8+ T cells was positively associated with better clinical response. Our findings highlight the potential of peripheral blood biomarkers to predict the effectiveness of PD-1-targeted treatments in patients with NSCLC. Larger prospective studies are warranted to further clarify the value of these biomarkers.

Peripheral blood inflammatory biomarkers dynamics reflect treatment response and predict prognosis in non-small cell lung cancer patients with neoadjuvant immunotherapy

Neoadjuvant immunotherapy has significantly changed the therapeutic approach for treating patients with surgically resectable non-small cell lung cancer (NSCLC). Here, peripheral blood inflammation-based biomarkers as well as previously less focused eosinophil fraction, modified Glasgow prognostic score (mGPS), and prognostic nutritional index (PNI) were systematically included to comprehensively analyze their potential in predicting neoadjuvant immunotherapy efficacy and prognosis. We enrolled 189 patients (94 in training and 95 in validation cohorts) with stage I-III B surgically resectable NSCLC treated with neoadjuvant immunotherapy from the National Cancer Center of China. Baseline and post-treatment eosinophils fraction, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), PNI, mGPS, and their changes were calculated and analyzed for correlation with neoadjuvant immunotherapy efficacy and prognosis. In patients in the major pathological response (MPR) group, the post-treatment eosinophil fraction was significantly high, and NLR, PLR, SII, and MLR were significantly lower compared to the non-MPR group in both the training and validation cohorts. The receiver operating characteristic curve showed that post-treatment, eosinophil fraction and SII and their changing were two of the most important factors. Univariate and multivariate logistic regression analyses showed that post-treatment eosinophil fraction, SII, mGPS, and ΔSII could independently predict MPR in patients treated with neoadjuvant immunotherapy. Survival analysis showed a significant correlation between high post-treatment NLR, PLR, SII, mGPS, and their changes in ΔNLR and ΔSII elevation with poor overall survival and event-free survival of patients. Our results suggest that inflammatory biomarkers could predict the patient's response to neoadjuvant immunotherapy and prognosis.

Association of systemic inflammatory markers and tertiary lymphoid structure with pathological complete response in gastric cancer patients receiving preoperative treatment: a retrospective cohort study

BACKGROUND

Assessment of systemic and local immune responses is crucial in determining the efficacy of cancer interventions. The identification of specific factors that correlate with pathological complete response (pCR) is essential for optimizing treatment decisions.

METHODS

In this retrospective study, a total of 521 patients diagnosed with gastric adenocarcinoma who underwent curative gastrectomy following preoperative treatment were reviewed. Of these patients, 463 did not achieve pCR (non-pCR) and 58 achieved pCR. Clinicopathological factors were evaluated to identify predictors for pCR using a logistic regression model. Additionally, a smaller cohort (n=76) was derived using propensity score matching to investigate local immune response, specifically the features of tertiary lymphoid structure (TLS) using H&E staining, immunohistochemistry, and multiplex immunofluorescence.

RESULTS

The multivariate regression analysis demonstrated a significant association between low systemic inflammatory status and pCR, as evidenced by reduced levels of the combined systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) (SII+NLR) (odds ratio: 3.33, 95% CI: 1.79-6.17, P<0.001). In the smaller cohort analysis, distinct TLS characteristics were correlated with the presence of pCR. Specifically, a higher density of TLS and a lower proportion of PD1+ cells and CD8+ cells within TLS in the tumor bed were strongly associated with pCR.

CONCLUSION

Both systemic and local immune profile were associated with pCR. A low level of SII+NLR served as an independent predictor of pCR, while distinct TLS features were associated with the presence of pCR. Focusing on the immune profile was crucial for optimal management of gastric cancer patients receiving preoperative treatment.

Immunotherapy for early-stage non-small cell lung cancer: A system review

With the addition of immunotherapy, lung cancer, one of the most common cancers with high mortality rates, has broadened the treatment landscape. Immune checkpoint inhibitors have demonstrated significant efficacy in the treatment of non-small cell lung cancer (NSCLC) and are now used as the first-line therapy for metastatic disease, consolidation therapy after radiotherapy for unresectable locally advanced disease, and adjuvant therapy after surgical resection and chemotherapy for resectable disease. The use of adjuvant and neoadjuvant immunotherapy in patients with early-stage NSCLC, however, is still debatable. We will address several aspects, namely the initial efficacy of monotherapy, the efficacy of combination chemotherapy, immunotherapy-related biomarkers, adverse effects, ongoing randomized controlled trials, and current issues and future directions for immunotherapy in early-stage NSCLC will be discussed here.

Association of early immune-related adverse events with treatment efficacy of neoadjuvant Toripalimab in resectable advanced non-small cell lung cancer

Background

Neoadjuvant immunotherapy with anti-PD-1 was proved promising in resectable non-small cell lung cancer (NSCLC). Immune-related adverse events (irAEs) have been preliminarily implicated their association with treatment efficacy. Here we elucidated the early onset of irAEs associated with better clinical outcomes in a prospective study (Renaissance study).

Methods

We conducted the prospective study of NSCLC patients treated by neoadjuvant Toripalimab (240mg, every 3 weeks) plus double platinum-based chemotherapy from December 2020 to March 2022 at Peking University Cancer Hospital. Patients were enrolled if they have resectable IIB-IIIB NSCLC without EGFR/ALK mutation. Data were analyzed to explore the relationship between clinical outcome and irAEs after neoadjuvant treatment. A multidisciplinary team including physicians, surgeons, and radiologists, confirmed the irAEs according to the clinical manifestation. The relationship between irAEs and pathological outcomes was analyzed. The Renaissance study was approved by the Peking University Ethic board (2020YJZ58) and registered at https://clinicaltrials.gov/ as NCT04606303.

Results

Fifty-five consecutive patients were enrolled with a male-to-female ratio of 10:1, the median age was 62 years old (IQR: 45-76), of which 44 patients (80%) were diagnosed with squamous cell carcinoma. Forty-eight of 55 patients finally received thoracic surgery with a median preoperative waiting time of 67 days (IQR 39-113 days). Pathological results demonstrated that 31 (64.6%) patients achieved major pathological response (MPR) and 24 (50.0%) achieved complete pathological response (pCR). Among 48 patients who received R0 resection, immunotherapy-related thyroid dysfunction, rash/pruritus and enteritis occurred in 11 patients (22.9%), 7 patients (14.6%), and 1 patient (2.1%), respectively. Six patients (54.5%) with thyroid dysfunction achieved MPR with 5 (45.5%) achieved pCR, and a median time to onset was 45 days (IQR 21-91 days). Six patients (85.7%) with rash or pruritus achieved MPR and 5 patients (71.4%) achieved pCR, with median time to onset being 8 days (IQR 6-29 days). Furthermore, irAEs had no significant influence on operation time (170.6 min vs 165.7 min, P=0.775), intraoperative blood loss (67.4 mL vs 64.3 mL, P=0.831) and preoperative waiting time (93 days vs 97 days, P=0.630) when comparing with patients without irAEs (Figure 1).Figure 1Comparison of operation time (A), intraoperative blood loss (B), and preoperative waiting time (C) between "with irAEs" and "without irAEs".

Conclusion

The immunotherapy-related rash is potentially associated with pathological outcomes in NSCLC patients after neoadjuvant chemo-immunotherapy, suggesting easy-to-find irAEs, such as rash, can be used as indicators to predict response to neoadjuvant chemo-immunotherapy.Clinical trial registration: clinicaltrials.gov/, identifier NCT04606303.

Predictive role of blood-based indicators in neuromyelitis optica spectrum disorders

Introduction

This study aimed to assess the predictive role of blood markers in neuromyelitis optica spectrum disorders (NMOSD).

Methods

Data from patients with NMOSD, multiple sclerosis (MS), and healthy individuals were retrospectively collected in a 1:1:1 ratio. The expanded disability status scale (EDSS) score was used to assess the severity of the NMOSD upon admission. Receiver operating characteristic (ROC) curve analysis was used to distinguish NMOSD patients from healthy individuals, and active NMOSD from remitting NMOSD patients. Binary logistic regression analysis was used to evaluate risk factors that could be used to predict disease recurrence. Finally, Wilcoxon signed-rank test or matched-sample t-test was used to analyze the differences between the indicators in the remission and active phases in the same NMOSD patient.

Results

Among the 54 NMOSD patients, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) (platelet × NLR) were significantly higher than those of MS patients and healthy individuals and positively correlated with the EDSS score of NMOSD patients at admission. PLR can be used to simultaneously distinguish between NMOSD patients in the active and remission phase. Eleven (20.4%) of the 54 patients had recurrence within 12 months. We found that monocyte-to-lymphocyte ratio (MLR) (AUC = 0.76, cut-off value = 0.34) could effectively predict NMOSD recurrence. Binary logistic regression analysis showed that a higher MLR at first admission was the only risk factor for recurrence (p = 0.027; OR = 1.173; 95% CI = 1.018-1.351). In patients in the relapsing phase, no significant changes in monocyte and lymphocyte count was observed from the first admission, whereas patients in remission had significantly higher levels than when they were first admitted.

Conclusion

High PLR is a characteristic marker of active NMOSD, while high MLR is a risk factor for disease recurrence. These inexpensive indicators should be widely used in the diagnosis, prognosis, and judgment of treatment efficacy in NMOSD.