Prognostic scores including peripheral blood-derived inflammatory indices in patients with advanced non-small-cell lung cancer treated with immune checkpoint inhibitors

Intracranial Efficacy of Atezolizumab, Bevacizumab, Carboplatin, and Paclitaxel in Real-World Patients with Non-Small-Cell Lung Cancer and EGFR or ALK Alterations

Contrary to Pemetrexed-containing chemo-immunotherapy studies, Atezolizumab, Bevacizumab, Carboplatin, and Paclitaxel (ABCP) treatment has consistently shown clinical benefit in prospective studies in patients with lung cancer and actionable mutations, where intracranial metastases are common. Here, we aimed to describe the real-life population of patients fit to receive ABCP after targeted therapy and quantify its clinical effect in patients with brain metastases. Patients treated in Cheshire and Merseyside between 2019 and 2022 were identified. Data were collected retrospectively. A total of 34 patients with actionable EGFR or ALK alterations had treatment with a median age of 59 years (range 32-77). The disease control rate was 100% in patients with PDL1 ≥ 1% (n = 10). In total, 19 patients (56%) had brain metastases before starting ABCP, 17 (50%) had untreated CNS disease, and 4 (22%) had PDL1 ≥ 1%. The median time to symptom improvement was 12.5 days (range 4-21 days), with 74% intracranial disease control rates and 89.5% synchronous intracranial (IC) and extracranial (EC) responses. IC median Progression Free Survival (mPFS) was 6.48 months, EC mPFS was 10.75 months, and median Overall Survival 11.47 months. ABCP in real-life patients with brain metastases (treated or untreated) was feasible and showed similar efficacy to that described in patients without actionable mutations treated with upfront chemo-immunotherapy.

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy

Aim

Limited data are available regarding ALI's clinical relevance and prognostic value in patients with hepatocellular carcinoma (HCC) after hepatectomy.

Materials and methods

HCC patients who received hepatectomy at the Meizhou People's Hospital from May 2011 to February 2022 were enrolled in the study cohort. The ALI was calculated as follows: ALI = BMI (kg/m2) × ALB (g/dL)/(absolute neutrophil count/absolute lymphocyte count). The primary outcome was overall survival (OS). The secondary outcome was cancer-specific survival (CSS). Univariate and multivariate Cox regression analyses were performed, followed by nomogram construction and decision curve analysis (DCA).

Results

425 HCC patients were enrolled for analyses. Lower preoperative ALI was significantly correlated with incomplete tumor capsule and advanced tumor stage. Lower preoperative ALI was an adverse independent prognostic factor for OS (HR: 1.512, 95% CI: 1.122-2.039, P 0.007) and CSS (HR: 1.754, 95% CI: 1.262-2.438, P <0.001) in HCC patients. The nomogram plot was built based on three (including age, TNM stage, and ALI) and two (including TNM stage and ALI) independent prognostic factors for OS and CSS, respectively. Further analyses indicated that the nomogram had better predictive value and some net benefit than the traditional TNM stage alone, especially in long-term OS.

Conclusions

Our study further indicated that ALI could be a prognostic marker for OS and CSS in HCC patients after hepatectomy, especially in long-term OS.

Integrative Oncology Approaches to Supporting Immune Checkpoint Inhibitor Treatment of Solid Tumours

PURPOSE OF REVIEW

The goal of this review was to examine the role and practical applications of integrative oncology strategies in supporting immune checkpoint inhibitor (ICI) treatment of adult solid tumours.

RECENT FINDINGS

Beyond tumour-intrinsic factors, several patient-associated factors affect ICI response, including germline genetics, systemic inflammation, the gut microbiota, and diet. Current promising supportive interventions include a Mediterranean-style diet with over 20 g of fibre, regular exercise, use of live biotherapeutics, minimisation of PPI and antibiotic use, and ensuring vitamin D repletion, with many other integrative oncology approaches under study. Caution around medical cannabis use in patients on ICIs is advised due to previously documented adverse impact on overall survival, while VAE (Viscum album extract) therapy studies have not highlighted any safety concerns so far. With expanding ICI use, it is important to investigate and apply low-cost integrative oncology strategies to support better treatment outcomes and minimise adverse events. Further research may lead to pre-treatment assessment of both tumour and patient-associated biomarkers and personalised multimodal prehabilitation care plans, as well as on-treatment support with targeted nutrition, physical activity, and supplementation regimes, including both systemic inflammation and gut microbiome modulating strategies. Given the emerging understanding of chronic stress impact on ICI treatment outcomes, mind-body approaches require further investigation.

Combining Classic and Novel Neutrophil-Related Biomarkers to Identify Non-Small-Cell Lung Cancer

BACKGROUND

Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC).

METHODS

We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement.

RESULTS

Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%.

CONCLUSIONS

IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease.

First-line immunotherapy in non-small cell lung cancer: how to select and where to go

INTRODUCTION

Immunotherapy (IO) has established a new milestone in lung cancer treatment. Several registrational studies have approved immune checkpoint inhibitors (ICIs) in different settings, including the metastatic nonsmall cell lung cancer (NSCLC). As well known, responders are just a certain proportion of patients; therefore, their selection by using predictive factors has stood out as a crucial issue to address in tailoring a patient-centered care.

AREAS COVERED

In our review we propose a detailed yet handy cross section on ICIs as first-line treatment in metastatic NSCLC, regarding indications, histological, clinical, and blood-based biomarkers, other than their mechanisms of resistance and new immunological actionable targets. We performed a literature search through PubMed entering keywords complying with crucial features of immunotherapy.

EXPERT OPINION

IO represents the backbone of lung cancer treatment. Trials are currently testing novel immune blockade agents assessing combinatorial approaches with standard ICIs, or antibody drug conjugates (ADC), harboring immunological targets. Perfecting patients' selection is an ongoing challenge and a more and more urgent need in order to best predict responders who will consistently benefit from it.

Using peripheral immune-inflammatory blood markers in tumors treated with immune checkpoint inhibitors: An INVIDIa-2 study sub-analysis

The neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammatory index (SII) have been reported as prognosticators in non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), and melanoma. This analysis of the INVIDIa-2 study on influenza vaccination in patients with cancer treated with immune checkpoint inhibitors (ICIs) assessed NLR and SII on overall survival (OS) by literature-reported (LR), receiver operating characteristic curve (ROC)-derived (ROC) cutoffs or as continuous variable (CV). NLR and SII with ROC cutoffs of <3.4 (p < 0.001) and <831 (p < 0.001) were independent factors for OS in multivariate analysis. SII with LR, ROC, or CV significantly predicted OS in NSCLC (p = 0.002, p = 0.003, p = 0.003), RCC (p = 0.034, p = 0.014, p = 0.014), and melanoma (p = 0.038, p = 0.022, p = 0.019). NLR with LR and ROC cutoffs predicted OS in first line (p < 0.001 for both) and second line or beyond (p = 0.006 for both); likewise SII (p < 0.001; p = 0.002 and p < 0.001). NLR and SII are prognosticators in NSCLC, RCC, and melanoma treated with ICIs.

Plasmacytoid Dendritic Cell, Slan+-Monocyte and Natural Killer Cell Counts Function as Blood Cell-Based Biomarkers for Predicting Responses to Immune Checkpoint Inhibitor Monotherapy in Non-Small Cell Lung Cancer Patients

The advent of immune checkpoint inhibitors (ICIs), for instance, programmed cell death 1 (PD-1)/PD-1 ligand 1 (PD-L1) blockers, has greatly improved the outcome of patients affected by non-small cell lung cancer (NSCLC). However, most NSCLC patients either do not respond to ICI monotherapy or develop resistance to it after an initial response. Therefore, the identification of biomarkers for predicting the response of patients to ICI monotherapy represents an urgent issue. Great efforts are currently dedicated toward identifying blood-based biomarkers to predict responses to ICI monotherapy. In this study, more commonly utilized blood-based biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR) and the lung immune prognostic index (LIPI) score, as well as the frequency/number and activation status of various types of circulating innate immune cell populations, were evaluated in NSCLC patients at baseline before therapy initiation. The data indicated that, among all the parameters tested, low plasmacytoid dendritic cell (pDC), slan+-monocyte and natural killer cell counts, as well as a high LIPI score and elevated PD-L1 expression levels on type 1 conventional DCs (cDC1s), were independently correlated with a negative response to ICI therapy in NSCLC patients. The results from this study suggest that the evaluation of innate immune cell numbers and phenotypes may provide novel and promising predictive biomarkers for ICI monotherapy in NSCLC patients.

Immune Checkpoint Inhibitors in "Special" NSCLC Populations: A Viable Approach?

Over the last decade, the therapeutic scenario for advanced non-small-cell lung cancer (NSCLC) has undergone a major paradigm shift. Immune checkpoint inhibitors (ICIs) have shown a meaningful clinical and survival improvement in different settings of the disease. However, the real benefit of this therapeutic approach remains controversial in selected NSCLC subsets, such as those of the elderly with active brain metastases or oncogene-addicted mutations. This is mainly due to the exclusion or underrepresentation of these patient subpopulations in most pivotal phase III studies; this precludes the generalization of ICI efficacy in this context. Moreover, no predictive biomarkers of ICI response exist that can help with patient selection for this therapeutic approach. Here, we critically summarize the current state of ICI efficacy in the most common "special" NSCLC subpopulations.

Assessing the role of colonic and other anatomical sites uptake by [18 F]FDG-PET/CT and immune-inflammatory peripheral blood indexes in patients with advanced non-small cell lung cancer treated with first-line immune checkpoint inhibitors

BACKGROUND

Inflammation in non-small cell lung cancer (NSCLC) may impair the response to immune checkpoint inhibitors (ICIs) and can be indicated by peripheral blood inflammatory indexes. 2-deoxy-2-[18 F]fluoro-D-glucose positron emission tomography/computed tomography ([18 F] FDG-PET/CT) may be used as a marker of inflammation by measuring glucose metabolism in different colonic sites.

METHODS

This retrospective analysis aimed to investigate the correlation between [18 F] FDGPET/CT SUVratio in six gastrointestinal districts, the spleen, the pharynx and the larynx alongside the most avid tumor lesion with peripheral blood inflammatory indexes, including the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammatory index (SII, i.e., NLR times platelets) and lactate dehydrogenase (LDH), in patients with [18 F] FDG-PET/CT staged IV NSCLC who received first-line immune checkpoint inhibitors (ICIs). The role of SUVratios and peripheral blood inflammatory indexes in predicting overall survival (OS) and progression-free survival (PFS) was then explored.

RESULTS

A total of 43 patients were treated with first-line ICI alone (58%) or in combination with chemotherapy (42%). A significant correlation was only found between the rectosigmoid SUVratio and NLR (p = 0.0465). NLR >5.5 and LDH > 333.5 were associated with a worse OS (p = 0.033 and p = 0.009, respectively). The SII was associated with a worse PFS in patients treated with ICI alone (p = 0.033). None of the SUVratios were significantly associated with OS or PFS, although a high left colon SUVratio showed a trend toward a worse PFS.

CONCLUSION

There was no significant correlation between [18 F]FDG PET/CT uptake in different anatomical sites, and in the tumor, and systemic immune-inflammatory indexes. The prognostic role of high left colon SUVratio deserves further investigation.

Neoadjuvant Immunotherapy: A Promising New Standard of Care

Neoadjuvant immunotherapy has emerged as a promising approach in the treatment of various malignancies, with preclinical studies showing improved immune responses in the preoperative setting. FDA-approved neoadjuvant-immunotherapy-based approaches include triple-negative breast cancer and early non-small cell lung cancer on the basis of improvement in pathological response and event free survival. Nevertheless, current trials have only shown benefits in a fraction of patients. It is therefore crucial to identify predictive biomarkers to improve patient selection for such approaches. This review aims to provide an overview of potential biomarkers of neoadjuvant immunotherapy in early triple-negative breast cancer, bladder cancer, melanoma, non-small cell lung cancer, colorectal cancer and gastric cancer. By the extrapolation of the metastatic setting, we explore known predictive biomarkers, i.e., PD-L1, mismatch repair deficiency and tumour mutational burden, as well as potential early-disease-specific biomarkers. We also discuss the challenges of identifying reliable biomarkers and the need for standardized protocols and guidelines for their validation and clinical implementation.

The predictive values of monocyte-lymphocyte ratio in postoperative acute kidney injury and prognosis of patients with Stanford type A aortic dissection

Objectives

Postoperative acute kidney injury (pAKI) is a serious complication of Stanford type A aortic dissection (TAAD) surgery, which is significantly associated with the inflammatory response. This study aimed to explore the relationship between blood count-derived inflammatory markers (BCDIMs) and pAKI and to construct a predictive model for pAKI.

Methods

Patients who underwent TAAD surgery were obtained from our center and the Medical Information Mart for Intensive Care (MIMIC)-IV database. The differences in preoperative BCDIMs and clinical outcomes of patients with and without pAKI were analyzed. Logistic regression was used to construct predictive models based on preoperative BCDIMs or white cell counts (WCCs). The performance of the BCDIMs and WCCs models was evaluated and compared using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), Hosmer-Lemeshow test, calibration plot, net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA). The Kaplan-Meier curves were applied to compare the survival rate between different groups.

Results

The overall incidence of pAKI in patients who underwent TAAD surgery from our center was 48.63% (124/255). The presence of pAKI was associated with longer ventilation time, higher incidence of cerebral complications and postoperative hepatic dysfunction, and higher in-hospital mortality. The results of the logistic regression indicated that the monocyte-lymphocyte ratio (MLR) was an independent risk factor for pAKI. The BCDIMs model had good discriminating ability, predictive ability, and clinical utility. In addition, the performance of the BCDIMs model was significantly better than that of the WCCs model. Analysis of data from the MIMIC-IV database validated that MLR was an independent risk factor for pAKI and had predictive value for pAKI. Finally, data from the MIMIC-IV database demonstrated that patients with a high MLR had a significantly poor 28-day survival rate when compared to patients with a low MLR.

Conclusion

Our study suggested that the MLR is an independent risk factor for pAKI. A predictive model based on BCDIMs had good discriminating ability, predictive ability, and clinical utility. Moreover, the performance of the BCDIMs model was significantly better than that of the WCCs model. Finally, a high MLR was significantly associated with poor short-term survival of patients who underwent TAAD surgery.

Immunotherapy-related adverse events in real-world patients with advanced non-small cell lung cancer on chemoimmunotherapy: a Spinnaker study sub-analysis

Background

The Spinnaker study evaluated survival outcomes and prognostic factors in patients with advanced non-small-cell lung cancer receiving first-line chemoimmunotherapy in the real world. This sub-analysis assessed the immunotherapy-related adverse effects (irAEs) seen in this cohort, their impact on overall survival (OS) and progression-free survival (PFS), and related clinical factors.

Methods

The Spinnaker study was a retrospective multicentre observational cohort study of patients treated with first-line pembrolizumab plus platinum-based chemotherapy in six United Kingdom and one Swiss oncology centres. Data were collected on patient characteristics, survival outcomes, frequency and severity of irAEs, and peripheral immune-inflammatory blood markers, including the neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII).

Results

A total of 308 patients were included; 132 (43%) experienced any grade irAE, 100 (32%) Grade 1-2, and 49 (16%) Grade 3-4 irAEs. The median OS in patients with any grade irAES was significantly longer (17.5 months [95% CI, 13.4-21.6 months]) than those without (10.1 months [95% CI, 8.3-12.0 months]) (p<0.001), either if Grade 1-2 (p=0.003) or Grade 3-4 irAEs (p=0.042). The median PFS in patients with any grade irAEs was significantly longer (10.1 months [95% CI, 9.0-11.2 months]) than those without (6.1 months [95% CI, 5.2-7.1 months]) (p<0.001), either if Grade 1-2 (p=0.011) or Grade 3-4 irAEs (p=0.036). A higher rate of irAEs of any grade and specifically Grade 1-2 irAEs correlated with NLR <4 (p=0.013 and p=0.018), SII <1,440 (p=0.029 ad p=0.039), response to treatment (p=0.001 and p=0.034), a higher rate of treatment discontinuation (p<0.00001 and p=0.041), and the NHS-Lung prognostic classes (p=0.002 and p=0.008).

Conclusions

These results confirm survival outcome benefits in patients with irAEs and suggest a higher likelihood of Grade 1-2 irAEs in patients with lower NLR or SII values or according to the NHS-Lung score.

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

Introduction

Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.

Methods

We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.

Results

Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.

Conclusions

In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.