Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab

Integrated modeling of biomarkers, survival and safety in clinical oncology drug development

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.

Prognostic factors for resected invasive mucinous lung adenocarcinoma: a systematic review and meta-analysis

BACKGROUND

Surgery is the optimal choice for early invasive mucinous lung adenocarcinoma (IMA). A systematic review and meta-analysis were conducted to explore the prognostic factors for resected IMA.

METHODS

We systematically reviewed the prognostic role of clinicopathological and genomic factors in resected IMA patients. Eligible studies on the treatment of IMA following the systematic search of PubMed, Embase and the Cochrane Library from January 2015 to January 2024 were identified. Outcomes of interest were overall survival (OS) and disease-free survival/recurrence-free survival (DFS/RFS). The hazard ratio (HR) and 95% confidence interval (CI) were used as impact indicators for systematic review and meta-analysis.

RESULTS

Sixteen studies involving 3,484 patients with IMA were included. The results of the combined analysis showed that male and smoking were associated with a worse prognosis. Furthermore, advanced clinical stage, poor differentiation grade, presence of visceral pleural invasion (VPI) and spread through air spaces (STAS), and presence of KRAS mutations were also associated with worse prognosis.

CONCLUSIONS

Gender, smoking, clinical stage, tumor size, differentiation grading, VPI, STAS and KRAS mutation affect DFS/RFS and OS of IMA patients after surgery. Identifying these factors may aid physicians in developing more individualized treatment plans for resectable IMA patients.

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment simple blood markers (albumin, C-reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients). It outperformed the current state-of-the-art for individual predictions with a test set C-index of 0.790, 12-months survival accuracy of 78.7% and hazard ratio of 25.2 (95% CI: 10.4-61.3, P < 0.0001) to identify long-term survivors. Critically, kML predicted the success of the phase III trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64-0.994) vs. final study HR = 0.778 (0.65-0.931)). Modeling on-treatment blood markers combined with predictive machine learning constitutes a valuable approach to support personalized medicine and drug development. The code is publicly available at https://gitlab.inria.fr/benzekry/nlml_onco.

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA

PURPOSE

To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts.

RESULTS

Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit.

CONCLUSION

Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.

A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients

Paclitaxel/platinum chemotherapy, the backbone of standard first-line treatment of advanced non-small cell lung cancer (NSCLC), exhibits high interpatient variability in treatment response and high toxicity burden. Baseline blood biomarker concentrations and tumor size (sum of diameters) at week 8 relative to baseline (RS8) are widely investigated prognostic factors. However, joint analysis of data on demographic/clinical characteristics, blood biomarker levels, and chemotherapy exposure-driven early tumor response for improved prediction of overall survival (OS) is clinically not established. We developed a Weibull time-to-event model to predict OS, leveraging data from 365 patients receiving paclitaxel/platinum combination chemotherapy once every three weeks for ≤six cycles. A developed tumor growth inhibition model, combining linear tumor growth and first-order paclitaxel area under the concentration-time curve-induced tumor decay, was used to derive individual RS8. The median model-derived RS8 in all patients was a 20.0% tumor size reduction (range from -78% to +15%). Whereas baseline carcinoembryonic antigen, cytokeratin fragments, and thyroid stimulating hormone levels were not significantly associated with OS in a subset of 221 patients, and lactate dehydrogenase, interleukin-6 and neutrophil-to-lymphocyte ratio levels were significant only in univariate analyses (p value < 0.05); C-reactive protein (CRP) in combination with RS8 most significantly affected OS (p value < 0.01). Compared to the median population OS of 11.3 months, OS was 128% longer at the 5th percentile levels of both covariates and 60% shorter at their 95th percentiles levels. The combined paclitaxel exposure-driven RS8 and baseline blood CRP concentrations enables early individual prognostic predictions for different paclitaxel dosing regimens, forming the basis for treatment decision and optimizing paclitaxel/platinum-based advanced NSCLC chemotherapy.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (k_g= 0.130 vs. 0.0551 week^-1, p-value < 0.0001), but similar for both groups (k_g = 0.0624 vs.0.0563 week^-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.

Five years after PACIFIC: Update on multimodal treatment efficacy based on real-world reports

INTRODUCTION

The growing body of real-life data on maintenance treatment with durvalumab suggests that immunological markers of the cancer-host interplay may have significant effects on the efficacy of multimodal therapy in patients with unresectable stage III NSCLC.

AREAS COVERED

We summarize real-world clinical data regarding this new tri-modal approach and report on potential biomarker landscape.

EXPERT OPINION

The obvious question posed in this context of a very heterogeneous inoperable stage III NSCLC disease is: How can we augment an ability to predict checkpoint inhibition success or failure? Which tools and biomarkers, which clinical metadata and genetic background are relevant and feasible? No single biomarker will ever fully dominate the unresectable stage III NSCLC space, so we advocate multilevel and multivariate analysis of biomarkers. In this particular opinion piece, we explore the impact of PD-L1 expression on tumor cells, neutrophil-to-lymphocyte ratio, EGFR and STK11 mutational status, interferon-gamma signature, and tumor-infiltrating lymphocytes among others.

Emerging precision neoadjuvant systemic therapy for patients with resectable non-small cell lung cancer: current status and perspectives

Over the past decade, targeted therapy for oncogene-driven NSCLC and immune checkpoint inhibitors for non-oncogene-driven NSCLC, respectively, have greatly improved the survival and quality of life for patients with unresectable NSCLC. Increasingly, these biomarker-guided systemic therapies given before or after surgery have been used in patients with early-stage NSCLC. In March 2022, the US FDA granted the approval of neoadjuvant nivolumab and chemotherapy for patients with stage IB-IIIA NSCLC. Several phase II/III trials are evaluating the clinical efficacy of various neoadjuvant immune checkpoint inhibitor combinations for non-oncogene-driven NSCLC and neoadjuvant molecular targeted therapies for oncogene-driven NSCLC, respectively. However, clinical application of precision neoadjuvant treatment requires a paradigm shift in the biomarker testing and multidisciplinary collaboration at the diagnosis of early-stage NSCLC. In this comprehensive review, we summarize the current diagnosis and treatment landscape, recent advances, new challenges in biomarker testing and endpoint selections, practical considerations for a timely multidisciplinary collaboration at diagnosis, and perspectives in emerging neoadjuvant precision systemic therapy for patients with resectable, early-stage NSCLC. These biomarker-guided neoadjuvant therapies hold the promise to improve surgical and pathological outcomes, reduce systemic recurrences, guide postoperative therapy, and improve cure rates in patients with resectable NSCLC.

A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics

Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early-phase studies and patient-reported outcomes as well as event risks or rates in late-phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non-small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness-of-fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed-effects models and software tools in an integrative and exhaustive manner.

Current Understanding and Future Perspectives on Hyperprogressive Disease Highlight the Tumor Microenvironment

Cancer immunotherapy with immune checkpoint inhibitors has revolutionized traditional cancer therapy. Although many patients have achieved long-term survival benefits from immune checkpoint inhibitors treatment, there are still some patients who develop rapid tumor progression after immunotherapy, known as hyperprogressive disease. Here we summarize current knowledge on hyperprogressive disease after immune checkpoint inhibitors treatment to promote more thorough understanding of the disease. This review focuses on multiple aspects of hyperprogressive disease, especially the tumor microenvironment, with the hope that more reliable biomarkers and therapeutics could be established for hyperprogressive disease in the future. This article is protected by copyright. All rights reserved.

Modelling the association between biomarkers and clinical outcome: an introduction to nonlinear joint models

Nonlinear joint models are a powerful tool to precisely analyze the association between a nonlinear biomarker and a time-to-event process, such as death. Here, we review the main methodological techniques required to build these models and to make inferences and predictions. We describe the main clinical applications and discuss the future developments of such models.

Small molecule tyrosine kinase inhibitors modulated blood immune cell counts in patients with oncogene-driven NSCLC

BACKGROUND

Lack of biomarkers and in vitro models has contributed to inadequate understanding of the mechanisms underlying the inferior clinical response to immune checkpoint inhibitors (ICIs) in patients with oncogene-driven non-small cell lung cancer (NSCLC).

METHODS

The effect of small molecule tyrosine kinase inhibitors (TKIs) on peripheral blood mononuclear cells (PBMCs) in 34 patients with oncogene-driven NSCLC (cohort A) was compared with those from 35 NSCLC patients without oncogene-driven mutations received ICI (cohort B) or from 22 treatment-naïve NSCLC patients (cohort C). Data for each blood biomarker were summarized by mean and standard deviation and compared by Wilcoxon rank sum tests or Kruskal-Wallis tests with significance at 2-sided p value < 0.05. Co-culture of PBMCs and pleural effusion-derived tumor cells from individual patients with oncogene-driven NSCLC was used to determine the in vitro cytotoxicity of TKI and ICI.

RESULTS

Except for low CD3% in cohort A, there were no significant differences in other 12 blood biomarkers among the 3 cohorts at baseline. TKI treatment in cohort A was associated with significant increase in CD3% and decrease in total and absolute neutrophils (p < 0.05). In cohort B, patients with good clinical response to ICI treatment (N = 18) had significant increases in absolute lymphocyte counts (ALCs), CD4 and/or CD8 cell counts. Conversely, those patients with poor clinical response to ICI (N = 17) had significant decreases in these cell counts. Of the 27 patients with pre- and post-treatment blood samples in cohort A, 11 had poor clinical response to TKIs and decreased lymphocyte counts. Of the remaining 16 patients who had good clinical response to TKI therapy, 10 (62.5%) patients had decreased, and 6 (37.5%) patients had increased lymphocyte counts. Multicolor immunophenotyping of PBMCs revealed ICI treatment activated additional immune cell types that need further validation. We confirmed that TKI treatment could either antagonize or enhance the effect of ICIs in the co-culture assay using patient's tumor cells and PBMCs.

CONCLUSIONS

To the best of our knowledge, this is the first study showing that TKIs can have various effects on blood immune cells, which may affect their response to ICIs. Further validation of the blood biomarker and in vitro assay is warranted.

Predicting immunotherapy outcomes under therapy in patients with advanced NSCLC using dNLR and its early dynamics

BACKGROUND

dNLR at the baseline (B), defined by neutrophils/[leucocytes-neutrophils], correlates with immune-checkpoint inhibitor (ICI) outcomes in advanced non-small-cell lung cancer (aNSCLC). However, dNLR is dynamic under therapy and its longitudinal assessment may provide data predicting efficacy. We sought to examine the impact of dNLR dynamics on ICI efficacy and understand its biological significance.

PATIENTS AND METHODS

aNSCLC patients receiving ICI at 17 EU/US centres were included [Feb/13-Jun/18]. As chemotherapy-only group was evaluated (NCT02105168). dNLR was determined at (B) and at cycle2 (C2) [dNLR≤3 = low]. B+C2 dNLR were combined in one score: good = low (B+C2), poor = high (B+C2), intermediate = other situations. In 57 patients, we prospectively explored the immunophenotype of circulating neutrophils, particularly the CD15+CD244-CD16^lowcells (immature) by flow cytometry.

RESULTS

About 1485 patients treatment with ICI were analysed. In ICI-treated patients, high dNLR (B) (~1/3rd) associated with worse progression-free (PFS)/overall survival (OS) (HR 1.56/HR 2.02, P < 0.0001) but not with chemotherapy alone (N = 173). High dNLR at C2 was associated with worse PFS/OS (HR 1.64/HR 2.15, P < 0.0001). When dNLR at both time points were considered together, those with persistently high dNLR (23%) had poor survival (mOS = 5 months (mo)), compared with high dNLR at one time point (22%; mOS = 9.2mo) and persistently low dNLR (55%; mOS = 18.6mo) (P < 0.0001). The dNLR impact remained significant after PD-L1 adjustment. By cytometry, high rate of immature neutrophils (B) (30/57) correlated with poor PFS/OS (P = 0.04; P = 0.0007), with a 12-week death rate of 49%.

CONCLUSION

The dNLR (B) and its dynamics (C2) under ICI associate with ICI outcomes in aNSCLC. Persistently high dNLR (B+C2) correlated with early ICI failure. Immature neutrophils may be a key subpopulation on ICI resistance.

High Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Are Associated with Poor Survival in Patients with Hemodialysis

Background

The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are markers for systemic inflammation condition. Although NLR has emerged as a risk factor for poor survival in end-stage renal disease (ESRD) patients, the relationship between PLR and mortality is still unknown. We aimed to explore the interaction of NLR and PLR in predicting mortality in hemodialysis (HD) patients.

Method

We enrolled 360 HD patients for a 71-month follow-up. The endpoint was all-cause and cardiovascular (CV) mortality. Pearson correlation analysis was conducted to evaluate the relationship between factors and NLR or PLR. Kaplan-Meier curves and Cox proportional analysis were used to assess the prognostic value of NLR and PLR.

Results

NLR was positively correlated with neutrophil and negatively correlated with lymphocyte, hemoglobin, and serum albumin. PLR was positively correlated with neutrophil and platelet and negatively correlated with lymphocyte and hemoglobin. In multivariate Cox regression, a higher NLR level was independently associated with all-cause mortality (OR 2.011, 95% CI 1.082-3.74, p = 0.027), while a higher PLR level might predict CV mortality (OR 2.768, 95% CI 1.147-6.677, p = 0.023) in HD patients.

Conclusion

NLR and PLR are cheap and reliable biomarkers for all-cause and CV mortality to predict survival in HD patients.