Pembrolizumab Plus Pemetrexed and Platinum in Nonsquamous Non–Small-Cell Lung Cancer: 5-Year Outcomes From the Phase 3 KEYNOTE-189 Study

Pembrolizumab; Non-small-cell lung cancerPembrolizumab; Cáncer de pulmón de células no pequeñasPembrolizumab; Càncer de pulmó de cèl·lules no petitesClinical trials frequently include multiple end points that mature at different times. The initial report, typically on the based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.We present 5-year outcomes from the phase 3 KEYNOTE-189 study (ClinicalTrials.gov identifier: NCT02578680). Eligible patients with previously untreated metastatic nonsquamous non-small-cell lung cancer without EGFR/ALK alterations were randomly assigned 2:1 to pembrolizumab 200 mg or placebo once every 3 weeks for up to 35 cycles with pemetrexed and investigator's choice of carboplatin/cisplatin for four cycles, followed by maintenance pemetrexed until disease progression or unacceptable toxicity. Primary end points were overall survival (OS) and progression-free survival (PFS). Among 616 randomly assigned patients (n = 410, pembrolizumab plus pemetrexed-platinum; n = 206, placebo plus pemetrexed-platinum), median time from random assignment to data cutoff (March 8, 2022) was 64.6 (range, 60.1-72.4) months. Hazard ratio (95% CI) for OS was 0.60 (0.50 to 0.72) and PFS was 0.50 (0.42 to 0.60) for pembrolizumab plus platinum-pemetrexed versus placebo plus platinum-pemetrexed. 5-year OS rates were 19.4% versus 11.3%. Toxicity was manageable. Among 57 patients who completed 35 cycles of pembrolizumab, objective response rate was 86.0% and 3-year OS rate after completing 35 cycles (approximately 5 years after random assignment) was 71.9%. Pembrolizumab plus pemetrexed-platinum maintained OS and PFS benefits versus placebo plus pemetrexed-platinum, regardless of programmed cell death ligand-1 expression. These data continue to support pembrolizumab plus pemetrexed-platinum as a standard of care in previously untreated metastatic non-small-cell lung cancer without EGFR/ALK alterations

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts

Associations of Tissue Tumor Mutational Burden and Mutational Status With Clinical Outcomes With Pembrolizumab Plus Chemotherapy Versus Chemotherapy For Metastatic NSCLC

INTRODUCTION: We evaluated tissue tumor mutational burden (tTMB) and mutations in STK11, KEAP1, and KRAS as biomarkers for outcomes with pembrolizumab plus platinum-based chemotherapy (pembrolizumab-combination) for NSCLC among patients in the phase 3 KEYNOTE-189 (ClinicalTrials.gov, NCT02578680; nonsquamous) and KEYNOTE-407 (ClinicalTrials.gov, NCT02775435; squamous) trials. METHODS: This retrospective exploratory analysis evaluated prevalence of high tTMB and STK11, KEAP1, and KRAS mutations in patients enrolled in KEYNOTE-189 and KEYNOTE-407 and the relationship between these potential biomarkers and clinical outcomes. tTMB and STK11, KEAP1, and KRAS mutation status was assessed using whole-exome sequencing in patients with available tumor and matched normal DNA. The clinical utility of tTMB was assessed using a prespecified cutpoint of 175 mutations/exome. RESULTS: Among patients with evaluable data from whole-exome sequencing for evaluation of tTMB (KEYNOTE-189, n = 293; KEYNOTE-407, n = 312) and matched normal DNA, no association was found between continuous tTMB score and overall survival (OS) or progression-free survival for pembrolizumab-combination (Wald test, one-sided p \u3e 0.05) or placebo-combination (Wald test, two-sided p \u3e 0.05) in patients with squamous or nonsquamous histology. Pembrolizumab-combination improved outcomes for patients with tTMB greater than or equal to 175 compared with tTMB less than 175 mutations/exome in KEYNOTE-189 (OS, hazard ratio = 0.64 [95% confidence interval (CI): 0.38‒1.07] and 0.64 [95% CI: 0.42‒0.97], respectively) and KEYNOTE-407 (OS, hazard ratio = 0.74 [95% CI: 0.50‒1.08 and 0.86 [95% CI: 0.57‒1.28], respectively) versus placebo-combination. Treatment outcomes were similar regardless of KEAP1, STK11, or KRAS mutation status. CONCLUSIONS: These findings support pembrolizumab-combination as first-line treatment in patients with metastatic NSCLC and do not suggest the utility of tTMB, STK11, KEAP1, or KRAS mutation status as a biomarker for this regimen

Nivolumab versus docetaxel in previously treated advanced non-small-cell lung cancer (CheckMate 017 and CheckMate 057): 3-year update and outcomes in patients with liver metastases

Abstract Background Long-term data with immune checkpoint inhibitors in non-small-cell lung cancer (NSCLC) are limited. Two phase III trials demonstrated improved overall survival (OS) and a favorable safety profile with the anti-programmed death-1 antibody nivolumab versus docetaxel in patients with previously treated advanced squamous (CheckMate 017) and nonsquamous (CheckMate 057) NSCLC. We report results from ≥3 years' follow-up, including subgroup analyses of patients with liver metastases, who historically have poorer prognosis among patients with NSCLC. Patients and methods Patients were randomized 1 : 1 to nivolumab (3 mg/kg every 2 weeks) or docetaxel (75 mg/m2 every 3 weeks) until progression or discontinuation. The primary end point of each study was OS. Patients with baseline liver metastases were pooled across studies by treatment for subgroup analyses. Results After 40.3 months' minimum follow-up in CheckMate 017 and 057, nivolumab continued to show an OS benefit versus docetaxel: estimated 3-year OS rates were 17% [95% confidence interval (CI), 14% to 21%] versus 8% (95% CI, 6% to 11%) in the pooled population with squamous or nonsquamous NSCLC. Nivolumab was generally well tolerated, with no new safety concerns identified. Of 854 randomized patients across both studies, 193 had baseline liver metastases. Nivolumab resulted in improved OS compared with docetaxel in patients with liver metastases (hazard ratio, 0.68; 95% CI, 0.50–0.91), consistent with findings from the overall pooled study population (hazard ratio, 0.70; 95% CI, 0.61–0.81). Rates of treatment-related hepatic adverse events (primarily grade 1–2 liver enzyme elevations) were slightly higher in nivolumab-treated patients with liver metastases (10%) than in the overall pooled population (6%). Conclusions After 3 years' minimum follow-up, nivolumab continued to demonstrate an OS benefit versus docetaxel in patients with advanced NSCLC. Similarly, nivolumab demonstrated an OS benefit versus docetaxel in patients with liver metastases, and remained well tolerated. Clinical trial registration CheckMate 017: NCT01642004; CheckMate 057: NCT01673867

Treatment Guidance for Patients With Lung Cancer During the Coronavirus 2019 Pandemic

The global coronavirus disease 2019 pandemic continues to escalate at a rapid pace inundating medical facilities and creating substantial challenges globally. The risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with cancer seems to be higher, especially as they are more likely to present with an immunocompromised condition, either from cancer itself or from the treatments they receive. A major consideration in the delivery of cancer care during the pandemic is to balance the risk of patient exposure and infection with the need to provide effective cancer treatment. Many aspects of the SARS-CoV-2 infection currently remain poorly characterized and even less is known about the course of infection in the context of a patient with cancer. As SARS-CoV-2 is highly contagious, the risk of infection directly affects the cancer patient being treated, other cancer patients in close proximity, and health care providers. Infection at any level for patients or providers can cause considerable disruption to even the most effective treatment plans. Lung cancer patients, especially those with reduced lung function and cardiopulmonary comorbidities are more likely to have increased risk and mortality from coronavirus disease 2019 as one of its common manifestations is as an acute respiratory illness. The purpose of this manuscript is to present a practical multidisciplinary and international overview to assist in treatment for lung cancer patients during this pandemic, with the caveat that evidence is lacking in many areas. It is expected that firmer recommendations can be developed as more evidence becomes available

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

IntroductionArtificial 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. MethodsWe 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. ResultsOf 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. ConclusionsIn 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

Maintenance therapy in NSCLC: why? To whom? Which agent?

Maintenance therapy is emerging as a treatment strategy in the management of advanced non small cell lung cancer (NSCLC). Initial trials addressing the question of duration of combination chemotherapy failed to show any overall survival benefit for the prolonged administration over a fixed number of cycles with an increased risk for cumulative toxicity. Nowadays several agents with different ways of administration and a different pattern of toxicity have been formally investigated in the maintenance setting. Maintenance strategies include continuing with an agent already present in the induction regimen or switching to a different one. Taking into consideration that no comparative trials of maintenance with different chemotherapy drugs or targeted agents have been conducted, the choice and the duration of maintenance agents is largely empirical. Furthermore, it is still unknown and it remains an open question if this approach needs to be proposed to every patient in the case of partial/complete response or stable disease after the induction therapy. Here, we critically review available data on maintenance treatment, discussing the possibility to tailor the right treatment to the right patient, in an attempt to optimize costs and benefits of an ever-growing panel of different treatment options

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

IntroductionArtificial 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.MethodsWe 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.ResultsOf 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.ConclusionsIn 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

Bringing onco‐innovation to Europe’s healthcare systems: The potential of biomarker testing, real world evidence, tumour agnostic therapies to empower personalised medicine

Rapid and continuing advances in biomarker testing are not being matched by uptake in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. The potential that genomics has brought to biomarker testing in diagnosis, prediction and research is being realised, pre‐eminently in many cancers, but also in an ever‐wider range of conditions— notably BRCA1/2 testing in ovarian, breast, pancreatic and prostate cancers. Nevertheless, the implementation of genetic testing in clinical routine setting is still challenging. Development is impeded by country‐related heterogeneity, data deficiencies, and lack of policy alignment on standards, approval—and the role of real‐world evidence in the process—and reimbursement. The acute nature of the problem is compellingly illustrated by the particular challenges facing the development and use of tumour agnostic therapies, where the gaps in preparedness for taking advantage of this innovative approach to cancer therapy are sharply exposed. Europe should already have in place a guarantee of universal access to a minimum suite of biomarker tests and should be planning for an optimum testing scenario with a wider range of biomarker tests integrated into a more sophisticated health system articulated around personalised medicine. Improving healthcare and winning advantages for Europe’s industrial competitiveness and innovation require an appropriate policy framework—starting with an update to outdated recommendations. We show herein the main issues and proposals that emerged during the previous advisory boards organised by the European Alliance for Personalized Medicine which mainly focus on possible scenarios of harmonisation of both oncogenetic testing and management of cancer patients

Targeted Toxicities: Protocols for Monitoring the Adverse Events of Targeted Therapies Used in the Treatment of Non-Small Cell Lung Cancer

Targeted therapies have revolutionized the treatment for many patients with non-small cell lung cancer (NSCLC). Multiple new oral targeted therapies have been approved in the last decade; however, their overall efficacy may be reduced by poor adherence, treatment interruptions, or dose reductions due to adverse events. Most institutions lack standard monitoring protocols for toxicities from these targeted agents. This review describes important adverse events observed in clinical trials and reported by the U.S. Food and Drug Administration for both currently approved and upcoming promising therapies in the treatment of NSCLC. These agents cause a range of toxicities, including dermatologic, gastroenteric, pulmonary, and cardiac toxicities. This review proposes protocols for routine monitoring of these adverse events, both prior to initiation of therapy and while on treatment