Differential treatment effect between younger and older adults for new cancer therapies in solid tumors supporting US Food and Drug Administration approval between 2010 and 2021

PD-L1 Testing, Treatment Patterns, and Clinical Outcomes Among Patients with Metastatic NSCLC at an Academic Medical Center, 2017-2021

Introduction

Targeted therapies and immune checkpoint inhibitors (ICIs) have revolutionized the management of metastatic non-small cell lung cancer (NSCLC) over the past decade.

Methods

This single-center observational study was conducted to describe programmed death-ligand 1 (PD-L1) testing, choice of therapy, and outcomes for adult patients with stage IV NSCLC initiating first-line therapy from 2017 through 2020, with follow-up through June 2021. Patient characteristics and study assessments were described according to four histomolecular subtypes, defined by histologic characteristics and availability of standard-of-care therapies for molecular subgroups at the time of study conduct.

Results

Of 507 eligible patients with metastatic NSCLC, 85 (17%) had squamous NSCLC; 288 (57%) had nonsquamous NSCLC with no actionable genomic alteration; 44 (9%) had nonsquamous NSCLC with KRAS G12C mutation; and 90 (18%) had nonsquamous NSCLC with ROS1, BRAF V600E, EGFR exon 20 insertion, or RET or NTRK genomic alteration. Most tumors were PD-L1 tested. After excluding 40 patients whose PD-L1 testing status was unknown, all but 55 tumors (12%) were tested for PD-L1 expression, and the percentages tested rose from 86% in 2017 to 100% in 2020. From 27% of nonsquamous NSCLC with no actionable genomic alteration to 46% of KRAS G12C-mutated NSCLC had PD-L1 expression ≥ 50%. Use of chemotherapy decreased and use of ICI-chemotherapy combinations increased from 2017 to 2020. In the squamous NSCLC group, single or combination chemotherapy was administered most commonly (42%), whereas ICI-chemotherapy combinations were the most common first-line regimens in the three nonsquamous NSCLC histomolecular groups. For patients with NSCLC and no actionable genomic alterations, ICI-chemotherapy combinations were the most common regimens in 2018-2020 in all but the PD-L1 ≥ 50% category, for whom ICI monotherapy was most common every year except 2020. Median overall survival was 25.0 months (95% CI, 19.1-28.3) for all patients, and, by histomolecular cohort, 14.3 months for squamous NSCLC, 25.3 months for nonsquamous NSCLC with no actionable genomic alteration, not reached for KRAS G12C-mutated NSCLC, and 27.7 months for nonsquamous NSCLC with other genomic alterations.

Conclusion

Study findings highlight the increased use of PD-L1 testing over the years from 2017 to 2020 and recent changes in therapy, with decreased use of chemotherapy and increased use of ICI-chemotherapy combinations during the study in each histomolecular group. Moreover, we observed improvements in survival for patients with metastatic NSCLC relative to historical real-world data.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Special populations in metastatic renal cell carcinoma

PURPOSE OF REVIEW

This review focuses on special populations poorly represented in current evidence-based practice for metastatic renal cell carcinoma (mRCC). This includes the elderly and frail, patients on immunosuppression or with autoimmune diseases, patients with brain, liver, and/or bone metastases, and RCC with sarcomatoid features.

RECENT FINDINGS

Certain populations are poorly represented in current trials for mRCC. Patients with central nervous system (CNS) metastases are often excluded from first-line therapy trials. Modern doublet systemic therapy appears to benefit patients with bone or liver metastases, but data supporting this conclusion is not robust. Post-hoc analyses on patients with sarcomatoid differentiation have shown improved response to modern doublet therapy over historical treatments. The elderly are underrepresented in current clinical trials, and most trials exclude all but high-performing (nonfrail) patients, though true frailty is likely poorly captured using the current widely adopted indices. It is difficult to make conclusions about the efficacy of modern therapy in these populations from subgroup analyses. Data from trials on other malignancies in patients with autoimmune diseases or solid organ transplant recipients on immunosuppression suggest that immune checkpoint inhibitors (ICIs) may still have benefit, though at the risk of disease flare or organ rejection. The efficacy of ICIs has not been demonstrated specifically for RCC in this group of patients.

SUMMARY

The elderly, frail, and immunosuppressed, those with tumors having aggressive histologic features, and patients with brain, bone, and/or liver metastases represent the populations least understood in the modern era of RCC treatment.