Association between depth of response and survival in patients with advanced-stage non-small cell lung cancer treated with first-line chemotherapy

A bootstrapping method to optimize go/no-go decisions from single-arm, signal-finding studies in oncology

Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti-PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (n = 30) with a combination of anti-PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (N = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (n = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials.

Statistical Interpretation and Comparison of Waterfall Plots

Waterfall plots have gained popularity as a visualization tool to present antitumor activity of treatments in oncology, especially for phase I and II trials. The typical waterfall plot in oncology is a bar plot with each bar representing the best percent tumor size reduction from baseline for a patient sorted in descending order along the x-axis. As new therapies are routinely developed in combination with standard of care or other investigational treatments, waterfall plot comparison between combination therapy and monotherapy may facilitate development decisions in addition to overall response rate or duration of response. However, waterfall plots are often assessed heuristically in practice with lack of statistical rigor. In this work, we examine the correspondence between the waterfall plot and the empirical cumulative distribution function. We demonstrate how to derive key summary statistics directly from the waterfall plot. Using real examples from published waterfall plots, we show how comparisons of waterfall plots can elucidate clinically meaningful information, such as treatment effect patterns in progression-free survival and overall survival.

Triplet Strategies in Metastatic Clear Cell Renal Cell Carcinoma: A Worthy Option in the First-Line Setting?

Significant strides have been made in the frontline treatment of patients with advanced clear cell renal cell carcinoma (ccRCC). There are multiple standard-of-care doublet regimens consisting of either the combined dual immune checkpoint inhibitors, ipilimumab and nivolumab, or combinations of a vascular endothelial growth factor receptor tyrosine kinase inhibitor and an immune checkpoint inhibitor. Currently, there is an emergence of clinical trials examining triplet combinations. In COSMIC-313, a randomized phase III trial for patients with untreated advanced ccRCC, the triplet combination of ipilimumab, nivolumab, and cabozantinib was compared with a contemporary control arm of ipilimumab and nivolumab. While patients receiving the triplet regimen demonstrated improved progression-free survival, these patients also experienced greater toxicity and the overall survival data are still maturing. In this article, we discuss the role of doublet therapy as standard of care, the current data available for the promise of triplet therapy, the rationale to continue pursuing trials with triplet combinations, and factors for clinicians and patients to consider when choosing among frontline treatments. We present ongoing trials with an adaptive design that may serve as alternative methods for escalating from doublet to triplet regimens in the frontline setting and explore clinical factors and emerging predictive biomarkers (both baseline and dynamic) that may guide future trial design and frontline treatment for patients with advanced ccRCC.

Characterizing tumor shrinkage as a measure of clinical benefit for immune checkpoint inhibitors

BACKGROUND

We explored whether the effectiveness of immune checkpoint inhibitors (ICIs) can be characterized by incorporating a composite of duration of response (DOR) to complement traditional Response Evaluation Criteria in Solid Tumors (RECIST) criteria for objective response rate (ORR) in an intent-to-treat (ITT) population. Furthermore, the correlation of this novel endpoint, characterized by the restricted mean time in response (RMTR), with overall survival (OS) will be examined.

METHODS

We analyzed ORR alone or in combination with DOR (RMTR) in available phase I, II, and III trials evaluating nivolumab monotherapy or in combination with ipilimumab across solid tumor types. ORR was evaluated per RECIST V.1.1. DOR was estimated using individual patient data in ITT populations regardless of RECIST response, with non-responders imputed as zero. Associations between ORR alone or RMTR and OS were evaluated in the ITT population. DOR curves were generated using the Kaplan-Meier product limit method, and 6-month RMTR, a measure of response durability, was derived from the area under the curves. For ORR and RMTR in the ITT population, the strength of association with OS was analyzed using Pearson correlation coefficients (r).

RESULTS

Nivolumab treatment was associated with longer response durations than active control in responder and ITT populations. Similarly, ORR and RMTR were both significantly correlated with OS (ORR vs OS: r=0.684, p=0.02; RMTR vs OS: r=0.695, p=0.018).

CONCLUSIONS

Combining ORR and DOR (RMTR) to objectively characterize tumor shrinkage in an ITT patient population is a novel approach that appears to correlate well with OS in patients treated with nivolumab monotherapy or in combination with ipilimumab. This endpoint may provide a more complete characterization of tumor shrinkage to incorporate into the design of future ICI clinical trials. However, confirmation of this approach will require further research.

Impact of Advantage in Tumor Response on the Correlation Between Progression-Free Survival and Overall Survival: Meta-Analysis of Clinical Trials in Patients with Advanced Non-Small Cell Lung Cancer

BACKGROUND

Progression-free survival (PFS) has not been validated as a surrogate endpoint for overall survival (OS) in patients with advanced non-small cell lung cancer.

OBJECTIVE

This study aimed to investigate an impact of advantage in tumor response on the correlation between PFS and OS in advanced non-small cell lung cancer.

METHODS

Based on a literature search, we identified randomized controlled trials of first-line therapy for advanced non-small cell lung cancer. The impact of absolute difference in objective response rate between treatment arms on the correlation between hazard ratios (HRs) for PFS and OS was evaluated based on Spearman rank correlation coefficients.

RESULTS

Sixty trials with a total of 29,134 patients were identified. The HR for PFS showed a relatively higher correlation with HR for OS (r_s = 0.75) when the trials were limited to those that demonstrated a larger advantage in objective response rate, compared with the case for trials that demonstrated a smaller advantage (r_s = 0.66). This tendency was also observed in the subgroup analysis stratified by the types of treatment agents (non-targeted, anti-angiogenic, and immunotherapy) except for the group of epidermal growth factor receptor-targeted agents.

CONCLUSIONS

The magnitude of advantage in tumor response was suggested to contribute to a better prediction of OS-HR based on PFS-HR in clinical trials in patients with advanced non-small cell lung cancer.

Metagenome association study of the gut microbiome revealed biomarkers linked to chemotherapy outcomes in locally advanced and advanced lung cancer

Background

The gut microbiome is important in the development and immunotherapy efficacy of lung cancer. However, the relationship between the intestinal flora and chemotherapy outcomes remains unclear and was investigated in this study.

Methods

We analyzed baseline stool samples from patients with locally advanced and advanced lung cancer before chemotherapy treatment, through metagenomics of the gut microbiota. The composition, diversity, function, and metabolic pathway analysis were compared among patients with different clinical outcomes.

Results

From 64 patients, 33 responded to treatment (responders) and 31 did not (nonresponders). Streptococcus mutans and Enterococcus casseliflavus were enriched in responders (P < 0.05), while 11 bacteria including Leuconostoc lactis and Eubacterium siraeum were enriched in nonresponders (P < 0.05) by variance analysis. Responders were associated with significantly higher Acidobacteria and Granulicella, while Streptococcus oligofermentans, Megasphaera micronuciformis, and Eubacterium siraeum were more abundant in nonresponders by Lefse analysis. Streptococcus mutans and Enterococcus casseliflavus were further identified as bacterial markers relevant to responders using unsupervised clustering, and Leuconostoc lactis and Eubacterium siraeum were related to nonresponders. The L‐glutamate degradation VIII pathway was enriched in responders (P = 0.014), and the C4 photosynthetic carbon assimilation cycle, reductive TCA cycle I, and hexitol fermentation to lactate, formate, ethanol, and acetate were enriched in nonresponders (P < 0.05). Additionally, significant associations of bacterial species with clinical phenotypes were observed by Spearman correlation analysis.

Conclusions

The specific gut microbiome of patients with lung cancer might be connected to the clinical outcomes of chemotherapy.

Key points