The design of clinical trials with outcomes reported in cohorts including nested subgroups is common in novel agents seeking new indications for approval. This structure represents a tension between drug companies that have an incentive to pursue broad biomarker-agnostic approvals and patients whose best interest is to identify the subgroup(s) most likely to benefit from the drug. Programmed death ligand 1 (PD-L1) and checkpoint inhibitors are a prominent example with early trials reporting efficacy of checkpoint inhibitors in cohorts with high levels of PD-L1. Subsequent analyses incrementally report outcomes in broader patient cohorts that include the nested subgroup of high PD-L1 expression which drives the positive outcome in the entire cohort. Comparing aggregate outcomes between groups of patients with known heterogeneous outcomes deters the effective analysis of all available data. Exploring the optimal treatment for individual patients with different levels of PD-L1 expression, whether it is checkpoint inhibitors only, checkpoint inhibitors combined with chemotherapy or chemotherapy only, requires a granular approach to trial design and reporting. Such grouping of patients with different biomarker findings is increasingly seen in the setting of adjuvant therapy, as well as in targeted therapies that show efficacy in a single gene mutation which however are studied in the setting of panels of mutations. Here we discuss the difference between nested and adjacent subgroups in oncology.

The current approval indications for pembrolizumab are complex, reflecting the inclusion criteria of numerous clinical trials that led to approvals. Here we argue that allowing the use of pembrolizumab to any advanced solid tumor in any tumor type in any line of therapy for a fixed duration may be preferable to the current assortment of indications. The aggregate response rate in landmark clinical trials for approved indications of pembrolizumab is low and even lower in real-world populations. Due to heterogeneity of response to checkpoint inhibitors and limited predictive biomarkers, there are subsets of patients without approved indications for pembrolizumab that may have response to checkpoint inhibitors. The current regulatory framework of numerous overlapping clinical trials leading to complex approval indications is redundant and inefficient. We conclude that giving pembrolizumab in any metastatic solid tumor in any setting may lead to better outcomes with minimal increase in cost. Randomized clinical trials should focus more on optimal duration of treatment based on tumor type and initial response to checkpoint inhibitors.

The US Food and Drug Administration granted acalabrutinib approval as the second Bruton tyrosine kinase (BTK) inhibitor to treat patients with chronic lymphocytic leukemia and small lymphocytic lymphoma as monotherapy or in combination with obinutuzumab. This approval was based on 2 phase 3 trials: ELEVATE-TN and ASCEND. There are several concerns with the design of these trials, including suboptimal treatment of patients in the control arm, expansion of the trial population, and lack of data regarding efficacy or tolerability compared with ibrutinib, a first-in-class drug. The Food and Drug Administration approval of acalabrutinib for patients with chronic lymphocytic leukemia and small lymphocytic lymphoma represents concerning drug approval patterns in the United States and a weakness in evidence generation.

The expanding list of treatment options available to patients with cancer is a source of excitement. Drugs with novel mechanisms of action receive attention at academic meetings and approval of novel drugs are cited as a victory of medical research. This is evidenced by the interest in number of new drug approvals each year. The Food and Drug Administration provides a yearly report of New Molecular Entities approved by the Center for Drug Evaluation and Research. High numbers of approved drugs is celebrated and equated with improvement in patient outcomes, as well as evidence of the effectiveness of regulatory agencies [1]. While more effective therapies lead to improved outcome, merely having more options may erode outcomes in unexpected ways. We discuss 3 different clinical scenarios where having more options can lead to worse outcomes.

Aim

Drug approvals for genome-informed indications have been increasing in recent years, but it is unknown how many of them have demonstrated an improvement in overall survival (OS). We assessed the frequency of approved genome-informed drugs demonstrating improvements in OS and progression-free survival (PFS) and whether the frequencies differed by cancer type.

Materials and methods

We searched all Food and Drug Administration approvals from 2006 to 2020, and for each drug that was approved for a genomic indication, we then searched on PubMed for randomised studies examining OS or PFS.

Results

We found 53 drugs approved for 92 unique indications from 2006 to 2020. We found that 50 drugs (55%) approved for a genomic indication had a randomised study evaluating OS benefit, and of those, only 22 demonstrated an improvement in OS. Similarly, 52 drugs (57%) evaluated PFS benefit, and 51 of these studies demonstrated an improvement in PFS. Drugs approved for BRAF V600 melanoma demonstrated an improvement in OS more often than drugs approved for ALK non-small cell lung cancer. The median improvement in OS was 4.7 months (range 1.5 months-49.1 months).

Conclusion

Although there is widespread enthusiasm for this class of agents, and many demonstrate impressive response rates, further trials or post-marketing studies are needed to ascertain the impact on survival and quality of life, the magnitude of these gains, and the cost-effectiveness of these agents.

Background

Rank preserving structural failure time (RPSFT) is a statistical method to correct or adjust for crossover in clinical trials, by estimating the counterfactual effect on overall survival (OS) when control arm patients do not receive the interventional drug when their tumor progresses. We sought to examine the strength of correlation between differences in uncorrected and corrected OS hazard ratios and percentage of crossover, and characterize instances of fundamental and sequential efficacy.

Methods

In a cross-sectional analysis (2003-2023), we reviewed oncology randomized trials that used RPSFT analysis to adjust the OS hazard ratio for patients who crossed over to an anti-cancer drug. We calculated the percentage of RPSFT studies evaluating a drug for fundamental efficacy (with or without a standard of care (SOC)) or sequential efficacy and the correlation between the OS hazard ratio difference (unadjusted and adjusted) and the percentage of crossover.

Results

Among 65 studies, the median difference between the uncorrected and corrected OS hazard ratio was -0.1 (quartile 1, quartile 3 : -0.3 to -0.06). The median percentage of crossover was 56% (quartile 1, quartile 3: 37% to 72%). All studies were funded by the industry or had authors who were employees of the industry. Twelve studies (19%) tested a drug's fundamental efficacy when there was no SOC; 34 studies (52%) tested a drug's fundamental efficacy when there was already a SOC; and 19 studies (29%) tested a drug's sequential efficacy. The correlation between the uncorrected and corrected OS hazard ratio difference and the percentage of crossover was 0.44 (95% CI: 0.21 to 0.63).

Conclusions

RPSFT is a common tactic used by the industry to reinterpret trial results. Nineteen percent of RPSFT use is appropriate. We recognize that while crossover can bias OS results, the allowance and handling of crossover in trials should be limited to appropriate circumstances.