Pembrolizumab for all.

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.

Quality of biomarker defined subgroups in FDA approvals of PD-1/PD-L1 inhibitors 2014 to 2020.

PD-L1 expression is associated with differential response in cancers treated with checkpoint inhibitors. Clinical trials for Food and Drug Administration (FDA) approvals of programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors include limited subgroup analyses based on PD-L1 expression. We aimed to define the characteristics of PD-L1 defined subgroups of clinical trials leading to FDA approvals for new indications of PD-1/PD-L1 inhibitors. FDA approvals for PD-1/PD-L1 inhibitors from January 2014 to December 2020 were identified and the clinical trials leading to each drug approval were reviewed. We collected key variables from publicly available information on FDA website and peer-reviewed publications of clinical trials. We assessed regulatory characteristics (approval date, approved drug[s], cancer type, line of therapy and biomarker-restricted approval criteria) of each approval. Clinical trials leading to approvals were reviewed for trial design (RCT vs single arm study, primary endpoint) and PD-L1 defined subgroup design (no subgroup analysis, single threshold 2-group analysis, nested subgroups and adjacent subgroups). We then compared regulatory and trials characteristics (trial design, primary endpoint and biomarker approval criteria) between studies with nested and adjacent subgroups. There were 60 approvals for PD-1/PD-L1 inhibitors between January 2014 and December 2020. Twelve of 60 (20%) did not include any PD-L1 subgroups. Twenty-five of 60 (42%) approvals reported only two subgroups, 14 (23%) included adjacent subgroups and 9 (15%) had nested subgroups. Twenty-five of 60 trials (42%) are single arm studies. Comparison of characteristics between trials with nested subgroup design and adjacent subgroup design did not show differences. We conclude that approvals for new indications of PD-1/PD-L1 inhibitors are based on studies that do not include comprehensive reporting of outcomes by PD-L1 biomarker subgroups.

The frequency of medical reversals in a cross-sectional analysis of high-impact oncology journals, 2009-2018.

BACKGROUND: Identifying ineffective practices that have been used in oncology is important in reducing wasted resources and harm. We sought to examine the prevalence of practices that are being used but have been shown in RCTs to be ineffective (medical reversals) in published oncology studies. METHODS: We cross-sectionally analyzed studies published in three high-impact oncology medical journals (2009-2018). We abstracted data relating to the frequency and characterization of medical reversals. RESULTS: Of the 64 oncology reversals, medications (44%) represented the most common intervention type (39% were targeted). Fourteen (22%) were funded by pharmaceutical/industry only and 56% were funded by an organization other than pharmaceutical/industry. The median number of years that the practice had been in use prior to the reversal study was 9 years (range 1-50 years). CONCLUSION: Here we show that oncology reversals most often involve the administration of medications, have been practiced for years, and are often identified through studies funded by non-industry organizations.

Nested and adjacent subgroups in cancer clinical trials: When the best interests of companies and patients diverge.

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.

New drugs and options can enhance patient outcomes: But can they also erode them?

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.

Persistent challenges with treating multiple myeloma early.

Over the past decade, 2 strategies have advanced the treatment of patients with multiple myeloma and its precursor diseases. First, the definition has changed to include patients without end organ damage, who previously would not have been treated. Second, there is widespread enthusiasm for treating high-risk, smoldering multiple myeloma. In this commentary, we explore the evidence supporting these therapeutic expansions. Although early treatment adds cost and therapeutic burden, it remains unknown whether survival and health-related quality of life are improved by early treatment. Herein, we consider the implications of diagnostic expansion in multiple myeloma.

Relative quantification in imaging of a peptide on a mouse brain tissue by matrix-assisted laser desorption ionization.

It is well-known that the number of analyte ions generated by matrix-assisted laser desorption ionization (MALDI) is not directly proportional to the analyte concentration at the irradiated spot. This is an obstacle to acquiring quantitatively meaningful maps for materials in a tissue by MALDI imaging. The problem worsens as the matrix suppression due to contaminants in the sample increases. In this work, we use a peptide as an example and show that we can overcome this problem by utilizing three guidelines derived from our recent studies on the generation of reproducible MALDI spectra. First is to acquire MALDI spectra under a temperature-controlled condition. Second is to keep the matrix suppression below an experimentally determined limit, and the third is to construct the image map using the peptide-to-matrix ion abundance ratio rather than the peptide ion abundance. The strategy works well for contaminated tissue samples and generates quantitatively meaningful maps. Also, it is demonstrated that a preposterous map can be generated when the peptide ion abundance is used in the construction of the map.