Relatlimab and Nivolumab versus Nivolumab in Untreated Advanced Melanoma

BACKGROUND

Lymphocyte-activation gene 3 (LAG-3) and programmed death 1 (PD-1) are distinct inhibitory immune checkpoints that contribute to T-cell exhaustion. The combination of relatlimab, a LAG-3-blocking antibody, and nivolumab, a PD-1-blocking antibody, has been shown to be safe and to have antitumor activity in patients with previously treated melanoma, but the safety and activity in patients with previously untreated melanoma need investigation.

METHODS

In this phase 2-3, global, double-blind, randomized trial, we evaluated relatlimab and nivolumab as a fixed-dose combination as compared with nivolumab alone when administered intravenously every 4 weeks to patients with previously untreated metastatic or unresectable melanoma. The primary end point was progression-free survival as assessed by blinded independent central review.

RESULTS

The median progression-free survival was 10.1 months (95% confidence interval [CI], 6.4 to 15.7) with relatlimab-nivolumab as compared with 4.6 months (95% CI, 3.4 to 5.6) with nivolumab (hazard ratio for progression or death, 0.75 [95% CI, 0.62 to 0.92]; P = 0.006 by the log-rank test). Progression-free survival at 12 months was 47.7% (95% CI, 41.8 to 53.2) with relatlimab-nivolumab as compared with 36.0% (95% CI, 30.5 to 41.6) with nivolumab. Progression-free survival across key subgroups favored relatlimab-nivolumab over nivolumab. Grade 3 or 4 treatment-related adverse events occurred in 18.9% of patients in the relatlimab-nivolumab group and in 9.7% of patients in the nivolumab group.

CONCLUSIONS

The inhibition of two immune checkpoints, LAG-3 and PD-1, provided a greater benefit with regard to progression-free survival than inhibition of PD-1 alone in patients with previously untreated metastatic or unresectable melanoma. Relatlimab and nivolumab in combination showed no new safety signals. (Funded by Bristol Myers Squibb; RELATIVITY-047 ClinicalTrials.gov number, NCT03470922.).

Relatlimab (RELA) plus nivolumab (NIVO) versus NIVO in first-line advanced melanoma: Primary phase III results from RELATIVITY-047 (CA224-047)

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Background: Immune checkpoint inhibitor therapy has revolutionized the treatment of patients with advanced melanoma. However, novel combinations are needed to optimize the benefit-risk profile. Lymphocyte-activation gene 3 (LAG-3) regulates an immune checkpoint pathway, which inhibits T-cell activity, and is upregulated in many tumor types including melanoma. Relatlimab (RELA), a human IgG4 LAG-3-blocking antibody, restores effector function of exhausted T cells. RELA in combination with nivolumab (NIVO; anti-programmed death [PD]-1) modulates potentially synergistic immune checkpoint pathways and can enhance antitumor immune responses. RELATIVITY-047 is a global, randomized, double-blind, phase II/III study evaluating a novel immune checkpoint inhibitor combination of RELA+NIVO as a fixed-dose combination (FDC) treatment in first-line advanced melanoma. Methods: Patients with previously untreated advanced melanoma were randomized 1:1 to receive RELA 160 mg + NIVO 480 mg FDC intravenously (IV) every 4 weeks (Q4W) or NIVO monotherapy 480 mg IV Q4W, stratified by LAG-3 expression, programmed death ligand 1 expression, BRAF mutation status, and AJCC (v8) M stage. The primary endpoint was progression-free survival (PFS) per RECIST v1.1 as assessed by blinded independent central review. Secondary endpoints were overall survival and objective response rate. PFS in prespecified subgroups and safety were additional objectives. Results: 714 patients were randomized to RELA+NIVO FDC (n = 355) or NIVO (n = 359). Patient characteristics were well balanced between treatment groups. Median follow-up was 13.2 months. Median PFS in the RELA+NIVO FDC group (10.1 months [95% CI, 6.4–15.7]) was significantly longer than in the NIVO group (4.6 months [95% CI, 3.4–5.6]; hazard ratio, 0.75 [95% CI, 0.6–0.9]; P = 0.0055). PFS rates at 12 months were 47.7% (95% CI, 41.8–53.2) and 36.0% (95% CI, 30.5–41.6) for RELA+NIVO FDC and NIVO, respectively. PFS favored RELA+NIVO FDC across key prespecified subgroups. The incidence of grade 3/4 treatment-related adverse events (TRAEs) was higher in the RELA+NIVO FDC group (18.9%) versus NIVO (9.7%). There were 3 treatment-related deaths with RELA+NIVO FDC and 2 with NIVO. TRAEs (any grade) led to treatment discontinuation in 14.6% and 6.7% of patients in the RELA+NIVO FDC and NIVO groups, respectively. Conclusions: First-line treatment with RELA+NIVO FDC demonstrated a statistically significant PFS benefit compared to NIVO monotherapy in patients with advanced melanoma. RELA+NIVO FDC was well tolerated with a manageable safety profile and without unexpected safety signals. This is the first phase III study of a novel FDC to demonstrate a clinically meaningful benefit by dual inhibition of the LAG-3 and PD-1 pathways. Clinical trial information: NCT03470922.

The Fast Real-time Assessment of Combination Therapies in Immuno-ONcology (FRACTION) program: innovative, high-throughput clinical screening of immunotherapies

BACKGROUND

The unprecedented success of immuno-oncology (I-O) agents targeting the cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/programmed death-ligand 1 pathways has stimulated the rapid development of other I-O agents against novel immune targets. Bristol-Myers Squibb has designed a novel phase II platform trial, the Fast Real-time Assessment of Combination Therapies in Immuno-ONcology (FRACTION) Program, to efficiently identify promising combinations for patients with specific malignancies. The concept and study design of the FRACTION Program-currently ongoing in patients with advanced non-small-cell lung cancer (FRACTION-Lung), gastric cancer (FRACTION-Gastric Cancer) and renal cell carcinoma (FRACTION-RCC)-are described.

METHODS

The FRACTION Program comprises open-label, phase II studies that use adaptive randomisation designs with rolling combination regimens. Master Protocols provide the overall study design framework, whereas Sub-Protocols introduced over time provide details on specific I-O combination therapies to which patients may be randomised. In a Master Protocol, patients are enrolled into different Study Tracks based on characteristics such as prior I-O therapy experience. Patients who progress may be rerandomised to other combination regimens from any ongoing Sub-Protocol. Primary objectives are to assess objective response rate, median duration of response and progression-free survival rate at 24 weeks; the secondary objective is to investigate safety and tolerability. Biomarker collection before and on treatment will facilitate identification of patient subsets who benefit most from each therapy.

CONCLUSIONS

The FRACTION Program allows for the evaluation of multiple I-O combinations through individual studies for specific tumours using an adaptive trial design and continuous enrolment.

Statistical tests of neutrality in the age of weak selection

Why review statistical tests of neutrality at a time when pan-selectionists and pan-neutralists alike seem to have been replaced by weak selectionists? First, we still don't actually know how variation is maintained at the molecular level; and second, tests of neutrality have a utility for evolutionary biologists beyond the neutralist/selectionist debate. New tests and variations on the existing tests are arising practically every month. From the complementary viewpoints of an empiricist and a theoretician, we sample the recent literature on tests of statistical neutrality and discuss the motivations, applications, assumptions, interpretations and future directions of these tests.

Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study

OBJECTIVES

We explored whether the benefit of intensive versus moderate statin therapy would be greater in carriers of KIF6 719Arg than in noncarriers.

BACKGROUND

The 719Arg variant of Trp719Arg (rs20455), a polymorphism in kinesin-like protein 6, is associated with greater risk of coronary events and greater benefit from pravastatin versus placebo.

METHODS

We genotyped 1,778 acute coronary syndrome patients within the PROVE IT-TIMI 22 (Pravastatin or Atorvastatin Evaluation and Infection Therapy: Thrombolysis in Myocardial Infarction 22) trial and investigated different intensities of statin therapy in carriers of 719Arg and in noncarriers using Cox proportional hazards models that adjusted for traditional risk factors.

RESULTS

Benefit from intensive, compared with moderate, statin therapy was significantly greater in the 59% of the cohort who were carriers (hazard ratio [HR] 0.59, 95% confidence interval [CI] 0.45 to 0.77) than in those who were noncarriers (HR 0.94, 95% CI 0.70 to 1.27; p = 0.018 for interaction between 719Arg carrier status and treatment). Absolute risk reduction was 10.0% in carriers versus 0.8% in noncarriers. The benefit of intensive therapy in carriers was significant as early as day 30 of therapy. Carriers and noncarriers did not differ in on-treatment low-density lipoprotein cholesterol, triglyceride, or C-reactive protein (CRP) levels.

CONCLUSIONS

Carriers of 719Arg receive significantly greater benefit from intensive statin therapy than do noncarriers, a superior benefit that appears to be due to a mechanism distinct from lipid or CRP lowering. Functional studies of the KIF6 kinesin are warranted, given the consistent association of Trp719Arg with risk of coronary events and statin benefit.

Model selection in binary trait locus mapping

Quantitative trait locus (QTL) mapping methodology for continuous normally distributed traits is the subject of much attention in the literature. Binary trait locus (BTL) mapping in experimental populations has received much less attention. A binary trait by definition has only two possible values, and the penetrance parameter is restricted to values between zero and one. Due to this restriction, the infinitesimal model appears to come into play even when only a few loci are involved, making selection of an appropriate genetic model in BTL mapping challenging. We present a probability model for an arbitrary number of BTL and demonstrate that, given adequate sample sizes, the power for detecting loci is high under a wide range of genetic models, including most epistatic models. A novel model selection strategy based upon the underlying genetic map is employed for choosing the genetic model. We propose selecting the "best" marker from each linkage group, regardless of significance. This reduces the model space so that an efficient search for epistatic loci can be conducted without invoking stepwise model selection. This procedure can identify unlinked epistatic BTL, demonstrated by our simulations and the reanalysis of Oncorhynchus mykiss experimental data.

Adding confidence to gene expression clustering

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.

Genomic haplotype blocks may not accurately reflect spatial variation in historic recombination intensity

Recently, genomic data have revealed a "block-like" structure of haplotype diversity on human chromosomes. This structure is anticipated to facilitate gene mapping studies, because strong associations among loci within a block may allow haplotype variation to be tagged with a limited number of markers. But its usefulness to mapping efforts depends on the consistency of the block structure within and among populations, which in turn depends on how the block structure arises. Recombination hot spots are generally thought to underlie the block structure, but haplotype blocks can also develop stochastically under random recombination, in which case the block structure will show limited consistency among populations. Using coalescent models, which we upscaled to simulate the evolution of haplotypes with many markers at fixed distances, we show that the relationship between block boundaries and historic recombination intensity may be surprisingly weak. The majority of historic recombinations do not leave a footprint in present-day linkage disequilibrium patterns, and the block structure is sensitive to factors that affect the timing of recombination relative to marker mutation events in the genealogy, such as marker frequency bias and historic population size changes. Our results give insight into the potential of stochastic events to affect haplotype block structure, which can limit the usefulness of the block structure to mapping studies.

Using alpha wisely: improving power to detect multiple QTL

The increase in the number of available markers for many experimental populations has led to QTL studies with ever increasing marker numbers and densities. The resulting conundrum is that as marker density increases, so does the multiple testing problem. It is important to re-examine the detection of multiple QTL in light of increasing marker density. We explore through simulation whether existing methods have achieved the maximum possible power for detecting multiple QTL and whether increasing the marker density is an effective strategy for locating multiple QTL. In addition to existing methods, such as the maximum, the CET, and the Benjamini-Hochberg and Benjamini-Yekutieli procedures, we propose and evaluate the complete set of order statistics with their corresponding empirical joint distribution. We examine these statistics in conjunction with a novel application of the alpha-spending approach, providing a less conservative solution to the problem of controlling the false discovery rate (FDR) in multiple tests. We conducted a simulation study to assess the relative power of these approaches as well as their ability to control FDR. We find that several of the new approaches have a reasonable FDR, and can substantially improve the experimenter's ability to detect multiple QTL compared to existing approaches in many cases; however, the Benjamini-Hochberg procedure remains a very reasonable choice. The methods are applied to a nine-trait Oat vernalization dataset.

Circumventing multiple testing: a multilocus Monte Carlo approach to testing for association

Advances in marker technology have made a dense marker map a reality. If each marker is considered separately, and separate tests for association with a disease gene are performed, then multiple testing becomes an issue. A common solution uses a Bonferroni correction to account for multiple tests performed. However, with dense marker maps, neighboring markers are tightly linked and may have associated alleles; thus tests at nearby marker loci may not be independent. When alleles at different marker loci are associated, the Bonferroni correction may lead to a conservative test, and hence a power loss. As an alternative, for tests of association that use family data, we propose a Monte Carlo procedure that provides a global assessment of significance. We examine the case of tightly linked markers with varying amounts of association between them. Using computer simulations, we study a family-based test for association (the transmission/disequilibrium test), and compare its power when either the Bonferroni or Monte Carlo procedure is used to determine significance. Our results show that when the alleles at different marker loci are not associated, using either procedure results in tests with similar power. However, when alleles at linked markers are associated, the test using the Monte Carlo procedure is more powerful than the test using the Bonferroni procedure. This proposed Monte Carlo procedure can be applied whenever it is suspected that markers examined have high amounts of association, or as a general approach to ensure appropriate significance levels and optimal power.

A Monte Carlo permutation approach to choosing an affection status model for bipolar affective disorder

A permutation test is proposed for assessing affection status models. The test uses marker data from regions with prior evidence of linkage to susceptibility genes, and three different test statistics are examined. We applied the test to the GAW10 data and found no evidence on chromosome 18 to reject the affection status model that groups individuals diagnosed with either bipolar I, bipolar II or unipolar. The chromosome 5 data gave similar results, and further suggested that individuals diagnosed with unipolar-single episode not be included as affected. A preliminary power study suggested that one of the proposed statistics, S, is to be preferred in certain circumstances.

A Markov Chain Model of Coalescence with Recombination

Trees that describe the ancestry of DNA sequences sampled from a population may differ between loci because of genetic recombination. We seek to understand the relationship between such trees for loci that are linked with non-zero recombination rate. We consider a coalescent process model with recombination, as described by Hudson (1983; 1990). For two loci and a sample size of two sequences, a detailed analysis of this process yields the joint distribution of the two trees (one at each locus). A number of interesting results follow from this analysis, including the distribution of the number of recombination events in the history of the sample. For the general case of m loci and samples of size n, we describe an algorithm for simulating the tree building process. Because analytic results are difficult to obtain in this case, we use simulation to study properties of trees at multiple linked loci such as total tree time and number of recombination events. Copyright 1997 Academic Press

Properties of statistical tests of neutrality for DNA polymorphism data

A class of statistical tests based on molecular polymorphism data is studied to determine size and power properties. The class includes Tajima's D statistic as well as the D* and F* tests proposed by Fu and Li. A new method of constructing critical values for these tests is described. Simulations indicate that Tajima's test is generally most powerful against the alternative hypotheses of selective sweep, population bottleneck, and population subdivision, among tests within this class. However, even Tajima's test can detect a selective sweep or bottleneck only if it has occurred within a specific interval of time in the recent past or population subdivision only when it has persisted for a very long time. For greatest power against the particular alternatives studied here, it is better to sequence more alleles than more sites.