Safety and activity of anti-mesothelin antibody-drug conjugate anetumab ravtansine in combination with pegylated-liposomal doxorubicin in platinum-resistant ovarian cancer: multicenter, phase Ib dose escalation and expansion study.

OBJECTIVES: Anetumab ravtansine is an antibody-drug conjugate consisting of a fully human anti-mesothelin monoclonal antibody conjugated to cytotoxic maytansinoid tubulin inhibitor DM4. Mesothelin is highly expressed in ovarian cancer. This phase Ib study determines the safety, pharmacokinetics, and anti-tumor activity of anetumab ravtansine and pegylated liposomal doxorubicin in mesothelin-expressing platinum-resistant ovarian cancer. METHODS: Anetumab ravtansine (5.5 or 6.5 mg/kg) and pegylated liposomal doxorubicin (30 mg/m2) were administered intravenously every 3 weeks to 65 patients with platinum-resistant epithelial ovarian cancer. Mesothelin expression was assessed by central immunohistochemistry. Adverse events, tumor response (RECIST 1.1), and progression-free survival were determined. Biomarker samples were assessed by ELISA and next-generation sequencing. RESULTS: In dose escalation, nine patients received anetumab ravtansine across two doses (5.5 or 6.5 mg/kg). The maximum tolerated dose of anetumab ravtansine was 6.5 mg/kg every 3 weeks and no dose-limiting toxicities were observed. In dose expansion, 56 patients were treated at the maximum tolerated dose. The most common treatment-emergent adverse events of any grade were nausea (47.7%), decreased appetite (43.1%), fatigue (38.5%), diarrhea (32.3%), and corneal disorder (29.2%). In all treated patients the objective response rate was 27.7% (95% CI 17.3% to 40.2%), including one complete (1.5%) and 17 partial responses (26.2%), with median duration of response of 7.6 (95% CI 3.3 to 10.2) months and median progression-free survival of 5.0 (95% CI 3.2 to 6.0) months. In an exploratory analysis of a sub-set of patients (n=19) with high mesothelin expression who received ≤3 prior lines of systemic therapy, the objective response rate was 42.1% (95% CI 20.3% to 66.5%) with a median duration of response of 8.3 (95% CI 4.1 to 12.0) months and median progression-free survival of 8.5 (95% CI 4.0 to 11.4) months. CONCLUSIONS: Anetumab ravtansine and pegylated liposomal doxorubicin showed tolerability and promising clinical activity. These results established the dose schedule and the mesothelin-positive target population of this combination for a phase III study in platinum-resistant ovarian cancer. TRIAL REGISTRATION NUMBER: NCT02751918.

Performing Meta-analyses with Very Few Studies.

This chapter contains a methodological framework for choosing a model for the meta-analysis of very few studies and selecting an estimation method for the chosen model by means of study characteristics and by comparing results yielded by different approaches. When the results are inconclusive between different estimation methods, it might be the best solution to refrain from a quantitative meta-analysis but to summarize the study results by means of a qualitative evidence synthesis.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective. METHODS: The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.

Effects of Rivaroxaban on Biomarkers of Coagulation and Inflammation: A Post Hoc Analysis of the X-VeRT Trial.

Introduction This X-VeRT (eXplore the efficacy and safety of once-daily oral riVaroxaban for the prevention of caRdiovascular events in patients with nonvalvular aTrial fibrillation scheduled for cardioversion) substudy evaluated the effects of treatment with rivaroxaban or a vitamin-K antagonist (VKA) on levels of biomarkers of coagulation (D-dimer, thrombin-antithrombin III complex [TAT] and prothrombin fragment [F1.2]) and inflammation (high sensitivity C-reactive protein [hs-CRP] and high-sensitivity interleukin-6 [hs-IL-6]) in patients with atrial fibrillation (AF) who were scheduled for cardioversion and had not received adequate anticoagulation at baseline (defined as, in the 21 days before randomization: no oral anticoagulant; international normalized ratio <2.0 with VKA treatment; or <80% compliance with non-VKA oral anticoagulant treatment). Methods Samples for biomarker analysis were taken at baseline ( n = 958) and treatment completion (42 days after cardioversion; n = 918). The influence of clinical characteristics on baseline biomarker levels and the effect of treatment on changes in biomarker levels were evaluated using linear and logistic models. Results Baseline levels of some biomarkers were significantly associated with type of AF (D-dimer and hs-IL-6) and with history of congestive heart failure (hs-CRP, D-dimer, and hs-IL-6). Rivaroxaban and VKA treatments were associated with reductions from baseline in levels of D-dimer (-32.3 and -37.6%, respectively), TAT (-28.0 and -23.1%, respectively), hs-CRP (-12.5 and -17.9%, respectively), and hs-IL-6 (-9.2 and -9.8%, respectively). F1.2 levels were reduced from baseline in patients receiving a VKA (-53.0%) but not in those receiving rivaroxaban (2.7%). Conclusion Anticoagulation with rivaroxaban reduced levels of key inflammation and coagulation biomarkers to a similar extent as VKAs, with the exception of F1.2. Further investigation to confirm the value of these biomarkers in patients with AF is merited.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective. METHODS: The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.

Left atrial thrombus resolution in non-valvular atrial fibrillation or flutter: biomarker substudy results from a prospective study with rivaroxaban (X-TRA).

BACKGROUND: Non-vitamin K antagonist oral anticoagulants including rivaroxaban are widely used for stroke prevention in patients with atrial fibrillation (AF). We investigated the relationship between plasma biomarkers (indicative of thrombogenesis, fibrinolysis and inflammation) and left atrial thrombus resolution after rivaroxaban treatment. METHODS: This was an ancillary analysis of the X-TRA study, which was a prospective interventional study evaluating the use of rivaroxaban for left atrial/left atrial appendage (LA/LAA) thrombus resolution in AF patients. We assessed various biomarkers of thrombogenesis/fibrinolysis [D-dimer, plasminogen activator inhibitor-1 (PAI-1), prothrombin fragment 1 + 2 (F1,2), thrombin-antithrombin (TAT) complexes, von Willebrand factor (vWF)] and inflammation [high-sensitivity interleukin-6 (hsIL-6), and high-sensitivity C-reactive protein (hsCRP)], measured at baseline and after 6 weeks' of rivaroxaban treatment. RESULTS: There was a significant decrease in the mean levels of hsCRP, D-dimer, vWF, and TAT from baseline to end of treatment with rivaroxaban. Although none of the thrombogenesis/fibrinolysis biomarkers showed a significant relationship with thrombus resolution, high inflammatory biomarkers at baseline were significantly associated with an increased chance of the thrombus being completely resolved (hsIL-6) or reduced/resolved (hsCRP). CONCLUSIONS: Biomarkers of inflammation are significantly associated with LA/LAA thrombus outcomes in AF patients prospectively treated with rivaroxaban.

Phase I/II Study of Refametinib (BAY 86-9766) in Combination with Gemcitabine in Advanced Pancreatic cancer.

BACKGROUND: Activating KRAS mutations are reported in up to 90% of pancreatic cancers. Refametinib potently inhibits MEK1/2, part of the MAPK signaling pathway. This phase I/II study evaluated the safety and efficacy of refametinib plus gemcitabine in patients with advanced pancreatic cancer. METHODS: Phase I comprised dose escalation, followed by phase II expansion. Refametinib and gemcitabine plasma levels were analyzed for pharmacokinetics. KRAS mutational status was determined from circulating tumor DNA. RESULTS: Ninety patients overall received treatment. The maximum tolerated dose was refametinib 50 mg twice daily plus standard gemcitabine (1000 mg/m2 weekly). The combination was well tolerated, with no pharmacokinetic interaction. Treatment-emergent toxicities included thrombocytopenia, fatigue, anemia, and edema. The objective response rate was 23% and the disease control rate was 73%. Overall response rate, disease control rate, progression-free survival, and overall survival were higher in patients without detectable KRAS mutations (48% vs. 28%, 81% vs. 69%, 8.8 vs. 5.3 months, and 18.2 vs. 6.6 months, respectively). CONCLUSION: Refametinib plus gemcitabine was well tolerated, with a promising objective response rate, and had an acceptable safety profile and no pharmacokinetic interaction. There was a trend towards improved outcomes in patients without detectable KRAS mutations that deserves future investigation.

AKT1 (E17K) mutation profiling in breast cancer: prevalence, concurrent oncogenic alterations, and blood-based detection.

BACKGROUND: The single hotspot mutation AKT1 [G49A:E17K] has been described in several cancers, with the highest incidence observed in breast cancer. However, its precise role in disease etiology remains unknown. METHODS: We analyzed more than 600 breast cancer tumor samples and circulating tumor DNA for AKT1 (E17K) and alterations in other cancer-associated genes using Beads, Emulsions, Amplification, and Magnetics digital polymerase chain reaction technology and targeted exome sequencing. RESULTS: Overall AKT1 (E17K) mutation prevalence was 6.3 % and not correlated with age or menopausal stage. AKT1 (E17K) mutation frequency tended to be lower in patients with grade 3 disease (1.9 %) compared with those with grade 1 (11.1 %) or grade 2 (6 %) disease. In two cohorts of patients with advanced metastatic disease, 98.0 % (n = 50) and 97.1 % (n = 35) concordance was obtained between tissue and blood samples for the AKT1 (E17K) mutation, and mutation capture rates of 66.7 % (2/3) and 85.7 % (6/7) in blood versus tissue samples were observed. Although AKT1-mutant tumor specimens were often found to harbor concurrent alterations in other driver genes, a subset of specimens harboring AKT1 (E17K) as the only known driver alteration was also identified. Initial follow-up survival data suggest that AKT1 (E17K) could be associated with increased mortality. These findings warrant additional long-term follow-up. CONCLUSIONS: The data suggest that AKT1 (E17K) is the most likely disease driver in certain breast cancer patients. Blood-based mutation detection is achievable in advanced-stage disease. These findings underpin the need for a further enhanced-precision medicine paradigm in the treatment of breast cancer.

Cytokeratin-19 as a biomarker in urine and in serum for the diagnosis of endometriosis--a prospective study.

Endometriosis compromises the quality of life of countless women worldwide and is a leading cause of disability. Clinical symptoms of endometriosis can be very heterogeneous leading to a long interval between onset of symptoms and surgical diagnosis. A noninvasive, rapid diagnostic test is urgently needed. In this prospective study, we evaluated the usefulness of Cytokeratin-19 (CK19) as a biomarker for the diagnosis of endometriosis through urine and serum ELISA. 76 reproductive-aged women undergoing laparoscopy for benign conditions were included to this study and divided into two groups by the presence (n = 44) or absence (n = 32) of endometriosis. There was no statistically significant correlation between the concentration of CK19 in urine (p = 0.51) or in serum (p = 0.77) and the diagnosis of endometriosis. Assigning the samples to the proliferative or secretory cycle stage did not sufficiently lower the p values. In this study, the promising data reported in the recent literature about CK19 serving as a sufficient biomarker for endometriosis could not be verified when tested in a larger sample size. Further studies are warranted to explore the usefulness of CK19 in the diagnosis of endometriosis.

Entropy-supported marker selection and Mantel statistics for haplotype sharing analysis.

Haplotype sharing analysis is a well-established option for the investigation of the etiology of complex diseases. The statistical power of haplotype association methods depends strongly on how the information of unobserved haplotypes can be captured by multilocus genotypes. In this study we combine an entropy-based marker selection algorithm (EMS), with a haplotype sharing-based Mantel statistics into a new algorithm. Genetic markers are iteratively selected by their multilocus linkage disequilibrium (LD), which is assessed by a normalized entropy difference. The initial marker set is gradually enlarged to increase the available information on the amount of sharing around a potential susceptibility marker. Markers are rejected from joint phasing if they do not increase the multilocus LD. In simulated candidate gene studies, the Mantel statistics combined with the new EMS performs as well or better at detecting the disease single nucleotide polymorphism-or in indirect association analysis its flanking markers-than the Mantel statistics without selection of markers prior to haplotype estimation and the Mantel statistics using sliding windows of size five. It is therefore appealing to apply our selection approach for haplotype-based association analysis, since marker selection driven by the observed data avoids both the arbitrary choice of markers when using a fixed window size, as well as the estimation of haplotype block structure.

Representation of genetic association via attributable familial relative risks in order to identify polymorphisms functionally relevant to rheumatoid arthritis.

The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signals on familial aggregation. This article investigates the possible advantage of a two-dimensional representation of genetic association in order to identify polymorphisms relevant to disease: a measure of evidence of association (the Bayes factor, BF) combined with the estimated contribution to familiality (the attributable sibling relative risk, lambdas). Simulation and data from the North American Rheumatoid Consortium (NARAC) were used to assess the possible benefit under several scenarios. Simulation indicated that the allele frequencies to reach the maximum BF and the maximum attributable lambdas diverged as the size of the genetic effect increased. The representation of BF versus attributable lambdas for selected regions of NARAC data revealed that SNPs involved in replicated associations clearly departed from the bulk of SNPs in these regions. In the 12 investigated regions, and particularly in the low-recombination major histocompatibility region, the ranking of SNPs according to BF differed from the ranking of SNPs according to attributable lambdas. The present results should be generalized using more extensive simulations and additional real data, but they suggest that a characterization of genetic association by both BF and attributable lambdas may result in an improved ranking of variants for further biological analyses.