Phase 1b/2a study of galunisertib, a small molecule inhibitor of transforming growth factor-beta receptor I, in combination with standard temozolomide-based radiochemotherapy in patients with newly diagnosed malignant glioma.

Purpose Galunisertib, a TGF-ß inhibitor, has demonstrated antitumor effects in preclinical and radiographic responses in some patients with malignant glioma. This Phase 1b/2a trial investigated the clinical benefit of combining galunisertib with temozolomide-based radiochemotherapy (TMZ/RTX) in patients with newly diagnosed malignant glioma (NCT01220271). Methods This is an open-label, 2-arm Phase 1b/2a study (N = 56) of galunisertib (intermittent dosing: 14 days on/14 days off per cycle of 28 days) in combination with TMZ/RTX (n = 40), versus a control arm (TMZ/RTX, n = 16). The primary objective of Phase 1b was to determine the safe and tolerable Phase 2 dose of galunisertib. The primary objective of Phase 2a was to confirm the tolerability and pharmacodynamic profile of galunisertib with TMZ/RTX, and the secondary objectives included determining the efficacy and pharmacokinetic (PK) profile of galunisertib with TMZ/RTX in patients with glioblastoma. This study also characterized the changes in the major T-cell subsets during TMZ/RTX plus galunisertib treatment. Results In the Phase 2a study, efficacy results for patients treated with galunisertib plus TMZ/RTX or TMZ/RTX were: median overall survival (18.2 vs 17.9 months), median progression-free survival (7.6 vs 11.5 months), and disease control rate (80% [32/40] vs 56% [9/16] patients) respectively. PK profile of galunisertib plus TMZ/RTX regimen was consistent with previously published PK data of galunisertib. The overall safety profile across treatment arms was comparable. Conclusion No differences in efficacy, safety or pharmacokinetic variables were observed between the two treatment arms.

Subgroups from regression trees with adjustment for prognostic effects and postselection inference.

Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal-theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored.

Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials.

BACKGROUND: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. METHODS: In this genome-wide analysis we included adults (aged ≥18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. FINDINGS: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G→A (Gly168Ser) in the GLP1R (0·08% [95% CI 0·04-0·12] or 0·9 mmol/mol lower reduction in HbA1c per serine, p=6·0 × 10-5) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6·7 × 10-8), largely driven by rs140226575G→A (Thr370Met; 0·25% [SE 0·06] or 2·7 mmol/mol  [SE 0·7] greater HbA1c reduction per methionine, p=5·2 × 10-6). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA1c than the 9% of the population with the worse response. INTERPRETATION: This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists. FUNDING: Innovative Medicines Initiative and the Wellcome Trust.

Correction: Biomarkers and overall survival in patients with advanced hepatocellular carcinoma treated with TGF-ßRI inhibitor galunisertib.

[This corrects the article DOI: 10.1371/journal.pone.0222259.].

Biomarkers and overall survival in patients with advanced hepatocellular carcinoma treated with TGF-ßRI inhibitor galunisertib.

BACKGROUND: Transforming growth factor beta (TGF-ß) signalling is involved in the development of hepatocellular carcinoma (HCC). We followed changes in biomarkers during treatment of patients with HCC with the TGF-ßRI/ALK5 inhibitor galunisertib. METHODS: This phase 2 study (NCT01246986) enrolled second-line patients with advanced HCC into one of two cohorts of baseline serum alpha-fetoprotein (AFP): Part A (AFP ≥1.5x ULN) or Part B (AFP <1.5x ULN). Baseline and postbaseline levels of AFP, TGF-ß1, E-cadherin, selected miRNAs, and other plasma proteins were monitored. RESULTS: The study enrolled 149 patients (Part A, 109; Part B, 40). Median OS was 7.3 months in Part A and 16.8 months in Part B. Baseline AFP, TGF-ß1, E-cadherin, and an additional 16 plasma proteins (such as M-CSF, IL-6, ErbB3, ANG-2, neuropilin-1, MIP-3 alpha, KIM-1, uPA, IL-8, TIMP-1, ICAM-1, Apo A-1, CA-125, osteopontin, tetranectin, and IGFBP-1) were found to correlate with OS. In addition, a range of miRs were found to be associated with OS. In AFP responders (21% of patients in Part A with decrease of >20% from baseline) versus non-responders, median OS was 21.5 months versus 6.8 months (p = 0.0015). In TGF-ß1 responders (51% of all patients) versus non-responders, median OS was 11.2 months versus 5.3 months (p = 0.0036). CONCLUSIONS: Consistent with previous findings, both baseline levels and changes from baseline of circulating AFP and TGF-ß1 function as prognostic indicators of survival. Future trials are needed to confirm and extend these results.

TGFß receptor inhibitor galunisertib is linked to inflammation- and remodeling-related proteins in patients with pancreatic cancer.

PURPOSE: Galunisertib, the first small molecule transforming growth factor beta (TGFß) receptor inhibitor, plus gemcitabine resulted in the improvement of survival in patients with unresectable pancreatic cancer, but markers to identify patients likely to respond are lacking. METHODS: In the Phase 1b/2 JBAJ study, 156 patients were randomized 2:1 to galunisertib + gemcitabine (N = 104) or placebo + gemcitabine (N = 52). Clinical outcome data were integrated with baseline markers and pharmacodynamic markers while patients were on treatment, including circulating proteins using a multi-analyte panel, T cell subset evaluation, and miRNA profiling. RESULTS: Baseline biomarkers associated with overall prognosis regardless of treatment included CA19-9 and TGF-ß1. In addition, IP-10, FSH, MIP-1α, and PAI-1 were potential predictive proteins. Baseline proteins that were changed during treatment included amphiregulin, CA15-3, cathepsin D, P-selectin, RAGE, sortilin, COMP, eotaxin-2, N-BNP, osteopontin, and thrombospondin-4. Plasma miRNA with potential prognostic value included miR-21-5p, miR-301a-3p, miR-210-3p, and miR-141-3p, while those with potential predictive value included miR-424-5p, miR-483-3p, and miR-10b-5p. CONCLUSIONS: Galunisertib + gemcitabine resulted in improvement of overall survival, and 4 proteins (IP-10, FSH, MIP-1α, PAI-1) were potentially predictive for this combination treatment. Future studies should also include baseline evaluation of miR-424-5p, miR-483-3p, and miR-10b-5p. TRIAL REGISTRATION: Clinicaltrials.gov NCT01373164.

Analytical Characterization of an Enzyme-Linked Immunosorbent Assay for the Measurement of Transforming Growth Factor ß1 in Human Plasma.

BACKGROUND: The transforming growth factor ß (TGF-ß)-signaling pathway has emerged as a promising therapeutic target for many disease states including hepatocellular carcinoma (HCC). Because of the pleiotropic effects of this pathway, patient selection and monitoring may be important. TGF-ß1 is the most prevalent isoform, and an assay to measure plasma levels of TGF-ß1 would provide a rational biomarker to assist with patient selection. Therefore, the objective of this study was to analytically validate a colorimetric ELISA for the quantification of TGF-ß1 in human plasma. METHODS: A colorimetric sandwich ELISA for TGF-ß1 was analytically validated per Clinical and Laboratory Standards Institute protocols by assessment of precision, linearity, interfering substances, and stability. A reference range for plasma TGF-ß1 was established for apparently healthy individuals and potential applicability was demonstrated in HCC patients. RESULTS: Precision was assessed for samples ranging from 633 to 10822 pg/mL, with total variance ranging from 28.4% to 7.2%. The assay was linear across the entire measuring range, and no interference of common blood components or similar molecules was observed. For apparently healthy individuals, the average TGF-ß1 level was 1985 ± 1488 pg/mL compared to 4243 ± 2003 pg/mL for HCC patients. Additionally, the TGF-ß1 level in plasma samples was demonstrated to be stable across all conditions tested, including multiple freeze-thaw cycles. CONCLUSIONS: The ELISA described in this report is suitable for the quantification of TGF-ß1 in human plasma and for investigational use in an approved clinical study.

Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data.

Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW.

Adaptive clustering and adaptive weighting methods to detect disease associated rare variants.

Current statistical methods to test association between rare variants and phenotypes are essentially the group-wise methods that collapse or aggregate all variants in a predefined group into a single variant. Comparing with the variant-by-variant methods, the group-wise methods have their advantages. However, two factors may affect the power of these methods. One is that some of the causal variants may be protective. When both risk and protective variants are presented, it will lose power by collapsing or aggregating all variants because the effects of risk and protective variants will counteract each other. The other is that not all variants in the group are causal; rather, a large proportion is believed to be neutral. When a large proportion of variants are neutral, collapsing or aggregating all variants may not be an optimal solution. We propose two alternative methods, adaptive clustering (AC) method and adaptive weighting (AW) method, aiming to test rare variant association in the presence of neutral and/or protective variants. Both of AC and AW are applicable to quantitative traits as well as qualitative traits. Results of extensive simulation studies show that AC and AW have similar power and both of them have clear advantages from power to computational efficiency comparing with existing group-wise methods and existing data-driven methods that allow neutral and protective variants. We recommend AW method because AW method is computationally more efficient than AC method.