Citations

312 Background: Silmitasertib (CX-4945), an oral small molecule inhibitor of casein kinase 2 (CK2), has exhibited preclinical antitumor activity and strong synergism with gemcitabine + cisplatin. We investigated the safety and efficacy of silmitasertib in combination with gemcitabine + cisplatin in patients with unresectable cholangiocarcinoma (CCA). Methods: S4-13-001 is a multicenter, open-label, phase Ib/II study of silmitasertib in combination with gemcitabine + cisplatin in patients with locally advanced or metastatic CCA. The phase Ib portion included dose-escalation, expansion, and exploratory cohorts of silmitasertib with doses ranging from 200 to 1000 mg bid (6 days for the escalation/expansion cohorts and 10 and 21 days’ continuous dosing for the exploratory cohorts). In the phase II portion patients received silmitasertib 1000 mg bid for 10 days in combination with gemcitabine + cisplatin on days 1 & 8 over a 21-day cycle. In this interim analysis, we present findings from the combined population of patients from the phase Ib and II portions of the study. Response to treatment was assessed by RECIST v1.1 every 6 weeks. Primary efficacy outcome measure was progression-free survival (PFS). ClinicalTrials.gov (NCT02128282). Results: A total of 87 patients were enrolled and received silmitasertib in the phase Ib (n=50) and phase II (n=37) portions of the study. Of these, 55 patients were evaluable for efficacy with details as follows: median PFS 11.1 (95% CI 7.6–14.7) months; median overall survival (OS) 17.4 (95% CI 13.4–25.7) months; overall response rate (ORR) 32.1%; and disease control rate (DCR) 79.3%. Almost all patients (79/87; 90.8%) evaluable for safety reported ≥1 treatment-related adverse event (TEAE). The most common TEAEs (all grades) with silmitasertib were diarrhea (65.5%), nausea (50.6%), vomiting (33.3%), fatigue (31.0%), and anemia (21.8%). The most common grade ≥3 TEAEs were diarrhea (13.8%), neutropenia (11.5%), nausea (9.2%), anemia (8.0%), and thrombocytopenia (8.0%). Eleven patients (12.6%) discontinued treatment due to TEAEs. Conclusions: Silmitasertib in combination with gemcitabine + cisplatin yields promising preliminary evidence of efficacy in patients with locally advanced or metastatic CCA. Based on these data a randomized phase III trial is planned. Clinical trial information: NCT02128282.

Abstract Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

Abstract To identify the genetic factors that influence overall survival in never smokers who have non–small cell lung carcinoma (NSCLC), we conducted a consistency meta-analysis study using genome-wide association approaches for overall survival in 327 never smoker patients with NSCLC from The University of Texas MD Anderson Cancer Center (Houston, TX) and 293 cases from the Mayo Clinic (Rochester, MN). We then conducted a two-pronged validation of the top 25 variants that included additional validation in 1,256 patients with NSCLC from Taiwan and assessment of expression quantitative trait loci (eQTL) and differential expression of genes surrounding the top loci in 70 tumors and matched normal tissues. A total of 94 loci were significant for overall survival in both MD Anderson and Mayo studies in the consistency meta-analysis phase, with the top 25 variants reaching a P value of 10−6. Two variants of these 25 were also significant in the Taiwanese population: rs6901416 [HR, 1.44; 95% confidence interval (CI), 1.01–2.06] and rs10766739 (HR, 1.23; 95%CI, 1.00–1.51). These loci resulted in a reduction of median survival time of at least eight and five months in three populations, respectively. An additional six variants (rs4237904, rs7976914, rs4970833, rs954785, rs485411, and rs10906104) were validated through eQTL analysis that identified significant correlations with expression levels of six genes (LEMD3, TMBIM, ATXN7L2, SHE, ITIH2, and NUDT5, respectively) in normal lung tissue. These genes were also significantly differentially expressed between the tumor and normal lung tissue. These findings identify several novel, candidate prognostic markers for NSCLC in never smokers, with eQTL analysis suggesting a potential biologic mechanism for a subset of these observed associations. Cancer Res; 73(13); 4028–38. ©2013 AACR.