Advances in next‐generation sequencing (NGS) technologies have enabled physicians to test for genomic alterations in multiple cancer‐related genes at once in daily clinical practice. In April 2015, we introduced clinical sequencing using an NGS‐based multiplex gene assay (OncoPrime) certified by the Clinical Laboratory Improvement Amendment. This assay covers the entire coding regions of 215 genes and the rearrangement of 17 frequently rearranged genes with clinical relevance in human cancers. The principal indications for the assay were cancers of unknown primary site, rare tumors, and any solid tumors that were refractory to standard chemotherapy. A total of 85 patients underwent testing with multiplex gene assay between April 2015 and July 2016. The most common solid tumor types tested were pancreatic (n = 19; 22.4%), followed by biliary tract (n = 14; 16.5%), and tumors of unknown primary site (n = 13; 15.3%). Samples from 80 patients (94.1%) were successfully sequenced. The median turnaround time was 40 days (range, 18–70 days). Potentially actionable mutations were identified in 69 of 80 patients (86.3%) and were most commonly found in TP53 (46.3%), KRAS (23.8%), APC (18.8%), STK11 (7.5%), and ATR (7.5%). Nine patients (13.0%) received a subsequent therapy based on the NGS assay results. Implementation of clinical sequencing using an NGS‐based multiplex gene assay was feasible in the clinical setting and identified potentially actionable mutations in more than 80% of patients. Current challenges are to incorporate this genomic information into better therapeutic decision making.

ObjectivesWe aimed to examine the association between homologous recombination repair (HRR)-related gene mutations and efficacy of oxaliplatin-based chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC).ResultsNon-synonymous mutations in HRR-related genes were found in 13 patients and only one patient had a family history of pancreatic cancer. Eight patients with HRR-related gene mutations (group A) and nine without HRR-related gene mutations (group B) received oxaliplatin-based chemotherapy. Median progression-free survival after initiation of oxaliplatin-based chemotherapy was significantly longer in group A than in group B (20.8 months vs 1.7 months, p = 0.049). Interestingly, two patients with inactivating HRR-related gene mutations who received FOLFIRINOX as first-line treatment showed exceptional responses with respect to progression-free survival for > 24 months.Materials and MethodsComplete coding exons of 12 HRR-related genes (ATM, ATR, BAP1, BRCA1, BRCA2, BLM, CHEK1, CHEK2, FANCA, MRE11A, PALB2, and RAD51) were sequenced using a Clinical Laboratory Improvement Amendment-certified multiplex next-generation sequencing assay. Thirty consecutive PDAC patients who underwent this assay between April 2015 and July 2017 were included.ConclusionsOur results suggest that inactivating HRR-related gene mutations are predictive of response to oxaliplatin-based chemotherapy in patients with PDAC.

Accurate prediction of binding affinities of drug candidates to their targets remains challenging because of protein flexibility in solution. Conformational flexibility of the ATP-binding site in the CDK2 and ERK2 kinases was identified using molecular dynamics simulations. The binding free energy (ΔG) of twenty-four ATP-competitive inhibitors toward these kinases was assessed using an alchemical free energy perturbation method, MP-CAFEE. However, large calculation errors of 2-3 kcal/mol were observed using this method, where the free energy simulation starts from a single equilibrated conformation. Here, we developed a new ΔG computation method, where the starting structure was set to multiconformations to cover flexibility. The calculation accuracy was successfully improved, especially for larger molecular size compounds, leading to reliable prediction of a broader range of drug candidates. The present study demonstrates that conformational flexibility of interactions between a compound and the glycine-rich loop in the kinases is a key factor in ΔG estimation.

Chemical genomics research has revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale. We also address the question of how to select plausible negative noninteraction pairs based on a statistical approach in order to develop reliable prediction models for GPCR-ligand interactions. The key interaction sites estimated by our approach can be of great use not only for screening of active compounds but also for modification of active compounds with the aim of improving activity or selectivity.