Implementation of whole-exome sequencing for pharmacogenomics profiling and exploring its potential clinical utilities.

Whole-exome sequencing (WES) is widely used in clinical settings; however, the exploration of its use in pharmacogenomic analysis remains limited. Our study compared the variant callings for 28 core absorption, distribution, metabolism and elimination genes by WES and array-based technology using clinical trials samples. The results revealed that WES had a positive predictive value of 0.71-0.92 and a sensitivity of single-nucleotide variants between 0.68 and 0.95, compared with array-based technology, for the variants in the commonly targeted regions of the WES and PhamacoScan™ assay. Besides the common variants detected by both assays, WES identified 200-300 exclusive variants per sample, totalling 55 annotated exclusive variants, including important modulators of metabolism such as rs2032582 (ABCB1) and rs72547527 (SULT1A1). This study highlights the potential clinical advantages of using WES to identify a wider range of genetic variations and enabling precision medicine.

First-in-Human Study of the Ataxia Telangiectasia and Rad3-Related (ATR) Inhibitor Tuvusertib (M1774) as Monotherapy in Patients with Solid Tumors.

PURPOSE: Tuvusertib (M1774) is a potent, selective, orally administered ataxia telangiectasia and Rad3-related (ATR) protein kinase inhibitor. This first-in-human study (NCT04170153) evaluated safety, tolerability, maximum tolerated dose (MTD), recommended dose for expansion (RDE), pharmacokinetics (PK), pharmacodynamics (PD), and preliminary efficacy of tuvusertib monotherapy. PATIENTS AND METHODS: Ascending tuvusertib doses were evaluated in 55 patients with metastatic or locally advanced unresectable solid tumors. A safety monitoring committee determined dose escalation based on PK, PD, and safety data guided by a Bayesian 2-parameter logistic regression model. Molecular responses (MR) were assessed in circulating tumor DNA samples. RESULTS: Most common grade ≥3 treatment-emergent adverse events were anemia (36%), neutropenia, and lymphopenia (both 7%). Eleven patients experienced dose-limiting toxicities, most commonly grade 2 (n = 2) or 3 (n = 8) anemia. No persistent effects on blood immune cell populations were observed. The RDE was 180 mg tuvusertib QD (once daily), 2 weeks on/1 week off treatment, which was better tolerated than the MTD (180 mg QD continuously). Tuvusertib median time to peak plasma concentration ranged from 0.5 to 3.5 hours and mean elimination half-life from 1.2 to 5.6 hours. Exposure-related PD analysis suggested maximum target engagement at ≥130 mg tuvusertib QD. Tuvusertib induced frequent MRs in the predicted efficacious dose range; MRs were enriched in patients with radiological disease stabilization, and complete MRs were detected for mutations in ARID1A, ATRX, and DAXX. One patient with platinum- and PARP inhibitor-resistant BRCA wild-type ovarian cancer achieved an unconfirmed RECIST v1.1 partial response. CONCLUSIONS: Tuvusertib demonstrated manageable safety and exposure-related target engagement. Further clinical evaluation of tuvusertib is ongoing.

Mapping combinatorial drug effects to DNA damage response kinase inhibitors.

One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.

Phase 1b study of berzosertib and cisplatin in patients with advanced triple-negative breast cancer.

Platinum derivatives are commonly used for the treatment of patients with metastatic triple-negative breast cancer (TNBC). However, resistance often develops, leading to treatment failure. This expansion cohort (part C2) of the previously reported phase 1b trial (NCT02157792) is based on the recommended phase 2 dose of the combination of the ataxia-telangiectasia and Rad3-related (ATR) inhibitor berzosertib and cisplatin observed in patients with advanced solid tumors, including TNBC. Forty-seven patients aged ≥18 years with advanced TNBC received cisplatin (75 mg/m2; day 1) and berzosertib (140 mg/m2; days 2 and 9), in 21-day cycles. Berzosertib was well tolerated, with a similar toxicity profile to that reported previously for this combination. The overall response rate (90% confidence interval) was 23.4% (13.7, 35.8). No relevant associations were observed between response and gene alterations. Further studies combining ATR inhibitors with platinum compounds may be warranted in highly selected patient populations.

A New Class of Selective ATM Inhibitors as Combination Partners of DNA Double-Strand Break Inducing Cancer Therapies.

Radiotherapy and chemical DNA-damaging agents are among the most widely used classes of cancer therapeutics today. Double-strand breaks (DSB) induced by many of these treatments are lethal to cancer cells if left unrepaired. Ataxia telangiectasia-mutated (ATM) kinase plays a key role in the DNA damage response by driving DSB repair and cell-cycle checkpoints to protect cancer cells. Inhibitors of ATM catalytic activity have been shown to suppress DSB DNA repair, block checkpoint controls and enhance the therapeutic effect of radiotherapy and other DSB-inducing modalities. Here, we describe the pharmacological activities of two highly potent and selective ATM inhibitors from a new chemical class, M3541 and M4076. In biochemical assays, they inhibited ATM kinase activity with a sub-nanomolar potency and showed remarkable selectivity against other protein kinases. In cancer cells, the ATM inhibitors suppressed DSB repair, clonogenic cancer cell growth, and potentiated antitumor activity of ionizing radiation in cancer cell lines. Oral administration of M3541 and M4076 to immunodeficient mice bearing human tumor xenografts with a clinically relevant radiotherapy regimen strongly enhanced the antitumor activity, leading to complete tumor regressions. The efficacy correlated with the inhibition of ATM activity and modulation of its downstream targets in the xenograft tissues. In vitro and in vivo experiments demonstrated strong combination potential with PARP and topoisomerase I inhibitors. M4076 is currently under clinical investigation.

RosettaSX: Reliable gene expression signature scoring of cancer models and patients.

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.

Pharmacologic Inhibitor of DNA-PK, M3814, Potentiates Radiotherapy and Regresses Human Tumors in Mouse Models.

Physical and chemical DNA-damaging agents are used widely in the treatment of cancer. Double-strand break (DSB) lesions in DNA are the most deleterious form of damage and, if left unrepaired, can effectively kill cancer cells. DNA-dependent protein kinase (DNA-PK) is a critical component of nonhomologous end joining (NHEJ), one of the two major pathways for DSB repair. Although DNA-PK has been considered an attractive target for cancer therapy, the development of pharmacologic DNA-PK inhibitors for clinical use has been lagging. Here, we report the discovery and characterization of a potent, selective, and orally bioavailable DNA-PK inhibitor, M3814 (peposertib), and provide in vivo proof of principle for DNA-PK inhibition as a novel approach to combination radiotherapy. M3814 potently inhibits DNA-PK catalytic activity and sensitizes multiple cancer cell lines to ionizing radiation (IR) and DSB-inducing agents. Inhibition of DNA-PK autophosphorylation in cancer cells or xenograft tumors led to an increased number of persistent DSBs. Oral administration of M3814 to two xenograft models of human cancer, using a clinically established 6-week fractionated radiation schedule, strongly potentiated the antitumor activity of IR and led to complete tumor regression at nontoxic doses. Our results strongly support DNA-PK inhibition as a novel approach for the combination radiotherapy of cancer. M3814 is currently under investigation in combination with radiotherapy in clinical trials.

Combining global and local measures for structure-based druggability predictions.

Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.

Analyzing the topology of active sites: on the prediction of pockets and subpockets.

Automated prediction of protein active sites is essential for large-scale protein function prediction, classification, and druggability estimates. In this work, we present DoGSite, a new structure-based method to predict active sites in proteins based on a Difference of Gaussian (DoG) approach which originates from image processing. In contrast to existing methods, DoGSite splits predicted pockets into subpockets, revealing a refined description of the topology of active sites. DoGSite correctly predicts binding pockets for over 92% of the PDBBind and the scPDB data set, being in line with the best-performing methods available. In 63% of the PDBBind data set the detected pockets can be subdivided into smaller subpockets. The cocrystallized ligand is contained in exactly one subpocket in 87% of the predictions. Furthermore, we introduce a more precise prediction performance measure by taking the pairwise ligand and pocket coverage into account. In 90% of the cases DoGSite predicts a pocket that contains at least half of the ligand. In 70% of the cases additionally more than a quarter of the respective pocket itself is covered by the cocrystallized ligand. Consideration of subpockets produces an increase in coverage yielding a success rate of 83% for the latter measure.