Age-related remodelling of oesophageal epithelia by mutated cancer drivers.

Clonal expansion in aged normal tissues has been implicated in the development of cancer. However, the chronology and risk dependence of the expansion are poorly understood. Here we intensively sequence 682 micro-scale oesophageal samples and show, in physiologically normal oesophageal epithelia, the progressive age-related expansion of clones that carry mutations in driver genes (predominantly NOTCH1), which is substantially accelerated by alcohol consumption and by smoking. Driver-mutated clones emerge multifocally from early childhood and increase their number and size with ageing, and ultimately replace almost the entire oesophageal epithelium in the extremely elderly. Compared with mutations in oesophageal cancer, there is a marked overrepresentation of NOTCH1 and PPM1D mutations in physiologically normal oesophageal epithelia; these mutations can be acquired before late adolescence (as early as early infancy) and significantly increase in number with heavy smoking and drinking. The remodelling of the oesophageal epithelium by driver-mutated clones is an inevitable consequence of normal ageing, which-depending on lifestyle risks-may affect cancer development.

Population-centered medical method: A population-centered medical model for public health medicine.

OBJECTIVES: Public health physicians (PHPs) are trained in both medicine and public health, yet practice models in each of these fields incompletely describe their work. A model of practice for public health physicians would better enable training and professional development in the specialty. The objective of this study was to develop an empirically grounded method of the practice of public health medicine by public health physicians. STUDY DESIGN: This was designed as a constructivist grounded theory (CGT) study. Semistructured interviews with 18 public health physicians in Canada were conducted over the course of 1 year. METHODS: Transcribed interviews were coded in three stages (line-by-line, focused, and theoretical). Constant comparison, theoretical sampling, reflective and analytic memos, and team discussion on reflexivity were used to ensure rigor and the proper application of CGT methods. RESULTS: The key finding of this study is the population-centered medical method (POP-CMM), an empirically grounded method of PHP practice. In this model, PHPs bring values, knowledge, and stances to their practice of medicine with populations as patients. They work to diagnose and intervene on public health issues, with a focus on prevention and systems. Essential to this work is knowledge sharing and relationship building between physicians and populations. CONCLUSIONS: POP-CMM represents a method of practice for PHPs. Further exploration of this method in other countries and systems would bring insight into PHP practice globally. The model has important connections to the practice of medicine and presents the possibility of developing a general model of physician practice for a range of patients, from individuals to populations.

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning's ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery.

Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks.

Previous studies have reported genome-wide mutation profile analyses in ovarian clear cell carcinomas (OCCCs). This study aims to identify specific novel molecular alterations by combined analyses of somatic mutation and copy number variation. We performed whole exome sequencing of 39 OCCC samples with 16 matching blood tissue samples. Four hundred twenty-six genes had recurrent somatic mutations. Among the 39 samples, ARID1A (62%) and PIK3CA (51%) were frequently mutated, as were genes such as KRAS (10%), PPP2R1A (10%), and PTEN (5%), that have been reported in previous OCCC studies. We also detected mutations in MLL3 (15%), ARID1B (10%), and PIK3R1 (8%), which are associations not previously reported. Gene interaction analysis and functional assessment revealed that mutated genes were clustered into groups pertaining to chromatin remodeling, cell proliferation, DNA repair and cell cycle checkpointing, and cytoskeletal organization. Copy number variation analysis identified frequent amplification in chr8q (64%), chr20q (54%), and chr17q (46%) loci as well as deletion in chr19p (41%), chr13q (28%), chr9q (21%), and chr18q (21%) loci. Integration of the analyses uncovered that frequently mutated or amplified/deleted genes were involved in the KRAS/phosphatidylinositol 3-kinase (82%) and MYC/retinoblastoma (75%) pathways as well as the critical chromatin remodeling complex switch/sucrose nonfermentable (85%). The individual and integrated analyses contribute details about the OCCC genomic landscape, which could lead to enhanced diagnostics and therapeutic options.

Association between UGT1A1*28*28 genotype and lung cancer in the Japanese population.

BACKGROUND: Lung cancer is the leading cause of cancer death and is closely linked to tobacco smoking. Genetic polymorphisms in genes that encode enzymes involved in metabolizing tobacco carcinogens could affect an individual's risk for lung cancer. While polymorphism of UDP-glucuronosyltransferase1A1 (UGT1A1) is involved in detoxification of benzo(a)pyrene-7,8-dihydrodiol(-), a major tobacco carcinogen, the association between UGT1A1 genotype and lung cancer has not been examined. METHODS: We retrieved the clinical data of 5,285 patients who underwent systemic chemotherapy at Kyoto University Hospital. A total of 765 patients (194 lung cancer patients and 671 patients with other malignancies) with UGT1A1 genotyping data were included in this analysis. We used logistic regression with recessive, dominant, and additive models to identify differences in genotype frequencies between lung cancer and other malignancies. RESULTS: In the recessive model, UGT1A1*28*28 genotype was significantly associated with lung cancer compared to other malignancies (odds ratio 5.3, P = 0.0083). Among lung cancer patients with a smoking history, squamous cell carcinoma was significantly predominant in patients with UGT1A1*28*28 compared to those with other UGT1A1 genotypes (P = 0.024). CONCLUSION: This is the first study to demonstrate a significant association between the homozygous UGT1A1*28 genotype and lung cancer.

Prediction of taxane and platinum sensitivity in ovarian cancer based on gene expression profiles.

OBJECTIVE: Prognoses of ovarian cancer (OC) have improved with the paclitaxel-carboplatin regimen. However, it remains unclear which cases exhibit a genuine benefit from taxane or from platinum. We aimed to predict taxane and platinum sensitivity in OC via gene expression. METHODS: We identified differentially expressed genes in responsive and resistant cases from advanced OC biopsy expression dataset GSE15622, containing responses to paclitaxel or carboplatin monotherapy. These genes generated a scoring system for prediction of drug response by applying single-sample gene set enrichment analysis. Discriminative metrics termed the T-score and C-score were derived. RESULTS: High C-score levels were significant in responders compared to non-responders in a separate cisplatin treatment dataset (GSE18864, p=0.043). High C-score groups also had significantly better progression-free survival in three OC datasets (The Cancer Genome Atlas, TCGA: p=0.02; GSE9891: p=0.03; GSE30161: p=0.001). In two additional datasets of advanced OC, high T-scores could associate taxane and platinum regimens with better survival than non-taxane and platinum regimens (GSE9891: p<0.0001; GSE3149: p=0.045), whereas in cases with low T-scores, different chemotherapeutic regimens did not result in a significant difference. Assessing TCGA and GSE9891, T-scores were elevated in the C1/Mesenchymal subtype, whereas C-scores were elevated in the C5/Proliferative subtype and were lower in the C1/Mesenchymal subtype (p<0.0001, respectively). C- and T-scores negatively correlated with each other, suggesting complementary roles of taxane and platinum. CONCLUSIONS: Our proposal and finding of a scoring system that could predict platinum or taxane response could be useful to develop individualized treatments to ovarian cancer.

Reliable pre-eclampsia pathways based on multiple independent microarray data sets.

Pre-eclampsia is a multifactorial disorder characterized by heterogeneous clinical manifestations. Gene expression profiling of preeclamptic placenta have provided different and even opposite results, partly due to data compromised by various experimental artefacts. Here we aimed to identify reliable pre-eclampsia-specific pathways using multiple independent microarray data sets. Gene expression data of control and preeclamptic placentas were obtained from Gene Expression Omnibus. Single-sample gene-set enrichment analysis was performed to generate gene-set activation scores of 9707 pathways obtained from the Molecular Signatures Database. Candidate pathways were identified by t-test-based screening using data sets, GSE10588, GSE14722 and GSE25906. Additionally, recursive feature elimination was applied to arrive at a further reduced set of pathways. To assess the validity of the pre-eclampsia pathways, a statistically-validated protocol was executed using five data sets including two independent other validation data sets, GSE30186, GSE44711. Quantitative real-time PCR was performed for genes in a panel of potential pre-eclampsia pathways using placentas of 20 women with normal or severe preeclamptic singleton pregnancies (n = 10, respectively). A panel of ten pathways were found to discriminate women with pre-eclampsia from controls with high accuracy. Among these were pathways not previously associated with pre-eclampsia, such as the GABA receptor pathway, as well as pathways that have already been linked to pre-eclampsia, such as the glutathione and CDKN1C pathways. mRNA expression of GABRA3 (GABA receptor pathway), GCLC and GCLM (glutathione metabolic pathway), and CDKN1C was significantly reduced in the preeclamptic placentas. In conclusion, ten accurate and reliable pre-eclampsia pathways were identified based on multiple independent microarray data sets. A pathway-based classification may be a worthwhile approach to elucidate the pathogenesis of pre-eclampsia.

Antipsychotics-associated serious adverse events in children: an analysis of the FAERS database.

OBJECTIVE: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess serious adverse events induced by the administration of antipsychotics to children. METHODS: Following pre-processing of FAERS data by elimination of duplicated records as well as adjustments to standardize drug names, reports involving haloperidol, olanzapine, quetiapine, clozapine, ziprasidone, risperidone, and aripiprazole were analyzed in children (age 0-12). Signals in the data that signified a drug-associated adverse event were detected via quantitative data mining algorithms. The algorithms applied to this study include the empirical Bayes geometric mean, the reporting odds ratio, the proportional reporting ratio, and the information component of a Bayesian confidence propagation neural network. Neuroleptic malignant syndrome (NMS), QT prolongation, leukopenia, and suicide attempt were focused on as serious adverse events. RESULTS: In regard to NMS, the signal scores for haloperidol and aripiprazole were greater than for other antipsychotics. Significant signals of the QT prolongation adverse event were detected only for ziprasidone and risperidone. With respect to leukopenia, the association with clozapine was noteworthy. In the case of suicide attempt, signals for haloperidol, olanzapine, quetiapine, risperidone, and aripiprazole were detected. CONCLUSIONS: It was suggested that there is a level of diversity in the strength of the association between various first- and second-generation antipsychotics with associated serious adverse events, which possibly lead to fatal outcomes. We recommend that research be continued in order to gather a large variety and quantity of related information, and that both available and newly reported data be placed in the context of multiple medical viewpoints in order to lead to improved levels of care.

A framework for developing rural academic general practices: a qualitative case study in rural Victoria.

INTRODUCTION: There is increasing pressure for Australian rural general practices to engage in educational delivery as a means of addressing workforce issues and accommodating substantial increases in learners. For practices that have now developed a strong focus on education, there is the challenge to complement this by engaging in research activity. This study develops a rural academic general practice framework to assist rural practices in developing both comprehensive educational activity and a strong research focus thus moving towards functioning as mature academic units. METHODS: A case study research design was used with the unit of analysis at the level of the rural general practice. Purposively sampled practices were recruited and individual interviews conducted with staff (supervisors, practice managers, nurses), learners (medical students, interns and registrars) and patients. Three practices hosted 'multi-level learners', two practices hosted one learner group and one had no learners. Forty-four individual interviews were conducted with staff, learners and patients. Audio recordings were transcribed for thematic analysis. After initial inductive coding, deductive analysis was undertaken with reference to recent literature and the expertise of the research team resulting in the rural academic general practice framework. RESULTS: Three key themes emerged with embedded subthemes. For the first theme, organisational considerations, subthemes were values/vision/culture, patient population and clinical services, staffing, physical infrastructure/equipment, funding streams and governance. For the second theme, educational considerations, subthemes were processes, clinical supervision, educational networks and learner presence. Third, for research considerations, there were the subthemes of attitude to research and research activity. The framework maps the development of a rural academic practice across these themes in four progressive stages: beginning, emerging, consolidating and established. CONCLUSIONS: The data enabled a framework to be constructed to map rural general practice activity with respect to activity characteristic of an academic general practice. The framework offers guidance to practices seeking to transition towards becoming a mature academic practice. The framework also offers guidance to educational institutions and funding bodies to support the development of academic activity in rural general practices. The strengths and limitations of the study design are outlined.

Computational chemogenomics: is it more than inductive transfer?

High-throughput assays challenge us to extract knowledge from multi-ligand, multi-target activity data. In QSAR, weights are statically fitted to each ligand descriptor with respect to a single endpoint or target. However, computational chemogenomics (CG) has demonstrated benefits of learning from entire grids of data at once, rather than building target-specific QSARs. A possible reason for this is the emergence of inductive knowledge transfer (IT) between targets, providing statistical robustness to the model, with no assumption about the structure of the targets. Relevant protein descriptors in CG should allow one to learn how to dynamically adjust ligand attribute weights with respect to protein structure. Hence, models built through explicit learning (EL) by including protein information, while benefitting from IT enhancement, should provide additional predictive capability, notably for protein deorphanization. This interplay between IT and EL in CG modeling is not sufficiently studied. While IT is likely to occur irrespective of the injected target information, it is not clear whether and when boosting due to EL may occur. EL is only possible if protein description is appropriate to the target set under investigation. The key issue here is the search for evidence of genuine EL exceeding expectations based on pure IT. We explore the problem in the context of Support Vector Regression, using more than 9,400 pKi values of 31 GPCRs, where compound-protein interactions are represented by the concatenation of vectorial descriptions of compounds and proteins. This provides a unified framework to generate both IT-enhanced and potentially EL-enabled models, where the difference is toggled by supplied protein information. For EL-enabled models, protein information includes genuine protein descriptors such as typical sequence-based terms, but also the experimentally determined affinity cross-correlation fingerprints. These latter benchmark the expected behavior of a quasi-ideal descriptor capturing the actual functional protein-protein relatedness, and therefore thought to be the most likely to enable EL. EL- and IT-based methods were benchmarked alongside classical QSAR, with respect to cross-validation and deorphanization challenges. A rational method for projecting benchmarked methodologies into a strategy space is given, in the aims that the projection will provide directions for the types of molecule designs possible using a given methodology. While EL-enabled strategies outperform classical QSARs and favorably compare to similar published results, they are, in all respects evaluated herein, not strongly distinguished from IT-enhanced models. Moreover, EL-enabled strategies failed to prove superior in deorphanization challenges. Therefore, this paper raises caution that, contrary to common belief and intuitive expectation, the benefits of chemogenomics models over classical QSAR are quite possibly due less to the injection of protein-related information, and rather impacted more by the effect of inductive transfer, due to simultaneous learning from all of the modeled endpoints. These results show that the field of protein descriptor research needs further improvements to truly realize the expected benefit of EL.

Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants.

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.

pyPept: a python library to generate atomistic 2D and 3D representations of peptides.

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept .

Adverse event profiles of 5-fluorouracil and capecitabine: data mining of the public version of the FDA Adverse Event Reporting System, AERS, and reproducibility of clinical observations.

OBJECTIVE: The safety profiles of oral fluoropyrimidines were compared with 5-fluorouracil (5-FU) using adverse event reports (AERs) submitted to the Adverse Event Reporting System, AERS, of the US Food and Drug Administration (FDA). METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving 5-FU and oral fluoropyrimidines were analyzed. Standardized official pharmacovigilance tools were used for the quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. RESULTS: Based on 22,017,956 co-occurrences, i.e., drug-adverse event pairs, found in 1,644,220 AERs from 2004 to 2009, it was suggested that leukopenia, neutropenia, and thrombocytopenia were more frequently accompanied by the use of 5-FU than capecitabine, whereas diarrhea, nausea, vomiting, and hand-foot syndrome were more frequently associated with capecitabine. The total number of co-occurrences was not large enough to compare tegafur, tegafur-uracil (UFT), tegafur-gimeracil-oteracil potassium (S-1), or doxifluridine to 5-FU. CONCLUSION: The results obtained herein were consistent with clinical observations, suggesting the usefulness of the FDA's AERS database and data mining methods used, but the number of co-occurrences is an important factor in signal detection.

ADMET Predictability at Boehringer Ingelheim: State-of-the-Art, and Do Bigger Datasets or Algorithms Make a Difference?

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an in-house ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorly-suited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

Utility of Homologous Recombination Deficiency Biomarkers Across Cancer Types.

PURPOSE: Homologous recombination DNA repair deficiency (HRD) is associated with sensitivity to platinum and poly (ADP-ribose) polymerase inhibitors in certain cancer types, including breast, ovarian, pancreatic, and prostate. In these cancers, BRCA1/2 alterations and genomic scar signatures are useful indicators for assessing HRD. However, alterations in other homologous recombination repair (HRR)-related genes and their clinical significance in other cancer types have not been adequately and systematically investigated. METHODS: We obtained data sets of all solid tumors in The Cancer Genome Atlas and comprehensively analyzed HRR pathway gene alterations, their loss-of-heterozygosity status, per-sample genomic scar scores, ie, the HRD score and mutational signature 3 ratio, DNA methylation profiles, gene expression profiles, somatic TP53 mutations, sex, and clinical information including chemotherapeutic regimens. RESULTS: Biallelic alterations in HRR genes other than BRCA1/2 were also associated with elevated genomic scar scores. The association between HRR-related gene alterations and genomic scar scores differed significantly by sex and the presence of somatic TP53 mutations. HRD cases determined by a combination of these indices also showed HRD features in gene expression analysis and were associated with better survival when treated with DNA-damaging agents. CONCLUSION: This study provides evidence for the usefulness of HRD analysis in all cancer types, improves chemotherapy decision making and its efficacy in clinical settings, and represents a substantial advancement in precision oncology.

Mutation burden-orthogonal tumor genomic subtypes delineate responses to immune checkpoint therapy.

BACKGROUND: In cancer therapy, higher-resolution tumor-agnostic biomarkers that predict response to immune checkpoint inhibitor (ICI) therapy are needed. Mutation signatures reflect underlying oncogenic processes that can affect tumor immunogenicity, and thus potentially delineate ICI treatment response among tumor types. METHODS: Based on mutational signature analysis, we developed a stratification for all solid tumors in The Cancer Genome Atlas (TCGA). Subsequently, we developed a new software (Genomic Subtyping and Predictive Response Analysis for Cancer Tumor ICi Efficacy, GS-PRACTICE) to classify new tumors submitted to whole-exome sequencing. Using existing data from 973 pan-cancer ICI-treated cases with outcomes, we evaluated the subtype-response predictive performance. RESULTS: Systematic analysis on TCGA samples identified eight tumor genomic subtypes, which were characterized by features represented by smoking exposure, ultraviolet light exposure, APOBEC enzyme activity, POLE mutation, mismatch repair deficiency, homologous recombination deficiency, genomic stability, and aging. The former five subtypes were presumed to form an immune-responsive group acting as candidates for ICI therapy because of their high expression of immune-related genes and enrichment in cancer types with FDA approval for ICI monotherapy. In the validation cohort, the samples assigned by GS-PRACTICE to the immune-reactive subtypes were significantly associated with ICI response independent of cancer type and TMB high or low status. CONCLUSIONS: The new tumor subtyping method can serve as a tumor-agnostic biomarker for ICI response prediction and will improve decision making in cancer treatment.

Comprehensive genomics in androgen receptor-dependent castration-resistant prostate cancer identifies an adaptation pathway mediated by opioid receptor kappa 1.

Castration resistance is a lethal form of treatment failure of prostate cancer (PCa) and is associated with ligand-independent activation of the androgen receptor (AR). It is only partially understood how the AR mediates survival and castration-resistant growth of PCa upon androgen deprivation. We investigated integrative genomics using a patient-derived xenograft model recapitulating acquired, AR-dependent castration-resistant PCa (CRPC). Sequencing of chromatin immunoprecipitation using an anti-AR antibody (AR-ChIP seq) revealed distinct profiles of AR binding site (ARBS) in androgen-dependent and castration-resistant xenograft tumors compared with those previously reported based on human PCa cells or tumor tissues. An integrative genetic analysis identified several AR-target genes associated with CRPC progression including OPRK1, which harbors ARBS and was upregulated upon androgen deprivation. Loss of function of OPRK1 retarded the acquisition of castration resistance and inhibited castration-resistant growth of PCa both in vitro and in vivo. Immunohistochemical analysis showed that expression of OPRK1, a G protein-coupled receptor, was upregulated in human prostate cancer tissues after preoperative androgen derivation or CRPC progression. These data suggest that OPRK1 is involved in post-castration survival and cellular adaptation process toward castration-resistant progression of PCa, accelerating the clinical implementation of ORPK1-targeting therapy in the management of this lethal disease.

[Splice sites are overrepresented in Pasilla binding motif clusters in D. melanogaster genes].

For RNA-binding protein Pasilla, which has been shown to play a role in alternative splicing regulation, binding sites and clusters of binding sites are found in silico in the whole genome of D. melanogaster. The current study analyzes the occurrence of splice sites in binding site clusters. Several hundred thousand binding site motifs and thousands of significant motif clusters were identified. It was discovered that exon-intron borders in D. melanogaster genes are reliably found within Pasilla binding motif clusters, with a higher frequency than could be otherwise expected based on a random model. Additionally, donor splice sites are found in Pasilla clusters twice as often as acceptor sites. This phenomena is observed both for exons annotated as alternatively spliced and for exons annotated as constitutive. These observations support the hypothesis that Pasilla plays a functional role in splicing regulation of D. melanogaster.

Utility of Homologous Recombination Deficiency Biomarkers Across Cancer Types.

Homologous recombination DNA repair deficiency (HRD) is associated with sensitivity to platinum and poly (ADP-ribose) polymerase inhibitors in certain cancer types, including breast, ovarian, pancreatic, and prostate. In these cancers, BRCA1/2 alterations and genomic scar signatures are useful indicators for assessing HRD. However, alterations in other homologous recombination repair (HRR)-related genes and their clinical significance in other cancer types have not been adequately and systematically investigated. METHODS: We obtained data sets of all solid tumors in The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, and comprehensively analyzed HRR pathway gene alterations, their loss-of-heterozygosity status, and per-sample genomic scar scores, that is, the HRD score and mutational signature 3 ratio, DNA methylation profiles, gene expression profiles, somatic TP53 mutations, sex, and clinical or in vitro response to chemical exposure. RESULTS: Biallelic alterations in HRR genes other than BRCA1/2 were also associated with elevated genomic scar scores. The association between HRR-related gene alterations and genomic scar scores differed significantly by sex and the presence of somatic TP53 mutations. HRD tumors determined by a combination of indices also showed HRD features in gene expression analysis and exhibited significantly higher sensitivity to DNA-damaging agents than non-HRD cases in both clinical samples and cell lines. CONCLUSION: This study provides evidence for the usefulness of HRD analysis in all cancer types, improves chemotherapy decision making and its efficacy in clinical settings, and represents a substantial advancement in precision oncology.A comprehensive pan-cancer analysis on the clinical significance of homologous recombination deficiency.

Combination of gene set signatures correlates with response to nivolumab in platinum-resistant ovarian cancer.

Based on our previous phase II clinical trial of anti-programmed death-1 (PD-1) antibody nivolumab for platinum-resistant ovarian cancer (n = 19, UMIN000005714), we aimed to identify the biomarkers predictive of response. Tumor gene expression was evaluated by proliferative, mesenchymal, differentiated, and immunoreactive gene signatures derived from high-grade serous carcinomas and a signature established prior for ovarian clear cell carcinoma. Resulting signature scores were statistically assessed with both univariate and multivariate approaches for correlation to clinical response. Analyses were performed to identify pathways differentially expressed by either the complete response (CR) or progressive disease (PD) patient groups. The clear cell gene signature was scored significantly higher in the CR group, and the proliferative gene signature had significantly higher scores in the PD group where nivolumab was not effective (respective p values 0.005 and 0.026). Combinations of gene signatures improved correlation with response, where a visual projection of immunoreactive, proliferative, and clear cell signatures differentiated clinical response. An applicable clinical response prediction formula was derived. Ovarian cancer-specific gene signatures and related pathway scores provide a robust preliminary indicator for ovarian cancer patients prior to anti-PD-1 therapy decisions.