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.

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 .

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.

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.

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.

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.

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.

Figla promotes secondary follicle growth in mature mice.

The in vitro growth (IVG) of human follicles is a potential fertility option for women for whom cryopreserved ovarian tissues cannot be transplanted due to the risk of cancer cell reintroduction; however, there is currently no established method. Furthermore, optimal IVG conditions may differ between the follicles of adult and pre-pubertal females due to molecular differences suggested by basic research. To systematically identify differences between the secondary follicles of adult and pre-pubertal females, a comparative transcriptomic study using mice was conducted herein. Among differentially expressed genes (DEGs), Figla was up-regulated in mature mice. We successfully down-regulated Figla expression in secondary follicle oocytes by a Figla siRNA microinjection, and the subsequent IVG of follicles showed that the diameter of these follicles was smaller than those of controls in mature mice, whereas no significant difference was observed in premature mice. The canonical pathways of DEGs between control and Figla-reduced secondary follicles suggest that Figla up-regulates VDR/RXR activation and down-regulates stem cell pluripotency as well as estrogen signaling. We demonstrated for the first time that folliculogenesis of the secondary follicles of premature and mature mice may be regulated by different factors, such as Figla with its possible target genes, providing insights into optimal IVG conditions for adult and pre-pubertal females, respectively.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Identification of Qk as a Glial Precursor Cell Marker that Governs the Fate Specification of Neural Stem Cells to a Glial Cell Lineage.

During brain development, neural stem cells (NSCs) initially produce neurons and change their fate to generate glias. While the regulation of neurogenesis is well characterized, specific markers for glial precursor cells (GPCs) and the master regulators for gliogenesis remain unidentified. Accumulating evidence suggests that RNA-binding proteins (RBPs) have significant roles in neuronal development and function, as they comprehensively regulate the expression of target genes in a cell-type-specific manner. We systematically investigated the expression profiles of 1,436 murine RBPs in the developing mouse brain and identified quaking (Qk) as a marker of the putative GPC population. Functional analysis of the NSC-specific Qk-null mutant mouse revealed the key role of Qk in astrocyte and oligodendrocyte generation and differentiation from NSCs. Mechanistically, Qk upregulates gliogenic genes via quaking response elements in their 3' untranslated regions. These results provide crucial directions for identifying GPCs and deciphering the regulatory mechanisms of gliogenesis from NSCs.

Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines.

The NCI-60 cancer cell line screening panel has provided insights for development of subtype-specific chemical therapies and repurposing. By extracting chemical structure and cytotoxicity patterns, virtual screening potentially complements the availability of high-throughput assay platforms and improves bioactive compound discovery rates by computational prefiltering of candidate compound libraries. Many groups report high prediction performances in computational models of NCI-60 data when using cross-validation or similar techniques, yet prospective therapy development in novel cancers may have little to no such data and further may not have the resources to perform hit identification using large compound libraries. In contrast to bulk screening and analysis, the active learning methodology has demonstrated how to identify compounds for screening in small batches and update computational models iteratively, leading to predictive models with a minimum number of compounds, and importantly clarifying data volumes at which limits in predictive ability are achieved. Here, in replicate per-cell line experiments using 50% of data (∼20 000 compounds) as the external prediction target, predictive limits are reproducibly demonstrated at the stage of systematic selection of 10-30% of the incorporable half. The pattern was consistent across all 60 cell lines. Limits of predictability are found to be correlated to the doubling times of cell lines and the number of cellular response discontinuities (activity cliffs) present per cell line. Organization into chemical scaffolds delineated degrees of predictive challenge. These results provide key insights for strategies in developing new inhibitors in existing cell lines or for future automated therapy selection in personalized oncotherapy.

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.

Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5-98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

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.

Fundamental Bioinformatic and Chemoinformatic Data Processing.

In order to execute more advanced computational chemogenomic workflows, it is essential to understand the basic data formats and options for processing them. In this chapter, de facto standards for compound and protein representation are explained, with procedures for processing them given. A walkthrough demonstrates the step-by-step processes of downloading a ligand-target database, parsing the bioactivity in the database, automatically retrieving its chemical structures and protein sequences from a command line, and finally converting the structures and sequences into representative machine-ready formats. A basic protocol to visualize the parsed database and look for patterns is also given.