The impact of immunosuppressive agents on immune checkpoint inhibitor efficacy in patients with advanced melanoma: A real-world, multicenter, retrospective study.

BACKGROUND: Immune-related adverse events (irAEs) associated with immune checkpoint inhibitors (ICIs) are often managed via immunosuppressive agents (ISAs); however, their impact on ICI efficacy is not well studied. The impact of the use of ISAs on ICI efficacy in patients with advanced melanoma was therefore investigated. METHODS: This is a real-world, multicenter, retrospective cohort study of patients with advanced melanoma who received ICIs (n = 370). Overall survival (OS) and time to treatment failure (TTF) from the time of ICI initiation were compared among patients in subgroups of interest by unadjusted and 12-week landmark sensitivity-adjusted analyses. The association of irAEs and their management with OS and TTF were evaluated using univariate and multivariable Cox proportional hazards regression models. RESULTS: Overall, irAEs of any grade and of grade ≥3 occurred in 57% and 23% of patients, respectively. Thirty-seven percent of patients received steroids, and 3% received other ISAs. Median OS was longest among patients receiving both (not reached [NR]), shorter among those receiving only systemic steroids (SSs) (84.2 months; 95% CI, 40.2 months to NR), and shortest among those who did not experience irAEs (10.3 months; 95% CI, 6-20.1 months) (p < .001). Longer OS was significantly associated with the occurrence of irAEs and the use of SSs with or without ISAs upon multivariable-adjusted analysis (p < .001). Similar results were noted with anti-programmed death 1 (PD-1) monotherapy and combination anti-PD-1 plus anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) therapy, and with 12-week landmark sensitivity analysis (p = .01). CONCLUSIONS: These findings in patients with melanoma who were treated with ICIs suggest that the use of SSs or ISAs for the management of irAEs is not associated with inferior disease outcomes, which supports the use of these agents when necessary.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial.

BACKGROUND: About 25% of pancreatic cancers harbour actionable molecular alterations, defined as molecular alterations for which there is clinical or strong preclinical evidence of a predictive benefit from a specific therapy. The Know Your Tumor (KYT) programme includes US patients with pancreatic cancer and enables patients to undergo commercially available multi-omic profiling to provide molecularly tailored therapy options and clinical trial recommendations. We sought to determine whether patients with pancreatic cancer whose tumours harboured such actionable molecular alterations and who received molecularly matched therapy had a longer median overall survival than similar patients who did not receive molecularly matched therapy. METHODS: In this retrospective analysis, treatment history and longitudinal survival outcomes were analysed in patients aged 18 years or older with biopsy-confirmed pancreatic cancer of any stage, enrolled in the KYT programme and who received molecular testing results. Since the timing of KYT enrolment varied for each patient, the primary outcome measurement of median overall survival was calculated from the initial diagnosis of advanced disease until death. We compared median overall survival in patients with actionable mutations who were treated with a matched therapy versus those who were not treated with a matched therapy. FINDINGS: Of 1856 patients with pancreatic cancer who were referred to the KYT programme between June 16, 2014, and March 31, 2019, 1082 (58%) patients received personalised reports based on their molecular testing results. Actionable molecular alterations were identified in 282 (26%) of 1082 samples. Among 677 patients for whom outcomes were available, 189 had actionable molecular alterations. With a median follow-up of 383 days (IQR 214-588), those patients with actionable molecular alterations who received a matched therapy (n=46) had significantly longer median overall survival than did those patients who only received unmatched therapies (n=143; 2·58 years [95% CI 2·39 to not reached] vs 1·51 years [1·33-1·87]; hazard ratio 0·42 [95% CI 0·26-0·68], p=0·0004). The 46 patients who received a matched therapy also had significantly longer overall survival than the 488 patients who did not have an actionable molecular alteration (2·58 years [95% CI 2·39 to not reached] vs 1·32 years [1·25-1·47]; HR 0·34 [95% CI 0·22-0·53], p<0·0001). However, median overall survival did not differ between the patients who received unmatched therapy and those without an actionable molecular alteration (HR 0·82 [95% CI 0·64-1·04], p=0·10). INTERPRETATION: These real-world outcomes suggest that the adoption of precision medicine can have a substantial effect on survival in patients with pancreatic cancer, and that molecularly guided treatments targeting oncogenic drivers and the DNA damage response and repair pathway warrant further prospective evaluation. FUNDING: Pancreatic Cancer Action Network and Perthera.

Dynamic Regulation of Caveolin-1 Phosphorylation and Caveolae Formation by Mammalian Target of Rapamycin Complex 2 in Bladder Cancer Cells.

The mammalian target of rapamycin (mTOR) and associated phosphatidylinositol 3-kinase/AKT/mTOR signaling pathway is commonly up-regulated in cancer, including bladder cancer. mTOR complex 2 (mTORC2) is a major regulator of bladder cancer cell migration and invasion, but the mechanisms by which mTORC2 regulates these processes are unclear. A discovery mass spectrometry and reverse-phase protein array-based proteomics dual approach was used to identify novel mTORC2 phosphoprotein targets in actively invading cancer cells. mTORC2 targets included focal adhesion kinase, proto-oncogene tyrosine-protein kinase Src, and caveolin-1 (Cav-1), among others. Functional testing shows that mTORC2 regulates Cav-1 localization and dynamic phosphorylation of Cav-1 on Y14. Regulation of Cav-1 activity by mTORC2 also alters the abundance of caveolae, which are specialized lipid raft invaginations of the plasma membrane associated with cell signaling and membrane compartmentalization. Our results demonstrate a unique role for mTORC2-mediated regulation of caveolae formation in actively migrating cancer cells.

SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants.

BACKGROUND: Molecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. The Python workflow utilizes molecular dynamics software (NAMD (Phillips et al., J Comput Chem 26(16):1781-802, 2005), VMD (Humphrey et al., J Mol Graph 14(1):33-8, 27-8, 1996)) to generate variant specific scaffolds for simulated small molecule docking (AutoDock Vina (Trott and Olson, J Comput Chem 31(2):455-61, 2010)). RESULTS: SNP2SIM is composed of three independent modules that can be used sequentially to generate the variant scaffolds of missense protein variants from the wildtype protein structure. The workflow first generates the mutant structure and configuration files required to execute molecular dynamics simulations of solvated protein variant structures. The resulting trajectories are clustered based on the structural diversity of residues involved in ligand binding to produce one or more variant scaffolds of the protein structure. Finally, these unique structural conformations are bound to small molecule ligand libraries to predict variant induced changes to drug binding relative to the wildtype protein structure. CONCLUSIONS: SNP2SIM provides a platform to apply molecular simulation based functional analysis of sequence variation in the protein targets of small molecule therapies. In addition to simplifying the simulation of variant specific drug interactions, the workflow enables large scale computational mutagenesis by controlling the parameterization of molecular simulations across multiple users or distributed computing infrastructures. This enables the parallelization of the computationally intensive molecular simulations to be aggregated for downstream functional analysis, and facilitates comparing various simulation options, such as the specific residues used to define structural variant clusters. The Python scripts that implement the SNP2SIM workflow are available (SNP2SIM Repository. https://github.com/mccoymd/SNP2SIM , Accessed 2019 February ), and individual SNP2SIM modules are available as apps on the Seven Bridges Cancer Genomics Cloud (Lau et al., Cancer Res 77(21):e3-e6, 2017; Cancer Genomics Cloud [ www.cancergenomicscloud.org ; Accessed 2018 November]).

Multi-omic molecular comparison of primary versus metastatic pancreatic tumours.

BACKGROUND: Molecular profiling is increasingly used to match patients with metastatic cancer to targeted therapies, but obtaining a high-quality biopsy specimen from metastatic sites can be difficult. METHODS: Patient samples were received by Perthera to coordinate genomic, proteomic and/or phosphoproteomic testing, using a specimen from either the primary tumour or a metastatic site. The relative frequencies were compared across specimen sites to assess the potential limitations of using a primary tumour sample for clinical decision support. RESULTS: No significant differences were identified at the gene or pathway level when comparing genomic alterations between primary and metastatic lesions. Site-specific trends towards enrichment of MYC amplification in liver lesions, STK11 mutations in lung lesions and ATM and ARID2 mutations in abdominal lesions were seen, but were not statistically significant after false-discovery rate correction. Comparative analyses of proteomic results revealed significantly elevated expression of ERCC1 and TOP1 in metastatic lesions. CONCLUSIONS: Tumour tissue limitations remain a barrier to precision oncology efforts, and these real-world data suggest that performing molecular testing on a primary tumour specimen could be considered in patients with pancreatic adenocarcinoma who do not have adequate tissue readily available from a metastatic site.

Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients.

BACKGROUND: Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients. METHODS: We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival. RESULTS: We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance. CONCLUSION: We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

G-DOC Plus - an integrative bioinformatics platform for precision medicine.

BACKGROUND: G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. RESULTS: G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability. CONCLUSION: G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .

The CPTAC Data Portal: A Resource for Cancer Proteomics Research.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC), under the auspices of the National Cancer Institute's Office of Cancer Clinical Proteomics Research, is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of proteomic technologies and workflows to clinical tumor samples with characterized genomic and transcript profiles. The consortium analyzes cancer biospecimens using mass spectrometry, identifying and quantifying the constituent proteins and characterizing each tumor sample's proteome. Mass spectrometry enables highly specific identification of proteins and their isoforms, accurate relative quantitation of protein abundance in contrasting biospecimens, and localization of post-translational protein modifications, such as phosphorylation, on a protein's sequence. The combination of proteomics, transcriptomics, and genomics data from the same clinical tumor samples provides an unprecedented opportunity for tumor proteogenomics. The CPTAC Data Portal is the centralized data repository for the dissemination of proteomic data collected by Proteome Characterization Centers (PCCs) in the consortium. The portal currently hosts 6.3 TB of data and includes proteomic investigations of breast, colorectal, and ovarian tumor tissues from The Cancer Genome Atlas (TCGA). The data collected by the consortium is made freely available to the public through the data portal.

AISAIC: a software suite for accurate identification of significant aberrations in cancers.

UNLABELLED: Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test; (iii) it estimates the fraction of normal contamination for each sample; (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations; and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines. AVAILABILITY AND IMPLEMENTATION: AISAIC is available as a Java application, with a user's guide and source code, at https://code.google.com/p/aisaic/.

Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine.

OBJECTIVES: Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic. METHODS: We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response. RESULTS: Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated. CONCLUSION: Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.

In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases.

BACKGROUND: Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System. RESULTS: This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more "Th17 T-cell subtype" genes in RA than the other AIDs, and more "Chemokine plus Receptors" genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS. CONCLUSIONS: Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.

Preclinical magnetic resonance imaging and systems biology in cancer research: current applications and challenges.

Biologically accurate mouse models of human cancer have become important tools for the study of human disease. The anatomical location of various target organs, such as brain, pancreas, and prostate, makes determination of disease status difficult. Imaging modalities, such as magnetic resonance imaging, can greatly enhance diagnosis, and longitudinal imaging of tumor progression is an important source of experimental data. Even in models where the tumors arise in areas that permit visual determination of tumorigenesis, longitudinal anatomical and functional imaging can enhance the scope of studies by facilitating the assessment of biological alterations, (such as changes in angiogenesis, metabolism, cellular invasion) as well as tissue perfusion and diffusion. One of the challenges in preclinical imaging is the development of infrastructural platforms required for integrating in vivo imaging and therapeutic response data with ex vivo pathological and molecular data using a more systems-based multiscale modeling approach. Further challenges exist in integrating these data for computational modeling to better understand the pathobiology of cancer and to better affect its cure. We review the current applications of preclinical imaging and discuss the implications of applying functional imaging to visualize cancer progression and treatment. Finally, we provide new data from an ongoing preclinical drug study demonstrating how multiscale modeling can lead to a more comprehensive understanding of cancer biology and therapy.

Integrative genomic analysis of the lung tissue microenvironment in SARS-CoV-2 and NL63 patients.

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects. In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus. Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients. Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data.

Rewriting the textbook for pharma: how to adapt and thrive in a digital, personalized and collaborative world.

The pharmaceutical industry is undergoing a sweeping transformation, driven by technological innovations, demographic shifts, regulatory changes and consumer expectations. For adaptive players in pharma to excel in this rapidly changing landscape, which will be markedly different from today by 2030 and beyond, they will require a different set of skills, capabilities and mindsets, as well as a willingness to collaborate and co-create value with multiple stakeholders. The industry needs to rewrite the textbook for pharma by embracing and implementing four key dimensions of change: digitalization, personalization, collaboration and innovation. In this article, we will examine how these dimensions of change are reshaping the industry, and provide practical and strategic guidance based on best practices and examples. Specifically, adaptive pharma companies should embrace the use of advanced digital technologies, such as artificial intelligence and machine learning, to streamline processes and solve challenges rapidly. Personalization, both in medicine and patient engagement, will also be key to success in the 'digital revolution', and a collaborative approach involving partnerships with tech start-ups, health-care providers and regulatory bodies will also be essential to create an integrated and responsive health-care ecosystem. Using these ideas for a rewritten textbook for pharma, adaptive players in pharma will evolve to be personalized and digitized health-focused organizations that provide comprehensive solutions which go beyond drugs and devices.

Using large language models for safety-related table summarization in clinical study reports.

Objectives: The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation. Materials and Methods: As part of a challenge initiated by Pfizer (organizer), several teams (participant) created a pilot for generating summaries of safety tables for clinical study reports (CSRs). Our evaluation framework used automated metrics and expert reviews to assess the quality of AI-generated documents. Results: The comparative analysis revealed differences in performance across solutions, particularly in factual accuracy and lean writing. Most participants employed prompt engineering with generative pre-trained transformer (GPT) models. Discussion: We discuss areas for improvement, including better ingestion of tables, addition of context and fine-tuning. Conclusion: The challenge results demonstrate the potential of LLMs in automating table summarization in CSRs while also revealing the importance of human involvement and continued research to optimize this technology.

Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.

BACKGROUND: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patient-reported data (PRD), and it remains unclear whether PRD can predict postoperative use behavior. The authors used a machine learning approach leveraging preoperative PRD and electronic health record data to predict persistent opioid use after upper extremity surgery. METHODS: Included patients underwent upper extremity surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. The authors trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. The authors assessed model performance using area under the receiver operating characteristic, sensitivity, specificity, and Brier score. RESULTS: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus electronic health record data achieved area under the receiver operating characteristic 0.73 at 6 months. Factors predictive of prolonged opioid use included income; education; tobacco, drug, or alcohol abuse; cancer; depression; and race. Protective factors included preoperative Patient-Reported Outcomes Measurement Information System Global Physical Health and Upper Extremity scores. CONCLUSIONS: This opioid use prediction model using preintervention data had good discriminative performance. PRD variables augmented electronic health record-based machine learning algorithms in predicting postsurgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

PUGSVM: a caBIG™ analytical tool for multiclass gene selection and predictive classification.

UNLABELLED: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. AVAILABILITY: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.