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BackgroundClinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.MethodsPatients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test). The primary outcome was a 30-day composite outcome including several postoperative major complications and mortality; secondary outcomes included common individual complications included in the primary composite outcome (sepsis, progressive renal insufficiency or renal failure, and mortality). Patients' EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance.ResultsOverall, 66,262 patients (median [IQR] age 78 [(70.9-84.0], female 53.9%, White race 68.5%) received urgent or emergency non-cardiac surgery (25.7% orthopedic cases, 21.9% general surgery cases). AUCROC ranged from 0.655 (Decision Tree) - 0.804 (XGBoost) for the primary composite outcome. XGBoost AUCROC was 0.823, 0.781, and 0.839 in predicting outcomes of sepsis, progressive renal insufficiency or renal failure, and mortality, respectively.ConclusionsWe developed a model to accurately predict major postoperative complications in geriatric patients undergoing urgent or emergency surgery using the patient's own existing EHR data. EHR implementation of this model could efficiently support clinicians' surgical risk assessment and perioperative decision-making discussions in this vulnerable patient population.

The recent trial Pediatric Neuro-Oncology Consortium 003 (PNOC003) utilized a molecular tumor board to recommend personalized treatment regimens based on tumor sequencing results in children with DIPG. We separately developed the Central Nervous System Targeted Agent Prediction (CNS-TAP) tool, which numerically scores targeted anticancer agents using preclinical, clinical, and patient-specific data. We hypothesized that highly scored agents from CNS-TAP would overlap with the PNOC003 tumor boards recommendations. For each of the 28 participants, actionable genetic alterations were derived from PNOC003 genomic reports and input to CNS-TAP to identify the highest scoring agents. These agents were then compared with PNOC003 recommendations, with a resultant concordance percentage calculated. Overall, 38% of the total agents recommended by the tumor board were also selected by CNS-TAP, with higher concordance (63%) in a subanalysis including only targeted anticancer agents. Furthermore, nearly all patients (93%) had at least 1 drug chosen by both methods. We demonstrate overlap between agents recommended by CNS-TAP and PNOC003 tumor board, though this does not appear to improve survival. We do observe some discordance, highlighting strengths and limitations of each method. We propose that a combination of expert opinion and data-driven tools may improve targeted treatment recommendations for children with DIPG.

ObjectivePatients with alcohol-associated hepatitis (AH) have a high mortality. Alcohol exacerbates liver damage by inducing gut dysbiosis, bacterial translocation and inflammation, which is characterised by increased numbers of circulating and hepatic neutrophils.DesignIn this study, we performed tandem mass tag (TMT) proteomics to analyse proteins in the faeces of controls (n=19), patients with alcohol-use disorder (AUD; n=20) and AH (n=80) from a multicentre cohort (InTeam). To identify protein groups that are disproportionately represented, we conducted over-representation analysis using Reactome pathway analysis and Gene Ontology to determine the proteins with the most significant impact. A faecal biomarker and its prognostic effect were validated by ELISA in faecal samples from patients with AH (n=70), who were recruited in a second and independent multicentre cohort (AlcHepNet).ResultFaecal proteomic profiles were overall significantly different between controls, patients with AUD and AH (principal component analysis p=0.001, dissimilarity index calculated by the method of Bray-Curtis). Proteins that showed notable differences across all three groups and displayed a progressive increase in accordance with the severity of alcohol-associated liver disease were predominantly those located in neutrophil granules. Over-representation and Reactome analyses confirmed that differentially regulated proteins are part of granules in neutrophils and the neutrophil degranulation pathway. Myeloperoxidase (MPO), the marker protein of neutrophil granules, correlates with disease severity and predicts 60-day mortality. Using an independent validation cohort, we confirmed that faecal MPO levels can predict short-term survival at 60 days.ConclusionsWe found an increased abundance of faecal proteins linked to neutrophil degranulation in patients with AH, which is predictive of short-term survival and could serve as a prognostic non-invasive marker.

PurposePatients with neurodevelopmental disorders (NDDs) have high rates of neuropsychiatric comorbidities. Genomic medicine may help guide care because pathogenic variants are identified in up to 50% of patients with NDDs. We evaluate the impact of a genomics-informed, multidisciplinary, neuropsychiatric specialty clinic on the diagnosis and management of patients with NDDs.MethodsWe performed a retrospective study of 316 patients from the University of California, Los Angeles Care and Research in Neurogenetics Clinic, a genomics-informed multidisciplinary clinic.ResultsAmong the 246 patients who underwent genetic testing, 41.8% had a pathogenic or likely pathogenic variant. Patients had 62 different genetic diagnoses, with 12 diagnoses shared by 2 or more patients, whereas 50 diagnoses were found in only single patients. Genetic diagnosis resulted in direct changes to clinical management in all patients with a pathogenic or likely pathogenic variant, including cascade testing (30.6%), family counseling (22.2%), medication changes (13.9%), clinical trial referral (2.8%), medical surveillance (30.6%), and specialty referrals (69.4%).ConclusionsA genomics-informed model can provide significant clinical benefits to patients with NDDs, directly affecting management across multiple domains for most diagnosed patients. As precision treatments advance, establishing a genetic diagnosis will be critical for proper management. With the growing number of rare neurogenetic disorders, clinician training should emphasize core principles of genomic medicine over individual syndromes.

IntroductionAlzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within non-Hispanic White (NHW) populations. Here we provide an extensive survey of the proteomic landscape of AD across diverse racial/ethnic groups.MethodsTwo cortical regions, from multiple centers, were harmonized by uniform neuropathological diagnosis. Among 998 unique donors, 273 donors self-identified as African American, 229 as Latino American, and 434 as NHW.ResultsWhile amyloid precursor protein and the microtubule-associated protein tau demonstrated higher abundance in AD brains, no significant race-related differences were observed. Further proteome-wide and focused analyses (specific amyloid beta [Aβ] species and the tau domains) supported the absence of racial differences in these AD pathologies within the brain proteome.DiscussionOur findings indicate that the racial differences in AD risk and clinical presentation are not underpinned by dramatically divergent patterns in the brain proteome, suggesting that other determinants account for these clinical disparities.HighlightsWe present a large-scale proteome (∼10,000 proteins) of DLPFC (998) and STG (244) across AD cases. About 50% of samples were from racially and ethnically diverse brain donors. Key AD proteins (amyloid and tau) correlated with CERAD and Braak stages. No significant race-related differences in amyloid and tau protein levels were observed in AD brains. AD-associated protein changes showed a strong correlation between the brain proteomes of African American and White individuals. This dataset advances understanding of ethnoracial-specific AD pathways and potential therapies.

PurposeWe examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.MethodsWe analyzed 203 deidentified radiology reports, manually annotated for disease status, location, and indeterminate nodules needing follow-up. Using generative pre-trained transformer (GPT)-4, GPT-3.5-turbo, and open models such as Gemma-7B and Llama3-8B, we employed strategies such as ablation and prompt engineering to boost accuracy. Discrepancies between human and model interpretations were reviewed by a secondary oncologist.ResultsAmong 164 patients with pancreatic tumor, GPT-4 showed the highest accuracy in inferring disease status, achieving a 75.5% correctness (F1-micro). Open models Mistral-7B and Llama3-8B performed comparably, with accuracies of 68.6% and 61.4%, respectively. Mistral-7B excelled in deriving correct inferences from objective findings directly. Most tested models demonstrated proficiency in identifying disease containing anatomic locations from a list of choices, with GPT-4 and Llama3-8B showing near-parity in precision and recall for disease site identification. However, open models struggled with differentiating benign from malignant postsurgical changes, affecting their precision in identifying findings indeterminate for cancer. A secondary review occasionally favored GPT-3.5's interpretations, indicating the variability in human judgment.ConclusionLLMs, especially GPT-4, are proficient in deriving oncologic insights from radiology reports. Their performance is enhanced by effective summarization strategies, demonstrating their potential in clinical support and health care analytics. This study also underscores the possibility of zero-shot open model utility in environments where proprietary models are restricted. Finally, by providing a set of annotated radiology reports, this paper presents a valuable data set for further LLM research in oncology.

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

PurposeThis study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes.MethodsA total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses.ResultsT2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P

BackgroundThe heterogeneity of Neuroblastoma (NB) leads to variation in response to treatment and outcomes. The aim of the current study is to discover AI-empowered cellular morphometric biomarkers (CMBs), to establish the corresponding CMB risk score (CMBRS), CMB risk group (CMBRG), large language model driven CMB risk score (CMB-LLM-RS), and large language model driven CMB risk group (CMB-LLM-RG), and to investigate and validate their prognostic and predictive power in NB.MethodsIn this study, the retrospective cohort enrolled 84 primary NBs between 1/2020 and 12/2021, followed up through 11/22/2024; the prospective cohort enrolled 67 primary NBs between 1/2022 and 7/2023, followed up through 11/22/2024.ResultsWe identified 9 CMBs from a retrospective NB cohort, enabling the CMBRS, CMBRG, CMB-LLM-RS, and CMB-LLM-RG. Both CMBRG and CMB-LLM-RG are significantly associated with prognosis (p

Groundbreaking strategies for preventive cardiology were showcased at the 2024 American Society for Preventive Cardiology (ASPC) Congress on Cardiovascular Disease (CVD) Prevention held in Salt Lake City, Utah, from August 2nd to 4th, 2024. The event featured 69 moderators and 13 scientific sessions comprised of 98 topics, 36 satellite events, 133 poster presentations, and 27 lifestyle classes. The conference highlighted innovative strategies focused on integrating cardiovascular, kidney, and metabolic health, presenting a cohesive approach for managing complex, interrelated conditions. Pivotal studies have addressed the role of lipid-lowering therapies, the benefits of early statin initiation, and the importance of precision medicine in preventing CVD. The ASPCs emphasis on translating this research into practical clinical tools has the potential to revolutionize preventive care strategies, making strides toward reducing the burden of CVD globally and improving long-term patient outcomes through personalized and early intervention approaches.

Individualized genetic therapies-medicines that precisely target a genetic variant that may only be found in a small number of individuals, as few as only one-offer promise for addressing unmet needs in genetic disease, but present unique challenges for trial design. By nature these new individualized medicines require testing in individualized N-of-1 trials. Here, we provide a framework for maintaining scientific rigor in N-of-1 trials. Building upon best practices from traditional clinical trial design, recent guidance from the United States Food and Drug Administration, and our own clinical research experience, we suggest key considerations including comprehensive baseline natural history, selection of appropriate clinical outcome assessments (COAs) individualized to the patient genotype-phenotype for safety and efficacy assessment over time, and specific statistical considerations. Standardization of N-of-1 trial designs in this fashion will maximize efficient learning from this next generation of targeted individualized therapeutics.

Background Chronic lymphocytic leukaemia (CLL) is much more common in Americans compared with Asians. In addition to the observed differences in incidence rate and onset age, our previous research has also documented significant differences in the IGHV mutation status, VDJ rearrangement, and gene mutation spectrum among patients with CLL between China and Western countries. The bases for these differences and implications for therapy outcomes are controversial and mostly unknown. Methods We compared baseline co-variates, therapies and outcomes of US and Chinese with CLL in the Flatiron Health (FH) (N = 15,786) and Tianjin CAMS databases (N = 2,996). Results Age at diagnosis was younger in the CAMS database where people had higher ECOG scores, more frequent lymph node enlargement and hepato-spleno-megaly, thrombocytopenia, higher Rai stage, a higher β2-microglobulin concentration and fewer unmutated IGHV, del(17p), and other cytogenetic abnormalities. Several of these differences persisted after adjusting for age, Rai stage and IGHV mutation state. There were also differences in therapies. We defined patients who received only traditional chemotherapy drugs as the traditional chemotherapy group, those treated with CD20 monoclonal antibodies as the immunotherapy group, and those receiving BTK inhibitors or BCL2 inhibitors as the targeted therapy group. This follows an upward classification principle where targeted therapy is considered superior to immunotherapy and traditional chemotherapy. Overall, CLL treatment patterns in China were lagged behind Western countries, with fewer patients receiving Rituximab and delayed adoption of targeted therapies. However, from 2019 onward, the proportions of patients receiving targeted therapy in both databases were approximately the same. Median survival in the CAMS dataset was 9.7 years versus 7.5 years in the FH database (P < 0.001). In sub-group analyses Chinese with CLL had better 5-year survivals with chemotherapy (69% [95% Confidence Interval (CI), 66, 72%] versus 49% [47, 52%]; P

Tissue biomarkers for immune checkpoint inhibitor (ICI) response are limited by tumor sample heterogeneity and availability. This study identifies clinically actionable pretreatment blood biomarkers that are associated with ICI treatment response and survival in recurrent/metastatic head and neck squamous cell carcinoma. A prospective multi-center study enrolled 100 patients before standard-of-care immunotherapy. Blood immune profiles, measured by methylation cytometry, were assessed alongside tumor mutational burden (TMB) and PD-L1 combined proportion score (CPS). TMB and PD-L1 CPS were available for 56 and 91 patients, respectively. High neutrophils, monocytes, and neutrophil-to-lymphocyte ratio were associated with worse survival, while high CD4T cells, especially naïve CD4T cells, and lymphocyte-to-monocyte ratio were associated with better survival. Significant interactions between TMB and peripheral immune profiles for both progression-free and overall survival were found. Clinically relevant pretreatment peripheral immune biomarkers were identified, demonstrating the potential of DNA-based immune profiling to predict ICI response before treatment.

ObjectivesIn 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration.Materials and methodsOversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals. The program supported centralized data management and genomic analysis including creation of a scalable approach to cataloging phenotypes. Cross-cutting methods including natural language processing, image processing, and reusable code were developed.ResultsThe 79.6 million dollar collaboration has supported centralized data management and genomic analysis including a scalable approach to cataloging phenotypes and launched over 10 collaborative scientific projects in health conditions highly prevalent in veterans. A ground-breaking analysis on the Summit and Andes supercomputers at the Oak Ridge National Laboratory (ORNL) of the genetic underpinnings of over 2000 health conditions across 44 million genetic variants which resulted in the identification of 38 270 independent genetic variants associating with one or more health traits. Of these, over 2000 identified associations were unique to non-European ancestry. Cross-cutting methods have advanced state-of-the-art artificial intelligence (AI) including large language natural language processing and a system biology study focused on opioid addiction awarded the 2018 Gordon Bell Prize for outstanding achievement in high-performance computing. The collaboration has completed work in prostate cancer, suicide prevention, and cardiovascular disease, and cross-cutting data science. Predictive models developed in these projects are being tested for application in clinical management.DiscussionEight new projects were launched in 2023, taking advantage of the momentum generated by the previous collaboration. A major challenge has been limitations in the scope of appropriated funds at DOE which cannot currently be used for health research.ConclusionExtensive multidisciplinary interactions take time to establish and are essential to continued progress. New funding models for maintaining high-performance computing infrastructure at the ORNL and for supporting continued collaboration by joint VA-DOE research teams are needed.

INTRODUCTION: Personalized and tumor-informed circulating tumor DNA (ctDNA) testing is feasible and allows for molecular residual disease (MRD) identification in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: In this retrospective analysis of commercial cases from multiple US institutions, personalized, tumor-informed, whole-exome sequenced, and germline-controlled ctDNA levels were quantified and analyzed in patients with PDAC. Plasma samples (n = 1329) from 298 clinically validated patients were collected at diagnosis, perioperatively (MRD-window; within 2-12 weeks after surgery, before therapy), and during surveillance (>12 weeks post-surgery if no ACT or starting 4 weeks post-ACT) from November 2019 to March 2023. RESULTS: Of the initially diagnosed patients with stages I-III PDAC who went for resection, the median follow-up time from surgery was 13 months (range 0.1-214). Positive ctDNA detection rates were 29% (29/100) and 29.6% (45/152) during the MRD and surveillance windows, respectively. Positive ctDNA detection was significantly associated with shorter DFS within the MRD window (median DFS of 6.37 months for ctDNA-positive vs 33.31 months for ctDNA-negative patients; HR: 5.45, P

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

IntroductionMulti-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD.MethodsTo bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors.ResultsWe generated multi-omics data and curated and harmonized phenotypic data from BA (n = 306), LA (n = 326), or BA and LA (n = 4) brain donors plus non-Hispanic White (n = 252) and other (n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures.DiscussionThe inclusion of traditionally underrepresented groups in multi-omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.HighlightsAccelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi-ethnic populations. Brain multi-omics data is generated from Black American, Latin American, and non-Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

BACKGROUND: Interindividual variability in oncology patients symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden. METHODS: Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression. RESULTS: Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership. CONCLUSIONS: Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.

Crohn's disease (CD) is a complex and heterogeneous condition with no perfect preclinical model or cure. To address this, we explore adult stem cell-derived organoids that retain their tissue identity and disease-driving traits. We prospectively create a biobank of CD patient-derived organoid cultures (PDOs) from colonic biopsies of 53 subjects across all clinical subtypes and healthy subjects. Gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and identified two major molecular subtypes: immune-deficient infectious CD (IDICD) and stress and senescence-induced fibrostenotic CD (S2FCD). Each subtype shows internal consistency in the transcriptome, genome, and phenome. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. Drug screens reverse subtype-specific phenotypes, suggesting phenotyped-genotyped CD PDOs can bridge basic biology and patient trials by enabling preclinical phase "0" human trials for personalized therapeutics.

BACKGROUND: Genetics has the potential to inform biologically relevant drug treatment and repurposing which may ultimately improve patient care. In this study, we combine methods which leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. METHODS: We used the largest available genome-wide association studies, in European ancestry, of four psychiatric disorders [i.e., attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] along with genes encoding drug targets. With this data, we conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We then conducted a series of molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. FINDINGS: We reveal candidate drug targets shared across multiple disorders as well as disorder-specific targets. Drug prioritization indicated genetic support for several currently used psychotropic medications including the antipsychotic paliperidone as the top ranked drug for schizophrenia. We also observed genetic support for other commonly used psychotropics (e.g., clozapine, risperidone, duloxetine, lithium, and valproic acid). Opportunities for drug repurposing were revealed such as cholinergic drugs for ADHD, estrogens for depression, and gabapentin enacarbil for schizophrenia. Our findings also indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. INTERPRETATION: Here we present a series of complimentary and comprehensive analyses that highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders. Our findings present novel opportunities for refining psychiatric treatment.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

Prostate cancer is frequently treated with radiotherapy. Unfortunately, aggressive radioresistant relapses can arise, and the molecular underpinnings of radioresistance are unknown. Modern clinical radiotherapy is evolving to deliver higher doses of radiation in fewer fractions (hypofractionation). We therefore analyzed genomic, transcriptomic, and proteomic data to characterize prostate cancer radioresistance in cells treated with both conventionally fractionated and hypofractionated radiotherapy. Independent of fractionation schedule, resistance to radiotherapy involved massive genomic instability and abrogation of DNA mismatch repair. Specific prostate cancer driver genes were modulated at the RNA and protein levels, with distinct protein subcellular responses to radiotherapy. Conventional fractionation led to a far more aggressive biomolecular response than hypofractionation. Testing preclinical candidates identified in cell lines, we revealed POLQ (DNA Polymerase Theta) as a radiosensitizer. POLQ-modulated radioresistance in model systems and was predictive of it in large patient cohorts. The molecular response to radiation is highly multimodal and sheds light on prostate cancer lethality.SignificanceRadiation is standard of care in prostate cancer. Yet, we have little understanding of its failure. We demonstrate a new paradigm that radioresistance is fractionation specific and identified POLQ as a radioresistance modulator.

PURPOSE OF REVIEW: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery. RECENT FINDINGS: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists oversight to ensure that MLs potential is fully realized in patient care, preserving the essential human element in medicine. SUMMARY: This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise.

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31-2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein-protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

The review article titled CD39 Transforming Cancer Therapy by Modulating Tumor Microenvironment published in June 2024 in Cancer Letters provides a comprehensive overview of CD39's multifaceted roles in cancer, particularly its influence on immunoregulation, angiogenesis, and metabolic reprogramming within the tumor microenvironment (TME). This commentary builds on that foundation by incorporating recent advancements in CD39 research, highlighting unresolved issues, and proposing future research directions. We delve into the therapeutic potential of targeting CD39, addressing clinical translation challenges, and exploring the integration of CD39-based strategies into precision oncology.

The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.

PurposeThe purpose of this study was to evaluate RB1 expression and survival across ovarian carcinoma histotypes and how co-occurrence of BRCA1 or BRCA2 (BRCA) alterations and RB1 loss influences survival in tubo-ovarian high-grade serous carcinoma (HGSC).Experimental designRB1 protein expression was classified by immunohistochemistry in ovarian carcinomas of 7,436 patients from the Ovarian Tumor Tissue Analysis consortium. We examined RB1 expression and germline BRCA status in a subset of 1,134 HGSC, and related genotype to overall survival (OS), tumor-infiltrating CD8+ lymphocytes, and transcriptomic subtypes. Using CRISPR-Cas9, we deleted RB1 in HGSC cells with and without BRCA1 alterations to model co-loss with treatment response. We performed whole-genome and transcriptome data analyses on 126 patients with primary HGSC to characterize tumors with concurrent BRCA deficiency and RB1 loss.ResultsRB1 loss was associated with longer OS in HGSC but with poorer prognosis in endometrioid ovarian carcinoma. Patients with HGSC harboring both RB1 loss and pathogenic germline BRCA variants had superior OS compared with patients with either alteration alone, and their median OS was three times longer than those without pathogenic BRCA variants and retained RB1 expression (9.3 vs. 3.1 years). Enhanced sensitivity to cisplatin and paclitaxel was seen in BRCA1-altered cells with RB1 knockout. Combined RB1 loss and BRCA deficiency correlated with transcriptional markers of enhanced IFN response, cell-cycle deregulation, and reduced epithelial-mesenchymal transition. CD8+ lymphocytes were most prevalent in BRCA-deficient HGSC with co-loss of RB1.ConclusionsCo-occurrence of RB1 loss and BRCA deficiency was associated with exceptionally long survival in patients with HGSC, potentially due to better treatment response and immune stimulation.

Abstract: Background: Next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) has emerged as a powerful complement to tissue NGS, offering a noninvasive and serially conductible test. Its application holds promise in enhancing the assessment of spatial and temporal molecular tumor heterogeneity, thus providing valuable insights into cancer progression and treatment response. In this study, we explore the mutational landscape of renal cell carcinoma (RCC) patients through comprehensive profiling of mutations in ctDNA and matched tissue samples, aiming to elucidate the concordance and clinical significance of molecular alterations detected in both circulating and tissue-derived DNA. Methods: From the Tempus multimodal database, we retrospectively analyzed de-identified NGS data from patients with RCC that had dual tissue (Tempus xT, 648 genes) and ctDNA testing (Tempus xF, 105 genes). Patients with matched samples (collected +/- 90 days of one another) were included. We evaluated socio-demographic and clinical characteristics and select pathogenic somatic short variants (PSSV) and copy number variants [(amplifications and deletions, two copy number losses (CNL)]. Concordance analyses were restricted to the 105 genes tested on the ctDNA panel and further restricted to short variants, with the exception of amplifications and CNL detected by both xF and xT. Analysis was further stratified by metastatic status (n=260, metastatic, n=120 non-metastatic) prior to collection of both xT and xF. Results: Among all patients (n=393), the median age was 61 years, and 71% were male. The patient cohort comprised a diverse population based on race, with 75% white, 12% African American, 4.8% Asian and 8% others. The median time from tissue to blood collection was 21 days (IQR, 7, 39). 67% (n=265) and 68% (n=266) had metastatic disease at the time of tissue and blood collection, respectively. The most common tissue sites were kidney (49%, n=189), bone (11%, n=43), lung (9%, n=34), lymph node (8%, n=29), liver (6%, n=23), and brain/CNS (4%, n=17). Genes harboring the most common PSSV in tissue included VHL (59% n=232), PBRM1 (31%, n=123), SETD2 (23%, n=91), TP53 (14%, n=54), BAP1 (12%, n=46) and TERT (11%, n=45). Genes with common PSSV in ctDNA included TP53 (23%, n=91), VHL (18%, n=69), BAP1 (6%, n=23), PBRM1 (5%, n=21), PTEN (4%, n=15), KRAS (4%, n=14) and NF2 (4%, n=14). The combination of tissue and ctDNA testing increased the detection of mutations (Table 1). There was higher concordance between somatic alterations in select genes among patients with metastases vs. non-metastases, including BAP1 (55.9% vs. 9.1%), TP53 (36.8% vs. 9.1%), VHL (32.3% vs. 12.5%), ARID1A (25% vs. 16.7%), and ATM (25% vs. 0%). Table 1: Detection of genetic mutations in tissue and ctDNA testing Conclusions: The analysis conducted in this study highlights the complementary nature of ctDNA profiling alongside tissue-based NGS in RCC, demonstrating an increased detection of mutations. Particularly, we observed a higher concordance between ctDNA and tissue profiling in individuals with metastatic disease, suggesting the potential utility of ctDNA analysis in advanced stages of RCC. Further research is warranted to elucidate how longitudinal ctDNA analysis can delineate biomarkers of response and resistance at both the mutation and ctDNA fraction levels. Understanding these dynamics could offer valuable insights into disease progression and guide personalized treatment strategies for RCC patients. DOD CDMRP Funding: no

Background: Metabolites in plasma form biosignatures of a range of common complex human diseases. Mapping the genetic architecture and discovering variants with pleiotropic effects across metabolites can reveal underlying mechanisms and potential targets for personalized interventions. Methods: We performed univariate and multivariate genome-wide association studies (GWAS) on the Nightingale panel of 249 circulating plasma metabolic markers, across 207,836 White British UK Biobank participants (mean age 57.4 years, 53.7% female), with replication conducted across 27,509 UK Biobank participants with different ancestries, and 92,661 Estonian Biobank participants (mean age 50.9 years, 65.7% female). We investigated rare variation through whole exome sequencing gene burden tests, quantified genetic architectures through Gaussian mixture modelling, analysed the causal role of body mass index (BMI) through Mendelian randomization, and performed genome-wide interaction analyses with sex. Results: We discovered 14,837 loci (497 unique), with shared and distinct effects on cardiometabolic traits, with high replication rates across populations. The loci explained over 70% of genetic variance for fatty acids. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. There was strong evidence for causal effects of BMI on cholesterol and amino acid levels. We discovered 31 loci interacting with sex, which mapped to genes involved in cholesterol processing, and to cardiometabolic conditions with sex differences in prevalence. Discussion: The findings offer new insights into the genetic architecture of circulating metabolites, revealing novel loci and plausible sex-specific molecular mechanisms of lipid metabolism. This improved understanding of the molecular biology of metabolism lays a foundation for personalized prevention and treatment strategies.

The under-representation of non-European cohorts in neurodegenerative disease genome-wide association studies (GWAS) hampers precision medicine efforts. Despite the inherent genetic and phenotypic diversity in these diseases, GWAS research consistently exhibits a disproportionate emphasis on participants of European ancestry. This study reviews GWAS up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. We conducted a systematic review of GWAS results and publications up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. Rigorous article inclusion and quality assessment methods were employed. Of 123 neurodegenerative disease (NDD) GWAS reviewed, 82% predominantly featured European ancestry participants. A single European study identified over 90 risk loci, compared to a total of 50 novel loci in identified in all non-European or multi-ancestry studies. Notably, only six of the loci have been replicated. The significant under-representation of non-European ancestries in NDD GWAS hinders comprehensive genetic understanding. Prioritizing genomic diversity in future research is crucial for advancing NDD therapies and understanding. HIGHLIGHTS: Eighty-two percent of neurodegenerative genome-wide association studies (GWAS) focus on Europeans. Only 6 of 50 novel neurodegenerative disease (NDD) genetic loci have been replicated. Lack of diversity significantly hampers understanding of NDDs. Increasing diversity in NDD genetic research is urgently required. New initiatives are aiming to enhance diversity in NDD research.

OBJECTIVES: Precision medicine is data-driven health care tailored to individual patients based on their unique attributes, including biologic profiles, disease expressions, local environments, and socioeconomic conditions. Emergency medicine (EM) has been peripheral to the precision medicine discourse, lacking both a unified definition of precision medicine and a clear research agenda. We convened a national consensus conference to build a shared mental model and develop a research agenda for precision EM. METHODS: We held a conference to (1) define precision EM, (2) develop an evidence-based research agenda, and (3) identify educational gaps for current and future EM clinicians. Nine preconference workgroups (biomedical ethics, data science, health professions education, health care delivery and access, informatics, omics, population health, sex and gender, and technology and digital tools), comprising 84 individuals, garnered expert opinion, reviewed relevant literature, engaged with patients, and developed key research questions. During the conference, each workgroup shared how they defined precision EM within their domain, presented relevant conceptual frameworks, and engaged a broad set of stakeholders to refine precision EM research questions using a multistage consensus-building process. RESULTS: A total of 217 individuals participated in this initiative, of whom 115 were conference-day attendees. Consensus-building activities yielded a definition of precision EM and key research questions that comprised a new 10-year precision EM research agenda. The consensus process revealed three themes: (1) preeminence of data, (2) interconnectedness of research questions across domains, and (3) promises and pitfalls of advances in health technology and data science/artificial intelligence. The Health Professions Education Workgroup identified educational gaps in precision EM and discussed a training roadmap for the specialty. CONCLUSIONS: A research agenda for precision EM, developed with extensive stakeholder input, recognizes the potential and challenges of precision EM. Comprehensive clinician training in this field is essential to advance EM in this domain.

Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), are developed and for whom treatments are optimized. In this study, we developed a genetic ancestry pipeline for the Cancer Genomics Cloud, which we used to assess the diversity of models currently available in the National Cancer Institute-supported PDX Development and Trial Centers Research Network (PDXNet). We showed that there is an under-representation of models derived from patients of non-European ancestry, consistent with other cancer model resources. We discussed these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US, as well as power analyses for biomarker discovery, to highlight the immediate need for developing models from minority populations to address cancer health equity in precision medicine. Our analyses identified key priority disparity-associated cancer types for which new models should be developed.SignificanceUnderstanding whether and how tumor genetic factors drive differences in outcomes among U.S. minority groups is critical to addressing cancer health disparities. Our findings suggest that many additional models will be necessary to understand the genome-driven sources of these disparities.

Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment. Using machine learning algorithms, including TreeBagger and deep neural networks, we systematically predicted and validated GBM patient survival terms based on the expression profiles of E2Fs across BVZ-responsive GBM subtypes. Our bioinformatics analyses suggested that a significant increase in E2F8 post-BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, implicating this factor as a candidate mechanism of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. These insights underscore that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers, therapeutic targets, and recommending precision BVZ treatment to individual GBM patients.

Adverse early-life experiences (ELA) affect a majority of the worlds children. Whereas the enduring impact of ELA on cognitive and emotional health is established, there are no tools to predict vulnerability to ELA consequences in an individual child. Epigenetic markers including peripheral-cell DNA-methylation profiles may encode ELA and provide predictive outcome markers, yet the interindividual variance of the human genome and rapid changes in DNA methylation in childhood pose significant challenges. Hoping to mitigate these challenges we examined the relation of several ELA dimensions to DNA methylation changes and outcome using a within-subject longitudinal design and a high methylation-change threshold. DNA methylation was analyzed in buccal swab/saliva samples collected twice (neonatally and at 12 months) in 110 infants. We identified CpGs differentially methylated across time for each child and determined whether they associated with ELA indicators and executive function at age 5. We assessed sex differences and derived a sex-dependent impact score based on sites that most contributed to methylation changes. Changes in methylation between two samples of an individual child reflected age-related trends and correlated with executive function years later. Among tested ELA dimensions and life factors including income to needs ratios, maternal sensitivity, body mass index and infant sex, unpredictability of parental and household signals was the strongest predictor of executive function. In girls, high early-life unpredictability interacted with methylation changes to presage executive function. Thus, longitudinal, within-subject changes in methylation profiles may provide a signature of ELA and a potential predictive marker of individual outcome.

Accurate diagnosis of lung cancer is important for treatment decision-making. Tumor biopsy and histologic examination are the standard for determining histologic lung cancer subtypes. Liquid biopsy, particularly cell-free DNA (cfDNA), has recently shown promising results in cancer detection and classification. In this study, we investigate the potential of cfDNA methylome for the noninvasive classification of lung cancer histologic subtypes. We focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcriptional activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in cross-validation and an AUC of 0.747 in the independent validation. Tumor copy-number alterations inferred from cfDNA methylome analysis revealed potential for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasive lung cancer subtyping, offering insights for cancer monitoring and early detection.SignificanceThis study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

PurposeWe previously demonstrated the clinical significance of circulating tumor DNA (ctDNA) in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy (NAC). Here, we compared its predictive and prognostic value with cell-free DNA (cfDNA) concentration measured in the same samples from the same patients.Experimental design145 patients with hormone receptor (HR)-positive/HER2-negative and 138 triple-negative breast cancer (TNBC) with ctDNA data from a previous study were included in the analysis. Associations of serial cfDNA concentration with residual cancer burden (RCB) and distant recurrence-free survival (DRFS) were examined.ResultsIn TNBC, we observed a modest negative correlation between cfDNA concentration 3 weeks after treatment initiation and RCB, but none of the other timepoints showed significant correlation. In contrast, ctDNA was significantly positively correlated with RCB at all timepoints (all R > 0.3 and P < 0.05). In the HR-positive/HER2-negative group, cfDNA concentration did not associate with response to NAC, but survival analysis showed that high cfDNA shedders at pretreatment had a significantly worse DRFS than low shedders (hazard ratio, 2.12; P = 0.037). In TNBC, the difference in survival between high versus low cfDNA shedders at all timepoints was not statistically significant. In contrast, as previously reported, ctDNA at all timepoints was significantly correlated with DRFS in both subtypes.ConclusionsIn TNBC, cfDNA concentrations during therapy were not strongly correlated with response or prognosis. In the HR-positive/HER2-negative group, pretreatment cfDNA concentration was prognostic for DRFS. Overall, the predictive and prognostic value of cfDNA concentration was more limited than that of ctDNA.

4585 Background: Advanced UC is a devastating disease, however recent advances with antibody drug conjugates (ADCs), including enfortumab vedotin (EV), have improved outcomes significantly. Currently no biomarkers are clinically available to predict response to therapy. We hypothesized that benefit from EV may correlate with mRNA expression of NECTIN4, the gene encoding the relevant cell surface antigen for EV. Therefore, we performed a landscape analysis of NECTIN4 in advanced UC and correlated expression with oncologic outcomes. Methods: Bladder and upper tract UC samples were tested at Caris Life Sciences (Phoenix, AZ) with NextGen Sequencing on DNA (592 genes or whole exome) and NovaSeq on RNA (whole transcriptome). UC samples were stratified by NECTIN4 mRNA levels into four quartiles. Tumor mutational burden (TMB) totaled somatic nonsynonymous mutations per tumor (high >10 mut/Mb). Insurance claims data were used to calculate survival outcomes using Kaplan-Meier estimates. Overall survival (OS) was calculated from the date of sample collection and date of treatment initiation to date of last follow up. Time on treatment (TOT) was calculated from date of treatment initiation to date of last treatment.Survival analysis was performed between top and bottom quartiles of NECTIN4 expression. Results: A total of 6,395 patient samples were analyzed [n=4335 from primary tumor (3496 bladder/urethra, 839 upper tract), n= 2060 from metastases]. Expression of NECTIN4 was associated with higher rates of mutations in FGFR3 (18.9% vs 6.9%), ERBB2 (11.8% vs 5.9%), pTERT (77.3% vs 62.6%), CCNE1 (5.6% vs 1.7%), SDHC (8.3% vs 0.6%) and with TMB-H status (46.2% vs 35.8%) but lower rates of TP53 mutations (54.6% vs 67.7%). NECTIN4 expression positively correlated with expression of TACSTD2 (TROP2), ERBB2 (HER2) and SLITRK6. Interestingly, NECTIN4 expression inversely correlated with PDL1 expression by IHC (22c3, 26.9% vs 59.2%). Similar findings were found when analysis was performed by primary bladder versus primary upper tract, as well as primary versus metastatic site. NECTIN4 expression was associated with longer OS in the overall population, and improvement in TOT (HR 0.53) and OS (HR 0.67) for patients treated with EV (Table). NECTIN4 expression was not associated with benefit with anti-PD1/L1 therapy. Conclusions: In the largest study investigating NECTIN4 expression in UC, we demonstrate co-expression of TACSTD2 (TROP2), ERBB2 (HER2) and SLITRK6 with NECTIN4. Expression of NECTIN4 correlated with favorable prognosis and predicted benefit from EV. NECTIN4 RNA expression could be a potential biomarker for selecting patients for treatment with EV. Further validation is required. [Table: see text]

TPS5128 Background: Patients with localized high-risk prostate cancer have an increased risk of relapse following RP. Approximately 6% of patients with high-risk disease harbor germline alterations in the DNA repair pathway, of which BRCA1/2 alterations are the most common. Patients with germline BRCA1/2 mutations have higher rates of aggressive disease, distant metastases, and worse survival compared to non-carriers. Olaparib is a PARP inhibitor which demonstrates improved overall survival in patients with metastatic castration resistant prostate cancer with BRCA1/2 germline and somatic alterations. Additionally, olaparib has demonstrated improved invasive disease-free survival as adjuvant therapy in patients with germline BRCA1/2 HER-2 negative breast cancer. Novel, multimodal treatment strategies for patients with high-risk localized prostate cancer with germline or somatic BRCA1/2 may improve outcomes for these patients. Methods: We designed a multicenter phase 2 single arm study evaluating neoadjuvant olaparib in combination with a LHRH agonist for 6 months followed by RP. Eligible patients include those with a Gleason score ≥4+3=7, PSA >20 ng/mL or T3 disease (by DRE or prostate MRI) and lymph node

Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all-in-one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation-induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette-based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning-driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high-multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost-effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics.

7009 Background: The phase 2 CAPTIVATE study evaluated first-line ibrutinib (Ibr) + venetoclax (Ven) for CLL/SLL in 2 cohorts: minimal residual disease (MRD)-guided randomized discontinuation (MRD cohort) and Fixed Duration (FD cohort). Ibr±Ven retreatment was allowed in patients (pts) who had progressive disease (PD). Here, we report outcomes for pts with high-risk genomic features from the FD cohort and retreatment outcomes in pts from the FD cohort and MRD cohort placebo arm. Methods: Pts aged ≤70 y with previously untreated CLL/SLL without restriction on genomic risk factors received 3 cycles of Ibr, then 12 cycles of Ibr+Ven (Ibr, 420 mg/d orally; Ven, 5-wk ramp up to 400 mg/d orally). On-study retreatment included single-agent Ibr (FD cohort or MRD cohort placebo arm); pts with PD >2 y after end of treatment (EOT) could be retreated with FD Ibr+Ven (FD cohort). Results: In the FD cohort (n=159) with a median follow-up of 61.2 mo (range, 0.8–66.3), 5-y PFS and OS rates (95% CI) were 67% (59–74) and 96% (91–98), respectively. 5-y PFS rates were higher in pts with undetectable MRD at 3 mo after EOT in peripheral blood (83%) or bone marrow (84%) vs those without (48% and 50%, respectively). 5-y PFS rates (95% CI) in pts with genomic risk factors were: del(17p)/mutated TP53 41% (21–59), complex karyotype 57% (37–72), del(11q) 64% (30–85), and unmutated IGHV 68% (50–80) (Table). In total, 18 second malignancies occurred in 13 pts (10 events in 8 pts during FD Ibr+Ven, 6 events in 4 pts after EOT and before retreatment, and 2 events in 2 pts during retreatment). Of 202 pts who completed Ibr+Ven (FD cohort, n=159; MRD cohort placebo arm, n=43), 63 have had PD to date; PD occurred >2 y after EOT in 43/63 pts (68%). 32/63 (51%) pts initiated retreatment with Ibr (n=25) or Ibr+Ven (n=7). With a median time on Ibr retreatment of 21.9 mo (range, 0.03–50.4), ORR was 86% in 22 evaluable pts (best response: 1 CR; 1 nodular PR; 17 PR; 2 SD; 1 PD [Richter transformation]). With a median time on Ibr+Ven retreatment of 13.8 mo (range, 3.7–15.1), ORR was 71% in 7 evaluable pts (best response: 1 CR; 4 PR; 1 PR with lymphocytosis; 1 SD). Conclusions: With up to 5.5 y of follow-up, FD Ibr+Ven continues to provide clinically meaningful PFS in pts with high-risk genomic features, as well as in the overall population. Ibr-based retreatment provides promising responses in pts needing subsequent therapy after the all-oral FD regimen of Ibr+Ven. Clinical trial information: NCT02910583 . [Table: see text]

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

Glioblastoma is a clinically and molecularly heterogeneous disease, and new predictive biomarkers are needed to identify those patients most likely to respond to specific treatments. Through prospective genomic profiling of 459 consecutive primary treatment-naïve IDH-wildtype glioblastomas in adults, we identified a unique subgroup (2%, 9/459) defined by somatic hypermutation and DNA replication repair deficiency due to biallelic inactivation of a canonical mismatch repair gene. The deleterious mutations in mismatch repair genes were often present in the germline in the heterozygous state with somatic inactivation of the remaining allele, consistent with glioblastomas arising due to underlying Lynch syndrome. A subset of tumors had accompanying proofreading domain mutations in the DNA polymerase POLE and resultant "ultrahypermutation". The median age at diagnosis was 50 years (range 27-78), compared with 63 years for the other 450 patients with conventional glioblastoma (p

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.