Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation.

BACKGROUND: Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives. OBJECTIVE: This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health records using deep learning-based NLP techniques. METHODS: We obtained data of 3281 industry-sponsored phase 2 or 3 interventional clinical trials recruiting patients with non-small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, and Crohn disease from ClinicalTrials.gov, spanning the period between 2013 and 2020. A customized bidirectional long short-term memory- and conditional random field-based NLP pipeline was used to extract all eligibility criteria attributes and convert hypernym concepts into computable hyponyms along with their corresponding values. To illustrate the simulation of clinical trial design for optimization purposes, we selected a subset of patients with non-small cell lung cancer (n=2775), curated from the Mount Sinai Health System, as a pilot study. RESULTS: We manually annotated the clinical trial eligibility corpus (485/3281, 14.78% trials) and constructed an eligibility criteria-specific ontology. Our customized NLP pipeline, developed based on the eligibility criteria-specific ontology that we created through manual annotation, achieved high precision (0.91, range 0.67-1.00) and recall (0.79, range 0.50-1) scores, as well as a high F1-score (0.83, range 0.67-1), enabling the efficient extraction of granular criteria entities and relevant attributes from 3281 clinical trials. A standardized eligibility criteria knowledge base, compatible with electronic health records, was developed by transforming hypernym concepts into machine-interpretable hyponyms along with their corresponding values. In addition, an interface prototype demonstrated the practicality of leveraging real-world data for optimizing clinical trial protocols and identifying eligible patients. CONCLUSIONS: Our customized NLP pipeline successfully generated a standardized eligibility criteria knowledge base by transforming hypernym criteria into machine-readable hyponyms along with their corresponding values. A prototype interface integrating real-world patient information allows us to assess the impact of each eligibility criterion on the number of patients eligible for the trial. Leveraging NLP and real-world data in a data-driven approach holds promise for streamlining the overall clinical trial process, optimizing processes, and improving efficiency in patient identification.

Divergent landscapes of A-to-I editing in postmortem and living human brain.

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.

Divergent landscapes of A-to-I editing in postmortem and living human brain.

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR1 and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries illuminate the nuanced functions and intricate regulatory mechanisms of RNA editing within the human brain.

Analysis of real-world data to investigate evolving treatment sequencing patterns in advanced non-small cell lung cancers and their impact on survival.

Background: Although optimal sequencing of systemic therapy in cancer care is critical to achieving maximal clinical benefit, there is a lack of analysis of treatment sequencing in advanced non-small cell lung cancer (aNSCLC) in real-world settings. Methods: A retrospective cohort study of 13,340 lung cancer patients within the Mount Sinai Health System (MSHS) was performed. Systemic therapy data of aNSCLC in 2,106 patients was the starting point in our analysis to investigate how treatment sequencing has evolved, the impact of sequencing patterns on clinical outcomes, and the effectiveness of 2nd line chemotherapy after patients progressed on immune checkpoint inhibitor (ICI)-based therapy as the 1st line of therapy (LOT). Results: There is a significant shift to more ICI-based therapy and multiple lines of targeted therapy after 2015. We compared clinical outcomes of two patient populations with different treatment sequencing patterns, with the 1st group receiving chemotherapy as the 1st LOT followed by ICI-based treatment, and the 2nd group treated in the opposite order receiving a 1st line ICI-containing regimen followed by a 2nd line chemotherapy. No statistically significant difference in overall survival (OS) was observed between the two groups [group 2 vs. group 1, adjusted hazard ratio (aHR) =1.36, P=0.39]. We assessed the efficacy of the 2nd line chemotherapy in three patient populations given either 1st line ICI single agent, 1st line ICI-chemotherapy combination, or 1st line chemotherapy alone, there was no statistically significant difference in time-to-next treatment (TTNT) and in OS among the three patient groups. Conclusions: Analysis of real-world data has shown two treatment sequencing patterns in aNSCLC, ICI followed by chemotherapy or chemotherapy followed by ICI, achieved similar clinical benefit. The chemotherapies routinely used following platinum doublet 1st LOT, is effective as the 2nd line option after ICI-chemotherapy combination in the 1st line setting.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Biopsy and blood-based molecular biomarker of inflammation in IBD.

OBJECTIVE: IBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments. DESIGN: Transcriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn's disease, n=421 UC and 243 controls) in the Mount Sinai Crohn's and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts. RESULTS: bMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time. CONCLUSION: Transcriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing.

BACKGROUND: Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE: We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS: We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS: Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS: Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.

Dual-Specificity Protein Phosphatase 4 (DUSP4) Overexpression Improves Learning Behavior Selectively in Female 5xFAD Mice, and Reduces ß-Amyloid Load in Males and Females.

Recent multiscale network analyses of banked brains from subjects who died of late-onset sporadic Alzheimer's disease converged on VGF (non-acronymic) as a key hub or driver. Within this computational VGF network, we identified the dual-specificity protein phosphatase 4 (DUSP4) [also known as mitogen-activated protein kinase (MAPK) phosphatase 2] as an important node. Importantly, DUSP4 gene expression, like that of VGF, is downregulated in postmortem Alzheimer's disease (AD) brains. We investigated the roles that this VGF/DUSP4 network plays in the development of learning behavior impairment and neuropathology in the 5xFAD amyloidopathy mouse model. We found reductions in DUSP4 expression in the hippocampi of male AD subjects, correlating with increased CDR scores, and in 4-month-old female and 12-18-month-old male 5xFAD hippocampi. Adeno-associated virus (AAV5)-mediated overexpression of DUSP4 in 5xFAD mouse dorsal hippocampi (dHc) rescued impaired Barnes maze performance in females but not in males, while amyloid loads were reduced in both females and males. Bulk RNA sequencing of the dHc from 5-month-old mice overexpressing DUSP4, and Ingenuity Pathway and Enrichr analyses of differentially expressed genes (DEGs), revealed that DUSP4 reduced gene expression in female 5xFAD mice in neuroinflammatory, interferon-gamma (IFNγ), programmed cell death protein-ligand 1/programmed cell death protein 1 (PD-L1/PD-1), and extracellular signal-regulated kinase (ERK)/MAPK pathways, via which DUSP4 may modulate AD phenotype with gender-specificity.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Management of de novo metastatic hormone-sensitive prostate cancer: A comprehensive report of a single-center experience.

BACKGROUND: Upfront docetaxel or novel hormonal agents (NHA) such as abiraterone and enzalutamide have become the standard of care for metastatic hormone sensitive prostate cancer (mHSPC). We evaluated real-world management of patients treated with these agents at a single center. PATIENTS AND METHODS: Patients with de novo mHSPC treated with upfront docetaxel or an NHA between January 2014 and April 2019 at Mount Sinai Health System were included. We evaluated time to next treatment (TTNT), PSA progression free survival (PFS) and overall survival (OS) after initial treatment with these drugs. Kaplan Meier method and multivariable Cox proportional hazards models were used for analysis. We additionally assessed the prognostic value of post-treatment PSA. RESULTS: We identified 94 de novo mHSPC patients; 52 and 42 treated with upfront docetaxel and NHAs, respectively. NHAs were associated with a median TTNT of 20.7 months compared to 10.1 months with docetaxel (log-rank p = 0.023). We also observed median PSA PFS of 19 months for NHAs and 13.2 months for docetaxel (p = 0.069). However, OS between the two treatment groups was unchanged. Among docetaxel treated patients, TTNT was shorter among those with high metastasis burden (9.63 vs 25.5 months, p = 0.026) which was not observed among NHA treated patients (25.1 vs 20.7 months, p = 0.79). Regardless of treatment, lower post-treatment PSA levels were associated with improved TTNT (58.95 vs. 11.57 vs. 9.4 months for PSA ≤0.2, 0.2-0.4, >0.4ng/ml, respectively; p<0.001). CONCLUSION: Real world data demonstrated a shorter duration of treatment with docetaxel than NHAs, reflecting the time-limited nature of docetaxel regimens compared to the treat-till-progression approach of NHAs. While TTNT was generally longer for NHAs than docetaxel, some docetaxel-treated patients achieved significant periods of time off treatment. In addition, the depth of PSA response following combination treatment may hold prognostic value for mHSPC outcomes.

Cardiac Targeted Adeno-Associated Virus Injection in Rats.

Gene therapy is a promising approach in the treatment of cardiovascular diseases. The vectors available for cardiovascular gene therapy have significantly improved over time. Cardiac tropism is a primary characteristic of an ideal vector along with a long-term expression profile and a minimal risk of cellular immune response. Preclinical and clinical studies have demonstrated that adeno-associated viral (AAV) vectors are one of the most attractive vehicles for gene transfer. AAV has gained great popularity in the last years because of its biological properties and advantages over other viral vector systems. In this chapter we will describe methods for intracardiac delivery of AAV vector in rats.

Incident Cancer Risk and Signatures Among Older MUTYH Carriers: Analysis of Population-Based and Genomic Cohorts.

MUTYH carriers have an increased colorectal cancer risk in case-control studies, with loss of heterozygosity (LOH) as the presumed mechanism. We evaluated cancer risk among carriers in a prospective, population-based cohort of older adults. In addition, we assessed if cancers from carriers demonstrated mutational signatures (G:C>T:A transversions) associated with early LOH. We calculated incident risk of cancer and colorectal cancer among 13,131 sequenced study participants of the ASPirin in Reducing Events in the Elderly cohort, stratified by sex and adjusting for age, smoking, alcohol use, BMI, polyp history, history of cancer, and aspirin use. MUTYH carriers were identified among 13,033 participants in The Cancer Genome Atlas and International Cancer Genome Consortium, and somatic signatures of cancers were analyzed. Male MUTYH carriers demonstrated an increased risk for overall cancer incidence [multivariable HR, 1.66; 95% confidence interval (CI), 1.03-2.68; P = 0.038] driven by increased colorectal cancer incidence (multivariable HR, 3.55; 95% CI, 1.42-8.78; P = 0.007), as opposed to extracolonic cancer incidence (multivariable HR, 1.40; 95% CI, 0.81-2.44; P = 0.229). Female carriers did not demonstrate increased risk of cancer, colorectal cancer, or extracolonic cancers. Analysis of mutation signatures from cancers of MUTYH carriers revealed no significant contribution toward early mutagenesis from widespread G:C>T:A transversions among gastrointestinal epithelial cancers. Among cancers from carriers, somatic transversions associated with base-excision repair deficiency are uncommon, suggestive of diverse mechanisms of carcinogenesis in carriers compared with those who inherit biallelic MUTYH mutations. PREVENTION RELEVANCE: Despite absence of loss of heterozygosity in colorectal cancers, elderly male MUTYH carriers appeared to be at increased of colorectal cancer.

Integrative network analysis of early-stage lung adenocarcinoma identifies aurora kinase inhibition as interceptor of invasion and progression.

Here we focus on the molecular characterization of clinically significant histological subtypes of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Within lung adenocarcinoma, histology is heterogeneous and associated with tumor invasion and diverse clinical outcomes. We present a gene signature distinguishing invasive and non-invasive tumors among esLUAD. Using the gene signatures, we estimate an Invasiveness Score that is strongly associated with survival of esLUAD patients in multiple independent cohorts and with the invasiveness phenotype in lung cancer cell lines. Regulatory network analysis identifies aurora kinase as one of master regulators of the gene signature and the perturbation of aurora kinases in vitro and in a murine model of invasive lung adenocarcinoma reduces tumor invasion. Our study reveals aurora kinases as a therapeutic target for treatment of early-stage invasive lung adenocarcinoma.

Targeted Next-Generation Sequencing Reveals Exceptionally High Rates of Molecular Driver Mutations in Never-Smokers With Lung Adenocarcinoma.

INTRODUCTION: Historically, high rates of actionable driver mutations have been reported in never-smokers with lung adenocarcinoma (ADC). In the era of modern, comprehensive cancer mutation sequencing, this relationship necessitates a more detailed analysis. METHODS: All Mount Sinai patients between January 1, 2015, and June 1, 2020, with a diagnosis of ADC of any stage with known smoking status who received genomic testing were included. Most patients were analyzed using the Sema4 hotspot panel or the Oncomine Comprehensive Assay version 3 next-generation sequencing (NGS) panel conducted at Sema4. Patients were considered fully genotyped if they were comprehensively analyzed for alterations in EGFR, KRAS, MET, ALK, RET, ROS1, BRAF, NTRK1-3, and ERBB2, otherwise they were considered partially genotyped. RESULTS: Two hundred and thirty-six never-smokers and 671 smokers met the above criteria. Of the never-smokers, 201 (85%) had a driver mutation with 167 (71%) considered actionable (ie, those with US Food and Drug Administration-approved agents). Among smokers, 439 (65%) had an identified driver mutation with 258 (38%) actionable (P < .0001). When comprehensively sequenced, 95% (70/74) of never-smokers had a driver mutation with 78% (58/74) actionable; whereas, for smokers, 75% (135/180) had a driver with only 47% (74/180) actionable (P < .0001). Within mutations groups, EGFR G719X and KRAS G12Cs were more common to smokers. For stage IV patients harboring EGFR-mutant tumors treated with EGFR-directed therapies, never-smokers had significantly improved OS compared to smokers (hazard ratio = 2.71; P = .025). In multivariable analysis, Asian ancestry and female sex remained significant predictors of (1) OS in stage IV patients and (2) likelihood of harboring a receptor of fusion-based driver. CONCLUSION: Comprehensive NGS revealed driver alterations in 95% of never-smokers, with the majority having an associated therapy available. All efforts should be exhausted to identify or rule out the presence of an actionable driver mutation in all metastatic lung ADC.

Impact of body mass index on the efficacy of aromatase inhibitors in patients with metastatic breast cancer.

PURPOSE: Higher levels of estrogen in obese patients may lead to incomplete inhibition by aromatase inhibitors (AIs). The aim of this study was to determine the impact of body mass index (BMI) on efficacy of AIs in patients with metastatic hormone receptor (HR)-positive breast cancer (BC). METHODS: We performed a retrospective chart review of all female patients with metastatic HR-positive BC on an AI in first- or second-line settings and seen at our academic institution between 2001 and 2020. The primary endpoint was progression-free survival (PFS), defined as the time from start of AI to disease progression or death from any cause. RESULTS: We identified 219 patients who had received an AI in the first- or second-line settings for metastatic HR-positive BC and with documented information on BMI. Of the 219 patients, 56% (123) had a low BMI (defined as < 27 kg/m2) and 44% (96) had a high BMI (≥ 27 kg/m2). The median PFS was 21.9 months (95% CI 14.5 to 28.4) in the low BMI group versus 20.2 months (95% CI 14.3 to 27.5) in the high BMI group (p = 0.73). CONCLUSION: While BMI influences efficacy of AIs in the adjuvant setting, our results suggest that in the metastatic setting, BMI may not impact the efficacy of AIs. This discrepancy could be due to other differences in disease characteristics that make complete aromatase inhibition more important in the adjuvant setting when disease burden is the lowest.

An Integrated Taxonomy for Monogenic Inflammatory Bowel Disease.

BACKGROUND & AIMS: Monogenic forms of inflammatory bowel disease (IBD) illustrate the essential roles of individual genes in pathways and networks safeguarding immune tolerance and gut homeostasis. METHODS: To build a taxonomy model, we assessed 165 disorders. Genes were prioritized based on penetrance of IBD and disease phenotypes were integrated with multi-omics datasets. Monogenic IBD genes were classified by (1) overlapping syndromic features, (2) response to hematopoietic stem cell transplantation, (3) bulk RNA-sequencing of 32 tissues, (4) single-cell RNA-sequencing of >50 cell subsets from the intestine of healthy individuals and patients with IBD (pediatric and adult), and (5) proteomes of 43 immune subsets. The model was validated by addition of newly identified monogenic IBD defects. As a proof-of-concept, we explore the intersection between immunometabolism and antimicrobial activity for a group of disorders (G6PC3/SLC37A4). RESULTS: Our quantitative integrated taxonomy defines the cellular landscape of monogenic IBD gene expression across 102 genes with high and moderate penetrance (81 in the model set and 21 genes in the validation set). We illustrate distinct cellular networks, highlight expression profiles across understudied cell types (e.g., CD8+ T cells, neutrophils, epithelial subsets, and endothelial cells) and define genotype-phenotype associations (perianal disease and defective antimicrobial activity). We illustrate processes and pathways shared across cellular compartments and phenotypic groups and highlight cellular immunometabolism with mammalian target of rapamycin activation as one of the converging pathways. There is an overlap of genes and enriched cell-specific expression between monogenic and polygenic IBD. CONCLUSION: Our taxonomy integrates genetic, clinical and multi-omic data; providing a basis for genomic diagnostics and testable hypotheses for disease functions and treatment responses.

Integrative Analysis of the Inflammatory Bowel Disease Serum Metabolome Improves Our Understanding of Genetic Etiology and Points to Novel Putative Therapeutic Targets.

BACKGROUND & AIMS: Polygenic and environmental factors are underlying causes of inflammatory bowel disease (IBD). We hypothesized that integration of the genetic loci controlling a metabolite's abundance, with known IBD genetic susceptibility loci, may help resolve metabolic drivers of IBD. METHODS: We measured the levels of 1300 metabolites in the serum of 484 patients with ulcerative colitis (UC) and 464 patients with Crohn's disease (CD) and 365 controls. Differential metabolite abundance was determined for disease status, subtype, clinical and endoscopic disease activity, as well as IBD phenotype including disease behavior, location, and extent. To inform on the genetic basis underlying metabolic diversity, we integrated metabolite and genomic data. Genetic colocalization and Mendelian randomization analyses were performed using known IBD risk loci to explore whether any metabolite was causally associated with IBD. RESULTS: We found 173 genetically controlled metabolites (metabolite quantitative trait loci, 9 novel) within 63 non-overlapping loci (7 novel). Furthermore, several metabolites significantly associated with IBD disease status and activity as defined using clinical and endoscopic indexes. This constitutes a resource for biomarker discovery and IBD biology insights. Using this resource, we show that a novel metabolite quantitative trait locus for serum butyrate levels containing ACADS was not supported as causal for IBD; replicate the association of serum omega-6 containing lipids with the fatty acid desaturase 1/2 locus and identify these metabolites as causal for CD through Mendelian randomization; and validate a novel association of serum plasmalogen and TMEM229B, which was predicted as causal for CD. CONCLUSIONS: An exploratory analysis combining genetics and unbiased serum metabolome surveys can reveal novel biomarkers of disease activity and potential mediators of pathology in IBD.

Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing.

Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing are urgently required. Most prior research has been based on women selected for high-risk features and more data is needed to make inference about breast cancer risk for women unselected for family history, an important consideration of population screening. We tested 1464 women diagnosed with breast cancer and 862 age-matched controls participating in the Australian Breast Cancer Family Study (ABCFS), and 6549 healthy, older Australian women enroled in the ASPirin in Reducing Events in the Elderly (ASPREE) study for rare germline variants using a 24-gene-panel. Odds ratios (ORs) were estimated using unconditional logistic regression adjusted for age and other potential confounders. We identified pathogenic variants in 11.1% of the ABCFS cases, 3.7% of the ABCFS controls and 2.2% of the ASPREE (control) participants. The estimated breast cancer OR [95% confidence interval] was 5.3 [2.1-16.2] for BRCA1, 4.0 [1.9-9.1] for BRCA2, 3.4 [1.4-8.4] for ATM and 4.3 [1.0-17.0] for PALB2. Our findings provide a population-based perspective to gene-panel testing for breast cancer predisposition and opportunities to improve predictors for identifying women who carry pathogenic variants in breast cancer predisposition genes.

Molecular Characterization of Limited Ulcerative Colitis Reveals Novel Biology and Predictors of Disease Extension.

BACKGROUND AND AIMS: Disease extent varies in ulcerative colitis (UC) from proctitis to left-sided colitis to pancolitis and is a major prognostic factor. When the extent of UC is limited there is often a sharp demarcation between macroscopically involved and uninvolved areas and what defines this or subsequent extension is unknown. We characterized the demarcation site molecularly and determined genes associated with subsequent disease extension. METHODS: We performed RNA sequence analysis of biopsy specimens from UC patients with endoscopically and histologically confirmed limited disease, of which a subset later extended. Biopsy specimens were obtained from the endoscopically inflamed upper (proximal) limit of disease, immediately adjacent to the uninvolved colon, as well as at more proximal, endoscopically uninflamed colonic segments. RESULTS: Differentially expressed genes were identified in the endoscopically inflamed biopsy specimens taken at each patient's most proximal diseased site relative to healthy controls. Expression of these genes in the more proximal biopsy specimens transitioned back to control levels abruptly or gradually, the latter pattern supporting the concept that disease exists beyond the endoscopic disease demarcation site. The gradually transitioning genes were associated with inflammation, angiogenesis, glucuronidation, and homeodomain pathways. A subset of these genes in inflamed biopsy specimens was found to predict disease extension better than clinical features and were responsive to biologic therapies. Network analysis revealed critical roles for interferon signaling in UC inflammation and poly(ADP-ribose) polymerase 14 (PARP14) was a predicted key driver gene of extension. Higher PARP14 protein levels were found in inflamed biopsy specimens of patients with limited UC that subsequently extended. CONCLUSION: Molecular predictors of disease extension reveal novel strategies for disease prognostication and potential therapeutic targeting.