Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.

A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality.

Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P < 0.05) in the joint meta-analysis with the HGI (Ncases = 44,820; Ncontrols = 1,775,827; OR, 0.97 [0.95-1.00]; P = 0.03). Associations were not observed with severe outcomes or mortality. Among individuals of European ancestry in the MVP, rs35705950-T was associated with fewer post-COVID-19 pneumonia events (OR, 0.82 [0.72-0.93]; P = 0.001). Conclusions: The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations.

Phosphoproteomic quantitation and causal analysis reveal pathways in GPVI/ITAM-mediated platelet activation programs.

Platelets engage cues of pending vascular injury through coordinated adhesion, secretion, and aggregation responses. These rapid, progressive changes in platelet form and function are orchestrated downstream of specific receptors on the platelet surface and through intracellular signaling mechanisms that remain systematically undefined. This study brings together cell physiological and phosphoproteomics methods to profile signaling mechanisms downstream of the immunotyrosine activation motif (ITAM) platelet collagen receptor GPVI. Peptide tandem mass tag (TMT) labeling, sample multiplexing, synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >1300 proteins over conditions initiating and progressing GPVI-mediated platelet activation. With literature-guided causal inference tools, >300 site-specific signaling relations were mapped from phosphoproteomics data among key and emerging GPVI effectors (ie, FcRγ, Syk, PLCγ2, PKCδ, DAPP1). Through signaling validation studies and functional screening, other less-characterized targets were also considered within the context of GPVI/ITAM pathways, including Ras/MAPK axis proteins (ie, KSR1, SOS1, STAT1, Hsp27). Highly regulated GPVI/ITAM targets out of context of curated knowledge were also illuminated, including a system of >40 Rab GTPases and associated regulatory proteins, where GPVI-mediated Rab7 S72 phosphorylation and endolysosomal maturation were blocked by TAK1 inhibition. In addition to serving as a model for generating and testing hypotheses from omics datasets, this study puts forth a means to identify hemostatic effectors, biomarkers, and therapeutic targets relevant to thrombosis, vascular inflammation, and other platelet-associated disease states.

High-Density Lipoprotein Carries Markers That Track With Recovery From Stroke.

RATIONALE: Prospective cohort studies question the value of HDL-C (high-density lipoprotein cholesterol) for stroke risk prediction. OBJECTIVE: Investigate the relationship between long-term functional recovery and HDL proteome and function. METHODS AND RESULTS: Changes in HDL protein composition and function (cholesterol efflux capacity) in patients after acute ischemic stroke at 2 time points (24 hours, 35 patients; 96 hours, 20 patients) and in 35 control subjects were measured. The recovery from stroke was assessed by 3 months, the National Institutes of Health Stroke Scale and modified Rankin scale scores. When compared with control subject after adjustments for sex and HDL-C levels, 12 proteins some of which participate in acute phase response and platelet activation (APMAP [adipocyte plasma membrane-associated protein], GPLD1 [phosphate inositol-glycan specific phospholipase D], APOE [apolipoprotein E], IHH [Indian hedgehog protein], ITIH4 [inter-alpha-trypsin inhibitor chain H4], SAA2 [serum amyloid A2], APOA4 [apolipoprotein A-IV], CLU [clusterin], ANTRX2 [anthrax toxin receptor 2], PON1 [serum paraoxonase/arylesterase], SERPINA1 [alpha-1-antitrypsin], and APOF [apolipoprotein F]) were significantly (adjusted P<0.05) altered in stroke HDL at 96 hours. The first 8 of these proteins were also significantly altered at 24 hours. Consistent with inflammatory remodeling, cholesterol efflux capacity was reduced by 32% (P<0.001) at both time points. Baseline stroke severity adjusted regression model showed that changes within 96-hour poststroke in APOF, APOL1, APMAP, APOC4 (apolipoprotein C4), APOM (apolipoprotein M), PCYOX1 (prenylcysteine oxidase 1), PON1, and APOE correlate with stroke recovery scores (R2=0.38-0.73, adjusted P<0.05). APOF (R2=0.73) and APOL1 (R2=0.60) continued to significantly correlate with recovery scores after accounting for tPA (tissue-type plasminogen activator) treatment. CONCLUSIONS: Changes in HDL proteins during early acute phase of stroke associate with recovery. Monitoring HDL proteins may provide clinical biomarkers that inform on stroke recuperation.

Platelet proteome dynamics in hibernating 13-lined ground squirrels.

Hibernating mammals undergo a dramatic drop in temperature and blood flow during torpor, yet avoid stasis blood clotting through mechanisms that remain unspecified. The effects of hibernation on hemostasis are especially complex, as cold temperatures generally activate platelets, resulting in platelet clearance and cold storage lesions in the context of blood transfusion. With a hibernating body temperature of 4°C-8°C, 13-lined ground squirrels (Ictidomys tridecemlineatus) provide a model to study hemostasis as well as platelet cold storage lesion resistance during hibernation. Here, we quantified and systematically compared proteomes of platelets collected from ground squirrels at summer (active), fall (entrance), and winter (topor) to elucidate how molecular-level changes in platelets may support hemostatic adaptations in torpor. Platelets were isolated from a total of 11 squirrels in June, October, and January. Platelet lysates from each animal were digested with trypsin prior to 11-plex tandem mass tag (TMT) labeling, followed by LC-MS/MS analysis for relative protein quantification. We measured >700 proteins with significant variations in abundance in platelets over the course of entrance, torpor, and activity-including systems of proteins regulating translation, secretion, metabolism, complement, and coagulation cascades. We also noted species-specific differences in levels of hemostatic, secretory, and inflammatory regulators in ground squirrel platelets relative to human platelets. Altogether, we provide the first ever proteomic characterization of platelets from hibernating animals, where systematic changes in metabolic, hemostatic, and other proteins may account for physiological adaptations in torpor and also inform translational effort to improve cold storage of human platelets for transfusion.

Application of PCSK9 Inhibitors in Practice.

PCSK9i (protein convertase subtilisin/kexin type 9 inhibitors) are set to revolutionize the treatment of hypercholesterolemia in the management of atherosclerotic risk, but numerous reports have detailed unprecedented barriers to access for these drugs. To overcome these challenges, our group created a model to facilitate provision of this new therapy for patients who qualify according to Food and Drug Administration criteria. This report details the real-world follow-up experience of PCSK9i use in a large patient cohort structured to ensure rigor in data collection, analysis, and interpretation. The 271 patients approved and actively followed in our PCSK9i clinic between July 2015 and August 2018 represent a 97% approval rate from insurance, with 28% of prescriptions requiring at least one appeal. Over 50% of patients were statin intolerant. On average, there was a median lapse of 15 days between initial visit and insurance approval. PCSK9i therapy was affordable for most patients, with an average monthly out-of-pocket expense of $58.05 (median $0). Only 2.3% of patients were unable to initiate or continue therapy because of cost. Reductions from baseline in LDL (low-density lipoprotein) cholesterol and Lp(a) (lipoprotein [a])were comparable to published reports with median reductions of 60% and 23% at 1 year, respectively. PCSK9i therapy was well-tolerated overall, though 9% of patients reported adverse events, and 5% of patients discontinued due mostly to musculoskeletal and flu-like symptoms. Our practice model demonstrates that PCSK9i therapy can be accessed easily and affordably for the majority of eligible patients, resulting in dramatic improvement in lipid profile results. Moreover, our registry data suggest that results from the prospective clinical trials of PCSK9i on LDL and Lp(a) reduction and on tolerability are applicable to a real-world cohort.

Automated feature selection of predictors in electronic medical records data.

The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Recently, more sophisticated machine learning algorithms have been developed to improve the robustness and accuracy of EHR phenotyping algorithms. These algorithms are typically trained via supervised learning, relating gold standard labels to a wide range of candidate features including billing codes, procedure codes, medication prescriptions and relevant clinical concepts extracted from narrative notes via Natural Language Processing (NLP). However, due to the time intensiveness of gold standard labeling, the size of the training set is often insufficient to build a generalizable algorithm with the large number of candidate features extracted from EHR. To reduce the number of candidate predictors and in turn improve model performance, we present an automated feature selection method based entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EHR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Relying on the results of Li and Duan (1989), we demonstrate that variable selection for the underlying phenotype model can be achieved by fitting the surrogate-based model. We explore the performance of our methods in numerical simulations and present the results of a prediction model for Rheumatoid Arthritis (RA) built on a large EHR data mart from the Partners Health System consisting of billing codes and NLP terms. Empirical results suggest that our procedure reduces the number of gold-standard labels necessary for phenotyping thereby harnessing the automated power of EHR data and improving efficiency.

Dexmedetomidine Sedation for Paroxysmal Supraventricular Tachycardia Ablation Is Not Associated With Alteration of Arrhythmia Inducibility.

BACKGROUND: Dexmedetomidine (Dex) is an attractive agent for procedural sedation due to its unique pharmacodynamic profile, specifically affording predictable sedation without concurrent respiratory depression. However, Dex has previously been reported to prevent or terminate arrhythmias. The purpose of this study was to investigate paroxysmal supraventricular tachycardia (PSVT) inducibility and homeostatic stability during electrophysiology studies (EPSs) and ablation when a standardized Dex protocol was used as the primary sedation agent. METHODS: We performed a retrospective review of 163 consecutive procedures for PSVT ablation that received Dex as the primary sedative with adjunct fentanyl and midazolam boluses (DEX-FENT-MIDAZ). This cohort was compared to 163 consecutive control procedures wherein strictly fentanyl and midazolam were used for sedation. The primary outcome reviewed was PSVT inducibility assessed before ablation. Reviewed secondary outcomes included level of sedation and intraprocedure hemodynamics and oxygenation. RESULTS: The arrhythmia profiles of the DEX-FENT-MIDAZ and control cohorts were very similar. The overall incidence of a "negative" EPSs in which arrhythmia was not induced was 24% in the DEX-FENT-MIDAZ group and 26% in the control group (P = .7). Unintended deep sedation was significantly less with DEX-FENT-MIDAZ (4.3% vs 27%; P ≤ .0001). However, DEX-FENT-MIDAZ use was associated with a higher incidence of intraprocedure hypotension. CONCLUSIONS: Dex sedation during EPSs is not associated with a reduction in PSVT inducibility. The therapeutic utility of Dex during EPS arises from the predictable sedation Dex affords but is associated with an increased incidence of intraprocedure hypotension.

Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue.

The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low versus high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism (e.g. AACS, ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g. ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g. GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle (e.g. ARG1, CPS1), immune response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.

Endothelial transcriptomics reveals activation of fibrosis-related pathways in hypertension.

Hypertension poses a significant challenge to vasculature homeostasis and stands as the most common cardiovascular disease in the world. Its effects are especially profound on endothelial cells that form the inner lining of the vasculature and are directly exposed to the effects of excess pressure. Here, we characterize the in vivo transcriptomic response of cardiac endothelial cells to hypertension by rapidly isolating these cells from the spontaneous hypertension mouse model BPH/2J and its normotensive BPN/3J control strain and performing and RNA sequencing on both. Comparison of transcriptional differences between these groups reveals statistically significant changes in cellular pathways consistent with cardiac fibrosis found in hypertensive animals. Importantly, many of the fibrosis-linked genes identified also differ significantly between juvenile prehypertensive and adult hypertensive BPH/2J mice, suggesting that these transcriptional differences are hypertension related. We examined the dynamic nature of these transcriptional changes by testing whether blood pressure normalization using either a calcium channel blocker (amlodipine) or a angiotensin II receptor blocker (losartan) is able to reverse these expression patterns associated with hypertension. We find that blood pressure reduction is capable of reversing some gene-expression patterns, while other transcripts are recalcitrant to therapeutic intervention. This illuminates the possibility that unmanaged hypertension may irreversibly alter some endothelial transcriptional patterns despite later intervention. This study quantifies how endothelial cells are remodeled at the molecular level in cardiovascular pathology and advances our understanding of the transcriptional events associated with endothelial response to hypertensive challenge.

The START App: a web-based RNAseq analysis and visualization resource.

Summary: Transcriptional profiling using RNA sequencing (RNAseq) has emerged as a powerful methodology to quantify global gene expression patterns in various contexts from single cells to whole tissues. The tremendous amount of data generated by this profiling technology presents a daunting challenge in terms of effectively visualizing and interpreting results. Convenient and intuitive data interfaces are critical for researchers to easily upload, analyze and visualize their RNAseq data. We designed the START (Shiny Transcriptome Analysis Resource Tool) App with these requirements in mind. This application has the power and flexibility to be resident on a local computer or serve as a web-based environment, enabling easy sharing of data between researchers and collaborators. Availability and Implementation: Source Code for the START App is written entirely in R and can be freely available to download at https://github.com/jminnier/STARTapp with the code licensed under GPLv3. It can be launched on any system that has R installed. The START App is also hosted on https://kcvi.shinyapps.io/START for researchers to temporarily upload their data. Contact: minnier@ohsu.edu

PCSK9 Association With Lipoprotein(a).

RATIONALE: Lipoprotein(a) [Lp(a)] is a highly atherogenic low-density lipoprotein-like particle characterized by the presence of apoprotein(a) [apo(a)] bound to apolipoprotein B. Proprotein convertase subtilisin/kexin type 9 (PCSK9) selectively binds low-density lipoprotein; we hypothesized that it can also be associated with Lp(a) in plasma. OBJECTIVE: Characterize the association of PCSK9 and Lp(a) in 39 subjects with high Lp(a) levels (range 39-320 mg/dL) and in transgenic mice expressing either human apo(a) only or human Lp(a) (via coexpression of human apo(a) and human apolipoprotein B). METHODS AND RESULTS: We show that PCSK9 is physically associated with Lp(a) in vivo using 3 different approaches: (1) analysis of Lp(a) fractions isolated by ultracentrifugation; (2) immunoprecipitation of plasma using antibodies to PCSK9 and immunodetection of apo(a); (3) ELISA quantification of Lp(a)-associated PCSK9. Plasma PCSK9 levels correlated with Lp(a) levels, but not with the number of kringle IV-2 repeats. PCSK9 did not bind to apo(a) only, and the association of PCSK9 with Lp(a) was not affected by the loss of the apo(a) region responsible for binding oxidized phospholipids. Preferential association of PCSK9 with Lp(a) versus low-density lipoprotein (1.7-fold increase) was seen in subjects with high Lp(a) and normal low-density lipoprotein. Finally, Lp(a)-associated PCSK9 levels directly correlated with plasma Lp(a) levels but not with total plasma PCSK9 levels. CONCLUSIONS: Our results show, for the first time, that plasma PCSK9 is found in association with Lp(a) particles in humans with high Lp(a) levels and in mice carrying human Lp(a). Lp(a)-bound PCSK9 may be pursued as a biomarker for cardiovascular risk.

Effect of PCSK9 inhibition on plasma levels of small dense low density lipoprotein-cholesterol and 7-ketocholesterol.

BACKGROUND: Oxidized forms of cholesterol (oxysterols) are implicated in atherogenesis and can accumulate in the body via direct absorption from food or through oxidative reactions of endogenous cholesterol, inducing the formation of LDL particles loaded with oxidized cholesterol. It remains unknown whether drastic reductions in LDL-cholesterol (LDL-C) are associated with changes in circulating oxysterols and whether small dense LDL (sdLDL) are more likely to carry these oxysterols and susceptible to the effects of PCSK9 inhibition (PCSK9i). OBJECTIVE: We investigate the effect of LDL-C reduction accomplished via PCSK9i on changes in plasma levels of sdLDL-cholesterol (sdLDL-C) and a common, stable oxysterol, 7-ketocholesterol (7-KC), among 134 patients referred to our Preventive Cardiology clinic. METHODS: Plasma lipid panel, sdLDL-C, and 7-KC measurements were obtained from patients before and after initiation of PCSK9i. RESULTS: The intervention caused a significant lowering of LDL-C (-55.4 %). The changes in sdLDL-C levels (mean reduction 51.4 %) were highly correlated with the reductions in LDL-C levels (R = 0.829, p < 0.001). Interestingly, whereas changes in plasma free 7-KC levels with PCSK9i treatment were much smaller than (-6.6 %) and did not parallel those of LDL-C and sdLDL-C levels, they did significantly correlate with changes in triglycerides and very low-density lipoprotein-cholesterol (VLDL-C) levels (R = 0.219, p = 0.025). CONCLUSION: Our findings suggest a non-preferential clearance of LDL subparticles as a consequence of LDL receptor upregulation caused by PCSK9 inhibition. Moreover, the lack of significant reduction in 7-KC with PCSK9i suggests that 7-KC may be in part carried by VLDL and lost during lipoprotein processing leading to LDL formation.

Mutation of SIVA, a candidate metastasis gene identified from clonally related bilateral breast cancers, promotes breast cancer cell spread in vitro and in vivo.

Metastasis is the most dreaded outcome after a breast cancer diagnosis, and little is known regarding what triggers or promotes breast cancer to spread distally, or how to prevent or eradicate metastasis effectively. Bilateral breast cancers are an uncommon form of breast cancers. In our study, a percentage of bilateral breast cancers were clonally related based on copy number variation profiling. Whole exome sequencing and comparative sequence analysis revealed that a limited number of somatic mutations were acquired in this "breast-to-breast" metastasis that might promote breast cancer distant spread. One somatic mutation acquired was SIVA-D160N that displayed pro-metastatic phenotypes in vivo and in vitro. Over-expression of SIVA-D160N promoted migration and invasion of human MB-MDA-231 breast cancer cells in vitro, consistent with a dominant negative interfering function. When introduced via tail vein injection, 231 cells over-expressing SIVA-D160N displayed enhanced distant spread on IVIS imaging. Over-expression of SIVA-D160N promoted invasion and anchorage independent growth of mouse 4T1 breast cancer cells in vitro. When introduced orthotopically via mammary fat pad injection in syngeneic Balb/c mice, over-expression of SIVA-D160N in 4T1 cells increased orthotopically implanted mammary gland tumor growth as well as liver metastasis. Clonally related bilateral breast cancers represented a novel system to investigate metastasis and revealed a role of SIVA-D160N in breast cancer metastasis. Further characterization and understanding of SIVA function, and that of its interacting proteins, may elucidate mechanisms of breast cancer metastasis, providing clinically useful biomarkers and therapeutic targets.

Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program.

Breast cancer is a leading cause of cancer and, therefore, a major health threat for women in the United States and worldwide. We have seen over the years major advances in breast cancer prevention and care. Breast cancer screening with mammography leads to reduction in breast cancer mortality, and breast cancer prevention treatment with antiestrogens results in reduction in breast cancer incidence. More progress, however, is urgently needed for this common cancer that affects 1 in 11 American women in their lifetime. Not all women have the same breast cancer risk. A personalized approach is highly desirable as women with higher breast cancer risk may benefit from more intense breast cancer screening and/or prevention intervention while lower risk women may be spared with the cost, inconvenience, and emotional burden of these procedures. In addition to age, demographics, family history, lifestyle, and personal health, genetics is an important determinant of an individual's risk for breast cancer. Over the past 10 years, advances in cancer genomics identified multiple common genetic variants from population studies that collectively can contribute significantly to an individual's breast cancer risk. The effects of these genetic variants can be summarized as a "polygenic risk score" (PRS). We are among the first groups to prospectively evaluate the performance of these risk prediction instruments among women veterans of the Million Veteran Program (MVP). A 313-variant PRS (PRS313) predicted incident breast cancer for a prospective cohort of European (EUR) ancestry women veterans with an area under the receiver operating characteristic curve (AUC) of 0.622. The PRS313 performed less well for AFR ancestry however, with an AUC of 0.579. This is not surprising as most genome-wide association studies were conducted in people of European ancestry. This is an important area of health disparity and unmet need. The large population size and diversity of the MVP provide a unique and important opportunity to explore novel approaches to produce accurate and clinically useful genetic risk prediction instruments for minority populations.

Disaggregating Latino nativity in equity research using electronic health records.

OBJECTIVE: To develop and validate prediction models for inference of Latino nativity to advance health equity research. DATA SOURCES/STUDY SETTING: This study used electronic health records (EHRs) from 19,985 Latino children with self-reported country of birth seeking care from January 1, 2012 to December 31, 2018 at 456 community health centers (CHCs) across 15 states along with census-tract geocoded neighborhood composition and surname data. STUDY DESIGN: We constructed and evaluated the performance of prediction models within a broad machine learning framework (Super Learner) for the estimation of Latino nativity. Outcomes included binary indicators denoting nativity (US vs. foreign-born) and Latino country of birth (Mexican, Cuban, Guatemalan). The performance of these models was compared using the area under the receiver operating characteristics curve (AUC) from an externally withheld patient sample. DATA COLLECTION/EXTRACTION METHODS: Census surname lists, census neighborhood composition, and Forebears administrative data were linked to EHR data. PRINCIPAL FINDINGS: Of the 19,985 Latino patients, 10.7% reported a non-US country of birth (5.1% Mexican, 4.7% Guatemalan, 0.8% Cuban). Overall, prediction models for nativity showed outstanding performance with external validation (US-born vs. foreign: AUC = 0.90; Mexican vs. non-Mexican: AUC = 0.89; Guatemalan vs. non-Guatemalan: AUC = 0.95; Cuban vs. non-Cuban: AUC = 0.99). CONCLUSIONS: Among challenges facing health equity researchers in health services is the absence of methods for data disaggregation, and the specific ability to determine Latino country of birth (nativity) to inform disparities. Recent interest in more robust health equity research has called attention to the importance of data disaggregation. In a multistate network of CHCs using multilevel inputs from EHR data linked to surname and community data, we developed and validated novel prediction models for the use of available EHR data to infer Latino nativity for health disparities research in primary care and health services research, which is a significant potential methodologic advance in studying this population.

Clinical Correlates of Venetoclax-Based Combination Sensitivities to Augment Acute Myeloid Leukemia Therapy.

The BCL2 inhibitor venetoclax combined with the hypomethylating agent azacytidine shows significant clinical benefit in a subset of patients with acute myeloid leukemia (AML); however, resistance limits response and durability. We prospectively profiled the ex vivo activity of 25 venetoclax-inclusive combinations on primary AML patient samples to identify those with improved potency and synergy compared with venetoclax + azacytidine (Ven + azacytidine). Combination sensitivities correlated with tumor cell state to discern three patterns: primitive selectivity resembling Ven + azacytidine, monocytic selectivity, and broad efficacy independent of cell state. Incorporation of immunophenotype, mutation, and cytogenetic features further stratified combination sensitivity for distinct patient subtypes. We dissect the biology underlying the broad, cell state-independent efficacy for the combination of venetoclax plus the JAK1/2 inhibitor ruxolitinib. Together, these findings support opportunities for expanding the impact of venetoclax-based drug combinations in AML by leveraging clinical and molecular biomarkers associated with ex vivo responses. SIGNIFICANCE: By mapping drug sensitivity data to clinical features and tumor cell state, we identify novel venetoclax combinations targeting patient subtypes who lack sensitivity to Ven + azacytidine. This provides a framework for a taxonomy of AML informed by readily available sets of clinical and genetic features obtained as part of standard care. See related commentary by Becker, p. 437 . This article is featured in Selected Articles from This Issue, p. 419.