Shahin, Mohamed H.

ABSTRACT Patient outcomes in advanced renal cell carcinoma (RCC) remain poor, with five‐year survival rates ranging from ~10% to 30%. Early projections of therapeutic outcomes could optimize precision medicine and accelerate drug development. While machine learning (ML) models integrating tumor growth inhibition (TGI) metrics have improved survival predictions over traditional models, their application in RCC remains unexplored. Herein, we used TGI metrics and baseline data to evaluate parametric (PM) and semi‐parametric (SPM) survival models alongside ML approaches for predicting progression‐free (PFS) and overall survival (OS) in 1839 RCC patients from four trials (evaluating sunitinib, axitinib, sorafenib, interferon‐alpha, and avelumab + axitinib). Data were split into training (70%) and testing (30%), and feature selection was used to determine parsimonious and robust models. Bootstrap resampling ( n = 100) was employed for models' validation, and performance was assessed using C‐index and Integrated Brier Score. In brief, training data results demonstrated that tree‐based ML models (random survival forest (RSF) and XGBoost) outperformed PM and SPM models in predicting PFS (C‐index: 0.783–0.785 vs. 0.725–0.738 for PM and SPM; p < 0.05) and OS (C‐index: 0.77–0.867 vs. 0.750–0.758 for PM and SPM; p < 0.05), with RSF achieving better prediction of PFS and OS using only 3–5 covariates, compared to 9–35 with other tested methods. Tree‐based methods were also superior in the testing data. SHapley Additive exPlanations revealed nonlinear relationships among top predictors, including TGI metrics, underscoring the ability of tree‐based methods to capture complex prognostic interactions. Further validation is required to confirm models' generalizability to additional therapies and patients with differing tumor severity.

Introduction: Hydrochlorothiazide (HCTZ) is among the most commonly prescribed antihypertensives in the US, yet <50% of HCTZ treated patients achieve blood pressure (BP) control. Hypothesis: Integrating metabolomic and genomic profiles of HCTZ treated patients could identify novel pathways/biomarkers for optimizing the use of HCTZ. Methods: Primary analysis included 228 white hypertensives treated with HCTZ from the Pharmacogenomic Evaluation of Antihypertensive Response (PEAR) study. Genotyping was determined via Illumina Omni 1M-Quad Chip and untargeted metabolomics was performed on baseline fasting plasma samples using a GC-TOF MS platform. Pathway analysis was used to integrate metabolites significantly associated with HCTZ BP response (FDR<.05) and genetic signals at p<5x10-5 from HCTZ BP genome-wide analysis. Replication was conducted in another 212 white HCTZ treated patients. Replicated genetic signals were used to create a BP response score by summing their BP lowering alleles. We validated this score by testing it in 196 white HCTZ treated patients in the Genetic Epidemiology of Responses to Antihypertensives (GERA) study. Results: Thirteen metabolites were significantly associated with HCTZ systolic BP (SBP) and diastolic BP (DBP) responses (FDR<.05). Metabolomics-genomics integration revealed significant metabolites along with rs2727563 PRKAG2, rs12604940 DCC, and rs13262930 EPHX2, in the netrin signalling pathway (p=1x10-5), as potential markers significantly influencing HCTZ BP response. We successfully replicated those 3 genetic signals and added their alleles to create a response score which explained 11.3% and 11.9% of the variability to HCTZ SBP and DBP responses, respectively. We found that patients carrying one “response” allele had a significantly worse response than carriers of six alleles (ΔSBP/ΔDBP: -1.5/1.2 vs -16.3/-10.4 mmHg, respectively, SBP score p=1x10-8 and DBP score p=3x10-9). We further validated this response score by testing it in the GERA (DBP score p=0.03, SBP score p=0.07). Conclusion: This study highlights the power of using different omics to identify novel pathways/biomarkers of drug response, and suggests that PRKAG2, DCC and EPHX2 might be important determinants of HCTZ BP response.

Introduction: β-blockers, including atenolol, are among the most widely prescribed antihypertensive drug classes. However, their use has been associated with hyperglycemia and incident diabetes, which poses concern for prescribers and patients. The Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study is a multicenter clinical trial with one of its objectives to identify genetic influences on adverse metabolic events in patients treated with atenolol. Here we report a genomics-metabolomics integrative approach to identify biomarkers associated with atenolol-induced glucose change. Methods: PEAR participants were genotyped using the Illumina Omni 1M-Quad Chip. Metabolite profiles were acquired through GC-TOF-MS. Association between baseline levels of metabolites and glucose change was conducted using a multivariate linear regression model adjusted for age, gender and baseline fasting glucose. Genomics-metabolomics integration included selecting SNPs within genes involved in the synthesis and degradation of top metabolites and testing their effect on glucose change in 234 atenolol-treated Caucasians in PEAR, adjusting for age, gender, baseline glucose and principal components 1 and 2. A false discovery rate (FDR) of .05 was used to adjust for multiple comparisons. Replication of significant SNPs was conducted in an additional 228 participants treated with atenolol in PEAR. Results: Metabolomics analysis revealed 3 metabolites associated with glucose change that passed FDR adjustment. The top metabolite signal was beta-alanine. Increasing levels of baseline beta-alanine was significantly correlated with greater glucose increases after atenolol treatment (p=.0003). A total of 79 SNPs in 12 genes in the beta-alanine pathway were explored. SNP rs2669429 in DPYS was significantly associated with glucose change, after FDR adjustment. We found that G allele carriers had significantly greater glucose change compared with non-carriers (p=.0006; β=+2.76 mg/dL). This finding was successfully replicated in the other 228 patients who received atenolol (p=.04; β=+1.86 mg/dL). Conclusions: These results suggest that beta alanine and DPYS rs2669429 SNP might be an important predictor of atenolol-induced dysglycemia.

Abstractβ‐Blockers’ heart rate (HR)‐lowering effect is an important determinant of the effectiveness for this class of drugs, yet it is variable among β‐blocker–treated patients. To date, genetic studies have revealed several genetic signals associated with HR response to β‐blockers. However, these genetic signals have not been consistently replicated across multiple independent cohorts. Here we sought to use data from 3 hypertension clinical trials to validate single‐nucleotide polymorphisms (SNPs) previously associated with the HR response to β‐blockers. Using linear regression analysis, we investigated the effects of 6 SNPs in 3 genes, including ADRB1, ADRB2, and GNB3, relative to the HR response following β‐blocker used in the PEAR (n = 757), PEAR‐2 (n = 368), and INVEST (n = 1401) trials, adjusting for baseline HR, age, sex, and ancestry. Atenolol was used in PEAR and INVEST, and metoprolol was used in PEAR‐2. We found that rs1042714 and rs1042713 in ADRB2 were significantly associated with HR response to both β‐blockers in whites (rs1042714 C‐allele carriers, meta‐analysis β = –0.95 beats per minute [bpm], meta‐analysis P = 3×10–4; rs1042713 A‐allele carriers, meta‐analysis β = –1.15 bpm, meta‐analysis P = 2×10–3). In conclusion, the results of our analyses provide strong evidence to support the hypothesis that rs1042714 and rs1042713 in the ADRB2 gene are important predictors of HR response to cardioselective β‐blockade in hypertensive patient cohorts.

Background Thiazide and thiazide‐like diuretics are first‐line medications for treating uncomplicated hypertension. However, their use has been associated with adverse metabolic events, including hyperglycemia and incident diabetes mellitus, with incompletely understood mechanisms. Our goal was to identify genomic variants associated with thiazide‐like diuretic/chlorthalidone‐induced glucose change. Methods and Results Genome‐wide analysis of glucose change after treatment with chlorthalidone was performed by race among the white (n=175) and black (n=135) participants from the PEAR ‐2 (Pharmacogenomic Evaluation of Antihypertensive Responses‐2). Single‐nucleotide polymorphisms with P <5×10 −8 were further prioritized using in silico analysis based on their expression quantitative trait loci function. Among blacks, an intronic single‐nucleotide polymorphism (rs9943291) in the HMGCS 2 was associated with increase in glucose levels following chlorthalidone treatment (ß=12.5; P =4.17×10 −8 ). G‐allele carriers of HMGCS 2 had higher glucose levels (glucose change=+16.29 mg/dL) post chlorthalidone treatment compared with noncarriers of G allele (glucose change=+2.80 mg/dL). This association was successfully replicated in an independent replication cohort of hydrochlorothiazide‐treated participants from the PEAR study (ß=5.54; P =0.023). A meta‐analysis of the 2 studies was performed by race in Meta‐Analysis Helper, where this single‐nucleotide polymorphism, rs9943291, was genome‐wide significant with a meta‐analysis P value of 3.71×10 −8 . HMGCS 2 , a part of the HMG ‐CoA synthase family, is important for ketogenesis and cholesterol synthesis pathways that are essential in glucose homeostasis. Conclusions These results suggest that HMGCS 2 is a promising candidate gene involved in chlorthalidone and Hydrochlorothiazide (HCTZ)‐induced glucose change. This may provide insights into the mechanisms involved in thiazide‐induced hyperglycemia that may ultimately facilitate personalized approaches to antihypertensive selection for hypertension treatment. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifiers: NCT 00246519 and NCT 01203852.

Background For many indications, the negative chronotropic effect of β‐blockers is important to their efficacy, yet the heart rate ( HR ) response to β‐blockers varies. Herein, we sought to use a genome‐wide association approach to identify novel single nucleotide polymorphisms ( SNP s) associated with HR response to β‐blockers. Methods and Results We first performed 4 genome‐wide association analyses for HR response to atenolol (a β1‐adrenergic receptor blocker) as: (1) monotherapy or (2) add‐on therapy, in 426 whites and 273 blacks separately from the PEAR (Pharmacogenomic Evaluation of Antihypertensive Responses) study. A meta‐analysis was then performed between the genome‐wide association analysis performed in PEAR atenolol monotherapy and add‐on therapy, in each race separately, using the inverse variance method assuming fixed effects. From this analysis, SNP s associated with HR response to atenolol at a P <1E‐05 were tested for replication in whites (n=200) and blacks (n=168) treated with metoprolol (a β1‐adrenergic receptor blocker). From the genome‐wide association meta‐analyses, SNP rs17117817 near olfactory receptor family10 subfamily‐p‐member1 ( OR 10P1 ), and SNP rs2364349 in sorting nexin‐9 ( SNX 9 ) replicated in blacks. The combined studies meta‐analysis P values for the rs17117817 and rs2364349 reached genome‐wide significance (rs17117817G‐allele; Meta‐β=5.53 beats per minute, Meta‐ P =2E‐09 and rs2364349 A‐allele; Meta‐β=3.5 beats per minute, Meta‐ P =1E‐08). Additionally, SNP s in the OR 10P1 and SNX 9 gene regions were also associated with HR response in whites. Conclusions This study highlights OR 10P1 and SNX 9 as novel genes associated with changes in HR in response to β‐blockers. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00246519.

Background Although hydrochlorothiazide ( HCTZ ) is a well‐established first‐line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure ( BP ) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response. Methods and Results First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway ( P =5.8E‐05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC 3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C‐allele carriers had a better BP response to HCTZ versus noncarriers (∆ SBP /∆ DBP : −11.4/−6.9 versus −6.8/−3.5 mm Hg; ∆ SBP P =6.7E‐04; ∆ DBP P =4.8E‐04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC 3 genomic region significantly associated with the BP response to HCTZ ( P <0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP ‐response ( r =−0.42; P =7E‐03) and SBP ‐response ( r =−0.36; P =2E‐02). Conclusions This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC 3 is a potential determinant of the BP response to HCTZ . Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00246519.