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BACKGROUND We constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting. METHODS PRSmix + , a multi-trait PRS framework, is used to train a composite PRS (PRS multi ) in COPDGene non-Hispanic White participants ( n = 6,647). Associations of PRS multi with COPD status (GOLD 2–4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American ( n = 2,466), ECLIPSE ( n = 1,858), Mass General Brigham Biobank ( n = 15,152), and All of Us ( n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRS multi and were validated with proteomic data in COPDGene ( n = 5,173) and UK Biobank ( n = 5,012). RESULTS PRSmix + selected 7 traits for PRS multi . In multivariable models, PRS multi was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28–1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11–0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS multi outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION COPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Abstract RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide. We previously published the polygenic transcriptome risk score (PTRS) which uses the cumulative effect of predicted gene expression to construct genetic predictors for complex diseases, and we demonstrated the value of PTRS for COPD built on Genotype-Tissue Expression (GTEx) Lung tissue with significantly improved cross-ancestry portability. However, the postmortem collection and lack of lung disease status on GTEx samples likely affect results. We hypothesized that the performance of PTRS will be improved by using disease-relevant expression quantitative trait loci (eQTLs) to construct the score. Here, we aimed to improve performance of PTRS by leveraging disease-relevant eQTLs. METHODS: We constructed two transcriptome prediction models, Elastic Net (EN) and Prediction Using Models Informed by Chromatin conformation and Epigenomics (PUMICE), using TOPMed lung RNA-seq data from the Lung Tissue Research Consortium (LTRC), which included COPD patients undergoing lung surgery. We compared their performance with two published models built on RNA-seq from GTEx Lung tissue, GTEx-EN and GTEx-PUMICE. We first integrated each of four transcriptome models with multi-ancestry GWAS of FEV1/FVC ratio (Shrine et al. 2023) to generate transcriptome-wide association study (TWAS) results, which produced trait-associated genes. Pathway analysis was then conducted on significant TWAS genes. Finally, the PTRS was computed on TWAS genes that were included in the top 20 significantly enriched pathways. We tested performance of PTRS in race-stratified analysis for moderate-to-severe and severe COPD in COPDGene. RESULTS: Our models produced more significant TWAS genes (FDR<0.05) and had more overlaps with genes identified for FEV1/FVC ratio from Shrine et al. 2023 compared to two GTEx models (Fig. A). Additionally, our models had significantly higher AUC for predicting both moderate-to-severe and severe COPD in race-stratified analysis. The mean AUC from LTRC-EN for predicting severe COPD was 0.57 and 0.55 respectively for EUR and AFA, which was significantly higher than mean AUC of 0.54 and 0.51 from GTEx-EN (Delong p-value=8.15x10-10 for EUR and 1.42x10-3 for AFA) (Fig. B). Similarly, the PTRS derived from our models produced higher Odds Ratios (OR) per standard deviation. The mean OR from LTRC-PUMICE for moderate-to-severe COPD was 1.25 and 1.18 respectively for EUR and AFA, while it was 1.17 and 1.12 from GTEx-PUMICE (Fig. C). CONCLUSIONS: Our study demonstrates the value of leveraging disease-relevant RNA-seq to construct transcriptome prediction models and their improvement on identifying lung function genes and predicting COPD across ancestries.

Abstract Background: We previouslyreported that invasive disease-free survival (IDFS), the primary outcome, and distantdisease-free survival (DDFS), a secondary outcome, differed by menopausalstatus in a pre-specified analysis. Here,we report updates on IDFS and DDFS with additional follow-up, as well as distantrecurrence-free interval (DRFI) and post hoc analyses in premenopausal women.Methods: Eligibilitycriteria included women > 18 years of age with HR+, HER2-BC and 1-3 +LN and no contraindications to taxane and/or anthracycline basedCT. DRFI was defined as time to distant recurrence or death from breast cancer.Analyses were intent-to-treat among eligible pts. We performed a post hocanalysis evaluating IDFS between treatment arms in premenopausal pts with pN1mi.In addition, we conducted a two-year landmarked IDFS analysis by ovarianfunction suppression (OFS) or not in the ET arm, as well as by regularmenstrual periods or not in both treatment arms.Results: Among the 4,984eligible pts, there were 553 IDFS events and median follow-up of 6.1 years.Postmenopausal women do not have any IDFS or DDFS benefit with CT; however, 5-yearabsolute benefits for IDFS and DDFS with CT for premenopausal pts were 5.9% and3.3%, respectively. In pre-menopausal pts, CT was associated with improved DRFIfor all RS values < 25, with an absolute improvement of 2.3% for RS0-13 and of 2.8% for RS 14-25. Among premenopausal pts, 12.4% (n=206) had pNmi)disease. In a post hoc analysis, there was a trend for CT benefit for thosewith pNmi [hazard ratio (HR)=0.44, confidence interval (CI)=0.18-1.08]. Therewere only 22 IDFS events. Only 17.2% of premenopausal pts assigned to ETunderwent OFS in the first 24 months and in two-year landmarked analysis, therewas no IDFS difference in those who underwent OFS or not (HR=0.88,CI=0.47-1.63). In premenopausal women assigned to ET, 58.9% stopped havingperiods within the first 24 months, and have anumerically improved IDFS compared to those who continued regular periods (HR=1.48,CI: 0.92-2.40). Among premenopausal assigned to CT followed by ET, 80.8%stopped having periods within the first 24 months and have a numericallyimproved IDFS compared to those who continue to have regular periods (HR=1.56, CI:0.85-2.86).Discussion: In accordancewith the differential IDFS and DDFS benefit based upon menopausal status inS1007, premenopausal pts with 1-3+LN and RS < 25 had a statisticallysignificant improvement in DRFI with the addition of CT. A small proportion of S1007premenopausal participants underwent OFS. The role of OFS as it relates to CTbenefit cannot be determined from this study. A future randomized trial should address the clinical question if OFScan replace CT in premenopausal pts with HR+/HER2-, node-positive breast cancer.Funding: Supported by National Cancer Institute grants U10CA180888, U10CA180819,U10CA180820, U10CA180821, U10CA180868, U10CA180863, and in part by Susan G.Komen for the Cure® Research Program, The Hope Foundation for Cancer Research,Breast Cancer Research Foundation, and Genomic Health, Inc. Citation Format: Kevin M Kalinsky, William E Barlow, Julie R Gralow, Funda Meric-Bernstam, Kathy S Albain, Daniel F Hayes, Nancy U Lin, Edith A Perez, Lori A Goldstein, Stephen KL Chia, Sukhbinder Dhesy-Thind, Priya Rastogi, Emilio Alba, Suzette Delaloge, Miguel Martin, Catherine M Kelly, Manuel Ruiz-Borrego, Miguel Gil-Gil, Claudia H Arce-Salinas, Etienne GC Brain, Eun Sook Lee, Jean-Yves Pierga, Begoña Bermejo, Manuel Ramos-Vasquez, Kyung Hae Jung, Jean-Marc Ferrero, Anne Schott, Steven Shak, Priyanka Sharma, Danika Lew, Jieling Miao, Debasish Tripathy, Lajos Pusztai, Gabriel Hortobagyi. Updated results from a phase 3 randomized clinical trial in participants (pts) with 1-3 positive lymph nodes (LN), hormone receptor-positive (HR+) and HER2-negative (HER2-) breast cancer (BC) with recurrence score (RS) ≤ 25 randomized to endocrine therapy (ET) +/- chemotherapy (CT): SWOG S1007 (RxPONDER) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS2-07.

Abstract Funding: Supported by National Cancer Institute grants U10CA180888, U10CA180819, U10CA180820, U10CA180821, U10CA180868, U10CA180863; and in part by Susan G. Komen for the Cure® Research Program, The Hope Foundation for Cancer Research, Breast Cancer Research Foundation, and Genomic Health, Inc. Acknowledgement: The authors wish to thank Dr. Ana M. Gonzalez-Angulo, MD, for her invaluable contributions to the design and implementation of this study. Background: The clinical utility of the RS to determine CT benefit is well established in pts with HR+, HER2-, axillary lymph node (LN)-negative BC. Retrospective analyses from SWOG S8814 support the potential prognostic and predictive role of RS for CT benefit in postmenopausal pts with LN+ BC. SWOG S1007 is a prospective, randomized trial of endocrine therapy (ET) vs. chemoendocrine therapy (CET) in women with 1-3 +LN and a RS < 25 (NCT01272037). Methods: Eligibility criteria included women > 18 years of age with HR+, HER2- BC and 1-3 +LN and no contraindications to taxane and/or anthracycline based CT. Women with a RS < 25 were randomized to receive ET or CET in 1:1 randomization using 3 stratification factors: (1) RS (0-13 vs.14-25); (2) menopausal status; and (3) axillary nodal dissection vs. sentinel node biopsy. The primary objective was to determine the effect of CT on invasive disease-free survival (IDFS) and whether the effect depended on the RS. The primary analysis was to test for a significant interaction of the treatment arm and continuous RS using a Cox regression model for IDFS, adjusting for treatment, RS, and menopausal status. A total of 832 IDFS events were expected for the final analysis. Secondary objectives included overall survival (OS). The protocol specified that interaction between treatment and the stratification variables was to be tested and, if significant, separate analyses performed by stratum. Annual interim analyses were planned starting at 24% of events. At the third interim analysis with 410 IDFS events, the Data and Safety Monitoring Committee recommended reporting results, with a decision by the NCI’s Cancer Therapy Evaluation Program, the study sponsor. Results: Of the 9,383 women screened from 2/28/11-9/29/17, 5,083 pts (54.2%) were randomized. With a median follow-up of 5.1 years, 447 IDFS events have been observed. For the primary analysis, the interaction test for CT benefit and continuous RS was not statistically significant, p=0.30. In a model with CT, RS, and menopausal status (no interaction term), higher continuous RS was associated with worse IDFS [HR 1.06, 2-sided p<0.001, 95% Confidence Interval (CI) 1.04-1.07], and CT was associated with an improvement in IDFS (HR 0.81, p=0.026, 95% CI 0.67-0.98). In a pre-specified analysis, a significant interaction was identified between CT and menopausal status (p=0.004), necessitating separate analyses by menopausal status. In postmenopausal pts (N=3350, 67%), adjusting for continuous RS, the HR for CET vs. ET was not significant (HR=0.97, p=0.82, 95% CI 0.78-1.22; 5-year IDFS 91.6% vs. 91.9%) indicating no benefit from CT. In premenopausal pts (N=1665, 33%), the HR (0.54) was statistically significant (p=0.0004, 95% CI 0.38-0.76; 5-year IDFS 94.2% vs. 89.0%), indicating CT benefit. In premenopausal pts, ovarian suppression was performed in 15.9% vs. 3.7% (ET vs. CET), and 47.9% vs. 26.4% reported menstruation after 6 months of treatment. Although the number of events is limited, the HR for treatment adjusted by RS for OS in premenopausal pts was 0.47 (p=0.032, 95% CI 0.24-0.94). At this time, there is no differential effect with CT in regard to other stratification factors. Conclusions: There is a significant differential treatment effect of CT benefit based on RS for premenopausal vs. postmenopausal women requiring separate analyses. While only 54% of the protocol specified events are recorded and pts will be followed for 15 years, the current data show that adjuvant therapy can be de-escalated to ET alone in postmenopausal pts with a RS < 25 and 1-3 +LN. However, there is a strong IDFS benefit for CET in premenopausal pts, with an early indication of an OS improvement. Citation Format: Kevin Kalinsky, William E Barlow, Funda Meric-Bernstam, Julie R Gralow, Kathy S Albain, Daniel Hayes, Nancy Lin, Edith A Perez, Lori J Goldstein, Stephen Chia, Subkhbinder Dhesy-Thind, Priya Rastogi, Emilio Alba, Suzette Delaloge, Miguel Martín, Miguel Gil Gil, Claudia Arce-Salinas, Etienne Brain, In Hae Park, Jean-Yves Pierga, Ana Hernandez Lluch, Manuel Ramos Vasquez, Manuel Ruiz Borrego, Kyung Hae Jung, Jean-Marc Ferrero, Anne Schott, Steve Shak, Priyanka Sharma, Danika L Lew, Jieling Miao, Debu Tripathy, Gabriel Hortobagyi, Lajos Pusztai. First results from a phase III randomized clinical trial of standard adjuvant endocrine therapy (ET) +/- chemotherapy (CT) in patients (pts) with 1-3 positive nodes, hormone receptor-positive (HR+) and HER2-negative (HER2-) breast cancer (BC) with recurrence score (RS) < 25: SWOG S1007 (RxPonder) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS3-00.

Abstract Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

Abstract Background: Large-scale genome-wide association studies (GWAS) have identified ~35 loci associated with epithelial ovarian cancer risk. However, these genetic loci together explain only a small portion of the heritability of this malignancy. The large majority of the GWAS-identified variants are located in non-coding regions, thus possible causal genes in these loci remain largely unknown. We performed a transcriptome-wide association study (TWAS) to search for novel genetic loci for ovarian cancer risk and plausible causal genes at GWAS-identified loci. Method: Transcriptome data from normal ovarian tissue samples (n=68) and all tissue samples (n = 369), along with their high-density genotyping data, obtained from only European descendants included in the Genotype-Tissue Expression Project (GTEx), were used to build ovarian linear prediction models and cross-tissue models (to increase statistical power) using the elastic net method. Based on model performance, we evaluated 17,121 genes for their cis-predicted gene expressions in relation to ovarian cancer risk using summary statistics data generated in GWAS of ovarian cancer from 25,509 cases and 40,941 controls. MetaXcan was used to integrate gene expression prediction model with summary statistics. Results: We identified 35 genes with predicted expression levels associated with ovarian cancer risk at P value < 2.2 × 10-6, the Bonferroni corrected significance level for multiple comparisons. Of these, 12 genes at 4 genetic loci are located ≥500kb away from risk SNPs previously reported in GWAS, representing potential novel genetic loci for ovarian cancer risk. The remaining 23 genes at 12 loci are located within known ovarian cancer risk loci. Fifteen of these genes at the 12 loci have not been reported in previous studies. Analyses by ovarian cancer histological subtypes showed that the majority of these 35 genes are associated with serous invasive carcinoma. Several new associations were identified in histological subtype analyses. Conclusion: In this TWAS we identified multiple genes with predicted expressions related to ovarian cancer risk and provide substantial new information to enhance the understanding of ovarian cancer biology and genetics. Citation Format: Yingchang Lu, Joellen M. Schildkraut, Thomas A. Sellers, Lang Wu, Xingyi Guo, Bingshan Li, Y. Ann Chen, Jennifer B. Doherty, Simon Gayther, Ellen L. Goode, Hae Kyung Im, Siddhartha Kar, Kate Lawrenson, Ani W. Manichaikul, Jennifer B. Permuth, Brett M. Reid, Jamie K. Teer, Paul Pharoah, Wei Zheng, Jirong Long. Transcriptome-wide association study among 66,450 women to identify candidate susceptible genes for ovarian cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1308. doi:10.1158/1538-7445.AM2017-1308

Abstract Purpose: Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy. Experimental Design: We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas. Results: In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P < 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (Pper-allele = 2 × 10−2), with a stronger effect in the subset of women with optimally debulked tumors (Pper-allele = 4 × 10−3). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (Pper-allele = 9 × 10−3). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts. Conclusion: This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect. Clin Cancer Res; 17(16); 5490–500. ©2011 AACR.