Aguet, François

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract The detection of genetic abnormalities is required during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this relies on invasive bone marrow (BM) biopsies, severely limiting early detection, frequent longitudinal monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. To address these needs, we recently launched GenoPredicta, a CLIA-approved LDT that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively characterizing MM genomes with whole genome sequencing (WGS) from as few as 50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Briefly, tumor cells are isolated from samples using fluorescence-activated cell sorting and subjected to WGS, from which copy number alterations, structural variants, and short variants (SNVs/indels) are identified using a fully automated pipeline generating physician-ready clinical reports from raw sequencing data in ∼6h. Analytical validation of GenoPredicta showed complete concordance with FISH results. Identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding MM immunotherapies is becoming increasingly important. Here, we describe GenoPredicta results from relapsed/refractory MM patients, highlighting resistance-conferring alterations that can only be detected by WGS, such as deletions in the kilo- to megabase scales that are observed in conjunction with SNVs and indels, leading to biallelic loss/inactivation of the gene, including at subclonal levels. In addition to biallelic loss of BCMA and GPRC5D in response to CAR T or T cell engager therapies, we observed similar resistance mechanisms for targets of immunomodulatory drugs, e.g., CRBN. The observation of these resistance mechanisms was consistent with patients’ clinical histories. In summary, we demonstrate that low input WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with CTC-based measurements enabling comprehensive profiling of the MM genome. Crucially, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection and guidance of therapy. The dramatically improved variant calling ability from WGS, especially in low-input CTC applications, extends to other malignancies and will gain wider adoption as sequencing costs continue to decrease. Citation Format: Bruno Paiva, Peter Voorhees, Patricia T. Greipp, Danielle Sookiasian, Julian Hess, Marisa DeMeo, Vicki Pounder, Sarah Calkins, Reid Meyer, Linda B. Baughn, Christine-Ivy Liacos, Meletios-Athanasios Dimopoulos, Alexandra Papadimou, Taouxi Konstantina, Efstathios Kastritis, Jesus Berdeja, Daniel Auclair, Valentina Nardi, Thomas Mullen, Francois Aguet, Shaji Kunnathu Kumar. Whole genome sequencing of multiple myeloma genomes with a novel clinical assay enables identification of genetic alterations underlying immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1067.

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of >60 viably frozen samples from >30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.

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 Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

PURPOSE Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models. METHODS We used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors. RESULTS We observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway ( KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort. CONCLUSION SMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.