Teer, Jamie K.

ABSTRACT Chimeric antigen receptor T-cell (CART) and T-cell engager (TCE) therapies targeting B-cell maturation antigen (BCMA) are transforming the treatment landscape for relapsed multiple myeloma (MM). However, despite impressive initial response rates, most patients eventually relapse. To investigate this unmet medical need, we applied whole-genome sequencing (WGS) to MM cells from cohorts of 102 relapsed patients treated with anti-BCMA CART and TCE therapies. Several genomic alterations were associated with clinical outcomes, particularly primary refractoriness, including high genomic complexity and mutations in genes regulating plasma cell identity, which predicted resistance to therapy. Single-cell RNA sequencing further revealed that MM cells from refractory patients exhibited high proliferation signatures and reduced expression of TNFRSF17 (encoding BCMA), while were less enriched for plasma cell–associated transcriptional programs, a phenomenon we term “plasma cell identity escape.” This profile was strongly associated with immune dysregulation of CD8 T cells including increased activation and exhaustion. This evolution of MM toward a more proliferative and lineage-divergent state, refractory to the anti-BCMA T-cell redirecting therapies, was functionally validated in preclinical MM mouse models. Collectively, our results comprehensively define the cellular and molecular mechanisms underlying primary resistance to anti-BCMA therapies.

Abstract Background: Multiple myeloma (MM) is always preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage or high tumor cell burden (i.e. SLiM criteria, SV Rajkumar Lancet Onc 2014), as morphology and cytogenetics cannot reliably distinguish them. Methods: To identify genomic features underlying clinical behavior in MGUS and SMM, a total of 374 patients with SMM (n= 290) or MGUS (n=84) with available whole exome (whole exome sequencing, WES, n=190) or whole genome sequencing (whole genome sequencing WGS, n=184) were included in the study. Overall, none of the patients had evidence of SLiM or CRAB criteria at the time of the biopsy. Based on the center of origin and availability of WES and WGS, 227 and 97 patients were used to form our study training and validation set. The study was designed in two parts: 1) defining a new classification for MM precursor conditions; 2) integrating genomics to existing prognostic score to improve accuracy in identifying patients at imminent risk of progression. Overall, 68 patients in the training set were either lost to follow-up or enrolled in a clinical trial and they were only included in the classification part of the study. Results: By leveraging the distribution of established MM-defining genomic events (Maura et al. JCO 2024) in the training set, we developed a workflow that differentiates MGUS and SMM into two genomically distinct entities: one with evidence of neoplastic transformation (i.e., genomic MM) and one without (i.e., genomic MGUS). Overall, 39% of MGUS and 91% of SMM cases had genomic evidence of neoplastic transformation in the training set (i.e., genomic MM). When we applied the same workflow and classification to the validation set, 46% and 83% of MGUS and SMM had evidence of neoplastic transformation, respectively. Importantly, none of the patients classified as genomic-MGUS progressed to MM in either the training or validation cohorts, with a median follow-up of 40 and 61 months, respectively. Moreover, all patients with IMWG 2/20/20 high risk SMM were classified as genomic MM in both the training and validation set. Interestingly, 5 out of 62 (8%) patients enrolled in early intervention trials for high risk SMM were genomically classified as genomic-MGUS. Overall, these data demonstrated that the MM genomic background and neoplastic transformation can be acquired very early in time, even before the SMM phase, in line with what observed in solid tumors. To avoid overestimating the prognostic impact of myeloma-defining genomic events—which are, by definition, enriched in the genomic-MM group compared to genomic-MGUS—we restricted our prognostic analysis to patients in whom transformation has already occurred (genomic-MM). In fact, distinct genomic drivers may predict malignant transformation but not necessarily the risk of imminent progression to MM once transformation has occurred. After running 5-fold cross validations corrected for IMWG 2/20/20, only presence of RAS mutations, genomic events involving MYC, presence of neoplastic copy number variant (CNV) signatures and APOBEC mutagenesis were predictive of early progression in the training set. Adding the presence of high-risk myeloma-defining genomic events to the IMWG 2/20/20 model (i.e., genomic-IMWG) significantly improves the accuracy of predicting MM progression in both the training (c-index 0.69 vs. 0.74) and validation sets (c-index 0.74 vs. 0.79), compared to the IMWG 2/20/20 alone. Conclusion: By leveraging comprehensive genomic profiling integrated with the clinical IMWG 2/20/20 risk score, we propose a novel classification and prognostication system for MM precursor conditions, which will better define patients who may benefit from early therapeutic intervention versus watch-and-wait monitoring.

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

ImportanceImmune checkpoint inhibitors (ICIs) have been approved for treatment of microsatellite instable (MSI-H) metastatic colorectal cancer (mCRC), but factors associated with receipt and efficacy of ICIs in routine clinical practice remain largely unknown.ObjectiveTo identify factors associated with receipt of ICIs and associated survival outcomes among patients with mCRC in routine clinical practice.Design, Setting, and ParticipantsThis population-based cohort study used deidentified data from a nationwide electronic health record–derived database to include 18 932 patients diagnosed with mCRC between January 2013 and June 2019. Population-based patients were diagnosed with de novo mCRC and had at least 2 documented clinical visits on or after the date of diagnosis. The study analyses were performed between September 2020 and April 2021.ExposurePatients received ICI therapy and/or chemotherapy as part of a systemic treatment for mCRC.Main Outcomes and MeasuresThe outcomes were receipt of ICI therapy, overall survival (OS), and time to treatment discontinuation (TTD).ResultsIn this cohort study of 18 932 patients diagnosed with mCRC (10 537 [55.7%] male; 546 [2.9%] Asian, 2005 [10.6%] Black or African American, 1674 [8.8%] Hispanic, 12 338 [65.2%] White, 4043 [21.4%] unknown race or ethnicity; median [IQR] age at metastatic diagnosis, 64.6 [55.0-73.3] years), patients with MSI-H tumors had a significantly higher probability of receiving ICIs than those with microsatellite stable (MSS) tumors (odds ratio [OR], 22.66 [95% CI, 17.30-29.73]; P < .001), whereas patients initially diagnosed with synchronous mCRC had significantly lower odds of receiving ICIs than patients with metachronous mCRC (OR, 0.57 [95% CI, 0.45-0.73]; P < .001). Patients with MSI-H tumors who received ICIs as first line of therapy had significantly longer OS than those receiving chemotherapy only (HR, 0.37 [95% CI, 0.25-0.56]; P < .001). Among patients with MSS tumors, ICI-based therapy was associated with significantly longer OS for patients with high albumin level (vs low: HR, 0.28 [95% CI, 0.18-0.45]; P < .001) and antibiotic use (vs nonuse: HR, 0.43 [95% CI, 0.27-0.67]; P < .001), but a significantly shorter OS for patients with synchronous mCRC (vs metachronous: HR, 1.90 [95% CI, 1.24-2.89]; P = .003). In addition, 29 out of 235 patients with MSS tumors (12.3%) experienced durable responses on ICI-based therapy. Similar patterns of associations with TTD were observed.Conclusions and RelevanceIn this cohort study of patients with mCRC, clinical characteristics were associated with different survival outcomes in patients treated with ICI-based therapy, with important clinical implications for patients with MSS tumors who are generally unresponsive to immunotherapy.

Abstract Introduction: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses to identify lncRNA expression patterns, provide insight into their cellular contexts and functions, and assess their relevance to HGSOC pathogenesis and genetic susceptibility. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cyst epithelia), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Reads were processed with a uniform bioinformatic and quality control pipeline, combined and batch corrected. Normalized TPM expression values were compared using the Wilcoxon rank sum test with >2-fold change and FDR <1% considered statistically significant. We computed weighted gene co-expression networks for each tissue type and tested for enrichment of differentially expressed genes (DEGs) and gene ontologies within co-expression modules. Results: Transcriptomes included 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most genes were expressed in all tissues, though 5% each showed HGSOC- or normal tissue-specific expression which comprised ≥50% lncRNA. Comparing HGSOCs to normal tissues identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4 × 10−39), SNHG1 (P=3.0 × 10−27), and HAGLR (P=2.0 × 10−24) at the HOXD risk locus. Lower proportions of lncRNA were seen in the HGSOC co-expression modules compared to those in normal tissues and less (3% vs. 15%) HGSOC modules were considered strongly lncRNA regulated (>50% lncRNA in module). Tissue-specific DElncRNA tended toward upregulation in HGSOC compared to ovarian epithelia with enriched modules associated with cell cycle regulation (P=1.5 × 10−38); and toward downregulation in HGSOC compared to bulk ovary with enriched modules associated with chemokine signaling/response (P=1.7 × 10−113). Conclusions: The lower proportion of lncRNA involved in HGSOC co-expression networks indicates a marked loss of regulation by lncRNA. Preliminary interrogation of HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional support for previously nominated risk genes. eQTL analysis has recently been completed and integration with GWAS are underway to elucidate lncRNA and lncRNA-regulated susceptibility genes and will be presented at the conference. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie K. Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson, Jonathan Tyrer. Patterns of lncRNA-regulated gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A004.

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Abstract Purpose: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cysts), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Raw reads were processed with a uniform bioinformatic and quality control pipeline; combined data were batch corrected. We tested for differences in median normalized CPM expression values using the Wilcoxon rank sum test with >2-fold change and a false discovery rate <1% considered statistically significant. We also conducted weighted gene co-expression network analysis in each tissue type. The hypergeometric test was used for enrichment of differentially expressed genes (DEGs) and gene ontologies within modules. Results: Transcriptomes comprised 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most (~90%) genes were expressed in all tissues; 5% each showed HGSOC- or normal tissue-specific expression and ≥50% were lncRNA. Comparing HGSOCs to ovarian epithelia and to bulk ovary identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4x10-39), SNHG1 (P=3.0x10-27), and HAGLR (P=2.0x10-24) at the HOXD risk locus. The highest expressed DElncRNA in precursor tissues was the HGSOC-downregulated MEG3 (P=1.7x10-67). DEGs were enriched in HGSOC co-expression modules associated with immune response, cell motility/localization, cell cycle regulation, angiogenesis, reproductive development, transcriptional regulation, and metabolic processes. Tissue-specific DElncRNA tended toward upregulation compared to ovarian epithelia with enriched modules associated with cell cycle regulation (hub=BUB1B; top DElncRNA TRPM2-AS, P=1.5x10-38); and toward downregulation compared to bulk ovary with enriched modules associated with chemokine signaling/response (hub= GADD45B, top DElncRNA RP11-87P3.1, P=1.7x10-113). Conclusion: HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional evidence to support previously nominated risk genes. Integration with eQTL and GWAS are underway to further elucidate novel HGSOC susceptibility genes. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson. Patterns of dysregulated coding and noncoding gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6065.

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 Background: Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P < 5 × 10−8). Most risk SNPs lie within non–protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets. Methods: Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10−5 > P > 5 × 10−8) overlapping lncRNAs. Results: Of 5,294 EOC-associated SNPs (P < 1.0 × 10−5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2 > 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10−4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression. Conclusions: lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC. Impact: lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116–25. ©2016 AACR.