Citations

766 Background: BASECAMP-1 (NCT04981119) is a pre-screening study to identify patients with tumor-associated human leukocyte antigen (HLA)-A*02 loss of heterozygosity (LOH) for interventional studies, such as EVEREST-2 (NCT06051695), a phase 1/2 study of logic-gated chimeric antigen receptor T-cell (CAR T) therapy for MSLN-expressing cancers. MSLN is a cell surface protein expressed in several cancer types, including mesothelioma (MESO), colorectal (CRC), non-small cell lung (NSCLC), ovarian (OVCA), and pancreatic (PANC) cancer, which can be associated with poor prognosis (1). Longitudinal clinical, genomic, and biomarker data were collected from patients enrolled in BASECAMP-1, providing a large dataset for translational discovery and propensity scoring. The aim of this analysis was to determine whether MSLN expression measured by RNAseq and IHC are correlated among patients with solid tumors. Methods: In BASECAMP-1, tumor tissue from patients with germline heterozygous HLA-A*02 is tested for HLA-A*02 LOH using an investigational next generation sequencing device codeveloped with Tempus Labs (Lozac'hmeur, et al. NPJ Precis Oncol. 2024) that detects somatic alterations, including HLA LOH, and generates RNAseq data (eg, MSLN expression). Gene-level transcripts per million read (TPM) values were determined and the log2 TPM +1 was displayed in a scatter plot (the mean and standard deviation [SD] are provided in the Table). Patients with HLA-A*02 LOH also submit tissue for IHC testing for exploratory biomarkers (eg, MSLN expression using anti-MSLN clone 5B2). Statistical significance was determined with ANOVA or t-tests. Results: As of June 1, 2024, 314 patients had been screened for BASECAMP-1 and had RNAseq results; 34 patients also had MSLN IHC results. MSLN expression by RNAseq was consistently higher in patients with PANC or OVCA vs CRC or NSCLC ( P <0.001). MSLN expression by RNAseq and IHC were correlated overall ( P <0.001), particularly among patients with PANC and OVCA. Among the 8 patients with OVCA or PANC who were IHC+ for MSLN, only 1 had an RNAseq value below 6 log2 TPM+1; this patient had mesonephric-like ovarian adenocarcinoma, which may account for the low value. The one MESO sample with both RNAseq and IHC available was IHC+ and had a high RNAseq value. Conclusions: This analysis demonstrated high correlation between RNAseq and IHC MSLN expression in tumor tissue. 1. Faust, et al. Cancers. 2022. Clinical trial information: NCT04981119 . MSLN expression by RNASeq by tumor type and IHC status. Tumor Overall (N=314) IHC data available (n=34) MSLN Positive MSLN Negative n Mean TPM a (SD) n Mean TPM a (SD) n Mean TPM a (SD) All 314 5.1 (2.3) 20 6.0 (2.1) 14 2.3 (1.7) adNSCLC 33 3.8 (2.4) 2 4.7 (1.7) 7 1.5 (1.3) sqNSCLC 4 3.1 (1.8) 0 0 MESO 4 4.5 (4.2) 1 5.7 0 CRC 148 4.4 (2.0) 8 5.1 (2.1) 7 3.1 (1.6) PANC 100 6.1 (1.8) 3 7.8 (0.6) 0 OVCA 25 7.2 (2.1) 5 6.6 (2.8) 0 a MSLN log2 transcripts per million+1. ad, adenocarcinoma; sq, squamous.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

TPS2698 Background: Chimeric antigen receptor (CAR) T-cell therapies for treating solid tumors are challenging due to a lack of tumor-specific targets that discriminate cancer from normal cells. Previous studies using CEA T-cell receptors and T-cell engagers have resulted in dose-limiting, on-target, off-tumor toxicities (1,2). A2B530 is an autologous logic-gated, CEA-targeted Tmod CAR T-cell therapy that addresses the challenges of on-target, off-tumor toxicity by combining a CAR-activating receptor with a blocking receptor to discriminate tumor from normal cells (3). The activator recognizes CEA on the surface of both tumor and normal cells while specificity for tumor cells is provided by a blocker that binds HLA-A*02. In patients with both germline HLA-A*02 and tumor-associated HLA-A*02 LOH, the blocker prevents on-target, off-tumor toxicity on normal cells owing to retained HLA-A*02 expression (4). HLA-A*02 LOH can be detected using next-generation sequencing (Tempus AI, Inc.). With this definitive discriminator target, A2B530 can potentially provide a therapeutic window to treat patients with CEA-expressing solid tumors exhibiting HLA LOH. Methods: EVEREST-1 (NCT05736731) is a seamless, phase 1/2, open-label, nonrandomized study to evaluate the safety and efficacy of A2B530 in adult patients. Patients are enrolled through BASECAMP-1 (NCT04981119), a master prescreening study that identifies patients with HLA LOH at any time in the course of their disease. BASECAMP-1–eligible patients undergo leukapheresis and, when clinically appropriate, their banked T cells are manufactured for the EVEREST-1 study. The key inclusion criteria include histologically confirmed recurrent unresectable, locally advanced, or metastatic cancers associated with CEA expression: non-small cell lung, colorectal, or pancreatic cancers. Patients should have received ≥1 line of prior therapy such as checkpoint inhibitor, molecular targeted, or chemotherapy. The primary objective of phase 1 is to evaluate the safety and tolerability of A2B530 and determine the recommended phase 2 dose (RP2D). The dose-escalation portion is based on the Bayesian optimal interval design. The dose-expansion phase will confirm RP2D and collect biomarker data to further characterize A2B530. Phase 2 will assess overall response rate per RECIST v1.1. As of January 29, 2024, 8 patients have been enrolled on EVEREST-1. A2B530 was successfully manufactured for all patients, and all patients have received A2B530 infusion, with the first patient dosed in May 2023. Dose escalation is ongoing. 1. Parkhurst, et al. Mol Ther. 2022. 2. Tabernero, et al. J Clin Oncol. 2017. 3. Hamburger, et al. Mol Immunol. 2020. 4. Sandberg, et al. Sci Transl Med. 2022. Clinical trial information: NCT05736731 .

Abstract Aims Ferric carboxymaltose (FCM) is guideline-recommended for iron deficiency (ID) in heart failure with reduced ejection fraction (HFrEF). Despite a well-established safety profile, the magnitude and clinical significance of FCM-induced hypophosphataemia in HFrEF remains unclear. This pre-specified substudy of HEART-FID evaluated serum phosphate, 1,25-dihydroxyvitamin D, and plasma parathyroid hormone (PTH) subsequent to FCM. Methods and results HEART-FID was a randomized, double-blind, placebo-controlled trial of ambulatory patients with HFrEF and ID randomized to FCM versus placebo. This substudy assessed mean change from baseline across eight visits over 6 months for the following endpoints: serum phosphate, 25-hydroxyvitamin D, 1,25-dihydroxyvitamin D, and PTH, in addition to the clinical severity of potential hypophosphataemia. Overall, 133 patients (n = 62 FCM, n = 71 placebo) were prospectively enrolled. Mean age was 68 ± 11 years, 55 (41.4%) were women, and 29 (21.8%) had chronic kidney disease. Phosphate levels decreased in 34 (57.6%) patients in the FCM group compared with 7 (10.3%) in the placebo group. Mean change in phosphate levels reached a nadir at day 21 (−0.36 ± 0.27 mmol/L) subsequent to FCM infusion with 28 (51%) having moderate-to-severe hypophosphataemia. Reductions in 1,25-dihydroxyvitamin D were also observed, whilst PTH increased. These biochemical changes returned to baseline levels by day 91. Serum levels of 25-hydroxyvitamin D remained stable throughout the study. No serious adverse events associated with hypophosphataemia were reported. Conclusions Transient moderate-to-severe hypophosphataemia was frequent subsequent to FCM infusion, accompanied by 1,25-dihydroxyvitamin D decrease and PTH increase. Serum levels of 25-hydroxyvitamin D remained stable. No evidence of symptomatic hypophosphataemia was reported, collectively indicating FCM-related hypophosphataemia to be clinically benign and transient in HFrEF.

Abstract Rearrangements that place the oncogenes MYC, BCL2, or BCL6 adjacent to superenhancers are common in mature B-cell lymphomas. Lymphomas with diffuse large B-cell lymphoma (DLBCL) or high-grade morphology with both MYC and BCL2 rearrangements are classified as high-grade B-cell lymphoma with MYC and BCL2 rearrangements (“double hit”; HGBCL-DH-BCL2) and are associated with aggressive disease and poor outcomes. Although it is established that MYC rearrangements involving immunoglobulin (IG) loci are associated with inferior outcomes relative to those involving other non-IG superenhancers, the frequency of and mechanisms driving IG vs non-IG MYC rearrangements have not been elucidated. Here, we used custom targeted capture and/or whole-genome sequencing to characterize oncogene rearrangements across 883 mature B-cell lymphomas including Burkitt lymphoma, follicular lymphoma, DLBCL, and HGBCL-DH-BCL2 tumors. We demonstrate that, although BCL2 rearrangement topology is consistent across entities, HGBCL-DH-BCL2 have distinct MYC rearrangement architecture relative to tumors with single MYC rearrangements or with both MYC and BCL6 rearrangements (HGBCL-DH-BCL6), including both a higher frequency of non-IG rearrangements and different architecture of MYC::IGH rearrangements. The distinct MYC rearrangement patterns in HGBCL-DH-BCL2 occur on the background of high levels of somatic hypermutation across MYC partner loci in HGBCL-DH-BCL2, creating more opportunity to form these rearrangements. Furthermore, because 1 IGH allele is already disrupted by the existing BCL2 rearrangement, the MYC rearrangement architecture in HGBCL-DH-BCL2 likely reflects selective pressure to preserve both BCL2 and B-cell receptor expression. These data provide new mechanistic explanations for the distinct patterns of MYC rearrangements observed across different lymphoma entities.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Abstract Importance Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. The most common forms of monogenic kidney disorders include autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN), caused by mutations in COL4A3, COL4A4, or COL4A5 genes. These disorders have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Objective Our objectives were to investigate the interplay of polygenic and monogenic risk of kidney disease and to test the utility of polygenic risk scores for risk stratification in ADPKD and COL4-AN. Design We combined exome/genome sequencing, SNP micro-array, and electronic health record data from the UK Biobank (N = 469,835 participants) and the All of Us (N = 98,622 participants) datasets to test the effects of the genome-wide polygenic score (GPS) for CKD in ADPKD and COL4-AN variant carriers defined by strict variant classification criteria. We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 or kidney failure) and controls (eGFR > 90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The GPS was tested as a predictor of CKD in qualifying variant carriers and non-carriers using logistic regression after adjustment for age, sex, diabetes, and genetic ancestry. Results The GPS was predictive of CKD in ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55–3.37, P = 2.6E-05) as well as non-carriers (ORmeta=1.72 per SD, 95% CI = 1.69–1.76, P < E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN carriers (ORmeta=1.78, 95% CI = 1.22–2.58, P = 2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68–1.73 P < E-300). The carriers in the top GPS tertile had a 2.5-fold higher risk of CKD while the risk for carriers in the bottom GPS tertile was not different from the average population risk. Conclusions and Relevance Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by the differences in polygenic risk profiles. Accounting for polygenic factors improves risk stratification in monogenic kidney disease and may have clinical implications for genetic counseling.