Citations

Abstract Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, and despite advances in treatment, immune suppression remains an obstacle to effective therapy. Effector CD4+ T cells (CD4+ Teff) are critical for antitumor immunity, but their function is often inhibited by regulatory T cells (Treg), which accumulate in lung tumors and mediate suppressive functions through multiple mechanisms. This suppression leads to tumor progression and poor patient outcomes. However, the mechanisms underlying Treg-mediated suppression are not fully understood. Herein, we identify the extracellular adenosine 5-triphosphate receptor P2RX7 as a key regulator of Treg function in lung tumors. In a murine lung cancer model induced by Lewis lung carcinoma cells, we found that P2RX7 enhanced the suppressive capacity of tumor-infiltrating Tregs, promoting tumor growth. In T cell–specific P2RX7-knockout (P2RX7-KO) mice, reduced Treg infiltration was accompanied by increased CD4+ Teff accumulation and improved tumor control. Treg-specific P2RX7-KO mice exhibited reduced tumor growth, confirming a Treg-intrinsic role of P2RX7. Suppression assays revealed that tumor-infiltrating wild-type Tregs had greater suppressive activity compared with P2RX7-KO Tregs, which failed to inhibit type 1 and follicular helper T–like responses. This was associated with increased tumor-specific IgG production by lung B cells in P2RX7-KO mice. We also observed that wild-type Tregs expressed higher levels of the immunosuppressive molecule CTLA-4 when compared with P2RX7-KO Tregs. Thus, we conclude that P2RX7 expression on Tregs is essential for their suppressive function in lung cancer and targeting P2RX7 may constitute a strategy to improve lung cancer treatment by alleviating Treg-mediated immune suppression.

Abstract Description The presence of Foxp3+CD4+ regulatory T cells (Tregs) hinders the protective antitumor T cell responses to lung cancer. The accumulation of Tregs is linked to lung cancer progression, resulting in poorer survival rates. However, the mechanisms behind this accumulation are not fully understood. Tregs express elevated levels of the extracellular ATP (eATP) receptor P2RX7 in both human and mouse lung cancer. Here, we show that P2RX7 is crucial for the accumulation of Tregs in lung tumor, promoting tumor growth in a murine lung cancer model. We observed, in T cell-specific P2RX7-KO mice (CD4-cre P2rx7fl/fl), reduced Treg infiltration and a concomitant increase in tumor CD4+ effector T cells, leading to better tumor control. In contrast, P2RX7-KO Tregs accumulated in mediastinal lymph nodes, suggesting that blocking P2RX7-KO signaling impairs the Tregs migratory ability. P2RX7+ Tregs had increased expression of T-bet and KLRG1, which are associated with greater suppressive function. Finally, Treg-specific P2RX7-KO mice (Foxp3-Cre-ERT2 P2rx7fl/fl) had reduced tumor growth and Treg tumor accumulation, indicating that P2RX7 controls lung tumor Tregs in a cell-intrinsic way. These results suggest that P2RX7 promotes lung tumor Treg infiltration and subsequent loss of cancer control. They also suggest that P2RX7 blockade can be used as a therapy to improve lung cancer control, which we are currently exploring in proof-of-concept experiments. Funding Sources Mayo Clinic and National Institutes of Health (NIH - H.B.d.S.: R01 AI170649) Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Abstract Description Developing a universal influenza vaccine requires understanding how lung-resident memory CD4+ T cells are formed. These cells remain in the lung for a long time and strengthen the immune response to secondary infections. Tissue-resident helper cells (TRH), a subset of memory T cells, share characteristics with follicular helper cells (TFH) and boost the IgA and IgG production by lung-resident B cells. TRH cells express elevated levels of the extracellular ATP (eATP) sensor P2RX7. Here we demonstrate that P2RX7 ablation on T cells impairs the establishment of flu-specific TRH cells. P2RX7-induced decrease of TRH cells negatively impacted the protection against homologous challenge, since T cell-specific P2RX7-KO mice exhibit worse control of viral load than WT mice. P2RX7-KO mice also showed a reduction in flu-specific lung-resident germinal center-like B cells and lower levels of IgA and IgG in the lungs. P2RX7-KO did not induce major changes in the numbers of TFH-induced responses in the draining lymph nodes. Our single-cell RNAseq showed reduced expression of mitochondrial genes in P2RX7-KO TRH cells, which reflects decreased mitochondrial content presence in these cells. We observed an aberrant metabolic pathway usage by P2RX7-KO TRH cells, which beyond having less mitochondria also cease to utilize glycolytic metabolism to support protein synthesis. Our findings suggest the eATP-P2RX7 axis is required to establish TRH cells that protect against influenza. Funding Sources Mayo Clinic and National Institutes of Health (NIH - H.B.d.S.: R01 AI170649) Topic Categories Mucosal and Regional Immunology (MUC)

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

Abstract Background Physical activity (PA) is an established protective factor for colorectal cancer (CRC), but it is unclear if genetic variants modify this effect. To investigate this possibility, we conducted a genome-wide gene–PA interaction analysis. Methods Using logistic regression and two-step and joint tests, we analyzed interactions between common genetic variants across the genome and PA in relation to CRC risk. Self-reported PA levels were categorized as active (≥ 8.75 MET-h/wk) vs. inactive (< 8.75 MET-h/wk) and as study- and sex-specific quartiles of activity. Results PA had an overall protective effect on CRC (OR [active vs. inactive] = 0.85; 95%CI = 0.81–0.90). The two-step GxE method identified an interaction between rs4779584, an intergenic variant near the GREM1 and SCG5 genes, and PA for CRC risk (p-interaction = 2.6×10 − 8 ). Stratification by genotype at this locus showed a significant reduction in CRC risk by 20% in active vs. inactive participants with the CC genotype (OR = 0.80; 95%CI = 0.75–0.85), but no significant PA–CRC association among CT or TT carriers. When PA was modeled as quartiles, the 1-d.f. GxE test identified that rs56906466, an intergenic variant near the KCNG1 gene, modified the association between PA and CRC (p-interaction = 3.5×10 − 8 ). Stratification at this locus showed that increase in PA (highest vs. lowest quartile) was associated with a lower CRC risk solely among TT carriers (OR = 0.77; 95%CI = 0.72–0.82). Conclusions In summary, we identified two genetic variants that modified the association between PA and CRC risk. One of them, related to GREM1 and SCG5 , suggests that the bone morphogenetic protein (BMP)-related, inflammatory, and/or insulin signaling pathways may be associated with the protective influence of PA on colorectal carcinogenesis.

ABSTRACT Lung cancer is the leading cause of cancer-related deaths worldwide and, despite treatment advances, immune suppression remains an obstacle to effective therapy. Effector CD4 + T cells (CD4 + Teffs) are critical for antitumor immunity, but their function is often inhibited by regulatory T cells (Tregs), which accumulate in lung tumors and perform suppressive functions through multiple mechanisms. This suppression leads to tumor progression and poor patient outcomes. However, the mechanisms underlying Treg-mediated suppression are not fully understood. Here, we identify the extracellular ATP receptor P2RX7 as a key regulator of Treg function in lung tumors. Using a murine lung cancer model induced by Lewis lung carcinoma cells, we demonstrate that P2RX7 enhances the suppressive capacity of tumor-infiltrating Tregs, promoting tumor growth. In T cell-specific P2RX7-KO mice, reduced Treg infiltration was accompanied by increased CD4 + Teff accumulation and improved tumor control. Treg-specific P2RX7-KO mice exhibit reduced tumor growth, confirming a cell-intrinsic role of P2RX7 in Tregs. Suppression assays revealed that tumor-infiltrating WT Tregs have greater suppressive activity compared to P2RX7-KO Tregs, which failed to inhibit type 1 and Tfh-like responses. This was associated with increased tumor-specific IgG production by lung B cells in P2RX7-KO mice. We also observed that WT Tregs express higher levels of the immunosuppressive surface molecule CTLA-4 when compared to P2RX7-KO Tregs. In summary, we show that P2RX7 expression on Tregs is essential for their suppressive function in lung cancer, and targeting of P2RX7 may constitute a novel strategy to improve lung cancer treatment by alleviating Treg-mediated immune suppression.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3583.

Abstract Background: Pancreatic cancer is a leading cause of cancer-related death globally. Risk factors for pancreatic cancer include common genetic variants and potentially heavy alcohol consumption. We assessed if genetic variants modify the association between heavy alcohol consumption and pancreatic cancer risk. Methods: We conducted a genome-wide interaction analysis of single-nucleotide polymorphisms (SNP) by heavy alcohol consumption (more than three drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies. Our analysis included 3,707 cases and 4,167 controls from case–control studies and 1,098 cases and 1,162 controls from cohort studies. Fixed-effect meta-analyses were conducted. Results: A potential novel region of association on 10p11.22, lead SNP rs7898449 (interaction P value (Pinteraction) = 5.1 × 10−8 in the meta-analysis; Pinteraction = 2.1 × 10−9 in the case–control studies; Pinteraction = 0.91 in the cohort studies), was identified. An SNP correlated with this lead SNP is an expression quantitative trait locus for the neuropilin 1 gene. Of the 17 genomic regions with genome-wide significant evidence of association with pancreatic cancer in prior studies, we observed suggestive evidence that heavy alcohol consumption modified the association for one SNP near LINC00673, rs11655237 on 17q25.1 (Pinteraction = 0.004). Conclusions: We identified a novel genomic region that may be associated with pancreatic cancer risk in conjunction with heavy alcohol consumption located near an expression quantitative trait locus for neuropilin 1, a protein that plays an important role in the development and progression of pancreatic cancer. Impact: This work can provide insights into the etiology of pancreatic cancer, particularly in heavy drinkers.

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769 ), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047 ), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.