Citations

Abstract Background: The cellular balance between saturated and monounsaturated fatty acids is a defining feature of tumor lipid metabolism, with SCD1 functioning as a key modulator through the production of MUFAs. Elevated SCD1 activity and MUFA accumulation have been documented in hepatocellular carcinomas, although the consequences of restricting MUFA synthesis have not been fully clarified. Methods: A selective SCD1 inhibitor, SSI-4, was evaluated across established human HCC cell lines and xenograft models. Measurements included cell viability, lipid compositional analyses, and characterization of stress response pathways at both molecular and biochemical levels. The effects of MUFA depletion were studied in vitro and in vivo, with attention to ER stress, apoptosis, ferroptosis markers, and autophagy induction. Results: SSI-4 treatment led to marked reduction in cellular MUFA content and an increase in saturated fatty acid pools. The resulting metabolic stress correlated with activation of unfolded protein response components, caspase-mediated cell death, and the upregulation of ferroptosis-associated genes. Triglyceride and MUFA levels declined precipitously, as confirmed by mass spectrometry-based lipidomics. Tumor growth was suppressed in PDX models and evidence of autophagic flux was apparent in sensitive cell lines. Variability in response highlights metabolic heterogeneity in HCC. Conclusions: Targeted disruption of MUFA biosynthesis inflicts metabolic vulnerability on HCC cells, precipitating membrane alterations and activation of multiple cell death programs. These findings recommend further investigation into metabolic interventions exploiting MUFA depletion, with consideration for stratifying patients according to lipid metabolic phenotypes. Funding: This work is supported by Mayo Clinic Comprehensive Cancer Center and the Jay and Deanie Stein Career Development Award for Cancer Research at Mayo Clinic Jacksonville and the Mayo Clinic Translational Hepatobiliary Cancer SPORE P50CA210964 Career Enhancement Program (CEP) and Florida Department of Health/Florida Cancer Innovation Fund C25C23. Citation Format: Justyna J. Gleba, Victoria N. Rios, Brian M. Necela, Skyeler M. Klinge, Matt L. Pawlush, Mike J. Redig, Aylin Alasonyalilar-Demirer, Han W. Tun, John A. Copland III, . Disrupting tumor lipid homeostasis by MUFA depletion drives cell death in HCC [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 7333.

Abstract Background: Digital access, health literacy, and confidence in obtaining cancer information are well-known factors that influence cancer beliefs and behaviors. However, less is understood about whether these information-related factors vary across regions within a cancer center's catchment area and how they collectively relate to cancer beliefs in large community samples. This study investigated regional differences in digital access, health literacy, and confidence in seeking information, and examined the connections between these factors and cancer risk perceptions, fatalistic beliefs, prevention confusion, and willingness to participate in cancer clinical trials. Methods: Data (N=2,334) from the 2023 Mayo Clinic Comprehensive Cancer Center’s Cancer-Focused Needs Assessment, conducted across catchment areas in Arizona, Florida, and the Midwest (Minnesota, Wisconsin, Iowa). Regional differences in digital access (Internet use and device ownership), health literacy, and confidence in obtaining cancer information were analyzed using multivariable ANCOVAs and logistic regressions, adjusting for sociodemographic covariates. Follow-up models examined whether these information precursors are associated with perceived cancer risk, fatalistic beliefs, confusion about recommendations, and willingness to participate in cancer clinical trials, adjusting for the same covariates. Results: Regions showed minimal differences in digital access and health literacy. Health literacy was instead associated with ethnicity, age, gender, and education (all p<.001). Confidence in obtaining cancer information followed similar trends, with significant influences of ethnicity and gender (p<.01). Internet use was strongly associated with sociodemographic factors (all p<.01). Additionally, the information precursors consistently correlated with cancer beliefs across the full sample: Higher health literacy and greater confidence in obtaining cancer information were associated with increased perceived cancer risk, reduced cancer fatalism, and less confusion regarding prevention guidelines (ps < .001). Internet use and device access were significantly related to a greater willingness to participate in clinical trials (ps < .01). These relationships persisted even after controlling for sociodemographic covariates. Conclusions: Regional differences in digital access and health literacy were minimal, indicating a consistent information environment. Within this context, individual sociodemographic and information-related factors played a larger role, strongly shaping cancer beliefs. These findings underscore technology access and information empowerment as key targets for enhancing cancer prevention knowledge and research engagement, and indicate that the effects of information empowerment are primarily driven at the individual rather than the geographic level. Citation Format: Tingyu Zou, Monica L. Albertie, Emelina Asto-Flores, Manisha Salinas, Farhia Omar, Adeline Abbenyi, Noreen Stephenson, Nuwanthi Heendeniya, James R. Cerhan, Kathleen J. Yost, Folakemi T. Odedina. Digital access, health literacy, and cancer beliefs: Findings from a multi-region community survey [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 7553.

Abstract Background: Symmetry principles, a long foundational framework in physics and chemistry, have rarely been applied to understand biological phenotypes especially in cancers. Here we examine whether symmetric relationships in gene expression can characterize and differentiate healthy from disease conditions. Method: To test this concept, we built a hybrid machine-learning framework, Learning-Based Invariant Feature Engineering (LIFE), that applies two symmetric invariant feature functions, IFF1 and IFF2, to all possible gene pairs in bulk transcriptomic datasets to identify invariant feature genes (IFGs) - gene pairs whose transformed expression values by either IFF1 or IFF2 produce quasi-constant single-value outputs within a phenotype despite inter-individual variability. Results: Using bulk transcriptomes from 25 normal organs (GTEx) and 25 cancer types (TCGA), we computed IFF values for all normally distributed gene pairs, selected the 1000 most stable pairs per phenotype, and evaluated their performance in multiclass classification with five-fold cross-validation and independent hold-out testing. IFGs generated >70% accuracy across organs and cancers, establishing the existence of robust phenotype-specific symmetry “fingerprints.” Mapping approved and experimental drug targets onto networks construction from IFGs (IF-Nets) showed strong enrichment of hubs in cancer networks and highlighted the use of IF-Nets as drug discovery platforms in cancer treatment. Conclusion: Our findings demonstrate that gene-expression symmetry as a unifying organizing principle for phenotype definition and illustrate how IFGs and IF-Nets can guide biomarker design and symmetry-aware pharmacological intervention via “symmetry breaking.” Citation Format: Cristina Correia, Choong-Yong Ung, Cheng Zhang, Shizhen Zhu, Hu Li, . Learning-based invariant feature engineering reveals symmetry-encoded fingerprints of cancers that facilitate drug discovery [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 5516.

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies due to its silent clinical onset, rapid progression, and limited responsiveness to current treatment options. These features underscore the urgent need for early detection strategies and for new molecular targets that can be exploited therapeutically. Epithelial Cell Transforming Sequence 2 (ECT2) is a Rho family guanine nucleotide exchange factor that was originally identified as an oncoprotein and later shown to regulate cytokinesis. In this study, we examined the clinical relevance of ECT2 expression in PDAC and determined its functional contribution to transformed growth and tumorigenicity. Our analyses revealed that ECT2 expression increases at the earliest stages of PDAC development, remains persistently elevated during disease progression, and associates with reduced overall patient survival. Furthermore, a substantial fraction of ECT2 is aberrantly localized to the cytoplasm of PDAC cells. Functional studies demonstrated that depletion of ECT2 suppresses three-dimensional growth, invasive behavior, and tumor formation in vivo, while having little impact on PDAC cell cytokinesis. Mechanistically, we found that ECT2 is required for activation of Rac1 and RhoA and downstream MEK/ERK and ROCK signaling, respectively. Consistent with these results, analyses of PDAC patient datasets revealed a strong positive association between ECT2 expression and Rho GTPase as well as MEK/ERK and ROCK pathway signatures. Together, our data identify ECT2 as an early driver of PDAC transformation and highlight it as a promising therapeutic target. Citation Format: Dania Al-Qasrawi, Nayya N. Murray, Ryan A. Argo, Alicia K. Fleming Martinez, Prita Pandya, Anaya Y. Clarke, Kayla C. Winter, Murli Krishna, Peter Storz, Nicole R. Murray, Verline Justilien, . The Rho-GEF ECT2 links Rho GTPases to MEK/ERK pathway activation in pancreatic ductal adenocarcinoma [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 5993.

Abstract Glioma stem cells (GSCs) are considered a major driver of glioblastoma (GBM) progression and are highly resistant to standard cytotoxic treatments. BMP4 has been shown to promote GSC differentiation, enhance radiosensitivity, slow tumor growth, and extend survival in animal models. Despite this promise, BMP4 has yet to achieve clinical impact, owing largely to heterogeneous and nonlinear responses across preclinical experimental systems. To elucidate how BMP4 could function as an effective targeted differentiation therapy in GBM, we develop a mathematical model that describes the growth of a GBM tumor via a hierarchy of GSCs, progenitor and terminally differentiated cells. We parameterize our model using new radiotherapy and proliferation assay data (with and without BMP4 exposure) from twelve patient-derived GSC lines. This integration of model and data allows us, for the first time, to quantitatively capture patient-specific heterogeneity in BMP4 sensitivity. We perform global sensitivity analysis on the model, identifying proliferation rate and GSC self-renewal sensitivity as key determinants of BMP4 efficacy. These parameters act as model-derived biomarkers that distinguish BMP4-responsive tumors from non-responsive ones. In an in silico analysis across a broad cohort of virtual patients, we find that continuous BMP4 delivery from surgical resection through radiotherapy consistently outperforms a single-dose strategy. Virtual clinical trials further show that, without stratification using these model-derived biomarkers, BMP4 yields little observable therapeutic benefit. In contrast, selecting patients with more proliferative, BMP4-responsive GSCs markedly increases the likelihood of observing a significant treatment effect. Citation Format: Nicholas Harbour, Lee Curtin, Loizos Michaelides, Matthew E. Hubbard, Pamela Jackson, Vinitha Rani, Rajappa Kenchappa, Virginea Farias, Anna Carrano, Markus Owen, Alfredo Quinones-Hinojosa, Kristin Swanson. Virtual clinical trials of BMP4 differentiation therapy [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 6831.

Abstract Phosphatase and Tensin Homolog (PTEN) is a critical tumor suppressor frequently mutated in cancers, notably endometrial cancer (EC), where it drives early carcinogenesis. PTEN mutations disrupt lipid phosphatase activity, causing PI3K/AKT/mTOR pathway hyperactivation. However, clinical trials targeting this pathway in advanced EC yield suboptimal outcomes, with PTEN loss showing poorly predictive value for therapeutic response, indicating other mechanisms are involved. TCGA Pan-Cancer Atlas data show a 39.1% co-mutation rate of PTEN and PIK3CA in EC, much higher than the 3.6% pan-cancer rate, challenging the idea of mutual exclusivity in driver mutations within the same pathway. PTEN missense mutations are most common in EC (41.44%), with R130 as a hotspot, especially among PIK3CA-mutant patients (45.28%), while other cancers with frequent PTEN mutations show much lower R130 rates. These findings suggest PTEN R130 missense mutants undergo positive selection and may have unique oncogenic effects in EC.To characterize these mutants, a Tet-on inducible system in PTEN-null SPAC-1-S EC cells compared wild-type PTEN (PTENWT), loss of function mutant PTENR173C, and ultrahigh frequency mutant PTENR130G. PTENWT suppressed proliferation, PTENR173C showed reduced tumor suppressive activity, while PTENR130G increased proliferation, indicating a PTENWT-independent tumor-promoting function. In MFE 296 cells with PTEN R130Q/N332fs mutations, knockdown of PTENR130Q suppressed proliferation, providing functional evidence that PTENR130G has PTENWT-independent oncogenic activity in EC. To elucidate the underlying mechanisms, we performed quantitative proteomic and BioSITe analyses in SPAC-1-S cells inducibly expressing PTENWT or PTENR130G, enabling comprehensive mapping of the unique signaling alterations specific to this ultrahigh-frequency PTENR130G mutant. Our initial analysis revealed that PTENWT induction caused broad transcriptional suppression of oncogenic and structural pathways, including MAPK/ERBB, focal adhesion, ECM-receptor interaction, and lysosomal function, consistent with restored tumor-suppressive activity. In contrast, PTENR130G induction led to a distinct transcriptional response; while partially repressing lysosomal and adhesion pathways, stress-adaptive and metabolic programs like TGF-β signaling and glutathione metabolism remained active or were reactivated. These results suggest PTENR130G not only loses PTEN’s inhibitory functions but may gain new transcriptional and metabolic activities that promote cell survival and tumor progression. Overall, our analysis indicates wildtype PTEN restores signaling restraint, while PTENR130G reprograms the transcriptome toward a pro-cancer, stress-resilient state, suggesting a gain-of-function oncogenic role. Citation Format: Xiangyi Kong, Li Wang, Md Kamrul Hasan Khan, Jidong Wang, Huaijun Zhou, Xinyan Wu. Unique oncogenic mechanisms of ultrahigh-frequency mutant PTENR130Gin endometrial cancer [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 7654.

Abstract INTRODUCTION: Colorectal cancer (CRC) is the 2nd most fatal cancer in the U.S.; most benefits of CRC screening are from detection of advanced pre-cancerous lesions (APLs). With a goal to improve non-invasive stool test APL sensitivity, we sought novel APL signatures in stool metaproteomes (host- and gut microbiome-derived proteins) among cancer-free controls (CTRL), APL, and CRC patients. Since dysbiosis in gut microbiota is associated with CRC initiation, we also examined the microbial source of differentially expressed proteins (DEPs). METHODS: Mass spectrometry was performed on archival stool supernatants (n=63 patients; 15 CTRL, 32 APL [16 adenomas & 16 sessile serrated lesions], 16 CRC). A sectioning and database-enrichment strategy was applied for peptide-spectrum matching. Microbial and human-derived proteins were searched against the Integrated Gene Catalogue (IGC) and UniProt human reference proteome. DEPs were identified using thresholds of P < 0.05 and fold-change ≥ 1.5. Pathway annotation of human DEPs was performed with the REVIGO webserver, while gut microbial DEPs were functionally categorized using the KEGG Automatic Annotation Server (KAAS). To assess the taxonomic distribution of microbial DEPs, sequences were aligned against the NCBI non-redundant database using BLASTP. RESULTS: Mass spectrometry detected 879 human and 32,514 microbiome-derived proteins. When limiting detection to proteins present in ≥ 75% of patients, only 117 human and 204 microbial proteins remained. There were 17 (4,875), 288 (9,638), and 63 (3,053) unique human (microbial) proteins detected in CTRL, APL, and CRC groups, respectively. When the protein presence cutoff was set at >30% of patients, there were 52 (13 human [11 upregulated, 2 downregulated], 39 microbial [28 upregulated, 11 downregulated]) DEPs detected for the CRC + APL vs CTRL comparison. Some pathways associated with upregulated human DEPs included: inflammatory response, cellular response to iron, and endothelial cell migration. The top upregulated microbial DEPs were derived from the Phocaeicola, Lachnospiraceae, and Oscillospiraceae genera. The top downregulated microbial DEPs belonged to the Bacteroides, Eubacterium, Agathobacter, Coprococcus, and Blautia genera. CONCLUSION: This work highlights the biological diversity and molecular heterogeneity of the stool proteome. We identified several host and microbial protein markers that differentiate APLs and early CRC from controls. These results provide a foundation for future validation studies, which are necessary to determine the clinical utility of these protein biomarkers prior to their integration into a noninvasive stool test. Citation Format: Emmalee J. Northrop-Albrecht, Vinod K. Gupta, William R. Taylor, Jason P. Sinnwell, Doug W. Mahoney, Patrick H. Foote, Kelli N. Burger, Jaeyun Sung, John B. Kisiel, . Comprehensive investigation of stool metaproteomes among patients with precancerous lesions and colorectal cancer [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 7656.

Abstract Introduction: The intricate cellular structure is a constantly evolving environment where cells interact, communicate, and adapt to their surroundings. The fate, role, and actions of each cell are shaped by its specific location within the tissue. Therefore, studying the spatial proteome is crucial for gaining a deeper understanding of physiological or pathological processes. With significant advancements in microscopy and mass spectrometry, it has become feasible to explore the cellular proteome at low or even single cell numbers. However, spatial proteomics is an evolving field with scope for newer sample preparation and data analysis techniques. Our objective was to develop a sample preparation workflow for analyzing proteome from limited cells extracted from formalin fixed paraffin embedded (FFPE) tissue using laser capture microdissection (LCM). Methods: One hundred cells from the FFPE normal colon tissue were extracted by LCM and collected into an AFA-compatible 96-well plate. Decrosslinking was performed at 900C for 50 minutes followed by lysis in a buffer composed of 100 mM triethylammonium bicarbonate and 0.1% n-dodecyl-β-D-maltoside using adaptive focused acoustic (AFA) energetics in scanning mode for 5 minutes. After lysis, the proteins were digested using Trypsin/Lys-C mix using AFA energetics for 1 hour. The resulting peptides were acidified with trifluoroacetic acid and analyzed on a timsTOF Ultra 2 mass spectrometer coupled to a nanoElute 2 liquid chromatography system using a 15 cm (75 μm) IonOpticks column, in DDA-PASEF mode for a total run time of 34 minutes. The data were searched against the UniProt Human Reviewed protein database using MSFragger. Results: We utilized AFA energetics to extract and digest proteins from a limited number of cells isolated from FFPE tissue using LCM. This experiment was conducted with technical replicates to ensure accuracy and reliability. A total of 3,723 proteins were identified across the three replicates, with replicate 1 identifying 3,895 proteins, replicate 2 identifying 3,754 proteins, and replicate 3 identifying 3,519 proteins. In total, we identified 21,005 peptides, with replicate 1 identifying 22,896 peptides, replicate 2 identifying 22,377 peptides, and replicate 3 identifying 17,741 peptides from 100 colon cells. Notably, we observed a 70% overlap in the proteins identified across the three replicates, which highlights the reproducibility of the approach. Conclusion: The entire sample preparation process, after cell collection, was completed within a concise four-hour timeframe, demonstrating the efficiency of this methodology for analyzing protein profiles from small cell populations. Further, this approach is highly adaptable to automation and high-throughput applications. Its adaptability and scalability make it a promising strategy for spatial proteomics applications. Citation Format: Dong-Gi Mun, Kiran Mangalaparthi, Daigo Gunji, Amy J. French, Raghavendra Pasupuleti, Cristine Charlesworth, Sameer Vasantgadkar, DEB BHATTACHARYYA, Akhilesh Pandey. An AFA assisted workflow for mass-spectrometry based proteomics of FFPE samples [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 7657.

Abstract Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles (<1.67%) for MIRAI+PRS. Calibration for invasive cancer was poor for MIRAI with or without PRS. In summary, the MIRAI+PRS risk model did not result in significant difference of discriminatory accuracy or overall calibration compared to the MIRAI model, but there was evidence for improved calibration for women with 5-year risk below 1.67%. For invasive BC, the model had poor calibration regardless of whether PRS was included, underscoring the importance of training AI models for BC outcomes that are associated with a clinical intervention. Citation Format: Christopher G. Scott, Peter Kraft, Imon Banerjee, Ramon Correa Medero, Aaron D. Norman, Fergus J. Couch, Karla Kerlikowske, Stacey J. Winham, Celine M. Vachon. Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score [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 2779.

Abstract Background: Aberrations in telomere length have important implications in cancer development and progression. This study aimed to determine whether leukocyte telomere length (LTL) in patients with colorectal cancer (CRC) is associated with survival outcomes. We also investigated whether genetic variants in telomere maintenance genes are associated with survival in these patients. Methods: Blood specimens were collected from 1,007 patients prior to receiving chemotherapy or radiation. Genomic DNA was extracted using the Promega Maxwell RSC instrument. LTL was measured in triplicate using monochrome multiplex PCR, where the average amplification value of telomeric repeats was termed T. Similarly, PCR amplification of a single-copy reference gene was performed, and its average amplification value was termed S. The telomere length for each sample was expressed as the T/S ratio. Genotyping of single-nucleotide polymorphisms (SNPs) in TERC, TERT, and OBFC1 genes was conducted at the institutional Genome Analysis Core facility. Kaplan-Meier survival curves were generated to evaluate patient survival outcomes. Results: Younger individuals (∼25 years) exhibited nearly a twofold longer LTL compared with older individuals (∼75 years). No significant difference in LTL was observed between stage II and stage III CRC patients. A strong inverse correlation was observed between patient age and LTL (Spearman’s r = -0.48; p = 1.13 × 10-58). Females had significantly longer LTL than males (p = 3.97 × 10-5). The TERC SNP rs1317082 was significantly associated with both overall survival (OS) and disease-free survival (DFS) in the combined stage II and stage III patient cohort (p = 0.017 and p = 0.023, respectively). Similarly, the OBFC1 SNP rs9419958 was significantly associated with OS (p = 0.016). Importantly, LTL itself was significantly associated with both OS and DFS (p = 0.008 and p = 0.044, respectively) among the combined stage II and stage III patients. Kaplan-Meier survival analyses demonstrated that age- and sex-adjusted LTL was predictive of long-term survival outcomes. Conclusions: Survival among patients with stage II and III colorectal cancer is significantly influenced by LTL. Additionally, specific allelic variants of telomere maintenance genes, including TERC and OBFC1, are independently associated with improved survival, irrespective of LTL. These findings suggest that peripheral blood LTL measurement, along with telomere-related genotyping, may serve as a valuable prognostic marker for colorectal cancer outcomes. Funding: Individualizing colorectal cancer patient care using the host and tumor telomere phenotype (RO1 CA204013), Curtiss Fund (92541775), C-SiG Core(s): Epigenomics & Spatial Biology Core, and Clinical Core of the Mayo Clinic Center for Cell Signaling in Gastroenterology (P30DK084567) Citation Format: Estela M. Cruz Garcia, Gobinda Sarkar, Jun Chen, Shubham Sood, Kim Kossick, Daniel Schupack, Rondell Graham, Brooke Druliner, Zahra Heydari, Lauren Helgeson, Richard G. Cawthon, Lisa A. Boardman. Longer leukocyte telomeres and specific alleles of telomere maintenance genes are independently associated with improved survival of colon cancer patients [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 1919.