Citations

Abstract Background: Combination immune checkpoint inhibitor (ICI) therapy represents a standard-of-care for patients with metastatic melanoma. However, >40% of patients do not respond to treatment and severe immune-related toxicity affects up to 60% of patients. Accordingly, we need more precise ways to select patients for combination ICIs. To address this, we developed LiquidTME, a liquid biopsy method based on Spatial EcoTyper (Cancer Res (2025) 85 (8_Supplement_1): 153) that leverages a deep learning approach to noninvasively assess the tumor microenvironment from cell-free DNA (cfDNA) methylation data. LiquidTME was previously trained to predict ICI response using 78 patients with advanced melanoma treated at Yale University. Here we performed a blinded validation of LiquidTME in coordination with clinicians at Washington University (WashU). Methods: The WashU cohort consisted of pre-ICI plasma from 34 patients with advanced melanoma, each treated with ipilimumab and nivolumab (n=27) or relatlimab and nivolumab (n=7). ICI response was classified as durable clinical benefit (DCB) or no durable benefit (NDB) by a board-certified oncologist. Tumor mutational burden (TMB) was determined for 27 patients using commercial CLIA assays and analyzed as nonsynonymous mutations (mt) per megabase (Mb), using the FDA-approved 10 mt/Mb cutpoint for TMB-high and -low groups. The WashU cohort was sent to a scientific team that was blinded to all clinical and TMB data. Cell-free DNA was extracted from 2 mL plasma per patient and subjected to enzymatic methyl-seq (EM-seq) at a median depth of 15x. LiquidTME was performed on the resulting profiles, yielding a binary response prediction and a continuous response score for each sample. Test results were locked down and returned. Both assays were compared by AUC and two-sided Wilcoxon rank-sum tests for response classification, and Kaplan-Meier analysis for progression-free survival (PFS). Results: Median follow-up time of the cohort was 21.6 months. LiquidTME performed on cycle 1 day 1 pre-ICI plasma significantly stratified PFS with a hazard ratio (HR) of 0.27 (P = 0.005), with LiquidTME(+) patients achieving a median PFS of 1.7 years longer than LiquidTME(-) patients. LiquidTME distinguished DCB from NDB with an AUC of 0.77 (P = 0.007). Similar performance was seen across distinct combination ICI regimens. In patients with TMB data, LiquidTME maintained whole-cohort performance with a PFS HR of 0.30 (P < 0.03; AUC = 0.77). In contrast, TMB high vs. low subsets failed to stratify PFS (HR = 0.49; P = 0.17) or response (AUC = 0.53; P = 0.8). Conclusion: LiquidTME identified durable responders to combination ICI from pretreatment plasma in this blinded validation cohort of melanoma patients. Given its favorable performance, LiquidTME shows promise as a clinical tool to guide personalized immunotherapy decision-making. Citation Format: Aadel A. Chaudhuri, David Y. Chen, Tucker Hansen, Mirna Jarosz, Vincent A. Miller, Aaron M. Newman. Blinded clinical validation of LiquidTME, a cell-free DNA assay for predicting response to immunotherapy by noninvasively profiling the tumor microenvironment [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 1040.

Abstract Background: Spatial cellular ecosystems in the tumor microenvironment (TME) form dynamic signaling hubs that critically influence cancer disease progression and response to therapy, including response to immune checkpoint inhibitors (ICIs). However, tumor sampling bias and the impracticality of acquiring serial tumor biopsies have made it challenging to profile the TME clinically. To address this challenge, we developed Liquid EcoTyper, a multi-analyte AI framework to quantify TME spatial cellular ecosystems—termed spatial ecotypes (SEs)—noninvasively from cell-free DNA (cfDNA) methylation data. Methods: In previous work, we discovered nine spatial ecotypes, including SEs associated with ICI response (e.g., SE7 with benefit, SE4 with resistance), and quantified them from 1,249 bulk tumor RNA-seq profiles of melanoma, non-small cell lung cancer (NSCLC), and bladder cancer (BC) patients (Cancer Res (2025) 85 (8_Supplement_1): 153). Here we trained Liquid EcoTyper, an interpretable deep learning model, to transfer SE profiling to cell-free DNA using CpG methylation profiles. Performance was quantified using simulated and real data, including methylation profiles (EM-seq) of plasma cfDNA paired with tumor biopsy-derived SE levels determined by EM-seq (n=20 pairs) or 10x Visium (n=15 pairs). Clinical outcome prediction was evaluated using pretreatment plasma from patients treated with ICI alone (n=78 melanoma pts), chemotherapy and ICI (n=25 NSCLC pts), and enfortumab vedotin and ICI (n=10 localized muscle-invasive BC (MIBC) pts). Tumor mutational burden (TMB), tumor PD-L1, or circulating tumor DNA (ctDNA) were profiled as a comparator where possible. Results: We observed striking concordance between plasma-derived SE levels and tumor biopsy-confirmed SE levels from the same patients, demonstrating that liquid biopsy analysis of cfDNA methylomes can recapitulate TME spatial biology. Underscoring specificity, no reliable SE signal was detectable from peripheral blood mononuclear cells. In pre-ICI plasma, liquid SE levels were significantly associated with ICI-based response (SE7) versus resistance (SE4) in patients with melanoma, NSCLC, and MIBC, with a mean cross-cancer area under the curve (AUC) of 0.87 for SE7 (range 0.8-0.97) and 0.81 for SE4 (range 0.76-0.85)—far surpassing TMB, tumor PD-L1, and ctDNA in evaluable patients, both in binary analyses of response and multivariable models of survival. Our results also showed potential to predict pathologic complete response in the neoadjuvant setting. Conclusion: Liquid EcoTyper is a scalable noninvasive AI-based framework for spatiotemporal assessment of the TME. By enabling TME-informed risk stratification without surgical biopsies, Liquid EcoTyper has potential to enable more precise and actionable decision making in patients with solid tumors. Citation Format: Erin L. Brown, Wubing Zhang, Abul Usmani, Noah Earland, Ayesha Hashmi, Chibuzor Olelewe, Anushka Viswanathan, Pradeep S. Chauhan, Minji Kang, Chloé B. Steen, Hyun Soo Jeon, Susanna Avagyan, Irfan Alahi, Nicholas P. Semenkovich, Matteo Bergsagel, Janella C. Schwab, Chloe M. Sachs, Faridi Qaium, Peter K. Harris, Antonella Bacchiocchi, Qingyuan Cai, Andrew J. Gentles, Rondell P. Graham, Peter C. Lucas, Ryan C. Fields, Jacob J. Orme, Aaron S. Mansfield, Mario Sznol, Ruth Halaban, David Y. Chen, Aaron M. Newman, Aadel A. Chaudhuri. Liquid biopsy profiling of the tumor microenvironment to determine response to immunotherapy regimens across solid tumors [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 94.

Abstract Cholesterol has long been viewed as a structural component of cellular membranes, yet its role in DNA damage response remains poorly defined. Here, we show that ionizing radiation (IR) induces broad transcriptional upregulation of cholesterol biosynthesis, transport, and storage pathways, leading to sustained intracellular accumulation of cholesterol. BODIPY-cholesterol imaging, Filipin III staining, and quantitative subcellular fractionation confirmed selective nuclear enrichment of cholesterol following IR, coinciding with extensive chromatin remodeling. Proteomic profiling of cholesterol-interacting proteins revealed a strong enrichment of chromatin-associated factors, suggesting a previously unrecognized lipid-chromatin interface in irradiated cells. Functionally, cholesterol accumulation promoted chromatin compaction and repressive histone modification patterns, impairing DNA damage sensing and checkpoint activation despite increased underlying DNA lesions. These alterations facilitated aberrant mitotic entry, formation of cytoplasmic chromatin fragments (CCFs), and activation of cGAS-STING-NF-κB signaling. Cholesterol exposure ultimately diverted damaged cells toward a senescent fate, marked by Lamin B1 loss, p21 induction, and a robust senescence-associated secretory phenotype (SASP) enriched for IL-1 and TNF family cytokines. In vivo, dietary cholesterol exacerbated radiation-induced inflammation and weakened systemic anti-tumor immunity. Transcriptomic analyses of immunotherapy-treated patients revealed that tumors enriched for cholesterol import and senescence signatures were associated with poor response to PD-L1 blockade and worse survival. Together, these findings identify a cholesterol-chromatin-senescence axis that links metabolic remodeling to defective DNA damage signaling and immune suppression after radiation, highlighting cholesterol metabolism as a potential therapeutic vulnerability in cancer therapy. Citation Format: Yiqun Han, Yaobin Ouyang, Shouhai Zhu, Hangcheng Xu, Bin Chen, Ping Yin, Qin Zhou, Jinzhou Huang, Kuntian Luo, Zheming Wu, Huaping Xiao, Xinyi Tu, Jake A. Kloeber, Jiajun Jing, Xiaofeng Huang, Meng Xu Welliver, Zhenkun Lou, Robert Mutter. Cholesterol triggers chromatin stress to drive senescence and inflammation [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 539.

Background: Hepatorenal syndrome–acute kidney injury (HRS-AKI) is associated with high mortality in cirrhosis. Recent FDA approval of terlipressin and updated clinical guidance have expanded management options, but real-world practice patterns remain unknown. Methods: We conducted a nationwide survey of U.S. hepatology providers regarding HRS-AKI management practices. The 33-question survey assessed provider demographics, vasoconstrictor selection, treatment timing, and adherence to guideline recommendations. Results: Among 162 respondents, most were hepatologists (94%) at academic centers (83%). Only 11% obtained a nephrology consultation at AKI detection, with 44% waiting until worsening renal function. Terlipressin was available at 80% of institutions but more commonly at academic centers (84% vs. 53%, p =0.003). Consistent with guideline recommendations, 77% of providers initiate vasoconstrictors only after completing a trial of volume expansion (if no renal improvement), particularly in academic centers (80% vs. 60% non-academic, p =0.03). Terlipressin (49%) and midodrine/octreotide (44%) were preferred first-line treatments, with providers at academic centers more likely to use midodrine/octreotide (46% vs. 30%, p =0.008). Mean arterial pressure was used by 62% of providers to guide vasoconstrictor dosing. Most providers (73%) discontinued vasoconstrictor treatment after 4 days if no improvement. Conclusions: Significant variations exist between guideline recommendations and real-world HRS-AKI management, especially first-line treatment choice, treatment monitoring, and nephrology consultation. These findings highlight opportunities to improve guideline implementation and identify areas where practice patterns might inform provider education and future guidance updates.

The underlying immunopathogenesis of inflammatory arthritis (IA) immune-related adverse event (irAE) remains obscure. Unlike rheumatoid arthritis (RA), where autoantibodies and B cell dysfunction are central features, the contribution of humoral immunity to IA-irAE is unclear. Here, we performed immunophenotyping of peripheral blood from patients with IA-irAE and compared them with patients with seronegative RA, immune checkpoint inhibition–treated patients without irAE, and healthy controls. IA-irAE was marked with increased cytotoxic gene expression and metabolic activation in T cells and reduced CXCR3 and CCR6 expression in CD4 + T cells. Contrary to seronegative RA, patients with IA-irAE displayed no substantial elevation in autoantibody levels or atypical CD11c + CD21 − B cells. IA-irAE was further characterized by elevated levels of interleukin-6 (IL-6), IL-12, and type I interferon, which correlated with the T cell activation phenotypes. Together, our findings define IA-irAE as a disease with certain immunological features distinctive from RA, representing a potentially T cell–driven, autoantibody-independent autoimmunity. These results offer insights into immune tolerance breakdown and therapeutic targeting in irAEs.

Abstract Methods that map genetic risk to cells do not directly test whether spatially organized ligand–receptor (LR) gene annotations carry conditional heritability association. Here we introduce EdgeMap, which scores each gene by the spatial activity of its LR contexts in spatial transcriptomics data, maps these scores alongside cell-intrinsic annotations to SNP-level LD scores, and tests both jointly against GWAS summary statistics. Across 17 traits and five human tissues, edge z -scores are systematically higher in biologically matched tissues: all 15 traits with both matched and unmatched tissue classes show this ordering (median Δ z = 1.51 ; sign P = 3.1 × 10 −5 ), and 11 of 13 nominal-positive associations concentrate in the biologically matched set ( P = 1 × 10 −4 ). The pattern survives broad LR-gene-class and cell-type composition controls. Donor-level cross-section summaries, independent GWAS replication for LDL and CAD, and cell-segmented Visium HD liver data provide convergent support. A secondary analysis prioritizes constituent genes within active LR contexts; sixteen of 31 prioritized genes are absent from standard gene-level methods. These results establish spatially weighted LR-gene annotations as a complementary layer for interpreting complex-trait genetic architecture.