Mansfield, Aaron S.

Abstract Understanding how immune checkpoint inhibitors (ICIs) reshape human T-cell responses requires models that move beyond static transcriptomic snapshots and discrete cell-state classifications. Here, we present an integrative AI-enabled virtual cell framework that represents human CD8⁺ T-cell responses as dynamic and computable systems during ICI therapy. By integrating single-cell RNA sequencing with paired T-cell receptor sequencing within the C2S-scale foundation model, we construct a virtual representation of individual T cells, in which each cell is encoded by a unique functional identity that captures its transcriptional, signaling, and clonal characteristics. Using this framework, we identify a previously unrecognized dynamic latent effector state of CD8⁺ T cells characterized by intermediate expression of effector genes, distinct signaling activity, and ongoing clonal expansion. Across independent patient cohorts, the virtual cell model consistently indicates that ICI therapy mainly acts by unmasking pre-existing effector potential rather than inducing de novo effector differentiation. Notably, this latent effector population remains transcriptionally restrained despite active signaling and clonal expansion, revealing a hidden reservoir of antitumor immune capacity. More broadly, our study demonstrates how AI-enabled virtual cell modeling can reconstruct latent cellular states and their dynamic transitions from multidimensional single-cell data. By incorporating functional identity into virtual cell model, this framework uncovers biologically meaningful yet non-obvious T-cell effector program during cancer immunotherapy and provides a generalizable approach for studying immune dynamics in human disease.

Abstract Exposure to asbestos is the primary cause of mesothelioma, however the processes underpinning its initiation and evolutionary trajectory remain elusive. To address this knowledge gap, we conducted whole exome sequencing and phylogenetic analysis of 277 mesotheliomas. Using multidimensional Dirichlet process clustering we inferred phylogenies, uncovering mutation order dominated by early 13q,1p, 8p, 6q and 22q somatic copy number alterations. Mesotheliomas exhibit a spectrum of genomic intra-tumor heterogeneity spanning linear (L) to highly branched (B) tree topologies, quantifiable by the Weighted Shannon Entropy (WSE). Somatic copy number alterations emerge earlier than single nucleotide variants (SNVs), and at a much faster relative rate in L-mesotheliomas. BAP1 is the only positively selected driver in L-mesotheliomas, whereas, B-mesotheliomas harbor multiple positively selected drivers and an aggressive phenotype, including a bias of loss of RPL5, and MTAP deletion linked to breakpoint enriched with long interspersed nuclear elements. L-trees had lower WSE, but greater T-cell inflammation and tertiary lymphoid structures, despite having a lower neoantigen burden. WSE is higher in larger volume, immunologically cold mesotheliomas, and is associated with shorter survival. In summary, progressive genomic heterogeneity is a defining feature of more aggressive mesothelioma yet illuminates opportunities for precision therapeutic intervention.

e20731 Background: Selective RET (REarranged during Transfection) inhibitors (RETi) have become standard treatment for RET fusion-positive non-small cell lung cancer ( RET + NSCLC), which represent 1–2% of NSCLC cases. Clinical factors that drive front-line versus later-line RETi use and outcomes are not well understood. This retrospective chart review evaluates physician decision drivers alongside real-world effectiveness and safety for pralsetinib (PRAL) in RET + NSCLC. Methods: A retrospective chart review was conducted via a standardized web-based form among US oncologists treating advanced RET + NSCLC. Eligible patients initiated PRAL for RET + NSCLC between October 2020 and December 2024. Data gathered included physician treatment considerations, patient baseline characteristics, and efficacy outcomes. Results: Eighty-two charts from patients who received PRAL as their first (index) RETi were collected from 43 physicians across both academic (41.9%) and community (58.1%) sites. Of the 82 patients, 51.2% received PRAL front line (1L) and 31.7% in the second line (2L). From a predefined list, physicians most often cited comorbidities (36.6%) and ECOG performance status (31.7%) as clinical factors when considering index PRAL therapy and efficacy profile (56.1%), followed by intracranial activity (24.4%) and hypersensitivity to immuno-oncology agents (24.4%) as therapy-related considerations. Although not a primary therapy selection driver, in 13 patients with safety as a consideration, 6 (46.2%) had myelosuppression noted as a concern for index PRAL treatment. RET + NSCLC patients treated in the 1L setting with PRAL had an overall response rate (ORR) of 73.8% (14 CR; 17 PR). For 2L line PRAL, the ORR was 61.5% (1 CR; 15 PR). Anemia (17.1%), diarrhea (13.4%), and constipation (13.4%) were the most frequently reported adverse events (AE). Discontinuation was mainly due to loss of/no response (80.6%), insurance (16.1%), and AEs (3.2%). The single PRAL patient that discontinued due to an AE had edema. There were an additional 7 patients that switched to PRAL due to an AE on a different RETi. The post-switch ORR was 57.1% (4/7); of these patients, 3 maintained their previous response and 1 improved their response. Conclusions: In this retrospective chart review, pralsetinib was used across treatment lines with high real-world response rates, consistent with results from the ARROW trial. Treatment selection was influenced by specific clinical factors including patient comorbidities, ECOG performance status, and safety concerns, with AE-driven switching uncommon yet associated with continued responses.

Abstract Purpose: Circulating tumor fraction estimate (ctFE) is a machine learning–derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothesized that pre-treatment and early on-treatment ctFE could robustly risk-stratify patients with locally advanced (LA) and oligometastatic non–small cell lung cancer (NSCLC) treated with radiotherapy (RT). Experimental Design: In a prospective phase II clinical trial (NCT03916419), 26 patients with unresectable stage IIB to IIIC NSCLC received magnetic resonance (MR)-guided hypofractionated chemoradiotherapy (chemoRT) followed by immunotherapy. Plasma ctDNA was profiled at baseline and mid-treatment (days 10–14) to derive ctFE and maximum variant allele frequency (Max VAF). A burden-based ctFE threshold derived from baseline samples was applied unchanged to mid-treatment samples and validated in two external cohorts: LA NSCLC treated with chemoRT (LA-RW; n = 94) and oligometastatic NSCLC treated with RT (OM-RW; n = 309). Results: Pre- and mid-treatment ctFE burden strongly stratified overall survival (OS) and progression-free survival (PFS), outperforming Max VAF and ctFE detectability. The baseline ctFE was prognostic for OS (HR 5.93, P = 0.005) and PFS (HR 11.08, P < 0.001) and remained significant at mid-treatment (OS: HR 7.08; PFS: HR 12.06; both P < 0.001). Early ctFE dynamics defined three molecular response groups with OS separation (median OS 60.8 vs. 13.0 vs. 2.9 months; P < 0.001). ctFE remained associated with survival in both validation cohorts. Conclusions: Early ctFE derived from a clinically available, tumor-naïve ctDNA assay enables noninvasive risk stratification in LA and oligometastatic NSCLC treated with RT, supporting its use as a practical biomarker for precision treatment adaptation.

Abstract Background: Diffuse pleural mesothelioma (DPM) is a highly aggressive malignancy with limited treatment options and poor prognosis. Although immune checkpoint inhibitors have improved outcomes, particularly for non-epithelioid subtypes, there remains no approved targeted therapy for mesothelioma. EGFR and MET are receptor tyrosine kinases frequently overexpressed in DPM, suggesting susceptibility to targeted antibody therapy. Amivantamab is a bispecific EGFR/MET antibody, originally developed for EGFR-mutant lung cancer, that blocks growth signals and engaging immune mechanisms. Given the high levels of EGFR and MET expression in mesothelioma, we evaluated the therapeutic potential of amivantamab in preclinical models. Methods: EGFR and MET transcript levels in DPM were analyzed using TCGA. Eight human mesothelioma cell lines were assessed by Western blotting and flow cytometry. Amivantamab binding, internalization, and signaling inhibition were examined using flow cytometry, confocal microscopy, and ligand-stimulated phosphorylation assays. Cytotoxicity and immune-effector activity were quantified using PBMCs, NK cells, and monocytes from healthy donors, along with an ADCC reporter assay. In vivo efficacy was tested in a DPM patient-derived xenograft (PDX) model in NOD/SCID-γ (NSG) mice supplemented with human NK cells and treated with amivantamab or control IgG. Results: TCGA analysis showed frequent EGFR/MET co-expression in DPM. Mesothelioma cell lines expressed higher EGFR and MET levels compared with mesothelial cells. Amivantamab strongly bound both receptors, induced receptor internalization, and inhibited EGF- and HGF-stimulated phosphorylation of EGFR, MET, and downstream AKT/ERK signaling. Direct cytotoxicity was minimal; however, the addition of PBMCs or NK cells resulted in robust, dose-dependent tumor cell killing consistent with antibody-dependent cellular cytotoxicity (ADCC). Reporter assays demonstrated 7-11-fold Fcγ receptor activation in tumor cells compared with ∼2-fold activation in non-malignant mesothelial cells. Co-culture with monocytes revealed amivantamab-induced trogocytosis, evidenced by loss of EGFR and MET from tumor-cell membranes. In the DPM PDX model, amivantamab produced 89.1% tumor-growth inhibition (TGI) relative to controls (P < 0.0001), with no overt toxicity observed in treated mice. Conclusions: Dual EGFR/MET targeting with amivantamab produced potent antitumor effects in mesothelioma through coordinated receptor-signaling blockade, ADCC, and monocyte-mediated trogocytosis. These results support amivantamab as a promising targeted therapeutic strategy for EGFR/MET-positive DPM, particularly epithelioid subtypes that currently derive limited benefit from immunotherapy. Citation Format: Shinichiro Suzuki, Kaushal Parikh, Ezequiel Tolosa, Kuan-Li Wu, Jennifer Ayers-Ringler, Lin Yang, Katherine E. Smith, Farhad Kosari, Aaron S. Mansfield, . Enhancing antibody-dependent cellular cytotoxicityin mesothelioma through concurrent EGFR and MET targeting withamivantamab [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 5547.

Abstract Background: Personalizing chemoradiotherapy (chemoRT) for locally advanced non-small cell lung cancer (LA-NSCLC) is limited by the lack of early biomarkers that inform response during treatment. Current circulating tumor DNA (ctDNA) molecular residual disease assays predict post-treatment outcomes but lack mid-treatment utility. ctDNA burden estimation has traditionally relied on variant-centric metrics such as maximum variant allele fraction (Max VAF), which rely on mutation-specific signals and may be confounded by histology-dependent shedding variability, allelic imbalance, and clonal hematopoiesis. Here we present circulating tumor fraction estimate (ctFE), a machine-learning composite score that integrates VAF distributions, copy-number alterations, and germline B-allele frequency deviations to approximate global tumor burden using a widely available, tumor-naïve clinical platform. Methods: A burden-based ctFE threshold was derived using pre-treatment plasma from a prospective phase II clinical trial of MR-guided hypofractionated chemoRT (LA-WU, n=26), locked, and applied unchanged to early mid-treatment samples (day 10-14). To test scalability and generalizability, its prognostic performance was validated in two real-world cohorts: 94 LA-NSCLC patients receiving definitive chemoRT (LA-RW) and 309 oligometastatic NSCLC patients receiving consolidative RT (OM-RW). ctFE and Max VAF were compared as continuous predictors across cohorts. Results: ctFE consistently outperformed Max VAF. In LA-WU, pre-treatment ctFE was associated with overall survival (OS HR=1.15, p=0.04) and progression-free survival (PFS HR=1.84, p=0.009), whereas Max VAF was not. Mid-treatment ctFE remained prognostic for PFS (HR=1.14, p=0.026). Early ctFE dynamics defined three molecular response groups with marked OS separation: consistently low, responder, and nonresponder groups (median OS 60.8 vs 13.0 vs 2.9 months, respectively; p<0.001). In multivariable models including both biomarkers, higher ctFE remained independently associated with worse survival in LA-RW (OS HR=1.88, p=0.010) and OM-RW (OS HR=1.37, p=0.040; PFS HR=1.45, p=0.008), whereas Max VAF did not. The locked ctFE threshold stratified OS across all cohorts (LA-WU HR=5.93; LA-RW HR=9.08; OM-RW HR=2.26; all p<0.001). Conclusion: ctFE provides clinically meaningful pre- and mid-treatment risk stratification and consistently outperforms Max VAF across locally advanced and oligometastatic NSCLC cohorts. We show that ctFE is a biologically informative, clinically generalizable and scalable ctDNA burden metric measurable using a tumor-naïve, off-the-shelf assay, supporting its practical utility in biomarker-adapted radiotherapy strategies. Citation Format: Ayesha Hashmi, Jessica Linford, Pradeep S. Chauhan, Kaushal Parikh, Rotem Ben-Shachar, John Guittar, Malvika Pillai, Jyoti Patel, Matteo Bergsagel, Nicholas P. Semenkovich, Kenneth R. Olivier, Sean S. Park, Dawn Owen, David M. Routman, Katie N. Lee, Alex D. Sherry, Aaron S. Mansfield, Daniel Morgensztern, Ramaswamy Govindan, Clifford G. Robinson, Carmen Bergom, Saiama N. Waqar, Bruna Pellini Ferreira, Gregory R. Vlacich, Aadel A. Chaudhuri. Early ctDNA quantification by ctFE outperforms Max VAF for survival stratification across locally advanced and oligometastatic NSCLC treated with radiotherapy [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 1183.

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.

RET fusions appear in 1%-2% of non–small cell lung cancers (NSCLCs). The results from the ARROW study (ClinicalTrials.gov identifier: NCT03037385 ) supported US Food and Drug Administration approval of pralsetinib, an oral selective RET inhibitor, for metastatic RET -altered NSCLC and RET fusion–positive thyroid cancers. ARROW was a phase I/II open-label study of pralsetinib 400 mg once daily in RET fusion–positive NSCLCs. Coprimary end points were overall response rate (ORR) and safety. Key secondary end points included duration of response, progression-free survival, and overall survival (OS). At data lock (May 20, 2024), 281 patients initiated pralsetinib (median treatment duration, 15.0 months). ORR (measurable disease patients; n = 259) was 78% (95% CI, 69 to 86) for treatment-naïve patients and 63% (95% CI, 54 to 71) for prior platinum-based chemotherapy patients. Median OS was 44.3 months (95% CI, 30.9 to 53.1), 50.1 months (95% CI, 28.3 to not reached) in treatment-naïve patients, and 39.7 months (95% CI, 27.8 to 53.2) in prior platinum patients. Common grade ≥3 treatment-related adverse events were anemia (21%), hypertension (15%), and decreased neutrophils (13%). Three treatment-related deaths occurred (pneumonia, n = 2; interstitial lung disease and rhabdomyolysis, n = 1 each). Safety was consistent with previous ARROW reports; no hypersensitivity was reported in patients receiving prior immunotherapies. Pralsetinib produced robust, durable responses with manageable safety in treatment-naïve and previously treated patients with RET fusion–positive NSCLCs, confirming previous findings with longer follow-up.