Chae, Young Kwang

Abstract Background: Tumor angiogenesis drives treatment resistance by fostering an immunosuppressive microenvironment. VEGF inhibitors (VEGFi) target the vasculature, while immune checkpoint inhibitors (ICI) do not address this mechanism directly. Quantitative Vessel Tortuosity (QVT) Score is a radiomic biomarker of vascularity previously shown to be associated with ICI response. Here, we introduce QVT Subscore, an interpretable panel that isolates distinct vascular attributes (e.g., branching, curvature, radius), enabling treatment-specific insight into vascularity MoA and vascular normalization. Methods: QVT Subscores are derived by grouping the 910 QVT features comprising QVT Score into six biological categories, summarized by principal component analysis. The panel was evaluated in 557 patients from the phase 3 SWOG S0819 trial and a real-world ICI monotherapy cohort (n=147). QVT score/subscore changes from baseline to first on-treatment CTs were assessed by paired t-test and substratified by objective response (OR). Results: While the mono-chemotherapy showed no vascularity changes, VEGFi regimens reduced QVT Score, with Branch and Curvature Subscores decreasing significantly but Vessel Volume unchanged. In contrast, ICI recipients exhibited significant increases in QVT Score and 5/6 Subscores, showing opposite changes to VEGFi in four subscores and differing effects on vessel Radius and Volume. VEGFi produced the greatest vascularity decrease in patients achieving OR, with milder decreases in non-OR. For ICI, OR showed negligible QVT change, while vascularity significantly increased in non-OR across all subscores except Radius. Conclusions: The QVT Subscore panel captures granular tumor vascular remodeling from different treatments, providing mechanistically grounded insights into clinical trial arm performance. Future work will assess the panel’s potential for identifying regimens that benefit from adding VEGFi. Citation Format: Haojia Li, Pushkar Mutha, Young Kwang Chae, Kai Zhang, Liam Il-Young Chung, Amogh Hiremath, Rhea Chitalia, Justin Yau, Omid Haji-Maghsoudi, Trishan Arul, Vamsidhar Velcheti, Anant Madabhushi, Nathaniel Braman. QVT Subscore: An interpretable radiomic panel of tumor vascularity reveals treatment-specific mechanisms-of-action (MoA) in NSCLC [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 3751.

Background Immune checkpoint inhibitors (ICIs) improve survival in advanced non-small cell lung cancer (NSCLC), yet current biomarkers such as programmed death-ligand 1 (PD-L1) expression and response criteria (Response Evaluation Criteria in Solid Tumors, RECIST, V.1.1) align poorly with long-term survival. Radiomics has been proposed as a source of novel biomarkers, but standard radiomic approaches suffer from limited biological interpretability and poor generalizability across treatment settings. We address these gaps by developing the Quantitative Vessel Tortuosity (QVT) score, a biologically interpretable imaging biomarker that quantifies tumor vascular complexity—a known mediator of immune evasion—from routine imaging. We hypothesized that QVT score would improve prognostication and enable treatment response monitoring in ICI-treated NSCLC, independent of current biomarkers. Methods This retrospective, multicenter study analyzed 1,301 CT scans from 682 patients with ICI-treated NSCLC. An automated pipeline segmented lesions and tumor-associated vasculature within each scan, extracting 910 QVT features measuring vascular shape and complexity. Unsupervised clustering of these features in a discovery cohort (N=375) was performed to identify fundamental vascular phenotypes. A continuous QVT score was then derived using regularized logistic regression to map patients along this phenotypic spectrum. QVT score was externally validated in ICI monotherapy (N=172) and chemoimmunotherapy (N=135) cohorts. In a longitudinal cohort (n=143), early on-treatment QVT score changes were evaluated for overall survival (OS) association. Results Two robust vascular phenotypes emerged in the discovery cohort: a highly vascularized, chaotic “QVT High” phenotype with poor post-ICI OS and a “QVT Low” phenotype with normalized vasculature and improved ICI outcomes. The continuous QVT score was prognostic for ICI monotherapy (HR=1.17 per 0.1 increase, p=0.0028) and chemoimmunotherapy (HR = 1.23 per 0.1 increase, p = 4.9×10⁻⁵). High QVT status remained prognostic for both treatments after adjustment for PD-L1 and clinical variables (adjusted HR range: 2.13–2.38, p≤0.002). Early decreases in QVT score during therapy, indicating vascular normalization, were associated with improved OS (HR=1.93, p=0.0022) independent of RECIST best overall response and tumor volume change. Conclusions QVT score is a novel, biologically interpretable imaging biomarker that quantifies vascular complexity. It enables automated, non-invasive prediction and monitoring of ICI outcomes by capturing treatment-induced vascular remodeling. Integrating QVT score into clinical decision-making and drug development can address critical gaps in precision oncology.

TPS4201 Background: Poorly differentiated, extrapulmonary NEC are rare cancers with median overall survival (OS) <1 year. Treatment is extrapolated from small cell lung cancer (SCLC) with use of P (cisplatin or carboplatin) + E. More effective treatment regimens and predictive biomarkers are needed to improve outcomes. In SCLC, induction therapy with combination of P/E + PD-L1 checkpoint inhibitor atezolizumab and maintenance atezolizumab improved OS (12.3 months vs 10.3 months; HR 0.70, 95% CI 0.54 – 0.91, P=0.007) vs P/E alone (1). No study to date has compared PD-1/PD-L1 inhibition during induction only vs during induction and maintenance therapy. In SCLC, distinct molecular subtypes can be identified by the presence of specific transcription factors (e.g., ASCL1, NEUROD1, POU2F3) or an inflamed gene signature (SCLC-I), with SCLC-I pts more likely to benefit clinically from the addition of immunotherapy (2). In this study we plan to test the benefit of adding atezolizumab to induction P/E as well as the role of adding maintenance atezolizumab after induction P/E plus atezolizumab. We also plan to correlate tumor- and blood-based subtype biomarkers with response to therapy. Methods: Eligible pts ≥18 years old have evaluable, histologically confirmed extrapulmonary NEC, Zubrod PS ≤2, and are allowed to have up to 1 cycle of P/E prior to enrollment. P (cisplatin 75 mg/m2 or carboplatin AUC 5, iv) on day 1, E 100 mg/m2 iv on days 1-3, and atezolizumab 1200 mg iv on day 1 of q21 day cycles. Pts are randomized to 1 of 3 arms: A) Induction P/E + atezolizumab -> maintenance atezolizumab B) Induction P/E + atezolizumab -> observation C) Induction P/E -> observation. The primary objective is to compare the overall survival (OS, from randomization) between arms in a fixed sequence: A vs C -> B vs C -> A vs B. Secondary objectives include comparing OS (from start of maintenance/observation), progression free survival, response rate, duration of response, and safety/tolerability across arms. Tumor and blood samples will be banked for future biomarker analyses, including immunohistochemistry of transcription factors on tissue and whole exome sequencing on tumor and circulating tumor DNA. With 189 pts (168 eligible), the study has 84% power to detect an improvement in 12-months OS from 35% to 57.5% (HR 0.53). This study was activated December 2021 with 3 pts enrolled over 1 year due to restricting enrollment of NEC of small cell histology only. The protocol was amended in January 2023 to broaden eligibility to all NEC subtypes (small cell, large cell, and undefined histology with Ki-67 ≥55%). As of January 2024, 35 patients have been enrolled. A protocol amendment is planned to remove the need for Ki-67 index for NEC of genitourinary origin to improve accrual. 1. Horn L, et al. N Engl J Med 2018. 2. Gay CM, et al. Cancer Cell 2021. Clinical trial information: NCT05058651 .

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value&lt;0.0001; irAE model: R2= 0.92; p-value &lt;0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value&lt;0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1208.

3040 Background: Pembrolizumab was recently FDA approved across solid tumors for TMB scores ≥ 10mut/Mb as assessed by next-generation sequencing (NGS) of tissue (tTMB). A prior study of advanced cancer patients treated with immunotherapy found that higher somatic TMB, as defined by the 80th percentile in each histology, was associated with better overall survival. Previously, bTMB assessed by ctDNA from patients with newly diagnosed advanced NSCLC at a score of 16 mut/MB correlated with a tTMB score of 10 mut/MB. TMB levels vary by cancer type, line of treatment, and therapy received; the distribution of bTMB scores across solid tumor types has not been well characterized. Here we report the distribution of bTMB scores in patients with advanced malignancies. Methods: We queried 5,610 samples from patients with different cancer types undergoing clinical cell-free DNA testing (Guardant360; Redwood City, CA) and assessed bTMB scores from October 2020 - January 2021. bTMB score was derived via a previously described computational algorithm examining the total number of synonymous and non-synonymous SNVs and indels across a 1.0MB genomic footprint. We assessed the success rate of bTMB evaluation, overlap with microsatellite instability (MSI) status, and defined the distribution of bTMB levels across indications in this dataset. Results: bTMB score was successfully assessed in 4,275/5,610 (76.3%) samples (Table). The majority of samples (58%) were tested at disease progression as compared to initial diagnosis (42%). The median turnaround time from sample receipt to clinical reporting was 11 days and decreased to 9 days over the course of the study. For the majority of cancer types the 80th percentile TMB was ≥ 16 mut/MB tissue equivalency. Conclusions: Our analysis demonstrates the feasibility of measuring bTMB using a commercially available liquid biopsy assay. bTMB scores trended higher than tTMB previously reported in these cancer types, reflecting the ability of ctDNA to better capture tumor heterogeneity. cfDNA may allow for exploration of bTMB evolution throughout treatment. TMB should be interpreted in the context of disease, treatment, and method; these data establish a pan-cancer benchmark for bTMB which will serve as a resource for further studies.[Table: see text]

PURPOSE NCI-MATCH is a nationwide, histology-agnostic, signal-finding, molecular profile–driven trial for patients with refractory cancers, lymphomas, or myelomas. Patients with tumors harboring actionable aberration(s) in fibroblast growth factor receptor ( FGFR) 1-3 were treated with AZD4547, an oral FGFR1-3 inhibitor. METHODS Patients’ tumors were screened by next-generation sequencing for predefined FGFR amplification, activating mutations, or fusions. Patients were treated with AZD4547, 80 mg orally twice daily until progression of disease or drug intolerance. A response rate of 16% was considered promising. RESULTS Between July 2016 and June 2017, 70 patients were assigned and 48 received protocol therapy and are eligible for analysis. Patients’ tumors harbored FGFR1 or FGFR2 amplification (n = 20), FGFR2 or FGFR3 single-nucleotide variants (n = 19), or FGFR1 or FGFR3 fusions (n = 9). The most common primary tumors were breast (33.3%), urothelial (12.5%), and cervical cancer (10.4%).Grade 3 adverse events were consistent with those described in previous clinical trials. Confirmed partial responses were seen in 8% (90% CI, 3% to 18%) and were observed only in patients whose tumors harbored FGFR1-3 point mutations or fusions. Stable disease was observed in 37.5% (90% CI, 25.8% to 50.4%). The median progression-free survival (PFS) was 3.4 months, and the 6-month PFS rate was 15% (90% CI, 8% to 31%). For patients with tumors harboring FGFR fusions, the response rate was 22% (90% CI, 4.1% to 55%), and 6-month PFS rate was 56% (90% CI, 31% to 100%). CONCLUSION Preliminary signals of activity appeared to be limited to cancers harboring FGFR activating mutations and fusions, although AZD4547 did not meet the primary end point. Different FGFR somatic alterations may confer different levels of signaling potency and/or oncogene dependence.