Guittar, John

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: 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.

2558 Background: Clinical validation studies have shown that early dynamic changes in circulating tumor DNA (ctDNA) tumor fraction (TF) can predict clinical outcomes. Yet few studies have evaluated the clinical value of longitudinal monitoring throughout the course of treatment. Here we evaluate longitudinal changes in ctDNA TF and clinical outcomes in an advanced real-world pan-cancer cohort of patients (pts) treated with immune checkpoint inhibitors (ICIs). Methods: The cohort included deidentified pts from the Tempus clinicogenomic database with stage IIIB or IV solid tumors who underwent ctDNA NGS and were treated with an FDA-approved ICI +/- chemotherapy (CT). Pts had a pre-treatment baseline liquid biopsy (T0) and ≥1 on-treatment sample Ti within 21-180 days of ICI. xM for treatment response monitoring estimates TF via an ensemble algorithm that incorporates variant and copy number information. Response status was determined at each on-treatment timepoint Ti relative to T0. Pts were classified as a Molecular Responder (MR) if TF < 1% at T0 and Ti or if TF decreased by ≥ 50% from T0 to Ti; otherwise, they were classified as a Molecular Non-Responder (nMR). Pts with TF < 0.09% at T0 and Ti were classified as ctDNA not detected. The longitudinal cohort included pts with ≥2 on-treatment timepoints, further classified based on the most frequent classification across each Ti as longitudinal MR, longitudinal nMR and longitudinal ctDNA not detected. If no classification was dominant, the most recent classification was used. Real-world overall survival (rwOS) was defined from T1 to death and assessed by log-rank test. Results: The full cohort of 84 pts with > 10 solid tumors included 34.5% (n = 29) NSCLC and 16.7% (n = 14) SCLC. The majority of pts (59.5%, n = 50) received ICI+CT, (64.0% first-line [1L]), and 40.5% (n = 34) ICI-only (35.3% 1L). rwOS was longer for 53 MRs vs. 18 nMRs (median not reached vs. 7.0 months, P < 0.005); 13 pts had no ctDNA detected. The longitudinal subcohort of 35 pts was 31.4% (n = 11) NSCLC, 20.0% (n = 7) prostate cancer, and 14.3% (n = 5) SCLC; 45.7% (n = 16) ICI+CT (56.3% 1L), 54.3% (n = 19) ICI-only (31.6% 1L). On average, pts had 3 on-treatment liquid biopsies, with a median time between on-treatment samples of 91 days. Twelve pts were longitudinal nMRs (10 MR or ctDNA not detected at T1), 18 longitudinal MRs, and 5 longitudinal no ctDNA detected. There were 6 death events in the longitudinal nMRs, 3 in the longitudinal MRs (all > 18 months after T1), and no death events in the longitudinal no ctDNA detected group. Longitudinal MRs had longer rwOS than longitudinal nMRs (median 37.4 months vs 8.7 months, P < 0.005). Conclusions: Longitudinal nMR was associated with worse survival compared to longitudinal MR. Longitudinal, molecular biomarker dynamics may be a useful clinical treatment decision tool for monitoring treatment response to ICI therapy.

Abstract Background: PDAC is a particularly fatal condition lacking established biomarkers for the choice of first-line treatment (1L). The PurIST algorithm is a gene signature (Rashid et al. Clin. Cancer Res., 2020) that categorizes PDAC tumors into basal or classical molecular subtypes and has been previously validated in a patient cohort of advanced PDAC (N=258). In this post-hoc analysis study, we examine whether the PurIST subtypes can distinguish patients likely to respond to FOLFIRINOX (FFX) versus Gemcitabine + nab-paclitaxel (GnP). Methods: A cohort of de-identified PDAC patients was selected from the Tempus Oncology Database following an IRB-approved protocol. Inclusion criteria required no prior systemic treatment, a surgically unresectable/metastatic status, available RNA-sequencing data from primary or metastatic tissue, and the use of FFX or GnP as 1L systemic therapy with documented outcomes. RNA-sequencing was generated by the CAP/CLIA validated Tempus xR assay. The PurIST gene signature was applied to normalized gene expression values to classify samples into basal or classical subtypes. Univariate and multivariate Cox PH analyzes comparing overall survival (OS) with adjustment for delayed entry, between two common 1L treatment regimens, FFX and GnP, were performed for classical patients with an ECOG performance status of 0 or 1. Results: Application of the PurIST model to the PDAC cohort (N=258) resulted in 67% (N=173) labeled as classical, 23% (N=59) as basal, and the remainder as no-call. Amongst classical patients, 35% (N=61) had a known ECOG score of 0 or 1 and were retained for subsequent analysis. Within this subset, 37 were treated with FFX and 24 with GnP as 1L, with an overall median OS of 11.4 months (95% CI: 9.5-16.5) and a 12-month survival rate of 47.2%. A univariate Cox PH analysis showed that classical patients treated with FFX had a significantly longer OS time (HR (95% CI) = 2.32 (1.29-4.17)). This result remained significant in a multivariate Cox PH, adjusting for age and ECOG, (HR=2.5 (1.15-5.44)). Conclusion: In this real-world cohort of advanced PDAC patients, we showed that classical patients with low ECOG scores had superior OS with 1L FFX treatment compared to 1L GnP. These findings demonstrate the potential for PurIST to aid in optimal treatment selection for patients with advanced PDAC. Citation Format: Stephane Wenric, James M. Davison, John Guittar, Gregory M. Mayhew, Kirk Beebe, Alia Zander, Seung Won Hyun, Kyle Beauchamp, Michael V. Milburn, Vincent Chung, Tanios S. Bekaii-Saab, Charles M. Perou. Real-world validation of the PurIST classifier demonstrates enhanced therapy selection for pancreatic ductal adenocarcinoma (PDAC) patients [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 2542.