Supplementary Table S7. Tissue informed clinical management in EGFR-mutant NSCLC.

Impact of pCGP and ctDNA dynamics on patient management. A, Swimmer’s plot illustrating the survival of 12 patients with EGFRm NSCLC; these patients were chosen to provide a representative example of situations in which pCGP beyond the first diagnosis was informative for patient management. Examples of how pCGP informed management include the identification of T790M alterations (patients PCGP1, PCGP4, PCGP5, PCGP7, PCGP12); identification of a BRAF alteration (PCGP6); identification of MET amplification (PCGP8 and PCGP9); and identification of a C797S resistance alteration (PCGP2). B and C, The emergence of resistance on pCGP; fish plots illustrate a dynamic, longitudinal representation of molecular alterations identified on pCGP in patients with serial sampling. The Y axis represents the aggregate VAF of all alterations identified at each time point. Each vertical dashed line represents the time point of a pCGP assay, with the number beneath representing the VAF of the most prevalent alteration at that time. B, pCGP serial profiling for a patient with EGFR L858R–driven NSCLC who had five iterations of pCGP. They were on erlotinib up to the second pCGP, which was obtained at the time of radiographic progression. This identified EGFR T790M, and the patient was switched to osimertinib with subsequent suppression of this clone. The third pCGP was at the time of further radiographic progression, no new driver of resistance was identified but EGFR C797S was identified on liver biopsy. They were transitioned from osimertinib to a clinical trial at this point, with corresponding re-expansion of T790M and L858R variants after. Fourth pCGP, after carboplatin/pemetrexed/bevacizumab therapy, identified both C797S and L718Q, an uncommon mutation in EGFR and an additional resistance mechanism. Their final pCGP demonstrated a significant increase in the aggregate VAF of all alterations, and the patient died 2 months later. Separate regimens are separated by semicolons. C, pCGP serial profiling for a patient with ALK-fusion NSCLC that had four iterations of pCGP. Their disease had become resistant to alectinib at the time of the first pCGP, likely attributable to ALK E1210K and I1171T alterations. I1171T is associated with sensitivity to ceritinib, and the patient was switched to ceritinib at this time. After some clinical response with a corresponding contraction of the I1171T variant, expansion of the E1210K variant was seen in addition to an acquired oncogenic PIK3CA resistance variant. The third pCGP is at the time of progression on ceritinib. The patient transitioned to brigatinib initially but progressed after 6 months and transitioned to lorlatinib. Fourth pCGP after 22 months on lorlatinib identified an additional acquired on-target mutation in ALK G1269A. Compound mutations of this kind are common in patients on lorlatinib. Separate regimens are separated by semicolons.

Figure S4. Categorization of EGFR driver variants identified in the EGFRm NSCLC subset.

e20069 Background: Neoadjuvant chemoIO is a standard treatment for resectable NSCLC, and PD-L1 expression correlates with outcomes. However, cross-trial comparative activity versus PD-(L)1-mono across PD-L1 subgroups remains unclear and may help identify patients for chemotherapy-free approaches, as in the metastatic setting. Methods: MEDLINE and SCOPUS (1/2018 to 8/2025) identified prospective neoadjuvant PD-(L)1 mono or chemoIO trials reporting pCR or MPR (excluding observational reports, CTLA-4 or dual ICB, and RT). Random-effects meta-analyses of arm-level proportions used inverse-variance logit models to estimate pooled pCR/MPR by regimen and PD-L1 status, with meta-regression including regimen, PD-L1, and their interaction. Individual patient data (IPD) were reconstructed from Kaplan-Meier curves for exploratory survival analyses using shared-frailty Cox models. Results: Thirty-four treatment arms comprising 2,640 patients were included. Pooled pCR/MPR increased from 6.4%/16.5% with PD-(L)1-mono to 19.7%/35.2% with chemoIO in PD-L1-negative tumors, and from 13.2%/25.8% to 36.1%/53.7% in PD-L1-positive tumors. In PD-L1 ≥50% disease, pooled MPR was 39.3% with PD-(L)1-mono versus 61.3% with chemoIO. Meta-regression estimated a 16.6% higher pCR with chemoIO (95% CI, 8.9 to 24.3) averaged across PD-L1 strata (Table), with a similar pattern for MPR. Exploratory KM-reconstructed IPD analyses, using a shared-frailty Cox model, suggested an apparent EFS signal favoring PD-(L)1-mono versus chemoIO in PD-L1-positive cohorts (HR 0.43; 95% CI, 0.19 to 0.98), noting cross-trial confounding. Conclusions: ChemoIO was associated with higher pCR/MPR than PD-(L)1-mono across PD-L1 strata. Exploratory EFS findings suggest some patients with PD-L1-positive disease may achieve favorable long-term outcomes with PD-(L)1-mono despite nominally lower pCR/MPR, supporting further prospective biomarker-directed studies to refine perioperative strategies. pCR by treatment regimen and PD-L1 status. Treatment regimen PD-L1 status No. of arms Meta-analysis pooled pCR rate % (95% CI) Meta-regression estimated pCR rate % (95% CI) Absolute difference from reference % (95% CI) P value PD-(L)1-mono PD-L1 negative 7 6.38 (2.66–14.54) 2.63 (-4.60–9.85) Reference — PD-(L)1-mono PD-L1 positive 7 13.20 (7.71–21.70) 16.40 (9.21–23.58) 13.77 (6.48–21.06) 0.0002 ChemoIO PD-L1 negative 13 19.69 (13.12–28.48) 19.24 (13.56–24.91) 16.61 (8.93–24.28) <0.0001 ChemoIO PD-L1 positive 13 36.07(28.83–44.00) 33.01 (27.10–38.91) 30.38 (19.64–41.11) <0.0001

Supplementary Table S1: Training cohort description with plasmaCHORD feature values. Supplementary Table S2: Description of the serial cohort. Supplementary Table S3: Validation cohort description with plasamCHORD feature values. Supplementary Table S4: Description of plasmaCHORD features. Supplementary Table S5: Correlation of cfDNA fragmentomic feature statistics between baseline and follow-up time points in the serial cohort. Supplementary Table S6: Possible CH-origin variants identified on retrospective analysis of 45 patients reviewed at the JH-MTB.

Performance of individual features in predicting variant origin in the training cohort. Receiver-operator curves and area-under the curve (AUC) values for individual model features as predictors for CH versus tumor-origin. The top performing individual features are gene heme fraction, length cluster mutant, and length max delta S. However, no individual feature performs as well as the plasmaCHORD model combining all features.

Description of the training cohort. (A) Distribution of cancer types, stage, and patient age in the training cohort. The mean age for patients in this cohort was 65 years old (range 19-87), the most common cancer type was colorectal, and most patients had early stage disease (22 stage I, 101 stage II, 48 stage III, 53 stage IV, and 1 unknown stage). (B) Distribution of WBC only versus WBC and tumor matched sequencing and determined reference variant origin. The proportion of variants with tumor-derived reference origin is slightly higher from samples that had both WBC and tumor sequencing versus WBC sequencing alone. (C) Distribution of variants included in training cohort by gene and reference variant origin. After DNMT3A, TP53 was the second-most commonly mutated gene (98 variants) of which 67 (68%) are tumor-derived and 31 are WBC-derived.

Figure S3. Actionable alterations identified on first pCGP at first diagnosis (treatment-naive setting).

BACKGROUND:Integration of immune checkpoint inhibitors with perioperative chemotherapy has significantly improved pathological response rates and survival outcomes in patients with resectable NSCLC. In this context, an international expert panel convened to discuss optimal use of neoadjuvant and perioperative chemo-immunotherapy in individuals with early-stage/locally advanced resectable NSCLC, based on emerging trial data. METHODS:A virtual expert panel meeting was held on July 10, 2025 under the auspices of the Italian Association of Thoracic Oncology (AIOT). Seven thoracic oncology experts reviewed current evidence from pivotal trials. The panel addressed predefined clinical questions spanning pre-treatment evaluation, choice of neoadjuvant vs perioperative strategies, and patient selection. Shared-opinion statements were developed through moderated discussion. RESULTS:The panel endorsed pathological mediastinal staging and molecular testing in all candidates before therapy. Neoadjuvant and perioperative chemo-immunotherapy were considered a new standard for resectable stage II-III NSCLC after thorough and thoughtful multidisciplinary board discussion, having shown improved pathological complete response (pCR), event-free survival (EFS), and overall survival (OS), and was deemed overall safe without compromising surgery. PD-L1-negative tumors were not excluded from immunotherapy benefit, although magnitude of benefit was lower. The panel recommended neoadjuvant or perioperative approaches especially for resectable stage III and N-positive disease, whereas upfront surgery could be considered for selected stage II N0 cases. No evidence currently favors perioperative over neoadjuvant strategy in efficacy, or viceversa. The panel emphasized that regimen choice should consider trial eligibility criteria, local drug approvals, and patient factors. High-priority research areas identified included trials comparing neoadjuvant vs perioperative strategies, studies of biomarker-driven therapy personalization, and novel combination approaches to further improve outcomes. CONCLUSIONS:Chemo-immunotherapy is an essential component of therapy for subjects with operable and resectable NSCLC. Based on current evidence, the expert panel formulated guideline recommendations for clinical practice. These expert statements aim to guide clinicians in integrating emerging perioperative immunotherapy regimens while awaiting further data from clinical trials.

Abstract Introduction: Genomic profiling of circulating tumor DNA (ctDNA) through liquid biopsies has become an important diagnostic method in clinical oncology. However, detection of variants related to clonal hematopoiesis (CH) is a major confounder that impairs the clinical utility of liquid biopsies. Strategies that reduce biological noise from CH in plasma NGS include deep sequencing of matched WBC DNA and/or tumor tissue sequencing. While these methods effectively distinguish most CH variants, the need for extra biospecimens and sequencing raises costs and limits feasibility. Methods: Using a training cohort of 426 variants identified in ctDNA NGS from 225 patients with stage I-IV solid tumors, we developed plasmaCHORD, a machine learning model (MLM) that includes fragmentomic, variant, and patient-level features to distinguish between tumor- and CH-origin for mutations detected by fixed gene panel hybrid capture NGS. Model performance was assessed by comparison to the reference origin of each plasma variant determined from matched WBC and tumor NGS. Following locking the model parameters, we applied plasmaCHORD to an independent validation cohort of 1,412 plasma variants detected in 114 patients with metastatic cancers, as well as to cfDNA NGS from patients enrolled in a prospective liquid biopsy-informed clinical trial (NCT05585684). Results: PlasmaCHORD predicted tumor versus CH-origin in the training set with high accuracy (cross-validated AUC=0.94), outperforming individual features such as variant allele frequency and canonical CH genes. Model performance remained robust when restricted to mutant DNA fragments supported by 3-5 mutant reads (AUC = 0.84). plasmaCHORD was locked for evaluation using a score of 0.5 as cutoff for distinguishing tumor- versus CH-origin variants. In the independent validation cohort, the locked model maintained similar overall accuracy (AUC=0.9) with a sensitivity of 82%, specificity 80.3% and accuracy of 80.2%. Our approach was shown to be highly reliable in classifying variant origin in clinically actionable genes not canonically associated with CH, including AKT1, ATM, BRCA1, BRCA2, and EGFR, as well as adjudicating cellular origin for TP53 mutations that are encountered in both solid and hematologic malignancies. Performance was consistent across cancer types, sequencing platforms, mutation classes, and a wide range of allele fractions. When applied to clinically challenging cases in the context of a precision oncology clinical trial, plasmaCHORD precisely determined variant origin, preventing mismatches with genotype-targeted therapies. Conclusions: plasmaCHORD, a multi-feature machine-learning classifier, can significantly enhance the ability to identify bona fide tumor variants in routine plasma-only NGS, addressing a critical need in implementing liquid biopsy-guided therapy by minimizing misinterpretation caused by CH. Citation Format: Daniel J. Rabizadeh, Jenna VanLiere Canzoniero, Ilias Ziakas, Jaime Wehr, Archana Balan, Amna Jamali, Blair V. Landon, Susan Combs Scott, Gavin Pereira, Vincent K. Lam, Christine L. Hann, Christine M. Lovly, Jessica Tao, Patrick M. Forde, Joseph C. Murray, Mark Sausen, Gerrit A. Meijer, Geraldine Vink, Remond J. A. Fijneman, Victor E. Velculescu, Jillian Ayn Phallen, Robert Scharpf, Valsamo Anagnostou. PlasmaCHORD- A machine learning method for identifying clonal hematopoiesis variants in liquid biopsies [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 95.

Supplementary Table S10. Drivers of EGFR TKI resistance.

Supplementary Table S1. Context of first pCGP assays.

Characteristics of study population.

Supplementary Table S5. Summary of tissue informed clinical management in patients with concurrent pCGP and tissue biopsy.

Abstract Plasma comprehensive genomic profiling (pCGP) is implemented in the clinical care of non–small cell lung cancer (NSCLC). Although less well documented, serial pCGP may also guide the management of oncogene-driven NSCLC after first progression. In this study, we assessed the clinical value of serial pCGP, focusing on EGFR-mutant NSCLC. We conducted a retrospective study of 718 patients with NSCLC who underwent pCGP between 2015 and 2022. Clinical genomic data were programmatically extracted from the data workflows of the Johns Hopkins Lung Cancer Precision Medicine Center of Excellence. After variant annotation and actionability characterization, we examined the prevalence and evolving comutation patterns across serial pCGP, focusing on genomic mechanisms of tyrosine kinase inhibitor (TKI)–acquired resistance in EGFR-mutant NSCLC. A total of 718 patients had 818 instances of pCGP, with 79 patients having longitudinal pCGP (range, 2–5). pCGP uniquely informed management in 13% of patients (n = 92), both at initial diagnosis and on serial genotyping. This occurred predominantly through the identification of actionable mutations when tissue testing was unavailable. Among 214 patients with EGFR-mutant NSCLC, pCGP identified PI3K pathway alterations in 11% after first-line therapy. BRAF V600E (3%) and MET exon 14 skipping mutations (3%) emerged after third-generation TKI therapy. After TKI progression, 31 patients (22%) with EGFR-mutant disease had actionable pCGP findings, of whom 18 (58%) were matched to targeted therapy. Serial pCGP can inform treatment decisions in patients with NSCLC. In those with EGFR-mutant disease, pCGP at progression identifies actionable drivers of therapy resistance, enabling therapeutic intervention. Significance: Our study provides critical insights into the routine implementation of serial pCGP within a thoracic oncology program, supported by a precision oncology informatics framework, in a tertiary healthcare institution. We show that pCGP enables genotyping when tissue testing is not feasible and identifies actionable mutations at resistance. The clinical implementation of pCGP can drive improved clinical outcomes by matching patients with effective interventions in a timely and minimally invasive manner.

Abstract Analysis of pre-treatment non-small cell lung cancers (NSCLCs) has helped identify predictors of response to immune checkpoint inhibitors (ICI), including low PD-L1 expression, targetable drivers alterations (EGFR, ALK), and STK11/KEAP1 mutations. To-date, however, few samples at treatment resistance have been studied, and consequently less is known about the mechanisms contributing to ICI resistance.Methods: Building on our previous analysis Stand Up 2 Cancer-Mark Foundation (SU2C-MARK) Cohort, we identified patients with on or post-treatment samples and performed whole exome (WES) and/or RNA-sequencing, based on tissue availability and quality. Genomic and transcriptomic features in post vs pre-treatment samples were compared. Subclonal evolution in paired samples was assessed using PhylogicNDT, and immune phenotypes were inferred using published gene signatures and deconvolution methods (CIBERSORTx).Results: Building on the original n=393 cohort, 41 patients with on and/or post-treatment samples were identified, with n=45 WES and n=35 RNA-seq on-treatment/post-treatment samples passing QC, for a total of n=445 samples included in the updated cohort, SU2C-MARKv2. N=28 patients had paired samples across treatment time, ranging from 2-5 time points. Only 3 samples had acquired mutations in B2M or JAK2; more commonly, post-treatment tumors showed persistence of clones containing STK11, KEAP1, and/or ARID1A/SMARCA4 alterations. The proportion of subclonal alterations decreased at resistance, suggesting elimination of passenger-rich antigenic subclones (median 7.9% vs1.9%, p<0.001). Acquired copy number loss in antigen presentation genes in 6p21 (STAT1/HLA/TAP1/TAP2) were observed in 5 treatment pairs, and amplification in 9p24.1 (JAK2/PD-L1/PD-L2) in 4 pairs. In the transcriptional space, resistant tumors demonstrated increased expression of gene sets associated with tumor-intrinsic biology, including MYC, oxidative phosphorylation, and EMT, and decrease in DNA repair genes (ATM). Alterations in immune gene sets were most prominent in on- rather than post-treatment specimens, with increase in T, B and myeloid cell signatures in both responding and non-responding tumors, though numbers were low (n=5). Immune clustering into hot, intermediate, and cold phenotypes confirmed an increase in hot tumors in on-treatment specimens, while post-treatment tumors had predominantly ‘intermediate’ immune phenotype.Conclusions: Integrated genomic/transcriptomic analysis of the expanded SU2C-MARKv2 cohort suggests that ICI resistance emerges through elimination of immunogenic subclones and selection for resistant subclones that maintain immune-suppressive transcriptomic phenotypes. Further integration of spatial and single-cell data will define the tumor-immune architecture and identify potential treatment targets. Citation Format: Natalie Vokes, Arvind Ravi, Mark M. Awad, Patrick M. Forde, Marta Luksza, Benjamin Dylan Greenbaum, Adam Jacob Schoenfeld, John V. Heymach, Alice T. Shaw, Pasi A. Jänne, Jedd D. Wolchok, Matt Hellman, Gad Getz, JUSTIN GAINOR. Clonal consolidation and transcriptional re-programming define non-small cell lung cancers resistant to immune checkpoint inhibitors [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 6473.

Figure S6. Plasma-only variants among patients with simultaneous plasma and tissue NGS.

Abstract Background: Circulating tumor DNA (ctDNA) has become a key biomarker for minimally invasive detection of residual disease during neoadjuvant immunotherapy. However, its integration into routine clinical decision-making remains limited by modest sensitivity and the practical constraints of current assays. Methods: We performed multi-modality matched tumor, white blood cell (WBC), and cell-free DNA (cfDNA) next-generation sequencing (NGS) of 520 biospecimens (56 tumor, 105 WBC, 359 plasma) from 32 patients with operable gastroesophageal (GE) cancer (NCT03044613) and 30 patients with resectable pleural mesothelioma (PM; NCT03918252). ctDNA residual disease analyses were performed at baseline, before each cycle of neoadjuvant immunotherapy, and preoperatively. For the tumor-informed approach, whole genome sequencing (WGS) data of matched tumor, WBC, and cfDNA (80x, 40x, and 30x coverage) were integrated through a random forest machine learning model and calibrated using a reference set of noncancerous cfDNA to determine cfDNA tumor fraction (TF). In parallel, we performed orthogonal tumor-naïve, fixed-gene-panel targeted error-correction NGS of cfDNA and WBC (30,000x), filtering germline and clonal hematopoiesis variants. Results: Overall, the tumor-informed assay showed significantly higher sensitivity, evidenced by a higher ctDNA detection rate at all evaluated timepoints compared to the tumor-naïve assay. In the GE cohort, the tumor-informed assay detected ctDNA for 22 of 25 (88%), 20 of 25 (80%), 18 of 26 (69%), and 5 of 21 (24%) patients at baseline, cycle 2, cycle 3, and preoperatively, respectively. By contrast, 13 of 30 (43%), 12 of 30 (40%), 11 of 30 (37%), and 5 of 25 (20%) had detectable ctDNA by the tumor-naïve assay at corresponding timepoints. In detectable cases, cfDNA TFs were highly concordant between approaches (R = 0.85, p < 0.001). In the PM cohort, 12 of 26 (46%), 11 of 25 (44%), 7 of 21 (33%), and 13 of 25 (52%) had ctDNA detected by the tumor-informed assay at baseline, cycle 2, cycle 3, and preoperatively, respectively. Having demonstrated higher analytical sensitivity with the tumor-informed approach in the GE cohort, we applied the tumor-naïve approach only in cases with detectable ctDNA by the tumor-informed assay. Of these, 6 of 14 (43%), 6 of 13 (46%), 5 of 8 (63%), and 7 of 15 (47%) had ctDNA detected at corresponding timepoints. cfDNA TFs were concordant at timepoints when ctDNA was detectable by both approaches (R = 0.63, p = 0.002). Tumor-informed ctDNA residual disease preoperatively was associated with shorter progression-free survival (log-rank, p = 0.0059). Conclusion: Tumor-informed WGS-based liquid biopsies reliably measure ctDNA residual disease during neoadjuvant immunotherapy, demonstrating greater sensitivity compared to a tumor-naïve approach, supporting their clinical value. Citation Format: Paul K. Lee, Blair V. Landon, Ezgi Oner, Jaime Wehr, Qiong Meng, Amna Jamali, Mimi Najjar, Gavin Pereira, Samira Hosseini-Nami, Rachel Keogh, Chen Hu, Ronan J. Kelly, Joshua E. Reuss, Patrick M. Forde, Mark Sausen, Vincent K. Lam, Robert B. Scharpf, Noushin Niknafs, Valsamo (Elsa) Anagnostou. Analytical and clinical sensitivity of tumor-informed and tumor-naïve ctDNA residual disease detection during neoadjuvant immune checkpoint inhibition in resectable cancers [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 1129.

Abstract Introduction: Overcoming immunotherapy (IO) failure in advanced NSCLC requires distinguishing molecular features of acquired vs primary resistance. Here we used comprehensive multi-omic analyses to define resistance mechanisms. Methods: Analyses were performed on the largest-to-date IO resistance cohort: 1819 biospecimens from 892 patients with NSCLC (371 primary, 521 acquired) from the Phase 2 HUDSON study (NCT03334617) of combination regimens after progression on anti-PD-(L)1/chemotherapy. Targeted next-generation sequencing of unpaired tumor biopsies pre- (n=226) and post-IO (n=497) was used to profile mutation signatures, clonality, aneuploidy and genomic instability. Post-IO tumor burden was assessed by plasma ctDNA (n=445); peripheral T-cell repertoires were analyzed (n=335). Bulk (n=205) and single-cell (sc, n=75) RNA sequencing (RNAseq) of unpaired post-IO tumor biopsies enabled gene set enrichment analysis (GSEA) and high-resolution cell type annotation. Results: Comprehensive analyses revealed acquisition of sub-clonal genomic alterations at the time of acquired resistance. Despite lower systemic ctDNA burden at progression, activating FGF10 and RICTOR and inactivating RBM10 and MSH6 mutations were enriched in acquired vs primary resistance (false discovery rate [FDR] p<0.05). Post-IO tumors harbored more CDK4, CDK6, and CD22 activating mutations, and inactivating mutations in KDM6A, SMARCA4, and CDKN2A (FDR p<0.05). Genomic instability (increased homologous recombination deficiency signatures, elevated large-scale transitions, telomeric allelic imbalance) was noted in both primary and acquired resistant tumors. Bulk RNAseq GSEA detected upregulation of epithelial-to-mesenchymal transition (EMT), IFNγ response, and inflammatory pathways (FDR p<0.05) in acquired vs primary resistant tumors. Single-cell transcriptomics showed enrichment of tumor-reactive, tissue-resident memory CD8+ T-cell clusters in acquired resistant tumors. Notably, a naïve/stem-like CD8+ T-cell cluster was also enriched in acquired resistant tumors. GSEA in early, central, and tissue-resident memory CD4+ T-cell clusters revealed an upregulation of naïve/stem-like gene sets and a downregulation of antigen processing/presentation gene sets in acquired resistant tumors. scRNAseq and differential expression analysis of epithelial populations highlighted pronounced EMT activation, increased lineage plasticity, and neuroendocrine differentiation gene signatures, implicating cellular reprogramming and phenotypic plasticity as potential contributors to acquired IO resistance. Conclusion: Acquired IO resistance in NSCLC involves dynamic and unique genomic and transcriptomic remodeling, encompassing EMT, lineage plasticity, stem-like programs, and immune reprogramming—highlighting potential avenues for therapeutic intervention. Citation Format: Archana Balan, Sonia Iyer, James Conway, Christopher Cherry, Noushin Niknafs, Mohamed Reda Keddar, Avinash Reddy, Robert McEwen, James White, Grace Kim, Anissa Dallmann, Nima Boluriaan, Sreeharsha Gunda, Mark Awad, Glenwood Goss, Se-Hoon Lee, Keunchil Park, Martin Reck, Michael Thomas, Rachel Karchin, Jane Peters, John F. Kurland, Giuseppe Galletti, Simon T. Barry, Jan Cosaert, J. Carl Barrett, Benjamin Besse, John V. Heymach, Patrick M. Forde, Valsamo Anagnostou. Multi-modal multi-omic analyses reveal mechanisms of immunotherapy resistance in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr CT233.

Supplementary Table S8. On-target acquired resistance in EGFR-mutant NSCLC.