TPS8665 Background: Tumor Treating Fields (TTFields) are electric fields that disrupt processes critical for cancer cell viability. TTFields are delivered by a noninvasive portable device that has the European CE Mark and FDA approval for glioblastoma and mesothelioma. Preclinical non-small cell lung cancer (NSCLC) studies demonstrated that TTFields enhance the antitumor immune response, through disruption of mitosis and subsequent induction of immunogenic cell death. The pivotal, phase III LUNAR study (NCT02973789) in metastatic NSCLC progressing on/after platinum-based therapy demonstrated that TTFields with an immune checkpoint inhibitor (ICI) or docetaxel provided a statistically significant and clinically meaningful 3.3-month improvement in median overall survival (OS) vs an ICI or docetaxel alone, with no added systemic toxicities and no clinically significant difference on quality of life between groups. Despite advances in the treatment of NSCLC, survival rates for stage IV disease remain poor and there is a need for effective and tolerable treatments. Methods: LUNAR-2 (NCT06216301) is a pivotal, global, randomized, trial investigating the efficacy and safety of TTFields concomitant with pembrolizumab (P) and platinum-based chemotherapy (C) in patients (pts) with metastatic NSCLC, with a planned enrollment of 734 pts at 134 sites. Pts with NSCLC, radiologically evaluable disease in the thorax, ECOG PS of 0–1, and no prior treatment for metastatic disease are eligible. Pts will be stratified by histology, PD-L1 Tumor Proportion Score (TPS) and prior treatment with immunotherapy. Randomization is 1:1 to TTFields/P+C or P+C alone. Standard doses of P+C will be administered to both arms on day 1 of a 21-day cycle for 4 cycles of induction followed by up to 31 cycles of maintenance with TTFields/P or P alone. TTFields generated by the NovoTTF-200T System, will be delivered to the thorax ≥ 18 h/day until local disease progression per iRECIST. The primary endpoints are OS and progression-free survival (PFS) per RECIST v1.1 as assessed by a blinded independent central review (BICR). Secondary endpoints include OS and PFS according to histology or PD-L1 TPS, objective response rate, duration of response, and disease control rate, all per RECIST v1.1 as assessed by BICR and by investigator, comparing TTFields/P+C vs. P+C alone. Other secondary endpoints include PFS rates at 6, 12, 24 and 36 months per RECIST v1.1 as assessed by BICR, 1-, 2-, and 3-year survival rates and safety. Device Support Specialists will provide technical and lifestyle integration training for pts and caregivers throughout TTFields therapy. Pts usage is tracked by the device and is provided to pts and physicians to facilitate discussions to optimize outcomes by maximizing time on therapy. The trial is currently recruiting. Clinical trial information: NCT06216301 .

There is an acute need to accurately identify patients with advanced melanoma who are most likely to respond to anti-PD1 immune checkpoint blockade (ICB) therapy. While anti-PD1 therapy can be highly effective in advanced melanoma patients, only 30-40% of patients respond well. In this study, we apply single-cell spatial proteomics together with statistical and machine learning (ML) methods to successfully predict advanced melanoma patient response to anti-PD1 ICB in a cohort of 12 patients with >8 million cells. While no single molecular feature is sufficient to predict ICB response in our cohort, ML models integrating multiple molecular features accurately predict response in 11 of 12 patients. A recurrent cellular neighborhood analysis revealed a tumor-infiltrating lymphocytes niche that was present in the tumors of most responders. This neighborhood, tumor microenvironment immune cell composition, and levels of nitric oxide synthases were all important features used by our ML models to make accurate predictions. Optimal predictive performance by our ML models—a ROC AUC of 0.76—was achieved when using all molecular features, including cellular spatial relationships, but limiting our analysis to only immune-rich tissue regions. This study demonstrates the feasibility of using machine learning models to accurately predict patient response to anti-PD1 ICB therapy using spatial proteomics datasets.

In preclinical models, PCSK9 mediates cancer immunotherapy resistance and may serve as a novel immuno-inhibitory target. Herein, we report the results from a multi-center, single arm, phase II study evaluating the clinical activity and safety of the PCSK9 inhibitor alirocumab, in combination with the anti-PD1 antibody cemiplimab, in non-small cell lung cancer (NSCLC) patients with disease progression after previous immune checkpoint blockade. The primary endpoint was objective response rate (ORR). Secondary endpoints were progression-free survival (PFS), overall survival (OS), duration of response (DOR), disease control rate (DCR) and safety, and an exploratory objective was to analyze potential biomarkers of response. Sixty patients were enrolled, and 58 were evaluable for ORR. The ORR was 14.78% (90% CI, 5.30, 25.43), the median PFS was 2.5 months (95% CI, 1.5-3), and the median OS was 7.3 months (95% CI, 5.4 - 12.3). The most common treatment related adverse events (all grades) were anemia and fatigue. Grade 3 adverse events occurred in seven (12%) patients and were anemia, Guillain-Barre Syndrome and elevated amino transferase. There were no treated related adverse events of grade 4 or greater in the study population. Biomarker analysis identified superior outcomes in NSCLC harboring PIK3CA, PTEN, or AKT1 alterations. The ORR in patients with PIK3CA, PTEN, or AKT1 alterations (n=17) was 29.4% (95% CI, 10.3% - 56.0%). No objective responses were observed in the absence of PIK3CA, PTEN or AKT1 alterations (n=39). The presence of PIK3CA, PTEN or AKT1 alterations was significantly associated with response, p 0.0032 (two-sided, Fisher's exact test). Further translational studies revealed the impact of PIK3CA, PTEN or AKT1 alterations on intratumoral PCSK9, providing a biological rationale for the pattern of response and clinical benefit. These findings provide clinical proof-of-principle that PCSK9 inhibition can overcome immunotherapy resistance in a subset of patients and suggest that PIK3CA/PTEN/AKT1 pathway plays a significant role in PCSK9 mediated immune evasion and could be a biomarker of response. These findings warrant further investigation in larger confirmatory studies.

BACKGROUND:The management of non-metastatic non-small-cell lung cancer (NSCLC) has become increasingly complex with the integration of multimodality strategies and biomarker-driven approaches. Several clinically relevant areas remain insufficiently defined by current evidence and international guidelines. We conducted an international multidisciplinary consensus to address major areas of uncertainty in real-world practice. METHODS:A modified Delphi process was conducted during a 3-day in-person meeting in Barcelona, Spain (3-5 September 2025). Eighty-nine thoracic oncology experts independently rated predefined clinical statements developed by working groups and refined by a steering committee. Agreement was assessed using a 9-point Likert scale. Consensus was predefined as ≥75% of ratings in the 7-9 range; rejection as ≥75% in the 1-3 range. RESULTS:Ninety-six statements were evaluated. Consensus was achieved for 62 statements (64%), 31 (32%) remained without consensus, and 3 (3%) were rejected. Consensus supported routine FDG PET-CT for staging, histologic confirmation of suspicious mediastinal nodes, reflex PD-L1 testing and DNA-based next-generation sequencing at diagnosis, standardized post-neoadjuvant pathologic assessment, and sublobar resection with systematic nodal evaluation for selected peripheral node-negative tumors ≤2 cm. Consolidation durvalumab after definitive chemoradiotherapy was supported irrespective of PD-L1 expression in unresectable stage II-III disease. Persistent areas of controversy included brain MRI in stage I disease, mediastinal restaging after induction therapy, routine RNA-based testing, and the use of circulating tumor DNA/minimal residual disease to guide perioperative decisions. CONCLUSIONS:This international consensus provides structured expert guidance in areas of uncertainty in non-metastatic NSCLC and highlights priorities for future prospective research.

Background: Former and current smokers in lung cancer screening remain at elevated risk for lung cancer despite smoking cessation. We and others have shown that curcumin (CUR) exhibits anti-inflammatory and antiproliferative effects but is limited by poor bioavailability. However, since CUR is lipophilic, co-administration with ω-3 FAs represents a mechanistically rational strategy to enhance delivery and target complementary pathways, including signal transducer and activator of transcription 3 (STAT3) and the transcription factor NF-κB (NF-κB) signaling for lung cancer chemoprevention. Methods: We conducted a randomized, single-blind, placebo-controlled Phase II pilot study evaluating CUR combined with ω-3 FAs in high-risk former and current smokers with CT-detected pulmonary nodules. Participants received intervention agents with active-dose groups (low dose = 3; high dose = 9) or a placebo (n = 7) for 6 months. Primary endpoints included radiologic changes in nodule size, number, and density. Secondary endpoints included safety, adherence to the study agent and exploratory biomarker analyses. Correlation analyses of imaging-derived metrics were performed to assess relationships among LDCT parameters. Results: Nineteen participants were enrolled (intervention, n = 12; placebo, n = 7). Eighteen (11 intervention, 7 placebo) subjects completed post-intervention imaging. One subject was unable to complete follow-up and study-related procedures. Data from the treatment arms were pooled for analysis and comparison with the placebo arm. No statistically significant between-group differences were observed in primary imaging endpoints. The intervention was well tolerated, with predominantly grade 1 adverse events. Exploratory analyses demonstrated consistent positive correlations among established imaging biomarkers, with clustering of size-based metrics (mean diameter, volume, sum of longest diameters) and density-based parameters. Multidimensional scaling supported this structure, indicating internal coherence among imaging-derived endpoints. Conclusions: Although no statistically significant treatment effect on the image biomarkers was observed, this pilot study demonstrates feasibility challenges and identifies coherent imaging biomarkers that may serve as intermediate endpoints in early-phase chemoprevention trials. These results support further investigation of strategies utilizing agent combinations with enhanced bioavailability and safety and refinement of trial design in high-risk lung cancer patient populations.

Resistance of cancers to targeted therapies is traditionally framed as a tumor-intrinsic phenomenon, mediated by tumor cell-intrinsic or microenvironmental mechanisms. Here, we identify a tumor-extrinsic, systemic resistance mechanism resulting from hyperactivation of the hepatic cytochrome P450 enzyme, CYP3A4. This tumor-extrinsic resistance mechanism can function independently of, or in tandem with, tumor-intrinsic resistance. Focusing on experimental mouse models of targetable lung cancer, we find that xenobiotic-mediated induction of CYP3A4 results in accelerated drug metabolism and a drastic reduction in systemic and tumor-drug exposure in vivo . CYP3A4 activation can be triggered by chemically unrelated xenobiotics, leading to resistance to a wide range of targeted therapies, including ALK, EGFR, and KRASG12C inhibitors. Retrospective analysis of clinical cohorts suggests that variability in CYP3A4 activity might be a major contributor to variability in clinical outcomes. While higher CYP3A4 activity leads to sub-therapeutic tumor drug exposure and shorter progression-free survival, reduced drug metabolism is expected to result in supratherapeutic exposure and increased systemic toxicity. To address the consequences of abnormal CYP3A4 activity, we utilized mathematical modeling to demonstrate that drug concentrations can be restored through the optimization of dosing amounts and intervals. Further, we show that tumor sensitivity to targeted therapies can be rescued through pharmacological inhibition of CYP3A4. Our findings establish systemic metabolic variability as a bona fide resistance and toxicity driver, providing a translational framework for personalized dosing to maximize both safety and efficacy.

Kaplan–Meier estimates showing OS in all trial patients with a valid baseline (detectable or non-detectable) plasma ctDNA result in A, patients from the AURA3 trial (n = 291) and B, patients from the FLAURA trial (n = 499). Censored data are indicated by tick marks. Abbreviations: CI, confidence interval; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R); HR, hazard ratio; mOS, median overall survival; NC, not calculable.

Serial low-dose computed tomography (LDCT) scans in patients who are diagnosed with lung cancer during screening offer a history of the densities of tumors and the tissues that surround them during carcinogenesis and cancer progression. We built a CT-scan resolution computational model to explore how variations in lung tissue density impact tumor growth and evolution in non-small cell lung cancer (NSCLC). Our findings indicate that tumors spread more rapidly through denser tissues when they upregulate glycolytic pathways and acid production, whilst tumors spread more rapidly through sparser tissues when they upregulate angiogenesis. We used data and images from the National Lung Screening Trial to calibrate our model for untreated lung cancer growth in patients and corroborated our findings in low-density environments. Significance Our lung lesion model supports prior studies that find tumors tend to “speciate” into angiogenic or glyoclytic phenotypes. We demonstrate that these evolutionary strategies may in part be driven by the surrounding normal tissue density. We also suggest predictive biomarkers of tumor phenotype so that these evolutionary strategies may be detected and targeted in patients. ### Competing Interest Statement The authors have declared no competing interest. National Cancer Institute via the Cancer Systems Biology Consortium (CSBC), U54CA274507 Moffitt Center of Excellence for Evolutionary Therapy

Integrated model for patient stratification and clinical outcome prediction with KRAS G12Ci monotherapy in KRASG12C-mutant NSCLC.

Response Evaluation Criteria in Solid Tumors (RECIST) is the primary tool for assessing tumor response in solid tumors. Immunotherapy elicits unique response patterns, and assessment of their contribution to overall survival (OS) is of interest. We evaluated tumor size changes (TSC) for association with OS, evaluated whether deeper response had greater association with OS than the 30

Univariable and multivariable analyses for OS from the AURA3 trial (ctDNA evaluable population, n = 291) and FLAURA trial (ctDNA evaluable population, n = 499).

Survival outcomes according to KEAP1 and STK11 co-mutation status: A) Cohort A; B) Cohort B; C) further subclassifying KEAP1MUT tumors according to STK11 mutation status; D) PFS and OS according to STK11 co-mutation status in KSCWT tumors in the overall study cohort.

BACKGROUND:Immune checkpoint modulators (ICMs) have revolutionized cancer treatment but have unique immune-related adverse events (irAEs). The aim of this study was to develop and validate a brief measure of the most common, distressing, and diagnostically useful symptomatic irAEs. METHODS:Items were generated to assess symptomatic irAEs through a multistep process of (1) literature review and iterative expert input and (2) qualitative interviews of patients, caregivers, and clinicians regarding ICM-related irAEs and quality of life (QOL) impacts. An initial item set was administered across five longitudinal or cross-sectional studies. The final item set was selected using a Delphi method; validity, reliability, minimally important differences (MIDs), and sensitivity to change were evaluated. RESULTS:Qualitative interviews with 14 patients, seven caregivers, and six clinicians informed an initial set of 46 symptomatic irAEs, which was administered to patients (N = 503, 52% female, mean age = 64) treated with ICMs for non-small cell lung cancer (n = 342), head and neck cancer (n = 72), renal cell carcinoma (n = 43), or melanoma (n = 46). A final item set was selected and mapped to FACIT library items. This produced the 17-item FACT-ICM Symptom Index, which showed reliability (α = 0.86), construct validity (comparative fit index = 0.93), convergent validity with validated measures of physical QOL (r = 0.69-0.73), discriminant validity with emotional and social QOL (r = 0.03-0.65), and criterion validity (i.e., better performance status was associated with fewer concerns). Response option anchors adequately captured MIDs and were sensitive to change. CONCLUSIONS:This brief 17-item FACT-ICM Symptom Index demonstrates initial construct, convergent, and divergent validity, reliability, MID, and sensitivity to change and is ready for use in research and clinical care.

While third-generation EGFR TKIs such as osimertinib have improved outcomes in EGFR-mutant non-small cell lung cancer (NSCLC), responses remain transient, and resistance develops often. We recently reported primary efficacy results from the phase II RAMOSE trial showing that the combination of osimertinib with the VEGFR2 antagonist ramucirumab improved progression-free survival (PFS) over osimertinib monotherapy (24.8 months [mo] vs 15.6 mo, HR 0.55 (95% CI, 0.32-0.93, p=0.023), establishing this regimen as a promising approach to improve clinical outcomes. However, biomarker strategies are needed to guide optimal treatment decisions and allow for personalized therapy stratification. Therefore, we explore EGFR mutation detection from blood plasma at baseline in patients enrolled in the RAMOSE trial. The randomized, open-label multicenter phase II RAMOSE trial (NCT03909334, HCRN LUN-18-335) compared osimertinib with ramucirumab to osimertinib for frontline treatment of metastatic EGFR-mutant NSCLC. Blood plasma was collected at baseline (BL). Circulating-tumor DNA (ctDNA) for analysis was isolated from up to 4ml of blood plasma using the Genexus purification system. EGFR mutations were analyzed using the Oncomine Precision Assay on the Ion Torrent Genexus System. EGFR mutations were assessed by variant allele frequency (VAF) and correlated to clinical outcomes, including PFS and OS. Eight-six patient samples were sequenced, and 81 (94%) yielded positive sequencing results at BL. No statistically significant differences in BL EGFR detection were detected between the two treatment arms (χ2 p = 0.33). EGFR mutations at any VAF were detected in 68/81 patients (84%). Patients with EGFR VAF < 0.5% had significantly longer PFS (17.9mo vs not reached; p = 0.016; HR = 0.37) and OS (33.4mo vs not reached; p = 0.028; HR = 0.14) compared to patients with VAF > 0.5%. In patients without detectable EGFR mutations at BL, no death occurred during median follow up of 21.4mo (OS p = 0.076). In patients with detectable EGFR mutation at BL, the PFS was 19.9 mo in combination arm versus 16.0 mo in the osimertinib arm (HR = 0.69, p = 0.3). Detection of EGFR mutations from blood plasma at baseline was prognostic of clinical outcome and associated with significantly shorter PFS and OS. Detection of EGFR VAF by liquid biopsy can identify patients with higher risk of treatment progression and warrants further investigation for stratification of patients to future clinical trials. Simon Heeke, Dzifa Duose, Srividya Arjuna, Monique Nilsson, Jyoti Patel, Elaine Shum, Christina Baik, Rachel Sanborn, Catherine Shu, Chul Kim, Mary Jo Fidler, Richard Hall, Jhanelle Gray, Andreas Saltos, John V. Heymach, Xiuning Le. Association of baseline ctDNA EGFR mutation detection with clinical outcome in the phase II RAMOSE trial assessing ramucirumab plus osimertinib versus osimertinib in EGFR mutant non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3776.

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive lung tumor marked by significant molecular heterogeneity. In a study of 590 patients across two independent cohorts, we observe comparable overall survival across treatment regimens (chemotherapy, chemoimmunotherapy, immunotherapy) without unexpected adverse events. Genomic analysis identifies distinct non-small cell lung cancer-like (NSCLC-like, KEAP1, KRAS, STK11 mutations) and SCLC-like (RB1, TP53 mutations) LCNEC subtypes, with 80% aligning with SCLC transcriptional profiles. Serial sampling reveals stable mutational but shifting transcriptomic landscapes over time. Here we show, elevated FGL-1 (a LAG-3 ligand) and SPINK1 expression in NSCLC-like LCNECs, and higher levels of DLL3 in SCLC-like LCNECs. Immunofluorescence confirms FGL-1 expression in NSCLC-like LCNECs, and H&E slide analyses indicates fewer tumor-infiltrating lymphocytes in LCNECs versus other lung cancers. These findings highlight LCNEC's distinct immunogenomic profile, supporting future investigations into LAG-3, SPINK1, and DLL3-targeted therapies.

Kaplan–Meier estimates showing investigator-assessed PFS in FLAURA trial patients by clearance or non-clearance of plasma EGFRm status at Weeks 3 or 6 in patients who had baseline detectable plasma EGFRm. For comparison, patients with baseline non-detectable plasma EGFRm are included. A, Osimertinib arm by Week 3 plasma EGFRm status (n = 238). B, Comparator EGFR-TKI arm by Week 3 plasma EGFRm status (n = 243). C, Osimertinib arm by Week 6 plasma EGFRm status (n = 240). D, Comparator EGFR-TKI arm by Week 6 plasma EGFRm status (n = 235). Censored data are indicated by tick marks. Abbreviations: CI, confidence interval; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R); NC, not calculable; mPFS, median PFS; TKI, tyrosine kinase inhibitor.

Supplementary Table 2. Representativeness of Study Participants