Lung Cancer Cohort Research Articles

EXPRESS study: A trial exploring the association of low level of genomic alteration with exceptional and unexpected response to targeted therapies in patients with advanced solid tumors.

e15140 Background: Small subsets of pts treated with targeted therapies (TT) present exceptional and unexpected response (1-10%), which could be driven and correlated by a low level of genomic alterations in genes identified as causally implicated in cancer. Conversely, the multiplication of the genomic alterations in such genes would decrease the oncogenic de-addiction process. Methods: EXPRESS is an exploratory, multicenter, prospective trial conducted in pts with locally advanced or metastatic solid tumors, who presented an exceptional and unexpected response to an antineoplastic TT. This research aimed to explore whether tumors with a low level genomic alteration (mutation, amplification, deletion) as assessed by whole exome sequencing (WES) are associated with exceptional and unexpected response. Secondary endpoints including the identification of novel candidate somatic molecular profiles and host immunologic characteristics susceptible to drive exceptional and unexpected response will be reported later. Results: 439 pts were identified over 32 centres from December 2016 to April 2021. Out of 270 pts confirmed with an exceptional response by our review committee of cases, 183 were included in the EXPRESS study. Exceptional responder population is summarised (Table). Out of 183, WES analysis was performed for 103 pts (56.3%). Genomic analysis failure was predominant in the breast and lung cohort (60.9% and 52.5%). The number of genomic alterations in the EXPRESS cohort was lower than in the TCGA control cohort (median[Q1-Q3]: 11[7-17] vs 19 [11-32], p<0.001). A lower number of genomic alterations was also reported in the melanoma (8[5-13] vs 33[17-51], p<0.001) and lung cancer cohort (8[5-17] vs 22[13-34], p<0.001) but not in the breast and kidney cohorts (13[10-21] vs 17[10-29], p=0.41 and 12[9-14] vs 11[7-17], p=0.89). Conclusions: Compared to the TCGA cohort, a low level of genomic alteration is associated with exceptional response in pts with lung cancer and melanoma. A comparison with a matched population based on genomic criteria in the TCGA is undergoing and will be presented at the conference. Clinical trial information: NCT02701907 . [Table: see text]

Predicting response of patients with NSCLC to immunotherapy using innate immune fitness (IIF) profiling.

8038 Background: Aberrant activity of AID/APOBEC deaminase enzymes can cause 'off-target' somatic mutations in cancerous cells. Mutations associated with deaminases and other mechanisms can be quantified using metrics relating to motif usage, strand bias, transitions/transversions, codon context, and amino acid changes. Collectively, these metrics form an Innate Immune Fitness (IIF) profile (US Patent 20200370124). The aim of this project was to conduct IIF profiling on a cohort of Non-Small Cell Lung Cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICI), and use the IIF profiles to build and evaluate a predictive model. Methods: Whole exome and progression-free survival (PFS) data was obtained from Rizvi et al. 2015, Hellmann et al. 2018, Miao et al. 2018, Fang et al. 2019, Frigola et al. 2021, and Ravi et al. 2023 (n = 515). IIF profiles were generated using CRIS (v5.0.0; GMDx Genomics Ltd). Patients were classified as a ‘Responder’ (PFS > 12 months or Complete Response; n = 148) or ‘Non-Responder’ (PFS ≤ 12 months; n = 367). Machine learning models were generated using the H2O.ai AutoML platform and evaluated using multiple rounds of cross-validation. Patient response predictions were collated for each patient and a consensus ‘IIF Score’ was calculated. Multivariable analysis of IIF Score, TMB (10mut/Mb) and PD-L1 TPS ( < 1%, 1% - 50%, > 50%) was conducted using a Cox proportional-hazards model. Results: The predictive accuracy of IIF Scores was 76% (Sensitivity = 58%; Specificity = 83% , NPV = 83% , PPV = 58%) with a Hazard Ratio (HR) of 0.39 (0.29-0.53; p < 0.001; corrected for TMB and PD-L1). In comparison, the predictive accuracy of TMB was 67% (Sensitivity = 53%; Specificity = 79% , NPV = 79% , PPV = 44%), HR = 0.77 (0.59-1.00; p = 0.049; corrected for IIF Score and PD-L1). ‘PD-L1 > 50%’ accuracy was 53% and HR = 0.64 (0.44-0.94; p = 0.023; corrected for IIF Score and TMB). The Area Under the Curve (AUC) for IIF Score was significantly higher than TMB (0.77 vs 0.70; DeLong test: p < 0.001). Conclusions: IIF Score was the strongest predictor of patient response to ICI. Despite the inherent limitations of combining data from multiple cohorts, IIF Score outperformed TMB and PD-L1 in predictive accuracy, HR and AUC. With a Negative Predictive Value of 83%, these results support the use of IIF Score as a biomarker for identifying ICI Non-Responders in NSCLC patients. Research Sponsor: GMDx Genomics Ltd.

Impact of trial eligibility criteria on enrollment to KRASG12C inhibitor trials in patients with non-small cell lung cancer.

11012 Background: Clinical trials of novel therapies should enroll a population that reflects patients (pts) who might receive the drug upon FDA approval. In 2017 ASCO proposed modifications to common eligibility criteria (EC) to increase the generalizability of trial findings. Despite this, barriers to trial enrollment in the recent era of molecularly-targeted therapies are not well-characterized. We examined a cohort of non-small cell lung cancer (NSCLC) pts with KRAS G12C mutations to determine whether EC for trials of KRAS G12C inhibitors allowed enrollment of patients seen as part of routine care at an academic medical center. Methods: We extracted EC for Phase I-III trials among six KRAS G12C inhibitors: sotorasib, adagrasib, LY3537982 , divarasib, JDQ443 and RMC-6291. We retrospectively reviewed pts with NSCLC and G12C mutations detected on universal, NGS-based testing of NSCLCs at Columbia University Irving Medical Center (CUIMC) from 2020 to 2023. We defined dates of disease progression and evaluated pts for clinical trial eligibility for each line of treatment. Pts were deemed trial-eligible if they met all EC, borderline if they had 1 laboratory value < 20% from cutoff, or ineligible. The association between baseline factors including self-reported race, sex, and age with rates of eligibility were evaluated with chi-squared tests. Results: EC criteria for 14 trials were characterized. EC were similar across trials regardless of agent or phase and did not substantially change over time; 1 Phase III trial had expanded EC from the Phase I/II trials. Of 1172 patients with NSCLC, we identified 185 pts with G12C mutations (15%), including 69 pts with advanced or metastatic disease. Of these, 19% (13/69) would have been eligible for any G12C trial, 15% (10/69) were borderline, and 67% (46/69) were ineligible. 9% (6) would have qualified for only 1 of the trials. The most common reasons for ineligibility were poor performance status (54% [30/56]), excluded comorbidities (25% [14/56]), and renal dysfunction (27% [15/56]). Among patients who received therapy after FDA accelerated approval (N = 22), only 23% (5) would have been eligible for the related Phase III trial. Conclusions: Our findings suggest that most patients with KRAS G12C mutations would not have been eligible for relevant trials, including > 75% of patients who received KRAS G12C inhibitors off-trial after accelerated FDA approval. EC did not expand after early phase trials demonstrated evidence of safety. These data should guide sponsor and FDA considerations in the development of trial protocols for targeted therapies, as fewer barriers to trial participation would enable trials to be completed more quickly and would improve the generalizability of trial results.

Enfortumab vedotin (EV) in non-squamous and squamous non–small cell lung cancer (NSCLC) cohorts of EV-202.

Enfortumab vedotin (EV) in non-squamous and squamous non–small cell lung cancer (NSCLC) cohorts of EV-202.

LINCATRA: Two-cycle method to amplify RNA for transcriptome analysis from formalin-fixed paraffin-embedded tissue

Whole transcriptome analysis (WTA) using RNA extracted from Formalin Fixed Paraffin Embedded (FFPE) tissue is an invaluable tool to understand the molecular pathology of disease. RNA extracted from FFPE tissue is either degraded and/or in very low quantities hampering gene expression analysis. Earlier studies described protocols applied for cellular RNA using poly-A primer-based linear amplification. The current study describes a method, LINCATRA (LINear amplifiCAtion of RNA for whole TRAnscriptome analysis). It employs random nonamer primer based method which can amplify short, fragmented RNA with high fidelity from as low as 5 ng to obtain enough material for WTA. The two-cycle method significantly amplified RNA at ∼1000 folds (p < 0.0001) improving the mean read lengths (p < 0.05) in WTA. Overall, increased mean read length positively correlated with on-target reads (Pearson's r = 0.71, p < 0.0001) in both amplified and unamplified RNA-seq analysis. Gene expression analysis compared between unamplified and amplified group displayed substantial overlap of the differentially expressed genes (DEGs) (log2 fold change cut-off < −2 and >2, p < 0.05) identified between lung cancer and asthma cohorts validating the method developed. This method is applicable in clinical molecular pathology field for both diagnostics and elucidation of disease mechanisms.

Correlation of immune phenotypes derived from H&amp;E-stained whole slide images with prognosis and response to checkpoint inhibitors in NSCLC.

8539 Background: The classification of tumors as inflamed, excluded or desert based on spatial patterns of tumor infiltrating lymphocytes (TILs) is a potential biomarker of patients likely to respond to checkpoint inhibitors (CPI). However, the subjectivity of manual methods to assess these immune phenotypes (IPs) and poor standardization in the methods and thresholds to define IPs have hampered their clinical adoption. Here, we describe a data-driven approach to inform IP threshold selection on hematoxylin and eosin (H&amp;E)-stained whole slide images (WSI) by maximizing differences in overall survival (OS) between IPs. Methods: A model to classify the IPs of NSCLC samples from H&amp;E images was developed using PathExplore models applied to a TCGA non-small cell lung cancer (NSCLC) cohort of LUAD (N=459) and LUSC (N=424). TIL densities were extracted within cancer and stroma for 0.01 mm2 patches tiled across each WSI. Cut-offs to define cancer and stroma patches as hot or cold were defined based on the 75th and 50th percentiles, respectively, of cancer TIL (cTIL) densities (386 cTIL/mm2) and stroma TIL (sTIL) densities (423 sTIL/mm2) in a TCGA cohort of 4,082 H&amp;E-stained WSI from 10 epithelial tumor types. Hierarchical fitting yielded optimal thresholds minimizing the p-values of OS differences between IPs, leading to classifications of inflamed (iIP, &gt;40% cancer hot patches), excluded (eIP, ≤40% cancer hot patches; &gt;45% stromal hot patches) and desert (dIP, ≤40% cancer hot patches; ≤45% stromal hot patches). The model was then deployed in a clinical cohort of PD-(L)1 inhibitor-treated NSCLC patients (N=95) enrolled in the BIP precision medicine study (NCT02534649; Institut Bergonié, Bordeaux, France). Model-predicted IPs were compared to progression-free survival (PFS) and OS. FDR correction was done with Benjamini-Hochberg. Results: In the TCGA NSCLC cohort, model-predicted iIP (N=196) and eIP (N=607) patients had significantly better OS compared to dIP (N=80; HR=0.53, p=0.003 and HR=0.59, p=0.003, respectively). In the clinical cohort, cTIL density and fraction of hot epithelial patches were significantly associated with PFS (HR=0.64, q=0.04 and HR=0.69, q=0.04, respectively). PFS was significantly shorter in model-predicted eIP patients (N=46) compared to iIP (N=39; HR=0.54, p=0.045). Notably, in PD-L1(-) patients (N=43, tumor proportion score ≤1%), iIP patients had longer PFS than eIP and dIP patients (HR=0.35, p=0.02). No difference in PFS was observed for PD-L1(+) patients. Conclusions: We developed a data-driven approach for predicting IPs using patch-level TIL features. Model-predicted IPs were prognostic in a TCGA NSCLC dataset and predictive of PFS in a CPI- treated clinical NSCLC cohort. Association of IP and PFS was independent of PD-L1 status, potentially allowing the identification of PD-L1(-) patients who may derive greater benefit from CPI.

A two-decade trend analysis of in-hospital outcomes and racial/ethnic disparities among hospitalized patients with lung cancer in the United States.

11035 Background: Previous research has focused largely on the disease and healthcare cost burden of lung cancer (LC),however, long-term trends of hospitalizations among LC patients (pts) in the US are limited. We aimed to describe longitudinal trends in in-hospital outcomes among hospitalized LC pts, overall and by race/ethnicity. Methods: We analyzed data from the National Inpatient Sample database, spanning 1998 to 2020. The study cohort included pts aged ≥18 years with a principal LC diagnosis. We assessed four in-hospital outcomes: 1) hospitalization, 2) mortality, 3) length of stay (LOS), and 4) total hospitalization charges (THCs). THCs were adjusted for inflation using the Medicare Hospital Care Consumer Price Index.To quantify trends, we performed weighted analyses (weighted means or proportions) and calculated P-trend using Cochran-Armitage or Jonckheere-Terpstra tests. Results: Of 791,100 pts (mean age 68.2 years), 80.4% were White, followed by 11.9% Black, 4.7% Hispanic, and 3.0% Asian or Pacific Islander (API). Among US hospitalized pts in 1998, 0.54% had a principal LC diagnosis, with hospitalization rates having decreased to 0.37% in 2020 ( P-trend&lt;.001). By race/ethnicity, the LC hospitalization rate for White pts declined from 0.53% in 1998 to 0.39% in 2020; for Black pts, it declined from 0.35% to 0.26%; and for Hispanic pts, it declined from 0.18% to 0.12% (all P-trend&lt;.001). In contrast, the hospitalization rate for API pts increased from 0.30% in 1998 to 0.38% in 2020 ( P-trend&lt;.001). The overall LC-specific in-hospital mortality rate declined drastically from 15.9% in 1998 to 6.2% in 2020 ( P-trend&lt;.001); a similar pattern was observed across racial/ethnic groups. The LOS reduced from an average of 7.9 days in 1998 to 5.9 days in 2020 ( P-trend&lt;.001). By race/ethnicity, the LOS for White pts decreased from 7.8 days in 1998 to 5.2 days in 2020; for Black pts, it decreased from 9.0 to 7.0 days; for Hispanic pts, it decreased from 8.8 to 6.7 days; and it declined from 8.7 to 5.9 days in API pts (all P-trend&lt;.001). THCs rose significantly from $39,706 in 1998 to $92,734 in 2020 ( P-trend&lt;.001). In White pts, THCs rose from $39782 to $89,681; from $41,235 to $91,792 in Black pts; from $49,444 to $119,386 in Hispanic pts; and from $52,779 to $112,365 in API pts (all P-trend&lt;.001). Moreover, the odds of female pts hospitalized with LC was higher (AOR=1.47, 95% CI=1.40-1.54, P&lt;.001) in 2020 compared to 1998. Conclusions: In this racially diverse hospitalized LC cohort, LOS, rates of hospitalizations, and mortality rates have declined, while THCs have risen significantly, over the last 20 years. API and female pts bore a heavier burden of hospitalizations. Our findings suggest the need for targeted healthcare interventions or policies to reduce THCs and hospitalization disparities in LC pts.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

Multicenter PET image harmonization using generative adversarial networks

PurposeTo improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches.MethodsGAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline 18F-FDG PET before and after harmonization.ResultsImage quality remained high (structural similarity: left kidney ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.800, right kidney ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.806, liver ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.780, lung ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.838, spleen ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.793, whole-body ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 5 ± 14% (first-order), ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 16 ± 7% (GLCM), ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 19 ± 5% (GLRLM), ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 16 ± 8% (GLSZM), ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 17 ± 6% (GLDM), and ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 23 ± 14% (NGTDM). In the head and neck cancer cohort, the outcome prediction improved from AUC 0.68 (95% CI 0.66–0.71) to AUC 0.73 (0.71–0.75) by application of GAN-harmonization.ConclusionsGANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners.

Correlation of Systemic Inflammation Parameters and Serum SLFN11 in Small Cell Lung Cancer-A Prospective Pilot Study.

The objective of this research was to analyze the correlation of the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), soluble programmed cell death ligand 1 (sPD-L1), and Schlafen 11 (SLFN11) with the response to first-line chemotherapy in a cohort of small cell lung cancer (SCLC) patients, and to determine their potential as predictive serum biomarkers. A total of 60 SCLC patients were included. Blood samples were taken to determine CRP, sPD-L1, and SLFN11 levels. The first sampling was performed before the start of chemotherapy, the second after two cycles, and the third after four cycles of chemotherapy. The patients who died earlier during the study had NLR and SLFN11 concentrations significantly higher compared to the survivor group. In the group of survivors, after two cycles of chemotherapy, the NLR ratio decreased significantly (p < 0.01), but after four cycles, the NLR ratio increased (p < 0.05). Their serum SLFN11 concentration increased significantly (p < 0.001) after two cycles of chemotherapy, but after four cycles, the level of SLFN11 fell significantly (p < 0.01). CRP, NLR, and SLFN11 were significant predictors of patient survival according to Kaplan-Meier analysis. The combination of inflammatory parameters and SLFN11 with a cutoff value above the 75th percentile of the predicted probability was associated with significantly lower overall survival in SCLC patients (average survival of 3.6 months vs. 4.8 months). The combination of inflammatory markers and the levels of two specific proteins (sPD-L1, SLFN11) could potentially serve as a non-invasive biomarker for predicting responses to DNA-damaging therapeutic agents in SCLC.

Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation

ObjectivesIn lung cancer, one of the main limitations for the optimal integration of the biological and anatomical information derived from Positron Emission Tomography (PET) and Computed Tomography (CT) is the time and expertise required for the evaluation of the different respiratory phases. In this study, we present two open-source models able to automatically segment lung tumors on PET and CT, with and without motion compensation.Materials and methodsThis study involved time-bin gated (4D) and non-gated (3D) PET/CT images from two prospective lung cancer cohorts (Trials 108237 and 108472) and one retrospective. For model construction, the ground truth (GT) was defined by consensus of two experts, and the nnU-Net with 5-fold cross-validation was applied to 560 4D-images for PET and 100 3D-images for CT. The test sets included 270 4D- images and 19 3D-images for PET and 80 4D-images and 27 3D-images for CT, recruited at 10 different centres.ResultsIn the performance evaluation with the multicentre test sets, the Dice Similarity Coefficients (DSC) obtained for our PET model were DSC(4D-PET) = 0.74 ± 0.06, improving 19% relative to the DSC between experts and DSC(3D-PET) = 0.82 ± 0.11. The performance for CT was DSC(4D-CT) = 0.61 ± 0.28 and DSC(3D-CT) = 0.63 ± 0.34, improving 4% and 15% relative to DSC between experts.ConclusionsPerformance evaluation demonstrated that the automatic segmentation models have the potential to achieve accuracy comparable to manual segmentation and thus hold promise for clinical application. The resulting models can be freely downloaded and employed to support the integration of 3D- or 4D- PET/CT and to facilitate the evaluation of its impact on lung cancer clinical practice.Clinical relevance statementWe provide two open-source nnU-Net models for the automatic segmentation of lung tumors on PET/CT to facilitate the optimal integration of biological and anatomical information in clinical practice. The models have superior performance compared to the variability observed in manual segmentations by the different experts for images with and without motion compensation, allowing to take advantage in the clinical practice of the more accurate and robust 4D-quantification.Key PointsLung tumor segmentation on PET/CT imaging is limited by respiratory motion and manual delineation is time consuming and suffer from inter- and intra-variability.Our segmentation models had superior performance compared to the manual segmentations by different experts.Automating PET image segmentation allows for easier clinical implementation of biological information.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias

ObjectiveTo develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. MethodsWe developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package – SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. ResultsWe developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. ConclusionsOur SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

A Multicenter Study on the Challenges and Real-World Utilization of Immune Checkpoint Inhibitors in Resource-Constrained Settings: Insights and Implications from India

Using immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment, but access and affordability remain significant challenges, particularly in resource-constrained settings. This multicenter study evaluated the utilization, outcomes, and challenges associated with ICIs in India. Data from multiple centers involving patients treated between January 2018 and December 2021 were retrospectively collected. Patient demographics, treatment indications, biomarker testing, financial coverage, toxicity, treatment discontinuation, clinical benefit, progression-free survival (PFS), and overall survival (OS) were analyzed. Ninety-one patients were analyzed; lung cancer (39.6%) and renal cancer (11%) were the main indications for ICI use. Programmed death ligand 1 expression was tested in 40.7% and tumor mutational burden in 3.3%. Financial constraints influenced 41.8% of patients with out-of-pocket expenses. Treatment discontinuation due to financial constraints occurred in 17.6%, with 50% showing ongoing responses. The median number of cycles was 4; the median PFS was 4.6 months, and the median OS was 15.4 months. The lung cancer cohort had a median PFS of 5.7 months and a 1-year OS of 57.6%. Limited biomarker testing and 6.6% grade ¾ toxicities were observed. This study revealed challenges in ICI utilization in resource-constrained settings driven by financial constraints. Compared with prior studies, improved outcomes reflect better patient selection and evolving understanding of ICI use. However, in the absence of biosimilars, cost remains a significant barrier. Solutions to increase access include using lower doses, which may be as effective.

PBMCs as Tool for Identification of Novel Immunotherapy Biomarkers in Lung Cancer.

Lung cancer (LC), including both non-small (NSCLC) and small (SCLC) subtypes, is currently treated with a combination of chemo- and immunotherapy. However, predictive biomarkers to identify high-risk patients are needed. Here, we explore the role of peripheral blood mononuclear cells (PBMCs) as a tool for novel biomarkers searching. We analyzed the expression of the cGAS-STING pathway, a key DNA sensor that activates during chemotherapy, in PBMCs from LC patients divided into best responders (BR), responders (R) and non-responders (NR). The PBMCs were whole exome sequenced (WES). PBMCs from BR and R patients of LC cohorts showed the highest levels of STING (p < 0.0001) and CXCL10 (p < 0.0001). From WES, each subject had at least 1 germline/somatic alteration in a DDR gene and the presence of more DDR gene mutations correlated with clinical responses, suggesting novel biomarker implications. Thus, we tested the effect of the pharmacological DDR inhibitor (DDRi) in PBMCs and in three-dimensional spheroid co-culture of PBMCs and LC cell lines; we found that DDRi strongly increased cGAS-STING expression and tumor infiltration ability of immune cells in NR and R patients. Furthermore, we performed FACS analysis of PBMCs derived from LC patients from the BR, R and NR cohorts and we found that cytotoxic T cell subpopulations displayed the highest STING expression. cGAS-STING signaling activation in PBMCs may be a novel potential predictive biomarker for the response to immunotherapy and high levels are correlated with a better response to treatment along with an overall increased antitumor immune injury.

Safety net hospital risk model demonstrates stronger, population-specific applicability in characterizing lung cancer risk.

Determining lung cancer (LC) risk using personalized risk stratification may improve screening effectiveness. While the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) is a well-established stratification model for LC screening, it was derived from a predominantly Caucasian population and its effectiveness in a safety net hospital (SNH) population is unknown. We have developed a model more tailored to the SNH population and compared its performance to the PLCO model in a SNH setting. Retrospective dataset was compiled from patients screened for LC at SNH from 2015 to 2019. Descriptive statistics were calculated using the following variables: age, sex, race, education, body mass index (BMI), smoking history, personal cancer history, family LC history, chronic obstructive pulmonary disease (COPD), and emphysema. Variables distribution was compared using t- and chi-square tests. LC risk scores were calculated using SNH and PLCO models and categorized as low (scores <0.65%), moderate (0.65-1.49%), and high (>1.5%). Linear regression was applied to evaluate the relationship between models and covariates. Of 896 individuals, 38 were diagnosed with LC. Data reflected the SNH patient demographics, which predominantly were African American (53.5%), current smokers (69.9%), and with emphysema (70.1%). Among the non-LC cohort, SNH model most frequently categorized patients as low risk, while PLCO model most frequently classified patients as moderate risk. Among the LC cohort, there was no significant difference between mean scores or risk stratification. SNH model showed 92.1% sensitivity and 96.8% specificity while PLCO model showed 89.4% sensitivity and 26.1% specificity. Emphysema demonstrated a strong association in SNH model (P<0.001) while race showed no relation. SNH model demonstrated greater specificity for characterizing LC risk in a SNH population. The results demonstrated the importance of study sample representation when identifying risk factors in a stratification model.

34 Predictive value of PD-L1 expression IHC on histology vs. cytology-specimen in non-small cell lung cancer (NSCLC) cohort in Leicester

34 Predictive value of PD-L1 expression IHC on histology vs. cytology-specimen in non-small cell lung cancer (NSCLC) cohort in Leicester

Carrying on with life as a lung cancer survivor: a qualitative study of Australian survivors' employment, finances, relationships, and healthcare experiences.

With novel therapies, more individuals are living longer with lung cancer (LC). This study aimed to understand the impacts of LC on life domains such as employment, finances, relationships, and healthcare needs. Individuals 18+, diagnosed with LC, 6-24 months post-treatment were recruited through an Australian LC cohort study (Embedding Research and Evidence in Cancer Healthcare-EnRICH). Demographic, clinical, quality-of-life and distress data were obtained through the EnRICH study database. Participants completed telephone interviews. Qualitative data were analysed via Framework methods. Twenty interviews (10 females) were conducted. Most participants were diagnosed with advanced LC (Stage III =8, Stage IV =6), and were on average 17 (range, 10-24) months post-diagnosis. Four themes related to "carrying on with life" as a LC survivor were identified: (I) the winding path back to work: those working pre-diagnosis discussed challenges of maintaining/returning to employment, and the meaning and satisfaction derived from work. (II) Vulnerability versus protection: managing the financial impacts of LC: wide variations in financial impacts, some described lost income and high healthcare expenses, others felt financially protected. (III) Connection and loneliness: navigating relationships as a survivor: some experienced lost friendships due to their diagnosis, others noted more meaningful connections. (IV) Still under the umbrella: healthcare during survivorship: participants noted the importance of ongoing oncology team connection and the vital role of cancer care coordinators. Many individuals living with LC want to "carry on" with life. Participants spoke of challenges and opportunities across life domains of relationships, work, and finances, and noted the importance of continued specialist healthcare throughout survivorship.

Wemics: A Single-Base Resolution Methylation Quantification Method for Enhanced Prediction of Epigenetic Regulation.

DNA methylation, an epigenetic mechanism that alters gene expression without changing DNA sequence, is essential for organism development and key biological processes like genomic imprinting and X-chromosome inactivation. Despite tremendous efforts in DNA methylation research, accurate quantification of cytosine methylation remains a challenge. Here, a single-base methylation quantification approach is introduced by weighting methylation of consecutive CpG sites (Wemics) in genomic regions. Wemics quantification of DNA methylation better predicts its regulatory impact on gene transcription and identifies differentially methylated regions (DMRs) with more biological relevance. Most Wemics-quantified DMRs in lung cancer are epigenetically conserved and recurrently occurred in other primary cancers from The Cancer Genome Atlas (TCGA), and their aberrant alterations can serve as promising pan-cancer diagnostic markers. It is further revealed that these detected DMRs are enriched in transcription factor (TF) binding motifs, and methylation of these TF binding motifs and TF expression synergistically regulate target gene expression. Using Wemics on epigenomic-transcriptomic data from the large lung cancer cohort, a dozen novel genes with oncogenic potential are discovered that are upregulated by hypomethylation but overlooked by other quantification methods. These findings increase the understanding of the epigenetic mechanism by which DNA methylation regulates gene expression.

Abstract 2490: Quantification of tumor fraction and outcomes association in a real-world non-small cell lung cancer (NSCLC) cohort using a tissue agnostic epigenomic circulating tumor DNA (ctDNA) assay

Abstract Background: The validity of ctDNA assays for evaluating molecular response to therapy by measuring changes in variant allele fraction (VAF) while on treatment is well established, but they are prone to limitations such as low ctDNA levels and interference from copy number variation and clonal hematopoeisis (CH), which may be overcome by methylation-based quantification. ctDNA levels may be monitored throughout a patient's (pts) journey to indicate when relapse or progression is present, often sooner than current methods (RECIST). Here we describe the performance of a methylation-based ctDNA assay to quantify ctDNA levels and correlate changes with outcomes in a real-world NSCLC cohort. Methods: RADIOHEAD is a pan-tumor observational study of 1200 patients on standard of care ICI treatment regimens with plasma samples collected prospectively for retrospective analysis. We randomly selected 241 NSCLC pts that have a combined total of 761 samples (236 baseline, 191 C3D1, and 128 at 6 months post first dose). Samples were analyzed using a commercial NGS assay, which quantifies circulating tumor fraction (cTF) via interrogating thousands of methylation sites from ctDNA, validated with LoB, LoD, LoQ, and linearity studies. Cox proportional hazards (CPH) were used for comparison of real-world progression free survival (rwPFS). Gender, age, disease stage, and blood-assessed tumor mutational burden (bTMB) were included as covariates. Median rwPFS was calculated using Kaplan Meier analysis. rwPFS ordinal groups were defined as PFS event within 3mo, 3-6mo, 6-12mo, and greater than 12mo, and association with early ctDNA changes was assessed with a chi-squared test. Results: In this cohort, 4, 11, 83 and 142 pts were stage I to IV, respectively (1 unknown). Methylation based detection of ctDNA at baseline or C3D1 was associated with shorter rwPFS (baseline: mPFS 12.7 mo [9.3-19.5] vs NR [16.4-NR]; HR=2.2 [1.3-3.7] p&amp;lt;0.005, C3D1: mPFS 12.4 mo [9.3-16.1] vs NR [19.5-NR] HR= 2.2 [1.3-3.5], p&amp;lt;0.005) independent of stage, gender, or age. Longer rwPFS was associated with &amp;gt;95% reduction in cTF from baseline to C3D1 or low ctDNA at both timepoints (rwPFS &amp;lt;3mo = 12.5%, 3-6 mo = 30.8%, 6-12 = 37.1%, ≥12 mo = 55.3%; N=153, p=0.015). For pts with ctDNA not detected at C3D1, ctDNA detection at 6 months post first dose was strongly associated with shorter rwPFS (mPFS 13.5 mo [9.5-NR] vs. NR [19.5-NR]; HR= 6.2 [1.9-20.1], p&amp;lt;0.005). Conclusions: These data demonstrate a significant association of ctDNA detection via methylation-based cTF and on-treatment changes in cTF with rwPFS. Furthermore, subsequent cTF detection 6-months on-treatment, without cTF detected early on-treatment was associated with worse outcomes, suggesting the value of serial monitoring to improve identification of patients likely to progress. Citation Format: Sara Wienke, Sean Gordon, Samantha Liang, Jing Wang, Reagan Barnett, Kyle Chang, Shile Zhang, Carin R. Espenschied, Katie Quinn, Kimberly Banks. Quantification of tumor fraction and outcomes association in a real-world non-small cell lung cancer (NSCLC) cohort using a tissue agnostic epigenomic circulating tumor DNA (ctDNA) assay [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 2490.