Prognosis Of NSCLC Research Articles

EP.06B.01 The Actual Status of Biomarker Testing and Prognosis in Postoperative Recurrent NSCLC: Subgroup Analyses of WJOG15421L (REVEAL)

EP.06B.01 The Actual Status of Biomarker Testing and Prognosis in Postoperative Recurrent NSCLC: Subgroup Analyses of WJOG15421L (REVEAL)

Comparison of methods for cancer stem cell detection in prognosis of early stages NSCLC

BackgroundDespite advances in diagnosis and treatment in lung cancer, therapies still fail to improve patient management due to resistance mechanisms and relapses. As Cancer stem cells (CSCs) directly contribute to tumor growth and therapeutic resistance, their clinical detection represents a major challenge. However specific and additional CSC markers lack. Thus, our aim was to achieve selective detection of CSCs with specific glycan patterns and assess the CSCs burden to predict the risk of relapse in NSCLC tumors.MethodsThe lung CSCs detection and sorting with a lectin MIX were assessed and compared to CD133 in vitro. Then, its putative role as CSC biomarker was evaluated in vivo and its clinical significance on 221 NSCLC patients.ResultsWe showed a significant CSCs enrichment in the MIX+ sorted fraction compared to CD133+ cells and confirmed its high tumorigenic capacity. The MIX prognostic value on the overall survival from early stages patients was validated suggesting its potential for detecting CSCs directly linked to tumor aggressiveness.ConclusionThe MIX could be more relevant for detecting and sorting CSCs than CD133. Moreover, its prognosis value could enable clinicians to better classify early-stage patients at high risk of relapse in order to tailor therapeutic decisions.

Identification of costimulatory molecule signatures for evaluating prognostic risk in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. Despite advances in treatment, prognosis remains poor, necessitating the identification of reliable prognostic biomarkers. Costimulatory molecules (CMs) have shown to enhance antitumor immune responses. We aimed to explore their prognostic signals in NSCLC. MethodsThis study is a combination of bioinformatics analysis and laboratory validation. Gene expression profiles from The Cancer Genome Atlas (TCGA), GSE120622, and GSE131907 datasets were collected. NSCLC samples in TCGA were clustered based on CMs using consensus clustering. We used LASSO regression to identify CMs-related signatures and constructed nomogram and risk models. Differences in immune cells and checkpoint expressions between risk models were evaluated. Enrichment analysis was performed for differentially expressed CMs between NSCLC and controls. Key results were validated using qRT-PCR and flow cytometry. ResultsNSCLC samples in TCGA were divided into two clusters based on CMs, with cluster 1 showing poor overall survival. Ten CMs-related signatures were identified using LASSO regression. NSCLC samples in TCGA were stratified into high- and low-risk groups based on the median risk score of these signatures, revealing differences in survival probability, drug sensitivity, immune cell infiltration and checkpoints expression. The area under the ROC curve values (AUC) for EDA, ICOS, PDCD1LG2, and VTCN1 exceeded 0.7 in both datasets and considered as hub genes. Expression of these hub genes was significance in GSE131907 and validated by qRT-PCR. Macrophage M1 and T cell follicular helper showed high correlation with hub genes and were lower in NSCLC than controls detected by flow cytometry. ConclusionThe identified hub genes can serve as prognostic biomarkers for NSCLC, aiding in treatment decisions and highlighting potential targets for immunotherapy. This study provides new insights into the role of CMs in NSCLC prognosis and suggests future directions for clinical research and therapeutic strategies.

Integrative analysis of aging-related genes reveals CEBPA as a novel therapeutic target in non-small cell lung cancer

BackgroundTo explore the impact of ARGs on the prognosis of NSCLC, and its correlation with clinicopathological parameters and immune microenvironment. Preliminary research on the biological functions of CEBPA in NSCLC.MethodsUsing consensus clustering analysis to identify molecular subtypes of ARGs in NSCLC patients; employing LASSO regression and multivariate Cox analysis to select 7 prognostic risk genes and construct a prognostic risk model; validating independent prognostic factors of NSCLC using forest plot analysis; analyzing immune microenvironment correlations using ESTIMATE and ssGSEA; assessing correlations between prognostic risk genes via qPCR and Western blot in NSCLC; measuring mRNA and protein expression levels of knocked down and overexpressed CEBPA in NSCLC using CCK-8 and EdU assays; evaluating the effects of knocked down and overexpressed CEBPA on cell proliferation using Transwell experiments; examining the correlation of CEBPA with T cells and B cells using mIHC analysis.ResultsConsensus clustering analysis identified three molecular subtypes, suggesting significant differential expression of these ARGs in NSCLC prognosis and clinical pathological parameters. There was significant differential expression between the two risk groups in the prognostic risk model, with P < 0.001. The risk score of the prognostic risk model was also P < 0.001. CEBPA exhibited higher mRNA and protein expression levels in NSCLC cell lines. Knockdown of CEBPA significantly reduced mRNA and protein expression levels of CEBPB, YWHAZ, ABL1, and CDK1 in H1650 and A549 cells. siRNA-mediated knockdown of CEBPA markedly inhibited proliferation, migration, and invasion of NSCLC cells, whereas overexpression of CEBPA showed the opposite trend. mIHC results indicated a significant increase in CD3 + CD4+, CD3 + CD8+, and CD20 + cell counts in the high CEBPA expression group.ConclusionsThe risk score of the prognostic risk model can serve as an independent prognostic factor, guiding the diagnosis and treatment of NSCLC. CEBPA may serve as a potential tumor biomarker and immune target, facilitating further exploration of the biological functions and immunological relevance in NSCLC.

Integrin αVβ1-activated PYK2 promotes the progression of non-small-cell lung cancer via the STAT3-VGF axis

BackgroundNon-small-cell lung cancer (NSCLC) accounts for 80–85% of all lung cancer and is the leading cause of cancer-related deaths globally. Although various treatment strategies have been introduced, the 5-year survival rate of patients with NSCLC is only 20–30%. Thus, it remains necessary to study the pathogenesis of NSCLC and develop new therapeutic drugs. Notably, PYK2 has been implicated in the progression of many tumors, including NSCLC, but its detailed mechanism remains unclear. In this study, we aimed to elucidate the mechanisms through which PYK2 promotes NSCLC progression.MethodsThe mRNA and protein levels of various molecules were measured using qRT-PCR, western blot (WB), and immunohistochemistry (IHC), respectively. We established stable PYK2 knockdown and overexpression cell lines, and CCK-8, EdU, and clonogenic assays; wound healing, transwell migration, and Matrigel invasion assays; and flow cytometry were employed to assess the phenotypes of tumor cells. Protein interactions were evaluated with co-immunoprecipitation (co-IP), immunofluorescence (IF)-based colocalization, and nucleocytoplasmic separation assays. RNA sequencing was performed to explore the transcriptional regulation mediated by PYK2. Secreted VGF levels were examined using ELISA. Dual-luciferase reporter system was used to detect transcriptional regulation site. PF4618433 (PYK2 inhibitor) and Stattic (STAT3 inhibitor) were used for rescue experiments. A public database was mined to analyze the effect of these molecules on NSCLC prognosis. To investigate the role of PYK2 in vivo, mouse xenograft models of lung carcinoma were established and examined.ResultsThe protein level of PYK2 was higher in human NSCLC tumors than in the adjacent normal tissue, and higher PYK2 expression was associated with poorer prognosis. PYK2 knockdown inhibited the proliferation and motility of tumor cells and caused G1-S arrest and cyclinD1 downregulation in A549 and H460 cells. Meanwhile, PYK2 overexpression had the opposite effect in H1299 cells. The siRNA-induced inhibition of integrins alpha V and beta 1 led to the downregulation of p-PYK2(Tyr402). Activated PYK2 could bind to STAT3 and enhance its phosphorylation at Tyr705, regulating the nuclear accumulation of p-STAT3(Tyr705). This further promoted the expression of VGF, as confirmed by RNA sequencing in a PYK2-overexpressing H1299 cell line and validated by rescue experiments. Two sites in promoter region of VGF gene were confirmed as binding sites of STAT3 by Dual-luciferase assay. Data from the TGCA database showed that VGF was related to the poor prognosis of NSCLC. IHC revealed higher p-PYK2(Tyr402) and VGF expression in lung tumors than in adjacent normal tissues. Moreover, both proteins showed higher levels in advanced TNM stages than earlier ones. A positive linear correlation existed between the IHC score of p-PYK2(Tyr402) and VGF. Knockdown of VGF inhibited tumor progression and reversed the tumor promoting effect of PYK2 overexpression in NSCLC cells. Finally, the mouse model exhibited enhanced tumor growth when PYK2 was overexpressed, while the inhibitors PF4618433 and Stattic could attenuate this effect.ConclusionsThe Integrin αVβ1-PYK2-STAT3-VGF axis promotes NSCLC development, and the PYK2 inhibitor PF4618433 and STAT3 inhibitor Stattic can reverse the pro-tumorigenic effect of high PYK2 expression in mouse models. Our findings provide insights into NSCLC progression and could guide potential therapeutic strategies against NSCLC with high PYK2 expression levels.

Real-world clinical outcomes in Chinese patients with EGFR-mutated NSCLC progressed from first-line EGFR-TKI: A prospective multicenter study.

e20547 Background: EGFR-TKIs is standard of care for treatment-naïve EGFR mutated ( EGFRm) advanced NSCLC (aNSCLC). The clinical outcome after 1L EGFR-TKI in clinical practice is not well studied prospectively. Herein, we reported the real-world clinical outcomes in Chinese EGFRm aNSCLC progressed from 1L EGFR-TKI. Methods: This prospective, observational study enrolled 300 Chinese patients (pts) from 16 sites between March, 2020 and August, 2021. At data cut-off date of August 27, 2023. 291 eligible pts were included into the full analysis set (FAS) and 225 pts in FAS received second-line EGFR-TKI or chemotherapy were included into outcome analysis set (OAS). The clinical outcome (PFS and OS, measured from 2nd line treatment) were presented. Results: The median age of 291 pts in FAS was 62 years, 57.0% was females and 29.3% pts with brain metastasis. EGFR mutation included 19del (143 pts, 49.1%), L858R (126 pts, 43.3%) and others (22 pts, 7.6%). 273 (93.8%) and 18 (6.2%) pts progressed from 1L 1G/2G EGFR-TKIs and 3G EGFR-TKIs, respectively. In OAS, overall mPFS and mOS in T790M + patients (90 pts) who received 2L 3G EGFR-TKIs were 14.7 (10.40-18.0) and 32 (25.20-NE) months, respectively. The mPFS and mOS by type of sample and T790M test results were presented in table. 85 pts with T790M- who progressed from 1L 1/2G EGFR-TKIs, including 51, 9 and 25 pts received 3G EGFR-TKIs, prior EGFR-TKIs plus local therapy, and chemotherapy as 2L therapy, respectively. The corresponding mPFS were 7.60 (95% CI, 4.70-10.40), 10.20 (2.30-14.90), and 4.90 (2.50-6.90) months, respectively. The corresponding mOS were 21.20 (12.10-NE), 16.60 (6.00-28.60), and 15.00 (9.40-NE) months, respectively. For T790M- patients with 19del (21 pts) and L858R (26 pts) mutation who received 2L 3G EGFR-TKIs, the mPFS was 21.00 (2.60-NE), and 8.00 (5.00-10.40) months, respectively. mOS was NE (21.00-NE) and 16.60 (8.20-24.60) months, respectively. 12 pts progressed from 1L 3G EGFR-TKIs, 10 and 2 pts received chemotherapy-based regimen and prior EGFR-TKIs plus local therapy as 2L therapy, respectively. The corresponding overall mPFS were 8.40 (95% CI: 1.20-11.80) months. The overall mOS was 14.20 (2.80-NE) months. Conclusions: The real-world treatment pattern and corresponding prognosis of EGFR-mutated NSCLC progressed from first-line EGFR-TKI were diverse, and among them those who received 3G EGFR-TKI at progression showed a better clinical outcome. These results supplemented the clinical evidence for the use of 3G EGFR-TKIs in EGFRm aNSCLC pts who progressed from 1L 1/2G EGFR-TKIs in the real-world setting. Clinical trial information: NCT04207775 . [Table: see text]

Abstract 5379: Inhibition of ULK1/2-mediated autophagy augments antigen processing and presentation in STK11 mutant NSCLC

Abstract BACKGROUND: Inactivating mutations and deletions in STK11 are present in 9-20% of NSCLC and result in immunologically cold tumors that respond poorly to therapy, despite having a high tumor mutational burden and neoantigen load. STK11 mutations in NSCLC are associated with higher levels of ULK1/2-mediated macroautophagy, resulting in localization of MHC-I to autophagosomes and lysosomes leading to its degradation and decreased antigen presentation. Knockdown of ULK1/2 can restore surface MHC-I in highly autophagic cell lines. Here, we show that inhibition of ULK1/2 decreases macroautophagy, resulting in increased antigen processing and presentation (APP) and MHC-I levels in cancer cells, supporting the hypothesis that inhibition of ULK1/2 will restore antigen presentation in NSCLC STK11mut tumors. METHODS: TCGA datasets were analyzed to determine transcriptional differences in autophagy and APP between STK11 WT and mutant NSCLC tumors. CRISPR was used to stably knockout STK11 in multiple human NSCLC cell lines to generate isogenic pairs. mRNA and protein levels of APP machinery in NSCLC cell lines were assessed using qPCR, western blots, and flow cytometry. CRISPR was additionally used to knockout ULK1 and ULK2 in human STK11 mutant A549 cells. ULK1/2 tool compounds were synthesized based on existing patent literature. Effects of the combination of ULK1/2 inhibition with low levels of IFNγ on surface MHC-I levels were evaluated in several NSCLC cell lines by flow cytometry. RESULTS: NSCLC tumors with loss of function mutations or deletion of STK11 display increased transcription of genes in the ULK complex and reduced transcription of genes necessary for APP. STK11mut and KO NSCLC cell lines also display lower levels of APP at the mRNA and protein levels. Double knockout of ULK1 and ULK2 in A549 cells is not cytotoxic and results in decreased autophagy as well as increased immunoproteasome components and cell surface MHC-I. ULK inhibition reduces autophagy and restores APP at the protein level, but not at the transcriptional level, in all NSCLC cell lines evaluated. Combination of very low concentrations of IFNγ with ULK inhibition demonstrates additive increases in MHC-I greater than either single-agent treatment alone. CONCLUSIONS: STK11mut/del is associated with poor prognosis in NSCLC and leads to increased ULK-induced autophagy and decreased APP. ULK inhibition increases APP and surface MHC-I levels in STK11mut cell lines. In the presence of low concentrations of IFNγ, ULK1/2 inhibition restores APP in NSCLC STK11mut cell lines to the levels seen in NSCLC STK11 WT cell lines. The data presented here provides a rationale for targeting ULK1/2 to amplify immune recognition in immunologically cold tumors with high autophagy to increase responses to immunotherapies. Citation Format: Samantha C. Schwager, Eugene Park, Anne Van Abbema, Yihong Guan, Connor Rosen, Sean Cho, Michael Black, Inna Kurilyak, Jenna Jeffrey, Juan Jose Fung, Susan L. Paprcka, Ester Fernandez-Salas. Inhibition of ULK1/2-mediated autophagy augments antigen processing and presentation in STK11 mutant NSCLC [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 5379.

Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer

Tertiary Lymphoid Structures (TLS) correlate with positive outcomes in patients with NSCLC and the efficacy of immune checkpoint blockade (ICB) in cancer. The actin regulatory protein hMENA undergoes tissue-specific splicing, producing the epithelial hMENA11a linked to favorable prognosis in early NSCLC, and the mesenchymal hMENAΔv6 found in invasive cancer cells and pro-tumoral cancer-associated fibroblasts (CAFs). This study investigates how hMENA isoforms in tumor cells and CAFs relate to TLS presence, localization and impact on patient outcomes and ICB response. Methods involved RNA-SEQ on NSCLC cells with depleted hMENA isoforms. A retrospective observational study assessed tissues from surgically treated N0 patients with NSCLC, using immunohistochemistry for tumoral and stromal hMENA isoforms, fibronectin, and TLS presence. ICB-treated patient tumors were analyzed using Nanostring nCounter and GeoMx spatial transcriptomics. Multiparametric flow cytometry characterized B cells and tissue-resident memory T cells (TRM). Survival and ICB response were estimated in the cohort and validated using bioinformatics pipelines in different datasets. Findings indicate that hMENA11a in NSCLC cells upregulates the TLS regulator LTβR, decreases fibronectin, and favors CXCL13 production by TRM. Conversely, hMENAΔv6 in CAFs inhibits LTβR-related NF-kB pathway, reduces CXCL13 secretion, and promotes fibronectin production. These patterns are validated in N0 NSCLC tumors, where hMENA11ahigh expression, CAF hMENAΔv6low, and stromal fibronectinlow are associated with intratumoral TLS, linked to memory B cells and predictive of longer survival. The hMENA isoform pattern, fibronectin, and LTβR expression broadly predict ICB response in tumors where TLS indicates an anti-tumor immune response. This study uncovers hMENA alternative splicing as an unexplored contributor to TLS-related Tumor Immune Microenvironment (TIME) and a promising biomarker for clinical outcomes and likely ICB responsiveness in N0 patients with NSCLC. This work is supported by AIRC (IG 19822), ACC (RCR-2019-23669120), CAL.HUB.RIA Ministero Salute PNRR-POS T4, "Ricerca Corrente" granted by the Italian Ministry of Health.

Immune checkpoint ligands expressed on mature high endothelial venules predict poor prognosis of NSCLC: have a relationship with CD8+ T lymphocytes infiltration.

An insufficient number of intratumoral CD8+ T lymphocytes is a major barrier to antitumor immunity and immunotherapy. High endothelial venules (HEVs) are the major sites through which lymphocytes enter tumors; however, the molecular mechanism through which HEVs mediate CD8+ T lymphocyte infiltration remains poorly understood. Forty-two patients with stage IIIA lung adenocarcinoma, who underwent surgery, were recruited. Multiplex immunohistochemical staining was conducted on tumor tissues to detect the immune checkpoint ligands (ICLs) expressed in the HEVs, blood vessels, and lymphatics. A new ICL score model was constructed to evaluate ligand expression. The relationship between ICL score, tumor-infiltrating CD8+ T cell frequency, and survival of patients was investigated. Mature HEVs, but not blood vessels or lymphatics, mediated CD8+ T cell infiltration. However, the ICLs expressed on mature HEVs could negatively regulate CD8+ T cell entry into tertiary lymphoid structures (TLSs). In addition, according to the results obtained using our ICLtotal score model, the expression of ICLs on HEVs was observed to be a predictor of both CD8+ T cell infiltration and survival, in which a high ICLtotal score > 1 represent a weak CD8+ T cell infiltration and a high ICLtotal score > 2 predicts poor survival. Using the ICL score model, we discovered that ICLs expressed on HEVs are indicative of CD8+ T cell subset infiltration in TLSs, as well as of patient survival with lung cancer.

Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients’ prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.

Construction of a radiomics-based model for predicting the efficacy of radiotherapy and chemotherapy for non-small cell lung cancer

ObjectivePre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). MethodsIn this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. ResultsA total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. ConclusionCT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.

Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer.

The objective of this study was to identify a kind of prognostic signature based on oxidative stress- and anoikis-related genes (OARGs) for predicting the prognosis and immune landscape of NSCLC. Initially, We identified 47 differentially expressed OARGs that primarily regulate oxidative stress and epithelial cell infiltration through the PI3K-Akt pathway. Subsequently, 10 OARGs related to prognosis determined two potential clusters. A cluster was associated with a shorter survival level, lower immune infiltration, higher stemness index and tumor mutation burden. Next, The best risk score model constructed by prognostic OARGs was the Random Survival Forest model, and it included SLC2A1, LDHA and PLAU. The high-risk group was associated with cluster A and poor prognosis, with a higher tumor mutation burden, stemness index and proportion of M0-type macrophages, and a lower immune checkpoint expression level, immune function score and IPS score. The calibration curve and decision-making curve showed that the risk score combined with clinical pathological characteristics could be used to construct a nomogram for guiding the clinical treatment strategies. Finally, We found that all three hub genes were highly expressed in tumor tissues, and LDHA expression was mainly regulated by has-miR-338-3p, has-miR-330-5p and has-miR-34c-5p. Altogether, We constructed an OARG-related prognostic signature to reveal potential relationships between the signature and clinical characteristics, TME, stemness, tumor mutational burden, drug sensitivity and immune landscape in NSCLC patients.

P1.24-12 Exploring the Stability of Gut Microbiome and Its Association with the Prognosis of NSCLC Treated with Chemoraditherapy

P1.24-12 Exploring the Stability of Gut Microbiome and Its Association with the Prognosis of NSCLC Treated with Chemoraditherapy

High levels of PD-L1 on platelets of NSCLC patients contributes to the pharmacological activity of Atezolizumab

Several studies have associated platelets (PLTs) to NSCLC prognosis. To understand the role of PLTs in immunotherapy-treated patients, we used blood samples of NSCLC patients at different TNM stage. We found that PLTs count and the expression of PD-L1 (pPD-L1) were significantly higher in NSCLC patients at Stage IV than Stage I-III and healthy subjects. The presence of high pPD-L1 was associated to upregulated genes for the extracellular matrix organization and tumor immunosuppression. When patients’ survival was correlated to the levels of pPD-L1, longer survival rate was observed, but not when progression disease occurred. The in vitro stimulation of pPD-L1 with Atezolizumab induced CXCL4 release, accompanied by higher levels of TGFβ at the time of drug resistance when the levels of CD16, CD32 and CD64 significantly increased. Leiden-clustering method defined the phenotype of PLTs which showed that the ezrin-radixin-moesin (ERM) family proteins, underlying the PD-L1 signalosome, were involved in high pPD-L1 and higher survival rate. These data imply that Stage IV NSCLC patients characterized by high pPD-L1 are associated with longer progression-free survival rate because the blockade of pPD-L1 by Atezolizumab avoids the exacerbation of a T cell-mediated immune-suppressive environment. pPD-L1 could be an easy-to-use clinical approach to predict ICI responsiveness.

Prognostic Significance of the Proposed Residual Tumor Classification in Patients With NSCLC After Sleeve Lobectomy

IntroductionTo validate the residual tumor (R) classification proposed by the International Association for the Study of Lung Cancer (IASLC) in NSCLC after sleeve lobectomy. MethodsA total of 682 patients were analyzed. The R status, on the basis of the Union for International Cancer Control (UICC) criteria, was recategorized according to the IASLC descriptors. Recurrence-free survival (RFS) and overall survival (OS) among different R classifications were assessed for the entire cohort and pathologic node (pN) subgroups. ResultsAll in all, 631 (92.5%), 48 (7.1%), and three patients (0.4%) were classified as R0, R1, and R2, respectively, by the UICC criteria, whereas 489 (71.7%), 110 (16.1%), and 83 patients (12.2%), received R0, uncertain resection (R[un]), and R1/2 resection, respectively, according to the IASLC criteria. There were 96 patients (15.2%) with UICC R0 who were reclassified as R(un), mainly because of the positive highest mediastinal node station (82 of 96, 85.4%). A total of 46 patients (7.3%) were reassigned from UICC R0 to IASLC R1/2 owing to extracapsular extension. For the entire cohort, patients with R(un) and R1/2 exhibited worse RFS (R[un], adjusted p = 0.023; R1/2, adjusted p = 0.001) and OS (R[un], adjusted p = 0.040; R1/2, adjusted p = 0.051) compared with R0. No significant differences were observed between R(un) and R1/2 (RFS, adjusted p = 0.586; OS, adjusted p = 0.781). Furthermore, subgroup analysis revealed a distinct prognostic impact of the IASLC R status—with prognostic significances in the pN1 and pN2 subgroups, but not in the pN0 subgroup. ConclusionsThe IASLC R descriptors helped to stratify the prognosis of NSCLC after sleeve lobectomy, with its prognostic impact varied among pN stages.

The Role of Lymphadenectomy in Early-Stage NSCLC.

Lung cancer remains the leading cause of cancer-related death worldwide. The involvement of lymph nodes by the tumor has a strong impact on survival of patients. For this reason, lymphadenectomy plays a crucial role in the staging and prognosis of NSCLC, to define the most appropriate therapeutic strategies concerning the stage of the disease. To date, the benefit, in terms of survival, of the different extents of lymphadenectomy remains controversial in the scientific community. It is recognized that metastatic involvement of mediastinal lymph nodes in lung cancer is one of the most significant prognostic factors, in terms of survival, and it is therefore mandatory to identify patients with lymph node metastases who may benefit from adjuvant therapies, to prevent distant disease and local recurrences. The purpose of this review is to evaluate the role of lymphadenectomy in early-stage NSCLC in terms of efficacy and accuracy, comparing systematic, sampling, and lobe-specific lymph node dissection and analyzing the existing critical issue, through a search of the most relevant articles published in the last decades.

Identification of A novel anoikis-related genes-based signature for non-small cell lung cancer

The prognostic value of anoikis in NSCLC and its mechanism in tumorigenesis and progress have not been fully elucidated. This study aimed to reveal the correlation between anoikis-related genes (ARGs) and tumor prognosis, to reveal molecular and immune features, and to evaluate the anticancer drug sensitivity and the efficacy of immunotherapy of NSCLC. ARGs were selected from both the GeneCards and Harmonizome databases and then were intersected with the Cancer Genome Atlas (TCGA) database by differential expression analysis, followed by functional analysis of the target ARGs. An ARGs-based prognostic signature was constructed using LASSO (least absolute shrinkage and selection operator) Cox regression analysis; Kaplan-Meier analysis, univariant and multivariant Cox analysis were used to validate the value of this model in NSCLC prognosis. Differential analyses on molecular and immune landscapes were applied in the model. Anticancer drug sensitivity and efficacy in immune-checkpoint inhibitors (ICI) therapy were analyzed. A total of 509 ARGs and 168 differentially expressed ARGs in NSCLC were generated. Functional analysis revealed enrichment in extracolonic apoptotic signaling pathway, collagen-containing ECM, and integrin binding, and indicated an association with the PI3K-Akt signaling pathway. Subsequently, a 14-genes signature was generated. The high-risk group had a worse prognosis, with higherM0 and M2 macrophage infiltration, and fewer CD8 T-cells and T follicular helper (TFH) cells. The high-risk group had higher expression of immune checkpoint genes, HLA-I genes, and higher TIDE scores than the low-risk group, leading to less benefit of ICI therapy. Additionally, an Immunohistochemical staining comparison revealed that FADD was highly expressed in tumor tissue, compared to normal tissue, consistent with the previous results.

TROP2 expression and response to immune checkpoint inhibition in patients with advanced non-small cell lung cancer.

9040 Background: Despite anti-monoclonal antibodies targeting programmed cell death-1 (PD-1) or its ligand PD-L1 revolution, most patients (pts) with advanced NSCLC displayed primary resistance to PD1/PD-L1 blockade. Identifying targetable alterations involved in resistance represent a promising approach to improve immunotherapy efficacy. TROP2 expression is associated with a poor prognosis in NSCLC, and offers a promising target for treatment with the development of new antibody drug conjugate. TROP2 has been suggested to play a role in innate immunity response, but, it's unclear whether TROP2 expression by tumor cells is detrimental specifically in the context of PD1/PD-L1 blockade. The aim of our study was to investigate the the role of TROP2 expression in outcome of NSCLC pts treated with ICI. Methods: We analyzed the largest NSCLC gene expression data set worldwide including the whole-transcriptome profile of 891 pre-treatment tumors from POPLAR and OAK trials, two randomized studies assessing the efficacy of atezolizumab versus docetaxel in NSCLC. We complemented this transcriptional approach with multiplex IF and digital pathology analysis to confirm our findings at the protein level We investigated whether a non-invasive approach focusing on the level of plasma levels of circulating TROP2 may be relevant to predict efficacy of ICI. Results: The PFS of pts with high expression of TROP2 was significantly lower than in pts with low expression 2.5 vs 4.1 months, p &lt; 0.001. Strikingly, high TROP2 expression was also associated with lower clinical benefit rate 15% vs 28%, p = 0.009 and worse overall survival (OS) 12.6 vs 16.3 months, p = 0.007 When adjusting for covariates such as histological subtype, TLS signature, PD-L1 expression, TROP2 remained independently associated with PFS and OS. None of these correlations were observed in the docetaxel arm, suggesting a predictive value of TROP2 gene expression specifically for response to ICI. By using a deconvolution approach, we also observed that TROP2-high tumors were characterized by a significant lowest expression of genes specific to immune populations (T effector memory cells,Th17 cells). Multiplex IF on an independent cohort 53 NSCLC tumors confirmed the correlation between high TROP2, microenvironment features and poor response to PD1 inhibition. Analysis of circulating plasma levels of TROP2 in 97 NSCLC patients revealed a strong correlation with expression in tumor tissue. High plasma levels of circulating TROP2 was also significantly associated with worse ORR, PFS and OS on treatment with PD1/PDL1 antagonist. Conclusions: Our study revealed that TROP2 expression by tumor cells in NSCLC is associated with worse outcome in patients with NSCLC independently of PDL1 expression. Targeting TROP2 in combination with ICI may represent a promising strategy to improve ICI efficacy in pts with TROP2-high NSCLC.

Effects of mutations in SWI/SNF subunits on context-specific prognosis in driver positive and driver negative NSCLC.

9039 Background: Mutations(mt) in the Switch/Sucrose-Nonfermentable chromatin remodeling complex (SWI/SNF) are commonly found in NSCLC and have been associated with worse prognosis. However, the overall prognostic role of the individual SWI/SNF subunits in all NSCLCs and in oncogenic driver-positive (D+) and driver-negative (D-) groups remains unclear. Our study attempts to clarify the prognostic role of SWI/SNF subunit alterations in these populations. Methods: 42,379 NSCLC tumor specimens were tested at Caris Life Sciences (Phoenix, AZ) with NextGen Sequencing (NGS) of DNA (592-gene panel or whole-exome sequencing). Based on the observed frequency of likely pathogenic/pathogenic (LP/P) mts, the four most commonly-altered SWI/SNF subunits – ARID1A (8.29%), SMARCA4 (6.29%), ARID2 (3.05%) and PBRM1 (1.98%) – were further analyzed. We divided the cohort by driver mts. D+ tumors possessed at least one of the following alterations: LP/P mts in KRAS, EGFR, BRAF, ERBB2 or MET; METex14 skipping; ERBB2 amplification, LP/P fusions in ALK (including IHC overexpression), RET, ROS1, NTRK (1-3) &amp; NRG1. D- tumors were devoid of mts/amplifications/fusions in all genes mentioned above. Kaplan-Meier analysis was performed on real world survival data (n = 24685) obtained from insurance claims. Statistical significance was determined using chi-square and Mann-Whitney U test adjusted for multiple comparisons (q &lt; 0.05). Results: Of the SWI/SNF genes, only SMARCA4mts were associated with worse survival in the overall (HR = 1.46), D+ (HR = 1.984) and D- (HR = 1.224) cohorts (all p &lt; 0.01). While SMARCA4mt was associated in the D- cohort with KEAP1 and STK11 mts, the worse prognosis persisted even in their absence (HR = 1.41, p &lt; 0.001). To understand the role of SWI/SNF mts in the context of driver co-mts, we compared the survival of D+ vs D- tumors for each SWI/SNF subunit. As expected, D+ tumors were associated with better survival in SWI/SNF wild-type (wt) tumors (HRs 0.76-0.78, p &lt; 0.00001). There was no survival effect of ARID1A, ARID2 or PBRM1 mts (HRs = 0.9-1, p = ns). In contrast, in SMARCA4mts, D+ tumors had much worse survival than D- (HR = 1.295, p = 0.003). This effect is driven by the KRASmt cohort, as KRASmt/SMARCA4mt tumors had significantly worse survival compared with D- tumors (HR = 1.882, p &lt; 0.001). Of note, KRASmts are enriched in the D+/SMARCA4mt subgroup (78% of all oncogenic drivers). When KRASmts were removed from the D+ group, no survival difference was observed between D+ and D- SMARCA4mt tumors (HR = 0.938, p = ns). Conclusions: In this largest-ever retrospective cohort of NSCLC patients with SWI/SNF mutations, SMARCA4mt is the only SWI/SNF alteration broadly associated with worse survival. SMARCA4mt is associated with particularly short survival in KRASmt tumors. These data suggest underlying biology which may inform further investigation and therapeutic development.

Abstract 5447: Artificial intelligence (AI)-based classification of stromal subtypes reveals associations between stromal composition and prognosis in NSCLC

Abstract Background: Many cancers are known to involve a desmoplastic stromal reaction. While cancer-associated stroma (CAS) has long been appreciated histologically, single-cell molecular analyses have revealed its heterogeneity, and the cellular composition of CAS has been linked to prognosis in several cancer types, including NSCLC. While pathologists have sought to manually classify CAS based on collagen architecture or nuclear density, this approach has not sufficiently captured the heterogeneity of CAS. To this end, we have developed an artificial intelligence (AI)-based model to predict stromal composition in NSCLC from hematoxylin and eosin (H&amp;E)-stained tissue Methods: We developed a convolutional neural network-based model to classify CAS as immature, mature, densely inflamed, densely fibroblastic, or elastosis. This model was trained using manual pathologist-derived annotations (N=3019) of H&amp;E-stained whole slide images (WSIs) of PDAC obtained from the TCGA (N=126). This stromal subdivision model was deployed on H&amp;E-stained LUAD (N=468) and LUSC (N=430) WSIs. Model performance was assessed by qualitative review by expert pathologists. Human interpretable features (HIFs) were extracted from the stromal subdivision model (e.g., proportional area of mature relative to total stroma) and were assessed to identify associations with overall survival (OS) using univariate Cox regression analysis after adjusting for age, sex, and tumor stage. Results: The stromal subdivision model successfully predicted areas of immature, mature, densely inflammatory, and densely fibrotic stroma, as well as elastosis, in LUAD and LUSC. In LUAD, higher combined proportional areas of mature and fibroblastic stroma relative to total cancer stroma was associated with poor OS (p=0.007), while higher combined proportional areas of densely inflamed stroma and elastosis relative to total cancer stroma was associated with improved OS (p=0.007). These findings were validated by stratified tertile analysis based on the corresponding risk direction. Notably, while the average stromal compositions did not differ significantly between NSCLC subtypes, the stromal HIFs were only prognostic in LUAD but not in LUSC. Conclusions: We developed a first of its kind model to characterize CAS subtypes in NSCLC tissue. Features extracted from this model are related to prognosis in LUAD, but not in LUSC, further confirming the importance of CAS to tumor biology and the importance of considering histologic subtypes. Work is ongoing to identify relationships between stromal HIFs and treatment response in NSCLC as well as other cancer indications. Citation Format: Fedaa Najdawi, Sandhya Srinivasan, Neel Patel, Michael G. Drage, Christian Kirkup, Chintan Parmar, Jacqueline Brosnan-Cashman, Michael Montalto, Andrew H. Beck, Archit Khosla, Ilan Wapinski, Ben Glass, Murray Resnick, Matthew Bronnimann. Artificial intelligence (AI)-based classification of stromal subtypes reveals associations between stromal composition and prognosis in NSCLC. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5447.