NSCLC Subtypes Research Articles

Biological and clinical characteristics of non-small cell lung cancer non-specific subtype

BackgroundNon-small cell lung cancer-not otherwise specified (NSCLC-NOS) is a rare subtype of NSCLC that cannot be classified specifically based on morphology and/or special staining. This study aimed to explore the clinical features, biological and pathological characteristics, treatment, and prognosis of NSCLC-NOS. MethodsThis retrospective study included NSCLC-NOS patients diagnosed and treated between 2010 and 2022. Clinical features, gene expression, first-line treatment, and prognosis were analyzed. Kaplan-Meier methods were calculated and log-rank tests and univariable and multivariable Cox regression analyses were performed to determine the relationship between prognostic factors and survival. ResultsOf 105 NSCLC-NOS patients, most were male (92.4%), smokers (78.1%), with a median age of 64 years, and advanced stage (IIIc-IV, 72.4%). Immunohistochemical analysis showed minimal expression of p40, NapsinA, and TTF-1, whereas cytokeratin (CK) was expressed in 100% of cases. 20.5% of 39 patients who underwent genetic testing had driver gene mutations, including EGFR, KRAS, and ROS1. Among 69 patients with complete treatment information, 58 received platinum-based chemotherapy, with paclitaxel being the most commonly used combination chemotherapy drug (n=25), followed by pemetrexed (n=21). The objective response rate (ORR) of paclitaxel was found to be higher compared to pemetrexed (83.3% vs. 54.5%, P=0.296). Furthermore, the combination of paclitaxel with immunotherapy demonstrated superior benefits in comparison to pemetrexed (76.9% vs. 50.0%, P=0.367). The median progression-free survival (PFS) for patients treated with monotherapy paclitaxel, the paclitaxel-immunotherapy combination, and the pemetrexed-immunotherapy combination were 6.6 months (95% CI: 1.508-11.692; P=0.017), 15.7 months (95% CI: 14.071-17.329; P=0.017), and 11.8 months (95% CI: 10.279-13.321; P=0.324), respectively. The median overall survival (OS) was 13.6 months. Anatomic location (P=0.026) and immunotherapy use (P=0.003) were associated with OS. Multivariate analysis confirmed that anatomical location and immunotherapy use were factors influencing the prognosis. ConclusionNSCLC-NOS is common in male smokers and often diagnosed at an advanced stage with low mutation rate. Paclitaxel with immunotherapy may have better benefits as a first-line treatment. Anatomic location and immunotherapy use are prognostic factors.

A nomogram for cancer-specific survival of lung adenocarcinoma patients: A SEER based analysis

BackgroundNon-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD. MethodsWe retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms. ResultsSeveral independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort. ConclusionsA dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

Tumor specimen cold ischemia time impacts molecular cancer drug target discovery

Tumor tissue collections are used to uncover pathways associated with disease outcomes that can also serve as targets for cancer treatment, ideally by comparing the molecular properties of cancer tissues to matching normal tissues. The quality of such collections determines the value of the data and information generated from their analyses including expression and modifications of nucleic acids and proteins. These biomolecules are dysregulated upon ischemia and decompose once the living cells start to decay into inanimate matter. Therefore, ischemia time before final tissue preservation is the most important determinant of the quality of a tissue collection. Here we show the impact of ischemia time on tumor and matching adjacent normal tissue samples for mRNAs in 1664, proteins in 1818, and phosphosites in 1800 cases (tumor and matching normal samples) of four solid tumor types (CRC, HCC, LUAD, and LUSC NSCLC subtypes). In CRC, ischemia times exceeding 15 min impacted 12.5% (mRNA), 25% (protein), and 50% (phosphosites) of differentially expressed molecules in tumor versus normal tissues. This hypoxia- and decay-induced dysregulation increased with longer ischemia times and was observed across tumor types. Interestingly, the proteomics analysis revealed that specimen ischemia time above 15 min is mostly associated with a dysregulation of proteins in the immune-response pathway and less so with metabolic processes. We conclude that ischemia time is a crucial quality parameter for tissue collections used for target discovery and validation in cancer research.

Functional elucidation of the EIF4A3–circR-4225–miR-507–TNFSF11 regulatory axis in LUAD and its role in tumor progression

Lung adenocarcinoma (LUAD) is the most common subtypes of NSCLC. However, the therapeutic effects for LUAD are unsatisfactory at current stage, so it is important to find new molecular targets and therapeutic strategies. circRNAs can regulate the expression of target genes by binding to microRNAs (miRNAs) to form competitive endogenous RNAs (ceRNAs). Therefore, we investigated the functions of circR-4225 in the tumor progression of LUAD and its molecular mechanism in this paper. circR-4225 is up-regulated in LUAD tissues. EIF4A3, a member of the eukaryotic translation initiation factor 4A (EIF4A) family, promotes the expression of circR-4225. circR-4225 acts as a molecular sponge to down-regulate miR-507, which promotes the up-regulation of the expression of its target gene–tumor necrosis factor superfamily member 11 (TNFSF11). Knockdown of circR-4225 in the LUAD cell lines can inhibit cell proliferation and viability, and promote apoptosis of the LUAD cell lines, which can be reverted by inhibiting miR-507 or overexpressing TNFSF11. To sum it up, this study demonstrated that circR-4225 was significantly up-regulated in LUAD tissues, and circR-4225 promoted LUAD progression by sponging miR-507 and up-regulating TNFSF11. This study can provide new molecular targets for early diagnosis and treatment of LUAD.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Development of Ex Vivo Analysis for Examining Cell Composition, Immunological Landscape, Tumor and Immune Related Markers in Non-Small-Cell Lung Cancer.

NSCLC is a very aggressive solid tumor, with a poor prognosis due to post-surgical recurrence. Analysis of the specific tumor and immune signatures of NSCLC samples is a critical step in prognostic evaluation and management decisions for patients after surgery. Routine histological assays have some limitations. Therefore, new diagnostic tools with the capability to quickly recognize NSCLC subtypes and correctly identify various markers are needed. We developed a technique for ex vivo isolation of cancer and immune cells from surgical tumor and lung tissue samples of patients with NSCLC (adenocarcinomas and squamous cell carcinomas) and their examination on ex vivo cell preparations and, parallelly, on histological sections after Romanovsky-Giemsa and immunofluorescent/immunochemical staining for cancer-specific and immune-related markers. As a result, PD-L1 expression was detected for some patients only by ex vivo analysis. Immune cell profiling in the tumor microenvironment revealed significant differences in the immunological landscapes between the patients' tumors, with smokers' macrophages with simultaneous expression of pro- and anti-inflammatory cytokines, neutrophils, and eosinophils being the dominant populations. The proposed ex vivo analysis may be used as an additional diagnostic tool for quick examination of cancer and immune cells in whole tumor samples and to avoid false negatives in histological assays.

Novel molecular subtypes of METex14 non-small cell lung cancer with distinct biological and clinical significance

Not all MET exon 14 skipping (METex14) NSCLC patients benefited from MET inhibitors. We hypothesized an inter-tumoral heterogeneity in METex14 NSCLC. Investigations at genomic and transcriptomic level were conducted in METex14 NSCLC samples from stage I-III and recurrent/metastatic patients as discovery and validation cohort. Four molecular subtypes were discovered. MET-Driven subtype, with the worst prognosis, displayed MET overexpression, enrichment of MET-related pathways, and higher infiltration of fibroblast and regulatory T cells. Immune-Activated subtype having the most idea long-term survival, had higher tertiary lymphoid structures, spatial co-option of PD-L1+ cancer cells, and GZMK+ CD8+ T cell. FGFR- and Bypass-Activated subtypes displayed FGFR2 overexpression and enrichments of multiple oncogenic pathways respectively. In the validation cohort, patients with MET-Driven subtype had better response to MET inhibitors than those with MET overexpression. Thus, molecular subtypes of METex14 NSCLC with distinct biological and clinical significance may indicate more precise therapeutic strategies for METex14 NSCLC patients.

PLEK2 activates the PI3K/AKT signaling pathway to drive lung adenocarcinoma progression by upregulating SPC25.

Lung adenocarcinoma (LUAD) is the most common subtype of NSCLC, characterized by poor prognosis and frequently diagnosed at advanced. While previous studies have demonstrated pleckstrin-2 (PLEK2) as aberrantly expressed and implicated in tumorigenesis across various tumor types, including LUAD, the molecular mechanisms underlying PLEK2-mediated LUAD progression remain incompletely understood. In this study, we obtained data from The Cancer Genome Atlas (TCGA) database to assess PLEK2 expression in LUAD, a finding further confirmed through analysis of human tissue specimens. PLEK2-silenced LUAD cellular models were subsequently constructed to examine the functional role of PLEK2 both in vitro and in vivo. Our results showed elevated PLEK2 expression in LUAD, correlating with poor patients'prognosis. PLEK2 knockdown led to a significant suppression of LUAD cell proliferation and migration, accompanied by enhanced apoptosis. Moreover, tumor growth in mice injected with PLEK2-silencing LUAD cells was impaired. Gene expression profiling and Co-IP assays suggested direct interaction between PLEK2 and SPC25, with downregulation of SPC25 similarly impairing cell proliferation and migration. Additionally, we revealed phosphoinositide 3-kinase (PI3K)/AKT signaling activation as requisite for PLEK2-induced malignant phenotypes in LUAD. Collectively, our findings underscore PLEK2's oncogenic potential in LUAD, suggesting its utility as a prognostic indicator and therapeutic target for LUAD management.

LIBRETTO-431: Confirming the Superiority of Selpercatinib to Chemotherapy and the Lack of Efficacy of Immune Checkpoint Inhibitors in Advanced RET Fusion-Positive (RET+) NSCLC, Another Unique Never-Smoker Predominant Molecular Subtype of NSCLC.

Selpercatinib, a potent and highly selective RET kinase inhibitor with significant CNS activity, has recently gained US approval for the treatment of NSCLC harboring RET fusions (RET+) based on a large-scale single-arm study. The LIBRETTO-431 trial was the global pivotal registration phase 3 trial comparing selpercatinib to platinum-based chemotherapy with or without pembrolizumab as the first-line treatment of patients with advanced RET+ NSCLC. Never-smokers constituted 67.4% of the RET+ NSCLC patients enrolled. KIF5B-RET made up the vast majority (77%) of the RET+ fusion variant with known fusion partner. The results of this study demonstrated significant improvement in progression-free survival (PFS) benefit as well as impressive intracranial disease response in participants treated with selpercatinib as compared to those treated with chemotherapy, with a HR [hazard ratio] of 0.46 (95% CI 0.33-0.70; P < 0.001) for the intention-to-treat (ITT)-pembrolizumab group and HR of 0.46 (95% CI 0.31-0.70, P < 0.001) for the overall ITT-group of patients. The addition of pembrolizumab to platinum/pemetrexed chemotherapy resulted in numerically identical PFS (11.2 months). These results point to selpercatinib's superiority to traditional chemotherapy regimens in the treatment of NSCLC harboring RET fusions and add to literature on the salience of targeted precision oncology and lack of efficacy of immune checkpoint inhibitor in NSCLC patients with never-smoker predominant actionable driver mutations. RET+ NSCLC should be added to the list of molecular subtypes (EGFR+, ALK+, ROS1+) of NSCLC to be excluded in chemoimmunotherapy trial.

Abstract 7088: MALDI-IHC high-plex protein biomarker imaging for fast, non-iterative identification of lung carcinoma subtypes

Abstract Lung cancer is the second most common cancer worldwide, with over 2.2 million new cases in 2020 and is the leading cause of cancer-related deaths worldwide. Most lung cancer (80-85%) is non-small cell (NSCLC), comprising mostly adenocarcinoma and squamous cell carcinoma, while roughly 10-15% of lung cancer is small cell lung cancer (SCLC), also termed neuroendocrine lung cancer. Subtype identification is critical for determining treatment, and with limited samples, it is critical to get the most data possible from each tissue section. NSCLC subtypes can be treated with chemotherapy or targeted drugs, whereas SCLC tends to respond best to a combination of chemotherapy and radiation. While there have been many improvements in the detection and treatment of lung carcinoma, the immuno-histochemical (IHC) confirmation of subtype remains a prolonged and often iterative process. To overcome this limitation, we have developed a new IHC approach which uses antibodies labeled with novel photocleavable mass-tags combined with matrix assisted laser desorption ionization (MALDI) mass spectrometry imaging. The approach, termed MALDI-IHC, provides fast (1 hr/cm2 imaging time) and highly multiplexed IHC (100+-plex) without the need for iterative cycling procedures. Here, we present a 20+-plex panel for the determination of subtype in lung cancer. An antibody panel comprised of established clinical biomarkers was developed to differentiate between the lung carcinoma subtypes and provide potentially prognostic information. This included markers for adenocarcinoma (TTF-1, Napsin A), squamous cell carcinoma (p40, CK5), and small cell lung cancer (CD56, Chromogranin A and Synaptophysin), as well as general carcinoma markers for t cells, b cells, macrophages, and fibroblasts These markers were first validated individually by immunofluorescence and then compared to MALDI-IHC on annotated tissues. Here we present an overview of the antibody validation for the 20+-plex lung carcinoma panel. Biomarker presence and peak signal intensity for the various subtypes was then determined using pathologist classified tissues for normal lung, adenocarcinoma, squamous cell carcinoma and small cell lung cancer. We report application of the MALDI-IHC approach to the simultaneous determination of lung carcinoma subtypes on a 99-core human lung tissue microarray (TMA) comprised of normal, normal adjacent, cancer adjacent, and cancerous tissues. Moreover, because MALDI-IHC is a non-destructive process, it can be followed by conventional H&amp;E staining on the same tissue section for accurate co-registration of traditional pathology annotations with the highly multiplexed MALDI-IHC results. In the future, even larger, 100+-plex antibody panels can be used for precision medicine on a wide range of cancers, in order to determine the molecular subtypes and guide treatment paths. Citation Format: Catherine A. Kita, Kristina Schwamborn, Gargey Yagnik, Kenneth J. Rothschild, Mark J. Lim. MALDI-IHC high-plex protein biomarker imaging for fast, non-iterative identification of lung carcinoma subtypes [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 7088.

Abstract 7504: Longitudinal multi-omics characterization of circulating tumor cells from ALK positive NSCLC patients

Abstract Introduction: Anaplastic lymphoma kinase positive non-small cell lung cancer (ALK+NSCLC) represents a distinct subtype of NSCLC characterized by oncogenic ALK gene mutations leading to ALK overexpression and activation in tumor cells. ALK+ NSCLC will frequently develop mutations during treatment, leading to nonresponse and eventually cancer relapse. Therefore, there is a critical need for sensitive technologies to monitor ALK+ NSCLC disease status in patients over time. To facilitate longitudinal monitoring, there has been a rapid evolution in diagnostic technologies leveraging circulating tumor cells (CTCs) as a liquid biopsy tool. CTCs can be collected non-invasively at various intervals throughout patient treatment. Moreover, they shed from solid tumors and disseminate into the bloodstream, which makes isolating CTCs a valuable source of genomic, transcriptomic, and proteomic insights pertaining to the primary tumor. In this study, we present a comprehensive workflow for conducting multi-omic analysis on CTCs derived from ALK+ NSCLC patients. Methods: Blood samples were collected from a cohort of ALK+ NSCLC patients, and CTCs were isolated using a high throughput, label free microfluidic device, Labyrinth. Enriched CTCs were immunofluorescently stained for cytokeratin, CD45, DAPI, epithelial cell adhesion molecule (EpCAM), and vimentin markers. Heterogeneous CTC populations with hybrid epithelial/mesenchymal (EM) phenotypes are reported. Fluorescence in situ hybridization (FISH) was performed on enriched CTC samples. DNA was extracted and sequenced using a 523 gene targeted sequencing panel. Single cell RNA-seq technology was performed in parallel on CTC samples to investigate the gene expression throughout treatment. Results: The median concentration of CTCs in the ALK+ NSCLC cohort (n=33) is 137 CTCs per ml of blood, and 64% of CTCs are in an EM hybrid state. In ALK+ NSCLC patients (n=3) with an average of 4.6 visits per sample, we observed a trend of decreasing CTC concentration for patients with partial response to the treatment. The presence of ALK fusion in ALK+ NSCLC patient’s CTC is confirmed by FISH. Oncogenic mutations (JAK1, SPEN, ERBB4, NTRK3) are observed in bulk CTC samples from two NSCLC patients. Single cell RNA sequencing was performed on three ALK+ NSCLC patients; 58, 199, and 700 potential CTCs were detected with positive cytokeratin expression and no white blood cell marker expression. Conclusions: This study examined the feasibility of using CTCs for studying and monitoring the clinical status of the ALK+ NSCLC patients as well as the genomic and transcriptomic signatures within their tumors. More patients will need to be processed in order to validate our workflow and reinforce our conclusions. Citation Format: Yuru Chen, Shamileh Fouladdel, Harrison Ball, Zhaoping Qin, Albert Liu, Xu Cheng, Liwei Bao, Habib Serhan, Bryce Vandenburg, Varun Kathawate, Larua Goo, Peter Ulintz, Nathan Merrill, Aaron Udager, Angel Qin, Sofia D. Merajver, Sunitha Nagrath. Longitudinal multi-omics characterization of circulating tumor cells from ALK positive NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7504.

Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer

The transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.

Allelic Context of EGFR C797X–Mutant Lung Cancer Defines Four Subtypes With Heterogeneous Genomic Landscape and Distinct Clinical Outcomes

IntroductionEGFR C797X (C797S or C797G) mutation is the most frequent on-target mechanism of resistance to osimertinib. The hypothesis that the allelic context of C797X/T790M has implications for treatment is on the basis of sporadic reports and needs validation with larger cohorts. MethodsWe identified patients with EGFR C797X–mutant NSCLC from nine centers who progressed on osimertinib, all analyzed in a single laboratory through next-generation sequencing. We analyzed genomic profiles and assessed associations between clinical outcomes and C797X status. ResultsA total of 365 EGFR C797X–mutant cases were categorized into four subtypes on the basis of allelic context: in cis (75.3%), in trans (6.4%), cis&trans (10.4%), and C797X-only (7.9%). Genomically, the cis&trans subtype displayed the highest frequency of concurrent alterations at osimertinib resistance sites (21.1%), while the in cis subtype had the lowest (8.4%). Clinically, cis&trans patients exhibited the worst progression-free survival (PFS) on both previous (median 7.7 mo) and subsequent treatment (median 1.0 mo) and overall survival (median 3.9 mo). In subsequent treatments, in cis patients exhibited superior PFS with combined brigatinib and cetuximab (median 11.0 mo) compared with other regimens (p = 0.005), while in trans patients exhibited variable outcomes with combined first or second- and third-generation EGFR inhibitor (PFS range: 0.7–8.1 mo, median 2.6 mo). Notably, subtype switching was observed after subsequent treatments, predominantly toward the in cis subtype. ConclusionsAllelic context could define four EGFR C797X–mutant NSCLC subtypes with heterogeneous genetic landscapes and distinct clinical outcomes. Subsequent treatments further complicate the scenario through subtype switching.

Identifying correlates of post treatment responses for unresectable locally advanced non-small cell lung cancer (LA NSCLC).

149 Background: Curative treatment for unresectable LA NSCLC comprises chemoradiotherapy (CRT) and immunotherapy (IO). However, this uniform approach does not take into account the intrinsic heterogeneity of NSCLC and nearly half of all pts relapse within the first year. This highlights the need to define distinct molecular and phenotypic subgroups of LA NSCLC so that we can interrogate the divergent responses to CRT-IO and identify biomarkers that can guide pt specific strategies. Methods: Consecutive pts treated with CRT +/- IO from May 2010 and recruited to an observational study were retrospectively analysed (N = 336). All pts with adenocarcinoma (ADC) were subject to reflex molecular profiling including NGS and FISH testing for ALK, ROS1, MET and RET. Of note, tracking of bespoke circulating tumour DNA (ctDNA) levels was performed for 25 pts. Plasma samples were collected at longitudinal timepoints pre, during and post CRT (N = 118; median 5 samples/ pt). WES of biopsies and matched normal DNA was used to design tumour informed ctDNA assays to track 16 single nucleotide variants in plasma samples. ctDNA levels were then quantified as mean tumour molecule per mL (MTM/mL). Primary endpoint was progression free survival (PFS). Results: Median age was 61 (IQR 48, 74). Of 336 pts, 74% were male; 32% never smokers; 54% were ADC, 33% squamous cell carcinoma (SCC); 18% had EGFR mutations (EGFRm), 37%EGFR wild type (EGFRw), 40% unknown EGFR status (EGFRu), 5% ALK rearranged, 1% ROS1 rearranged. At median follow-up of 44 months (m), median PFS for the entire cohort was 16.2m. Pts who received consolidation IO had longer PFS [IO, not reached vs no IO, 14.9m; P = 0.05]. Median PFS was shorter for pts with EGFRm [EGFRm, 13m vs EGFRw, 22.5m vs EGFRu, 15.8m]. Patterns of failure were analysed according to local in-field, regional out-field and distant. EGFRm pts were less likely to fail locally in-field [EGFRm, 12% vs EGFRw,18% vs EGFRu, 28%] and significantly more likely to develop distant metastases as first site of failure post CRT [EGFRm, 51% vs EGFRw, 28% vs EGFRu, 19%; P = 0.01]. For the 25 pts with ctDNA analysed, 24 pts had evaluable ctDNA post CRT. Significant heterogeneity was noted at baseline pre CRT [EGFRm, 6.45 vs EGFRw, 7.49 vs SCC, 37.67 MTM/mL; P = 0.05]. No difference was noted in PFS between pts with high vs low baseline ctDNA. Of note, 14 of 24 (58%) pts achieved undetectable ctDNA post CRT and had significantly longer PFS [undetectable, 24.1m vs detectable, 3.5m; P = 0.02]. Conclusions: Here, we demonstrate distinct phenotypic responses to CRT based on molecular subtypes of NSCLC. Crucially, we demonstrate clinical feasibility of deploying tumour-informed ctDNA assays in LA NSCLC with detection of ctDNA post CRT serving as a potential actionable biomarker to guide intensification strategies, providing a window into developing patient-specific combinatorial approaches.

Tumor-infiltrating Leukocyte Profiling Defines Three Immune Subtypes of NSCLC with Distinct Signaling Pathways and Genetic Alterations.

The precise TIL profiling classified NSCLC into novel three immune subtypes that correlates with patient outcome, identifying subtype-specific molecular pathways and genomic alterations that should play important roles in constructing subtype-specific immune tumor microenvironments. These classifications of NSCLC based on TIL status are useful for developing personalized immune therapies for NSCLC.

Molecular characterization of nectin-4 in non-small cell lung cancer subtypes.

e21119 Background: Nectin-4 (Nectin cell adhesion molecule-4) is a tumor associated antigen overexpressed in multiple solid tumor types. Studies show that over 60% of NSCLC are nectin-4 overexpressed. As enfortumab vedotin, an antibody-drug conjugate directed against nectin-4, has been FDA approved for advanced urothelial carcinoma, this holds treatment implications. Here, we investigated nectin-4 expression in NSCLC subtypes. Methods: 27,607 NSCLC tumors were tested for whole transcriptome sequencing (WTS, NovaSeq) and NextGen DNA sequencing (592 gene panel or whole exome sequencing, WES) at Caris Life Sciences (Phoenix, AZ). Tumors were analyzed overall in quartiles by nectin-4 expression (Q4-greatest, Q1-least) and further analyzed in the adenocarcinoma (AD) and squamous carcinoma (SQ) cohorts. PD-L1 expression was tested by IHC (Dako 22C3). Immune cell populations were estimated by QuanTISeq. Statistical significance was determined using Chi-square/Fisher-Exact/Mann-Whitney U tests and adjusted for multiple comparisons (q &lt; 0.05). Results: We saw significantly greater expression of nectin-4 in SQ (median TPM- 15.68) versus AD tumors (median TPM - 9.88) (p &lt; 0.05). We saw a greater prevalence of NFE2L2, STK11, ERBB2 mutations and genomic loss of heterozygosity (LOH) but a lower percentage of PD-L1+ tumors in Q4 vs Q1 tumors (all q-value &lt; 0.05, Table 1). mRNA expression of immune checkpoints PDL1, CTLA4, CD80, CD86, HAVCR2, and IDO1 was increased in Q4 tumors (1.1-1.3-fold, all q &lt; 0.05). Q4 tumors had reduced estimates of M1 macrophages, regulatory T cells (Tregs), and CD8+ T cells and increased estimates of neutrophils and NK cells. The association of nectin-4 expression with the immune landscapes of AD and SQ was similar. Q4 tumors in AD and SQ displayed increased expression of the above-mentioned immune checkpoints (q &lt; 0.05). In AD and SQ, estimates of M1 macrophages, Tregs, and CD8+ T cells were reduced, but neutrophils and NK cells were increased in Q4 tumors (q &lt; 0.05). The pattern of molecular alterations was different in AD and SQ. While KEAP1, STK11 and ERBB2 mutations were significantly enriched in AD Q4 tumors, NFE2L2 mutations were significantly enriched in SQ Q4 tumors (Table 1). Prevalence of LOH was higher and PD-L1+ tumors was lower in Q4 tumors in both AD and SQ (Table 1). Conclusions: NFE2L2 alterations in SQ and STK11, KEAP1 mutations in AD, which are associated with poor immunotherapy (IO) response, were enriched in Q4 tumors. This suggests that the immune landscape associated with high nectin-4 expression in NSCLC may be driven by the dysregulated KEAP1-NFE2L2 pathway. Targeted therapy towards nectin-4 may benefit these NSCLC subtypes that do not respond well to IO. [Table: see text]

Overcoming MET-mediated resistance in oncogene-driven NSCLC

This study evaluates the efficacy of combining targeted therapies with MET or SHP2 inhibitors to overcome MET-mediated resistance in different NSCLC subtypes. A prevalence study was conducted for MET amplification and overexpression in samples from patients with NSCLC who relapsed on ALK, ROS1, orRET tyrosine kinase inhibitors. MET-mediated resistance was detected in 37.5% of tissue biopsies, which allow the detection of MET overexpression, compared to 7.4% of liquid biopsies. The development of drug resistance by MET overexpression was confirmed in EGFRex19del-, KRASG12C-, HER2ex20ins-, and TPM3-NTRK1-mutant cell lines. The combination of targeted therapy with MET or SHP2 inhibitors was found to overcome MET-mediated resistance in both invitro and invivo assays. This study highlights the importance of considering MET overexpression as a resistance driver to NSCLC targeted therapies to better identify patients who could potentially benefit from combination approaches with MET or SHP2 inhibitors.

Abstract 2578: Phosphorylation of AHR by PLK1 promotes metastasis of LUAD via upregulation of DIO2

Abstract Non-small cell lung cancer (NSCLC) is one of the most aggressive cancer types, and metastasis of cancer cells is the major reason leading to death of patients. Aryl hydrocarbon receptor (AHR) is an important transcription factor involved in the initiation and progression of lung cancer. Polo-like kinase 1 (PLK1), a serine/threonine kinase, is another oncogene that promotes the malignancy of multiple cancer types. Preliminary examination indicates that AHR is a potential substrate of PLK1. Nonetheless, the phosphorylation and interaction of these two factors, as well as the subsequent biological significance in lung cancer remain to be determined. Here we prove that PLK1 phosphorylates AHR at serine 489 (S489) in lung adenocarcinoma (LUAD), a subtype of NSCLC. Overexpressing of exogenous AHR with mutation of phosphomemetic amino acid (S489D), enhances migration and invasion in transwell assays compared to cells harboring AHR that is unable to be phosphorylated (S489A), indicating that phosphorylation of AHR by PLK1 induces the metastatic potential of lung cancer. This is further confirmed in mouse studies using intravenous or subcutaneous inoculation of cancer cells. RNA-seq analyses of cells with S489D or S489A show that type 2 deiodinase (DIO2) is underrepresented in S489A but overexpressed in S489D. DIO2 converts thyroxine (T4) to triiodothyronine (T3), which is the more active form of thyroid hormone and activates downstream effect. Treatment with T3 or T4 promotes the metastasis of LUAD, whereas depletion of DIO2 or treatment with deiodinase inhibitor iopanoic acid disrupts this property. Furthermore, this phenomenon is verified by in vivo mouse experiment using intravenous injection of DIO2-depleted cancer cells. Taken together, these results identify phosphorylation of AHR by PLK1 as a mechanism that leads to progression of LUAD. Citation Format: Chaohao Li, Daheng He, Fengyi Mao, Xinyi Wang, Yanning Hao, Yifan Kong, Christine F. Brainson, Jinghui Liu, Yanquan Zhang, Ruixin Wang, Qiongsi Zhang, Zhiguo Li, Xiongjian Rao, Sai Wu, Chi Wang, Qiou Wei, Jianlin Wang, Xiaoqi Liu. Phosphorylation of AHR by PLK1 promotes metastasis of LUAD via upregulation of DIO2 [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 2578.

Abstract 4633: Quantitative spatial profiling of NSCLC subtypes across tumor stages using 6-plex multiplex imaging technology and AI-powered phenotyping analysis

Abstract Non-small cell lung cancer (NSCLC) is a complex disease with varying pathological subtypes including adenocarcinomas and squamous cell carcinomas. After diagnosis, tumor staging provides important information about the extent of cancer in the body and anticipated response to treatment. NSCLC patients can have impaired immune responses within the tumor microenvironment (TME), leading to a progression of tumor growth and poorer prognosis. Accurate cell phenotyping combined with spatial profiling of the immune contexture and checkpoint expression, can provide a deeper understanding of complex cellular interactions underpinning the tumor-immune response. The aim of this study was to utilize spatial multiplexed imaging technology and associated data analysis methods to profile the immune contexture, as well as their spatial interactions with the tumor, in a set of tissue cores covering a range of NSCLC subtypes and tumor staging. Formalin-fixed paraffin-embedded (FFPE) NSCLC tissue microarrays (TMA), comprised of n=41 cores containing a range of carcinomas and pathological Tumor-Node-Metastasis (pTNM) stages (I-IV), were stained on a Leica Bond RX™ using the Akoya PhenoCode™ Signature Immuno-contexture Human Protein Panel, which includes markers for CD8, CD68, PD-1, PD-L1, FoxP3, and PanCK as a tumor indicator. Stained TMAs were scanned at 20x magnification on a PhenoImager HT multispectral imaging system. Image analysis was performed using Visiopharm software. Deep learning algorithms were applied to segment tumor and stroma regions of interest (ROI) and to accurately detect and classify specific cell phenotypes. Cell object data files per core were exported for spatial and neighborhood analysis using OracleBio’s proprietary python-based program, PhenoXplore. Immune cell counts, phenotypes and spatial interactions were generated within tumor and stroma ROI per core. Data included total and negative cell phenotype counts, cell density in tumor and stroma, as well as cell distance and neighborhood spatial interactions per core across NSCLC subtypes and stages in the TMA set. The combination of high-quality staining provided by the PhenoCode™ panel, coupled with deep learning quantitative phenotyping and spatial pattern analysis enables detailed characterization of the complex cellular interactions, at both the functional and spatial level, within the TME of NSCLC tissue. Data generated will support a greater understanding of the complex cellular interactions that can contribute to progression of NSCLC and may help guide future precision medicine strategies. Citation Format: Lorcan Sherry, Nicole Couper, Bethany Remeniuk, Karen McClymont, Bei Hopkins, Natalie Monteiro, Gabriel Reines March, Darren Locke. Quantitative spatial profiling of NSCLC subtypes across tumor stages using 6-plex multiplex imaging technology and AI-powered phenotyping analysis. [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 4633.

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.