Subtype Of Non-small Cell Lung Cancer Research Articles

Targeting KRASG12D mutation in non-small cell lung cancer: molecular mechanisms and therapeutic potential.

Lung malignant tumors are a type of cancer with high incidence and mortality rates worldwide. Non-small cell lung cancer (NSCLC) accounts for over 80% of all lung malignant tumors, and most patients are diagnosed at advanced stages, leading to poor prognosis. Over the past decades, various oncogenic driver alterations associated with lung cancer have been identified, each of which can potentially serve as a therapeutic target. Rat sarcoma (RAS) genes are the most commonly mutated oncogenes in human cancers, with Kirsten rat sarcoma (KRAS) being the most common subtype. The role of KRAS oncogene in NSCLC is still not fully understood, and its impact on prognosis remains controversial. Despite the significant advancements in targeted therapy and immune checkpoint inhibitors (ICI) that have transformed the treatment landscape of advanced NSCLC in recent years, targeting KRAS (both directly and indirectly) remains challenging and is still under intensive research. In recent years, significant progress has been made in the development of targeted drugs targeting the NSCLC KRASG12C mutant subtype. However, research progress on target drugs for the more common KRASG12D subtype has been slow, and currently, no specific drugs have been approved for clinical use, and many questions remain to be answered, such as the mechanisms of resistance in this subtype of NSCLC, how to better utilize combination strategies with multiple treatment modalities, and whether KRASG12D inhibitors offer substantial efficacy in the treatment of advanced NSCLC patients.

Focal adhesion kinase-YAP signaling axis drives drug-tolerant persister cells and residual disease in lung cancer

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant persister cells (DTPs) which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response.

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes.

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

Comprehensive genomic profiling of pulmonary spindle cell carcinoma using tissue and plasma samples: insights from a real-world cohort analysis.

Pulmonary spindle cell carcinoma (PSCC) is a rare and aggressive non-small cell lung cancer (NSCLC) subtype with a dismal prognosis. The molecular characteristics of PSCC are largely unknown due to its rarity, which limits the diagnosis and treatment of this historically poorly characterized malignancy. We present comprehensive genomic profiling results of baseline tumor samples from 22 patients histologically diagnosed with PSCC, representing the largest cohort to date. Somatic genetic variant detection was compared between paired plasma samples and primary tumors from 13 patients within our cohort. The associations among genomic features, treatment, and prognosis were also analyzed in representative patient cases. TP53 (54.5%), TERT (36.4%), CDKN2A (27.3%), and MET (22.7%) were most frequently mutated. Notably, 81.8% of patients had actionable targets in their baseline tumors, including MET (22.7%), ERBB2 (13.6%), EGFR (9.1%), KRAS (9.1%), ALK (9.1%), and ROS1 (4.5%). The median tumor mutation burden (TMB) for PSCC tumors was 5.5 mutations per megabase (muts/Mb). TMB-high tumors (>10 muts/Mb) exhibited a significantly higher mutation frequency in genes such as KRAS, ARID2, FOXL2, and LRP1B, as well as within the DNA mismatch repair pathway. The detection rates for single nucleotide variants and structural variants were comparable between matched tumor and plasma samples, with 48.6% of genetic variants being mutually identified in both sample types. Additionally, a patient with a high mutation load and positive PD-L1 expression demonstrated a 7-month survival benefit from chemoimmunotherapy. Furthermore, a patient with an ALK-rearranged tumor achieved a remarkable 3-year progression-free survival following crizotinib treatment. Overall, our findings deepen the understanding of the complex genomic landscape of PSCC, revealing actionable targets amenable to tailored treatment of this poorly characterized malignancy.

Endobronchial Ultrasound Guided Transbronchial Needle Aspiration and PD-L1 Yields.

Immunotherapy is a leading approach for treating advanced non-small cell lung cancer (NSCLC) by targeting the PD-1/PD-L1 checkpoint signaling pathway, particularly in tumors expressing high levels of PD-L1 (Jug et al. in J Am Soc Cytopathol 9:485-493, 2020; Perrotta et al. in Chest 158: 1230-1239, 2020). Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive method to obtain tissue for molecular studies, including PD-L1 analysis, in unresectable tumors (Genova et al. in Front Immunol 12: 799455, 2021; Wang et al. in Ann Oncol 29: 1417-1422, 2018). This study aimed to assess the adequacy of PD-L1 assessment in EBUS-TBNA cytology specimens. Data was collected retrospectively from patients who underwent EBUS-TBNA between 2017 and 2021 for suspected lung cancer biopsy. Samples positive for NSCLC were examined for PD-L1 expression. EBUS was performed by experienced practitioners, following institutional guidelines of a minimum of five aspirations from positively identified lesions. Sample adequacy for molecular testing was determined by the pathology department. The analysis involved 387 NSCLC cases (149 squamous cell, 191 adenocarcinoma, 47 unspecified). Of the 263 EBUS-TBNA specimens tested for PD-L1, 237 (90.1%) were deemed adequate. While 84% adhered to the protocol, adherence did not yield better results. Significantly higher PD-L1 adequacy was observed in squamous cell carcinomas (93.2%) compared to adenocarcinoma (87.6%). The number of aspirations and sedation type did not correlate with PD-L1 adequacy in either cancer type, but lesion size and location had a significant impact in adenocarcinomas. Adenocarcinoma exhibited higher PD-L1 expression (68%) compared to squamous cell carcinoma (48%). EBUS-TBNA offers high yields for assessing immunotherapy markers like PD-L1, with satisfactory adequacy regardless of NSCLC subtype, lesion size, or location.

NCI 159456 PERK Inhibitor as a Targeted Therapy for Lung Cancer: An In Vitro Study.

Non-small cell lung cancer (NSCLC) represents the most common histological type of lung cancer, characterized by a five-year survival rate of 15% and poor prognosis. Accumulating evidence indicates a prominent role of endoplasmic reticulum (ER) stress and the protein kinase RNA-like ER kinase (PERK)-dependent pathway of the unfolded protein response (UPR) in the pathogenesis of NSCLC. Increased expression of downstream targets of PERK was observed in various subtypes of NSCLC, and it was associated with a more aggressive phenotype, high risk of recurrence, and poor prognosis. Therefore, the present study aimed to investigate the biological effect of the selective PERK inhibitor NCI 159456 on A549 NSCLC cells and Human Pulmonary Fibroblasts (HPF) in vitro. Treatment of both normal and ER-stressed A549 cells with NCI 159456 resulted in a significant increase in the mRNA expression level of pro-apoptotic genes like activating transcription factor 4 (ATF4), DNA damage inducible transcript 3 (DDIT3), and BCL2 Associated X, Apoptosis Regulator (BAX) as well as a decreased level of the anti-apoptotic gene B-cell lymphoma 2 (Bcl-2). Cytotoxicity and genotoxicity analyses revealed that NCI 159456 significantly decreased viability and increased DNA damage in A549 cells under normal and ER stress conditions. Caspase-3 and reactive oxygen species (ROS) detection assays demonstrated that NCI 159456 significantly induced apoptosis and increased the ROS level in normal and ER-stressed A549 cells. Importantly, treatment with the inhibitor did not affect substantially normal HPF cells at any used concentration. The results indicate that PERK inhibitors could potentially be applied as a targeted therapy for NSCLC.

Cancer-associated fibroblasts expressing fibroblast activation protein and podoplanin in non-small cell lung cancer predict poor clinical outcome.

Cancer-associated fibroblasts (CAFs) are a dominant cell type in the stroma of non-small cell lung cancer (NSCLC). Fibroblast heterogeneity reflects subpopulations of CAFs, which can influence prognosis and treatment efficacy. We describe the subtypes of CAFs in NSCLC. Primary human NSCLC resections were assessed by flow cytometry and multiplex immunofluorescence for markers of fibroblast activation which allowed identification of CAF subsets. Survival data were analysed for our NSCLC cohort consisting of 163 patients to understand prognostic significance of CAF subsets. We identified five CAF populations, termed CAF S1-S5. CAF-S5 represents a previously undescribed population, and express FAP and PDPN but lack the myofibroblast marker αSMA, whereas CAF-S1 populations express all three. CAF-S5 are spatially further from tumour regions then CAF-S1 and scRNA data demonstrate an inflammatory phenotype. The presence of CAF-S1 or CAF-S5 is correlated to worse survival outcome in NSCLC, despite curative resection, highlighting the prognostic importance of CAF subtypes in NSCLC. TCGA data suggest the predominance of CAF-S5 has a poor prognosis across several cancer types. This study describes the fibroblast heterogeneity in NSCLC and the prognostic importance of the novel CAF-S5 subset where its presence correlates to worse survival outcome.

Histology-driven hypofractionated radiation therapy schemes for early-stage lung adenocarcinoma and squamous cell carcinoma

Background and PurposeHistology was found to be an important prognostic factor for local tumor control probability (TCP) after stereotactic body radiotherapy (SBRT) of early-stage non-small-cell lung cancer (NSCLC). A histology-driven SBRT approach has not been explored in routine clinical practice and histology-dependent fractionation schemes remain unknown. Here, we analyzed pooled histologic TCP data as a function of biologically effective dose (BED) to determine histology-driven fractionation schemes for SBRT and hypofractionated radiotherapy of two predominant early-stage NSCLC histologic subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Material and MethodsThe least-χ2 method was used to fit the collected histologic TCP data of 8510 early-stage NSCLC patients to determine parameters for a well-developed radiobiological model per the Hypofractionated Treatment Effects in the Clinic (HyTEC) initiative. ResultsA fit to the histologic TCP data yielded independent radiobiological parameter sets for radiotherapy of early-stage lung ADC and SCC. TCP increases steeply with BED and reaches an asymptotic maximal plateau, allowing us to determine model-independent optimal fractionation schemes of least doses in 1–30 fractions to achieve maximal tumor control for early-stage lung ADC and SCC, e.g., 30, 44, 48, and 51 Gy for ADC, and 32, 48, 54, and 58 Gy for SCC in 1, 3, 4, and 5 fractions, respectively. ConclusionWe presented the first determination of histology-dependent radiobiological parameters and model-independent histology-driven optimal SBRT and hypofractionated radiation therapy schemes for early-stage lung ADC and SCC. SCC requires substantially higher radiation doses to maximize tumor control than ADC, plausibly attributed to tumor genetic diversity and microenvironment. The determined optimal SBRT schemes agree well with clinical practice for early-stage lung ADC. These proposed optimal fractionation schemes provide first insights for histology-based personalized radiotherapy of two predominant early-stage NSCLC subtypes ADC and SCC, which require further validation with large-scale histologic TCP data.

“Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer”

Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components underlying NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background holds a great potential for the identification of epi-biomarkers for treatment response however, it is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin.We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the transcription factor (TF) LRF, a known epigenetic silencer, was examined as a modulator of non-coding RNAs expression. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060–109,747,960 mouse chromosomal region to micro-RNA upregulated species. Furthermore, the novel epigenetic miR-148b-3p/lncPVT1/ZBTB7A axis was identified, which differentiates human cell line of lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes.The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease progression, NSCLC subtypes discrimination and advancement to patient-tailored treatments.

Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study.

Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.

Abstract 4286: Proteolytic degradation of BIGH3 can be quantified non-invasively in serum with biomarker potential for patients with non-small cell lung cancer

Abstract Introduction: Transforming growth factor beta induced protein ig-h3 (BIGH3/TGFBI) is expressed widely by multiple cell types, including macrophages and fibroblasts, and is participating in various biological processes including adhesion, migration, angiogenesis and wound healing. BIGH3 is known to bind multiple collagens and are often embedded in the matrix where it seems to function as a linker between ECM and cell surfaces. The increased proteolytic activity found in non-small cell lung cancer (NSCLC) may degrade BIGH3 that affect this interaction as well as generate small peptide fragments that can serve as novel non-invasive biomarker targets if released to circulation. The aim of this study is to develop a tool to quantify degraded BIGH3 non-invasively and explore its potential as a biomarker in NSCLC. Methods: An ELISA, named nordicBIGH3M-N targeting a cleaved fragment of BIGH3 was developed to reflect BIGH3 degradation and enable serological quantification. We incubated recombinant BIGH3 with and without collagenase to confirm that nordicBIGH3M-N only measured degraded BIGH3. In addition, we generated matrixes from normal fibroblasts and cancer associated fibroblasts (CAFs) grown with or without tgf-b. These matrixes were cleaved with collagenase to confirm that NordicBIGH3M-N could be generated from degradation of matrix. nordicBIGH3M-N was also measured in serum from 39 patients with NSCLC (18 patients with adenocarcinoma and 21 patients with squamous cell carcinoma) and 35 healthy controls. BIGH3M-N levels in serum from patients with the two subtypes of NSCLC and healthy controls was compared using Dunn’s multiple comparison test and area under receiver operation characteristic curves (AUROC). Results: BIGH3M-N was only detectable after incubating recombinant BIGH3 and fibroblasts matrices with collagenase. A 7-fold increase of BIGH3M-N levels was seen between the collagenase degraded matrix from tgf-b treated fibroblasts compared to the degraded matrix from untreated fibroblasts. No difference in BIGH3M-N levels was observed between degraded matrix from tgf-b treated or untreated CAFs. BIGH3M-N was significantly elevated in patients with squamous cell carcinoma compared to healthy controls (p = 0.006, AUROC = 0.73) and patients with adenocarcinoma (p = 0.022, AUROC = 0.78). However, there was no significant difference between BIGH3M-N levels in serum from patients with adenocarcinoma and healthy controls (p>0.99, AUROC = 0.51). Conclusion: Degradation of BIGH3 can be reflected by non-invasive quantification of the cleaved fragment of BIGH3, BIGH3M-N, in serum. BIGH3M-N is a promising biomarker in NSCLC with potential for discriminating between subtypes. Moreover, BIGH3M-N is connected to fibroblast matrix biology so the optimal use for this biomarker might be in combination with other ECM biomarkers. Citation Format: Rasmus S. Pedersen, Morten Karsdal, Nicholas Willumsen. Proteolytic degradation of BIGH3 can be quantified non-invasively in serum with biomarker potential for patients with non-small cell lung cancer [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 4286.

Abstract 4888: Multi-modal knowledge graphs enhance patient stratification & biomarker discovery

Abstract Background: Multi-modal analysis is crucial for deeper understanding of disease subtypes and more meaningful patient selection. We developed a flexible Knowledge Graph (KG) framework that enables deep multi-omic analysis. It can be used to uncover the interrelationships between the layers of data in a population to inform patient selection or biomarker discovery. We present an application of our framework to non-small cell lung cancer (NSCLC) to identify and separate communities of patients based on their survival and identify the associated biomarkers. We identified potentially mislabeled patients that do not share all the characteristics of the cancer subtype to which they are assigned: either lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC). Crucially, the community-based biomarkers for poor or long survivors were validated on the whole population. Methods: Our KG framework was leveraged by applying supervised community detection to NSCLC data from TCGA, specifically RNA expression and DNA methylation, with overall survival (OS) as the endpoint (n=999 subjects). Biomarkers associated to each community were ranked based on their prevalence inside against their prevalence outside the community. Results: We obtained 3 communities (391 + 229 + 379 patients) that are all significantly separated by their OS (p<0.05). While KG-derived communities largely overlapped with histology-labelled LUAD or LUSC (concordant LUAD (n=330)/concordant LUSC (n=342)), a small number of patients did not (discordant LUAD (n=37)/discordant LUSC (n=61)). Discordant LUAD patients had significantly lower OS than concordant LUAD (p=0.0198), despite being both labeled as LUAD. Many of the discordant LUSC lacked the 3q26 amplification commonly seen in LUSC and other squamous cell carcinomas. The results from our KG framework highlight its increased sensitivity in relation to existing tools (Cline, Sci Rep 3, 2652 (2013)) as we identified a discordant LUAD group in addition to a discordant LUSC group. Moreover, our tool can select the biomarkers most prevalent in each community, and these significantly separated long from poor survivors on the whole population (p=3.53e-5). Some of these KG-identified biomarkers are known regulators of progression and survival in NSCLC, whereas others are still not extensively studied. This highlights another advantage of our approach in prospective target discovery. Conclusions: Our KG framework allowed the observation of potential misclassifications of tumor subtypes in NSCLC (TCGA). This approach is a proof-of-concept of the value KGs have in identifying signals in muti-omic data that may improve patient stratification and uncover associated biomarker signatures. Our flexible end-to-end framework can take any type of ‘omic data and can be applied to any tumor type for such findings, which represents an alternative to pre-defined KG architectures with defined relationships. Citation Format: Miguel Goncalves, Jake Cohen-Setton, Ioannis Kagiampakis, Ben Sidders, Krishna Bulusu. Multi-modal knowledge graphs enhance patient stratification & biomarker discovery [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 4888.

Abstract 1463: Characterizing inflammation-induced epigenetic and transcriptomic alterations driving NSCLC using lung organoid models

Abstract Chronic inflammation is a major driver of lung cancer and contributes to its pathogenesis by inducing genetic and epigenetic alterations. These, in turn, modulate the microenvironment to facilitate emergence of tumor promoting immune evasive mechanisms. The evolution of epigenetic events and their role, both alone, and in association with lung cancer specific genetic drivers in initiation of lung cancer is not well delineated. Specific pathological subtypes of non-small cell lung cancer (NSCLC) have distinct cells of origin, suggestive of cell type-specific changes in driving their development. We employed lung organoid models with chronic exposure to cigarette smoke condensate (CSC) to elucidate sequential, cell type-specific epigenetic and transcriptomic alterations and their role in co-operating with genetic events to drive development of NSCLC. We examined epigenomic and transcriptomic alterations over a 6-month exposure period by DNA methylation arrays, ATAC-seq, and RNA-seq. We observe that CSC exposure causes distinct morphological changes in organoid structure and composition, accompanied by an increase in proliferative potential. Using flow cytometry and immunofluorescent staining approaches, we demonstrate a chronic inflammation-driven shift in stem cell populations accompanied by reduction in differentiated cell types. Bulk and single-cell RNA sequencing approaches identified distinct temporal changes in transcriptional signatures in CSC exposed organoids suggestive of evolution of an immune evasive and pro-tumorigenic phenotype. Gene set enrichment analysis revealed a downregulation of interferon signaling and upregulation of Myc targets and oxidative phosphorylation pathways in CSC treated organoids. Notably, co-culturing organoids with key immune cell populations, demonstrate the ability of the CSC exposed organoids to modulate shifts in immune cells from a pro- to anti- inflammatory state. In accordance with this shift in immune cell phenotype, CSC treated organoids secreted decreased levels of pro-inflammatory cytokines as measured by multiplex ELISA assays. Genome wide DNA methylation analysis reveal that CSC methylated genes were associated with differentiation and cell fate commitment as well as transcription factor activity. Finally, we demonstrate that introduction of key NSCLC-specific driver events at the 6-month time point sensitize only CSC treated organoids to tumor formation in vivo, leading to the development of distinct genetic driver-specific subtypes of NSCLC. Altogether, our results demonstrate the ability of chronic inflammation to drive key cell type-specific transcriptomic and epigenetic changes leading to the development of distinct subtypes of NSCLC. Our findings will help identify molecular correlates for early detection and aid development of novel therapeutic strategies for disease interception. Citation Format: Na Wang, Kara Lombardo, Raksha Padaki, Ray-Whay Chiu Yen, Malcolm V. Brock, Leslie Cope, Hariharan Easwaran, Stephen B. Baylin, Michelle Vaz. Characterizing inflammation-induced epigenetic and transcriptomic alterations driving NSCLC using lung organoid models [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 1463.

Abstract 986: Mathematical modeling of ctDNA dynamics to predict utility of ultrasensitive MRD assays in early stage non-small cell lung cancer (NSCLC)

Abstract Background: ctDNA assay use for minimal residual disease (MRD) detection in early stage NSCLC has been limited by suboptimal sensitivity of first generation assays. It is unclear how improving assay limit of detection (LOD) would translate to improvements in clinical sensitivity. Here, we performed mathematical modeling of ctDNA dynamics after curative-intent treatment in early stage NSCLC to predict the clinical impact of MRD assays with improved analytical sensitivity. Methods: We analyzed a recently reported dataset of post-surgical ctDNA MRD surveillance in early stage NSCLC (Abbosh et al., Nature 2023) to investigate ctDNA growth dynamics. Of 70 relapsed patients in this cohort, 23 had 3 consecutive ctDNA-positive samples with no intervening therapy. We used these to generate patient-specific mathematical models for ctDNA variant allele fractions (VAF) over time, initially assuming exponential growth. We assessed this assumption by creating log-linear models for ctDNA increase in these cases. Using slopes of the log-linear models, we calculated patient-specific doubling times and assessed their relation to stage, histology, and outcome. We used the log-linear models to simulate a VAF distribution each day after surgery, enabling projection of clinical sensitivities and estimation of lead times for recurrence detection at varying LODs. Results: For the 23 cases with 3+ consecutive ctDNA positive samples, we developed log-linear models for ctDNA VAF growth, which were strongly correlated (r > 0.5) in 18/23 (78%) patients (median r = 0.89), supporting that ctDNA can be modeled as exponential growth. We calculated ctDNA doubling times for the 18 patients with exponential ctDNA kinetics and 10 patients with 2 consecutive ctDNA positive samples. Doubling times were remarkably consistent with median 51 days (IQR 40-79). Doubling times were longer for stage I tumors compared to stage II/III (p = 0.04) but were not different between NSCLC subtypes. Doubling time correlated with time to clinical relapse, with patients having more rapid ctDNA growth relapsing earlier (r = 0.52, p=0.03). Finally, we used patient-specific log-linear models to project ctDNA VAFs at a landmark 30 days after surgery. By improving the LOD for ctDNA detection from 10−4 to 10−6, the projected clinical sensitivity doubled (from 43% to 86%). Simulations predicted that this improvement in sensitivity would increase median lead time from 183 days for LOD=10−4 to 349 days for LOD=10−6 (p<0.0001). Conclusion: For most relapsed NSCLC patients, the natural history of ctDNA MRD growth can be reliably modeled with exponential growth kinetics. ctDNA doubling time is associated with stage and clinical outcome. Ultrasensitive MRD assays with LOD of 10−6 are predicted to meaningfully improve clinical sensitivity at the post-surgical landmark and to increase the lead time for MRD detection before relapse. Citation Format: Jordan S. Goldstein, Christopher Abbosh, Ash A. Alizadeh, Maximilian Diehn, David M. Kurtz. Mathematical modeling of ctDNA dynamics to predict utility of ultrasensitive MRD assays in early stage non-small cell lung cancer (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 986.

Abstract 2607: Dual payload immunochemotherapy to treat CD276 positive cancers

Abstract The Cancer Genome Atlas (TCGA) dataset showed higher level of CD276 (B7-H3) transcript in multiple cancers, including triple-negative breast cancers (TNBCs), non-small cell lung cancers (NSCLCs) and other cancers, than normal tissues. Our immunohistochemistry staining of hundreds of patient tissue microarrays confirmed the high expression of CD276 surface receptor in >60% of TNBC and >70% of NSCLC while minimal or low expression in 33 normal organs or tissues. This study aims to develop and evaluate a novel treatment strategy, i.e. anti-CD276 mAb-dual payloads conjugate (DualADC), as immune-chemotherapy to treat CD276-positive TNBCs and NSCLCs. First, a new CD276-targeting monoclonal antibody (mAb) capable of delivering payloads and upregulating tumoral immunity was developed and engineered. Then, a new platform to concurrently conjugate both traditional cytotoxic payload and immunoregulating regent with one CD276 mAb (DualADC) was established. Our evaluations showed that this targeted therapy effectively killed multiple subtypes of TNBC and NSCLC, significantly upregulated immune functions in tumor microenvironment, and reduced tumor burden by up to 90-100% in seven animal models (including patient-derived xenograft). The post-treatment analysis using single-cell RNA sequencing, Luminex multiplex cytokine assay, whole blood, histology and histopathology analysis demonstrated an integrated anti-cancer mechanisms and minimal systematic toxicity. Altogether, our study suggested that the 276-DualADC could provide a promising targeted chemo-immunotherapy for CD276-postive cancer patients. In future, translational investigations will be performed to determine the toxicology, optimal dose, and treatment strategy, and the combination with other therapy will also be evaluated. Citation Format: Zhuoxin Zora Zhou, Jiashuai Zhang, Lufang Zhou, Xiaoguang Margaret Liu. Dual payload immunochemotherapy to treat CD276 positive cancers [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 2607.

Abstract 1536: Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach

Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [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 1536.

Abstract 3499: Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies

Abstract Background: Non-small cell lung cancer (NSCLC) makes up ~85% of lung cancers, and its major subtypes are adenocarcinoma (AD) and squamous cell carcinoma (SC). The histology classification of NSCLC has significant therapeutic and prognostic implications. As liquid biopsies are rapid and non-invasive, computational methods to classify NSCLC subtypes using cell-free DNA (cfDNA) are desired. Methods: Using The Cancer Genome Atlas (TCGA) NSCLC methylation data, we developed a method to classify AD and SC subtypes from cfDNA samples. We mapped TCGA methylation data to Guardant Infinity’s promoter regions and transformed them to allow for the transfer of the TCGA trained model to Infinity cfDNA epigenomic data. An advantage of this approach is that TCGA training data originates from the primary tumor, while cfDNA samples have low tumor fraction (TF) and may also contain signals from metastatic tumors. We trained a penalized Logistic Regression model with LASSO. To better fit the model to Infinity cfDNA data, we performed region filtering. Results: Our model effectively classified AD and SC subtypes from TCGA samples (AUC 0.97) (10-fold cross validation; Table 1). When evaluating on cfDNA samples with epigenomic TF ≥ 1%, the final model shows reasonable performance (AUC 0.94) (Table 1); the performance decreases when reduced to TF > 0.05% (AUC 0.89) as expected (Table 1). When evaluating cfDNA from uncategorized lung samples, the predicted AD probabilities have significant association with KRAS mutations (p-value = 0.027). Conclusion: Our results show that the penalized Logistic Regression model trained with tissue TCGA methylation data through mapping, transformation, and region selection can classify NSCLC subtypes from cfDNA samples. This method may offer a fast, non-invasive tool to inform subtype-based clinical decisions, especially in the setting of poorly-differentiated carcinomas, and to identify subtype switches upon resistance to targeted therapies. Table 1: Model evaluation on tissue TCGA samples and cfDNA Infinity samples (different TF cutoffs) Sample type N samples AUC Accuracy Sensitivity Specificity Precision F1 score tissue TCGA 907 0.97 0.93 0.94 +/- 0.02 0.93 +/- 0.02 0.94 +/- 0.02 0.94 +/- 0.01 cfDNA Infinity 17 (TF ≥ 1%) 0.94 0.88 0.83 +/- 0.3 0.91 +/- 0.17 0.83 +/- 0.3 0.83 +/- 0.21 cfDNA Infinity 29 (TF > 0.05%) 0.89 0.83 0.88 +/- 0.23 0.81 +/- 0.17 0.64 +/- 0.28 0.74 +/- 0.2 Citation Format: Laura Tung, Denis Tolkunov, Catalin Barbacioru, Deepika Kale, Scott Shell, Jill Tsai, Leylah Drusbosky, Kaushal Parikh, Han-Yu Chuang. Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies [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 3499.

Abstract 2151: Additional impact of genetic ancestry over self-reported race/ethnicity to prevalence of KRAS mutations and allele-specific subtypes in non-small cell lung cancer

Abstract Background: KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, the interplay between self-reported race and/or ethnicity (SIRE) and genetically inferred ancestry (GIA) on KRAS mutations and allele-specific subtypes is largely unknown. Methods: A total of 3918 NSCLC patients from the Chinese OrigiMed (OM) cohort (n=2039) and Boston Lung Cancer Survival (BLCS) cohort (n=1879) were included, all of whom had baseline covariates including age, sex, clinical stage, SIRE, histology and smoking status. KRAS mutation and subtypes were determined by targeted NGS panels in each cohort, and KRAS mutation outcomes, including KRAS mutation, KRAS subtypes, and KRAS transversion and transition were analyzed. GIA was inferred from paired germline data for patients in the BLCS cohort. Multivariable and multinomial logistic regressions were employed to assess the associations between SIRE, GIA and KRAS outcomes. Results: In the entire cohort, 804 (20.5%) patients harbored KRAS mutation, with a considerably higher prevalence of 31.5% observed in BLCS compared to 10.4% in OM cohort. Patients with self-identified Asian was associated with a significantly lower rate of KRAS mutation (OR = 0.44, 95% CI:0.22 - 0.81, P = 0.01), transversion substitutions (OR = 0.24, 95% CI:0.10 - 0.62, P = 0.003) and KRASG12C (OR = 0.17, 95% CI:0.04 - 0.72, P = 0.02) compared to SIRE-White patients after adjusting for confounders including smoking status, sex and histology. The association of SIRE and KRAS driver mutation was further modified by sex, with Asian women demonstrating lower risk compared to Asian men, while the opposite trend was observed in SIRE-White patients. Increased European ancestry was associated with a higher risk of KRAS mutation (OR per 10% increase = 1.05, 95% CI:1.00 - 1.09, P = 0.03), particularly with more transition substitutions (OR per 10% increase = 1.10, 95% CI:1.01 - 1.21, P = 0.03) and KRASG12D (OR per 10% increase = 1.18, 95% CI:1.04 - 1.34, P = 0.01). This association was even more pronounced in patients of increased Northern European ancestry. Furthermore, even among SIRE-White patients, a 10% increase in European ancestry was associated with an additional 6% increase in the likelihood of KRAS mutations (OR = 1.06, 95% CI: 1.01 - 1.12, P = 0.03), while a 10% increase in Admixed American ancestry was associated with a 6% decreased likelihood of KRAS mutations (OR = 0.94, 95% CI: 0.88 - 0.99, P = 0.04). Results were validated in an independent cohort of 1450 patients. Conclusion: Self-reported race/ethnicity and genetic ancestry are both associated with KRAS driver mutations and allele-specific subtypes of NSCLC. This work highlights the significance of integrating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities. Citation Format: Xinan Wang, Kangcheng Hou, Biagio Ricciuti, Joao V. Alessi, Xihao Li, Federica Pecci, Rounak Dey, Jia Luo, Mark M. Awad, Alexander Gusev, Xihong Lin, Bruce E. Johnson, David C. Christiani. Additional impact of genetic ancestry over self-reported race/ethnicity to prevalence of KRAS mutations and allele-specific subtypes in non-small cell lung cancer [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 2151.

Abstract 2750: Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma

Abstract Background: Non-small cell lung cancer (NSCLC) survival is closely linked to metastatic progression. 5-year survival rates drop from >65% when localized to ~9% when distant metastases occur. Despite this impact on mortality, drivers of overall metastasis and site-specific organotropism are not well understood in NSCLC. To investigate these questions, we focused on Lung adenocarcinoma (LUAD), the most common subtype of NSCLC. Although previous efforts have been made to examine LUAD organotropism, we sought to expand upon longitudinal modeling approaches and explore associations in an independent large clinical and genomic annotated cohort. Methods: Using the artificial intelligence model from (Kehl et al. 2021), we annotated a clinico-genomic cohort of 2777 patients with lung adenocarcinoma, resulting in 59,177 annotated imaging reports. This resulted in time to metastatic site annotations for each patient, with median of 2 years follow up. Metastatic sites included brain, bone, adrenal, liver, lymph node, and mesentery. All patients were evaluated at the Dana-Farber Cancer Institute, and each patient had at least one tumor biopsy sequenced with targeted panel sequencing of 400+ cancer related genes. To infer genomic and clinical correlates of site-specific metastasis, we performed cause-specific and Fine-Gray hazard modeling in this cohort. We used the recurrent event Andersen-Gill model to infer predictors of longitudinal metastasis rates. Results: Site-specific analyses yielded genes whose mutant status were significantly associated with a change in risk and/or rate of certain metastatic sites. We found that NOTCH1 is significantly associated with an increased rate and risk of brain metastases (HR=2.46, P<0.001). SETD2 is associated with decreased incidence of brain (HR=0.42, P<0.001). SMARCA4 is significantly associated with increased incidence and rate of adrenal metastasis (HR=1.64, P<0.01), while NF1 is significantly associated with decreased rate of adrenal metastasis (HR=0.72, P<0.01). In a multivariable model, Female patients and younger patients had a lower rate of new metastatic sites. As previously seen, TP53 and SMARCA4 mutations associated with higher rates of new metastatic sites (HR=1.17, P<0.001; HR=1.36, P<0.001 respectively), and SETD2 is associated with a lower rate of new metastatic sites (HR=0.71, P<0.001). Conclusions: By using time-to-event analysis with longitudinal metastatic annotations, we identify potential drivers of overall metastasis and site-specific organotropism in LUAD patients. Identification of new associations and concordance of our results with previous studies also supports the utility of artificial-intelligence-annotated datasets, allowing for larger cohorts without the need for manual annotation. Citation Format: Tyler Aprati, Michael Manos, Giuseppe Tarantino, Marc Glettig, Maryclare Griffin, Alexander Gusev, Kenneth Kehl, David Liu. Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma [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 2750.

Abstract 1534: The tumor immune microenvironment at different stages of non-small cell lung cancer

Abstract Introduction: To better understand the immune microenvironment in non-small cell lung cancer (NSCLC), we evaluated immune cell populations within the tumor parenchyma and their interaction at the periphery of the tumors at different stages of disease. Methods: 27 whole tumor slides were collected from NSCLC patients of all stages after surgical resection. Tissue slides were stained with multiplex Opal immunofluorescence for CD8, CD68, CK, PD-1, PD-L1, FoxP3, and DAPI using a Leica Bond automated stainer. Slides were scanned at 20x magnification by Akoya Polaris whole slide scanner and spectrally unmixed. Spatial analyses were performed on the HALO software. Results: Density of CD8 T cells, T regulatory (Treg) cells, and macrophages were significantly higher (p=0.001) in the interface region compared to the tumor and stroma. There is abundant CD8 T cell infiltration in the tumor at early stage I and this decreased significantly (p=0.05) at higher stages. PD-1− CD8 T cells significantly dropped with higher stage, while PD-1+ CD8 T cells did not. On the other hand, macrophage infiltration increased as stage increased, with most of the increase driven by PD-L1− macrophages. No statistically significant trends were observed in Treg cells across stages, but they appeared to be higher in stage II. No differences in immune infiltration were observed between the NSCLC subtypes adenocarcinoma and squamous cell carcinoma. No correlation was found between proximity of immune cell subsets to tumor cells and stage. Conclusion: The tumor microenvironment in NSCLC is rich in CD8+ cells at early stage and their density decreases as the stage of the disease increases, while macrophage infiltration increases as the disease stage progresses. Citation Format: Mackenzie Hansen, Qin Zhou, Yoko S. DeRose, Magdalena Kovacsovics, Benjamin Brintz, Benjamin L. Witt, Kim L. O'Neill, Siwen Hu-Lieskovan. The tumor immune microenvironment at different stages of non-small cell lung cancer [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 1534.