Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined. Methods: Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes. Results: In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs. Conclusions: We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Circulating microRNA Panel for Prediction of Recurrence and Survival in Early-Stage Lung Adenocarcinoma.

Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p < 0.001, hazard ratio [HR] = 1.64, 95% confidence interval [CI] = 1.51-4.22) and overall survival (p = 0.001, HR = 1.51, 95% CI = 1.17-1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis.

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.

Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer.

Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes. Our results demonstrate two cellular distribution patterns-an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells. Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions.

Analysis of Immune Intratumor Heterogeneity Highlights Immunoregulatory and Coinhibitory Lymphocytes as Hallmarks of Recurrence in Stage I Non-Small Cell Lung Cancer.

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1+ macrophages (CD68+PD-L1+) or antigen-experienced cytotoxic T cells (CD3+CD8+PD-1+) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC.

Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung cancer.

Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) are effective for many patients with lung cancer with EGFR mutations. However, not all patients are responsive to EGFR TKIs, including even those harboring EGFR-sensitizing mutations. In this study, we quantified the cells and cellular interaction features of the tumor microenvironment (TME) using routine H&E-stained biopsy sections. These TME features were used to develop a prediction model for survival benefit from EGFR TKI therapy in patients with lung adenocarcinoma and EGFR-sensitizing mutations in the Lung Cancer Mutation Consortium 1 (LCMC1) and validated in an independent LCMC2 cohort. In the validation data set, EGFR TKI treatment prolonged survival in the predicted-to-benefit group but not in the predicted-not-to-benefit group. Among patients treated with EGFR TKIs, the predicted-to-benefit group had prolonged survival outcomes compared with the predicted not-to-benefit group. The EGFR TKI survival benefit positively correlated with tumor-tumor interaction image features and negatively correlated with tumor-stroma interaction. Moreover, the tumor-stroma interaction was associated with higher activation of the hepatocyte growth factor/MET-mediated PI3K/AKT signaling pathway and epithelial-mesenchymal transition process, supporting the hypothesis of fibroblast-involved resistance to EGFR TKI treatment.

Immunogenomic profiling of lung adenocarcinoma reveals poorly differentiated tumors are associated with an immunogenic tumor microenvironment.

OBJECTIVES: Pathologists have routinely observed distinct histologic patterns of growth in early-stage lung adenocarcinoma (LUAD), which have been suggested to be associated with prognosis. Herein, we investigated the relationship between LUAD patterns of growth, as defined by the updated international association for the study of lung cancer (IASLC) grading criteria, and differences in the tumor immune microenvironment to identify predictors of response to immunotherapy. METHODS: 174 resected stage I-III LUAD tumors were classified by histologic pattern of growth (i.e. solid, micropapillary, acinar, papillary, and lepidic) and then grouped as well differentiated, moderately differentiated, and poorly differentiated. Comprehensive multiplatform analysis including whole exome sequencing, gene expression profiling, immunohistochemistry, CIBERSORT, and T-cell receptor sequencing was performed and groups were compared for differences in genomic drivers, immune cell infiltrate, clonality, and survival. Finally, multivariate analysis was performed adjusting for pathologic stage and smoking status. RESULTS: Poorly differentiated tumors demonstrated a strong association with smoking relative to moderately differentiated or well differentiated tumors. However, unlike in prior reports, poorly differentiated tumors were not associated with a worse survival after curative-intent resection. Genomic analysis revealed that poorly differentiated tumors are associated with high tumor mutation burden but showed no association with oncogenic drivers. Immune analyses revealed that poorly differentiated tumors are associated with increased T-cell clonality, expression of PD-L1, and infiltration by cytotoxic CD8 T-cells, activated CD4 T-cells, and pro-inflammatory (M1) macrophages. Finally, multivariate analysis controlling for stage and smoking status confirmed independence of immune differences between IASLC grade groups. CONCLUSIONS: Poorly differentiated tumors, as defined by the updated IASLC grading criteria, are associated with a distinct immunogenic tumor microenvironment that predicts for therapeutic response to immune agents, including checkpoint inhibitors, and should be included in the clinical trial design of immunotherapy studies in early-stage lung adenocarcinoma.

Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set.

PURPOSE: Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining. METHODS: We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project. RESULTS: Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype. CONCLUSION: The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.

Targeting de novo lipogenesis and the Lands cycle induces ferroptosis in KRAS-mutant lung cancer.

Mutant KRAS (KM), the most common oncogene in lung cancer (LC), regulates fatty acid (FA) metabolism. However, the role of FA in LC tumorigenesis is still not sufficiently characterized. Here, we show that KMLC has a specific lipid profile, with high triacylglycerides and phosphatidylcholines (PC). We demonstrate that FASN, the rate-limiting enzyme in FA synthesis, while being dispensable in EGFR-mutant or wild-type KRAS LC, is required for the viability of KMLC cells. Integrating lipidomic, transcriptomic and functional analyses, we demonstrate that FASN provides saturated and monounsaturated FA to the Lands cycle, the process remodeling oxidized phospholipids, such as PC. Accordingly, blocking either FASN or the Lands cycle in KMLC, promotes ferroptosis, a reactive oxygen species (ROS)- and iron-dependent cell death, characterized by the intracellular accumulation of oxidation-prone PC. Our work indicates that KM dictates a dependency on newly synthesized FA to escape ferroptosis, establishing a targetable vulnerability in KMLC.

Mutational Activation of the NRF2 Pathway Upregulates Kynureninase Resulting in Tumor Immunosuppression and Poor Outcome in Lung Adenocarcinoma.

Activation of the NRF2 pathway through gain-of-function mutations or loss-of-function of its suppressor KEAP1 is a frequent finding in lung cancer. NRF2 activation has been reported to alter the tumor microenvironment. Here, we demonstrated that NRF2 alters tryptophan metabolism through the kynurenine pathway that is associated with a tumor-promoting, immune suppressed microenvironment. Specifically, proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. The siRNA-mediated knockdown of NFE2L2, the gene encoding for NRF2, or activation of the NRF2 pathway through siRNA-mediated knockdown of KEAP1 or via chemical induction with the NRF2-activator CDDO-Me confirmed that NRF2 is a regulator of KYNU expression in LUAD. Metabolomic analyses confirmed KYNU to be enzymatically functional. Analysis of multiple independent gene expression datasets of LUAD, as well as a LUAD tumor microarray demonstrated that elevated KYNU was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and resulted in poorer survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.

Immunogenomic intertumor heterogeneity across primary and metastatic sites in a patient with lung adenocarcinoma.

BACKGROUND: Lung cancer is the leading cause of cancer death, partially owing to its extensive heterogeneity. The analysis of intertumor heterogeneity has been limited by an inability to concurrently obtain tissue from synchronous metastases unaltered by multiple prior lines of therapy. METHODS: In order to study the relationship between genomic, epigenomic and T cell repertoire heterogeneity in a rare autopsy case from a 32-year-old female never-smoker with left lung primary late-stage lung adenocarcinoma (LUAD), we did whole-exome sequencing (WES), DNA methylation and T cell receptor (TCR) sequencing to characterize the immunogenomic landscape of one primary and 19 synchronous metastatic tumors. RESULTS: We observed heterogeneous mutation, methylation, and T cell patterns across distinct metastases. Only TP53 mutation was detected in all tumors suggesting an early event while other cancer gene mutations were later events which may have followed subclonal diversification. A set of prevalent T cell clonotypes were completely excluded from left-side thoracic tumors indicating distinct T cell repertoire profiles between left-side and non left-side thoracic tumors. Though a limited number of predicted neoantigens were shared, these were associated with homology of the T cell repertoire across metastases. Lastly, ratio of methylated neoantigen coding mutations was negatively associated with T-cell density, richness and clonality, suggesting neoantigen methylation may partially drive immunosuppression. CONCLUSIONS: Our study demonstrates heterogeneous genomic and T cell profiles across synchronous metastases and how restriction of unique T cell clonotypes within an individual may differentially shape the genomic and epigenomic landscapes of synchronous lung metastases.

AXL targeting restores PD-1 blockade sensitivity of STK11/LKB1 mutant NSCLC through expansion of TCF1+ CD8 T cells.

Mutations in STK11/LKB1 in non-small cell lung cancer (NSCLC) are associated with poor patient responses to immune checkpoint blockade (ICB), and introduction of a Stk11/Lkb1 (L) mutation into murine lung adenocarcinomas driven by mutant Kras and Trp53 loss (KP) resulted in an ICB refractory syngeneic KPL tumor. Mechanistically this occurred because KPL mutant NSCLCs lacked TCF1-expressing CD8 T cells, a phenotype recapitulated in human STK11/LKB1 mutant NSCLCs. Systemic inhibition of Axl results in increased type I interferon secretion from dendritic cells that expanded tumor-associated TCF1+PD-1+CD8 T cells, restoring therapeutic response to PD-1 ICB in KPL tumors. This was observed in syngeneic immunocompetent mouse models and in humanized mice bearing STK11/LKB1 mutant NSCLC human tumor xenografts. NSCLC-affected individuals with identified STK11/LKB1 mutations receiving bemcentinib and pembrolizumab demonstrated objective clinical response to combination therapy. We conclude that AXL is a critical targetable driver of immune suppression in STK11/LKB1 mutant NSCLC.

Distinct Immune Gene Programs Associated with Host Tumor Immunity, Neoadjuvant Chemotherapy, and Chemoimmunotherapy in Resectable NSCLC.

PURPOSE: Our understanding of the immunopathology of resectable non-small cell lung cancer (NSCLC) is still limited. Here, we explore immune programs that inform of tumor immunity and response to neoadjuvant chemotherapy and chemoimmunotherapy in localized NSCLC. EXPERIMENTAL DESIGN: Targeted immune gene sequencing using the HTG Precision Immuno-Oncology panel was performed in localized NSCLCs from three cohorts based on treatment: naïve (n = 190), neoadjuvant chemotherapy (n = 38), and neoadjuvant chemoimmunotherapy (n = 21). Tumor immune microenvironment (TIME) phenotypes were based on the location of CD8+ T cells (inflamed, cold, excluded), tumoral PD-L1 expression (<1% and ≥1%), and tumor-infiltrating lymphocytes (TIL). Immune programs and signatures were statistically analyzed on the basis of tumoral PD-L1 expression, immune phenotypes, and pathologic response and were cross-compared across the three cohorts. RESULTS: PD-L1-positive tumors exhibited increased signature scores for various lymphoid and myeloid cell subsets (P < 0.05). TIME phenotypes exhibited disparate frequencies by stage, PD-L1 expression, and mutational burden. Inflamed and PD-L1+/TILs+ NSCLCs displayed overall significantly heightened levels of immune signatures, with the excluded group representing an intermediate state. A cytotoxic T-cell signature was associated with favorable survival in neoadjuvant chemotherapy-treated NSCLCs (P < 0.05). Pathologic response to chemoimmunotherapy was positively associated with higher expression of genes involved in immune activation, chemotaxis, as well as T and natural killer cells (P < 0.05 for all). Among the three cohorts, chemoimmunotherapy-treated NSCLCs exhibited the highest scores for various immune cell subsets including T effector and B cells (P < 0.05). CONCLUSIONS: Our findings highlight immune gene programs that may underlie host tumor immunity and response to neoadjuvant chemotherapy and chemoimmunotherapy in resectable NSCLC.

MTAP deficiency creates an exploitable target for antifolate therapy in 9p21-loss cancers.

Methylthioadenosine phosphorylase, an essential enzyme for the adenine salvage pathway, is often deficient (MTAPdef) in tumors with 9p21 loss and hypothetically renders tumors susceptible to synthetic lethality by antifolates targeting de novo purine synthesis. Here we report our single arm phase II trial (NCT02693717) that assesses pemetrexed in MTAPdef urothelial carcinoma (UC) with the primary endpoint of overall response rate (ORR). Three of 7 enrolled MTAPdef patients show response to pemetrexed (ORR 43%). Furthermore, a historic cohort shows 4 of 4 MTAPdef patients respond to pemetrexed as compared to 1 of 10 MTAP-proficient patients. In vitro and in vivo preclinical data using UC cell lines demonstrate increased sensitivity to pemetrexed by inducing DNA damage, and distorting nucleotide pools. In addition, MTAP-knockdown increases sensitivity to pemetrexed. Furthermore, in a lung adenocarcinoma retrospective cohort (N = 72) from the published BATTLE2 clinical trial (NCT01248247), MTAPdef associates with an improved response rate to pemetrexed. Our data demonstrate a synthetic lethal interaction between MTAPdef and de novo purine inhibition, which represents a promising therapeutic strategy for larger prospective trials.

SRGN-Triggered Aggressive and Immunosuppressive Phenotype in a Subset of TTF-1-Negative Lung Adenocarcinomas.

BACKGROUND: Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1-negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1-negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1-negative LUAD. METHODS: Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1-negative and 4 TTF-1-positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided. RESULTS: SRGN was markedly overexpressed at mRNA and protein levels in TTF-1-negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1-positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase-mediated impairment of methionine metabolism. CONCLUSIONS: Our findings suggest that SRGN plays a pivotal role in tumor-stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1-negative LUAD.

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics.

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conduct whole-exome sequencing and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations are shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrate similar subclonal architecture. On the other hand, transcriptomic profiling reveals shared pathways between the same histologic subtypes from different patients, which is supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles in most tumors.