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.

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.

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.

Augmented Lipocalin-2 Is Associated with Chronic Obstructive Pulmonary Disease and Counteracts Lung Adenocarcinoma Development.

Rationale: Early pathogenesis of lung adenocarcinoma (LUAD) remains largely unknown. We found that, relative to wild-type littermates, the innate immunomodulator Lcn2 (lipocalin-2) was increased in normal airways from mice with knockout of the airway lineage gene Gprc5a (Gprc5a-/-) and that are prone to developing inflammation and LUAD. Yet, the role of LCN2 in lung inflammation and LUAD is poorly understood.Objectives: Delineate the role of Lcn2 induction in LUAD pathogenesis.Methods: Normal airway brushings, uninvolved lung tissues, and tumors from Gprc5a-/- mice before and after tobacco carcinogen exposure were analyzed by RNA sequencing. LCN2 mRNA was analyzed in public and in-house data sets of LUAD, lung squamous cancer (LUSC), chronic obstructive pulmonary disease (COPD), and LUAD/LUSC with COPD. LCN2 protein was immunohistochemically analyzed in a tissue microarray of 510 tumors. Temporal lung tumor development, gene expression programs, and host immune responses were compared between Gprc5a-/- and Gprc5a-/-/Lcn2-/- littermates.Measurements and Main Results:Lcn2 was progressively elevated during LUAD development and positively correlated with proinflammatory cytokines and inflammation gene sets. LCN2 was distinctively elevated in human LUADs, but not in LUSCs, relative to normal lungs and was associated with COPD among smokers and patients with LUAD. Relative to Gprc5a-/- mice, Gprc5a-/-/Lcn2-/- littermates exhibited significantly increased lung tumor development concomitant with reduced T-cell abundance (CD4+) and richness, attenuated antitumor immune gene programs, and increased immune cell expression of protumor inflammatory cytokines.Conclusions: Augmented LCN2 expression is a molecular feature of COPD-associated LUAD and counteracts LUAD development in vivo by maintaining antitumor immunity.

CD73 expression defines immune, molecular, and clinicopathological subgroups of lung adenocarcinoma.

INTRODUCTION: CD73 is a membrane-bound enzyme crucial in adenosine generation. The adenosinergic pathway plays a critical role in immunosuppression and in anti-tumor effects of immune checkpoint inhibitors (ICI). Here, we interrogated CD73 expression in a richly annotated cohort of human lung adenocarcinoma (LUAD) and its association with clinicopathological, immune, and molecular features to better understand the role of this immune marker in LUAD pathobiology. MATERIALS AND METHODS: Protein expression of CD73 was evaluated by immunohistochemistry in 106 archived LUADs from patients that underwent surgical treatment without neoadjuvant therapy. Total CD73 (T +) was calculated as the average of luminal (L +) and basolateral (BL +) percentage membrane expression scores for each LUAD and was used to classify tumors into three groups based on the extent of T CD73 expression (high, low, and negative). RESULTS: CD73 expression was significantly and progressively increased across normal-appearing lung tissue, adenomatous atypical hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and LUAD. In LUAD, BL CD73 expression was associated with an increase in PD-L1 expression in tumor cells and increase of tumor-associated immune cells. Stratification of LUADs based on T CD73 extent also revealed that tumors with high expression of this enzyme overall exhibited significantly elevated immune infiltration and PD-L1 protein expression. Immune profiling demonstrated that T-cell inflammation and adenosine signatures were significantly higher in CD73-expressing lung adenocarcinomas relative to those lacking CD73. CONCLUSION: Our study suggests that higher CD73 expression is associated with an overall augmented host immune response, suggesting potential implications in the immune pathobiology of early stage lung adenocarcinoma. Our findings warrant further studies to explore the role of CD73 in immunotherapeutic response of LUAD.

Interplay between estrogen and Stat3/NF-κB-driven immunomodulation in lung cancer.

K-ras mutant lung adenocarcinoma (LUAD) is the most common type of lung cancer, displays abysmal prognosis and is tightly linked to tumor-promoting inflammation, which is increasingly recognized as a target for therapeutic intervention. We have recently shown a gender-specific role for epithelial Stat3 signaling in the pathogenesis of K-ras mutant LUAD. The absence of epithelial Stat3 in male K-ras mutant mice (LR/Stat3Δ/Δ mice) promoted tumorigenesis and induced a nuclear factor-kappaB (NF-κB)-driven pro-tumor immune response while reducing tumorigenesis and enhancing anti-tumor immunity in female counterparts. In the present study, we manipulated estrogen and NF-κB signaling to study the mechanisms underlying this intriguing gender-disparity. In LR/Stat3Δ/Δ females, estrogen deprivation by bilateral oophorectomy resulted in higher tumor burden, an induction of NF-κB-driven immunosuppressive response, and reduced anti-tumor cytotoxicity, whereas estrogen replacement reversed these changes. On the other hand, exogenous estrogen in males successfully inhibited tumorigenesis, attenuated NF-κB-driven immunosuppression and boosted anti-tumor immunity. Mechanistically, genetic targeting of epithelial NF-κB activity resulted in reduced tumorigenesis and enhanced the anti-tumor immune response in LR/Stat3Δ/Δ males, but not females. Our data suggest that estrogen exerts a context-specific anti-tumor effect through inhibiting NF-κB-driven tumor-promoting inflammation and provide insights into developing novel personalized therapeutic strategies for K-ras mutant LUAD.

18F-fluorodeoxyglucose positron emission tomography correlates with tumor immunometabolic phenotypes in resected lung cancer.

Enhanced tumor glycolytic activity is a mechanism by which tumors induce an immunosuppressive environment to resist adoptive T cell therapy; therefore, methods of assessing intratumoral glycolytic activity are of considerable clinical interest. In this study, we characterized the relationships among tumor 18F-fluorodeoxyglucose (FDG) retention, tumor metabolic and immune phenotypes, and survival in patients with resected non-small cell lung cancer (NSCLC). We retrospectively analyzed tumor preoperative positron emission tomography (PET) 18F-FDG uptake in 59 resected NSCLCs and investigated correlations between PET parameters (SUVMax, SUVTotal, SUVMean, TLG), tumor expression of glycolysis- and immune-related genes, and tumor-associated immune cell densities that were quantified by immunohistochemistry. Tumor glycolysis-associated immune gene signatures were analyzed for associations with survival outcomes. We found that each 18F-FDG PET parameter was positively correlated with tumor expression of glycolysis-related genes. Elevated 18F-FDG SUVMax was more discriminatory of glycolysis-associated changes in tumor immune phenotypes than other 18F-FDG PET parameters. Increased SUVMax was associated with multiple immune factors characteristic of an immunosuppressive and poorly immune infiltrated tumor microenvironment, including elevated PD-L1 expression, reduced CD57+ cell density, and increased T cell exhaustion gene signature. Elevated SUVMax identified immune-related transcriptomic signatures that were associated with enhanced tumor glycolytic gene expression and poor clinical outcomes. Our results suggest that 18F-FDG SUVMax has potential value as a noninvasive, clinical indicator of tumor immunometabolic phenotypes in patients with resectable NSCLC and warrants investigation as a potential predictor of therapeutic response to immune-based treatment strategies.

Driver Mutations in Normal Airway Epithelium Elucidate Spatiotemporal Resolution of Lung Cancer.

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non-small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown.Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC.Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis.Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared "two-hit" alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1).Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

YES1 Drives Lung Cancer Growth and Progression and Predicts Sensitivity to Dasatinib.

Rationale: The characterization of new genetic alterations is essential to assign effective personalized therapies in non-small cell lung cancer (NSCLC). Furthermore, finding stratification biomarkers is essential for successful personalized therapies. Molecular alterations of YES1, a member of the SRC (proto-oncogene tyrosine-protein kinase Src) family kinases (SFKs), can be found in a significant subset of patients with lung cancer.Objectives: To evaluate YES1 (v-YES-1 Yamaguchi sarcoma viral oncogene homolog 1) genetic alteration as a therapeutic target and predictive biomarker of response to dasatinib in NSCLC.Methods: Functional significance was evaluated by in vivo models of NSCLC and metastasis and patient-derived xenografts. The efficacy of pharmacological and genetic (CRISPR [clustered regularly interspaced short palindromic repeats]/Cas9 [CRISPR-associated protein 9]) YES1 abrogation was also evaluated. In vitro functional assays for signaling, survival, and invasion were also performed. The association between YES1 alterations and prognosis was evaluated in clinical samples.Measurements and Main Results: We demonstrated that YES1 is essential for NSCLC carcinogenesis. Furthermore, YES1 overexpression induced metastatic spread in preclinical in vivo models. YES1 genetic depletion by CRISPR/Cas9 technology significantly reduced tumor growth and metastasis. YES1 effects were mainly driven by mTOR (mammalian target of rapamycin) signaling. Interestingly, cell lines and patient-derived xenograft models with YES1 gene amplifications presented a high sensitivity to dasatinib, an SFK inhibitor, pointing out YES1 status as a stratification biomarker for dasatinib response. Moreover, high YES1 protein expression was an independent predictor for poor prognosis in patients with lung cancer.Conclusions: YES1 is a promising therapeutic target in lung cancer. Our results provide support for the clinical evaluation of dasatinib treatment in a selected subset of patients using YES1 status as predictive biomarker for therapy.

5 protein-based signature for resectable lung squamous cell carcinoma improves the prognostic performance of the TNM staging.

INTRODUCTION: Prognostic biomarkers have been very elusive in the lung squamous cell carcinoma (SCC) and none is currently being used in the clinical setting. We aimed to identify and validate the clinical utility of a protein-based prognostic signature to stratify patients with early lung SCC according to their risk of recurrence or death. METHODS: Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (eighth edition, 2018). Three independent retrospective cohorts of 117, 96 and 105 patients with lung SCC were analysed to develop and validate a prognostic signature based on immunohistochemistry for five proteins. RESULTS: We identified a five protein-based signature whose prognostic index (PI) was an independent and significant predictor of disease-free survival (DFS) (p<0.001; HR=4.06, 95% CI 2.18 to 7.56) and overall survival (OS) (p=0.004; HR=2.38, 95% CI 1.32 to 4.31). The prognostic capability of PI was confirmed in an external multi-institutional cohort for DFS (p=0.042; HR=2.01, 95% CI 1.03 to 3.94) and for OS (p=0.031; HR=2.29, 95% CI 1.08 to 4.86). Moreover, PI added complementary information to the newly established IASLC TNM 8th edition staging system. A combined prognostic model including both molecular and anatomical (TNM) criteria improved the risk stratification in both cohorts (p<0.05). CONCLUSION: We have identified and validated a clinically feasible protein-based prognostic model that complements the updated TNM system allowing more accurate risk stratification. This signature may be used as an advantageous tool to improve the clinical management of the patients, allowing the reduction of lung SCC mortality through a more accurate knowledge of the patient's potential outcome.

A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients.

Each of the pathological stages (I-IIIa) of surgically resected non-small-cell lung cancer has hidden biological heterogeneity, manifested as heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers with which to assess individual patient risk is an unmet medical need. Here, we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI, and SLC2A1) to stratify early-stage lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged according to the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8th edition, 2018). A test cohort (n = 239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression with the use of stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of 5-year outcome for disease-free survival (p < 0.001) and overall survival (p < 0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n = 114, p = 0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging, with a highly significant improvement of the likelihood ratio. We subsequently developed a combined PI including both the molecular and the pathological data that improved the risk stratification in both cohorts (p ≤ 0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with a high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even when the new IASLC 8th edition staging criteria are applied. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Immunoproteasome deficiency is a feature of non-small cell lung cancer with a mesenchymal phenotype and is associated with a poor outcome.

The immunoproteasome plays a key role in generation of HLA peptides for T cell-mediated immunity. Integrative genomic and proteomic analysis of non-small cell lung carcinoma (NSCLC) cell lines revealed significantly reduced expression of immunoproteasome components and their regulators associated with epithelial to mesenchymal transition. Low expression of immunoproteasome subunits in early stage NSCLC patients was associated with recurrence and metastasis. Depleted repertoire of HLA class I-bound peptides in mesenchymal cells deficient in immunoproteasome components was restored with either IFNγ or 5-aza-2'-deoxycytidine (5-aza-dC) treatment. Our findings point to a mechanism of immune evasion of cells with a mesenchymal phenotype and suggest a strategy to overcome immune evasion through induction of the immunoproteasome to increase the cellular repertoire of HLA class I-bound peptides.

The oncogenic RNA-binding protein SRSF1 regulates LIG1 in non-small cell lung cancer.

In recent years, the relevance of RNA metabolism has been increasingly recognized in a variety of diseases. Modifications in the levels of RNA-binding proteins elicit changes in the expression of cancer-related genes. Here we evaluate whether SRSF1 regulates the expression of DNA repair genes, and whether this regulation has a relevant role in lung carcinogenesis. An in silico analysis was performed to evaluate the association between the expression of SRSF1 and DNA repair genes. In vitro functional analyses were conducted in SRSF1 or DNA ligase 1 (LIG1)-downregulated non-small cell lung cancer (NSCLC) cell lines. In addition, the prognostic value of LIG1 was evaluated in NSCLC patients by immunohistochemistry. We found a significant correlation between the DNA repair gene LIG1 and SRSF1 in NSCLC cell lines. Moreover, SRSF1 binds to LIG1 mRNA and regulates its expression by increasing its mRNA stability and enhancing its translation in an mTOR-dependent manner. Furthermore, siRNA-mediated LIG1 inhibition reduced proliferation and increased apoptosis of NSCLC cells. Finally, the expression of LIG1 was an independent prognostic factor for NSCLC, as confirmed in a series of 210 patients. These results show that LIG1 is regulated by the oncoprotein SRSF1 and plays a relevant role in lung cancer cell proliferation and progression. LIG1 is associated with poor prognosis in non-small lung cancer patients.

Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer.

Intra-tumor heterogeneity may be present at all molecular levels. Genomic intra-tumor heterogeneity at the exome level has been reported in many cancer types, but comprehensive gene expression intra-tumor heterogeneity has not been well studied. Here, we delineated the gene expression intra-tumor heterogeneity by exploring gene expression profiles of 35 tumor regions from 10 non-small cell lung cancer tumors (three or four regions/tumor), including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and pleomorphic carcinoma of the lung. Using Affymetrix Gene 1.0 ST arrays, we generated the gene expression data for every sample. Inter-tumor heterogeneity was generally higher than intra-tumor heterogeneity, but some tumors showed a substantial level of intra-tumor heterogeneity. The analysis of various clinically relevant gene expression signatures including molecular subtype, epithelial-to-mesenchymal transition, and anti-PD-1 resistance signatures also revealed heterogeneity between different regions of the same tumor. The gene expression intra-tumor heterogeneity we observed was associated with heterogeneous tumor microenvironments represented by stromal and immune cells infiltrated. Our data suggest that RNA-based prognostic or predictive molecular tests should be carefully conducted in consideration of the gene expression intra-tumor heterogeneity.

Long-circulating siRNA nanoparticles for validating Prohibitin1-targeted non-small cell lung cancer treatment.

RNA interference (RNAi) represents a promising strategy for identification and validation of putative therapeutic targets and for treatment of a myriad of important human diseases including cancer. However, the effective systemic in vivo delivery of small interfering RNA (siRNA) to tumors remains a formidable challenge. Using a robust self-assembly strategy, we develop a unique nanoparticle (NP) platform composed of a solid polymer/cationic lipid hybrid core and a lipid-poly(ethylene glycol) (lipid-PEG) shell for systemic siRNA delivery. The new generation lipid-polymer hybrid NPs are small and uniform, and can efficiently encapsulate siRNA and control its sustained release. They exhibit long blood circulation (t1/2 ∼ 8 h), high tumor accumulation, effective gene silencing, and negligible in vivo side effects. With this RNAi NP, we delineate and validate the therapeutic role of Prohibitin1 (PHB1), a target protein that has not been systemically evaluated in vivo due to the lack of specific and effective inhibitors, in treating non-small cell lung cancer (NSCLC) as evidenced by the drastic inhibition of tumor growth upon PHB1 silencing. Human tissue microarray analysis also reveals that high PHB1 tumor expression is associated with poorer overall survival in patients with NSCLC, further suggesting PHB1 as a therapeutic target. We expect this long-circulating RNAi NP platform to be of high interest for validating potential cancer targets in vivo and for the development of new cancer therapies.

Interleukin-11 Receptor Is a Candidate Target for Ligand-Directed Therapy in Lung Cancer: Analysis of Clinical Samples and BMTP-11 Preclinical Activity.

We previously isolated an IL-11-mimic motif (CGRRAGGSC) that binds to IL-11 receptor (IL-11R) in vitro and accumulates in IL-11R-expressing tumors in vivo. This synthetic peptide ligand was used as a tumor-targeting moiety in the rational design of BMTP-11, which is a drug candidate in clinical trials. Here, we investigated the specificity and accessibility of IL-11R as a target and the efficacy of BMTP-11 as a ligand-targeted drug in lung cancer. We observed high IL-11R expression levels in a large cohort of patients (n = 368). In matching surgical specimens (i.e., paired tumors and nonmalignant tissues), the cytoplasmic levels of IL-11R in tumor areas were significantly higher than in nonmalignant tissues (n = 36; P = 0.003). Notably, marked overexpression of IL-11R was observed in both tumor epithelial and vascular endothelial cell membranes (n = 301; P < 0.0001). BMTP-11 induced in vitro cell death in a representative panel of human lung cancer cell lines. BMTP-11 treatment attenuated the growth of subcutaneous xenografts and reduced the number of pulmonary tumors after tail vein injection of human lung cancer cells in mice. Our findings validate BMTP-11 as a pharmacologic candidate drug in preclinical models of lung cancer and patient-derived tumors. Moreover, the high expression level in patients with non-small cell lung cancer is a promising feature for potential translational applications.

ASCL1 is a lineage oncogene providing therapeutic targets for high-grade neuroendocrine lung cancers.

Aggressive neuroendocrine lung cancers, including small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), represent an understudied tumor subset that accounts for approximately 40,000 new lung cancer cases per year in the United States. No targeted therapy exists for these tumors. We determined that achaete-scute homolog 1 (ASCL1), a transcription factor required for proper development of pulmonary neuroendocrine cells, is essential for the survival of a majority of lung cancers (both SCLC and NSCLC) with neuroendocrine features. By combining whole-genome microarray expression analysis performed on lung cancer cell lines with ChIP-Seq data designed to identify conserved transcriptional targets of ASCL1, we discovered an ASCL1 target 72-gene expression signature that (i) identifies neuroendocrine differentiation in NSCLC cell lines, (ii) is predictive of poor prognosis in resected NSCLC specimens from three datasets, and (iii) represents novel "druggable" targets. Among these druggable targets is B-cell CLL/lymphoma 2, which when pharmacologically inhibited stops ASCL1-dependent tumor growth in vitro and in vivo and represents a proof-of-principle ASCL1 downstream target gene. Analysis of downstream targets of ASCL1 represents an important advance in the development of targeted therapy for the neuroendocrine class of lung cancers, providing a significant step forward in the understanding and therapeutic targeting of the molecular vulnerabilities of neuroendocrine lung cancer.

Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations.

BACKGROUND: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. RESULTS: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC). CONCLUSION: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.

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.