The distinct metabolic phenotype of lung squamous cell carcinoma defines selective vulnerability to glycolytic inhibition.
Goodwin Justin,Neugent Michael L,Lee Shin Yup,Choe Joshua H,Choi Hyunsung,Jenkins Dana M R,Ruthenborg Robin J,Robinson Maddox W,Jeong Ji Yun,Wake Masaki,Abe Hajime,Takeda Norihiko,Endo Hiroko,Inoue Masahiro,Xuan Zhenyu,Yoo Hyuntae,Chen Min,Ahn Jung-Mo,Minna John D,Helke Kristi L,Singh Pankaj K,Shackelford David B,Kim Jung-Whan
Adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) are the two predominant subtypes of non-small cell lung cancer (NSCLC) and are distinct in their histological, molecular and clinical presentation. However, metabolic signatures specific to individual NSCLC subtypes remain unknown. Here, we perform an integrative analysis of human NSCLC tumour samples, patient-derived xenografts, murine model of NSCLC, NSCLC cell lines and The Cancer Genome Atlas (TCGA) and reveal a markedly elevated expression of the GLUT1 glucose transporter in lung SqCC, which augments glucose uptake and glycolytic flux. We show that a critical reliance on glycolysis renders lung SqCC vulnerable to glycolytic inhibition, while lung ADC exhibits significant glucose independence. Clinically, elevated GLUT1-mediated glycolysis in lung SqCC strongly correlates with high F-FDG uptake and poor prognosis. This previously undescribed metabolic heterogeneity of NSCLC subtypes implicates significant potential for the development of diagnostic, prognostic and targeted therapeutic strategies for lung SqCC, a cancer for which existing therapeutic options are clinically insufficient.
The GSK3 Signaling Axis Regulates Adaptive Glutamine Metabolism in Lung Squamous Cell Carcinoma.
Momcilovic Milica,Bailey Sean T,Lee Jason T,Fishbein Michael C,Braas Daniel,Go James,Graeber Thomas G,Parlati Francesco,Demo Susan,Li Rui,Walser Tonya C,Gricowski Michael,Shuman Robert,Ibarra Julio,Fridman Deborah,Phelps Michael E,Badran Karam,St John Maie,Bernthal Nicholas M,Federman Noah,Yanagawa Jane,Dubinett Steven M,Sadeghi Saman,Christofk Heather R,Shackelford David B
Altered metabolism is a hallmark of cancer growth, forming the conceptual basis for development of metabolic therapies as cancer treatments. We performed in vivo metabolic profiling and molecular analysis of lung squamous cell carcinoma (SCC) to identify metabolic nodes for therapeutic targeting. Lung SCCs adapt to chronic mTOR inhibition and suppression of glycolysis through the GSK3α/β signaling pathway, which upregulates glutaminolysis. Phospho-GSK3α/β protein levels are predictive of response to single-therapy mTOR inhibition while combinatorial treatment with the glutaminase inhibitor CB-839 effectively overcomes therapy resistance. In addition, we identified a conserved metabolic signature in a broad spectrum of hypermetabolic human tumors that may be predictive of patient outcome and response to combined metabolic therapies targeting mTOR and glutaminase.
circTP63 functions as a ceRNA to promote lung squamous cell carcinoma progression by upregulating FOXM1.
Cheng Zhuoan,Yu Chengtao,Cui Shaohua,Wang Hui,Jin Haojie,Wang Cun,Li Botai,Qin Meilin,Yang Chen,He Jia,Zuo Qiaozhu,Wang Siying,Liu Jun,Ye Weidong,Lv Yuanyuan,Zhao Fangyu,Yao Ming,Jiang Liyan,Qin Wenxin
Circular RNAs (circRNAs) are identified as vital regulators in a variety of cancers. However, the role of circRNA in lung squamous cell carcinoma (LUSC) remains largely unknown. Herein, we explore the expression profiles of circRNA and mRNA in 5 paired samples of LUSC. By analyzing the co-expression network of differentially expressed circRNAs and dysregulated mRNAs, we identify that a cell cycle-related circRNA, circTP63, is upregulated in LUSC tissues and its upregulation is correlated with larger tumor size and higher TNM stage in LUSC patients. Elevated circTP63 promotes cell proliferation both in vitro and in vivo. Mechanistically, circTP63 shares miRNA response elements with FOXM1. circTP63 competitively binds to miR-873-3p and prevents miR-873-3p to decrease the level of FOXM1, which upregulates CENPA and CENPB, and finally facilitates cell cycle progression.
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.
Coudray Nicolas,Ocampo Paolo Santiago,Sakellaropoulos Theodore,Narula Navneet,Snuderl Matija,Fenyö David,Moreira Andre L,Razavian Narges,Tsirigos Aristotelis
Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .
Mutation profiles in early-stage lung squamous cell carcinoma with clinical follow-up and correlation with markers of immune function.
Choi M,Kadara H,Zhang J,Parra E R,Rodriguez-Canales J,Gaffney S G,Zhao Z,Behrens C,Fujimoto J,Chow C,Kim K,Kalhor N,Moran C,Rimm D,Swisher S,Gibbons D L,Heymach J,Kaftan E,Townsend J P,Lynch T J,Schlessinger J,Lee J,Lifton R P,Herbst R S,Wistuba I I
Annals of oncology : official journal of the European Society for Medical Oncology
Background:Lung squamous cell carcinoma (LUSC) accounts for 20–30% of non-small cell lung cancers (NSCLCs). There are limited treatment strategies for LUSC in part due to our inadequate understanding of the molecular underpinnings of the disease. We performed whole-exome sequencing (WES) and comprehensive immune profiling of a unique set of clinically annotated early-stage LUSCs to increase our understanding of the pathobiology of this malignancy. Methods:Matched pairs of surgically resected stage I-III LUSCs and normal lung tissues (n = 108) were analyzed by WES. Immunohistochemistry and image analysis-based profiling of 10 immune markers were done on a subset of LUSCs (n = 91). Associations among mutations, immune markers and clinicopathological variables were statistically examined using analysis of variance and Fisher’s exact test. Cox proportional hazards regression models were used for statistical analysis of clinical outcome. Results:This early-stage LUSC cohort displayed an average of 209 exonic mutations per tumor. Fourteen genes exhibited significant enrichment for somatic mutation: TP53, MLL2, PIK3CA, NFE2L2, CDH8, KEAP1, PTEN, ADCY8, PTPRT, CALCR, GRM8, FBXW7, RB1 and CDKN2A. Among mutated genes associated with poor recurrence-free survival, MLL2 mutations predicted poor prognosis in both TP53 mutant and wild-type LUSCs. We also found that in treated patients, FBXW7 and KEAP1 mutations were associated with poor response to adjuvant therapy, particularly in TP53-mutant tumors. Analysis of mutations with immune markers revealed that ADCY8 and PIK3CA mutations were associated with markedly decreased tumoral PD-L1 expression, LUSCs with PIK3CA mutations exhibited elevated CD45ro levels and CDKN2A-mutant tumors displayed an up-regulated immune response. Conclusion(s):Our findings pinpoint mutated genes that may impact clinical outcome as well as personalized strategies for targeted immunotherapies in early-stage LUSC.
Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.
Jurmeister Philipp,Bockmayr Michael,Seegerer Philipp,Bockmayr Teresa,Treue Denise,Montavon Grégoire,Vollbrecht Claudia,Arnold Alexander,Teichmann Daniel,Bressem Keno,Schüller Ulrich,von Laffert Maximilian,Müller Klaus-Robert,Capper David,Klauschen Frederick
Science translational medicine
Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.
BCL11A interacts with SOX2 to control the expression of epigenetic regulators in lung squamous carcinoma.
Lazarus Kyren A,Hadi Fazal,Zambon Elisabetta,Bach Karsten,Santolla Maria-Francesca,Watson Julie K,Correia Lucia L,Das Madhumita,Ugur Rosemary,Pensa Sara,Becker Lukas,Campos Lia S,Ladds Graham,Liu Pentao,Evan Gerard I,McCaughan Frank M,Le Quesne John,Lee Joo-Hyeon,Calado Dinis,Khaled Walid T
Patients diagnosed with lung squamous cell carcinoma (LUSC) have limited targeted therapies. We report here the identification and characterisation of BCL11A, as a LUSC oncogene. Analysis of cancer genomics datasets revealed BCL11A to be upregulated in LUSC but not in lung adenocarcinoma (LUAD). Experimentally we demonstrate that non-physiological levels of BCL11A in vitro and in vivo promote squamous-like phenotypes, while its knockdown abolishes xenograft tumour formation. At the molecular level we found that BCL11A is transcriptionally regulated by SOX2 and is required for its oncogenic functions. Furthermore, we show that BCL11A and SOX2 regulate the expression of several transcription factors, including SETD8. We demonstrate that shRNA-mediated or pharmacological inhibition of SETD8 selectively inhibits LUSC growth. Collectively, our study indicates that BCL11A is integral to LUSC pathology and highlights the disruption of the BCL11A-SOX2 transcriptional programme as a novel candidate for drug development.
Factor XIIIA-expressing inflammatory monocytes promote lung squamous cancer through fibrin cross-linking.
Porrello Alessandro,Leslie Patrick L,Harrison Emily B,Gorentla Balachandra K,Kattula Sravya,Ghosh Subrata K,Azam Salma H,Holtzhausen Alisha,Chao Yvonne L,Hayward Michele C,Waugh Trent A,Bae Sanggyu,Godfrey Virginia,Randell Scott H,Oderup Cecilia,Makowski Liza,Weiss Jared,Wilkerson Matthew D,Hayes D Neil,Earp H Shelton,Baldwin Albert S,Wolberg Alisa S,Pecot Chad V
Lung cancer is the leading cause of cancer-related deaths worldwide, and lung squamous carcinomas (LUSC) represent about 30% of cases. Molecular aberrations in lung adenocarcinomas have allowed for effective targeted treatments, but corresponding therapeutic advances in LUSC have not materialized. However, immune checkpoint inhibitors in sub-populations of LUSC patients have led to exciting responses. Using computational analyses of The Cancer Genome Atlas, we identified a subset of LUSC tumors characterized by dense infiltration of inflammatory monocytes (IMs) and poor survival. With novel, immunocompetent metastasis models, we demonstrated that tumor cell derived CCL2-mediated recruitment of IMs is necessary and sufficient for LUSC metastasis. Pharmacologic inhibition of IM recruitment had substantial anti-metastatic effects. Notably, we show that IMs highly express Factor XIIIA, which promotes fibrin cross-linking to create a scaffold for LUSC cell invasion and metastases. Consistently, human LUSC samples containing extensive cross-linked fibrin in the microenvironment correlated with poor survival.