Lung Adenocarcinoma Cohort Research Articles

Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma.

BackgroundPrognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer.MethodsThe RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell.ResultsIn this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Identification and Validation of the Immune Subtypes of Lung Adenocarcinoma: Implications for Immunotherapy.

Lung adenocarcinoma (LUAD) is a devastating disease with poor patient survival. Cancer immunotherapy has revolutionized the treatment of LUAD, but only a limited number of patients effectively respond to this treatment. Thus, the work to elucidate the LUAD immune heterogeneity could be crucial in developing new immunotherapeutic strategies with better efficacy. Non-negative matrix factorization-based deconvolution was performed to identify robust clusters of 489 LUAD patients in The Cancer Genome Atlas (TCGA) and verify their reproducibility and stability in an independent LUAD cohort of 439 patients from the Gene Expression Omnibus (GEO). We used the graph learning-based dimensionality reduction to visualize the distribution of individual patients. In this study, four reproducible immune subtypes, Clusters 1–4 (C1–C4) associated with distinct gene module signatures, clinicopathological features, molecular and cellular characteristics were identified and validated. The immune-cold subtype, C3, was associated with the Dead event, the most advanced T stage, N stage, TNM stage and the worst prognosis for LUAD patients. Moreover, C3 exhibited the lowest infiltrating levels of B cells, T cell receptor (TCR) repertoire diversity and the highest level of neoantigen and mutation rate among C1–C4. On the other hand, the immune-hot subtype (C4) exhibited the highest infiltration of six types of infiltrating immune cells as well as the greatest leukocyte fraction, TCR and B cell receptor (BCR) repertoire diversity. C1 and C2 subtypes showed diverse clinicopathological and immunological features. Finally, our investigations discovered a complex immune landscape with a scattered immune subtype profile. This work may help inform immunotherapeutic decision-making and design advanced immunotherapy strategies for the treatment of lung cancer.

Mutational Portrait of Lung Adenocarcinoma in Brazilian Patients: Past, Present, and Future of Molecular Profiling in the Clinic.

Objectives: Approximately 60% of lung adenocarcinomas (LAs) carry mutations that can guide treatment with tyrosine-kinase inhibitors (TKI) and other targeted therapies. Data on activating mutations in EGFR and other tyrosine-kinase receptor (TKR) genes in highly admixed populations, such as that of Brazil, are scarce. In this study, we comprehensively analyzed the actionable alteration profile of LA in Brazilian patients.Materials and Methods: EGFR driver mutation data were collected from a large Brazilian LA cohort covering an 8-year period of molecular testing in a single institution. Tests were performed using three distinct methods, and demographic and histopathological data were analyzed. For a subset of patients, driver mutations in KRAS, NRAS, and BRAF and gene fusions involving TKR genes (before TKI treatment) and EGFR T790M (after TKI treatment) were assessed.Results: EGFR mutations were detected in 25% of 1,316 LAs evaluated, with exon 19 deletions and exon 21 L858R TKI sensitizing mutations representing 72.5% of all mutations. Mutation rates were higher in women and non-smokers (p < 0.001). Next-generation sequencing was very sensitive, with a lower rate of inconclusive results compared with Sanger sequencing and pyrosequencing. EGFR/RAS/BRAF hotspot gene panels were applied in 495 LA cases and detected oncogenic mutations in 51.3% of samples, most frequently in EGFR (22.4%) and KRAS (26.9%). In subgroups of 36 and 35 patients, gene fusions were detected in 11.1% of tumors and EGFR T790M resistance mutations were detected in 59% of plasma samples from patients previously treated with TKI, respectively.Conclusion: This report provides the first comprehensive actionable alteration portrait of LA in Brazil. The high rate of actionable alterations in EGFR and other driver genes in LA reinforces the need to incorporate TKI guided by molecular diagnostics into clinical routines for patients in both public and private healthcare systems.

High tumor mutation burden predicts favorable outcome among patients with aggressive histological subtypes of lung adenocarcinoma: A population-based single-institution study

ObjectivesTumor mutation burden (TMB) is an emerging predictive cancer biomarker. Few studies have addressed the prognostic role of TMB in non-small cell lung carcinoma, with conflicting results. Moreover, the association of TMB with different histological subtypes of lung adenocarcinoma has hitherto not been systematically evaluated. Here we studied the prognostic value of TMB and its distribution in different histological subtypes of lung adenocarcinomas in a retrospective cohort using the most recent updated classification guidelines. Materials and methods176 surgically resected stage I–IV lung adenocarcinomas were histologically reclassified according to WHO 2015 guidelines. A modified classification subdividing the acinar subtype into classic acinar, complex glandular and cribriform subtypes was further applied and potentially prognostic histopathological characteristics such as tumor-infiltrating lymphocytes were evaluated. 148 patients with stage I–III tumors and complete follow-up data were included in the survival analyses. TMB was determined by a commercial next generation sequencing panel from 131 tumors, out of which 105 had survival data available. ResultsPredominant micropapillary, solid and complex glandular as well as nonpredominant cribriform histological subtypes were associated with significantly shorter survival. High TMB concentrated in micropapillary, solid and acinar predominant subtypes. Interestingly, TMB ≥ 14 mutations/MB conferred a stage- and histology-independent survival benefit compared to TMB < 14 in multivariable analysis for overall (HR 0.284, 95% CI 0.14–0.59, P=0.001) and disease-specific survival (HR 0.213, 95% CI 0.08–0.56, P=0.002). ConclusionTMB was an independent biomarker of favorable prognosis in our cohort of lung adenocarcinoma despite being associated with predominant histological subtypes considered aggressive.

Identification and validation of tumor microenvironment-related genes of prognostic value in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer. Despite significant progress in its diagnosis and treatment, the mortality and morbidity rate of LUAD remains high worldwide. The aim of the present study was to perform a systematic investigation of the tumor microenvironment (TME) and identify TME-related genes of prognostic value in patients with LUAD. Firstly, the immune scores and stromal scores of patients with LUAD from The Cancer Genome Atlas were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data algorithm, and a total of 281 prognostic TME-related genes were identified. Subsequently, functional analysis and protein-protein interaction network analysis revealed that these genes were mainly related to immune response, inflammatory response and chemotaxis. Finally, two independent LUAD cohorts from the Gene Expression Omnibus database were used to validate these genes, and 4 genes (GTPase IMAP family member 1, T-cell surface glycoprotein CD1b, integrin alpha-L and leukocyte surface antigen CD53) were identified, and downregulation of these genes was indicated to be associated with poor overall survival rate in patients with LUAD. In conclusion, a comprehensive analysis of TME was performed and 4 prognostic TME-related genes in patients with LUAD were identified.

Profiles of Immune Infiltration and Prognostic Immunoscore in Lung Adenocarcinoma.

Lung tissue is abundant with immune cells that form a powerful first defense against exotic particles and microbes. The malignant phenotype of lung adenocarcinoma (LUAD) is defined not only by intrinsic tumor cells but also by tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment. Understanding more about the immune microenvironment of LUAD could function in sorting out patients more likely with high risk and benefit from immunotherapy. Twenty-two types of TIICs were estimated based on large public LUAD cohorts from the TCGA and GEO datasets using the CIBERSORT algorithm. Then principal component analysis (PCA), meta-analysis, and single-sample gene set enrichment analysis (ssGSEA) were used to measure and evaluate the specific immune responses and relative mechanisms. Moreover, an immunoscore model based on the percent of immune cells was constructed via the univariate and multivariate Cox regression models, which provided an in-depth overview of the LUAD immune microenvironment and shed light on the immune regulatory mechanism. The differential expression genes (DEGs) were acquired based on the immunoscore model, and prognostic immune-related genes were further identified. GSEA and the protein–protein interaction network (PPI) further revealed that these genes were mostly enriched in many immune-related biological processes. It is hoped that this immune landscape could provide a more accurate understanding for LUAD development and tumor immune therapy.

Development and Validation of a 12-Gene Immune Relevant Prognostic Signature for Lung Adenocarcinoma Through Machine Learning Strategies.

Background: Although immunotherapy with checkpoint inhibitors is changing the face of lung adenocarcinoma (LUAD) treatments, only limited patients could benefit from it. Therefore, we aimed to develop an immune-relevant-gene-based signature to predict LUAD patients' prognosis and to characterize their tumor microenvironment thus guiding therapeutic strategy.Methods and Materials: Gene expression data of LUAD patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were systematically analyzed. We performed Cox regression and random survival forest algorithm to identify immune-relevant genes with potential prognostic value. A risk score formula was then established by integrating these selected genes and patients were classified into high- and low-risk score group. Differentially expressed genes, infiltration level of immune cells, and several immune-associated molecules were further compared across the two groups.Results: Nine hundred and fifty-four LUAD patients were enrolled in this study. After implementing the 2-steps machine learning screening methods, 12 immune-relevant genes were finally selected into the risk-score formula and the patients in high-risk group had significantly worse overall survival (HR = 10.6, 95%CI = 3.21–34.95, P < 0.001). We also found the distinct immune infiltration patterns in the two groups that several immune cells like cytotoxic cells and immune checkpoint molecules were significantly enriched and upregulated in patients from the high-risk group. These findings were further validated in two independent LUAD cohorts.Conclusion: Our risk score formula could serve as a powerful and accurate tool for predicting survival of LUAD patients and may facilitate clinicians to choose the optimal therapeutic regimen more precisely.

EGFR-mutated lung adenocarcinomas from patients who progressed on EGFR-inhibitors show high engraftment rates in xenograft models

ObjectivePatient-derived xenografts (PDX) are useful preclinical models to study cancer biology and mechanisms of drug response/resistance, particularly in molecularly targetable tumors. However, PDX engraftment may not be stochastic. We investigated clinical, histological and molecular features associated with PDX engraftment in a large cohort of EGFR-mutated lung adenocarcinoma (LUAD). Material And MethodsSamples were collected by different methods from patients at various disease stages and phases of treatment. PDX engraftment was defined as an ability to passage tumors twice in NOD-SCID mice. Uni- and multivariate logistic regression evaluated factors associated with engraftment. ResultsAmong 138 EGFR-mutated LUAD implanted into NOD-SCID mice, the overall engraftment rate was only 10% (14/138). However, engraftment was significantly higher in specimens from surgical resections or core-needle biopsies collected from metastatic sites (5/5; 100%) or from patients who had progressed on EGFR-inhibitors (7/10; 70%). Engrafted tumors usually showed poor histological differentiation, a solid morphologic pattern, and presence of either EGFR T790 M and/or TP53 mutations. ConclusionsPopulation level analyses of mutant EGFR-PDX show that these models might not fully recapitulate the inter-patient heterogeneity of EGFR-LUAD. However, mutant EGFR-PDXs may be useful to address key clinical questions, notably development of resistance to EGFR-inhibitors and disease progression to distant metastases.

Immunoscore Predicts Survival in Early-Stage Lung Adenocarcinoma Patients.

Background: The lung cancer staging system is insufficient for a comprehensive evaluation of patient prognosis. We constructed a novel immunoscore model to predict patients with high risk and poor survival.Method: Immunoscore was developed based on z-score transformed enrichment score of 11 immune-related gene sets of 109 immune risk genes. The immunoscore model was trained in lung adenocarcinoma cohort from The Cancer Genome Atlas (TCGA-LUAD) (n = 400), and validated in other two independent cohorts from Gene Expression Omnibus (GEO), GSE31210 (n = 219) and GSE68465 (n = 356). Meta-set (n = 975) was formed by combining all training and testing sets.Result: High immunoscore conferred worse prognosis in all sets. It was an independent prognostic factors in multivariate Cox analysis in training, testing and meta-set [hazard ratio (HR) = 2.96 (2.24–3.9), P < 0.001 in training set; HR = 1.99 (1.21–3.26), P = 0.006 in testing set 1; HR = 1.48 (1.69–2.39), P = 0.005 in testing set 2; HR = 2.01 (1.69–2.39), P < 0.001 in meta-set]. Immunoscore-clinical prognostic signature (ICPS) was developed by integrating immunoscore and clinical characteristic, and had higher C-index than immunoscore or stage alone in all sets [0.72 (ICPS) vs. 0.7 (immunoscore) or 0.59 (stage) in training set; 0.75 vs. 0.72 or 0.7 in testing set 1; 0.65 vs. 0.61 or 0.62 in testing set 2; 0.7 vs. 0.66 or 0.64 in meta-set]. Genome analysis revealed that immunoscore was positively correlated with tumor mutation burden (R = 0.22, P < 0.001). Besides, high immunoscore was correlated with high proportion of carcinoma-associated fibroblasts (R = 0.32, P < 0.001) in tumor microenvironment but fewer CD8+ cells infiltration (R = −0.28, P < 0.001).Conclusion: The immunoscore and ICPS are potential biomarkers for evaluating patient survival. Further investigations are required to validate and improve their prediction accuracy.

Ground-glass opacity-featured lung adenocarcinoma has no response to chemotherapy.

We aimed to investigate the treatment effect of chemotherapy on ground-glass opacity (GGO)-featured lung adenocarcinoma radiologically and pathologically. This retrospective study included patients who met the following criteria: (1) presence of lung GGO lesions before chemotherapy for other concurrent malignancies; (2) underwent surgical resection of GGO-featured primary lung adenocarcinoma. The last computed tomography images before chemotherapy (CT1) and the last images before GGO resection (CT2) were reviewed to assess radiologic response. Specimens of the resected tumors were reviewed to evaluate the histopathologic response. Immunohistochemical staining of ki-67, caspase-3 and β-gal was performed and compared between these tumors and a propensity score-matched (1:1) cohort of GGO-featured lung adenocarcinoma without prior chemotherapy. Forty-four patients with 55 GGO lesions were included. There were 20 mixed GGOs and 22 invasive adenocarcinomas. These patients all received at least three cycles of chemotherapy for other concurrent malignancies in breast, lung, cervix, ovary or rectum. Thirty-four (77%) patients received chemotherapy regimens that contained platinum, pemetrexed, paclitaxel, docetaxel or gemcitabine. The median interval between CT1 and CT2 was 10months. Radiologically, all the GGO lesions either remained unchanged or enlarged. There was no chemotherapy-induced histopathologic response (necrosis, fibrosis or inflammation) in any of these tumors. The protein expression of ki-67, caspase-3 and β-gal was comparable between GGO-featured lung adenocarcinoma with or without prior chemotherapy. GGO-featured lung adenocarcinoma has no response to chemotherapy. For these patients, chemotherapy should not be a treatment option.

Prevalence of the EGFR T790M and other resistance mutations in the Australian population and histopathological correlation in a small subset of cases

We sought to review the prevalence of EGFR T790M and other EGFR mutations associated with either proven or probable tyrosine kinase inhibitor (TKI) resistance in the Australasian lung cancer population and to perform histopathological correlation in a subset of cases. Retrospective statistical analysis was performed on a set of targeted lung cancer gene mutation tests (FIND IT gene panel) performed at Sonic Healthcare during 2018 and early 2019. A total of 1833 lung adenocarcinoma tumour samples underwent somatic mutation testing. EGFR mutations were found in 28% (n=514) of patients, in whom 9.3% (n=48) T790M mutations were present (always combined with other EGFR mutations) and 4.8% (n=25) exon 20 insertions were found. We also compared the prevalence of EGFR mutations identified in our population with that of the four largest publicly available lung cancer cohorts (total n=576 samples). Finally, a subset of 38 samples of primary/and or metastatic lung adenocarcinomas from 23 patients, including five with serial biopsies, underwent detailed morphological analysis. No reproducible morphological correlates were found to be associated with T790M, exon 20 resistance mutations or rarer co-occurring EGFR mutations. Although this may be subject to referral bias towards patients with resistant disease, the incidence of EGFR and T790M mutations is higher in this series from an Australasian population than in other similar publicly available lung adenocarcinoma cohorts. We conclude that histopathological features cannot be used to predict the acquisition of EGFR resistance.

Long non-coding RNA LCAL62 / LINC00261 is associated with lung adenocarcinoma prognosis

BackgroundMore than half of non-small cell lung cancer (NSCLC) patients present with metastatic disease at initial diagnosis with an estimated five-year survival rate of ~5%. Despite advances in understanding primary lung cancer oncogenesis metastatic disease remains poorly characterized. Recent studies demonstrate important roles of long non-coding RNAs (lncRNAs) in tumor physiology and as prognostic markers. Therefore, we present the first transcriptome analysis to identify lncRNAs altered in metastatic lung adenocarcinoma leading to the discovery and characterization of the lncRNA LCAL62 as a prognostic biomarker. Patients and methodsRNA-Seq, microarray, nanoString expression, and clinical data from 1,116 LUAD patients across six independent cohorts and 83 LUAD cell lines were used to discover and evaluate the survival association of metastasis associated lncRNAs. Coexpression and gene set enrichment analyses were used to establish gene regulatory networks and implicate metastasis associated lncRNAs in specific biological processes. ResultsOur integrative analysis discovered LCAL62 as the most down-regulated lncRNA in metastasis. Further low LCAL62 expression promoted aggressive phenotypes and regulated genes associated with metastasis (such as metastasis repressor FOXA2). Low LCAL62 expression corresponded to poor overall patient survival across five independent lung adenocarcinoma cohorts (n = 881) including our own nanoString validation cohort. ConclusionWe discovered that LCAL62 was down-regulated in lung cancer progression to promote invasive phenotypes, and lower expression was significantly associated with poor patient outcome and aggressive lung adenocarcinoma.

Elevated TOP2A and UBE2C expressions correlate with poor prognosis in patients with surgically resected lung adenocarcinoma: a study based on immunohistochemical analysis and bioinformatics.

Lung cancer has the highest morbidity and mortality among all cancer types. Reliable prognostic biomarkers are needed to identify high-risk patients apart from TNM system for precision medicine. The present study is designed to identify robust prognostic biomarkers in lung adenocarcinoma (LUAD) based on integration of multiple GEO datasets, The Cancer Genome Atlas (TCGA) database and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Four LUAD GEO datasets (GSE10072, GSE2514, GSE43458, and GSE32863) and TCGA database were implemented to analyze the differently expressed genes (DEGs). Gene ontology, KEGG pathway, and protein-protein interaction network (PPI) were conducted based on the above DEGs. Hub genes were selected based on connectivity degree in the PPI network. Expression analysis and Kaplan-Meier survival analysis were conducted in CPTAC lung adenocarcinomas cohort. Kaplan-Meier survival analysis and Cox proportional hazards regression were performed on these hub genes using TCGA and our own cohort. A total of 430 shared genes in all five datasets were identified as DEGs. Based on their PPI network, nine hub genes were selected and all of them were significantly associated with overall survival using GEPIA analysis. Two hub genes, TOP2A and UBE2C, were further combined and showed poorer prognosis in both TCGA dataset and our validated cohort. Analysis in CPTAC revealed that TOP2A and UBE2C were significantly highly expressed in tumor sample. Multivariable analysis suggested TOP2A and UBE2C as independent prognostic factors in LUAD. Using data mining approach, we identified TOP2A and UBE2C as two robust prognostic factors in LUAD. We also demonstrated the TOP2A/UBE2C co-expression status in LUAD, and TOP2A/UBE2C co-expression correlated with poorer prognosis. More in-depth research is needed for transforming this result into clinical setting.

Elevated SLC2A1 Expression Correlates with Poor Prognosis in Patients with Surgically Resected Lung Adenocarcinoma: A Study Based on Immunohistochemical Analysis and Bioinformatics.

Lung adenocarcinoma (LUAD) accounts for an increasing proportion of non-small-cell lung cancer and an increasing number of cancer-related deaths worldwide. However, few biomarkers are available for prognosis and patient stratification. In all eight datasets from the Oncomine and The Cancer Genome Atlas (TCGA) LUAD cohorts, solute carrier family 2 member 1 (SLC2A1) was significantly more highly expressed in LUAD tissue than in normal lung tissue. High SLC2A1 expression was also significantly (p < 0.05) associated with a poor prognosis in stage I, II, and III subgroups using the Kaplan-Meier plotter. In the National Cancer Center of China (NCC) cohort, SLC2A1 expression correlated significantly (p < 0.05) with several parameters, including sex, smoking history, tumor size, tumor differentiation, T stage, N stage, and pathologic TNM stage. Moreover, multivariate Cox regression indicated SLC2A1 to be an independent prognostic factor (p < 0.05) in both TCGA and NCC cohorts. Eleven hallmark pathways were significantly enriched (p < 0.01, false discovery rate <0.25) in the high-SLC2A1 expression group. SLC2A1 is a promising biomarker that can be used to predict the prognosis of LUAD.

Analysis of immune-related signatures of lung adenocarcinoma identified two distinct subtypes: implications for immune checkpoint blockade therapy.

Immune checkpoint blockade (ICB) therapies have revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD). However, our understanding of the immune subtyping of LUAD and its association with clinical response of immune checkpoint inhibitor remains incomplete. Here we performed molecular subtyping and association analysis of LUAD from the Cancer Genome Atlas (TCGA) and validated findings from TCGA cohort in 9 independent validation cohorts. We conducted consensus molecular subtyping with nonnegative matrix factorization (NMF). Potential response of ICB therapy was estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. We identified 2 distinct subtypes of LUAD in TCGA cohort that were characterized by significantly different survival outcomes (i.e., high- and low-risk subtypes). The high-risk subtype was featured by lower TIDE score, upregulation of programmed death-ligand 1 (PD-L1) expression, and higher tumor mutation burden (TMB). The high-risk subtype also harbored significantly elevated cell cycle modulators CDK4/CDK6 and TP53 mutation. These observations were validated in 9 independent LUAD cohorts. Our findings suggest that immune checkpoint blockade therapy may be efficacious for high-risk subtype of LUAD patients.

The Lung Adenocarcinoma Microenvironment Mining and Its Prognostic Merit.

Background:As a common pathological type of lung cancer, lung adenocarcinoma (LUAD) is mainly treated by surgery, chemotherapy, targeted therapy and radiotherapy. Although a relatively mature treatment system has been established, there are few studies on the microenvironment of LUAD.Material and Methods:The immune and stromal scores of patients from the LUAD cohort in the TCGA database were obtained by using ESTIMATE. The relationship of immune and stromal scores with the clinicopathological characteristics and overall survival of LUAD patients was assessed by R. GO, KEGG and Cox regression analyses were employed to analyze intersecting genes and to identify reliable prognostic markers. The identified genes were also analyzed in the GEPIA database to assess their correlations with survival, and these relationships were verified with the Kaplan-Meier Plotter database.Results:The immune score was related to the survival time and tumor topography of LUAD patients. There was a significant correlation between stromal score and tumor metastasis. Through multivariate analysis, stage (HR = 1.640, 95% CI = 1.019-2.642, P = 0.042) and risk score (HR = 1.036, 95% CI = 1.026-1.046, P < 0.001). The genes (ARHGAP15, BTLA, CASS4, CLECL1, FAM129C, STAP1, TESPA1, and S100P) showed credible prognostic value in LUAD patients in TCGA through GEPIA database online analysis and verification in the Kaplan-Meier plotter database.Conclusions:In the microenvironment of lung adenocarcinoma, the differentially expressed genes screened by immune score and stromal score have certain value in evaluating the survival/prognosis of patients, as well as the invasion and progression of tumors.

The landscape of bacterial presence in tumor and adjacent normal tissue across 9 major cancer types using TCGA exome sequencing

Identification of microbial composition directly from tumor tissue permits studying the relationship between microbial changes and cancer pathogenesis. We interrogated bacterial presence in tumor and adjacent normal tissue strictly in pairs utilizing human whole exome sequencing to generate microbial profiles. Profiles were generated for 813 cases from stomach, liver, colon, rectal, lung, head & neck, cervical and bladder TCGA cohorts. Core microbiota examination revealed twelve taxa to be common across the nine cancer types at all classification levels. Paired analyses demonstrated significant differences in bacterial shifts between tumor and adjacent normal tissue across stomach, colon, lung squamous cell, and head & neck cohorts, whereas little or no differences were evident in liver, rectal, lung adenocarcinoma, cervical and bladder cancer cohorts in adjusted models. Helicobacter pylori in stomach and Bacteroides vulgatus in colon were found to be significantly higher in adjacent normal compared to tumor tissue after false discovery rate correction. Computational results were validated with tissue from an independent population by species-specific qPCR showing similar patterns of co-occurrence among Fusobacterium nucleatum and Selenomonas sputigena in gastric samples. This study demonstrates the ability to identify bacteria differential composition derived from human tissue whole exome sequences. Taken together our results suggest the microbial profiles shift with advanced disease and that the microbial composition of the adjacent tissue can be indicative of cancer stage disease progression.

Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma.

Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD).Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29).Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005).Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.

STK11 and KEAP1 mutations as prognostic biomarkers in an observational real-world lung adenocarcinoma cohort

IntroductionSomatic mutations in STK11 and KEAP1, frequently comutated in non-squamous non-small cell lung cancer (NSQ NSCLC), have been associated with poor response to immune checkpoint blockade (ICB). However, previous reports lack non-ICB controls needed to properly ascertain the predictive nature of those biomarkers. The objective of this study was to evaluate the predictive versus prognostic effect of STK11 or KEAP1 mutations in NSQ NSCLC.MethodsPatients diagnosed with stage IIIB, IIIC, IVA or IVB NSQ NSCLC from a real-world data cohort from the Flatiron Health Network linked with genetic testing from Foundation Medicine were retrospectively assessed. Real-world, progression-free survival (rwPFS) and overall survival (OS) were calculated from time of initiation of first-line treatment.ResultsWe analysed clinical and mutational data for 2276 patients including patients treated with anti-programmed death-1 (PD-1)/anti-programmed death ligand 1 (PD-L1) inhibitors at first line (n=574). Mutations in STK11 or KEAP1 were associated with poor outcomes across multiple therapeutic classes and were not specifically associated with poor outcomes in ICB cohorts. There was no observable interaction between STK11 mutations and anti-PD-1/anti-PD-L1 treatment on rwPFS (HR, 1.05; 95% CI 0.76 to 1.44; p=0.785) or OS (HR, 1.13; 95% CI 0.76 to 1.67; p=0.540). Similarly, there was no observable interaction between KEAP1 mutations and treatment on rwPFS (HR, 0.93; 95% CI 0.67 to 1.28; p=0.653) or OS (HR, 0.98; 95% CI 0.66 to 1.45; p=0.913).ConclusionOur results show that STK11-KEAP1 mutations are prognostic, not predictive, biomarkers for anti-PD-1/anti-PD-L1 therapy.

Integrative analyses of noncoding RNAs reveal the potential mechanisms augmenting tumor malignancy in lung adenocarcinoma.

Precise noncoding RNA (ncRNA)-based network prediction is necessary to reveal ncRNA functions and pathological mechanisms. Here, we established a systemic pipeline to identify prognostic ncRNAs, predict their functions and explore their pathological mechanisms in lung adenocarcinoma (LUAD). After in silico and experimental validation based on evaluations of prognostic value in multiple LUAD cohorts, we selected the PTTG3P pseudogene from among other prognostic ncRNAs (MIR497HG, HSP078, TBX5-AS1, LOC100506990 and C14orf64) for mechanistic studies. PTTG3P upregulation in LUAD cells shortens the metaphase to anaphase transition in mitosis, increases cell viability after cisplatin or paclitaxel treatment, facilitates tumor growth that leads to poor survival in orthotopic lung models, and is associated with a poor survival rate in LUAD patients in the TCGA cohort who received chemotherapy. Mechanistically, PTTG3P acts as an ncRNA that interacts with the transcription factor FOXM1 to regulate the transcriptional activation of the mitotic checkpoint kinase BUB1B, which augments tumor growth and chemoresistance and leads to poor outcomes for LUAD patients. Overall, we established a systematic strategy to uncover prognostic ncRNAs with functional prediction methods suitable for pan-cancer studies. Moreover, we revealed that PTTG3P, due to its upregulation of the PTTG3P/FOXM1/BUB1B axis, could be a therapeutic target for LUAD patients.