The expression of four genes as a prognostic classifier for stage I lung adenocarcinoma in 12 independent cohorts

Tumor suppressor gene DLC1: Its modifications, interactive molecules, and potential prospects for clinical cancer application

Deleted in liver cancer 1 (DLC1) is a recognized tumor suppressor gene that negatively regulates Rho family proteins by hydrolyzing the active GTP-bound state to its inactive GDP-bound state. Active Rho proteins play a positive role in tumorigenesis. Numerous in vitro and in vivo experiments have shown that DLC1 is downregulated or inactivated in various solid tumors, which may be due to the following five reasons: genomic deletion, epigenetic modification and ubiquitin-dependent proteasomal degradation may cause DLC1 underexpression; phosphorylation at the post-translation level may cause DLC1 inactivation; and failure to localize at focal adhesions (FAs) may prevent DLC1 from exerting full activity. All of the causes could be attributed to molecular binding. Experimental evidence suggests that direct or indirect targeting of DLC1 is feasible for cancer treatment. Therefore, elucidating the interaction of DLC1 with its binding partners might provide novel targeted therapies for cancer. In this review, we summarized the binding partners of DLC1 at both the gene and protein levels and expounded a variety of anticancer drugs targeting DLC1 to provide information about DLC1 as a cancer diagnostic indicator or therapeutic target.

Bioinformatics-Based Identification of HDAC Inhibitors as Potential Drugs to Target EGFR Wild-Type Non-Small-Cell Lung Cancer

Patients with EGFR-mutant non-small-cell lung cancer (NSCLC) greatly benefit from EGFR-tyrosine kinase inhibitors (EGFR-TKIs) while the prognosis of patients who lack EGFR-sensitive mutations (EGFR wild type, EGFR-WT) remains poor due to a lack of effective therapeutic strategies. There is an urgent need to explore the key genes that affect the prognosis and develop potentially effective drugs in EGFR-WT NSCLC patients. In this study, we clustered functional modules related to the survival traits of EGFR-WT patients using weighted gene co-expression network analysis (WGCNA). We used these data to establish a two-gene prognostic signature based on the expression of CYP11B1 and DNALI1 by combining the least absolute shrinkage and selection operator (LASSO) algorithms and Cox proportional hazards regression analysis. Following the calculation of risk score (RS) based on the two-gene signature, patients with high RSs showed a worse prognosis. We further explored targeted drugs that could be effective in patients with a high RS by the connectivity map (CMap). Surprisingly, multiple HDAC inhibitors (HDACis) such as trichostatin A (TSA) and vorinostat (SAHA) that may have efficacy were identified. Also, we proved that HDACis could inhibit the proliferation and metastasis of NSCLC cells in vitro. Taken together, our study identified prognostic biomarkers for patients with EGFR-WT NSCLC and confirmed a novel potential role for HDACis in the clinical management of EGFR-WT patients.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View

Starting from the work of Ulivi and colleagues, we aim to summarize the research area of biomarkers for early diagnosis and early stage lung cancer.

An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy

Gene expression and DNA methylation levels affect the outcomes of patients with cancer. The present study aimed to establish a multigene risk model for predicting the outcomes of patients with cervical cancer (CerC) treated with or without radiotherapy. RNA sequencing training data with matched DNA methylation profiles were downloaded from The Cancer Genome Atlas database. Patients were divided into radiotherapy and non‑radiotherapy groups according to the treatment strategy. Differently expressed and methylated genes between the two groups were identified, and 8 prognostic genes were identified using Cox regression analysis. The optimized risk model based on the 8‑gene signature was defined using the Cox's proportional hazards model. Kaplan‑Meier survival analysis indicated that patients with higher risk scores exhibited poorer survival compared with patients with lower risk scores (log‑rank test, P=3.22x10‑7). Validation using the GSE44001 gene set demonstrated that patients in the high‑risk group exhibited a shorter survival time comprared with the low‑risk group (log‑rank test, P=3.01x10‑3). The area under the receiver operating characteristic curve values for the training and validation sets were 0.951 and 0.929, respectively. Cox regression analyses indicated that recurrence and risk status were risk factors for poor outcomes in patients with CerC treated with or without radiotherapy. The present study defined that the 8‑gene signature was an independent risk factor for the prognosis of patients with CerC. The 8‑gene prognostic model had predictive power for CerC prognosis.

HLA-DQB1 expression on tumor cells is a novel favorable prognostic factor for relapse in early-stage lung adenocarcinoma

Background: Postoperative recurrence is the main cause of a poor prognosis in early-stage lung adenocarcinoma (LUAD). Factors that can predict recurrence risk are critically needed.

Materials and methods: In this study, we designed a screening procedure based on gene profile data and performed validation using TCGA and Daping hospital’s cohorts. Differentially expressed genes (DEGs) between patients with recurrence-free survival (RFS) <1 year and RFS >3 years were identified, overlapping genes among these DEGs were selected as candidate biomarkers. A Cox proportional hazards model, immunohistochemistry and Kaplan-Meier survival analysis were performed to validate these biomarkers in two distinct validation sets.

Results:SFTPB, SFTPD, SFTA1P, HLA-DQB1, ITGB8, ANLN, and LRRN1 were overlapped both in TCGA and Daping discovery sets. The Cox proportional hazards model analysis of the TCGA validation set showed that HLA-DQB1 was an independent prognostic factor for RFS (HR=0.686, 95% CI, 0.542–0.868). Immunohistochemistry and Kaplan-Meier analysis in Daping validation sets confirmed HLA-DQB1 expression on tumor cells (not interstitial cells) to be an effective predictor of postoperative recurrence. Further examination revealed that the level of HLA-DQB1 expression on tumor cells was positively correlated with CD4- and CD8-positive lymphocyte infiltration into the tumor.

Conclusion: All results indicate that high expression of HLA-DQB1 on tumor cells is a good prognostic marker in early-stage LUAD, and the mechanism may be related to anti-tumor immune activity.

Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I–IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

Integration of multiple "OMIC" biomarkers: A precision medicine strategy for lung cancer

More than half of all new lung cancer diagnoses are made in patients with locally advanced or metastatic disease, at which point therapeutic options are scarce. It is anticipated, however, that the widespread use of Low-Dose Computed Tomography (LDCT) screening, will lead to a greater proportion of lung cancers being diagnosed at an early, operable, stage. Still, the overall rate of recurrence for surgically treated Stage I lung cancer patients is up to 30% within 5 years of diagnosis. Thus, the identification and clinical application of biomarkers of early stage lung cancer are a pressing medical need. The integrative analysis of "omic," clinical and epidemiological data for single patients is a core principle of precision medicine. Through rigorous bioinformatics and statistical analyses we have identified biomarkers of early-stage lung cancer based on DNA methylation, expression of mRNA and miRNA, inflammatory cytokines, and urinary metabolites. Beyond a more comprehensive understanding of the molecular taxonomy of lung cancer, these biomarkers can have very practical implications in the context of unmet clinical needs of early stage lung cancer patients: First, current guidelines for LDCT screening broadly include individuals based on age and history of heavy smoking. Tumor-derived circulating biomarkers in the blood and urine associated with lung cancer risk could narrow and prioritize individuals for LDCT screening. Second, a high number of nodules are identified by LDCT, of which fewer than 5% are finally diagnosed as lung cancer. Biomarkers may help discriminate malignant nodules from benign or indolent lesions. Third, the expected rise in the numbers of lung cancer patients diagnosed at an early stage will necessitate new treatment options. Circulating, urinary and tissue-based biomarkers that molecularly categorize Stage I patients after tumor resection can help identify high-risk patients who may benefit from adjuvant chemotherapy or innovative immunotherapy regimens.

Excerpts from the 1st international NTNU symposium on current and future clinical biomarkers of cancer: innovation and implementation, June 16th and 17th 2016, Trondheim, Norway

The goal of biomarker research is to identify clinically valid markers. Despite decades of research there has been disappointingly few molecules or techniques that are in use today. The “1st International NTNU Symposium on Current and Future Clinical Biomarkers of Cancer: Innovation and Implementation”, was held June 16th and 17th 2016, at the Knowledge Center of the St. Olavs Hospital in Trondheim, Norway, under the auspices of the Norwegian University of Science and Technology (NTNU) and the HUNT biobank and research center. The Symposium attracted approximately 100 attendees and invited speakers from 12 countries and 4 continents. In this Symposium original research and overviews on diagnostic, predictive and prognostic cancer biomarkers in serum, plasma, urine, pleural fluid and tumor, circulating tumor cells and bioinformatics as well as how to implement biomarkers in clinical trials were presented. Senior researchers and young investigators presented, reviewed and vividly discussed important new developments in the field of clinical biomarkers of cancer, with the goal of accelerating biomarker research and implementation. The excerpts of this symposium aim to give a cutting-edge overview and insight on some highly important aspects of clinical cancer biomarkers to-date to connect molecular innovation with clinical implementation to eventually improve patient care.

Liquid biopsy in early stage lung cancer

Lung cancer is the leading cause of cancer-associated deaths worldwide. Surgery is the standard treatment for early-stage non-small cell lung cancer (NSCLC). However, 30% to 80% of these patients will die within 5 yearS of diagnosis. Circulating cell-free DNA (cfDNA) harbors pathologic characteristics of the original tumor, such as gene mutations or epigenetic alterations. Analysis of cfDNA has revolutionized the clinical care of advanced lung cancer patients undergoing targeted therapies. However, the low concentration of cfDNA in the blood of early-stage NSCLC patients has hampered its use for management of early disease. Continuing development of more specific and sensitive techniques for detection and analysis of cfDNA will soon enable its leverage in early stage and, perhaps, even screening settings. Therefore, cfDNA analysis may become a tool used for routine NSCLC diagnosis and for monitoring tumor burden, as well as for identifying hidden residual disease. In this review, we will focus on the current evidence of cfDNA in patients with early-stage NSCLC, new and upcoming approaches to identify circulating-tumor biomarkers, their clinical applications and future directions.

A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts

INTRODUCTION

There are no validated molecular methods that prospectively identify patients with surgically resected lung squamous cell carcinoma (SCC) at high risk for recurrence. By focusing on the expression of genes with known functions in development of lung SCC and prognosis, we sought to develop a robust prognostic classifier of early-stage lung SCC.

METHODS

The expression of 253 genes selected by literature search was evaluated in microarrays from 107 stage I/II tumors. Associations with survival were evaluated by Cox regression and Kaplan-Meier survival analyses in two independent cohorts of 121 and 91 patients with SCC, respectively. A classifier score based on multivariable Cox regression was derived and examined in six additional publicly available data sets of stage I/II lung SCC expression profiles (n = 358). The prognostic value of this classifier was evaluated in meta-analysis of patients with stage I/II (n = 479) and stage I (n = 326) lung SCC.

RESULTS

Dual specificity phosphatase 6 gene (DUSP6) and actinin alpha 4 gene (ACTN4) were associated with prognostic outcome in two independent patient cohorts. Their expression values were utilized to develop a classifier that identified patients with stage I/II lung SCC at high risk for recurrence (hazard ratio [HR] = 4.7, p = 0.018) or cancer-specific mortality (HR = 3.5, p = 0.016). This classifier also identified patients at high risk for recurrence (HR = 2.7, p = 0.008) or death (HR = 2.2, p = 0.001) in publicly available data sets of stage I/II and in meta-analysis of stage I patients.

CONCLUSIONS

We have established and validated a prognostic classifier to inform clinical management of patients with lung SCC after surgical resection.

The prognostic potential of alternative transcript isoforms across human tumors

BACKGROUND

Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival.

METHODS

Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor (ER) status and melanoma tumors with proliferative and invasive phenotypes.

RESULTS

Transcript isoform signatures accurately separate early from late-stage groups and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures in retrospective data and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to ER status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup.

CONCLUSIONS

Our analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis and may potentially contribute towards current strategies of precision cancer medicine.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Translational Research to Improve the Efficacy of Carbon Ion Radiotherapy: Experience of Gunma University

Carbon ion radiotherapy holds great promise for cancer therapy. Clinical data show that carbon ion radiotherapy is an effective treatment for tumors that are resistant to X-ray radiotherapy. Since 1994 in Japan, the National Institute of Radiological Sciences has been heading the development of carbon ion radiotherapy using the Heavy Ion Medical Accelerator in Chiba. The Gunma University Heavy Ion Medical Center (GHMC) was established in the year 2006 as a proof-of-principle institute for carbon ion radiotherapy with a view to facilitating the worldwide spread of compact accelerator systems. Along with the management of more than 1900 cancer patients to date, GHMC engages in translational research to improve the treatment efficacy of carbon ion radiotherapy. Research aimed at guiding patient selection is of utmost importance for making the most of carbon ion radiotherapy, which is an extremely limited medical resource. Intratumoral oxygen levels, radiation-induced cellular apoptosis, the capacity to repair DNA double-strand breaks, and the mutational status of tumor protein p53 and epidermal growth factor receptor genes are all associated with X-ray sensitivity. Assays for these factors are useful in the identification of X-ray-resistant tumors for which carbon ion radiotherapy would be beneficial. Research aimed at optimizing treatments based on carbon ion radiotherapy is also important. This includes assessment of dose fractionation, normal tissue toxicity, tumor cell motility, and bystander effects. Furthermore, the efficacy of carbon ion radiotherapy will likely be enhanced by research into combined treatment with other modalities such as chemotherapy. Several clinically available chemotherapeutic drugs (carboplatin, paclitaxel, and etoposide) and drugs at the developmental stage (Wee-1 and heat shock protein 90 inhibitors) show a sensitizing effect on tumor cells treated with carbon ions. Additionally, the efficacy of carbon ion radiotherapy can be improved by combining it with cancer immunotherapy. Clinical validation of preclinical findings is necessary to further improve the treatment efficacy of carbon ion radiotherapy.

An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma Based on mRNA, microRNA, and DNA Methylation Biomarkers

INTRODUCTION

Up to 30% stage I lung cancer patients suffer recurrence within 5 years of curative surgery. We sought to improve existing protein-coding gene and microRNA expression prognostic classifiers by incorporating epigenetic biomarkers.

METHODS

Genome-wide screening of DNA methylation and pyrosequencing analysis of HOXA9 promoter methylation were performed in two independently collected cohorts of stage I lung adenocarcinoma. The prognostic value of HOXA9 promoter methylation alone and in combination with mRNA and miRNA biomarkers was assessed by Cox regression and Kaplan-Meier survival analysis in both cohorts.

RESULTS

Promoters of genes marked by polycomb in embryonic stem cells were methylated de novo in tumors and identified patients with poor prognosis. The HOXA9 locus was methylated de novo in stage I tumors (p < 0.0005). High HOXA9 promoter methylation was associated with worse cancer-specific survival (hazard ratio [HR], 2.6; p = 0.02) and recurrence-free survival (HR, 3.0; p = 0.01), and identified high-risk patients in stratified analysis of stages IA and IB. Four protein-coding gene (XPO1, BRCA1, HIF1α, and DLC1), miR-21 expression, and HOXA9 promoter methylation were each independently associated with outcome (HR, 2.8; p = 0.002; HR, 2.3; p = 0.01; and HR, 2.4; p = 0.005, respectively), and when combined, identified high-risk, therapy naive, stage I patients (HR, 10.2; p = 3 × 10). All associations were confirmed in two independently collected cohorts.

CONCLUSION

A prognostic classifier comprising three types of genomic and epigenomic data may help guide the postoperative management of stage I lung cancer patients at high risk of recurrence.

The hallmarks of premalignant conditions: a molecular basis for cancer prevention

The hallmarks of premalignant lesions were first described in the 1970s, a time when relatively little was known about the molecular underpinnings of cancer. Yet it was clear there must be opportunities to intervene early in carcinogenesis. A vast array of molecular information has since been uncovered, with much of this stemming from studies of existing cancer or cancer models. Here, examples of how an understanding of cancer biology has informed cancer prevention studies are highlighted and emerging areas that may have implications for the field of cancer prevention research are described. A note of caution accompanies these examples, in that while there are similarities, there are also fundamental differences between the biology of premalignant lesions or premalignant conditions and invasive cancer. These differences must be kept in mind, and indeed leveraged, when exploring potential cancer prevention measures.

Epigenetics in Personalized Management of Lung Cancer

In last several years, the focus on the origin and progression of human cancers has shifted from genetic to epigenetic regulation, with particular attention to methylation and acetylation events that have profound effect on the eventual expression of oncogenes and the suppression of tumor suppressors. A few drugs targeting these epigenetic changes have already been approved for treatment, albeit not for lung cancer. With the recent advances in the push towards personalized therapy, questions have been asked about the possible targeting of epigenetic events for personalized lung cancer therapy. Some progress has been made but a lot needs to be done. In this chapter, a succinct review of these topics is provided.