Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma

The pathological distinction between malignant pleural mesothelioma (MPM)and adenocarcinoma (ADCA) of the lung can be cumbersome using established methods. We propose that a simple technique, based on the expression levels of a small number of genes, can be useful in the early and accurate diagnosis of MPM and lung cancer. This method is designed to accurately distinguish between genetically disparate tissues using gene expression ratios and rationally chosen thresholds. Here we have tested the fidelity of ratio-based diagnosis in differentiating between MPM and lung cancer in 181 tissue samples (31 MPM and 150 ADCA). A training set of 32 samples (16 MPM and 16 ADCA) was used to identify pairs of genes with highly significant, inversely correlated expression levels to form a total of 15 diagnostic ratios using expression profiling data. Any single ratio of the 15 examined was at least 90% accurate in predicting diagnosis for the remaining 149 samples (e.g., test set). We then examined (in the test set) the accuracy of multiple ratios combined to form a simple diagnostic tool. Using two and three expression ratios, we found that the differential diagnoses of MPM and lung ADCA were 95% and 99% accurate, respectively. We propose that using gene expression ratios is an accurate and inexpensive technique with direct clinical applicability for distinguishing between MPM and lung cancer. Furthermore, we provide evidence suggesting that this technique can be equally accurate in other clinical scenarios.     

Gene expression signature predicts recurrence in lung adenocarcinoma.

PURPOSE: Improving outcomes for early-stage lung cancer is a major research focus at present because a significant proportion of stage I patients develop recurrent disease within 5 years of curative-intent lung resection. Within tumor stage groups, conventional prognostic indicators currently fail to predict relapse accurately. EXPERIMENTAL DESIGN: To identify a gene signature predictive of recurrence in primary lung adenocarcinoma, we analyzed gene expression profiles in a training set of 48 node-negative tumors (stage I-II), comparing tumors from cases who remained disease-free for a minimum of 36 months with those from cases whose disease recurred within 18 months of complete resection. RESULTS: Cox proportional hazards modeling with leave-one-out cross-validation identified a 54-gene signature capable of predicting risk of recurrence in two independent validation cohorts of 55 adenocarcinomas [log-rank P=0.039; hazard ratio (HR), 2.2; 95% confidence interval (95% CI), 1.1-4.7] and 40 adenocarcinomas (log-rank P=0.044; HR, 3.3; 95% CI, 1.4-7.9). Kaplan-Meier log-rank analysis found that predicted poor-outcome groups had significantly shorter survival, and furthermore, the signature predicted outcome independently of conventional indicators of tumor stage and node stage. In a subset of earliest stage adenocarcinomas, generally expected to have good outcome, the signature predicted samples with significantly poorer survival. CONCLUSIONS: We describe a 54-gene signature that predicts the risk of recurrent disease independently of tumor stage and which therefore has potential to refine clinical prognosis for patients undergoing resection for primary adenocarcinoma of the lung.     

MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 19-25 nt and play important roles in gene regulation in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis of stage I NSCLC. MiRNAs extracted from 527 stage I NSCLC patients were profiled on the human miRNA expression profiling v2 panel (Illumina). The expression profiles were analyzed for their association with cancer subtypes, lung cancer brain metastasis and recurrence/relapse free survival (RFS). MiRNA expression patterns between lung adenocarcinoma and squamous cell carcinoma differed significantly with 171 miRNAs, including Let-7 family members and miR-205. Ten miRNAs associated with brain metastasis were identified including miR-145*, which inhibit cell invasion and metastasis. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 34 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype, whereas the second containing 27 miRNAs was adenocarcinoma specific. Both signatures were validated using formalin-fixed paraffin embedded and/or fresh frozen tissues in independent data set with 170 stage I patients. Our findings have important prognostic or therapeutic implications for the management of stage I lung cancer patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.     

A gene expression signature from peripheral whole blood for stage I lung adenocarcinoma

Affordable early screening in subjects with high risk of lung cancer has great potential to improve survival from this deadly disease. We measured gene expression from lung tissue and peripheral whole blood (PWB) from adenocarcinoma cases and controls to identify dysregulated lung cancer genes that could be tested in blood to improve identification of at-risk patients in the future. Genome-wide mRNA expression analysis was conducted in 153 subjects (73 adenocarcinoma cases, 80 controls) from the Environment And Genetics in Lung cancer Etiology study using PWB and paired snap-frozen tumor and noninvolved lung tissue samples. Analyses were conducted using unpaired t tests, linear mixed effects, and ANOVA models. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the identified biomarkers. We identified 50 dysregulated genes in stage I adenocarcinoma versus control PWB samples (false discovery rate ≤0.1, fold change ≥1.5 or ≤0.66). Among them, eight (TGFBR3, RUNX3, TRGC2, TRGV9, TARP, ACP1, VCAN, and TSTA3) differentiated paired tumor versus noninvolved lung tissue samples in stage I cases, suggesting a similar pattern of lung cancer-related changes in PWB and lung tissue. These results were confirmed in two independent gene expression analyses in a blood-based case-control study (n = 212) and a tumor-nontumor paired tissue study (n = 54). The eight genes discriminated patients with lung cancer from healthy controls with high accuracy (AUC = 0.81, 95% CI = 0.74-0.87). Our finding suggests the use of gene expression from PWB for the identification of early detection markers of lung cancer in the future.     

Signaling pathway-based identification of extensive prognostic gene signatures for lung adenocarcinoma

Tumor recurrence is the major cause of death in lung cancer treatment. To date, there is no clinically applied gene expression-based model to predict the risk for tumor recurrence in non-small cell lung cancer (NSCLC). We sought to embed crosstalk with major signaling pathways into biomarker identification. Three approaches were used to identify prognostic gene signatures from 442 lung adenocarcinoma samples. Candidate genes co-expressed with 6 or 7 major NSCLC signaling hallmarks were identified from genome-wide coexpression networks specifically associated with different prognostic groups. From these candidate genes, the first approach selected genes significantly associated with disease-specific survival using univariate Cox model. The second approach used random forests to refine the gene signatures; and the third approach used Relief algorithm to form the final gene sets. A total of 21 gene signatures were identified using these three approaches. These gene signatures generated significant prognostic stratifications (log-rank P<0.05 in Kaplan-Meier analyses; hazard ratio >1, P<0.05) in all tumors, stage I only, and in stage I patients not receiving chemotherapy in all training and test sets. In multivariate analyses with age, gender, race, smoking history, cancer stage, and tumor differentiation, a 10-gene signature had a hazard ratio of 3.23 (95% CI: [1.48, 7.06]), which was a more significant prognostic factor than other clinical factors, except cancer stage (III vs. I; with no significant difference). All identified 21 gene signatures outperformed other lung cancer signatures evaluated in the Director's Challenge Study. This study is an important step toward personalized prognosis of tumor recurrence and patient selection for adjuvant chemotherapy, with significant impact on down-stream clinical applications.     

Prediction of breast cancer prognosis by gene expression profile of TP53 status

TP53 mutations are a poor prognostic factor in breast cancers. The present study sets out to identify the gene set that determines the expression signature of the TP53 status ( TP53 signature) and to correlate it with clinical outcome. Using comprehensive expression analysis and DNA sequencing of the TP53 gene in 38 Japanese breast cancer patients, a gene set from differentially expressed genes was isolated, depending on the TP53 status. As independent external datasets, two published datasets were introduced for validation of TP53 status predictions (251 Swedish samples) and survival analysis (both the Swedish and 295 Dutch samples). Thirty-three gene sets were identified from microarray analysis. Predictive accuracy of the TP53 status by gene expression profiling was 83.3% in the test set ( n  = 12). TP53 signature has the ability to predict recurrence-free survival (RFS) of 29 stage I and stage II Japanese breast cancers (log rank, P  = 0.032), and RFS, overall survival of two independently published datasets (log rank, both P  < 0.0001). Multivariate analysis has shown that the TP53 signature an independent and significant prognostic factor with a hazard ratio (HR) for recurrence and survival in two external datasets ( P  < 0.0001). The TP53 signature is also a strong prognostic factor in the subgroups: estrogen-receptor positive, lymph node positive and negative, intermediate/high risk in St. Gallen criteria, and high risk in National Cancer Institute (NCI) criteria (log rank, P  < 0.0001). TP53 signature is a reliable and independent predictor of the outcome of disease in operated breast cancer. ( Cancer Sci 2008; 99: 324–332)     

Overexpression of T-LAK cell-originated protein kinase predicts poor prognosis in patients with stage I lung adenocarcinoma

Tumor recurrence is the most common cause of disease failure after surgical resection in early-stage lung adenocarcinoma. Identification of clinically relevant prognostic markers could help to predict patients with high risk of disease recurrence. A meta-analysis of available lung adenocarcinoma microarray datasets revealed that T-LAK cell-originated protein kinase (TOPK), a serine/threonine protein kinase, is overexpressed in lung cancer. Using stable cell lines with overexpression or knockdown of TOPK, we have shown that TOPK can promote cell migration, invasion, and clonogenic activity in lung cancer cells, suggesting its crucial role in lung tumorigenesis. To evaluate the prognostic value of TOPK expression in resected stage I lung adenocarcinoma, a retrospective analysis of 203 patients diagnosed with pathological stage I lung adenocarcinoma was carried out to examine the expression of TOPK by immunohistochemistry (IHC). The prognostic significance of TOPK overexpression was examined. Overexpression of TOPK (IHC score >3) was detected in 67.0% of patients, and these patients were more frequently characterized with disease recurrence and angiolymphatic invasion. Using multivariate analysis, patient age (>65 years old; P = 0.002) and TOPK overexpression (IHC score >3; P < 0.001) significantly predicted a shortened overall survival. Moreover, TOPK overexpression (IHC score >3; P = 0.005) also significantly predicted a reduced time to recurrence in the patients. Our results indicate that overexpression of TOPK could predetermine the metastatic capability of tumors and could serve as a significant prognostic predictor of shortened overall survival and time to recurrence.     

IASLC分级系统与Ⅰ期浸润性非黏液型肺腺癌患者预后关系的回顾性研究

目的：回顾性分析国际肺癌研究协会(IASLC)分级系统与Ⅰ期浸润性非黏液型肺腺癌临床病理特征的相关性及与患者预后的关系。方法：回顾性分析2015年1月至2018年12月就诊于天津市胸科医院的204例Ⅰ期浸润性非黏液型肺腺癌患者的临床病理资料及随访资料,根据IASLC分级系统对患者分组,采用单因素方差分析、χ²检验和Fisher精确检验分析IASLC分级与Ⅰ期浸润性非黏液型肺腺癌临床病理特征的相关性,及与肺腺癌患者预后的关系。通过Kaplan-Meier法计算浸润性非黏液型肺腺癌患者总生存率(overall survival,OS)、无复发生存率(recurrence-free survival,RFS);采用Log-rank法比较不同组间的差异性。使用单因素Cox回归、多因素Cox回归分析独立危险因素。结果：204例患者中IASLC分级为Ⅰ级108例,Ⅱ级66例,Ⅲ级30例。IASLC分级与性别(P=0.022)、吸烟史(P=0.041)、脉管侵犯(P=0.004)、胸膜累及(P=0.001)、病理分期(P<0.001)、肿瘤直径(P<0.001)均显...     

A molecular signature in superficial bladder carcinoma predicts clinical outcome.

PURPOSE: Cancer of the urinary bladder is a common malignant disease in the western countries. The majority of patients presents with superficial tumors with a high recurrence frequency, a minor fraction of these patients experience disease progression to a muscle invasive stage. No clinical useful molecular markers exist to identify patients showing later disease progression. The purpose of this study was to identify markers of disease progression using full-genome expression analysis. EXPERIMENTAL DESIGN: We did a full-genome expression analysis (59,619 genes and expressed sequence tags) of superficial bladder tumors from 29 bladder cancer patients (13 without later disease progression and 16 with later disease progression) using high-density oligonucleotide microarrays. We used supervised learning for identification of the optimal genes for predicting disease progression. The identified genes were validated on an independent test set (74 superficial tumor samples) using in house-fabricated 60-mer oligonucleotide microarrays. RESULTS: We identified a 45-gene signature of disease progression. By monitoring this progression signature in an independent test set, we found a significant correlation between our classifications and the clinical outcome (P < 0.03). The genes identified as differentially expressed were involved in regulating apoptosis, cell differentiation, and cell cycle and hence may represent potential therapeutic targets. CONCLUSIONS: Our results indicate that it may be possible to identify patients with a high risk of disease progression at an early stage using a molecular signature present already in the superficial tumors. In this way, better treatment and follow-up regimens could be assigned to patients suffering from superficial bladder cancer.     

Low expression of polypeptide GalNAc N-acetylgalactosaminyl transferase-3 in lung adenocarcinoma: impact on poor prognosis and early recurrence

Initial glycosylation of mucin-type O-linked protein is catalysed by one of the UDP-GalNAc: polypeptide N-acetyl-galactosaminyl transferase-3 (GalNAc-T3). O-glycosylation is important in the binding of cell adhesion molecules, cell differentiation, invasion, and metastasis in tumours. This study was designed to detect GalNAc-T3 expression in lung adenocarcinoma by using immunohistochemical staining, and to evaluate the relationship between the GalNAc-T3 expression level and prognosis and recurrence in completely resected lung adenocarcinoma patients. A low expression of GalNAc-T3 was detected in the cytoplasm of tumour cells in 79 of 148 patients (53.4%) with lung adenocarcinoma. The low expression of GalNAc-T3 was associated with poorly differentiated tumour (P<0.0001), poor pathologic stage (P<0.0001), lymph node metastasis (P<0.0001), and tumour recurrence (P=0.016). The lung carcinoma patients with low GalNAc-T3 expression had a poorer prognosis than those with high GalNAc-T3 expression, using both univariate and multivariate analyses (overall survival: P<0.0001 and P=0.011, respectively). In addition, multivariate analysis of the clinicopathological characteristics of stage I lung adenocarcinoma indicated that the low expression of GalNAc-T3 was a significant independent factor for predicting poor prognosis and early recurrence (P=0.006, rr=2.87 and P=0.019, rr=3.05, respectively). The low expression of GalNAc-T3 may be a useful marker for predicting poor prognosis and early recurrence in completely resected lung carcinoma patients, particularly patients with stage I diseases.     

A multigene assay is prognostic of survival in patients with early-stage lung adenocarcinoma

PURPOSE: Clinical staging does not adequately risk stratify patients with early stage non-small cell lung cancer. We sought to generate a real-time PCR (RT-PCR)-based prognostic model in patients with early stage lung adenocarcinoma, the dominant histology of lung cancer in the United States. EXPERIMENTAL DESIGN: We studied gene expression of 61 candidate genes in 107 patients with completely surgically resected lung adenocarcinoma using RT-PCR. We used crossvalidation methods to select and validate a prognostic model based on the expression of a limited number of genes. A risk score was generated based on model coefficients, and survival of patients with high- and low-risk scores were analyzed. RESULTS: We generated a four-gene model based on expression of WNT3a, ERBB3, LCK, and RND3. Risk score predicted mortality better than clinical stage or tumor size (adjusted hazard ratio, 6.7; 95% confidence interval, 1.6-28.9; P=0.001). Among 70 patients with stage I disease, 5-year overall survival was 87% among patients with low-risk scores, and 38% among patients with high-risk scores (P=0.0002). Among all patients, 5-year overall survival was 62% and 41%, respectively (P=0.0054). Disease-free survival was also significantly different among low- and high-risk score patients. CONCLUSIONS: This multigene assay predicts overall and disease-free survival significantly better than clinical stage and tumor size in patients with early stage lung adenocarcinoma and performs especially well in patients with stage I disease. Prospective clinical trials are needed to determine whether high-risk patients with stage I disease benefit from adjuvant chemotherapy.     

Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicates ZEB2 in tumor progression and prognosis

To elucidate the mechanisms of rapid progression of serous ovarian cancer, gene expression profiles from 43 ovarian cancer tissues comprising eight early stage and 35 advanced stage tissues were carried out using oligonucleotide microarrays of 18 716 genes. By non-negative matrix factorization analysis using 178 genes, which were extracted as stage-specific genes, 35 advanced stage cases were classified into two subclasses with superior ( n  = 17) and poor ( n  = 18) outcome evaluated by progression-free survival (log rank test, P  = 0.03). Of the 178 stage-specific genes, 112 genes were identified as showing different expression between the two subclasses. Of the 48 genes selected for biological function by gene ontology analysis or Ingenuity Pathway Analysis, five genes ( ZEB2 , CDH1 , LTBP2 , COL16A1 , and ACTA2 ) were extracted as candidates for prognostic factors associated with progression-free survival. The relationship between high ZEB2 or low CDH1 expression and shorter progression-free survival was validated by real-time RT-PCR experiments of 37 independent advanced stage cancer samples. ZEB2 expression was negatively correlated with CDH1 expression in advanced stage samples, whereas ZEB2 knockdown in ovarian adenocarcinoma SKOV3 cells resulted in an increase in CDH1 expression. Multivariate analysis showed that high ZEB2 expression was independently associated with poor prognosis. Furthermore, the prognostic effect of E-cadherin encoded by CDH1 was verified using immunohistochemical analysis of an independent advanced stage cancer samples set ( n  = 74). These findings suggest that the expression of epithelial–mesenchymal transition-related genes such as ZEB2 and CDH1 may play important roles in the invasion process of advanced stage serous ovarian cancer. ( Cancer Sci 2009)     

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

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

Biological prognostic factors for early stage completely resected non-small cell lung cancer

BACKGROUND AND OBJECTIVES: The different and unpredictable outcomes in early-stage non-small cell lung cancer patients requires urgent research concerning the biological pathway of this neoplasm. Our study investigated the frequency of expression and the clinicopathologic and prognostic significance of a series of biological markers in stage I and II resected non-small cell lung cancer. METHODS: A total of 99 cases of pathologic stage I and II were analyzed. The mean follow-up of surviving patients was 41 months. The expressions of the following biological markers were tested: bcl-2, p53, Ki-67, angiogenesis, and tumor vessel invasion. Kaplan-Meier estimates of survival and time to recurrence were calculated for clinical variables and biological markers using Cox's model for multivariate analysis. RESULTS: Tumoral vessel invasion was present in 22 (22%) pathologic samples, the angiogenesis mean value was 37 +/- 13, and median was 35; 13 (13%) patients showed positive immunostaining for bcl-2 oncoprotein. P53 oncoprotein expression was present in 48 patients (48.5%). All samples presented Ki-67 expression (mean value = 25.3 +/- 19.3, median = 20). The pathologic staging of the tumor was the most important independent prognostic factor for survival (P = 0.037) and for recurrence of disease (P = 0.040). Tumoral vessel invasion was the only marker with an independent predictive factor for survival and recurrence of disease in the group of patients without lymph node involvement (P = 0.02). CONCLUSION: Our data do not support a relevant prognostic role for p53, bcl-2, or Ki-67 immunohistochemical markers in non-small cell cancer. Tumor vessel invasion was an independent predictive factor of poor outcome in the group of patients without lymph node involvement. Pathological stage was confirmed as the most important independent prognostic factor.