Somatic POLE exonuclease domain mutations elicit enhanced intratumoral immune responses in stage II colorectal cancer

Previous studies found patients with POLE exonuclease domain mutations (EDMs) in targeted exons were related to significant better outcomes in stage II-III colorectal cancer (CRC). The detailed mutational profile of the entire POLE exonuclease domain, tumor mutation burden (TMB) and immune cell infiltration in POLE EDMs tumors, and the prognostic value of such mutations in stage II CRCs were largely unknown to us. This study was to clarify the characteristics, immune response and prognostic value of somatic POLE EDMs in stage II CRC. A total of 295 patients with stage II CRC were sequenced by next-generation sequencing with a targeted genetic panel. Simultaneous detection of the immune cells was conducted using a five-color immunohistochemical multiplex technique. The detailed molecular characteristics, tumor-infiltrating lymphocyte (TIL) and prognostic effect of POLE EDMs in stage II CRC were analyzed. For stage II CRCs, the POLE EDMs were detected in 3.1% of patients. Patients with POLE EDMs were more prone to be microsatellite instability-high (MSI-H) (33.3% vs 11.2%, p=0.043), younger at diagnosis (median 46 years vs 62 years, p<0.001) and more common at right-sided location (66.7% vs 23.1%; p=0.003). All patients with POLE EMDs were assessed as extremely high TMB, with a mean TMB of 200.8. Compared with other stage II CRCs, POLE EDMs displayed an enhanced intratumoral cytotoxic T cell response, evidenced by increased numbers of CD8+TILs and CD8A expression. Patients with stage II CRCs could be classified into three risk subsets, with significant different 5 years disease-free survival rates of 100% for POLE EDMs, 82.0% for MSI-H and 63.0% for MSS, p=0.013. In conclusion, characterized by a robust intratumoral T cell response, ultramutated POLE EDMs could be detected in a small subset of stage II CRCs with extremely high TMB. Patients with POLE EDMs had excellent outcomes in stage II CRCs, regardless of MSI status. Sequencing of all the exonuclease domain of POLE gene is recommended in clinical practice.

A Validation Study for Recurrence Risk Stratification of Stage II Colon Cancer Using the 55-Gene Classifier

INTRODUCTION

DNA microarrays, such as the consensus molecular subtype (CMS) classification using >600 genes, are used to predict cancer patient prognosis. We recently constructed a simple 55-gene classifier (55GC) system to risk stratify colon cancer (CC).

OBJECTIVE

Here, we validate the 55GC specifically for stage II CC and compare it with CMS categories.

METHODS

Tissue sections from 232 stage II CC patients who underwent curative surgery without adjuvant chemotherapy between 2009 and 2012 were subjected to DNA microarray analysis.

RESULTS

Based on the 55GC, patients were classified into microsatellite instability-like (27%), chromosomal instability-like (41%), and stromal (32%) subtypes with 5-year relapse-free survival (RFS) rates of 88.5, 83.3, and 71.2%, respectively (stromal vs. others: p = 0.0049). Multivariate analysis by Cox's proportional hazard model revealed that the stromal subtype, pT4, and the number of lymph nodes examined (<12) were independent poor prognostic factors. The overall concordance rate between 55GC and CMS was 72%, and 5-year RFS rates of patients with CMS1, CMS2, CMS3, and CMS4 cancers were 100, 85.5, 92.3, and 73.0%, respectively (p = 0.0113).

CONCLUSIONS

We conclude that the 55GC is a useful and reproducible grading system for stage II CC recurrence risk stratification.

Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study

BACKGROUND

Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally.

METHODS

532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value.

RESULTS

The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA.

CONCLUSION

Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

Is Nasal Polyposis Related to Levels of Serum Vitamin D and Vitamin D Receptor Gene Expression?

BACKGROUND Nasal polyposis (NP) is the most frequent cause of nasal masses. Despite considerable research on the subject, its etiology has not been fully elucidated, and effective treatment methods have not been developed. Some etiological factors causing low or high expression of genes in genetically predisposed individuals may play a role in the pathogenesis of the disease. The purpose of this study was to assess the relation between levels of vitamin D receptor (VDR) gene expression and serum vitamin D with NP. MATERIAL AND METHODS The study included 46 subjects with NP (NP group) and 40 volunteers (control group). Nasal polyp tissue samples were taken from the NP group and nasal mucosa samples were taken from the control group. Levels of VDR gene expression in the tissue samples were assessed using the real-time polymerase chain reaction (RT-PCR) method. RESULTS Mean serum 25(OH)D levels were 13.38±14.08 ng/ml in the NP group and 10.57±6.44 ng/ml in the control group (p=0.249). VDR gene expression was present in 17.5% of the NP group and 3.3% of the control group, and the difference between the 2 groups was statistically significant (likelihood ratio χ²=3.887; p=0.049). CONCLUSIONS This is the first study to assess levels of VDR gene expression in subjects with NP. Our results suggest that VDR gene expression may be associated with the pathogenesis or progression of NP.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

A 19-Gene expression signature as a predictor of survival in colorectal cancer

Background

Histopathological assessment has a low potential to predict clinical outcome in patients with the same stage of colorectal cancer. More specific and sensitive biomarkers to determine patients’ survival are needed. We aimed to determine gene expression signatures as reliable prognostic marker that could predict survival of colorectal cancer patients with Dukes’ B and C.

Methods

We examined microarray gene expression profiles of 78 archived tissues of patients with Dukes’ B and C using the Illumina DASL assay. The gene expression data were analyzed using the GeneSpring software and R programming.

Results

The outliers were detected and replaced with randomly chosen genes from the 90 % confidence interval of the robust mean for each group. We performed three statistical methods (SAM, LIMMA and t-test) to identify significant genes. There were 19 significant common genes identified from microarray data that have been permutated 100 times namely NOTCH2, ITPRIP, FRMD6, GFRA4, OSBPL9, CPXCR1, SORCS2, PDC, C12orf66, SLC38A9, OR10H5, TRIP13, MRPL52, DUSP21, BRCA1, ELTD1, SPG7, LASS6 and DUOX2. This 19-gene signature was able to significantly predict the survival of patients with colorectal cancer compared to the conventional Dukes’ classification in both training and test sets (p < 0.05). The performance of this signature was further validated as a significant independent predictor of survival using patient cohorts from Australia (n = 185), USA (n = 114), Denmark (n = 37) and Norway (n = 95) (p < 0.05). Validation using quantitative PCR confirmed similar expression pattern for the six selected genes.

Conclusion

Profiling of these 19 genes may provide a more accurate method to predict survival of patients with colorectal cancer and assist in identifying patients who require more intensive treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0218-1) contains supplementary material, which is available to authorized users.

ColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients

Colorectal cancer (CRC) is a heterogeneous disease with a high mortality rate and is still lacking an effective treatment. Our goal is to develop a robust prognosis model for predicting the prognosis in CRC patients. In this study, 871 stage II and III CRC samples were collected from six gene expression profilings. ColoFinder was developed using a 9-gene signature based Random Survival Forest (RSF) prognosis model. The 9-gene signature recurrence score was derived with a 5-fold cross validation to test the association with relapse-free survival, and the value of AUC was gained with 0.87 in GSE39582(95% CI [0.83-0.91]). The low-risk group had a significantly better relapse-free survival (HR, 14.8; 95% CI [8.17-26.8]; P < 0.001) than the high-risk group. We also found that the 9-gene signature recurrence score contributed more information about recurrence than standard clinical and pathological variables in univariate and multivariate Cox analyses when applied to GSE17536(p = 0.03 and p = 0.01 respectively). Furthermore, ColoFinder improved the predictive ability and better stratified the risk subgroups when applied to CRC gene expression datasets GSE14333, GSE17537, GSE12945and GSE24551. In summary, ColoFinder significantly improves the risk assessment in stage II and III CRC patients. The 9-gene prognostic classifier informs patient prognosis and treatment response.

A multigene prognostic assay for selection of adjuvant chemotherapy in patients with T3, stage II colon cancer: impact on quality-adjusted life expectancy and costs

OBJECTIVES

Uncertainty exists regarding appropriate and affordable use of adjuvant chemotherapy in stage II colon cancer (T3, proficient DNA mismatch repair). This study aimed to estimate the effectiveness and costs from a US societal perspective of a multigene recurrence score (RS) assay for patients recently diagnosed with stage II colon cancer (T3, proficient DNA mismatch repair) eligible for adjuvant chemotherapy.

METHODS

RS was compared with guideline-recommended clinicopathological factors (tumor stage, lymph nodes examined, tumor grade, and lymphovascular invasion) by using a state-transition (Markov) lifetime model. Data were obtained from published literature, a randomized controlled trial (QUick And Simple And Reliable) of adjuvant chemotherapy, and rates of chemotherapy use from the National Cooperative Cancer Network Colon/Rectum Cancer Outcomes study. Life-years, quality-adjusted life expectancy, and lifetime costs were examined.

RESULTS

The RS is projected to reduce adjuvant chemotherapy use by 17% compared with current treatment patterns and to increase quality-adjusted life expectancy by an average of 0.035 years. Direct medical costs are expected to decrease by an average of $2971 per patient. The assay was cost saving for all subgroups of patients stratified by clinicopathologic factors. The most influential variables affecting treatment decisions were projected years of life remaining, recurrence score, and patients' disutilities associated with adjuvant chemotherapy.

CONCLUSIONS

Use of the multigene RS to assess recurrence risk after surgery in stage II colon cancer (T3, proficient DNA mismatch repair) may reduce the use of adjuvant chemotherapy without decreasing quality-adjusted life expectancy and be cost saving from a societal perspective. These findings need to be validated in additional cohorts, including studies of clinical practice as assay use diffuses into nonacademic settings.

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Introduction

The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.

Methods

A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.

Results

Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.

Conclusions

The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

ColoGuidePro: a prognostic 7-gene expression signature for stage III colorectal cancer patients

PURPOSE

Improved prognostic stratification of patients with stage II and III colorectal cancer is warranted for postoperative clinical decision making. This study was conducted to develop a clinically feasible and robust prognostic classifier for these patients independent of adjuvant treatment.

EXPERIMENTAL DESIGN

Global gene expression profiles from altogether 387 stage II and III colorectal cancer tissue samples from three independent patient series were included in the study. ColoGuidePro, a seven-gene prognostic classifier, was developed from a selected Norwegian learning series (n = 95; no adjuvant treatment) using lasso-penalized multivariate survival modeling with cross-validation.

RESULTS

The expression signature significantly stratified patients in a consecutive Norwegian test series, in which patients were treated according to current standards [HR, 2.9 (1.1-7.5); P = 0.03; n = 77] and an external validation series [HR, 3.7 (2.0-6.8); P < 0.001; n = 215] according to survival. ColoGuidePro was also an independent predictor of prognosis in multivariate models including tumor stage in both series (HR, ≥ 3.1; P ≤ 0.03). In the validation series, which consisted of patients from other populations (United States and Australia), 5-year relapse-free survival was significantly predicted for stage III patients only (P < 0.001; n = 107). Here, prognostic stratification was independent of adjuvant treatment (P = 0.001).

CONCLUSIONS

We present ColoGuidePro, a prognostic classifier developed for patients with stage II and III colorectal cancer. The test is suitable for transfer to clinical use and has best prognostic prediction potential for stage III patients.

Semi-supervised learning improves gene expression-based prediction of cancer recurrence

MOTIVATION

Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequently, a large number of microarray data that do not have sufficient follow-up information are disregarded. To fully leverage all of the precious data in public databases, we turned to a semi-supervised learning technique, low density separation (LDS).

RESULTS

Using a clinically important question of predicting recurrence risk in colorectal cancer patients, we demonstrated that (i) semi-supervised classification improved prediction accuracy as compared with the state of the art supervised method SVM, (ii) performance gain increased with the number of unlabeled samples, (iii) unlabeled data from different institutes could be employed after appropriate processing and (iv) the LDS method is robust with regard to the number of input features. To test the general applicability of this semi-supervised method, we further applied LDS on human breast cancer datasets and also observed superior performance. Our results demonstrated great potential of semi-supervised learning in gene expression-based outcome prediction for cancer patients.

CONTACT

bing.zhang@vanderbilt.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prognostic and predictive markers in stage II colon cancer: is there a role for gene expression profiling?

Conventional clinical and pathologic risk factors in stage II colon cancer provide limited prognostic information and do not predict response to adjuvant 5-fluorouracil-based chemotherapy. New prognostic and predictive biomarkers are needed to identify patients with highest recurrence risk who will receive the greatest absolute risk reduction from adjuvant chemotherapy. We review below the evidence for conventional risk factors in patients with node-negative colon cancer, followed by a discussion of promising new molecular and genetic markers in this malignancy. Gene expression profiling is an emerging tool with both prognostic and predictive potential in oncology. For patients with stage II colon cancer, the Oncotype DX Colon Cancer test is now commercially available as a prognostic marker, and the ColoPrint assay is expected to be released later this year. Current evidence for both of these assays is described below, concluding with a discussion of potential future directions for gene expression profiling in colon cancer risk stratification and treatment decision making.

Enhancement of deoxyribonucleic acid microarray performance using post-hybridization signal amplification

Microarray performance depends upon the ability to screen samples against a vast array of probes with the appropriate sensitivity and selectivity. While these factors are significantly influenced by probe design, they are also subject to the particular detection methodology and reagents employed. Herein we describe the incorporation of super avidin-biotin system (SABS) and secondary enzymatic enhancement (SEE) as post-hybridization signal amplification techniques to improve the sensitivity of oligonucleotide microarrays. To these ends, we tested these methods on electrochemically interrogated arrays using both purified influenza A PCR products and randomly amplified genomic Francisella tularensis DNA as targets. While SABS treatment did not improve sensitivity for CombiMatrix ElectraSense(®) arrays using purified influenza A cDNA, chip sensitivity was improved 10-fold for randomly amplified targets. SEE improved performance to a greater degree and was able to lower the detection limits 10-fold for influenza A and 100-fold for F. tularensis DNA. These results indicate the promising capability of post-hybridization amplification techniques for enhancing microarray performance.