Molecular Signature for Lymphatic Metastasis in Colorectal Carcinomas

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

FBXW7 and Its Downstream NOTCH Pathway Could be Potential Indicators of Organ-Free Metastasis in Colorectal Cancer

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths globally. Metastasis is associated with a poor prognosis, yet the underlying molecular mechanism(s) remained largely unknown. In this study, a total of 85 CRC patients were included and the primary tumor lesions were evaluated by next-generation sequencing using a targeted panel for genetic aberrations. Patients were sub-divided according to their metastasis pattern into the non-organ metastases (Non-OM) and organ metastases (OM) groups. By comparing the genetic differences between the two groups, we found that mutations in FBXW7 and alterations in its downstream NOTCH signaling pathway were more common in the Non-OM group. Moreover, correlation analysis suggested that FBXW7 mutations were independent of other somatic alterations. The negative associations of alterations in FBXW7 and its downstream NOTCH signaling pathway with CRC organ metastasis were validated in a cohort of 230 patients in the TCGA CRC dataset. Thus, we speculated that the genomic alterations of FBXW7/NOTCH axis might be an independent negative indicator of CRC organ metastases.

Development of a prognostic gene signature for hepatocellular carcinoma

Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns. Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package "limma". Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan-Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the "rms" package. Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes, IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. In conclusion, the prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients

Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma

OBJECTIVE

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC).

MATERIALS AND METHODS

Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated.

RESULTS

In the comparison of the Radiomics values by stages, "minimum", "Long Run Low Gray-level Emphasis" values were higher in the stage 1-2 group, while "Energy", "Total energy", "Range", "Joint Average", "Sum Average", "Gray-Level Non-Uniformity", "Short-Run High Gray-level Emphasis ", "Run Length Non-Uniformity "and "High Gray-Level Run Emphasis "values were higher in the stage 3-4 group. Of these parameters, only "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity'' values were significantly lower in tumors with low Fuhrman grade (1-2) and low TNM stage (1-2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage.

CONCLUSION

This study demonstrates that "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity "parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

Functions and Clinical Significance of UPF3a Expression in Human Colorectal Cancer

Background

Nonsense-mediated mRNA decay (NMD) can degrade mRNAs with a premature termination codon (PTC), and undegraded mRNAs with PTC mutations can induce a genetic compensation response (GCR) by upregulating its compensatory genes. UPF3a refers to up-frame shift 3A (UPF3a) participating in NMD pathway and GCR. It inhibits the NMD pathway while it stimulates GCR. Notably, the role of UPF3a in cancer remains unclear.

Purpose

The identification and discovery of prognostic markers for colorectal cancer (CRC) are of great clinical significance. The aim of this study was to investigate clinical significance of UPF3a expression in CRC.

Materials and Methods

UPF3a expression was examined in fresh CRC tissues and pared distant metastatic tissues using quantitative real-time PCR, Western blotting and immunohistochemistry staining. Tissue microarray immunohistochemical staining was used to study the relationship of UPF3a with clinicopathological features in 158 CRC patient samples collected from January 2008 to December 2012, and prognosis of CRC was analyzed.

Results

The expression of UPF3a was higher in metastatic tissues than that in primary sites. Moreover, high expression of UPF3a was significantly associated with TNM stage (p=0.009), liver metastasis and recurrence (p<0.001) in CRC patients. The Cancer Genome Atlas (TCGA) database showed the same trend. In CRC cells, knockdown of UPF3a led to a decline in the migration potential. Kaplan-Meier survival analysis revealed that high UPF3a expression, TNM stage were significantly associated (all P<0.01) with poor prognosis for patients. Furthermore, univariate and multivariate Cox analysis revealed that high UPF3a expression was independent risk factor for both overall survival and disease-free survival of CRC patients (all P<0.01).

Conclusion

Results showed that high levels of UPF3a could lead to aggressiveness and poor CRC prognosis. Targeted UPF3a can act as a novel and effective gene therapy for CRC patients to make a better prognosis.

Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer

PURPOSE

The diagnostic value of nomogram in pancreatic cancer (PC) with liver metastasis (PCLM) is still largely unknown. We sought to develop and validate a novel nomogram for the prediction of liver metastasis in patients with PC.

METHOD

About 604 pathologically confirmed PC patients from the Sun Yat-sen University Cancer Center (SYSUCC) between July, 2001 and December, 2013 were retrospectively studied. The SYSUCC cohort was randomly assigned to as the training set and internal validation set. Using these two sets, we derived and validated a prognostic model by using concordance index and calibration curves. Another two independent cohorts between August, 2002 and December, 2013 from the Sun Yat-sen Memorial Hospital (SYSMH, n = 335) and Guangdong General Hospital (GDGH, n = 503) was used for external validation.

RESULT

Computed tomography (CT) reported liver metastasis status, carcinoembryonic antigen (CEA) level and differentiation type were identified as risk factors for PCLM in the training set. The final diagnostic model demonstrated good calibration and discrimination with a concordance index of 0.97 and had a robust internal validation. The score ability to diagnose PCLM was further externally validated in SYSMH and GDGH with a concordance index of 0.93. The model showed better calibration and discrimination than CT, CEA and differentiation in each cohort.

CONCLUSION

Based on a large multi-institution database and on the routinely observed CT-reported status, CEA level and tumor differentiation in clinical practice, we developed and validated a novel nomogram to predict PLCM.

Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer

BACKGROUND

Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer.

PURPOSE

To establish a multiparametric MRI (mpMRI)-based radiomics nomogram for preoperatively predicting LNM status.

STUDY TYPE

Retrospective.

POPULATION

Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.

FIELD STRENGTH

Radiomic features were extracted from a 1.5T mpMRI scan (T₁ -weighted imaging [T₁ WI], fat-saturated T₂ -weighted imaging [FS-T₂ WI], contrast-enhanced [CE], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).

ASSESSMENT

The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively.

RESULTS

The radiomic signature allowed a good discrimination between the LNM and non-LNM groups, with a C-index of 0.856 (95% confidence interval [CI], 0.794-0.918) in the primary cohort and 0.883 (95% CI, 0.809-0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C-index, 0.882 [95% CI, 0.827-0.937], C-index, 0.893 [95% CI, 0.822-0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.

DATA CONCLUSION

A radiomics nomogram was developed by incorporating the radiomics signature with the MRI-reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early-stage cervical cancer.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores

To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers.

The clinical significance of CCBE1 expression in human colorectal cancer

Purpose

The identification and discovery of prognostic markers for colorectal cancer (CRC) are of great clinical significance. CCBE1 is expressed in various tumors and its expression correlates with lymphangiogenesis and angiogenesis. However, the association between CCBE1 expression and CRC outcome has not been reported. The aim of this study was to investigate clinical significance of CCBE1 expression in CRC.

Patients and methods

CCBE1 expression was examined in 30 pairs of fresh CRC tissues and compared with adjacent normal (AN) tissues using quantitative real-time PCR (qRT-PCR), Western blotting and immunohistochemistry (IHC) staining. Tissue microarray immunohistochemical staining was used to study the CCBE1 expression characteristics of 204 CRC patient samples collected from January 2002 to December 2007, and the relationship of CCBE1 with clinicopathological features and prognosis of CRC was analyzed.

Results

CCBE1 was highly expressed in CRC tissues compared with matched AN tissues (P=0.001). Moreover, high expression of CCBE1 was significantly associated with tumor differentiation, lymph node metastasis, vascular invasion, liver metastasis and TNM stage in CRC patients (P≤0.01). Kaplan-Meier survival analysis revealed that high CCBE1 expression, poor tumor differentiation, lymph node metastasis and vascular invasion were significantly associated (all P<0.001) with poor prognosis for patients. Furthermore, univariate and multivariate Cox analysis revealed that high CCBE1 expression, poor tumor differentiation, lymph node metastasis and vascular invasion were independent risk factors for both overall survival (OS) and disease-free survival (DFS) of CRC patients (all P<0.05). OS and DFS of 267 CRC patients from The Cancer Genome Atlas (TCGA) database showed the same trend (log-rank P=6e-04, HR [high] =2.4; log-rank P=0.0081, HR [high] =1.9).

Conclusion

High levels of CCBE1 contribute to the aggressiveness and poor prognosis of CRC. CCBE1 can serve as a novel potential biomarker to predict CRC patients' prognosis.

Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas

Objectives

This study aims to define a radiomic signature for pre-operative differentiation between subclinical pheochromocytoma (sPHEO) and lipid-poor adrenal adenoma (LPA) in adrenal incidentaloma. The goal was to apply a predictive, preventive, and personalized medical approach to the management of adrenal tumors.

Patients and methods

This retrospective study consisted of 265 consecutive patients (training cohort, 212 (LPA, 145; sPHEO, 67); validation cohort, 53 (LPA, 36; sPHEO, 17)). Computed tomography (CT) imaging features were evaluated, including long diameter (LD), short diameter (SD), pre-enhanced CT value (CT_pre), enhanced CT value (CT_post), shape, homogeneity, necrosis or cystic degeneration (N/C). Radiomic features were extracted and then were used to construct a radiomic signature (Rad-score) and radiomic nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate their performance.

Results

Sixteen of three hundred forty candidate features were used to build a radiomic signature. The signature was significantly different between the sPHEO and LPA groups (AUC: training, 0.907; validation, 0.902). The radiomic nomogram based on enhanced CT features (M1) consisted of Rad-score, LD, SD, CT_pre, shape, homogeneity and N/C (AUC: training, 0.957; validation, 0.967). The pre-enhanced CT features based radiomic nomogram (M2) included Rad-score, LD, SD, CT_pre, shape, and homogeneity (AUC: training, 0.955; validation, 0.958).

Conclusions

Our radiomic nomograms based on pre-enhanced and enhanced CT images distinguished sPHEO from LPA. In addition, the promising result using pre-enhanced CT images for predictive diagnostics is important because patients could avoid the additional radiation and risk associated with enhanced CT.

Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer

Purpose: To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings.Experimental Design: We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training (n = 194) and validation (n = 100) sets. A radiomics signature (Rad-score) was generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high- and low-risk groups was determined using receiver-operating characteristic curve analysis. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of the radiomics signature, MRI findings, and clinicopathological variables with DFS. A radiomics nomogram combining the Rad-score and MRI and clinicopathological findings was constructed to validate the radiomic signatures for individualized DFS estimation.Results: Higher Rad-scores were significantly associated with worse DFS in both the training and validation sets (P = 0.002 and 0.036, respectively). The radiomics nomogram estimated DFS [C-index, 0.76; 95% confidence interval (CI); 0.74-0.77] better than the clinicopathological (C-index, 0.72; 95% CI, 0.70-0.74) or Rad-score-only nomograms (C-index, 0.67; 95% CI, 0.65-0.69).Conclusions: The radiomics signature is an independent biomarker for the estimation of DFS in patients with invasive breast cancer. Combining the radiomics nomogram improved individualized DFS estimation. Clin Cancer Res; 24(19); 4705-14. ©2018 AACR.

CT-based radiomic model predicts high grade of clear cell renal cell carcinoma

PURPOSE

To compare the predictive models that can incorporate a set of CT image features for preoperatively differentiating the high grade (Fuhrman III-IV) from low grade (Fuhrman I-II) clear cell renal cell carcinoma (ccRCC).

MATERIAL AND METHODS

One hundred and fourteen patients with ccRCC treated with a partial or radical nephrectomy were enrolled in the training cohort. The six non-texture features, including Pseudocapsule, Round mass, maximal tumor diameter (Diameter_max), intratumoral artery (Artery_tumor), enhancement value of the tumor (TEV) and relative TEV (rTEV), were assessed for each tumor. The texture features were extracted from the CT images of the section with the largest area of renal mass at both corticomedullary and nephrographic phases. The least absolute shrinkage and selection operator (LASSO) was used to screen the most valuable texture features to calculate a texture score (Texture-score) for each patient. A logistic regression model was used in the training cohort to discriminate the high from low grade ccRCC at nephrectomy. The predictors would include all non-texture features in Model 1, all non-texture features and Texture-score in Model 2, and Texture-score in Model 3. The performance of the predictive models were tested and compared in an independent validation cohort composed of 92 cases with ccRCC.

RESULTS

Inter-rater agreement was good for each non-texture feature and Texture-score (the concordance correlation coefficient or Kappa coefficient > 0.70). The Texture-score was calculated via a linear combination of the 4 selected texture features. The three models shown good discrimination of the high from low grade ccRCC in the training cohort and the area under receiver operating characteristic curve (AUC) was 0.826 in Mode 1, 0.878 in Model 2 and 0.843 in Model 3, and a significant different AUC was found between Model 1 and Model 2. Application of the predictive models in the validation cohort still gave a discrimination (AUC > 0.670), and the Texture-score based models with or without the non-texture features (Model 2 and 3) showed a better discrimination of the high from low grade ccRCC (P < 0.05).

CONCLUSION

This study presented the Texture-score based models can facilitate the preoperative discrimination of the high from low grade ccRCC.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

PURPOSE

To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).

PATIENTS AND METHODS

The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011.

RESULTS

The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.

CONCLUSION

This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background

Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature’s relevance to a classification task.

Results

We apply POS, along‐with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.

Conclusions

A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along‐with a novel gene score are exploited to produce the selected subset of genes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-274) contains supplementary material, which is available to authorized users.

Molecular staging of lymph node-negative colon carcinomas by one-step nucleic acid amplification (OSNA) results in upstaging of a quarter of patients in a prospective, European, multicentre study

Background:

Current histopathological staging procedures in colon carcinomas depend on midline division of the lymph nodes with one section of haematoxylin & eosin (H&E) staining only. By this method, tumour deposits outside this transection line may be missed and could lead to understaging of a high-risk group of stage UICC II cases, which recurs in ∼20% of cases. A new diagnostic semiautomated system, one-step nucleic acid amplification (OSNA), detects cytokeratin (CK) 19 mRNA in lymph node metastases and enables the investigation of the whole lymph node. The objective of this study was to assess whether histopathological pN0 patients can be upstaged to stage UICC III by OSNA.

Methods:

Lymph nodes from patients who were classified as lymph node negative after standard histopathology (single (H&E) slice) were subjected to OSNA. A result revealing a CK19 mRNA copy number >250, which makes sure to detect mainly macrometastases and not isolated tumour cells (ITC) or micrometastases only, was regarded as positive for lymph node metastases based on previous threshold investigations.

Results:

In total, 1594 pN0 lymph nodes from 103 colon carcinomas (median number of lymph nodes per patient: 14, range: 1–46) were analysed with OSNA. Out of 103 pN0 patients, 26 had OSNA-positive lymph nodes, resulting in an upstaging rate of 25.2%. Among these were 6/37 (16.2%) stage UICC I and 20/66 (30.3%) stage UICC II patients. Overall, 38 lymph nodes were OSNA positive: 19 patients had one, 3 had two, 3 had three, and 1 patient had four OSNA-positive lymph nodes.

Conclusions:

OSNA resulted in an upstaging of over 25% of initially histopathologically lymph node-negative patients. OSNA is a standardised, observer-independent technique, allowing the analysis of the whole lymph node. Therefore, sampling bias due to missing investigation of certain lymph node tissue can be avoided, which may lead to a more accurate staging.

High-throughput arrays identify distinct genetic profiles associated with lymph node involvement in rectal cancer

BACKGROUND

Preoperative clinical diagnosis of lymph node involvement guides treatment decisions for rectal cancer. Unfortunately, clinical staging still suffers from a lack of accuracy.

OBJECTIVE

The aim of this study was to evaluate objective genetic differences in primary rectal cancers with and without associated lymph node metastasis.

DESIGN

cDNA microarrays were generated from fresh-frozen tumors. Normalized data underwent global unsupervised hierarchical clustering analysis, and discriminating genes were mapped. Top discriminating genes were compared between stage II and III rectal cancers by use of an empirical Bayes 2 group t test with the Statistical Analysis of Microarrays and the Reproducibility-Optimized Test Statistic software separately to guide data reduction and deal with the difficulties of simultaneous statistical inference. Ingenuity Pathways Analysis software was used to analyze discriminating genes in terms of function and biological processes.

PATIENTS

Fifty-five patients with stage II and 22 patients with stage III rectal adenocarcinomas not treated with chemoradiation were included.

RESULTS

Two major unsupervised clusters emerged representing stage II and III cancers. In 1 cluster, 11 of 12 patients (92%) had stage III cancer; in the other cluster, 54 of 65 patients (83%) had stage II (p < 0.001). Five significantly differentially expressed genes characterized the stage III cluster: interleukin-8, 3-hydroxy-3-methylglutaryl coenzyme A synthase, carbonic anhydrase, ubiquitin, and cystatin (all p < 0.05). Of the 12 patients with differential expression of the 5 marker genes, only one had stage II cancer. Fifty-four of 55 stage II patients clustered with alternative expression patterns of the predictor genes. Differentially expressed genes are related to cancer-associated processes, pathways, and networks.

LIMITATIONS

The identified gene signatures have not yet been validated in independent patient populations.

CONCLUSIONS

Distinct gene expression signatures from primary rectal adenocarcinomas can help differentiate the presence or absence of lymph node metastases. These data are informative, and validation of this gene signature may provide a novel approach for more appropriate individualized treatment selection.

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.

[Individualizing treatment for locally advanced rectal cancer]

Based on results of the German Rectal Cancer Study Group CAO/ARO/AIO-94 trial, long-term chemoradiotherapy (RT/CTx) is recommended as standard treatment for locally advanced rectal cancer (UICC stages II/III) in the lower two thirds of the rectum (0-12 cm from the anocutaneous verge). Tumor response to neoadjuvant therapy is very heterogeneous, ranging from complete remission to total resistance to RT/CTx. To fulfill the clinical requirement of individual and risk-adapted multimodal treatment, distinct progress in translational research has been achieved (e.g. gene profiling). However, in clinical reality "individualization" of the therapy of rectal cancer patients has not actually been realized. This can be achieved only on the basis of successful randomized clinical trials (e.g. the CAO/ARO/AIO-04 and GAST-05 trials) translationally combined with basic scientific approaches. One simple first step toward individualizing rectal cancer therapy is being made with the ongoing GAST-05 trial. This investigator initiated phase II trial funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) excludes preoperative RT/CTx for patients with rectal cancer localized in the upper third of the rectum, using only quality controlled principles of radical surgery (partial vs total mesorectal excision) followed by adjuvant chemotherapy.

Comparability of microarray data between amplified and non amplified RNA in colorectal carcinoma

Microarray analysis reaches increasing popularity during the investigation of prognostic gene clusters in oncology. The standardisation of technical procedures will be essential to compare various datasets produced by different research groups. In several projects the amount of available tissue is limited. In such cases the preamplification of RNA might be necessary prior to microarray hybridisation. To evaluate the comparability of microarray results generated either by amplified or non amplified RNA we isolated RNA from colorectal cancer samples (stage UICC IV) following tumour tissue enrichment by macroscopic manual dissection (CMD). One part of the RNA was directly labelled and hybridised to GeneChips (HG-U133A, Affymetrix), the other part of the RNA was amplified according to the “Eberwine” protocol and was then hybridised to the microarrays. During unsupervised hierarchical clustering the samples were divided in groups regarding the RNA pre-treatment and 5.726 differentially expressed genes were identified. Using independent microarray data of 31 amplified vs. 24 non amplified RNA samples from colon carcinomas (stage UICC III) in a set of 50 predictive genes we validated the amplification bias. In conclusion microarray data resulting from different pre-processing regarding RNA pre-amplification can not be compared within one analysis.

An expression module of WIPF1-coexpressed genes identifies patients with favorable prognosis in three tumor types

Wiskott–Aldrich syndrome (WAS) predisposes patients to leukemia and lymphoma. WAS is caused by mutations in the protein WASP which impair its interaction with the WIPF1 protein. Here, we aim to identify a module of WIPF1-coexpressed genes and to assess its use as a prognostic signature for colorectal cancer, glioma, and breast cancer patients. Two public colorectal cancer microarray data sets were used for discovery and validation of the WIPF1 co-expression module. Based on expression of the WIPF1 signature, we classified more than 400 additional tumors with microarray data from our own experiments or from publicly available data sets according to their WIPF1 signature expression. This allowed us to separate patient populations for colorectal cancers, breast cancers, and gliomas for which clinical characteristics like survival times and times to relapse were analyzed. Groups of colorectal cancer, breast cancer, and glioma patients with low expression of the WIPF1 co-expression module generally had a favorable prognosis. In addition, the majority of WIPF1 signature genes are individually correlated with disease outcome in different studies. Literature gene network analysis revealed that among WIPF1 co-expressed genes known direct transcriptional targets of c-myc, ESR1 and p53 are enriched. The mean expression profile of WIPF1 signature genes is correlated with the profile of a proliferation signature. The WIPF1 signature is the first microarray-based prognostic expression signature primarily developed for colorectal cancer that is instrumental in other tumor types: low expression of the WIPF1 module is associated with better prognosis.