Circulating tumor DNA dynamics and recurrence risk in patients undergoing curative intent resection of colorectal cancer liver metastases: A prospective cohort study

BackgroundIn patients with resectable colorectal liver metastases (CRLM), the role of pre- and postoperative systemic therapy continues to be debated. Previous studies have shown that circulating tumor DNA (ctDNA) analysis, as a marker of minimal residual disease, is a powerful prognostic factor in patients with nonmetastatic colorectal cancer (CRC). Serial analysis of ctDNA in patients with resectable CRLM could inform the optimal use of perioperative chemotherapy. Here, we performed a validation study to confirm the prognostic impact of postoperative ctDNA in resectable CRLM observed in a previous discovery study.Methods and findingsWe prospectively collected plasma samples from patients with resectable CRLM, including presurgical and postsurgical samples, serial samples during any pre- or postoperative chemotherapy, and serial samples in follow-up. Via targeted sequencing of 15 genes commonly mutated in CRC, we identified at least 1 somatic mutation in each patient’s tumor. We then designed a personalized assay to assess 1 mutation in plasma samples using the Safe-SeqS assay. A total of 380 plasma samples from 54 patients recruited from July 2011 to Dec 2014 were included in our analysis. Twenty-three (43%) patients received neoadjuvant chemotherapy, and 42 patients (78%) received adjuvant chemotherapy after surgery. Median follow-up was 51 months (interquartile range, 31 to 60 months). At least 1 somatic mutation was identified in all patients’ tumor tissue. ctDNA was detectable in 46/54 (85%) patients prior to any treatment and 12/49 (24%) patients after surgery. There was a median 40.93-fold (19.10 to 87.73, P < 0.001) decrease in ctDNA mutant allele fraction with neoadjuvant chemotherapy, but ctDNA clearance during neoadjuvant chemotherapy was not associated with a better recurrence-free survival (RFS). Patients with detectable postoperative ctDNA experienced a significantly lower RFS (HR 6.3; 95% CI 2.58 to 15.2; P < 0.001) and overall survival (HR 4.2; 95% CI 1.5 to 11.8; P < 0.001) compared to patients with undetectable ctDNA. For the 11 patients with detectable postoperative ctDNA who had serial ctDNA sampling during adjuvant chemotherapy, ctDNA clearance was observed in 3 patients, 2 of whom remained disease-free. All 8 patients with persistently detectable ctDNA after adjuvant chemotherapy have recurred. End-of-treatment (surgery +/− adjuvant chemotherapy) ctDNA detection was associated with a 5-year RFS of 0% compared to 75.6% for patients with an undetectable end-of-treatment ctDNA (HR 14.9; 95% CI 4.94 to 44.7; P < 0.001). Key limitations of the study include the small sample size and the potential for false-positive findings with multiple hypothesis testing.ConclusionsWe confirmed the prognostic impact of postsurgery and posttreatment ctDNA in patients with resected CRLM. The potential utility of serial ctDNA analysis during adjuvant chemotherapy as an early marker of treatment efficacy was also demonstrated. Further studies are required to define how to optimally integrate ctDNA analyses into decision-making regarding the use and timing of adjuvant therapy for resectable CRLM.Trial registrationACTRN12612000345886.     

Classification of Epidermal Growth Factor Receptor Gene Mutation Status Using Serum Proteomic Profiling Predicts Tumor Response in Patients with Stage IIIB or IV Non-Small-Cell Lung Cancer

Objectives

Epidermal growth factor receptor (EGFR) gene mutations in tumors predict tumor response to EGFR tyrosine kinase inhibitors (EGFR-TKIs) in non-small-cell lung cancer (NSCLC). However, obtaining tumor tissue for mutation analysis is challenging. Here, we aimed to detect serum peptides/proteins associated with EGFR gene mutation status, and test whether a classification algorithm based on serum proteomic profiling could be developed to analyze EGFR gene mutation status to aid therapeutic decision-making.

Patients and Methods

Serum collected from 223 stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors prior to therapy was analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Differences in serum peptides/proteins between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were detected in a training group of 100 patients; based on this analysis, a serum proteomic classification algorithm was developed to classify EGFR gene mutation status and tested in an independent validation group of 123 patients. The correlation between EGFR gene mutation status, as identified with the serum proteomic classifier and response to EGFR-TKIs was analyzed.

Results

Nine peptide/protein peaks were significantly different between NSCLC patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes in the training group. A genetic algorithm model consisting of five peptides/proteins (m/z 4092.4, 4585.05, 1365.1, 4643.49 and 4438.43) was developed from the training group to separate patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes. The classifier exhibited a sensitivity of 84.6% and a specificity of 77.5% in the validation group. In the 81 patients from the validation group treated with EGFR-TKIs, 28 (59.6%) of 47 patients whose matched samples were labeled as “mutant” by the classifier and 3 (8.8%) of 34 patients whose matched samples were labeled as “wild” achieved an objective response (p<0.0001). Patients whose matched samples were labeled as “mutant” by the classifier had a significantly longer progression-free survival (PFS) than patients whose matched samples were labeled as “wild” (p=0.001).

Conclusion

Peptides/proteins related to EGFR gene mutation status were found in the serum. Classification of EGFR gene mutation status using the serum proteomic classifier established in the present study in patients with stage IIIB or IV NSCLC is feasible and may predict tumor response to EGFR-TKIs.     

Artificial neural networks and decision tree model analysis of liver cancer proteomes

Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and decision tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.     

Serum microRNA-29a is a promising novel marker for early detection of colorectal liver metastasis

Background: Colorectal cancer (CRC) metastasis occurs in various organs, most frequently in liver. Serological examination including tumor and biochemical markers for liver function evaluation is routinely performed, though its accuracy is not high. MicroRNAs (miRNAs) have been implicated in a variety of human diseases including cancer, and have many characteristics of an ideal biomarker most notably their inherent stability and resilience. Recently, several studies have indicated that circulating miRNAs hold much potential as novel noninvasive biomarkers for cancer and other disease processes. The objective of this study was to investigate the potential of serum miRNAs as novel biomarkers for CRC with liver metastasis. Methods: This study was divided into three phases: (I) 3 candidate serum miRNAs were detected by using real-time RT-PCR, corresponding 38 CRC patients with liver metastasis and 36 CRC patients without metastasis. (II) Marker validation by real-time RT-PCR on a similar cohort of age- and sex-matched CRC patients without (n = 20) and with liver metastasis (n = 20). (III) We examined the correlation between the expressions of candidate serum miRNAs with clinical parameters of CRC patients. Results: Serum miR-29a was significantly higher in colorectal liver metastasis (CRLM) patients than in CRC patients. This marker yielded a receiver operating characteristic curve area of 80.3%. At a cutoff value of 0.155, the sensitivity was 75% and the specificity was 75% in discriminating metastatic from non-metastatic patients. In addition, increased levels of miR-29a expression were also observed in colorectal tumors from CRLM patients compared with CRC patients. No significant difference was observed in the levels of serum miR-92a between metastatic and non-metastatic patients. Conclusions: These findings suggest that serum miR-29a has strong potential as a novel noninvasive biomarker for early detection of CRC with liver metastasis.     

Effective Predictor of Colorectal Cancer Survival Based on Exclusive Expression Pattern Among Different Immune Cell Infiltration

Tumor-infiltrating immune/inflammatory cells, the important components of the tumor microenvironment (TME), remarkably affect the progression of human cancers. To understand the actual conditions within the TME of colorectal cancer (CRC), the interrelationship among tumor-infiltrating neutrophils, M2 macrophages, and regulatory T-cells (T_regs) was systematically analyzed. The infiltration conditions of CD66b⁺ neutrophils, CD163⁺ M2 macrophages, and FOXP3⁺ T_regs in tissue microarrays including 1021 cases of CRC were determined by immunohistochemical analysis. The prediction power of these immune cells for CRC prognosis was evaluated by subgroup analysis of the CRC cohort. Results revealed the existence pattern of infiltrating neutrophils, and T_regs/M2 macrophages fulfilled a “X-low implies Y-high” Boolean relationship, indicative of a mutually exclusive correlation between neutrophils and M2 macrophages, and between neutrophils and T_regs in the TME of CRC. What’s more, the tumor-infiltrating M2 macrophages and T_regs were associated with adverse prognostic factors, whereas neutrophils were corelated with favorable factors. The high infiltration of neutrophils predicted longer survival and better chemotherapeutic response. Nonetheless, high infiltration of M2 macrophages and T_regs predicted poor prognosis. The combination of these tumor-infiltrating immune cells can serve as an effective predictor for the survival of CRC and for the chemotherapeutic outcomes of stage II–III patients.      

Intraoperative Blood Loss Independently Predicts Survival and Recurrence after Resection of Colorectal Cancer Liver Metastasis

Background

Although numerous prognostic factors have been reported for colorectal cancer liver metastasis (CRLM), few studies have reported intraoperative blood loss (IBL) effects on clinical outcome after CRLM resection.

Methods

We retrospectively evaluated the clinical and histopathological characteristics of 139 patients who underwent liver resection for CRLM. The IBL cutoff volume was calculated using receiver operating characteristic curves. Overall survival (OS) and recurrence free survival (RFS) were assessed using the Kaplan–Meier and Cox regression methods.

Results

All patients underwent curative resection. The median follow up period was 25.0 months (range, 2.1–88.8). Body mass index (BMI) and CRLM number and tumor size were associated with increased IBL. BMI (P=0.01; 95% CI = 1.3–8.5) and IBL (P<0.01; 95% CI = 1.6–12.5) were independent OSOs predictors. Five factors, including IBL (P=0.02; 95% CI = 1.1–4.1), were significantly related to RFS via multivariate Cox regression analysis. In addition, OSOs and RFS significantly decreased with increasing IBL volumes. The 5-year OSOs of patients with IBL≤250, 250–500, and >500mL were 71%, 33%, and 0%, respectively (P<0.01). RFS of patients within three IBL volumes at the end of the first year were 67%, 38%, and 18%, respectively (P<0.01).

Conclusions

IBL during CRLM resection is an independent predictor of long term survival and tumor recurrence, and its prognostic value was confirmed by a dose–response relationship.     

Proteomic-based identification of the APC-binding protein EB1 as a candidate of novel tissue biomarker and therapeutic target for colorectal cancer

Novel candidates of biomarker and therapeutic target in colorectal cancer (CRC) were investigated using a proteomic approach. The proteome of normal colorectal epithelial tissues was compared with that of the tumor ones in 59 CRC patients using two-dimensional difference gel electrophoresis. Of 3458 protein spots, 110 exhibited statistically significant (p<0.01) differences in intensity (more than 2.5-folds) between the normal and tumor tissue groups. Of 67 unique gene products that were identified for 105 of the 110 protein spots, we focused on the higher expression of the adenoma polyposis coli-binding protein EB1 (EB1). EB1 was originally discovered as a binding protein of APC, which is a tumor suppressor gene product, and the expression of EB1 has been associated with poor prognosis in several malignancies but not in CRC. Immunohistochemical analysis of the 132 CRC cases revealed that EB1 was overexpressed in tumor cells in correlation with poor prognosis. Suppression of EB1 by RNAi inhibited CRC cell proliferation and invasion. In this study, the overexpression of EB1 in CRC tissues correlating with prognosis, and its functional contribution to the malignant phenotypes of CRC cells are described. The present findings indicate that EB1 is a potential biomarker and therapeutic target in CRC.     

Digital Detection of Multiple Minority Mutants and Expression Levels of Multiple Colorectal Cancer-Related Genes Using Digital-PCR Coupled with Bead-Array

To simultaneously analyze mutations and expression levels of multiple genes on one detection platform, we proposed a method termed “multiplex ligation-dependent probe amplification–digital amplification coupled with hydrogel bead-array” (MLPA–DABA) and applied it to diagnose colorectal cancer (CRC). CRC cells and tissues were sampled to extract nucleic acid, perform MLPA with sequence-tagged probes, perform digital emulsion polymerase chain reaction (PCR), and produce a hydrogel bead-array to immobilize beads and form a single bead layer on the array. After hybridization with fluorescent probes, the number of colored beads, which reflects the abundance of expressed genes and the mutation rate, was counted for diagnosis. Only red or green beads occurred on the chips in the mixed samples, indicating the success of single-molecule PCR. When a one-source sample was analyzed using mixed MLPA probes, beads of only one color occurred, suggesting the high specificity of the method in analyzing CRC mutation and gene expression. In gene expression analysis of a CRC tissue from one CRC patient, the mutant percentage was 3.1%, and the expression levels of CRC-related genes were much higher than those of normal tissue. The highly sensitive MLPA–DABA succeeds in the relative quantification of mutations and gene expressions of exfoliated cells in stool samples of CRC patients on the same chip platform. MLPA–DABA coupled with hydrogel bead-array is a promising method in the non-invasive diagnosis of CRC.     

Sex Differences in Colorectal Cancer Survival: Population-Based Analysis of 164,996 Colorectal Cancer Patients in Germany

Risk of colorectal cancer (CRC) is considerably higher in men compared to women; however, there is inconclusive evidence of sex differences in CRC prognosis. We aimed to assess and explain sex differences in 5-year relative survival using standard and model-based period analysis among 164,996 patients diagnosed with CRC from 1997 to 2006 and reported to 11 German cancer registries covering a population of 33 million inhabitants. Age-adjusted 5-year relative survival was higher in women (64.5% vs. 61.9%, P<0.0001). A substantial survival advantage of women was confirmed in multivariate analysis after adjusting for CRC stage and subsite in subjects under 65 years of age (relative excess risk, RER 0.86, 95% CI 0.82–0.90), but not in older subjects (RER 1.01, 95% CI 0.98–1.04); this pattern was similar in the 1st and in the 2nd to 5th year after diagnosis. The survival advantage of women varied by CRC stage and age and was most pronounced for localized disease (RERs 0.59–0.88 in various age subgroups) and in patients under 45 years of age (RERs 0.59, 0.72 and 0.76 in patients with localized, regional or advanced disease, respectively). On the contrary, sex differences in survival did not vary by location of CRC. In conclusion, our large population-based study confirmed a survival advantage of female compared to male CRC patients, most notably in young and middle aged patients and patients with localized disease. The effect of sex hormones, either endogenous or through hormonal replacement therapy, might be the most plausible explanation for the observed patterns.     

Stool Microbiome and Metabolome Differences between Colorectal Cancer Patients and Healthy Adults

In this study we used stool profiling to identify intestinal bacteria and metabolites that are differentially represented in humans with colorectal cancer (CRC) compared to healthy controls to identify how microbial functions may influence CRC development. Stool samples were collected from healthy adults (n = 10) and colorectal cancer patients (n = 11) prior to colon resection surgery at the University of Colorado Health-Poudre Valley Hospital in Fort Collins, CO. The V4 region of the 16s rRNA gene was pyrosequenced and both short chain fatty acids and global stool metabolites were extracted and analyzed utilizing Gas Chromatography-Mass Spectrometry (GC-MS). There were no significant differences in the overall microbial community structure associated with the disease state, but several bacterial genera, particularly butyrate-producing species, were under-represented in the CRC samples, while a mucin-degrading species, Akkermansia muciniphila, was about 4-fold higher in CRC (p<0.01). Proportionately higher amounts of butyrate were seen in stool of healthy individuals while relative concentrations of acetate were higher in stools of CRC patients. GC-MS profiling revealed higher concentrations of amino acids in stool samples from CRC patients and higher poly and monounsaturated fatty acids and ursodeoxycholic acid, a conjugated bile acid in stool samples from healthy adults (p<0.01). Correlative analysis between the combined datasets revealed some potential relationships between stool metabolites and certain bacterial species. These associations could provide insight into microbial functions occurring in a cancer environment and will help direct future mechanistic studies. Using integrated “omics” approaches may prove a useful tool in identifying functional groups of gastrointestinal bacteria and their associated metabolites as novel therapeutic and chemopreventive targets.     

Cell Budding from Normal Appearing Epithelia: A Predictor of Colorectal Cancer Metastasis?

Background: Colorectal carcinogenesis is believed to be a multi-stage process that originates with a localized adenoma, which linearly progresses to an intra-mucosal carcinoma, to an invasive lesion, and finally to metastatic cancer. This progression model is supported by tissue culture and animal model studies, but it is difficult to reconcile with several well-established observations, principally among these are that up to 25% of early stage (Stage I/II), node-negative colorectal cancer (CRC) develop distant metastasis, and that circulating CRC cells are undetectable in peripheral blood samples of up to 50% of patients with confirmed metastasis, but more than 30% of patients with no detectable metastasis exhibit such cells. The mechanism responsible for this diverse behavior is unknown, and there are no effective means to identify patients with pending, or who are at high risk for, developing metastatic CRC.Novel findings: Our previous studies of human breast and prostate cancer have shown that cancer invasion arises from the convergence of a tissue injury, the innate immune response to that injury, and the presence of tumor stem cells within tumor capsules at the site of the injury. Focal degeneration of a capsule due to age or disease attracts lymphocyte infiltration that degrades the degenerating capsules resulting in the formation of a focal disruption in the capsule, which selectively favors proliferating or “budding” of the underlying tumor stem cells. Our recent studies suggest that lymphocyte infiltration also triggers metastasis by disrupting the intercellular junctions and surface adhesion molecules within the proliferating cell buds causing their dissociation. Then, lymphocytes and tumor cells are conjoined through membrane fusion to form tumor-lymphocyte chimeras (TLCs) that allows the tumor stem cell to avail itself of the lymphocyte''s natural ability to migrate and breach cell barriers in order to intravasate and to travel to distant organs. Our most recent studies of human CRC have detected nearly identical focal capsule disruptions, lymphocyte infiltration, budding cells, and the formation of TLCs. Our studies have further shown that age- and type-matched node-positive and -negative CRC have a significantly different morphological and immunohistochemical profile and that the majority of lymphatic ducts with disseminated cells are located within the mucosa adjacent to morphologically normal appearing epithelial structures that express a stem cell-related marker.New hypothesis: Based on these findings and the growth patterns of budding cells revealed by double immunohistochemistry, we further hypothesize that metastatic spread is an early event of carcinogenesis and that budding cells overlying focal capsule disruptions represent invasion- and metastasis-initiating cells that follow one of four pathways to progress: (1) to undergo extensive in situ proliferation leading to the formation of tumor nests that subsequently invade the submucosa, (2) to migrate with associated lymphocytes functioning as “seeds” to grow in new sites, (3) to migrate and intravasate into pre-existing vascular structures by forming TLCs, or (4) to intravasate into vascular structures that are generated by the budding cells themselves. We also propose that only node-positive cases harbor stem cells with the potential for multi-lineage differentiation and unique surface markers that permit intravasation.     

Predicting Outcome on Admission and Post-Admission for Acetaminophen-Induced Acute Liver Failure Using Classification and Regression Tree Models

Background/Aim

Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King’s College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.

Methods

CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998–09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).

Results

Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease), lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade) offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.

Conclusion

CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.

Key Points

• Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission• Little has been published regarding the use of King’s College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies• Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians• Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity• KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points     

Pharmacological targeting of the novel β-catenin chromatin-associated kinase p38α in colorectal cancer stem cell tumorspheres and organoids

The prognosis of locally advanced colorectal cancer (CRC) is currently unsatisfactory. This is mainly due to drug resistance, recurrence, and subsequent metastatic dissemination, which are sustained by the cancer stem cell (CSC) population. The main driver of the CSC gene expression program is Wnt signaling, and previous reports indicate that Wnt3a can activate p38 MAPK. Besides, p38 was shown to feed into the canonical Wnt/β-catenin pathway. Here we show that patient-derived locally advanced CRC stem cells (CRC-SCs) are characterized by increased expression of p38α and are “addicted” to its kinase activity. Of note, we found that stage III CRC patients with high p38α levels display reduced disease-free and progression-free survival. Extensive molecular analysis in patient-derived CRC-SC tumorspheres and APC^Min/+ mice intestinal organoids revealed that p38α acts as a β-catenin chromatin-associated kinase required for the regulation of a signaling platform involved in tumor proliferation, metastatic dissemination, and chemoresistance in these CRC model systems. In particular, the p38α kinase inhibitor ralimetinib, which has already entered clinical trials, promoted sensitization of patient-derived CRC-SCs to chemotherapeutic agents commonly used for CRC treatment and showed a synthetic lethality effect when used in combination with the MEK1 inhibitor trametinib. Taken together, these results suggest that p38α may be targeted in CSCs to devise new personalized CRC treatment strategies.Subject terms: Cancer stem cells, Colorectal cancer, Post-translational modifications     

Identification of Kininogen-1 as a Serum Biomarker for the Early Detection of Advanced Colorectal Adenoma and Colorectal Cancer

Background

Serum markers represent potential tools for the detection of colorectal cancer (CRC). The aim of this study was to obtain proteomic expression profiles and identify serum markers for the early detection of CRC.

Methods

Proteomic profiles of serum samples collected from 35 healthy volunteers, 35 patients with advanced colorectal adenoma (ACA), and 40 patients with CRC were compared using Clinprot technology. Using enzyme-linked immunosorbent assays (ELISAs), 366 sera samples were additionally analyzed, and immunohistochemistry studies of 400 tissues were used to verify the expression of kininogen-1 and its value in the early detection of CRC.

Results

Predicting models were established among the three groups, and kininogen-1 was identified as a potential marker for CRC using Clinprot technology. ELISAs also detected significantly higher serum kininogen-1 levels in ACA and CRC patients compared to controls (P<0.05). Furthermore, the area under the receiver operating characteristic curve (AUC) for serum kininogen-1 in the diagnosis of ACA was 0.635 (P = 0.003), and for serum carcinoembryonic antigen (CEA) was 0.453 (P = 0.358). The sensitivity, specificity, and accuracy of serum kininogen-1 for diagnosing Duke’s stage A and B CRC was 70.13%, 65.88%, and 67.90%, respectively, whereas serum CEA was 38.96%, 85.88%, and 63.58%, respectively. Moreover, immunohistochemistry showed that expression of kininogen-1 was significantly higher in CRC and ACA tissues than in normal mucosa (48.39% vs. 15.58% vs. 0%, P<0.05).

Conclusions

These results suggest that Clinprot technology provides a useful tool for the diagnosis of CRC, and kininogen-1 is a potential serum biomarker for the early detection of advanced colorectal adenoma and CRC.     

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.     

Detection of bladder cancer using proteomic profiling of urine sediments

We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.     

DNA Methylation Combinations in Adjacent Normal Colon Tissue Predict Cancer Recurrence: Evidence from a Clinical Cohort Study

Accumulating evidence has suggested the requirement for further stratification of patients in the same tumor stage according to molecular factors. We evaluate the combination of cancer stage and DNA methylation status as an indicator of the risk of recurrence and mortality among patients with colorectal cancer (CRC). A cohort study of 215 patients with CRC (mean age 64.32 years; 50.5% of men) from Tri-Service General Hospital in Taiwan examined the association between cancer stage and risk of CRC recurrence and mortality. A Cox proportional hazard model was used to analyze patient methylation status and clinical information at study entry, and their associations with CRC recurrence and mortality during follow-up. The advanced stage patients with p16, hMLH1, and MGMT methylation were associated with higher risk of CRC recurrence compared with the local stage patients with unmethylation status in tumor tissues, with adjusted hazard ratios (HRs) (95% confidence interval [CI]) of 9.64 (2.92–31.81), 8.29 (3.40–20.22), and 11.83 (3.49–40.12), respectively. When analyzing normal tissues, we observed similar risk of CRC recurrence with adjusted HRs (95% CI) of 10.85 (4.06–28.96), 9.04 (3.79–21.54), and 12.61 (4.90–32.44), respectively. For combined analyses, the risk of recurrence in the patients in advanced stage with DNA methylation in both normal and tumor tissues, compared with local stage with unmethylation, was increased with adjusted HR (95% CI) of 9.37 (3.36–26.09). In the advanced stage patients, methylation status and tissue subtype were associated with increased risk of 5-year cumulative CRC recurrence (p < 0.001). This study demonstrates that clustering DNA methylation status according to cancer stage and tissue subtype is critical for the assessment of risk of recurrence in CRC patients and also indicated an underlying mechanism.