Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy

Circulating Biomarkers in Predicting Pathological Response to Neoadjuvant Therapy for Colorectal Cancer

Circulating biomarkers show promise in the management of many cancers. They have become the novel non-invasive approach to complement the current strategies in colorectal cancer (CRC) management. Their ability in guiding diagnosis, evaluating response to treatment, screening and prognosis is phenomenal, especially when it comes to their minimally invasive nature. These “liquid biopsies,” which show potential for replacing invasive surgical biopsies, provide useful information on the primary and metastatic disease by providing an insight into cancer biology. Analysis of blood and body fluids for circulating tumour DNA (ctDNA), carcinoembryonic antigen (CEA), circulating tumour cells (CTC), or circulating micro RNA (miRNA) shows potential for improving CRC management. Recognizing a predictive model to assess response to neoadjuvant chemotherapy would help in better patient selection. This review was conducted with the aim of outlining the use of circulatory biomarkers in current practice and their effectiveness in the management of patients having CRC with a focus on response to neoadjuvant therapy.

Precise Detection of Cataracts with Specific High-Risk Factors by Layered Binary Co-Ionizers Assisted Aqueous Humor Metabolic Analysis

Diabetes and high myopia as well-known high-risk factors can aggravate cataracts, yet clinical coping strategy remains a bottleneck. Metabolic analysis tends to be powerful for precisely detection and mechanism exploration since most of diseases including cataracts are accompanied by metabolic disorder. Herein, a layered binary co-ionizers assisted aqueous humor metabolic analysis tool is proposed for potentially etiological typing and detection of cataracts, including age-related cataracts (ARC), cataracts with diabetes mellitus (CDM), and cataracts with high myopia (CHM). Startlingly, taking advantage of the optimal machine learning algorithm and all metabolic fingerprints, 100% of accuracy, precision, and recall rates are achieved for arbitrary comparison between groups. Moreover, 11, 9, and 7 key metabolites with explicit identities are confirmed as markers of discriminating CDM from ARC, CHM from ARC, and CDM from CHM, and the corresponding area under the curve values of validation cohorts are 0.985, 1.000, and 1.000. Finally, the critical impact of diabetes/high myopia on cataracts is revealed by excavating the change levels and metabolic pathways of key metabolites. This work updates the insights of prevention and treatment about cataracts at metabolic level and throws out huge surprises and progresses metabolic diagnosis toward a reality.

The Role of Micro-RNAs and Circulating Tumor Markers as Predictors of Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

The response to neoadjuvant chemoradiation (nCRT) is a critical step in the management of locally advanced rectal cancer (LARC) patients. Only a minority of LARC patients responds completely to neoadjuvant treatments, thus avoiding invasive radical surgical resection. Moreover, toxic side effects can adversely affect patients' survival. The difficulty in separating in advances responder from non-responder patients affected by LARC highlights the need for valid biomarkers that guide clinical decision-making. In this context, microRNAs (miRNAs) seem to be promising candidates for predicting LARC prognosis and/or therapy response, particularly due to their stability, facile detection, and disease-specific expression in human tissues, blood, serum, or urine. Although a considerable number of studies involving potential miRNA predictors to nCRT have been conducted over the years, to date, the identification of the perfect miRNA signatures or single miRNA, as well as their use in the clinical practice, is still representing a challenge for the management of LARC patients. In this review, we will first introduce LARC and its difficult management. Then, we will trace the scientific history and the key obstacles for the identification of specific miRNAs that predict responsiveness to nCRT. There is a high potential to identify non-invasive biomarkers that circulate in the human bloodstream and that might indicate the LARC patients who benefit from the watch-and-wait approach. For this, we will critically evaluate recent advances dealing with cell-free nucleic acids including miRNAs and circulating tumor cells as prognostic or predictive biomarkers.

Preoperative Radiotherapy Leads to Significant Differences in the Plasma Protein Profile of Rectal Cancer Patients

INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer worldwide, accounting for 10% of the global cancer burden. Rectal cancer accounts for around 30% of CRC cases, and patients with resectable rectal cancer are often given preoperative radiotherapy (PRT) to reduce the rate of local recurrence. The human plasma proteome is an exceptionally complex proteome and ideal to study due to its ability to reflect the presence of diseases such as cancer and the ease of obtaining blood samples. Previous proteomic studies involving rectal cancer patients have mostly focused on the identification of proteins involved in resistance to radiotherapy.

OBJECTIVE

The aim of this study was to investigate the overall effects of PRT on plasma protein expression in rectal cancer patients, as there is a lack of such studies.

METHODS

Here, we have used mass spectrometry and subsequent statistical analyses to analyze the plasma samples of 30 rectal cancer patients according to PRT status (positive or negative) and tumor stage (II or III).

RESULTS AND CONCLUSIONS

We discovered 42 proteins whose levels differed significantly between stage II and III rectal cancer patients who did or did not receive PRT. This study shows that PRT, although localized to the pelvis, leads to measurable, tumor stage-specific changes in plasma protein expression. Future studies of plasma proteins should, when relevant, take this into account and be aware of the widespread effects that PRT has on the plasma proteome.

Circulating Biomarkers for Response Prediction of Rectal Cancer to Neoadjuvant Chemoradiotherapy

Rectal cancer response to neoadjuvant Chemoradiotherapy (pCRT) is highly variable. In fact, it has been estimated that only about 21 % of patients show pathologic Complete Response (pCR) after therapy, while in most of the patients a partial or incomplete tumour regression is observed. Consequently, patients with a priori chemoradioresistant tumour should not receive the treatment, which is associated with substantial adverse effects and does not guarantee any clinical benefit. For Locally Advanced Rectal Cancer Patients (LARC), a standardized neoadjuvant treatment protocol is applied, the identification and the usefulness of prognostic or predictive biomarkers can improve the antitumoural treatment strategy, modifying the sequence, dose, and combination of radiotherapy, chemotherapy and surgical resection. For these reasons, a growing number of studies are actually focussed on the discovery and investigation of new predictive biomarkers of response to pCRT. In this review, we have selected the most recent literature (2012-2017) regarding the employment of blood-based biomarkers potentially predicting pCR in LARC patients and we have critically discussed them to highlight their real clinical benefit and the current limitations of the proposed methodological approaches.

Identification of protein clusters predictive of tumor response in rectal cancer patients receiving neoadjuvant chemo-radiotherapy

Preoperative neoadjuvant chemoradiotherapy (nCRT) is the gold standard in locally advanced rectal cancer, only 10–30% of patients achieving benefits. Currently, there is a need of a reliable selection of markers for the identification of poor or non-responders prior to therapy. In this work, we compared protein profiles before therapy of patients differing in their responses to nCRT to find novel predictive markers of response to therapy. Patients were grouped into 3 groups according to their tumor regression grading (TRG) after surgery: 'TRG 1–2′, good responders, 'TRG 3′ and 'TRG 4′, poor responders. Paired surgical specimens of rectal cancer and healthy (histologically confirmed) rectal tissues from 15 patients were analysed before nCRT by two dimensional difference in gel electrophoresis followed by mass spectrometry. Thirty spots were found as differentially expressed (p < 0.05). Among them, 3 spots (spots 471, 683 and 684) showed an increased amount of protein in poor responders compared with good responders, and they were more tumor associated compared with healthy tissues. Proteins of these spots were identified as fibrinogen ß chain fragment D, actin isoforms, B9 and B5 serpins, cathepsin D isoforms and peroxiredoxin-4. In an independent validation set of 20 rectal carcinomas we validated the increased fibrinogen ß chain abundance before nCRT in poor responders by immunoblotting. In conclusion, we propose a risk-stratification tool in predicting the response to nCRT treatment in rectal cancer based on the quantity of these proteins.

Bulk evaluation and comparison of radiotherapy treatment plans for breast cancer

This study provides a bulk, retrospective analysis of 151 breast and chest wall radiotherapy treatment plans, as a small-scale demonstration of the potential breadth and value of the information that may be obtained from clinical data mining. The treatments were planned at three centres belonging to one organisation over a period of 3 months. All 151 plans were used to evaluate inter-centre consistency and compliance with a local planning protocol. A subset of 79 plans, from one centre, were used in a more detailed evaluation of the effects of anatomical asymmetry on heart and lung dose, the effects of a metallic temporary tissue expander port on dose homogeneity and the overall conformity and homogeneity achieved in routine breast treatment planning. Differences in anatomical structure contouring and nomenclature were identified between the three centres, with all centres showing some non-compliance with the local planning protocol. When evaluated against standard conformity indices, these breast plans performed relatively poorly. However, when evaluated against recommended organ-at-risk tolerances, all evaluated plans performed sufficiently well that tighter planning tolerances could be recommended for future planning. Heart doses calculated in left breast and chest wall treatments were significantly higher than heart doses calculated in right sided breast and chest wall treatments (p < 0.001). In the treatment involving a temporary tissue expander, the inflated implant effectively pushed the targeted breast tissue away from the healthy tissues, leading to a dose distribution that was relatively conformal, although attenuation through the tissue expander's metallic port may have been underestimated by the treatment planning system. The results of this study exemplify the use of bulk treatment planning data to evaluate clinical workloads and inform ongoing treatment planning.

A survey of computational tools for downstream analysis of proteomic and other omic datasets

Proteomics is an expanding area of research into biological systems with significance for biomedical and therapeutic applications ranging from understanding the molecular basis of diseases to testing new treatments, studying the toxicity of drugs, or biotechnological improvements in agriculture. Progress in proteomic technologies and growing interest has resulted in rapid accumulation of proteomic data, and consequently, a great number of tools have become available. In this paper, we review the well-known and ready-to-use tools for classification, clustering and validation, interpretation, and generation of biological information from experimental data. We suggest some rules of thumb for the reader on choosing the best suitable learning method for a particular dataset and conclude with pathway and functional analysis and then provide information about submitting final results to a repository.

Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy

Preoperative chemoradiotherapy is worldwide accepted as a standard treatment for locally advanced rectal cancer. Current standard of treatment includes administration of ionizing radiation for 45-50.4 Gy in 25-28 fractions associated with 5-fluorouracil administration during radiation therapy. Unfortunately, 40% of patients have a poor or absent response and novel predictive biomarkers are demanding. For the first time, we apply a novel peptidomic methodology and analysis in rectal cancer patients treated with preoperative chemoradiotherapy. Circulating peptides (Molecular Weight <3 kDa) have been harvested from patients' plasma (n = 33) using nanoporous silica chip and analyzed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass spectrometer. Peptides fingerprint has been compared between responders and non-responders. Random Forest classification selected three peptides at m/z 1082.552, 1098.537, and 1104.538 that were able to correctly discriminate between responders (n = 16) and non-responders (n = 17) before therapy (T0) providing an overall accuracy of 86% and an area under the receiver operating characteristic (ROC) curve of 0.92. In conclusion, the nanoporous silica chip coupled to mass spectrometry method was found to be a realistic method for plasma-based peptide analysis and we provide the first list of predictive circulating biomarker peptides in rectal cancer patients underwent preoperative chemoradiotherapy.

Biomarkers and Molecular Imaging as Predictors of Response to Neoadjuvant Chemoradiotherapy in Patients With Locally Advanced Rectal Cancer

Standard treatment of patients with locally advanced rectal cancer (LARC) includes neoadjuvant chemoradiotherapy (NCRT) followed by surgery. Tumor regression after NCRT varies substantially among individuals and pathological complete response is a known prognostic factor for LARC. The identification of a predictive model for response to chemoradiotherapy would help clinicians to identify patients who would probably benefit from multimodal treatment and to perform an early assessment of individual prognosis. Carcinoembryonic antigen has proven to be a good predictor of response in several clinical trials. Other widely studied predictive models in LARC include molecular biomarkers, analyzed at various levels and by different techniques, and molecular imaging, in particular magnetic resonance imaging and positron emission tomography/computed tomography. Although none of the studied markers have been approved in clinical practice, their evaluation in larger, prospective trials and in combined predictive models could be of use to define tailored therapeutic strategies.

Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

Clinical criteria underestimate complete pathological response in rectal cancer treated with neoadjuvant chemoradiotherapy

BACKGROUND

Published criteria define specific mucosal features of clinical complete response for rectal cancer after neoadjuvant chemoradiotherapy.

OBJECTIVE

The aim of this study was to determine the performance of these criteria to identify a pathological complete response.

DESIGN

Histopathology reports were retrieved for consecutive rectal cancers treated with neoadjuvant therapy followed by proctectomy. The mucosal appearance of residual disease was compared with the final pathological stage.

SETTING

This study was conducted at a single-institution, tertiary referral center.

PATIENTS

The study included 238 patients.

INTERVENTIONS

All patients underwent neoadjuvant chemoradiation followed by proctectomy between 1997 and 2007.

MAIN OUTCOME MEASURES

Gross mucosal appearance was compared with the final pathological stage.

RESULTS

Following neoadjuvant chemoradiation, 61 of 238 (25%) patients were downstaged to ypT0. Forty-five of these 61 patients (74%) had a residual mucosal abnormality that precluded assignment of a complete response. Of these, 40 had residual ulcers (up to 10 mm in depth) and 5 had exophytic lesions. The remaining 16 patients with pathological complete response fulfilled criteria for clinical complete response and had either no visible abnormality or a scar. Although mucosal complete clinical response was statistically associated with ypT0 status (p < 0.0001), 6 of 22 (27%) patients with mucosal complete clinical response still had residual disease. Smaller size of residual mucosal abnormality was also associated with ypT0 status (p = 0.002).

LIMITATIONS

This is a retrospective study and preoperative clinical assessment of response is not recorded for comparison to resected specimens.

CONCLUSIONS

The majority of patients attaining ypT0 status do not display mucosal features of complete response. When present, a mucosal complete response is statistically associated with ypT0 status, but is poorly sensitive. If rectal conservation after chemoradiation is to be pursued, alternative means of restaging are required to maximize the number who might benefit from this approach.

Analysis of serum proteome profiles in nasopharyngeal carcinoma with different radiosensitivity

PURPOSE

The research is endeavored to establish a serum protein fingerprint model for predicting radiosensitivity of nasopharyngeal carcinoma through the analysis of the serum expressed proteins of the pre- and post-treatment nasopharyngeal carcinoma patients treated with radiotherapy.

METHODS

Surface-enhanced laser desorption ionization-time of flight-mass spectrometry and CM-10 protein chip were used to detect the serum proteomic patterns of 50 nasopharyngeal carcinoma patients with different radiosensitivity. Thirty-eight of the 50 patients after the treatment were also studied. Biomarker Wizard 3.01 and Biomarker Pattern 5.01 were used in combination to analyze the data and to develop predicting models.

RESULTS

At the molecular weight range of 2,000-20,000, the software identified an average of 83 mass peaks between radiation sensitive group and radiation resistant group. 11 protein peaks were significantly different. Of the 83 mass peaks, 4 mass peaks (mass/charge ratio [m/z] 2,575, 3,942, 6,117 and 6,778) were chosen automatically to construct a classification tree. The diagnostic accuracy was 78.0 %. M/z 6,117 and 6,778 of the radiation sensitive group after the treatment trended to those of the radiation resistant group.

CONCLUSIONS

The technology of surface-enhanced laser desorption ionization-time of flight-mass spectrometry can be used to screen and identify differentially expressed proteins associated with different radiosensitivity in nasopharyngeal cancer. It should be a very useful tool for predicting radiosensitivity of nasopharyngeal carcinoma.

Improving colorectal cancer management: the potential of proteomics

Colorectal cancer (CRC) is the third most common cancer worldwide. Successful treatment is heavily dependent on tumor stage at the time of detection, but unfortunately CRC is often only detected in advanced stages. New biomarkers in the form of genes or proteins that can be used for diagnosis, prognostication, follow-up, and treatment selection and monitoring could be of great benefit for the management of CRC. Furthermore, proteins could prove valuable new targets for therapy. Therefore, clinical proteomics has gained a lot of scientific interest in this regard. To get an overall insight into the extent to which this research has contributed to a better management of CRC, we give a comprehensive overview of the results of proteomics research on CRC, focusing on expression proteomics, in other words, protein profiling studies. Furthermore, we evaluate the potential of the discriminating proteins identified in this research for clinical use as biomarkers for (early) diagnosis, prognosis and follow-up of CRC or as targets for new therapeutic regimens.

MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and classification of cancer. Several published studies have shown that multipeptide signatures selected in numerical tests have potential values for diagnostics of different types of cancer. However due to apparent problems with standardization of methodological details, both experimental and computational, none of the proposed peptide signatures analyzed directly by MALDI/SELDI-ToF spectrometry has been approved for routine diagnostics. Noteworthy, several components of proposed cancer signatures, especially those characteristic for advanced cancer, were identified as fragments of blood proteins involved in the acute phase and inflammatory response. This indicated that among cancer biomarker candidates to be possibly identified by serum proteome profiling were rather those reflecting overall influence of a disease (and the therapy) upon the human organism, than products of cancer-specific genes. Current paper focuses on changes in serum proteome that are related to response of patient's organism to progressing malignancy and toxicity of anticancer treatment. In addition, several methodological issues that affect robustness and interlaboratory reproducibility of MS-based serum proteome profiling are discussed.

Biological and methodical challenges of blood-based proteomics in the field of neurological research

Biomarker discovery is an application of major importance in today's proteomic research. There is an urgent need for suitable biomarkers to improve diagnostic tools and treatment in various neurological diseases, such as neurodegenerative disorders. Recent years have witnessed an enormous interest in proteomics, which is currently seen as an invaluable tool to shed more light on complex interacting signalling pathways and molecular networks involved in several neuropathological conditions. However, while first results of proteomic research studies have sparked much public attention, the momentum of further proteomic biomarker research in neurological disorders may suffer by its very complex methodology which is sensitive to various sources of artefacts. A major source of variability is proteome perturbation caused by sample handling/preservation (preanalytical phase) and processing/measurement (analytical phase). The aim of the present review is to summarize the current literature focusing on the crucial role played by preanalytical and analytical factors that affect the quality of samples and the reliability of the data produced in blood-based proteomic biomarker research in neurology, which may apply to Alzheimer's disease (AD) as well as other neurological disorders. Procedures for sample preparation and protocols for the analysis of serum and plasma samples will be delineated. Finally, the potential usefulness of bioinformatics--allowing for the assembly, store, and processing of data--as well as its contribution to the execution of proteomic studies will be critically discussed.

The use of molecular markers as a method to predict the response to neoadjuvant therapy for advanced stage rectal adenocarcinoma

AIM

The response to combined neoadjuvant therapy for advanced stage rectal adenocarcinoma is predictive of outcome. In addition to both clinical and pathological features, the expression of a variety of molecules may provide another method of identifying tumour responsiveness to pre-operative therapy. The aim of this study was to evaluate several markers in the apoptotic pathway as well as expression of Cox-2 and vascular endothelial growth factor (VEGF) to determine their ability to predict response to neoadjuvant therapy.

METHOD

In total, 152 patients with advanced rectal adenocarcinoma were treated with neoadjuvant therapy followed by resection. Paraffin-embedded sections obtained before and after therapy were assessed by immunohistochemical staining for Cox-2, VEGF, p53, p21, p27, Bax, BCL-2 and apoptosis protease-activating factor 1 (APAF-1). These stains were correlated with tumour regression grade, complete pathological response and T-downstaging of the surgical specimen. Clinical and pathological data were also collected. Data were analysed using the χ2 and Spearman's correlation tests.

RESULTS

Pathological complete response was seen in 24.5% of patients. Amongst the apoptosis-associated markers, only APAF-1 expression was found to be significantly associated with tumour regression grade (P<0.001), complete pathological response (P<0.031) and T-downstaging (P<0.004). On multivariate analysis, APAF-1 expression was found to be independently associated with good tumour regression grade. In contrast, overexpression of Cox-2 and VEGF in pretreatment biopsies was related to less tumour regression (P<0.003) and less likelihood of T-downstaging (P<0.03).

CONCLUSION

Immunohistochemical evaluation of initial biopsy specimens of rectal cancer with APAF-1, Cox-2 and VEGF may predict tumour response to neoadjuvant therapy in patients with advanced rectal adenocarcinoma. Those with an expected limited response may be considered for other investigational neoadjuvant protocols.

The surgical significance of residual mucosal abnormalities in rectal cancer following neoadjuvant chemoradiotherapy

BACKGROUND

Local excision of rectal cancer after neoadjuvant chemoradiotherapy (CRT) has been proposed as an alternative to radical surgery in selected patients. However, little is known about the significance of the morphological and histological features of residual tumour.

METHODS

Patients who had undergone CRT at the authors' institution between 1997 and 2010 were identified. Multiple features were assessed as putative markers of pathological response. These included: gross residual disease, diameter of residual mucosal abnormalities, tumour differentiation, presence of lymphovascular/perineural invasion and lymph node ratio.

RESULTS

Data from 220 of 276 patients were suitable for analysis. Diameter of residual mucosal abnormalities correlated strongly with pathological tumour category after CRT (ypT) (P < 0·001). Forty of 42 tumours downstaged to ypT0/1 had residual mucosal abnormalities of 2·99 cm or less after CRT. Importantly, 19 of 31 patients with a complete pathological response had evidence of a residual mucosal abnormality consistent with an incomplete clinical response. The ypT category was associated with both pathological node status after CRT (P < 0·001) and lymph node ratio (P < 0·001). Positive nodes were found in only one of 42 patients downstaged to ypT0/1. The risk of nodal metastases was associated with poor differentiation (P = 0·027) and lymphovascular invasion (P < 0·001).

CONCLUSION

In this series, the majority of patients with a complete pathological response did not have a complete clinical response. In tumours downstaged to ypT0/1 after CRT, residual mucosal abnormalities were predominantly small and had a 2 per cent risk of positive nodes, thus potentially facilitating transanal excision. The presence of adverse histological characteristics risk stratified tumours for nodal metastases.

Serum proteomic profiling reveals that pretreatment complement protein levels are predictive of esophageal cancer patient response to neoadjuvant chemoradiation

OBJECTIVE

To identify serum-based biomarkers predicting response to neoadjuvant chemoradiotherapy (neo-CRT) in esophageal cancer.

PURPOSE

Increasingly, the standard of care for esophageal cancer involves neo-CRT followed by surgery. The identification of biomarkers predicting response to therapy may represent a major advance, enabling clinical trials and improved outcomes.

BACKGROUND DATA

Patients with esophageal cancer (n = 31) received a standard neo-CRT regimen. Histopathologic response to therapy was assessed by using the Mandard tumor regression grade (TRG) classification. Serum was collected pretreatment and at 24-hour and 48-hour time points into treatment. Serum samples were analyzed by using Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and enzyme-linked immunosorbent assay. A leave-one-out cross-validation predictive algorithm assessed the ability of validated biomarkers to correctly predict therapeutic outcome.

RESULTS

Fifty-one percent (16) of patients were poor responders (TRG 3-5), whereas 49% (15) responded well (TRG 1-2). On CM10 biochips, serum expression of 9 protein peaks was significantly different between the response groups. Two differential spectrum peaks were identified as complement C4a and C3a and were subsequently analyzed by enzyme-linked immunosorbent assay. Pretreatment serum C4a and C3a levels were significantly higher in poor responders versus good responders. Subdivision of the response groups by TRG indicated an inverse correlation between levels of C4a and C3a and pathological response to neo-CRT. The leave-one-out cross-validation analysis revealed that these serum proteins could predict response to neo-CRT with a sensitivity and specificity of 78.6% and 83.3%, respectively.

CONCLUSIONS

This translational application of proteomics technology identifies pretreatment serum levels of C4a and C3a as predictive biomarkers of response. Large validation studies in an independent cohort are merited.

Screening for colorectal cancer: established and emerging modalities

It has been estimated that >95% of cases of colorectal cancer (CRC) would benefit from curative surgery if diagnosis was made at an early or premalignant polyp stage of disease. Over the past 10 years, most developed nation states have implemented mass population screening programs, which are typically targeted at the older (at-risk) age group (>50-60 years old). Conventional screening largely relies on periodic patient-centric investigation, particularly involving colonoscopy and flexible sigmoidoscopy, or else on the fecal occult blood test. These methods are compromised by either low cost-effectiveness or limited diagnostic accuracy. Advances in the development of diagnostic molecular markers for CRC have yielded an expanding list of potential new screening modalities based on investigations of patient stool (for colonocyte DNA mutations, epigenetic changes or microRNA expression) or blood specimens (for plasma DNA mutations, epigenetic changes, heteroplasmic mitochondrial DNA mutations, leukocyte transcriptome profile, plasma microRNA expression or protein and autoantibody expression). In this Review, we present a critical evaluation of the performance data and relative merits of these various new potential methods. None of these molecular diagnostic methods have yet been evaluated beyond the proof-of-principle and pilot-scale study stage and it could be some years before they replace existing methods for population screening in CRC.

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

[Proteomic technologies for identification of serum potential biomarkers of autoimmune demyelinating polyneuropathies]

Time-of-flight MALDI mass spectrometry (MALDI-TOF-MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples) and practically healthy donors (HD) (35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis samples were pre-fractionated on magnetic microparticles with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks, agreed change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS values of these characteristics reached 100 and 100, and for CIDP 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP, allowed to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.

Serum biomarkers which correlate with failure to respond to immunotherapy and tumor progression in a murine colorectal cancer model

PURPOSE

To advance our understanding of mechanisms involved in tumor progression/regression, a CT26 colorectal mouse model treated intra-tumorally with DISC-herpes simplex virus as immunotherapy was used in the discovery and validation phases to investigate and ultimately identify biomarkers correlating with the failure to respond to immunotherapy.

EXPERIMENTAL DESIGN

For the discovery phase, serum protein/peptide profiles of a retrospective sample collection (total n=70) were analyzed using MALDI-TOF-MS combined with artificial neural networks. Following identification of the key predictive peptides using ESI-MS/MS, validation of the identified proteins was carried out on serum and tissues collected in an independent sample set (total n=60).

RESULTS

Artificial neural network analysis resulted in four discriminatory peaks with an accuracy of 86%, sensitivity of 90% and specificity of 81% between the progressor/regressor groups. Three of the identified discriminatory markers were upregulated and demonstrated a positive correlation with tumor progression following DISC-herpes simplex virus therapy. Immunovalidation studies corroborated the MALDI-TOF-MS findings. Immunohistochemistry revealed that serum amyloid A-1 and serum amyloid P produced in the liver localized intracellularly in CT26 tumor tissue.

CONCLUSIONS

MALDI-TOF-MS and BI analysis of the serum proteome of tumor-bearer mice undergoing immunotherapy, identified biomarkers associating with "failure to respond" and biological arrays confirmed these findings.

New method for peptide desorption from abundant blood proteins for plasma/serum peptidome analyses by mass spectrometry

This report describes a new method for desorption of low-molecular weight (LMW) peptides from abundant blood proteins for use in subsequent mass spectrometry analyses. Heating of diluted blood serum to 98°C for 15min resulted in dissociation of LMW peptides from the most abundant blood proteins. Application of blood plasma/serum fractionation using magnetic beads with a functionalized surface followed by heating of the resultant fractions significantly increases the number of LMW peptides detected by MALDI-TOF MS, enhances the general reproducibility of mass spectrometry profiles and considerably increases the number of identified blood serum peptides by LC-MS/MS using an Agilent 6520 Accurate-Mass Q-TOF.

Rectum-conserving surgery in the era of chemoradiotherapy

BACKGROUND

A complete pathological response occurs in 10-30 per cent of patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy (CRT). The standard of care has been radical surgery with high morbidity risks and the challenges of stomata despite the favourable prognosis. This review assessed minimalist approaches (transanal excision or observation alone) to tumours with a response to CRT.

METHODS

A systematic review was performed using PubMed and Embase databases. Keywords included: 'rectal', 'cancer', 'transanal', 'conservative', 'complete pathological response', 'radiotherapy' and 'neoadjuvant'. Original articles from all relevant listings were sourced. These were hand searched for further articles of relevance. Main outcome measures assessed were rates of local recurrence and overall survival, and equivalence to radical surgery.

RESULTS

Purely conservative 'watch and wait' strategies after CRT are still controversial. Originally used for elderly patients or those who refused surgery, the data support transanal excision of rectal tumours showing a good response to CRT. A complete pathological response in the T stage (ypT0) indicates < 5 per cent risk of nodal metastases.

CONCLUSION

Rectal tumours showing an excellent response to CRT may be suitable for local excision, with equivalent outcomes to radical surgery. This approach should be the subject of prospective clinical trials in specialist centres.

Proteomic strategies and challenges in tumor metastasis research

The rapidly evolving field of proteomics offers new approaches to understanding the pathogenesis of cancer and metastatic disease. Although numerous tumor markers have been identified with different genomic methods in the past, most are either not specific or sensitive enough to be used in routine clinical setting. The rationale for proteomic profiling is based on the fact that proteins represent the dynamic state of the cells, reflecting pathophysiological changes in the disease more accurately than genomic and epigenetic alterations. Emerging proteomic techniques allow simultaneous assessment of a large number of proteins at one time. The study of protein profiles in complex systems, such as plasma, serum or tissues of cancer patients is likely to become valuable for monitoring the response of patients during treatment or for detecting recurrence of the disease.

Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study

BACKGROUND

Mass spectrometry-based protein expression profiling of blood sera can be used to discriminate colorectal cancer (CRC) patients from unaffected individuals. In a pilot methodological study, we have evaluated the changes in protein expression profiles of sera from CRC patients that occur following surgery to establish the potential of this approach for monitoring post-surgical response and possible early prediction of disease recurrence.

METHODS

In this initial pilot study, serum specimens from 11 cancer patients taken immediately prior to surgery and at approximately 6 weeks following surgery were analysed alongside 10 normal control sera by matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Using a two-sided t-test the top 20 ranked protein peaks that discriminate normal from pre-operative sera were identified. These were used to classify post-operative sera by hierarchical clustering analysis (Spearman's Rank correlation) and, as an independent 'test' dataset, by k-nearest neighbour and weighted voting supervised learning algorithms.

RESULTS

Hierarchical cluster analysis classified post-operative sera from all six early Dukes' stage (A and B) patients as normal. The remaining five post-operative sera from more advanced Dukes' stages (C1 and C2) were classified as cancer. Analysis by supervised learning algorithms similarly grouped all advanced Dukes' stages as cancer, with four of the six post-operative sera from early Dukes' stages being classified as normal (P = 0.045; Fisher's exact test).

CONCLUSIONS

The results of this pilot methodological study illustrate the proof-of-concept of using protein expression profiling of post-surgical blood sera from individual patients to monitor disease course. Further validation on a larger patient cohort and using an independent post-operative sera dataset would be required to evaluate the potential clinical relevance of this approach. Prospective data, including follow-up on patient survival, could in the future, then be evaluated to inform decisions on individualised treatment modalities.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Prediction of outcome of non-small cell lung cancer patients treated with chemotherapy and bortezomib by time-course MALDI-TOF-MS serum peptide profiling

BACKGROUND

Only a minority of patients with advanced non-small cell lung cancer (NSCLC) benefit from chemotherapy. Serum peptide profiling of NSCLC patients was performed to investigate patterns associated with treatment outcome.Using magnetic bead-assisted serum peptide capture coupled to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), serum peptide mass profiles of 27 NSCLC patients treated with cisplatin-gemcitabine chemotherapy and bortezomib were obtained. Support vector machine-based algorithms to predict clinical outcome were established based on differential pre-treatment peptide profiles and dynamic changes in peptide abundance during treatment.

RESULTS

A 6-peptide ion signature distinguished with 82% accuracy, sensitivity and specificity patients with a relatively short vs. long progression-free survival (PFS) upon treatment. Prediction of long PFS was associated with longer overall survival. Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity. A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity. Differential peptide profiles were also found when comparing the NSCLC serum profiles to those from cancer-free control subjects.

CONCLUSION

This study shows that serum peptidome profiling using MALDI-TOF-MS coupled to pattern diagnostics may aid in prediction of treatment outcome of advanced NSCLC patients treated with chemotherapy.

MaSDA: a system for analyzing mass spectrometry data

Mass spectrometry (MS) approaches have been recently coupled with advanced data analysis techniques in order to enable clinicians to discover useful knowledge from MS data. However, effectively and efficiently handling and analyzing MS data requires to take into account a number of issues. In particular, the huge dimensionality and the variety of noisy factors present in MS data require careful preprocessing and modeling phases in order to make them amenable to the further analysis. In this paper we present MaSDA, a system performing advanced analysis on MS data. MaSDA has the following main features: (i) it implements an approach of MS data representation that exploits a model based on low dimensional, dense time series; (ii) it provides a wide set of MS preprocessing operations which are accomplished by means of a user-friendly graphical tool; (iii) it embeds a number of tools implementing various tasks of data mining and knowledge discovery, in order to assist the user in taking critical clinical decisions. Our system has been experimentally tested on several publicly available datasets, showing effectiveness and efficiency in supporting advanced analysis of MS data.

Genomic and oncoproteomic advances in detection and treatment of colorectal cancer

Aims

We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers.

Methods

An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library.

Results

Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment.

Conclusion

If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.

Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers

Despite substantial improvements in the diagnosis and treatment of many gastrointestinal cancers, particularly colorectal cancer, numerous patients are only diagnosed in advanced stages of disease, which can preclude curative treatment. Screening and early diagnosis of high-risk individuals might be the most promising approach to improve prognosis; however, molecular biomarkers for early diagnosis of most gastrointestinal cancers are not yet available. The prognosis of patients with advanced gastrointestinal cancers has improved through the development of multimodal treatments and the introduction of targeted therapies. Nonetheless, not all patients benefit equally from these treatment approaches, and toxicity can be substantial. The ability to predict whether a patient will respond to therapy early in their treatment for gastrointestinal cancer may be of particular value to stratify and individualize patient treatment strategies. Despite improvement in the understanding of cancer pathogenesis and progression at the molecular level, the molecular changes that underlie treatment response and/or drug resistance are still largely unknown. PET is the first technique to show promise in prediction of response to therapy, and has resulted in promising advancements, particularly in esophageal and gastric cancers. Tissue-based and blood-based molecular biomarkers are still subject to validation. Prediction of response to treatment could ultimately lead to an overall improvement in prognosis.

The current state of proteomics in GI oncology

Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy.

A simplified tumor regression grade correlates with survival in locally advanced rectal carcinoma treated with neoadjuvant chemoradiotherapy

BACKGROUND

Locally advanced rectal cancer is frequently treated with neoadjuvant chemoradiotherapy to reduce local recurrence and possibly improve survival. The tumor response to chemoradiotherapy is variable and may influence the prognosis after surgery. This study assessed tumor regression and its influence on survival in patients with rectal cancer treated with chemoradiotherapy followed by curative surgery.

METHODS

One hundred twenty-six patients with locally advanced rectal cancer (T3/T4 or N1/N2) were treated with chemoradiotherapy followed by total mesorectal excision. Patients received long-course radiotherapy (50 Gy in 25 fractions) in combination with 5-flourouracil over 5 weeks. By means of a standardized approach, tumor regression was graded in the resection specimen using a 3-point system related to tumor regression grade (TRG): complete or near-complete response (TRG1), partial response (TRG2), or no response (TRG3).

RESULTS

The 5-year disease-free survival was 72% (median follow-up 37 months), and 7% of patients had local recurrence. Chemoradiotherapy produced downstaging in 60% of patients; 21% of patients experienced TRG1. TRG1 correlated with a pathological T0/1 or N0 status. Five-year disease-free survival after chemoradiotherapy and surgery was significantly better in TRG1 patients (100%) compared with TRG2 (71%) and TRG3 (66%) (P = .01).

CONCLUSION

Tumor regression grade measured on a 3-point system predicts outcome after chemoradiotherapy and surgery for locally advanced rectal cancer.

Colorectal cancer progression correlates with upregulation of S100A11 expression in tumor tissues

BACKGROUND AND AIMS

Early detection and treatment of human colorectal cancers remain a challenge. Identification of new potential markers may help in the diagnosis of colorectal cancer.

MATERIALS AND METHODS

By comparative two-dimensional gel electrophoresis using extracts from colorectal tumor and adjacent normal tissues, we identified a calcium-binding protein, S100A11, which was highly expressed in colorectal cancer compared with adjacent normal tissues. We expanded our study in 89 clinical colorectal tumor samples to validate this finding and correlates S100A11 expression in human colorectal cancer tissues with various stages of the tumor by Western blotting and immunohistochemical staining.

RESULTS

We identified a calcium-binding protein, S100A11, which was highly expressed in colorectal cancer compared with adjacent normal tissues. S100A protein was expressed predominantly in the cytoplasm of normal tissue; however, it was expressed in both the nuclei and cytoplasm of colorectal cancer. S100A11 level in colorectal cancer tissue was increased following stage progression of the disease.

CONCLUSION

These findings suggest S100A11 could be helpful in the pathological study of colorectal cancer, especially for the classification of different stages in colorectal cancer.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

Technology insight: the application of proteomics in gastrointestinal disease

Analysis of the human genome has increased our knowledge of the genes that are associated with disease. At the same time, however, it has become clear that having complete DNA sequences alone is not sufficient to elucidate the biological functions of the proteins that they encode. For this reason, proteomics-the analysis of proteins-has become increasingly attractive, because the proteome reflects both the intrinsic genetic programming of a cell and the impact of its immediate environment. The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention. Other goals include the identification of biomarkers for the early detection of disease recurrence (relapse) and how they might be combined with diagnostic imaging techniques to improve the sensitivity for detecting disease. This Review describes conventional proteomic technologies, their strengths and limitations, and demonstrates their application to clinical practice, with specific reference to their use in the gastroenterology field.