Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction: Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics. Objectives: Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs. Methods: A literature search of the current relevant primary research was performed. Results: Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance. Conclusion: Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies

TP53 is not a prognostic markerâ clinical consequences of a generally disregarded fact

Technological progress within the last 15â 20 years has significantly increased our knowledge about the molecular basis of cancer development, tumor progression, and treatment response. As a consequence, a vast number of biomarkers have been proposed, but only a small fraction of them have found their way into clinical use. The aim of this paper is to describe the specific demands a clinically relevant biomarker should meet and how biomarkers can be tested stepwise. We name this procedure the â tripleâ R principleâ : robustness, reproducibility, and relevance. The usefulness of this principle is illustrated with the marker TP53. Since it is mutated in a broad spectrum of cancer entities, TP53 can be considered a very promising marker. Thus, TP53 has been studied in detail but there is still no explicit consensus about its clinical value. By considering our own experience and reviewing the literature, we demonstrate that a major problem of current biomarker research is disregard of whether the biomarker is prognostic or predictive. As an example, it is demonstrated that TP53 is not a prognostic marker, but rather a purely predictive marker, and that disregard of this fact has made this otherwise strong biomarker appear as not being clinically useful so far.Many biomarkers have been proposed for cancer, but only a small fraction of them are clinically useful. This paper describes the specific demands a clinically relevant biomarker should meet and how biomarkers can be tested stepwise. This is illustrated with the marker TP53, which has been studied in detail but for which there is still no explicit consensus about its clinical value.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146810/1/nyas13947.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146810/2/nyas13947_am.pd

DNA damage predicts prognosis and treatment response in colorectal liver metastases superior to immunogenic cell death and T cells

Preclinical models indicate that DNA damage induces type I interferon (IFN), which is crucial for the induction of an anti-tumor immune response. In human cancers, however, the association between DNA damage and an immunogenic cell death (ICD), including the release and sensing of danger signals, the subsequent ER stress response and a functional IFN system, is less clear. Methods: Neoadjuvant-treated colorectal liver metastases (CLM) patients, undergoing liver resection in with a curative intent, were retrospectively enrolled in this study (n=33). DNA damage (gammaH2AX), RNA and DNA sensors (RIG-I, DDX41, cGAS, STING), ER stress response (p-PKR, p-eIF2alpha, CALR), type I and type II IFN- induced proteins (MxA, GBP1), mature dendritic cells (CD208), and cytotoxic and memory T cells (CD3, CD8, CD45RO) were investigated by an immunohistochemistry whole-slide tissue scanning approach and further correlated with recurrence-free survival (RFS), overall survival (OS), radiographic and pathologic therapy response. Results: gammaH2AX is a negative prognostic marker for RFS (HR 1.32, 95% CI 1.04-1.69, p=0.023) and OS (HR 1.61, 95% CI 1.23-2.11, p<0.001). A model comprising of DDX41, STING and p-PKR predicts radiographic therapy response (AUC=0.785, p=0.002). gammaH2AX predicts prognosis superior to the prognostic value of CD8. CALR positively correlates with GBP1, CD8 and cGAS. A model consisting of gammaH2AX, p-eIF2alpha, DDX41, cGAS, CD208 and CD45RO predicts pathological therapy response (AUC=0.944, p<0.001). Conclusion: In contrast to preclinical models, DNA damage inversely correlated with ICD and its associated T cell infiltrate and potentially serves as a therapeutic target in CLM

PROTEOMIC IDENTIFICATION OF NOVEL MARKERS IN BREAST AND COLON CANCER

Background: Discovery of new biomarker represent the greatest promise for the detection and management of cancer. Although progress in cancer biology has been rapid during the past few years, the complete understanding of molecular basis for cancer initiation, progression and efficacious treatments is still lacking. In this context, the application of proteomic strategies is now holding a focal position. The main reason is that proteins are the functional players that drive cancer phenotypes. Among cancers, breast and colon represent the most frequent forms. The evolution of these type of cancer are not easily predictable since there are several types that behave differently among patients. The biological heterogeneity is consistent with observed varied responses to therapies across patients, also. On the other hand, drug delivery is an emergent field focused on targeting drugs to a desirable group of cells, in order to minimize undesirable side-effects and maximize the therapeutic activity. Metallic nanoparticles, in particular silver nanoparticles (Ag-NPs) exhibit low toxicity to mammalian cells (Mahapatra and Karak, 2008) and are good candidate as smart therapeutics. Based on these evidences, the first part of the study was aimed to discover new potential protein biomarkers in breast and colon cancer tissues and sera, using proteomic techniques, useful as diagnostic and prognostic factors in vivo. The second part of the study was focused on the in vitro cytotoxic effects of silver nanoparticles Ag-NPs embedded on Klebsiella Oxytoca DSM29614 (KO) Exopolysaccaride (EPS), produced in aerobic versus anaerobic conditions. Methods: Diagnostic biomarkers in breast and colon cancer: Taken advantage from previous results by the proteomic analysis performed on 13 breast cancer tissues and their matched non-tumoral adjacent tissues (Pucci-Minafra et al., 2007), we first analyze by 2D-DIGE pool of both breast and colon cancer tissues extracts compared to the matched pool of non tumoral adjacent tissues extracts. Differentially expressed proteins, identified by Maldi-TOF/TOF, were functionally clustered. We also investigate the activity levels of MMP-2 and MMP-9 in breast and colon tissues as well as in sera of the same patients. Prognostic biomarkers in breast and colon cancer: In order to identity putative proteomic signatures for colorectal cancer (CRC) metastasis, a comparative profiling of a colon cancer tissue paired with the non tumoral adjacent mucosa and with the liver metastasis from the same patient was performed. A three-step approach (normal versus tumoral versus metastasis) was used to select unique proteins involved in liver metastasis. For breast cancer, a large proteomic investigation performed on a large sample set of breast cancer patients (Cancemi et al., 2010, 2012), pointed the important role of S100 protein members in breast cancer progression. Using on line tools, for instance GOBO and breast cancer Kaplan Meir-plotter we assessed gene expression levels and clinical correlations of S100 proteins in breast patients. Cytotoxic effects of silver nanoparticles biosynthesized from KO (Ag-NPs-EPS) in SK-BR3 breast cancer cell line: We monitored cell proliferation inhibition rate by MTT assay, morphological changes and proteomic modulation. Results: Diagnostic biomarkers in breast and colon cancer: Differentially breast and bolon proteomic profiling revealed several proteins involved in common pathways among the type of cancer. The important role of MMPs in tumorigenesis was confirmed by our observations regarding their major expressions in cancer tissues compared to the normal tissues. Prognostic biomarkers in breast and colon cancer: Among the differentially expressed proteins between normal-tumor and liver metastasis, Cathepsin D expression was further analyzed as prognostic factor in CRC. Moreover, integrating results obtained by bioinformatics analysis performed on breast cancer gene expression dataset confirmed the important role of S100 proteins in breast cancer progression. Cytotoxic effects of silver nanoparticles (AgNPs) biosynthesized from KO in SK-BR3 breast cancer cell line: The most important effects were obtained by aerobically AgNPs-EPS treatment, due to the major release of Ag+1, as verified by voltammetry analysis. Morphological alteration were consistent with apoptotic features. Proteomic analysis showed modulation of several proteins related to oxidative stress and apoptotic and mitochondrial pathways. Conclusions: Conclusively, the present study contribute to the implementation of the panel of new proteomic biomarkers useful for diagnostic and prognostic applications in breast and colon cancer, providing new informations about the effects of the biosynthesized Ag-NPs-EPS on breast cancer cells

Developing an individualized survival prediction model for colon cancer

In this work a 5-year survival prediction model was developed for colon cancer using machine learning methods. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Survival prediction models for colon cancer are not widely and easily available. Results showed that the performance of the model using fewer features is close to that of the model using a larger set of features recommended by an expert physician, which indicates that the first may be a good compromise between usability and performance. The purpose of such a model is to be used in Ambient Assisted Living applications, providing decision support to health care professionals.info:eu-repo/semantics/publishedVersio

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients. METHODS Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action. RESULTS Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds). CONCLUSION Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care