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Title: Deep top-down proteomics revealed significant proteoform-level differences between metastatic and nonmetastatic colorectal cancer cells
Top-down proteomics of colorectal cancer cells provides proteoform-level knowledge about cancer metastasis.  more » « less
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Science Advances
Medium: X
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National Science Foundation
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  1. Background

    Rates of contralateral prophylactic mastectomy (CPM)—removal of the healthy breast following breast cancer diagnosis—have increased, particularly among women for whom CPM provides no survival benefit. Affective (i.e., emotional) decision making is often blamed for this increase. We studied whether greater negative breast cancer affect could motivate uptake of CPM through increased cancer risk perceptions and biased treatment evaluations.


    We randomly assigned healthy women with average breast-cancer risk ( N = 1,030; Mage= 44.14, SD = 9.23 y) to 1 of 3 affect conditions (negative v. neutral v. positive narrative manipulation) in a hypothetical online experiment in which they were asked to imagine being diagnosed with cancer in one breast. We assessed 1) treatment choice, 2) affect toward CPM, and 3) perceived risk of future breast cancer in each breast (cancer affected and healthy) following lumpectomy, single mastectomy, and CPM.


    The manipulation caused women in the negative and neutral narrative conditions (26.9% and 26.4%, respectively) to choose CPM more compared with the positive narrative condition (19.1%). Across conditions, women’s CPM affect did not differ. However, exploratory analyses addressing a possible association of affect toward cancer-related targets suggested that women in the negative narrative condition may have felt more positively toward CPM than women in the positive narrative condition. The manipulation did not have significant effects on breast cancer risk perceptions.


    The manipulation of affect had a small effect size, possibly due to the hypothetical nature of this study and/or strong a priori knowledge and attitudes about breast cancer and its treatment options.


    Increased negative affect toward breast cancer increased choice of CPM over other surgical options and might have motivated more positive affective evaluations of CPM.


    This study used narratives to elicit different levels of negative integral affect toward breast cancer to investigate the effects of affect on breast cancer treatment choices. Increased negative affect toward breast cancer increased the choice of double mastectomy over lumpectomy and single mastectomy to treat a hypothetical, early-stage cancer. The narrative manipulation of negative affect toward breast cancer did not change the perceived risks of future cancer following any of the surgical interventions. Negative affect toward breast cancer may have biased affective evaluations of double mastectomy.

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  2. Summary

    Cancer is a major public health burden and is the second leading cause of death in the USA. The US National Cancer Institute estimated overall costs of cancer in 2007 at $219.2 billion. Breast cancer has the highest cancer incidence rates among women and is the second leading cause of cancer death among women. The ‘Surveillance, epidemiology, and end results’ programme of the National Cancer Institute collects and publishes cancer survival data from 17 population-based cancer registries. The CANSURV software of the National Cancer Institute analyses cancer survival data from the programme by using parametric and semiparametric mixture cure models. Another popular approach in cancer survival is the competing risks approach which considers the simultaneous risks from cancer and various other causes. The paper develops a model that unifies the mixture cure and competing risks approaches and that can handle the masked causes of death in a natural way. Markov chain sampling is used for Bayesian analysis of this model, and modelling and computational issues of general and restricted structures are discussed. The various model structures are compared by using Bayes factors. This Bayesian model is used to analyse survival data for the approximately 620000 breast cancer cases from the programme. The estimated cumulative probabilities of death from breast cancer from the proposed mixture cure competing risks model is found to be lower than the estimates that are obtained from the CANSURV software. Whereas the estimate of the cure fraction is found to be dependent on the modelling assumptions, the survival and cumulative probability estimates are not sensitive to these assumptions. Breast cancer survival in different ethnic subgroups, in different age subgroups and in patients with localized, regional and distant stages of the disease are compared. The risk of mortality from breast cancer is found to be the dominant cause of death in the beginning part of the follow-up whereas the risk from other competing causes often became the dominant cause in the latter part. This interrelation between breast cancer and other competing risks varies among the different ethnic groups, the different stages and the different age groups.

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  3. Breast cancer is highly sporadic and heterogeneous in nature. Even the patients with same clinical stage do not cluster together in terms of genomic profiles such as mRNA expression. In order to prevent and cure breast cancer completely, it is essential to decipher the detailed heterogeneity of breast cancer at genomic level. Putting the cancer patients on a time scale, which represents the trajectory of cancer development, may help discover the detailed heterogeneity. This in turn would help establish the mechanisms for prevention and complete cure of breast cancer. The goal of this study is to discover the heterogeneity of breast cancer by ordering the cancer patients using pseudotime. This is achieved through two objectives: First, a computational framework is developed to place the cancer patients on a time scale, meaning construct a trajectory of cancer development, by inferring pseudotime from static mRNA expression data; Second, discovering breast cancer heterogeneity at different time periods of the trajectory using statistical and machine learning techniques. In this study, the trajectory of breast cancer progression was constructed using static mRNA expression profiles of 1072 breast cancer patients by inferring pseudotime. Three sets of key genes discovered using supervised machine learning techniques are used to develop the trajectories. The first set of genes are PAM50 genes which is available in literature. The second and third sets of genes were discovered in the present study using the clinical stages of breast cancer (Stage-I, Stage-II, Stage-III, and Stage-IV). The proposed computational framework has the capability of deciphering heterogeneity in breast cancer at a granular level. The results also show the existence of multiple parallel trajectories at different time periods of cancer development or progression. 
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  4. The cancer stem cell hypothesis has been used to explain many cancer complications resulting in poor patient outcomes including induced drug resistance, metastases to distant organs, and tumor recurrence. While the validity of the cancer stem cell model continues to be the cause of much scientific debate, a number of putative cancer stem cell markers have been identified making studies concerning the targeting of cancer stem cells possible. In this review, a number of identifying properties of cancer stem cells have been outlined including properties contributing to the drug resistance and metastatic potential commonly observed in supposed cancer stem cells. Due to cancer stem cells' numerous survival mechanisms, the diversity of cancer stem cell markers between cancer types and tissues, and the prevalence of cancer stem cell markers among healthy stem and somatic cells, it is likely that currently utilized treatments will continue to fail to eradicate cancer stem cells. The successful treatment of cancer stem cells will rely upon the development of anti-neoplastic drugs capable of influencing many cellular mechanisms simultaneously in order to prevent the survival of this evasive subpopulation. Natural compounds represent a historically rich source of novel, biologically active compounds which are able to interact with a large number of cellular targets while limiting the painful side-effects commonly associated with cancer treatment. A brief review of select natural products that have been demonstrated to diminish the clinically devastating properties of cancer stem cells or to induce cancer stem cell death is also presented. 
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  5. Abstract Motivation Detecting cancer gene expression and transcriptome changes with mRNA-sequencing (RNA-Seq) or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. Results Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer specific molecular signatures detected by multi-task learning frameworks on TCGA ovarian cancer, breast cancer, and prostate cancer datasets are correlated with the known marker genes and enriched in cancer relevant KEGG pathways and Gene Ontology terms. Availability and Implementation Source code is available at: Supplementary information Supplementary data are available at Bioinformatics 
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