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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
Award ID(s):
1846913
NSF-PAR ID:
10412930
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Science Advances
Volume:
8
Issue:
51
ISSN:
2375-2548
Format(s):
Medium: X
Sponsoring Org:
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.

    Methods

    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.

    Results

    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.

    Limitations

    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.

    Conclusion

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

    Highlights

    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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