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      Are rare cancer survivors at elevated risk of subsequent new cancers?

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          Abstract

          Background

          Although rare cancers account for 27% of cancer diagnoses in the US, there is insufficient research on survivorship issues in these patients. An important issue cancer survivors face is an elevated risk of being diagnosed with new primary cancers. The primary aim of this analysis was to assess whether a history of rare cancer increases the risk of subsequent cancer compared to survivors of common cancers.

          Methods

          This was a prospective cohort study of 16,630 adults with personal and/or family history of cancer who were recruited from cancer clinics at 14 geographically dispersed US academic centers of the NIH-sponsored Cancer Genetics Network (CGN). Participants’ self-reported cancer histories were collected at registration to the CGN and updated annually during follow-up. At enrollment, 14% of participants reported a prior rare cancer. Elevated risk was assessed via the cause-specific hazard ratio on the time to a subsequent cancer diagnosis.

          Results

          After a median follow-up of 7.9 years , relative to the participants who were unaffected at enrollment, those with a prior rare cancer had a 23% higher risk of subsequent cancer (95% CI: -1 to 52%), while those with a prior common cancer had no excess risk. Patients having two or more prior cancers were at a 53% elevated risk over those with fewer than two (95% CI: 21 to 94%) and if the multiple prior cancers were rare cancers, risk was further elevated by 47% (95% CI: 1 to 114%).

          Conclusion

          There is evidence suggesting that survivors of rare cancers, especially those with multiple cancer diagnoses, are at an increased risk of a subsequent cancer. There is a need to study this population more closely to better understand cancer pathogenesis.

          Electronic supplementary material

          The online version of this article (10.1186/s12885-019-5358-1) contains supplementary material, which is available to authorized users.

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          Most cited references10

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          Does the Immune System Naturally Protect Against Cancer?

          The importance of the immune system in conferring protection against pathogens like viruses, bacteria, and parasitic worms is well established. In contrast, there is a long-lasting debate on whether cancer prevention is a primary function of the immune system. The concept of immunological surveillance of cancer was developed by Lewis Thomas and Frank Macfarlane Burnet more than 50 years ago. We are still lacking convincing data illustrating immunological eradication of precancerous lesions in vivo. Here, I present eight types of evidence in support of the cancer immunosurveillance hypothesis. First, primary immunodeficiency in mice and humans is associated with increased cancer risk. Second, organ transplant recipients, who are treated with immunosuppressive drugs, are more prone to cancer development. Third, acquired immunodeficiency due to infection by human immunodeficiency virus (HIV-1) leads to elevated risk of cancer. Fourth, the quantity and quality of the immune cell infiltrate found in human primary tumors represent an independent prognostic factor for patient survival. Fifth, cancer cells harbor mutations in protein-coding genes that are specifically recognized by the adaptive immune system. Sixth, cancer cells selectively accumulate mutations to evade immune destruction (“immunoediting”). Seventh, lymphocytes bearing the NKG2D receptor are able to recognize and eliminate stressed premalignant cells. Eighth, a promising strategy to treat cancer consists in potentiating the naturally occurring immune response of the patient, through blockade of the immune checkpoint molecules CTLA-4, PD-1, or PD-L1. Thus, there are compelling pieces of evidence that a primary function of the immune system is to confer protection against cancer.
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            Highly penetrant hereditary cancer syndromes.

            The past two decades have brought many important advances in our understanding of the hereditary susceptibility to cancer. Approximately 5-10% of all cancers are inherited, the majority in an autosomal dominant manner with incomplete penetrance. While this is a small fraction of the overall cancer burden worldwide, the molecular genetic discoveries that have resulted from the study of families with heritable cancer have not only changed the way these families are counselled and managed, but have shed light on molecular regulatory pathways important in sporadic tumour development as well. In this review, we consider 10 of the more highly penetrant cancer syndromes, with emphasis on those predisposing to breast, colon, and/or endocrine neoplasia. We discuss the prevalence, penetrance, and tumour spectrum associated with these syndromes, as well as their underlying genetic defects.
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              Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications.

              An evaluation of the effect of a healthcare intervention (or an exposure) must consider multiple possible outcomes, including the primary outcome of interest and other outcomes such as adverse events or mortality. The determination of the likelihood of benefit from an intervention, in the presence of other competing outcomes, is a competing risks problem. Although statistical methods exist for quantifying the probability of benefit from an intervention while accounting for competing events, these methods have not been widely adopted by clinical researchers. (1) To demonstrate the importance of considering competing risks in the evaluation of treatment effectiveness, and (2) to review appropriate statistical methods, and recommend how they might be applied. We reviewed 3 statistical approaches for analyzing the competing risks problem: (a) cause-specific hazard (CSH), (b) cumulative incidence function (CIF), and (c) event-free survival (EFS). We compare these methods using a simulation study and a reanalysis of a randomized clinical trial. Simulation studies evaluating the statistical power to detect the effect of intervention under different scenarios showed that: (1) CSH approach is best for detecting the effect of an intervention if the intervention only affects either the primary outcome or the competing event; (2) EFS approach is best only when the intervention affects both primary and competing events in the same manner; and (3) CIF approach is best when the intervention affects both primary and competing events, but in opposite directions. Using data from a randomized controlled trial, we demonstrated that a comprehensive approach using all 3 approaches provided useful insights on the effect of an intervention on the relative and absolute risks of multiple competing outcomes. CSH is the fundamental measure of outcome in competing risks problems. It is appropriate for evaluating treatment effects in the presence of competing events. Results of CSH analysis for primary and competing outcomes should always be reported even when EFS or CIF approaches are called for. EFS is appropriate for evaluating the composite effect of an intervention, only when combining different endpoints is clinically and biologically meaningful, and the treatment has similar effects on all event types. CIF is useful for evaluating the likelihood of benefit from an intervention over a meaningful period. CIF should be used for absolute risk calculations instead of the widely used complement of the Kaplan-Meier (1 - KM) estimator.
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                Author and article information

                Contributors
                (617) 726-5970 , dfinkelstein@mgh.harvard.edu
                nhorick@mgh.harvard.edu
                rramchan@sdac.harvard.edu
                kristina.lynn.boyd@gmail.com
                humaq_rana@dfci.harvard.edu
                bbychkovsky@partners.org
                Journal
                BMC Cancer
                BMC Cancer
                BMC Cancer
                BioMed Central (London )
                1471-2407
                21 February 2019
                21 February 2019
                2019
                : 19
                : 166
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Massachusetts General Hospital Biostatistics Center & Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; 50 Staniford Street, Suite 560, Boston, MA 02114 USA
                [2 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Massachusetts General Hospital Biostatistics Center, ; Boston, MA USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, USA
                [4 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Medicine, , Dana-Farber Cancer Institute & Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0001-9564-8181
                Article
                5358
                10.1186/s12885-019-5358-1
                6385466
                30791872
                8dc4e66e-33f2-4e23-9ba7-154c4f37ca33
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 6 November 2018
                : 7 February 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: CA078284
                Award ID: HHSN2612007440000C
                Award ID: CA144706
                Award ID: CA160233
                Award ID: CA167570
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Oncology & Radiotherapy
                rare cancer,subsequent cancer risk,multiple cancers,survivorship
                Oncology & Radiotherapy
                rare cancer, subsequent cancer risk, multiple cancers, survivorship

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