Increasing incidence of Epstein‐Barr virus–related nasopharyngeal carcinoma in the United States

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152902/1/cncr32517_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152902/2/cncr32517.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152902/3/cncr32517-sup-0001-FigS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152902/4/cncr32517-sup-0002-FigS2.pd

Optimal Baseline Prostate-Specific Antigen Level to Distinguish Risk of Prostate Cancer in Healthy Men Between 40 and 69 Years of Age

The present study evaluated optimal baseline prostate-specific antigen (PSA) level at different ages in order to determine the risk of developing prostate cancer (CaP). We analyzed 6,651 Korean men, aged 40-69 yr. The serum PSA levels for these men were measured at one institute from 2000 to 2004 and were determined to be between 0-4 ng/mL. Patients were divided into 4 groups of 25th-percentile intervals, based on initial PSA level. Of these, the group with an increased risk was selected, and the optimal value was determined by the maximal area under a receiver-operating characteristic curve within the selected group. The risk of CaP diagnosis was evaluated by Cox regression. The mean follow-up period was 8.3 yr. CaP was detected in 27 of the 6,651 subjects. CaP detection rate was increased according to age. The optimal PSA value to distinguish the risk of CaP was 2.0 ng/mL for 50- to 69-yr-olds. Patients with a baseline PSA level greater than the optimal value had a 27.78 fold increase in the prostate cancer risk. Baseline PSA values are useful for determining the risk of developing CaP in Korean men for 50- and 69-yr-old. We suggest that PSA testing intervals be modified based on their baseline PSA levels

Disparities in breast cancer survival in the United States (2001-2009): Findings from the CONCORD-2 study.

BACKGROUND: Reducing breast cancer incidence and achieving equity in breast cancer outcomes remains a priority for public health practitioners, health care providers, policy makers, and health advocates. Monitoring breast cancer survival can help evaluate the effectiveness of health services, quantify inequities in outcomes between states or population subgroups, and inform efforts to improve the effectiveness of cancer management and treatment. METHODS: We analyzed breast cancer survival using individual patient records from 37 statewide registries that participated in the CONCORD-2 study, covering approximately 80% of the US population. Females were diagnosed between 2001 and 2009 and were followed through December 31, 2009. Age-standardized net survival at 1 year, 3 years, and 5 years after diagnosis was estimated by state, race (white, black), stage at diagnosis, and calendar period (2001-2003 and 2004-2009). RESULTS: Overall, 5-year breast cancer net survival was very high (88.2%). Survival remained remarkably high from 2001 through 2009. Between 2001 and 2003, survival was 89.1% for white females and 76.9% for black females. Between 2004 and 2009, survival was 89.6% for white females and 78.4% for black females. CONCLUSIONS: Breast cancer survival was more than 10 percentage points lower for black females than for white females, and this difference persisted over time. Reducing racial disparities in survival remains a challenge that requires broad, coordinated efforts at the federal, state, and local levels. Monitoring trends in breast cancer survival can highlight populations in need of improved cancer management and treatment. Cancer 2017;123:5100-18. Published 2017. This article is a U.S. Government work and is in the public domain in the USA

A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates

<p>Abstract</p> <p>Background</p> <p>The purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.</p> <p>Methods</p> <p>The equation [<it>p </it>= 1 - (1 - (1 - (1 - <it>u</it>)^<it>d</it>)^<it>k</it>)^{<it>Nm </it>}] calculates the probability of cancer (<it>p</it>) and contains five parameters: the number of divisions (<it>d</it>), the number of stem cells (<it>N </it>× <it>m</it>), the number of critical rate-limiting pathway driver mutations (<it>k</it>), and the mutation rate (<it>u</it>). In this model progression to cancer "starts" at conception and mutations accumulate with cell division. Transformation occurs when a critical number of rate-limiting pathway mutations first accumulates within a single stem cell.</p> <p>Results</p> <p>When applied to several colorectal cancer data sets, parameter values consistent with crypt stem cell biology and normal mutation rates were able to match the increase in cancer with aging, and the mutation frequencies found in cancer genomes. The equation can help explain how cancer risks may vary with age, height, germline mutations, and aspirin use. APC mutations may shorten pathways to cancer by effectively increasing the numbers of stem cells at risk.</p> <p>Conclusions</p> <p>The equation illustrates that age-related increases in cancer frequencies may result from relatively normal division and mutation rates. Although this equation does not encompass all of the known complexity of cancer, it may be useful, especially in a teaching setting, to help illustrate relationships between small and large cancer features.</p