The Palpable Prostate

Prostate cancer topics, links and more. Now at 100+ posts!

News: Health Day, Medical News Today, ScienceDaily, Urol Times, Urotoday, Zero Cancer Papers: Pubmed (all), Pubmed (Free only), Amedeo
Journals: Eur Urol, J Urol, JCO, The Prostate Others Pubmed Central Journals (Free): Adv Urol, BMC Urol, J Endourol, Kor J Urol, Rev Urol, Ther Adv Urol, Urol Ann
Reviews: Cochrane Summaries, PC Infolink Newsletters: PCRI, US Too General Medical Reviews: f1000, Health News Review

Tuesday, January 5, 2016

Metformin and prostate cancer

A 2014 article in the Journal of the National Cancer Institute by Azvolinksy [PMID: 24511112] [Full Free Text] and a 2014 article by Fleshner in the Canadian Urological Association Journal [PMID: 25243043] [Full Free Text] discuss observational studies on diabetic prostate cancer patients which have shown reduced risk of death from prostate cancer as well as reduced all cause mortality among those taking the widely used inexpensive diabetes drug, metformin.

An undated overview by Peter Wehrwein speculates that the biological mechanism may be the lowering of insulin, lowering of blood sugar output of the liver or via interruption of signalling pathways.

A 2015 meta-analysis by Wu et al found that the risk of developing prostate cancer (as opposed to the risk of death for those who have prostate cancer) is unaffected by metformin. [PMID: 26550231]

Confirmation of these results in diabetics and non-diabetics through randomized clinical trails is underway. See: []

Monday, November 30, 2015

From Pilot to System Solution

It seems that so much is known in medicine and health care that is not widely enough applied. The video below of the keynote talk by Dr. Danielle Martin at the Health Quality Transformation 2015 conference focused on health care in Ontario notes that "less than 40% of health improvement projects successfully expand beyond the early group of adopters who first bring them to life" and discusses how innovation can move from pilot to system-wide application.

It discusses the concepts of spread and scale:

- Spread is about innovations diffusing to other teams or organizations, one at a time, as successive teams learn of the successful innovation and individually decide to adopt it.

- Scale is about large scale structural change involving policies that are implemented all at once across an entire region. It requires significant resources and political capital.

She discusses the application of these concepts and many associated issues and examples.

Sunday, November 1, 2015

Peto's Paradox

The larger an animal is the more cells it has and the longer lived an animal has the more time cells have to mutate so one would think that larger and longer lived animals would have more cancer; however, that is not the case.   Science World Report quotes researchers as saying that "less than 5 percent of elephants develop cancer compared to 25 percent of humans". That larger animals do not have higher rates of cancer is called Peto's paradox. [Wikipedia] [PMID: 21296451] [Full text] While it may be that larger animals do not get more cancer larger, or at least taller, humans have higher rates of cancer than shorter humans so this observation does seem to hold on the scale of an individual species. Evidently methods of cancer prevention have evolved in larger animals; however, taller humans may not have evolved over a sufficiently long period to elicit an evolutionary response.

We can get a bit more insight by reviewing the simple probability model of cancer developed by Calabrese and Shibata. [PMID: 20051132] [Full text] [Excel model] [Powerpoint slides] [Also see Box 2 of this paper] Assume that mutations must occur in k critical genes for cancer to occur. Assume each gene divides into d copies in its cell lineage and that there are M cells at risk in a particular organ such as the prostate -- all cells in an organ are not necessarily at risk so M is, in general, less than the number of cells in an organ.  Let u be the prob of a mutation in one critical gene. Then as shown by Calabrese and Shibata (also see proof at end of this post) the probability of cancer, p, is:

       p = 1 - (1 - (1 - (1 - u)^d)^k)^M

Larger animals have more cells, M, and longer lived animals undergo more cell divisions d.  The number of mutations needed for cancer, k, could vary among animals and the even by the type of cancer within a species.  Thus a number of other factors may be at play as well as size and life expectancy.

In the case of elephants two independent papers have concluded that they have multiple copies of the p53 tumor suppressor gene (which kills cancer cells) whereas humans only have a single set.  This had been previously hypothesized; however, these two 2015 papers seem to have independently determined this to be the case.

Abegglen et al

Sulak et al

An interview with Joshua Schiffman, an author of the first paper, appears here:

In a New York Times article
Dr. Patricia Muller mentions that this research does not establish the mechanism of action so further work needs to be done even before attempting to replicate this in humans.  Furthermore, the following paper: indicates that in
some contexts p53 accelerates aging in mice whereas in other contexts it promotes longevity so there is some question as to whether it would be feasible to apply p53 at all to humans.

Other large or long lived animals with low cancer rates might have evolved different mechanisms of combating cancer.  Discovering the various mechanisms that evolved over millions of years might lead to cancer treatments in humans.  Schiffman is quoted in the same New York Times article as speculating "that parrots, tortoises and whales may all have special longevity tactics of their

The formula given above can be derived as follows:

p = 1 - Prob of no cancer in any of the M cells at risk)
       = 1 - (Prob of no cancer in one cell at risk)^M
       = 1 - (1 - Prob of cancer in one cell at risk)^M
       = 1 = (1 - Prob of one cell accumulating k mutations)^M
       = 1 - (1 - (Prob of one mutation)^k)^M
       = 1 - (1 - (Prob of mutation in 1 critical gene)^k)^M
       = 1 - (1 - (1 - Prob of no mutation in one cell lineage)^k))^M
       = 1 - (1 - (1 - (1 - Prob of no mutation in one of d divisions)^d)^k)^M
       = 1 - (1 - (1 - (1 - u)^d)^k)^M

Tuesday, September 29, 2015

Blog Updates for September 2015

Sep 27, 2015. In Prostate Cancer Calculators we added:
  • Date of Death The web site asks for your birthday, country and gender and based on this input estimates what percentage of people in your country and on earth are younger than you and your date of death.
  • Mortality Simulator The flowingdata web site shows a chart with a blue curve part way down the page. The curve represents the probability of living for at least another year given your age. The probability is on the vertical axis and age is on the horizontal axis. Moving dots traverse the blue curve representing lifetimes of a single person. They drop off the curve at death and accumulate in piles below the curve. These piles represent the distribution of life spans.

Friday, August 14, 2015

Nutritional Supplements and Prostate Cancer

[Updated August 17, 2015]

Literature Review

A 2015 survey of nutritional approaches to prostate cancer published in BMC Medicine contains a table of approaches for which the authors found research. See table 1 in [Full Free Text of paper].


A 2014 six month randomized placebo-controlled double blind UK study published in Prostate Cancer and Prostatic Diseases of a mixture of polyphenols (pomegranate, green tea, turmeric and brocolli) with 199 prostate cancer patients on Active Surveillance or Watchful Waiting found that the treatment arm had an increase in PSA of 14% vs. 79% for the control arm (lesser increase is more favorable). The paper notes that the study received no funding from the manufacturer of the supplement. [Full Free Text of Paper] [Cancernet UK Charity info] [video interview with researcher Robert Thomas] [Manufacturer's FAQ].

PSA Doubling Time. Note that a 14% increase in PSA over a 6 month period (as found in the treatment arm) represents a doubling time of 6 / log2(1.14) = 31.74 months whereas an increase of 79% over 6 months (as found in the control arm) represents a doubling time of 6 / log2(1.79) = 7.14 months. Thus the doubling time was 31.74 / 7.14 = 4.4x times longer in the treatment arm (higher is more favorable).

Disease Progression. MRI scans were not part of the protocol but many of the patients had them anyways as part of their routine medical visits and these scans were consistent with lesser disease progression in the treatment arm (as opposed to just a matter of just modifying the PSA kinetics).

Group Composition. The treatment and control groups were assigned randomly subject to assigning 2 patients in the treatment group for every patient in the control group. By chance the average age in the treatment group was 5 years less than the average age in the control group so statistical methods were used to adjust the results to correct for this difference. On other measured variables the two groups were statistically similar.

A 12 month US study on effects of pomegranate extract on rising PSA levels

A randomized double blind trial of pomegranate extract on 183 men with rising PSA after primary therapy published in Prostate Cancer and Prostatic Disease 2015 found no effect on PSA doubling time. It did find some indications that patients with the MnSOD AA genotype might benefit but this would require a further study to confirm. Some of the study authors received funding from the manufacturer of the extract. See [PMID: 26169045] [Full Free Text].

Key differences between this study and the UK NCRN POMI-T study are that:
  • this study was performed after primary therapy whereas the UK study was performed on patients undergoing Active Surveillance and Watchful waiting.
  • this study examined only pomegranate extract whereas the prior study examined pomegranate extract in combination with 3 other polyphenols.
  • this study followed men for one year and the UK study for 6 months

Saturday, August 1, 2015

Blog updates for July 2015

August 1, 2015.  In PSADT Part 1 we add: PSA Doubling Time (PSADT) was found to be the most consistent of the top prognostic factors for a variety of endpoints (prostate cancer-specific survival, metastases-free survival, overall or all cause survival) in a 2015 literature review of relevant studies so we focus this 5 part series of posts specifically on it. [PMID: 26180662] [Full Free Text]. (The other consistent key factors were Gleason Score and Time to Biochemical Failure.)

Wednesday, July 1, 2015

Demographic Factors Affecting Prostate Cancer Mortality

In a June 25, 2015 PLoS One paper [full text] Yao et al perform a regression of prostate cancer mortality (one point per county) using the following explanatory variables:

Significant at 0.1% level:

- more urologists per 100,000 people decreases prostate cancer mortality
- being in a metropolitan country decreases prostate cancer mortality
- greater percentage of population being non-white increases prostate cancer mortality
- greater percentage of population with high school diploma decreases prostate cancer mortality

Significant at 1% level:
- greater prostate cancer incidence per 100,000 men increases prostate cancer mortality

Significant at 5% level:
- if county is a designated Health Professional Shortage Area it increases prostate cancer mortality
- if per capita income is higher then it reduces prostate cancer mortality

Factors that were not significant were:
- radiation oncologists per 100,000 people
- beds per 100,000 people
- percent of population over 65