The present study aims to evaluate the reproducibility
and performance of a genetic risk prediction model in Japanese and estimate its
utility as a diagnostic biomarker in a clinical scenario. We created a logistic
regression model incorporating 16 SNPs that were significantly associated with
PC in a genome-wide association study of Japanese population using 689 cases
and 749 male controls. The model was validated by two independent sets of
Japanese samples comprising 3,294 cases and 6,281 male controls. The areas
under curve (AUC) of the model were 0.679, 0.655, and 0.661 for the samples
used to create the model and those used for validation.
The researchers focused on the set of 16 Single Nucleotide
Polymorphisms, SNPs, and those nucleotides which are different in a single
spot. The authors calculated an odds ratio using logistic models. Specifically
they posited:
log (OR) =b0+b1x1+b2x2+b3x3+…
and here the bs are the
regression coefficients and the x the number of the risk alleles at each SNP
locus. The result was a ROC, receiver operating characteristic, whose area
under the curve was nearing 0.7, a reasonable number.
Now the authors state:
PSA is a protein secreted specifically from the prostate
gland, and has been widely accepted as a serum biomarker for PC. However, other
medical conditions, such as benign prostatic hypertrophy and inflammation can
cause serum PSA elevation. Hence, the diagnostic specificity of PSA is quite
low, especially at boarder-line levels of PSA, or ‘gray-zone’. Patients
suspected to have PC by PSA screening usually undergo prostate needle biopsy,
which is an invasive procedure that accompany complications, some of which are
severe. In addition, recent randomized controlled trials have shown no or
little benefit of PSA screening in extending cancer-specific survival,
Economic burden of prostate needle biopsies, followed by overdiagnosis and
overtreatment for PC, is another serious issue since it is estimated that each
year, more than one million patients undergo prostate needle biopsies in the
US, a procedure which costs $500–1,000 for each. Therefore, there is a
world-wide controversy over PSA screening, and additional biomarkers which can
better identify the patients that need prostate needle biopsies are definitely
required.
As we have discussed before, the “recent randomized
controlled trials” were not really that good of a trial. We refer the reader to
our analysis of this issue.
The authors state:
There is still a large debate over the clinical utility
of genetic risk prediction models. The overall predictive performances of
genetic risk prediction models as assessed by ROC analysis are usually modest,
since the distribution of the ORs between the case and controls largely
overlap. However, it has been implicated in breast cancer that genetic risk
prediction models could be clinically useful among a subset of high risk
patients. In case of PC, patients can be risk-stratified using PSA, and
genetic risk prediction models can be a useful compensatory marker at gray-zone
PSA, where patients have relatively high risk of PC, and the diagnostic ability
of PSA is the lowest. Furthermore, PCs are generally slow growing, and even if
patients with PC are false negatively classified as low risk by a genetic
prediction model, they can still be followed with serial PSA measurements, and
can have prostate biopsy with increasing PSA before reaching advanced stages
except in rare cases of very aggressive tumor. Identification of aggressive
PCs is another important issue in PC diagnosis. Most of the PC susceptibility
variants identified by GWAS have fallen short of discriminating aggressive from
non-aggressive PCs, and there was no significant difference in the distribution
of ORs between the aggressive and non-aggressive PCs in our genetic risk
prediction model as well. Additional biomarkers that can discriminate
aggressive and indolent PCs should be explored.
The above is another generalization which can result in
serious complications. Yes, many PCa are slow growing. We see the 85 year old
patient who we have been watching with a Gleason 7 for fifteen years. The PSA
may now be 20. But he most likely will never die of this disease. On the other hand
we all have seen the patient with a PSA of 4, then two years later it is 40,
and two years later after painful bone mets he is dead. How do we tell the
difference?
They conclude:
We have shown that while the genetic risk model may not be helpful clinically in all the patients with gray-zone PSA, it may largely influence decision making in a portion of patients. In our clinical simulation, 24.2% of the patients had OR,0.5, and these patients had 10.7% chance of being positive after prostate needle biopsy. Considering the complications of prostate needle biopsies, these patients might chose serial PSA follow-up rather than immediate prostate needle biopsy. On the other hand, 9.7% of the patients with OR.2, who have more than 42.4% chance of being positive for prostate cancer, may choose to undergo immediate prostate needle biopsy. Although the genetic risk prediction model should further be evaluated prospectively in clinics, our data suggests that it can be an additional biomarker that can risk stratify individuals at gray-zone PSA in Japanese, leading to personalized medicine.
Yes, this is a form of personalized medicine. Yet the key
question is still what does the somatic cell have that the germ line does not
and why. We want to control the somatic, not the germ line. Causality, and
causality relationships are essential, they are the sine qua non of
understanding cancer.
Observations
This paper raises several compelling issues:
1. The SNPs are germ line not somatic. They are there from
the very beginning. The question then should be “why these SNPs” and “how do
they create the malignancy?” The authors just thrown them in the pile with so many
others and causation is totally neglected. Unlike what we seen in more main
line analysis, we see a PTEN problem, an AKT issue, and AR fault. We then know
what these do and we can then do something to reverse the process.
2. These are genome wide surveys, GWAS, and what we have
seen is that one may catch a lot of things in a GWAS; they just seem to sweep
everything in. Is that good, or is it just a distraction? I really do not know.
3. Personal genomics has become a new catch phrase. This
raises the issue that in the germ line gene we can predict our medical future.
It is not at all clear that such is the fact.
4. Just what do the SNPs do? What makes the changes
causative, if at all?
Notwithstanding my concerns here, the work is of substantial
interest.
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