Saturday, February 9, 2013

PSA, SNPs, and Prostate Cancer

In a recent PLOS article there was an analysis of obtaining better diagnostic results using PSA and SNPs from germline cells[1]. The authors conclude:

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.