Performance of an adipokine pathway-based multilocus genetic risk score for prostate cancer risk prediction

Few biomarkers are available to predict prostate cancer risk. Single nucleotide polymorphisms (SNPs) tend to have weak individual effects but, in combination, they have stronger predictive value. Adipokine pathways have been implicated in the pathogenesis. We used a candidate pathway approach to investigate 29 functional SNPs in key genes from relevant adipokine pathways in a sample of 1006 men eligible for prostate biopsy. We used stepwise multivariate logistic regression and bootstrapping to develop a multilocus genetic risk score by weighting each risk SNP empirically based on its association with disease. Seven common functional polymorphisms were associated with overall and high-grade prostate cancer (Gleason≥7), whereas three variants were associated with high metastatic-risk prostate cancer (PSA≥20 ng/mL and/or Gleason≥8). The addition of genetic variants to age and PSA improved the predictive accuracy for overall and high-grade prostate cancer, using either the area under the receiver-operating characteristics curves (P<0.02), the net reclassification improvement (P<0.001) and integrated discrimination improvement (P<0.001) measures. These results suggest that functional polymorphisms in adipokine pathways may act individually and cumulatively to affect risk and severity of prostate cancer, supporting the influence of adipokine pathways in the pathogenesis of prostate cancer. Use of such adipokine multilocus genetic risk score can enhance the predictive value of PSA and age in estimating absolute risk, which supports further evaluation of its clinical significance.     

Updating risk prediction tools: a case study in prostate cancer

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.     

A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer

Patients with hormone receptor(HR)-positive tumors breast cancer usually experience a relatively low pathological complete response(p CR) to neoadjuvant chemotherapy(NAC). Here, we derived a 10-micro RNA risk score(10-mi RNA RS)-based model with better performance in the prediction of p CR and validated its relation with the disease-free survival(DFS) in 755 HRpositive breast cancer patients(273, 265, and 217 in the training, internal, and external validation sets, respectively). This model,pres...     

Monitoring response, prediction methodology, staging, and imaging in prostate cancer

The predictive values of various tests and examinations are assessed as they relate to prostate cancer progression and treatment. The usefulness of post-treatment biopsy specimens is greatest 2 years after radiation therapy completion. Gleason grading is not reliable in the setting of hormonal ablation therapy. For patients with extracapsular extension, the survival curves separate depending on whether positive or negative surgical margins are obtained. Prostate-specific antigen doubling time is increasingly used as an indicator of disease recurrence after local therapy and prostate cancer-specific survival.     

Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients

BackgroundNew biomarkers are needed to identify different clinical outcomes for HER2+ breast cancer (BC).MethodsDifferential genes of HER2+ BC were screened based on TCGA database. We used WGCNA to identify the genes related to the survival. Genetic Algorithm was used to structure risk prediction model. The prognostic model was validated in GSE data.ResultsWe constructed a risk prediction model of 6 genes to identify prognosis of HER2+ BC, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27. Kaplan-Meier curve showed that the model effectively distinguished the survival of HER2+ BC patients. The multivariate Cox regression suggested that the risk model was an independent predictor for HER2+ BC. Analysis related to immune showed that significant differences in immune infiltration between high- and low-risk groups classified by the prognostic model.ConclusionsOur study identified a risk prediction model of 6 genes that could distinguish the prognosis of HER2+ BC.     

A stochastic model for cancer risk

We propose a simple stochastic model based on the two successive mutations hypothesis to compute cancer risks. Assume that only stem cells are susceptible to the first mutation and that there are a total of D stem cell divisions over the lifetime of the tissue with a first mutation probability mu(1) per division. Our model predicts that cancer risk will be low if m = mu(1)D is low even in the case of very advantageous mutations. Moreover, if mu(1)D is low the mutation probability of the second mutation is practically irrelevant to the cancer risk. These results are in contrast with existing models but in agreement with a conjecture of Cairns. In the case where m is large our model predicts that the cancer risk depends crucially on whether the first mutation is advantageous or not. A disadvantageous or neutral mutation makes the risk of cancer drop dramatically.     

Computational protein biomarker prediction: a case study for prostate cancer

Background  

Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.     

The Estradiol-Dihydrotestosterone model of prostate cancer

Background  

The exact relationship between hormonal activity and prostate cancer(PCa) has not yet been clearly defined. One of the key hormones associated with PCa is testosterone(T). However, both in vitro and in vivo studies have shown that under some conditions T is capable of either promoting PCa growth or death. This article proposes a theory which resolves this apparent paradox.     

DNA damage phenotype and prostate cancer risk

The capacity of an individual to process DNA damage is considered a crucial factor in carcinogenesis. The comet assay is a phenotypic measure of the combined effects of sensitivity to a mutagen exposure and repair capacity. In this paper, we evaluate the association of the DNA repair kinetics, as measured by the comet assay, with prostate cancer risk. In a pilot study of 55 men with prostate cancer, 53 men without the disease, and 71 men free of cancer at biopsy, we investigated the association of DNA damage with prostate cancer risk at early (0-15 min) and later (15-45 min) stages following gamma-radiation exposure. Although residual damage within 45 min was the same for all groups (65% of DNA in comet tail disappeared), prostate cancer cases had a slower first phase (38% vs. 41%) and faster second phase (27% vs. 22%) of the repair response compared to controls. When subjects were categorized into quartiles, according to efficiency of repairing DNA damage, high repair-efficiency within the first 15 min after exposure was not associated with prostate cancer risk while higher at the 15-45 min period was associated with increased risk (OR for highest-to-lowest quartiles=3.24, 95% CI=0.98-10.66, p-trend=0.04). Despite limited sample size, our data suggest that DNA repair kinetics marginally differ between prostate cancer cases and controls. This small difference could be associated with differential responses to DNA damage among susceptible individuals.     

Preliminary evaluation of prostate cancer metastatic risk biomarkers

Prostate cancer patients at high risk of metastasis need to be identified as early as possible since metastasis is invariably fatal. Treatment could be tailored to risk. Recent array comparative genomic hybridization (aCGH) studies of primary and metastatic prostate tumors identified 39 BAC clones capable of detecting genomic signatures of metastasis. We termed these loci the genomic evaluators of metastatic CaP (GEMCaP). Risk assessments were made on a set of men who were managed with radical prostatectomy. We compared the utility of GEMCaP loci and the Kattan nomogram, a common risk assessment tool, in relation to biochemical outcome. This preliminary evaluation experiment suggests we can use aCGH to detect genomic signatures of metastasis in primary tumors with an accuracy of 78%. The classification accuracy for the Kattan nomogram was 75%. Therefore, validation of GEMCaP is warranted in a larger, appropriately designed cohort.     

Serum methionine metabolites are risk factors for metastatic prostate cancer progression

Background

Clinical decision for primary treatment for prostate cancer is dictated by variables with insufficient specificity. Early detection of prostate cancer likely to develop rapid recurrence could support neo-adjuvant therapeutics and adjuvant options prior to frank biochemical recurrence. This study compared markers in serum and urine of patients with rapidly recurrent prostate cancer to recurrence-free patients after radical prostatectomy. Based on previous identification of urinary sarcosine as a metastatic marker, we tested whether methionine metabolites in urine and serum could serve as pre-surgical markers for aggressive disease.

Methodology/Principal Findings

Urine and serum samples (n = 54 and 58, respectively), collected at the time of prostatectomy were divided into subjects who developed biochemical recurrence within 2 years and those who remained recurrence-free after 5 years. Multiple methionine metabolites were measured in urine and serum by GC-MS. The role of serum metabolites and clinical variables (biopsy Gleason grade, clinical stage, serum prostate specific antigen [PSA]) on biochemical recurrence prediction were evaluated. Urinary sarcosine and cysteine levels were significantly higher (p = 0.03 and p = 0.007 respectively) in the recurrent group. However, in serum, concentrations of homocysteine (p = 0.003), cystathionine (p = 0.007) and cysteine (p<0.001) were more abundant in the recurrent population. The inclusion of serum cysteine to a model with PSA and biopsy Gleason grade improved prediction over the clinical variables alone (p<0.001).

Conclusions

Higher serum homocysteine, cystathionine, and cysteine concentrations independently predicted risk of early biochemical recurrence and aggressiveness of disease in a nested case control study. The methionine metabolites further supplemented known clinical variables to provide superior sensitivity and specificity in multivariable prediction models for rapid biochemical recurrence following prostatectomy.     

Incorporating non-genetic risk factors and behavioural modifications into risk prediction models for colorectal cancer

Background: Epidemiological studies have identified potentially modifiable risks for colorectal cancer, including alcohol intake, diet and a sedentary lifestyle. Modelling these environmental factors alongside genetic risk is critical in obtaining accurate estimates of disease risk and improving our understanding of behavioural modifications. Methods: 14 independent single nucleotide polymorphisms identified though GWAS studies and reported on by the international consortium COGENT were used to model genetic disease risk at a population level. Six well validated environmental risks were selected for modelling together with the genetic risk factors (alcohol intake; smoking; exercise levels; BMI; fibre intake and consumption of red and processed meat). Through a simulation study using risk modelling software, we assessed the potential impact of behavioural modifications on disease risk. Results: Modelling the genetic data alone leads to 24% of the population being classified as reduced risk; 60% average risk; 10% elevated risk and 6% high risk for colorectal cancer. Adding alcohol consumption to the model reduced the elevated and high risk categories to 9% and 5% respectively. The simulation study suggests that a substantial proportion of individuals could reduce their disease risk profile by altering their behaviour, including reclassification of over 62% of heavy drinkers. Conclusion: Modelling lifestyle factors alongside genetic risk can provide useful strategies to select individuals for screening for colorectal cancer risk. Impact: Quantifying the impact of moderating behaviour, particularly related to alcohol intake and obesity levels, is beneficial for informing health campaigns and tailoring prevention strategies.