Deep Sequencing of Cell-Free Peripheral Blood DNA as a Reliable Method for Confirming the Diagnosis of Myelodysplastic Syndrome.

BACKGROUND: Demonstrating the presence of myelodysplastic syndrome (MDS)-specific molecular abnormalities can aid in diagnosis and patient management. We explored the potential of using peripheral blood (PB) cell-free DNA (cf-DNA) and next-generation sequencing (NGS). MATERIALS AND METHODS: We performed NGS on a panel of 14 target genes using total nucleic acid extracted from the plasma of 16 patients, all of whom had confirmed diagnoses for early MDS with blasts <5%. PB cellular DNA from the same patients was sequenced using conventional Sanger sequencing and NGS. RESULTS: Deep sequencing of the cf-DNA identified one or more mutated gene(s), confirming the diagnosis of MDS in all cases. Five samples (31%) showed abnormalities in cf-DNA by NGS that were not detected by Sanger sequencing on cellular PB DNA. NGS of PB cell DNA showed the same findings as those of cf-DNA in four of five patients, but failed to show a mutation in the RUNX1 gene that was detected in one patient's cf-DNA. Mutant allele frequency was significantly higher in cf-DNA compared with cellular DNA (p = 0.008). CONCLUSION: These data suggest that cf-DNA when analyzed using NGS is a reliable approach for detecting molecular abnormalities in MDS and should be used to determine if bone marrow aspiration and biopsy are necessary.

Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System.

BACKGROUND: Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa). To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa. METHODS: Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies. We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine. Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7). RESULTS: Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87. The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively. We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7). This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively. Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa. Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected. CONCLUSIONS: This model of a biomarker panel with algorithmic interpretation can be used as a "liquid biopsy" to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions.

Synonymous Polymorphisms in HOXB13 as a Protective Factor for Prostate Cancer.

BACKGROUND: Genomic association and linkage studies, as well as epidemiological data have implicated both the HOXB13 gene and single nucleotide polymorphisms (SNPs) in the development of prostate cancer (PCa). The recent association between the G84E polymorphism in the HOXB13 gene and PCa has been shown to result in a more aggressive cancer with an earlier onset of the disease. We examined the frequency of this mutation and other recurrent HOXB13 SNPs in patients with PCa and those with benign prostatic hyperplasia (BPH) or no cancer. METHODS: Reverse transcriptase-polymerase chain reaction (RT-PCR) was performed on exons 1 and 2 of HOXB13 gene, followed by bidirectional Sanger Sequencing on peripheral blood from 232 PCa (age 46-92) and 110 BPH (age 45-84) patients. Statistical analysis was used to correlate between recurrent SNPs and PCa. RESULTS: The G84E mutation was found at a low frequency in randomly selected PCa and BPH (both 0.9%). Two recurrent, synonymous SNPs, rs8556 and rs900627, were also detected. rs8556 was detected in 48 PCa (20.7%) and 26 BPH (23.6%) subjects; rs9900627was detected in 27 PCa (11.6%) and 19 BPH (17.3%) subjects. Having both rs8556 and rs9900627 or being homozygous for either one was associated with being 2.9 times less likely to develop PCa (p=0.05). CONCLUSIONS: Although a larger study in order to confirm our findings, our data suggests a significant negative correlation between two SNPs, rs8556 and rs9900627, and the presence of PCa.

Diagnostic and prognostic scoring system for prostate cancer using urine and plasma biomarkers.

AIMS: To avoid relying solely on serum prostate-specific antigen (sPSA) in screening for prostate cancer (PCa), we developed a scoring system for detecting PCa and the prediction of aggressiveness. We analyzed urine and plasma specimens from 121 patients with PCa or benign prostatic hyperplasia (BPH) for the levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, and B2M genes. Patient age, sPSA level, and polymerase chain reaction data were entered through multiple algorithms to determine models most useful for the detection of cancer and predicting aggressiveness. RESULTS: In the first algorithm, we distinguished PCa from BPH (area under the receiver operating characteristic curve [AUROC] of 0.78). Another algorithm distinguished patients with the Gleason score (GS) of ≥7 from GS of <7 cancer or BPH (AUROC of 0.88). By incorporating the two algorithms into a scoring system, 75% of the analyzed samples showed concordance between the two models (99% specificity and 68% sensitivity for predicting GS ≥7 in this group). CONCLUSION: A scoring system incorporating two algorithms using urine and plasma biomarkers highly predicts the presence of GS ≥7 PCa in 75% of patients. Our algorithms may assist with both biopsy indication and patient prognosis.