Comprehensive Genomic Profiling Alters Clinical Diagnoses in a Significant Fraction of Tumors Suspicious of Sarcoma.

PURPOSE: Tumor classification is a key component in personalized cancer care. For soft tissue and bone tumors, this classification is currently based primarily on morphology assessment and immunohistochemical staining. However, these standard-of-care methods can pose challenges for pathologists. We therefore assessed how whole-genome and whole-transcriptome sequencing (WGTS) impacted tumor classification and clinical management when interpreted together with histomorphology. EXPERIMENTAL DESIGN: We prospectively evaluated WGTS in routine diagnostics of 200 soft tissue and bone tumors suspicious for malignancy, including DNA and RNA isolation from the tumor, and DNA isolation from a peripheral blood sample or any non-tumor tissue. RESULTS: Based on specific genomic alterations or absence of presumed findings, WGTS resulted in reclassification of 7% (13/197) of the histopathological diagnoses. Four cases were downgraded from low-grade sarcomas to benign lesions, and two cases were reclassified as metastatic malignant melanomas. Fusion genes associated with specific tumor entities were found in 30 samples. For malignant soft tissue and bone tumors, we identified treatment relevant variants in 15% of cases. Germline pathogenic variants associated to a hereditary cancer syndrome were found in 22 participants (11%). CONCLUSION: We conclude that WGTS provides an important dimension of data which aids in the classification of soft tissue and bone tumors, correcting a significant fraction of clinical diagnoses, and identifies molecular targets relevant for precision medicine. However, genetic findings need to be evaluated in their morphopathological context, just as germline findings need to be evaluated in the context of patient phenotype and family history.

Whole exome and targeted sequencing reveal novel mutations associated with inherited PCOS condition in an Indian cohort.

A cohort of polycystic ovary syndrome (PCOS) women presents themselves with persistent abnormal reproductive hormone levels and has a familial representation of characteristics. In our study, we have aimed to identify genetic variants which are inherited across such PCOS families and also validate them among Indian population. Independent discovery was done by whole exome sequencing in a three-generation family (Family P01). Validation was done by targeted sequencing at 30,000x using HaloPlex panel in 9 families (P01-P09). The variants were filtered and reported according to American College of Medical Genetics and Genomics (ACMG) guidelines. Mutation burden analysis and in-silico functional analyses were performed. After careful annotation analyses, we report 24 likely pathogenic variants from 21 genes, out of which 8 are novel structural variants, 14 missense variants and 2 intronic variants. Out of these, 3 variants from the genes FSHR, SCARB1, and INSR are involved in the ovarian steroidogenesis pathway and 5 variants from genes DFFB, ACTG1, GPX4, CYC1 and ALDOA directly or indirectly trigger the apoptotic pathways. Three ovarian steroidogenesis variants, FSHR, SCARB1 and INSR were screened among Indian women using a case-control approach to validate these variant's pathogenicity in Indian PCOS women. Variants of SCARB1 and INSR were found to be pathogenic to Indian PCOS women, while FSHR variants did not show significant association to PCOS cases.

External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort.

Background: External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed. Objective: To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC). Design setting and participants: We used data from the prospective, population-based STHLM3 screening study, performed in 2012-2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included. Outcome measurements and statistical analysis: Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models. Results and limitations: Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1-7.1%) and 20% (IQR 15-28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman's r = 0.55). Using the RPCRC's recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72-0.76) and the PBCG-RC of 0.70 (95% CI 0.68-0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03-1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68-0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs decreased. Conclusions: Assessment of calibration is essential to ensure the clinical value of risk prediction tools. The PBCG-RC provided clinical benefit in its current version online. On the contrary, the RPCRC cannot be recommended in this setting. Patient summary: Predicting the probability of finding prostate cancer on biopsy differed between two assessed risk calculators. After recalibration, the agreement of the models improved, and both were shown to be clinically useful.

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients.

Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15-30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.

Ductal and acinar components of mixed prostatic adenocarcinoma frequently have a common clonal origin.

BACKGROUND: Ductal adenocarcinoma (DA) is an aggressive subtype of prostate cancer. It is most commonly seen in mixed tumors together with conventional acinar adenocarcinoma (AA). The genetic profile of DA and its clonal origin is not fully characterized. OBJECTIVE: To investigate whether DA represents a distinct genetic subtype and to investigate the somatic relationship between the ductal and acinar components of mixed cancers. DESIGN, SETTING, AND PARTICIPANTS: In 17 radical prostatectomy specimens ductal and acinar tumor components from the same tumor foci were dissected. DNA was extracted and genomic sequencing performed. After exclusion of two cases with low cell yield, 15 paired samples remained for analysis. RESULTS: In 12 of 15 cases a common somatic denominator was identified, while three cases had clonally separate components. In DA, TMPRSS2-ERG gene fusions were detected in 47% (7/15), clonal FOXA1 alterations in 33% (5/15) and SPOP alterations in 27% (4/15) of cases. In one case KIAA1549-BRAF fusion was identified. Genome doubling events, resulting in an increased ploidy, were identified in the DA in 53% (8/15) of cases, but not seen in any AA. PTEN and CTNNB1 alterations were enriched in DA (6/15) but not seen in any AA. No cancers showed microsatellite instability or high tumor mutation burden. CONCLUSIONS: Ductal and acinar prostate adenocarcinoma components of mixed tumors most often share the same origin and are clonally related. DA components in mixed tumor often exhibit genome doubling events resulting in aneuploidy, consistent with the aggressive nature of high grade prostate cancer.

Head-to-head Comparison of Conventional, and Image- and Biomarker-based Prostate Cancer Risk Calculators.

BACKGROUND: A new generation of risk calculators (RCs) for prostate cancer (PCa) incorporating magnetic resonance imaging (MRI) data have been introduced. However, these have not been validated externally, and their clinical benefit compared with alternative approaches remains unclear. OBJECTIVE: To assess previously published PCa RCs incorporating MRI data, and compare their performance with traditional RCs (European Randomized Study of Screening for Prostate Cancer [ERSPC] 3/4 and Prostate Biopsy Collaborative Group [PBCG]) and the blood-based Stockholm3 test. DESIGN, SETTING, AND PARTICIPANTS: RCs were tested in a prospective multicenter cohort including 532 men aged 45-74 yr participating in the Stockholm3-MRI study between 2016 and 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The probabilities of detection of clinically significant PCa (csPCa) defined as Gleason score ≥3 + 4 were calculated for each patient. For each RC and the Stockholm3 test, discrimination was assessed by area under the curve (AUC), calibration by numerical and graphical summaries, and clinical usefulness by decision curve analysis (DCA). RESULTS AND LIMITATIONS: The discriminative ability of MRI RCs 1-4 for the detection of csPCa was superior (AUC 0.81-0.87) to the traditional RCs (AUC 0.76-0.80). The observed prevalence of csPCa in the cohort was 37%, but calibration-in-the-large predictions varied from 14% to 63% across models. DCA identified only one model including MRI data as clinically useful at a threshold probability of 10%. The Stockholm3 test achieved equivalent performance for discrimination (AUC 0.86) and DCA, but was underpredicting the actual risk. CONCLUSIONS: Although MRI RCs discriminated csPCa better than traditional RCs, their predicted probabilities were variable in accuracy, and DCA identified only one model as clinically useful. PATIENT SUMMARY: Novel risk calculators (RCs) incorporating imaging improved the ability to discriminate clinically significant prostate cancer compared with traditional tools. However, all but one predicted divergent compared with actual risks, suggesting that regional modifications be implemented before usage. The Stockholm3 test achieved performance comparable with the best MRI RC without utilization of imaging.