Selective sequencing or in silico analysis for differences in DNA fragment size can improve the detection of circulating tumor DNA.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample. According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively. We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

Ecto-5′-nucleotidase (CD73) is a membrane-bound enzyme, which catalyzes the conversion of adenosine monophosphate to adenosine. CD73 has been postulated to play an important role in carcinogenesis, as adenosine promotes tumor progression and CD73-expressing cancer cell lines are more aggressive. However, other studies have shown that activated adenosine receptors may also inhibit cell proliferation. This study investigated the clinical significance of CD73 expression in breast cancer. The study group included 136 consecutive stage I-III breast cancer patients treated between 2001 and 2008 at 2 institutions. CD73 expression was examined by immunohistochemistry (IHC) on tissue microarrays, using antihuman mouse monoclonal antibody. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. CD73 staining was expressed as the score calculated by multiplying the staining intensity (0=negative, 1=weak, 2=intermediate, 3=strong) and percentage of positive cells (0% to 100%). The median score among all samples was 100. Positive CD73 staining (defined as score equal or higher than 100) occurred in 74% of the cases. No correlation was found between CD73 expression and grading, tumor size, lymph node status, histologic type, estrogen receptor, or progesterone receptor status. Positive CD73 expression strongly correlated with longer disease-free survival (hazard ratio=0.26; 95% confidence interval, 0.1-0.66; P=0.0044) and overall survival (hazard ratio =0.24; 95% confidence interval, 0.07-0.85; P=0.027). Multivariate analysis for disease-free survival revealed correlation with tumor size and CD73 status. Elevated CD73 expression in breast cancer can predict a good prognosis. However, the actual role of CD73 in cancerogenesis remains unclear and requires further analysis.

Blood platelet RNA-sequencing is increasingly used among the scientific community. Aberrant platelet transcriptome is common in cancer or cardiovascular disease, but reference data on platelet RNA content in healthy individuals are scarce and merit complex investigation. We sought to explore the dynamics of platelet transcriptome. Datasets from 204 healthy donors were used for the analysis of splice variants, particularly with regard to age, sex, blood storage time, unit of collection or library size. GenesB2M, PPBP, TMSB4X, ACTB, FTL, CLU, PF4, F13A1, GNAS, SPARC, PTMA, TAGLN2, OAZ1andOST4demonstrated the highest expression in the analysed cohort, remaining substantial transcription consistency.CSF3Rgene was found upregulated in males (fold change 2.10, FDR q < 0.05). Cohort dichotomisation according to the median age, showed upregulatedKSR1in the older donors (fold change 2.11, FDR q < 0.05). Unsupervised hierarchical clustering revealed two clusters which were irrespective of age, sex, storage time, collecting unit or library size. However, when donors are analysed globally (as vectors), sex, storage time, library size, the unit of blood collection as well as age impose a certain degree of between- and/or within-group variability. Healthy donor platelet transcriptome retains general consistency, with very few splice variants deviating from the landscape. Although multidimensional analysis reveals statistically significant variability between and within the analysed groups, biologically, these changes are minor and irrelevant while considering disease classification. Our work provides a reference for studies working both on healthy platelets and pathological conditions affecting platelet transcriptome.

Background: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. Methods: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. Results: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. Conclusions: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.