Normal and pathogenic variation of RFC1 repeat expansions: implications for clinical diagnosis.

Cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS) is an autosomal recessive neurodegenerative disease, usually caused by biallelic AAGGG repeat expansions in RFC1. In this study, we leveraged whole genome sequencing data from nearly 10 000 individuals recruited within the Genomics England sequencing project to investigate the normal and pathogenic variation of the RFC1 repeat. We identified three novel repeat motifs, AGGGC (n = 6 from five families), AAGGC (n = 2 from one family) and AGAGG (n = 1), associated with CANVAS in the homozygous or compound heterozygous state with the common pathogenic AAGGG expansion. While AAAAG, AAAGGG and AAGAG expansions appear to be benign, we revealed a pathogenic role for large AAAGG repeat configuration expansions (n = 5). Long-read sequencing was used to characterize the entire repeat sequence, and six patients exhibited a pure AGGGC expansion, while the other patients presented complex motifs with AAGGG or AAAGG interruptions. All pathogenic motifs appeared to have arisen from a common haplotype and were predicted to form highly stable G quadruplexes, which have previously been demonstrated to affect gene transcription in other conditions. The assessment of these novel configurations is warranted in CANVAS patients with negative or inconclusive genetic testing. Particular attention should be paid to carriers of compound AAGGG/AAAGG expansions when the AAAGG motif is very large (>500 repeats) or the AAGGG motif is interrupted. Accurate sizing and full sequencing of the satellite repeat with long-read sequencing is recommended in clinically selected cases to enable accurate molecular diagnosis and counsel patients and their families.

Evaluation of Semen Self-Sampling Yield Predictors and CTC Isolation by Multi-Color Flow Cytometry for Liquid Biopsy of Localized Prostate Cancer.

Liquid biopsy (LB) for prostate cancer (PCa) detection could represent an alternative to biopsy. Seminal fluid (SF) is a source of PCa-specific biomarkers, as 40% of ejaculate derives from the prostate. We tested the feasibility of an SF-based LB by evaluating the yield of semen self-sampling in a cohort of >750 patients with clinically localized PCa. The overall SF collection yield was 18.2% (39% when considering only compliant patients), with about a half of the patients (53.15%) not consenting to SF donation. Independent favorable predictors for SF collection were younger age and lower prostate volume. We implemented a protocol to enrich prostate-derived cells by multi-color flow cytometry and applied it on SF and urine samples from 100 patients. The number of prostate-enriched cells (SYTO-16+ PSMA+ CD45-) was variable, with higher numbers of cells isolated from SF than urine (p value < 0.001). Putative cancer cells (EpCAMhigh) were 2% of isolated cells in both specimens. The fraction of EpCAMhigh cells over prostate-enriched cells (PSMA+) significantly correlated with patient age in both semen and urine, but not with other clinical parameters, such as Gleason Score, ISUP, or TNM stage. Hence, enumeration of prostate-derived cells is not sufficient to guide PCa diagnosis; additional molecular analyses to detect patient-specific cancer lesions will be needed.

Post-Biopsy Cell-Free DNA From Blood: An Open Window on Primary Prostate Cancer Genetics and Biology.

Circulating cell-free DNA (ccfDNA), released from normal and cancerous cells, is a promising biomarker for cancer detection as in neoplastic patients it is enriched in tumor-derived DNA (ctDNA). ctDNA contains cancer-specific mutations and epigenetic modifications, which can have diagnostic/prognostic value. However, in primary tumors, and in particular in localized prostate cancer (PCa), the fraction of ctDNA is very low and conventional strategies to study ccfDNA are unsuccessful. Here we demonstrate that prostate biopsy, by causing multiple injuries to the organ, leads to a significant increase in plasma concentration of ccfDNA (P<0.0024) in primary PCa patients. By calculating the minor allele fraction at patient-specific somatic mutations pre- and post-biopsy, we show that ctDNA is significantly enriched (from 3.9 to 164 fold) after biopsy, representing a transient "molecular window" to access and analyze ctDNA. Moreover, we show that newly released ccfDNA contains a larger fraction of di-, tri- and multi-nucleosome associated DNA fragments. This feature could be exploited to further enrich prostate-derived ccfDNA and to analyze epigenetic markers. Our data represent a proof-of-concept that liquid tumor profiling from peripheral blood performed just after the biopsy procedure can open a "valuable molecular metastatic window" giving access to the tumor genetic asset, thus providing an opportunity for early cancer detection and individual genomic profiling in the view of PCa precision medicine.

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.

OBJECTIVE: The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). METHODS: In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. RESULTS: Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. CONCLUSIONS: Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.