EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

Robust methylation-based classification of brain tumours using nanopore sequencing.

BACKGROUND: DNA methylation-based classification of cancer provides a comprehensive molecular approach to diagnose tumours. In fact, DNA methylation profiling of human brain tumours already profoundly impacts clinical neuro-oncology. However, current implementation using hybridisation microarrays is time consuming and costly. We recently reported on shallow nanopore whole-genome sequencing for rapid and cost-effective generation of genome-wide 5-methylcytosine profiles as input to supervised classification. Here, we demonstrate that this approach allows us to discriminate a wide spectrum of primary brain tumours. RESULTS: Using public reference data of 82 distinct tumour entities, we performed nanopore genome sequencing on 382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of 55 cases, we found that a minimum set of 1000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as a confidence measure for interpretation in a clinical context and empirically determined a platform-specific threshold in a randomly sampled discovery cohort (N = 185). Applying this cut-off to an independent validation series (n = 184) yielded 148 classifiable cases (sensitivity 80.4%) and demonstrated 100% specificity. Cross-lab validation demonstrated robustness with concordant results across four laboratories in 10/11 (90.9%) cases. In a prospective benchmarking (N = 15), the median time to results was 21.1 h. CONCLUSIONS: In conclusion, nanopore sequencing allows robust and rapid methylation-based classification across the full spectrum of brain tumours. Platform-specific confidence scores facilitate clinical implementation for which prospective evaluation is warranted and ongoing.

Analysis of AR/ARV7 Expression in Isolated Circulating Tumor Cells of Patients with Metastatic Castration-Resistant Prostate Cancer (SAKK 08/14 IMPROVE Trial).

Despite several treatment options and an initial high response rate to androgen deprivation therapy, the majority of prostate cancers will eventually become castration-resistant in the metastatic stage (mCRPC). Androgen receptor splice variant 7 (ARV7) is one of the best-characterized androgen receptor (AR) variants whose expression in circulating tumor cells (CTCs) has been associated with enzalutamide resistance. ARV7 expression analysis before and during enzalutamide treatment could identify patients requiring alternative systemic therapies. However, a robust test for the assessment of the ARV7 status in patient samples is still missing. Here, we implemented an RT-qPCR-based assay for detection of AR full length (ARFL)/ARV7 expression in CTCs for clinical use. Additionally, as a proof-of-principle, we validated a cohort of 95 mCRPC patients initiating first line treatment with enzalutamide or enzalutamide/metformin within a clinical trial. A total of 95 mCRPC patients were analyzed at baseline of whom 27.3% (26/95) had ARFL+ARV7+, 23.1% (22/95) had ARFL+ARV7-, 23.1% (22/95) had ARFL-ARV7-, and 1.1% (1/95) had ARFL-ARV7+ CTCs. In 11.6% (11/95), no CTCs could be isolated. A total of 25/95 patients had another CTC analysis at progressive disease, of whom 48% (12/25) were ARV7+. Of those, 50% (6/12) were ARV7- and 50% (6/12) were ARV7+ at baseline. Our results show that mRNA analysis of isolated CTCs in mCRPC is feasible and allows for longitudinal endocrine agent response monitoring and hence could contribute to treatment optimization in mCRPC.

Liquid Biopsy in Clinical Management of Breast, Lung, and Colorectal Cancer.

Examination of tumor molecular characteristics by liquid biopsy is likely to greatly influence personalized cancer patient management. Analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and tumor-derived exosomes, all collectively referred to as "liquid biopsies," are not only a modality to monitor treatment efficacy, disease progression, and emerging therapy resistance mechanisms, but they also assess tumor heterogeneity and evolution in real time. We review the literature concerning the examination of ctDNA and CTC in a diagnostic setting, evaluating their prognostic, predictive, and monitoring capabilities. We discuss the advantages and limitations of various leading ctDNA/CTC analysis technologies. Finally, guided by the results of clinical trials, we discuss the readiness of cell-free DNA and CTC as routine biomarkers in the context of various common types of neoplastic disease. At this moment, one cannot conclude whether or not liquid biopsy will become a mainstay in oncology practice.

New polyene macrolide family produced by submerged culture of Streptomyces durmitorensis.

A new polyene macrolide family, closely related to the pentaene macrolide antibiotic roflamycoin, was isolated from the both fermentation broth and biomass of Streptomyces durmitorensis wild-type strain MS405. The main compound was identified by NMR and Fourier transform ion cyclotron resonance mass spectrometry as 32,33-didehydroroflamycoin (1; DDHR). Additional four structurally related compounds were determined solely by MS analysis. DDHR induces cell death by apoptosis in various cancer cell lines as demonstrated by DNA fragmentation. Striking feature of DDHR is its internal fluorescence allowing visualization of labeled plasma membranes and internal membrane structures.