Buffy coat signatures of breast cancer risk in a prospective cohort study

BACKGROUND

Epigenetic alterations are a near-universal feature of human malignancy and have been detected in malignant cells as well as in easily accessible specimens such as blood and urine. These findings offer promising applications in cancer detection, subtyping, and treatment monitoring. However, much of the current evidence is based on findings in retrospective studies and may reflect epigenetic patterns that have already been influenced by the onset of the disease.

METHODS

Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).

RESULTS

We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.

CONCLUSIONS

Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.

Abstract 4956: Discovering novel targetable pathways by combining functional and multi-omic data from primary ovarian cancer samples

Background: There is a critical necessity to reveal novel and tractable targets for anti-cancer treatments in indications with high unmet medical need, such as high grade serous ovarian cancer (HGSOC). However, standard process for target discovery using models such as outgrown cell lines and well-averaged readouts has yielded a less than 5% approval rate for drugs entering trials (Thomas et al. 2016 Bio.org).

Here, we describe patient-centric target discovery through the use of disease relevant primary OC samples and single cell functional characterization using a platform with proven hemonc translatability (Kornauth et al. 2021, Snijder et al. 2017). We integrate data from our functional drug testing platform under multiple drug perturbations with matching genomic and transcriptomic data to reveal associations with novel downstream regulators of sensitivity.

Methods: Sensitivity of the cancer cell compartment in primary malignant ascites samples (n = 20; 75% HGSOC) to 85 small molecule drugs, was evaluated using a proprietary and translatable deep learning-driven single cell imaging platform (Vladimer et al. 2017). Cancer cell sensitivity from the drugs was combined with WES, bulk-RNAseq and drug induced changes in phosphoproteome, and single cell RNAseq transcriptome to identify perturbed targets and pathways.

Results: Here we describe a family of TKIs including ALKi that induce cytotoxicity of cancer cells in primary samples, not previously captured in publicly available cell line drug sensitivity screening data (Iorio et al. 2016). We report novel sensitivity of OC driven by non-canonical targets of ceritinib such as FAK1 or IGF1R, mediated by the downstream signaling hub YBX1 (Kuenzi et al. 2017), involved in NFB pathway regulation (Motolani et al. 2021). Indeed, transcriptomic scRNA analysis upon ceritinib treatment of primary OC cells revealed rapid perturbation of numerous NFB pathway members, alongside YBX1 inactivation.

Conclusions: Combining functional endpoints and single cell-based differential expression analysis of primary OC samples, we have identified the NFB pathway and the regulator YBX1 as a promising novel sensitivity for HGSOC treatment development. These and several other important targetable nodes identified, sit outside the recently suggested JAK/STAT pathway (Izar et al. 2020), thereby demonstrating a pipeline towards novel drug target and pathway discovery driven by patient-centric, disease relevant models of high-need indications.

Citation Format: Irene Gutierrez-Perez, Bekir Ergüner, Pisanu Buphamalai, Joost Van Ham, Paul Heinz, Valentin Aranha, Rin Okumura, Elisabeth Waltenberger, Isabella Alt, Claudia Baumgaertler, Maja Stulic, Edgar Petru, Christoph Minichsdorfer, Judith Lafleur, Lukas Hefler, Laudia Hadjari, Lucia Dzurillova, Jozef Sufliarsky, Nikolaus Krall, Thorsten Füreder, Gregory Ian Vladimer, Bojan Vilagos, Robert Sehlke. Discovering novel targetable pathways by combining functional and multi-omic data from primary ovarian cancer samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4956.

Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species

Methylation of cytosines is a prototypic epigenetic modification of the DNA. It has been implicated in various regulatory mechanisms across the animal kingdom and particularly in vertebrates. We mapped DNA methylation in 580 animal species (535 vertebrates, 45 invertebrates), resulting in 2443 genome-scale DNA methylation profiles of multiple organs. Bioinformatic analysis of this large dataset quantified the association of DNA methylation with the underlying genomic DNA sequence throughout vertebrate evolution. We observed a broadly conserved link with two major transitions-once in the first vertebrates and again with the emergence of reptiles. Cross-species comparisons focusing on individual organs supported a deeply conserved association of DNA methylation with tissue type, and cross-mapping analysis of DNA methylation at gene promoters revealed evolutionary changes for orthologous genes. In summary, this study establishes a large resource of vertebrate and invertebrate DNA methylomes, it showcases the power of reference-free epigenome analysis in species for which no reference genomes are available, and it contributes an epigenetic perspective to the study of vertebrate evolution.

Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma

Background

The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification.

Methods

In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.

Results

The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.

Conclusions

The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.

Identifying disease-causing mutations with privacy protection

Motivation

The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease–gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private.

Results

Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively.

Availability and implementation

https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden

Sequencing of cell-free DNA in the blood of cancer patients (liquid biopsy) provides attractive opportunities for early diagnosis, assessment of treatment response, and minimally invasive disease monitoring. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we introduce an integrated genetic/epigenetic analysis method and demonstrate its utility on 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sarcomas. Our method achieves sensitive detection and classification of circulating tumor DNA in peripheral blood independent of any genetic alterations. Moreover, we benchmark different metrics for cell-free DNA fragmentation analysis, and we introduce the LIQUORICE algorithm for detecting circulating tumor DNA based on cancer-specific chromatin signatures. Finally, we combine several fragmentation-based metrics into an integrated machine learning classifier for liquid biopsy analysis that exploits widespread epigenetic deregulation and is tailored to cancers with low mutation rates. Clinical associations highlight the potential value of cfDNA fragmentation patterns as prognostic biomarkers in Ewing sarcoma. In summary, our study provides a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations, and it renders the benefits of liquid biopsy more readily accessible for childhood cancers.

Finding underlying genetic mechanisms of two patients with autism spectrum disorder carrying familial apparently balanced chromosomal translocations

BACKGROUND

Genetic etiologies of autism spectrum disorders (ASD) are complex, and the genetic factors identified so far are very diverse. In complex genetic diseases such as ASD, de novo or inherited chromosomal abnormalities are valuable findings for researchers with respect to identifying the underlying genetic risk factors. With gene mapping studies on these chromosomal abnormalities, dozens of genes have been associated with ASD and other neurodevelopmental genetic diseases. In the present study, we aimed to idenitfy the causative genetic factors in patients with ASD who have an apparently balanced chromosomal translocation in their karyotypes.

METHODS

For mapping the broken genes as a result of chromosomal translocations, we performed whole genome DNA sequencing. Chromosomal breakpoints and large DNA copy number variations (CNV) were determined after genome alignment. Identified CNVs and single nucleotide variations (SNV) were evaluated with VCF-BED intersect and Gemini tools, respectively. A targeted resequencing approach was performed on the JMJD1C gene in all of the ASD cohorts (220 patients). For molecular modeling, we used a homology modeling approach via the SWISS-MODEL.

RESULTS

We found that there was no contribution of the broken genes or regulator DNA sequences to ASD, whereas the SNVs on the JMJD1C, CNKSR2 and DDX11 genes were the most convincing genetic risk factors for underlying ASD phenotypes.

CONCLUSIONS

Genetic etiologies of ASD should be analyzed comprehensively by taking into account of the all chromosomal structural abnormalities and de novo or inherited CNV/SNVs with all possible inheritance patterns.

Detection of allele frequencies of common c. 511C>T and c.625G>A variants in the ACADS gene in the Turkish population

Short-chain acyl-CoA dehydrogenase deficiency (SCADD) is a rare inborn error of mitochondrial fatty acid oxidation and protein misfolding disorder. Our aim was to detect the number of Turkish patients diagnosed with SCADD in the literature and to determine the allele frequencies of two common variants (c.511C > T and c.625G > A) in the Turkish population. Five Turkish patients with SCADD were reported in the literature from four unrelated families. We also investigated allele frequencies of common variants of c.511C > T and c.625G > A, which confer susceptibility to SCADD, which were found to be 1.7% and 20.2%, respectively. Both of these susceptibility variants were found to be high in the Turkish population as they are worldwide.