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Reimand, J.

Publications and source records attributed to Reimand, J..

At least 19 recordsLinked to original sources

Multi-platform reassessment of human mitochondrial DNA methylation reveals signals consistent with technical artifacts

The existence and functional relevance of mitochondrial DNA methylation remain controversial. Here, we systematically profiled cytosine methylation and hydroxymethylation across human brain and blood tissues spanning healthy and malignant states using orthogonal sequencing approaches that avoid chemical conversion during library preparation. While nuclear DNA exhibited canonical methylation patterns, mitochondrial DNA consistently showed negligible signal, indistinguishable from background technical noise. By mapping cytosine-guanine sites between mitochondrial DNA and nuclear-embedded mitochondrial sequences, we demonstrate the potential of these nuclear counterparts to confound not only cytosine methylation but also hydroxymethylation measurements, corroborating and extending prior findings implicating nuclear contamination as a potential source of apparent mitochondrial epigenetic signals. Additional technical factors that inflate apparent mtDNA methylation signals were identified, including sequence context biases, flow cell chemistries, and coverage-dependent discrepancies between the heavy and light strands. Collectively, these results provide convergent evidence against the presence of biologically meaningful cytosine methylation or hydroxymethylation in mitochondrial DNA. These findings caution against interpreting apparent mtDNA methylation signals in human adult tissues as meaningful without rigorous orthogonal validation and comprehensive consideration of technical and analytical confounding factors.

bioinformatics↗

Chromatin accessibility of primary cancers informs regional mutagenesis in metastases through multi-scale deep learning

Tissue-specific chromatin states shape regional mutation density in primary tumors, but whether this relationship persists in metastases is unclear. We integrated whole-genome sequencing data from 2,507 metastatic tumors across six cancer types with 892 chromatin accessibility and replication timing profiles, and developed CAMM, a multi-scale deep learning model, to jointly predict SNV and indel density across multiple genomic resolutions. The model explained regional variance in mutation density across metastatic cancers and preserved mutagenesis patterns in an independent primary tumor cohort. Chromatin accessibility profiles from tissue-matched primary cancers were informative predictors of regional mutagenesis in metastases, supporting a contribution of lineage-linked chromatin context, as indicated by feature attribution analyses. Genomic windows with mutation burdens exceeding epigenome-based expectations were enriched for known and candidate cancer loci. These results link metastatic regional mutagenesis to tissue-of-origin chromatin accessibility and provide a framework for interpreting mutational processes and prioritizing mutation-enriched loci.

cancer biology↗

Therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity

Genotoxic cancer therapies introduce DNA damage that can be fixed as somatic mutations in surviving tumor cells. However, the impact of therapy-associated mutagenesis on regulatory elements remains unclear. CTCF binding sites (CBS) are chromatin architectural elements that exhibit recurrent localized mutation enrichment in cancer genomes. We asked whether treatment exposure is associated with increased mutagenesis at CBS in 4,870 whole-genome sequences from metastatic tumors across 17 cancer types and 45 therapies. Radiotherapy and trifluridine exposure in metastatic colorectal cancer were associated with increased mutation enrichment at CBS. This enrichment was pronounced at motif-containing sites and in low-expression or late-replicating genomic contexts. Alterations in DNA damage response genes, including BRCA2, were associated with increased CBS mutation enrichment following radiotherapy. Together, these findings indicate that therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity, extending the mutational consequences of cancer treatment to regulatory genome architecture.

cancer biology↗

Pairwise genomic alterations identify prognostic tumor states in multiple cancer types

Genomic models of cancer prognosis usually rely on individual genomic alterations, potentially overlooking clinically meaningful combinations of events. We analyzed genomic and clinical data from nearly 10,000 primary tumors across major cancer types to identify prognostic genomic interactions (PGIs), defined as pairs of genomic alterations whose joint status was associated with patient outcome beyond either alteration alone. By systematically integrating survival associations with pairwise combinations of recurrent copy-number alterations and frequently mutated driver genes, we identified 57 PGIs. These PGIs refined prognostic stratification and were linked to distinct transcriptomic programs representing immune-response, epithelial-mesenchymal transition, and proliferation-related themes. Gene-level mapping highlighted dosage-sensitive candidate genes within recurrent copy-number regions, and gene essentiality profiles supported subsets of PGI-derived gene pairs. Two PGIs were validated in independent datasets. Together, these results establish a framework for identifying prognostic combinations of genomic alterations and connecting them to pathway programs, candidate genes, and functional dependencies in human tumors.

cancer biology↗

Mutational signatures in cancer genomes alter protein sequence motifs in cellular signaling networks

Somatic mutations in cancer genomes arise from distinct, context-specific mutational processes, yet their functional consequences at the protein and network level remain incompletely understood. Here, we show that mutational processes of single-nucleotide variants (SNVs) can systematically rewire signaling networks by inducing amino acid substitutions in short linear motifs (SLiMs) that mediate interactions with kinases and other signaling proteins. By analysing 11,000 cancer genomes and 144 classes of SLiMs, we identify motif-rewiring SNVs (rwSNVs) that create or disrupt SLiMs or remove phosphorylated residues. Mutational processes of methylcytosine deamination, APOBEC activity, and ultraviolet light exposure emerge as major contributors to motif rewiring. rwSNVs are enriched in cancer driver genes and pathways, linking mutation etiology to functional consequences. rwSNVs at the BRAF V600E hotspot associated with UV-related mutagenesis are predicted to generate a phosphorylation motif recognized by PLK1 kinase. Together, these findings reveal how mutational processes shape oncogenic signaling and tumor heterogeneity.

cancer biology↗

Combinations of genomic alterations and immune microenvironmental features associate with patient survival in multiple cancer types

Oncogenesis and tumor progression are shaped by somatic alterations in the cancer genome and features of the tumor immune microenvironment (TME). How interactions of these two systems influence tumor development and clinical outcomes remains incompletely understood. To address this challenge, we developed the multi-omics analysis framework PACIFIC to systematically integrate genetic cancer drivers and infiltration profiles of immune cells with clinical information. In an analysis of 8500 cancer samples, we report 34 immunogenomic interactions (IGXs) in 13 cancer types in which context-specific combinations of genomic alterations and immune cell activities associate with disease outcomes. Risk associations of IGXs are potentially explained by tumor-intrinsic and microenvironmental metrics of immunogenicity and differential expression of therapeutic targets. In luminal-A breast cancer, MEN1 deletion combined with reduced neutrophils is associated with poor prognosis and deregulation of immune signalling pathways. These findings help elucidate how cancer drivers interact with TME to contribute to tumorigenesis.

cancer biology↗

Topoisomerase IIb binding underlies frequently mutated elements in cancer genomes

Type-II topoisomerases resolve topological stress in DNA through controlled double-strand breaks. While TOP2A is a chemotherapy target in proliferating cells, the ubiquitously expressed TOP2B is a potential off-target. Here we explore roles of TOP2B in mutagenesis by generating DNA-binding maps of TOP2B, CTCF, and RAD21 in human cancer samples and analysing these maps for driver mutations and mutational processes in 6500 whole cancer genomes. TOP2B-CTCF-RAD21 and TOP2B-RAD21 sites are enriched in somatic mutations and structural variants (SVs), especially at evolutionary conserved sites displaying high transcription and long-range chromatin interactions. TOP2B binding underlies SVs and hotspot mutations in cancer-driving genes such as TP53, MYC, FOXA1, and VHL, and many cis-regulatory elements. We show that the TOP2B-bound mutational hotspot at RMRP drives tumor initiation and growth in vivo. These data highlight TOP2B as a protector of the genome from topological challenges whose aberrant activity promotes driver and passenger mutations in cancer genomes.

cancer biology↗

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of the data requires joint multi-omics analyses that are challenging. Here we present DPM, a data fusion method for combining multiple omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally, reflecting the initial experimental design or regulatory relationships between the datasets. DPM statistically prioritises genes and pathways that change consistently across the datasets, while penalising those violating the constraints. Joint analyses of transcriptomic, proteomic, DNA methylation, and clinical datasets of cancer samples demonstrate how directional integration identifies genes and pathways modulated across omics datasets, highlights those with inconsistent evidence, and reveals candidate biomarkers with prognostic signals in multiple datasets. DPM is implemented in the ActivePathways method and provides a general framework for testing detailed hypotheses in multi-omics data.

bioinformatics↗

Pan-cancer analysis of the ion permeome reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-permeating proteins, collectively comprising the ion permeome (IP), are common drug targets. However, their roles in cancer are understudied. Our integrative pan-cancer analysis shows that IP genes display highly-elevated expression patterns in subsets of cancer samples significantly more often than expected transcriptome-wide. To enable target identification, we identified 410 survival-associated IP genes in 29 cancer types using a machine learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and associate with poor prognosis in glioblastoma (GBM), the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived GBM cells induces transcriptome-wide changes involving neural projection and proliferation pathways, impairs cell viability and tumor sphere formation, mitigates tunneling nanotube formation, and extends the survival of GBM-bearing mice. Thus, aberrant activation of IP genes appears as a pan-cancer feature of tumor heterogeneity that can be exploited for mechanistic insights and therapy development.

cancer biology↗

Mutational processes of tobacco smoking and APOBEC activity generate protein-truncating mutations in cancer genomes

Mutational signatures represent a footprint of tumor evolution and its endogenous and exogenous mutational processes. However, their functional impact on the proteome remains incompletely understood. We analysed the protein-coding impact of single base substitution signatures in 12,341 cancer genomes from 18 cancer types. Stop-gain mutations (SGMs) were strongly enriched in the signatures of tobacco smoking, APOBEC cytidine deaminases, and reactive oxygen species. These mutational processes affect specific trinucleotide contexts to substitute serine and glutamic acid residues with stop codons. SGMs are enriched in cancer hallmark pathways and tumor suppressors such as TP53, FAT1, and APC. Tobacco-driven SGMs in lung cancer correlate with lifetime smoking history and highlight a preventable determinant of these harmful mutations. Our study exposes SGM expansion as a genetic mechanism by which endogenous and carcinogenic mutational processes contribute to protein loss-of-function, oncogenesis, and tumor heterogeneity, providing potential translational and mechanistic insights.

cancer biology↗

Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

Whole genome sequencing of primary breast tumors enabled the identification of cancer driver genes 1,2 and non-coding cancer driver plexuses from somatic mutations 3-6. However, differentiating driver and passenger events among non-coding genetic variants remains a challenge to understand the etiology of cancer and inform delivery of personalized cancer medicine. Herein, we reveal an enrichment of non-coding mutations in cis-regulatory elements that cover a subset of transcription factors linked to tumor progression in luminal breast cancers. Using a cohort of 26 primary luminal ER+PR+ breast tumors, we compiled a catalogue of [~]100,000 unique cis-regulatory elements from ATAC-seq data. Integrating this catalogue with somatic mutations from 350 publicly available breast tumor whole genomes, we identified four recurrently mutated individual cis-regulatory elements. By then partitioning the non-coding genome into cistromes, defined as the sum of binding sites for a transcription factor, we uncovered cancer driver cistromes for ten transcription factors in luminal breast cancer, namely CTCF, ELF1, ESR1, FOSL2, FOXA1, FOXM1 GATA3, JUND, TFAP2A, and TFAP2C in luminal breast cancer. Nine of these ten transcription factors were shown to be essential for growth in breast cancer, with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of non-coding mutations over cis-regulatory elements concatenated into a functional unit drawn from an accessible chromatin catalogue derived from primary cancer tissues.

cancer biology↗

Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin

BackgroundRegional distribution of somatic mutations in cancer genomes associates with DNA replication timing (RT) and chromatin accessibility (CA), however normal tissues and cell lines have contributed these insights while associations with the epigenomes of primary cancers remain uncharacterized. ResultsHere we model megabase-scale mutation burden in whole cancer genomes using [~]900 CA and RT profiles of primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than normal tissues, predict regional mutagenesis in most cancer types. Regional mutation burden associates with the CA profiles of matching cancer types, indicating tissue-specific determinants of mutagenesis. However, mutagenesis in squamous cell and lymphoid cancers instead associates with RT profiles. Mutational signatures also show tissue-specific associations with cancer epigenomes, especially for carcinogen-induced and unannotated signatures. Lastly, while each cancer type includes certain frequently-mutated genomic regions exceeding epigenome-informed predictions of mutation burden, these regions show a pan-cancer convergence to biological processes involved in development and cancer. Thus, modelling excess mutations using epigenomes highlights known cancer driver genes as well as frequently mutated non-coding regions. ConclusionsThe dominant association of regional mutation burden with cancer epigenomes suggests that many passenger mutations are determined by the epigenetic landscapes of transformed cells and may occur later in tumor evolution. CA-informed models help find cancer genes and pathways with positive selection and highlight regions where additional mutation burden is contributed by local mutational processes. This study underlines the complex interplay of mutational processes, genome function and evolution in cancer and tissues of origin.

genomics↗

Independent information from PET, CSF and plasma biomarkers of tau pathology in Alzheimer's disease

PET, CSF and plasma biomarkers of tau pathology may be differentially associated with Alzheimers disease (AD) related demographic, cognitive, genetic and neuroimaging markers. We examined 771 participants with normal cognition, mild cognitive impairment or dementia from BioFINDER-2 (n=400) and ADNI (n=371). All had tau-PET ([18F]RO948 in BioFINDER-2, [18F]flortaucipir in ADNI) and CSF p-tau181 biomarkers available. Plasma p-tau181 and plasma/CSF p-tau217 were available in BioFINDER-2 only. Concordance between PET, CSF and plasma tau biomarkers ranged between 66% and 95%. Across the whole group, ridge regression models showed that increased CSF and plasma p-tau181 and p-tau217 levels were independently of tau PET associated with higher age, and APOE{varepsilon}4-carriership and A{beta}-positivity, while increased tau-PET signal in the temporal cortex was associated with worse cognitive performance and reduced cortical thickness. We conclude that biofluid and neuroimaging markers of tau pathology convey partly independent information, with CSF and plasma p-tau181 and p-tau217 levels being more tightly linked with early markers of AD (especially A{beta} pathology), while tau-PET shows the strongest associations with cognitive and neurodegenerative markers of disease progression.

neuroscience↗

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

BackgroundCancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mutational mechanisms with functional and genetic determinants remain poorly understood. ResultsWe characterised the mutational landscape of 1.3 million gene regulatory and chromatin architectural elements in 2,419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We developed RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. We show that increased mutational frequency in TSSs correlates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identified [~]10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of local mutagenesis. We detected site-specific mutational signatures, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. We also proposed candidate drivers of localised mutagenesis: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutations in pancreatic cancer. ConclusionsOur method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver discovery. Functional and genetic correlates of localised mutagenesis provide mechanistic hypotheses for future studies.

genomics↗

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer pathways and powerful molecular biomarkers, however most lncRNAs remain uncharacterized. Here we performed a systematic discovery of prognostic lncRNAs in 9,326 patient tumors of 29 types using a proportional-hazards elastic net machine-learning framework. lncRNAs showed highly tissue-specific transcript abundance patterns. We identified 179 prognostic lncRNAs whose abundance correlated with patient risk and improved the performance of common clinical variables and molecular tumor subtypes. Pathway analysis revealed a large diversity of the high-risk tumors stratified by lncRNAs and suggested their functional associations. In lower-grade gliomas, discrete activation of HOXA10-AS indicated poor patient prognosis, neurodevelopmental pathway activation and a transcriptomic similarity to glioblastomas. HOXA10-AS knockdown in patient-derived glioblastoma cells caused decreased cell proliferation and deregulation of glioma driver genes and proliferation pathways. Our study underlines the pan-cancer potential of the non-coding transcriptome for developing molecular biomarkers and innovative therapeutic strategies.

cancer biology↗

PET and CSF amyloid-β status are differently predicted by patient features: Information from discordant cases

BackgroundAmyloid-{beta} PET and CSF A{beta}42 yield discordant results in 10-20% of patients, possibly providing unique information. Although the predictive power of demographic, clinical, genetic and imaging features for amyloid-positivity has previously been investigated, it is unknown whether these features differentially predict amyloid-{beta} status based on PET or CSF, or whether this differs by disease stage.\n\nMethodsWe included 768 patients (subjective cognitive decline (SCD, n=194), mild cognitive impairment (MCI, n=127), dementia (AD and non-AD, n=447) with amyloid-{beta} PET and CSF A{beta}42 measurement within one year. 97(13%) patients had discordant PET/CSF amyloid-{beta} status. We performed parallel random forest models predicting separately PET and CSF status using 17 patient features (demographics, APOE4 positivity, CSF (p)tau, cognitive performance, and MRI visual ratings) in the total patient group and stratified by syndrome diagnosis. Thereafter, we selected features with the highest variable importance measure (VIM) as input for logistic regression models, where amyloid status on either PET or CSF was predicted by (i) the selected patient feature, and (ii) the patient feature adjusted for the status of the other amyloid modality.\n\nResultsAPOE4, CSF tau and p-tau had highest VIM for PET and CSF in all groups. In the amyloid-adjusted logistic regression models, p-tau was a significant predictor for PET-amyloid in SCD (OR=1.02[1.01-1.04], pFDR=0.03), MCI (OR=1.05[1.02-1.07], pFDR<0.01) and dementia (OR=1.04[1.03-1.05], pFDR<0.001), but not for CSF-amyloid. APOE4 (OR=3.07[1.33-7.07], punc<0.01) was associated with CSF-amyloid in SCD, while it was only predictive for PET-amyloid in MCI (OR=9.44[2.93,30.39], pFDR<0.01). Worse MMSE scores (OR=1.21[1.03-1.41], punc=0.02) were associated to CSF-amyloid status in SCD, whereas worse memory (OR=1.17[1.05-1.31], pFDR=0.02) only predicted PET positivity in dementia.\n\nConclusionAmyloid status based on either PET or CSF was predicted by different patient features and this varied by disease stage, suggesting that PET-CSF discordance yields unique information. The stronger associations of both APOE4 carriership and worse memory z-scores with CSF-amyloid in SCD suggests that CSF-amyloid is more sensitive early in the disease course. The higher predictive value of CSF p-tau for a positive PET scan suggests that PET is more specific to AD pathology. These findings can influence the choice between amyloid biomarkers in future studies or trials.

neuroscience↗

BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions

Transforming data from genome-scale assays into knowledge of affected molecular functions and pathways is a key challenge in biomedical research. Using vocabularies of functional terms and databases annotating genes with these terms, pathway enrichment methods can identify terms enriched in a gene list. With data that can refer to intergenic regions, however, one must first connect the regions to the terms, which are usually annotated only to genes. To make these connections, existing pathway enrichment approaches apply unwarranted assumptions such as annotating non-coding regions with the terms from adjacent genes. We developed a computational method that instead links genomic regions to annotations using data on long-range chromatin interactions. Our method, Biological Enrichment of Hidden Sequence Targets (BEHST), finds Gene Ontology (GO) terms enriched in genomic regions more precisely and accurately than existing methods. We demonstrate BEHSTs ability to retrieve more pertinent and less ambiguous GO terms associated with results of in vivo mouse enhancer screens or enhancer RNA assays for multiple tissue types. BEHST will accelerate the discovery of affected pathways mediated through long-range interactions that explain non-coding hits in genome-wide association study (GWAS) or genome editing screens. BEHST is free software with a command-line interface for Linux or macOS and a web interface (http://behst.hoffmanlab.org/).

bioinformatics↗

Integrative pathway enrichment analysis of multivariate omics data

Multi-omics datasets quantify complementary aspects of molecular biology and thus pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple omics datasets using a statistical data fusion approach, rationalizes contributing evidence and highlights associated genes. We demonstrate its utility by analyzing coding and non-coding mutations from 2,583 whole cancer genomes, revealing frequently mutated hallmark pathways and a long tail of known and putative cancer driver genes. We also studied prognostic molecular pathways in breast cancer subtypes by integrating genomic and transcriptomic features of tumors and tumor-adjacent cells and found significant associations with immune response processes and anti-apoptotic signaling pathways. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

bioinformatics↗