Biology subjects

Kristensen, V. N.

Publications and source records attributed to Kristensen, V. N..

16 recordsLinked to original sources

bioRxiv2025

CLONAL SELECTION SUPPORTED BY SINGLE CELL DNA SEQUENCING REVEALS HORMONAL ADAPTATION AND RESISTANCE IN LOCALLY ADVANCED BREAST CANCER DURING NEOADJUVANT AROMATASE INHIBITION

BackgroundThe aromatase inhibitors (AI) letrozole and exemestane, are often used in sequence in targeting ER+ breast cancers. However resistance to AI poses a major barrier to sustained clinical benefit, while the biological mechanisms underlying the phenomenon remain largely unknown. In this study, we build on our clinical NeoLetExe trial, with the aim to investigate the molecular basis of resistance to AI, by analysing subclonal evolutionary dynamics during sequential treatment. MethodsWe use whole-exome sequencing (WES) data from 11 ER+ breast cancer patients and 3 timepoints of the Neoletexe trial to reconstruct cancer cell fraction-based subclonal composition. Single-cell DNA sequencing from matched tumour samples is used for validating identified clones and variants. Subclonal variants were annotated to genes by integrating evidence from public data and ExpectoSc. Pathway enrichment analysis using Human Base was conducted. ResultsHigher cancer cell fraction clone trajectories were significantly associated with reduced treatment response (p = 0.023). Clones reconstructed by WES were validated at 81% using single-cell DNA sequencing. Clones resistant to both letrozole and exemestane demonstrated PIK3CA/AKT/mTOR signaling activation, KRAS pathway dysregulation, hedgehog signaling, and androgen receptor pathways, alongside extensive immune activation and metabolic reprogramming. Drug-specific resistance patterns showed exemestane-resistant clones enriched for epigenetic control and miRNA-mediated silencing, while letrozole-resistant clones displayed metabolic dysregulation but notably lacked immune pathway activation. In contrast, treatment-sensitive clones maintained coordinated cell cycle control, preserved DNA damage responses, and retained immune signaling capacity. Analysis of FDA-approved breast cancer targets identified actionable alterations in PIK3CA (4 patients) and AKT1 (1 patient) that persisted through AI treatment, with RNA expression analysis revealing 48 additional therapeutic targets spanning PI3K/AKT/mTOR, CDK4/6, DNA repair (BRCA1/2, ATM), and immune checkpoint pathways. ConclusionWES-based cancer cell fraction analysis successfully captured subclonal evolutionary trajectories during AI treatment, revealing drug-specific mechanisms and identifying key molecular players in endocrine therapy resistance. This work establishes a framework for precision oncology approaches by providing actionable therapeutic targets and advancing our understanding of resistance mechanisms to improve clinical outcomes in sequential AI therapy.

cancer biology↗

bioRxiv2025

HiFIseek: gene-specific enrichment of high-impact mutations in associated genomic regions

Transcription is regulated through the sequence-specific binding of transcription factors (TFs) to cis-regulatory regions (CRRs). Although scattered along the genome, multiple CRRs are brought in close spatial proximity to the genes they regulate through the formation of DNA loops. The sequence component of transcriptional regulation suggests that DNA variants in CRRs could disrupt TF-DNA interactions and gene regulatory networks through a cascading effect. To date, only a few cases of recurrent cis-regulatory variants have been described. As an alternative to variant recurrence, some methods utilize genomic annotations that indicate the functional impact (FI) of a variant and the CRRs of each gene to detect the enrichment of high-impact cis-regulatory variants (CRVs). However, the agreement across the different associations and FI scoring methods remains unexplored. This work demonstrates that gene-CRR and FI scoring methods exhibit little consensus, highlighting the impact of the choice of a specific CRR-score combination in a given analysis. In addition, we demonstrate that cancer genes exhibit a higher frequency of FI variants compared to non-cancer genes. Based on this, we developed HiFIseek, a Snakemake pipeline exploring the enrichment of FI variants in associated genomic regions using all region-score combinations and two enrichment detection methods. We apply HiFIseek to detect genes exhibiting high FI CRVs in eight cancer cohorts and a set of breast cancer risk-associated single-nucleotide polymorphisms (SNPs). In the cancer cohorts, we observe a small degree of agreement across CRR-score combinations. Although HiFIseek detected known cancer-related genes with FI CRVs, most of them did not show significant changes in their expression. In the SNPs cohort, HiFIseek found small consensus across CRR-score combinations. However, one of the combinations returned 18 known cancer-related genes, including BRCA1, showing an enrichment of high-impact CRVs.

bioinformatics↗

bioRxiv2024

Integrated multiomics analysis unveils how macrophages drive immune suppression in breast tumors and affect clinical outcomes

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis are still poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devised an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying our pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we created a comprehensive catalog of the TMEs role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) analysis, we discovered that macrophages tend to accumulate in the vicinity of HLA-ABChi epithelial cells as their frequency increases in tumor tissues and also express elevated levels of HLA-ABC protein. Combining IMC with single-cell RNA sequencing (scRNA-seq) data, we uncovered a significant association between these HLA-ABChi macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicted interactions between HLA-ABChi macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, our research enhances the characterization of macrophage-driven immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

bioinformatics↗

bioRxiv2024

Loss of chromosome cytoband 13q14.2 orchestrates breast cancer pathogenesis and drug response

Breast cancer (BCa) is a major global health challenge, characterized by chromosomal instability (CIN) and subsequent acquisition of extensive somatic copy number alterations (CNAs). CNAs including amplifications and deletions, significantly influence intra-tumor heterogeneity and the tumor microenvironment (TME). Among these, the loss of chromosome 13q14.2 emerges as a considerable factor in BCa pathogenesis and treatment responses. We provide evidence that this genomic alteration is under positive selective pressure, correlating with poorer patient survival. Furthermore, through multi-omic and in vitro analyses, we uncover a dual role of 13q14.2 loss: it confers a survival advantage to tumor cells and modulate the cell cycle and pro-apoptotic pathways in cancer cells, affecting macrophages population in the TME, while paradoxically increasing tumor susceptibility to BCL2 inhibitors. These findings suggest that targeting 13q14.2 as a biomarker in BCa could enhance the efficacy of existing treatments and offer a new avenue for improving clinical outcomes in BCa. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/599481v1_fig1.gif" ALT="Figure 1"> View larger version (93K): org.highwire.dtl.DTLVardef@123f961org.highwire.dtl.DTLVardef@c952f0org.highwire.dtl.DTLVardef@16430eborg.highwire.dtl.DTLVardef@c7c954_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical abstract C_FIG

cancer biology↗

bioRxiv2023

Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis; specifically, we identified three types of emQTL networks associated with alterations linked to the regulation of proliferation, metabolism, and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in transcription factor binding regions (TFBRs) located in enhancers. The underlying CpGs were topologically linked to upregulated genes through chromatin loops. Loss of enhancer methylation and target genes were exemplified in pancreatic cancer. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL. Taken together, our integrative pan-cancer analysis reveals a common architecture of aberrant DNA demethylation that illustrates a convergence of pathological regulatory mechanisms across cancer types.

cancer biology↗

bioRxiv2023

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled the identification of phenotypic diversity within breast tumor tissues. However, the contribution of these cell phenotypes to tumor biology and treatment response has remained less understood. This is primarily due to the limited number of available samples and the inherent heterogeneity of breast tumors. To address this limitation, we leverage a state-of-the-art scRNA-seq atlas and employ CIBER-SORTx to estimate cell phenotype fractions by de-convolving bulk expression profiles in more than 2000 samples from patients who have undergone Neoad-juvant Chemotherapy (NAC). We introduce a pipeline based on explainable Machine Learning (XML) to robustly explore the associations between different cell phenotype fractions and the response to NAC in the general population as well as different subtypes of breast tumors. By comparing tumor subtypes, we observe that multiple cell types exhibit a distinct association with pCR within each subtype. Specifically, Dendritic cells (DCs) exhibit a negative association with pathological Complete Response (pCR) in Estrogen Receptor positive, ER+, (Luminal A/B) tumors, while showing a positive association with pCR in ER-(Basal-like/HER2-enriched) tumors. Analysis of new spatial cyclic immunoflu-orescence data and publicly available imaging mass cytometry data showed significant differences in the spatial distribution of DCs between ER subtypes. These variations underscore disparities in the engagement of DCs within the tumor microenvironment (TME), potentially driving their divergent associations with pCR across tumor subtypes. Overall, our findings on 28 different cell types provide a comprehensive understanding of the role played by cellular compo-nents of the TME in NAC outcomes. They also highlight directions for further experimental investigations at a mechanistic level.

bioinformatics↗

bioRxiv2022

An integrated 'omics approach highlights the role of epigenetic events to explain and predict response to neoadjuvant chemotherapy and bevacizumab

Here we present an integrated omics approach for DNA methylation profiling using copy number alteration, gene expression and proteomic data to predict response to therapy and to pinpoint response-related epigenetic events. Fresh frozen tumor biopsies taken before, during and after treatment from patients receiving neoadjuvant chemotherapy with or without the anti-angiogenic drug bevacizumab were subjected to molecular profiling. Our previous studies have shown that administration of bevacizumab in addition to chemotherapy (combination treatment) may confer improved response for patients; here we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for ER positive patients with high fidelity (AUC=0.874), and we validate this observation in an independent patient cohort with similar treatment regimen (AUC=0.762). When combining the DNA methylation score with a previously reported proteomic score (ViRP), the prediction accuracy further improved in the validation cohort (AUC=0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations than tumors receiving only chemotherapy. Finally, we performed an integrative emQTL analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-EMT axis through the activity of the transcription factor GRHL2. Taken together, these results illustrate the clinical benefit of the addition of bevacizumab to chemotherapy if administered to the correct patients.

cancer biology↗

bioRxiv2022

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present retriever, a tool that extracts robust disease-specific transcrip-tional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.

bioinformatics↗

bioRxiv2021

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesized that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression-methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF-binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

cancer biology↗

bioRxiv2021

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer

Breast cancer is a highly heterogeneous disease driven by multiple factors including genetic and epigenetic alterations. DNA methylation patterns have been shown to be altered on a genome-wide scale and previous studies have highlighted the critical role of aberrant DNA methylation on gene expression and breast cancer pathogenesis. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL), a method for integration of CpG methylation and gene expression to identify disease-driving genes under epigenetic control. By grouping these emQTLs by biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis such as proliferation and tumor infiltrating fibroblasts. We report hypomethylation at enhancers carrying transcription factor binding sites of key proliferation-driving transcription factors such as CEBP-{beta}, FOSL1, and FOSL2, with concomitant high expression of cell cycle- and proliferation-related genes in aggressive breast tumors. The identified CpGs and genes were found to be connected through chromatin loops, together indicating that proliferation in aggressive breast tumors is under epigenetic regulation by DNA methylation. Interestingly, there was a significant correlation between proliferation-related DNA methylation and gene expression also within subtypes of breast cancer, thereby showing that varying proliferation may be explained by epigenetic profiles across breast cancer subtypes. Indeed, the identified proliferation gene signature was prognostic both in the Luminal A and Luminal B subtypes. Taken together, we show that proliferation in breast cancer is linked to hypomethylation at specific enhancers and transcription factor binding mediated through chromatin loops.

cancer biology↗

bioRxiv2020

Spatial Transcriptomics Inferred from Pathology Whole-Slide Images Using Deep Learning Links Tumor Heterogeneity to Survival in Breast and Lung Cancer

Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized on pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.

bioinformatics↗

bioRxiv2020

Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

BackgroundAbnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and have been shown to play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. ResultsmiRNA expression and DNA methylation data from two breast cancer cohorts were subjected to genome-wide correlation analysis. Clustering of the miRNA expression-DNA methylation association pairs significant in both cohorts identified distinct clusters of miRNAs and CpGs. These clusters recapitulated important biological processes associated with breast cancer pathogenesis. Notably, two major clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding the CpGs associated with the estrogen-signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2 and ER-alpha. Further, at the hub of the estrogen-cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of the DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER positive breast cancer. ConclusionsWe describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

cancer biology↗

bioRxiv2018

Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention

DNA methylation has been extensively linked to alterations in gene expression, playing a key role in the manifestation of multiple diseases, most notably cancer. For this reason, researchers have long been measuring DNA methylation in living organisms. The relationship between methylation and expression, and between methylation in different genomic regions is of great theoretical interest from a molecular biology perspective. Therefore, several models have been suggested to support the prediction of methylation status in samples. These models, however, have two main limitations: (a) they heavily rely on partially measured methylation levels as input, somewhat defeating the object as one is required to collect measurements from the sample of interest before applying the model; and (b) they are largely based on human mediated feature engineering, thus preventing the model from unveiling its own representations. To address these limitations we used deep learning, with an attention mechanism, to produce a general model that predicts DNA methylation for a given sample in any CpG position based solely on the sample's gene expression profile and the sequence surrounding the CpG.\n\nWe show that our model is capable of generalizing to a completely separate test set of CpG positions and subjects. Depending on gene-CpG proximity conditions, our model can attain a Spearman correlation of up to 0.8 and MAE of 0.14 for thousands of CpG sites in the test data. We also identify and analyze several motifs and genes that our model suggests may be linked to methylation activity, such as Nodal and Hand1. Moreover, our approach, and most notably the use of attention mechanisms, offers a novel framework with which to extract valuable insights from gene expression data when combined with sequence information.\n\nThe code and trained models are available at: https://github.com/YakhiniGroup/Methylation

bioinformatics↗

bioRxiv2018

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.

cancer biology↗

bioRxiv2018

Haplotypes associated to gene expression in breast cancer: can they lead us to the susceptibility markers?

We have undertaken a systematic haplotype analysis of the positional type of biclusters analysing samples collected from 164 breast cancer patients and 86 women with no known history of breast cancer. We present here the haplotypes and LD patterns in more than 80 genes distributed across all chromosomes and how they differ between cases and controls. We aim by this to 1) identify genes with different haplotype distribution or LD patterns between breast cancer patients and controls and 2) to evaluate the intratumoral mRNA expression patterns in breast cancer associated particularly to the cancer susceptibility haplotypes. A significant difference in haplotype distribution between cases and controls was observed for a total of 35 genes including ABCC1, AKT2, NFKB1, TGFBR2 and XRCC4. In addition we see a negative correlation between LD patterns in cases and controls for neighboring markers in 8 genes such as CDKN1A, EPHX1 and XRCC1.

genomics↗

bioRxiv2018

Candidate SNP analyses integrated with mRNA expression and hormone levels reveal influence on mammographic density and breast cancer risk

Introduction Introduction Materials and methods Results Discussion Globaltest Conclusion Competing interests Authors contributions Reference List Mammographic density (MD) is a well-documented risk factor for breast cancer[1-9], and thus a promising target for early detection of the disease. Based on mammographic results, the breast is given a percent density (PDEN) score ranging from 0-100% with 0 being the least dense (primarily adipose tissue). It is estimated that the risk of developing breast cancer is four to six times higher in women with an MD score of at least 75% [2,9] and that one-third of breast cancers occur in patients with PDEN scores of at least 50% [2]. MD is ...

genomics↗