Aims Chemotherapy resistance remains a major challenge in breast cancer (BC) treatment. This study aimed to investigate the role of DNA methylation in this complex process and evaluate the potential of the DNA methyltransferase inhibitor decitabine (DAC) in restoring chemosensitivity. Methods Paclitaxel (PAC)- and doxorubicin (DOX)- resistant BC cell lines were derived from luminal A (T-47D), triple-negative (MDA-MB-231), and HER2-positive (JIMT-1) models and characterized by molecular profiling and functional assays. The therapeutic effects of DAC and DOX were assessed in MDA-MB-231 xenografts, and integrative analyses of DNA methylation and gene expression identified pathways associated with resistance. Follow-up analyses were performed in PAC-resistant MAS98.12 patient-derived xenografts (PDX) and in clinical samples from the NeoAva trial (NCT00773695). Results Resistant cells exhibited a slow-cycling phenotype, reduced tumorigenicity, and widespread genomic alterations. PAC-resistant xenografts showed extensive methylation and transcriptomic reprogramming, partly restored by DAC, which increased Ki-67 expression and enhanced DOX responsiveness. In contrast, PDX tumors displayed less pronounced changes, predominantly hypomethylation, indicating distinct resistance mechanisms. Importantly, xenograft-derived CpG signatures stratified NeoAva patients by treatment response. Conclusions Chemoresistance in BC involves extensive genomic and epigenetic remodeling. Although DAC can modulate methylation and tumor phenotype, rational drug combinations will be required to overcome resistance.

With the increasing availability of ranking data, there has been a growing demand for appropriate unsupervised rank-based inferential frameworks capable of handling high-dimensional datasets and providing uncertainty quantification for all estimates. Rank-based methods have also seen a growing popularity in -omics pipelines, as ranking continuous measurements provides a robust means of handling non-normally distributed data. The Bayesian Mallows model (BMM) has emerged as a promising choice because of its adaptability to various types of ranking data and its flexible framework, integrating cluster-wise rank aggregation with inference at the individual level. However, the scalability of BMM to ultra-high-dimensional settings, such as -omics analyses, has remained limited. The present paper addresses this issue by introducing the first rank-based model generalizing BMM to jointly handle clustering and variable selection, namely the lower-dimensional Bayesian Mallows Model Mixture (lowBM3). The proposed method provides a novel Bayesian framework that simultaneously handles heterogeneity in the sample, unsupervised parameter estimation, and model selection in a scalable manner for ultra-high-dimensional data. Additionally, a companion postprocessing framework is introduced to provide posterior summaries of the discrete posterior distributions of both the consensus ranking and the variable selector. Simulation studies are performed to assess the performance of the method. The usefulness of the method is also shown in an application to signature discovery for cancer genomics, where RNA-seq bulk gene expression data obtained from breast cancer patients are clustered genome-wide.

A comprehensive understanding of the underlying molecular mechanisms of prostate cancer is essential for the development of precise diagnostic biomarkers. In this study, we applied the unsupervised multi-omics factor analysis framework (MOFA) to integrate DNA methylation, gene expression, and metabolic profiles derived from the same individuals, aiming to characterize the biological landscape of normal, malignant, and aggressive prostate tissue. Our analysis identified distinct molecular pathways associated with aggressive disease, specifically those involved in zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology. Key metabolites within the TCA cycle, amino acid metabolism, and lipid pathways were central to these signatures. Furthermore, we observed a consistent co-enrichment of SP1 and CTCFL binding regions among factor-associated CpGs, suggesting a model of global epigenetic reprogramming. These findings indicate a novel interplay between Polycomb deregulation, CTCFL-mediated chromatin remodeling, and SP1-driven transcriptional activation in shaping the prostate cancer epigenome. Apart from immune activation, the identified molecular signatures were validated in the TCGA cohort and demonstrated significant predictive value for disease recurrence. Overall, these results underscore the power of multi-omics integration in providing a holistic understanding of prostate cancer biology and its potential for clinical translation into prognostic biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, since most PDAC tumors develop resistance to the standard of care (SoC) treatments like chemotherapy, radiation, and targeted therapies. All tumors consist of multiple, genetically related subpopulations of cancer cells that evolve in parallel and display heterogeneity at genomic, epigenetic, or phenotypic levels. As cancer develops, some subpopulations of cancer cells may show faster growth, increased metastatic potential, and resistance toward SoC treatment. A key challenge in PDAC management is understanding chemoresistance driven by cancer subpopulation dynamics. This would broaden our understanding of tumor adaptation to treatments and guide targeting resistant subpopulations to improve therapeutic outcomes. In this study, we identified PDAC cell subpopulations resistant to chemotherapy using our DNA barcoding system B-GLI (barcode-guide lineage isolation). The B-GLI system leverages a highly complex DNA barcode library and CRISPR activation (CRISPRa) to trace and isolate sub-lineages within heterogeneous cell populations by their DNA barcodes. PDAC cell lines PANC-1 and Mia-PaCa-2 were barcoded with the B-GLI barcode library, which consist of two optimized guide RNA binding sites in each barcode. After DNA barcoding, we performed treatments with two SoC chemotherapeutics, gemcitabine + paclitaxel and FOLFIRINOX, to induce a selection pressure in the PDAC cell lines, and then identified the resistant subpopulations through differentially represented barcodes and sequencing. Furthermore, we designed barcode-specific single guide RNAs (sgRNAs) targeting the resistant subpopulations and activated the expression of the puromycin resistance gene by CRISPRa. This allowed us to enrich and isolate chemoresistant subpopulations by puromycin treatment. After having isolated the resistant PDAC subpopulations, we performed ATAC-seq and RNA-seq for molecular characterization of the resistant and parental cell populations, along with phenotypic drug screening with 384 compounds to identify novel treatment vulnerabilities. The compound testing confirmed that the resistant cells were less sensitive to the two SoC treatments, as well as identified selective sensitivity to specific compounds. ATAC-seq and RNA-seq data will allow us to decode whether and how chromatin structure and transcriptomic changes are associated with the treatment resistance, and the confirmation screen of the drug sensitivities will identify potential compounds for future pre-clinical and clinical testing in PDAC models and patients with resistant disease. In summary, this study will identify targeted treatment alternatives, accompanied by molecular biomarkers for chemotherapy resistant PDAC. Subhendu Roy Choudhury, Shixiong Wang, Yevhen Akimov, Katarina Willoch, Biswajyoti Sahu, Alfonso Urbanucci, Thomas Fleischer, Tero Aittokallio. Identification, isolation and molecular characterization of drug-resistant sub-populations of pancreatic cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5544.

There is a need for more precise biomarkers and understanding on the development of aggressive prostate cancer. In this study, we analyzed DNA methylation in 64 prostate cancer tissue samples, using tissue from radical prostatectomy patients (n = 16) with up to 16 years of clinical follow-up. We used several samples from each patient including both normal and cancer tissue to study DNA methylation patterns in relation to aggressiveness measured by follow-up data of biochemical recurrence and metastasis status as clinical endpoints. We identified differentially methylated CpGs associated with recurrence and metastasis, regardless of whether the tissue was normal, cancer-adjacent normal, or cancer. The identified CpG sites were over-represented in promoter regions and transcription factor binding regions, suggesting their influence on gene expression regulation. They further exhibited low intrapatient heterogeneity both between normal, normal adjacent, and cancer tissue, making them favorable as potential biomarkers for aggressive prostate cancer. However, validation of a subset of these CpGs in an external dataset was unsuccessful.

Patients with pancreatic ductal adenocarcinoma (PDAC) have the lowest survival rate among all cancer patients in Europe. Since western societies have the highest incidence of pancreatic cancer, it has been projected that PDAC will soon become the second leading cause of cancer-related deaths. The main challenge of PDAC treatment is that patients with similar somatic genotypes exhibit a wide range of disease phenotypes. Artificial Intelligence (AI) is currently transforming the field of healthcare and represents a promising technology for integrating various datasets and optimizing evidence-based decision making. However, the interpretability of most AI models is limited and it is challenging to understand how and why a decision is made. In this study, we developed a deep clustering model for PDAC patient stratification using integrated methylation and gene expression data. We placed a specific emphasis on model explainability, with the aim to understand the hidden patterns learned by the model. The results showed two subgroups of PDAC patients with different prognoses and biological factors. The multi-omics profile analysis revealed the important role of DNA methylation. We also showed how the model was able to learn underlying patterns using both single modalities and their combinations. We hope that this study will help to promote more explainable AI in real-world clinical applications, where the knowledge of decision factors is crucial. The code of this project is publicly available in GitHub (https://github.com/albertolzs/edc_mo_pdac).

Treatment with the anti‐angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response‐related epigenetic events. Fresh‐frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non‐metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression–methylation quantitative trait loci 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–epithelial‐to‐mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell‐specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.

Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in the onset of epithelial ovarian cancer (OvC). TL in either target or surrogate tissue (blood) is currently being investigated for use as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is currently an urgent need for an appropriate approach to chemotherapy response prediction.We performed a monochrome multiplex qPCR measurement of TL in peripheral blood leukocytes (PBL) and tumor tissues of 209 OvC patients. The methylation status and gene expression of the shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n = 46) had shorter telomeres in PBL compared to treatment-resistant patients (n = 93; P = 0.037). In the patients with a different therapy response, transcriptomic analysis showed alterations in the peroxisome proliferator-activated receptor (PPAR) signaling pathway (q = 0.001). Moreover, tumor TL shorter than the median corresponded to better overall survival (OS) (P = 0.006). TPP1 gene expression was positively associated with TL in tumor tissue (P = 0.026).TL measured in PBL could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' OS.

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 including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

Supplementary Figure 2. Canonical pathway:Gαi Signalling

Supplementary Figure 1. Canonical pathway:Communication between Innate and Adaptive Immune Cells

A limited number of studies are devoted to regulating TRIP6 expression in cancer. Hence, we aimed to unveil the regulation of TRIP6 expression in MCF-7 breast cancer cells (with high TRIP6 expression) and taxane-resistant MCF-7 sublines (manifesting even higher TRIP6 expression). We found that TRIP6 transcription is regulated primarily by the cyclic AMP response element (CRE) in hypomethylated proximal promoters in both taxane-sensitive and taxane-resistant MCF-7 cells. Furthermore, in taxane-resistant MCF-7 sublines, TRIP6 co-amplification with the neighboring ABCB1 gene, as witnessed by fluorescence in situ hybridization (FISH), led to TRIP6 overexpression. Ultimately, we found high TRIP6 mRNA levels in progesterone receptor-positive breast cancer and samples resected from premenopausal women.

Table S1: Genes significantly differentially expressed in samples with pathological complete response compared to those without in the Combination arm. Table S2: DAVID analysis of 720 differentially expressed genes in tumors that achieved pathological complete reponse compared to those that did not, in the combination arm. Table S3 : Genes significantly differentially expressed in samples with pathological complete response compared to those without in the Chemotherapy arm. Table S4: DAVID analysis of 1243 differentially expressed genes in tumors that achieved pathological complete reponse compared to those that did not, in the chemotherapy arm. Table S5 : List of genes with significant lower expression in week 12 samples treated with combination therapy compared to those treated with chemotherapy Table S6: DAVID analysis of 42 differentially expressed genes in week-12 tumors in the combination arm compared to chemotherapy arm. Table S7 : Differentially expressed genes between week-0 and week-12 in Basal-like tumors treated with combination therapy Table S8 : Differentially expressed genes between week-0 and week-12 Luminal B tumors treated with combination therapy Table S9 : Differentially expressed genes between week-0 and week-12 Luminal A tumors treated with combination therapy Table S10: Differentially expressed genes between week-0 and week-12 Basal-like tumors treated with chemotherapy Table S11 : Differentially expressed genes between week-0 and week-12 Luminal B tumors treated with chemotherapy Table S12 : Differentially expressed genes between week-0 and week-12 Luminal A tumors treated with chemotherapy Table S13: Pathways significantly altered between Chemotherapy and Combination therapy arms from week-0 to week-12 LuminalB samples Table S14: Pathways significantly altered between anthracycline and taxane treatment in the Luminal A samples in chemotherapy arm

The analysis of whole genomes of pan‐cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank‐based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top‐ranked genomic features. We applied our method to RNA‐seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan‐cancer clusters. Importantly, we identified three pan‐squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over‐represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan‐cancer samples.

Anti-VEGF (vascular endothelial growth factor) treatment improves response rates, but not progression-free or overall survival in advanced breast cancer. It has been suggested that subgroups of patients may benefit from this treatment; however, the effects of adding anti-VEGF treatment to a standard chemotherapy regimen in breast cancer patients are not well studied. Understanding the effects of the anti-vascular treatment on tumor vasculature may provide a selection of patients that can benefit. The aim of this study was to study the vascular effect of bevacizumab using clinical dynamic contrast-enhanced MRI (DCE-MRI). A total of 70 women were randomized to receive either chemotherapy alone or chemotherapy with bevacizumab for 25 weeks. DCE-MRI was performed at baseline and at 12 and 25 weeks, and in addition 25 of 70 patients agreed to participate in an early MRI after one week. Voxel-wise pharmacokinetic analysis was performed using semi-quantitative methods and the extended Tofts model. Vascular architecture was assessed by calculating the fractal dimension of the contrast-enhanced images. Changes during treatment were compared with baseline and between the treatment groups. There was no significant difference in tumor volume at any point; however, DCE-MRI parameters revealed differences in vascular function and vessel architecture. Adding bevacizumab to chemotherapy led to a pronounced reduction in vascular DCE-MRI parameters, indicating decreased vascularity. At 12 and 25 weeks, the difference between the treatment groups is severely reduced.

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.

Supplementary text to the main article detailing mathematical model, model initialisation and parametrisation, computational details and overview of clinical data .

Supplementary Table 1-6. Supplementary Table 1: Differentially methylated genes and functions of the genes before/after treatment with doxorubicin and FUMI Supplementary Table 2: Ingenuity Pathway analysis Supplementary Table 3: List and function of the 46 differentially methylated genes Supplementary Table 4: List of the 333 differentially methylated genes Supplementary Table 5: Ingenuity Pathway analysis of the 333 differentially methylated genes Supplementary Table 6: Associations between methylation status and TP53 mutation status

Supplementary Figure legends. supplementary data