bims-tumhet 2025-10-26 papers

bims-tumhet

Biomed News

on Tumor heterogeneity

Issue of 2025–10–26
nine papers selected by
Sergio Marchini, Humanitas Research

Circulating tumor DNA in the diagnosis of ovarian cancer: a systematic review.
Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard.
Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer.
Rucaparib maintenance for newly diagnosed advanced ovarian cancer: interim overall survival, progression-free survival, and safety at 5 years of follow-up from the phase III ATHENA-MONO/GOG-3020/ENGOT-ov45.
Sacituzumab Tirumotecan in EGFR-TKI-Resistant, EGFR-Mutated Advanced NSCLC.
Read-level DNA methylation deconvolution enhances circulating tumor DNA detection.
Integrating mutation, copy number, and gene expression data to identify driver genes of recurrent chromosome-arm losses.
How to get the most out of your cancer spatial transcriptomics data.
Digital droplet PCR is an accurate and precise method to measure DNA copy number.

Int J Gynecol Cancer. 2025 Oct 06. pii: S1048-891X(25)01806-7. [Epub ahead of print] 102686

Circulating tumor DNA in the diagnosis of ovarian cancer: a systematic review.

Cristina Taliento, Pantaleo Greco, Giulia Bruni, Ina Marie Dueholm Hjorth, An Coosemans, Wouter Froyman, Dirk Timmerman, John Charles Rotondo, Chiara Mazziotta, Martina Arcieri, Stefano Restaino, Francesco Multinu, Giuseppe Vizzielli, Carlotta Giorgi, Lars Dyrskjøt, Paolo Pinton, Giampaolo Morciano.

   OBJECTIVE: Ovarian cancer (OC) remains a leading cause of gynecologic cancer mortality worldwide, largely due to late-stage diagnosis and limited early detection tools. Circulating tumor DNA (ctDNA) has emerged as a promising non-invasive biomarker with the potential to improve diagnostic accuracy through detection of tumor-specific genetic and epigenetic alterations.
METHODS: This systematic review aimed to evaluate the diagnostic accuracy of ctDNA in detecting OC compared to healthy controls or benign conditions. A comprehensive literature search was conducted across PubMed, Web of Science, and EBSCO databases through April 2024, including studies that assessed sensitivity, specificity of ctDNA assays in plasma or serum samples. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. PROSPERO registration number: CRD42024590089.
RESULTS: Nineteen studies met inclusion criteria, employing a variety of molecular techniques including polymerase chain reaction-based methylation assays (73.7%) and sequencing methods (whole genome sequencing/next-generation sequencing) (21%), targeting single genes or multi-gene panels. Diagnostic accuracy of ctDNA varied, with sensitivity (40.6%-94.7%) and specificity (56%-100%) ranging broadly, but often outperforming CA125, particularly in early-stage. Concordance between ctDNA and tumor tissue ranged from moderate (r = 0.428) to strong (r = 0.771).
CONCLUSIONS: Although heterogeneity across studies precluded meta-analysis, narrative synthesis suggests that ctDNA may offer an improved early detection capability over CA125, through methylation and copy number variation analyses. Further controlled prospective studies are needed to validate the clinical utility of ctDNA as a complementary tool in OC detection.

Keywords:  Circulating Tumor DNA; Diagnosis; Diagnostic Accuracy; Early Detection; Ovarian Cancer

DOI:  https://doi.org/10.1016/j.ijgc.2025.102686
Nucleic Acids Res. 2025 Oct 14. pii: gkaf970. [Epub ahead of print]53(19):

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard.

Yu-Yu Lin, Kersten Breuer, Dieter Weichenhan, Pascal Lafrenz, Antonella Sarnataro, Agata Wilk, Maryna Chepeleva, Oliver Mücke, Maximilian Schönung, Franziska Petermann, Philip Reiner Kensche, Lena Weiser, Frank Thommen, Gideon Giacomelli, Karl Nordstroem, Edahi Gonzalez-Avalos, Angelika Merkel, Helene Kretzmer, Jonas Fischer, Stephen Krämer, Murat Iskar, Stephan Wolf, Ivo Buchhalter, Manel Esteller, Christian Lawerenz, Sven Twardziok, Marc Zapatka, Volker Hovestadt, Matthias Schlesner, Marcel H Schulz, Steve Hoffmann, Clarissa Gerhauser, Jörn Walter, Mark Hartmann, Daniel B Lipka, Yassen Assenov, Christoph Bock, Christoph Plass, Reka Toth, Pavlo Lutsik.

DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five whole-genome profiling protocols. As an evaluation reference, we employed accurate locus-specific measurements from our previous benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and multiple performance metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To ensure the long-term utility of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and readily expandable to future software.

DOI: https://doi.org/10.1093/nar/gkaf970
Sci Rep. 2025 Oct 22. 15(1): 36869

Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer.

Hafiza Bushra Manzoor, Melisa Acun, Hamed Yasavoli-Sharahi, Chantal Matar, Arvind Mer, Benjamin K Tsang, Meshach Asare-Werehene.

Ovarian cancer (OVCA) is third most lethal gynecologic cancers and acquired chemoresistance is the key link in the high mortality rate of OVCA patients. Currently, there are no reliable methods to predict chemoresistance in OVCA. In our study, we identify genes, pathways and networks altered by DNA methylation in high-grade serous ovarian carcinoma (HGSC) cells that are associated with chemoresistance and prognosis of HGSC patients. We performed methylome-wide profiling using Illumina Infinium MethylationEPIC BeadChip (HM850K) methylation array on a set of HGSC chemoresistant and chemosensitive cell lines. Differentially Methylated CpG Probes (DMPs) were identified between the resistant and sensitive groups in HGSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) over-representation analyses were conducted to identify both common and unique pathways between resistant and sensitive cells. While the HM850K array was used for the discovery phase to identify differentially methylated probes and regions in HGSC cell lines, the publicly available The Cancer Genome Atlas ovarian cancer (TCGA-OV) dataset generated using the Illumina Infinium HumanMethylation27 BeadChip (27 K array) methylation array served as an independent validation cohort for downstream survival and drug sensitivity analyses. Machine learning methods were applied to our dataset to predict drug sensitivity in the TCGA-OV cohort and to investigate associations with overall survival and progression-free survival. Kaplan-Meier analysis was performed to assess the relationship between differentially methylated genes and patient survival outcomes. The overlapping CpG probes shared between the two Illumina platforms were used for machine learning and survival analyses. Data visualization was performed using various R/Bioconductor packages. Our analysis identified a total of 3,641 DMPs spanning 1,617 differentially methylated genes between chemoresistant and sensitive HGSC cells, whereas 80% of them were hypermethylated CpG sites associated with HGSC resistant cells. Approximately half of the DMPs were distributed on chromosomes 1-3, 6, 11-12 and 17 and top identified hypermethylated CpGs were cg21226224 (SOX17, ∆β = 79%, adj.P = 7.73E-03), cg02538901 (ATP1A1, ∆β = 75%, adj.P = 7.6E-03), and cg17032184 (CD58, ∆β = 64%, adj.P = 4.39E-02). Machine learning analysis identified significant association of global hypermethylation in the HGSC chemoresistant cells with poor overall and progression-free survival of HGSC patients. Further analysis identified four differentially methylated genes (CD58, SOX17, FOXA1, ETV1) that were also positively associated with poor prognosis of HGSC OC patients. Functional enrichment analysis showed enrichment of several cancer-related pathways, including phosphatidylinositol signaling, homologous recombination and ECM-receptor interaction pathways. This study supplements the current knowledge of the underlying mechanism behind acquired chemoresistance in OVCA. Four differentially methylated genes identified in this study may have the potential to serve as promising epigenetic clinical biomarkers for HGSC chemotherapy resistance.

DOI: https://doi.org/10.1038/s41598-025-20827-8
Ann Oncol. 2025 Oct 18. pii: S0923-7534(25)04950-6. [Epub ahead of print]

Rucaparib maintenance for newly diagnosed advanced ovarian cancer: interim overall survival, progression-free survival, and safety at 5 years of follow-up from the phase III ATHENA-MONO/GOG-3020/ENGOT-ov45.

R S Kristeleit, S Ghamande, A Lisyanskaya, A Oaknin, E Prendergast, Y B Kim, J Fuentes Pradera, R D Littell, B Gao, G Valabrega, D M O'Malley, A Dean, C Pisano, J Buscema, D Provencher, F Zagouri, L P Martin, H-H Chou, F Demirkiran, F Ueland, A Chudecka-Głaz, O Yeku, M Beiner, C Anderson, V Drochytek, R N Eskander, R Agarwal, L Maloney, D Despain, C Connor, M Craib, D Shih, K Fujiwara, B J Monk.

   BACKGROUND: We report long-term efficacy and safety from the multicenter, randomized, double-blind, placebo-controlled, phase III ATHENA-MONO/GOG-3020/ENGOT-ov45 (NCT03522246) study of first-line rucaparib maintenance for advanced ovarian cancer.
PATIENTS AND METHODS: Patients were randomized 4:1 to oral rucaparib + intravenous (IV) placebo or oral + IV placebo. Stratification factors were homologous recombination deficiency (HRD; BRCA mutation and loss of heterozygosity status) classification, residual disease post-chemotherapy and surgical timing. Primary endpoint was investigator-assessed progression-free survival (invPFS) in HRD and intent-to-treat (ITT) populations. Overall survival (OS) and safety were secondary endpoints. Second event of progression (PFS2) and time to first subsequent treatment (TFST) were exploratory. Interim OS and final safety analyses data cutoff was March 9, 2023. Updated invPFS, PFS2, and TFST analyses data cutoff was May 5, 2025.
RESULTS: Median invPFS follow-up was approximately 59 months for both rucaparib (HRD, n = 185; ITT, n = 427) and placebo (HRD, n = 49; ITT, n = 111). invPFS was significantly longer with rucaparib versus placebo in the HRD (31.4 versus 12.0 months; HR 0.52, 95% CI 0.35-0.76) and ITT (20.2 versus 9.2 months; HR 0.53, 95% CI 0.42-0.69) populations. Interim OS was immature (OS maturity: ITT, 35%) with the median (95% CI) OS not reached with rucaparib and 46.2 (34.6-NR) months with placebo for the ITT population (HR 0.83, 95% CI 0.58-1.17). ITT TFST (median, 23.6 versus 12.1 months) and PFS2 (35.1 versus 26.9 months) were longer with rucaparib versus placebo. Overall, 34.6% of patients receiving rucaparib completed the 24-month treatment cap versus 17.3% receiving placebo. As of May 5, 2025, 40.0% of patients on rucaparib were still on study and in long-term follow-up. Safety remained consistent with the primary analysis.
CONCLUSIONS: Rucaparib monotherapy provides significant and durable long-term benefit as first-line maintenance for patients with advanced ovarian cancer with and without HRD.

Keywords:  PARP inhibitor; advanced ovarian cancer; long-term follow-up; maintenance therapy; rucaparib

DOI:  https://doi.org/10.1016/j.annonc.2025.10.007
N Engl J Med. 2025 Oct 19.

Sacituzumab Tirumotecan in EGFR-TKI-Resistant, EGFR-Mutated Advanced NSCLC.

Wenfeng Fang, Lin Wu, Xiangjiao Meng, Yu Yao, Wei Zuo, Wenxiu Yao, Yanyan Xie, Yu Zhang, Jiuwei Cui, Yongchang Zhang, Xingya Li, Wu Zhuang, Jian Fang, Qiming Wang, Wei Jiang, Kai Li, Yuju Bai, Yongzhong Luo, Fang Ma, Yan Yu, Wei Zheng, Zhentian Liu, Bin Yang, Rui Ma, Yong Fang, Runxiang Yang, Liyan Jiang, Jie Hu, Jiacheng Yang, Yina Diao, Xiaoping Jin, Junyou Ge, Yunpeng Yang, Li Zhang.

BACKGROUND: Sacituzumab tirumotecan (sac-TMT) is an antibody-drug conjugate targeting trophoblast cell-surface antigen 2 that has shown significant survival benefits in patients with EGFR-mutated non-small-cell lung cancer (NSCLC) that has progressed after epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy and platinum-based chemotherapy.
METHODS: In this phase 3 trial, we enrolled patients with EGFR-mutated locally advanced or metastatic nonsquamous NSCLC that had progressed after EGFR-TKI therapy. The patients were randomly assigned, in a 1:1 ratio, to receive sac-TMT monotherapy or pemetrexed plus platinum-based chemotherapy. The primary end point was progression-free survival as assessed by blinded independent review. Overall survival was a hierarchically tested key secondary end point. In the interim analysis of progression-free survival as assessed by blinded independent review, sac-TMT monotherapy met the prespecified criterion for significance (two-sided P<0.0001); we report here the prespecified final analysis of progression-free survival and the preplanned interim analysis of overall survival.
RESULTS: Overall, 376 patients underwent randomization, with 188 assigned to each group. After a median follow-up of 18.9 months, the median progression-free survival was 8.3 months in the sac-TMT group and 4.3 months in the chemotherapy group (hazard ratio for disease progression or death, 0.49; 95% confidence interval [CI], 0.39 to 0.62). Overall survival was significantly longer with sac-TMT than with chemotherapy (hazard ratio for death, 0.60; 95% CI, 0.44 to 0.82; two-sided P = 0.001); 18-month overall survival was 65.8% and 48.0%, respectively. Treatment-related adverse events of grade 3 or higher occurred in 58.0% of patients receiving sac-TMT and in 53.8% of those receiving chemotherapy, with the most common being a decreased neutrophil count (39.9% vs. 33.0%); treatment-related serious adverse events occurred in 9.0% and 17.6%, respectively.
CONCLUSIONS: In patients with EGFR-mutated advanced or metastatic NSCLC that had progressed after previous EGFR-TKI therapy, progression-free survival and overall survival outcomes were significantly better with sac-TMT than with platinum-based chemotherapy. (Funded by Sichuan Kelun-Biotech Biopharmaceutical; OptiTROP-Lung04 ClinicalTrials.gov number, NCT05870319.).

DOI: https://doi.org/10.1056/NEJMoa2512071
Brief Bioinform. 2025 Aug 31. pii: bbaf551. [Epub ahead of print]26(5):

Read-level DNA methylation deconvolution enhances circulating tumor DNA detection.

Ting Qi, Lakshmi Narayanan Lakshmanan, Yuwei Yang, Ying Zhou, Min Pan, Anders Jacobsen Skanderup, Qinyu Ge.

  DNA methylation is a key epigenetic modification underlying cellular identity. Conventional methods based on CpG site-level data often lack sensitivity in detecting low-frequency methylation signals. Here, we present Alpha, a novel method combining unbiased segmentation with robust read-level identification of low frequency cell-type-specific methylation signals. Methylation markers identified by Alpha exhibited significant enrichment in regulatory genomic elements such as enhancers, active promoters, and transcription factor binding sites. In simulated cell-type admixtures, Alpha-derived markers demonstrated improved deconvolution performance, exhibiting lower error metrics compared to beta-value based methods (DSS), even with limited marker numbers (N < 50). We combined Alpha with a non-negative least squares approach (Alpha-NNLS) to enable sensitive detection of circulating tumor DNA (ctDNA) in simulated cell-free DNA from breast and colon cancers, outperforming existing read-level methylation-based tumor fraction estimation methods (CelFEER and UXM). We applied Alpha-NNLS to targeted bisulfite sequencing data from early-stage colon cancer plasma samples and demonstrated strong concordance with existing approaches (R2 = 0.98), supporting its potential for sensitive detection of ctDNA.

Keywords:  DNA methylation; cell-free DNA; circulating tumor DNA; deconvolution

DOI:  https://doi.org/10.1093/bib/bbaf551
Cell Rep. 2025 Oct 22. pii: S2211-1247(25)01226-4. [Epub ahead of print]44(11): 116455

Integrating mutation, copy number, and gene expression data to identify driver genes of recurrent chromosome-arm losses.

Ron Saad, Ron Shamir, Uri Ben-David.

  Aneuploidy is a hallmark of cancer, yet the genes driving recurrent chromosome-arm losses remain largely unknown. We present a systematic framework integrating mutation, copy number, and gene expression data to identify candidate driver genes of cancer type-specific recurrent chromosome-arm losses across 20 cancer types, using ∼7,500 tumors from The Cancer Genome Atlas. By analyzing focal deletions and point mutations that co-occur, or are mutually exclusive, with chromosome-arm losses, we pinpoint 322 candidate drivers associated with 159 recurring events. Our approach identifies known aneuploidy drivers such as TP53 and PTEN, while revealing multiple additional candidates, including tumor suppressors not previously linked to aneuploidy. We leverage expression changes associated with chromosome-arm losses to propose cancer-promoting pathway-level alterations. Integrating these findings highlights key candidate drivers that underlie the observed expression alterations, reinforcing their biological relevance. We provide a comprehensive catalog of candidate driver genes for recurrently lost chromosome-arms in human cancer.

Keywords:  CP: Cancer; CP: Genomics; aneuploidy; cancer genomics; chromosomal instability; copy number alterations; driver genes; gene expression; mutations

DOI:  https://doi.org/10.1016/j.celrep.2025.116455
Semin Cell Dev Biol. 2025 Oct 17. pii: S1084-9521(25)00067-9. [Epub ahead of print]175 103657

How to get the most out of your cancer spatial transcriptomics data.

David I Kaplan, Xiang Guo, Sasuni D Hirimuthugoda, Lachlan Cain, Sirui Weng, David Le, James Comben, Anna S Trigos.

  Spatial transcriptomics (ST) has emerged as a powerful tool in cancer research, significantly expanding our capacity to study the complexity of tumour ecosystems. Together with the diversity of ST platforms, a plethora of analysis approaches and tools have been developed with the goal of extracting distinct aspects of biological information contained in the data. From visualizing gene expression in the context of tissue structure and cell morphology, to the exploitation of machine learning and spatial statistics to identify cell neighbourhoods, quantify tumour heterogeneity and map cell-cell signalling networks, there is a current explosion of novel analyses techniques. Unfortunately, this makes it challenging to develop workflows and strategies for data analysis, especially for those new to the field. This review serves to offer a path to cancer researchers who recognise the potential of ST and would like to start their data analysis journey. We cover the main analysis approaches used to address common research questions associated with ST data in cancer, highlighting commonly used tools, as well as discuss emerging analysis techniques that hold the potential to leverage the richness of the data at an unprecedented scale. Finally, we end by highlighting considerations when designing ST projects, from experimental design, to assembling teams and managing the rapid flux of ST technologies. We anticipate this review will be useful resource for researchers to not just seek analysis strategies to answer their current research questions, but also provide inspiration to further take advantage of the wealth of information provided by ST data.

Keywords:  Bioinformatics; Cancer; Data analysis; Spatial transcriptomics

DOI:  https://doi.org/10.1016/j.semcdb.2025.103657
Sci Rep. 2025 Oct 22. 15(1): 36958

Digital droplet PCR is an accurate and precise method to measure DNA copy number.

Shaobo Zhang, Evan A Rajadhyaksha, Farooq Syed, Jorge Canas, Vijay Saxena, Andrew L Schwaderer, David S Hains.

  DNA copy number research is impeded by limited methodology to determine true DNA copy numbers accurately and precisely. Human alpha defensin 1-3 (DEFA1A3) is a multiallelic gene with DNA copy numbers generally ranging from 2 to 12 copies per diploid genome. In this study, we developed a digital droplet PCR (ddPCR) protocol using DEFA1A3 as a model locus. We compared these results to DNA copy numbers determined by pulsed field gel electrophoresis (PFGE), which is considered a gold standard in CNV identification, on 40 DNA samples from a clinical study cohort. Taqman real-time quantitative PCR (qPCR) was also compared, being the other major available low cost, high-throughput system. The copy number measurements of 40 genomic samples were highly concordant between ddPCR and PFGE, while copy number by qPCR correlated only weakly with PFGE copy number. In conclusion, ddPCR is a low-cost, high-throughput technique with accurate resolution of CNV at both low and high DNA copy numbers. This makes it an ideal model to adapt for CNV testing in clinical practice.

Keywords:  CNV; Copy typing; DdPCR; Genetic; PFGE; qPCR

DOI:  https://doi.org/10.1038/s41598-025-20944-4