bims-tumhet Biomed News
on Tumor heterogeneity
Issue of 2025–10–19
eleven papers selected by
Sergio Marchini, Humanitas Research
  1. Emerging AI Approaches for Cancer Spatial Omics.
  2. Decoding tumor heterogeneity: A spatially informed pan-cancer analysis of the tumor microenvironment.
  3. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors.
  4. cfMethDB: A Comprehensive cfDNA Methylation Data Resource for Cancer Biomarkers.
  5. CNAdjust: enhancing CNA calling accuracy through systematic baseline adjustment.
  6. Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing.
  7. Survival with Osimertinib plus Chemotherapy in EGFR-Mutated Advanced NSCLC.
  8. Analysis of cfDNA fragmentomics metrics and commercial targeted sequencing panels.
  9. Methods for analysis of tertiary lymphoid structures and immune activity by multiplex immunofluorescence histology.
  10. stTransfer enables transfer of single-cell annotations to spatial transcriptomics with single-cell resolution.
  11. Endometrial cancer: from clinical reality to molecular treatment.
  1. Gigascience. 2025 Oct 16. pii: giaf128. [Epub ahead of print]
    Emerging AI Approaches for Cancer Spatial Omics.
    Javad Noorbakhsh, Ali Foroughi Pour, Jeffrey Chuang.
      Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms - in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling - as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.
    Keywords:  artificial intelligence; deep learning; foundation models; spatial proteomics; spatial transcriptomics; tissue biophysics
    DOI:  https://doi.org/10.1093/gigascience/giaf128
  2. Cell Rep Med. 2025 Oct 10. pii: S2666-3791(25)00489-6. [Epub ahead of print] 102416
    Decoding tumor heterogeneity: A spatially informed pan-cancer analysis of the tumor microenvironment.
    Francesca Lodi, Sam Vanmassenhove, Duojiao Chen, Bram Boeckx, Lucas Ferreira Maciel, Frederik Peeters, Elena Donders, Heesoo Song, Luuk Harbers, Joanna Poźniak, Liselore Loverix, Yiduo Zhou, Michel Bila, Ayse Bassez, Sarah Cappuyns, Tom Venken, Siel Olbrecht, Amelie Franken, Pierre Van Mol, Rogier Schepers, Thomas van Brussel, Gino Philips, Hanne Vos, Machteld Keupers, Frederik De Smet, Sabine Tejpar, Oliver Bechter, Gabriele Bergers, Ann Smeets, Paul Clement, Els Wauters, Jeroen Dekervel, Toon Van Gorp, Junbin Qian, Jean-Christophe Marine, Diether Lambrechts.
      Pan-cancer single-cell atlases explore the heterogeneity of cell types residing within the tumor microenvironment (TME). So far, atlases focused on individual cell types, failing to capture the full complexity of the TME. Here, we present a single-cell atlas that simultaneously considers heterogeneity in 5 cell types, collected from 230 treatment-naive samples across 9 cancer types. We identify 70 pan-cancer single-cell subtypes, investigate their patterns of co-occurrence and show an enrichment of specific subtypes in certain TMEs, e.g., immune-reactive versus immune-suppressive TME. We observe two TME hubs of strongly co-occurring subtypes: one hub resembling tertiary lymphoid structures (TLSs), another consisting of immune-reactive PD1+/PD-L1+ immune-regulatory T cells and B cells, dendritic cells and inflammatory macrophages. Subtypes belonging to each hub are spatially co-localized, while their abundance associates with early and long-term checkpoint immunotherapy response. We publicly share our atlas using a Shiny app, allowing others to explore TME heterogeneity in different biological contexts.
    Keywords:  ICB; TLS; TME; immune checkpoint blockade; pan-cancer; scRNA-seq; single-cell RNA sequencing; tertiary lymphoid structure; tumor microenvironment
    DOI:  https://doi.org/10.1016/j.xcrm.2025.102416
  3. Nat Commun. 2025 Oct 17. 16(1): 9232
    Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors.
    Pengfei Ren, Rui Zhang, Yunfeng Wang, Peng Zhang, Ce Luo, Suyan Wang, Xiaohong Li, Zongxu Zhang, Yanping Zhao, Yufeng He, Haorui Zhang, Yufeng Li, Zhidong Gao, Xiuping Zhang, Yahui Zhao, Zhihua Liu, Yuanguang Meng, Zhe Zhang, Zexian Zeng.
      Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. Here, we generate serial tissue sections from colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer samples for systematic evaluation. Using these uniformly processed samples, we generate spatial transcriptomics data across four high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profile proteins on tissue sections adjacent to all platforms using CODEX and perform single-cell RNA sequencing on the same samples. Leveraging manual nuclear segmentation and detailed annotations, we systematically assess each platform's performance across capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and concordance with adjacent CODEX. The uniformly generated and processed multi-omics dataset could advance computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download.
    DOI:  https://doi.org/10.1038/s41467-025-64292-3
  4. Genomics Proteomics Bioinformatics. 2025 Oct 14. pii: qzaf092. [Epub ahead of print]
    cfMethDB: A Comprehensive cfDNA Methylation Data Resource for Cancer Biomarkers.
    Yuanhui Sun, Zhixian Zhu, Qiangwei Zhou, Zhe Wang, Yuying Hou, Xionghui Zhou, Guoliang Li.
      Cancer is a major global health threat, and early detection is crucial for improving patient outcomes. DNA methylation in circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for non-invasive cancer diagnosis. However, the integration and utilization of existing cfDNA methylation data have been limited, hindering comprehensive research efforts, particularly in the discovery of cfDNA methylation biomarkers. To address this challenge, we introduce cfMethDB, a comprehensive database dedicated to cfDNA methylation in cancer that encompasses 4828 publicly available datasets. Through standardized analysis, we identified 1,048,770 differentially methylated cytosines (DMCs) as candidate biomarkers across seven cancer types. With cfMethDB, we not only identified known cfDNA methylation biomarkers, but also discovered several genes, such as ZIC4, that could be novel biomarkers. Moreover, cfMethDB offers a suite of user-friendly tools, including biomarker evaluation, pan-cancer search, and end motif analysis. We hope that cfMethDB will serve as a valuable platform for the discovery of novel cancer cfDNA methylation biomarkers and will facilitate cancer research and clinical applications. cfMethDB is publicly available at: https://cfmethdb.hzau.edu.cn/home.
    Keywords:  Biomarker; DNA methylation; Database; Pan-cancer; cfDNA
    DOI:  https://doi.org/10.1093/gpbjnl/qzaf092
  5. Front Genet. 2025 ;16 1674138
    CNAdjust: enhancing CNA calling accuracy through systematic baseline adjustment.
    Hangjia Zhao, Michael Baudis.
      Accurate determination of the genomic copy number baseline is crucial for identifying copy number alterations (CNAs) in cancer, yet it remains a significant challenge in tumors with complex karyotypes. To address this, we present CNAdjust, an integrated method to systematically detect and correct baseline inaccuracies in CNA data. CNAdjust employs a Bayesian framework that integrates cohort-specific CNA frequency priors with a data-driven plausibility score, ensuring that adjusted calls align with both biological cohort patterns and study-specific data. Performance validation using the TCGA pan-cancer dataset demonstrated improved alignment with absolute copy number estimates and enhanced CNA pattern interpretation. Furthermore, we revealed a strong correlation between chromosomal aneuploidy and baseline abnormalities, underscoring the prevalence of this issue in cancer genomics. By systematically improving the precision of CNA calls, CNAdjust serves as a critical tool for constructing harmonized reference datasets and advancing the progress of precision oncology. Its implementation as a standard, portable workflow enables the reproducible and scalable analysis of large, heterogeneous datasets, supporting large-scale genomic research. Source codes are available at: https://github.com/baudisgroup/CNAdjust.
    Keywords:  baseline correction; bayesian framework; cancer genomics; copy number alterations; nextflow workflow
    DOI:  https://doi.org/10.3389/fgene.2025.1674138
  6. Nat Commun. 2025 Oct 16. 16(1): 9202
    Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing.
    Xiaorou Guo, Qingwang Chen, Yuanfeng Zhang, Yujing Zhang, Yaqing Liu, Shumeng Duan, Yu Ma, Peng Ni, Jianxin Wang, Bo He, Luyao Ren, Ruiwen Ma, Wanwan Hou, Ying Yu, Bingsi Li, Fujun Qiu, Yuan Sun, Zhihong Zhang, Weihong Xu, Xiang Fang, Jinming Li, Leming Shi, Rui Zhang, Yuanting Zheng, Lianhua Dong.
      The lack of quantitative methylation reference datasets (ground truth) and cross-laboratory reproducibility assessment hinders clinical translation of epigenome-wide sequencing technologies. Using certified Quartet DNA reference materials, here we generate 108 epigenome-sequencing datasets across three mainstream protocols (whole-genome bisulfite sequencing, enzymatic methyl-seq, and TET-assisted pyridine borane sequencing) with triplicates per sample across laboratories. We observe strand-specific methylation biases across all protocols and libraries. Cross-laboratory reproducibility analyses reveal high quantitative methylation levels agreement (mean Pearson correlation coefficient (PCC) = 0.96) but low detection concordance (mean Jaccard index = 0.36). Using consensus voting, we construct genome-wide quantitative methylation reference datasets serving as ground truth for proficiency testing. Key technical parameters-including mean CpG depth, coverage, and strand consistency-correlate strongly with reference-dependent quality metrics (recall, PCC, and RMSE). Collectively, these resources establish foundational standards for benchmarking emerging epigenomic technologies and analytical pipelines, enabling robust, standardized quality control in research and clinical applications.
    DOI:  https://doi.org/10.1038/s41467-025-64250-z
  7. N Engl J Med. 2025 Oct 17.
    Survival with Osimertinib plus Chemotherapy in EGFR-Mutated Advanced NSCLC.
    FLAURA2 Investigators
       BACKGROUND: The primary analysis of this trial showed that first-line treatment with osimertinib plus chemotherapy with a platinum-based agent and pemetrexed led to significantly longer progression-free survival than osimertinib monotherapy among patients with epidermal growth factor receptor (EGFR)-mutated advanced non-small-cell lung cancer (NSCLC). Results from the planned final analysis of overall survival are needed.
    METHODS: In this phase 3, international, open-label trial, we randomly assigned in a 1:1 ratio patients with EGFR-mutated (exon 19 deletion or L858R mutation) advanced NSCLC who had not previously received treatment for advanced disease to receive either osimertinib (80 mg once daily) plus chemotherapy with pemetrexed (500 mg per square meter of body-surface area) and a platinum-based agent (cisplatin [75 mg per square meter] or carboplatin [pharmacologically guided dose]) or osimertinib monotherapy (80 mg once daily). The key secondary end point was overall survival.
    RESULTS: A total of 557 patients were randomly assigned to the osimertinib plus platinum-pemetrexed group (279 patients) or the osimertinib monotherapy group (278 patients). The median overall survival was 47.5 months in the osimertinib plus platinum-pemetrexed group and 37.6 months in the osimertinib monotherapy group (hazard ratio for death, 0.77; 95% confidence interval, 0.61 to 0.96; P = 0.02). Grade 3 or higher adverse events of any cause were reported in 70% of the patients in the osimertinib plus platinum-pemetrexed group and in 34% of the patients in the osimertinib monotherapy group; adverse events leading to the discontinuation of osimertinib were reported in 12% and 7%, respectively.
    CONCLUSIONS: Among patients with EGFR-mutated advanced NSCLC, first-line treatment with osimertinib plus platinum-pemetrexed led to significantly longer overall survival than osimertinib monotherapy and was associated with an increased risk of reversible adverse events of grade 3 or higher. (Funded by AstraZeneca; FLAURA2 ClinicalTrials.gov number, NCT04035486.).
    DOI:  https://doi.org/10.1056/NEJMoa2510308
  8. Nat Commun. 2025 Oct 14. 16(1): 9122
    Analysis of cfDNA fragmentomics metrics and commercial targeted sequencing panels.
    Kyle T Helzer, Marina N Sharifi, Jamie M Sperger, Matthew R Chrostek, Matthew L Bootsma, Shannon R Reese, Amy Taylor, Katie R Kaufmann, Hannah Krause, Jennifer Schehr, Nan Sethakorn, David Kosoff, Christos E Kyriakopoulos, Michael Bassetti, Grace Blitzer, John Floberg, Martin Sjöström, Andrew J Armstrong, Himisha Beltran, Rana R McKay, Felix Y Feng, Ruth O'Regan, Kari B Wisinski, Hamid Emamekhoo, Alex W Wyatt, Joshua M Lang, Shuang G Zhao.
      Fragmentomics based analysis of cell-free DNA (cfDNA) has recently emerged as a method to infer epigenetic and transcriptional data. Many of these reports analyze whole genome sequencing (WGS) which is not readily available clinically. Targeted exon panels are used for clinical cfDNA variant calling. In this report, we conduct an investigation of multiple published fragmentomics methods for WGS, but on cancer exon panels. We find that strategies utilizing normalized depth metrics, as well as all exons present on the panel, generally allow for better prediction of cancer phenotypes across a range of tumor fractions, though other metrics work particularly well in specific applications. Additionally, genes from commercial clinical targeted sequencing panels could be similarly employed for cancer phenotyping with a minimal decrease in performance despite their smaller genomic coverage. These results suggest that fragmentomics-based analysis of cfDNA can utilize targeted sequencing panels and does not necessarily require additional WGS.
    DOI:  https://doi.org/10.1038/s41467-025-64153-z
  9. Methods Cell Biol. 2025 ;pii: S0091-679X(25)00089-5. [Epub ahead of print]199 37-49
    Methods for analysis of tertiary lymphoid structures and immune activity by multiplex immunofluorescence histology.
    Leon Zheng, Priya Katyal, Ileana Soto Mauldin.
      Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that are correlated with improved patient outcomes in several solid cancers, including melanoma. Multiplex immunofluorescent histology (mIFH) has been used in numerous studies to identify and characterize TLS. However, detailed studies evaluating immune cell subsets and markers of immune activity at TLS sites have been limited. Here, we introduce multiplex immunofluorescence histology methods to identify TLS, their associated immune cell components, and markers of immune activity. We outline two mIFH panels for evaluating and quantifying TLS, and markers of immune activity, offering methodologies that can be used to gain a more nuanced understanding of the role and immunological activity of TLS in cancer prognosis and therapy.
    Keywords:  IHC; Immune infiltrates; Immunofluorescence histology; Immunology; Melanoma; Multiplex; Tertiary lymphoid structures
    DOI:  https://doi.org/10.1016/bs.mcb.2025.03.008
  10. Cell Rep Methods. 2025 Oct 15. pii: S2667-2375(25)00241-3. [Epub ahead of print] 101205
    stTransfer enables transfer of single-cell annotations to spatial transcriptomics with single-cell resolution.
    Tao Zhou, Lin Xiang, Kuo Liao, Youzhe He, Zhenkun Zhuang, Shiping Liu.
      Spatial transcriptomics (ST) enables in situ analysis of gene expression patterns and spatial microenvironments. However, current ST technologies are limited by detection sensitivity and gene coverage, posing significant challenges for precise cell type annotation at the single-cell level. To address this, we present stTransfer, a method that integrates reference single-cell RNA sequencing (scRNA-seq) data with ST context using a graph autoencoder and transfer learning. This approach minimizes information transfer loss between scRNA-seq and ST datasets. Benchmark analyses on publicly available spatial transcriptomic datasets demonstrate that stTransfer outperforms existing methods in both accuracy and robustness for cell type annotation. Lastly, we apply stTransfer to annotate neuronal populations in a high-precision Stereo-seq dataset of the zebra finch optic tectum.
    Keywords:  CP: Computational biology; CP: Systems biology; Stereo-seq; cell type transfer; graph autoencoder; spatial transcriptomics
    DOI:  https://doi.org/10.1016/j.crmeth.2025.101205
  11. Discov Oncol. 2025 Oct 14. 16(1): 1874
    Endometrial cancer: from clinical reality to molecular treatment.
    Léa-Isabelle Renaud, Elisabeth St-Laurent, Coralie Compare, Raphaëlle Proulx, Jasmine Nolan, Eric Asselin.
      Endometrial cancer (EC) is the most prevalent gynecological malignancy in developed countries, with incidence rates steadily increasing due to factors such as obesity, aging populations, and changes in reproductive behavior. While mortality rates have remained relatively stable, survival outcomes have improved little in recent years, underscoring the need for advancements in prevention, diagnosis, and treatment strategies. This review provides a comprehensive overview of both the clinical and fundamental aspects of EC, aiming to bridge the gap between biological research and clinical practice. On the clinical side, we assess genetic predispositions, hormonal and metabolic risk factors, classification systems, detection methods, treatment options, and their impact on survival and quality of life. From a research perspective, we highlight the models commonly used to study EC, including cell lines and animal models, and delve into the PI3K/AKT/mTOR signaling pathway, a critical driver of tumor progression and a promising therapeutic target. By synthesizing these insights, this review aims to inform future efforts to improve patient outcomes and advance the understanding of EC.
    DOI:  https://doi.org/10.1007/s12672-025-03652-3
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