bims-tumhet Biomed News
on Tumor heterogeneity
Issue of 2025–06–15
fifteen papers selected by
Sergio Marchini, Humanitas Research
  1. ctDNA Analysis in ERBB2-Amplified Colorectal Cancer: Biomarker Analysis of the MyPathway Trial.
  2. Improving cell-free DNA detection in advanced-stage high-grade serous ovarian cancer using combined TP53 mutational status and copy number changes.
  3. Improved tumor-type informed compared to tumor-informed mutation tracking for ctDNA detection and microscopic residual disease assessment in epithelial ovarian cancer.
  4. Dogme: A nextflow pipeline for reprocessing nanopore RNA and DNA modifications.
  5. Growth kinetics of high-grade serous ovarian cancer: implications for early detection.
  6. SpotSweeper: spatially aware quality control for spatial transcriptomics.
  7. Monitoring the rate and variability of somatic genomic alterations using long-read sequencing.
  8. Roles and functions of tumor-infiltrating lymphocytes and tertiary lymphoid structures in gastric cancer progression.
  9. Analysis of clonal evolution in cancer: A computational perspective.
  10. Ultrasensitive detection and tracking of circulating tumor DNA to predict relapse and survival in patients with locally advanced cervical cancer: phase III CALLA trial analyses.
  11. Molecular Insights into Pleural Mesothelioma: Unveiling Pathogenic Mechanisms and Therapeutic Opportunities.
  12. CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics.
  13. Liquid Biopsy: The Challenges of a Revolutionary Approach in Oncology.
  14. Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data.
  15. Gene Spatial Integration: enhancing spatial transcriptomics analysis via deep learning and batch effect mitigation.
  1. Clin Cancer Res. 2025 Jun 13. OF1-OF10
    ctDNA Analysis in ERBB2-Amplified Colorectal Cancer: Biomarker Analysis of the MyPathway Trial.
    Funda Meric-Bernstam, Kanwal Pratap Singh Raghav, Christopher J Sweeney, Charles Swanton, David R Spigel, Ron Bose, Howard A Burris, Claire F Friedman, Carin R Espenschied, Jessica M Grindheim, Julia Malato, Katja Schulze, Richard Price, Razelle Kurzrock.
       PURPOSE: A combination of two HER2-directed antibodies, pertuzumab and trastuzumab (P + T), has antitumor activity in HER2-positive colorectal cancer. Although liquid biopsies are increasingly being used in clinical oncology, the association between tumor and ctDNA ERBB2 status and ctDNA monitoring for early response and resistance are unknown.
    PATIENTS AND METHODS: Eighty-five patients with ERBB2-amplified and/or -overexpressed colorectal cancer were treated with P + T in the MyPathway trial; 42 had ctDNA testing at cycle (C) 1 day (D) 1, and 38 had longitudinal plasma tested for ctDNA. We analyzed the ctDNA versus tissue ERBB2 concordance, genomic co-alterations, and ctDNA dynamics and association with response.
    RESULTS: Forty-one (98%) of 42 patients had genomic alterations detected in ctDNA at C1D1, and 29 (69%) had ERBB2 amplification in ctDNA. There was a strong correlation between the ERBB2 copy number on next-generation sequencing in tissue and C1D1 ERBB2 ctDNA copy number. Thirty-seven percent achieved a molecular response by C3D1 on P + T, which was associated with prolonged progression-free survival and overall survival. CDKN2A and KRAS mutations were associated with shorter overall survival, and a trend was seen with PIK3CA mutations. Several emerging co-alterations were identified in ctDNA at progression, including in the MAPK and PI3K pathways and other tyrosine receptor kinases.
    CONCLUSIONS: ctDNA can detect ERBB2 amplification in many, but not all, patients with ERBB2 amplification detected in tumor samples. ctDNA molecular response was associated with better survival, and ctDNA co-alterations may offer insights into mechanisms of intrinsic and acquired resistance.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-24-2763
  2. Pathol Res Pract. 2025 May 31. pii: S0344-0338(25)00231-6. [Epub ahead of print]272 156038
    Improving cell-free DNA detection in advanced-stage high-grade serous ovarian cancer using combined TP53 mutational status and copy number changes.
    Hein S Zelisse, Ymke van der Pol, Florent Mouliere, Frederike Dijk, Johannes B G Halfwerk, Banafsche Mearadji, Ludo F M Beenen, Roy J Reinten, Saskia A G M Cillessen, Gerrit K J Hooijer, Malou L H Snijders, Marc J van de Vijver, Mignon D J M van Gent, Constantijne H Mom.
      Circulating tumor DNA (ctDNA) is a promising biomarker in patients with high-grade serous ovarian cancer (HGSOC). However, the detection rate of TP53 mutations in ctDNA of HGSOC patients has previously been shown to be inadequate. Given the prevalence of copy number aberrations (CNAs) in HGSOC, this study aimed to improve ctDNA detection by combining TP53 sequencing with shallow whole-genome sequencing (sWGS), and to evaluate the correlation with clinicopathological features and survival outcomes. This exploratory, retrospective cohort study included 53 advanced-stage HGSOC patients, comprising 18 treatment-naive patients and 35 patients treated with two neoadjuvant chemotherapy cycles. TP53 targeted sequencing was integrated with sWGS (<5x coverage) for CNA estimation using ichor copy number aberration tumor fraction (ichorCNA TF). TP53 mutations were detected in 28 patients (52.8 %), and 17 patients (32.1 %) showed positive ichorCNA TF. Combining TP53 mutation detection with ichorCNA TF identified 62.3 % (n = 33) of patients as ctDNA-positive, showing a trend towards improved detection compared to TP53 mutation alone (p = .063). Treatment-naive patients exhibited higher TP53 mutation (72.2 % vs. 42.9 %, p = .043) and ichorCNA TF (66.7 % vs. 14.3 %, p < .001) detection rates compared to chemotherapy-treated patients. No correlations between ctDNA metrics and clinicopathological characteristics or survival outcomes were found. In conclusion, the integration of ichorCNA TF with TP53 mutation analysis showed a trend towards improved ctDNA detection in advanced-stage HGSOC patients. Future studies should further explore ctDNA detection rates by ichorCNA TF and its potential clinical implications in HGSOC.
    Keywords:  Circulating tumor DNA; Copy number aberrations; Epithelial ovarian cancer; Shallow whole-genome sequencing
    DOI:  https://doi.org/10.1016/j.prp.2025.156038
  3. J Exp Clin Cancer Res. 2025 Jun 12. 44(1): 174
    Improved tumor-type informed compared to tumor-informed mutation tracking for ctDNA detection and microscopic residual disease assessment in epithelial ovarian cancer.
    Mehdi Ben Sassi, Henri Azais, Charles Marcaillou, Sylvain Guibert, Emmanuel Martin, Jérôme Alexandre, Louise Benoit, Aurélien de Reynies, Emilie Laude, Cam Duong, Jacques Medioni, Bruno Borghese, Anne-Sophie Bats, Valerie Taly, Pierre Laurent-Puig.
       BACKGROUND: Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality in women, often diagnosed at advanced stages. While first-line treatments improve survival, relapses remain common, with 5-year survival rates below 40%. Circulating tumor DNA (ctDNA) is a promising biomarker for non-invasive EOC detection and monitoring. It may help assess treatment response, notably microscopic residual disease. Our objective was to compare two ctDNA characterization strategies in EOC for assessing tumor burden during first-line treatment: a tumor-informed approach based on somatic mutations and a tumor-type informed approach utilizing DNA methylation patterns.
    METHODS: In the tumor-informed approach, whole exome sequencing (WES) was performed on EOC tumor DNA and matched PBMCs from 22 patients to identify tumor-specific mutations. Personalized panels were then designed to track these mutations in plasma cfDNA. In the tumor-type informed approach, differentially methylated loci (DMLs) were identified by comparing EOC samples, healthy ovarian tissues, and PBMCs. A unique custom methylation panel was designed, and a support vector machine classifier was trained to distinguish between methylation profiles in plasma cfDNA from healthy donors and from EOC patients. Plasma samples from 47 advanced-stage EOC patients receiving chemotherapy and 54 healthy subjects were analyzed.
    RESULTS: For the tumor-informed approach, WES identified an average of 72 somatic mutations per patient. For the tumor-type informed approach, 52,173 DMLs were identified as tumor-specific markers. In 47 plasma samples tested by both approaches, ctDNA levels were significantly correlated (R = 0.56, p = 4.3 × 10-5), with 70.2% concordance in detection. At baseline, ctDNA was detected in 21/22 patients with the tumor-informed approach, and in 11/12 non-training baseline samples with the tumor-type-informed classifier. At end-of-treatment, the latter detected ctDNA in 16/22 samples, outperforming the former. Detection using this more sensitive approach was significantly associated with relapse (log-rank p = 0.009; hazard ratio = 9.44; 95% CI 1.22-73.26) and poorer overall survival (log-rank p = 0.041).
    CONCLUSION: The tumor-type informed classifier demonstrated sensitivity and specificity for ctDNA detection, outperforming the tumor-informed approach in monitoring EOC progression. Requiring fewer sequencing data, it offers a practical, efficient solution for clinical management of EOC.
    DOI:  https://doi.org/10.1186/s13046-025-03433-4
  4. bioRxiv. 2025 Jun 08. pii: 2025.06.04.657941. [Epub ahead of print]
    Dogme: A nextflow pipeline for reprocessing nanopore RNA and DNA modifications.
    Elnaz Abdollahzadeh, Ali Mortazavi.
       Motivation: The Oxford Nanopore Technologies (ONT) platform allows for the direct detection of RNA and DNA modifications from unamplified nucleic acids, which is a significant advantage over other platforms. However, the rapid updates to ONT basecalling models and the evolving landscape of computational tools for modification detection bring about challenges for reproducible and standardized analyses. To address these challenges, we developed Dogme, which is a Nextflowbased workflow that automates the processing of ONT data, including basecalling, alignment, modification detection, and transcript quantification. Dogme automates the reprocessing of ONT POD5 files by integrating basecalling using Dorado, read mapping using minimap2 and subsequent analysis steps such as running modkit. The pipeline supports three major types of ONT sequencing data - direct RNA (dRNA), complementary DNA (cDNA), and genomic DNA (gDNA) - enabling comprehensive analyses across different library preparations. Dogme facilitates detection of diverse RNA modifications supported by Dorado such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), inosine, pseudouridine, 2'-Omethylation (Nm) and DNA methylation, while concurrently quantifying full-length transcript isoforms LR-Kallisto for transcript quantification for dRNA and cDNA.
    Results: We applied Dogme to three separate mouse C2C12 myoblast replicates using direct RNA sequencing on MinION flow cells. We detected an average of 147,879 m6A, 86,673 m5C, 21,242 inosine, 24,540 pseudouridine, and 83,841 2'- O-methylation sites per replicate with 96,581 m6A, 43,446 m5C, 8,825 inosine, 10,048 pseudouridine, and 30,157 2'-O- methylation sites detected in all three biological replicates. The pipeline produced reproducible modification profiles and transcript expression levels across replicates, demonstrating its utility for integrative long-read transcriptomic and epigenomic analyses.
    Availability: Dogme is implemented in Nextflow and is freely available under the MIT license at https://github.com/mortazavilab/dogme , with documentation provided for installation and usage.
    DOI:  https://doi.org/10.1101/2025.06.04.657941
  5. Br J Cancer. 2025 Jun 12.
    Growth kinetics of high-grade serous ovarian cancer: implications for early detection.
    Bharath Narayanan, Thomas Buddenkotte, Hayley Smith, Mitul Shah, Susan Freeman, David Hulse, Gabriel Funingana, Marie-Lyne Alcaraz, Mireia Crispin-Ortuzar, James Brenton, Paul Pharoah, Nora Pashayan.
       BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most lethal gynaecological cancer with patients routinely diagnosed at advanced stages. Evidence from randomized controlled trials indicates that annual screening may not reduce cancer-related deaths. We aim to characterise the growth kinetics of HGSOC to understand why early detection failed and under what conditions it might prove fruitful.
    METHODS: We analysed data from 597 HGSOC patients and identified 34 cases with serial CT scans. We calculated the growth rates of lesions in the ovaries/pelvis and the omentum and estimated the time to metastasis using a Gompertz model. Finally, we simulated ultrasound and CA125 based screening in a virtual population of patients.
    RESULTS: Growing lesions in the ovaries and the omentum doubled in volume every 2.2 months and 1.8 months respectively. The 11 cases with growing lesions in both sites had a median interval of 13.1 months between disease initiation and the onset of metastasis. Our simulations suggested that 27% of tumours would metastasise before screen detection. The remainder would provide a median window of 4.2 months for detection before metastasis.
    CONCLUSION: Our results suggest that HGSOC lesions have short times to metastasis, preventing effective early detection using current screening approaches.
    DOI:  https://doi.org/10.1038/s41416-025-03082-6
  6. Nat Methods. 2025 Jun 06.
    SpotSweeper: spatially aware quality control for spatial transcriptomics.
    Michael Totty, Stephanie C Hicks, Boyi Guo.
      Quality control (QC) is a crucial step to ensure the reliability of data obtained from RNA sequencing experiments, including spatially resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-cell or single-nucleus RNA sequencing methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts that are unique to SRT. Here, we introduce SpotSweeper, a spatially aware QC method that leverages local neighborhoods to correct for spatial confounding in order to identify both local outliers and regional artifacts in SRT. Using SpotSweeper on publicly available data, we identify a consistent set of Visium barcoded spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts. SpotSweeper represents a substantial advancement in spatially resolved transcriptomics QC for SRT, providing a robust, generalizable framework to ensure data reliability across diverse experimental conditions and technologies.
    DOI:  https://doi.org/10.1038/s41592-025-02713-3
  7. Sci Rep. 2025 Jun 11. 15(1): 18397
    Monitoring the rate and variability of somatic genomic alterations using long-read sequencing.
    Xingyao Chen, Hagai Ligumsky, Charlie Ambrose, Denisse Sibrian, Brian Tran, Daoud Arif, Olga Castellanos, Darren Kessner, Hanyi Luo, Mukta Ubale, Abigail Coleman, Vaidhyanathan Mahaganapathy, Thomas J Jönsson, Reva K Basho, Jerry S H Lee, Naim Matasci, David B Agus.
      Cancer initiation occurs when a cell acquires and accumulates mutations in genes involved in the regulation of cell processes: each cell division throughout a person's life introduces novel mutations in the cells' DNA and under normal circumstances, the body is primed to prevent those from leading to cancer. Occasionally, a subset of those mutations escapes those safeguards and might eventually result in the emergence of the disease. To understand the dynamics of accumulation of somatic mutations, we have performed longitudinal whole genome sequencing of DNA obtained from whole blood from healthy individuals and cancer patients using Oxford Nanopore Technologies' Long Read Sequencing. Here we show that the number of somatic single nucleotide variants detected increases with their age and that for specific mutational processes, changes can be detected within months. We computed aggregated metrics for unique participants at each timepoint across types of variants (based on single based substitution molecular signatures) and identified patterns of change both over an individual's lifespan (age) and over the sampling period (months). This study showcases the suitability of long read sequencing of blood DNA for detecting coarse-grained differences over time and enable future development of "state of the system" personalized prevention programs.
    DOI:  https://doi.org/10.1038/s41598-025-01690-z
  8. Front Immunol. 2025 ;16 1595070
    Roles and functions of tumor-infiltrating lymphocytes and tertiary lymphoid structures in gastric cancer progression.
    Zhiyuan Yao, Gengchen Li, Di Pan, Zichen Pei, Yan Fang, Haonan Liu, Zhengxiang Han.
      Gastric cancer (GC), a leading cause of cancer mortality, exhibits profound molecular heterogeneity and immunosuppressive tumor microenvironment (TME) features that limit therapeutic efficacy. This review elucidates the dual roles of tertiary lymphoid structures (TLS) and tumor-infiltrating lymphocytes (TILs) in GC progression. TLS, ectopic lymphoid organs formed under chronic inflammation, correlate with improved survival and immunotherapy sensitivity by fostering effector T/B cell interactions and antigen presentation. Conversely, immunosuppressive TME components like regulatory T cells (Tregs) and tumor-associated macrophages (TAMs) drive immune evasion via cytokine-mediated suppression and checkpoint activation (PD-1/PD-L1). CD8+ T cells exert context-dependent effects, with high infiltration reducing recurrence risk but paradoxically inducing exhaustion in PD-L1-rich microenvironments. Th17 and memory T cells further modulate disease through IL-17-driven angiogenesis and CD45RO+ immune memory dynamics. Multi-omics-based TLS scoring and combinatorial therapies emerge as promising strategies to overcome resistance.
    Keywords:  biomarkers; gastric cancer; immune checkpoint inhibitors; progression; tertiary lymphoid structures; tumor microenvironment; tumor-infiltrating lymphocytes
    DOI:  https://doi.org/10.3389/fimmu.2025.1595070
  9. J Bioinform Comput Biol. 2025 Apr;23(2): 2531001
    Analysis of clonal evolution in cancer: A computational perspective.
    Paulo Henrique Ribeiro, Adenilso Simao.
      Cancer is a complex disease that progresses through Darwinian evolution in cells with genetic mutations, leading to the development of multiple distinct cell populations within tumors, a process known as clonal evolution. While computational methods aid in the analysis of clonal evolution in cancer samples using genetic sequencing data, accurately identifying the clonal structure of tumor samples remains one of the biggest challenges in Cancer Genomics. Several computational methods for analyzing clonal evolution in cancer have been developed in recent years. However, the algorithms of these computational methods are complex and often described at a high level of abstraction. This paper provides a detailed explanation of some computational methods for clonal evolution analysis from a computational perspective, aiding in understanding their mechanisms. Additionally, some methods have been implemented on an online platform, enabling researchers to easily run and analyze the algorithms, as well as adapt these methods to their specific needs.
    Keywords:  Clonal evolution; computational methods; driver mutations
    DOI:  https://doi.org/10.1142/S0219720025310018
  10. Ann Oncol. 2025 Jun 02. pii: S0923-7534(25)00738-0. [Epub ahead of print]
    Ultrasensitive detection and tracking of circulating tumor DNA to predict relapse and survival in patients with locally advanced cervical cancer: phase III CALLA trial analyses.
    J Mayadev, J C Vázquez Limón, F J Ramírez Godinez, M Leiva, L D C Cetina-Pérez, S Varga, A Molina Alavez, A E Alarcon-Rozas, N Valdiviezo, C Acevedo, A Figueroa, A Santini, L Vera, F Rey, Z Kahán, P Galaz, G Meléndez Mier, X Wu, M Mandai, R Shapira-Frommer, M D P Estevez-Diz, S Limaye, W Xin, H Dry, M A S Broggi, D Y Yuan, R A Stewart, B J Monk.
       BACKGROUND: After chemoradiotherapy (CRT), 30%-50% of patients with locally advanced cervical cancer (LACC) relapse, highlighting the unmet need for prognostic biomarkers. In the global randomized CALLA trial (NCT03830866), the addition of durvalumab during and after CRT did not significantly improve progression-free survival (PFS) in a biomarker-unselected intent-to-treat population. We analyzed the association of ultrasensitive circulating tumor DNA (ctDNA) and circulating human papillomavirus (cHPV) DNA detection with relapse and survival in the largest dataset in LACC to date.
    PATIENTS AND METHODS: In CALLA, adult women with stage IB2-IIB node-positive or IIIA-IVA any node-status LACC were randomized 1 : 1 to receive durvalumab + CRT or CRT alone. The NeXT Personal® (Personalis) ultrasensitive tumor-informed assay with up to 1800 patient-specific variants was used for ctDNA and cHPV DNA analysis at baseline, cycle 3 day 1 (C3D1, post-CRT), and C6D1 (3 months post-CRT). Correlations were analyzed between ctDNA/cHPV DNA detection and outcomes [PFS, overall survival (OS)].
    RESULTS: ctDNA was detected in 98.9% (183/185) of baseline samples, with no difference between treatment arms. Detection levels of ctDNA were predictive of disease progression and survival at baseline: hazard ratios (95% confidence intervals) comparing PFS and OS, respectively, in the ctDNA less than median versus ctDNA greater than median subgroups were 0.61 (0.28-1.35) and 0.55 (0.23-1.35) with durvalumab + CRT, and 0.49 (0.26-0.95) and 0.65 (0.33-1.28) with CRT. Post-treatment trends were similar and independent of stage or lymph node status. ctDNA detection at C3D1 occurred a median of 164 days (95% confidence interval 85-250) days before clinical progression. Baseline cHPV DNA levels were similar but were only predictive following treatment.
    CONCLUSIONS: This study demonstrates the potential utility of ultrasensitive detection of ctDNA as a predictive and prognostic marker of disease progression and OS in LACC independent of disease stage.
    Keywords:  cHPV; ctDNA; efficacy outcomes; locally advanced cervical cancer; predictive marker; ultrasensitive
    DOI:  https://doi.org/10.1016/j.annonc.2025.05.533
  11. Diagnostics (Basel). 2025 May 24. pii: 1323. [Epub ahead of print]15(11):
    Molecular Insights into Pleural Mesothelioma: Unveiling Pathogenic Mechanisms and Therapeutic Opportunities.
    Teodora Zahiu, Carmen Mihaela Mihu, Bianca A Bosca, Mariana Mărginean, Lavinia Patricia Mocan, Roxana-Adelina Ștefan, Rada Teodora Suflețel, Carina Mihu, Carmen Stanca Melincovici.
      Pleural mesothelioma (PM) is a rare disease, which is going to be a global medical concern in the 21st century, because of its aggressiveness, late diagnosis, and insufficient therapies. This review seeks to enhance the comprehension of medical professionals regarding the risk factors and environmental influences that contribute to the development of the disease, as well as its underlying mechanisms. In addition, we aim to provide a schematic yet thorough overview of diagnostic techniques in PM, emphasizing the significance of the immunohistochemical markers BAP1 and MTAP, with the latter serving as an almost ideal surrogate for the gold-standard diagnostic approach, FISH p16/CDKN2A deletion. The scientific world is grappling with BAP1, MTAP, and the tumour inflammatory microenvironment, because they are the key for personalized treatments and palliative care in this disease. Considering that the survival rate for patients with PM seldom surpasses five years, every moment is significant. Therefore, our article also highlights recent advancements in clinical assessments related to prognostic scoring and treatment options. PM is a complex disease, with gradual progression over decades, which requires further investigation covering the prevention, mutations, diagnosis and treatment.
    Keywords:  BAP1; MTAP; asbestos; diagnosis; immunohistochemical markers; immunohistochemistry; inflammatory microenvironment; pleural mesothelioma; prognosis
    DOI:  https://doi.org/10.3390/diagnostics15111323
  12. Nat Methods. 2025 Jun 06.
    CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics.
    Fatema Tuz Zohora, Deisha Paliwal, Eugenia Flores-Figueroa, Joshua Li, Tingxiao Gao, Faiyaz Notta, Gregory W Schwartz.
      Dysregulation of communication between cells mediates complex diseases such as cancer and diabetes; however, detecting cell-cell communication at scale remains one of the greatest challenges in transcriptomics. Most current single-cell RNA sequencing and spatial transcriptomics computational approaches exhibit high false-positive rates, do not detect signals between individual cells and only identify single ligand-receptor communication. To overcome these challenges, we developed Cell Neural Networks on Spatial Transcriptomics (CellNEST) to decipher patterns of communication. Our model introduces a new type of relay-network communication detection that identifies putative ligand-receptor-ligand-receptor communication. CellNEST detects T cell homing signals in human lymph nodes, identifies aggressive cancer communication in lung adenocarcinoma and colorectal cancer, and predicts new patterns of communication that may act as relay networks in pancreatic cancer. Along with CellNEST, we provide a web-based, interactive visualization method to explore in situ communication. CellNEST is available at https://github.com/schwartzlab-methods/CellNEST .
    DOI:  https://doi.org/10.1038/s41592-025-02721-3
  13. Int J Mol Sci. 2025 May 23. pii: 5013. [Epub ahead of print]26(11):
    Liquid Biopsy: The Challenges of a Revolutionary Approach in Oncology.
    Claudio Antonio Coppola, Simona De Summa, Giuseppina Matera, Brunella Pilato, Debora Traversa, Stefania Tommasi.
      Liquid biopsy has gained attention in oncology as a non-invasive diagnostic tool, offering valuable insights into tumor biology through the analysis of circulating nucleic acid (cfDNA and cfRNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and tumor-educated platelets (TEPs). In this review, we summarize the clinical use of liquid biopsies in cancer now and look forward to its future, with a particular emphasis on some the methods used to isolate the liquid biopsy analytes. This technique provides real-time information on tumor dynamics, treatment response, and disease progression, with the potential for early diagnosis and personalized treatment. Despite its advantages, liquid biopsy faces several challenges, particularly in detecting analytes in early-stage cancers and evaluating the tumor molecular fraction. Tumor burden, molecular fraction, and the presence of subclones can impact the sensitivity and specificity of the analysis. Recent advancements in artificial intelligence (AI) have enhanced the diagnostic accuracy of liquid biopsy by integrating data, and multimodal approaches that combine multiple biomarkers such as ctDNA, CTCs, EVs, and TEPs show promise in providing a more comprehensive view of tumor characteristics. Liquid biopsy has the potential to revolutionize cancer care by providing rapid, non-invasive, and cost-effective diagnostics, enabling timely interventions and personalized treatment strategies.
    Keywords:  CTCs; cfDNA; cfRNA; ctDNA; liquid biopsy; precision medicine; tumor-educated platelets
    DOI:  https://doi.org/10.3390/ijms26115013
  14. Brief Bioinform. 2025 May 01. pii: bbaf264. [Epub ahead of print]26(3):
    Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data.
    Su Xu, Duan Chen, Xue Wang, Shaoyu Li.
      This study benchmarks the robustness and resilience of computational deconvolution methods for estimating cell-type proportions in bulk tissues, with a focus on comparing reference-based and reference-free methods. Robustness is evaluated by generating in silico pseudo-bulk tissue RNA sequencing data from cell-level gene expression profiles derived from four different tissue types, with simulated cellular composition at varying levels of heterogeneity. To assess resilience, we intentionally alter single-cell RNA profiles to create pseudo-bulk tissue RNA-seq data. Deconvolution estimates are compared with ground truth using Pearson's correlation coefficient, root mean squared deviation, and mean absolute deviation. The results show that reference-based methods are more robust when reliable reference data are available, whereas reference-free methods excel in scenarios lacking suitable reference data. Furthermore, variations in cell-level transcriptomic profiles and cell composition have emerged as critical factors influencing the performance of deconvolution methods. This study provides significant insights into the factors affecting bulk tissue deconvolution performance, which are essential for guiding users and advancing the development of more powerful and reliable algorithms in the future.
    Keywords:  cellular composition; deconvolution; resilience; robustness
    DOI:  https://doi.org/10.1093/bib/bbaf264
  15. Bioinformatics. 2025 Jun 13. pii: btaf350. [Epub ahead of print]
    Gene Spatial Integration: enhancing spatial transcriptomics analysis via deep learning and batch effect mitigation.
    Rian Pratama, Jason Hilton, J Michael Cherry, Giltae Song.
       MOTIVATION: Spatial transcriptomics (ST) is a groundbreaking technique for studying the correlation between cellular organization within a tissue and their physiological and pathological properties. Every facet of spatial information, including cell/spot proximity, distribution, and dimensionality, is significant. Most methods lean heavily on proximity for ST analysis, each resulting in useful insights but still leaving other aspects untapped. In addition, samples procured at different times, different donors, and by different technologies introduce a batch effects problem that hinders the statistical approach employed by most analysis tools. Addressing these challenges, we have developed a deep learning method for analyzing integrated multiple ST data, focusing on the distribution aspect. Furthermore, our method aims to leverage single-cell analysis tools.
    RESULTS: Our study introduces Gene Spatial Integration (GSI), a data integration pipeline utilizing representation learning approach to extract spatial distribution of genes into the same feature space as gene expression features. We employ Autoencoder network to extract spatial embedding, facilitating the projection of spatial features into gene expression feature space. Our approach allows for seamless integration of multiple samples with minimum detriment, increasing the performance of the ST data analysis tool. We show application of our method on human DLPFC dataset. Our method consistently improves the performance of the clustering of Seurat tools, with the most significant increase observed in sample 151673, almost doubling the ARI score from 0.225 to 0.405. We also combine our pipeline with the clustering of GraphST, achieving a significantly higher ARI score in sample 151672 from 0.614 to 0.795. This result reveals the potential of gene distribution spatial aspect, also emphasizes the impact of integration and batch effect removal in developing a refined analysis in understanding tissue characteristics.
    AVAILABILITY: Implementation of GSI is accessible at https://github.com/Riandanis/Spatial_Integration_GSI.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaf350
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