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Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential

Genome Biology 2025年第1期26.0卷 1-25页

作者： Bao, Pengfei Wang, Taiwei Liu, Xiaofan Xing, Shaozhen Ruan, Hanjin Ma, Hongli Tao, Yuhuan Zhan, Qing Belmonte-Reche, Efres Qin, Lizheng Han, Zhengxue Mao, Minghui Li, Mengtao Lu, Zhi John MOE Key Laboratory of Bioinformatics State Key Lab of Green Biomanufacturing Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China Institute for Precision Medicine Tsinghua University Beijing 100084 China Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program School of Life Sciences Tsinghua University Beijing China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China National Clinical Research Center for Dermatologic and Immunologic Diseases (Ministry of Science & Technology) MOE Key Laboratory of Rheumatology and Clinical Immunology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Beijing 100730 China Academy for Advanced Interdisciplinary Studies (AAIS)and Sciences Joint Graduate Program (PTN) Peking University Beijing China Department of Oral and Maxillofacial & Head and Neck Oncology Beijing Stomatological Hospital Capital Medical University Beijing China Centre for Genomics and Oncological Research (GENYO) Avenida de La Ilustración 114 Granada 18016 Spain Department of Biochemistry and Molecular Biology II Faculty of Pharmacy University of Granada Granada Spain Instituto de Investigación Biosanitaria Ibs.GRANADA Hospital Virgen de Las Nieves Granada Spain The Center for Regeneration Aging and Chronic Diseases School of Basic Medical Sciences Tsinghua University Beijing China

Background: Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications. Results: We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients’ plasma that enable cancer detection and are informative of cancer types and metastasis. Conclusions: Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy. © The Author(s) 2025.

关键词： Cell-free RNA CfRNA fragment Liquid biopsy biomarker Noninvasive cancer detection Peak calling SncRNA Tissue-of-origin

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Facilitating the design of combination therapy for cancer using multipartite network models: Emphasis on acute myeloid leukaemia

Research Square

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research Square 2021年

作者： Jafari, Mohieddin Mirzaie, Mehdi Bao, Jie Barneh, Farnaz Zheng, Shuyu Eriksson, Johanna Tang, Jing Research Program in Systems Oncology Faculty of Medicine University of Helsinki Finland Prinses Maxima Center for Pediatric Oncology Utrecht3584 CS Netherlands

From the drug discovery perspective, combination therapy is recommended for cancer treatment due to its efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time-and cost-consuming. Here, we offer a novel strategy for predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug-response data on patient samples. In this study, we used Beat AML and GDSC, two comprehensive datasets based on patient-derived and cell line-based samples, to show the potential of multipartite network modelling in combinatorial cancer therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network that models the interaction of drugs and biological samples. Then, clusters of network communities were identified in two projected networks based on the topological structure of the networks. Chemical structures, drug-target networks, protein–protein interactions, and signalling networks were used to corroborate the intra-cluster homogeneity. We further leveraged the community structures within the drug-based multipartite networks to discover effective multi-targeted drug combinations and synergy levels, which were supported with more evidence using the DrugComb and ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapy in in vitro experiments using three acute myeloid leukaemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve AML treatment. © 2021, CC BY.

关键词： Cell culture

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Asymptotic supervised predictive classifiers under partition exchangeability

arXiv

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arXiv 2021年

作者： Amiryousefi, Ali Research Program in Systems Oncology Faculty of Medicine University of Helsinki PO BOX 63 FI-00014 Finland

The convergence of simultaneous and marginal predictive classifiers under partition exchangeability in supervised classification is obtained. The result shows the asymptotic convergence of these classifiers under infinite amount of training or test data, such that after observing umpteen amount of data, the differences between these classifiers would be negligible. This is an important result from the practical perspective as under the presence of sufficiently large amount of data, one can replace the simpler marginal classifier with computationally more expensive simultaneous one. © 2021, CC BY-NC-SA.

关键词： Classification (of information)

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Image Processing Pipeline to Compute Homologous Recombination Score 22

Image Processing Pipeline to Compute Homologous Recombinatio...

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12th International Conference on Biomedical Engineering and Technology, ICBET 2022

作者： Vundavilli, Haswanth Tumiati, Manuela Hautaniemi, Sampsa Datta, Aniruddha Kauppi, Liisa Texas A&M University United States The University of Texas MD Anderson Cancer Center United States Research Program in Systems Oncology Faculty of Medicine University of Helsinki Finland Department of Electrical and Computer Engineering The Center for Bioinformatics and Genomic Systems Engineering Texas A&M University United States

ISBN: (纸本)9781450395779

DNA double-strand breaks (DSBs) occur frequently in eukaryotic cells, and the homologous recombination pathway (HR) is one of the major pathways required to repair these breaks. However, tumor cells that are able to repair DSBs are unlikely to die due to damage incurred by DNA damaging chemotherapies, such as platinum compounds. While platinum-based therapies have been effective in treating various cancers, they also carry harsh side effects, and thus ideally platinum should be used when the probability of treatment resistance is low. HR scores provide a measure for patients' tumor's HR capacity and have been shown to predict their chemotherapy response and long-term survival. Calculating this score manually from immunofluorescence microscopy images for each patient is error-prone and time-consuming. Herein, we propose an image processing pipeline that takes as input imaging data from three emission channels (representing nuclei, S-phase cells, and HR-mediated repair in a tumor slice) from an epifluorescence microscope and computes the HR score. Our open-source methodology forms a rationale to develop similar approaches in predicting chemotherapeutic responses and facilitating to make treatment decisions. © 2022 ACM.

关键词： Chemotherapy

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Resolving Network Clusters Disparity Based on Dissimilarity Measurements with Nonmetric Analysis of Variance

SSRN

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SSRN 2022年

作者： Malyutina, Alina Tang, Jing Amiryousefi, Ali Research Program in Systems Oncology Faculty of Medicine University of Helsinki PO Box 63 HelsinkiFI-00014 Finland

Classic Analysis of Variance (ANOVA;cA) tests the explanatory power of apartitioning on a set of objects. Nonparametric ANOVA (npA) extends toa case where instead of the object values themselves, their mutual distancesare available. While considerably widening the applicability of the cA, thenpA does not provide a statistical framework for the cases where the mutualdissimilarity measurements between objects are *** on the central limit theorem (CLT), we introduce nonmetric ANOVA(nmA) as an extension of the cA and npA models where metric properties(identity, symmetry, and subadditivity) are relaxed. Our model allows anydissimilarity measures to be defined between objects where a disparity of aspecific clustering imposed on those are of interest. This derivation accommodatesan ANOVA-like framework of judgment, indicative of significant dispersionof the partitioned outputs in nonmetric space. We present a statisticwhich under the null hypothesis of no differences between the mean of theimposed partitioning, follows an exact F-distribution allowing to obtain theconsequential p-value. Three biological examples are provided and the performanceof our method in relation to the cA and npA is discussed. The R codefor implementing the cA, npA, and nmA as well as the supplementary data forthe case studies are available from ***/AmiryousefiLab/nmANOVA. © 2022, The Authors. All rights reserved.

关键词： Analysis of variance (ANOVA)

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Impact of HLA Evolutionary Divergence on Clinical Features of Patients with Aplastic Anemia and Paroxysmal Nocturnal Hemoglobinuria

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BLOOD 2020年第Sup1期136卷 2-3页

作者： Pagliuca, Simona Gurnari, Carmelo Chowell, Diego Krishna, Chirag Patel, Bhumika J. Awada, Hassan Kerr, Cassandra M. Kongkiatkamon, Sunisa Kishtagari, Ashwin Laframboise, Thomas Saunthararajah, Yogenthiran Jha, Babal K. Visconte, Valeria Chan, Timothy Maciejewski, Jaroslaw Université de Paris Paris France Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Foundation Cleveland OH Department of Biomedicine and Prevention University of Rome Tor Vergata Rome Italy Immunogenomics and Precision Oncology Platform Memorial Sloan Kettering Cancer Center New York Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York Leukemia Program Department of Hematology and Medical Oncology Cleveland Clinic Cleveland OH Department of hematology King Chulalongkorn Memorial Hospital Bangkok Thailand Genetics and Genome Sciences Department Case Western Reserve University Cleveland OH Immunotherapy and Precision Immuno-Oncology Cleveland Clinic Cleveland

HLA evolutionary divergence (HED) is a continuous metric that quantifies the breadth of the immunopeptidome that can be presented by an individual's HLA allotype. High HED scores have been shown to be associated with better outcome after immune check point inhibitor therapy in solid tumors.1 However, the role of HED has never been assessed in autoimmune disorders, and may have an impact on disease susceptibility and outcomes. Here we explored the impact of HED on clinical features of aplastic anemia (AA) and paroxysmal nocturnal hemoglobinuria (PNH). The immunological landscape of those diseases is characterized by an HLA-restricted CD8+ and CD4+ mediated autoimmunity which leads to hematopoietic stem cell destruction, with a crucial role played by immune-dominant PNH clones and immune escape mechanisms likely associated with the down-regulation of the HLA-antigen presentation. Since higher HED scores reflect a wider antigenic spectrum and potentially more diverse peptide presentation capabilities, we hypothesize that this context not only may elicit T-cellular responses by auto-antigen presentation but also that HLA structural diversity can impact on disease phenotype and have a role within the definition of an immune-dominant (higher HED) or immune-escape (lower HED) environment. Using an NGS-based platform targeting the entire HLA locus, we have sequenced DNA samples of a cohort of 98 well-annotated AA, PNH, or AA/PNH patients. We then calculated HED at the classical HLA class I and II loci (A, B, C, DRB1, DQA1, DQB1, DPB1, DPA1), and investigated its impact on disease course and outcomes (Fig. A, B ). Homozygosity for at least 1 out of 8 loci was frequently found (70%), especially for class II, however only 2% of patients had >1 homozygous locus). HLA-B and HLA-DQA1 genotypes demonstrated the highest HED among HLA-I and II loci, respectively (Fig. C ). While for class I loci no difference was detected across diagnostic subgroups, mean HED for class II loci was s

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R-BERT-CNN: drug-target interactions extraction from biomedical literature

arXiv

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arXiv 2021年

作者： Aldahdooh, Jehad Tanoli, Ziaurrehman Tang, Jing Research Program in Systems Oncology Faculty of medicine University of Helsinki Finland Doctoral Program in Computer Science University of Helsinki Finland Department of Mathematics and Statistics University of Helsinki

In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art language model, with Convolutional Neural Networks (CNN) to extract these relations. Despite the class imbalances in the BioCreative VII DrugProt test corpus, our model achieves a good performance compared to the average of other submissions in the challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI ChemProt test corpus). The results show the potential of deep learning in extracting various types of DTIs. © 2021, CC BY-NC-SA.

关键词： Extraction

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Acquired Mutations within the JAK2 Kinase Domain Confer Resistance to JAK Inhibitors in an in Vitro model of a High-Risk Acute Lymphoblastic Leukemia

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BLOOD 2020年第Sup1期136卷 5-6页

作者： Downes, Charlotte E. J. McClure, Barbara J. Rehn, Jacqueline Breen, James Bruning, John B. Yeung, David T. White, Deborah L. Cancer Program Precision Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Biological Sciences The University of Adelaide Adelaide Australia Adelaide Medical School The University of Adelaide Adelaide Australia Cancer Program Precision Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia Computational and Systems Biology Program Precision Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia Robinson Research Institute The University of Adelaide Adelaide Australia Precision Medicine Theme South Australian Health and Medical Research Institute Adelaide Australia Department of Haematology Royal Adelaide Hospital and SA Pathology Adelaide Australia Australian and New Zealand Childrens Haematology/Oncology Group (ANZCHOG) Melbourne Australia

Introduction Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype of ALL associated with high relapse rates and poor survival. Rearrangements of Janus kinase 2 (JAK2 r) are present in approximately 5% and 14% of pediatric and young adult Ph-like ALL cases respectively. The resultant JAK2 gene fusions drive leukemogenesis through constitutive activation of the JAK/STAT signaling pathway and are associated with very poor outcomes in patients with Ph-like ALL. All JAK inhibitors in clinical development are type I inhibitors, which bind in the ATP-binding site of JAK2. A phase II clinical trial is currently assessing the only FDA-approved JAK1/2 inhibitor, ruxolitinib in high-risk B-cell ALL cases harboring JAK2 alterations. The development of treatment resistance to targeted inhibitors in other diseases is well documented and often results in disease relapse. Elucidating mechanisms of ruxolitinib resistance in JAK2 r ALL will inform approaches to monitor the emergence of resistance in ongoing clinical trials and enable the development of therapeutic strategies to overcome or avert resistance. Methods JAK2 r B-ALL was modelled in the pro-B cell line, Ba/F3, by expressing the high-risk B-ALL fusion, ATF7IP-JAK2 . Ruxolitinib resistance in three independent ATF7IP-JAK2 Ba/F3 cell lines was achieved following dose escalation to a clinically relevant dose of 1 μM ruxolitinib. Sanger sequencing of the RT-PCR amplified JAK2 fusion revealed each resistant line had acquired a different mutation within the JAK2 kinase domain. Therapeutic sensitives were assessed by staining with Fixable Aqua Dead Cell Stain (Invitrogen) and Annexin V, and analysis by flow cytometry. Alterations in signaling pathways were determined using phosphoflow cytometry and western blot analysis. Computational modelling of acquired JAK2 mutations and subsequent influence on ruxolitinib binding was performed using ICM-Pro (Molsoft L.C.C.). Results In addition to the

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Plain language summary: does enzalutamide treatment with or without leuprolide improve outcomes and affect quality of life in patients with high-risk biochemical recurrence?

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Future oncology 2025年第5期21卷 501-514页

作者： Freedland, Stephen J. Gleave, Martin De Giorgi, Ugo Rannikko, Antti Pieczonka, Christopher M. Tutrone Jr, Ronald F. Venugopal, Balaji Woo, Henry H. Ramirez-Backhaus, Miguel Tarazi, Jamal Tang, Yiyun Ganguli, Arijit Haas, Gabriel P. Shore, Neal D. Department of Urology Cedars-Sinai Medical Center Los Angeles CA United States Section of Urology Durham VA Medical Center Durham NC United States The Vancouver Prostate Centre University of British Columbia Vancouver BC Canada IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) Meldola Italy Department of Urology and Research Program in Systems Oncology University of Helsinki Helsinki Finland Helsinki University Hospital Helsinki Finland US Urology Partners and Associated Medical Professionals of New York Syracuse NY United States Chesapeake Urology Research Associates Towson MD United States Beatson West of Scotland Cancer Centre University of Glasgow UK Glasgow United Kingdom College of Health and Medicine Australian National University Canberra ACT Australia SAN Prostate Centre of Excellence Sydney Adventist Hospital Sydney NSW Australia Servicio de Urología Fundación Instituto Valenciano de Oncología Valencia Spain Global Product Development Pfizer Collegeville PA United States Global Product Development San Francisco Pfizer CA United States Medical Affairs Astellas Pharma Northbrook IL United States Global Development Astellas Pharma Northbrook IL United States Carolina Urologic Research Center/AUC Urologists Myrtle Beach SC United States

Plain Language Summary: What is this summary about? This is a plain language summary of two research articles originally published in the New England Journal of Medicine and NEJM Evidence. The EMBARK study included patients with a type of advanced prostate cancer called non-metastatic castration-sensitive prostate cancer (nmCSPC) and high-risk biochemical recurrence (BCR). These patients experience a quick rise in prostate-specific antigen (PSA) levels after prostate cancer surgery or radiation treatment. Patients with a shorter PSA doubling time (PSADT) have quickly rising PSA levels. The patients from EMBARK had a short PSADT of 9 months or less, and their prostate cancer had not spread to other parts of the body. Prostate cancer treatments that lower testosterone levels, such as leuprolide, are widely used in patients with nmCSPC and high-risk BCR. In the EMBARK study, researchers wanted to know if enzalutamide treatment with or without leuprolide improved outcomes compared with leuprolide and placebo in patients with nmCSPC and high-risk BCR. researchers also looked at the safety of these medicines and their effect on quality of life. What were the results? researchers found that patients who received enzalutamide with or without leuprolide had a longer time until cancer spread to other parts of the body or death. It also took longer for their PSA levels to rise further and for them to start a new prostate cancer treatment. No new side effects were seen in the enzalutamide groups beyond those already known for enzalutamide. The most common side effects in all groups were hot flash, tiredness, and joint pain. The common side effects in the enzalutamide alone group were gynecomastia and breast tenderness. The median time that it took until patients had a confirmed drop in overall quality of life was similar between the enzalutamide combination, the enzalutamide alone, and the leuprolide plus placebo groups. What do the results of the study mean? Enzalutamide with

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Retraction notice to “Efficacy of Sunitinib and Radiotherapy in Genetically Engineered Mouse Model of Soft-Tissue Sarcoma” Int J Radiat Oncol Biol Phys , 2009;74(4), pp 1207-1216

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International Journal of Radiation oncology*Biology*Physics 2025年第2期122卷 524-524页

作者： Sam S. Yoon Lars Stangenberg Yoon-Jin Lee Courtney Rothrock Jonathan M. Dreyfuss Kwan-Hyuck Baek Peter R. Waterman G. Petur Nielsen Ralph Weissleder Umar Mahmood Peter J. Park Tyler Jacks Rebecca D. Dodd Carolyn J. Fisher Sandra Ryeom David G. Kirsch Department of Surgery Division of Surgical Oncology Massachusetts General Hospital and Harvard Medical School Boston MA Center for Molecular Imaging Research Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA Harvard-Partners Center for Genetics and Genomics Brigham and Women's Hospital and Harvard Medical School Boston MA Vascular Biology Program and Department of Surgery Boston Children's Hospital and Harvard Medical School Boston MA Center for Systems Biology Massachusetts General Hospital and Harvard Medical School Boston MA Department of Pathology Massachusetts General Hospital and Harvard Medical School Boston MA Center for Cancer Research Department of Biology and Howard Hughes Medical Institute Massachusetts Institute of Technology Boston MA Departments of Radiation Oncology and Pharmacology and Cancer Biology Duke University School of Medicine Durham NC

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