bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024‒11‒10
twelve papers selected by
Xiao Qin, University of Oxford
  1. Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.
  2. Empowering biomedical discovery with AI agents.
  3. A beginner's guide to supervised analysis for mass cytometry data in cancer biology.
  4. Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.
  5. Identification and genetic dissection of convergent persister cell states.
  6. Origin, development and therapy of colorectal cancer from the perspective of a biologist and an oncologist.
  7. Sticky situation: how adhesive bacteria drive colon cancer.
  8. Recapitulating the adenoma-carcinoma sequence by selection of four spontaneous oncogenic mutations in mismatch-repair-deficient human colon organoids.
  9. Development of compact transcriptional effectors using high-throughput measurements in diverse contexts.
  10. ChatGPT is transforming peer review - how can we use it responsibly?
  11. KRAS and EGFR inhibitors: a new step in the management of colorectal cancer.
  12. Automated real-world data integration improves cancer outcome prediction.
  1. Genes Dev. 2024 Nov 04.
    Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.
    Hirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, Yibin Kang.
      Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.
    Keywords:  cancer stem cell; cell-to-cell interaction; deconvolution; next-generation sequencing; normal stem cell; segmentation; spatial transcriptomics; stem cell niche
    DOI:  https://doi.org/10.1101/gad.351956.124
  2. Cell. 2024 Oct 31. pii: S0092-8674(24)01070-5. [Epub ahead of print]187(22): 6125-6151
    Empowering biomedical discovery with AI agents.
    Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, Marinka Zitnik.
      We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
    Keywords:  AI agent; agent systems; artificial intelligence; biomedical discovery; foundation models; large language models
    DOI:  https://doi.org/10.1016/j.cell.2024.09.022
  3. Cytometry A. 2024 Nov 01.
    A beginner's guide to supervised analysis for mass cytometry data in cancer biology.
    Julia Wlosik, Samuel Granjeaud, Laurent Gorvel, Daniel Olive, Anne-Sophie Chretien.
      Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.
    Keywords:  cancer; machine learning; mass cytometry; predictive models; supervised analysis
    DOI:  https://doi.org/10.1002/cyto.a.24901
  4. Bioinform Biol Insights. 2024 ;18 11779322241287120
    Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.
    Karamveer, Yasin Uzun.
      Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.
    Keywords:  Gene regulatory networks; benchmarking; epigenomics; ground truth; single-cell genomics
    DOI:  https://doi.org/10.1177/11779322241287120
  5. Nature. 2024 Nov 06.
    Identification and genetic dissection of convergent persister cell states.
    Sydney B Blattman, Wenyan Jiang, E Riley McGarrigle, Menghan Liu, Panos Oikonomou, Saeed Tavazoie.
      Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections1. However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease4, and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections.
    DOI:  https://doi.org/10.1038/s41586-024-08124-2
  6. Crit Rev Oncol Hematol. 2024 Oct 25. pii: S1040-8428(24)00287-7. [Epub ahead of print] 104544
    Origin, development and therapy of colorectal cancer from the perspective of a biologist and an oncologist.
    Jiri Svec, Jakub Onhajzer, Vladimir Korinek.
      The intestinal epithelium, a rapidly renewing tissue, is characterized by a continuous cell turnover that occurs through a well-coordinated process of cell proliferation and differentiation. This dynamic is crucial for the long-term function of the gastrointestinal tract. Disruption of this process can lead to colorectal carcinoma, a common malignancy worldwide. The first part of the review focuses on the cellular composition of the epithelium and the molecular mechanisms that control its functions, and describes the pathways that lead to epithelial transformation and tumor progression. This forms the basis for understanding the development and progression of advanced colorectal cancer. The second part deals with current therapeutic approaches and presents the latest treatment options, ongoing clinical trials and new drugs. In addition, the biological and medical perspectives of the adverse effects of therapies and models of regeneration of the intestinal epithelium are highlighted and, finally, future treatment options are discussed.
    Keywords:  chemotherapy; colorectal cancer; immunotherapy; intestinal epithelium; regeneration; signaling pathways; targeted therapy
    DOI:  https://doi.org/10.1016/j.critrevonc.2024.104544
  7. Nature. 2024 Nov 06.
    Sticky situation: how adhesive bacteria drive colon cancer.
    Kyanna S Ouyang, Jens Puschhof.
      
    Keywords:  Cancer; Microbiology
    DOI:  https://doi.org/10.1038/d41586-024-03285-6
  8. Nat Cancer. 2024 Nov 01.
    Recapitulating the adenoma-carcinoma sequence by selection of four spontaneous oncogenic mutations in mismatch-repair-deficient human colon organoids.
    Tomohiro Mizutani, Matteo Boretto, Sangho Lim, Jarno Drost, Diego Montiel González, Rurika Oka, Maarten H Geurts, Harry Begthel, Jeroen Korving, Johan H van Es, Ruben van Boxtel, Hans Clevers.
      Carcinogenesis results from the sequential acquisition of oncogenic mutations that convert normal cells into invasive, metastasizing cancer cells. Colorectal cancer exemplifies this process through its well-described adenoma-carcinoma sequence, modeled previously using clustered regularly interspaced short palindromic repeats (CRISPR) to induce four consecutive mutations in wild-type human gut organoids. Here, we demonstrate that long-term culture of mismatch-repair-deficient organoids allows the selection of spontaneous oncogenic mutations through the sequential withdrawal of Wnt agonists, epidermal growth factor (EGF) agonists and the bone morphogenetic protein (BMP) antagonist Noggin, while TP53 mutations were selected through the addition of Nutlin-3. Thus, organoids sequentially acquired mutations in AXIN1 and AXIN2 (Wnt pathway), TP53, ACVR2A and BMPR2 (BMP pathway) and NRAS (EGF pathway), gaining complete independence from stem cell niche factors. Quadruple-pathway (Wnt, EGF receptor, p53 and BMP) mutant organoids formed solid tumors upon xenotransplantation. This demonstrates that carcinogenesis can be recapitulated in a DNA repair-mutant background through in vitro selection that targets four consecutive cancer pathways.
    DOI:  https://doi.org/10.1038/s43018-024-00841-x
  9. Nat Biotechnol. 2024 Nov 01.
    Development of compact transcriptional effectors using high-throughput measurements in diverse contexts.
    Josh Tycko, Mike V Van, Aradhana, Nicole DelRosso, Hanrong Ye, David Yao, Raeline Valbuena, Alun Vaughan-Jackson, Xiaoshu Xu, Connor Ludwig, Kaitlyn Spees, Katherine Liu, Mingxin Gu, Venya Khare, Adi Xiyal Mukund, Peter H Suzuki, Sophia Arana, Catherine Zhang, Peter P Du, Thea S Ornstein, Gaelen T Hess, Roarke A Kamber, Lei S Qi, Ahmad S Khalil, Lacramioara Bintu, Michael C Bassik.
      Transcriptional effectors are protein domains known to activate or repress gene expression; however, a systematic understanding of which effector domains regulate transcription across genomic, cell type and DNA-binding domain (DBD) contexts is lacking. Here we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator called NFZ, by combining NCOA3, FOXO3 and ZNF473 domains, which enables efficient CRISPRa with better viral delivery and inducible control of chimeric antigen receptor T cells.
    DOI:  https://doi.org/10.1038/s41587-024-02442-6
  10. Nature. 2024 Nov;635(8037): 10
    ChatGPT is transforming peer review - how can we use it responsibly?
    James Zou.
      
    Keywords:  Computer science; Conferences and meetings; Machine learning; Publishing
    DOI:  https://doi.org/10.1038/d41586-024-03588-8
  11. Transl Gastroenterol Hepatol. 2024 ;9 56
    KRAS and EGFR inhibitors: a new step in the management of colorectal cancer.
    Khalil Saleh, Rebecca Ibrahim, Rita Khoury, Zamzam Tikriti, Nadine Khalife.
      
    Keywords:  KRASG12C mutation; adagrasib; colorectal cancer (CRC); panitumumab; sotorasib
    DOI:  https://doi.org/10.21037/tgh-24-73
  12. Nature. 2024 Nov 06.
    Automated real-world data integration improves cancer outcome prediction.
    MSK Cancer Data Science Initiative Group
      The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcomes with unprecedented richness. Patient data are often stored in unstructured text and siloed datasets. Here we combine natural language processing annotations1,2 with structured medication, patient-reported demographic, tumour registry and tumour genomic data from 24,950 patients at Memorial Sloan Kettering Cancer Center to generate a clinicogenomic, harmonized oncologic real-world dataset (MSK-CHORD). MSK-CHORD includes data for non-small-cell lung (n = 7,809), breast (n = 5,368), colorectal (n = 5,543), prostate (n = 3,211) and pancreatic (n = 3,109) cancers and enables discovery of clinicogenomic relationships not apparent in smaller datasets. Leveraging MSK-CHORD to train machine learning models to predict overall survival, we find that models including features derived from natural language processing, such as sites of disease, outperform those based on genomic data or stage alone as tested by cross-validation and an external, multi-institution dataset. By annotating 705,241 radiology reports, MSK-CHORD also uncovers predictors of metastasis to specific organ sites, including a relationship between SETD2 mutation and lower metastatic potential in immunotherapy-treated lung adenocarcinoma corroborated in independent datasets. We demonstrate the feasibility of automated annotation from unstructured notes and its utility in predicting patient outcomes. The resulting data are provided as a public resource for real-world oncologic research.
    DOI:  https://doi.org/10.1038/s41586-024-08167-5
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒17 at 20:40.