bims-tumhet 2024-11-03 papers

Issue of 2024‒11‒03
nine papers selected by
Sergio Marchini, Humanitas Research

J Clin Invest. 2024 Oct 29. pii: e179501. [Epub ahead of print]

Immunological and molecular features of the tumor microenvironment of long-term survivors of ovarian cancer.

Brad H Nelson, Phineas T Hamilton, Minh Tung Phung, Katy Milne, Bronwyn Harris, Shelby Thornton, Donald Li Stevens, Shreena Kalaria, Karanvir Singh, Céline M Laumont, Elena Moss, Aliya Alimujiang, Nicola S Meagher, Adelyn Bolithon, Sian Fereday, Catherine J Kennedy, Joy Hendley, Dinuka Ariyaratne, Kathryn Alsop, Nadia Traficante, Ellen L Goode, Anthony N Karnezis, Hui Shen, Jean Richardson, Cindy McKinnon Deurloo, Anne Chase, Bronwyn Grout, Jennifer A Doherty, Holly R Harris, Kara L Cushing-Haugen, Michael S Anglesio, Karolin Heinze, David Huntsman, Aline Talhouk, Gillian E Hanley, Jennifer Alsop, Mercedes Jimenez-Linan, Paul Dp Pharoah, Jessica Boros, Alison H Brand, Paul R Harnett, Raghwa Sharma, Jonathan L Hecht, Naoko Sasamoto, Kathryn L Terry, Beth Y Karlan, Jenny Lester, Michael E Carney, Marc T Goodman, Brenda Y Hernandez, Lynne R Wilkens, Sabine Behrens, Renée Turzanski Fortner, Peter A Fasching, Christiani Bisinotto, Francisco José Candido Dos Reis, Prafull Ghatage, Martin Köbel, Esther Elishaev, Francesmary Modugno, Linda S Cook, Nhu D Le, Aleksandra Gentry-Maharaj, Usha Menon, María J García, Cristina Rodriguez-Antona, Kyo M Farrington, Linda E Kelemen, Stefan Kommoss, Annette Staebler, Dale W Garsed, James D Brenton, Anna M Piskorz, David Dl Bowtell, Anna DeFazio, Susan J Ramus, Malcolm C Pike, Celeste Leigh Pearce.

BACKGROUND: Despite an overall poor prognosis, about 15% of patients with advanced-stage tubo-ovarian high-grade serous carcinoma (HGSC) survive ten or more years after standard treatment.METHODS: We evaluated the tumor microenvironment of this exceptional, understudied group using a large international cohort enriched for long-term survivors (LTS; 10+ years; n = 374) compared to medium-term (MTS; 5-7.99 years; n = 433) and short-term survivors (STS; 2-4.99 years; n = 416). Primary tumor samples were immunostained and scored for intra-epithelial and intra-stromal densities of 10 immune-cell subsets (including T cells, B cells, plasma cells, myeloid cells, PD-1+ cells, and PD-L1+ cells) and epithelial content.
RESULTS: Positive associations with LTS compared to STS were seen for 9/10 immune-cell subsets. In particular, the combination of intra-epithelial CD8+ T cells and intra-stromal B cells showed near five-fold increased odds of LTS compared to STS. All of these associations were stronger in tumors with high epithelial content and/or the C4/Differentiated molecular subtype, despite immune-cell densities generally being higher in tumors with low epithelial content and/or the C2/Immunoreactive molecular subtype.
CONCLUSIONS: The tumor microenvironment of HGSC long-term survivors is distinguished by the intersection of T and B cell co-infiltration, high epithelial content and C4/Differentiated molecular subtype, features which may inspire new approaches to immunotherapy.
FUNDING: Ovarian Cancer Research Program (OCRP) of the Congressionally Directed Medical Research Program (CDMRP), U.S. Department of Defense (DOD); American Cancer Society; BC Cancer Foundation; Canada's Networks of Centres of Excellence; Canadian Cancer Society; Canadian Institutes of Health Research; Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania, Cancer Foundation of Western Australia; Cancer Institute NSW; Cancer Research UK; Deutsche Forschungsgesellschaft; ELAN Funds of the University of Erlangen-Nuremberg; Fred C. and Katherine B. Andersen Foundation; Genome BC; German Cancer Research Center; German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research; Instituto de Salud Carlos III; Mayo Foundation; Minnesota Ovarian Cancer Alliance; Ministerio de Economía y Competitividad; MRC; National Center for Advancing Translational Sciences; National Health and Medical Research Council of Australia (NHMRC); Ovarian Cancer Australia; Peter MacCallum Foundation; Sydney West Translational Cancer Research Centre; Terry Fox Research Institute; The Eve Appeal (The Oak Foundation); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; University of Pittsburgh School of Medicine; U.S. National Cancer Institute of the National Institutes of Health; VGH & UBC Hospital Foundation; Victorian Cancer Agency.

Keywords: Cancer; Cellular immune response; Epidemiology; Immunology; Oncology

DOI: https://doi.org/10.1172/JCI179501

Nat Methods. 2024 Oct 30.

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.

Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Michael J Wilson, Christopher H Sun, Li Ding, Benjamin J Raphael.

Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs-including copy-neutral loss of heterozygosity and mirrored subclonal CNAs-that are invisible to total copy number analysis. Using nine patients' data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

DOI: https://doi.org/10.1038/s41592-024-02438-9

Comput Methods Programs Biomed. 2024 Sep 21. pii: S0169-2607(24)00424-3. [Epub ahead of print]257 108431

STGAT: Graph attention networks for deconvolving spatial transcriptomics data.

Wei Li, Huixia Zhang, Linjie Wang, Pengyun Wang, Kun Yu.

BACKGROUND AND OBJECTIVE: Spatially resolved gene expression profiles are crucial for understanding tissue structure and function. However, the lack of single-cell resolution in these profiles demands their integration with single-cell RNA sequencing data for accurate dataset deconvolution. We propose STGAT, an innovative deconvolution method that leverages graph attention networks to enhance spatial transcriptomic (ST) data analysis.METHODS: STGAT generates pseudo-ST data that more comprehensively represents the cell-type composition within real-ST data by using three different sampling probabilities. A comprehensive combined graph is then constructed to capture the complex relationships both across pseudo- and real-ST data and within each dataset. Moreover, integrating a graph attention network further enables STGAT to dynamically assign the weights to the connections between spots, significantly enhancing the accuracy of cell-type composition predictions.
RESULTS: Extensive comparative experiments on simulated and real-world datasets, demonstrate the superior performance of STGAT for cell-type deconvolution. The method outperforms six established methods and is robust across various biological contexts.
CONCLUSION: STGAT exhibits more precise results in cell-type composition inference that are more consistent with the known knowledge, suggesting its potential utility in improving the resolution and accuracy of spatial transcriptomics data analysis.

Keywords: Cell type deconvolution; Graph attention networks; Single-cell RNA sequencing; Spatial transcriptomics

DOI: https://doi.org/10.1016/j.cmpb.2024.108431

Nature. 2024 Oct;634(8036): 1178-1186

Tumour evolution and microenvironment interactions in 2D and 3D space.

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

DOI: https://doi.org/10.1038/s41586-024-08087-4

Mol Oncol. 2024 Oct 29.

When molecular biology transforms clinical oncology: the EGFR journey in colorectal cancer.

Pietro Paolo Vitiello, Nadia Saoudi González, Alberto Bardelli.

The discovery of growth factors and their involvement in cancer represents the foundation of precision oncology. The preclinical and clinical development of agents targeting epidermal growth factor receptor (EGFR) in colorectal cancer (CRC) were accompanied by big hype and hopes, though the clinical testing of such agents clashed with intrinsic and acquired resistance, greatly limiting their therapeutic value. However, a better understanding of the biology of the EGFR signaling pathway in CRC, coupled with the development of liquid biopsy methodologies to study cancer evolution in real time, fostered the clinical refinement of anti-EGFR treatment in CRC. Such a workflow, based on the co-evolution of biology knowledge and clinical development, allowed to couple the discovery of relevant therapy resistance mechanisms to the development of strategies to bypass this resistance. A broader application of this paradigm could prove successful and create an effective shortcut between the bench and the bedside for treatment strategies other than targeted therapy.

Keywords: EGFR; colorectal cancer; drug resistance; precision medicine; translational research

DOI: https://doi.org/10.1002/1878-0261.13754

Int J Gynecol Cancer. 2024 Oct 26. pii: ijgc-2024-005916. [Epub ahead of print]

Validation and clinical performance of a single test, DNA based endometrial cancer molecular classifier.

OBJECTIVES: We have previously shown that DNA based, single test molecular classification by next generation sequencing (NGS) (Proactive Molecular risk classifier for Endometrial cancer (ProMisE) NGS) is highly concordant with the original ProMisE classifier and maintains prognostic value in endometrial cancer. Our aim was to validate ProMisE NGS in an independent cohort and assess the performance of ProMisE NGS in real world clinical practice to address if there were any practical challenges or learning points for implementation.METHODS: We evaluated DNA extracted from an external research cohort of 211 endometrial cancer cases diagnosed in 2016 from Germany, Switzerland, and Austria, across seven European centers, comparing standard molecular classification (NGS for POLE status, immunohistochemistry for mismatch repair and p53) with ProMisE NGS (NGS for POLE and TP53, microsatellite instability assay) for concordance metrics and Kaplan-Meier survival statistics across molecular subtypes. In parallel, we assessed all patients who had undergone a new NGS based molecular classification test (n=334) comparing molecular subtype assignment with the original ProMisE classifier.
RESULTS: A total of 545 endometrial cancers were compared. Prognostic differences in progression free, disease specific, and overall survival between the four molecular subtypes were observed for the NGS classifier, recapitulating the survival curves of original ProMisE. In 28 of 545 (5%) discordant cases (8/211 (4%) in the validation set, 20/334 (6%) in the real world cohort), molecular subtype was able to be definitively assigned in all, based on review of the histopathological features and/or additional immunohistochemistry. DNA based molecular classification identified twice as many 'multiple classifier' endometrial cancers; 37 of 545 (7%) compared with 20 of 545 (4%) with original ProMisE.
CONCLUSION: External validation confirmed that single test, DNA based molecular classification was highly concordant (95%) with original ProMisE classification, with prognostic value maintained, representing an acceptable alternative for clinical practice. Careful consideration of reasons for discordance and knowledge of how to correctly assign multiple classifier endometrial cancers is imperative for implementation.

Keywords: Endometrial Neoplasms

DOI: https://doi.org/10.1136/ijgc-2024-005916

Nat Rev Cancer. 2024 Oct 28.

The present and future of the Cancer Dependency Map.

Rand Arafeh, Tsukasa Shibue, Joshua M Dempster, William C Hahn, Francisca Vazquez.

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

DOI: https://doi.org/10.1038/s41568-024-00763-x

Trends Genet. 2024 Oct 24. pii: S0168-9525(24)00236-1. [Epub ahead of print]

The distinction between epigenetics and epigenomics.

Kevin Struhl.

'Epigenetics' is the process by which distinct cell types or cell states are inherited through multiple cell divisions. 'Epigenomics' refers to DNA-associated physical and functional entities including histone modifications and DNA methylation, not concepts of inheritance. Conflating epigenetics and epigenomics is confusing and causes misunderstanding of a fundamental biological process.

DOI: https://doi.org/10.1016/j.tig.2024.10.002

Annu Rev Pathol. 2024 Oct 30.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.

Tancredi Massimo Pentimalli, Nikos Karaiskos, Nikolaus Rajewsky.

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

DOI: https://doi.org/10.1146/annurev-pathmechdis-111523-023417