bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022–12–04
nine papers selected by
Sergio Marchini, Humanitas Research
  1. Spatially resolved epigenomics.
  2. Cellular functions of cGAS-STING signaling.
  3. SPATIAL RNA SEQUENCING METHODS SHOWED HIGH RESOLUTION OF SINGLE CELL IN CANCER METASTASIS AND THE FORMATION OF TME.
  4. Circulating T-cell receptor diversity as predictive biomarker for PARP inhibitors maintenance therapy in high grade serous ovarian cancer.
  5. Assessment of spatial transcriptomics for oncology discovery.
  6. The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer.
  7. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.
  8. Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.
  9. varAmpliCNV: Analyzing variance of amplicons to detect CNVs in targeted NGS data.
  1. Nat Methods. 2022 Dec;19(12): 1516
    Spatially resolved epigenomics.
    Rita Strack.
      
    DOI:  https://doi.org/10.1038/s41592-022-01718-6
  2. Trends Cell Biol. 2022 Nov 24. pii: S0962-8924(22)00252-5. [Epub ahead of print]
    Cellular functions of cGAS-STING signaling.
    Chen Chen, Pinglong Xu.
      Cyclic GMP-AMP (cGAMP) synthase (cGAS) senses misplaced genomic, mitochondrial, and microbial double-stranded DNA (dsDNA) to synthesize 2'3'-cGAMP that mobilizes stimulator of interferon genes (STING) to unleash innate immune responses, constituting a ubiquitous and effective surveillance system against tissue damage and pathogen invasion. However, imbalanced cGAS-STING signaling tethers considerably in infectious, autoimmune, malignant, fibrotic, and neurodegenerative diseases. Recently, multifaceted roles for cGAS-STING signaling at the cellular scale have emerged; these include autophagy, translation, metabolism homeostasis, cellular condensation, DNA damage repair, senescence, and cell death. These dominances adaptively shape cellular physiologies and impact disease pathogenesis. However, understanding how DNA sensing-initiated responses trigger these diverse cellular processes remains an outstanding challenge. In this review we discuss recent developments of cellular physiological states controlled by cGAS-STING machinery, as well as their disease relevance and underlying mechanisms, canonical or noncanonical. Ultimately, exploiting these cellular functions and mechanisms may represent promising targets for disease therapeutics.
    Keywords:  autophagy; cGAMP; cGAS-STING; condensation; innate immunity; metabolism; organelle; pathogenesis; senescence; translation
    DOI:  https://doi.org/10.1016/j.tcb.2022.11.001
  3. Biosci Rep. 2022 Dec 02. pii: BSR20221680. [Epub ahead of print]
    SPATIAL RNA SEQUENCING METHODS SHOWED HIGH RESOLUTION OF SINGLE CELL IN CANCER METASTASIS AND THE FORMATION OF TME.
    Yue Zheng, Xiaofeng Yang.
      Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors, to illustrate recent developments in the imaging resolution of micro-tissues.
    Keywords:  cancer metastasis; in situ RNA sequencing; multimodal analysis; single cell RNA sequencing; spatial transcriptomics; tumor microenvironments
    DOI:  https://doi.org/10.1042/BSR20221680
  4. Gynecol Oncol. 2022 Nov 25. pii: S0090-8258(22)01927-8. [Epub ahead of print]168 135-143
    Circulating T-cell receptor diversity as predictive biomarker for PARP inhibitors maintenance therapy in high grade serous ovarian cancer.
    Tong Shu, Zhipeng Zhou, Jing Bai, Xiao Xiao, Min Gao, Nan Zhang, Hongguo Wang, Xuefeng Xia, Yunong Gao, Hong Zheng.
       OBJECTIVE: T-cell receptor (TCR) repertoire diversity is getting increasing attention as a predictive biomarker in cancer patients. However, the characteristics of the TCR together with its predictive significance for high grade serous ovarian cancer (HGSOC) patients receiving poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy remain unknown.
    METHODS: Twenty-seven patients with HGSOC were analyzed including 22 patients receiving PARPi maintenance therapy and 5 untreated patients as control. Peripheral blood samples were collected for TCR sequencing at baseline as well as one month and three months after the exposure to PARPi. To determine whether TCR diversity was related to PARPi efficacy, we compared the TCR repertoire between patients who had received PARPi and those who had not.
    RESULTS: For patients receiving PARPi treatment or not, we evaluated changes in clone abundance during PARPi maintenance and the similarity of the TCR repertoire before and after the treatment. The results revealed that patients receiving PARPi had TCR repertoires that were more stable than those of untreated cases. We next correlated TCR diversity with the efficacy of PARPi in the treatment group. The rising trend of TCR diversity after three months with PARPi treatment was associated with a longer PFS (21.7 vs 7.4 months, hazard ratio = 0.19, p < 0.001) and a better response to PARPi (91.7% vs 25.0%, p = 0.004). Furthermore, we discovered that the primary characteristic with predictive value for the effectiveness of PARPi is the considerable reduction of the high-frequency T cell clones.
    CONCLUSION: We suggested that the circulating TCR diversity could be a potential predictive biomarker for PARPi maintenance therapy in HGSOC.
    Keywords:  Maintenance therapy; Ovarian cancer; PARP inhibitor; Predictive biomarker; TCR diversity
    DOI:  https://doi.org/10.1016/j.ygyno.2022.11.013
  5. Cell Rep Methods. 2022 Nov 21. 2(11): 100340
    Assessment of spatial transcriptomics for oncology discovery.
    Anna Lyubetskaya, Brian Rabe, Andrew Fisher, Anne Lewin, Isaac Neuhaus, Constance Brett, Todd Brett, Ethel Pereira, Ryan Golhar, Sami Kebede, Alba Font-Tello, Kathy Mosure, Nicholas Van Wittenberghe, Konstantinos J Mavrakis, Kenzie MacIsaac, Benjamin J Chen, Eugene Drokhlyansky.
      Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.
    Keywords:  biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors
    DOI:  https://doi.org/10.1016/j.crmeth.2022.100340
  6. Nat Genet. 2022 Dec 01.
    The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer.
    Dale W Garsed, Ahwan Pandey, Sian Fereday, Catherine J Kennedy, Kazuaki Takahashi, Kathryn Alsop, Phineas T Hamilton, Joy Hendley, Yoke-Eng Chiew, Nadia Traficante, Pamela Provan, Dinuka Ariyaratne, George Au-Yeung, Nicholas W Bateman, Leanne Bowes, Alison Brand, Elizabeth L Christie, Julie M Cunningham, Michael Friedlander, Bronwyn Grout, Paul Harnett, Jillian Hung, Bryan McCauley, Orla McNally, Anna M Piskorz, Flurina A M Saner, Robert A Vierkant, Chen Wang, Stacey J Winham, Paul D P Pharoah, James D Brenton, Thomas P Conrads, George L Maxwell, Susan J Ramus, Celeste Leigh Pearce, Malcolm C Pike, Brad H Nelson, Ellen L Goode, Anna DeFazio, David D L Bowtell.
      Fewer than half of all patients with advanced-stage high-grade serous ovarian cancers (HGSCs) survive more than five years after diagnosis, but those who have an exceptionally long survival could provide insights into tumor biology and therapeutic approaches. We analyzed 60 patients with advanced-stage HGSC who survived more than 10 years after diagnosis using whole-genome sequencing, transcriptome and methylome profiling of their primary tumor samples, comparing this data to 66 short- or moderate-term survivors. Tumors of long-term survivors were more likely to have multiple alterations in genes associated with DNA repair and more frequent somatic variants resulting in an increased predicted neoantigen load. Patients clustered into survival groups based on genomic and immune cell signatures, including three subsets of patients with BRCA1 alterations with distinctly different outcomes. Specific combinations of germline and somatic gene alterations, tumor cell phenotypes and differential immune responses appear to contribute to long-term survival in HGSC.
    DOI:  https://doi.org/10.1038/s41588-022-01230-9
  7. Bioinformatics. 2022 Dec 02. pii: btac775. [Epub ahead of print]
    Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.
    Micha Sam Brickman Raredon, Junchen Yang, Neeharika Kothapalli, Wesley Lewis, Naftali Kaminski, Laura E Niklason, Yuval Kluger.
       MOTIVATION: Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level.
    RESULTS: NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters, and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution.
    AVAILABILITY: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btac775
  8. Cell Rep Methods. 2022 Nov 21. 2(11): 100348
    Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.
    Huw D Summers, John W Wills, Paul Rees.
      Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.
    Keywords:  cell imaging; cytometry; spatial statistics; tissue analysis
    DOI:  https://doi.org/10.1016/j.crmeth.2022.100348
  9. Bioinformatics. 2022 Nov 28. pii: btac756. [Epub ahead of print]
    varAmpliCNV: Analyzing variance of amplicons to detect CNVs in targeted NGS data.
    Ajay Anand Kumar, Bart Loeys, Gerarda Van De Beek, Nils Peeters, Wim Wuyts, Lut Van Laer, Geert Vandeweyer, Maaike Alaerts.
       MOTIVATION: Computational identification of copy number variants (CNVs) in sequencing data is a challenging task. Existing CNV-detection methods account for various sources of variation and perform different normalization strategies. However, their applicability and predictions are restricted to specific enrichment protocols. Here, we introduce a novel tool named varAmpliCNV, specifically designed for CNV-detection in amplicon-based targeted resequencing data (HaloplexTM enrichment protocol) in the absence of matched controls. VarAmpliCNV utilizes principal component analysis (PCA) and/or metric dimensional scaling (MDS) to control variances of amplicon associated read counts enabling effective detection of CNV signals.
    RESULTS: Performance of VarAmpliCNV was compared against three existing methods (ConVaDING, ONCOCNV, DECoN) on data of 167 samples run with an aortic aneurysm gene panel (n = 30), including 9 positive control samples. Additionally, we validated the performance on a large deafness gene panel (n = 145) run on 138 samples, containing 4 positive controls. VarAmpliCNV achieved higher sensitivity (100%) and specificity (99.78%) in comparison to competing methods. In addition, unsupervised clustering of CNV segments and visualization plots of amplicons spanning these regions is included as a downstream strategy to filter out false positives.
    AVAILABILITY: https://hub.docker.com/r/cmgantwerpen/varamplicnv.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btac756
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