bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023‒02‒19
twelve papers selected by
Sergio Marchini
Humanitas Research
  1. Homologous Recombination Repair Deficiency: An Overview for Pathologists.
  2. A biomarker-driven therapy for ovarian cancer.
  3. Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review.
  4. Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.
  5. Optimizing the design of spatial genomic studies.
  6. Spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface in long-term ovarian cancer survivors.
  7. Patient Assessment and Therapy Planning Based on Homologous Recombination Repair Deficiency.
  8. Methylation Signature Implicated in Immuno-Suppressive Activities in Tubo-Ovarian High-Grade Serous Carcinoma.
  9. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.
  10. Liquid biopsies: the future of cancer early detection.
  11. SODB facilitates comprehensive exploration of spatial omics data.
  12. Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer.
  1. Mod Pathol. 2023 Jan 10. pii: S0893-3952(22)00480-X. [Epub ahead of print]36(3): 100049
    Homologous Recombination Repair Deficiency: An Overview for Pathologists.
    Kenneth D Doig, Andrew P Fellowes, Stephen B Fox.
      The repair of DNA double-stranded breaks relies on the homologous recombination repair pathway and is critical to cell function. However, this pathway can be lost in some cancers such as breast, ovarian, endometrial, pancreatic, and prostate cancers. Cancer cells with homologous recombination deficiency (HRD) are sensitive to targeted inhibition of poly-ADP ribose polymerase (PARP), a key component of alternative backup DNA repair pathways. Identifying patients with cancer with HRD biomarkers allows the identification of patients likely to benefit from PARP inhibitor therapies. In this study, we describe the causes of HRD, the underlying molecular changes resulting from HRD that form the basis of different molecular HRD assays, and discuss the issues around their clinical use. This overview is directed toward practicing pathologists wishing to be informed of this new predictive biomarker, as PARP inhibitors are increasingly used in standard care settings.
    Keywords:  HRD; PARPi; clinical sequencing; homologous recombination deficiency; homologous recombination repair; molecular diagnostics
    DOI:  https://doi.org/10.1016/j.modpat.2022.100049
  2. Nat Med. 2023 Feb 16.
    A biomarker-driven therapy for ovarian cancer.
    Karen O'Leary.
      
    Keywords:  Clinical trials; Ovarian cancer; Targeted therapies
    DOI:  https://doi.org/10.1038/d41591-023-00018-6
  3. Clin Epigenetics. 2023 Feb 14. 15(1): 24
    Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review.
    Simone Karlsson Terp, Malene Pontoppidan Stoico, Karen Dybkær, Inge Søkilde Pedersen.
      Patients diagnosed with epithelial ovarian cancer (OC) have a 5-year survival rate of 49%. For early-stage disease, the 5-year survival rate is above 90%. However, advanced-stage disease accounts for most cases as patients with early stages often are asymptomatic or present with unspecific symptoms, highlighting the need for diagnostic tools for early diagnosis. Liquid biopsy is a minimal invasive blood-based approach that utilizes circulating tumor DNA (ctDNA) shed from tumor cells for real-time detection of tumor genetics and epigenetics. Increased DNA methylation of promoter regions is an early event during tumorigenesis, and the methylation can be detected in ctDNA, accentuating the promise of methylated ctDNA as a biomarker for OC diagnosis. Many studies have investigated multiple methylation biomarkers in ctDNA from plasma or serum for discriminating OC patients from patients with benign diseases of the ovaries and/or healthy females. This systematic review summarizes and evaluates the performance of the currently investigated DNA methylation biomarkers in blood-derived ctDNA for early diagnosis of OC. PubMed's MEDLINE and Elsevier's Embase were systematically searched, and essential results such as methylation frequency of OC cases and controls, performance measures, as well as preanalytical factors were extracted. Overall, 29 studies met the inclusion criteria for this systematic review. The most common method used for methylation analysis was methylation-specific PCR, with half of the studies using plasma and the other half using serum. RASSF1A, BRCA1, and OPCML were the most investigated gene-specific methylation biomarkers, with OPCML having the best performance measures. Generally, methylation panels performed better than single gene-specific methylation biomarkers, with one methylation panel of 103,456 distinct regions and 1,116,720 CpGs having better performance in both training and validation cohorts. However, the evidence is still limited, and the promising methylation panels, as well as gene-specific methylation biomarkers highlighted in this review, need validation in large, prospective cohorts with early-stage asymptomatic OC patients to assess the true diagnostic value in a clinical setting.
    Keywords:  Biomarker; Cell-free DNA; Circulating tumor DNA; DNA methylation; Diagnosis; Epigenetics; Liquid biopsy; Ovarian cancer; Systematic review
    DOI:  https://doi.org/10.1186/s13148-023-01440-w
  4. Brief Bioinform. 2023 Feb 13. pii: bbad048. [Epub ahead of print]
    Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.
    Yuansong Zeng, Rui Yin, Mai Luo, Jianing Chen, Zixiang Pan, Yutong Lu, Weijiang Yu, Yuedong Yang.
      Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.
    Keywords:  contrastive learning; histopathological image; multi-modality; spatial clustering; spatial transcriptomics
    DOI:  https://doi.org/10.1093/bib/bbad048
  5. bioRxiv. 2023 Jan 31. pii: 2023.01.29.526115. [Epub ahead of print]
    Optimizing the design of spatial genomic studies.
    Andrew Jones, Diana Cai, Didong Li, Barbara E Engelhardt.
      Spatially-resolved genomic technologies have shown promise for studying the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. Thus, when performing spatial genomics experiments using multiple tissue slices, there is a need to select the tissue cross sections that will be maximally informative for the purposes of the experiment. In this work, we formalize the problem of experimental design for spatial genomics experiments, which we generalize into a problem class that we call structured batch experimental design . We propose approaches for optimizing these designs in two types of spatial genomics studies: one in which the goal is to construct a spatially-resolved genomic atlas of a tissue and another in which the goal is to localize a region of interest in a tissue, such as a tumor. We demonstrate the utility of these optimal designs, where each slice is a two-dimensional plane, on several spatial genomics datasets.
    DOI:  https://doi.org/10.1101/2023.01.29.526115
  6. Cancer Res. 2023 Feb 14. pii: CAN-22-1821. [Epub ahead of print]
    Spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface in long-term ovarian cancer survivors.
    Sammy Ferri-Borgogno, Ying Zhu, Jianting Sheng, Jared K Burks, Javier A Gomez, Kwong Kwok Wong, Stephen T C Wong, Samuel C Mok.
      Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in advanced HGSC patients. Also, increased APOE-LRP5 crosstalk occurred at the stroma-tumor interface in tumor tissues from STS compared to LTS. These findings were validated using multiplex immunohistochemistry. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor crosstalk signaling networks in the ovarian TME that are associated with long-term survival of HGSC patients. Further studies to confirm whether such crosstalk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-22-1821
  7. Genomics Proteomics Bioinformatics. 2023 Feb 13. pii: S1672-0229(23)00036-0. [Epub ahead of print]
    Patient Assessment and Therapy Planning Based on Homologous Recombination Repair Deficiency.
    Wenbin Li, Lin Gao, Xin Yi, Shuangfeng Shi, Jie Huang, Leming Shi, Xiaoyan Zhou, Lingying Wu, Jianming Ying.
      Defects in genes involved in the DNA damage response cause homologous recombination repair deficiency (HRD). HRD is found in a subgroup of patients with cancer with several tumor types, and it has a clinical relevance in cancer prevention and therapies. Accumulating evidence has identified HRD as a biomarker for assessing the therapeutic response of tumor cells to poly (ADP-ribose) polymerase inhibitors and platinum-based chemotherapies. Nevertheless, the biology of HRD is complex, and its applications and the benefits of different HRD biomarker assays are controversial. This is primarily due to inconsistencies in HRD assessments and definitions (gene-level tests, genomic scars, mutational signatures, or a combination of these methods) and difficulties in assessing the contribution of each genomic event. Therefore, we aimed to review the biological rationale and clinical evidence of HRD as a biomarker. This review provides a blueprint for the standardization and harmonization of HRD assessments.
    Keywords:  Biomarker; DNA damage response; Harmonization; Homologous recombination repair deficiency; Poly (ADP-ribose) polymerase inhibitor
    DOI:  https://doi.org/10.1016/j.gpb.2023.02.004
  8. Cancer Epidemiol Biomarkers Prev. 2023 Feb 15. OF1-OF8
    Methylation Signature Implicated in Immuno-Suppressive Activities in Tubo-Ovarian High-Grade Serous Carcinoma.
    Australian Ovarian Cancer Study Group
      BACKGROUND: Better understanding of prognostic factors in tubo-ovarian high-grade serous carcinoma (HGSC) is critical, as diagnosis confers an aggressive disease course. Variation in tumor DNA methylation shows promise predicting outcome, yet prior studies were largely platform-specific and unable to evaluate multiple molecular features.METHODS: We analyzed genome-wide DNA methylation in 1,040 frozen HGSC, including 325 previously reported upon, seeking a multi-platform quantitative methylation signature that we evaluated in relation to clinical features, tumor characteristics, time to recurrence/death, extent of CD8+ tumor-infiltrating lymphocytes (TIL), gene expression molecular subtypes, and gene expression of the ATP-binding cassette transporter TAP1.
    RESULTS: Methylation signature was associated with shorter time to recurrence, independent of clinical factors (N = 715 new set, hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.10-2.46; P = 0.015; N = 325 published set HR, 2.87; 95% CI, 2.17-3.81; P = 2.2 × 10-13) and remained prognostic after adjustment for gene expression molecular subtype and TAP1 expression (N = 599; HR, 2.22; 95% CI, 1.66-2.95; P = 4.1 × 10-8). Methylation signature was inversely related to CD8+ TIL levels (P = 2.4 × 10-7) and TAP1 expression (P = 0.0011) and was associated with gene expression molecular subtype (P = 5.9 × 10-4) in covariate-adjusted analysis.
    CONCLUSIONS: Multi-center analysis identified a novel quantitative tumor methylation signature of HGSC applicable to numerous commercially available platforms indicative of shorter time to recurrence/death, adjusting for other factors. Along with immune cell composition analysis, these results suggest a role for DNA methylation in the immunosuppressive microenvironment.
    IMPACT: This work aids in identification of targetable epigenome processes and stratification of patients for whom tailored treatment may be most beneficial.
    DOI:  https://doi.org/10.1158/1055-9965.EPI-22-0941
  9. Comput Biol Med. 2023 Feb 09. pii: S0010-4825(23)00099-9. [Epub ahead of print]155 106634
    scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.
    Yusuf Baran, Berat Doğan.
      Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
    Keywords:  Marker gene selection; Spatial transcriptomics; scRNA-seq
    DOI:  https://doi.org/10.1016/j.compbiomed.2023.106634
  10. J Transl Med. 2023 02 11. 21(1): 118
    Liquid biopsies: the future of cancer early detection.
    Siobhan Connal, James M Cameron, Alexandra Sala, Paul M Brennan, David S Palmer, Joshua D Palmer, Haley Perlow, Matthew J Baker.
      Cancer is a worldwide pandemic. The burden it imposes grows steadily on a global scale causing emotional, physical, and financial strains on individuals, families, and health care systems. Despite being the second leading cause of death worldwide, many cancers do not have screening programs and many people with a high risk of developing cancer fail to follow the advised medical screening regime due to the nature of the available screening tests and other challenges with compliance. Moreover, many liquid biopsy strategies being developed for early detection of cancer lack the sensitivity required to detect early-stage cancers. Early detection is key for improved quality of life, survival, and to reduce the financial burden of cancer treatments which are greater at later stage detection. This review examines the current liquid biopsy market, focusing in particular on the strengths and drawbacks of techniques in achieving early cancer detection. We explore the clinical utility of liquid biopsy technologies for the earlier detection of solid cancers, with a focus on how a combination of various spectroscopic and -omic methodologies may pave the way for more efficient cancer diagnostics.
    Keywords:  Cancer; Diagnostics; Early detection; Liquid biopsy; Multi-cancer
    DOI:  https://doi.org/10.1186/s12967-023-03960-8
  11. Nat Methods. 2023 Feb 16.
    SODB facilitates comprehensive exploration of spatial omics data.
    Zhiyuan Yuan, Wentao Pan, Xuan Zhao, Fangyuan Zhao, Zhimeng Xu, Xiu Li, Yi Zhao, Michael Q Zhang, Jianhua Yao.
      Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
    DOI:  https://doi.org/10.1038/s41592-023-01773-7
  12. Lung Cancer. 2023 Feb 01. pii: S0169-5002(23)00044-2. [Epub ahead of print]178 28-36
    Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer.
    Esther Visser, Sylvia A A M Genet, Remco P P A de Kock, Ben E E M van den Borne, Maggy Youssef-El Soud, Huub N A Belderbos, Gerben Stege, Marleen E A de Saegher, Susan C van 't Westeinde, Luc Brunsveld, Maarten A C Broeren, Daan van de Kerkhof, Birgit A L M Deiman, Federica Eduati, Volkher Scharnhorst.
      OBJECTIVES: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice.MATERIALS AND METHODS: In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination.
    RESULTS: Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively.
    CONCLUSION: In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete.
    Keywords:  Decision–support algorithm; Liquid biopsy; Lung cancer; Protein tumor markers; circulating tumor DNA
    DOI:  https://doi.org/10.1016/j.lungcan.2023.01.014
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