bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023–01–22
nine papers selected by
Sergio Marchini, Humanitas Research
  1. DNA Damage Response Alterations in Ovarian Cancer: From Molecular Mechanisms to Therapeutic Opportunities.
  2. Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST.
  3. Can circulating cell free DNA be a promising marker in ovarian cancer? - a genome-scale profiling study in a single institution.
  4. Shallow Whole-Genome Sequencing of Cell-Free DNA (cfDNA) Detects Epithelial Ovarian Cancer and Predicts Patient Prognosis.
  5. DIST: spatial transcriptomics enhancement using deep learning.
  6. MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.
  7. National Cancer Institute Combination Therapy Platform Trial with Molecular Analysis for Therapy Choice (ComboMATCH).
  8. DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation.
  9. Epigenetic Regulations in Autoimmunity and Cancer: from Basic Science to Translational Medicine.
  1. Cancers (Basel). 2023 Jan 10. pii: 448. [Epub ahead of print]15(2):
    DNA Damage Response Alterations in Ovarian Cancer: From Molecular Mechanisms to Therapeutic Opportunities.
    María Ovejero-Sánchez, Rogelio González-Sarmiento, Ana Belén Herrero.
      The DNA damage response (DDR), a set of signaling pathways for DNA damage detection and repair, maintains genomic stability when cells are exposed to endogenous or exogenous DNA-damaging agents. Alterations in these pathways are strongly associated with cancer development, including ovarian cancer (OC), the most lethal gynecologic malignancy. In OC, failures in the DDR have been related not only to the onset but also to progression and chemoresistance. It is known that approximately half of the most frequent subtype, high-grade serous carcinoma (HGSC), exhibit defects in DNA double-strand break (DSB) repair by homologous recombination (HR), and current evidence indicates that probably all HGSCs harbor a defect in at least one DDR pathway. These defects are not restricted to HGSCs; mutations in ARID1A, which are present in 30% of endometrioid OCs and 50% of clear cell (CC) carcinomas, have also been found to confer deficiencies in DNA repair. Moreover, DDR alterations have been described in a variable percentage of the different OC subtypes. Here, we overview the main DNA repair pathways involved in the maintenance of genome stability and their deregulation in OC. We also recapitulate the preclinical and clinical data supporting the potential of targeting the DDR to fight the disease.
    Keywords:  ATM; ATR; DNA damage response; DNA repair; base excision repair; direct reversal repair; homologous recombination; mismatch repair; nonhomologous end joining; nucleotide excision repair; ovarian cancer; p53
    DOI:  https://doi.org/10.3390/cancers15020448
  2. Nat Commun. 2023 Jan 18. 14(1): 296
    Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST.
    Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Yeong, Jin Liu.
      Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.
    DOI:  https://doi.org/10.1038/s41467-023-35947-w
  3. J Ovarian Res. 2023 Jan 14. 16(1): 11
    Can circulating cell free DNA be a promising marker in ovarian cancer? - a genome-scale profiling study in a single institution.
    Huimei Zhou, Xueying Zhang, Qian Liu, Jiaxin Yang, Jian Bai, Min Yin, Dongyan Cao, Qingzheng Zhang, Lu Zheng.
       BACKGROUND: Cell-free DNA (cfDNA) is emerging as a potential biomarker for the detection of ovarian cancer (OC). Recently, we reported a method based upon cfDNA whole-genome sequencing data including the nucleosome distribution (nucleosome footprinting NF), terminal signature sequence (motif), DNA fragmentation (fragment), and copy number variation (CNV).In the present study, we explored whether multiomics early screening technology in cfDNA can be applied for early screening of ovarian cancer.
    METHODS: Fifty-nine patients with OC and 100 healthy controls were included in this prospective study. Cell-free DNA was extracted from plasma and analyzed by low-pass whole-genome sequencing. Genomic features were obtained for all samples of the cohort, including copy number variation (CNV), 5'-end motifs, fragmentation profiles, and nucleosome footprinting (NF). An integrated scoring system termed the OC score was developed based on the performance of these four features.
    RESULTS: All four features showed diagnostic potential for OC. Based on the unique genome features of cfDNA, the OC score has high accuracy in distinguishing OC patients from healthy controls (AUC 97.7%; sensitivity 94.7%; specificity 98.0%) as a new comprehensive diagnostic method for OC. The OC score showed a gradual trend from healthy controls to OC patients with different stages, especially for early OC monitoring of concern, which achieved a satisfactory sensitivity (85.7%) at a high specificity.
    CONCLUSIONS: This is the first study evaluating the potential of cell-free DNA for the diagnosis of primary OC using multidimensional early screening technology. We present a promising method to increase the accuracy of prediction in patients with OC.
    DOI:  https://doi.org/10.1186/s13048-022-01068-z
  4. Cancers (Basel). 2023 Jan 15. pii: 530. [Epub ahead of print]15(2):
    Shallow Whole-Genome Sequencing of Cell-Free DNA (cfDNA) Detects Epithelial Ovarian Cancer and Predicts Patient Prognosis.
    Seong Eun Bak, Hanwool Kim, Jung Yoon Ho, Eun-Hae Cho, Junnam Lee, Sung Min Youn, Seong-Woo Park, Mi-Ryung Han, Soo Young Hur, Sung Jong Lee, Youn Jin Choi.
      Despite the progress in diagnostics and therapeutics, epithelial ovarian cancer (EOC) remains a fatal disease. Using shallow whole-genome sequencing of plasma cell-free DNA (cfDNA), we investigated biomarkers that could detect EOC and predict survival. Plasma cfDNA from 40 EOC patients and 20 healthy subjects were analyzed by shallow whole-genome sequencing (WGS) to identify copy number variations (CNVs) and determine the Z-scores of genes. In addition, we also calculated the genome-wide scores (Gi scores) to quantify chromosomal instability. We found that the Gi scores could distinguish EOC patients from healthy subjects and identify various EOC histological subtypes (e.g., high-grade serous carcinoma). In addition, we characterized EOC CNVs and demonstrated a relationship between RAB25 amplification (alone or with CA125), and disease-free survival and overall survival. This study identified RAB25 amplification as a predictor of EOC patient survival. Moreover, we showed that Gi scores could detect EOC. These data demonstrated that cfDNA, detected by shallow WGS, represented a potential tool for diagnosing EOC and predicting its prognosis.
    Keywords:  cfDNA; epithelial ovarian cancer; plasma; prognosis; shallow whole-genome sequencing
    DOI:  https://doi.org/10.3390/cancers15020530
  5. Brief Bioinform. 2023 Jan 18. pii: bbad013. [Epub ahead of print]
    DIST: spatial transcriptomics enhancement using deep learning.
    Yanping Zhao, Kui Wang, Gang Hu.
      Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we present DIST, a deep learning-based method that imputes the gene expression profiles on unmeasured locations and enhances the gene expression for both original measured spots and imputed spots by self-supervised learning and transfer learning. We evaluate the performance of DIST for imputation, clustering, differential expression analysis and functional enrichment analysis. The results show that DIST can impute the gene expression accurately, enhance the gene expression for low-quality data, help detect more biological meaningful differentially expressed genes and pathways, therefore allow for deeper insights into the biological processes.
    Keywords:  denoising; imputation; self-supervised learning; spatial transcriptomics; transfer learning
    DOI:  https://doi.org/10.1093/bib/bbad013
  6. Brief Bioinform. 2023 Jan 18. pii: bbad015. [Epub ahead of print]
    MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.
    Dario Romagnoli, Agostina Nardone, Francesca Galardi, Marta Paoli, Francesca De Luca, Chiara Biagioni, Gian Marco Franceschini, Marta Pestrin, Giuseppina Sanna, Erica Moretti, Francesca Demichelis, Ilenia Migliaccio, Laura Biganzoli, Luca Malorni, Matteo Benelli.
      DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.
    Keywords:  DNA-methylation; bioinformatics; breast cancer; cancer; cell-free DNA; deconvolution; liquid biopsy; precision medicine; tumor content; tumor subtype
    DOI:  https://doi.org/10.1093/bib/bbad015
  7. Clin Cancer Res. 2023 Jan 20. pii: CCR-22-3334. [Epub ahead of print]
    National Cancer Institute Combination Therapy Platform Trial with Molecular Analysis for Therapy Choice (ComboMATCH).
    Funda Meric-Bernstam, James M Ford, Peter J O'Dwyer, Geoffrey I Shapiro, Lisa M McShane, Boris Freidlin, Roisin E O'Cearbhaill, Suzanne George, Julia Glade Bender, Gary H Lyman, James V Tricoli, David Patton, Stanley R Hamilton, Robert J Gray, Douglas S Hawkins, Bhanumati Ramineni, Keith T Flaherty, Petros Grivas, Timothy A Yap, Jordan Berlin, James H Doroshow, Lyndsay N Harris, Jeffrey A Moscow.
      Over the past decade, multiple trials, including the precision medicine trial NCI-MATCH (National Cancer Institute-Molecular Analysis for Therapy Choice, EAY131, NCT02465060) have sought to determine if treating cancer based on specific genomic alterations is effective, irrespective of the cancer histology. Although many therapies are now approved for the treatment of cancers harboring specific genomic alterations, most patients do not respond to therapies targeting a single alteration. Further, when antitumor responses do occur, they are often not durable due to the development of drug resistance. Therefore, there is a great need to identify rational combination therapies that may be more effective. To address this need, the National Cancer Institute (NCI) and National Clinical Trials Network have developed NCI-ComboMATCH, the successor to NCI-MATCH. Like the original trial, NCI-ComboMATCH is a signal-seeking study. The goal of ComboMATCH is to overcome drug resistance to single-agent therapy and/or utilize novel synergies to increase efficacy by developing genomically-directed combination therapies, supported by strong preclinical in vivo evidence. While NCI-MATCH was mainly comprised of multiple single-arm studies, NCI-ComboMATCH tests combination therapy, evaluating both combination of targeted agents as well as combinations of targeted therapy with chemotherapy. While NCI-MATCH was histology agnostic with selected tumor exclusions, ComboMATCH has histology-specific and histology-agnostic arms. While NCI-MATCH consisted of single arm studies, ComboMATCH utilizes single-arm as well as randomized designs. NCI-MATCH had a separate, parallel Pediatric MATCH trial, whereas ComboMATCH will include children within the same trial. We present rationale, scientific principles, study design and logistics supporting the ComboMATCH study.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-22-3334
  8. Nat Commun. 2023 Jan 18. 14(1): 287
    DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation.
    Yunyun An, Xin Zhao, Ziteng Zhang, Zhaohua Xia, Mengqi Yang, Li Ma, Yu Zhao, Gang Xu, Shunda Du, Xiang'an Wu, Shuowen Zhang, Xin Hong, Xin Jin, Kun Sun.
      Plasma cell-free DNA (cfDNA) are small molecules generated through a non-random fragmentation procedure. Despite commendable translational values in cancer liquid biopsy, however, the biology of cfDNA, especially the principles of cfDNA fragmentation, remains largely elusive. Through orientation-aware analyses of cfDNA fragmentation patterns against the nucleosome structure and integration with multidimensional functional genomics data, here we report a DNA methylation - nuclease preference - cutting end - size distribution axis, demonstrating the role of DNA methylation as a functional molecular regulator of cfDNA fragmentation. Hence, low-level DNA methylation could increase nucleosome accessibility and alter the cutting activities of nucleases during DNA fragmentation, which further leads to variation in cutting sites and size distribution of cfDNA. We further develop a cfDNA ending preference-based metric for cancer diagnosis, whose performance has been validated by multiple pan-cancer datasets. Our work sheds light on the molecular basis of cfDNA fragmentation towards broader applications in cancer liquid biopsy.
    DOI:  https://doi.org/10.1038/s41467-023-35959-6
  9. Eur J Immunol. 2023 Jan 16. e2048980
    Epigenetic Regulations in Autoimmunity and Cancer: from Basic Science to Translational Medicine.
    Li Hui, Zhao Ziyue, Liu Chao, Yu Bin, Li Aoyu, Wu Haijing.
      Epigenetics, as a discipline that aims to explain the differential expression of phenotypes arising from the same gene sequence and the heritability of epigenetic expression, has received much attention in medicine. Epigenetic mechanisms are constantly being discovered, including DNA methylation, histone modifications, noncoding RNAs and m6A. The immune system mainly achieves immune response through the differentiation and functional expression of immune cells, in which epigenetic modification will have an important impact. Because of the immune infiltration in tumor microenvironment, immunotherapy has become a research hotspot in tumor therapy. Epigenetics will play an important role in autoimmune diseases and cancers through immunology. An increasing number of drugs targeting epigenetic mechanisms, like DNA methyltransferase inhibitors, histone deacetylase inhibitors, and drug combinations are being evaluated in clinical trials for treatment of various cancers (including leukaemia and osteosarcoma) and autoimmune diseases (systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis). This review summarized the progress of epigenetic regulation for cancers and autoimmune diseases to date, shedding light on potential therapeutic strategies. This article is protected by copyright. All rights reserved.
    Keywords:  Autoimmune diseases; Cancers; Epigenetic drugs; Epigenetic modifications
    DOI:  https://doi.org/10.1002/eji.202048980
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:30.