bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023‒12‒03
eleven papers selected by
Sergio Marchini, Humanitas Research
  1. Serial circulating-tumor DNA analysis with a tumor naïve next-generation sequencing panel detects minimal residual disease and predicts outcome in ovarian cancer.
  2. scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.
  3. Lighting up cancer initiation with mutant p53 reporters.
  4. STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.
  5. Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability.
  6. Preoperative ctDNA Levels Are Associated With Poor Overall Survival in Patients With Ovarian Cancer.
  7. Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer.
  8. Decoding the tumor microenvironment with spatial technologies.
  9. Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening.
  10. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.
  11. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.
  1. Cancer Res. 2023 Dec 01.
    Serial circulating-tumor DNA analysis with a tumor naïve next-generation sequencing panel detects minimal residual disease and predicts outcome in ovarian cancer.
    Jinho Heo, Yoo-Na Kim, Saeam Shin, Kyunglim Lee, Ji-Hyun Lee, Yong Jae Lee, Zisun Choi, Jihyang Park, Seungki Min, Sang Wun Kim, Jong Rak Choi, Sunghoon Kim, Seung-Tae Lee, Jung-Yun Lee.
      Circulating tumor DNA (ctDNA) may aid in personalizing ovarian cancer therapeutic options. Here, we aimed to assess the clinical utility of serial ctDNA testing using tumor-naïve, small-sized next-generation sequencing (NGS) panels. A total of 296 patients, including 201 with ovarian cancer and 95 with benign or borderline disease, were enrolled. Samples were collected at baseline (initial diagnosis or surgery) and every 3 months after that, resulting in a total of 811 blood samples. Patients received adjuvant therapy based on the current standard of care. Cell-free DNA was extracted and sequenced using an NGS panel of nine genes: TP53, BRCA1, BRCA2, ARID1A, CCNE1, KRAS, MYC, PIK3CA, and PTEN. Pathogenic somatic mutations were identified in 69.2% (139/201) of patients with ovarian cancer at baseline but not in those with benign or borderline disease. Detection of ctDNA at baseline and/or at 6 months follow-up was predictive of Progression-free survival (PFS). PFS was significantly poorer in patients with detectable pathogenic mutations at baseline that persisted at follow-up than in patients that converted from having detectable ctDNA at baseline to being undetectable at follow-up; survival did not differ between patients without pathogenic ctDNA mutations in baseline or follow-up samples and those that converted from ctDNA positive to negative. Disease recurrence was also detected earlier with ctDNA than with conventional radiological assessment or CA125 monitoring. These findings demonstrate that serial ctDNA testing could effectively monitor patients and detect minimal residual disease, facilitating early detection of disease progression and tailoring of adjuvant therapies for ovarian cancer treatment.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-23-1429
  2. BMC Bioinformatics. 2023 Nov 27. 24(1): 445
    scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.
    Valentina Nale, Alice Chiodi, Noemi Di Nanni, Ingrid Cifola, Marco Moscatelli, Cinzia Cocola, Matteo Gnocchi, Eleonora Piscitelli, Ada Sula, Ileana Zucchi, Rolland Reinbold, Luciano Milanesi, Alessandra Mezzelani, Paride Pelucchi, Ettore Mosca.
      INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data.RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics.
    CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.
    Keywords:  Cancer; Cell identity; Single-cell transcriptomics; Tumor heterogeneity
    DOI:  https://doi.org/10.1186/s12859-023-05563-y
  3. Nat Rev Cancer. 2023 Nov 30.
    Lighting up cancer initiation with mutant p53 reporters.
    Xiwen Tang.
      
    DOI:  https://doi.org/10.1038/s41568-023-00649-4
  4. Genome Biol. 2023 Nov 30. 24(1): 273
    STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.
    Limin Chen, Darwin Chang, Bishal Tandukar, Delahny Deivendran, Joanna Pozniak, Noel Cruz-Pacheco, Raymond J Cho, Jeffrey Cheng, Iwei Yeh, Chris Marine, Boris C Bastian, Andrew L Ji, A Hunter Shain.
      Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.
    DOI:  https://doi.org/10.1186/s13059-023-03121-6
  5. Proc Natl Acad Sci U S A. 2023 Dec 05. 120(49): e2310367120
    Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability.
    Yunlong Cao, Yali Bai, Tianjiao Yuan, Liyang Song, Yu Fan, Liuhao Ren, Weiliang Song, Jiahui Peng, Ran An, Qingqing Gu, Yinghui Zheng, Xiaoliang Sunney Xie.
      Existing single-cell bisulfite-based DNA methylation analysis is limited by low DNA recovery, and the measurement of 5hmC at single-base resolution remains challenging. Here, we present a bisulfite-free single-cell whole-genome 5mC and 5hmC profiling technique, named Cabernet, which can characterize 5mC and 5hmC at single-base resolution with high genomic coverage. Cabernet utilizes Tn5 transposome for DNA fragmentation, which enables the discrimination between different alleles for measuring hemi-methylation status. Using Cabernet, we revealed the 5mC, hemi-5mC and 5hmC dynamics during early mouse embryo development, uncovering genomic regions exclusively governed by active or passive demethylation. We show that hemi-methylation status can be used to distinguish between pre- and post-replication cells, enabling more efficient cell grouping when integrated with 5mC profiles. The property of Tn5 naturally enables Cabernet to achieve high-throughput single-cell methylome profiling, where we probed mouse cortical neurons and embryonic day 7.5 (E7.5) embryos, and constructed the library for thousands of single cells at high efficiency, demonstrating its potential for analyzing complex tissues at substantially low cost. Together, we present a way of high-throughput methylome and hydroxymethylome detection at single-cell resolution, enabling efficient analysis of the epigenetic status of biological systems with complicated nature such as neurons and cancer cells.
    Keywords:  5hmC sequencing; 5mC sequencing; bisulfite-free conversion; single-cell
    DOI:  https://doi.org/10.1073/pnas.2310367120
  6. Cancer Genomics Proteomics. 2023 Dec;20(6suppl): 763-770
    Preoperative ctDNA Levels Are Associated With Poor Overall Survival in Patients With Ovarian Cancer.
    Arturas Dobilas, Yilun Chen, Christian Brueffer, Pia Leandersson, Lao H Saal, Christer Borgfeldt.
      BACKGROUND/AIM: Circulating tumor DNA (ctDNA), which is shed from cancer cells into the bloodstream, offers a potential minimally invasive approach for cancer diagnosis and monitoring. This research aimed to assess the preoperative ctDNA levels in ovarian tumors patients' plasma and establish correlations with clinicopathological parameters and patient prognosis.PATIENTS AND METHODS: Tumor DNA was extracted from ovarian tumor tissue from 41 patients. Targeted sequencing using a panel of 127 genes recurrently mutated in cancer was performed to identify candidate somatic mutations in the tumor DNA. SAGAsafe digital PCR (dPCR) assays targeting the candidate mutations were used to measure ctDNA levels in patient plasma samples, obtained prior to surgery, to evaluate ctDNA levels in terms of mutant copy number/ml and variant allele frequency.
    RESULTS: Somatic mutations were found in 24 tumor samples, 17 of which were from ovarian cancer patients. The most frequently mutated gene was TP53. Preoperative plasma ctDNA levels were detected in 14 of the 24 patients. With higher stage, plasma ctDNA mutant concentration increased (p for trend <0.001). The overall survival of cancer patients with more than 10 ctDNA mutant copies/ml in plasma was significantly worse (p=0.008).
    CONCLUSION: Pre-operative ctDNA measurement in ovarian cancer patients' plasma holds promise as a predictive biomarker for tumor staging and prognosis.
    Keywords:  ctDNA; ovarian cancer; overall survival
    DOI:  https://doi.org/10.21873/cgp.20423
  7. Nat Commun. 2023 Nov 27. 14(1): 7780
    Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer.
    Tumor Profiler Consortium
      Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.
    DOI:  https://doi.org/10.1038/s41467-023-43387-9
  8. Nat Immunol. 2023 Dec;24(12): 1982-1993
    Decoding the tumor microenvironment with spatial technologies.
    Logan A Walsh, Daniela F Quail.
      Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
    DOI:  https://doi.org/10.1038/s41590-023-01678-9
  9. Chem Sci. 2023 Nov 15. 14(44): 12747-12766
    Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening.
    Yifeng Tang, Jeremiah Y Kim, Carman K M Ip, Azadeh Bahmani, Qing Chen, Matthew G Rosenberger, Aaron P Esser-Kahn, Andrew L Ferguson.
      The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.
    DOI:  https://doi.org/10.1039/d3sc03613h
  10. Nat Commun. 2023 Dec 01. 14(1): 7930
    Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.
    Zixiang Zhou, Yunshan Zhong, Zemin Zhang, Xianwen Ren.
      Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
    DOI:  https://doi.org/10.1038/s41467-023-43600-9
  11. Nat Commun. 2023 Nov 29. 14(1): 7848
    Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.
    Xiaomeng Wan, Jiashun Xiao, Sindy Sing Ting Tam, Mingxuan Cai, Ryohichi Sugimura, Yang Wang, Xiang Wan, Zhixiang Lin, Angela Ruohao Wu, Can Yang.
      The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.
    DOI:  https://doi.org/10.1038/s41467-023-43629-w
This page is being maintained by Thomas Krichel. It was last updated on 2023‒12‒05 at 9:58.