scholarly journals Single-cell analysis of hepatoblastoma identifies distinct tumor cell signatures that predict susceptibility to chemotherapy using patient-specific tumor spheroids

2021 ◽  
Author(s):  
Hanbing Song ◽  
Simon Bucher ◽  
Katherine Rosenberg ◽  
Margaret Tsui ◽  
Deviana Burhan ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Tumor Heterogeneity ◽  
Single Cell Analysis ◽  
Primary Liver Cancer ◽  
Patient Specific ◽  
Precision Oncology ◽  
Cell Of Origin ◽  
Specific Tumor ◽  
Transcriptomic Signature

Pediatric hepatoblastoma (HB) is the most common primary liver cancer in infants and children. Studies of HB that focus exclusively on tumor cells demonstrate sparse somatic mutations and a common cell of origin, the hepatoblast, across patients. In contrast to the homogeneity these studies would suggest, HB tumors have a high degree of heterogeneity that can portend poor prognosis. In this study, we used single- cell genomic techniques to analyze resected human pediatric HB specimens. This study establishes that tumor heterogeneity can be defined by the relative proportions of five distinct subtypes of tumor cells. Notably, patient-derived HB spheroid cultures predict differential responses to treatment based on the transcriptomic signature of each tumor, suggesting a path forward for precision oncology for these tumors. Collectively, these results define HB tumor heterogeneity with single-cell resolution and demonstrate that patient-derived spheroids can be used to evaluate responses to chemotherapy.

scholarly journals Single-Cell Analysis of Circulating Tumor Cells as a Window into Tumor Heterogeneity

2016 ◽  
Vol 81 ◽  
pp. 269-274 ◽  
Author(s):  
David T. Miyamoto ◽  
David T. Ting ◽  
Mehmet Toner ◽  
Shyamala Maheswaran ◽  
Daniel A. Haber
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Tumor Heterogeneity ◽  
Single Cell Analysis ◽  
Cell Analysis

scholarly journals Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform

2020 ◽  
pp. 736-748
Author(s):  
Mireia Crispin-Ortuzar ◽  
Marcel Gehrung ◽  
Stephan Ursprung ◽  
Andrew B. Gill ◽  
Anne Y. Warren ◽  
...  
Keyword(s):  
Open Source ◽  
Tumor Heterogeneity ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Patient Specific ◽  
Sampling Location ◽  
Precision Oncology ◽  
Similarity Coefficients ◽  
Tissue Samples ◽  
Manual Intervention

PURPOSE Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data. METHODS We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional–printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows. RESULTS We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSION Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.

Integrated multi-omic analysis to reveal comprehensive tumor heterogeneity and novel immunophenotypic classification in primary liver cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15662-e15662
Author(s):  
Qi Zhang ◽  
Yu Lou ◽  
Xueli Bai ◽  
Tingbo Liang
Keyword(s):  
Tumor Heterogeneity ◽  
Single Cell Analysis ◽  
Primary Liver Cancer ◽  
Treatment Strategies ◽  
Local Immunity ◽  
Expression Levels ◽  
Chain Reactions ◽  
Prognostic Prediction ◽  
Practice Methods

e15662 Background: Hepatocellular carcinoma (HCC) is heterogeneous,especially in multifocal tumors, which decreases the efficacy of clinical treatments. Understanding tumor heterogeneity is critical when developingnovel treatment strategies. However, a comprehensive investigation of tumor heterogeneity in HCC is lacking, and the available evidence regarding tumor heterogeneity has not ledto improvements inclinical practice. Methods: We harvested 42 samples from eight HCC patients and evaluatedtumor heterogeneity using whole-exome sequencing, RNA sequencing, mass spectrometry-based proteomics and metabolomics, cytometry by time-of-flight, and single-cell analysis. Immunohistochemistry and quantitative polymerase chain reactions (qPCRs) were performed to confirm the expression levels of genes. Results: Tumor heterogeneity is considerable with regard to the genomes, transcriptomes, proteomes, and metabolomes of lesions and tumors. The immune status oftheHCC microenvironment had a relatively low level of heterogeneity. Targeting local immunity could be a suitable intervention with balanced precision and practicability. By clustering immune cells in the HCC microenvironment, we identified three distinctive HCC subtypes with immunocompetent, immunodeficient, and immunosuppressive features. We further revealed the specific metabolic features and cytokine/chemokine expression levels of the different subtypes. Determiningthe expression levels of PTPRCand FOXP3using qPCR and immunohistochemistry in two independent HCC cohorts facilitated the correct classification of HCC patients and the prediction of their prognosis. Conclusions: There is comprehensive intratumoral and intertumoral heterogeneity inall dimensions ofHCC. Based on the results, we propose a novel immunophenotypic classification of HCCs that facilitates prognostic prediction and may support decision making with regard to the choice of therapy.

scholarly journals Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer

2021 ◽  
Author(s):  
Tania Velletri ◽  
Carlo Emanuele Villa ◽  
Domenica Cilli ◽  
Bianca Barzaghi ◽  
Pietro Lo Riso ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Single Cell ◽  
Single Cells ◽  
Unmet Need ◽  
3D Culture ◽  
Patient Specific ◽  
Precision Oncology ◽  
Cell Subpopulations ◽  
Cancer Spheroids ◽  
2D And 3D

AbstractHigh Grade Serous Ovarian cancer (HGSOC) is a major unmet need in oncology, due to its precocious dissemination and the lack of meaningful human models for the investigation of disease pathogenesis in a patient-specific manner. To overcome this roadblock, we present a new method to isolate and grow single cells directly from patients’ metastatic ascites, establishing the conditions for propagating them as 3D cultures that we refer to as single cell-derived metastatic ovarian cancer spheroids (sMOCS). By single cell RNA sequencing (scRNAseq) we define the cellular composition of metastatic ascites and trace its propagation in 2D and 3D culture paradigms, finding that sMOCS retain and amplify key subpopulations from the original patients’ samples and recapitulate features of the original metastasis that do not emerge from classical 2D culture, including retention of individual patients’ specificities. By enabling the enrichment of uniquely informative cell subpopulations from HGSOC metastasis and the clonal interrogation of their diversity at the functional and molecular level, this method provides a powerful instrument for precision oncology in ovarian cancer.

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