Functional cell profiling (FCP) of ~100,000 CTCs from multiple cancer types identifies morphologically distinguishable CTC subtypes within and between cancer types.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14553-e14553
Author(s):  
Gordon Vansant ◽  
Adam Jendrisak ◽  
Ramsay Sutton ◽  
Sarah Orr ◽  
David Lu ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Digital Pathology ◽  
Disease Patient ◽  
Therapy Response ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Multiple Cancer ◽  
Cluster Assignment ◽  
Cancer Types

e14553 Background: Different cancers subtypes can often be effectively treated with similar Rx classes (i.e. platinum or taxane Rx). Yet, within a disease patient therapy benefit can be variable. The origins of precision medicine derive from pathologic sub-stratification to guide therapy (e.g. SCLC vs. NSCLC). Using the Epic Sciences platform, we performed FPC analysis of ~100,000 single CTCs from multiple indications and sought to utilize high resolution digital pathology and machine learning to index metastatic cancers for the purpose of improving our understanding of therapy response and precision medicine. Methods: 92,300 CTCs underwent FCP analysis (single cell digital pathology features of cellular and sub-cellular morphometrics) were collected from prostate (1641 pts, 70,747 CTCs), breast (268 pts, 8,718 CTCs), NSCLC ( 110 pts, 1884 CTCs), SCLC ( 141 pts, 8,872 CTCs) and bladder (65 pts, 2079 CTCs) cancer pts. After pre-processing the raw data, a training set was balanced by sampling the same number of CTCs from each indication. K-means clustering was applied on the training set and optimized number of clusters were determined by using the elbow approach. After generating the clusters on the training set, the cluster centers were extracted from k-means, and used to train a k-Nearest Neighbor (k-NN) classifier to predict the cluster assignment for the remaining CTCs (test set). Results: The optimized # of clusters was 9. The % and characteristics of CTCs in each indication are listed below. BCa CTCs were more enriched in cluster c1, which had higher CK expression, while SCLC and some of mCRPC shared the small cell features (c5). Conclusions: Heterogeneous CTC phenotypic subtypes were observed across multiple indications. Each indication harbored subtype heterogeneity and shared clusters with other disease subtypes. Patient cluster subtype analysis to prognosis and therapy benefit are on-going. Analysis of linking of CTC subtypes genotypes (by single cell sequencing) and to patient survival on multiple indications is ongoing.[Table: see text]

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Vol 49 (D1) ◽  
pp. D1420-D1430
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Jun Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

scholarly journals Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Erdogan Taskesen ◽  
Sjoerd M. H. Huisman ◽  
Ahmed Mahfouz ◽  
Jesse H. Krijthe ◽  
Jeroen de Ridder ◽  
...  
Keyword(s):  
Therapy Response ◽  
Visual Exploration ◽  
Molecular Characteristics ◽  
Cancer Type ◽  
Breast Cancers ◽  
Data Types ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Cancer Types

Abstract The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

scholarly journals Deep Learning Features Encode Interpretable Morphologies within Histological Images

2021 ◽  
Author(s):  
Ali Foroughi pour ◽  
Brian White ◽  
Jonghanne Park ◽  
Todd Sheridan ◽  
Jeffrey Chuang
Keyword(s):  
Gene Expression ◽  
Digital Pathology ◽  
Colon Adenocarcinoma ◽  
Patient Characteristics ◽  
Multiple Cancer ◽  
Class Prediction ◽  
Linear Classifiers ◽  
Individual Gene ◽  
Cancer Types ◽  
Class Labels

Abstract Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture), which we show can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. We observe that many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC=97.1%±2.8% for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC=99.2%±0.12%). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values.

scholarly journals Single-cell epigenomics in cancer: charting a course to clinical impact

Epigenomics ◽  
2020 ◽  
Vol 12 (13) ◽  
pp. 1139-1151 ◽  
Author(s):  
Danielle R Bond ◽  
Kumar Uddipto ◽  
Anoop K Enjeti ◽  
Heather J Lee
Keyword(s):  
Single Cell ◽  
Patient Outcomes ◽  
Cancer Biology ◽  
Cell Types ◽  
Disease Relapse ◽  
Multiple Cancer ◽  
Heterogeneous Nature ◽  
Numerous Cell ◽  
Cancer Types ◽  
Exciting Possibility

Cancer is a disease of global epigenetic dysregulation. Mutations in epigenetic regulators are common events in multiple cancer types and epigenetic therapies are emerging as a treatment option in several malignancies. A major challenge for the clinical management of cancer is the heterogeneous nature of this disease. Cancers are composed of numerous cell types and evolve over time. This heterogeneity confounds decisions regarding treatment and promotes disease relapse. The emergence of single-cell epigenomic technologies has introduced the exciting possibility of linking genetic and transcriptional heterogeneity in the context of cancer biology. The next challenge is to leverage these tools for improved patient outcomes. Here we consider how single-cell epigenomic technologies may address the current challenges faced by cancer clinicians.

scholarly journals Deep Learning Features Encode Interpretable Morphologies within Histological Images

2021 ◽  
Author(s):  
Ali Foroughi Pour ◽  
Brian White ◽  
Jonghanne Park ◽  
Todd B. Sheridan ◽  
Jeffrey H. Chuang
Keyword(s):  
Gene Expression ◽  
Digital Pathology ◽  
Colon Adenocarcinoma ◽  
Patient Characteristics ◽  
Multiple Cancer ◽  
Class Prediction ◽  
Linear Classifiers ◽  
Individual Gene ◽  
Cancer Types ◽  
Class Labels

ABSTRACTConvolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture), which we show can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. We observe that many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC=97.1% ± 2.8% for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC=99.2% ± 0.12%). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values.

High Diagnostic and Prognostic Value of miRNAs Compared with the Carcinoembryonic Antigen as a Traditional Tumor Marker

2021 ◽  
Vol 21 ◽  
Author(s):  
Neda Yaghoubi ◽  
Farnaz Zahedi Avval ◽  
Majid Khazaei ◽  
Amirhossein Sahebkar ◽  
Seyed Hamid Aghaee-Bakhtiari
Keyword(s):  
Tumor Marker ◽  
Carcinoembryonic Antigen ◽  
Survival Rates ◽  
Therapy Response ◽  
Multiple Cancer ◽  
Diagnostic Significance ◽  
Therapeutic Goals ◽  
Cancer Types ◽  
Diagnostic And Prognostic Value ◽  
Limited Sensitivity

: A significant challenge in cancer detection and treatment is early diagnosis and accurate prognosis of the disease that enables effective therapies and interventions to improve the patient’s condition. Up to now, many parts of research have tended to focus on the carcinoembryonic antigen (CEA) to detect cancers and estimate the survival rates of patients with multiple cancer types, including colorectal, breast, non-small cell lung, and pancreas cancer. Limited sensitivity and specificity of this traditional tumor marker make it an inappropriate biomarker to diagnose cancer, especially in the early stages, while several lines of research have introduced miRNAs as reliable indicators of tumor initiation, development, and therapy response. Indeed, miRNAs have unique properties that provide considerable benefits, such as discriminating benign diseases from malignancies, prediction of cancer possibility and progress, checking sensitivity to treatment, and initial detection of tumors. This review summarizes the relationships between miRNAs and CEA, the diagnostic significance of CEA in combination with miRNAs, and the distinct advantages of miRNAs over CEA as tumor biomarkers. Advancement in our current understanding of miRNAs is essential to discover new and effective biomarkers for diagnostic, prognostic, and therapeutic goals of cancer patients.

scholarly journals TMExplorer: A Tumour Microenvironment Single-cell RNAseq Database and Search Tool

2020 ◽  
Author(s):  
Erik Christensen ◽  
Alaine Naidas ◽  
Mia Husic ◽  
Parisa Shooshtari
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
R Package ◽  
Tumour Microenvironment ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Stromal Fibroblasts ◽  
Search Tool ◽  
Cancer Types ◽  
Easy Integration

ABSTRACTTumour microenvironments (TME) contain a variety of cells including but not limited to stromal fibroblasts, endothelial cells, immune cells, malignant cells, and cells of the tissues of origin, whose interactions likely influence tumour behaviour and response to cancer treatment. The specific composition of the TME can be elucidated using single-cell RNA sequencing (scRNA-seq) by measuring expression profiles of individual cells. Several scRNA-seq datasets from multiple cancer types have been published in recent years, yet we still lack a comprehensive database for the collection and presentation of TME data from these studies in an easily accessible format. We have thus built a database of TME scRNA-seq data, containing 21 TME scRNA-seq datasets from 12 different cancer types. We have also created an R package called TMExplorer, which provides an interface to easily search and access all available datasets and their metadata. Data and metadata are kept in a consistent format across all datasets, with multiple expression formats available depending on the use case. Users can view a table of metadata and select individual datasets or filter them by specific characteristics. Users may also select a specific type of cancer and view all published scRNA-seq data for that cancer type available in our database. Users are provided with an option to save the data in multiple formats in order to view or process it outside of R. Thus, the TMExplorer database and search tool allows for thorough examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Rongbin Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

AbstractCancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors, but also posed computational challenges on how to integrate and utilize the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly two million cells from 76 high-quality tumor datasets across 28 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, malignant cell classification, cell clustering, cell-type annotation, differential expression analysis, and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching, and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

scholarly journals Development of Molecularly Targeted Agents and Immunotherapies in Glioblastoma: A Personalized Approach

2018 ◽  
Vol 12 ◽  
pp. 117955491875907 ◽  
Author(s):  
Niamh Coleman ◽  
Malaka Ameratunga ◽  
Juanita Lopez
Keyword(s):  
Precision Medicine ◽  
Complex Disease ◽  
Targeted Agents ◽  
Multiple Cancer ◽  
Genomic Landscape ◽  
Molecularly Targeted Agents ◽  
Critical Issues ◽  
Cancer Types ◽  
Personalized Approach ◽  
The Impact

Over the past decade, precision cancer medicine has driven major advances in the management of advanced solid tumours with the identification and targeting of putative driver aberrations transforming the clinical outcomes across multiple cancer types. Despite pivotal advances in the characterization of genomic landscape of glioblastoma, targeted agents have shown minimal efficacy in clinical trials to date, and patient survival remains poor. Immunotherapy strategies similarly have had limited success. Multiple deficiencies still exist in our knowledge of this complex disease, and further research is urgently required to overcome these critical issues. This review traces the path undertaken by the different therapeutics assessed in glioblastoma and the impact of precision medicine in this disease. We highlight challenges for precision medicine in glioblastoma, focusing on the issues of tumour heterogeneity, pharmacokinetic-pharmacodynamic optimization and outline the modern hypothesis-testing strategies being undertaken to address these key challenges.

Anticancer Immunotoxins, Challenges, and Approaches

2020 ◽  
Vol 26 ◽  
Author(s):  
Maryam Dashtiahangar ◽  
Leila Rahbarnia ◽  
Safar Farajnia ◽  
Arash Salmaninejad ◽  
Arezoo Gowhari Shabgah ◽  
...  
Keyword(s):  
Therapeutic Strategy ◽  
Antibody Fragment ◽  
Clinical Use ◽  
Multiple Cancer ◽  
Main Obstacle ◽  
Cancer Types ◽  
Recombinant Immunotoxins ◽  
Cancerous Cells ◽  
High Immunogenicity ◽  
Novel Therapeutic

: The development of recombinant immunotoxins (RITs) as a novel therapeutic strategy has made a revolution in the treatment of cancer. RITs are resulting from the fusion of antibodies to toxin proteins for targeting and eliminating cancerous cells by inhibiting protein synthesis. Despite indisputable outcomes of RITs regarding inhibiting multiple cancer types, high immunogenicity has been known as the main obstacle in the clinical use of RITs. Various strategies have been proposed to overcome these limitations, including immunosuppressive therapy, humanization of the antibody fragment moiety, generation of immunotoxins originated from endogenous human cytotoxic enzymes, and modification of the toxin moiety to escape the immune system. This paper devoted to reviewing recent advances in the design of immunotoxins with lower immunogenicity.

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