Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling
24 Oct 2012-Science Translational Medicine (American Association for the Advancement of Science)-Vol. 4, Iss: 157
TL;DR: A predictor for survival in estrogen receptor–negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures is devised.
Abstract: Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.
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TL;DR: It is suggested that bold approaches to drug development, harnessing the adaptive properties of the immune-microenvironment while limiting those of the tumor, combined with advances in clinical trial-design, will improve patient outcome.
1,800 citations
Cites methods from "Quantitative Image Analysis of Cell..."
...Through the integration of gene expression and somatic copy number data with automated microenvironment analysis from standard hematoxylin and eosin slides, Yuan et al. (2012) demonstrated that survival predictions in ER negative breast cancer can be optimized....
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...Through the integration of gene expression and somatic copy number data with automated microenvironment analysis from standard hematoxylin and eosin slides, Yuan and colleagues demonstrated that survival predictions in ER negative breast cancer can be optimized (Yuan et al., 2012)....
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Francis Crick Institute1, Fox Chase Cancer Center2, Washington University in St. Louis3, Cold Spring Harbor Laboratory4, Howard Hughes Medical Institute5, Salk Institute for Biological Studies6, Cornell University7, Goethe University Frankfurt8, Fred Hutchinson Cancer Research Center9, Massachusetts Institute of Technology10, Harvard University11, University of Manchester12, New York University13, University of Texas Health Science Center at Houston14, University of Pennsylvania15, Stony Brook University16, Hofstra University17, Weizmann Institute of Science18, Oregon Health & Science University19, University of California, San Francisco20, King's College London21, Johns Hopkins University22
TL;DR: This Consensus Statement issues a call to action for all cancer researchers to standardize assays and report metadata in studies of cancer-associated fibroblasts to advance the understanding of this important cell type in the tumour microenvironment.
Abstract: Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with diverse functions, including matrix deposition and remodelling, extensive reciprocal signalling interactions with cancer cells and crosstalk with infiltrating leukocytes. As such, they are a potential target for optimizing therapeutic strategies against cancer. However, many challenges are present in ongoing attempts to modulate CAFs for therapeutic benefit. These include limitations in our understanding of the origin of CAFs and heterogeneity in CAF function, with it being desirable to retain some antitumorigenic functions. On the basis of a meeting of experts in the field of CAF biology, we summarize in this Consensus Statement our current knowledge and present a framework for advancing our understanding of this critical cell type within the tumour microenvironment.
1,616 citations
TL;DR: The current state of the art in this field is summarized, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and a European consortium of PDX models is introduced.
Abstract: Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models. Significance: PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field. Cancer Discov; 4(9); 998–1013. ©2014 AACR .
1,309 citations
TL;DR: A Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection and a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei are proposed.
Abstract: Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the centers of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and potentially lead to a better understanding of cancer.
1,043 citations
Cites background or methods from "Quantitative Image Analysis of Cell..."
...[9] classified nuclei into cancer, lymphocyte or stromal based on the morphological features in H&E stained breast cancer images....
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...Moreover, the proposed approaches for detection and classification do not require the difficult step of nucleus segmentation [9] which can be fairly challenging due to the reasons mentioned above....
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...Note that this thresholding scheme does not apply to CRImage [9]....
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...[9] proposed the use of hierarchical spatial smoothing to correct misclassification....
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...Lastly, CRImage [9] segments all nuclei with help of thresholding, followed by morphological operation, distance transform, and watershed....
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TL;DR: The current understanding of T cell dysfunction in cancer, the value of novel technologies to dissect such dysfunction at the single cell level, and how the emerging understanding may be utilized to develop personalized strategies to restore antitumor immunity are discussed.
674 citations
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TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
7,879 citations