Emerging Understanding of Multiscale Tumor Heterogeneity

Gerdes, Michael J.; Sood, Anup; Sevinsky, Christopher J.; Pris, Andrew D.; Zavodszky, Maria I.; Ginty, Fiona

doi:10.3389/fonc.2014.00366

Cited by 100 publications

(73 citation statements)

References 127 publications

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“…The differential sensitivity to drugs by subpopulations of cells, or cells of different types, can provide an indication of compound mechanism of action [2, 7-9, 12, 13, 66] 1 . In one study it was shown that cells treated with drugs of similar mechanism of action induce similar heterogeneity [64], while in another study, the analysis of the heterogeneity induced by a drug led to the development of a hypothesis of interacting networks [61].…”

Section: Discussionmentioning

confidence: 99%

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Gough

Shun

Taylor

et al. 2016

Methods

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Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and to quantify heterogeneity in tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

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Section: Discussionmentioning

confidence: 99%

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Gough

Shun

Taylor

et al. 2016

Methods

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“…This microenvironment, which defines an additional barrier or an asset for therapy, is responsible for differently directed selective pressure within the same tumour, changing the evolutional trajectories of the tumour cells [118]. This opens up new areas of research for developing techniques focusing on regulating the tumour microenvironment in order to control tumour progression.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

Tumour Heterogeneity: The Key Advantages of Single-Cell Analysis

Gabriel

Ory

Lamoureux

et al. 2016

IJMS

160

102

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Tumour heterogeneity refers to the fact that different tumour cells can show distinct morphological and phenotypic profiles, including cellular morphology, gene expression, metabolism, motility, proliferation and metastatic potential. This phenomenon occurs both between tumours (inter-tumour heterogeneity) and within tumours (intra-tumour heterogeneity), and it is caused by genetic and non-genetic factors. The heterogeneity of cancer cells introduces significant challenges in using molecular prognostic markers as well as for classifying patients that might benefit from specific therapies. Thus, research efforts for characterizing heterogeneity would be useful for a better understanding of the causes and progression of disease. It has been suggested that the study of heterogeneity within Circulating Tumour Cells (CTCs) could also reflect the full spectrum of mutations of the disease more accurately than a single biopsy of a primary or metastatic tumour. In previous years, many high throughput methodologies have raised for the study of heterogeneity at different levels (i.e., RNA, DNA, protein and epigenetic events). The aim of the current review is to stress clinical implications of tumour heterogeneity, as well as current available methodologies for their study, paying specific attention to those able to assess heterogeneity at the single cell level.

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“…4, 68 The differential sensitivity to drug treatment of subpopulations of cells may well provide an indication of compound mechanism(s) of action. 1, 5, 9, 64, 65, 68, 69 Differential sensitivity measurements in vitro also provide insights into how effective a therapy might be in vivo . For example, if the half-maximal response represents all cells showing 50% inhibition, then treatment cycles in vivo may produce a different response rate than if the half maximal response is a result of 100% inhibition in half of the cells.…”

Section: Introduction: Biological Heterogeneity Is a Fundamental Propmentioning

confidence: 99%

Biologically Relevant Heterogeneity: Metrics and Practical Insights

et al. 2017

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Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications including basic biomedical research, drug discovery, diagnostics and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell level data, as well as the need for standard metrics of the spatial, temporal and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics and the design of optimal therapeutic strategies for individual patients.

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Emerging Understanding of Multiscale Tumor Heterogeneity

Cited by 100 publications

References 127 publications

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Tumour Heterogeneity: The Key Advantages of Single-Cell Analysis

Biologically Relevant Heterogeneity: Metrics and Practical Insights

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