Cancer heterogeneity: implications for targeted therapeutics

By R. Fisher, L. Pusztai, Charles Swanton. 2013

935 citations recorded by the source corpus.

Developments in genomic techniques have provided insight into the remarkable genetic complexity of malignant tumours. There is increasing evidence that solid tumours may comprise of subpopulations of cells with distinct genomic alterations within the same tumour, a phenomenon termed intra-tumour heterogeneity. Intra-tumour heterogeneity is likely to have implications for cancer therapeutics and biomarker discovery, particularly in the era of targeted treatment, and evidence for a relationship between intra-tumoural heterogeneity and clinical outcome is emerging. Our understanding of the processes that exacerbate intra-tumoural heterogeneity, both iatrogenic and tumour specific, is likely to increase with the development and more widespread implementation of advanced sequencing technologies, and adaptation of clinical trial design to include comprehensive tissue collection protocols. The current evidence for intra-tumour heterogeneity and its relevance to cancer therapeutics will be presented in this mini-review.

Subfields: Cancer Research, Modeling and Simulation, Oncology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Hugo J. W. L. Aerts
2. David Craft
3. David Krane
4. Elaine R. Mardis
5. Jerzy K. Kulski

In Modeling and Simulation

1. David Craft
2. David Krane

In Oncology

1. Hugo J. W. L. Aerts
2. Philippe Lambin
3. David Craft
4. David Krane

Citations in the corpus

• Simulation-assisted machine learning (2018, positive): “Bioinformatic analysis has revealed that there is heterogeneity both within a patient tumor and across tumors; no two tumors are the same genomically (Felipe De Sousa et al., 2013; Fisher et al., 2013).”
• Machine Learning methods for Quantitative Radiomic Biomarkers (2015, background): “Moreover, it can also capture the intra-tumor heterogeneity, which is often considered as an important biomarker in oncology 12,35–37 .”
• LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. (2018, background): “Recently, there has been renewed interest in texture analysis in the context of tumor imaging (1), assuming that textural features could reflect intratumor heterogeneity, which is known to have important implications in cancer research (2).”
• SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution (2014, background): “Recent results suggest that accurately describing the subclonal composition and evolution of tumors requires sampling cancer cells across multiple time points or spatially-distinct regions [3,23,25].”
• Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer (2015, background): “It has been stated that intra-tumor heterogeneity could have profound implications in clinical predictions (e.g., treatment response, survival outcomes, disease progression, etc.), and therefore it is considered as a crucial factor for precision oncology and related research (3–6).”