scholarly journals When, why and how clonal diversity predicts future tumour growth

2019 ◽  
Author(s):  
Robert Noble ◽  
John T Burley ◽  
Cécile Le Sueur ◽  
Michael E Hochberg
Keyword(s):  
Growth Rate ◽  
Tumour Growth ◽  
Evolutionary Process ◽  
Clonal Diversity ◽  
Driver Mutations ◽  
Multiple Cancer ◽  
Intratumour Heterogeneity ◽  
Cancer Types ◽  
The Relationship ◽  
Tumour Growth Rate

AbstractIntratumour heterogeneity holds promise as a prognostic biomarker in multiple cancer types. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate, an important predictor of clinical progression. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggest that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we find that, for predicting tumour growth, clonal diversity is likely to perform worse than conventional measures of tumour stage and grade. We thus offer explanations – grounded in evolutionary theory – for empirical findings in various cancers. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

scholarly journals Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

2022 ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
Tumour Progression ◽  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
Driver Mutations ◽  
Cancer Type ◽  
Expression Data ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Types ◽  
Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

Ki67 in the Assessment of Tumour Growth Rate: A Study on Xenografts

1989 ◽  
Vol 56 (5) ◽  
pp. 797-800 ◽  
Author(s):  
P. Price ◽  
C. Bush ◽  
C.S. Parkins ◽  
P. Imrie ◽  
M.G. Ormerod ◽  
...  
Keyword(s):  
Growth Rate ◽  
Tumour Growth ◽  
Tumour Growth Rate

The Therapeutic Potential of miR-7 in Cancers

2019 ◽  
Vol 19 (20) ◽  
pp. 1707-1716 ◽  
Author(s):  
Miao Li ◽  
Meng Pan ◽  
Chengzhong You ◽  
Jun Dou
Keyword(s):  
Tumor Suppressor ◽  
Future Study ◽  
Therapeutic Potential ◽  
Biological Processes ◽  
Multiple Cancer ◽  
Current Review ◽  
Cancer Types ◽  
The Relationship ◽  
Potent Tumor

MiRNAs play an important role in cancers. As a potent tumor suppressor, miRNA-7(miR-7) has been demonstrated to inhibit the diverse fundamental biological processes in multiple cancer types including initiation, growth and metastasis by targeting a number of molecules and signaling pathways. This current review summarizes and discusses the relationship between miR-7 and cancers and the therapeutic potential of miR-7 in cancers. It may provide new integrative understanding for future study on the role of miR-7 in cancers.

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