Molecular hallmarks of cancer: Stemness

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Conference Proceeding

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Publication Title

Cancer Res


Stemness, defined as the potential for self-renewal and de-differentiation from the cell-of-origin, has been initially attributed to normal stem cells that possess the ability to give rise to all cell types in the adult organism. Cancer stem cells have been identified in most if not all hematological malignancies as well as in solid tumors. CSCs are postulated to be amongst the most resistant cells to various forms of chemotherapy and radiotherapy and contribute to relapse and metastasis and hence inferior clinical outcomes. Here, we have performed a comprehensive and multi-platform analyses of the stemness features in 33 cancer types totalling more than 10,000 mostly primary, but also some metastatic and recurrent samples. In the first step, we have derived signatures to measure the stemness using molecular profiles of normal cells with various degrees of stemness from publicly available datasets. By multiplatform analyses of the transcriptome, methylome, and chromatin markers using different machine learning computational approaches, we have obtained four independent stemness scores. Initial validation revealed comparable performance of these computational metrics for sorting the TCGA samples. The cancer types with previously documented features of de-differentiation have higher stemness score in contrast to more differentiated cancer types. More detailed analyses of recently published molecular subtypes of gliomas have revealed a strong association of high stemness scores with the worst clinical outcome, further confirming our initial hypotheses. By adopting machine learning algorithms, we have counted percentage of immune cells of both innate and acquired response infiltrating tumor tissue. Further deconvolution of the TCGA cancers allowed quantification of the tumour microenvironment composed of cancer-associated fibroblasts and the endothelial cells. Integration of gene expression and DNA methylation datasets defines classic hallmarks of pluripotent signatures which shed new light into biological processes regulating and maintaining oncogenic dedifferentiation. Our ongoing analyses of the TCGA samples sorted by the obtained metrics involve: (1) activity of the selected hallmarks of cancer that have been shown as features of the CSCs; (2) correlation with the tumor pathology grading and clinical outcomes; (3) identification of potential drivers for development and/or repositioning of drugs targeting tumor self-renewal and de-differentiation potential; and (4) development of novel biomarkers for selection of patients that will respond to these novel therapies. In summary, we defined molecular signatures of cancer stem cells that enabled classification of TCGA cancer types and identification of tumors with stem/progenitor-like phenotypes, associated with poor clinical outcomes. Understanding stemness-like hallmark of cancer will pave the way for novel diagnostics and therapies for cancer patients.




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