| Abstract | Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile , as an estimate of the stemness of a tumour sample. |
| Abstract | By considering over 500 mixtures of diverse cellular expression profiles , we reveal that signalling entropy also associates with infra-tumour heterogeneity. |
| Introduction | Although putative CSCs have been identified by surface marker eXpression for several malignancies, isolated, and demonstrated to be chemotherapeutic resistant [7—11], it remains a significant challenge to obtain a prognostic measure of their abundance from tumour bulk gene eXpression profiles across multiple malignancies. |
| Introduction | Specifically, we consider signalling entropy which is computed from the integration of a sample’s genome-wide gene eXpression profile with an interactome, and provides an overall measure of the signalling promiscuity in the sample [16]. |
| Introduction | We derived a sufficient condition on the eXpression profiles of homogeneous cell populations for signalling entropy to be a measure of intra-sample heterogeneity on average. |
| Rationale of signalling entropy as a prognostic measure | Signalling entropy is derived from the integration of a sample’s gene expression profile with a human protein interactome, and provides a rough proxy for the overall level of signalling promiscuity in the sample. |
| Rationale of signalling entropy as a prognostic measure | It is clear that if cell type x has an expression profile that maximises signalling entropy and cell type y does not, then the signalling entropy of the mixture Will be lower than the signalling entropy of x, thus signalling entropy is not a point-wise measure of heterogeneity. |
| Rationale of signalling entropy as a prognostic measure | However, as most biologically realistic cell types have distinct expression profiles , corresponding to the existence of non-overlapping active pathways between cell type pairs [29] , we posited that the signalling entropy of a mixed sample may be higher than that of a homogeneous sample on average. |
| ANG-2 and PIGF survival analysis on breast cancer patients | To this end we analyzed a dataset including tumor expression profiles and clinical data of 1809 breast cancer patients [50] and compared two subsets of patients: those with lowest and highest expression values for ANG-2, PIGF and CD14 (as TEM marker), using as threshold the first and fourth quartile respectively. |
| Abstract | In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. |
| Acknowledgments | We thank the nurses of the breast center (CHUV), Dr. Iulien Dorier (SIB, CIG) for stimulating discussions and the Genomic Technologies Facility (GTF at CIG) for RNA preparation, gene expression profiling and for printing assistance. |
| Combining computational and experimental approaches to delineate the pathways controlling TEM pro-angiogenic function | Finally, to help shed light on possible molecular mechanisms underlying TEM pro-angiogenic transformation, we selected several treatment combinations and measured genome wide expression profiles for the TEM differentiated in vitro, comparing the state of the cells before and after treatment. |
| Gene expression profiling | Gene expression profiling |
| Introduction | Finally, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM infiltrating carcinoma of the breast remain plastic cells that can be reverted from pro-angiogenic and protumoral cells to immunological potent monocytes. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | To this end, we selected VEGF/TNF-oc, ANG-2/TGF-[3 and PlGF/TGF-B treatments for gene expression profiling using Affimetrix whole genome microarrays, because these treatments were present in 17, 16 and 14, respectively of the 74 links (treatment/receptor/cytokine) retained in TEM regulatory network (S4 Table and Fig. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | Similar expression profiles were obtained for untreated and TNF-oc/VEGF treated cells consistent with their weak impact on TEM functional angiogenic phenotype (Fig. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | Taken together, our results suggest that ANG-2/TGF-[3 and PlGF/TGF-B treatments are not only anti-angiogenic but also shift the gene expression profile of monocytes toward the one of cells promoting immune surveillance, thereby limiting tumor growth. |
| Introduction | Similarly, it is known that bacterial organisms undergoing rapid adaptations to varying environments, such as heat-shock and osmotic stress, produce differential expression profiles that are indicative of the corresponding stress [4—9]. |
| Introduction | More recently, a probabilistic human tissue and cell type predictor was built based solely on gene expression profiles [28]. |
| Introduction | In this work, we investigate how well we can predict cellular and environmental state from genome-wide expression, using known gene expression profiles as our only training data. |