Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.

Motivation: Identification of differential expressed genes has led to countless new discoveries. However, differentially expressed genes are only a proxy for finding dysregulated pathways. The problem is to identify how the network of regulatory and physical interactions rewires in different conditions or in disease. Results: We developed a procedure named DINA (DIfferential Network Analysis), which is able to identify set of genes, whose co-regulation is condition-specific, starting from a collection of condition-specific gene expression profiles. DINA is also able to predict which transcription factors (TFs) may be responsible for the pathway condition-specific co-regulation. We derived 30 tissue-specific gene networks in human and identified several metabolic pathways as the most differentially regulated across the tissues. We correctly identified TFs such as Nuclear Receptors as their main regulators and demonstrated that a gene with unknown function (YEATS2) acts as a negative regulator of hepatocyte metabolism. Finally, we showed that DINA can be used to make hypotheses on dysregulated pathways during disease progression. By analyzing gene expression profiles across primary and transformed hepatocytes, DINA identified hepatocarcinoma-specific metabolic and transcriptional pathway dysregulation. Availability: We implemented an on-line web-tool http://dina.tigem.it enabling the user to apply DINA to identify tissue-specific pathways or gene signatures. Contact: dibernardo@tigem.it Supplementary information: Supplementary data are available at Bioinformatics online.

In response to starvation, cells tune protein synthesis to promote autophagy, thus ensuring nutrient recycling and survival.

Synchronous colorectal cancers (syCRCs) are physically separated tumours that develop simultaneously. To understand how the genetic and environmental background influences the development of multiple tumours, here we conduct a comparative analysis of 20 syCRCs from 10 patients. We show that syCRCs have independent genetic origins, acquire dissimilar somatic alterations, and have different clone composition. This inter- and intratumour heterogeneity must be considered in the selection of therapy and in the monitoring of resistance. SyCRC patients show a higher occurrence of inherited damaging mutations in immune-related genes compared to patients with solitary colorectal cancer and to healthy individuals from the 1,000 Genomes Project. Moreover, they have a different composition of immune cell populations in tumour and normal mucosa, and transcriptional differences in immune-related biological processes. This suggests an environmental field effect that promotes multiple tumours likely in the background of inflammation.

During the development of the central nervous system (CNS), combinations of transcription factors and signalling molecules orchestrate patterning, specification and differentiation of neural cell types. In vertebrates, three types of melanin-containing pigment cells, exert a variety of functional roles including visual perception. Here we analysed the mechanisms underlying pigment cell specification within the CNS of a simple chordate, the ascidian Ciona intestinalis. Ciona tadpole larvae exhibit a basic chordate body plan characterized by a small number of neural cells. We employed lineage-specific transcription profiling to characterize the expression of genes downstream of fibroblast growth factor signalling, which govern pigment cell formation. We demonstrate that FGF signalling sequentially imposes a pigment cell identity at the expense of anterior neural fates. We identify FGF-dependent and pigment cell-specific factors, including the small GTPase, Rab32/38 and demonstrated its requirement for the pigmentation of larval sensory organs.