The transcription factor GATA6 enables self-renewal of colon adenoma stem cells by repressing BMP gene expression

Aberrant activation of WNT signalling and loss of BMP signals represent the two main alterations leading to the initiation of colorectal cancer (CRC). Here we screen for genes required for maintaining the tumour stem cell phenotype and identify the zinc-finger transcription factor GATA6 as a key regulator of the WNT and BMP pathways in CRC. GATA6 directly drives the expression of LGR5 in adenoma stem cells whereas it restricts BMP signalling to differentiated tumour cells. Genetic deletion of Gata6 from mouse colon adenomas increases the levels of BMP factors, which signal to block self-renewal of tumour stem cells. In human tumours, GATA6 competes with ?-catenin/TCF4 for binding to a distal regulatory region of the BMP4 locus that has been linked to increased susceptibility to development of CRC. Hence, GATA6 creates an environment permissive for CRC initiation by lowering the threshold of BMP signalling required for tumour stem cell expansion

Detecting repeated cancer evolution from multiregion tumor sequencing data

This work is supported by the Wellcome Trust (202778/B/16/Z to A.S.; 202778/Z/16/Z to T.A.G.; 105104/Z/14/Z to the Centre for Evolution and Cancer, Institute of Cancer Research), Cancer Research UK (A22909 to A.S.; A19771 to T.A.G.), the Institute of Cancer Research (Chris Rokos Fellowship in Evolution and Cancer to A.S.), and ERC (MLCS 306999 to G.S.)

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

Carcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patient’s tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution.This work is supported by the Wellcome Trust (202778/B/16/Z to A.S.; 202778/Z/16/Z to T.A.G.; 105104/Z/14/Z to the Centre for Evolution and Cancer, Institute of Cancer Research), Cancer Research UK (A22909 to A.S.; A19771 to T.A.G.), the Institute of Cancer Research (Chris Rokos Fellowship in Evolution and Cancer to A.S.), and ERC (MLCS 306999 to G.S.)