Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.

Background: Understanding the molecular control of cell lineages and fate determination in complex tissues is key to not only understanding the developmental biology and cellular homeostasis of such tissues but also for our understanding and interpretation of the molecular pathology of diseases such as cancer. The prerequisite for such an understanding is detailed knowledge of the cell types that make up such tissues, including their comprehensive molecular characterisation. In the mammary epithelium, the bulk of the tissue is composed of three cell lineages, namely the basal/myoepithelial, luminal epithelial estrogen receptor positive and luminal epithelial estrogen receptor negative cells. However, a detailed molecular characterisation of the transcriptomic differences between these three populations has not been carried out.

BRCA1 mutation-associated breast cancer originates in oestrogen receptor-alpha-negative (ER À ) progenitors in the mammary luminal epithelium. These cells also express high levels of the Kit gene and a recent study demonstrated a correlation between Brca1 loss and Kit over-expression in the mammary epithelium. However, the functional significance of c-Kit expression in the mammary gland is unknown. To address this, c-Kit À and c-Kit þ mammary epithelial subsets were isolated by flow cytometry, characterised for expression of lineage-specific cell markers and functionally analysed by in vitro colony forming and in vivo transplantation assays. The results confirm that the majority of luminal ER À progenitors are c-Kit þ , but also that most stem cells and the differentiated cell populations are c-Kit À . A subset of c-Kit þ cells with high proliferative potential was found in the luminal ER þ population, however, suggesting the existence of a distinct luminal ER þ progenitor cell type. Analysis of mouse Brca1 mammary tumours demonstrated that they expressed Kit and its downstream effector Lyn at levels comparable to the most strongly c-Kit þ luminal ER À progenitors. Consistent with c-Kit being a progenitor cell marker, in vitro threedimensional differentiation of c-Kit þ cells resulted in a loss of c-Kit expression, whereas c-Kit over-expression prevented normal differentiation in vivo. Furthermore, c-Kit was a functional marker of proliferative potential, as c-Kit inhibition by short hairpin knockdown prevented normal epithelial growth and caused cells to undergo apoptosis. Therefore, c-Kit defines distinct progenitor populations in the mammary epithelium and is critical for mammary progenitor survival and proliferation. Importantly, c-Kit is only the second mammary epithelial stem/progenitor marker to be shown to have a functional role in the mammary epithelium and the first marker to be shown to be required for progenitor cell function. The c-Kit signalling network has potential as a target for therapy and/or prevention in BRCA1-associated breast cancer.

Colon cancer is a heterogeneous tumor driven by a subpopulation of cancer stem cells (CSCs). To study CSCs in colon cancer, we used limiting dilution spheroid and serial xenotransplantation assays to functionally define the frequency of CSCs in a panel of patient-derived cancer organoids. These studies demonstrated cancer organoids to be enriched for CSCs, which varied in frequency between tumors. Whole-transcriptome analysis identified WNT and Hedgehog signaling components to be enhanced in CSC-enriched tumors and in aldehyde dehydrogenase (ALDH)-positive CSCs. Canonical GLI-dependent Hedgehog signaling is a negative regulator of WNT signaling in normal intestine and intestinal tumors. Here, we show that Hedgehog signaling in colon CSCs is autocrine SHH-dependent, non-canonical PTCH1 dependent, and GLI independent. In addition, using small-molecule inhibitors and RNAi against SHH-palmitoylating Hedgehog acyltransferase (HHAT), we demonstrate that non-canonical Hedgehog signaling is a positive regulator of WNT signaling and required for colon CSC survival.

Cell fate determination in the progeny of mammary epithelial stem/progenitor cells remains poorly understood. Here, we have examined the role of the mitotic kinase Aurora A (AURKA) in regulating the balance between basal and luminal mammary lineages. We find that AURKA is highly expressed in basal stem cells and, to a lesser extent, in luminal progenitors. Wild-type AURKA expression promoted luminal cell fate, but expression of an S155R mutant reduced proliferation, promoted basal fate, and inhibited serial transplantation. The mechanism involved regulation of mitotic spindle orientation by AURKA and the positioning of daughter cells after division. Remarkably, this was NOTCH dependent, as NOTCH inhibitor blocked the effect of wild-type AURKA expression on spindle orientation and instead mimicked the effect of the S155R mutant. These findings directly link AURKA, NOTCH signaling, and mitotic spindle orientation and suggest a mechanism for regulating the balance between luminal and basal lineages in the mammary gland.

Isolation of mammary epithelial subpopulations, including stem and progenitor cells, has become a standard technique in recent years. However, a number of methods and approaches for this have developed and the relative benefits of the different approaches, and the reason for their development, have not always been clear. Here, three of the leading laboratories working on the separation of mammary cell subpopulations have summarised their methods, highlighted their differences and similarities and also discussed the reasoning behind the approaches they have taken. This article will assist workers establishing mammary cell separation protocols in their laboratories to make informed choices about the methods they should use.

Introduction In humans, an early full-term pregnancy reduces lifetime breast cancer risk by up to 50% whereas a later pregnancy (>35 years old) can increase lifetime risk. Several mechanisms have been suggested, including changes in levels of circulating hormones, changes in the way the breast responds to these hormones, changes in gene expression programmes which may alter susceptibility to transformation and changes to mammary stem cell numbers or behaviour. Previous studies have shown that the mammary tissue isolated from both virgin and parous mice has the ability to repopulate a cleared mammary fat pad in transplant experiments. Limited dilution transplant assays have demonstrated that early pregnancy (at 5 weeks of age) reduces stem/progenitor cell numbers in the mouse mammary epithelium by twofold. However, the effects on stem/progenitor cell numbers in the mammary epithelium of a pregnancy in older animals have not yet been tested.

Introduction To understand which signalling pathways become deregulated in breast cancer, it is necessary to identify functionally significant gene expression patterns in the stem, progenitor, transit amplifying and differentiated cells of the mammary epithelium. We have previously used the markers 33A10, CD24 and Sca-1 to identify mouse mammary epithelial cell subpopulations. We now investigate the relationship between cells expressing these markers and use gene expression microarray analysis to identify genes differentially expressed in the cell populations.