Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia

Intestinal homeostasis is underpinned by LGR5+ve crypt-base columnar stem cells (CBCs), but following injury, dedifferentiation results in the emergence of LGR5-ve regenerative stem cell populations (RSCs), characterized by fetal transcriptional profiles. Neoplasia hijacks regenerative signaling, so we assessed the distribution of CBCs and RSCs in mouse and human intestinal tumors. Using combined molecular-morphological analysis, we demonstrate variable expression of stem cell markers across a range of lesions. The degree of CBC-RSC admixture was associated with both epithelial mutation and microenvironmental signaling disruption and could be mapped across disease molecular subtypes. The CBC-RSC equilibrium was adaptive, with a dynamic response to acute selective pressure, and adaptability was associated with chemoresistance. We propose a fitness landscape model where individual tumors have equilibrated stem cell population distributions along a CBC-RSC phenotypic axis. Cellular plasticity is represented by position shift along this axis and is influenced by cell-intrinsic, extrinsic, and therapeutic selective pressures. Keywords: cell plasticity; colorectal cancer; colorectal neoplasia; intestinal polyps; intestinal stem cells; molecular phenotyping; stem cells

Trends and emissions of six perfluorocarbons in the Northern Hemisphere and Southern Hemisphere

Perfluorocarbons (PFCs) are potent greenhouse gases with global warming potentials up to several thousand times greater than CO2 on a 100-year time horizon. The lack of any significant sinks for PFCs means that they have long atmospheric lifetimes of the order of thousands of years. Anthropogenic production is thought to be the only source for most PFCs. Here we report an update on the global atmospheric abundances of the following PFCs, most of which have for the first time been analytically separated according to their isomers: c-octafluorobutane (c-C4F8), n-decafluorobutane (n-C4F10), n-dodecafluoropentane (n-C5F12), n-tetradecafluorohexane (n-C6F14), and n-hexadecafluoroheptane (n-C7F16). Additionally, we report the first data set on the atmospheric mixing ratios of perfluoro-2-methylpentane (i-C6F14). The existence and significance of PFC isomers have not been reported before, due to the analytical challenges of separating them. The time series spans a period from 1978 to the present. Several data sets are used to investigate temporal and spatial trends of these PFCs: time series of air samples collected at Cape Grim, Australia, from 1978 to the start of 2018; a time series of air samples collected between July 2015 and April 2017 at Tacolneston, UK; and intensive campaign-based sampling collections from Taiwan. Although the remote “background” Southern Hemispheric Cape Grim time series indicates that recent growth rates of most of these PFCs are lower than in the 1990s, we continue to see significantly increasing mixing ratios that are between 6 % and 27 % higher by the end of 2017 compared to abundances measured in 2010. Air samples from Tacolneston show a positive offset in PFC mixing ratios compared to the Southern Hemisphere baseline. The highest mixing ratios and variability are seen in air samples from Taiwan, which is therefore likely situated much closer to PFC sources, confirming predominantly Northern Hemispheric emissions for most PFCs. Even though these PFCs occur in the atmosphere at levels of parts per trillion molar or less, their total cumulative global emissions translate into 833 million metric tonnes of CO2 equivalent by the end of 2017, 23 % of which has been emitted since 2010. Almost two-thirds of the CO2 equivalent emissions within the last decade are attributable to c-C4F8, which currently also has the highest emission rates that continue to grow. Sources of all PFCs covered in this work remain poorly constrained and reported emissions in global databases do not account for the abundances found in the atmosphere

E-cadherin can limit the transforming properties of activating β-catenin mutations

Wnt pathway deregulation is a common characteristic of many cancers. But only Colorectal Cancer predominantly harbours mutations in APC, whereas other cancer types (hepatocellular carcinoma, solid pseudopapillary tumours of pancreas) have activating mutations in β-catenin (CTNNB1). We have compared the dynamics and the potency of β-catenin mutations in vivo. Within the murine small intestine (SI), an activating mutation of β-catenin took much longer to achieve a Wnt deregulation and acquire a crypt-progenitor-cell (CPC) phenotype than Apc or Gsk3 loss. Within the colon, a single activating mutation of β-catenin was unable to drive Wnt deregulation or induce the CPC phenotype. This ability of β-catenin mutation to differentially transform the SI versus the colon correlated with significantly higher expression of the β-catenin binding partner E-cadherin. This increased expression is associated with a higher number of E-cadherin:β-catenin complexes at the membrane. Reduction of E-cadherin synergised with an activating mutation of β-catenin so there was now a rapid CPC phenotype within the colon and SI. Thus there is a threshold of β-catenin that is required to drive transformation and E-cadherin can act as a buffer to prevent β-catenin accumulation

Molecular mechanism of BMP signal control by Twisted gastrulation

Twisted gastrulation (TWSG1) is an evolutionarily conserved secreted glycoprotein which controls signaling by Bone Morphogenetic Proteins (BMPs). TWSG1 binds BMPs and their antagonist Chordin to control BMP signaling during embryonic development, kidney regeneration and cancer. We report crystal structures of TWSG1 alone and in complex with a BMP ligand, Growth Differentiation Factor 5. TWSG1 is composed of two distinct, disulfide-rich domains. The TWSG1 N-terminal domain occupies the BMP type 1 receptor binding site on BMPs, whereas the C-terminal domain binds to a Chordin family member. We show that TWSG1 inhibits BMP function in cellular signaling assays and mouse colon organoids. This inhibitory function is abolished in a TWSG1 mutant that cannot bind BMPs. The same mutation in the Drosophila TWSG1 ortholog Tsg fails to mediate BMP gradient formation required for dorsal-ventral axis patterning of the early embryo. Our studies reveal the evolutionarily conserved mechanism of BMP signaling inhibition by TWSG1

Abrupt reversal in emissions and atmospheric abundance of HCFC-133a (CF3CH2Cl)

Hydrochlorofluorocarbon HCFC-133a (CF3CH2Cl) is an anthropogenic compound whose consumption for emissive use is restricted under the Montreal Protocol. A recent study showed rapidly increasing atmospheric abundances and emissions. We report that, following this rise, the at- mospheric abundance and emissions have declined sharply in the past three years. We find a Northern Hemisphere HCFC-133a increase from 0.13 ppt (dry air mole fraction in parts-per-trillion) in 2000 to 0.50 ppt in 2012–mid-2013 followed by an abrupt reversal to 0.44 ppt by early 2015. Global emissions derived from these observations peaked at 3.1 kt in 2011, followed by a rapid decline of 0.5 kt yr−2 to 1.5 kt yr−1 in 2014. Sporadic HCFC-133a pollution events are detected in Europe from our high-resolution HCFC-133a records at three European stations, and in Asia from sam- ples collected in Taiwan. European emissions are estimated to be <0.1 kt yr−1 although emission hotspots were identi- fied in France

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data

PURPOSE Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. RESULTS Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples

Evolutionary history of human colitis-associated colorectal cancer

Objective: IBD confers an increased lifetime risk of developing colorectal cancer (CRC), and colitis-associated CRC (CA-CRC) is molecularly distinct from sporadic CRC (S-CRC). Here we have dissected the evolutionary history of CA-CRC using multiregion sequencing. Design: Exome sequencing was performed on fresh-frozen multiple regions of carcinoma, adjacent non-cancerous mucosa and blood from 12 patients with CA-CRC (n=55 exomes), and key variants were validated with orthogonal methods. Genome-wide copy number profiling was performed using single nucleotide polymorphism arrays and low-pass whole genome sequencing on archival non-dysplastic mucosa (n=9), low-grade dysplasia (LGD; n=30), high-grade dysplasia (HGD; n=13), mixed LGD/HGD (n=7) and CA-CRC (n=19). Phylogenetic trees were reconstructed, and evolutionary analysis used to reveal the temporal sequence of events leading to CA-CRC. Results: 10/12 tumours were microsatellite stable with a median mutation burden of 3.0 single nucleotide alterations (SNA) per Mb, ~20% higher than S-CRC (2.5 SNAs/Mb), and consistent with elevated ageing-associated mutational processes. Non-dysplastic mucosa had considerable mutation burden (median 47 SNAs), including mutations shared with the neighbouring CA-CRC, indicating a precancer mutational field. CA-CRCs were often near triploid (40%) or near tetraploid (20%) and phylogenetic analysis revealed that copy number alterations (CNAs) began to accrue in non-dysplastic bowel, but the LGD/HGD transition often involved a punctuated ‘catastrophic’ CNA increase. Conclusions: Evolutionary genomic analysis revealed precancer clones bearing extensive SNAs and CNAs, with progression to cancer involving a dramatic accrual of CNAs at HGD. Detection of the cancerised field is an encouraging prospect for surveillance, but punctuated evolution may limit the window for early detection

Exploiting differential Wnt target gene expression to generate a molecular biomarker for colorectal cancer stratification

OBJECTIVE Pathological Wnt pathway activation is a conserved hallmark of colorectal cancer. Wnt-activating mutations can be divided into: i) ligand-independent (LI) alterations in intracellular signal transduction proteins (, β-catenin), causing constitutive pathway activation and ii) ligand-dependent (LD) mutations affecting the synergistic R-Spondin axis (, -fusions) acting through amplification of endogenous Wnt signal transmembrane transduction. Our aim was to exploit differential Wnt target gene expression to generate a mutation-agnostic biomarker for LD tumours. DESIGN We undertook harmonised multi-omic analysis of discovery (n=684) and validation cohorts (n=578) of colorectal tumours collated from publicly available data and the Stratification in Colorectal Cancer Consortium. We used mutation data to establish molecular ground truth and subdivide lesions into LI/LD tumour subsets. We contrasted transcriptional, methylation, morphological and clinical characteristics between groups. RESULTS Wnt disrupting mutations were mutually exclusive. Desmoplastic stromal upregulation of may compensate for absence of epithelial mutation in a subset of stromal-rich tumours. Key Wnt negative regulator genes were differentially expressed between LD/LI tumours, with targeted hypermethylation of some genes (, ) occurring even in CIMP-negative LD cancers. mRNA expression was used as a discriminatory molecular biomarker to distinguish LD/LI tumours (area under the curve >0.93). CONCLUSIONS Epigenetic suppression of appropriate Wnt negative feedback loops is selectively advantageous in LD tumours and differential expression in LD/LI lesions can be exploited as a molecular biomarker. Distinguishing between LD/LI tumour types is important; patients with LD tumours retain sensitivity to Wnt ligand inhibition and may be stratified at diagnosis to clinical trials of Porcupine inhibitors

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows