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In colorectal cancer (CRC), aberrant Wnt signalling is essential for tumorigenesis and maintenance of cancer stem cells. However, how other oncogenic pathways converge on Wnt signalling to modulate stem cell homeostasis in CRC currently remains poorly understood. Using large-scale compound screens in CRC, we identify MEK1/2 inhibitors as potent activators of Wnt/β-catenin signalling. Targeting MEK increases Wnt activity in different CRC cell lines and murine intestine in vivo. Truncating mutations of APC generated by CRISPR/Cas9 strongly synergize with MEK inhibitors in enhancing Wnt responses in isogenic CRC models. Mechanistically, we demonstrate that MEK inhibition induces a rapid downregulation of AXIN1. Using patient-derived CRC organoids, we show that MEK inhibition leads to increased Wnt activity, elevated LGR5 levels and enrichment of gene signatures associated with stemness and cancer relapse. Our study demonstrates that clinically used MEK inhibitors inadvertently induce stem cell plasticity, revealing an unknown side effect of RAS pathway inhibition. Wnt signaling is necessary for colorectal cancer tumorigenesis and stem cell maintenance. Here, the authors identify MEK1/2 inhibitors as potent activators of Wnt/β-catenin signalling and show that clinically approved MEK inhibitors inadvertently induce stem cell plasticity in colorectal cancer

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

Patient-derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases. They have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we use high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with >500 small molecules. Integration of data using multi-omics modeling identifies axes of morphological variation across organoids: Organoid size is linked to IGF1 receptor signaling, and cystic vs. solid organoid architecture is associated with LGR5 + stemness. Treatment-induced organoid morphology reflects organoid viability, drug mechanism of action, and is biologically interpretable. Inhibition of MEK leads to cystic reorganization of organoids and increases expression of LGR5 , while inhibition of mTOR induces IGF1 receptor signaling. In conclusion, we identify shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them. The heterogeneity underlying cancer organoid phenotypes is not yet well understood. Here, the authors develop an imaging analysis assay for high throughput phenotypic screening of colorectal organoids that allows to define specific morphological changes that occur following different drug treatments.

The geroscience hypothesis states that a therapy that prevents the underlying aging process should prevent multiple aging related diseases. The mTOR (mechanistic target of rapamycin)/insulin and NAD+ (nicotinamide adenine dinucleotide) pathways are two of the most validated aging pathways. Yet, it's largely unclear how they might talk to each other in aging. In genome-wide CRISPRa screening with a novel class of N-O-Methyl-propanamide-containing compounds we named BIOIO-1001, we identified lipid metabolism centering on SIRT3 as a point of intersection of the mTOR/insulin and NAD+ pathways. In vivo testing indicated that BIOIO-1001 reduced high fat, high sugar diet-induced metabolic derangements, inflammation, and fibrosis, each being characteristic of non-alcoholic steatohepatitis (NASH). An unbiased screen of patient datasets suggested a potential link between the anti-inflammatory and anti-fibrotic effects of BIOIO-1001 in NASH models to those in amyotrophic lateral sclerosis (ALS). Directed experiments subsequently determined that BIOIO-1001 was protective in both sporadic and familial ALS models. Both NASH and ALS have no treatments and suffer from a lack of convenient biomarkers to monitor therapeutic efficacy. A potential strength in considering BIOIO-1001 as a therapy is that the blood biomarker that it modulates, namely plasma triglycerides, can be conveniently used to screen patients for responders. More conceptually, to our knowledge BIOIO-1001 is a first therapy that fits the geroscience hypothesis by acting on multiple core aging pathways and that can alleviate multiple conditions after they have set in.

Maintenance of homeostatic processes ensure curtailment of intestinal tumorigenesis. Inactivating mutations to Adenomatous Polyposis Coli (Apc) result in aberrantly activated Wnt signalling and initiates colorectal cancer (CRC) in approx. 80% of cases, yet our understanding of the subcellular mechanisms that modulate dysregulated pathway activity is limited. Here, using a conditional in vivo genetic screen, we identify Rab35 GTPase as a novel tumour suppressor that modulates regional Wnt activity after loss of Apc in progenitor cells. Single cell analysis revealed that progenitor cells respond to Apc depletion by increasing the expression of a GTPase activating protein, which we named blackbelt, and triggering Rab35 disassociation from the plasma membrane. Mechanistically, we demonstrate that Rab35 controls the localisation and activation of the Rho GTPase, Cdc42, which functions as a relay to regulate JNK signalling. This in turn tunes the Wnt pathway upstream of β catenin to direct proliferation and differentiation of progenitor cells. Importantly, we show that maintaining active JNK signalling is important for the propagation of Apc mutant mouse colon organoids. Our findings highlight a novel GTPase cascade that sustains aberrant Wnt activity in specific segments of the intestine and provides impetus to therapeutically exploit this pathway to target CRC.Competing Interest StatementThe authors have declared no competing interest.

Precision oncology, the genetic sequencing of tumors to identify druggable targets, has emerged as the standard of care in the treatment of many cancers. Nonetheless, due to the pace of therapy development and variability in patient information, designing effective protocols for individual treatment assignment in a sample-efficient way remains a major challenge. One promising approach to this problem is to frame precision oncology treatment as a contextual bandit problem and to apply sequential decision-making algorithms designed to minimize regret in this setting. However, a clear prerequisite for considering this methodology in high-stakes clinical decisions is careful benchmarking to understand realistic costs and benefits. Here, we propose a benchmark dataset to evaluate contextual bandit algorithms based on real in vitro drug response of approximately 900 cancer cell lines. Specifically, we curated a dataset of complete treatment responses for a subset of 7 treatments from prior in vitro studies. This allows us to compute the regret of proposed decision policies using biologically plausible counterfactuals. We ran a suite of Bayesian bandit algorithms on our benchmark, and found that the methods accumulate less regret over a sequence of treatment assignment tasks than a rule-based baseline derived from current clinical practice. This effect was more pronounced when genomic information was included as context. We expect this work to be a starting point for evaluation of both the unique structural requirements and ethical implications for real-world testing of bandit based clinical decision support.

Patient derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases from primary patient samples. Organoids can be used as models for drug discovery and are being explored to guide clinical decision making. Patient derived organoids can have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we used high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with more than 500 small molecules. Integration of data using a joint multi-omics modelling framework identified organoid size and cystic vs. solid organoid architecture as axes of morphological variation across organoids. Mechanistically, we found that organoid size was linked to IGF1 receptor signaling, while a cystic organoid architecture was associated with an LGR5+ stemness program. Treatment-induced organoid morphology reflected organoid viability, drug mechanism of action, and was biologically interpretable using joint modelling. Inhibition of MEK led to cystic reorganization of organoids and increased expression of LGR5, while inhibition of mTOR induced IGF1 receptor signaling. In conclusion, we identified shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them. Image-based profiling of patient derived organoids coupled with multi-omics integration facilitates drug discovery by linking drug responses with underlying biological mechanisms. Competing Interest Statement The authors declare no competing interests. M.B. and M.E. received a research grant within the Merck Heidelberg Innovation Program, which did not support this study. Footnotes * Updated several figures and new analysis was added to the new version of the manuscript. Supplemental files were updated.