Biology subjects

Robertson, A. G.

Publications and source records attributed to Robertson, A. G..

6 recordsLinked to original sources

bioRxiv2025

Predicting microsatellite instability from whole slide images using texture features

Identifying MSI in whole slide images (WSIs), one of the most widely used diagnostic imaging formats, is of great importance and in demand. In this study we employed color-based texture features to predict MSI on both a tile and sample based level. We found that within cohorts of hematoxylin and eosin (H&E) stained WSIs, texture morphology is able to predict MSI on a tile level with an AUC of up to 0.95 and on a sample level with an AUC of up to 0.98. This runs in contrast to other methods for predicting MSI in H&E WSIs which either utilized artificial intelligence based models, or achieved lower accuracy scores. Our results demonstrate that texture morphology is a significantly notable factor when it comes to identifying MSI in H&E WSIs, and should be used when constructing future models for MSI identification in a clinical setting.

bioinformatics↗

bioRxiv2022

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

MotivationCancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. ResultsWe show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. Availabilityhttps://bioconductor.org/packages/devel/bioc/html/SPONGE.html Contactmarkus.daniel.hoffmann@tum.de; markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2021

cSurvival: a web resource for biomarker interactions in cancer outcomes

Survival analysis is a technique to identify prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11,000 adult and >4,000 paediatric tumor cases with clinical outcomes and multi-omics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database, and offers three main advances: (a) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (b) survival analysis not only at the gene, but also the GS level; and (c) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Together, these analyses demonstrate cSurvivals ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies. Key pointsO_LIWe developed cSurvival, an advanced framework using clinical correlations to study biomarker interactions in cancers, with source code and curated datasets freely available for all C_LIO_LIcSurvival includes new algorithms to identify optimal cutoffs for two continuous predictors to stratify patients into risk groups, enabling for the first time joint analysis with two genomic predictors; C_LIO_LIcSurvival allows survival analysis at the gene set (GS) level with comprehensive and up-to-date GS libraries C_LIO_LIThe cSurvival pipeline integrates clinical outcomes and experimental cancer cell line data to generate synergistic biological insights and to mine for appropriate preclinical cell line tools C_LIO_LIcSurvival is built on a manually curated cancer outcomes database C_LI

genomics↗

bioRxiv2020

IFN-{lambda}4 may contribute to HCV persistence by increasing ER stress and enhancing IRF1 signaling

Chronic hepatitis C virus (HCV) infection and cirrhosis are major risk factors for developing hepatocellular carcinoma (HCC). Genetic polymorphisms in the IFNL3/IFNL4 locus have been associated both with impaired clearance of HCV and protection from liver fibrosis, an early stage of cirrhosis. Here, we aimed to address the genetic and functional relationships between IFNL3/IFNL4 polymorphisms, HCV-related cirrhosis, and HCC risk. We evaluated associations between IFNL4 genotype, defined as the presence of rs368234815-dG or rs12979860-T alleles, with cirrhosis and HCC risk in patients with chronic HCV - 2,931 from Taiwan and 3,566 from Japan. We detected associations between IFNL4 genotype and decreased risk of cirrhosis (OR=0.66, 95%CI=0.46-0.93, P=0.018, in Taiwan), but increased risk of HCC (OR=1.28, 95%CI=1.07-1.52, P=0.0058, in Japan). In-vitro, IFN-{lambda}4 expression increased ER stress, and enhanced positive regulation of IFN responses via IRF1 induction, which mediated antiproliferative effects in hepatic cells. Our data present novel IFN-{lambda}4-associated pathways that may be contributing to HCV persistence and development of HCC.

cancer biology↗

bioRxiv2019

Quantum and Quantum-inspired Methods for de novo Discovery of Altered Cancer Pathways

The combinatorial calculations for de novo discovery of altered pathways in cancer cohorts involve both coverage (i.e. recurrence) and mutual exclusivity, and solving mutual exclusivity problems is NP-hard for classical computers. Advances in quantum computing systems and our classical, quantum-inspired algorithm GAMA (Graver Aug-mented Multi-seed Algorithm) motivated us to revisit methods for identifying altered pathways. Using different types of quantum and classical algorithms, we report novel formulations of the problem that are tailored to these new computational models. Our formulations require fewer binary variables than available methods, and offer a tuning parameter that allows a trade-off between coverage and exclusivity; varying this parameter returns a variety of solutions. We illustrate our formulations and methods with TCGA mutation data for Acute Myeloid Leukemia (AML). Both the D-Wave quantum annealing solver and the classical GAMA solver returned altered pathways that are known to be important in AML, with different tuning parameter values returning alternative altered pathways. Our reduced-variable algorithm and QUBO problem for-mulations demonstrate how quantum annealing-based binary optimization solvers can be used now in cancer genomics.

bioinformatics↗

bioRxiv2018

The consensus molecular classification of muscle-invasive bladder cancer

Muscle-Invasive Bladder Cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, yielding diverse sets of subtypes, which hampers the clinical implications of such knowledge. Here, we report the results of a large international effort to reach a consensus on MIBC molecular subtypes. Using 1750 MIBC transcriptomes and a network-based analysis of six independent MIBC classification systems, we identified a consensus set of six molecular classes: Luminal Papillary (24%), Luminal Non-Specified (8%), Luminal Unstable (15%), Stroma-rich (15%), Basal/Squamous (35%), and Neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. This consensus system offers a robust framework that will enable testing and validating predictive biomarkers in future clinical trials.

cancer biology↗