Biology subjects

Berenguer Llergo, A.

Publications and source records attributed to Berenguer Llergo, A..

3 recordsLinked to original sources

bioRxiv2022

MmCMS: Mouse models' Consensus Molecular Subtypes of colorectal cancer

BACKGROUNDColorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODSUsing transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n=577) and mouse (n=57 across n=8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTSWe developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONSWhen applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package with three options helps researchers select suitable mouse models of human CRC subtype for their experimental testing.

cancer biology↗

bioRxiv2021

A comparison of rotation-based scores for gene set analysis

Gene-wise differential expression is usually the first major step in the statistical analysis of high-throughput data obtained from tech-niques such as microarrays or RNA-sequencing. The analysis at gene level is often complemented by the screening of the data in a broader biological context that considers as unit of analysis meaningful groups of genes that may have functions in certain biological processes. Among the vast number of publications about gene set analysis, the rotation test for gene set analysis, also referred by roast, is a general sample randomization approach that maintains the integrity of the intra-gene set correlation structure in defining the null distribution of the test. In this work we compare the performance of several enrichment score functions using such rotational approach for hypothesis testing. We find that computationally intensive measures based on Kolmogorov-Smirnov statistics fail to improve the rates of simpler measures of GSA like mean and maxmean scores. We also show the importance of ac-counting for the gene linear dependence structure of the testing set, which it is linked to the loss of effective signature size. In this regard, weighted statistics are introduced with the aim of maximizing the ef-fective signature size. These are found to out-power other usual scores in some simulations scenarios. The average of absolute values is found to be the most powerful score using both simulated and benchmarking data. All tools are available in the roastgsa R package.

bioinformatics↗

bioRxiv2018

Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts

In recent years, many efforts in clinical and basic research have focused on finding molecular features of tumor samples with prognostic or classification potential. Among these, the association of the expression of gene signatures with survival probability is of special interest given its relatively direct applicability in the clinic and its power to shed insights into the molecular basis of cancer.\n\nAlthough great efforts have been invested in data processing to control for unknown sources of variability in a gene-wise manner, little is known about the behaviour of gene signatures with respect to the effect of technical variables.\n\nHere we show that the association of signatures with survival may be biased due to technical reasons and propose a simple and low intensive methodology based on correction by expectation under gene randomization. The resulting estimates are centred around zero and ensure correct asymptotic inference. Moreover, our methodology is robust against spurious correlations between global dataset tendencies and clinical outcome.\n\nAll tools (will be soon) available in the \"HRunbiased\" R package as well as processed datasets for colorectal and breast cancer.

bioinformatics↗