Biology subjects

Lo, B.

Publications and source records attributed to Lo, B..

5 recordsLinked to original sources

bioRxiv2024

MUC2 Expression Modulates Immune Infiltration in Colorectal Cancer

IntroductionColorectal cancer (CRC) is a prevalent malignancy with significant morbidity and mortality worldwide. A deeper understanding of the interaction of cancer cells with other cells in the tumor microenvironment is crucial for devising effective therapeutic strategies. MUC2, a major component of the protective mucus layer in the gastrointestinal tract, has been implicated in CRC progression and immune response regulation. MethodIn this study, we sought to elucidate the relationship between MUC2 expression and immune infiltration within CRC, using in-vitro models involving two well-established cell lines, HT-29 and LS-174T. By employing CRISPR-mediated MUC2 knockout, we investigated the influence of MUC2 on tumor immune infiltration and its interplay with T cells and NK cells enriched peripheral blood mononuclear cells (PBMCs) in 3D spheroid cultures. ResultsWhile MUC2 was more abundant in LS-174T cell lines compared to HT-29, its knockout resulted in increased immune infiltration solely in the HT-29 cell line, but not in LS-174T. We revealed that the removal of MUC2 protein was compensated in LS-174T by the expression of other gel forming mucin proteins (Muc6, Muc5B) commonly expressed in gastrointestinal epithelium, while this was not observed in HT-29 cell line. DiscussionWe propose that the role of MUC2 documented in CRC progression can partially be explained by impairing immune infiltration due to physical barrier established by the gel forming proteins such as MUC2 in mucinous CRC. On the other hand, the removal of MUC2 expression can be compensated by alternative gel forming mucin proteins, thereby impeding any increase in tumor immune infiltration.

cancer biology↗

bioRxiv2023

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9{+/-}18.0 years; time since stroke 12.2{+/-}0.2 months; normalised motor score 0.7{+/-}0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

neuroscience↗

bioRxiv2021

Chronic stroke sensorimotor impairment is related to smaller hippocampal volumes: An ENIGMA analysis

Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is involved in sensorimotor behavior but has not been widely studied within the context of post-stroke upper limb sensorimotor impairment. The hippocampus is vulnerable to secondary degeneration after stroke, and damage to this region could further weaken sensorimotor circuits, leading to greater chronic sensorimotor impairment. The purpose of this study was to investigate the cross-sectional association between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke. We hypothesized that smaller ipsilesional hippocampal volumes would be associated with worse upper-limb sensorimotor impairment. Cross-sectional T1-weighted brain MRIs were pooled from 357 participants at the chronic stage after stroke (>180 days post-stroke) compiled from 18 research cohorts worldwide in the ENIGMA Stroke Recovery Working Group (age: median = 61 years, interquartile range = 18, range = 23-93; 135 women and 222 men). Sensorimotor impairment was estimated from the Fugl-Meyer Assessment of Upper Extremity scores. Robust mixed-effects linear models were used to test associations between post-stroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, p-value < 0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. We also performed an exploratory analysis to test whether sex differences influence the relationship between sensorimotor impairment and hippocampal volume. Upper limb sensorimotor impairment was positively associated with ipsilesional (p = 0.005; d = 0.33) but not contralesional (p = 0.96; d = 0.01) hippocampal volume, such that impairment was worse for participants with smaller ipsilesional hippocampal volume. This association remained significant independent of lesion volume or other covariates (p = 0.001; d = 0.36). Evidence indicates an interaction between sensorimotor impairment and sex for both ipsilesional (p = 0.008; d = -0.29) and contralesional (p = 0.006; d = -0.30) hippocampal volumes, whereby women showed progressively worsening sensorimotor impairment with smaller hippocampal volumes compared to men. The present study has identified a novel association between chronic post-stroke sensorimotor impairment and ipsilesional, but not contralesional, hippocampal volume. This finding was not due to lesion size and may be stronger in women. We also provide supporting evidence that smaller hippocampal volume post-stroke is likely a consequence of ipsilesional damage, which could provide a link between vascular disease and other disorders, such as dementia.

neuroscience↗

bioRxiv2019

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data

BackgroundTreating cancer depends in part on identifying the mutations driving each patients disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant-calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed. ResultsBecause ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illuminas TruSight 170 panel and the Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple biological replicates of each assay, to test reproducibility. Finally, we applied five different public and freely-available somatic single-nucleotide variant (SNV) callers to the data, MuTect2, SAMtools, VarScan2, Pisces and VarDict. Although the TruSight 170 and Oncomine Focus panels cover different amounts of the genome, we did not observe major differences in variant calling success within the regions that each covers. We observed substantial discrepancies between the five variant callers. All had high sensitivity, detecting known SNVs, but highly varying and non-overlapping false positive detections. Harmonizing variant caller parameters or intersecting the results of multiple variant callers reduced disagreements. However, intersecting results from biological replicates was even better at eliminating false positives. ConclusionsReproducibility and accuracy of targeted clinical sequencing results depends less on sequencing platform and panel than on downstream bioinformatics and biological variability. Differences in variant callers default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend analyzing replicate samples, as this greatly decreases false positive calls.

bioinformatics↗

bioRxiv2018

Creating Standards for Evaluating Tumour Subclonal Reconstruction

Tumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.

bioinformatics↗