Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration

PURPOSE: Neovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor (anti-VEGF) treatment is effective, response varies considerably between individuals. Thus, patients face substantial uncertainty regarding their future ability to perform daily tasks. In this study, we evaluate the performance of an automated machine learning (AutoML) model which predicts visual acuity (VA) outcomes in patients receiving treatment for nAMD, in comparison to a manually coded model built using the same dataset. Furthermore, we evaluate model performance across ethnic groups and analyse how the models reach their predictions. METHODS: Binary classification models were trained to predict whether patients' VA would be 'Above' or 'Below' a score of 70 one year after initiating treatment, measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The AutoML model was built using the Google Cloud Platform, whilst the bespoke model was trained using an XGBoost framework. Models were compared and analysed using the What-if Tool (WIT), a novel model-agnostic interpretability tool. RESULTS: Our study included 1631 eyes from patients attending Moorfields Eye Hospital. The AutoML model (area under the curve [AUC], 0.849) achieved a highly similar performance to the XGBoost model (AUC, 0.847). Using the WIT, we found that the models over-predicted negative outcomes in Asian patients and performed worse in those with an ethnic category of Other. Baseline VA, age and ethnicity were the most important determinants of model predictions. Partial dependence plot analysis revealed a sigmoidal relationship between baseline VA and the probability of an outcome of 'Above'. CONCLUSION: We have described and validated an AutoML-WIT pipeline which enables clinicians with minimal coding skills to match the performance of a state-of-the-art algorithm and obtain explainable predictions

Loss of SigB in Listeria monocytogenes strains EGD-e and 10403S leads to hypersensitivity to hydrogen peroxide in stationary phase under aerobic conditions

SigB is the main stress gene regulator in L. monocytogenes affecting the expression of more than 150 genes and thus contributing in multiple stress resistance. Despite its clear role in most stresses, its role in oxidative stress is uncertain as results accompanying the loss of sigB range from hyperresistance to hypersensitivity. Previously, these differences have been attributed to strain variation. In this study, we show conclusively that in contrast to all other stresses, loss of sigB results in hyperresistance against H2O2 (more than 8 log CFU ml-1 compared to the wild type) in aerobically-grown stationary phase cultures of 10403S and EGD-e.. Furthermore, growth at 30°C resulted in higher resistance to oxidative stress than at 37°C. Oxidative stress resistance seemed to be higher with higher levels of oxygen. Under anaerobic conditions, loss of SigB in 10403S did not affect survival against H2O2 while in EGD-e it resulted in a sensitive phenotype. During exponential phase, minor differences occurred as expected due to the absence of sigB transcription. Catalase tests were performed under all conditions and stronger catalase results corresponded well with higher survival underpinning the important role of catalase in this phenotype. Furthermore, we assessed the catalase activity in protein lysates which corresponded with the catalase tests and survival. In addition, RT-PCR showed no differences in transcription between the wild type and the ΔsigB in various oxidative stress genes. Further investigation of the molecular mechanism behind this phenotype and its possible consequences for the overall phenotype of L. monocytogenes are underway

The MyD88+ phenotype is an adverse prognostic factor in epithelial ovarian cancer

The prognosis of epithelial ovarian cancer is poor in part due to the high frequency of chemoresistance. Recent evidence points to the Toll-like receptor-4 (TLR4), and particularly its adaptor protein MyD88, as one potential mediator of this resistance. This study aims to provide further evidence that MyD88 positive cancer cells are clinically significant, stem-like and reproducibly detectable for the purposes of prognostic stratification. Expression of TLR4 and MyD88 was assessed immunohistochemically in 198 paraffin-embedded ovarian tissues and in an embryonal carcinoma model of cancer stemness. In parallel, expression of TLR4 and MyD88 mRNA and regulatory microRNAs (miR-21 and miR-146a) was assessed, as well as in a series of chemosensitive and resistant cancer cells lines. Functional analysis of the pathway was assessed in chemoresistant SKOV-3 ovarian cancer cells. TLR4 and MyD88 expression can be reproducibly assessed via immunohistochemistry using a semi-quantitative scoring system. TLR4 expression was present in all ovarian epithelium (normal and neoplastic), whereas MyD88 was restricted to neoplastic cells, independent of tumour grade and associated with reduced progression-free and overall survival, in an immunohistological specific subset of serous carcinomas, p<0.05. MiR-21 and miR-146a expression was significantly increased in MyD88 negative cancers (p<0.05), indicating their participation in regulation. Significant alterations in MyD88 mRNA expression were observed between chemosensitive and chemoresistant cells and tissue. Knockdown of TLR4 in SKOV-3 ovarian cells recovered chemosensitivity. Knockdown of MyD88 alone did not. MyD88 expression was down-regulated in differentiated embryonal carcinoma (NTera2) cells, supporting the MyD88+ cancer stem cell hypothesis. Our findings demonstrate that expression of MyD88 is associated with significantly reduced patient survival and altered microRNA levels and suggest an intact/functioning TLR4/MyD88 pathway is required for acquisition of the chemoresistant phenotype. Ex vivo manipulation of ovarian cancer stem cell (CSC) differentiation can decrease MyD88 expression, providing a potentially valuable CSC model for ovarian cancer

Heterogeneous expression of monoclonal anti-TLR4.

<p>A: variable staining observed in adjacent epithelium within a benign serous cystadenoma (20x). B: focal strong staining within a serous carcinoma (40x).</p

The effect of silencing MyD88 and TLR4 mRNA on the chemoresponsive properties of SKOV-3 cells.

<p>SKOV-3 cells were left untransfected (Unt), transfected with negative control siRNA (siNeg), MyD88 targeting siRNA (siMyD88) or TLR4 targeting siRNA (siTLR4). The transfected cells were incubated for 72 hrs before either harvesting for mRNA analysis (A), for protein analysis (B) or treatment with paclitaxel (C). (A) MyD88 and TLR4 mRNA expression levels were evaluated by TaqMan RT-PCR. MyD88 and TLR4 mRNA expression was normalised to that of an endogenous control, B2M, and calibrated to that of untreated cells to establish the relative percentage of mRNA expression (n = 3, mean +SD). (B) MyD88 and TLR4 mRNA expression levels were evaluated by western blot analysis. GAPDH was used as a loading control. (C) Transfected cells were either left untreated, treated with DMSO (vehicle control) or 3.5 nM of paclitaxel (IC25). 48 hrs post treatment, cell viability was assessed by means of the CCK-8 assay. % cell viability rate was calculated by comparing the absorbance values for the vehicle control to the corresponding paclitaxel treated samples. Results are expressed as mean +SD, n = 3; *p<0.05, **p<0.01 (un-paired Student's t-test).</p

MyD88 expression and survival (Kaplan-Meier curves: median survival time shown in months).

<p>MyD88 positive tumours (n = 12) had significantly reduced progression-free survival (A) and overall survival (B) (p = 0.018 and p = 0.008, respectively).</p

TLR4/MyD88 and progression-free survival (Kaplan-Meier curves; median survival shown in months).

<p>TLR4 (A) or MyD88 (B) negative cases had significantly better PFS (15 & 18 months longer; p<0.05).</p

miR-21 (A) and miR-146a (B) expression in chemosensitive and chemoresistant cancer cells.

<p>Data are expressed as fold change in expression with respect to A2780 cancer cells (with standard deviation).</p

Photomicrographs illustrating the process of laser capture microdissection

<p>, including pre-dissection (A), post-dissection with a malignant gland removed from the surrounding stroma (B) and the isolated epithelial sample for genetic analysis (C).</p