PathAI and Gilead Report Data from Machine Learning Model Predictions of Liver Disease Progression and Treatment Response at AASLD’s The Liver Meeting…
BOSTON (PRWEB) November 06, 2020
PathAI, a global provider of AI-powered technology applied to pathology research, today announced the results of a research collaboration with Gilead that retrospectively analyzed liver biopsies from participants in clinical trials evaluating treatments for NASH or CHB (1). Using digitized hematoxylin and eosin (H&E)-, picrosirius red-, and trichrome-stained biopsy slides, PathAIs machine learning (ML) models were able to accurately predict changes in features traditionally used as markers for liver disease progression in clinical practice and clinical trials, including fibrosis, steatosis, hepatocellular ballooning, and inflammation. The new results will be presented in an oral presentation and 4 poster sessions at The Liver Meeting Digital Experience (TLMdX) that will be held from November 13-16, 2020.
The data builds upon PathAIs previous success in retrospectively staging liver biopsies from clinical trials by showing that ML models may uncover patterns of histological features that correlate with disease progression or treatment response. Furthermore, ML models were able to estimate the hepatic venous pressure gradient (HVPG) in study subjects with NASH related cirrhosis and quantify fibrosis heterogeneity from digitized slides, which are measures that are not reliably captured by traditional pathology methods. After appropriate clinical validation, these new tools could be useful in staging disease more accurately than can be done with current approaches.
"We continue to use machine learning to advance our understanding of liver diseases, including NASH and hepatitis B, as a foundation for developing new methods to track disease progression and assess response to therapeutics, said PathAI co-founder and Chief Executive Officer Andy Beck MD, PhD. Our long-standing partnership with Gilead continues to demonstrate the power of AI-based pathology to support development efforts to bring new therapies to patients."
Highlights include:
Data presented at AASLD demonstrate the potential of machine learning approaches to improve our assessment of liver disease severity, reduce the variability of human interpretation of liver biopsies, and identify novel features associated with disease progression, said Rob Myers, MD, Vice President, Liver Inflammation/Fibrosis, Gilead Sciences. We are proud of our ongoing partnership with PathAI and look forward to continued collaboration toward our shared goals of enhancing research efforts and improving outcomes of patients with liver disease.
The antiviral drug TDF effectively suppresses hepatitis B virus in patients with CHB, but a small subset of patients have persistently elevated serum ALT despite virologic suppression. ML-models were applied to biopsy data from registrational studies of TDF to examine this small subgroup of non-responders. Analyses of the ML-model predicted histologic features showed that persistently elevated ALT after five years of TDF treatment is associated with a higher steatosis score at BL and increases in steatosis during follow-up. These data suggest that subjects with elevated ALT despite TDF treatment may have underlying fatty liver disease that impacts biochemical response.Machine Learning Enables Quantitative Assessment of Histopathologic Signatures Associated with ALT Normalization in Chronic Hepatitis B Patients Treated with Tenofovir Disoproxil Fumarate (TDF) Oral Abstract #18
ML-models were deployed on biopsies from registrational trials of TDF in CHB to identify cellular and tissue-based phenotypes associated with HBV DNA and hepatitis B e-antigen (HBeAg). The study demonstrated that proportionate areas of ML-model-predicted hepatocellular ballooning at BL and Yr 5, and lobular inflammation at Yr 5 were higher in subjects that did not achieve virologic suppression. In addition, lymphocyte density across the tissue and within regions of lobular inflammation correlated with HBeAg loss, supporting the importance of an early immune response for viral clearance.Machine Learning Based Quantification of Histology Features from Patients Treated for Chronic Hepatitis B Identifies Features Associated with Viral DNA Suppression and dHBeAg Loss Poster Number #0848
Standard manual methods for staging liver fibrosis have limited sensitivity and reproducibility. Application of a ML-model to evaluate changes in fibrosis in response to treatment in the STELLAR and ATLAS trials enabled development of the DELTA (Deep Learning Treatment Assessment) Liver Fibrosis Score. This scoring method accounts for the heterogeneity in fibrosis severity that can be detected by ML-models and reflects changes in fibrotic patterns that occur in response to treatment. Application of the DELTA Liver Fibrosis Score to biopsies from the Phase 2b ATLAS trial demonstrated a reduction in fibrosis in response to treatment with the investigational combination of cilofexor and firsocostat that was not detected by standard staging methods. Validation of a Machine Learning-Based Approach (DELTA Liver Fibrosis Score) for the Assessment of Histologic Response in Patients with Advanced Fibrosis Due to NASH Poster Number #1562
Integration of tissue transcriptomic data with histologic information is likely to reveal new insights into disease. Using liver tissue obtained during the STELLAR trials evaluating NASH subjects with advanced fibrosis, RNA-seq-generated, tissue-level gene expression profiles were integrated with ML-predicted histology. This analysis revealed five key genes strongly correlated with proportionate areas of portal inflammation and bile ducts, features that are themselves predictive of disease progression in NASH. High levels of expression of these genes was associated with an increased risk of progression to cirrhosis in subjects with bridging (F3) fibrosis (hazard ratio [HR] 2.1; 95% CI 1.25, 3.49) and liver-related clinical events among those with cirrhosis (HR 4.05; 95% CI 1.4, 14.36). Integration of Machine Learning-Based Histopathology and Hepatic Transcriptomic Data Identifies Genes Associated with Portal Inflammation and Ductular Proliferation as Predictors of Disease progression in Advanced Fibrosis Due to NASH Poster Number #595
The severity of portal hypertension as assessed by HPVG predicts the risk of hepatic complications in patients with liver disease but is not simple to measure. ML-models were trained on images of 320 trichrome-stained liver biopsies from a phase 2b trial of investigational simtuzumab in subjects with compensated cirrhosis due to NASH to recognize patterns of fibrosis that correlate with centrally-read HVPG measurements. Deployed on a test set of slides, ML-calculated HVPG scores strongly correlated with measured HVPG and could discriminate subjects with clinically-significant portal hypertension (HVPG 10 mm Hg).A Machine Learning Model Based on Liver Histology Predicts the Hepatic Venous Pressure Gradient (HVPG) in Patients with Compensated Cirrhosis Due to Nonalcoholic Steatohepatitis (NASH) Poster Number #1471
(1) Trials include STELLAR, ATLAS, and NCT01672879 for investigation of NASH therapies, and registrational studies GS-US-174-102/103 for tenofovir disoproxil fumarate [TDF] for CHB.
About PathAIPathAI is a leading provider of AI-powered research tools and services for pathology. PathAIs platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit pathai.com.
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PathAI and Gilead Report Data from Machine Learning Model Predictions of Liver Disease Progression and Treatment Response at AASLD's The Liver Meeting...