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Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies

Unsupervised machine learning and 3 PM recommendations
: Goldstein, Elisha; Yeghiazaryan, Kristina; Ahmad, Ashar; Giordano, Frank A.; Fröhlich, Holger; Golubnitschaja, Olga

Fulltext urn:nbn:de:0011-n-6023031 (1.1 MByte PDF)
MD5 Fingerprint: acf0b309315fe0da33786c99efa19f90
Created on: 16.9.2020

The EPMA journal 11 (2020), No.3, pp.505-515
ISSN: 1878-5085 (online)
ISSN: 1878-5077 (print)
Journal Article, Electronic Publication
Fraunhofer SCAI ()
Precision Medicine; Data Science; Artificial Intelligence; Biomarkers; Predictive preventive personalised medicine (PPPM/3 PM); Liver diseases; Liver malignancy; Palliative medicine; Unsupervised machine learning; Multiparametric modelling; Patient stratification; Hepatocellular carcinoma; Colorectal cancer; Breast cancer; Prostate cancer; Metastasis; Liquid biopsy; Ex vivo; Circulating leucocytes; Multi-omics; Biomarker patterns; Multi-level diagnostics; Survival; Prognosis; Metalloproteinase; Comet assay; Calgranulin A; S100; Catalase; Superoxide-dismutase 2; SOD-2; Profilin; Rho A; Thioredoxin; Scavenger; Antioxidant compounds; ROS inhibition; Selective internal radiation therapy (SIRT); Trans-arterial chemo-embolisation (TACE); Individual outcomes; Covid-19; Viral infection; Hepatitis; Hypoxia; Inflammation; Detoxification; Impairments; Individualised patient profile; Redox status; Genoprotection; Phytochemicals; Redox-based therapy; Riskmitigation

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from “just end of the life” care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, “comet assay” patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin—all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of parameters measured, clear differences were found for “comet assay” and S100 expression patterns. In conclusion, multi-faceted cancer control within the palliative care of liver malignancies is crucial for improved disease outcomes including individualised patient profiling, predictive models and implementation of corresponding cost-effective risks mitigating measures detailed in the paper. The “proof-of-principle” model is presented.