Working Group 404/06
Mathematical & Computational Modelling
No of members
Prof Gabriel FERNANDEZ CALVO
To discover liquid biopsy biomarkers and neuroimaging markers for iagnosis and prognosis of brain cancers. Relevant to RCO1, RCO4 and RCO5
T4.1 Data resource collection & data integration strategies; T4.2 Create a pan-European brain cancer FAIR-aware database and biobank; T4.3 WG4 meetings
Involved in all MSC and WG4 meetings including organising a WG4/WG5-focused conference, contribute to annual and final reports, the final year conference; 2 peer reviewed joint publications, contribute to dissemination, exploitation, training schools
M4.1: Mid-term report in month 25; M4.2: WG4/WG5 conference in month 34; M4.3: Database development in month 44.
MATHEMATICAL & COMPUTATIONAL MODELLING (Theme-4) Although ML/DL approaches such
as Support Vector Machine (SVM), Random Forest (RF), as well as DL approaches, e.g. Convolutional Neural Network (CNN) and DeepCC, and subtype classification using a single type of omics or imaging data, have been successfully applied in cancer diagnosis and subtype classification, current existing methods may not be adequate to address the challenge of integrating genotypic data from patients, multi-omics data from drug treatments, pharmacology data from bioactivity and toxicity assays, as well
as radiomics imaging data for diagnosis and classification of brain tumours, therefor, developing new algorithms are highly demanded. Pioneer studies have been conducted to integrate scRNAseq, miRNAseq, and DNAm data from TCGA using ML/DL for pan-cancer classification and survival prediction where multi-omics and clinical data are both considered, this Action aims to extend such strategies by integrating genotype and radiomics data. Specifically, omics-specific DL models including radiomics model will be built-up for a single type or multiple types of brain cancer depending on the data availability, then the VCDN approach will be utilized to integrate these models for classification, and novel approaches will be developed for optimisation. Biomarkers can be identified by their importance to classification performance which has the potential to interpret the results and understand the
underlying cancer biology and pathology, especially when the correlation between radiomic and otheromics biomarkers is detected (radiogenomics study). To make ML/DL explainable and enhance its success in clinical practice, combining domain expertise with AI algorithms is essential. Therefore, this Action also aims to encourage clinicians, neuro-oncologist, radiologists, etc. to participate in the early development of AI algorithms and the whole training and validation process.
Moreover, five mathematical models will be developed to understand and overcome resistance to therapies by integrating multi-omics data as well as biomarkers discovered in liquid biopsies:
1) to optimise the scheduling/therapy combinations allowing to mitigate or overcome the problem of the emergence of resistances; 2) to simulate tumour heterogeneity influence the response to drug therapies in pre-clinical models developed during this Action; 3) to reveal “macroscopic” heterogeneity metrics correlate with tumour cell biology (genotypes and/or phenotypes i.e., invasive vs angiogenic growth); 4) to estimate prognosis in brain metastasis and understand the response of these secondary tumours to radiotherapy focusing on lung and breast cancers. 5) to develop a tumour growth model (TGM) by estimating cell density.
Finally, open-source big data processing and bioinformatics solutions — e.g., cBioportal, tranSMART, and Arvados — will be deployed for creating a pan-European brain cancer database implementing the FAIR Data Principles during this Action and be publicly accessible after this Action. Although the general limited data/sample size due to the cost in biomedical research is one of the key challenges for the application of ML/DL, it is believed through data integration/combination along with more and more data becoming available, it may not be a significant concern in the future.