Periodic Reporting for period 1 - QIandQD (The first IVDR-approved commercial software solutions for AI-powered RNA-based companion and precision cancer diagnostics of acute myeloid leukaemia and bladder cancer)
Periodo di rendicontazione: 2024-05-01 al 2025-04-30
The molecular profiling of tumours is important for providing cancer patients with the correct diagnosis, most effective treatment and accurate prognosis. Next-generation sequencing (NGS)-based technologies are increasingly being included in clinical guidelines as a complement to more traditional laboratory techniques. NGS-based tumour profiling, such as RNA-sequencing (RNA-seq), offers a more comprehensive picture of the processes that control tumour heterogeneity compared to the single biomarker assays that are currently state of the art in the clinic.
Qlucore is developing visualisation-based analytical solutions with an established position in the NGS market. We aim to develop software solutions integrating machine learning-based classification models for gene expression signatures and gene fusion analysis from RNA-seq. In the current project we focus on models for classifying two cancer types, acute myeloid leukaemia (AML) and bladder cancer (BC), into clinically relevant subtypes to provide a more accurate diagnosis and prognosis.
A software-based setup speeds up the introduction of new clinical guidelines with the ultimate purpose of increasing survival rates and overall health.
Website reference: https://qlucore.com/collaborations-and-partners(si apre in una nuova finestra)
Qlucore is developing visualisation-based analytical solutions with an established position in the NGS market. We aim to develop software solutions integrating machine learning-based classification models for gene expression signatures and gene fusion analysis from RNA-seq. In the current project we focus on models for classifying two cancer types, acute myeloid leukaemia (AML) and bladder cancer (BC), into clinically relevant subtypes to provide a more accurate diagnosis and prognosis.
A software-based setup speeds up the introduction of new clinical guidelines with the ultimate purpose of increasing survival rates and overall health.
Website reference: https://qlucore.com/collaborations-and-partners(si apre in una nuova finestra)
Work package (WP) 1 focuses on project management and has so far delivered plans for an efficient management structure, such as data management and scientific management plans, and a risk assessment and change management plan. WP1 continuously works to ensure that the project delivers on time.
WP2 focuses on communication and dissemination activities and has delivered a dissemination and communication plan, as well as produced several press releases, newsletters and webinars promoting the project.
WP3 is responsible for generating RNA-seq data and has secured ethics approvals for the AML and BC cohorts for use of samples and data within the project. It has also produced standard operating procedures (SOPs) to ensure that laboratory workflow and bioinformatic pipelines generate RNA-seq data of high quality. Qlucore is working in close collaboration with two reasearch groups at Lund University that generate the gene expression data. A subset of 153 (AML) and 518 (BC) samples respectively, have already been processed at high quality and are used for training prototype classifiers in WP4. The remaining samples are scheduled for delivery on time.
WP4 is responsible for data analysis and training classifier models based on gene-expression profiles for subtyping AML and BC. The data generated in WP3 is used to train the first prototype classifiers.
WP2 focuses on communication and dissemination activities and has delivered a dissemination and communication plan, as well as produced several press releases, newsletters and webinars promoting the project.
WP3 is responsible for generating RNA-seq data and has secured ethics approvals for the AML and BC cohorts for use of samples and data within the project. It has also produced standard operating procedures (SOPs) to ensure that laboratory workflow and bioinformatic pipelines generate RNA-seq data of high quality. Qlucore is working in close collaboration with two reasearch groups at Lund University that generate the gene expression data. A subset of 153 (AML) and 518 (BC) samples respectively, have already been processed at high quality and are used for training prototype classifiers in WP4. The remaining samples are scheduled for delivery on time.
WP4 is responsible for data analysis and training classifier models based on gene-expression profiles for subtyping AML and BC. The data generated in WP3 is used to train the first prototype classifiers.