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Deciphering the developmental roots of childhood sarcoma cells – combining single cell sequencing technologies and machine learning

Location: United Kingdom

Source: EU Funding & Tenders Portal

Project
Ended

Each year more than 35,000 European children and young people are diagnosed with cancer. Childhood cancer remains a major public health and socioeconomic issue in Europe and around the world. Cancer arises when a single cell transforms and divides uncontrollably, resulting in a malignant mass of tumour cells. To study cancer, we must understand how these normal cells change. Our current understand

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The project “Deciphering the developmental roots of childhood sarcoma cells – combining single cell sequencing technologies and machine learning” is an infrastructure initiative, located in United Kingdom. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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Description

Description

Each year more than 35,000 European children and young people are diagnosed with cancer. Childhood cancer remains a major public health and socioeconomic issue in Europe and around the world. Cancer arises when a single cell transforms and divides uncontrollably, resulting in a malignant mass of tumour cells. To study cancer, we must understand how these normal cells change. Our current understanding of how normal cells vary across the many tissues of our body is poorly understood. Child development represents a unique challenge in understanding our cells, as children’s bodies change at a cellular level entirely different than adults. This is reflected in the spectrum of cancers diagnosed in children compared to adults, particularly in bone and soft tissue cancers (sarcomas) where they present in less than 1% of adults' cancers and nearly 21% of children's cancers. With the advent of high-throughput single-cell RNA sequencing (scRNA-seq), it is now possible to analyze cell populations at remarkable scale and resolution. The primary purpose of this project is to use scRNA-seq to reconstruct the phylogenetic cellular lineage of childhood and adult sarcomas, and corresponding normal tissue. This fellowship aims to (1) discover and define differences between normal and cancer cell biology at single-cell resolution and (2) use machine learning to determine the cell type (cell-of-origin), the somatic changes and transcriptional trajectories of normal cells that lead to malignant transformation in children compared to adults. This project represents the highest resolution map of the intratumour genetic heterogeneity and clonal evolution of sarcoma ever produced. Another outcome of this project will be a reference map of all of the somatic mutations and expression profiles of normal bone and cartilaginous tissue. This will be a pivotal resource for the global research community - the ‘Bone’ and ‘Cartilage’ branches of the ‘Developmental Human Cell Atlas’.

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100%

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