Association of dystrophic epidermolysis bullosa and neuroblastoma in a newborn.

n/

Report from the 4th European Bone Sarcoma Networking meeting: focus on osteosarcoma

Abstract This report summarizes the proceedings of the 4th European Bone Sarcoma Networking Meeting, held in London, England, on 21 June 2017. The meeting brought together scientific and clinical researchers and representatives from sarcoma charities from 19 countries representing five networks across Europe, to present and discuss new developments on bone sarcoma. In view of the challenges is poses, the meeting focussed primarily on osteosarcoma with presentations on developments in our understanding of osteosarcoma genetics and immunology as well as results from preclinical investigations and discussion of recent and ongoing clinical trials. These include studies examining the efficacy of multi-targeted tyrosine kinase inhibitors and checkpoint inhibitors, as well as those with molecular profiling to stratify patients for specific therapies. Discussion was centred on generation of new hypotheses for collaborative biological and clinical investigations, the ultimate goal being to improve therapy and outcome in patients with bone sarcomas

Precision Medicine in Osteosarcoma: MATCH Trial and Beyond

Osteosarcoma (OS) is a rare bone malignant tumour with a poor prognosis in the case of recurrence. So far, there is no agreement on the best systemic therapy for relapsed OS. The availability of next generation sequencing techniques has recently revolutionized clinical research. The sequencing of the tumour and its matched normal counterpart has the potential to reveal a wide landscape of genetic alterations with significant implications for clinical practice. The knowledge that the genomic profile of a patient’s tumour can be precisely mapped and matched to a targeted therapy in real time has improved the development of precision medicine trials (PMTs). PMTs aiming at determining the effectiveness of targeted therapies could be advantageous for patients with a tumour refractory to standard therapies. Development of PMTs for relapsed OS is largely encouraging and is in its initial phase. Assessing OS features, such as its rarity, its age distribution, the technical issues related to the bone tissue origin, and its complex genomic landscape, represents a real challenge for PMTs development. In this light, a multidisciplinary approach is required to fully exploit the potential of precision medicine for OS patients

Precision Medicine in Osteosarcoma: MATCH Trial and Beyond

Osteosarcoma (OS) is a rare bone malignant tumour with a poor prognosis in the case of recurrence. So far, there is no agreement on the best systemic therapy for relapsed OS. The availability of next generation sequencing techniques has recently revolutionized clinical research. The sequencing of the tumour and its matched normal counterpart has the potential to reveal a wide landscape of genetic alterations with significant implications for clinical practice. The knowledge that the genomic profile of a patient’s tumour can be precisely mapped and matched to a targeted therapy in real time has improved the development of precision medicine trials (PMTs). PMTs aiming at determining the effectiveness of targeted therapies could be advantageous for patients with a tumour refractory to standard therapies. Development of PMTs for relapsed OS is largely encouraging and is in its initial phase. Assessing OS features, such as its rarity, its age distribution, the technical issues related to the bone tissue origin, and its complex genomic landscape, represents a real challenge for PMTs development. In this light, a multidisciplinary approach is required to fully exploit the potential of precision medicine for OS patients

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments