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The genomic landscape of pediatric cancers: Implications for diagnosis and treatment

Science

The past decade has witnessed a major increase in our understanding of the genetic underpinnings of childhood cancer. Genomic sequencing studies have highlighted key differences between pediatric and adult cancers. Whereas many adult cancers are characterized by a high number of somatic mutations, pediatric cancers typically have few somatic mutations but a higher prevalence of germline alterations in cancer predisposition genes. Also noteworthy is the remarkable heterogeneity in the types of genetic alterations that likely drive the growth of pediatric cancers, including copy number alterations, gene fusions, enhancer hijacking events, and chromoplexy. Because most studies have genetically profiled pediatric cancers only at diagnosis, the mechanisms underlying tumor progression, therapy resistance, and metastasis remain poorly understood. We discuss evidence that points to a need for more integrative approaches aimed at identifying driver events in pediatric cancers at both diagnosis and relapse. We also provide an overview of key aspects of germline predisposition for cancer in this age group. Approximately 300,000 children from infancy to age 14 are diagnosed with cancer worldwide every year (1). Some of the cancer types affecting the pediatric population are also seen in adolescents and young adults (AYA), but it has become increasingly clear that cancers in the latter age group have unique biological characteristics that can affect prognosis and therapy (2).


Crispr Therapeutics Plans to Launch Its First Clinical Trial in 2018

WIRED

In late 2012, French microbiologist Emmanuelle Charpentier approached a handful of American scientists about starting a company, a Crispr company. They included UC Berkeley's Jennifer Doudna, George Church at Harvard University, and his former postdoc Feng Zhang of the Broad Institute--the brightest stars in the then-tiny field of Crispr research. Back then barely 100 papers had been published on the little-known guided DNA-cutting system. It certainly hadn't attracted any money. But Charpentier thought that was about to change, and to simplify the process of intellectual property, she suggested the scientists team up.


Blood Diseases Could Show Crispr's Potential as Therapy

WIRED

You know you've struck marketing gold when a brand becomes a so-called "proprietary eponym." Need to blow your nose? In biology, Crispr is the proprietary eponym of the moment. The gene-editing technique is so inexpensive and easy to use that, in just four years, it's become a ubiquitous tool in labs across the world. And soon, it could jump from bench-top workhorse to human therapeutic.


Three people with inherited diseases successfully treated with CRISPR

New Scientist

Two people with beta thalassaemia and one with sickle cell disease no longer require blood transfusions, which are normally used to treat severe forms of these inherited diseases, after their bone marrow stem cells were gene-edited with CRISPR. Result of the ongoing trial, which is the first to use CRISPR to treat inherited genetic disorders, were announced today at a virtual meeting of the European Hematology Association. Beta thalassaemia and sickle cell are diseases caused by mutations that affect haemoglobin, the protein that carries oxygen in red blood cells. Those with severe forms require regular blood transfusions. However, a few people with the disease-causing mutations never show any symptoms, because they keep producing fetal haemoglobin in adulthood.


Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network

arXiv.org Machine Learning

Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempt to determine a set of critical proteins that are associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning network consisting of autoencoders forming a hierarchical model from which high-level features are extracted without labeled training data. Dimensional reduction reduced the number of critical proteins from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD mutation with the levels of these 20 critical proteins (accuracy 97%, sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on 20 proteins with the strongest association with FLT3-ITD. The results of this study allow a novel approach to determine critical protein pathways in the FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to model big data in cancer proteomics and genomics.