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Pfizer this week announced that it had filed a new drug application with the US Food and Drug Administration for the pharmacogenomically guided non-small cell lung cancer drug crizotinib.

The company is seeking approval for crizotinib in advanced NSCLC patients whose tumors harbor the EML4 and ALK fusion oncogene, and as such a genetic test needs to be marketed with the drug to pick out patients expressing this gene marker.

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Bloomberg reports that the DNA-for-cash deal reported in Kentucky might be a more widespread scam.

St. Jude Children's Research Hospital scientists have treated infants with X-linked severe combined immunodeficiency using gene therapy in an early phase study.

St. Louis Public Radio reports that some African Americans are turning to DNA ancestry testing to help guide genealogical searches.

In Nature this week: a genomic analysis of the snailfish Pseudoliparis swirei, ancient DNA analysis gives insight into the introduction of farming to England, and more.

Apr
30
Sponsored by
Lexogen

This webinar will discuss novel long-read transcript sequencing (LRTseq) methods for transcriptome annotation that could increase the efficiency and accuracy of future sequencing projects.

May
08
Sponsored by
Sysmex Inostics

This webinar will present recent evidence that demonstrates how incorporating circulating tumor DNA (ctDNA) assessments into real-world patient management can influence patient care decisions, alter radiographic interpretations, and impact clinical outcomes.

May
15
Sponsored by
Thermo Fisher Scientific

This webinar will discuss how Radboud University Medical Center’s Department of Human Genetics is using exon-level copy number variant (CNV) detection by microarray to assist its efforts in constitutional genome testing. 

May
16
Sponsored by
PerkinElmer

This webinar will discuss a comprehensive end-to-end workflow for soil metagenomic shotgun sequencing that offers an unbiased alternative to amplicon-based approaches to assess the composition of culture-free microbial communities and predict functional profiles.