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Bioinformatics Training and Education Program

may, 2021

03may3:00 pm4:00 pmSignatera – A Personalized Tumor-informed Approach to Detect Molecular Residual Disease


Event Details

We will discuss technical advantages of a personalized and tumor-informed multiplex PCR next generation sequencing assay, called Signatera™, that enables a sensitive, specific, and dynamic detection of molecular disease burden in cell-free DNA (cfDNA) samples. The tumor-informed approach offers detection of circulating tumor DNA (ctDNA) by tracking tumor-specific clonal variants in plasma based on up front tumor tissue and matched normal sequencing data. Signatera test performance has been clinically validated in multiple cancer types including colorectal, non-small cell lung, breast, and bladder cancers. Unlike a standard liquid biopsy, Signatera is not intended to match patients with any particular therapy; rather it is intended to detect and quantify residual disease, detect recurrence earlier, assess therapy effectiveness, and help optimize treatment decisions.

Raheleh Salari is the Senior Director of Bioinformatics at Natera, where she leads a team of Bioinformatics Scientists working on cfDNA-based diagnostic tests. Natera is a global leader in cell-free DNA testing with a mission to transform the diagnosis and management of genetic diseases. Natera’s Signatera is at the forefront of innovation, representing a breakthrough technology that provides clinicians with an invaluable tool for sensitive, specific, and dynamic detection of molecular disease burden. Raheleh holds a PhD in Computer Science from Simon Fraser University, Canada, with a background on data structure and algorithm design. After her PhD, she continued her research at the National Center for Biotechnology and Information (NCBI) for slightly over a year.

Lecture by Dr. Raheleh Salari from Natera Inc.


Meeting details:

Topic: CDSL webinar
Time: May 3, 2021 03:00 PM Eastern Time (US and Canada)

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Meeting ID: 979 4193 1766
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(Monday) 3:00 pm - 4:00 pm




CDSLNCI CCR Cancer Data Science Lab

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