OCT 16, 2013 11:00 AM PDT

SuperSelective PCR Primers for the Detection of Rare Mutant Cancer Cells in Clinical Samples Containing Abundant Normal Cells

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Abstract

"SuperSelective" primers, by virtue of their unique design, enable only a few molecules of a mutant sequence to generate amplicons in conventional, real-time PCR assays without interference from extremely abundant wild-type molecules, even if the only difference between the mutant sequence and the wild-type sequence is a single-nucleotide polymorphism. The basis of this extraordinary selectivity is two-fold: (1) short, perfectly complementary mutant hybrids formed by the primers are thermodynamically more likely to be present at any given moment than the even shorter, mismatched wild-type hybrids; and (2) because the mean persistence time of mismatched wild-type hybrids (measured in milliseconds) is significantly shorter than the mean persistence time of perfectly complementary mutant hybrids, DNA polymerase molecules are kinetically more likely to encounter mutant hybrids before those hybrids dissociate, compared to the probability of their encountering the shorter-lived wild-type hybrids before they dissociate. Consequently, as few as 10 mutant molecules can routinely be distinguished and quantitated in samples containing 1,000,000 wild-type molecules. Learner Objectives: After completing this activity, the learner will be able to design polymerase chain reaction assays for the detection of rare mutant cells for the sensitive detection of circulating tumor cell DNA, for the quantitation of minimal residual disease, and for the early detection of mutations associated with resistance to therapeutic drugs.


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OCT 16, 2013 11:00 AM PDT

SuperSelective PCR Primers for the Detection of Rare Mutant Cancer Cells in Clinical Samples Containing Abundant Normal Cells


Specialty

Antibodies

Virology

Immunology

Dna Sequencing

Personalized Medicine

Clinical Diagnostics

Immunity

Cancer Diagnostics

Immunotherapy

Bioinformatics

Flow Cytometry

Immuno-Oncology

Gene Expression

Dna

Big Data

Geography

Europe100%

Registration Source

Website Visitors100%

Job Title

Student100%

Organization

Academic Institution100%


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