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Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Here, we present a protocol to accurately quantify multiple genetic alterations of a target region in a single reaction using drop-off ddPCR and a unique pair of hydrolysis...

Mutations

Mutations

Mutations

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

Viral Mutations

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

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Related Experiment Video

Updated: Jan 19, 2026

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Published on: September 25, 2018

Passenger Hotspot Mutations in Cancer.

Julian M Hess¹, Andre Bernards², Jaegil Kim¹

¹The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

|September 19, 2019

Summary

This summary is machine-generated.

Statistical models often misidentify cancer mutation hotspots. This study introduces a new Log-normal-Poisson (LNP) model to accurately distinguish driver mutations from passenger events, reducing false positives in large patient cohorts.

Keywords:

Log-normal-Poisson cancer drivers genomics hotspots mutability mutations passengers selection

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Published on: September 20, 2016

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Last Updated: Jan 19, 2026

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Published on: September 25, 2018

Point Mutations and Frameshift Mutations

Point Mutations and Frameshift Mutations

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Area of Science:

Genomics
Cancer Research
Statistical Bioinformatics

Background:

Current statistical methods for identifying cancer mutation hotspots are limited.
These models fail to account for site-specific mutability, leading to numerous false-positive results.
Accurate identification of true driver mutations is crucial for understanding cancer development.

Purpose of the Study:

To develop an improved statistical model for hotspot significance assessment.
To differentiate true cancer driver hotspots from passenger events.
To reduce false-positive rates in mutation hotspot identification.

Main Methods:

Detailed a Log-normal-Poisson (LNP) background model accounting for site-specific mutability.
Applied the LNP model to a large cohort of approximately 10,000 cancer patients.
Compared LNP model results against conventional methods for hotspot nomination.

Main Results:

The Log-normal-Poisson (LNP) model accurately accounts for mutational variability.
Passenger hotspots were shown to arise from common mutational processes.
The LNP model identified driver hotspots with significantly fewer false positives than traditional approaches.

Conclusions:

Many recurring cancer hotspot mutations are passenger events, not driver mutations.
These passenger events occur at inherently mutable genomic sites without positive selection.
The developed LNP model offers a more precise method for cancer driver hotspot discovery.