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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

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Published on: July 25, 2020

Next-Generation Sequencing Analysis and Algorithms for PDX and CDX Models.

Garima Khandelwal¹, María Romina Girotti², Christopher Smowton³

¹RNA Biology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, United Kingdom.

Molecular Cancer Research : MCR

|April 27, 2017

Summary

This summary is machine-generated.

Accurately distinguishing host from xenograft DNA and RNA sequences is crucial for cancer research using patient-derived xenograft (PDX) and circulating tumor cell-derived explant (CDX) models. This study presents a sensitive tool to remove host contamination, ensuring reliable genomic and transcriptomic analysis.

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Area of Science:

Genomics
Cancer Research
Bioinformatics

Background:

Patient-derived xenograft (PDX) and circulating tumor cell-derived explant (CDX) models are vital for studying human diseases, particularly cancer.
These models recapitulate tumor heterogeneity and drug responses, making them valuable for genomic, transcriptional, and epigenetic analyses.
Accurate identification of sequencing reads originating from the host versus the xenograft is essential for reliable data interpretation.

Purpose of the Study:

To demonstrate that failure to identify host contamination in PDX and CDX sequencing data leads to incorrect mutation calls and missed tumorigenicity signatures.
To introduce a sensitive, open-source algorithm and software tool for identifying and removing host-derived sequences from xenograft sequencing data.
To validate the tool's utility in correcting whole-exome and RNA-sequencing data from melanoma PDX and CDX models.

Main Methods:

Analysis of DNA- and RNA-sequencing (DNA-Seq and RNA-Seq) data from PDX and CDX models.
Development and application of a sensitive algorithm for host sequence identification.
Utilizing an open-source software tool to remove contaminating host reads.

Main Results:

Failure to remove host reads significantly confounds mutation calls and obscures cancer-associated genomic signatures.
The developed algorithm and tool effectively identify and remove host sequences with high sensitivity and selectivity.
Application to melanoma PDX and CDX models confirmed the tool's accuracy in correcting sequencing data.

Conclusions:

Correctly identifying and removing host contamination is critical for accurate genomic and transcriptomic profiling of PDX and CDX models.
The presented open-source tool provides a reliable solution for data correction, enhancing the utility of xenograft models in cancer research.
This method is broadly applicable to various deep sequencing applications involving xenograft or explant models.