Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
July 25th, 2020
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Here we present a protocol to test the efficacy of targeted therapies selected based on the genomic makeup of a tumor. The protocol describes identification and validation of structural DNA rearrangements, engraftment of patients’ tumors into mice and testing responses to corresponding drugs.
Overview
This protocol demonstrates an algorithm for testing the efficacy of targeted therapies based on the genomic makeup of tumors. It includes the identification of structural DNA rearrangements and the engraftment of patient tumors into mice for drug response testing.
Key Study Components
Area of Science
- Oncology
- Genomics
- Pharmacology
Background
- Targeted therapies are selected based on genomic alterations.
- Understanding DNA rearrangements can inform treatment options.
- In vivo models are essential for testing drug efficacy.
- Immunocompromised mice are used for tumor engraftment.
Purpose of Study
- To validate the efficacy of drugs selected through genomic analysis.
- To establish a protocol for in vivo testing of targeted therapies.
- To enhance understanding of tumor responses to specific treatments.
Methods Used
- Whole genome bio-sequencing of human tumor specimens.
- Identification of genomic alterations, including gene rearrangements.
- Selection of potentially druggable changes based on analysis.
- In vivo treatment of tumors in immunocompromised mice.
Main Results
- Successful identification of structural DNA rearrangements.
- Validation of selected drugs in mouse models.
- Demonstration of treatment efficacy based on genomic analysis.
- Insights into tumor behavior in response to targeted therapies.
Conclusions
- The protocol provides a framework for testing targeted therapies.
- Genomic analysis is crucial for selecting effective treatments.
- In vivo models are valuable for understanding drug responses.
Frequently Asked Questions
What is the main focus of this protocol?
The protocol focuses on testing the efficacy of targeted therapies based on genomic alterations in tumors.
How are genomic alterations identified?
Genomic alterations are identified through whole genome bio-sequencing of human tumor specimens.
What type of animal models are used?
Immunocompromised mice are used for engrafting patient tumors.
What is the significance of structural DNA rearrangements?
Structural DNA rearrangements can indicate potential druggable targets for therapy.
How does this study contribute to cancer treatment?
It provides a systematic approach to selecting and testing targeted therapies based on genomic data.
What are the expected outcomes of the treatment?
The expected outcomes include improved understanding of tumor responses and validation of effective therapies.
Executive Industry Relevance
This protocol enables biopharma R&D teams to evaluate targeted therapies using patient-derived xenograft models guided by genomic profiling, supporting hypothesis-driven drug selection and mechanistic de-risking in preclinical development. By linking structural DNA alterations to functional drug response, it enhances predictive confidence in target validation and informs go/no-go decisions for combination therapies. The approach is particularly relevant for tumors with high genomic instability where standard therapies fail and off-label drug use is considered.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by linking structural DNA alterations such as gene amplifications and rearrangements to druggable targets like ERBB2/HER2 and AKT2.
- Operational Value: Supports functional target validation in vivo using PDX models, reducing mechanistic ambiguity before lead optimization.
- Predictive Value: Demonstrates correlation between genomic predictions and observed tumor responses, aiding portfolio triage based on target confidence.
Screening & Assay Development
- Scientific Value: Prepares validated biological systems (PDX models) with characterized genomic profiles for reliable compound evaluation in downstream screening.
- Operational Value: Standardizes tumor engraftment and treatment protocols, improving reproducibility across studies and enabling scalable preclinical testing.
- Assay Readiness: Generates quantitative molecular readouts (e.g., phosphorylated S6, AKT, mTOR) via immunoblotting to assess pathway inhibition and treatment response.
Translational & Preclinical Research
- Translational Continuity: Uses patient-derived tumors to maintain disease relevance, bridging genomic findings from human specimens to in vivo models.
- Preclinical Validation: Supports risk-adjusted advancement decisions by demonstrating tumor burden reduction and pathway-specific drug effects in vivo.
- Mechanistic De-risking: Clarifies whether observed efficacy is due to monotherapy or combination treatment, informing clinical trial design.
Pipeline & Workflow Integration
The method integrates genomic analysis with in vivo efficacy testing, positioning it between target identification and lead optimization in the discovery continuum to enable data-driven progression into preclinical development.
- Discovery Biology: Supports hypothesis testing and pathway clarification by validating whether structurally altered genes are functionally druggable in vivo.
- Screening: Creates assay-ready PDX models with known genomic drivers, enabling reliable compound screening and combination therapy evaluation.
- Analytics: Provides quantitative dependent variable measurements (tumor burden, protein phosphorylation) that allow teams to compare drug effects and assess target engagement.
- Translational Research: Maintains continuity from patient tumor to preclinical model, supporting biomarker alignment and predictive modeling of clinical response.
- Enterprise Reuse: Establishes a reusable workflow for testing targeted therapies across tumor types, reducing redundant model development.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in target validation by confirming DNA-level alterations translate to functional drug sensitivity in vivo.
- Operational Value: Enhances reproducibility through standardized tissue processing, engraftment, and drug administration protocols.
- Strategic Value: Improves capital efficiency by prioritizing targets with demonstrated in vivo efficacy, reducing late-stage biological risk.
- Portfolio Impact: Enables risk-adjusted prioritization of drug candidates based on concordance between genomic prediction and PDX response.
Implementation Considerations
- Requires expertise in cancer genomics, pathology, and in vivo modeling to accurately isolate viable tumor tissue and interpret genomic alterations.
- Dependent on next-generation sequencing infrastructure and bioinformatics tools (e.g., Panda tool) for structural alteration analysis and pathway enrichment.
- Necessitates standardized procedures for sterile tumor fragmentation, Matrigel mixing, and subcutaneous engraftment to ensure engraftment success.
- Requires immunoblotting capabilities to assess downstream pathway modulation (e.g., AKT/mTOR/S6) as pharmacodynamic readouts.
- Limited by the time and resource intensity of PDX model generation, which may restrict throughput for large-scale screening campaigns.
Biopharma FAQs
Why does null hypothesis testing matter for target validation in genomic analysis?
Null hypothesis testing, such as Fisher's exact test for pathway enrichment, determines whether observed gene alterations occur more frequently than by chance, helping distinguish driver from passenger events and increasing confidence in target selection.
How does independent variable isolation fit the discovery pipeline in this protocol?
Isolating specific genomic alterations (e.g., ERBB2 amplification) as independent variables allows researchers to test their causal relationship to drug response, supporting mechanistic de-risking and hypothesis-driven target validation.
What quantitative dependent variable measurements enable assessment of drug response in PDX models?
Tumor burden reduction and immunoblot-based quantification of phosphorylated signaling proteins (e.g., p-S6, p-AKT, p-mTOR) serve as dependent variables to measure drug efficacy and pathway inhibition following treatment.
Why do replication requirements matter for cross-functional collaboration in preclinical studies?
Replicating tumor engraftment and treatment across multiple mice ensures statistical robustness and reproducibility, enabling reliable data sharing between discovery, preclinical, and translational teams for go/no-go decisions.
What statistical analysis capabilities are required before implementing this genomic-to-PDX workflow?
Teams must be able to perform enrichment analysis (e.g., Fisher's exact test) to identify significantly altered pathways and validate genomic findings via PCR or immunoblotting to confirm target modulation before initiating in vivo studies.
