Drug Response QTL Profiling
Overview
To build better personalized treatments, we need a full picture of how genetics guides drug response. Our methods work together to provide this. It starts with Drug Response QTL Profiling, which pinpoints the genetic reasons for different drug reactions—from how the body breaks down a drug to how the drug hits its target. But genetics isn't static. That's where Dynamic QTL Mapping comes in. It lets us see how those genetic associations evolve, for example, as a patient undergoes therapy.
Cellular Genomics allows us to look at individual cells to complete the picture. This helps us understand the hidden diversity of responses within a single tumor or tissue, which is often the key to overcoming treatment resistance. Leveraging these techniques brings us closer to highly predictive and tailored therapeutic interventions.
Our Drug Response QTL Profiling, Dynamic QTL Mapping & Cellular Genomics Solution Elevates Your Research with:
Comprehensive QTL Analysis
We use advanced statistical and computational methods to pinpoint the genetic factors (QTLs) that affect how a person responds to drugs. Our approach combines genome-wide studies and machine learning to find both common and rare variants linked to drug efficacy or side effects. We can also track how these genetic associations change with different doses, treatment lengths, or combination therapies.
High-Resolution Cellular Profiling
Using single-cell sequencing, we look closely at how genetic differences influence drug responses within specific cell types. This helps identify which cells are sensitive or resistant to a treatment—enabling more targeted therapies and a better understanding of tumor diversity in cancer.
Integration of Multi-Omics Data
We bring together genomic, transcriptomic, proteomic, and metabolomic data to get a full picture of how a drug works. By linking QTL findings with functional genomics, we can identify key causal genes, discover new drug targets, and anticipate potential adverse effects.
Accelerated Drug Development Pipeline
Our insights into drug response's genetic and cellular drivers help speed up drug discovery. The platform supports new target identification, drug repurposing, and patient stratification for clinical trials—making therapeutic development more efficient and successful.
What Is Drug Response QTL Profiling
Drug Response QTL (Quantitative Trait Loci) Profiling utilizes dynamic QTL mapping and cellular genomics to understand how genetic variations influence individual responses to drugs. This approach identifies genetic markers associated with drug efficacy and toxicity by integrating genomic data with cellular-level responses. Dynamic QTL mapping tracks these associations across different conditions or time points, offering a nuanced view of drug-gene interactions. This solution enhances precision medicine by predicting patient-specific drug responses, and optimizing therapeutic strategies
How We Deliver This Solution
Sample Collection & Preparation
1. Recruiting Patient Cohorts
We assemble a diverse group of patients with different responses to a drug—responders versus non-responders. This helps us uncover genetic factors that may explain why people react differently in terms of treatment efficacy or side effects.
By including participants from various backgrounds, we can also identify genetic markers that are specific to certain populations or ancestral groups, offering insights into drug metabolism, immune response, and how well a therapy engages its target.
2. Collecting Multi-Omics Samples
To connect genetic variation with molecular activity, we collect several types of biological data—including genomic, transcriptomic, and proteomic information. This multi-omics approach allows us to link inherited genetic variants (germline DNA) with functional readouts like gene expression (eQTLs) and protein levels (pQTLs), helping pinpoint which variants truly influence drug response.
For example, we often collect matched tumor and normal DNA, whole blood RNA, and plasma proteomes before treatment begins and at specific points during therapy.
Sequencing
1. Whole Genome Sequencing (WGS)
- We use WGS to detect a wide range of genetic changes across the entire genome—including single nucleotide variants (SNVs), structural variations (SVs), and copy number alterations (CNAs). This helps uncover both rare and common variants that influence how a person responds to drugs.
- A key application is discovering new regulatory elements in non-coding regions (such as promoters or enhancers) that affect genes involved in drug metabolism (e.g., CYP2D6) or drug targets (e.g., EGFR).
- Link: /pop-genomics/seq/whole-genome-reseq.html
2. Whole Exome Sequencing (WES)
- WES focuses on protein-coding regions of the genome, helping us pinpoint functional variants that have a strong impact on drug response.
- It is especially useful for identifying loss-of-function mutations in pharmacogenes—like DPD variants linked to toxicity from fluoropyrimidine chemotherapy—or clinically actionable alterations such as the BRAF V600E mutation in melanoma.
- Link: /pop-genomics/seq/whole-exome-sequencing.html
Bioinformatics & Functional Analysis
1. QTL Mapping & Colocalization
- We link genetic variants to drug response outcomes—such as progression-free survival or drug concentration levels—using genome-wide association (GWAS) and fine-mapping methods. This helps identify whether the same genetic signal influences both drug response and an intermediate trait—like a gene expression QTL (eQTL) in liver tissue that may explain statin-induced myopathy risk.
- Tool Example: Use FastQTL or TensorQTL for cis/trans-QTL mapping, followed by coloc for colocalization analysis.
2. Polygenic Risk Scores (PRS) for Drug Response
- Purpose: Aggregate multiple QTLs into a single score to predict individualized drug efficacy/toxicity.
- Application: Prioritizes patients for high-cost therapies (e.g., PD-1 inhibitors in PD-L1-high tumors with favorable PRS) or flags high-risk individuals for alternative regimens.
- Method: Train PRS models using PRSice-2 or LDpred on drug response GWAS summary statistics.
3. Pathway & Functional Enrichment
- This analysis places genetic findings into biological context—highlighting pathways involved in drug metabolism, immune signaling, and other key processes—to reveal potential therapeutic targets.
- For example, it can uncover druggable alterations in treatment-resistant cases, such as ERBB2 amplification in trastuzumab-resistant breast cancer.
- Tool Example: GSEA or Ingenuity Pathway Analysis (IPA) for pathway overrepresentation.
Service flowchart
Figure 1: Drug Response QTL Profiling Workflow
Our Advantages
1. Dynamic QTL Mapping: See the Full Picture of Treatment Response.
We track how a patient's genetics influence drug response over time and under different conditions. This reveals why treatments work for some but not others, empowering doctors to adjust therapies for truly personalized care dynamically.
2. Cellular Genomics: Understand the "Why" Behind Drug Action.
We analyze individual cell-level data to uncover the precise biological pathways that connect genetics to drug effects. This clarity helps us identify new drug targets and design more effective treatment strategies.
3. A Scalable Workflow: One Solution for Diverse Needs.
Our platform is built to adapt. From initial genetic profiling to in-depth cellular analysis, our flexible workflow can be applied across many diseases and patient groups, ensuring broad relevance and impact.
Applications
Immunology & Autoimmunity: Enhancing Immune-Modulating Therapies
We use genetic profiling (QTL) to find the DNA differences that affect how patients respond to immunomodulatory drugs in autoimmune diseases. Our dynamic mapping approach monitors how these genetic factors interact as the immune system changes, while cellular genomics helps pinpoint effects in specific immune cells. Together, these insights help optimize biologic treatments—reducing trial-and-error and improving care for conditions like rheumatoid arthritis and lupus.
Neuropsychiatric Drug Development: Unraveling Treatment Variability
Our platform identifies QTLs linked to variable responses in neuropsychiatric drugs, such as antidepressants or antipsychotics. Dynamic QTL mapping captures how genetic influences change with disease progression or treatment duration, and cellular genomics reveals neuronal pathway alterations.
Precision Oncology: Tailoring Cancer Therapies
Our platform helps personalize therapy in oncology by identifying the genetic drivers behind treatment response. We continuously track how these factors change during therapy—uncovering resistance mechanisms—while single-cell genomics exposes diversity within tumors. These insights enable more effective, combination treatments tailored to each patient's cancer.
Demo
Figure 2: The three-stage protein QTL model used to identify the mechanism by which SNP rs1800566 influences drug response. (Akhtari, 2021)
Case Study
Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.
Journal：Sci Adv.
Published：2024
Immunotherapy drugs called immune checkpoint inhibitors (ICIs) have shown great results for some patients with advanced metastatic cancers. However, only 10–40% of people actually respond to them. We know that factors within the tumor—like its mutations and the surrounding immune environment—affect treatment success, but they don't fully explain why responses vary so much. A person's own genetic makeup also likely plays a key role, since it influences immune-related conditions such as autoimmune disease and transplant rejection. But it's been difficult to study this in humans because studies often have small sample sizes, diverse populations, and rely on methods that can produce false leads. What's more, the genetic complexity of each tumor makes it hard to pinpoint which host genes influence ICI response using standard genetic association approaches.
To tackle these issues, we turned to a special mouse model system called the Collaborative Cross (CC), which captures a wide range of genetic diversity similar to humans. By using a controlled breeding strategy, we implanted the same type of colon tumor into mice with well-defined but varied genetic backgrounds.
This setup allowed us to systematically study how the host's genetics affects response to anti-PD-1 immunotherapy, across different tumor models. We discovered four distinct regions in the genome (QTLs) that influence treatment outcome—some acting independently and others working together.
The study developed a gene expression-based biomarker by carefully narrowing down the candidate genes in these regions. This biomarker proved better at predicting overall survival in ICI-treated patients than commonly used measures. Importantly, it worked especially well in patients whose tumor immune environment matched the types we modeled in mice—showing how crucial it is to use pre-clinical models that reflect real patient diversity.
Our study establishes a key role of host genetics in shaping ICI response. We identified four quantitative trait loci (QTL) that reveal new insights into the genetic control of anti-tumor immunity. From these findings, we developed a transcriptional biomarker that robustly predicts ICI efficacy, especially in patients whose tumor immune profile matches our model. These findings underscore the power of genetically controlled murine models to uncover host genes influencing ICI response, addressing the confounding variability of human tumors. By integrating QTL mapping with immune subtype stratification, our approach advances precision immunotherapy and provides a framework for discovering novel genetic determinants of ICI efficacy, bridging preclinical and clinical translational gaps.
Figure 3: Table reporting results of statistical testing of epistatic interactions between QTL. Pairs of QTL with significant evidence of interaction are shown with p-value in bold.
FAQ
Absolutely. Our approach is adaptable to small cohort sizes and diverse populations, making it ideal for rare diseases with limited patient numbers. By prioritizing functionally relevant variants and leveraging dynamic mapping, we uncover genetic modifiers of drug response even in understudied groups, accelerating orphan drug development and equitable therapeutic access.
Cellular Genomics helps us understand how different cell types in the body respond to drugs. By combining single-cell sequencing, transcriptomics, and proteomics, we can pinpoint drug effects at the level of individual cell types—not just averaged across tissues. This allows us to see which molecular pathways are influenced by genetic variants, uncover new biomarkers, and identify why some treatments succeed while others fail. It adds a layer of biological context to genetic findings, complementing broader approaches like QTL mapping with high-resolution, mechanism-driven insight.
References
- Akhtari FS, Green AJ, Small GW, et al. High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs. PLoS Genet. 2021 Aug 26;17(8):e1009732. doi: 10.1371/journal.pgen.1009732.
- Skelly DA, Graham JP, Cheng M, et al. Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans. bioRxiv [Preprint]. 2024 Jul 16:2024.07.11.603136. doi: 10.1101/2024.07.11.603136.
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