From Biomarkers to Precision Trials: The Expanding Role of NGS in Pharma
Dr. Kalpana Katiyar, Assistant professor, Department of Biotechnology, Dr. Ambedkar Institute of Technology for Divyangjan
Ms. Shreeya Arora, Researcher, Department of Biotechnology, Dr. Ambedkar Institute of Technology for Divyangjan
Next-Generation Sequencing (NGS) is transforming pharmaceutical research and development by enabling the discovery of biomarkers, implementing adaptive designs in clinical trials, and advancing pharmacogenomics strategies. This article explores sophisticated NGS workflows—including single-cell and spatial transcriptomics, liquid biopsy, and AI-assisted interpretation—along with a case study centered on NGS-integrated breast cancer trials. We offer a distinct mapping of NGS workflows to pharmaceutical applications and evaluate regulatory trends as well as future opportunities.
Introduction
Next-generation sequencing (NGS) has evolved from being an experimental technique to a crucial element in drug research and development. Rather than only pinpointing targets, NGS now affects clinical trial design, regulatory strategies, and the management of precision therapeutics throughout their lifecycle.
Contemporary pharmaceutical development processes utilize NGS at multiple phases:
- To discover and validate predictive biomarkers
- To enable adaptive designs in clinical research
- To evaluate treatment outcomes using liquid biopsies
- To include pharmacogenomics in decisions about dosing and patient enrollment
This article examines the innovative uses of NGS in the pharmaceutical industry, offering a comprehensive mapping of NGS techniques to specific clinical trial situations, along with a case study that illustrates their use in adaptive oncology trials.
2. Advanced NGS Workflows for Biomarker Discovery
A. Single-Cell and Spatial Omics
Single-Cell RNA-seq (scRNA-seq)
scRNA-seq offers an unmatched level of detail in identifying the diversity present within tumor and immune cells. Its uses include:
- Investigating the tumor microenvironment (TME)
- Identifying clonal changes in cancer in response to treatment
- Profiling different immune cell subsets to guide the development of immunotherapy trials
For example, in melanoma treatments involving anti-PD-1, T cell exhaustion markers identified through single-cell RNA sequencing (scRNA-seq) aid in classifying patients [1].
Spatial Transcriptomics
Spatial transcriptomics allows for the in-situ mapping of gene expression while preserving the tissue's structural integrity. This enables:
- Insights into the interactions among tumor, stroma, and immune cells
- The identification of spatially restricted predictive biomarkers
- The visualization of drug distribution and response variations
In studies combining anti-TGFβ with anti-PD-L1 therapies, spatial profiling can indicate which tumors exhibit immunosuppressive stroma traits that correlate with treatment resistance [2].
B. Workflow Example
Table I: Mapping NGS Workflows to Pharma R&D Use Cases
| NGS Workflow / Modality | Primary Application in Pharma Trials | Example Impact / Case |
| Whole Exome Sequencing (WES) | Discovery of somatic mutations for target identification and trial stratification | I-SPY2 adaptive trial enrolling HER2-/HR- high-risk breast cancer patients based on PIK3CA mutations [3] |
| RNA-Sequencing (RNA-seq) | Identification of predictive expression signatures and dynamic response biomarkers | BRAF-MEK inhibitor trials stratifying patients by immune-related gene expression profiles [4] |
| Single-cell RNA-seq (scRNA-seq) | Immune landscape profiling for immunotherapy trial stratification and mechanistic insights | Anti-PD-1 trials in melanoma guided by T cell exhaustion markers derived from scRNA-seq [1 |
| Spatial Transcriptomics | Mapping tumor-stroma interaction and drug penetration for combination therapy optimization | Spatial profiling used to select patients likely to respond to anti-TGFβ + anti-PD-L1 combinations [2] |
| Ultra-deep ctDNA Sequencing | Non-invasive MRD monitoring and early detection of acquired resistance | NSCLC trials using serial ctDNA NGS to detect EGFR T790M resistance and guide 3rd-line osimertinib therapy [5] |
| Pharmacogenomics (PGx) NGS Panels | Dosing optimization and pre-enrollment screening for toxicity risk | DPYD PGx NGS panels used to screen for 5-FU toxicity risk in colorectal cancer trials [6] |
3. Case Study: NGS-Driven Precision Trials in Breast Cancer (I-SPY2)
A. Background
The I-SPY2 adaptive platform trial utilizes NGS workflows to successfully match new treatments with molecularly characterized subgroups in high-risk early-stage breast cancer [3].
B. NGS Integration Points
Before enrollment: WES and RNA-seq reveal actionable molecular signatures.
Throughout the trial: Serial ctDNA sequencing monitors minimal residual disease (MRD) and the effectiveness of treatment.
Adaptive design: Bayesian algorithms adjust patient allocation based on real-time biomarker responses.
C. Impact of NGS Integration
| Metric | Traditional Trials | I-SPY2 NGS-Integrated Trials |
| Trial duration | ~5–7 years | ~2–3 years (adaptive design) |
| Response monitoring | Imaging + biopsy | Serial ctDNA NGS |
| Success probability for novel agents | ~15–20% | ~30–40% in molecularly matched arms [3] |
D. Key Insights
- Dynamic NGS endpoints improve the educational experience.
- ctDNA acts as an effective, non-invasive endpoint.
- Multi-modal NGS workflows enhance patient selection and boost trial efficiency.
4. Pharmacogenomics at Scale
A. Population-Scale PGx Screening
Pharmaceutical company-backed projects in population genetics are now enabling proactive pharmacogenomics (PGx) screening:
- UK Biobank (500,000 whole genome sequencing samples)
- All of Us (U.S. cohort)
- Genomics England
The PGx variants that are being screened consist of:
- DPYD related to 5-FU toxicity
- CYP2C9/19 for determining anticoagulant dosages
- SLCO1B1 associated with the risk of statin intolerance.
B. Impact of Trials
In AstraZeneca’s GENETIC study, PGx screening via next-generation sequencing (NGS) has been proven to reduce major bleeding events in patients on anticoagulants by identifying individuals who are poor metabolizers of CYP2C9 [7].
C. Discovery of Rare Variants
NGS aids in:
- The identification of highly rare PGx variants
- The utilization of polygenic risk scores (PRS) for trial participant stratification [8].
5. Liquid Biopsy: Evolving Biomarkers in Clinical Trials
A. Advances in Technology
Ultra-sensitive ctDNA sequencing:
- Utilizes Unique Molecular Identifiers (UMIs) for correcting errors.
- Can detect variants with an allele frequency of less than 0.1%.
- Duplex sequencing enhances specificity further [5].
B. Clinical Applications
| Use Case | NGS Application |
| MRD detection | Ultra-deep targeted ctDNA sequencing |
| Resistance monitoring | Longitudinal ctDNA NGS |
| Enrollment biomarker | ctDNA NGS when tissue biopsy is infeasible |
C. Example
In non-small cell lung cancer (NSCLC), serial ctDNA next-generation sequencing (NGS) identifies the emergence of EGFR T790M resistance mutations several months before imaging progression, facilitating an early transition to osimertinib [5].
6. AI-Augmented NGS Data Analysis
A. Variant Interpretation Challenges
Pharmaceutical pipelines face:
- Roughly 10–15% of variants classified as Variants of Uncertain Significance (VUS).
- A variety of bioinformatics processes that lack standardization globally.
- Regulatory requirements for transparency in AI-assisted decision-making.
B. AI Tools in Use
| Tool | Function |
| SpliceAI | Predicts impact of variants on RNA splicing [9] |
| PolyPhen-2 | Predicts impact of missense variants |
| OncoKB | Precision oncology knowledge base |
| DeepVariant | AI-driven variant calling pipeline |
Workflows enhanced by AI enhance the prioritization of variants and the discovery of biomarkers in clinical trials.
7. Regulatory Landscape
A. Regulatory Trends
| Agency | Guidance on NGS in Trials |
| FDA | Draft guidance on ctDNA-based endpoints in oncology trials [10] |
| EMA | Reflection papers supporting biomarker-driven adaptive designs [11] |
| EU IVDR | Strict compliance requirements for NGS-based companion diagnostics [12] |
B. Challenges in Validation
When validating NGS for clinical trials, it is crucial to demonstrate:
- Analytical sensitivity and consistency
- The stability of assays over multi-year adaptive trials
- The clarity of AI-driven bioinformatics processes
- The standardization across international trial location.
8. Prospective Pathways
A. Real-Time Sequencing in Clinical Trials
Nanopore sequencing is facilitating near-instantaneous sequencing:
- For adaptive trial stratification
- To promote point-of-care pharmacogenomics
- For monitoring Minimal Residual Disease (MRD) in rapidly advancing conditions [13].
B. Single-Cell Multi-Omics in Clinical Trials
New trials are incorporating:
- Single-cell immune profiling
- Endpoints in spatial transcriptomics
- Tracking T-cell/B-cell receptor repertoire dynamics in cell therapy research [14].
These advancements bolster next-generation precision trial designs and enhance biomarker development processes.
9. Summary
Next-Generation Sequencing has become integral to the planning and implementation of contemporary pharmaceutical clinical trials. Beyond the identification of static biomarkers, NGS processes facilitate:
- Adaptive randomization in trials
- Validation of dynamic biomarker endpoints
- Enrollment guided by pharmacogenomics
- Real-time tracking of disease progression
The combination of NGS with AI and multi-modal omics will continue to reshape pharmaceutical trial strategies, hastening the creation of precision therapies and enhancing patient outcomes.
Pharmaceutical companies that effectively incorporate NGS into their global trial frameworks will secure a significant competitive edge in the realm of precision medicine.
References
- S. Sade-Feldman et al., “Defining T cell states associated with response to checkpoint immunotherapy in melanoma,” Cell, vol. 175, no. 4, pp. 998–1013.e20, 2018.
- S. Mariathasan et al., “TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells,” Nature, vol. 554, pp. 544–548, 2018.
- I-SPY2 Investigators, “Accelerating anticancer drug development—adaptive trial design in action,” Nature Medicine, vol. 26, pp. 1035–1040, 2020.
- E. M. Van Allen et al., “Genomic correlates of response to combination immunotherapy in melanoma,” Cancer Discovery, vol. 5, no. 8, pp. 827–837, 2015.
- G. R. Oxnard et al., “Assessment of resistance mechanisms and clinical implications in EGFR-mutant NSCLC using circulating tumor DNA,” JAMA Oncology, vol. 2, no. 8, pp. 1014–1022, 2016.
- U. Amstutz et al., “Clinical pharmacogenetics implementation consortium (CPIC) guideline for DPYD genotype and fluoropyrimidine dosing: 2017 update,” Clinical Cancer Research, vol. 24, no. 10, pp. 2489–2493, 2018.
- AstraZeneca, “GENETIC trial,” ClinicalTrials.gov, 2023. [Online]. Available: https://clinicaltrials.gov/
- M. Inouye et al., “Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention,” Nature Genetics, vol. 50, no. 9, pp. 1219–1224, 2018.
- K. Jaganathan et al., “Predicting splicing from primary sequence with deep learning,” Cell, vol. 176, no. 3, pp. 535–548.e24, 2019.
- U.S. Food and Drug Administration (FDA), “Use of circulating tumor DNA for early clinical trial endpoint evaluation,” Draft guidance, 2022.
- European Medicines Agency (EMA), “Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design,” EMA/286914/2012, 2020.
- European Commission, “EU In Vitro Diagnostic Medical Devices Regulation (IVDR),” Regulation (EU) 2017/746, 2022.
- Oxford Nanopore Technologies, “Real-time sequencing in clinical trials,” Technical Note, 2023.
- M. Stubbington et al., “Single-cell transcriptomics to explore the immune system in health and disease,” Nature Reviews Drug Discovery, vol. 20, pp. 305–321, 2021.
Dr. Kalpana Katiyar is an Assistant Professor in the Department of Biotechnology at Dr. Ambedkar Institute of Technology for Divyangjan, Kanpur, Uttar Pradesh, India. She received her Ph.D. in Biotechnology from Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, in 2022. Her teaching interests include Biochemistry, Immunology, and Genetic Engineering. She co-authored this article with Ms. Shreeya Arora, a passionate undergraduate student exploring various facets of Biotechnology. Together, they aim to bridge academic insights with the emerging interests of young scholars.
Ms. Shreeya Arora is pursuing a B.Tech (Hons.) in Biotechnology, with a specialization in Computational Biology and Bioinformatics. Her research interests include next-generation sequencing, biomarker discovery, and translational genomics. She is particularly focused on applying bioinformatics in pharmaceutical research, designing clinical trials, and advancing precision medicine initiatives.
