Tracking Clonal Evolution and Resistance with Next-Generation Sequencing

Up to 80% of relapses following targeted therapy originate from pre-existing resistant subclones.

Detecting these rare variants at diagnosis typically requires deep next-generation sequencing (NGS).^1,2,3

In this blog, we outline how you can overcome key challenges in tracking tumor evolution and resistance, supported by best practices and real-world examples.

Targeted therapies and the challenge of solid tumors

Targeted therapies have transformed outcomes in certain cancers, such as chronic myeloid leukemia. However, overall response rates in solid tumors are often 50% or lower, largely due to primary resistance mechanisms that are difficult to fully capture using traditional tissue analyses alone.⁴

Immunohistochemistry (IHC), while valuable, cannot always provide the resolution needed to fully characterize tumor biology. For example, low HER2 expression can vary across tumor regions, leading to interpretation variability and potential misclassification when relying on single biopsies.⁵

A similar challenge exists in non-small cell lung cancer (NSCLC), where for example, PD-L1 expression can fluctuate spatially within tumors. Further, small biopsy samples may not reflect the full tumor landscape, increasing the risk of inaccurate patient stratification.⁶

NGS addresses some of these limitations by enabling high-resolution tracking of tumor clonal evolution and potential resistance mechanisms. Across both solid tumors and hematological malignancies, it provides critical insight into subclonal expansion, mutation acquisition, and treatment-driven adaptation that conventional assays may miss.⁴

Common pitfalls in NGS trials

NGS is a powerful tool, but unlocking its full value requires careful consideration of several challenges.

At the core of tumor progression and treatment resistance are intratumoral heterogeneity and clonal evolution. Tumors often begin as polyclonal populations, but under therapeutic pressure, resistant subclones, fueled by driver mutations in genes like TP53 or KRAS, create resistant cell populations that can expand and cause the cancer to relapse.^7,8

A key challenge in clinical trials is determining whether resistance is pre-existing or acquired during treatment. This distinction is critical for treatment decisions, yet difficult to resolve without sufficiently sensitive and longitudinal approaches.⁹

Additional complexity arises from the need to distinguish tumor-derived variants from clonal hematopoiesis (CH) in blood-based analyses, as well as from variability in assay sensitivity at low variant allele frequencies (VAFs <1%). Longitudinal monitoring is also essential to capture real-time tumor evolution without placing excessive burden on patients.¹⁰

Key challenges include:

• Operational variability across sites: Inconsistent sensitivity for low-VAF subclones (<0.5%) and long turnaround times can disrupt trial timelines.
• Data interpretation risks: Misclassification of CH as tumor-derived mutations and under-detection of polyclonal resistance can compromise insights.
• Limited protocol adaptability: Lack of real-time triggers (e.g., ctDNA-guided biopsy) can delay response to emerging resistance.
• Increased trial risk: Incomplete understanding of resistance evolution may lead to inconclusive outcomes and delayed decision-making.

Our approach to best practice in NGS precision oncology

To address these challenges, a standardized, integrated approach to NGS is essential. Our methodology combines scientific rigor with clinical relevance, enabling you to generate reliable, actionable insights throughout your trial. We use:

• Longitudinal NGS across tissue and ctDNA: Enables early detection of resistance-driving subclones and supports adaptive trial design.
• Structured serial sampling protocols: Baseline, on-treatment (cycles 2/6/12), and progression sampling using tissue and liquid biopsy.
• Optimized low-VAF variant detection: Supported by orthogonal validation methods such as digital droplet PCR (ddPCR).
• Phylogenetic analysis: Differentiates trunk and branch mutations to predict resistance trajectories.
• Integrated multi-omics approaches: Combines NGS with IHC and flow cytometry to provide spatial and immune context.
• CH-aware bioinformatics pipelines: Uses matched normal controls and targeted filtering (e.g., DNMT3A, TET2) to improve accuracy.
• Standardized, guideline-aligned assays: Designed in line with AMP/ASCO and regulatory expectations.
• Adaptive trial frameworks: Enables real-time protocol adjustments based on emerging molecular insights.

Real-world results from clinical trials

In practice, this approach enables deeper insight into tumor evolution and resistance.

In one melanoma study, NGS-based phylogenetic analysis mapped tumor evolution, distinguishing an early BRAF mutation from a later-emerging NRAS-driven resistance pathway.¹¹

Circulating tumor DNA (ctDNA) analysis has demonstrated over 90% sensitivity for detecting minimal residual disease (MRD) and resistance at variant allele frequencies above 0.1%, using standardized pipelines aligned with AMP guidelines.¹²

In a recent phase II NSCLC trial, serial ctDNA NGS identified expansion of a KRAS G12C subclone by cycle six. This enabled real-time treatment adaptation, supporting a switch to a next-generation inhibitor.¹³

Illuminating the future of precision oncology

Understanding tumor evolution is critical to anticipating resistance before it impacts trial outcomes.

By identifying early signals of subclonal expansion, NGS enables you to move from reactive to proactive decision-making. Instead of relying on incomplete snapshots, you gain a continuous, data-driven view of how tumors respond and adapt.

This approach combines methodological rigor, CH-aware analysis, and multi-omics integration to transform complex genomic data into actionable insight.

As oncology trials become more complex, the ability to track tumor evolution in real time becomes a key differentiator; supporting more informed decisions, more resilient trial designs, and ultimately better patient outcomes.

Key takeaways

• Clonal evolution drives therapeutic resistance: Most relapses originate from pre‑existing subclones, making early detection essential.
• Tumor heterogeneity limits traditional assays: Spatial variation can lead to misclassification or missing actionable biology.
• NGS enables high resolution insight: Deep sequencing and longitudinal monitoring reveal evolving resistance pathways.
• CH interference must be addressed: Accurate interpretation requires matched controls and specialized filtering.
• Multi‑omics strengthens decision-making: Integrating NGS with complementary assays improves patient stratification.
• Standardization reduces trial risk: Robust pipelines and adaptive designs support more reliable and actionable outcomes.

Ready to gain clarity on tumor evolution?

If you are advancing precision oncology programs, our integrated NGS, biomarker, and spatial capabilities can help you uncover resistance mechanisms earlier and make more confident trial decisions.

Explore our oncology solutions or connect with our experts to see how we can support your next study.

References

1. Li, B., Brady, S.W., Ma, X., Shen, S., Zhang, Y., Li, Y., Szlachta, K., Dong, L., Liu, Y., Yang, F. and Wang, N. 2020. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood, The Journal of the American Society of Hematology. 135(1), pp.41-55.
2. Schmitt, M.W., Loeb, L.A. and Salk, J.J. 2016. The influence of subclonal resistance mutations on targeted cancer therapy. Nature reviews Clinical oncology. 13(6), pp.335-347.
3.  Vander Velde, R., Yoon, N., Marusyk, V., Durmaz, A., Dhawan, A., Miroshnychenko, D., Lozano-Peral, D., Desai, B., Balynska, O., Poleszhuk, J. and Kenian, L. 2020. Resistance to targeted therapies as a multifactorial, gradual adaptation to inhibitor specific selective pressures. Nature communications. 11(1), p.2393.
4. Pogrebniak, K.L. and Curtis, C. 2018. Harnessing tumor evolution to circumvent resistance. Trends in Genetics. 34(8), pp.639-651.
5. Qin, Q. 2025. Advances in research and current challenges in the treatment of advanced HER2-low breast cancer. Frontiers in Cell and Developmental Biology. 13, p.1451471.
6. Dori, S.B., Aizic, A., Sabo, E. and Hershkovitz, D. 2020. Spatial heterogeneity of PD-L1 expression and the risk for misclassification of PD-L1 immunohistochemistry in non-small cell lung cancer. Lung Cancer. 147, pp.91-98.
7. Wild, S.A., Cannell, I.G., Nicholls, A., Kania, K., Bressan, D., Hannon, G.J., Sawicka, K. and CRUK IMAXT Grand Challenge Team. 2022. Clonal transcriptomics identifies mechanisms of chemoresistance and empowers rational design of combination therapies. elife, 11, p.e80981.
8. Fu, Y.C., Liang, S.B., Luo, M. and Wang, X.P. 2025. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell International. 25(1), p.103.
9. Umkehrer, C. 2022. Functional lineage tracing to study the clonal evolution of therapy resistance. Nature Reviews Cancer. 22(6), pp.321-321.
10. Buttigieg, M.M. and Rauh, M.J. 2023. Clonal hematopoiesis: updates and implications at the solid tumor-immune interface. JCO Precision Oncology, 7, p.e2300132.
11. Shang, Y., Cao, T., Li, J., Li, J., Zhang, L., Ma, Q., Feng, L. and Zhao, H. 2026. BRAF inhibitor resistance in melanoma: from resistance mechanisms to therapeutic innovations. Molecular Biomedicine, 7(1), p.27.
12. Lee, J.S., Cho, E.H., Kim, B., Hong, J., Kim, Y.G., Kim, Y., Jang, J.H., Lee, S.T., Kong, S.Y., Lee, W. and Shin, S., 2024. Clinical practice guideline for blood-based circulating tumor DNA assays. Annals of laboratory medicine, 44(3), pp.195-209.
13. Choi, Y., Dharia, N.V., Jun, T., Chang, J., Royer-Joo, S., Yau, K.K., Assaf, Z.J., Aimi, J., Sivakumar, S., Montesion, M. and Sacher, A. 2024. Circulating tumor DNA dynamics reveal KRAS G12C mutation heterogeneity and response to treatment with the KRAS G12C inhibitor divarasib in solid tumors. Clinical Cancer Research, 30(17), pp.3788-3797.