In oncology drug development, biomarkers play a critical role in connecting scientific discovery with clinical decision-making. For Hua Gong, Vice President of Translational Medicine at BridgeBio Oncology Therapeutics, this work is about building the bridge between preclinical research, clinical trials, and real-world patient application.
Gong’s team focuses on developing biomarker assays, working with clinical and real-world evidence teams, and generating data that helps explain how drugs behave in patients. The goal is to close translational gaps and ensure that biomarker insights can meaningfully inform oncology drug development, from early discovery through to clinical strategy.
One of the biggest challenges is that preclinical findings do not always translate into humans. Biomarker methods that work well in laboratory settings may not be suitable for patient samples. For example, techniques such as Western blotting may be useful in preclinical pharmacodynamic testing, but they are not practical for routine clinical sample analysis. This creates a clear hurdle when moving biomarkers into early-phase oncology trials.
Different biomarker use cases also require different validation approaches. Predictive biomarkers for patient selection often rely on baseline, pre-treatment samples. Pharmacodynamic biomarkers require both pre-dose and post-dose samples to show whether a drug is having its intended biological effect. Resistance monitoring adds another challenge, as samples are often needed at disease progression. In practice, obtaining these tissue samples can be difficult, especially when repeat biopsies are invasive, patients do not consent, or tumours are located in organs that are hard to biopsy.
For translational medicine teams, strong clinical study design begins with a clear scientific hypothesis. Preclinical data from cell lines and animal models may have limitations, but it can still provide an important foundation for deciding which samples to collect, when to collect them, and which biomarker questions to prioritize. A strong preclinical biomarker package can increase the probability of success in clinical development and help teams make better decisions around dose, indication selection, and combination strategies.
Looking ahead, Gong sees major promise in multi-omics technologies, ctDNA, AI-enabled analysis, real-world evidence, and improved assay platforms. These advances could help generate richer datasets, support more predictive models, and improve the efficiency of evidence generation. As testing costs for technologies such as next-generation sequencing continue to fall, there is growing potential to gather broader and deeper biomarker data across patient populations.
However, tissue collection remains a major challenge. As Gong notes, “the tissue is the issue.” Clinical trial sample sizes can also limit the strength of biomarker findings, which is why real-world evidence may become increasingly important. By combining clinical trial data with larger real-world datasets, translational teams may be able to better validate findings and build models that more accurately reflect patient biology.
Ultimately, the future of translational medicine in oncology will depend on combining strong biological hypotheses with better technologies, smarter trial design, and broader evidence sources. By integrating clinical trial data with real-world evidence and advanced analytics, biomarker strategies can become more powerful tools for guiding patient selection, dose decisions, indication strategy, and combination approaches. This integrated approach could help make oncology drug development more efficient, more evidence-driven, and more closely aligned with the needs of patients.
