Elements in precision oncology

cancer treatment

Validating the regulatory expectations on evidential requirements for predictive biomarkers is not a trivial process, but quite conceptually challenging. In this commentary, we highlight some critical issues in the clinical development of predictive biomarkers for precision oncology and offer insights on key elements of study designs for data collection and analysis. 

With innovative clinical laboratory measurements for nucleic acids and proteins, we can now begin to understand and address the complexity of inherent heterogeneities in cancer, at least from an analytical standpoint. 

Aberrant but actionable genomic variants from gene mutations usually occur in no more than 20-30% of malignant lesions of the same tissue origin.1 Genomic variations in germline and somatic mutations in DNA levels, as well as other structural alterations of unknown significance, pose significant challenges to clinicians, cancer researchers, and clinical developers.

Designing therapies to treat or manage cancer in which the underlying molecular profiles are evolving constantly is inefficient, if not impossible. These molecular alterations could occur due to pressure of treatments or the effects of weakening human immunity from anti-cancer therapeutics. They both highlight the unmet medical needs in the assessment of ever-changing structural modifications for better clinical and patient outcomes.

Using a tumour’s genetic features as predictive biomarkers

In 2017, the FDA approved Pembrolizumab for adult and paediatric patients with solid tumours who have microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) biomarkers in cancer cells, and who progressed on previous therapy and have no other treatment options. This marked a milestone and the first oncology approval using a tumour’s genetic features as predictive biomarkers, rather than its location in the human body.2 

This provides a proof-of concept case study for a new regulatory pathway. In human cancers, abnormal somatic mutations are mostly not tissue-specific, therefore, predictive biomarkers are not only informative, but expected to offer specific guidance for treatment decisions.

The evidence required for use in predictive lab assays is more stringent than evidence needed for prognostic purposes, which help foretasting natural history of cancer without implications for guiding treatment selections.3 In contrast, predicting a treatment response is quite a different clinical task in which clinicians need information in a timely manner to prescribe a therapeutic agent or combination regimen with a variety of classes of therapeutic agents with different modes of action, based on test results.4-5

New strategies, workflow management, and study designs

Differentiating the fundamental clinical utilities of predictive and prognostic biomarkers in clinical utility or usefulness requires new strategies, workflow management, and study designs for verification and validation.6-7

If sampling is practical for clinical measurements, many molecular targets could be potential predictive biomarkers. Biomolecules, such as cell surface receptors or components in intracellular signal transduction pathways, could be useful target candidates. Current platforms for clinical measurements can reliably handle the clinical analysis of DNA, RNA, protein, or metabolites for confident determination with high sensitivity and specificity. Platforms like Next Generation Sequencing (NGS), automated chemistry or immunoassay analysers, mass spectrometry, or cell sorting with flow cytometry use different sampling techniques and materials, such as with cell-free or circulating DNA.

The analytical sensitivity in clinical labs has reached single-cell or unique molecule level, thanks to the assistance of robust labelling technologies and highly specific targeting of monoclonal antibodies. We have crossed the analytical threshold to reliably measure the expression or level of biomarkers intended for predictive purpose in directing treatment selections. This allows for the provision of sensitive and specific indications of treatment response possibilities for prescribing anti-cancer treatments. 

However, in precision oncology, the assay format for an appropriate instrument platform should be carefully considered or adaptable for the source materials to determine its practicality in the context of practice settings, especially considering sampling, as the operational requirements of predictive biomarkers are fundamentally different from the ones used for clinical diagnosis or epidemiological surveys.

Biomarkers as a predictive tool: The need for evidence

Nevertheless, for a potential biomarker to be used as a meaningful predictive tool, there are other basic characteristics and performance criteria to consider, as well as evidence for demonstrating clinical utilities and/or usefulness in preclinical and clinical validations. Aside from the need for practical and convenient sampling, a reasonable turn-around-time (TAT) is of the highest importance. For example, NGS with either biopsy or surgical tissues could be useful for molecular profiling, but if the lag time from sample processing to reporting is unacceptable, cell-free DNA could be a potential alternative for a molecular target being developed as a predictive clinical assay. 

The performance requirements for quantitative measurements should be designed or configured with high specificity for any specific molecular target with an appropriate cut-off threshold. Similarly, for qualitative assays, target specificity should be crucial for definitive positive identification.

Statistically speaking, the clinical validations should demonstrate treatment-by-marker interaction between the predictive biomarkers with the investigational medicinal products (IMPs) with appropriate study design.8-9 To do so, the clinical study is required to use randomised treatment allocation, especially in later phase clinical trials, for the marker positive interventional cohort balanced with controls.10-11

Cancer heterogeneity would be more pronounced, with treatments over time hampering the success of clinical trials in human malignancies, especially in retreatment settings with later line of therapeutic agents. Therefore, patient selections for study population enrichments according to proposed indication use during enrolment are advisable for targeted therapeutics. Some innovative, but expensive, agents such as immunotherapies or gene therapies are also similar applicable strategies. All, though, would be helpful to ensure clinical trials are on budget and on time for conclusions. 

Predictive biomarkers: Moving forwards

In summary, predictive biomarkers present largely untapped opportunities for therapeutic success in oncology trials and clinical practice. 

The inherent heterogeneity in cancer biology justifies precise identification of patients who could more likely benefit from a novel cancer treatment and/or deescalating standard dosing to prevent toxicity; it justifies the design of an appropriate regimen for a subtype of malignant lesion within a seemingly homogenous patient population according to clinical presentations. 

Predictive biomarkers are unique in performance characteristics, requiring specialised workflow management and study designs during clinical development to demonstrate their usefulness in oncology trials for future convincing clinical utilities. In this regard, we emphasise the importance of randomisations in study designs to show treatment-by-marker interactions. 

Moving forwards, coordinated efforts should involve the device and biopharmaceutical companies, as well as regulatory authorities in qualification, verification, and validations of the potential predictive biomarkers for future success of clinical studies and beyond.


  1. Mendiratta G, Jones MK, Stites EC. How often is each gene mutated within the cancer patient population? Mol Cell Oncol. 2022 May 1;9(1):2065176. doi: 10.1080/23723556.2022.2065176. PMID: 35529901. 
  2. Goldberg KB, Blumenthal GM, and McKee AE et al. The FDA Oncology Center of Excellence and precision medicine. Exp Biol Med (Maywood). 2018 Feb;243(3):308-312. DOI: 10.1177/1535370217740861. PMID: 29105511
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  4. Gromova M, Vaggelas A, and Dallmann G et al. Biomarkers: Opportunities and Challenges for Drug Development in the Current Regulatory Landscape. Biomarker Insights. 2020 Dec 8. DOI: 10.1177/1177271920974652. PMID: 33343195
  5. Jørgensen JT. Predictive biomarkers and clinical evidence. Basic Clin Pharmacol Toxicol. 2021 May;128(5):642-648. doi: 10.1111/bcpt.13578. PMID: 33665955
  6. Janes, H., Pepe, M. S., Bossuyt, P. M. & Barlow, W. E. Measuring the Performance of Markers for Guiding Treatment Decisions. Ann. Intern. Med. 2011, 154, 253–259.
  7. Amur, S., LaVange, L., Zineh, I., Buckman-Garner, S. & Woodcock, J. Biomarker qualification: Toward a multiple stakeholder framework for biomarker development, regulatory acceptance, and utilization. Clin. Pharmacol. Ther. 2015, 34–46, doi:10.1002/cpt.136.
  8. Vivot A, Boutron I, and Béraud-Chaulet G, et al. Evidence for Treatment-by-Biomarker interaction for FDA-approved Oncology Drugs with Required Pharmacogenomic Biomarker Testing. Sci Rep. 2017 Jul 31;7(1):6882. doi: 10.1038/s41598-017-07358-7. PMID: 287610699.    Težak Ž, Kondratovich MV, Mansfield E. US FDA and personalized medicine: in vitro diagnostic regulatory perspective. Per Med. 2010 Sep;7(5):517-530. doi: 10.2217/pme.10.53. PMID: 29776248
  9. Mandrekar SJ and Sargent DJ. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J Clin Oncol. 2009 Aug 20;27(24):4027-34. DOI: 10.1200/JCO.2009.22.3701. PMID: 19597023. 
  10. Sargent DJ, Conley BA, Allegra C et al. Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol. 2005 Mar 20;23(9):2020-7. doi: 10.1200/JCO.2005.01.112. PMID: 15774793.

About the authors

Dave LiDr Dave Li is a principal consultant and clinical research physician with KCR Consulting. He is a medical oncologist and regulatory scientist, and an expert in molecular medicine, immuno-oncology, and clinical informatics. Dr Li was on the faculty of John Hopkins Medicine and served as a medical officer with the US/HHS FDA before joining KCR. He obtained his medical degree from the Sun Yat-sen University, and MSc/PhD at the University of Texas M.D. Anderson Cancer Center in Houston, Texas.  

Dr Anna BaranDr Anna Baran is the chief medical officer and oversees all stages of clinical trial operations and KCR Consulting services. She brings expert clinical and medical experience to the company, having conducted research for pre-clinical and clinical projects for many years, working as a sub investigator on endocrinology and dermatology therapies, and holding drug development consultant positions. Dr Baran holds a medical degree and a postgraduate degree in healthcare management.