Biomarker Stratification in Clinical Trials

Starting around 2007, investigators began to appreciate the tremendous potential of biomarkers for improving the design of clinical trials. This article will discuss the emerging practice of stratifying clinical trial participants based on the presence of various diagnostic and prognostic biomarkers. Furthermore, we will highlight the wide variety of evolving technologies―including proximity extension assays (PEAs), mass spectrometry, multiplex genotyping, liquid biopsy, and next-generation sequencing―that may be useful in discovering and detecting biomarkers.

Proteomic biomarkers for interstitial lung disease

A recent study on interstitial lung disease (ILD) demonstrates how biomarkers may be used to pre-stratify patients for clinical trials. In the absence of biomarker evidence, it is often difficult to identify which ILD patients are likely to have progressive forms of the disease―referred to as progressive fibrosing ILD. Further complicating matters, many immunosuppressant therapies can actually be harmful to patients with progressive ILD.

Although genomic, transcriptomic, and proteomic methods have been employed to understand ILD prognosis, the ability to identify progressive forms of the disease has remained elusive. Most recently, a 2022 ILD trial employed the Explore Inflammation panel from Olink to quantify 368 inflammation-related proteins for each participant.

With the use of machine learning, a proteomic signature comprising 12 biomarkers was identified. Ultimately, this signature was quite effective (with a sensitivity of 90%) at predicting which participants would experience ILD progression in the year after the blood draw. The authors of the study estimated that the identification of high-risk patients through proteomic signatures allowed for 80% fewer participants compared with a trial designed without biomarkers. This resulted in an estimated $26.7 million of savings. Furthermore, participants with progressive forms of the disease were spared inappropriate treatments.

Proximity extension assays for fluid samples

In clinical laboratory assays, blood―which can easily be obtained through phlebotomy―is the most widely used human body fluid in disease diagnosis, prognosis, and treatment outcomes. The vast structural complexity of the plasma proteome in blood samples can best be explored with one of two methods: a highly sensitive targeted immunoassay such as proximity extension assays (PEAs) or mass spectrometry (such as LC/MS/MS). However, a major advantage of PEA―which was used in the ILD trial―is its specificity.

In addition to blood samples, PEA technology can now also be used for other sample matrices such as cerebral spinal fluid, cell and tissue lysate, saliva, urine, interstitial fluid, cell culture media, peritoneal fluid, and synovial fluid. The technology can also be applied to small-volume samples―including single cells, exosomes, dried blood spots, aqueous humor, and fine-needle biopsies.

Olink Explore for proximity extension assays

With Olink Explore, Olink Proteomics has coupled PEA with quantitative real-time PCR. Olink Explore offers a high-multiplex protein biomarker platform, high-throughput capacity, and high specificity and sensitivity. Furthermore, the Olink Explore uses less than one drop of blood to measure the protein profile in each patient’s sample.

The basis of PEA involves a dual recognition of a targeted biomarker through a matched pair of antibodies that have been labeled with unique DNA oligonucleotides. Biomarker-specific DNA barcodes are subsequently quantified using quantitative PCR. This allows for the relative quantification of over 1000 human plasma proteins with as little as 1 μL of blood. Furthermore, Olink currently has at least 14 available human panels, each comprising 92 biomarkers. These panels cover a protein concentration range of over 10 orders of magnitude and provide accurate quantification below the picogram-per-milliliter (pg/mL) level.

Emerging technologies for biomarker identification

Although the ILD trial used PEA to identify proteins in blood samples, many other techniques are also possible. After all, biomarkers are diverse, and various approaches can be used to investigate molecular signatures―including proteomic (proteins), genomics (DNA and RNA), and metabolomics (metabolites). Ultimately, the optimal technique will depend on the field. For example, both genomics and proteomics are heavily used in oncology.

Although the field of protein diagnostics has relied strongly on antibody-based detection strategies, mass spectrometry also enables comprehensive insight into the proteome. Mass spectrometry-based proteomic analysis is a particularly powerful approach for discovering new disease biomarkers. Furthermore, analysis in 96-well plates has dramatically increased the throughput of methods such as LC/MS/MS for clinical applications.

High-throughput tissue analysis of scant material is also possible with multiplex genotyping and genomic profiling. This may be achieved using technologies such as next-generation sequencing and even mass spectrometry of nucleic acids. Alternatively, liquid biopsies from fluids like blood can be used to identify prognostic factors, including somatic germline mutations, changes in DNA methylation, varying levels of miRNA, and circulating abnormal proteins or tumor cells. Molecular diagnostics leading to personalized therapeutics―which have already been implemented with excellent results in oncology―are likely to catch on for other conditions such as interstitial lung disease.

Conclusions

The stratification of study subjects prior to enrollment in clinical trials has the potential to save resources, money, and time. This practice may ultimately accelerate the drug development process so that superior treatments can reach patients faster. In addition to ILD, proteomic approaches have been used to identify key blood biomarkers for atrial fibrillation, chronic fatigue syndrome, myocardial infarction, acute graft-versus-host disease, type 1 diabetes, oral squamous cell carcinoma, chronic kidney disease, and ovarian cancer. As analytical techniques continue to evolve, biomarker pre-stratification is likely to become a more common practice in clinical trials.