How Tissue Microarrays (TMA) Contribute to Translational Research: Bridging the Gap Between Lab and Clinic
What is Translational Research?
In today’s rapidly advancing medical field, translational research is the engine that turns fundamental scientific findings into useful medical therapies. The development of novel diagnostic techniques, treatment plans, and customized medical approaches is accelerated by this “bench to bedside” strategy, which is crucial for enhancing patient outcomes.
Tissue Microarrays (TMAs) in Translational Research
Here is where Tissue Microarray (TMA) technology comes into place. Through TMAs, they allow high-throughput analysis that is both cost-effective and efficient which helps translational researchers to test hypotheses and validate potential biomarkers more quickly than ever before.
The Roles of TMAs in Clinical Trials
Clinical trials benefit greatly from TMAs, especially in the early phases when the goal is to find biomarkers that can predict how a patient will react to a particular drug. TMAs are frequently used to identify tumours that express a specific protein of interest, enabling clinicians to stratify patients for individualized treatment. This ability to test multiple tissue samples from various patients at once enables researchers to determine which biomarkers are clinically relevant, helping guide the direction of trials.
Researchers are able to expedite the process of verifying whether a putative biomarker is useful for detecting or forecasting illness outcomes by integrating TMAs into clinical trial design. This integration is key in advancing therapies and diagnostic tools from the lab to the clinic in a cost-effective and time-efficient manner.
Here’s a more thorough breakdown of TMAs’ role in clinical trials and the reasons they matter in this situation:
- Biomarker Discovery and Stratification of Patients
- High-Throughput Screening of Potential Therapies
- Accelerating the Validation Process
- Exploring Tissue Heterogeneity
1. Biomarker Discovery and Stratification of Patients
- Biomarker Identification: Finding biomarkers that can forecast a patient’s reaction to therapy is essential in clinical trials, particularly for personalized medicine or cancer therapies. Using TMAs, researchers may analyse several patient samples simultaneously, which aids in the discovery of biomarkers that are consistently expressed in particular tumour types or illnesses.
- Patient Stratification: TMAs facilitate the evaluation of biomarker expression in a sizable patient cohort by researchers. They may now divide patients into several groups according to their molecular profiles thanks to this. Patients may respond better to a specific targeted medication, for instance, if their tumours express a particular protein. TMAs allow more individualized and successful clinical trial designs.
2. High-Throughput Screening of Potential Therapies
- Drug Target Validation: With TMAs, researchers can confirm whether a target is broadly expressed in a variety of patients or tumour types. This is important because it helps determine whether a medication can be used extensively or if it can only be used in a certain patient population.
3. Accelerating the Validation Process
- Efficiency in Validation: Extensive validation of biomarkers or treatment targets is frequently necessary for clinical studies. TMAs significantly cut down on the amount of time needed for this validation. Through the use of TMAs, all samples can be processed and analysed at the same time as opposed to processing hundreds of tissue slices separately. As a result, determining whether a biomarker is clinically important across various patient populations is made simpler and faster.
- Cost-Effectiveness: Testing and analysing tissue samples is a major expense associated with conducting clinical studies. By combining the task of analysing hundreds of tissue samples onto a single slide, TMAs reduce this expense. When researchers are still investigating a wide range of biomarkers or therapeutic targets, such as in early-phase clinical trials, this efficiency is extremely helpful.
4. Exploring Tissue Heterogeneity
- Tumour Heterogeneity: Tumour heterogeneity, or the variance in genetic and molecular features within a tumour, is a major factor in how patients react to treatment in diseases like cancer. TMAs allow for the inclusion of several cores from various tumour regions, which enables researchers to examine this heterogeneity. Clinical trials can learn more about how distinct molecular characteristics affect treatment outcomes by examining diverse locations of the same tumour or a variety of tumour types.
- Multiple Sample Comparison: Comparing samples from various individuals or tissue types (healthy vs. sick) is made possible by TMAs. This enables clinical trials to investigate the behaviour of diseases such as cancer in various patients, which can help develop treatment plans customized to each patient’s unique profile.
The Future of TMA in Translational Research
TMAs have the potential to improve translational research even more as technology advances. TMA analysis is already incorporating artificial intelligence (AI) and machine learning to enable quicker and more precise tissue sample interpretation. The ultimate objective of individualized healthcare for every patient may be closer to these developments as they may result in even more accuracy in biomarker discovery and validation.
Conclusion
Are Tissue Microarrays Important in Clinical Trials?
Yes. TMAs have transformed the way clinical trials are conducted, particularly in the fields of cancer and personalized medicine. By enabling high-throughput analysis of tissue samples, TMAs accelerate the discovery and validation of biomarkers, help stratify patients, and ensure that treatments are tailored to individual molecular profiles. In doing so, TMAs make clinical trials more efficient, cost-effective, and scientifically robust, ultimately bringing new treatments to patients faster.
At MYmAb Biologics, we expedite bench-to-bedside translation by supplying top-notch tissue microarrays (TMAs) to the entire scientific community worldwide to bridge the gap between laboratory discoveries and clinical applications that improve patient care.
Contact us for further details.
References:
- Hewitt S. M. (2012). Tissue microarrays as a tool in the discovery and validation of predictive biomarkers. Methods in molecular biology (Clifton, N.J.), 823, 201–214. https://doi.org/10.1007/978-1-60327-216-2_13
- Lee, A. T. J., Chew, W., Wilding, C. P., Guljar, N., Smith, M. J., Strauss, D. C., Fisher, C., Hayes, A. J., Judson, I., Thway, K., Jones, R. L., & Huang, P. H. (2019). The adequacy of tissue microarrays in the assessment of inter- and intra-tumoural heterogeneity of infiltrating lymphocyte burden in leiomyosarcoma. Sci Rep 9, 14602. https://doi.org/10.1038/s41598-019-50888-5
- Lubbock, A. L., Katz, E., Harrison, D. J., & Overton, I. M. (2013). TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data. Nucleic acids research, 41(Web Server issue), W562–W568. https://doi.org/10.1093/nar/gkt529
- Pati, P., Karkampouna, S., Bonollo, F., Comperat, E., Radic, M., Spahn, M., Martinelli, A., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2024). Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nat Mach Intell 6, 1077–1093. https://doi.org/10.1038/s42256-024-00889-5
- Seyhan, A.A. (2019). Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. transl med commun 4, 18. https://doi.org/10.1186/s41231-019-0050-7
- Shafi, S., & Parwani, A.V. (2023). Artificial intelligence in diagnostic pathology. Diagn Pathol 18, 109. https://doi.org/10.1186/s13000-023-01375-z
- Stopsack, K. H., Tyekucheva, S., Wang, M., Gerke, T. A., Vaselkiv, J. B., Penney, K. L., Kantoff, P. W., Finn, S. P., Fiorentino, M., Loda, M., Lotan, T. L., Parmigiani, G., & Mucci, L. A. (2021) Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays. eLife 10:e71265. https://doi.org/10.7554/eLife.71265
- Torhorst, J., Bucher, C., Kononen, J., Haas, P., Zuber, M., Köchli, O. R., Mross, F., Dieterich, H., Moch, H., Mihatsch, M., Kallioniemi, O. P., & Sauter, G. (2001). Tissue microarrays for rapid linking of molecular changes to clinical endpoints. The American journal of pathology, 159(6), 2249–2256. https://doi.org/10.1016/S0002-9440(10)63075-1