Quantitative Analysis of Tissue Biomarkers: Taking Pathology One Step Further

The analysis of biomarkers in tissue sections is a powerful tool to study the molecular and histological features of healthy and pathological tissues, diagnose diseases, and monitor—or even predict—treatment response. Traditionally, the expression status of certain biomarkers, such as growth factor receptors, has been used to distinguish molecular subtypes of diseases and determine the best treatment for each patient.

However, it has become evident that not only the expression status of biomarkers but also their expression levels can provide key information about the molecular profile of the tissue and better predict treatment response.1 Therefore, the use of quantitative image analysis methods to determine the expression levels of biomarkers in research and diagnostic laboratories has increased exponentially over the last few years.

Current technologies for quantitative image analysis of tissue biomarkers

Immunohistochemistry (IHC) and different variations of in situ hybridization (ISH) are the most commonly used methods to analyze the expression of molecular tissue biomarkers. Although non-targeted histochemical stains, such as hematoxylin and eosin (H&E), do not provide information about the expression status of specific biomarkers, artificial intelligence (AI) frameworks can be used to predict the expression of biomarkers based on the morphologic characteristics of tissues.2

Immunofluorescence can also be used to detect tissue biomarkers and determine their expression levels.3 Compared to standard IHC, immunofluorescent methods provide a higher signal-to-noise ratio and a wider and more linear dynamic range of detection. As such, immunofluorescent methods present a great tool for obtaining reliable quantitative information on tissue biomarkers.

A key drawback of immunofluorescence is the requirement for technical experience and special—and often costly—equipment.4 Autofluorescence and the short duration of the signal should also be taken into account when performing quantitative image analysis of tissue biomarkers using immunofluorescent methods.

Key considerations for the detection and quantification of tissue biomarkers

The ability to detect multiple biomarkers on the same tissue section—or multiplexing—is often essential to obtain more thorough insight into the molecular features of tissues, as well as study the interactions between different biomarkers and cell types. The ability for multiplexing differs considerably among technologies and often defines which method is the most appropriate for each application.

When IHC is used, singleplex staining of consecutive serial sections is often required to evaluate the expression of numerous tissue biomarkers.5 This approach also requires the alignment and overlay of registered digital images of consecutive serial sections.

On the other hand, when antibodies labeled with different fluorophores are used, fluorescent methods allow the detection of several biomarkers on the same tissue section.6 Careful selection of antibodies and fluorophores can minimize the overlap in excitation and emission spectra. The use of elaborate deconvolution methods can increase the number of biomarkers analyzed within the same tissue. Multiplexed fluorescence methods are often the method of choice when tissue material is limited.

Another critical consideration when choosing the method of detection and quantitation of tissue biomarkers, especially when used for clinical applications, is staining consistency and repeatability.3 Several steps in the tissue preparation and staining process can affect staining intensity; hence, variations in these steps can limit the repeatability of image analysis results.

Tissue preparation (including tissue thickness) and the material and quality of coverslips should also be taken into account when choosing the most appropriate method to detect and quantify tissue biomarkers.3

Clinically useful biomarkers for quantitative analysis      

Oncology is by far the field with the most clinical applications of methods for quantitative biomarker analysis because the expression levels of oncogenes can predict response to treatment and indicate prognosis.7

The expression status of estrogen receptor (ER) and progesterone receptor (PR) in breast tumors can predict response to tamoxifen and other hormonal therapies; hence, ER/PR status analysis is standard practice for patients with breast cancer.8 Traditionally, ER/PR staining intensity in IHC-stained tissues is scored manually by a pathologist; however, objectivity and variations in staining intensity in different parts of the tissue limit the reproducibility of manual scoring.

HER2 is overexpressed in 1 out of 5 breast cancers, and its expression levels can predict response to HER2-targeted therapies.9 HER2 status is typically determined by IHC, followed by manual semi-quantification by a pathologist, who scores the level of membranous HER2 staining. Algorithms for automated analysis of HER2 IHC-stained images can reduce interobserver variability in HER2 staining intensity.10,11 Additionally or alternatively, FISH can be used to assess for HER2 gene amplification. Similar algorithms have also been developed to automate HER2 FISH analysis.12

Ki-67 levels determined by IHC can indicate cellular proliferation, which has a prognostic value in various cancers.13 PD-1/PD-L1 status has a similar prognostic value has, in addition to predicting response to immunotherapies targeting the PD-1/PD-L1 axis.14 Currently, the expression of Ki-67 and PD-L1 is determined by IHC, followed by a manual examination of stained slides by a pathologist. Several automated algorithms for PD-L1 and Ki-76 scoring are currently under development and validation.

New trends in quantitative image analysis

Several efforts are underway to increase the multiplexing ability of technologies to detect and quantify tissue biomarkers. Most conventional fluorescent technologies are limited to five markers, although several methods can be used to minimize the spectral overlap between different fluorophores.3 For instance, Opal technology can be used to deconvolve the overlapping spectra of up to nine fluorophores.

The use of antibodies with non-fluorescent tags is also gaining popularity because of the increased multiplexing ability of this approach. For example, mass spectrometry entails the use of metal-tagged antibodies, overcoming the limitations of overlapping spectra.15 Mass spectrometry allows the concurrent analysis of over 40 biomarkers.

Additionally, optimization and standardization of tissue preparation, staining, and tissue biomarker interpretation are warranted to ensure accuracy, consistency, and reproducibility of quantitative analysis of tissue biomarkers. This is particularly important when detecting and quantifying tissue biomarkers for clinical applications.3 Emerging signal analysis software with predefined “turn-key algorithms” enables the automated analysis of staining area and staining intensity, improving the accuracy and repeatability of biomarker quantification.16 The clinical validation of such algorithms is warranted before their clinical implementation in patient care.



  1. Malone ER, Oliva M, Sabatini PJB, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome Med. 2020;12(1):8. doi:10.1186/s13073-019-0703-1
  2. Sha L, Osinski BL, Ho IY, et al. Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images. J Pathol Inform. 2019;10:24. doi:10.4103/jpi.jpi_24_19
  3. Lara H, Li Z, Abels E, et al. Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association. Appl Immunohistochem Mol Morphol  AIMM. 2021;29(7):479-493. doi:10.1097/PAI.0000000000000930
  4. Meyerholz DK, Beck AP. Principles and approaches for reproducible scoring of tissue stains in research. Lab Investig. 2018;98(7):844-855. doi:10.1038/s41374-018-0057-0
  5. Stack EC, Wang C, Roman KA, Hoyt CC. Multiplexed immunohistochemistry, imaging, and quantitation: A review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods. 2014;70(1):46-58. doi:https://doi.org/10.1016/j.ymeth.2014.08.016
  6. Viratham Pulsawatdi A, Craig SG, Bingham V, et al. A robust multiplex immunofluorescence and digital pathology workflow for the characterisation of the tumour immune microenvironment. Mol Oncol. 2020;14(10):2384-2402. doi:10.1002/1878-0261.12764
  7. Ben-Hamo R, Jacob Berger A, Gavert N, et al. Predicting and affecting response to cancer therapy based on pathway-level biomarkers. Nat Commun. 2020;11(1):3296. doi:10.1038/s41467-020-17090-y
  8. Mosly D, Turnbull A, Sims A, Ward C, Langdon S. Predictive markers of endocrine response in breast cancer. World J Exp Med. 2018;8(1):1-7. doi:10.5493/wjem.v8.i1.1
  9. Wang J, Xu B. Targeted therapeutic options and future perspectives for HER2-positive breast cancer. Signal Transduct Target Ther. 2019;4(1):34. doi:10.1038/s41392-019-0069-2
  10. Brügmann A, Eld M, Lelkaitis G, et al. Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res Treat. 2012;132(1):41-49. doi:10.1007/s10549-011-1514-2
  11. Skaland I, Øvestad I, Janssen EAM, et al. Comparing subjective and digital image analysis HER2/neu expression scores with conventional and modified FISH scores in breast cancer. J Clin Pathol. 2008;61(1):68-71. doi:10.1136/jcp.2007.046763
  12. Furrer D, Jacob S, Caron C, Sanschagrin F, Provencher L, Diorio C. Validation of a new classifier for the automated analysis of the human epidermal growth factor receptor 2 (HER2) gene amplification in breast cancer specimens. Diagn Pathol. 2013;8(1):17. doi:10.1186/1746-1596-8-17
  13. Inwald EC, Klinkhammer-Schalke M, Hofstädter F, et al. Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry. Breast Cancer Res Treat. 2013;139(2):539-552. doi:10.1007/s10549-013-2560-8
  14. Yi M, Jiao D, Xu H, et al. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Mol Cancer. 2018;17(1):1-14. doi:10.1186/s12943-018-0864-3
  15. Baharlou H, Canete NP, Cunningham AL, Harman AN, Patrick E. Mass Cytometry Imaging for the Study of Human Diseases—Applications and Data Analysis Strategies. Front Immunol. 2019;10:2657. doi:10.3389/fimmu.2019.02657
  16. Goldstein NS, Hewitt SM, Taylor CR, Yaziji H, Hicks DG. Recommendations for improved standardization of immunohistochemistry. Appl Immunohistochem Mol Morphol AIMM. 2007;15(2):124-133. doi:10.1097/PAI.0b013e31804c7283


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