Rethinking Biomarkers: Quantitative Continuous Scoring as an Emerging Tool for Oncologic Decision-Making – Medical News Asia

Key learnings:

Traditional tissue staining by immunohistochemistry (IHC) may be sufficient to detect (ultra-) low levels of biomarker expression for guiding intervention with some targeted therapies,^1,2 however, due to limitations it may not be an effective method to evaluate biomarkers for some novel therapeutics.^3-6
Quantitative continuous scoring (QCS) is a computational pathology approach utilizing artificial intelligence (AI)-driven algorithms to analyze images of IHC-stained specimens and allow quantification of the levels, heterogeneity, and patterns of target expression.^5,6
QCS can be used to determine the normalized membrane ratio (NMR), which represents the ratio of membrane target protein intensity to combined cytoplasmic plus membrane target intensity and represents the proportion of target protein at the membrane.⁷
An ongoing phase III study of a targeted agent against trophoblast cell surface antigen 2 (TROP2) is now prospectively enrolling non-small cell lung cancer (NSCLC) patients testing positive to TROP2 by QCS-NMR.^8-10

Introduction

The use of IHC staining of tissue samples to guide targeted treatment is widely used in clinical practice, and is recommended by expert groups for many tumor types and biomarkers.^11,12 The classical approach to IHC scoring for biomarkers, such as human epidermal growth factor receptor-2 (HER2), classifies specimens as either ‘positive’ or ‘negative’, scoring samples as IHC 0, 1+, 2+ or 3+ per pathologists’ visual interpretation.^11,12 However, with the availability of novel therapeutics, the concept of ‘positivity’ for some biomarkers is becoming more nuanced as some of the new targeted treatments are active against tumors with low levels of biomarker expression.⁵ Moreover, classical approaches to biomarker scoring may be subjective, semiquantitative, and do not account for heterogeneity of expression.⁵ With the ongoing development of new targeted therapies in oncology and the advent of new diagnostic technologies, there is a clinical need and opportunity for novel biomarker-driven approaches that complement classical IHC methods and enhance patient selection for targeted treatments.⁵ One such approach is utilizing computational AI-driven image analysis to develop and validate novel biomarkers – an approach known as QCS.

QCS: The technology and its applications

By utilizing computational pathology for biomarker scoring, QCS analyzes whole-slide IHC-stained tumor tissues and quantifies target protein expression in individual cells as well as subcellular compartments (Figure 1).^5,13

The QCS procedure starts with the capture of high-quality whole-slide images of IHC-stained tumor specimens, which are turned into digital images by scanners.^5,6 These images are analyzed by AI-driven algorithms, with quality control by a pathologist, who ensures the algorithm’s accurate identification of the tumor cells.^5,6 During the analysis, tumor and non-tumor cells are identified and counted, as are other structures such as the nuclei, cytoplasm, membranes and subcellular compartments.^5,6 Additionally, measurements of the optical density within these images can allow quantification of the levels, heterogeneity, and patterns of target expression.^5,6

Figure 1. Overview of how QCS is performed and comparison with conventional IHC scoring

The advantage of QCS is that it generates a large volume of quantitative data on the distribution and expression of the target protein in the entire tissue section at the level of subcellular compartments,^5,6 providing various additional measurements that may serve as potential biomarkers.^5,6 Although there is currently no standardized approach, several notable examples of QCS-derived biomarkers are undergoing preclinical and clinical development.^5,7,13 These include (Figure 2):

1. The percentage of tumor cells that are IHC positive (fraction of tumor cells expressing the target protein).⁵
2. Spatial proximity scores: These use data on proximity and density of target-positive and target-negative cells, and can be used to estimate the ‘bystander effect’ of ADCs, i.e. the likelihood that a target-negative tumor cell near a target-positive cell will be killed by the ADC.⁵ Spatial proximity can be scored as continuous (total signal intensity level contributing to a tumor cell’s susceptibility to a given drug due to their own target expression or their neighbors’ target expression) or binary (the percentage of cells that are positive or have at least one positive neighbor).⁵
3. NMR: Calculated as the ratio of membrane target protein-intensity to overall (cytoplasmic + membrane) target-intensity. It represents the proportion of target protein at the membrane (discussed in further detail below).⁷

Figure 2: Selected examples of novel features measurable by QCS

Through precise quantification and mapping of a target protein across subcellular compartments of tumor cells, QCS can uncover biological insights for a specific target protein associated with a specific drug response.^6,14Furthermore, because QCS analysis can provide a variety of different potential biomarkers, such a biomarker could be selected based on the mechanism of action of a drug.¹⁴

TROP2 NMR as a potential biomarker in lung cancer

A practical implementation of a QCS biomarker currently under investigation is testing the NMR of TROP2, a glycoprotein whose expression is increased in the cells of many solid tumors relative to the normal tissues.^15-17 TROP2 regulates cancer growth and spread via multiple signaling pathways.^15-17 Due to its increased expression and involvement of tumor proliferation, it is an attractive target for targeted treatment including antibody-drug conjugates (ADCs).^15-17

Despite different expression levels in tumor cells compared with normal cells, conventional TROP2 IHC does not predict efficacy of targeted therapies,^3,4 indicating the need for novel biomarkers to direct TROP2-targeted treatment. Indeed, several phase III studies of TROP2 ADCs exploring TROP2 NMR as a novel biomarker are currently ongoing.^8,9,18-20

Figure 3. The use of TROP2 NMR as a predictive biomarker

TROP2 NMR is calculated by dividing the optical density of a target protein at the cell membrane by the overall (cytoplasmic plus membrane) target protein intensity (Figure 3).⁷ By differentiating membrane and cytoplasmic expression, TROP2 NMR may better predict the efficacy of TROP2-directed therapy, suggesting a more accurate evaluation of internalization of the drug-receptor complex.²¹ This is supported by data from in vitro models suggesting that the TROP2 NMR score can predict the capacity of tumor cells to internalize TROP2-directed ADCs (Figure 4).^21,22 The statistical distribution of a QCS-derived biomarker in a patient population, such as TROP2 NMR, can be assessed to identify cutoff values per tumor-type that are associated with the response to a specific targeted therapy.⁵ In an exploratory analysis, a TROP2-directed ADC showed strong efficacy in NSCLC patients with TROP2 NMR below a specific value, indicating that relatively higher cytoplasmic TROP2 may be associated with higher TROP2-receptor internalization capacity.^7,21 Ongoing studies will provide more clarity on the utilization of TROP2 NMR as a biomarker for guiding TROP2-directed ADCs.^10,18-20 At least one of the ongoing trials is prospectively enrolling patients using TROP2 NMR.^8,9

Figure 4. Relationship between TROP2 NMR and TROP2-directed ADC internalization capacity

The future development of QCS biomarkers

Currently, the use of QCS biomarkers is being investigated in clinical trials. Notably, QCS technology is not limited to TROP2 NMR, and it can be adapted to apply tailored scoring schemes that are based on the mechanism of action of a drug of interest in various tumor types, e.g. QCS analysis of HER2 IHC to further optimize identification of patients who may benefit from HER2 ADCs.⁵ If QCS is to be deployed into clinical practice, rigorous validation will be needed and standardized protocols for tissue preparation (e.g. section thickness), IHC staining, and slide digitization must be implemented.

References

Venetis K, Crimini E, Sajjadi E, et al. HER2 Low, Ultra-low, and Novel Complementary Biomarkers: Expanding the Spectrum of HER2 Positivity in Breast Cancer. Front Mol Biosci. 2022;9:834651.

College of American Pathologists. Template for Reporting Results of Biomarker Testing of Specimens from Patients with Carcinoma of the Breast (Version 1.6.1.0, June 2025). https://documents.cap.org/documents/New-Cancer-Protocols-June-2025/Breast.Bmk_1.6.1.0.-REL_CAPCP.pdf. Accessed 15 July, 2025.

Heist RS, Guarino MJ, Masters G, et al. Therapy of Advanced Non-Small-Cell Lung Cancer With an SN-38-Anti-Trop-2 Drug Conjugate, Sacituzumab Govitecan. J Clin Oncol. 2017;35(24):2790–2797.

Sands J, Ahn MJ, Lisberg A, et al. Datopotamab Deruxtecan in Advanced or Metastatic Non-Small Cell Lung Cancer With Actionable Genomic Alterations: Results From the Phase II TROPION-Lung05 Study. J Clin Oncol. 2025;43(10):1254–1265.

Kapil A, Spitzmüller A, Brieu N, et al. HER2 quantitative continuous scoring for accurate patient selection in HER2 negative trastuzumab deruxtecan treated breast cancer. Sci Rep. 2024;14(1):12129.

Reis-Filho JS, Scaltriti M, Kapil A, Sade H, Galbraith S. Shifting the paradigm in personalized cancer care through next-generation therapeutics and computational pathology. Mol Oncol. 2024;18(11):2607–2611.

Garassino MC, Sands J, Paz-Ares L, et al. PL02.11 Normalized Membrane Ratio of TROP2 by Quantitative Continuous Scoring is Predictive of Clinical Outcomes in TROPION-Lung 01. J Thorac Oncol. 2024;19(10):S2–S3 (Abstract PL02.11).

ClinicalTrials.gov. NCT07291037. https://clinicaltrials.gov/study/NCT07291037. Accessed 21 January, 2026.

Daiichi-Sankyo. TROPION-Lung17 TROP2 Biomarker Directed Phase 3 Trial of DATROWAY^® Initiated in Patients with Previously Treated Advanced Nonsquamous Non-Small Cell Lung Cancer. https://daiichisankyo.us/press-releases/-/article/tropion-lung17-trop2-biomarker-directed-phase-3-trial-of-datroway-initiated-in-patients-with-previously-treated-advanced-nonsquamous-non-small-cell-lung-cancer. Accessed 21 January, 2026.

Targeted Oncology. Phase 3 TROPION-Lung17 Trial Initiates TROP2-Directed Therapy in NSCLC. https://www.targetedonc.com/view/phase-3-tropion-lung17-trial-initiates-trop2-directed-therapy-in-nsclc. Accessed 26 March, 2026.

Bartley AN, Washington MK, Colasacco C, et al. HER2 Testing and Clinical Decision Making in Gastroesophageal Adenocarcinoma: Guideline From the College of American Pathologists, American Society for Clinical Pathology, and the American Society of Clinical Oncology. J Clin Oncol. 2017;35(4):446–464.

Wolff AC, Hammond MEH, Allison KH, et al. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J Clin Oncol. 2018;36(20):2105–2122.

Spitzmueller A, Schmidt G, Triltsch N, Kapil A. Patent WO2023175483. A scoring method for an anti-TROP2 antibody‑drug conjugate therapy. https://patentscope.wipo.int/search/en/WO2023175483. Published 2023. Accessed 3 December, 2025.

Sade H, Reis-Filho JS, Schmidt G. Transforming cancer diagnostics with computational pathology. https://www.astrazeneca.com/what-science-can-do/topics/data-science-ai/computational-pathology-potential-transform-cancer-diagnostics.html. Published 2023. Accessed 3 December, 2025.

Shvartsur A, Bonavida B. Trop2 and its overexpression in cancers: regulation and clinical/therapeutic implications. Genes Cancer. 2015;6(3-4):84–105.

Goldenberg DM, Stein R, Sharkey RM. The emergence of trophoblast cell-surface antigen 2 (TROP-2) as a novel cancer target. Oncotarget. 2018;9(48):28989–29006.

Lenárt S, Lenárt P, Šmarda J, Remšík J, Souček K, Beneš P. Trop2: Jack of All Trades, Master of None. Cancers (Basel). 2020;12(11).

ClinicalTrials.gov. NCT05687266. https://www.clinicaltrials.gov/study/NCT05687266. Accessed 3 December, 2025.

ClinicalTrials.gov. NCT06357533. https://www.clinicaltrials.gov/study/NCT06357533. Accessed 3 December, 2025.

ClinicalTrials.gov. NCT05555732. https://www.clinicaltrials.gov/study/NCT05555732. Accessed 3 December, 2025.

Sung M, Wilson M, Vonficht D, et al. The role of TROP2 in the MoA of Dato-DXd and how it underpins the biologic rationale of the novel AI-guided biomarker TROP2 normalized membrane ratio. Cancer Research. 2025;85(Suppl 1):(Abstract 7149).

Okajima D, Yasuda S, Maejima T, et al. Datopotamab Deruxtecan, a Novel TROP2-directed Antibody-drug Conjugate, Demonstrates Potent Antitumor Activity by Efficient Drug Delivery to Tumor Cells. Mol Cancer Ther. 2021;20(12):2329–2340.

The article is sponsored by Daiichi Sankyo and AstraZeneca.

MED-HK-DATO-00049 2026/06
HK-13107 06/2026

Disclaimer

This article is not medical advice. Patients should seek personal assessment by a licenced specialist. Physicians are recommended to read the full publication(s) as cited in the article before making medical decisions. This article does not supersede nor replace the published article(s).