What does this research mean for the field?

RNA-sequencing algorithms can predict immunohistochemistry (IHC) positivity for HER2, TROP2, and NECTIN4 biomarkers with varying levels of positive and negative percent agreement, providing a viable screening approach for biomarker-directed clinical trials. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to validate algorithms that predict IHC positivity for HER2, TROP2, and NECTIN4 in advanced tumors using RNA-seq data.

April 5, 2026

Abstract 6942: Validation of HER2, TROP2, and NECTIN4 IHC prediction algorithms for the ADC MATCH trial

Key Points

The study aims to validate algorithms that predict IHC positivity for HER2, TROP2, and NECTIN4 in advanced tumors using RNA-seq data.
Collected three retrospective, de-identified RNA+IHC datasets for analysis.
Conducted RNA-seq and IHC tests on samples from specified laboratories.
Defined IHC positivity using specific scoring systems and gene expression thresholds.
Positive percent agreement (PPA) was 47% for HER2, 93% for TROP2, and 35% for NECTIN4.
Negative percent agreement (NPA) was 89% for HER2, 55% for TROP2, and 92% for NECTIN4.
Performance varied by cancer type, notably lower PPA for TROP2 in colorectal cancer.

Abstract

Abstract Introduction Antibody-drug conjugates (ADC) have recently emerged as a leading class of targeted oncology therapies. Their usage remains complex, with approvals ranging from broad, tumor-agnostic immunohistochemistry (IHC) companion diagnostic approvals to narrower indication-specific labels. The ongoing ADC MATCH clinical trial (NCT06311214) examines whether a treatment algorithm defined by reflex RNA/IHC testing of the HER2, TROP2, and NECTIN4 gene/proteins can lead to successful biomarker-directed treatment of advanced solid tumors. This study describes the validation of an RNA-seq algorithm for identifying likely IHC-positive patients as required by the ADC MATCH study. Methods Three retrospective, de-identified RNA+IHC datasets were collected to enable accuracy studies of CAP/CLIA lab-developed tests for HER2 IHC prediction, TROP2 IHC prediction, and NECTIN4 IHC prediction. RNA-seq was performed by Tempus AI, Inc (Tempus xR), while IHC was performed by Neogenomics, Inc (HER2; clone 4B5), Tempus AI, Inc. (TROP2; clone SP294), or Histologix (NECTIN4; AB192033). IHC positivity was defined as 2+/3+ score groups (i.e., approximately an H-score 100). High expressors were defined as binary thresholds using log2 gene tpm values of 7.9 (HER2), 4.9 (TROP2), and 6.8 (NECTIN4). Results The sample sizes for the three accuracy studies were 2,018 (HER2), 184 (TROP2), and 478 (NECTIN4), respectively. The performance of IHC prediction was assessed for these three targets using positive percent agreement (PPA) and negative percent agreement (NPA). The PPA of each target was found to be 47% (HER2; n=545), 93% (TROP2; n=146), and 35% (NECTIN4; n=71). The corresponding NPA of each target was found to be 89% (HER2; n=1473), 55% (TROP2; n = 38), 92% (NECTIN4; n=407). PPA and NPA varied among cancer types; for example, the TROP2 PPA in colorectal cancer was substantially lower than other cancers (62% vs 95%; p .05). Conclusion IHC prediction using RNA-seq data is a promising approach to identify patients who are likely to test positive for specific protein biomarkers, potentially aiding in clinical trial screening and treatment decisions. Future studies, such as the ADC MATCH clinical trial, will assess the clinical value and outcomes of this approach. Citation Format: Kyle A. Beauchamp, Elizabeth Mauer, Kaveri Nadhamuni, Elizabeth morency, Alia Zander, Xingyu Zheng, Katherine Mclean, Sayantoni Mukhopadhyay, Seung Won Hyun, Chithra Sangli, Kate Sasser, Halla Nimeiri, Charles Koyias, Michelle Ting-Lin, Funda Meric-Bernstam. Validation of HER2, TROP2, and NECTIN4 IHC prediction algorithms for the ADC MATCH trial abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6942.

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