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Title: HER2 Heterogeneity: Why Some Tumors Escape Targeted Therapy (and What New Targets Emerge)
Type: A (mechanism/basic-translational)
Tags: HER2, intratumoral heterogeneity, resistance, models, subclones
PMID: 41925564
DOI: 10.1158/2159-8290.CD-25-1459
Journal: Cancer Discovery (2026);16(8):1691-1710
Category: Oncology

⚖️ Transparency Notice: This article was written with AI assistance and reviewed by the author, a medical oncologist.

Resistance is not uniform

HER2 is one of the most exploited targets in oncology, but tumors find ways to escape. This work from Dana-Farber/Harvard (Polyak, Michor) builds breast cancer models with intratumoral HER2 heterogeneity to see how resistance to anti-HER2 therapies evolves and what new targets emerge in resistant subclones [PMID: 41925564].

What they contribute

Models that recapitulate HER2 expression heterogeneity within the same tumor, allowing tracking of subclonal dynamics during treatment exposure. They identify novel therapeutic targets that arise specifically in resistant populations—not in the naïve tumor [PMID: 41925564].

Critical reading

It is basic/translational research using models (not patients). The promise is to identify “rescue targets” before resistance becomes clinically established. The leap to the clinic is long, but the subclone map is exactly the kind of biology that explains why T-DXd works in some and not others [PMID: 41925564].

💡 Implications for clinical practice

Explains why resistance to anti-HER2 is multifocal and not a single mutation. Reinforces the idea that resistance testing must be deep (not just one clone). Direct connection with our post on T-cell engagers/ADCs (ID 39).

Reference: Goyette MA, et al. Cancer Discov. 2026;16(8):1691-1710. doi:10.1158/2159-8290.CD-25-1459. PMID: 41925564.

— This analysis was generated by ANGIE (Always Next to Guide, Inspire and Empower), an artificial intelligence system with SOUL profiles, designed by Dr. Javier Pumares Pérez.

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