The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer
Michalina Janiszewska, … , Franziska Michor, Kornelia Polyak
Michalina Janiszewska, … , Franziska Michor, Kornelia Polyak
Published April 22, 2021
Citation Information: JCI Insight. 2021;6(11):e147617. https://doi.org/10.1172/jci.insight.147617.
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Research Article Oncology

The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer

Abstract

Despite the availability of multiple human epidermal growth factor receptor 2–targeted (HER2-targeted) treatments, therapeutic resistance in HER2+ breast cancer remains a clinical challenge. Intratumor heterogeneity for HER2 and resistance-conferring mutations in the PIK3CA gene (encoding PI3K catalytic subunit α) have been investigated in response and resistance to HER2-targeting agents, while the role of divergent cellular phenotypes and tumor epithelial-stromal cell interactions is less well understood. Here, we assessed the effect of intratumor cellular genetic heterogeneity for ERBB2 (encoding HER2) copy number and PIK3CA mutation on different types of neoadjuvant HER2-targeting therapies and clinical outcome in HER2+ breast cancer. We found that the frequency of cells lacking HER2 was a better predictor of response to HER2-targeted treatment than intratumor heterogeneity. We also compared the efficacy of different therapies in the same tumor using patient-derived xenograft models of heterogeneous HER2+ breast cancer and single-cell approaches. Stromal determinants were better predictors of response than tumor epithelial cells, and we identified alveolar epithelial and fibroblastic reticular cells as well as lymphatic vessel endothelial hyaluronan receptor 1–positive (Lyve1+) macrophages as putative drivers of therapeutic resistance. Our results demonstrate that both preexisting and acquired resistance to HER2-targeting agents involve multiple mechanisms including the tumor microenvironment. Furthermore, our data suggest that intratumor heterogeneity for HER2 should be incorporated into treatment design.

Authors

Michalina Janiszewska, Shayna Stein, Otto Metzger Filho, Jennifer Eng, Natalie L. Kingston, Nicholas W. Harper, Inga H. Rye, Maša Alečković, Anne Trinh, Katherine C. Murphy, Elisabetta Marangoni, Simona Cristea, Benjamin Oakes, Eric P. Winer, Ian E. Krop, Hege G. Russnes, Paul T. Spellman, Elmar Bucher, Zhi Hu, Koei Chin, Joe W. Gray, Franziska Michor, Kornelia Polyak

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Figure 1

Cellular genetic heterogeneity in neoadjuvant HER2-targeted treatment patient cohorts.

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Cellular genetic heterogeneity in neoadjuvant HER2-targeted treatment pa...
(A) Representative image of STAR-FISH analysis. Nuclear outline image and topological map of the sample are shown. Scale bars: 50 μm. WT, PIK3CA wild-type STAR-FISH signal; CEP17, chromosome 17 centromeric probe; ERBB2, ERBB2-specific FISH probe; MUT, PIK3CA mutant; Amp, amplification of ERBB2. (B) Summary of frequencies of cells with distinct genotypes in Norwegian (NOR) and T-DM1 cohorts. pCR, pathological complete response; No pCR, no pathological complete response. (C) Frequencies of cells with distinct genotypes in each analyzed sample from NOR cohort. Each row corresponds to a single image analyzed (n = 3 per case). Gray represents a frequency of 0. Images are grouped according to the patient ID, and patient IDs are grouped according to response (left). For nonresponders, frequency of genotypes after treatment is also shown (right). (D) Average genotype frequency in pre- versus posttreatment samples from NOR cohort. P values from a Wilcoxon test comparing the change in frequency pre- and posttreatment to 0. (E) Unsupervised clustering of frequencies of cells with distinct genotypes per patient in pretreatment samples from Norwegian cohort. Samples are colored according to response. (F) Differences in genotype frequencies between groups identified in (E). P values from Kruskal-Wallis test. (G) Frequencies of cells with distinct genotypes in each analyzed sample from T-DM1 cohort. Images are grouped according to the patient ID, and patient IDs are grouped according to response.