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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

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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 4

Gene expression profiles of HER2+ PDX models.

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Gene expression profiles of HER2+ PDX models.
(A) MetaCore Gene Ontology...
(A) MetaCore Gene Ontology (GO) Processes overrepresented in expression profiles of untreated PDX1 compared with untreated PDX2 (top) and untreated PDX2 compared with untreated PDX1 (bottom). n = 3 independent tumors per group. The x axis corresponds to –log P value of the significance of enrichment, calculated using the MetaCore enrichment analysis. (B) MetaCore GO Processes upregulated and downregulated upon treatment compared with untreated controls. n = 3 independent tumors per group. The color scale corresponds to –log P value of the significance of enrichment, calculated using the MetaCore enrichment analysis. (C and D) Alternative splicing analysis of CD46 (C) and GRB7 (D) in PDX2. Boxes represent the exons between which significant alternative splicing events were detected. Red lines, splicing occurred more frequently than in untreated samples; blue lines, splicing occurred less frequently than in untreated samples. The dPSI (change in percentage spliced in) is indicated above each line. n = 3 independent tumors per group. P values from the LeafCutter (52) algorithm are shown. *Exons 6–10 correspond to the exons in the canonical CD46 transcript. Exon 1 is the first exon in transcript ENST00000636114.1 and is not included in the canonical CD46 transcript. Exon 9 in the canonical transcript corresponds to the second exon in ENST00000636114.1.

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