Usage Information
Citation Information: JCI Insight. 2021;6(11):e147617. https://doi.org/10.1172/jci.insight.147617.
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
Usage data is cumulative from April 2022 through April 2023.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 3,022 | 534 |
| 300 | 137 | |
| Figure | 404 | 12 |
| Supplemental data | 453 | 89 |
| Citation downloads | 54 | 0 |
| Totals | 4,233 | 772 |
| Total Views | 5,005 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
