Abstract 1310: Targeting pro-metastatic stress networks in triple-negative breast cancer using patient-derived models
Metastatic triple-negative breast cancer (mTNBC) remains one of the most challenging forms of breast cancer to treat due to profound heterogeneity; chemotherapy-based regimens continue to be the mainstay of treatment. Kinases are attractive therapeutic targets, but while kinase inhibitors have improved outcomes in many metastatic solid cancers including HER2/Neu-amplified and kinase-driven hormone receptor positive breast cancers kinase inhibitors have not shown durable efficacy in mTNBC and many have failed in clinical trials. Furthermore, the high doses of kinase inhibitors to achieve cytotoxicity have unacceptable side effects for patients limiting real-world use. Preclinical research and secondary analyses from clinical trial data from our group and others demonstrate important differences in how kinase signaling networks, and not individual pathways, drive TNBC metastatic transformation compared to other breast cancer subtypes. Chronic stress states such as those initiated by hypoxia, poor nutrient delivery and higher metabolic demands promote pro-metastatic transcriptional programs in TNBC. Our research focuses on identifying and targeting regulators of the chronic stress response to suppress TNBC metastasis. Our previous work showed that activation of the physiologic metastasis suppressor RKIP effectively reduced TNBC metastasis in cell line models through inhibition of stress MAPK networks (p38, JNK, ERK1/2, MLK) resulting in decreased transcription of the pro-metastatic transcription factor BACH1. We demonstrated that low dose inhibition of multiple kinase nodes of networks, rather than single kinase nodes, more effectively reduces oncogenic signaling output and prevents compensatory signaling activation. Here we take the next step to translate this concept into the clinic. We show that combinations of kinase inhibitors used in early phase clinical trials (Ralimetinib (p38), JNK-in-8 (JNK) or FDA approved (Trametinib (MEK & ERK1/2)) can inhibit stress kinase networks, pro-motility gene expression and TNBC cell migration. Using patient-derived organoids that recapitulate heterogeneous TNBC molecular characteristics we successfully generated stable BACH1-knockout TNBC organoids. We utilized mass spectrometry-based techniques with a kinase inhibitor system (MIB-MS) and proteomics to characterize the activation state of kinase signaling networks in organoids and are investigating the effect of metastatic inhibitors on these pathways. Inhibition of these stress kinase networks should enable effective reprogramming of metastatic or high-risk cancers to more benign states and make tumors more susceptible to approved anti-proliferative therapies such as chemotherapy or immunotherapy while limiting toxicities from kinase inhibitors using a low-dose, multi-drug combination approach. Citation Format: Margarite D. Matossian, Ali Yesilkanal,Michael East, Denis Okumu, Geetha P. Yerradoddi, Rita Nanda, Gary Johnson, Marsha R. Rosner. Targeting pro-metastatic stress networks in triple-negative breast cancer using patient-derived models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1310.
Authors
- Rita Nanda (ORCID: https://orcid.org/0000-0001-5248-0876)
- Marsha Rich Rosner (ORCID: https://orcid.org/0000-0001-6586-8335)
- Margarite D. Matossian (ORCID: https://orcid.org/0000-0002-9527-1043)
- Ali E. Yesilkanal (ORCID: https://orcid.org/0000-0003-4988-1294)
- Denis O. Okumu (ORCID: https://orcid.org/0000-0002-4791-0453)
- Michael P. East (ORCID: https://orcid.org/0000-0002-1994-6117)
- Geetha Priyanka Yerradoddi
- Gary A. Johnson
Institutions
- University of Chicago (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-1310
- Primary Topic
- Nanoplatforms for cancer theranostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00