Abstract 5881: Identification of resistance mechanisms to IGF-IR targeting in triple negative breast cancer
Bibliographic record
Abstract
Abstract The triple negative subtypes of breast cancer (TNBC) are associated with poor prognosis. Unlike HER2+ and hormone receptor-positive BC, TNBC do not respond to targeted therapy and chemotherapy remains the primary treatment option. There is therefore an unmet need to develop effective therapy for TNBC. The insulin-like growth factor 1 (IGF-I) axis plays a critical role in BC progression by conveying survival and growth signals. Our laboratory reported on the production of a soluble fusion protein comprised of the extracellular domain of human IGF-IR fused to the Fc portion of human IgG (the IGF-Trap). The IGF-Trap reduces the bioavailability of circulating and locally produced IGF-I, thereby limiting tumor growth. When human TNBC MDA-MB-231 cells were xenotransplanted into nude mice and treated with the IGF-Trap, we observed variability in the response as it ranged from complete tumor regression to disease stabilization and tumor progression in some mice. This suggested that MDA-MB-231 cells are heterogeneous in respect to their sensitivity to IGF-IR signaling blockade. The aim of the present study was to identify resistance mechanisms that allow the cells to progress in the face of IGF-IR signaling blockade by the IGF-Trap. We first analyzed the tyrosine kinase receptor profile of these cells and confirmed by PCR that in addition to IGF-IR, they express epidermal growth factor receptor (EGFR), c-Met, and fibroblast growth factor receptor 1 (FGFR1). They also produce IGF-I, EGF and relatively high level of FGF1 that could provide potential autocrine signaling to compensate for IGF signaling blockade. Using limiting dilution cloning, we isolated MDA-MB-231 cells with a range of IGF-IR expression levels, as confirmed by qPCR and Western blotting. We found that clones with higher basal IGF-IR activation levels, independently of expression levels had increased sensitivity to IGF-Trap treatment in the presence of serum, identifying them as IGF-addicted clonal subpopulations. Furthermore, an IGF-Trap resistant population selected from MDA-MB-231 cells by prolonged exposure to the IGF-Trap had an increased proliferation rate in the presence of the IGF-Trap as compared to unselected cells, as assessed by MTT and showed higher p-EGFR and p-ERK levels, suggesting that prolonged IGF-Trap treatment enriched an IGF-I-independent population with increased aggressiveness. Collectively these results showed that MDA-MB-231 cells are heterogeneous in respect to IGF-IR expression levels and that IGF-Trap sensitivity correlated with constitutive IGF-IR activation levels. Moreover, IGF-Trap resistance in these cells was associated with increased EGFR signaling and proliferation. Further interrogation of the gene expression profile of these cells will define a “resistance signature” with potential relevance to personalized treatment with IGF-targeting drugs. Supported by the CIHR and Mitacs. Citation Format: Jennifer Tsui, George Vaniotis, Maria Celia Fernandez, Pnina Brodt. Identification of resistance mechanisms to IGF-IR targeting in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5881. doi:10.1158/1538-7445.AM2017-5881
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".