Abstract 4124: Potent anti-tumor and metastatic breast cancer efficacy of bevacizumab with CRLX101, an investigational chemotherapy nanoparticle-drug conjugate that secondarily suppresses HIF-1α
Bibliographic record
Abstract
Abstract Despite the approval of seven different VEGF-pathway targeting agents, such as bevacizumab, for ten different cancer types, VEGF inhibition has shown only modest survival benefits - especially in breast cancer. One hypothesis for this is the adaptive resistance that emerges, such as increased tumor hypoxia and elevated hypoxia-inducible factor 1α (HIF-1α), which up-regulates genes causing tumor angiogenesis, resistance and metastasis. Here we evaluated bevacizumab paired with a potent cytotoxic investigational drug called CRLX101, a nanoparticle-drug conjugate containing the payload camptothecin that secondarily suppresses HIF-1α. In a preclinical mouse model of primary human breast tumor xenografts grown in the mammary fat pad after cell-line implantation, CRLX101 monotherapy was highly efficacious. More importantly, CRLX101 with bevacizumab resulted in dramatic primary tumor shrinkages and greatly improved mice survival, despite bevacizumab alone having no activity in this model. This potent anti-tumor efficacy was further confirmed in a patient-derived xenograft (PDX) model, where again CRLX101 and bevacizumab led to obvious shrinkage of established primary tumors. HIF-1α suppression was confirmed by immunohistochemistry while changes in tumor hypoxia and perfusion were evaluated using photoacoustic imaging and contrast-enhanced ultrasound, respectively. To better reflect the clinical treatment setting, the combination was next evaluated in our preclinical model of post-surgical, overt metastatic disease. Both CRLX101 monotherapy and CRLX101 in combination with bevacizumab showed shrinkage of existing metastatic masses and prevented the emergence of new metastases. In conclusion, we showed that pairing an anti-angiogenic agent with a potent chemotherapy backbone that is able to suppress HIF-1α up-regulation induced by anti-angiogenic therapy greatly improve efficacy in both primary and metastatic breast cancer models. The data from these preclinical experiments demonstrate that further research of the combination of bevacizumab and other anti-angiogenic drugs with CRLX101 is warranted in solid tumors. Citation Format: Elizabeth Pham, Christina R. Lee, Ping Xu, Shan Man, Melissa Yin, F. Stuart Foster, Christian G. Peters, Douglas Lazarus, Scott Eliasof, Robert S. Kerbel. Potent anti-tumor and metastatic breast cancer efficacy of bevacizumab with CRLX101, an investigational chemotherapy nanoparticle-drug conjugate that secondarily suppresses HIF-1α. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4124. doi:10.1158/1538-7445.AM2015-4124
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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) | 0.001 | 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".