Abstract IA22: Preclinical modeling of adjuvant and metastatic antiangiogenic therapy: Relevance for better predicting clinical outcomes
Bibliographic record
Abstract
Abstract Over the last decade my lab has developed a number of models involving treatment of mice with either early stage microscopic metastatic disease for adjuvant therapy or late stage overt metastatic disease for metastatic therapy studies1-3. More recently, models of neoadjuvant therapy have been developed as well with the lab of Dr. John Ebos4. The rationale for utilizing the first two models is that they may be superior in predicting future activity in patients enrolled in randomized phase III adjuvant or metastatic therapy clinical trials, in comparison to conventional treatment models involving mice with unresected established primary tumors, and evaluating the effect on the primary tumor growth only1. As an example, we reported that sunitinib (or pazopanib) or DC101, the VEGFR-2 antibody were all devoid of anti-tumor activity when treating mice with advanced metastatic breast cancer after primary tumor resection, whereas in contrast, all showed efficacy when treating orthotopic primary tumors in control experiments5. Combining chemotherapy with sunitinib also did not improve outcomes in the metastatic setting. In contrast, combining chemotherapy with DC101 caused a small but statistically significant benefit in survival. These results retrospectively correlated with outcomes of four metastatic breast cancer phase III trials evaluating sunitinib alone or in combination with chemotherapy in the metastatic setting (all were negative) or multiple phase III trials evaluating the VEGF antibody, bevacizumab, with chemotherapy, which showed variable benefits in improving PFS in metastatic breast cancer5. With respect to adjuvant therapy modelling, we reported in 2009 that adjuvant sunitinib therapy of mice with microscopic metastases after resection of orthotopic primary human breast cancer xenografts resulted in a worsened survival outcome, with accelerated progression of metastatic disease2. On the basis of the results we raised a cautionary “flag” about the rationale of antiangiogenic drugs in the clinic for adjuvant setting6. As is now well known, there have been multiple adjuvant trials evaluating bevacizumab plus chemotherapy in postsurgical early stage colorectal or breast cancer, as well as sorafenib in hepatocellular carcinoma, and all of these trials have failed to meet their primary endpoint of a benefit in disease free survival7. This has provoked considerable discussion and debate about the basis for such failures in contrast to the same drugs/therapies showing efficacy in the more advanced metastatic settings of the same malignancies. One basis for the modest positive effects noted of such therapies in the metastatic setting, and for the failure in the adjuvant setting, concerns the impact that “vessel co-option”, especially in distant metastases, may have on therapeutic outcomes. Evidence is growing that a variety of tumors and especially overt metastases in certain sites such as the lungs, liver, and brain are minimally or non-angiogenic and instead “hijack” the existing vasculature in such organ sites8. The same may be the case for microscopic metastases. Consequently, there will be growing interest in evaluating whether vessel co-option can be therapeutically targeted (and also what the implications may be for drug-induced vascular normalization). In this regard there are a number of strategies being evaluated such as the impact of metronomic chemotherapy and targeting other pro-angiogenic factors/pathways beyond VEGF such as ang2/tie29, which may be effective as an adjuvant therapy strategy9. References: 1. Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS. Perspective: Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nature Reviews Cancer 2011; 11:135-41. 2. Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 2009; 15:232-9. 3. Jedeszko C, Paez-Ribes M, Di Desidero T, Bocci G, Man S, Lee CR, et al. Orthotopic primary and postsurgical adjuvant or metastatic renal cell carcinoma therapy models reveal potent anti-tumor activity of minimally toxic metronomic oral topotecan with pazopanib. Sci Transl Med 2015; in press. 4. Ebos JM, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med 2014; 6:1561-76. 5. Guerin E, Man S, Xu P, Kerbel RS. A model of postsurgical advanced metastatic breast cancer more accurately replicates the clinical efficacy of antiangiogenic drugs. Cancer Res 2013; 73:2743-8. 6. Ebos JML and Kerbel RS. Impact of antiangiogenic therapy on invasion, disease progression, and metastasis. Nat Rev Clin Oncol 2011; 8:210-21. 7. Sledge GW. Anti-vascular endothelial growth factor therapy in breast cancer: game over? J Clin Oncol 2015; 33:133-5. 8. Donnem T, Hu J, Ferguson M, Adighibe O, Snell C, Harris AL, et al. Vessel co-option in primary human tumors and metastases: an obstacle to effective anti-angiogenic treatment? Cancer Med 2013; 2:427-36. 9. Srivastava K, Hu J, Korn C, Savant S, Teichert M, Kapel SS, et al. Postsurgical adjuvant tumor therapy by combining anti-angiopoietin-2 and metronomic chemotherapy limits metastatic growth. Cancer Cell 2014; 26:880-95. Citation Format: Robert S. Kerbel. Preclinical modeling of adjuvant and metastatic antiangiogenic therapy: Relevance for better predicting clinical outcomes. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Angiogenesis and Vascular Normalization: Bench to Bedside to Biomarkers; Mar 5-8, 2015; Orlando, FL. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl):Abstract nr IA22.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".