List of publications at My NCBI:

https://www.ncbi.nlm.nih.gov/sites/myncbi/david.axelrod.1/bibliography/48445282/public/?sort=date&direction=descending

Selection of publications:

Cockrell, C. and D.E. Axelrod. Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations. Cancer Informatics 18:1-8(2019) https://doi.org/10.1177/1176935118822804

Yang, J., D.E. Axelrod, and N. L. Komarova. Determining the control networks regulating stem cell lineages in colonic crypts, Journal of Theoretical Biology 429:190-203 (2017). https://doi.org/10.1016/j.thbi.2017.06.033. 

Axelrod, D.E. and R. Bravo. 2017. Chemoprevention of colon cancer: Advantage of intermittent pulse treatment schedules quantified by computer simulation of human colon crypts. Converg. Sci. Phys. Oncol. 3:035004 (2017). https://doi.org/10.1088/2057-1739/aa82e6

Axelrod, D.E., Vedula, S. & Obaniyi, J. Effective Chemotherapy of Heterogeneous and Drug-Resistant Early Colon Cancers by Intermittent Dose Schedules: A Computer Simulation Study. Cancer Chemother Pharmacol (2017) 79: 889-898.http://rdcu.be/qn25 

Bravo, R. and D.E. Axelrod. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoretical Biology and Medical Modeling 10:66 (2013), DOI: 10.1186/1742-4682-10-66, http://www.tbiomed.com/content/10/1/66

Axelrod, D.E., Shah, K, Yang, Q., Haffty, B.G. 2012. Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry. Cancer and Clinical Oncology 1:52-64. URL: http://dx.doi.org/10.5539/cco.v1n1p52

Miller, N.A., J.-A. Chapman, J. Qian, W.A. Christens-Barry, Y. Fu, Y. Yuan, H.L.A. Lickley, D.E. Axelrod. 2010. Heterogeneity Between Ducts of the Same Nuclear Grade Involved by In Situ Duct Carcinoma (DCIS) of the Breast. Cancer Informatics 9:201-216. URL http://www.la-press.com/heterogeneity-between-ducts-of-the-same-nuclear-grad e-involved-by-article-a2250

Axelrod, D.E., N. Miller, and J.-A. Chapman. 2009. Avoiding pitfalls in the statistical analysis of heterogeneous tumors. Biomedical Informatics Insights 2:11-18. URL http://la-press.com/article.php?article_id=1374

Pienta, K., N. McGregor, R. Axelrod, and D.E. Axelrod. 2008. Ecological therapy for cancer: Defining tumors utilizing an ecosystem paradigm suggests new opportunities for novel cancer treatments. Translational Oncology 1:158-164. doi 10.1593/tlo.08178

Axelrod, D.E., N.A. Miller, H.L. Lickely J.Qian, W.A. Christens-Barry, Y. Yuan, Y. Fu,. J.-A.W. Chapman. 2008. Effect of quantitative nuclear image features on recurrence of ductal carcinoma in situ (DCIS) of the breast. Cancer Informatics 4:99-109.
URL http://la-press.com/article.php?article_id=583. PubMed Central ID 2531292.

Chapman, J.-A.W., N.A. Miller, H.L.A. Lickley, J. Qian, W.A. Christens-Barry, Y. Fu, Y. Yuan, and D.E. Axelrod. 2007. Ductal carcinoma in situ of the breast (DCIS) with heterogeneity of nuclear grade: Prognostic effects of quantitative nuclear assessment. BMC Cancer 2007, 7:174 doi: 10.1186/1471-2407-7-174.
URL http://www.biomedcentral.com/1471-2407/7/174

Cardiff, R.D., J.P. Gregg, J.W. Miller, D.E. Axelrod, and A.D. Borowsky. 2006. Histopathology as a predictive biomarker: Strengths and limitations. J. Nutrition 136:2673S-2675S.

Axelrod, R., D.E. Axelrod, and K.J. Pienta. 2006. Evolution of cooperation between tumor cells. Proc. Natl. Acad. Sci. USA 103:13474-12479. doi/10.1073/pnas.0606053103

Alexe, G., S. Alexe, D.E. Axelrod, E. Boros, M. Reiss, and P.L. Hammer. 2006 Combinatorial analysis of breast cancer data from gene expression microarrays. Breast Cancer Res 8:R41. doi: 10.1186/bcr1512.

Alexe, G., S. Alexe, D.E. Axelrod, P.L. Hammer, and D.J. Weissman. 2005. Logical analysis of diffuse large B-cell lymphomas. Artificial Intell. Med. 34: 235-267.

Sontag, L. and D.E. Axelrod. 2005. Evaluation of pathways for progression of heterogeneous breast tumors. J. Theoret. Biol. 232: 179-189.

We are interested in the initiation, progression, and heterogeneity of cancer. Our goal is to contribute to the improvement of cancer diagnosis, prognosis, prevention, and therapy, specifically of breast cancer and colon cancer. We have measured DNA and proteins in human biopsy specimens by computer-aided image analysis, and analyzed the quantitative data with multivariate statistics, computer simulation, and mathematical modeling.

To aid in the differential diagnosis of human breast tumors and normal tissues, discriminant functions have been derived. To predict the disease free survival of patients with pre-malignant lesions (ductal carcinoma in situ), and to predict the overall survival of young patients with invasive breast cancer, quantitative algorithms have been developed.

To study the initiation and progression of colon cancer, an agent-based computer model of human colon crypts has been developed. The virtual crypt model has been calibrated with measurements of human biopsy specimens, and its behavior verified to reproduce experimentally observed stem cell dynamics in colon crypts. Simulations are being used to evaluate different protocols for chemotherapy and chemoprevention, including drug combinations and intermittent schedules.