Functional subclone profiling for prediction of treatment-induced intratumor population shifts and discovery of rational drug combinations in human glioblastoma

Authors: REINARTZ, ROMAN - University Of Bonn; WANG, SHANSHAN - University Of Florida; KEBIR, SIED - University Of Bonn; SILVER, DANIEL - Lerner Research Institute; WIELAND, ANJA - University Of Bonn; ZHENG, TONG - University Of Florida; KUPPER, MARIUS - University Of Bonn; RAUSCHENBACH, LAUREL - University Of Bonn; FIMMERS, ROLF - University Of Bonn; SHEPHERD, TIMOTHY - New York University; TRAGESER, DANIEL - University Of Bonn; TILL, ANDREAS - University Of Bonn; SCHAFER, NIKLAS - University Of Bonn; GLAS, MARTIN - University Of Bonn; HILLMER, AXEL - Genome Institute Of Singapore; CICHON, SVEN - University Of Basel; SMITH, AMY - University Of Florida; PIETSCH, TORSTEN - University Of Bonn; LIU, YING - University Of Texas; REYNOLDS, BRENT - University Of Florida; YACHNIS, ANTHONY - University Of Florida; PINCUS, DAVID - University Of Florida; SIMON, MATTHIAS - University Of Bonn; BRUSTLE, OLIVER - University Of Bonn; STEINDLER, DENNIS - Jean Mayer Human Nutrition Research Center On Aging At Tufts University; SCHEFFLER, BJORN - University Of Bonn

Submitted to: Clinical Cancer Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/4/2016
Publication Date: 8/12/2016
Citation: Reinartz, R., Wang, S., Kebir, S., Silver, D.J., Wieland, A., Zheng, T., Kupper, M., Rauschenbach, L., Fimmers, R., Shepherd, T.M., Trageser, D., Till, A., Schafer, N., Glas, M., Hillmer, A.M., Cichon, S., Smith, A.A., Pietsch, T., Liu, Y., Reynolds, B.A., Yachnis, A., Pincus, D.W., Simon, M., Brustle, O., Steindler, D., Scheffler, B. 2016. Functional subclone profiling for prediction of treatment-induced intratumor population shifts and discovery of rational drug combinations in human glioblastoma. Clinical Cancer Research. 23(2):562-574. https://doi.org/10.1158/1078-0432.CCR-15-2089.
DOI: https://doi.org/10.1158/1078-0432.CCR-15-2089

Interpretive Summary: Most malignant types of solid cancers share the diagnostic feature of many different populations of cancer-initiating cells within single tumors, which leads to limitations in the design of a therapeutic protocol that must target many different contributors to the disease process. In this study we investigated the most common and aggressive human brain cancer, glioblastoma, for variability of response to different drugs and nutrients that contribute to treatment-associated populations of different and dynamic brain cancer stem cells that our laboratory originally discovered and characterized. Distinct populations of these cancer stem cells gave rise to different clones (cells derived from one mother cell) that mirrored those that contributed to the patient's tumor. We used experimental and commercial drug compound libraries to define variability of drug responses in association with different clones of tumor-generating cells. Selected drugs enabled both targeted enrichment and depletion of specific tumor-initiating populations that could then be verified in the original, heterogeneous mixture of primary patient cells, both in vitro and upon application in the human brain tumor cells being grafted into the brains of recipient host mice. Our data provide a way for studying the functional consequences of different brain cancer stem-like cells that contribute to current drug and biological treatment failure in brain and other solid cancers, and design more efficacious, targeted molecular therapies based on the understanding of different cell populations within a cancerous tissue target that require their own treatment regimens.

Technical Abstract: Purpose: Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design: Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and interindividual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacologic sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results: Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential antiglioblastoma compounds. A more comprehensive intratumoral analysis revealed that stable genetic and phenotypic characteristics of coexisting subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naive parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacologic profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions: Our data provide a previously unrecognized strategy for revealing functional consequences of intratumor heterogeneity by enabling predictive modeling of treatment related subclone dynamics in human glioblastoma.