Despite decades of research, brain tumours remain among the deadliest of all forms of cancer. The ability of these tumours to resist almost all conventional and novel treatments relates, in part, to the unique cell-intrinsic and microenvironmental properties of neural tissues. In an attempt to encourage progress in our understanding and ability to successfully treat patients with brain tumours, Cancer Research UK convened an international panel of clinicians and laboratory-based scientists to identify challenges that must be overcome if we are to cure all patients with a brain tumour. The seven key challenges summarized in this Position Paper are intended to serve as foci for future research and investment.

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Challenges to curing primary brain tumours

Intratumoral heterogeneity: pathways to treatment resistance and relapse in human glioblastoma

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.

Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity

Glioblastoma is an aggressive and heterogeneous tumor in which glioblastoma stem cells (GSCs) are at the apex of an entropic hierarchy and impart devastating therapy resistance. The high entropy of GSCs is driven by a permissive epigenetic landscape and a mutational landscape that revokes crucial cellular checkpoints. The GSC population encompasses a complex array of diverse microstates that are defined and maintained by a wide variety of attractors including the complex tumor ecosystem and therapeutic intervention. Constant dynamic transcriptional fluctuations result in a highly adaptable and heterogeneous entity primed for therapy evasion and survival. Analyzing the transcriptional, epigenetic, and metabolic landscapes of GSC dynamics in the context of a stochastically fluctuating tumor network will provide novel strategies to target resistant populations of GSCs in glioblastoma.

Glioblastoma Stem Cells: Driving Resilience through Chaos.

Combination therapy is increasingly becoming the cornerstone of current day antitumor therapy. Glioblastoma multiforme is an aggressive brain tumor with a dismal median survival post diagnosis and a high rate of disease recurrence. The poor prognosis can be attributed to unique treatment limitations, which include the infiltrative nature of tumor cells, failure of anti-glioma drugs to cross the blood–brain barrier, tumor heterogeneity and the highly metastatic and angiogenic nature of the tumor making cells resistant to chemotherapy. Combination therapy approach is being developed against glioblastoma with new innovative combination drug regimens being tested in preclinical and clinical trials. In this review, we discuss the pathophysiology of glioblastoma, diagnostic markers, therapeutic targeting strategies, current treatment limitations, novel combination therapies in the context of current treatment options and the ongoing clinical trials for glioblastoma therapy.

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Combination therapy to checkmate Glioblastoma: clinical challenges and advances

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. Clin Cancer Res; 23(2); 562–74. ©2016 AACR.

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Functional Subclone Profiling for Prediction of Treatment-Induced Intratumor Population Shifts and Discovery of Rational Drug Combinations in Human Glioblastoma.

Ying Liu

Papers

Functional Subclone Profiling for Prediction of Treatment-Induced Intratumor Population Shifts and Discovery of Rational Drug Combinations in Human Glioblastoma.