(497a) Multi-Modal Single-Cell Analysis As a Basis for Precision Therapy Targeting Intratumoral Heterogeneity in Glioblastoma

Glioblastoma (GBM), the most aggressive and lethal form of primary brain cancer in adults, is a prominent example of how a heterogeneous tumor cell population hinders our ability to treat cancer^1–4. An unfortunate consequence of this intratumoral heterogeneity is the failure to improve survival rates using precision medicine approaches that rely on bulk-level DNA sequencing. Bulk-level profiling methods fail to capture the heterogeneous composition of a tumor. However, advances in single-cell level genomic profiling technologies (e.g. single-cell ATAC-seq, single-cell RNA-seq) and integration of such data enable the multi-modal characterization of ‘cell state’, i.e., the metastable phenotype of a cell at a given moment. In GBM, tumor cells evolve during therapeutic intervention, possibly into drug-resistant states, along trajectories that are as yet unknown. Towards understanding these cell-state changes and identifying novel targets to eliminate the various tumor cell subpopulations, we created a patient-derived xenograft (PDX) mouse model of an individual patient’s GBM tumor biopsy to simulate patient-derived tumor cell response to chemotherapy (temozolomide) and radiotherapy, which are part of the standard of care. We characterized tumor cell response using single-cell ATAC-seq and RNA-seq analysis and compared the cell states of xenograft samples 1) prior to treatment, 2) 24hrs post-treatment, and 3) 72hrs post-treatment to those of the corresponding parental tumor. Moreover, we applied the Systems Genetics Network AnaLysis (SYGNAL) platform⁴ to identify distinct epigenetic programs that distinguish phenotypically diverse tumor samples across conditions. Further, we identified several drugs that target specific regulators associated with these epigenetic programs, which we will be testing in corresponding tissue-culture slices for validation. This proof-of-concept work will provide the basis for our envisioned goal: the development of a modeling and analytical system that enables single-cell characterization of an individual patient’s tumor and its response to drugs. Ultimately, the information gathered from this systematic analysis of an individual tumor would inform clinical treatment and enable the ultimate goal of precision medicine.

References

1. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (80-. ). 344, 1396–1401 (2014).
2. Weathers, S. P. & Gilbert, M. R. Current challenges in designing GBM trials for immunotherapy. J. Neurooncol. 123, 331–337 (2015).
3. Ellis, H. P. et al. Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Front. Oncol. 5, 251 (2015).
4. Plaisier, C. L. et al. Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. Cell Syst. 3, 172–186 (2016).