The 2019 mathematical oncology roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

A Random Matrix Theory Approach to Denoise Single-Cell Data.

Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition, we explain how sparsity can cause spurious eigenvector localization, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory, about 3% is spurious signal induced by sparsity, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations.

Distinct genomic profile and specific targeted drug responses in adult cerebellar glioblastoma.

Background: Despite extensive efforts on the genomic characterization of gliomas, very few studies have reported the genetic alterations of cerebellar glioblastoma (C-GBM), a rare and lethal disease. Here, we provide a systematic study of C-GBM to better understand its specific genomic features. Methods: We collected a cohort of C-GBM patients and compared patient demographics and tumor pathologies with supratentorial glioblastoma (S-GBM). To uncover the molecular characteristics, we performed DNA and mRNA sequencing and DNA methylation arrays on 19, 6, and 4 C-GBM cases, respectively. Moreover, chemical drug screening was conducted to identify potential therapeutic options for C-GBMs. Results: Despite differing anatomical origins of C-GBM and S-GBM, neither histological, cytological, nor patient demographics appeared significantly different between the 2 types. However, we observed striking differences in mutational patterns, including frequent alterations of ATRX, PDGFRA, NF1, and RAS and absence of EGFR alterations in C-GBM. These results show a distinct evolutionary path in C-GBM, suggesting specific therapeutic targeted options. Targeted-drug screening revealed that C-GBMs were more responsive to mitogen-activated protein kinase kinase (MEK) inhibitor and resistant to epidermal growth factor receptor inhibitors than S-GBMs. Also, differential expression analysis indicated that C-GBMs may have originated from oligodendrocyte progenitor cells, suggesting that different types of cells can undergo malignant transformation according to their location in brain. Master regulator analysis with differentially expressed genes between C-GBM and proneural S-GBM revealed NR4A1 as a potential therapeutic target. Conclusions: Our results imply that unique gliomagenesis mechanisms occur in adult cerebellum and new treatment strategies are needed to provide greater therapeutic benefits for C-GBM patients. Key Points: 1. Distinct genomic profiles of 19 adult cerebellar GBMs were characterized. 2. MEK inhibitor was highly sensitive to cerebellar GBM compared with supratentorial GBM. 3. Master regulator analysis revealed NR4A1 as a potential therapeutic target in cerebellar GBM.

Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy.

Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.