Biology subjects

Hankinson, T. C.

Publications and source records attributed to Hankinson, T. C..

4 recordsLinked to original sources

bioRxiv2024

Multi-pronged analysis of pediatric low-grade glioma reveals a unique tumor microenvironment associated with BRAF alterations

Pediatric low-grade gliomas (pLGG) comprise 35% of all brain tumors. Despite favorable survival, patients experience significant morbidity from disease and treatments. A deeper understanding of pLGG biology is essential to identify novel, more effective, and less toxic therapies. We utilized single cell RNA sequencing (scRNA-seq), spatial transcriptomics, and cytokine analyses to characterize and understand tumor and immune cell heterogeneity across pLGG. scRNA-seq revealed tumor and immune cells within the tumor microenvironment (TME). Tumor cell subsets revealed a developmental hierarchy with progenitor and mature cell populations. Immune cells included myeloid and lymphocytic cells. There was a significant difference between the prevalence of two major myeloid subclusters between pilocytic astrocytoma (PA) and ganglioglioma (GG). Bulk and single-cell cytokine analyses evaluated the immune cell signaling cascade with distinct immune phenotypes among tumor samples. KIAA1549-BRAF tumors appeared more immunogenic, secreting higher levels of immune cell activators and chemokines, compared to BRAF V600E tumors. Spatial transcriptomics revealed the differential gene expression of these chemokines and their location within the TME. A multi-pronged analysis of pLGG demonstrated the complexity of the pLGG TME and differences between genetic drivers that may influence their response to immunotherapy. Further investigation of immune cell infiltration and tumor-immune interactions is warranted. Key pointsO_LIThere is a developmental hierarchy in neoplastic population comprising of both progenitor-like and mature cell types in both PA and GG. C_LIO_LIA more immunogenic, immune activating myeloid population is present in PA compared to GG. C_LIO_LIFunctional analysis and spatial transcriptomics show higher levels of immune mobilizing chemokines in KIAA1549-BRAF fusion PA tumor samples compared to BRAF V600E GG samples. C_LI Importance of the StudyWhile scRNA seq provides information on cellular heterogeneity within the tumor microenvironment (TME), it does not provide a complete picture of how these cells are interacting or where they are located. To expand on this, we used a three-pronged approach to better understand the biology of pediatric low-grade glioma (pLGG). By analyzing scRNA-seq, secreted cytokines and spatial orientation of cells within the TME, we strove to gain a more complete picture of the complex interplay between tumor and immune cells within pLGG. Our data revealed a complex heterogeneity in tumor and immune populations and identified an interesting difference in the immune phenotype among different subtypes.

cancer biology↗

bioRxiv2020

Neoplastic and Immune single cell transcriptomics define subgroup-specific intra-tumoral heterogeneity of childhood medulloblastoma.

Medulloblastoma (MB) is a heterogeneous disease in which neoplastic cells and associated immune cells contribute to disease progression. To better understand cellular heterogeneity in MB we use single-cell RNA sequencing, immunohistochemistry and deconvolution of transcriptomic data to profile neoplastic and immune populations in childhood MB samples. Neoplastic cells cluster primarily according to individual sample of origin which is in part due to the effect of chromosomal copy number gains and losses. Harmony alignment reveals novel MB subgroup/subtype-associated subpopulations that recapitulate neurodevelopmental processes and are associated with clinical outcomes, including discrete photoreceptor-like cells in MB subgroups GP3 and GP4 and nodule-associated neuronally-differentiated cells in subgroup SHH. We definitively chart the spectrum of MB immune cell infiltrates, which include subgroup/subtype-associated developmentally-related neuron-pruning and antigen presenting myeloid cells. MB cellular diversity is recapitulated in genetically engineered mouse subgroup-specific models of MB. These findings provide a clearer understanding of both the neoplastic and immune cell heterogeneity in MB.

cancer biology↗

bioRxiv2019

Linnaeus: Interpretable Deep Learning Classification of Single Cell Transcript Data

High throughput data is commonplace in biomedical research as seen with technologies such as single-cell RNA sequencing (scRNA-seq) and other Next Generation Sequencing technologies. As these techniques continue to be increasingly utilized it is critical to have analysis tools that can identify meaningful complex relationships between variables (i.e., in the case of scRNA-seq: genes) in a way such that human bias is absent. Moreover, it is equally paramount that both linear and non-linear (i.e., one-to-many) variable relationships be considered when contrasting datasets. HD Spot is a deep learning-based framework that generates an optimal interpretable classifier a given high-throughput dataset using a simple genetic algorithm as well as an autoencoder to classifier transfer learning approach. Using four unique publicly available scRNA-seq datasets with published ground truth, we demonstrate the robustness of HD Spot and the ability to identify ontologically accurate gene lists for a given data subset. HD Spot serves as a bioinformatic tool to allow novice and advanced analysts to gain complex insight into their respective datasets enabling novel hypotheses development.

bioinformatics↗

bioRxiv2019

Comprehensive molecular characterization of pediatric treatment-induced high-grade glioma: A distinct entity despite disparate etiologies with defining molecular characteristics and potential therapeutic targets

Treatment-induced high-grade gliomas (TIHGGs) are an incurable late complication of cranial radiation therapy or combined radiation/chemotherapy used to treat pediatric cancer. We assembled a cohort of 33 TIHGGs from multiple institutions. The primary antecedent malignancies were medulloblastoma, acute lymphoblastic leukemia, astrocytoma, and ependymoma. We performed methylation profiling, RNA-seq, and genomic sequencing (whole-genome or whole-exome) on TIHGG samples. Methylation profiling revealed that TIHGGs cluster primarily with the pediatric receptor tyrosine kinase I subtype (26/31 samples). Common TIHGG copy-number alterations include Chromosome (Ch.) 1p loss/1q gain, Ch. 4 loss, Ch. 6q loss, and Ch. 13 and Ch. 14 loss; focal alterations include PDGFRA and CDK4 gain and loss of CDKN2A and BCOR. Relative to de novo pediatric high-grade glioma (pHGG), BCOR loss (p=0.004) and CDKN2A loss (p=0.005) were significantly increased. Transcriptomic analysis identified two distinct TIHGG subgroups, one with a lesser mutation burden (0.12 mut/Mb), Ch. 1p loss/1q gain (5/6 samples), and stem cell characteristics, and one with a greater mutation burden (1.08 mut/Mb, p<0.0002), depletion of DNA repair pathways, and inflammatory characteristics. We observed increased chromothripsis in TIHGG versus pHGG (67% vs. 31%, p=0.036), which was associated with extrachromosomal circular DNA-mediated amplification of PDGFRA and CDK4. In vitro drug screening in one primary, patient-derived TIHGG cell line from each expression subgroup identified microtubule inhibitors/stabilizers, DNA-damaging agents, MEK inhibition, and, in the inflammatory subgroup, proteasome inhibitors as potentially effective therapies. This study provides a comprehensive molecular profile of TIHGG, including mechanistic insights to TIHGG oncogenesis, and identifies potentially effective therapeutic modalities for further investigation.

cancer biology↗