Transcriptional fidelity enhances cancer cell line selection in pediatric cancers
Luck, C.; Yu, K.; Okimoto, R. A.; Sirota, M.
Show abstract
Multi-omic technologies have allowed for comprehensive profiling of patient-derived tumor samples and the cell lines that are intended to model them. Yet, our understanding of how cancer cell lines reflect native pediatric cancers in the age of molecular subclassification remains unclear and represents a clinical unmet need. Here we use Treehouse public data to provide an RNA-seq driven analysis of 799 cancer cell lines, focusing on how well they correlate to 1,655 pediatric tumor samples spanning 12 tumor types. For each tumor type we present a ranked list of the most representative cell lines based on correlation of their transcriptomic profiles to those of the tumor. We found that most (8/12) tumor types best correlated to a cell line of the closest matched disease type. We furthermore showed that inferred molecular subtype differences in medulloblastoma significantly impacted correlation between medulloblastoma tumor samples and cell lines. Our results are available as an interactive web application to help researchers select cancer cell lines that more faithfully recapitulate pediatric cancer.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transcriptomic landscape identifies two unrecognized ependymoma subtypes and novel pathways in medulloblastoma 95%
- Lineage-dependence of the neuroblastoma surfaceome defines tumor cell state-dependent and independent immunotherapeutic targets 93%
- Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment 93%
Similar papers in this journal
- In silico drug sensitivity predicts subgroup-specific therapeutics in medulloblastoma patients 94%
- Mebendazole for Differentiation Therapy of Acute Myeloid Leukemia Identified by a Lineage Maturation Index 93%
- Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications in a common base culture media system 93%
Similar papers in this journal
- Cell state transitions drive the evolution of disease progression in B-lymphoblastic leukemia 93%
- EZH2 inhibition promotes tumor immunogenicity in lung squamous cell carcinomas 92%
- Structurally complex osteosarcoma genomes exhibit limited heterogeneity within individual tumors and across evolutionary time 92%
Similar papers in this journal
- High risk glioblastoma cells revealed by machine learning and single cell signaling profiles 94%
- Detection of malignant peripheral nerve sheath tumors in patients with neurofibromatosis using aneuploidy and mutation identification in plasma 93%
- Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.