Biology subjects

Klisch, T. J.

Publications and source records attributed to Klisch, T. J..

4 recordsLinked to original sources

bioRxiv2024

Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data

Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ~8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. To integrate interpretable features from the bulk (n=78 samples) and single-cell profiling (~10K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models with RNA-inferred copy number variation (CNV) signals and the initial bulk model to create a meta-model, which exhibited the strongest performance in meningioma classification. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n=792 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (~200K cells) and bulk TCGA glioma data (n=711 samples). Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

bioinformatics↗

bioRxiv2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, the most common intracranial tumor, though mostly benign can be recurrent and fatal. WHO grading does not always identify high risk meningioma and better characterizations of their aggressive biology is needed. To approach this problem, we combined 13 bulk RNA-Seq datasets to create a dimension-reduced reference landscape of 1298 meningiomas. Clinical and genomic metadata effectively correlated with landscape regions which led to the identification of meningioma subtypes with specific biological signatures. Time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape where nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcome. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

cancer biology↗

bioRxiv2023

NF2 Loss-of-Function and Hypoxia Drive Radiation Resistance in Grade 2 Meningiomas

BackgroundWorld Health Organization Grade 2 meningiomas (G2Ms) exhibit an aggressive natural history characterized by recurrence and therapy resistance. G2Ms with histopathological necrosis have been associated with worse local control (LC) following radiation therapy, but drivers and biomarkers of radiation resistance in these G2Ms remain unknown. MethodsWe performed genetic sequencing and histopathological analysis of 113 G2Ms and investigated the role of intratumoral hypoxia as well as genes of interest through knockdown and clonogenic survival following ionizing radiation. Lastly, we performed transcriptional profiling of our in vitro model and 18 G2M tumors using RNA sequencing. ResultsNF2 loss-of-function (LOF) mutations were associated with necrosis in G2Ms (p=0.0127). Tumors with NF2 mutation and necrosis had worse post-radiation LC compared to NF2 wildtype tumors without necrosis (p=0.035). Under hypoxic conditions, NF2 knockdown increased radiation resistance in vitro (p<0.001). Bulk RNA sequencing of our in vitro model revealed NF2- and hypoxia-specific changes and a 50-gene set signature specific to radiation resistant, NF2 knockdown and hypoxic cells, which could distinguish NF2 mutant and necrotic patient G2Ms by unsupervised clustering. Gene set enrichment analysis of patient tumor and in vitro data revealed downregulation of apoptosis and upregulation of proliferation in NF2-deficient and hypoxic cells, which we validated with functional assays. ConclusionsNF2 LOF in the setting of hypoxia confers radiation resistance through transcriptional programs that reduce apoptosis and promote proliferation. These pathways may identify tumors resistant to radiation and represent therapeutic targets that in the future could improve LC in patients with radiation resistant G2Ms. KEY POINTS1. Spontaneous necrosis with NF2 mutations is associated with radio-resistance in WHO G2Ms. 2. NF2 knockdown in the setting of hypoxia confers radio-resistance to meningioma cells in vitro and is driven by increased cell proliferation and decreased apoptosis. IMPORTANCE OF THE STUDYWorld Health Organization Grade 2 meningiomas (G2M) are often treated with surgical resection followed by radiation, especially in the case of recurrence. However, the mechanisms underlying radiation resistance in G2Ms remain to be identified, and moreover, we lack biomarkers to distinguish G2Ms that will respond to radiotherapy from those that are refractory. In this study we perform histological and molecular analysis of a large cohort of G2Ms to identify predictors of radiation resistance. Using these data and an in vitro model of radiation therapy, we demonstrate that radiation resistance in G2Ms is likely driven by the combination of NF2 gene mutations and the hypoxia that accompanies tumor necrosis. Patients whose tumors bear these two features may therefore benefit from alternative treatments that target specific pathways implicated in radiation resistance.

molecular biology↗

bioRxiv2019

Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system, there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all three WHO grades (I-III) using clinical, gene expression and sequencing data. Unsupervised clustering analysis identified three molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which would classify more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.\n\nSignificance StatementMeningiomas are the most common primary brain tumors. Although most of these tumors are benign, one-fifth will recur despite apparently complete resection. Several studies have demonstrated that genomic approaches can yield important insights into the biology of these tumors. We performed RNA sequencing and whole-exome sequencing of 160 tumors from 140 patients, which identified three distinct groups of meningioma that correlate with recurrence better than the current WHO grading system. Our analysis also revealed that the most aggressive type was characterized by loss of the repressive DREAM complex. These findings should improve prognostication for patients and lead to viable therapeutic targets.

cancer biology↗