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Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity.

ABSTRACT: Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

SUBMITTER: Lam KHB

PROVIDER: S-EPMC8748638 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and objectiveGlioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.MethodsWe proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.ResultsWSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.ConclusionsThis study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Project description:Intra-individual tumoral heterogeneity (ITH) is a hallmark of solid tumors and impedes accurate genomic diagnosis and selection of proper therapy. The purpose of this study was to identify ITH of ovarian serous adenocarcinomas (OSAs) and to determine the utility of ascitic cancer cells as a resource for mutation profiling in spite of ITH. We performed whole-exome sequencing, copy number profiling, and DNA methylation profiling of four OSA genomes using multiregional biopsies from 13 intraovarian lesions, 12 extraovarian tumor lesions (omentum/peritoneum), and ascitic cells. We observed substantial levels of heterogeneity in mutations and copy number alterations (CNAs) of the OSAs. We categorized the mutations into 'common', 'shared' and 'private' according to the regional distribution. Six common, 8 shared, and 24 private mutations were observed in known cancer-related genes,. but common mutations had a higher mutant allele frequency and included TP53 mutations in all four OSAs. Region-specific chromosomal amplifications and deletions involving BRCA1, PIK3CA, and RB1 were also identified. Of note, the mutations detected in ascitic cancer cells represented 92.3-100% of overall somatic mutations in the given case. Phylogenetic analyses of ascitic genomes predicted a polyseeding origin of somatic mutations in ascitic cells. Our results demonstrate that despite ITH, somatic mutations, CNAs, and DNA methylations in both â??commonâ?? category and cancer-related genes were highly conserved in ascitic cells of OSAs, highlighting the clinical relevance of genome analysis of ascitic cells. Ascitic tumor cells may serve as a potential resource to discover somatic mutations of primary OSA with diagnostic and therapeutic relevance. Genome wide DNA methylation profiling of ascitic cells as well as biopsies from ovarian serous adenocarcinomas cases obtained by Illumina Infinium 450k Human DNA methylation Beadchip Bisulphite converted DNA from the 16 samples were hybridized to the Illumina Infinium 450k Human Methylation Beadchip

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Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity.

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