Targeted Proteomics to Assess the Response to Anti-Angiogenic Treatment in Human Glioblastoma (GBM)

Abstract

Glioblastoma (GBM) is a highly aggressive primary brain tumor with dismal outcome for affected patients. Because of the significant neo-angiogenesis exhibited by GBMs, anti-angiogenic therapies have been intensively evaluated during the past years. Recent clinical studies were however disappointing, although a subpopulation of patients may benefit from such treatment. We have previously shown that anti-angiogenic targeting in GBM increases hypoxia and leads to a metabolic adaptation toward glycolysis, suggesting that combination treatments also targeting the glycolytic phenotype may be effective in GBM patients. The aim of this study was to identify marker proteins that are altered by treatment and may serve as a short term readout of anti-angiogenic therapy. Ultimately such proteins could be tested as markers of efficacy able to identify patient subpopulations responsive to the treatment. We applied a proteomics approach based on selected reaction monitoring (SRM) to precisely quantify targeted protein candidates, selected from pathways related to metabolism, apoptosis and angiogenesis. The workflow was developed in the context of patient-derived intracranial GBM xenografts developed in rodents and ensured the specific identification of human tumor versus rodent stroma-derived proteins. Quality control experiments were applied to assess sample heterogeneity and reproducibility of SRM assays at different levels. The data demonstrate that tumor specific proteins can be precisely quantified within complex biological samples, reliably identifying small concentration differences induced by the treatment. In line with previous work, we identified decreased levels of TCA cycle enzymes, including isocitrate dehydrogenase, whereas malectin, calnexin, and lactate dehydrogenase A were augmented after treatment. We propose the most responsive proteins of our subset as potential novel biomarkers to assess treatment response after anti-angiogenic therapy that warrant future analysis in clinical GBM samples.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

“…Calnexin was revealed to inhibit the proliferation and activation of CD4 + T and CD8 + T cells, and it may impair the function of T cells by upregulating the expression of PD-1 in oral squamous cancer (Chen et al, 2019). Consistent with our results, it was found that decreased expression of CANX was associated with favorable survival outcome (Patel et al, 2013) and served as a biomarkers for tumor response in glioblastoma (Demeure et al, 2016). TAP1, an essential component of the major histocompatability complex (MHC) class I antigen-presenting pathway.…”

Molecular crosstalk between tumour and brain parenchyma instructs histopathological features in glioblastoma

“…We have previously shown that ‘intermediate’ (P3) and ‘angiogenic’ (P13) PDX tumors adapt to anti-angiogenic treatment by switching to a glycolytic metabolism and increasing invasion [6, 21]. Interestingly, the morphological response to bevacizumab was more pronounced in tumors with a high level of angiogenesis [22], and our preliminary data indicate that this may also be true with regard to survival (unpublished), suggesting that the level of angiogenesis (and presence of angiogenic markers) may indicate whether patients could benefit from anti-angiogenic treatment.…”

Peptide Selection for Targeted Protein Quantitation

“…Indeed, several previous works had successfully used peptides with missed cleavages or containing several potential sites of chemical modification for targeted proteomics analyses, as these peptides were shown to be reproducibly generated from replicate samples. [15][16][17] Therefore, given these evidences it seems advisable to not rely on the aminoacidic composition of a peptide but rather on experimental data-i.e. pilot shotgun or data-independent experiments with the system under study-to assess the quantitative behaviour of the peptides to be targeted, and thus select peptides that correctly represent true protein fold-changes in targeted proteomics studies.…”