Search Results (2)

Glioblastoma (GB) is the most aggressive form of glioma and is characterized by poor prognosis and high recurrence despite intensive clinical interventions. To retrieve the key factors underlying the high malignancy of GB with potential diagnosis utility, we combined the analysis of The Cancer Gene Atlas and the REMBRANDT datasets plus a molecular examination of our own collection of surgical tumor resections. We determined a net reduction in the levels of the non-canonical histone H3 variant H3.3 in GB compared to lower-grade astrocytomas and oligodendrogliomas with a concomitant increase in the levels of the canonical histone H3 variants H3.1/H3.2. This increase can be potentially useful in the clinical diagnosis of high-grade gliomas, as evidenced by an immunohistochemistry screening of our cohort and can be at least partially explained by the induction of multiple histone genes encoding these canonical forms. Moreover, GBs showing low bulk levels of the H3.1/H3.2 proteins were more transcriptionally similar to low-grade gliomas than GBs showing high levels of H3.1/H3.2. In conclusion, this study identifies an imbalanced ratio between the H3 variants associated with glioma malignancy and molecular patterns relevant to the biology of gliomas, and proposes the examination of the H3.3 and H3.1/H3.2 levels to further refine diagnosis of low- and high-grade gliomas in future studies. Full article

Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5–10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (CNGB1, GGH, HIST1H4F, IDO1, KIF5A, MRPL4, NCDN, PRRT3, SLC35B3) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, n = 26, Log rank test, p = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, CNGB1, as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort (n = 30, p = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, n = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival (p < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients. Full article