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Immunohistochemical assessment of phosphorylated mTORC1-pathway proteins in human brain tumors
(2015)
Background: Current pathological diagnostics include the analysis of (epi-)genetic alterations as well as oncogenic pathways. Deregulated mammalian target of rapamycin complex 1 (mTORC1) signaling has been implicated in a variety of cancers including malignant gliomas and is considered a promising target in cancer treatment. Monitoring of mTORC1 activity before and during inhibitor therapy is essential. The aim of our study is to provide a recommendation and report on pitfalls in the use of phospho-specific antibodies against mTORC1-targets phospho-RPS6 (Ser235/236; Ser240/244) and phospho-4EBP1 (Thr37/46) in formalin fixed, paraffin embedded material.
Methods and findings: Primary, established cell lines and brain tumor tissue from routine diagnostics were assessed by immunocyto-, immunohistochemistry, immunofluorescent stainings and immunoblotting. For validation of results, immunoblotting experiments were performed. mTORC-pathway activation was pharmacologically inhibited by torin2 and rapamycin. Torin2 treatment led to a strong reduction of signal intensity and frequency of all tested antibodies. In contrast phospho-4EBP1 did not show considerable reduction in staining intensity after rapamycin treatment, while immunocytochemistry with both phospho-RPS6-specific antibodies showed a reduced signal compared to controls. Staining intensity of both phospho-RPS6-specific antibodies did not show considerable decrease in stability in a timeline from 0–230 minutes without tissue fixation, however we observed a strong decrease of staining intensity in phospho-4EBP1 after 30 minutes. Detection of phospho-signals was strongly dependent on tissue size and fixation gradient. mTORC1-signaling was significantly induced in glioblastomas although not restricted to cancer cells but also detectable in non-neoplastic cells.
Conclusion: Here we provide a recommendation for phospho-specific immunohistochemistry for patient-orientated therapy decisions and monitoring treatment response.
Background: Hypoxia is a key driver for infiltrative growth in experimental gliomas. It has remained elusive whether tumor hypoxia in glioblastoma patients contributes to distant or diffuse recurrences. We therefore investigated the influence of perioperative cerebral ischemia on patterns of progression in glioblastoma patients.
Methods: We retrospectively screened MRI scans of 245 patients with newly diagnosed glioblastoma undergoing resection for perioperative ischemia near the resection cavity. 46 showed relevant ischemia nearby the resection cavity. A control cohort without perioperative ischemia was generated by a 1:1 matching using an algorithm based on gender, age and adjuvant treatment. Both cohorts were analyzed for patterns of progression by a blinded neuroradiologist.
Results: The percentage of diffuse or distant recurrences at first relapse was significantly higher in the cohort with perioperative ischemia (61.1%) compared to the control cohort (19.4%). The results of the control cohort matched well with historical data. The change in patterns of progression was not associated with a difference in survival.
Conclusions: This study reveals an unrecognized association of perioperative cerebral ischemia with distant or diffuse recurrence in glioblastoma. It is the first clinical study supporting the concept that hypoxia is a key driver of infiltrative tumor growth in glioblastoma patients.
Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.