Five independent predictors of survival were identified: age, Karnofsky Performance Scale (KPS) score, extent of resection, and the degree of necrosis and enhancement on preoperative MR imaging studies. A significant survival advantage was associated with resection of 98% or more of the tumor volume (median survival 13 months, 95% confidence interval [CI] 11.4-14.6 months), compared with 8.8 months (95% CI 7.4-10.2 months; p < 0.0001) for resections of less than 98%. Using an outcome scale ranging from 0 to 5 based on age, KPS score, and tumor necrosis on MR imaging, we observed significantly longer survival in patients with lower scores (1-3) who underwent aggressive resections, and a trend toward slightly longer survival was found in patients with higher scores (4-5). Gross-total tumor resection is associated with longer survival in patients with GBM, especially when other predictive variables are favorable.

Glioblastoma multiforme (GBM) comprises several molecular subtypes including proneural GBM. Most therapeutic approaches targeting glioma cells have failed. An alternative strategy is to target cells in the glioma microenvironment, such as tumor-associated macrophages and microglia (TAMs). Macrophages depend upon colony stimulating factor (CSF)-1 for differentiation and survival. A CSF-1R inhibitor was used to target TAMs in a mouse proneural GBM model, which dramatically increased survival, and regressed established tumors. CSF-1R blockade additionally slowed intracranial growth of patient-derived glioma xenografts. Surprisingly, TAMs were not depleted in treated mice. Instead, glioma-secreted factors including GM-CSF and IFN-γ facilitated TAM survival in the context of CSF-1R inhibition. Alternatively activated/ M2 macrophage markers decreased in surviving TAMs, consistent with impaired tumor-promoting functions. These gene signatures were associated with enhanced survival in proneural GBM patients. Our results identify TAMs as a promising therapeutic target for proneural gliomas, and establish the translational potential of CSF-1R inhibition for GBM.

The vexing difficulty in delineating brain tumor margins represents a major obstacle toward better outcome of brain tumor patients. Current imaging methods are often limited by inadequate sensitivity, specificity, and spatial resolution. Here we show that a unique triple-modality Magnetic resonance imaging - Photoacoustic imaging – surface enhanced Raman scattering (SERS) nanoparticle (MPR) can accurately help delineate the margins of brain tumors in living mice both pre- and intra-operatively. The MPRs were detected by all three modalities with at least picomolar sensitivity both in vitro and in living mice. Intravenous injection of MPRs into glioblastoma-bearing mice led to specific MPR accumulation and retention by the tumors, allowing for non-invasive tumor delineation by all three modalities through the intact skull. Raman imaging allowed guidance of intra-operative tumor resection, and histological correlation validated that Raman imaging is accurately delineating brain tumor margins. This novel triple-modality nanoparticle approach holds promise to enable more accurate brain tumor imaging and resection.

Recent studies have established distinctive serum polypeptide patterns through mass spectrometry (MS) that reportedly correlate with clinically relevant outcomes. Wider acceptance of these signatures as valid biomarkers for disease may follow sequence characterization of the components and elucidation of the mechanisms by which they are generated. Using a highly optimized peptide extraction and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS-based approach, we now show that a limited subset of serum peptides (a signature) provides accurate class discrimination between patients with 3 types of solid tumors and controls without cancer. Targeted sequence identification of 61 signature peptides revealed that they fall into several tight clusters and that most are generated by exopeptidase activities that confer cancer type-specific differences superimposed on the proteolytic events of the ex vivo coagulation and complement degradation pathways. This small but robust set of marker peptides then enabled highly accurate class prediction for an external validation set of prostate cancer samples. In sum, this study provides a direct link between peptide marker profiles of disease and differential protease activity, and the patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer. Our findings also have important implications for future peptide biomarker discovery efforts.