Glioblastoma (GBM) is the most common adult brain cancer. Despite extensive treatment protocols comprised of maximal surgical resection and adjuvant chemo–radiation, all glioblastomas recur and are eventually fatal. Emerging as a novel investigation for GBM treatment, photodynamic therapy (PDT) is a light-based modality that offers spatially and temporally specific delivery of anti-cancer therapy with limited systemic toxicity, making it an attractive option to target GBM cells remaining beyond the margins of surgical resection. Prior PDT approaches in GBM have been predominantly based on 5-aminolevulinic acid (5-ALA), a systemically administered drug that is metabolized only in cancer cells, prompting the release of reactive oxygen species (ROS), inducing tumor cell death via apoptosis. Hence, this review sets out to provide an overview of current PDT strategies, specifically addressing both the potential and shortcomings of 5-ALA as the most implemented photosensitizer. Subsequently, the challenges that impede the clinical translation of PDT are thoroughly analyzed, considering relevant gaps in the current PDT literature, such as variable uptake of 5-ALA by tumor cells, insufficient tissue penetrance of visible light, and poor oxygen recovery in 5-ALA-based PDT. Finally, novel investigations with the potential to improve the clinical applicability of PDT are highlighted, including longitudinal PDT delivery, photoimmunotherapy, nanoparticle-linked photosensitizers, and near-infrared radiation. The review concludes with commentary on clinical trials currently furthering the field of PDT for GBM. Ultimately, through addressing barriers to clinical translation of PDT and proposing solutions, this review provides a path for optimizing PDT as a paradigm-shifting treatment for GBM.

Background Diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Our objective was to identify diagnostic and prognostic cerebrospinal fluid (CSF) proteomic signatures in glioblastoma (GBM), brain metastases (BM), and primary central nervous system lymphoma (CNSL). Methods CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics. Proteomic signatures were identified using machine learning classifiers and survival analyses. Results Using 30 µL CSF volumes, we recovered 755 unique proteins across 73 samples. Proteomic-based classifiers identified malignancy with area under the receiver operating characteristic (AUROC) of 0.94 and distinguished between tumor entities with AUROC ≥0.95. More clinically relevant triplex classifiers, comprised of just 3 proteins, distinguished between tumor entities with AUROC of 0.75-0.89. Novel biomarkers were identified, including GAP43, TFF3 and CACNA2D2, and characterized using single-cell RNA sequencing. Survival analyses validated previously implicated prognostic signatures, including blood brain barrier disruption. Conclusions Reliable classification of intra-axial malignancies using low CSF volumes is feasible, allowing for longitudinal tumor surveillance.

Background Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches. Methods Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N=197) on these entities and normal brain parenchyma were used for validation and model optimization. Results Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC]=0.93, 95% confidence interval [CI]: 0.71-1.0), GBM (AUROC=0.83, 95% CI: 0.63-1.0), and CNSL (AUROC=0.91, 95% CI: 0.66-1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy. Conclusions CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.

Low-grade gliomas (LGGs) comprise 13–16% of glial tumors. As survival for LGG patients has been gradually improving, it is essential that the effects of diagnosis and disease progression on mental health be considered. This retrospective cohort study queried the IBM Watson Health MarketScan® Database to describe the incidence and prevalence of mental health disorders (MHDs) among LGG patients and identify associated risk factors. Among the 20,432 LGG patients identified, 12,436 (60.9%) had at least one MHD. Of those who never had a prior MHD, as documented in the claims record, 1915 (16.7%) had their first, newly diagnosed MHD within 12 months after LGG diagnosis. Patients who were female (odds ratio (OR), 1.14, 95%, 1.03–1.26), aged 35–44 (OR, 1.20, 95%, 1.03–1.39), and experienced glioma-related seizures (OR, 2.19, 95%, confidence intervals (CI), 1.95–2.47) were significantly associated with MHD incidence. Patients who underwent resection (OR, 2.58, 95% CI, 2.19–3.04) or biopsy (OR, 2.17, 95% CI, 1.68–2.79) were also more likely to develop a MHD compared to patients who did not undergo a first-line surgical treatment. These data support the need for active surveillance, proactive counseling, and management of MHDs in patients with LGG. Impact of surgery on brain networks affecting mood should also be considered.

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