Segmentation of pre- and posttreatment diffuse glioma tissue subregions including resection cavities.

Background: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities. Methods: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks). Results: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94 < R 2 values < 0.98). The Combined network performed similarly to the Pre-Rx network on pretreatment cases and the Post-Rx network on posttreatment cases with fewer false positive resection cavities (7% vs 59%). Conclusions: Neural networks that accurately segment pre- and posttreatment diffuse gliomas have the potential to improve response assessment in clinical trials and reduce provider burden and errors in measurement.

Mesenchymal Stromal Cell Implants for Chronic Motor Deficits After Traumatic Brain Injury: Post Hoc Analysis of a Randomized Trial.

BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is frequently characterized by chronic motor deficits. Therefore, this clinical trial assessed whether intracranial implantation of allogeneic modified mesenchymal stromal (SB623) cells can improve chronic motor deficits after TBI. METHODS: Post hoc analysis of the double-blind, randomized, prospective, surgical sham-controlled, phase 2, STEMTRA clinical trial (June 2016 and March 2019) with 48 weeks of follow-up was conducted. In this international, multicenter clinical trial, eligible participants had moderate-to-severe TBI, were ≥12 months postinjury, and had chronic motor deficits. Participants were randomized in a 1:1:1:1 ratio to stereotactic surgical intracranial implantation of SB623 cells (2.5 × 106, 5.0 × 106, 10 × 106) or surgical sham-controlled procedure. The prespecified primary efficacy end point was significantly greater change from baseline of the Fugl-Meyer Motor Scale (FMMS) score, a measure of motor status, for the SB623 pooled vs control arm at 24 weeks. RESULTS: A total of 211 participants were screened, 148 were excluded, and 63 underwent randomization, of which 61 (97%; mean age, 34 [SD, 12] years; 43 men [70.5%]) completed the trial. Single participants in the SB623 2.5 × 106 and 5.0 × 106 cell dose groups discontinued before surgery. Safety and efficacy (modified intent-to-treat) were assessed in participants who underwent surgery (N = 61; SB623 = 46, controls = 15). The primary efficacy end point (FMMS) was achieved (least squares mean [SE] SB623: +8.3 [1.4]; 95% CI 5.5-11.2 vs control: +2.3 [2.5]; 95% CI -2.7 to 7.3; p = 0.04), with faster improvement of the FMMS score in SB623-treated groups than in controls at 24 weeks and sustained improvement at 48 weeks. At 48 weeks, improvement of function and activities of daily living (ADL) was greater, but not significantly different in SB623-treated groups vs controls. The incidence of adverse events was equivalent in SB623-treated groups and controls. There were no deaths or withdrawals due to adverse events. DISCUSSION: Intraparenchymal implantation of SB623 cells was safe and significantly improved motor status at 24 weeks in participants with chronic motor deficits after TBI, with continued improvement of function and ADL at 48 weeks. Cell therapy can modify chronic neurologic deficits after TBI. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT02416492. Submitted to registry: April 15, 2015. First participant enrolled: July 6, 2016. Available at: classic.clinicaltrials.gov/ct2/show/NCT02416492. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that intracranial implantation of allogeneic stem (SB623) cells in adults with motor deficits from chronic TBI improves motor function at 24 weeks.

Efficient use of binned data for imputing univariate time series data.

Time series data are recorded in various sectors, resulting in a large amount of data. However, the continuity of these data is often interrupted, resulting in periods of missing data. Several algorithms are used to impute the missing data, and the performance of these methods is widely varied. Apart from the choice of algorithm, the effective imputation depends on the nature of missing and available data. We conducted extensive studies using different types of time series data, specifically heart rate data and power consumption data. We generated the missing data for different time spans and imputed using different algorithms with binned data of different sizes. The performance was evaluated using the root mean square error (RMSE) metric. We observed a reduction in RMSE when using binned data compared to the entire dataset, particularly in the case of the expectation-maximization (EM) algorithm. We found that RMSE was reduced when using binned data for 1-, 5-, and 15-min missing data, with greater reduction observed for 15-min missing data. We also observed the effect of data fluctuation. We conclude that the usefulness of binned data depends precisely on the span of missing data, sampling frequency of the data, and fluctuation within data. Depending on the inherent characteristics, quality, and quantity of the missing and available data, binned data can impute a wide variety of data, including biological heart rate data derived from the Internet of Things (IoT) device smartwatch and non-biological data such as household power consumption data.

Development of a diagnostic model for pancreatic ductal adenocarcinoma using machine learning and blood-based miRNAs.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, an early diagnosis of PDAC is critical. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aims to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC. METHODS: Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model. RESULTS: Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC. CONCLUSIONS: Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be implicated in PDAC diagnostics.

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.

A real-world observation of patients with glioblastoma treated with a personalized peptide vaccine.

Current treatment outcome of patients with glioblastoma (GBM) remains poor. Following standard therapy, recurrence is universal with limited survival. Tumors from 173 GBM patients are analysed for somatic mutations to generate a personalized peptide vaccine targeting tumor-specific neoantigens. All patients were treated within the scope of an individual healing attempt. Among all vaccinated patients, including 70 treated prior to progression (primary) and 103 treated after progression (recurrent), the median overall survival from first diagnosis is 31.9 months (95% CI: 25.0-36.5). Adverse events are infrequent and are predominantly grade 1 or 2. A vaccine-induced immune response to at least one of the vaccinated peptides is detected in blood samples of 87 of 97 (90%) monitored patients. Vaccine-specific T-cell responses are durable in most patients. Significantly prolonged survival is observed for patients with multiple vaccine-induced T-cell responses (53 months) compared to those with no/low induced responses (27 months; P = 0.03). Altogether, our results highlight that the application of personalized neoantigen-targeting peptide vaccine is feasible and represents a promising potential treatment option for GBM patients.

Neoadjuvant combination treatment with checkpoint inhibitors, chemotherapy, and BRAF/MEK inhibitors for BRAFV600E glioblastoma results in sustained response: A case report.

Radiation's confounding and adverse effects on tumor microenvironment and normal brain could potentially be delayed by upfront combination treatment. We present a patient with newly diagnosed BRAF V600E-mutant, PD-L1-positive glioblastoma treated with off-label RAF/MEK inhibitors encorafenib/binimetinib after progressing on postoperative immune checkpoint blockade and temozolomide (no radiation administered: NCT03425292). Complete response occurred 6 months after adding encorafenib/binimetinib, and clinical benefit was sustained for over 20 months. Treatment was well tolerated with manageable toxicities, with quality of life and cognitive function maintained throughout treatment. Adding encorafenib/binimetinib to immunotherapy and temozolomide conferred favorable and lasting efficacy for our BRAF V600E -mutant glioblastoma patient, justifying future studies.

Drug delivery in leptomeningeal disease: Navigating barriers and beyond.

Leptomeningeal disease (LMD) refers to the infiltration of cancer cells into the leptomeningeal compartment. Leptomeninges are the two membranous layers, called the arachnoid membrane and pia mater. The diffuse nature of LMD poses a challenge to its effective diagnosis and successful management. Furthermore, the predominant phenotype; solid masses or freely floating cells, has altering implications on the effectiveness of drug delivery systems. The standard of care is the intrathecal delivery of chemotherapy drugs but it is associated with increased instances of treatment-related complications, low patient compliance, and suboptimal drug distribution. An alternative involves administering the drugs systemically, after which they must traverse fluid barriers to arrive at their destination within the leptomeningeal space. However, this route is known to cause off-target effects as well as produce subtherapeutic drug concentrations at the target site within the central nervous system. The development of new drug delivery systems such as liposomal cytarabine has improved drug delivery in leptomeningeal metastatic disease, but much still needs to be done to effectively target this challenging condition. In this review, we discuss about the anatomy of leptomeninges relevant for drug penetration, the conventional and advanced drug delivery methods for LMD. We also discuss the future directions being set by different clinical trials.

3D Brain Vascular Niche Model Captures Invasive Behavior and Gene Signatures of Glioblastoma.

Glioblastoma (GBM) is a lethal brain cancer with no effective treatment; understanding how GBM cells respond to tumor microenvironment remains challenging as conventional cell cultures lack proper cytoarchitecture while in vivo animal models present complexity all at once. Developing a culture system to bridge the gap is thus crucial. Here, we employed a multicellular approach using human glia and vascular cells to optimize a 3-dimensional (3D) brain vascular niche model that enabled not only long-term culture of patient derived GBM cells but also recapitulation of key features of GBM heterogeneity, in particular invasion behavior and vascular association. Comparative transcriptomics of identical patient derived GBM cells in 3D and in vivo xenotransplants models revealed that glia-vascular contact induced genes concerning neural/glia development, synaptic regulation, as well as immune suppression. This gene signature displayed region specific enrichment in the leading edge and microvascular proliferation zones in human GBM and predicted poor prognosis. Gene variance analysis also uncovered histone demethylation and xylosyltransferase activity as main themes for gene adaption of GBM cells in vivo . Furthermore, our 3D model also demonstrated the capacity to provide a quiescence and a protective niche against chemotherapy. In summary, an advanced 3D brain vascular model can bridge the gap between 2D cultures and in vivo models in capturing key features of GBM heterogeneity and unveil previously unrecognized influence of glia-vascular contact for transcriptional adaption in GBM cells featuring neural/synaptic interaction and immunosuppression.

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

BACKGROUND: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. METHODS: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. RESULTS: Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. CONCLUSIONS: The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach.

Colorectal Cancer Detection via Metabolites and Machine Learning.

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

A Microfluidic, Multi-Antibody Cell Capture Method to Evaluate Tumor Cells in Cerebrospinal Fluid in Patients With Suspected Leptomeningeal Metastases.

CONTEXT.­: Leptomeningeal disease (LMD) is a clinical sequela of central nervous system metastasis involving the cerebrospinal fluid (CSF), often seen in late-stage solid tumors. It has a grave prognosis without urgent treatment. Standard of care methodologies to diagnose LMD include CSF cytology, magnetic resonance imaging, and clinical evaluation. These methods offer limited sensitivity and specificity for the evaluation of LMD. Here, we describe the analytic performance characteristics of a microfluidic-based tumor cell enrichment and detection assay optimized to detect epithelial cells in CSF using both contrived samples as well as CSF from patients having suspected or confirmed LMD from carcinomas. OBJECTIVE.­: To demonstrate the feasibility of using a microfluidic, multi-antibody cell capture assay to identify and quantify tumor cells in CSF. DESIGN.­: An artificial CSF solution was spiked with 34 different human carcinoma cell lines at different concentrations and assayed for the ability to detect tumor cells to assess analytic accuracy. Two cell lines were selected to assess linearity, intra-assay precision, interinstrument precision, and sample stability. Clinical verification was performed on 65 CSF specimens from patients. Parameters assessed included the number of tumor cells, coefficient of variation percentage, and percentage of tumor cell capture (TCC). RESULTS.­: Among contrived samples, average tumor cell capture ranged from 50% to 82% (261 of 522; 436 of 531), and coefficients of variation ranged from 7% to 67%. The cell capture assay demonstrated a sensitivity of 92% and a specificity of 95% among clinical samples. CONCLUSIONS.­: This assay demonstrated the ability to detect and enumerate epithelial cells in contrived and clinical specimens in an accurate and reproducible fashion. The use of cell capture assays in CSF may be useful as a sensitive test for the diagnosis and longitudinal monitoring of LMD from solid tumors.

A phase I dose-escalation study of pulsatile afatinib in patients with recurrent or progressive brain cancer.

Background: Afatinib (BIBW2992; Gilotrif®) is a selective and irreversible inhibitor of the epidermal growth factor receptor (ErbB; EGFR) family. It inhibits EGFR, HER2, and HER4 phosphorylation, resulting in tumor growth inhibition and regression. This phase I dose-escalation trial of pulsatile afatinib examined the safety, drug penetration into the central nervous system, preliminary antitumor activity, and recommended phase II dose in patients with progressive or recurrent brain cancers. Methods: Afatinib was taken orally once every 4 days or once every 7 days depending on dose cohort, until disease progression or unacceptable toxicity. Results: A total of 24 patients received the investigational agent and were evaluable for safety analyses, and 21 patients were evaluable for efficacy. Dosing was administered at 80 mg every 4 days, 120 mg every 4 days, 180 mg every 4 days, or 280 mg every 7 days. A recommended phase II dose of pulsatile afatinib was established at 280 mg every 7 days as there were no dose-limiting toxicities in any of the dosing cohorts and all toxicities were deemed manageable. The most common drug-related toxicities were diarrhea, rash, nausea, vomiting, fatigue, stomatitis, pruritus, and limb edema. Out of the 21 patients evaluable for efficacy, 2 patients (9.5%) exhibited partial response based on Response Assessment in Neuro-Oncology criteria and disease stabilization was seen in 3 patients (14.3%). Conclusions: Afatinib taken orally was safe and well-tolerated up to 280 mg every 7 days in brain cancer patients.

Evaluation of the EMulate Therapeutics Voyager's ultra-low radiofrequency energy in murine model of glioblastoma.

BACKGROUND: Glioblastoma (GBM) presents as an aggressive brain cancer, notorious for its recurrence and resistance to conventional treatments. This study aimed to assess the efficacy of the EMulate Therapeutics Voyager®, a non-invasive, non-thermal, non-ionizing, battery-operated, portable experimental medical device, in treating GBM. Using ultra-low radiofrequency energy (ulRFE) to modulate intracellular activity, previous preliminary results in patients have been encouraging. Now, with a focus on murine models, our investigation seeks to elucidate the device's mechanistic impacts, further optimizing its therapeutic potential and understanding its limitations. METHODS: The device employs a silicone over molded coil to deliver oscillating magnetic fields, which are believed to interact with and disrupt cellular targets. These fields are derived from the magnetic fluctuations of solvated molecules. Xenograft and syngeneic murine models were chosen for the study. Mice were injected with U-87 MG or GL261 glioma cells in their flanks and were subsequently treated with one of two ulRFE cognates: A1A, inspired by paclitaxel, or A2, based on murine siRNA targeting CTLA4 + PD1. A separate group of untreated mice was maintained as controls. RESULTS: Mice that underwent treatments with either A1A or A2 exhibited significantly reduced tumor sizes when compared to the untreated cohort. CONCLUSION: The EMulate Therapeutics Voyager® demonstrates promising potential in inhibiting glioma cells in vivo through its unique ulRFE technology and should be further studied in terms of biological effects in vitro and in vivo.

Hypoxia drives shared and distinct transcriptomic changes in two invasive glioma stem cell lines.

Glioblastoma (GBM) is the most common primary malignant cancer of the central nervous system. Insufficient oxygenation (hypoxia) has been linked to GBM invasion and aggression, leading to poor patient outcomes. Hypoxia induces gene expression for cellular adaptations. However, GBM is characterized by high intertumoral (molecular subtypes) and intratumoral heterogeneity (cell states), and it is not well understood to what extent hypoxia triggers patient-specific gene responses and cellular diversity in GBM. Here, we surveyed eight patient-derived GBM stem cell lines for invasion phenotypes in 3D culture, which identified two GBM lines showing increased invasiveness in response to hypoxia. RNA-seq analysis of the two patient GBM lines revealed a set of shared hypoxia response genes concerning glucose metabolism, angiogenesis, and autophagy, but also a large set of patient-specific hypoxia-induced genes featuring cell migration and anti-inflammation, highlighting intertumoral diversity of hypoxia responses in GBM. We further applied the Shared GBM Hypoxia gene signature to single cell RNA-seq datasets of glioma patients, which showed that hypoxic cells displayed a shift towards mesenchymal-like (MES) and astrocyte-like (AC) states. Interestingly, in response to hypoxia, tumor cells in IDH-mutant gliomas displayed a strong shift to the AC state, whereas tumor cells in IDH-wildtype gliomas mainly shifted to the MES state. This distinct hypoxia response of IDH-mutant gliomas may contribute to its more favorable prognosis. Our transcriptomic studies provide a basis for future approaches to better understand the diversity of hypoxic niches in gliomas.

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.

BACKGROUND: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD. METHODS: We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers. RESULTS: The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis. CONCLUSIONS: The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.

Pilot Study of High-Dose Pemetrexed in Patients with Progressive Chordoma.

PURPOSE: Chordomas are ultrarare tumors of the axial spine and skull-base without approved systemic therapy. Most chordomas have negative expression of thymidylate synthase (TS), suggesting a potential for responding to the antifolate agent pemetrexed, which inhibits TS and other enzymes involved in nucleotide biosynthesis. We evaluated the therapeutic activity and safety of high-dose pemetrexed in progressive chordoma. PATIENTS AND METHODS: Adult patients with previously treated, progressive chordoma participated in an open-label, single-institution, single-arm, pilot clinical trial of intravenous pemetrexed 900 mg/m2 every 3 weeks and supportive medications of folic acid, vitamin B12, and dexamethasone. The primary endpoint was objective response rate according to RECIST v1.1. Secondary endpoints included adverse events, progression-free survival (PFS), tumor molecular profiles, and alterations in tissue and blood-based biomarkers. RESULTS: Fifteen patients were enrolled and the median number of doses administered was 15 (range, 4-31). One patient discontinued treatment due to psychosocial issues after four cycles and one contracted COVID-19 after 13 cycles. Of the 14 response-evaluable patients, 2 (14%) achieved a partial response and 10 (71%) demonstrated stable disease. Median PFS was 10.5 months (95% confidence interval: 9 months-undetermined) and 6-month PFS was 67%. Adverse events were expected and relatively mild, with one grade 3 creatinine increased, and one each of grade 3 and 4 lymphopenia. No grade 5 adverse events, unexpected toxicities, or dose-limiting toxicities were observed. Several patients reported clinical improvement in disease-related symptoms. CONCLUSIONS: High-dose pemetrexed appears tolerable and shows objective antitumor activity in patients with chordoma. Phase II studies of high-dose pemetrexed are warranted.

BETA prime: a first-in-man phase 1 study of AdAPT-001, an armed oncolytic adenovirus for solid tumors.

AdAPT-001 is an oncolytic adenovirus (OAV) with a transforming growth factor beta (TGF-ß) trap, which neutralizes the immunosuppressive and profibrotic cytokine, TGF-ß. The aim or purpose of this phase 1 study was to assess the safety and tolerability and, secondarily, the efficacy of AdAPT-001 after single intratumoral injection (IT) (Part 1) and multidose IT injection (Part 2) in patients with superficially accessible, advanced refractory solid tumors. Part 1 enrolled 9 patients with a 3 + 3 single dose-escalation safety run-in involving 2.5 × 1011, 5.0 × 1011, 1.0 × 1012 viral particles (vps). No dose-limiting toxicities or treatment-related serious adverse events (SAEs) were seen. In Part 2, a dose-expansion phase, 19 patients received AdAPT-001 at 1.0 × 1012 vps until disease progression according to Response Evaluation Criteria in Solid Tumors or RECIST 1.1. The overall responses to treatment included confirmed partial responses (3), durable stable disease ≥ 6 months (5), and progressive disease (13). AdAPT-001 is well tolerated. Evidence of an anti-tumor effect was seen in both injected and uninjected lesions. The recommended Phase 2 dose was 1.0 × 1012 vp administered by intratumoral injection once every 2 weeks. Combination of AdAPT-001 with a checkpoint inhibition is enrolling.

Response to OKN-007 and NAC in a Patient with Unilateral Hearing Loss and Chronic Tinnitus from Vestibular Schwannoma.

BACKGROUND: Tinnitus is the perception of sound in the absence of external acoustic stimulation. Being one of the most common diseases of the ear, it has a global prevalence ranging from 4.1 to 37.2%. To date, it has been difficult to treat tinnitus as its pathophysiology is poorly understood and there are limited treatment options. OBJECTIVE: To investigate the effect of OKN-007 (also known as HPN-07), a nitrone-based investigational drug, in combination with oral N-acetylcycsteine (NAC), for the treatment of hearing loss and chronic tinnitus under an individual expanded access protocol. PATIENT CASE: We report the case of a patient who presented with left-sided ear fullness, mild tinnitus, and mild high frequency sensorineural hearing loss with 100% word recognition. A large enhancing mass seen on MRI revealed a vestibular schwannoma. He underwent subtotal resection of the tumor resulting in a moderate-to-profound sensorineural hearing loss and catastrophic tinnitus. The patient was treated with intravenous OKN-007 at 60 mg/kg dosed three times per week and oral NAC 2500 mg twice daily. RESULTS: Post-treatment audiometric testing revealed an average of 16.66 dB in hearing threshold improvement in three frequencies (125, 250 and 500 Hz) with residual hearing in the affected left ear. His tinnitus loudness matching improved from 90 dB to 19 dB post-treatment. His Tinnitus Handicap Inventory improved from 86/100 (Catastrophic) to 40/100 (Moderate). He also experienced improvements in sleep, concentration, hearing, and emotional well-being, and reported significantly decreased levels of tinnitusrelated distress. CONCLUSIONS: This case report highlights the feasibility and therapeutic potential of the combination of OKN-007 and NAC in treating hearing loss and tinnitus that warrants further investigation.

Advancing personalized medicine in brain cancer: exploring the role of mRNA vaccines.

Advancing personalized medicine in brain cancer relies on innovative strategies, with mRNA vaccines emerging as a promising avenue. While the initial use of mRNA vaccines was in oncology, their stunning success in COVID-19 resulted in widespread attention, both positive and negative. Regardless of politically biased opinions, which relate more to the antigenic source than form of delivery, we feel it is important to objectively review this modality as relates to brain cancer. This class of vaccines trigger robust immune responses through MHC-I and MHC-II pathways, in both prophylactic and therapeutic settings. The mRNA platform offers advantages of rapid development, high potency, cost-effectiveness, and safety. This review provides an overview of mRNA vaccine delivery technologies, tumor antigen identification, combination therapies, and recent therapeutic outcomes, with a particular focus on brain cancer. Combinatorial approaches are vital to maximizing mRNA cancer vaccine efficacy, with ongoing clinical trials exploring combinations with adjuvants and checkpoint inhibitors and even adoptive cell therapy. Efficient delivery, neoantigen identification, preclinical studies, and clinical trial results are highlighted, underscoring mRNA vaccines' potential in advancing personalized medicine for brain cancer. Synergistic combinatorial therapies play a crucial role, emphasizing the need for continued research and collaboration in this area.