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.

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.

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.

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.

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.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics.

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.