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Integrating multisector molecular characterization into personalized peptide vaccine design for patients with newly diagnosed glioblastoma.

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE238012 | GEO | 2024/04/12

REPOSITORIES: GEO

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Integrating multisector molecular characterization into personalized peptide vaccine design for patients with newly diagnosed glioblastoma.

Project description:Integrating multisector molecular characterization into personalized peptide vaccine design for patients with newly diagnosed glioblastoma.

| PRJNA997365 | ENA

Integration of multisector molecular characterization into a personalized peptide vaccine approach for patients with newly diagnosed glioblastoma; Gene expression using NanoString CAR-T Characterization Panel

Project description:Incorporation of multisector sampling in glioblastoma increases the candidate pool of potentially targetable neoantigens that can be successfully developed into a peptide-based vaccine

2024-04-12 | GSE238010 | GEO

Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma

Project description:Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma

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Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma

Project description:Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma

| PRJNA429519 | ENA

EPCO-22. IDENTIFYING NEOANTIGENS FOR A PERSONALIZED MUTATION-DERIVED GENOMIC VACCINE IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

Project description:Abstract BACKGROUND Glioblastomas (GBM) are known for having a lower mutational burden than many other tumor types. Nevertheless, somatic variants that occur in GBM mutational processes may give rise to neoantigens, which can be targeted in a personalized genomic vaccine to elicit a patient-specific anti-tumor response. METHODS Normal and tumor DNA and tumor RNA are extracted from tumor specimens and PBMCs. Exome and RNA sequencing and HLA typing is performed for each patient. Neoantigens are identified using the OpenVax computational pipeline, which calls somatic variants in the DNA and prioritizes corresponding candidate neoantigens for vaccination based on tumor RNA expression and predicted MHC class I binding affinity for each of the patient’s HLA alleles. This work is the base for a phase I trial of personalized neoantigen vaccines in combination with Tumor Treating Fields for GBM (NCT03223103). RESULTS For each of the 9 patients enrolled in the trial, an average of 1005 somatic mutations were identified (range 299–2441), of which 118 were coding variants (range 52–198), 20 were coding and expressed in the tumor RNA (range 9–45), and 16 were coding, expressed and resulted in predicted MHC class I ligands (range 7–33). An average of 2.3% of all somatic variants identified in each tumor gave rise to predicted neoantigens. Sufficient numbers of neoantigens were identified in all tumor samples of the patients enrolled in the study. The overall somatic mutation landscape for the tumors revealed 4/9 PIK3R1 deletions, 2/9 IDH1 substitutions, as well as disruptions in PTEN, TP53, and ATRX, among others. CONCLUSIONS Identifying sufficient neoantigens for inclusion in the personalized genomic vaccine is computationally feasible despite a typically low GBM mutational burden. Driver and passenger mutations can be identified through the same computational pipeline utilized for other tumor types.

| S-EPMC7651423 | biostudies-literature

Integration of multisector molecular characterization into a personalized peptide vaccine approach for patients with newly diagnosed glioblastoma Gene expression using NanoString CAR-T Characterization Panel

Project description:Integration of multisector molecular characterization into a personalized peptide vaccine approach for patients with newly diagnosed glioblastoma Gene expression using NanoString CAR-T Characterization Panel

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T cell receptor repertoire as a prognosis marker for heat shock protein peptide complex-96 vaccine trial against newly diagnosed glioblastoma.

Project description:Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults with a dismal prognosis. We previously reported that vaccination with heat shock protein peptide complex-96 (HSPPC-96) improves survival in patients with newly diagnosed GBM (NCT02122822). Especially for patients with a strong antitumor immune response after vaccination, a durable survival benefit can be achieved. Here, we conducted T cell receptor (TCR) sequencing to retrospectively examine the TCR repertoires of tumor-infiltrating lymphocytes in long-term survivors (LTS) and short-term survivors (STS). We found that LTS exhibit lower TCR repertoire diversity compared with STS, indicating the prevalence of dominant TCR clones in LTS tumors. Accordingly, the LTS group showed increased inter-patient similarity, especially among high-frequency TCR clones, implying some of these dominant clones are shared among LTS. Indeed, we discovered four TCR clones significantly enriched in the LTS group: the presence of these clones has predictive value for stratifying patients prior to vaccination. Together, these findings uncover a group of preexisting TCR clones shared in LTS that can be utilized as candidate biomarkers to select GBM patients most likely to durably respond to HSPPC-96 treatment.

| S-EPMC7153824 | biostudies-literature

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Project description:Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

| S-EPMC7170051 | biostudies-literature

What next for newly diagnosed glioblastoma?

Project description:Glioblastoma is the most common primary brain tumor in adults. Despite current multimodality treatment including surgical resection and temozolomide-based chemoradiotherapy, median survival is only 14-16 months. Characterization of molecular alterations in glioblastoma has identified prognostic subgroups and therapeutic opportunities for clinical trials across glioblastoma subsets. Following a number of negative Phase III trials testing temozolomide dose intensification and angiogenesis inhibition, recent interim analysis data indicate survival prolongation with use of a device (Optune™) delivering alternating electrical field therapy in newly diagnosed glioblastoma patients. In this review, we present an overview of the data supporting the current standard of care and discuss novel experimental therapies in early and late phase clinical testing including devices, small molecule drugs, angiogenesis inhibitors, oncolytic virotherapy and immunotherapy.

| S-EPMC4976851 | biostudies-literature

Newly Diagnosed Glioblastoma in Elderly Patients.

Project description:Purpose of reviewElderly patients with newly diagnosed glioblastoma (eGBM) carry a worse prognosis compared with their younger counterparts. eGBM garners special attention due to the unique challenges, including increased treatment-associated toxicity, less relative benefit from aggressive therapy, medical comorbidities, and immunosuppression. The pivotal GBM trials excluded patients > 70 years old and the optimal treatment approach remains unsettled for eGBM. In this review, we analyze the historical evidence-based data for treating eGBM and discuss the future direction for managing this vulnerable population.Recent findingsTreatment for eGBM continues to evolve. Therapy choice is guided by performance status and presence of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation. For eGBM with good performance status, combinatorial hypofractionated radiation therapy (hRT) and temozolomide should be recommended. For those with poor performance status, further stratification based on MGMT promoter methylation test result is recommended. Single-agent temozolomide is a viable treatment option for MGMT methylated tumors (mMGMT); in particular, those classified with receptor tyrosine kinase II methylation. hRT alone can be considered in MGMT unmethylated (uMGMT) eGBM patients. As precision oncology continues to advance, effective targeted and immunotherapy may emerge as new treatment options for eGBM. Management of elderly patients with newly diagnosed GBM carries a unique set of challenges. Progress has been made in defining the optimal therapeutic approach for these patients, but many questions remain to be answered.

| S-EPMC8817659 | biostudies-literature

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