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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ORGANISM(S): Homo sapiens
PROVIDER: GSE238010 | GEO | 2024/04/12
REPOSITORIES: GEO
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Project description:Glioblastoma (GBM) patient outcomes remain low despite surgery, radiation, and chemotherapy. There are few immunotherapy options due to the lack of tumor immunogenicity. Several clinical trials have reported promising results with cancer vaccines, although most trials have used single tumor region sequencing to identify targetable antigens. This approach limits the antigen pool due to the heterogeneity of GBM tumors. We have implemented multisector sequencing to identify targetable neoantigens across the GBM tumor landscape that were then incorporated into personalized peptide vaccines called NeoVax. Here, we report the findings of four subjects enrolled on the trial. Following NeoVax, we observed a clonal expansion of CD8+ T cells in the peripheral blood that, when expanded in vitro, were reactive to, on average, half of the NeoVax neoantigens tested by IFN-g ELISPOT, including BRAF V600E in one subject. A post-NeoVax tumor biopsy was resected from one subject and also showed evidence of infiltrating CD8+ T cell clonal expansion. Although the patients enrolled in this trial did not achieve remission, this pilot study demonstrates the feasibility of NeoVax and the importance of multisector sampling for neoantigen screening.
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Project description:Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma
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Project description:Personalized NeoAntigen Cancer Vaccine with RT for Newly Diagnosed Glioblastoma
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Project description:Genomic abnormalities in MM are common and can affect a patients outcome. Here we have performed targeted sequencing on xx patient tumor samples and matched control DNA. The targeted panel consists of ~160 genes and copy number regions, as well as key regions of chromosomal translocation including IGH, IGK, IGL and MYC. Using mutation, copy number, and translocation information we have been able to identify abnormalities that affect prognosis including bi-allelic inactivation of TP53 and rearrangements involving MYC.
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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.
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Project description:Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10-5). We posit that multi-omic models using the limited set of 'omic' features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.
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Project description:BackgroundHypertension and diabetes mellitus are often jointly present, especially in early onset cases of either disease. We investigated clinical characteristics of hypertensive patients with newly diagnosed diabetes and diabetic patients with newly diagnosed hypertension.MethodsOur study subjects were recruited in a China nationwide multicenter registry of hypertension and diabetes (n?=?2510). We performed logistic regression to compare patients seen for hypertension in cardiology, with newly diagnosed diabetes (n?=?137) and patients seen for diabetes mellitus in endocrinology, with newly diagnosed hypertension (n?=?155). Albuminuria was defined as a urinary albumin-to-creatinine ratio of???30 mg/g, and left ventricular hypertrophy according to the Cornell product index.ResultsThese two groups of patients with both hypertension and diabetes mellitus were similar in most of the characteristics (P???0.06). However, hypertensive patients with newly diagnosed diabetes, compared to diabetic patients with newly diagnosed hypertension, had a significantly greater body mass index (26.3 vs. 25.4 kg/m2, P?=?0.03) and slower heart rate (73.7 vs. 78.1 beats/min, P?=?0.01). In logistic regression analyses adjusted for sex (48.3% women) and age (mean 60.0?±?11.5 years), the odds ratio for newly diagnosed diabetes mellitus versus newly diagnosed hypertension was 1.27 (95% CI 1.03-1.56) and 0.80 (95% CI 0.66-0.96) for body mass index (+?3 kg/m2) and heart rate (+?10 beat/min), respectively. Hypertensive patients with newly diagnosed diabetes also had a lower prevalence of albuminuria (16.0% vs. 30.1%, P?=?0.02) and slightly and non-significantly higher prevalence of left ventricular hypertrophy (5.1% vs. 1.9%, P?=?0.14) than diabetic patients with newly diagnosed hypertension.ConclusionsEarlier or later onset of hypertension than diabetes mellitus may have different risk factors and organ damage.
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