Dataset Information

ACTR-13. EPIDERMAL GROWTH FACTOR RECEPTOR (EGFR) AMPLIFICATION RATES OBSERVED IN SCREENING PATIENTS FOR RANDOMIZED CLINICAL TRIALS IN GLIOBLASTOMA

ABSTRACT: Abstract BACKGROUND EGFR is a key oncogenic target for the treatment of glioblastoma. EGFR amplification is detected in ~50% of tumors. Depatuxizumab mafodotin (depatux-m), formerly called ABT-414, is a tumor-specific antibody-drug conjugate that preferentially targets tumors with EGFR amplification. Herein, we report the frequency of EGFR amplification in 2 large-scale glioblastoma clinical trials of depatux-m. METHODS As of May 1, 2017, testing for EGFR amplification status was performed centrally in 2077 glioblastoma samples during screening for 2 randomized glioblastoma trials with depatux-m: INTELLANCE 1 (NCT02573324) for newly diagnosed patients (n=1001), and INTELLANCE 2 (NCT02343406) for recurrent disease (n=1076). EGFR amplification, required for eligibility in both trials, was determined by fluorescence in situ hybridization and defined as at least 2 copies in at least 15% of cells. EGFRvIII mutation data will be reported at the meeting. RESULTS Out of 2077 total patients, 1084 were positive for EGFR amplification (52.2%), similar to published rates. Per world region number and frequency of EGFR amplification was as follows: Africa (4/9; 44.4%), Americas (238/451; 52.8%), Asia (69/201; 34.3%), Europe (672/1190; 56.5%), Middle East (21/40; 52.5%), Oceania (80/186; 43.0). CONCLUSIONS To our knowledge, this study represents the first report of lower EGFR amplification rates in patients from Asia with glioblastoma. This observation requires further study.

SUBMITTER: van den Bent M

PROVIDER: S-EPMC5693085 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Abstract Glioblastoma (GBM) is the most common and aggressive primary brain cancer. Current standard-of-care includes surgery, radiation and treatment with temozolomide (TMZ), however nearly all tumors recur and the prognosis for recurrent GBM is dismal. Resistance to TMZ is correlated with expression of the DNA repair enzyme O6-methylguanine-DNA-methyltransferase (MGMT), which is highly expressed in a majority of GBM tumors. Dianhydrogalactitol (VAL-083) is a first-in-class bi-functional DNA-targeting agent that exhibited activity against GBM in NCI-sponsored clinical trials both as a single agent and in combination with radiotherapy. VAL-083 readily crosses the blood-brain barrier and accumulates in brain tumor tissue. We have demonstrated that VAL-083 targets N7-Guanine and rapidly induces interstrand DNA cross-links, leading to DNA double-strand breaks, S/G2 cell-cycle arrest and cell death in GBM cell lines and GBM cancer stem cells (CSCs) in vitro. This unique N7-guanine targeting mechanism not only circumvents MGMT-mediated chemo-resistance but also maintains cytotoxic activity in cancer cells deficient in mismatch repair (MMR). These data suggest VAL-083 may offer a superior chemotherapeutic alternative in the treatment of MGMT-unmethylated or MMR deficient GBM. Here, we provide an update of ongoing clinical trials with VAL-083 in MGMT-unmethylated GBM: i) a single-arm, biomarker-driven, Phase II study to determine if VAL-083 treatment of MGMT-unmethylated adult GBM patients at first recurrence/progression, prior to bevacizumab improves survival compared to historical lomustine control (clinicaltrials.gov identifier: NCT02717962); ii) a single-arm, biomarker-driven, Phase II study to confirm the tolerability and efficacy of VAL-083 in combination with radiotherapy in newly diagnosed MGMT-unmethylated GBM patients (clinicaltrials.gov identifier: NCT03050736). The results of these studies may support a new treatment paradigm in for the treatment of MGMT-unmethylated GBM.

Project description:Abstract BACKGROUND Depatuxizumab mafodotin (depatux-m, formerly ABT-414), is an antibody-drug conjugate comprised of an EGFR-targeted antibody, a non-cleavable linker maleimidocaproyl, and the microtubule inhibitor monomethylauristatin F. Promising antitumor activity of depatux-m was observed in patients with glioblastoma (GBM) in Phase 12 studies. Dosage of depatux-m is limited by ocular side effects (OSE), such as blurred vision, dry eye, and photophobia from corneal epitheliopathy, which are generally reversible after dose reduction or drug discontinuation. This Phase 3b study evaluates depatux-m-related OSE management strategies used in depatux-m clinical trials. METHODS This open-label study will enroll approximately 90 patients with newly diagnosed, histologically confirmed, grade IV GBM that is epidermal growth factor receptor (EGFR)-amplified. Patients will receive depatux-m during the chemoradiation phase (radiation and temozolomide [TMZ]), and during adjuvant therapy with TMZ. Patients are randomized to one of three prophylactic ocular treatments: standard steroids (SS), SS with vasoconstrictors and cold compress (VC), or enhanced steroids (ES) with VC. Primary objective is to evaluate these prophylactic strategies for their effect on the proportion of patients requiring a change in OSE management due to inadequate control of OSEs, defined as either a 3-line decline in visual acuity from baseline or Grade 3 OSE severity on the Corneal Epithelial Adverse Event (CEAE) activities of daily living-based scale. Inadequate control with initial prophylactic regimen will trigger a switch to the addition of bandage contact lenses (BCL). Secondary objective assesses change in OSE management due to inadequate control of OSEs by BCL, defined as percentage of patients with Grade 3 CEAE that will trigger transition of patient to investigator discretion regimen (depatux-m interruption/dose reduction, VC prophylaxis, or ES prophylaxis). ClinicalTrials.gov: NCT03419403.

Project description:Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Dataset Information

ACTR-13. EPIDERMAL GROWTH FACTOR RECEPTOR (EGFR) AMPLIFICATION RATES OBSERVED IN SCREENING PATIENTS FOR RANDOMIZED CLINICAL TRIALS IN GLIOBLASTOMA

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets