Identification of Predictive Biomarker of Regorafenib in Refractory Colorectal Cancer
Ontology highlight
ABSTRACT:
DISEASE(S): Metastatic Colorectal Cancer,Colorectal Neoplasms
PROVIDER: 2155773 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Background: Patients with glioblastoma (GBM) have a dramatically poor prognosis. The recent REGOMA trial suggested an overall survival benefit of regorafenib in recurrent GBM patients. Considering the extreme genetic heterogeneity of GBMs, we aimed to identify molecular biomarkers predictive of differential response to the drug. The study provides evidence that a signature based on the expression of five biomarkers could help identifying a subgroup of GBM patients exhibiting a striking survival advantage when treated with regorafenib. Despite the presented results must be confirmed in larger replication cohorts, the study highlights potential biomarker options to help guiding the clinical decision among regorafenib and other treatments in patients with relapsing GBM. Methods.Total RNA was extracted from tumor samples of patients enrolled in the REGOMA trial. Genome-wide transcriptome and miRNA profiles were associated with patients' Overall Survival (OS) and Progression Free Survival (PFS). Results. At first step, a set of 11 gene transcripts (HIF1A, CTSK, SLC2A1, KLHL12, CDKN1A, CA12, WDR1, CD53, CBR4, NIFK-AS1, RAB30-DT) and 10 miRNAs (miR-93-5p, miR-203a-3p, miR-17-5p, let-7c-3p, miR-101-3p, miR-3607-3p, miR-6516-3p, miR-301a-3p, miR-23b-3p, miR-222-3p) was filtered by comparing survival between regorafenib and lomustine arms. As second step, a minisignature of two gene transcripts (HIF1A, CDKN1A) and three miRNAs (miR-3607-3p, miR-301a-3p, miR-93-5p) identified a subgroup of patients showing prolonged survival after regorafenib administration (median OS range 10.6 - 20.8 months). Conclusions. The study provides evidence that a signature based on the expression of five biomarkers could help identifying a subgroup of GBM patients exhibiting a striking survival advantage when treated with regorafenib. Despite the presented results must be confirmed in larger replication cohorts, the study highlights potential biomarker options to help guiding the clinical decision among regorafenib and other treatments in patients with relapsing GBM.
2020-12-31 | GSE154042 | GEO
Project description:Background: Patients with glioblastoma (GBM) have a dramatically poor prognosis. The recent REGOMA trial suggested an overall survival benefit of regorafenib in recurrent GBM patients. Considering the extreme genetic heterogeneity of GBMs, we aimed to identify molecular biomarkers predictive of differential response to the drug. The study provides evidence that a signature based on the expression of five biomarkers could help identifying a subgroup of GBM patients exhibiting a striking survival advantage when treated with regorafenib. Despite the presented results must be confirmed in larger replication cohorts, the study highlights potential biomarker options to help guiding the clinical decision among regorafenib and other treatments in patients with relapsing GBM. Methods.Total RNA was extracted from tumor samples of patients enrolled in the REGOMA trial. Genome-wide transcriptome and miRNA profiles were associated with patients' Overall Survival (OS) and Progression Free Survival (PFS). Results. At first step, a set of 11 gene transcripts (HIF1A, CTSK, SLC2A1, KLHL12, CDKN1A, CA12, WDR1, CD53, CBR4, NIFK-AS1, RAB30-DT) and 10 miRNAs (miR-93-5p, miR-203a-3p, miR-17-5p, let-7c-3p, miR-101-3p, miR-3607-3p, miR-6516-3p, miR-301a-3p, miR-23b-3p, miR-222-3p) was filtered by comparing survival between regorafenib and lomustine arms. As second step, a minisignature of two gene transcripts (HIF1A, CDKN1A) and three miRNAs (miR-3607-3p, miR-301a-3p, miR-93-5p) identified a subgroup of patients showing prolonged survival after regorafenib administration (median OS range 10.6 - 20.8 months). Conclusions. The study provides evidence that a signature based on the expression of five biomarkers could help identifying a subgroup of GBM patients exhibiting a striking survival advantage when treated with regorafenib. Despite the presented results must be confirmed in larger replication cohorts, the study highlights potential biomarker options to help guiding the clinical decision among regorafenib and other treatments in patients with relapsing GBM.
2020-12-31 | GSE154041 | GEO
Project description:Developing effective model systems to evaluate new potential chemotherapeutic reagents is critical. Three-dimensional cell cultures, such as spheroids, aid in bridging the gap between commonly used monolayer cell cultures and significantly more complex and expensive animal models. Spheroid model systems contain pathophysiological and chemical gradients similar to an in vivo tumor, which ultimately form distinct cellular subpopulations. In the spatial SILAC model, labels are pulsed into the spheroid at discrete time windows during development. These media pulses result in labeled proteins in distinct regions of the spheroid, which allows for tracing of quantitative proteomic changes to be correlated to different cellular subregions. In this study, we use the Spatial SILAC model to evaluate the proteomic response of a colon carcinoma spheroid to the multikinase inhibitor Regorafenib. We determined regorafenib to be an effective kinase inhibitor which significantly altered whole spheroid proliferation and resulted in increased apoptosis when the signal was averaged for all cells in the culture. However, when regorafenib treatment is more closely examined among the cellular subpopulations, drastic differences are observed for how the drug impacted the proteome of the necrotic spheroid core and the proliferating outer regions. Whole spheroid and outer region analysis shows that regorafenib treatment inhibited critical pathways such as mTOR signaling, ERK/MAPK signaling, and colorectal cancer metastasis signaling. However, analysis of the core shows that regorafenib had an entirely different effect, including upregulation of MAPK1 and KRAS, possibly indicating drug resistance within these late apoptotic cells. Ultimately, these combinatory studies can be used to further understanding of drug metabolism is different cellular subpopulations and provide valuable information to ultimately improve the accuracy of therapeutic testing.
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Project description:Regorafenib has anti-tumor activity in patients with unresectable hepatocellular carcinoma (uHCC) with potential immunomodulatory effects, suggesting that its combination with immune checkpoint inhibitor may have clinically meaningful benefits in patients with uHCC. The multicenter, single-arm, phase 2 RENOBATE trial tested regorafenib-nivolumab as front-line treatment for uHCC. Forty-two patients received nivolumab 480 mg every 4 weeks and regorafenib 80 mg daily (3-weeks-on/1-week-off schedule). The primary endpoint was the investigator-assessed objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. The secondary endpoints included safety, progression-free survival (PFS) and overall survival (OS). ORR per RECIST version 1.1 was 31.0%, meeting the primary endpoint. The most common adverse events were palmar-plantar erythrodysesthesia syndrome (38.1%), alopecia (26.2%) and skin rash (23.8%). Median PFS was 7.38 months. The 1-year OS rate was 80.5%, and the median OS was not reached. Exploratory single-cell RNA sequencing analyses of peripheral blood mononuclear cells showed that long-term responders exhibited T cell receptor repertoire diversification, enrichment of genes representing immunotherapy responsiveness in MKI67+ proliferating CD8+ T cells and a higher probability of M1-directed monocyte polarization. Our data support further clinical development of the regorafenib-nivolumab combination as front-line treatment for uHCC and provide preliminary insights on immune biomarkers of response. ClinicalTrials.gov identifier: NCT04310709 .
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Project description:Current methods for subgroup analyses of data collected from randomized clinical trials (RCTs) may lead to false-positives from multiple testing, lack power to detect moderate but clinically meaningful differences, or be too simplistic in characterizing patients who may benefit from treatment. Herein, we present a general procedure based on a set of newly developed statistical methods for the identification and evaluation of complex multivariate predictors of treatment effect. Furthermore, we implemented this procedure to identify a subgroup of patients who may receive the largest benefit from bevacizumab treatment using a panel of 10 biomarkers measured at baseline in patients enrolled on two RCTs investigating bevacizumab in metastatic breast cancer. Data were collected from patients with human epidermal growth factor receptor 2 (HER2)-negative (AVADO) and HER2-positive (AVEREL) metastatic breast cancer. We first developed a classification rule based on an estimated individual scoring system, using data from the AVADO study only. The classification rule takes into consideration a panel of biomarkers, including vascular endothelial growth factor (VEGF)-A. We then classified the patients in the independent AVEREL study into patient groups according to "promising" or "not-promising" treatment benefit based on this rule and conducted a statistical analysis within these subgroups to compute point estimates, confidence intervals, and p-values for treatment effect and its interaction. In the group with promising treatment benefit in the AVEREL study, the estimated hazard ratio of bevacizumab versus placebo for progression-free survival was 0.687 (95% confidence interval [CI]: 0.462-1.024, p = 0.065), while in the not-promising group the hazard ratio (HR) was 1.152 (95% CI: 0.526-2.524, p = 0.723). Using the median level of VEGF-A from the AVEREL study to divide the study population, then the HR becomes 0.711 (95% CI: 0.435-1.163, p = 0.174) in the promising group and 0.828 (95% CI: 0.496-1.380, p = 0.468) in the not-promising group. Similar results were obtained with the median VEGF-A levels from the AVADO study ("promising" group: HR = 0.709, 95%CI: 0.444-1.133, p = 0.151; "not-promising" group: HR = 0.851, 95% CI: 0.497-1.458, p = 0.556). Our analysis shows it is feasible to employ statistical methods for empirically constructing and validating a scoring system based on a panel of biomarkers. This scoring system can be used to estimate the treatment effect for individual patients and identify a subgroup of patients who may benefit from treatment. The proposed procedure can provide a general framework to organize many statistical methods (existing or to be developed) into a coherent set of analyses for the development of personalized medicines and has the potential of broad applications.
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