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Genomic Correlates of Disease Progression and Treatment Response in Prospectively Characterized Gliomas.

ABSTRACT: PURPOSE:The genomic landscape of gliomas has been characterized and now contributes to disease classification, yet the relationship between molecular profile and disease progression and treatment response remain poorly understood.Experimental Design: We integrated prospective clinical sequencing of 1,004 primary and recurrent tumors from 923 glioma patients with clinical and treatment phenotypes. RESULTS:Thirteen percent of glioma patients harbored a pathogenic germline variant, including a subset associated with heritable genetic syndromes and variants mediating DNA repair dysfunctions (29% of the total) that were associated with somatic biallelic inactivation and mechanism-specific somatic phenotypes. In astrocytomas, genomic alterations in effectors of cell-cycle progression correlated with aggressive disease independent of IDH mutation status, arose preferentially in enhancing tumors (44% vs. 8%, P < 0.001), were associated with rapid disease progression following tumor recurrence (HR = 2.6, P = 0.02), and likely preceded the acquisition of alkylating therapy-associated somatic hypermutation. Thirty-two percent of patients harbored a potentially therapeutically actionable lesion, of whom 11% received targeted therapies. In BRAF-mutant gliomas, response to agents targeting the RAF/MEK/ERK signaling axis was influenced by the type of mutation, its clonality, and its cellular and genomic context. CONCLUSIONS:These data reveal genomic correlates of disease progression and treatment response in diverse types of glioma and highlight the potential utility of incorporating genomic information into the clinical decision-making for patients with glioma.

SUBMITTER: Jonsson P

PROVIDER: S-EPMC6753053 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Existing cognitive and clinical predictors of treatment response to date are not of sufficient strength to meaningfully impact treatment decision making and are not readily employed in clinical settings. This study investigated whether clinical and cognitive markers used in a tertiary care clinic could predict response to usual treatment over a period of 4 to 6 months in a sample of 75 depressed adults. Patients (N = 384) were sequentially tested in 2 half-day clinics as part of a quality improvement project at an outpatient tertiary care center between August 2003 and September 2007; additional subjects evaluated in the clinic between 2007 and 2009 were also included. Diagnosis was according to DSM-IV-TR criteria and completed by residents and attending faculty. Test scores obtained at intake visits on a computerized neuropsychological screening battery were the Parametric Go/No-Go task and Facial Emotion Perception Task. Treatment outcome was assessed using 9-item Patient Health Questionnaire (PHQ-9) self-ratings at follow-up (n = 75). Usual treatment included psychotropic medication and psychotherapy. Decline in PHQ-9 scores was predicted on the basis of baseline PHQ-9 score and education, with neuropsychological variables entered in the second step. PHQ-9 scores declined by 46% at follow-up (56% responders). Using 2-step multiple regression, baseline PHQ-9 score (P ≤ .05) and education (P ≤ .01) were significant step 1 predictors of percent change in PHQ-9 follow-up scores. In step 2 of the model, faster processing speed with interference resolution (go reaction time) independently explained a significant amount of variance over and above variables in step 1 (12% of variance, P < .01), while other cognitive and affective skills did not. This 2-step model accounted for 28% of the variance in treatment change in PHQ-9 scores. Processing speed with interference resolution also accounted for 12% variance in treatment and follow-up attrition. Use of executive functioning assessments in clinics may help identify individuals with cognitive weaknesses at risk for not responding to standard treatments.

Project description:BackgroundGliomas are tumours arising mostly from astrocytic or oligodendrocytic precursor cells. These tumours are classified according to the updated WHO classification from 2021 in 4 grades depending on molecular and histopathological criteria. Despite novel multimodal therapeutic approaches, the vast majority of gliomas (WHO grade III and IV) are not curable. The circadian clock is an important regulator of numerous cellular processes and its dysregulation had been found during the progression of many cancers, including gliomas.ResultsIn this study, we explore expression patterns of clock-controlled genes in low-grade glioma (LGG) and glioblastoma multiforme (GBM) and show that a set of 45 clock-controlled genes can be used to distinguish GBM from normal tissue. Subsequent analysis identified 17 clock-controlled genes with a significant association with survival. The results point to a loss of correlation strength within elements of the circadian clock network in GBM compared to LGG. We further explored the progression patterns of mutations in LGG and GBM, and showed that tumour suppressor APC is lost late both in LGG and GBM. Moreover, HIF1A, involved in cellular response to hypoxia, exhibits subclonal losses in LGG, and TERT, involved in the formation of telomerase, is lost late in the GBM progression. By examining multi-sample LGG data, we find that the clock-controlled driver genes APC, HIF1A, TERT and TP53 experience frequent subclonal gains and losses.ConclusionsOur results show a higher level of disrgulation at the gene expression level in GBM compared to LGG, and indicate an association between the differentially expressed clock-regulated genes and patient survival in both LGG and GBM. By reconstructing the patterns of progression in LGG and GBM, our data reveals the relatively late gains and losses of clock-regulated glioma drivers. Our analysis emphasizes the role of clock-regulated genes in glioma development and progression. Yet, further research is needed to asses their value in the development of new treatments.

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Genomic Correlates of Disease Progression and Treatment Response in Prospectively Characterized Gliomas.

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