Prognostic stratification of adult primary glioblastoma multiforme patients based on their tumor gene amplification profiles

Targeting chromosome 12q amplification in relapsed glioblastoma: the use of computational biological modeling to identify effective therapy-a case report

Background

Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies.

Case Description

We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months.

Conclusions

Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.

Gene expression-based biomarkers designating glioblastomas resistant to multiple treatment strategies

Despite advances in molecular characterization of glioblastoma multiforme (GBM), only a handful of predictive biomarkers exist with limited clinical relevance. We aimed to identify differentially expressed genes in tumor samples collected at surgery associated with response to subsequent treatment, including temozolomide (TMZ) and nitrosoureas. Gene expression was collected from multiple independent datasets. Patients were categorized as responders/nonresponders based on their survival status at 16 months postsurgery. For each gene, the expression was compared between responders and nonresponders with a Mann-Whitney U-test and receiver operating characteristic. The package 'roc' was used to calculate the area under the curve (AUC). The integrated database comprises 454 GBM patients from 3 independent datasets and 10 103 genes. The highest proportion of responders (68%) were among patients treated with TMZ combined with nitrosoureas, where FCGR2B upregulation provided the strongest predictive value (AUC = 0.72, P < 0.001). Elevated expression of CSTA and MRPS17 was associated with a lack of response to multiple treatment strategies. DLL3 upregulation was present in subsequent responders to any treatment combination containing TMZ. Three genes (PLSCR1, MX1 and MDM2) upregulated both in the younger cohort and in patients expressing low MGMT delineate a subset of patients with worse prognosis within a population generally associated with a favorable outcome. The identified transcriptomic changes provide biomarkers of responsiveness, offer avenues for preclinical studies and may enhance future GBM patient stratifications. The described methodology provides a reliable pipeline for the initial testing of potential biomarker candidates for future validation studies.

Tumor cell and immune cell profiles in primary human glioblastoma: Impact on patient outcome

The distribution and role of tumor-infiltrating leucocytes in glioblastoma (GBM) remain largely unknown. Here, we investigated the cellular composition of 55 primary (adult) GBM samples by flow cytometry and correlated the tumor immune profile with patient features at diagnosis and outcome. GBM single-cell suspensions were stained at diagnosis (n = 44) and recurrence following radiotherapy and chemotherapy (n = 11) with a panel of 8-color monoclonal antibody combinations for the identification and enumeration of (GFAP+ CD45- ) tumor and normal astrocytic cells, infiltrating myeloid cells -i.e. microglial and blood-derived tumor-associated macrophages (TAM), M1-like, and M2-like TAM, neutrophils. and myeloid-derived suppressor cells (MDSC)- and tumor-infiltrating lymphocytes (TIL) -i.e. CD3+ T-cells and their TCD4+ , TCD8+ , TCD4- CD8- , and (CD25+ CD127lo ) regulatory (T-regs) subsets, (CD19+ CD20+ ) B-cells, and (CD16+ ) NK-cells-. Overall, GBM samples consisted of a major population (mean ± 1SD) of tumor and normal astrocytic cells (73% ± 16%) together with a significant but variable fraction of immune cells (24% ± 18%). Within myeloid cells, TAM predominated (13% ± 12%) including both microglial cells (10% ± 11%) and blood-derived macrophages (3% ± 5%), in addition to a smaller proportion of neutrophils (5% ± 9%) and MDSC (4% ± 8%). Lymphocytes were less represented and mostly included TCD4+ (0.5% ± 0.7%) and TCD8+ cells (0.6% ± 0.7%), together with lower numbers of TCD4- CD8- T-cells (0.2% ± 0.4%), T-regs (0.1% ± 0.2%), B-lymphocytes (0.1% ± 0.2%) and NK-cells (0.05% ± 0.05%). Overall, three distinct immune profiles were identified: cases with a minor fraction of leucocytes, tumors with a predominance of TAM and neutrophils, and cases with mixed infiltration by TAM, neutrophils, and T-lymphocytes. Untreated GBM patients with mixed myeloid and lymphoid immune infiltrates showed a significantly shorter patient overall survival versus the other two groups, in the absence of gains of the EGFR gene (p = 0.02). Here we show that immune cell infiltrates are systematically present in GBM, with highly variable levels and immune profiles. Patients with mixed myeloid and T-lymphoid infiltrates showed a worse outcome.

Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer

Background

Lymphovascular invasion (LVI) is a vital risk factor for prognosis across cancers. We aimed to develop a scoring system for stratifying LVI risk in patients with breast cancer.

Methods

A total of 301 consecutive patients (mean age, 49.8 ± 11.0 years; range, 29–86 years) with breast cancer confirmed by pathological reports were retrospectively evaluated at the authors’ institution between June 2015 and October 2018. All patients underwent contrast-enhanced Magnetic Resonance Imaging (MRI) examinations before surgery. MRI findings and histopathologic characteristics of tumors were collected for analysis. Breast LVI was confirmed by postoperative pathology. We used a stepwise logistic regression to select variables and two cut-points were determined to create a three-tier risk-stratification scoring system. The patients were classified as having low, moderate and high probability of LVI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the scoring system.

Results

Tumor margins, lobulation sign, diffusion-weighted imaging appearance, MRI-reported axillary lymph node metastasis, time to signal intensity curve pattern, and HER-2 were selected as predictors for LVI in the point-based scoring system. Patients were considered at low risk if the score was < 3.5, moderate risk if the score was 3.5 to 6.0, and high risk if the score was ≥6.0. LVI risk was segmented from 0 to 100.0% and was positively associated with an increase in risk scores. The AUC of the scoring system was 0.824 (95% confidence interval [CI]: 0.776--0.872).

Conclusion

This study shows that a simple and reliable score-based risk-stratification system can be practically used in stratifying the risk of LVI in breast cancer.

Heterogeneous EGFR, CDK4, MDM4, and PDGFRA Gene Expression Profiles in Primary GBM: No Association with Patient Survival

BACKGROUND

The prognostic impact of the expression profile of genes recurrently amplified in glioblastoma multiforme (GBM) remains controversial.

METHODS

We investigated the RNA gene expression profile of epidermal growth factor receptor (EGFR), cyclin-dependent kinase 4 (CDK4), murine doble minute 4 (MDM4), and platelet derived growth factor receptor alpha (PDGFRA) in 83 primary GBM tumors vs. 42 normal brain tissue samples. Interphase FISH (iFISH) analysis for the four genes, together with analysis of intragenic deletions in EGFR and PDGFRA, were evaluated in parallel at the DNA level. As validation cohort, publicly available RNA gene expression data on 293 samples from 10 different GBM patient series were also studied.

RESULTS

At the RNA level, CDK4 was the most frequently overexpressed gene (90%) followed by EGFR (58%) and PDGFRA (58%). Chromosome 7 copy number alterations, i.e., trisomy (49%) and polysomy (44%), showed no clear association with EGFR gene expression levels. In turn, intragenic EGFR deletions were found in 39 patients (47%), including EGFRvIII (46%) in association with EGFRvIVa (4%), EGFRvII (2%) or other EGFR deletions (3%) and PDGFRA deletion of exons 8-9 was found in only two tumors (2%).

CONCLUSIONS

Overall, none of the gene expression profiles and/or intragenic EGFR deletions showed a significant impact on overall survival of GBM supporting the notion that other still unraveled features of the disease might play a more relevant prognostic role in GBM.