Glioblastoma Multiforme and Breast Cancer: Report on 11 Cases and Clinico-Pathological Remarks

Risk of developing glioblastoma following non-CNS primary cancer: a SEER analysis between 2000 and 2018

BACKGROUND

Patients with a prior malignancy are at elevated risk of developing subsequent primary malignancies (SPMs). However, the risk of developing subsequent primary glioblastoma (SPGBM) in patients with a prior cancer history is poorly understood.

METHODS

We used the Surveillance, Epidemiology, and End Results (SEER) database and identified patients diagnosed with non-CNS malignancy between 2000 and 2018. We calculated a modified standardized incidence ratio (M-SIR), defined as the ratio of the incidence of SPGBM among patients with initial non-CNS malignancy to the incidence of GBM in the general population, stratified by sex latency, and initial tumor location.

RESULTS

Of the 5,326,172 patients diagnosed with a primary non-CNS malignancy, 3559 patients developed SPGBM (0.07%). Among patients with SPGBM, 2312 (65.0%) were men, compared to 2,706,933 (50.8%) men in the total primary non-CNS malignancy cohort. The median age at diagnosis of SPGBM was 65 years. The mean latency between a prior non-CNS malignancy and developing a SPGBM was 67.3 months (interquartile range [IQR] 27-100). Overall, patients with a primary non-CNS malignancy had a significantly elevated M-SIR (1.13, 95% CI 1.09-1.16), with a 13% increased incidence of SPGBM when compared to the incidence of developing GBM in the age-matched general population. When stratified by non-CNS tumor location, patients diagnosed with primary melanoma, lymphoma, prostate, breast, renal, or endocrine malignancies had a higher M-SIR (M-SIR ranges: 1.09-2.15). Patients with lung cancers (M-SIR 0.82, 95% CI 0.68-0.99), or stomach cancers (M-SIR 0.47, 95% CI 0.24-0.82) demonstrated a lower M-SIR.

CONCLUSION

Patients with a history of prior non-CNS malignancy are at an overall increased risk of developing SPGBM relative to the incidence of developing GBM in the general population. However, the incidence of SPGBM after prior non-CNS malignancy varies by primary tumor location, with some non-CNS malignancies demonstrating either increased or decreased predisposition for SPGBM depending on tumor origin. These findings merit future investigation into whether these relationships represent treatment effects or a previously unknown shared predisposition for glioblastoma and non-CNS malignancy.

Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis

Purpose

The authors’ purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis.

Materials and methods

Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere^® 3.0. Lesions’ volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed.

Results

According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10⁻³ mm² s⁻¹ GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%).

Conclusion

Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning

MOTIVATION

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages.

RESULTS

In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems.

AVAILABILITY

http://sbbi-panda.unl.edu/pin/.

Widely metastatic glioblastoma with BRCA1 and ARID1A mutations: a case report

Background

Glioblastoma (GBM) is a highly malignant brain neoplasm with poor survival. Despite its aggressive nature, metastatic spread of GBM is identified only rarely. While the molecular alterations associated with GBM and its subtypes are well-described, there remains a gap in understanding which alterations may predispose towards metastasis. In this report, we present a case of GBM with multi-organ metastases and discuss its genomic alterations.

Case presentation

A 74-year-old woman was diagnosed with left occipital glioblastoma (IDH-wildtype, MGMT-unmethylated), for which she underwent resection, standard chemoradiation, and then stereotactic radiosurgery (SRS) for local recurrence. One month after SRS, work-up for a pathologic hip fracture revealed a left breast mass, lytic lesions involving pelvic bones, and multiple pulmonary and hepatic lesions. Biopsies of the breast and bone lesions both demonstrated metastatic IDH-wildtype GBM. For worsening neurologic symptoms, the patient underwent debulking of a large right temporal lobe recurrence and expired shortly thereafter. Autopsy confirmed metastatic GBM in multiple systemic sites, including bilateral lungs, heart, liver, thyroid, left breast, small bowel, omentum, peritoneal surfaces, visceral surfaces, left pelvic bone, and hilar lymph nodes. Targeted sequencing was performed on tissue samples obtained pre- and postmortem, as well as on cell cultures and an orthotopic mouse xenograft derived from premortem surgical specimens. A BRCA1 mutation (p.I571T) was the only variant found in common among the primary, recurrence, and metastatic specimens, suggesting its likely status as an early driver mutation. Multiple subclonal ARID1A mutations, which promote genomic instability through impairment of DNA mismatch repair, were identified only in the recurrence. Mutational spectrum analysis demonstrated a high percentage of C:G to T:A transitions in the post-treatment samples but not in the primary tumor.

Conclusion

This case report examines a rare case of widely metastatic IDH-wildtype GBM with a clonal somatic mutation in BRCA1. Post-treatment recurrent tumor in the brain and in multiple systemic organs exhibited evidence of acquired DNA mismatch repair deficiency, which may be explained by functional loss of ARID1A. We identify a potential role for immune checkpoint and PARP inhibitors in the treatment of metastatic GBM.

Glioblastoma multiforme presenting as postpartum depression: a case report

Background

Alterations of mental status are characteristic of psychiatric disorders but may also result from a multitude of organic causes. Generally, physical examination and blood analysis are a part of basic psychiatric differential diagnostics, whereas more sophisticated procedures (for example, brain imaging) are applied only in cases with pathologic diagnostic findings. Our report challenges this approach by describing a case of glioblastoma multiforme presenting as postpartum depression without abnormalities in basic differential diagnostics.

Case presentation

A 28-year-old white woman who had been in outpatient treatment for postpartum depression was taken to the psychiatric emergency room. The psychopathological assessment, however, showed mild disorientation and severe deficits of long-term memory. Moreover, she complained of stabbing, bilateral headaches, but results of her physical examination and blood analysis were unremarkable. Magnetic resonance imaging of the brain was performed, which showed a contrast-enhanced mass lesion in the left frontal lobe. The patient underwent urgent tumor resection, and histologic results revealed an IDH-mutant glioblastoma multiforme. The patient was discharged with a substantially improved psychopathology and without neurological deficits.

Conclusions

This report adds to the evidence that postpartum depression may have organic causes in some cases, a fact that needs to be considered in the clinical setting. Atypical neurocognitive findings in a psychiatric interview may alone justify brain imaging, despite normal physical examination and blood analysis results.

Prior malignancies in patients harboring glioblastoma: an institutional case-study of 2164 patients

More patients are surviving long-term following a cancer diagnosis and as such are at risk for second malignancies. As the most common primary brain tumor, glioblastoma (GBM) will not infrequently occur in this population. No study has examined the incidence of prior cancer (PC) in patients harboring GBM. Here we evaluate the epidemiological features, as well as the molecular and clinical characteristics of GBM as a second cancer. Utilizing a web-based cancer data management system at our institution, we identified 2164 patients harboring GBM from 2007 to 2014. We collected baseline demographic, molecular, and clinical data. Univariate analysis was performed to compare the cohort of GBM patients with and without PC diagnosis. Survival differences were analyzed with Kaplan-Meier and log-rank testing. A Cox-proportional hazards model was fit for multivariable analysis. 170 patients (7.9%) harboring GBM had a PC diagnosis. The median interval between diagnoses was 79 months. The most common pathologies were breast (18.8%) and prostate (18.8%) cancer. Patients with a PC were older at the time of GBM diagnosis than those without PC (66 vs. 59 years, p < 0.001) and were more likely to be white (88.2 vs. 72.8%, p < 0.001). Patients with PC were more likely to harbor an EGFR (20 vs. 12.3%, p < 0.001) or MGMT mutation (17.6 vs. 11.6%, p < 0.001). Median survival was 13 months in the PC cohort and 15 months in the cohort without PC (p = NS). Age, KPS, and diagnosis year were the only factors which influenced outcome in multivariable analysis. Patients who develop GBM following a prior malignancy constitute ~8% of patients with GBM. Despite significant molecular differences these two cohorts appear to have a similar overall prognosis and clinical course. Thus, whether or not a patient harbors a malignancy prior to diagnosis of GBM should not exclude him or her from aggressive treatment or for consideration of novel investigational therapies.

Literature-based condition-specific miRNA-mRNA target prediction

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Bilateral Triple-Negative Invasive Breast Cancer with a BRCA2 Mutation, and Glioblastoma: A Case Report and Literature Review

Breast cancer is the second leading cause of death among women in North America. Glioblastoma is the most common primary malignant central nervous system tumor in adults. The majority of hereditary breast cancers are associated with deleterious mutations in the BRCA1 and BRCA2 genes. Although few case reports have described the incidence of glioblastoma in patients previously diagnosed with breast cancer, any association between BRCA2 mutations and glioblastoma has not been demonstrated to date. Herein, we report a woman who is a carrier of a familial BRCA2 mutation, and was previously diagnosed with triple-negative breast cancer (TNBC) and subsequently with a second primary TNBC and glioblastoma. Further investigation is required to define the possible relationship between these two aggressive malignances and the BRCA2 mutation, which might be critical for the proper management and treatment of this disease.

Outcome of patients with malignant glioma and synchronous or metachronous non-central nervous system primary neoplasms

Patients with malignant glioma who are also diagnosed with one or more primary neoplasms of other organs present a unique challenge in both determining prognosis and clinical management. The overlapping impact of the malignancies and their treatment result in confounding variables that may adversely affect optimal management of such patients. Additionally, the glioma-related characteristics and survival outcome of these patients is not well-defined. In this retrospective chart and data review from our longitudinal database, we identified patients with malignant glioma including anaplastic glioma and glioblastoma, diagnosed between January 2005 and June 2011, who were also diagnosed with other non-CNS primary neoplasms. Patients with known genetic syndromes were excluded. The data was analyzed to determine the clinical characteristics and glioma-related survival. A total of 204 patients with malignant glioma (165 glioblastoma and 39 anaplastic glioma) were identified. There was no significant difference in the overall survival or progression-free survival between patients with malignant glioma plus non-CNS primary neoplasm when compared with patients with malignant glioma only. In patients with glioblastoma and non-CNS malignancy, the duration between diagnosis of glioblastoma and non-CNS neoplasms did not significantly alter glioma-related survival. Patients with malignant glioma who were diagnosed with other non-CNS malignancy have survival outcome comparable to those with malignant glioma only. The duration between diagnosis of glioblastoma and diagnosis of non-CNS neoplasms did not affect survival. Further prospective studies specifically addressing survival and molecular characteristics of patients with malignant glioma plus non-CNS cancers are recommended.

Comparison of Glioblastomas and Brain Metastases using Dynamic Contrast-Enhanced Perfusion MRI

PURPOSE

To compare glioblastoma and brain metastases using T1-weighted dynamic contrast-enhanced (DCE)-MRI perfusion technique.

METHODS

26 patients with glioblastoma and 32 patients with metastatic brain lesions with no treatment who underwent DCE-MRI were, retrospectively, analyzed. DCE perfusion parameters K(trans) and Vp were calculated for the whole tumor. Signal intensity time curves were quantified by calculating the area under the curve (AUC) and the logarithmic slope of the washout phase to explore the heterogeneous tumor characteristics.

RESULTS

Glioblastoma did not differ from all brain metastases in K(trans) (P = .34) or Vp (P = .47). Glioblastoma and melanoma metastases differed from hypovascular metastases in AUC and log slope of the washout phase of the signal intensity time curve (P < .05); however, glioblastoma and melanoma metastases did not differ from each other (AUC: P = .78, Log slope: P = .77). Glioblastoma and melanoma metastases differed from hypovascular metastases in the ratio of Voxelneg /Voxelpos (P< .03); however, they did not differ from each other. Glioblastoma and melanoma metastases differed from each other in Voxelneg_threshold at higher negative log slope threshold.

CONCLUSION

DCE-MRI showed that it has a potential to differentiate glioblastomas, melanoma metastases and hypovascular brain tumors. Logarithmic slope of the washout phase and AUC of the signal intensity time curve were shown to be the best discriminator between hypervascular and hypovascular neoplasms.

[Gliomas and BRCA genes mutations: fortuitous association or imputability?]

BRCA is a tumor suppressor gene implicated in the major mechanisms of cellular stability in every type of cell. Its mutations are described in numerous cancers, mainly breast and ovarian in women. It was also found an increase of lifetime risk of pancreas, colon, prostate cancer or lymphoma in men carriers. We report the cases of two female patients aged 40 and 58-years-old female patients and one 35-years-old male patient, with brain or medullar gliomas, carriers of a germline mutation of BRCA gene. Those gliomas were particularly aggressive and were not responding to the standard treatment, with chemo and radiotherapy. The very unusual characteristics in location and evolutive profile of these central nervous system tumors raise the question of a genetical underlying mechanism, maybe linked to the BRCA gene mutation that carry these patients. In addition, a non-fortuitous association between germline mutation of BRCA and occurrence of a glioma can be evoked according to the embryological, epidemiological and biomolecular findings noted in the literature. Other clinical and experimental studies are necessary to precise the physiopathological link existing between BRCA mutations and the occurrence of a glioma; this could have therapeutical and clinical implications in the future.

BRCA1 germline mutation and glioblastoma development: report of cases

BACKGROUND

Germline mutations in breast cancer susceptibility gene 1 (BRCA1) increase the risk of breast and ovarian cancers. However, no association between BRCA1 germline mutation and glioblastoma malignancy has ever been highlighted. Here we report two cases of BRCA1 mutated patients who developed a glioblastoma multiform (GBM).

CASES PRESENTATION

Two patients diagnosed with triple negative breast cancer (TNBC) were screened for BRCA1 germline mutation. They both carried a pathogenic mutation introducing a premature STOP codon in the exon 11 of the BRCA1 gene. Few years later, both patients developed a glioblastoma and a second breast cancer. In an attempt to clarify the role played by a mutated BRCA1 allele in the GBM development, we investigated the BRCA1 mRNA and protein expression in breast and glioblastoma tumours for both patients. The promoter methylation status of this gene was also tested by methylation specific PCR as BRCA1 expression is also known to be lost by this mechanism in some sporadic breast cancers.

CONCLUSION

Our data show that BRCA1 expression is maintained in glioblastoma at the protein and the mRNA levels, suggesting that loss of heterozygosity (LOH) did not occur in these cases. The protein expression is tenfold higher in the glioblastoma of patient 1 than in her first breast carcinoma, and twice higher in patient 2. In agreement with the high protein expression level in the GBM, BRCA1 promoter methylation was not observed in these tumours. In these two cases, despite of a BRCA1 pathogenic germline mutation, the tumour-suppressor protein expression is maintained in GBM, suggesting that the BRCA1 mutation is not instrumental for the GBM development.

Functional characterization of breast cancer using pathway profiles

BACKGROUND

The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.

METHODS

We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.

RESULTS

Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.

CONCLUSIONS

Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

High-grade glioma in a patient with breast cancer

Breast cancer is one of the most common origins of metastatic lesions in the central nervous system. Many patients with a breast cancer and concurrent brain tumor(s) were diagnosed to have a metastatic lesion or lesions in the brain, based exclusively on their image findings without further pathologic verification, and received radiotherapy alone thereafter. It is, however, possible that a different pathology such as primary brain malignancy, which actually warrants a specific treatment modality, may occur in such patients with an already known malignancy. We, herein, reported a 61-year-old female patient who suffered from an anaplastic oligodendroglioma 1 year after her diagnosis of breast cancer. Demographic data, characteristic imaging findings, treatment, and outcome of the patient were discussed.

Multicentric Glioblastoma Multiforme in a Patient with BRCA-1 Invasive Breast Cancer

Breast cancer is the most common cancer in women. Germline mutations in BRCA-1 and BRCA-2 significantly increase the risk of developing breast and ovarian cancers, and are also associated with an increased incidence of primary cancers at other sites. We report the case of a 46-year-old woman previously treated for ductal adenocarcinoma of the breast with BRCA-1 who subsequently was diagnosed with multicentric glioblastoma multiforme (GBM) in the right temporal and right occipital lobes. We briefly discuss the incidence of BRCA-1 mutations in breast cancer as well as other primary neoplasms and consider potential mechanisms shared in the pathogenesis of breast and glial tumors.