Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

Perioperative imaging predictors of tumor progression and pseudoprogression: A systematic review

In high-grade gliomas, pseudoprogression after radiation treatment might dramatically impact patient's management. We searched for perioperative imaging predictors of pseudoprogression in high-grade gliomas according to PRISMA guidelines, using MEDLINE/Pubmed and Embase (until January 2024). Study design, sample size, setting, diagnostic gold standard, imaging modalities and contrasts, and differences among variables or measures of diagnostic accuracy were recorded. Study quality was assessed through the QUADAS-2 tool. Twelve studies (11 with MRI, one with PET; 1058 patients) were reviewed. Most studies used a retrospective design (9/12), and structural MRI (7/12). Studies were heterogeneous in metrics and diagnostic reference standards; patient selection bias was a frequent concern. Pseudoprogression and progression showed some significant group differences in perioperative imaging metrics, although often with substantial overlap. Radiomics showed moderate accuracy but requires further validation. Current literature is scarce and limited by methodological concerns, highlighting the need of new predictors and multiparametric approaches.

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

Clinical and Genomic Predictors of Adverse Events in Newly Diagnosed Glioblastoma

PURPOSE

Adverse clinical events cause significant morbidity in patients with GBM (GBM). We examined whether genomic alterations were associated with AE (AE) in patients with GBM.

EXPERIMENTAL DESIGN

We identified adults with histologically confirmed IDH-wild-type GBM with targeted next-generation sequencing (OncoPanel) at Dana Farber Cancer Institute from 2013 to 2019. Seizure at presentation, lymphopenia, thromboembolic events, pseudoprogression, and early progression (within 6 months of diagnosis) were identified as AE. The biologic function of genetic variants was categorized as loss-of-function (LoF), no change in function, or gain-of-function (GoF) using a somatic tumor mutation knowledge base (OncoKB) and consensus protein function predictions. Associations between functional genomic alterations and AE were examined using univariate logistic regressions and multivariable regressions adjusted for additional clinical predictors.

RESULTS

Our study included 470 patients diagnosed with GBM who met the study criteria. We focused on 105 genes that had sequencing data available for ≥ 90% of the patients and were altered in ≥10% of the cohort. Following false-discovery rate (FDR) correction and multivariable adjustment, the TP53, RB1, IGF1R, and DIS3 LoF alterations were associated with lower odds of seizures, while EGFR, SMARCA4, GNA11, BRD4, and TCF3 GoF and SETD2 LoF alterations were associated with higher odds of seizures. For all other AE of interest, no significant associations were found with genomic alterations following FDR correction.

CONCLUSIONS

Genomic biomarkers based on functional variant analysis of a routine clinical panel may help identify AE in GBM, particularly seizures. Identifying these risk factors could improve the management of patients through better supportive care and consideration of prophylactic therapies.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Evolution and emergence of primate-specific interferon regulatory factor 9

The binding of interferon (IFN) to its receptors leads to formation of IFN-stimulated gene factor 3 (ISGF3) complex that activates the transcription of cellular IFN-regulated genes. IFN regulatory factor 9 (IRF9, also called ISGF3γ or p48) is a key component of ISGF3. However, there is limited knowledge regarding the molecular evolution of IRF9 among vertebrates. In this study, we have identified the existence of the IRF9 gene in cartilaginous fish (sharks). Among primates, several isoforms unique to old world moneys and great apes are identified. These IRF9 isoforms are named as primate-specific IRF9 (PS-IRF9) to distinguish from canonical IRF9. PS-IRF9 originates from a unique exon usage and differential splicing in the IRF9 gene. Although the N-terminus are identical for all IRF9s, the C-terminal regions of the PS-IRF9 are completely different from canonical IRF9. In humans, two PS-IRF9s are identified and their RNA transcripts were detected in human primary peripheral blood mononuclear cells. In addition, human PS-IRF9 proteins were detected in human cell lines. Sharing the N-terminal exons with the canonical IRF9 proteins, PS-IRF9 is predicted to bind to the same DNA sequences as the canonical IRF9 proteins. As the C-terminal regions of IRFs are the determinants of IRF functions, PS-IRF9 may offer unique biological functions and represent a novel signaling molecule involved in the regulation of the IFN pathway in a primate-specific manner.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

Liquid Biopsy in Glioblastoma

Simple Summary

Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma.

Abstract

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

TULP3 silencing suppresses cell proliferation, migration and invasion in gastric cancer via the PTEN/Akt/Snail pathway

BACKGROUND

Tubby-like protein 3 (TULP3) is a member of the tubby family, has been related to the development of nervous system by gene knockout researches. Nevertheless, the role of TULP3 in the gastric cancer is not clear.

METHODS

Western blotting and real-time polymerase chain reaction (PCR) were employed for the quantitative detection of TULP3 expression in the gastric cancer and consecutive non-cancerous tissues, and gastric cancer cells. The roles of TULP3 in invasion, migration as well as proliferation of the gastric cancer cell in vivo and in vitro through utilizing colony formation, MTT, wound-healing, transwell and mouse xenograft model. Western blotting assay was implemented in order to clarify the potential molecular mechanisms. Furthermore, electron microscopy and western blot were evaluated TULP3 expression in gastric cancer patient extracted serum exosomes.

RESULTS

TULP3 expression levels were remarkably upregulated in the gastric cancer tissues and cells. Subsequent functional assays demonstrated that TULP3 downregulation suppressed invasion, migration as well as the proliferation of the gastric cancer cell. Mechanism assays depicted that the PTEN/Akt/Snail signaling pathway can inhibit invasion, migration as well as the proliferation of the gastric cancer cell via TULP3 silencing. Finally, we found that the expression of TULP3 could be determined in the extracted serum exosomes. The expression of TULP3 in gastric cancer group was higher in comparison with normal group.

CONCLUSIONS

Our results reveal that TULP3 might serve as a potential prognostic biomarker and therapeutic target for the treatment of gastric cancer.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers

After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features

With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.

Liquid Biopsy Strategies to Distinguish Progression from Pseudoprogression and Radiation Necrosis in Glioblastomas

Liquid biopsy for the detection and monitoring of central nervous system tumors is of significant clinical interest. At initial diagnosis, the majority of patients with central nervous system tumors undergo magnetic resonance imaging (MRI), followed by invasive brain biopsy to determine the molecular diagnosis of the WHO 2016 classification paradigm. Despite the importance of MRI for long-term treatment monitoring, in the majority of patients who receive chemoradiation therapy for glioblastoma, it can be challenging to distinguish between radiation treatment effects including pseudoprogression, radiation necrosis, and recurrent/progressive disease based on imaging alone. Tissue biopsy-based monitoring is high risk and not always feasible. However, distinguishing these entities is of critical importance for the management of patients and can significantly affect survival. Liquid biopsy strategies including circulating tumor cells, circulating free DNA, and extracellular vesicles have the potential to afford significant useful molecular information at both the stage of diagnosis and monitoring for these tumors. Here, current liquid biopsy-based approaches in the context of tumor monitoring to differentiate progressive disease from pseudoprogression and radiation necrosis are reviewed.

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

BACKGROUND

Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.

METHODS

The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).

RESULTS

Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.

CONCLUSIONS

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Circulating biomarkers in patients with glioblastoma

Gliomas are the most common tumours of the central nervous system and the most aggressive form is glioblastoma (GBM). Despite advances in treatment, patient survival remains low. GBM diagnosis typically relies on imaging techniques and postoperative pathological diagnosis; however, both procedures have their inherent limitations. Imaging modalities cannot differentiate tumour progression from treatment-related changes that mimic progression, known as pseudoprogression, which might lead to misinterpretation of therapy response and delay clinical interventions. In addition to imaging limitations, tissue biopsies are invasive and most of the time cannot be performed over the course of treatment to evaluate 'real-time' tumour dynamics. In an attempt to address these limitations, liquid biopsies have been proposed in the field. Blood sampling is a minimally invasive procedure for a patient to endure and could provide tumoural information to guide therapy. Tumours shed tumoural content, such as circulating tumour cells, cell-free nucleic acids, proteins and extracellular vesicles, into the circulation, and these biomarkers are reported to cross the blood-brain barrier. The use of liquid biopsies is emerging in the field of GBM. In this review, we aim to summarise the current literature on circulating biomarkers, namely circulating tumour cells, circulating tumour DNA and extracellular vesicles as potential non-invasively sampled biomarkers to manage the treatment of patients with GBM.

Machine learning and glioma imaging biomarkers

AIM

To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

MATERIALS AND METHODS

The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction.

RESULTS

Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).

CONCLUSION

Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

Association of XPG rs2094258 polymorphism with gastric cancer prognosis

BACKGROUND

The xeroderma pigmentosum group G (XPG) gene at chromosome 13q33 consists of 15 exons, which may be related to the occurrence and development of gastric cancer (GC).

AIM

To examine the association of several common single nucleotide polymorphisms (SNPs) of the XPG gene with GC risk and survival.

METHODS

Five SNPs of XPG (rs2094258, rs751402, rs873601, rs2296147, and rs1047768) were genotyped by PCR restriction fragment length polymorphism in 956 histologically confirmed GC cases and 1012 controls in North China. GC patients were followed for survival status and, if deceased, cause of death. Logistic regression and Cox regression were used for analysing associations of XPG SNPs with risk of GC and prognosis, respectively. For rs2094258, heterozygous model (CT vs CC), homozygous model (TT vs CC), recessive model (TT vs CT + CC), and dominant model (TT + CT vs CC) were analyzed.

RESULTS

None of the examined loci were statistically associated with GC risk, although rs2296147 was marginally associated with GC risk (P = 0.050). GC patients with the rs2094258 CT + CC genotype showed worse survival than those with the TT genotype (log-rank test, P = 0.028), and patients with the CC genotype had a tendency of unfavourable prognosis compared with those with the TT + CT genotype (log-rank test, P = 0.039). The increase in C alleles of rs2094258 [hazard ratio (HR) = 1.19, 95% confidence interval (CI): 1.02-1.45, P = 0.037] were associated with the long-term survival of GC cases. Other risk factors for survival included tumor differentiation (HR = 4.51, 95%CI: 1.99-8.23, P < 0.001), lymphovascular invasion (HR = 1.97, 95%CI: 1.44-3.01, P < 0.001), and use of chemotherapy (HR = 0.81, 95%CI: 0.63-0.98, P = 0.041).

CONCLUSION

The XPG rs2094258 polymorphism may be associated with overall survival in GC patients.

How to differentiate pseudoprogression from true progression in cancer patients treated with immunotherapy

Immunotherapy has achieved unprecedented clinical efficacy in patients with various types of advanced tumors; however, some patients experience delayed tumor shrinkage following an increase in tumor burden after such a therapeutic method. This phenomenon is called pseudoprogression and can lead to premature cessation of efficacious immunotherapeutic agents. Consequently, we summarized the available data on methods to differentiate pseudoprogression from true progression in patients who have been treated with immunotherapy including biomarkers, medical imaging techniques and biopsy. We also introduce hyperprogression and special pseudoprogression for improved evaluation of immunotherapy.

Towards Immunotherapy for Pediatric Brain Tumors

Pediatric brain tumors are the leading cause of childhood cancer-related death. Immunotherapy is a powerful new approach for treating some refractory cancers; applying this 'fourth pillar' of cancer treatment to pediatric brain tumors is an exciting but challenging prospect. This review offers new perspectives on moving towards successful immunotherapy for pediatric brain tumors, focusing on pediatric high-grade glioma (HGG), a subgroup with universally poor outcomes. We cover chimeric antigen receptor T cell (CAR-T) therapy, vaccine therapy, and checkpoint inhibition in this context, and focus on the need for intimately understanding the growing brain and its immune system. We highlight the challenges associated with the application of immunotherapy in pediatric neuro-oncology, as well as the tissue-specific challenges to be overcome, to achieve improved outcomes.

TULP3: A potential biomarker in colorectal cancer?

Colorectal cancer (CRC) is the second most common cancer in women and the third most common cancer in men globally. The identification of differentially expressed genes associated to patient's clinical data may represent a useful approach to find important genes in CRC carcinogenesis. Previously, the TULP3 transcription factor was identified as a possible prognostic biomarker in pancreatic ductal adenocarcinoma. Considering that pancreatic and colorectal tissues have the same embryonic origin, we investigated the profile of TULP3 expression in CRC hypothesizing that it may have a role in its development. We comparatively analysed TULP3 gene expression in CRC and normal adjacent colonic tissue and assessed association of expression profiles with survival and clinicopathological information, using publicly available datasets. TULP3 expression levels were increased in CRC when compared to the adjacent non-tumoral tissue. In addition, higher TULP3 gene expression was associated to lymphatic and vascular invasion in colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), respectively. In summary, our results point to a possible role of TULP3 as a diagnostic and prognostic biomarker in CRC. Additional studies are necessary to confirm these preliminary findings.

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11-22.

Interferon Regulatory Factor 9 Structure and Regulation

Interferon regulatory factor 9 (IRF9) is an integral transcription factor in mediating the type I interferon antiviral response, as part of the interferon-stimulated gene factor 3. However, the role of IRF9 in many important non-communicable diseases has just begun to emerge. The duality of IRF9’s role in conferring protection but at the same time exacerbates diseases is certainly puzzling. The regulation of IRF9 during these conditions is not well understood. The high homology of IRF9 DNA-binding domain to other IRFs, as well as the recently resolved IRF9 IRF-associated domain structure can provide the necessary insights for progressive inroads on understanding the regulatory mechanism of IRF9. This review sought to outline the structural basis of IRF9 that guides its regulation and interaction in antiviral immunity and other diseases.

Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns?

PURPOSE

For glioblastoma (GBM), imaging response (IR) or pseudoprogression (PSP) is frequently observed after chemoradiation and may connote a favorable prognosis. With tumors categorized by the Cancer Genome Atlas Project (mesenchymal, classical, neural, and proneural) and by methylguanine-methyltransferase (MGMT) methylation status, we attempted to determine if certain genomic or molecular subtypes of GBM were specifically associated with IR or PSP.

METHODS

Patients with GBM treated at two institutions were reviewed. Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Mantel-cox test determined effect of IR and PSP on OS and PFS. Fisher's exact test was utilized to correlate IR and PSP with genomic subtypes and MGMT status.

RESULTS

Eighty-two patients with GBM were reviewed. The median OS and PFS were 17.9 months and 8.9 months. IR was observed in 28 (40%) and was associated with improved OS (median 29.4 vs 14.5 months p < 0.01) and PFS (median 17.7 vs 5.5 months, p < 0.01). PSP was observed in 14 (19.2%) and trended towards improved PFS (15.0 vs 7.7 months p = 0.08). Tumors with a proneural component had a higher rate of IR compared to those without a proneural component (IR 60% vs 28%; p = 0.03). MGMT methylation was associated with IR (58% vs 24%, p = 0.032), but not PSP (34%, p = 0.10).

CONCLUSION

IR is associated with improved OS and PFS. The proneural subtype and MGMT methylated tumors had higher rates of IR.

Association between the XPG gene rs2094258 polymorphism and risk of gastric cancer

BACKGROUND

Xeroderma pigmentosum group G (XPG) plays an important role in maintaining the stability and integrity of genomic DNA. Previous studies demonstrate some XPG gene polymorphisms are associated with susceptibility to gastric cancer (GC).

METHODS

The association between XPG rs2094258 polymorphism and GC risk was investigated first by a hospital-based case-control study involving 386 patients and 439 controls and then by a meta-analysis. The polymorphism was genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLR).

RESULTS

Xeroderma pigmentosum group G rs2094258 polymorphism was associated with an increased risk of GC in a Chinese population. The meta-analysis did not reveal any significant difference in the overall population. Subgroup analysis of geographic locations showed a significant association between the XPG gene rs2094258 polymorphism and GC risk in Southern China. Stratification analysis further indicated significant associations in hospital-based studies and studies using PCR-RFLR.

CONCLUSION

Xeroderma pigmentosum group G gene rs2094258 polymorphism may be associated with an increased risk of GC in Southern China. Nevertheless, the findings of this meta-analysis should be validated by well-designed large-scale case-control studies among other ethnicities.

Sequencing the next generation of glioblastomas

The most aggressive brain malignancy, glioblastoma, accounts for 60-70% of all gliomas and is uniformly fatal. According to the molecular signature, glioblastoma is divided into four subtypes (proneural, neural, classical, and mesenchymal), each with its own genetic background. The Cancer Genome Atlas project provides information about the most common genetic changes in glioblastoma. They involve mutations in TP53, TERT, and PTEN, and amplifications in EFGR, PDGFRA, CDK4, CDK6, MDM2, and MDM4. Recently, epigenetics was used to demonstrate the oncogenic roles of miR-124, miR-137, and miR-128. The most important findings so far are mutations in IDH1/2 and MGMT promoter methylation, which are routinely used as predictive biomarkers in patient care. Current clinical treatment leaves patients with only a 10% chance for 5-year survival. Attempts to define the mutational profile of glioblastoma to identify clinically relevant changes have not yet yielded significant results. This can be attributed to inter- and intra-tumor heterogeneity that is present in most glioblastomas, as well as hypermutation that appears as a consequence of chemotherapy. The evolving field of radiogenomics aims to classify glioblastoma using a combination of magnetic resonance imaging and genomic information. In the era of genomic medicine, next-generation sequencing is extensively used in glioblastoma research because it can detect multiple changes in a single biological sample; its potential in detecting circulating cell-free DNA has been tested in cerebrospinal fluid and plasma, and it shows promise in the examination of the cellular content of extracellular vesicles as a potential source of biomarkers. Next-generation sequencing is making its way into glioblastoma diagnostics. Gene panels like GlioSeq, which includes the most commonly mutated genes, are currently being tested on snap frozen and formalin fixed paraffin embedded tissues. This new methodology is helping to define the "next generation of glioblastomas" - clinically defined and better understood, with greater potential to improve patient care. However, limitations of the necessary infrastructure, space for data storage, technical expertise, and data ownership need to be considered carefully.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

XPG gene rs751402 C>T polymorphism and cancer risk: Evidence from 22 publications

The Xeroderma pigmentosum group G (XPG) gene promotes recognition and excision of damaged DNA during the DNA repair process. We conducted a comprehensive search of the MEDLINE, EMBASE, and Chinese Biomedical databases for publications evaluating the association XPG gene rs751402 C>T polymorphism and overall cancer risk. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were adopted to assess the strength of the association. A total of 22 publications encompassing 10538 cases and 10511 control subjects were included in the final meta-analysis. We found the polymorphism to be associated with increased cancer risk (TT vs. CC: OR = 1.18, 95% CI = 1.01-1.38, P = 0.040; CT vs. CC: OR = 1.12, 95% CI = 1.01-1.24, P = 0.040; and CT/TT vs. CC: OR = 1.12, 95% CI = 1.002-1.26, P = 0.045). Stratification by cancer type indicated that this polymorphism may increase the risk of gastric cancer and hepatocellular carcinoma, which was further confirmed by a false-positive report probability analysis. Genotype-based mRNA expression provides further evidence that this polymorphism is associated with altered XPG mRNA expression. This meta-analysis suggests XPG gene rs751402 C>T polymorphism correlates with overall cancer risk, especially for gastric cancer and hepatocellular carcinoma.

Association between genetic variants in the XPG gene and gastric cancer risk in a Southern Chinese population

Xeroderma pigmentosum group G (XPG) recognizes and excises DNA damage on the 3' side during the DNA repair process. Previous studies indicated that XPG gene polymorphisms may associate with gastric cancer susceptibility, but results were inconsistent. We evaluated the association of five potentially functional XPG polymorphisms (rs2094258 C>T, rs751402 C>T, rs2296147 T>C, rs1047768 T>C, and rs873601 G>A) with gastric cancer susceptibility in 1142 gastric cancer cases and 1173 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression models. Overall, no significant association was detected between any of selected polymorphism and gastric cancer risk. However, we found that individuals carrying 3-4 risk genotypes were at significantly higher risk of gastric cancer than those with 0-2 risk genotypes (OR=1.32, 95% CI=1.04-1.68, P=0.021). The stratification analysis revealed that the cumulative effect of risk genotypes (3-4 vs. 0-2) on gastric cancer were more prominent among subgroups older than 58 years and men. In conclusion, our results indicated that none of the selected XPG polymorphism could significantly alter gastric cancer susceptibility alone. These polymorphisms might collectively confer increased gastric cancer susceptibility. These findings would be strengthened by larger prospective multicenter studies involving different ethnic populations.