Patient-derived glioblastoma organoids reflect tumor heterogeneity and treatment sensitivity.

Background: Treatment resistance and tumor relapse are the primary causes of mortality in glioblastoma (GBM), with intratumoral heterogeneity playing a significant role. Patient-derived cancer organoids have emerged as a promising model capable of recapitulating tumor heterogeneity. Our objective was to develop patient-derived GBM organoids (PGO) to investigate treatment response and resistance. Methods: GBM samples were used to generate PGOs and analyzed using whole-exome sequencing (WES) and single-cell karyotype sequencing. PGOs were subjected to temozolomide (TMZ) to assess viability. Bulk RNA sequencing was performed before and after TMZ. Results: WES analysis on individual PGOs cultured for 3 time points (1-3 months) showed a high inter-organoid correlation and retention of genetic variants (range 92.3%-97.7%). Most variants were retained in the PGO compared to the tumor (range 58%-90%) and exhibited similar copy number variations. Single-cell karyotype sequencing demonstrated preservation of genetic heterogeneity. Single-cell multiplex immunofluorescence showed maintenance of cellular states. TMZ treatment of PGOs showed a differential response, which largely corresponded with MGMT promoter methylation. Differentially expressed genes before and after TMZ revealed an upregulation of the JNK kinase pathway. Notably, the combination treatment of a JNK kinase inhibitor and TMZ demonstrated a synergistic effect. Conclusions: Overall, these findings demonstrate the robustness of PGOs in retaining the genetic and phenotypic heterogeneity in culture and the application of measuring clinically relevant drug responses. These data show that PGOs have the potential to be further developed into avatars for personalized adaptive treatment selection and actionable drug target discovery and as a platform to study GBM biology.

Temporal muscle thickness as an independent prognostic imaging marker in newly diagnosed glioblastoma patients: A validation study.

Background: Previous studies have recognized temporal muscle thickness (TMT) as a prognostic marker in glioblastoma, but clinical implementation is hampered due to studies' heterogeneity and lack of established cutoff values. The aim of this study was to assess the validity of recent proposed sex-specific TMT cutoff values in a real-world population of genotyped primary glioblastoma patients. Methods: We measured TMT in preoperative MR images of 328 patients. Sex-specific TMT cutoff values were used to divide patients into "at risk of sarcopenia" or "normal muscle status". Kaplan-Meier analyses and stepwise multivariate Cox-Regression analyses were used to assess the association with overall survival (OS) and progression-free survival (PFS). The association with occurrence of complications and discontinuation of glioblastoma treatment was investigated using odds ratios (OR). Results: Patients at risk of sarcopenia had a significantly higher risk of progression and death than patients with normal muscle status, which remained significant in the multivariate analyses (OS HR = 1.437; 95%CI: 1.046-1.973; P = .025 and PFS HR = 1.453; 95%CI: 1.037-2.036; P = .030). Patients at risk of sarcopenia also had a significantly higher risk of early discontinuation of treatment (OR = 2.45; 95%CI: 1.011-5.952; P = .042) and a significantly lower chance of receiving second-line treatment (OR = 0.23; 95%CI: 0.09-0.60; P = .001). There was no association with the occurrence of complications. Conclusions: Our study confirms external validity of the use of proposed sex-specific TMT cutoff values as an independent prognostic marker in newly diagnosed glioblastoma patients. This simple, noninvasive marker could improve patient counseling and aid in treatment decision processes or trial stratification.

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers.

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

Deciphering the glioblastoma phenotype by computed tomography radiomics.

INTRODUCTION: Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. MATERIALS AND METHODS: Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated. RESULTS: Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71. CONCLUSION: In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.

Patient-Derived Cancer Organoids as Predictors of Treatment Response.

Patient-derived cancer organoids have taken a prominent role in pre-clinical and translational research and have been generated for most common solid tumors. Cancer organoids have been shown to retain key genetic and phenotypic characteristics of their tissue of origin, tumor subtype and maintain intratumoral heterogeneity and therefore have the potential to be used as predictors for individualized treatment response. In this review, we highlight studies that have used cancer organoids to compare the efficacy of standard-of-care and targeted combination treatments with clinical patient response. Furthermore, we review studies using cancer organoids to identify new anti-cancer treatments using drug screening. Finally, we discuss the current limitations and improvements needed to understand the full potential of cancer organoids as avatars for clinical management of cancer therapy.

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma.

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64-0.78) and used to stratify Kaplan-Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582-8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522-0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436-0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response.

Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma.

Use of Systemic Therapy Concurrent With Cranial Radiotherapy for Cerebral Metastases of Solid Tumors.

The incidence of brain metastases of solid tumors is increasing. Local treatment of brain metastases is generally straightforward: cranial radiotherapy (e.g., whole-brain radiotherapy or stereotactic radiosurgery) or resection when feasible. However, treatment becomes more complex when brain metastases occur while other metastases, outside of the central nervous system, are being controlled with systemic therapy (chemotherapeutics, molecular targeted agents, or monoclonal antibodies). It is known that some anticancer agents can increase the risk for neurotoxicity when used concurrently with radiotherapy. Increased neurotoxicity decreases quality of life, which is undesirable in this predominantly palliative patient group. Therefore, it is of utmost importance to identify the compounds that should be temporarily discontinued when cranial radiotherapy is needed.This review summarizes the (neuro)toxicity data for combining systemic therapy (chemotherapeutics, molecular targeted agents, or monoclonal antibodies) with concurrent radiotherapy of brain metastases. Because only a limited amount of high-level data has been published, a risk assessment of each agent was done, taking into account the characteristics of each compound (e.g., lipophilicity) and the microenvironment of brain metastasis. The available trials suggest that only gemcitabine, erlotinib, and vemurafenib induce significant neurotoxicity when used concurrently with cranial radiotherapy. We conclude that for most systemic therapies, the currently available literature does not show an increase in neurotoxicity when these therapies are used concurrently with cranial radiotherapy. However, further studies are needed to confirm safety because there is no high-level evidence to permit definitive conclusions. The Oncologist 2017;22:222-235Implications for Practice: The treatment of symptomatic brain metastases diagnosed while patients are receiving systemic therapy continues to pose a dilemma to clinicians. Will concurrent treatment with cranial radiotherapy and systemic therapy (chemotherapeutics, molecular targeted agents, and monoclonal antibodies), used to control intra- and extracranial tumor load, increase the risk for neurotoxicity? This review addresses this clinically relevant question and evaluates the toxicity of combining systemic therapies with cranial radiotherapy, based on currently available literature, in order to determine the need to and interval to interrupt systemic treatment.