The Role of Prophylactic Cranial Irradiation in Patients With Non-small Cell Lung Cancer: An Updated Systematic Review and Meta-Analysis

Prevention of Brain Metastases: A New Frontier

This review discusses the topic of prevention of brain metastases from the most frequent solid tumor types, i.e., lung cancer, breast cancer and melanoma. Within each tumor type, the risk of brain metastasis is related to disease status and molecular subtype (i.e., EGFR-mutant non-small cell lung cancer, HER2-positive and triple-negative breast cancer, BRAF and NRAF-mutant melanoma). Prophylactic cranial irradiation is the standard of care in patients in small cell lung cancer responsive to chemotherapy but at the price of late neurocognitive decline. More recently, several molecular agents with the capability to target molecular alterations driving tumor growth have proven as effective in the prevention of secondary relapse into the brain in clinical trials. This is the case for EGFR-mutant or ALK-rearranged non-small cell lung cancer inhibitors, tucatinib and trastuzumab-deruxtecan for HER2-positive breast cancer and BRAF inhibitors for melanoma. The need for screening with an MRI in asymptomatic patients at risk of brain metastases is emphasized.

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Synthesis of novel benzothiophene derivatives as protectors against cranial irradiation-induced neuroinflammation

Aim: Cranial irradiation results in many deleterious effects to normal tissues, including neuroinflammation. There is a need to explore radioprotective agents that could be safely used to ameliorate these effects. Method: Nine novel benzothiophene derivatives bearing pyrimidinone, pyrazolidinone, triazole and other active moieties were synthesized and evaluated as antioxidants in an in vitro screening experiment. The most potent compounds were then tested as protectors against radiation-induced neuroinflammation and oxidative stress in rat brains following cranial irradiation. Results: The most potent antioxidant compounds were compounds 3–5 and 10 . P-fluro,p- bromo and pyrido benzothiophene derivatives offered good antioxidant and anti-inflammatory effects. Conclusion: Compounds 3–5 may be introduced as nontoxic candidates for adjuvant therapeutic protocols used in head and neck tumor radiotherapeutic management.

Metastasis prevention: targeting causes and roots

The spread of tumor cells from the primary focus, metastasis, is the main cause of cancer mortality. Therefore, anticancer therapy should be focused on the prevention of metastatic disease. Key targets can be conditions in the primary tumor that are favorable for the appearance of metastatic cells and the first steps of the metastatic cascade. Here, we discuss different approaches for targeting metastasis causes (hypoxia, metabolism changes, and tumor microenvironment) and roots (angiogenesis, epithelial-mesenchymal transition, migration, and invasion). Also, we emphasize the challenges of the existing approaches for metastasis prevention and suggest opportunities to overcome them. In conclusion, we highlight the importance of clinical evaluation of the agents showing antimetastatic effects in vivo, especially in patients with early-stage cancers, the identification of metastatic seeds, and the development of therapeutics for their eradication.

Integrated genomic and DNA methylation analysis of patients with advanced non-small cell lung cancer with brain metastases

BACKGROUND

Brain metastasis is a common and lethal complication of non-small cell lung cancer (NSCLC). It is mostly diagnosed only after symptoms develop, at which point very few treatment options are available. Therefore, patients who have an increased risk of developing brain metastasis need to be identified early. Our study aimed to identify genomic and epigenomic biomarkers for predicting brain metastasis risk in NSCLC patients.

METHODS

Paired primary lung tumor tissues and either brain metastatic tissues or cerebrospinal fluid (CSF) samples were collected from 29 patients with treatment-naïve advanced NSCLC with central nervous system (CNS) metastases. A control group comprising 31 patients with advanced NSCLC who died without ever developing CNS metastasis was also included. Somatic mutations and DNA methylation levels were examined through capture-based targeted sequencing with a 520-gene panel and targeted bisulfite sequencing with an 80,672 CpG panel.

RESULTS

Compared to primary lung lesions, brain metastatic tissues harbored numerous unique copy number variations. The tumor mutational burden was comparable between brain metastatic tissue (P = 0.168)/CSF (P = 0.445) and their paired primary lung tumor samples. Kelch-like ECH-associated protein (KEAP1) mutations were detected in primary lung tumor and brain metastatic tissue samples of patients with brain metastasis. KEAP1 mutation rate was significantly higher in patients with brain metastasis than those without (P = 0.031). DNA methylation analysis revealed 15 differentially methylated blocks between primary lung tumors of patients with and without CNS metastasis. A brain metastasis risk prediction model based on these 15 differentially methylated blocks had an area under the curve of 0.94, with 87.1% sensitivity and 82.8% specificity.

CONCLUSIONS

Our analyses revealed 15 differentially methylated blocks in primary lung tumor tissues, which can differentiate patients with and without CNS metastasis. These differentially methylated blocks may serve as predictive biomarkers for the risk of developing CNS metastasis in NSCLC. Additional larger studies are needed to validate the predictive value of these markers.

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Purpose

The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment.

Patients and Methods

Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model.

Results

Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively.

Conclusion

Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

Late Sequelae of Radiotherapy—The Effect of Technical and Conceptual Innovations in Radiation Oncology

BACKGROUND

Approximately half of all patients with tumors need radiotherapy. Long-term survivors may suffer from late sequelae of the treatment. The existing radiotherapeutic techniques are being refined so that radiation can be applied more precisely, with the goal of limiting the radiation exposure of normal tissue and reducing late sequelae.

METHODS

This review is based on the findings of a selective search in PubMed for publications on late sequelae of conventional percutaneous radiotherapy, January 2000 to May 2020. Late sequelae affecting the central nervous system, lungs, and heart and the development of second tumors are presented, and radiobiological mechanisms and the relevant technical and conceptual considerations are discussed.

RESULTS

The current standard of treatment involves the use of linear accelerators, intensity-modulated radiotherapy (IMRT), image-guided and respiratory-gated radiotherapy, and the integration of positron emission tomography combined with computed tomography (PET-CT) in radiation treatment planning. Cardiotoxicity has been reduced with regard to the risk of coronary heart disease after radiotherapy for Hodgkin's lymphoma (hazard ratio [HR] 0.44 [0.23; 0.85]). It was also found that the rate of radiation- induced pneumonitis dropped from 7.9% with conformal treatment to 3.5% with IMRT in a phase III lung cancer trial. It is hoped that neurocognitive functional impairment will be reduced by hippocampal avoidance in modern treatment planning: an initial phase III trial yielded a hazard ratio of 0.74 [0.58; 0.94]. It is estimated that 8% of second solid tumors in adults are induced by radiotherapy (3 additional tumors per 1000 patients at 10 years).

CONCLUSION

Special challenges for research in this field arise from the long latency of radiation sequelae and the need for largescale, well-documented patient collectives in order to discern dose-effect relationships, and take account of cofactors, when the overall number of events is small. It is hoped that further technical and conceptual advances will be made in the areas of adaptive radiotherapy, proton and heavy-ion therapy, and personalized therapy.