Early Detection Of Brain Metastases Research Articles

ENPP1 induces blood-brain barrier dysfunction and promotes brain metastasis formation in HER2-positive breast cancer.

Brain metastasis (BrM) is a devastating end-stage neurological complication that occurs in up to 50% of HER2+ breast cancer patients. Understanding how disseminating tumor cells manage to cross the blood-brain barrier (BBB) is essential for developing effective preventive strategies. We identified the ecto-nucleotidase ENPP1 as specifically enriched in the secretome of HER2+ brain metastatic cells, prompting us to explore its impact on BBB dysfunction and BrM formation. We used in vitro BBB and in vivo premetastatic mouse models to evaluate the effect of tumor-secreted ENPP1 on brain vascular permeability. BBB integrity was analyzed by real-time fluorescence imaging of 20kDa Cy7.5-dextran extravasation and immunofluorescence staining of adherens and tight junction proteins. Pro-metastatic effects of ENPP1 were evaluated in an experimental brain metastatic model. Systemically secreted ENPP1 from primary breast tumors impaired the integrity of BBB with loss of tight and adherens junction proteins early before the onset of BrM. Mechanistically, ENPP1 induced endothelial cell dysfunction by impairing insulin signaling and its downstream AKT/GSK3β/β-catenin pathway. Genetic ablation of ENPP1 from HER2+ brain metastatic cells prevented endothelial cell dysfunction and reduced metastatic burden while prolonging the overall and metastasis-free survival of mice. Furthermore, plasmatic ENPP1 levels correlate with brain metastatic burden and inversely with overall survival. We demonstrated that metastatic breast cancer cells exploit the ENPP1 signaling for cell transmigration across the BBB and brain colonization. Our data implicate ENPP1 as a potential biomarker for poor prognosis and early detection of BrM in HER2+ breast cancer.

Abstract 7019: Identifying cell free DNA methylation signatures in cerebrospinal fluids for the early detection of brain metastasis in non-small cell lung cancer

Abstract Brain metastasis (BM) is major complication for adult cancer patients. Up to 2% of all cancer patients develop BM. Non-small cell lung cancer (NSCLC) contributes up to 55% of all BM. Approximately 15-20% of NSCLC patients show metastases at diagnosis, and approximately half will develop BM during their clinical course. Even after treatments like surgery, radiation and immunotherapy, the prognosis is dismal with median overall survival less than 13 months. No standard screening or early diagnostic testing exists for BM now. There is an urgent need to identify genomic features that predispose towards brain metastasis - these biomarkers could be used for early detection and disease monitoring. The cfDNA methylation and fragmentation patterns provide potential epigenetic biomarkers for detecting cancer. These patterns are specific to cell/tissue origin and cancer types. DNA methylation refers to a chemical modification of a specific carbon with an added methyl group in the cytosine. Hypomethylation in the gene body and hypermethylation in the promoter region lead to cancer development. We hypothesize that there are specific cfDNA methylation and fragmentation signatures in CSF that reflect the epigenetic states of BMs, and cancer predisposition for brain metastasis. We applied a novel single molecule sequencing approach to study CSF cfDNA methylation of BM from NSCLC. Our cohort consisted of ten patients with NSCLC-BM (samples N=14) and eleven healthy controls (N=11). Briefly, extracted cfDNA was obtained from CSF samples. Multiplexed sequencing libraries were prepared following an optimized protocol, and sequenced on PromethION flow cell for 72hr by Oxford nanopore technologies. Data were processed with the onboard base and methylation calling, followed by our bioinformatic pipelines for cfDNA size and methylation analysis. Distinct methylation patterns were found on significant CpG sites (fdr-corrected p<0.01) between cancer patients and controls, in both gene-body and promoter region. Biological pathway analysis showed enrichment in essential functions involving PI3K, ERBB2, TNF-α signaling (p<0.03), all of which play a role in NSCLC. Enriched mono-nucleosome levels (p = 1.07e-03) and differentiated mono-/tri- nucleosome ratios (p = 1.34e-03) were found in cancer vs. controls. Overall, our results suggest that a combination of cfDNA methylation and fragmentation can detect NSCLC-BM. This study will benefit BM patients with early detection and better prognosis, and extend our understanding in brain metastases in NSCLC. Citation Format: Tianqi (Kiki) Chen, Billy T. Lau, Xiangqi Bai, Melanie Hayden Gephart, Hanlee P. Ji. Identifying cell free DNA methylation signatures in cerebrospinal fluids for the early detection of brain metastasis in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7019.

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics. Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance. The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1. The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Early detection of brain metastases and appropriate local therapy followed by systemic chemotherapy may improve the prognosis of gastric cancer

Brain metastases develop in 0.5–0.7% of patients with gastric/gastroesophageal junction (G/GEJ) cancer. Although rare, brain metastasis is often identified when the patient is already symptomatic; hence prognosis is poor. Given the therapeutic developments for G/GEJ cancer, overall survival is prolonged, thereby the incidence of brain metastases is predicted to increase. We retrospectively surveyed the rate of brain metastasis among 1257 patients diagnosed with G/GEJ cancer who received chemotherapy between January 2011 and April 2021. We investigated the time of onset of brain metastasis, treatments administered, and impact of the metastasis on the overall treatment course and prognosis. Of the 741 patients included in the analysis, brain metastasis was confirmed in 16 (2.2%). The median survival time (MST) from G/GEJ cancer diagnosis was 14.9 months in patients with brain metastasis detected during the treatment period, and the MST from the diagnosis of brain metastasis was 2.8 months. Patients who received chemotherapy exhibited prolonged survival compared with those who did not (12.4 months vs 1.0 months, p < 0.001). Our findings suggest that the early detection of brain metastases and local therapy for poor responders to chemotherapy enable the continuation of chemotherapy and prolong survival.

Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records.

Early detection of brain metastases (BMs) is critical for prompt treatment and optimal control of the disease. In this study, we seek to predict the risk of developing BM among patients diagnosed with lung cancer on the basis of electronic health record (EHR) data and to understand what factors are important for the model to predict BM development through explainable artificial intelligence approaches accurately. We trained a recurrent neural network model, REverse Time AttentIoN (RETAIN), to predict the risk of developing BM using structured EHR data. To interpret the model's decision process, we analyzed the attention weights in the RETAIN model and the SHAP values from a feature attribution method, Kernel SHAP, to identify the factors contributing to BM prediction. We developed a high-quality cohort with 4,466 patients with BM from the Cerner Health Fact database, which contains over 70 million patients from more than 600 hospitals. RETAIN uses this data set to achieve the best area under the receiver operating characteristic curve at 0.825, a significant improvement over the baseline model. We also extended a feature attribution method, Kernel SHAP, to structured EHR data for model interpretation. Both RETAIN and Kernel SHAP can identify important features related to BM prediction. To the best of our knowledge, this is the first study to predict BM using structured EHR data. We achieved decent prediction performance for BM prediction and identified factors highly relevant to BM development. The sensitivity analysis demonstrated that both RETAIN and Kernel SHAP could discriminate unrelated features and put more weight on the features important to BM. Our study explored the potential of applying explainable artificial intelligence for future clinical applications.

Interactive effects of molecular subtypes with tumor size and extracranial metastatic pattern on risk of brain metastasis in breast cancer patients: A population-based study.

Early detection of brain metastasis (BM) is essential for prognostic improvement in breast cancer (BC) patients. The aim was to identify predictors of BCBM in different molecular subtypes on a population-based level. The Surveillance, Epidemiology, and End Results database was used to select BC patients diagnosed from 2010 to 2018. We evaluated the incidence and risk factors of BCBM and tested the interaction effects between molecular subtypes and other risk factors. Among the 527,525 selected patients, molecular subtypes significantly interacted with T stage and extracranial metastasis (ECM) patterns on the risk of BM in the whole BC population (interaction p=0.002, <0.001, respectively) and after excluding patients with unknown states of key factors. BM development was independent of the T stage only in HR-/HER2- patients (trend p=0.126). We selected BC patients with single-organ ECM and found a significant interaction between molecular subtypes and ECM patterns (interaction p=0.013). The impact of ECM patterns on the risk of BM was limited to HR-/HER2- patients (trend p < 0.001), for whom using bone metastasis as a reference, lung metastasis increased the risk of BM (OR=1.936, 95% CI: 1.300-2.882, p=0.001). T stage and ECM patterns had different associations with BM in different molecular subtypes. HR-/HER2- BC had distinct features on BM development, manifested as a lack of tumor size effect and is associated with lung metastasis. Close surveillance for BM should be considered for HR-/HER2- BC patients.

Predictive Model to Guide Brain Magnetic Resonance Imaging Surveillance in Patients With Metastatic Lung Cancer: Impact on Real-World Outcomes.

Brain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there are no prediction models to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our goal is to develop a machine learning-based clinicogenomic prediction model to estimate patient-level brain metastasis risk. A penalized regression competing risk model was developed using 330 patients diagnosed with lung cancer between January 2014 and June 2019 and followed through June 2021 at Stanford HealthCare. The main outcome was time from the diagnosis of distant metastatic disease to the development of brain metastasis, death, or censoring. Among the 330 patients, 84 (25%) developed brain metastasis over 627 person-years, with a 1-year cumulative brain metastasis incidence of 10.2% (95% CI, 6.8 to 13.6). Features selected for model inclusion were histology, cancer stage, age at diagnosis, primary site, and RB1 and ALK alterations. The prediction model yielded high discrimination (area under the curve 0.75). When the cohort was stratified by risk using a 1-year risk threshold of > 14.2% (85th percentile), the high-risk group had increased 1-year cumulative incidence of brain metastasis versus the low-risk group (30.8% v 6.1%, P < .01). Of 48 high-risk patients, 24 developed brain metastasis, and of these, 12 patients had brain metastasis detected more than 7 months after last brain MRI. Patients who missed this 7-month window had larger brain metastases (58% v 33% largest diameter > 10 mm; odds ratio, 2.80, CI, 0.51 to 13) versus those who had MRIs more frequently. The proposed model can identify high-risk patients, who may benefit from more intensive brain MRI surveillance to reduce morbidity of subsequent treatment through early detection.

Augmented networks for faster brain metastases detection in T1-weighted contrast-enhanced 3D MRI.

Early detection of brain metastases (BM) is one of the determining factors for the successful treatment of patients with cancer; however, the accurate detection of small BM lesions (< 15mm) remains a challenging task. We previously described a framework for the detection of small BM in single-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combined classical image processing (IP) with a dedicated convolutional neural network, taking approximately 30s to process each dataset due to computation-intensive IP stages. To overcome the speed limitation, this study aims to reformulate the framework via an augmented pair of CNNs (eliminating the IP) to reduce the processing times while preserving the BM detection performance. Our previous implementation of the BM detection algorithm utilized Laplacian of Gaussians (LoG) for the candidate selection portion of the solution. In this study, we introduce a novel BM candidate detection CNN (cdCNN) to replace this classical IP stage. The network is formulated to have (1) a similar receptive field as the LoG method, and (2) a bias for the detection of BM lesion loci. The proposed CNN is later augmented with a classification CNN to perform the BM detection task. The cdCNN achieved 97.4% BM detection sensitivity when producing 60K candidates per 3D MRI dataset, while the LoG achieved 96.5% detection sensitivity with 73K candidates. The augmented BM detection framework generated on average 9.20 false-positive BM detections per patient for 90% sensitivity, which is comparable with our previous results. However, it processes each 3D data in 1.9s, presenting a 93.5% reduction in the computation time.

Liquid biopsy markers for early diagnosis of brain metastasis patients with breast cancer by metabolomics.

Introduction: Breast cancer is the most common cancer in women and is the second most common cause of cancer related mortality. Metabolomics, the identification of small metabolites, is a technique for determining the amount of these metabolites. Objectives: This study aimed to identify markers for the early diagnosis of brain metastasis by metabolomic methods in breast cancer patients. Methods: A total of 88 breast cancer patients with distant metastases were included in the study. The patients were divided into two groups according to their metastasis status: patients with brain metastases and distant metastases without any brain metastases. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) and gas chromatography-mass spectrometry (GC-MS) analysis methods were used for metabolomic analyses. Results: 33 of them, 88 patients had brain metastasis, and 55 patients had distant metastases without brain metastasis. A total of 72 and 35 metabolites were identified by the GC-MS and LC-qTOF-MS analysis, respectively. 47 of them were found to be significantly different in patients with brain metastasis. The pathway analysis, performed with significantly altered metabolites, showed that aminoacyl tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, glycine, serine, and threonine metabolism pathways significantly altered in patients with brain metastasis. Predictive accuracies for have identifying the brain metastasis were performed with receiver operating characteristic (ROC) analysis, and the model with fifteen metabolites has 96.9% accuracy. Conclusions: While these results should be supported by prospective studies, these data are promising for early detection of brain metastasis with markers in liquid biopsy samples.

Abstract OT1-07-01: MRI screening versus symptom-directed surveillance for brain metastases among patients with triple negative or HER2+ metastatic breast cancer: A pilot study (nct03881605)

Abstract Background: The incidence of brain metastases (BrM) has steadily increased over time as women with metastatic breast cancer (MBC) live longer and survive to experience spread of cancer to the central nervous system (CNS). Women with triple negative and HER2+ MBC, which represent 30-40% of the MBC population, are at particularly high risk of BrM. At present, MBC patients are not screened for BrM; rather, they undergo imaging of the brain only after symptoms suggestive of CNS involvement develop. Unfortunately, by the time that patients experience potentially debilitating symptoms of BrM, they often have a significant burden of disease with limited treatment options and a poor prognosis. We hypothesize that early detection of BrM via magnetic resonance imaging (MRI)-based screening may allow for early intervention and, ultimately, improved outcomes for MBC patients. Methods: A multi-centre, open-label prospective phase II study, randomizing 50 women with triple negative or HER2+ MBC to either MRI-based BrM screening or symptom-directed surveillance. Randomization is stratified for tumor subtype. Intervention arm: Contrast-enhanced MRI of the brain at baseline, 4-, 8- and 12-months with concurrent chemical exchange saturation transfer (CEST) imaging, a new metabolic MRI sequence that may detect BrM even earlier than standard MRI. Control arm: Symptom-directed surveillance (brain imaging only if patients develop symptoms suggestive of BrM). All participants complete questionnaires at baseline, 6- and 15-months to assess overall (EORTC QLQ BN20) and neurologic-specific (FACT-BR tools) quality of life (QoL) as well as cancer-related anxiety (NCI PRO-CTCAE). A blood sample is collected for ctDNA analysis at baseline in all patients at diagnosis of BrM, if applicable. Key inclusion criteria: 1) Age ≥18; 2) Triple negative MBC, with metastatic disease diagnosed ≤12 weeks prior to study entry OR HER2+ MBC with no restrictions regarding timeline of diagnosis; 3) No symptoms of BrM or known asymptomatic BrM at study entry. Key exclusion criteria: 1) ECOG&amp;gt;2; 2) Inability to complete an MRI (e.g., claustrophobia). Analyses: The primary goal of this study is to determine the feasibility of a randomized trial of BrM screening versus symptom-directed surveillance in the proposed patient population. The pilot study will be considered “not feasible” if &amp;lt;30% of eligible patients enroll, &amp;lt;50% complete the study protocol, and/or &amp;gt;50% of patients allocated to the control arm are screened for BrM with CT or MRI. Overall survival will be assessed as a secondary endpoint. We will also investigate how screening for BrM influences the detection rate of BrM and explore how subsequent intervention affects both overall and neurologic-specific QoL. Our data will enable a power calculation to determine a sample size for a future, larger trial. 28 of 50 planned patients have enrolled in the study to-date. Citation Format: Katarzyna J Jerzak, Arjun Sahgal, Gregory Pond, Priscilla K Brastianos, Orit Freedman, Gregory Stanisz, Ellen Warner. MRI screening versus symptom-directed surveillance for brain metastases among patients with triple negative or HER2+ metastatic breast cancer: A pilot study (nct03881605) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-07-01.

Value of screening and follow-up brain MRI scans in patients with metastatic melanoma.

BackgroundNovel treatments make long‐term survival possible for subsets of patients with melanoma brain metastases. Brain magnetic resonance imaging (MRI) may aid in early detection of brain metastases and inform treatment decisions. This study aimed to determine the impact of screening MRI scans in patients with metastatic melanoma and follow‐up MRI scans in patients with melanoma brain metastases.MethodsThis retrospective cohort study included patients diagnosed with metastatic melanoma or melanoma brain metastases between June 2015 and January 2018. The impact of screening MRI scans was evaluated in the first 2 years after metastatic melanoma diagnosis. The impact of follow‐up MRI scans was examined in the first year after brain metastases diagnosis. The number of MRI scans, scan indications, scan outcomes, and changes in treatment strategy were analyzed.ResultsIn total, 116 patients had no brain metastases at the time of the metastatic melanoma diagnosis. Twenty‐eight of these patients (24%) were subsequently diagnosed with brain metastases. Screening MRI scans detected the brain metastases in 11/28 patients (39%), of which 8 were asymptomatic at diagnosis. In the 96 patients with melanoma brain metastases, treatment strategy changed after 75/168 follow‐up MRI scans (45%). In patients treated with immune checkpoint inhibitors, the number of treatment changes after follow‐up MRI scans was lower when patients had been treated longer.Conclusion(s)Screening MRI scans aid in early detection of melanoma brain metastases, and follow‐up MRI scans inform treatment strategy. In patients with brain metastases responding to immune checkpoint inhibitors, treatment changes were less frequently observed after follow‐up MRI scans. These results can inform the development of brain imaging protocols for patients with immune checkpoint inhibitor sensitive tumors.

Investigating diffusion patterns of brain metastases pre- and post-stereotactic radiosurgery: a feasibility study

Purpose. Metastatic complications are responsible for 90% of cancer-associated mortality. Magnetic resonance imaging (MRI) can be used to observe the brain’s microstructure and potentially correlate changes with metastasis occurrence. Diffusion weighted imaging (DWI) is an MRI technique that utilizes the kinetics of water molecules within the body. The aim of this study is to use DWI to characterize diffusion changes within brain metastases in cancer patients pre- and post-stereotactic radiosurgery (SRS). Methods. We retrospectively analyzed 113 metastases from 13 patients who underwent SRS for brain metastasis recurrence. Longitudinal apparent diffusion coefficient (ADC) maps were registered to Gd-T1 images and CT, and clinical metastasis ROIs from all SRS treatments were retrospectively transferred onto these ADC maps for analysis. Metastases were characterized based on pre-SRS diffusion pattern, primary cancer site, and post-SRS outcome. ADC values were calculated pre- and post-SRS. Results.ADC values were significantly elevated (980.2 × 10−6 mm2 s−1 and 1040.3 × 10−6 mm2 s−1 pre- and post-SRS, respectively) when compared to healthy brain tissue (826.8 × 10−6 mm2 s−1) for all metastases. Three identified pre-SRS patterns were significantly different before SRS and within 6 months post-SRS. No significant differences were observed between different primaries pre-SRS. Post-SRS, Lung metastases ADC decreased by 86.2 × 10−6 mm2 s−1, breast metastases increased by 116.7 × 10−6 mm2 s−1, and genitourinary metastases showed no significant ADC change. SRS outcomes showed ADC variability pre-treatment but no significant differences pre- and post-SRS, except at 6–9 months post-SRS where progressing metastases were elevated when compared to other response groups. Conclusion. This study provided a unique opportunity to characterize diffusion changes in brain metastases before their manifestation on standard Gd-T1 images and post-SRS. Identified patterns may improve early detection of brain metastases as well as predict their response to treatment.

Brain metastases in gastroesophageal cancers\u2014an underestimated complication

BackgroundBrain metastases represent a severe complication in many gastrointestinal malignancies especially those arising from the upper gastrointestinal tract, including cancer of the esophagus, gastroesophageal junction, and stomach (GEC). However, there is little knowledge about the onset or potential risk factors for brain metastases (BRMs) in upper gastrointestinal cancers resulting in a lack of screening guidelines for BRMs.MethodsWe analyzed 827 patients from our cancer registry suffering from gastroesophageal cancer (GEC) and treated at the University Medical Center Göttingen between January 2013 and December 2019 for the presence of BRMs.ResultsFrom 827 patients with GEC we found 54 patients with BRMs, resulting in an incidence of 6.5%. BRMs are more frequent in male patients (90.74% vs 9.26%, p = 0.0051) and in adenocarcinomas (90.74% vs 9.26%, p = 0.0117). Mean duration for the onset of BRMs from initial cancer diagnoses was 20.9 months in limited disease (curative approach) and 9.3 months in advanced disease (palliative approach) (p = 0.0026). However, early detection of BRMs is a prognostic factor since patients with successful resection of BRMs have a better prognosis compared to those with unresectable BRMs (5.93 vs 2.07 months, p = 0.0091).ConclusionIn this single-center retrospective study, brain metastases (BRMs) occur with a high frequency (6.5%) in gastroesophageal cancer (GEC), significantly more often in male patients and adenocarcinomas. Since survival of these patients considerably correlates with successful BRMs resection, our observations propose further prospective trails to validate our hypothesis and ultimately the implementation of routine screening procedures to detect asymptomatic brain metastases.

Usefulness of assessment of circulating tumor DNA(ctDNA) of cerebrospinal fluid(CSF) samples for early detection of brain metastasis (BrM) in patients with triple-negative breast cancer (TNBC).

Retraction The abstract by Raimondi et al entitled, “Usefulness of assessment of circulating tumor DNA(ctDNA) of cerebrospinal fluid(CSF) samples for early detection of brain metastasis (BrM) in patients with triple-negative breast cancer (TNBC),” published in Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021) 507-507, was retracted by the Journal of Clinical of Oncology (JCO). An Expression of Concern was previously issued by JCO on June 16, 2021. Subsequent to publication of the abstract, questions about the data were brought to JCO’s attention. The data were provided by author Giuseppe Naso, who has since died. As the source of the data and methods of data collection cannot be verified, the abstract is being retracted. A copy of this Retraction Notice was sent to the last known email addresses for the authors. Authors Lucrezia Raimondi, Laura Giaconi, and Gian Paolo Spinelli agreed to the retraction. Authors Rachele Lazzeroni, Laura Di Benedetto, Filippo Maria Raimondi, Arianna Di Rocco, and Luigi Rossi did not respond to our queries. Author Giuseppe Naso is deceased. This abstract was retracted on November 9, 2021. 507 Expression of Concern The abstract by Raimondi et al entitled, “Usefulness of assessment of circulating tumor DNA(ctDNA) of cerebrospinal fluid(CSF) samples for early detection of brain metastasis (BrM) in patients with triple-negative breast cancer (TNBC),” published in Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021) 507-507, is under further review. Questions have been raised regarding the integrity of the methods, results, and analysis of the reported abstract. Until the authors and their institutions can fully provide additional information, readers should interpret the findings presented with caution. An update will be provided when our investigation is complete. Background: Despite improvements in treatments, patients diagnosed with TNBC still have poor prognosis for a higher tendency of developing BrM. Identifying patients at high risk of BrM, enabling to predict who will take advantage from appropriate additional treatment, remains a critical problem. ctDNA represents a valuable tool associated with the outcome and the aggressiveness of breast cancer but no prognostic and predictive biomarker has been identified to predict the development of BrM in TNBC. We studied the usefulness of assessment of CSF-ctDNA for early identification of the risk of BrM in TNBC. Methods: Between January 2016 and December 2020, 323 newly diagnosed non-metastatic TNBC patients who underwent neoadjuvant therapy+surgery(NACT) with complete response(CR)were prospectively enrolled. After surgery, samples of CSF measuring ctDNA were obtained from all patients: CSF-ctDNA was extracted with the QIAamp Circulating Nucleic Acid Kit (Qiagen, Valencia, CA, USA) and ctDNA levels were measured. Survival curves were estimated using the Kaplan-Meier method and compared with the Log-rank test. Multivariate Cox regression was used to identify the risk of mortality at three years. Results: After NACT, CSF-ctDNA was detectable in 126/323 (39%) patients, 101/126 (80%) were diagnosed at III stage. 124 of 126 (98.4%) ctDNA+ patients subsequently developed BrM. In contrast, only 2 (2/197, 1%) ctDNA- patients subsequently developed BrM and the 195 other patients remain in a CR (p &lt; 0.001, Fisher's exact test). CSF-ctDNA did associate with PFS and OS: undetectable ctDNA was associated with superior PFS (HR 0.3; p = 0.002) and OS (HR 0.2; p &lt; 0.01), indicating survival is largely determined by the onset of BrM. With a median follow-up of 3 years, median PFS of ctDNA+ vs ctDNA- patients was 13 months vs not reach, p = 0.004 (by Log-rank test). Median OS for ctDNA+ vs ctDNA- patients was 16 months after NACT vs not reach, p = 0.0016 (by Log-rank test). At multivariate analysis detectable CSF-ctDNA emerged as the best predictor of the develop of BrM and 24-month mortality (HR:3.62; p &lt; 0.0001). Age, stage, Ki67% and response to chemotherapy were not significantly associated with the prognosis. Conclusions: After NACT, detectable CSF-ctDNA significantly associates with PFS and OS, identifying early at-risk patients to develop BrM in TNBC.

Current status of gastrointestinal tract cancer brain metastasis and the use of blood-based cancer biomarker biopsy

Brain metastasis (BM) frequently occurs in patients with cutaneous melanoma, lung, and breast cancer; although, BM rarely arises from cancers of the gastrointestinal tract (GIT). The reported incidence of GIT cancer BM is less than 4%. In the last few years, effective systemic therapy has prolonged the survival of GIT patients and consequently, the incidence of developing BM is rising. Therefore, the epidemiology and biology of BM arising from GIT cancer requires a more comprehensive understanding. In spite of the development of new therapeutic agents for patients with metastatic GIT cancers, survival for patients with BM still remains poor, with a median survival after diagnosis of less than 4months. Limited evidence suggests that early detection of isolated intra-cranial lesions will enable surgical resection plus systemic and/or radiation therapy, which may lead to an increase in overall survival. Novel diagnostic methods such as blood-based biomarker biopsies may play a crucial role in the early detection of BM. Circulating tumor cells and circulating cell-free nucleic acids are known to serve as blood biomarkers for early detection and treatment response monitoring of multiple cancers. Blood biopsy may improve early diagnosis and treatment monitoring of GIT cancers BM, thus prolonging patients' survivals.

Construction and Validation of a Convenient Clinical Nomogram to Predict the Risk of Brain Metastasis in Renal Cell Carcinoma Patients.

Brain metastasis (BM) is a typical type of metastasis in renal cell carcinoma (RCC) patients. The early detection of BM is likely a crucial step for RCC patients to receive appropriate treatment and prolong their overall survival. The aim of this study was to identify the independent predictors of BM and construct a nomogram to predict the risk of BM. Demographic and clinicopathological data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database for RCC patients between 2010 and 2015. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors, and then, a visual nomogram was constructed. Multiple parameters were used to evaluate the discrimination and clinical value. We finally included 42577 RCC patients. Multivariate logistic regression analysis showed that histological type, tumor size, bone metastatic status, and lung metastatic status were independent BM-associated risk factors for RCC. We developed a nomogram to predict the risk of BM in patients with RCC, which showed favorable calibration with a C-index of 0.924 (0.903-0.945) in the training cohort and 0.911 (0.871-0.952) in the validation cohort. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. The nomogram was shown to be a practical, precise, and personalized clinical tool for identifying the RCC patients with a high risk of BM, which not only will contribute to the more reasonable allocation of medical resources but will also enable a further improvements in the prognosis and quality of life of RCC patients.

Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response.

Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases–specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.

Clinical Value of Susceptibility Weighted Imaging of Brain Metastases

MRI is used for screening, initial diagnosis and follow-up of brain metastases. Multiparametric MRI protocols encompass an array of image sequences to depict key aspects of metastases morphology and biology. Given the recent safety concerns of Gd-administration and the retention of linear Gd-agents in the brain, non-contrast sequences are currently evaluated regarding their diagnostic value for brain imaging studies. Susceptibility weighted imaging has been established as a valuable clinical and research tool that is heavily used in clinical practice and utilized in diverse pathologies ranging from neuroinflammation, neurovascular disease to neurooncology. We review the value of SWI in the field of brain metastases with an emphasis on its role in early diagnosis, determination of the primary tumor entity, treatment monitoring and discuss therapy-associated changes that can affect SWI. We also review recent insights on the role of “isolated SWI signals” and the controversy on the specificity of SWI for the early detection of brain metastases.

Breast Cancer With Brain Metastases: Perspective From a Long-Term Survivor.

The purpose of this essay is to inform others that it is possible to survive breast cancer with brain metastases. The second author is the subject patient and a long-term survivor of systemic metastatic breast cancer with numerous brain metastases (corresponding to 8% survivor group). We credit her survival to a combination of (1) medicine as practiced by an excellent oncologist with whom we developed a partnership to manage the patient’s health, (2) our informed exploration of the available scientific knowledge including a review of scientific research articles that go beyond conventional care, and (3) the patient’s supplementation with numerous repurposed drugs and other substances reported to have antitumor properties. Alongside her conventional treatment (the medical standard of care), it seems likely that this supplementation has been a key factor in the patient’s long-term survival. We also point out that the lack of follow-up magnetic resonance imaging brain scans for early detection of brain metastases poses substantial risks for patients with HER2+ metastatic breast cancer in non–central nervous system locations. Thus, we suggest that research be conducted on such early detection for possible inclusion in the recommendations for the medical standard of care. Finally, medical doctors and also patients with backgrounds in biological science may wish to consider potential options and advantages of repurposed drugs and other substances reported in scientific publications when the medical standard of care has limited options for advanced cancer and other severe chronic health conditions. However, any efforts along this line by patients should be in collaboration with their medical doctors.

Over-expression of S100B protein as a serum marker of brain metastasis in non-small cell lung cancer and its prognostic value

Validated serum biomarkers for patients suffering from non-small cell lung cancer (NSCLC) brain metastasis are urgently needed for early diagnosis, treatment monitoring, and prognostic classification in daily clinical practice. Serum S100B was reported to be a marker of leaky blood-brain barrier (BBB), which was often caused by brain tumors. This study aimed to investigate the role of S100B in NSCLC brain metastasis. The results showed that serum S100B correlated significantly with NSCLC brain metastasis (P < 0.001). When evaluated by the ROC curve, at the cutoff point 13.83 pg/ml, the sensitivity and specificity were 94% and 93%, respectively (AUC= 0.938, P < 0.001). High level of serum S100B was significantly correlated with a higher number of brain metastases and significantly worse prognosis (P < 0.05). In addition, S100B was an independent prognostic factor (P < 0.001). In conclusion, serum S100B was a sensitive and specific marker for early detection of brain metastasis in NSCLC and could be used as a surveillance tool for prognosis evaluation.