The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.

Lymph node-targeting adjuvant/neoantigen-codelivering vaccines for combination glioblastoma radioimmunotherapy.

Glioblastoma multiforme (GBM) is the most common and lethal type of adult brain cancer. Current GBM standard of care, including radiotherapy, often ends up with cancer recurrence, resulting in limited long-term survival benefits for GBM patients. Immunotherapy, such as immune checkpoint blockade (ICB), has thus far shown limited clinical benefit for GBM patients. Therapeutic vaccines hold great potential to elicit anti-cancer adaptive immunity, which can be synergistically combined with ICB and radiotherapy. Peptide vaccines are attractive for their ease of manufacturing and stability, but their therapeutic efficacy has been limited due to poor vaccine co-delivery and the limited ability of monovalent antigen vaccines to prevent tumor immune evasion. To address these challenges, here, we report GBM radioimmunotherapy that combines radiotherapy, ICB, and multivalent lymph-node-targeting adjuvant/antigen-codelivering albumin-binding vaccines (AAco-AlbiVax). Specifically, to codeliver peptide neoantigens and adjuvant CpG to lymph nodes (LNs), we developed AAco-AlbiVax based on a Y-shaped DNA scaffold that was site-specifically conjugated with CpG, peptide neoantigens, and albumin-binding maleimide-modified Evans blue derivative (MEB). As a result, these vaccines elicited antitumor immunity including neoantigen-specific CD8+ T cell responses in mice. In orthotopic GBM mice, the combination of AAco-AlbiVax, ICB, and fractionated radiation enhanced GBM therapeutic efficacy. However, radioimmunotherapy only trended more efficacious over radiotherapy alone. Taken together, these studies underscore the great potential of radioimmunotherapy for GBM, and future optimization of treatment dosing and scheduling would improve the therapeutic efficacy.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.

OBJECTIVES: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.

Background and purpose: Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods: A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion: No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net.

In Vivo Imaging to Measure Spontaneous Lung Metastasis of Orthotopically-injected Breast Tumor Cells.

Metastasis remains the primary cause of cancer-related death. The succession of events that characterize the metastatic cascade presents multiple opportunities for therapeutic intervention, and the ability to accurately model them in mice is critical to evaluate their effects. Here, a step-by-step protocol is presented for the establishment of orthotopic primary breast tumors and the subsequent monitoring of the establishment and growth of metastatic lesions in the lung using in vivo bioluminescence imaging. This methodology allows for the evaluation of treatment or its biological effects along the entire range of metastatic development, from primary tumor escape to outgrowth in the lungs. Breast orthotopic tumors are generated in mice via injection of a luciferase-labeled cell suspension in the 4th mammary gland. Tumors are allowed to grow and disseminate for a specific amount of time and are then surgically resected. Upon resection, spontaneous lung metastasis is detected, and the growth over time is monitored using in vivo bioluminescence imaging. At the desired experimental endpoint, lung tissue can be collected for downstream analysis. The treatment of established, clinically evident metastasis is critical to improve outcomes for stage IV cancer patients, and it can be evaluated through tail vein models of experimental lung metastasis. However, metastatic dissemination occurs early in breast cancer, and many patients have latent, subclinical disseminated disease after surgery. Utilization of spontaneous models such as this one provides the opportunity to study the whole spectrum of the disease, especially the systemic effects driven by treatment of the primary tumor such as pre-metastatic niche priming, and evaluate treatments on dormant and subclinical disease after surgery.

Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer.

PURPOSE: Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. RESULTS: Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features. CONCLUSION: Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models.

Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist's performance for T staging?

PURPOSE: To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. METHODS: We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. RESULTS: The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720-0.839 for combined model and AUC = 0.697, 95% CI = 0.538-0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621-0.777 for combined model and AUC = 0.628, 95% CI = 0.558-0.689 for CT staging only, Boot CI = 0.099). CONCLUSION: CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.

Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer.

BACKGROUND AND PURPOSE: Manual delineation of head and neck tumor contours for radiomics analyses is tedious and time consuming. This study investigates if fast or readily available tumor contours can substitute full tumor contours by an experienced observer for an MR-based radiomics model to predict locoregional control (LRC) in oropharyngeal squamous cell carcinoma (OPSCC) tumors. MATERIALS AND METHODS: Radiomic features were extracted from postcontrast T1-weighted MRIs of 177 OPSCC primary tumors using six different manual delineation strategies. LRC prediction models based on recursive feature elimination combined with logistic regression were built. Models were trained and tested on data from each separate delineation. Additionally, the model derived from segmentations from the experienced reader was tested by each of the alternative delineations. Complementary, this was repeated with removal of size and shape features. Model performance was evaluated using area under the curve (AUC). RESULTS: Prediction performance of the experienced radiologist tumor delineation (AUC: 0.74) was superior compared to all other delineations when trained and tested (AUCs: 0.41-0.56) or trained on experienced delineations and tested (AUC: 0.56-0.67) on alternative segmentations. Removal of size and shape features considerably decreases prediction performance (AUC: 0.54). Applying the model based on expert delineations to spherical or single slice delineations makes prediction worthless since these models predict one class. CONCLUSION: Fast or readily available contours cannot substitute full expert tumor delineations in radiomics models predictive of LRC in OPSCC.

Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review.

OBJECTIVE: Functional MR imaging has demonstrated potential for predicting treatment response. This systematic review gives an extensive overview of the current level of evidence for pre-treatment MR-based perfusion and diffusion imaging parameters that are prognostic for treatment outcome in head and neck squamous cell carcinoma (HNSCC) (PROSPERO registrationCRD42020210689). MATERIALS AND METHODS: According to the PRISMA statements, Medline, Embase and Scopus were queried for articles with a maximum date of October 19th, 2020. Studies investigating the predictive performance of pre-treatment MR-based perfusion and/or diffusion imaging parameters in HNSCC treatment response were included. All prognosticators were extracted from the primary tumor. Risk of bias was assessed using the QUIPS tool. Results were summarized in tables and forest plots. RESULTS: 31 unique studies met the inclusion criteria; among them, 11 articles described perfusion (n = 529 patients) and 28 described diffusion (n = 1626 patients) MR-imaging, eight studies were included in both categories. Higher Ktrans and Kep were associated with better treatment response for OS and DFS, respectively. Study findings for Vp and Ve were inconsistent or not significant. High-level controversy was observed between studies examining the MR diffusion parameters mean and median ADC. CONCLUSION: For HNSCC patients, the accurate and consistent results of pre-treatment MR-based perfusion parameters Ktrans and Kep are potential for clinical applicability predictive of OS and DFS and treatment decision guidance. Significant heterogeneity in study designs might affect high discrepancy in study results for parameters extracted from diffusion imaging. Furthermore, recommendations for future research were summarized.

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

BACKGROUND AND PURPOSE: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic. MATERIALS AND METHODS: We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performance using different combinations of MRI sequences as input. Finally, a semi-automatic approach by two human observers defining clipboxes around the tumor was tested. Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). RESULTS: The 3D UNet trained with full context and all sequences as input yielded a median Dice of 0.55, HD of 8.7 mm and MSD of 2.7 mm. Combining all MRI sequences was better than using single sequences. The semi-automatic approach with all sequences as input yielded significantly better performance (p < 0.001): a median Dice of 0.74, HD of 4.6 mm and MSD of 1.2 mm. CONCLUSION: Reducing the amount of context around the tumor and combining multiple MRI sequences improved the segmentation performance. A semi-automatic approach was accurate and clinically feasible.

Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models.

OBJECTIVES: New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors. METHODS: 177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup. RESULTS: A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction. CONCLUSION: Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS.

Autophagy-Dependent Sensitization of Triple-Negative Breast Cancer Models to Topoisomerase II Poisons by Inhibition of the Nucleosome Remodeling Factor.

Epigenetic regulators can modulate the effects of cancer therapeutics. To further these observations, we discovered that the bromodomain PHD finger transcription factor subunit (BPTF) of the nucleosome remodeling factor (NURF) promotes resistance to doxorubicin, etoposide, and paclitaxel in the 4T1 breast tumor cell line. BPTF functions in promoting resistance to doxorubicin and etoposide, but not paclitaxel, and may be selective to cancer cells, as a similar effect was not observed in embryonic stem cells. Sensitization to doxorubicin and etoposide with BPTF knockdown (KD) was associated with increased DNA damage, topoisomerase II (TOP2) crosslinking and autophagy; however, there was only a modest increase in apoptosis and no increase in senescence. Sensitization to doxorubicin was confirmed in vivo with the syngeneic 4T1 breast tumor model using both genetic and pharmacologic inhibition of BPTF. The effects of BPTF inhibition in vivo are autophagy dependent, based on genetic autophagy inhibition. Finally, treatment of 4T1, 66cl4, 4T07, MDA-MB-231, but not ER-positive 67NR and MCF7 breast cancer cells with the selective BPTF bromodomain inhibitor, AU1, recapitulates genetic BPTF inhibition, including in vitro sensitization to doxorubicin, increased TOP2-DNA crosslinks and DNA damage. Taken together, these studies demonstrate that BPTF provides resistance to the antitumor activity of TOP2 poisons, preventing the resolution of TOP2 crosslinking and associated autophagy. These studies suggest that BPTF can be targeted with small-molecule inhibitors to enhance the effectiveness of TOP2-targeted cancer chemotherapeutic drugs. IMPLICATIONS: These studies suggest NURF can be inhibited pharmacologically as a viable strategy to improve chemotherapy effectiveness.

Unexpected PD-L1 immune evasion mechanism in TNBC, ovarian, and other solid tumors by DR5 agonist antibodies.

Lack of effective immune infiltration represents a significant barrier to immunotherapy in solid tumors. Thus, solid tumor-enriched death receptor-5 (DR5) activating antibodies, which generates tumor debulking by extrinsic apoptotic cytotoxicity, remains a crucial alternate therapeutic strategy. Over past few decades, many DR5 antibodies moved to clinical trials after successfully controlling tumors in immunodeficient tumor xenografts. However, DR5 antibodies failed to significantly improve survival in phase-II trials, leading in efforts to generate second generation of DR5 agonists to supersize apoptotic cytotoxicity in tumors. Here we have discovered that clinical DR5 antibodies activate an unexpected immunosuppressive PD-L1 stabilization pathway, which potentially had contributed to their limited success in clinics. The DR5 agonist stimulated caspase-8 signaling not only activates ROCK1 but also undermines proteasome function, both of which contributes to increased PD-L1 stability on tumor cell surface. Targeting DR5-ROCK1-PD-L1 axis markedly increases immune effector T-cell function, promotes tumor regression, and improves overall survival in animal models. These insights have identified a potential clinically viable combinatorial strategy to revive solid cancer immunotherapy using death receptor agonism.

Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer.

BACKGROUND: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables. METHODS: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC). RESULTS: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors. CONCLUSION: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.

Regulatory T Cells Support Breast Cancer Progression by Opposing IFN-γ-Dependent Functional Reprogramming of Myeloid Cells.

Regulatory T (Treg) cell infiltration of solid tumors often correlates with poor prognosis, but their tumor-suppressive function lacks mechanistic understanding. Through a combination of transgenic mice, cell fate mapping, adoptive transfer, and co-injection strategies, we demonstrate that Treg cell ablation-dependent anti-tumor effects in murine breast cancer require intratumoral recruitment of CCR2+ inflammatory monocytes, which primarily differentiate into tumor-associated macrophages (TAMs), and lead to reprogramming of their function in an IFN-γ-dependent manner. Furthermore, transcriptomic signatures from murine TAMs in Treg cell-ablated conditions correlate with increased overall survival in human breast cancer. Our studies highlight the strong myeloid dependency of breast cancer and provide the basis for the development of therapeutic strategies based on manipulation of the IFN-γ signaling pathway in monocytes.

Local Targeting of Lung-Tumor-Associated Macrophages with Pulmonary Delivery of a CSF-1R Inhibitor for the Treatment of Breast Cancer Lung Metastases.

The lungs are major sites of metastases for several cancer types, including breast cancer (BC). Prognosis and quality of life of BC patients that develop pulmonary metastases are negatively impacted. The development of strategies to slow the growth and relieve the symptoms of BC lung metastases (BCLM) is thus an important goal in the management of BC. However, systemically administered first line small molecule chemotherapeutics have poor pharmacokinetic profiles and biodistribution to the lungs and significant off-target toxicity, severely compromising their effectiveness. In this work, we propose the local delivery of add-on immunotherapy to the lungs to support first line chemotherapy treatment of advanced BC. In a syngeneic murine model of BCLM, we show that local pulmonary administration (p.a.) of PLX-3397 (PLX), a colony-stimulating factor 1 receptor inhibitor (CSF-1Ri), is capable of overcoming physiological barriers of the lung epithelium, penetrating the tumor microenvironment (TME), and decreasing phosphorylation of CSF-1 receptors, as shown by the Western blot of lung tumor nodules. That inhibition is accompanied by an overall decrease in the abundance of protumorigenic (M2-like) macrophages in the TME, with a concomitant increase in the amount of antitumor (M1-like) macrophages when compared to the vehicle-treated control. These effects with PLX (p.a.) were achieved using a much smaller dose (1 mg/kg, every other day) compared to the systemic doses typically used in preclinical studies (40-800 mg/kg/day). As an additive in combination with intravenous (i.v.) administration of paclitaxel (PTX), PLX (p.a.) leads to a decrease in tumor burden without additional toxicity. These results suggested that the proposed immunochemotherapy, with regional pulmonary delivery of PLX along with the i.v. standard of care chemotherapy, may lead to new opportunities to improve treatment, quality of life, and survival of patients with BCLM.

Brain Metastasis Cell Lines Panel: A Public Resource of Organotropic Cell Lines.

Spread of cancer to the brain remains an unmet clinical need in spite of the increasing number of cases among patients with lung, breast cancer, and melanoma most notably. Although research on brain metastasis was considered a minor aspect in the past due to its untreatable nature and invariable lethality, nowadays, limited but encouraging examples have questioned this statement, making it more attractive for basic and clinical researchers. Evidences of its own biological identity (i.e., specific microenvironment) and particular therapeutic requirements (i.e., presence of blood-brain barrier, blood-tumor barrier, molecular differences with the primary tumor) are thought to be critical aspects that must be functionally exploited using preclinical models. We present the coordinated effort of 19 laboratories to compile comprehensive information related to brain metastasis experimental models. Each laboratory has provided details on the cancer cell lines they have generated or characterized as being capable of forming metastatic colonies in the brain, as well as principle methodologies of brain metastasis research. The Brain Metastasis Cell Lines Panel (BrMPanel) represents the first of its class and includes information about the cell line, how tropism to the brain was established, and the behavior of each model in vivo. These and other aspects described are intended to assist investigators in choosing the most suitable cell line for research on brain metastasis. The main goal of this effort is to facilitate research on this unmet clinical need, to improve models through a collaborative environment, and to promote the exchange of information on these valuable resources.

Gross tumour volume delineation in anal cancer on T2-weighted and diffusion-weighted MRI - Reproducibility between radiologists and radiation oncologists and impact of reader experience level and DWI image quality.

PURPOSE: To assess how gross tumour volume (GTV) delineation in anal cancer is affected by interobserver variations between radiologists and radiation oncologists, expertise level, and use of T2-weighted MRI (T2W-MRI) vs. diffusion-weighted imaging (DWI), and to explore effects of DWI quality. METHODS AND MATERIALS: We retrospectively analyzed the MRIs (T2W-MRI and b800-DWI) of 25 anal cancer patients. Four readers (Senior and Junior Radiologist; Senior and Junior Radiation Oncologist) independently delineated GTVs, first on T2W-MRI only and then on DWI (with reference to T2W-MRI). Maximum Tumour Diameter (MTD) was calculated from each GTV. Mean GTVs/MTDs were compared between readers and between T2W-MRI vs. DWI. Interobserver agreement was calculated as Intraclass Correlation Coefficient (ICC), Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). DWI image quality was assessed using a 5-point artefact scale. RESULTS: Interobserver agreement between radiologists vs. radiation oncologists and between junior vs. senior readers was good-excellent, with similar agreement for T2W-MRI and DWI (e.g. ICCs 0.72-0.94 for T2W-MRI and 0.68-0.89 for DWI). There was a trend towards smaller GTVs on DWI, but only for the radiologists (P = 0.03-0.07). Moderate-severe DWI-artefacts were observed in 11/25 (44%) cases. Agreement tended to be lower in these cases. CONCLUSION: Overall interobserver agreement for anal cancer GTV delineation on MRI is good for both radiologists and radiation oncologists, regardless of experience level. Use of DWI did not improve agreement. DWI artefacts affecting GTV delineation occurred in almost half of the patients, which may severely limit the use of DWI for radiotherapy planning if no steps are undertaken to avoid them.

Tracing bone marrow-derived microglia in brain metastatic tumors.

Microglia are the resident macrophages in the central nervous system (CNS), and they constitute 15-20% of the total glial populations. They have wide developmental and protective functions during brain injury, infection and tumorigenesis. Originally thought to derive from postnatal hematopoietic progenitors, it has recently been demonstrated that microglia originate from primitive myeloid progenitor cells that arise during early development from the embryonic yolk sac. Circulating monocytes infiltrate the CNS upon inflammatory conditions, such as cancer, primarily differentiating into macrophages and dendritic cells. Both resident and recruited microglia respond to environmental cues and actively participate in pathogenic processes, albeit their transcriptomic profiles contain significant differences suggesting distinctive roles. Metastatic brain tumors are the most common intracranial neoplasm in adults, with an estimate incidence 10 times higher than all primary brain neoplasms combined, and with dismal prognosis. Microglia is a major immune population associated with brain metastatic tumors in patients. They are proposed to play multiple, and sometimes opposing roles, in tumor progression. However, our ability to evaluate individual contribution of resident and recruited populations is hindered by the fact that they express overlapping sets of surface markers. Tracking and interrogating tissue-resident vs recruited microglia in the brain tumor microenvironment becomes critical to dissect their respective roles and gain a better understanding of the mechanism governing their interaction. In this chapter, we describe the utilization of genetic reporter mice to identify recruited brain microglia, offer a comparison between the genetic method and the most widely used flow cytometric approach, and discuss potential downstream applications to interrogate BMDM function in brain metastatic disease.