Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.https://github.com/amin20/GBM_WSSM.This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub:  MVP typically appears as glomeruloid tufts of multi-layered endothelial cells that are mitotically active along with smooth muscle cells or pericytes. Necrosis is a fundamental feature of glioblastoma and, together with the presence of blood vessels, is the strongest predictor of aggressiveness.11Gliomas are graded and categorised according to the World Health Organization (WHO) guidelines based on a combination of histologic and molecular features.12 GBM treatment is further complicated by the fact that glioblastoma tumours also exhibit a high level of cancer cell heterogeneity and plasticity.19 Indeed, tumour plasticity and the capacity of cancer stem cells to partially and/or reversibly differentiate into different cancer cell populations is believed to be the main cause of resistance to therapy and the development of tumour recurrence.20 Understanding the mechanisms that contribute to glioblastoma plasticity and how it is shaped by the tumour microenvironment (i.e. stem cell niches) has been difficult. This is partly due to the lack of appropriate tools to unravel the spatial and functional interactions that occur between tumour cells and cells in the tumour niche, and to predict which interactions are important regulators of cancer stem cell plasticity and the capacity of cancer cells to infiltrate the surrounding healthy brain tissue.12Although analysis of gene-expression data (bulk RNA-seq) available from The Cancer Genome Atlas (TCGA) has enabled the successful identification of the molecular signatures/biomarkers associated with the different GBM tumour subtypes, to date, GBM treatment based on this information alone has not resulted in improved patient survival.21 The Ivy GAP data constituted a significant advance in the field and has allowed the identification of signatures associated with different tumour regions.21 However, it has limitations, as the results were highly dependent on the segmentation algorithms that were implemented for the segmentation of the different tumour regions25 and inherently biased due to the small number (n = 32) of patient samples analysed.21 Furthermore, although multiple sections/regions were processed for data augmentation and the creation of the Ivy GAP database, the small number of patients recruited to the project did not permit the identification of genetic signatures or key demographic characteristics associated with survival and/or particular molecular profiles of the tumours.21 Moreover, the gain in insight into spatial resolution by attributing gene signatures to specific tumour regions was not translated into an understanding of the expression of these signatures by specific cell types, as limited cellular ontology analysis was performed on this data, due to the limited number of datasets and bioinformatic tools at the moment of its publication. Consequently, there is currently no available data that permits the identification of the cell populations that are present in each of these tumour anatomic locations and the corresponding transcriptional programs that underpin tumour-stroma interactions that may contribute to poor survival. Gaining insight into these aspects would be critical to progress our understanding on the biology of these very aggressive brain tumours.12Recently, a Decision Forests statistical machine learning-driven algorithm (“Mill”) was used by the Ivy GAP to identify and segment tumour regions and label the anatomic features in ~12,000 histological images, which were then laser-capture micro-dissected and subjected to bulk RNA-seq.Here, we implemented a deep convolutional neural network (DCNN) model for semantic segmentation of histopathological  images trained on the Ivy GAP dataset to improve the quality of segmentation of different brain tumour regions . We sought a pathologist’s opinion to advise on the removal of outliers  in the input dataset and for verification of results, that together contribute to improve the accuracy of segmentation of different brain tumour regions. The resulting model was then employed to segment TCGA histopathological images and correlation analysis between gene expression and tumour region size was used for the identification of different gene signatures associated with different tumour regions as well as the different cell types that express them. We further combined these results with single-cell RNA sequencing (scRNA-seq) data to investigate tumour-stroma interactions that contribute to poor prognosis in glioblastoma.21 Three distinct datasets  were created by randomly splitting the data in the ratio of 14:1:1. Twenty eight patients with 687 WSIs were allocated to the training dataset, two patients with 52 WSIs were allocated to the validating dataset and two patients with 48 WSIs were allocated to the testing dataset. The validation set was used to evaluate the performance of our models during the training phase while the test set includes unseen samples for checking the model after training. An additional dataset was downloaded from TCGA26 and includes 640 whole-slides H&E histopathological images from 329 glioblastoma tumours. The GBM_WSSM was applied to this dataset to produce the masks corresponding to all patients. Those masks were then used for further biological investigation as described in the paper.We used the Ivy GAP dataset, as the main dataset for training our DCNN semantic segmentation model (GBM_WSSM). This dataset includes 32 patients diagnosed by primary surgery type with a total of 805 whole-slide images (WSIs) along with their corresponding ground truths.https://m.ensembl.org/biomart/martview/e5476740b357b1b968ca8507fd4d853e) using Homo sapiens (human) genome assembly GRCh38.p13 from Genome Reference Consortium.The following filters were applied to extract RNA glioma datasets from TCGA for correlation analysis. Project: TCGA GBM, Disease Type: gliomas, Vital Status: dead; Data Category, transcriptome profiling; Data Type: RNA-Seq. The resulting cases were further filtered by the availability of matched and successfully segmented whole-slide images from the same tissue portion used for RNA-seq analysis  and 18 of 805 were removed as outliers  because of high-level noise presenting poor segmentation in the masks. All images and masks were then re-sized to four different scales: 4096 × 4096, 2048 × 2048, 1024 × 1024 and 512 × 512 from the original size 18,000 × 15,000. Afterwards, different sizes of patches  were systematically extracted (not randomly) from the re-sized images and their masks. Therefore, the extracted patches were the entire datasets obtained from images and used in the training phase. Patches were then normalised by using a pixel-normalisation method into the domain  by dividing all pixel values by the largest pixel value among all patches i.e. 255. For data augmentation purposes, random crops and vertical flips were applied in the training phase to all models.23 was trained and validated on the normalised patches. These regions include  the p-value distribution is not the same across all brain tumour regions and is not always uniformly distributed ude Fig.  leading 23 and by using pixel-wise classification accuracy, where each predicted pixel in the mask produced by a DCNN model is compared with the corresponding pixel in the original ground truth.We trained 49 different networks and used the random search approach for hyperparameter tuning in each one , Clustergrams were generated using Morpheus (https://software.broadinstitute.org/morpheus) and Matlab2018  for training deep learning models have been generated by non-experts or automated methods and hence, the level of noise in such labels is typically higher than the labels annotated by experts. However, recent studies confirm that DCNNs are extremely robust to handle the high level of noisy labels in supervised learning approaches since well-designed DCNNs applied to sufficiently large and diverse dataset do not memorise the data and they learn the dominant patterns shared among all samples.http://glioblastoma.alleninstitute.org/) is a freely accessible online database that contains GBM slides and their corresponding ground truths annotated using a statistical machine learning method. Although this dataset is very helpful for further research, we identified variable segmentation accuracy in some masks by manual inspection.The Ivy GAP portal  architecture and obtain a semantic segmentation model of glioblastoma histopathological images. To increase the accuracy of the semantic segmentation model, we took the advantage of DCNN models in addressing noisy labelsThe DCNN model can segment unclear regions in the original slide Fig. , iiiThe DCNN model is able to properly detect the regions corresponding to the Infiltrating Tumour (specified by the purple colour) and Cellular Tumour Necrosis (specified by the black colour) regions without over-segmenting the Leading-Edge region Fig. , ivThe DCNN model can tackle the problem of over-segmentation of the Cellular Tumour Microvascular Proliferation region  region .Our results indicated that WSI and mask re-sizing from its original size 18,000 × 15,000 to 1024 × 1024 and extracted patches with the size of 512 × 512 led to the highest segmentation accuracy, reaching ~70% determined by pixel by pixel comparison  obtained by the experiments with the better performance  with regard to the total pixels (TP + TN + FP + FN). We have used the accuracy metric as an evaluator to compare between our 49 different DCNN experiments during the training phase, hyper-parameters tuning, and to select the best models. For this purpose, we applied the Human_In_The_Loop (HITL) approach, which is a setting in a loop where an expert can insert prior knowledge into an AI machine to enhance its output.43 was then applied on this numerical dataset and 27 outliers were removed in order to obtain a normally distributed survival data, which is required for the analysis of the relationship between segmentation results and patient survival through the calculation of correlation coefficients45 . To analyse the relationship between segmentation results and patient survival, we next performed Pearson correlation analysis between the area of different brain tumour regions and patient survival across (i) all TCGA glioblastomas, and (ii) brain tumours harbouring the 13 most frequent mutations  but little or no correlation with the other infiltrative regions LE, IT and CTmvp). This probably reflects non-invasive and therefore more benign tumours. Overall, these results highlight the varying influence of gene mutations on histopathological features and reveal tumour features associated with prognosis. These data also highlight critical differences that significantly influence the biology of these tumours and their capacity to escape complete resection during surgery as well as to develop therapy resistance.We have previously shown that DCNN models can be trained and applied to TCGA H&E histopathological images to predict brain cancer patient survivalp < 0.05) correlated genes  associated with each of the different regions. We reason was that genes specific to one region would show higher expression in tumour samples where the area for that region  is bigger. For this, we used Spearman correlation analysis results between gene expression  and brain tumour area for different brain tumour regions. The results indicated that for different regions, there are both positively and negatively correlated genes . We found that most of the genes in the CT and CTpnz signatures positively correlated with survival, which agreed with our previous observation in cohorts of patients with a specific set of mutations  as well as CT and CTpnz gene markers whose expression is positively correlated with patient survival (CTpos and CTpnzpos). Gene ontology (GO) biological processes analysis allowed us to investigate the cellular processes associated with these gene signatures , again reflecting properties of the microenvironment to which tumour and inflammatory cells are exposed.16These results revealed different gene signatures for different regions that have different correlations with patient survival. In order to gain insight on these, we then focus on IT and CTmvp gene markers whose expression is negatively correlated with patient survival  organisation’. These are related to the regulation of ECM production and the interaction of that ECM with cells and is consistent with the capacity of tumour cells to interact with the ECM surrounding the vasculature and infiltrate healthy brain tissue along the microvasculature.54GO biological processes analysis revealed a more complex microenvironment in the CTmvpneg and CTmvpneg signatures, the CTpos and CTpnzpos gene signatures showed a strong connection to those processes related to DNA, RNA and protein synthesis through different metabolic pathways, that align overall with higher macromolecule synthesis rates required by highly proliferative tumour cells . This match score is calculated for each cell type and correspond to the sum of rank-based scores calculated for overlapping genes between the query and the cell type. The match score thus accounts for the gene rank, the difference in gene ranks and the total number of significant genes in the cell type. Using CellKb, we obtained a list of cell types and for each cell type an associated match score and a list of genes specific for that cell type  are predicted to be highly expressed by microglial cells (CL:0000129) and monocytes (CL:0000576). This is consistent with the GO processes discussed above but highlighted a key non-cell autonomous role of the tumour microenvironment (microglia) in the poor prognosis of glioblastoma. Cells in CTmvpneg compartment also exhibited monocyte/microglia characteristics. Moreover, this compartment also included cells expressing genes that are specific for macrophages (CL:0000235), endothelial cells (CL:0000115), dendritic cells (CL:0001056), cerebral cortex endothelial cells . Fibroblasts are probably representative of pericytes, which coat blood vessels and contribute to the architecture and function of the blood–brain barrier. The results thus indicate that the genes characteristic of these different regions, that are associated with poor patient prognosis, are more likely expressed by cells of the tumour microenvironment than the cancer cells.In this list, a cell type may be predicted more than once, since multiple databases (scRNA-seq experiments) were used. Thus, to uniquely assign a cell type to our gene signature, we weighted the analysis for not only using the cell type match score but also taking into account the number of times a particular cell type was predicted using this analysis Fig. . The respos and CTpnzpos regions. These regions are expected to be enriched in tumour cells, which was supported by our analysis revealing that neoplastic cells are more likely to express the gene signature corresponding to each region. Interestingly, T cells were only associated with the CTpnz, a potential inflammatory region that might chemoattract T cells , lymphocytes, endothelial cells, pericytes and tumour cells.38 In order to map the gene signatures identified by semantic segmentation to this data, we first re-analysed our scRNA-seq data using CellKb  receptors identified in the IT or CTmvp gene signatures, and;—(ii) expression of the ligand–receptor pair in all three patient-derived scRNA-seq datasets; we identified the reticulon 4 (RTN4): Gap junction beta-2 protein (GJB2) paracrine ligand–receptor pair as a potential mediator of cell–cell interactions between tumour cells and the surrounding microenvironment56  encodes three isoforms (isoforms A-C) with Nogo-A enriched in the central nervous system (CNS). NOGO isoforms contain two transmembrane domains and are localised through the exocytic pathway, plasma membrane and cell–cell junctions where it also interacts with cadherin 5 (Cdh557). NOGO proteins have been implicated in glioma cell invasion,58 tumour angiogenesis59 and regulation of blood vessel homeostasis.61 In contrast, gap junction protein 2B  is a structural component of gap junctions67 and is expressed at the plasma membranes of cells. GJB2 has previously been identified as a gene associated with prognostic value in brain69 and pancreatic cancers71 and contribute to tumorigenesis in breast cancer.72 Survival plots of TCGA GBM data for patients with low and high expression of GJB2 and RTN4 reveal their association with a poor prognosis in glioblastoma . Although our gene signatures exhibited some overlap with the gene signatures in the reported in the Ivy GAP database, our gene signatures had fewer genes. This is probably a consequence of the thresholds we applied for the correlation coefficient and the p-value, to define the different gene signatures and the fact that Ivy GAP tumour regions markers were defined by differences in expression between regions, which is distinct from the correlation versus no correlation approach between gene expression and brain tumour regions taken in this study. Interestingly, our analysis permitted the identification of a large number of genes that did not overlap with the results from Ivy GAP database. This reflects again the power of the combination of AI for segmentation and the possibility of analysing large datasets to be able to perform robust correlation analysis for the identification of gene signatures.Several studies have been undertaken to characterise gene signatures relating to the tumour microenvironment.77 as well as bioinformatics approaches designed to extract microenvironment features from bulk RNA-seq data in TCGA78 have significantly advanced the characterisation of the genetic signatures and cellular composition of the tumour microenvironment in glioblastoma. These previous studies have provided significant insight into the tumour cell heterogeneity in glioblastoma as well as the cellular composition of the tumour microenvironment.79 Moreover, these experiments have not only provided a better understanding of tumour plasticity but also the transcriptional program changes that occur in different cell types in the tumour microenvironment.77 However, it has not been possible until now to investigate the spatial organisation of non-cancer cell types that populate the microenvironment and pinpoint their occurrence within the context of different brain tumour regions. In a recent study, Rajapakse et al. used computational modelling to define cancer cell states and allocate active and dormant cancer cell types within these different tumour regions.80 In contrast, in this work, we combined publicly available scRNA-seq data (using the CellKb platform) and our own experiments to determine the gene signatures expressed by different cell types in the microenvironment and how these relate to different tumour compartments. Although spatial transcriptomics83 and multiplex immunofluorescence imaging75 approaches can offer similar outcomes to that presented here, these techniques are still prohibitively expensive and time-consuming. We expect these results to facilitate the spatial analysis of tumour biopsies and promote the adoption of complementary techniques such as spatial transcriptomics83 and highly multiplexed immunofluorescence imaging.75 Although expensive and time-consuming, these approaches enable high-resolution detection of RNA or protein in tissue sections, which facilitates precise spatial analysis of the tumour microenvironment and measurements of distances (and possible interactions) between different cells of interest when combined with single-cell RNA sequencing analysis.84The results of multiple laboratories performing scRNA-seq experiments using cellular suspensions derived from resected tumour tissue85 Using these methods, we investigated membrane receptors expressed on tumour cells that can interact with secreted and/or membrane proteins, and determined whether these ligand–receptor interactions are part of the gene signatures that we have identified. Interestingly, we found the GJB2 : RTN4  pair in the CTmvp signature, with both genes being indicative of poor prognosis in glioblastoma.scRNA-seq data have also proved to be a powerful tool for assessing potential protein–protein interactions that can control tumour cell behaviour.86 In our data, the expression of GJB2  was restricted mostly to cancer cells, whereas the expression of RTN4 was more widely distributed between cells in the tumour microenvironment including microglial cells, endothelial cells, pericytes and B cells. GJB2 is a tetraspan membrane protein with two extracellular domains and is expressed in the plasma membrane. GJB2 has previously been identified as a gene with prognostic significance in brain,69 pancreatic71 and breast cancer.72 RTN4 has two long (32 aa) transmembrane domains and the region between these domains (NOGO-66) has been shown to mediate protein interaction with cells expressing RTN4 receptors.88 The long transmembrane domains have been also postulated to confer distinct topological organisations of this protein, particularly exposing the N-terminus (the more divergent domain between RTN proteins) extracellularly.Although the role of connexins has been primarily associated with its ability to function as hemichannels forming a direct transmembrane communication pathway between neighbouring cells, recent studies of clinical samples suggested connexins can contribute to cancer progression through multiple pathways, namely (1) gap junction intercellular communication, (2) C-terminal tail-mediated signalling and (3) cell–cell adhesion during gap junction formation.89 suggesting a role as a tumour suppressor. However, more recent data in prostate cancer has shown that RTN4 regulates cell fate and its overexpression led to cell cycle arrest and senescence.90 Besides, RTN4 binding to RTN4R, the more established receptor RTN4, contributes to ROCK and STAT3 activation in the cell expressing RTN4R,88 which can contribute to glioblastoma tumour cells invasion (through ROCK91) and chronic microglia activation (through ROCK and STAT393). It is still unclear how RNT4’s interaction with GJB2 and RTN4R contributes to poor prognosis in glioblastoma, but redistributing RTN4 from the ER to the plasma membrane and preventing it to interact with other RTN4 intracellular binding partners could be a possible therapeutic strategy that needs further investigation.In terms of downstream signalling, RTN4 localises both at the endoplasmic reticulum and at the plasma membrane. In the endoplasmic reticulum, it is known to interact with and suppress the anti-apoptotic functions of Bcl2 and BclXL,In summary, our analysis of the TCGA GBM database using segmentation of histopathological images provides new opportunities for the characterisation of the tumour microenvironment in glioblastoma within distinctly spatial regions within these very heterogeneous brain tumours. These results have led to the identification of novel prognosis-associated region-specific gene signatures and targets for treating glioblastoma and are now available to the rest of the brain cancer research community.Supplementary materialSupplementary legends


Melanoma is the deadliest type of skin cancer, due to its invasiveness and limited treatment efficacy. The main therapy for primary melanoma and solitary organ metastases is wide excision. Adjuvant therapy, such as chemotherapy and targeted therapies are mainly used for disseminated disease. Radiotherapy (RT) is a powerful treatment option used in more than 50% of cancer patients, however, conventional RT alone is unable to eradicate melanoma. Its general radioresistance is attributed to overexpression of repair genes in combination with cascades of biochemical repair mechanisms. A novel sophisticated technique based on synchrotron-generated, spatially fractionated RT, called Microbeam Radiation Therapy (MRT), has been shown to overcome these treatment limitations by allowing increased dose delivery. With MRT, a collimator subdivides the homogeneous radiation field into an array of co-planar, high-dose microbeams that are tens of micrometres wide and spaced a few hundred micrometres apart. Different preclinical models demonstrated that MRT has the potential to completely ablate tumours, or significantly improve tumour control while dramatically reducing normal tissue toxicity. Here, we discuss the role of conventional RT-induced immunity and the potential for MRT to enhance local and systemic anti-tumour immune responses. Comparative gene expression analysis from preclinical tumour models indicated a specific gene signature for an ‘MRT-induced immune effect’. This focused review highlights the potential of MRT to overcome the inherent radioresistance of melanoma which could be further enhanced for future clinical use with combined treatment strategies, in particular, immunotherapy. Melanoma is a highly malignant type of skin cancer that develops from melanocytes, the cells that produce the UV-absorbing pigment melanin. Although melanoma constitutes only a minority of skin cancers, it is the deadliest . During Several subtypes of melanomas can be distinguished as mucosal, uveal, anorectal, meningeal, and the most common, cutaneous melanoma. Cutaneous findings such as superficial spreading, nodular, and lentigo maligna account for the vast majority of melanoma cases. The difference in prognosis between these subtypes is mainly determined by the stage of growth at the time of diagnosis . Non-cutMost melanomas are detected clinically and primarily diagnosed by physical examination, dermoscopy, histopathology, and imaging, with the detection of circulating biomarkers being less crucial. Depending on the age of the patient and tumour thickness, sentinel lymph node biopsy is a powerful staging tool . In addiThe severity of the disease is classified using four different stages according to the American Joint Committee on Cancer (AJCC) . The staThe main therapy for primary cutaneous melanoma is wide local excision. Depending on tumour thickness, a clinical margin of 0.5–2 cm is recommended for the excision in order to prevent local tumour recurrence . To treaThe concept of melanoma radioresistance is based on early studies involving radiation cell-survival curves for rodent and human melanoma cell lines, which suggested an enhanced capacity to repair radiation-induced DNA damage ,16. GeneThe radiosensitivity of different melanoma cell lines varies significantly . The radAdjuvant RT has been shown to improve locoregional disease control in melanoma, but not overall survival, however, this still remains under investigation ,25. CurrTechnological advancements in RT have contributed to the improved efficacy of cancer treatment. These advancements are increasing the therapeutic ratio by successfully eradicating tumours while improving normal tissue sparing. Historically used to treat large, bulky tumours, spatially fractionated radiotherapy (SFRT) is a type of external beam radiation treatment that allows for the delivery of higher dose fractions of radiation in order to achieve local tumour control with minimal toxicity ,31. ClinWith advancements in technology, more sophisticated techniques were developed to deliver SFRT. GRID therapy could then be administered with megavoltage X-ray tubes where doses between 10 and 25 Gy were deliverable with minimal toxicity ,33. LATT4 Gy/s up to 16 kGy/s [Another type of SFRT that adopts submillimetre fractionation is MRT. Experimental synchrotron X-ray-generated MRT is delivered with the same dose deposition pattern of the minibeams, but using sub-millimetric beam widths of 25–100 μm spaced 200–400 μm apart, which drastically increases the normal tissue tolerance to radiation doses in the range of hundreds of Gy. This greatly exceeds the thresholds of conventional RT and the previously discussed SFRT regimens . This i16 kGy/s ,48.Although IR is a type of sterile tissue injury, and IR-induced inflammation differs from the infectious inflammatory response, it is still a part of a basic immunological process; a protective response that aims to eliminate the cause of injury, remove damaged cells, and repair tissues. It is regulated by complex interactions among a variety of immune components and soluble mediators . When tiIt was long believed that the immune system did not contribute to the anti-cancer effects of IR. However, there is sufficient evidence showing that irradiated tumour cells become a robust source of antigen with adjuvant properties . ThroughConventional RT, however, is known to have a dual effect on the tumour immune response, where it can also elicit changes in the immune system that support tumour progression. Such changes may counterbalance the release of factors that stimulate the immune system to fight tumour cells. For example, radiation can enhance the release of factors that recruit immune-suppressing myeloid cells, including myeloid-derived suppressor cells (MDSCs), tumour-associated macrophages (TAMs), and tumour-associated neutrophils (TANs) . RT alsoA review of Haikerwal et al.  comparinActivation of naïve T cells and their differentiation into effector T cells is regulated by the signals generated from the T cell receptor (TCR) complex and co-receptors  and program death 1 (PD-1)) . CTLA-4 CTLA-4 is expressed on all subpopulations of activated T cells and has a dampening effect on both CD4+ and CD8+ T cell-driven immune responses . CTLA-4 In addition to activated CD4+ and CD8+ T cells, CTLA-4 is constitutively expressed on immunosuppressive FoxP3+ Tregs and plays a crucial role in Treg-driven suppression of anti-tumour immunity . Tregs, Although RT induces a generation of neo-antigens in irradiated tumours and, consequently, induces activation of neo-antigen-specific CD8+ CTLs, it also induces cell death in rapidly proliferating cells, including T lymphocytes . Among TSince CTLA-4 and PD-1 negatively affect proliferation and cytotoxicity of CTLs and, at the same time, promote the generation and immunosuppressive properties of Tregs in irradiated tumours, the use of monoclonal antibodies, which induce a blockade of CTLA-4 (Ipilimumab) and PD-1 (Nivolumab and Pembrolizumab) opened a completely new avenue in cancer RT  radiation and un-irradiated controls . ImportaSimilarly, early transcriptional changes following MRT of rat glioma revealed an increase in genes associated with inflammation, including those associated with NK or CD8+ lymphocytes, in irradiated tumours and surrounding normal tissues . SubsequPrior to the genomic studies, evidence that the immune system may be involved in the MRT therapeutic effect came from a study reporting that gene-mediated immunotherapy enhances the efficacy of MRT in terms of survival for rat gliosarcoma . One of CD11c transcript, which might be indicative of a higher infiltration of DCs into the tumour post-MRT, relative to the BB control. Lastly, the mRNA expression level for Arg1, a classic marker for a subset of M2–like immunosuppressive TAMs [Mrc1 and CD163, markers for M2-like macrophages [Important work contributing to the understanding of MRT-induced immune responses came from the Rogers group in 2019 . In thisive TAMs , is highive TAMs  in the srophages , also inrophages , as wellrophages . In the rophages  and in rrophages  frequencFinally, it is not clear yet if the suppression of pro-tumourigenic immune responses elicited distinctly by MRT in the murine EMT6.5 model is reproduced in all other types of cancer when irradiated with MRT, but it is obvious that there is a better anti-tumourigenic response (increase of T cells) relative to BB treatment. The shifted balance towards a more anti-tumourigenic and pro-inflammatory phenotype would explain the better tumour control observed so far in response to MRT treatment. Future studies are required to give a detailed phenotypic characterization of all the immune subpopulations that play a role in the tumour, including B cells, DCs, etc., with an emphasis on the temporal dynamics.The immune response after MRT has also been investigated in normal tissues. Leukocyte infiltration was observed to be higher after BB than after MRT irradiation in normal mouse skin at 5 days after irradiation . On the Ccl2, Cxcl10 (Interferon gamma-induced protein 10), Cxcl11 , Gbp2 (interferon-induced guanylate-binding protein 2), Ccl9 , Sele , and Ccl7 (monocyte-chemotactic protein 3) [In rat brain tissues, 6 h after MRT irradiation, the following genes  were overexpressed compared to un-irradiated controls: otein 3) . All theotein 3) .−/− (NOD SCID gamma) mice and mice with disrupted macrophage recruitment (CCL2 KO and injected with antibodies to CSF-1R), compared to their immunocompetent counterparts [In a non-tumour setting, MRT irradiation has been shown to generate systemic effects in mice ,105,106.terparts .p-value < 0.05. Following this filtration, gene lists coming from the group 9L glioblastoma evaluated at 6 h post-irradiation (9LGB 6 h PI) and from the three groups of EMT6.5 mammary tumours investigated at 4-, 24- and 48-h post-irradiation  were intersected in a Venn diagram , also known as macrophage inhibitory cytokine-1 has been recently reviewed as a possible target for cancer immunotherapy [Irf7 and Rsda2.In Cluster I, we found three genes that are significantly overexpressed after MRT relative to control in all the four groups analyzed. These genes are adiation . In Clusruitment ,112, whiA damage . Clusterotherapy . In ClusCcl9 and Rsad2 . In the same settings, Ccl2 and Ccl7, found in our Cluster II, and Irf7 in our Cluster IV together with Rsda2, were upregulated [The comparison only assessed MRT vs. un-irradiated tumours, since in the study by Bouchet and colleagues  no treategulated .Ccl9 mRNA expression was validated by qPCR in Ibahim et al. [CCL9 is a chemokine that induces recruitment of myeloid progenitor cells at tumour sites and it has been detected as a possible target for treating metastatic disease . Remarkam et al. , and it If we consider that we examined two different animal species (rat vs. mouse) and two different tumour types (glioblastoma vs. mammary carcinoma) located in different organs (brain vs. skin), the similarities are quite remarkable. Certainly, what stands out is the similarities between the early time point of 6 h post-irradiation in the glioblastoma model and the later time points of 24 and 48 h in the EMT6.5 model. It seems that in rats the transcriptional changes induced by MRT happen more quickly than in mice.A recent research interest of our group is exploring the efficacy of MRT for treating melanoma. An ad hoc preclinical B16F10 radioresistant melanoma model for testing a microbeam array was established in our laboratory . In the In another study, Fernandez-Palomo and colleagues compared the efficacy of delivering three fractions of 133 Gy MRT, administered in three consecutive days versus only one MRT fraction of 400 Gy in a single day . RemarkaThe importance of this result is not only that radioresistant B16F10 melanoma does not lose sensitivity to the radiation treatment after several fractions, but also that there is no local tumour recurrence and metastasis for a long period of time. This could be explained by the presence of the anti-tumour abscopal effect specifically triggered by fractionated MRT. Therefore, this work offers a unique model to further study MRT-induced abscopal effects and is a starting point for optimizing a treatment protocol that could increase the rate of tumour remission above 50% in this preclinical model.In Despite the progress made in the last decades, the efficacy of RT remains suboptimal for many radioresistant tumours and invariably leads to a host of normal tissue complications. MRT is an innovative strategy that could shift the current RT practices into a new era. The therapy has the potential to significantly improve tumour control or even result in complete ablation, while preserving the normal/healthy tissues, even after receiving MRT peak doses of thousands of Gy. MRT, therefore, has the potential to overcome radioresistance, modulate the local immune response and trigger abscopal effects. These unique features prompt us to propose MRT as a novel radiotherapeutic approach with the potential to treat inoperable radioresistant lesions. MRT veterinary trials should be the first step taken towards clinical translation. Thereafter, the use of MRT as a primary treatment strategy or as a neoadjuvant in combination with systemic therapies, in particular, immunotherapies, should be explored in extensive clinical trials. In an ideal future scenario, MRT would reduce the size of the primary tumour and prevent organ metastases by triggering the systemic anti-tumour response before surgical removal.


Social media utilization is on the rise globally, and the potential of social media for health behavior campaigns is widely recognized. However, as the landscape of social media evolves, so do techniques used to optimize campaign dissemination.The primary aim of this study was to evaluate the impact of 4 material dissemination paths for a breastfeeding social media marketing campaign in Ghana on exposure and engagement with campaign material.Campaign materials (n=60) were posted to a Facebook and Twitter campaign page over 12 weeks . The top 40 performing materials were randomized to 1 of 4 redissemination arms . Key performance indicator data  were extracted from both Facebook and Twitter 2 days after the material was posted. A difference-in-difference model was used to examine the impact of the dissemination paths on performance.P<.001), and performance of materials shared by either type of influencer did not differ significantly from the control arm. No differences in Twitter performance were detected across arms.At baseline, campaign materials received an average (SD) exposure of 1178 (670) on Facebook and 1071 (905) on Twitter (n=60). On Facebook, materials posted with paid advertisements had significantly higher exposure and engagement compared with the control arm (Paid advertisements are an effective mechanism to increase exposure and engagement of campaign posts on Facebook, which was achieved at a low cost. These pAs social media has evolved, so have the marketing techniques used by businesses to push their products and ideas on the population. Social marketing of public health goods is also finding its place on this environment, with social media becoming widely recognized as “an unprecedented opportunity...to deliver socially influential online behavior change interventions” . Social Specifically, social media has been identified as an opportunity for a potentially cost-effective approach to improve breastfeeding outcomes relative to traditional social marketing, yet research for social marketing for breastfeeding promotion is needed to identify best practices and approaches ,12,13. TIn social media marketing, consumer-to-consumer interaction and word-of-mouth dissemination of information is widely used in the form of influencers . Social Alternatively, social media platforms have established mechanisms for businesses to pay for advertisements to appear on the news feed of targeted consumer groups. These paid advertisements have been effective for business, especially when paired with creative marketing, and as a result are considered essential in business social media marketing plans . This isBetween 2008 and 2014, the rate of children under 6 months of age who were exclusively breastfed in Ghana declined from 63% to 52% . In respTherefore, the primary aim of this study is to evaluate the impact of different dissemination paths on exposure and engagement with campaign material and to examine the relationship of acceptability of campaign material with material performance.We implemented a 6-month long Facebook  and Twitter  campaign that targeted the protection, promotion, and support of breastfeeding in Ghana, on the basis of evidence from recommendations from the BBF Initiative in Ghana . The metCampaign performance for platforms  and individual material performance  were monitored using data extracted from Facebook Insights and Twitter Analytics and entered into a Microsoft Access database . Platform data were collected weekly and included the number of followers and likes on both Facebook and Twitter; and reach, engagements, views, and follower and engagement demographics by age, sex, and country on Facebook. Data on the campaign posts, such as the core campaign materials, were extracted at 3 timepoints: 1 day, 1 week, and 2 weeks after the material was posted. These data included material impressions or reach, likes, share or retweets, and comments or replies. Materials were selected for redissemination in the test phase based on a composite indicator of the amplification and applause rates per 100 followers for each Facebook and Twitter at the 1 day time point. Thus, materials were ranked from highest to lowest performance, by theme, and the top performing 40 campaign materials (13 or 14 per theme) were selected for redissemination across an 8-week period.Stratified by theme, materials were randomized to 1 of 4 redissemination paths: (1) posted as usual ; (2) shared by 6 key influencers on Facebook and 6 key influencers on Twitter after being posted; (3) shared by 6 random influencers on Facebook and 6 random influencers on Twitter after being posted; or (4) paid advertisement with US $6 on each Facebook and Twitter after being posted . Each difriends from the Breastfeed4Ghana campaign coordinator’s Facebook profile and followers from the Twitter page. The sampling frame for the influencers was the Breastfeed4Ghana Facebook friends and Twitter followers, excluding individuals who did not reside in Ghana. The size of an individual’s reach was calculated based on the size of their network that did not overlap with the campaign’s network based on the number of friend or follower that the individual had that were not in common with the campaign . This indicator of reach was used as an approximation of the significance of each individual in these social networks [To select the influencers, publicly available details  from Facebook and Twitter user profiles were extracted from the respective platform among networks ,26.after the key influencers had been selected were numbered. We randomly selected individuals from this list using a random number generator. Selected individuals were vetted by the campaign coordinator to ensure they did not have inappropriate content on their page and invited to be a campaign influencer. This was repeated until 6 randomly selected influencers were confirmed for each Facebook and Twitter. To reach 6 randomly selected influencers on each platform, we invited 13 individuals on Facebook and 14 individuals on Twitter. Influencers were sent weekly instructions to share or retweet specific posts on Facebook and Twitter, respectively.The selection of key influencers focused on influencer reach and network ties, and therefore, the previous level of involvement with the campaign was not a selection criterion, although all influencers were at least following the campaign. Those with the highest reach were vetted by the campaign coordinator to make sure that they did not have inappropriate content  on their social media page and were then invited to be a key influencer for the campaign. It was explained to potential influencers that as an influencer they would be asked to share or retweet 1 to 2 campaign posts per week for 8 weeks and would receive a small incentive for their time of 80 Ghana cedis (~ US $20 or US $2.50 per week) for sharing the posts. This was repeated until a total of 6 key influencers on each of Facebook and Twitter were selected. To achieve this sample of 6 key influencers per platform, a total of 19 individuals were approached on Facebook and 9 on Twitter. To select random influencer, the remaining individuals in the sampling frame boost. The objective of the advertisement was set to post engagement, which aims to gain more views and engagement, such as likes and shares, for the post [Scheduled, targeted advertisements for the selected campaign materials were achieved through business accounts on both Facebook and Twitter, equating to US $6 per platform for each material randomly assigned to the paid advertisement dissemination arm. On Facebook, advertisements targeted 18 to 49 years old women residing in Ghana and were conducted in the form of a post the post .For Twitter, acquiring a business account in Ghana required going through a third-party company, which required meeting a minimum quarterly advertising budget. Similar to Facebook, advertisements targeted individuals 18 to 49 years old residing in Ghana. For both Facebook and Twitter, the paid advertisement was schedule along with the posts being scheduled, which were generally scheduled at least 1 week before the post date.likes and share on Facebook and likes and retweets on Twitter. For each material, participants were asked questions regarding their understanding and acceptability of the image, the message, and the overall material; 10 questions were asked, based on a 5-point Likert scale ranging from strongly disagree to strongly agree (5 questions regarding the image and 5 regarding the message); 3 questions asked about the overall rating on a 5-point Likert scale (from very bad to very good) of each of the image, message, and material, respectively. Performance was calculated as a composite indicator of the amplification and applause rates per 100 followers for each Facebook and Twitter at the 1-day time point. A total of 9 campaign materials were evaluated  and asked about the core campaign materials from that particular theme. Survey participants were a convenient sample of women ≥ 18 years of age residing in Ghana. The survey was promoted through an advertisement post on the campaign’s Facebook and Twitter pages and completed through Qualtrics . Materials presented in the surveys were chosen on the basis of material performance to represent the bottom, median, and top performance within the respective campaign theme, with performance on the basis of the number of valuated .All data were imported into Stata 14.1  for cleaning and analysis.Key performance indicators (KPIs) for platform and material exposure and engagement were developed from available data and based on Neiger et al 2012 definition of KPIs .Platform Engagement was defined as the number of followers on each Facebook and Twitter. For this analysis, this KPI was used to adjust material KPIs by follower based at the time the material was posted, to make them comparable across time and platform. As reported elsewhere, the campaign started with 3061 Facebook followers and 27 Twitter followers and ended with 4096 Facebook followers and 736 Twitter followers .Material Exposure was defined as reach on Facebook, which is the number of unique people who saw the material; and as impressions on Twitter, which is the number of times the material appeared on a Twitter timeline. These values were converted into rates per 100 followers on the respective platform at the time of the material posting to make them comparable across time and platform. These rates were used at the primary KPI for material exposure on each Facebook and Twitter.Material Engagement comprises 3 subindicators: applause, amplification, and conversation. Applause was defined as the number of likes on each Facebook and Twitter; amplification was defined as the number of shares on Facebook and retweets on Twitter; and conversation was the number of comments on Facebook and replies on Twitter. Each of these subindicators by platform were converted to a rate per 100 followers on the respective platform at the time of the material posting  × 100). Applause and amplification rates per 100 followers from all material posts and timepoints (n=300) for each Facebook and Twitter were standardized , and summed by platform and collectively to generate 3 material engagement scores for each material: total engagement score, Facebook engagement score, and Twitter engagement score.positive response options, thus agreement included extremely agree and agree, and not in agreement included neutral, disagree, and extremely disagree for analysis. For negative statements, such as the picture is confusing, the responses were collapsed to emphasize disagreement (extremely disagree and disagree); and for the overall rating questions, responses were collapsed to good (very good and good) and not good .In the content analysis survey, 13 statements examined the material image, message, and overall acceptability. The 5-point-Likert response options were collapsed to emphasize To determine the impact of dissemination path on material performance, a series of difference-in-difference models that accounted for material performance at baseline were run for each of the following material KPI: Facebook exposure, Twitter exposure, total engagement score; Facebook engagement score, and Twitter engagement score. Given the difference in the indicator for exposure on Facebook versus Twitter, a total engagement score was not tested.good in the respective 3 questions. Logistic regression models, controlling for the survey taken , and the respondent examined the odds of binary material acceptability across low, middle, and high material performance.To determine characteristics of campaign materials that related to higher material performance, content survey data was pooled across the 3 timepoints, and material performance  was examined in relation to material acceptability defined as agreement with each of 6 acceptability statements, disagreement with 4 negative statements, and rating the image, message, and material as This study was approved by the Yale University Institutional review board and the review board for Ghana University hosted by the Noguchi Institute. Influencers and content survey participants provided their electronic consent to participate in the study before their respective participation.During the 12-week long baseline period, the 60 core campaign materials received an average (SD) exposure of 1178 (670) on Facebook and 1071 (905) on Twitter . On bothP<.05.The top 40 performing core campaign materials were selected for the dissemination test period, with baseline characteristics summarized in P<.01). Similarly, the Facebook engagement score in the paid advertisement group increased by 953% (0.40 engagement per 100 followers to 4.21), compared with an increase of 147% in the control arm . There were no statistically significant differences between the 2 influencer arms and the control arm.KPI for exposure on Facebook and all engagement indicators tending to increase from baseline to the test period, though these increases were only significant in the paid advertisement arm . When thAll 40 posts were disseminated via scheduled posts on both Facebook and Twitter at baseline and during the test period. Among the 10 materials randomly assigned to the paid advertisement arm, all had the paid advertisements directed at adults (18-49 years) in Ghana scheduled alongside the post schedule, as planned. Among the 20 materials that were randomized to the key influencers  and random influencer  arms, influencers were requested to share the material within 48 hours; 2 of the 6 key influencers on Facebook did not share all 10 materials that were requested and 1 of the 6 key influencers on Twitter did not share all the materials requested. All of the random influencers on Facebook (n=6) and Twitter (n=6) shared all 10 materials requested.A total of 44 female participants completed the content survey . The majI like this picture to 91% with the statement this message is informative. The odds of acceptability of the material image were significantly greater among the high-performance material compared with the low-performance materials, based on 3 out of the 5 acceptability statements at P<.05  for 12 out of 13 acceptability statements across all materials . Agreemeat P<.05 . Similarsocial media ecosystem [Breastfeed4Ghana core campaign materials achieved higher exposure and engagement on Facebook than on Twitter, and higher exposure and engagement rates per 100 followers on Twitter than on Facebook, because of the relatively small number of followers on Twitter. In this study, paid advertisements significantly increased material exposure and engagement on Facebook. Although neither influencer type had a significant impact on material performance on Facebook, there was a trend in higher engagement and exposure as a result of random influencers, compared with baseline. Conversely, there were no significant differences across the 4 dissemination paths on Twitter , and it is important to consider the variations in purposes of different social media platforms and how they are used and how to consider this variation as part of the cosystem ,17.cost US $12 per platform for promotion. Furthermore, there was more personnel time required to share the materials with the influencers, follow-up with influencers, and provide the incentives to influencers. Influencer management accounted for approximately 90 min per week of the campaign coordinators times, compared with approximately 10 min/week to manage the advertisements. Thus, the findings from this study indicate that paid post advertisements, through Facebook’s business account, was not only most effective in increasing material performance but also at a lower cost than our model for either key or random influencers.A total of US $6 per material per platform was allocated to advertisement for each material in the paid advertisement arm. Accounting for the airtime incentives provided to influencers across the test period, each material in the influencer arm Word-of-mouth is a recognized marketing approach for the expansion of a product or idea , and theThere is no prescribed way to recruit, select, and manage influencers, as Keller and Fay describe various case studies in their business marketing report, and influencers generally can impact various outcomes such as message amplification and product sales . In thishighly influential individuals. As well, in this study, the randomly selected influencers were more adherent to sharing posts compared with the key influencers. Customer-to-customer interactions, such as those prescribed to influencers, has further been modeled in the marketing literature to be able to start a chain effect among consumers, with lasting impacts [Our key influencers were selected with consideration for social network targeting, and also aimed to examine the difference in selecting key influencers  as is done often with micro- and macroinfluencers versus randomly selected influencers. Similar to findings within a community health program in Honduras, between social network targeting and randomly selecting influencers, there were not significantly different outcomes; however, in Honduras, both social network targeting (akin to macroinfluencers) and random selection yielded significantly higher adoption of the intervention than the control group . The lac impacts , which m impacts . Yet, finot confusing. Research which aims to shed light on social media consumer behavior and interaction with content provides insights into findings from this study. Indeed, Berger postulated a framework for the drivers of viral content: social currency, triggers, emotion, public, practical information, and stories [In our examination of domains of material acceptability and material performance, overall acceptability of the image, message, and material were associated with performance in terms of engagement. Most notable was that highest performing materials were those that had images viewed as (1) promoting breastfeeding; (2) informative; and (3)  stories . Yuki ex stories . FindingWhile this campaign generated 60 core campaign materials that were disseminated 1 to 2 times during the active campaign period, and as a result varied the images so as not to be too repetitive . Despite the variety of images presented in the campaign materials, the highest performing material for each theme was that of a woman breastfeeding. This may suggest that such variety  was not necessary, and generating images most aligned with the campaign focus and message will yield the greatest engagement and acceptability among the target population. As such, other social marketing campaigns have generated a small number of messages and materials that are widely and repeatedly disseminated .data gap, both in terms of reporting and consideration for consistency in metrics. Although different campaigns and studies will have different goals that should drive indicators of performance or success, work by Neiger and metrics reported in this study can provide a guide for other campaigns.This study also reports on KPIs of campaign materials. In public health, return on investment is not always a useful indicator, and Neiger et al summarized KPIs and evaluation metrics for health promotion on social media . AlthougTo our knowledge, similar studies have not been published. However, paid advertisements, as well as paid influencers, are widespread social marketing techniques used across businesses and industry. It is important to recognize the limitation in lack of comparable studies and results for us to consider. These results also reflect a short-term study and small number of influencers. Therefore, it is possible that in a study of longer duration with a higher number of influencers could yield different results. The lack of differences across dissemination path arms on Twitter could be the result of a small follower based on that platform, as well as a significant increase in follower (approximately doubled) right before the test period of the study. Finally, we would like to acknowledge that these results come from a breastfeeding social media–based campaign that targeted the population of Ghana. There are variations in how social media is used in different contexts across geographic space, demographics, and time, and therefore, generalizability of these results beyond the context should be done with caution. Similarly, for context, it is important to note the dates of from this campaign (March to September 2018). Social media platforms are continually updating their business platforms, advertising option, and algorithms for what is viewed on user’s news feeds, and therefore, comparability of the findings from this study may be limited based on how the landscape of social media changed and evolves with time.Paid advertisements are an effective mechanism to increase exposure and engagement of campaign posts on Facebook, achieved at a low cost. Although influencers are used in marketing and are generally considered effective at increasing consumer engagement or sales, microinfluencers were not effective at increasing exposure and engagement in this study. Furthermore, the use of influencers to promote materials required a greater financial cost compared with paid advertisements in our study.For social marketing, there are challenges with how to compete against product advertisers with bigger budgets and more ways to reach consumers  . As soci


Sharp declines in influenza and respiratory syncytial virus (RSV) circulation across the U.S. have been described during the pandemic in temporal association with community mitigation for control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to determine relative frequencies of rhinovirus/enterovirus (RV/EV) and other respiratory viruses in children presenting to emergency departments or hospitalized with acute respiratory illness (ARI) prior to and during the COVID-19 pandemic. We conducted a multi-center active prospective ARI surveillance study in children as part of the New Vaccine Surveillance Network (NVSN) from December 2016 through January 2021. Molecular testing for RV/EV, RSV, influenza, and other respiratory viruses  were performed on specimens collected from children enrolled children. Cumulative percent positivity of each virus type during March 2020–January 2021 was compared from March-January in the prior seasons  using Pearson’s chi-squared. Data are provisional.Among 69,403 eligible children, 37,676 (54%) were enrolled and tested for respiratory viruses. The number of both eligible and enrolled children declined in early 2020 , but 4,691 children (52% of eligible) were enrolled and tested during March 2020-January 2021. From March 2020-January 2021, the overall percentage of enrolled children with respiratory testing who had detectable RV/EV was similar compared to the same time period in 2017-2018 and 2019-2020 . In contrast, the percent positivity of RSV, influenza, and other respiratory viruses combined declined compared to prior years, .Figure 1. Percentage of Viral Detection Among Enrolled Children Who Received Respiratory Testing, New Vaccine Surveillance Network (NVSN), United States, December 2016 – January 2021Table 1. Percent of Respiratory Viruses Circulating in March 2020– January 2021, compared to March-January in Prior Years, New Vaccine Surveillance Network (NVSN), United States, March 2017 – January 2021During 2020, RV/EV continued to circulate among children receiving care for ARI despite abrupt declines in other respiratory viruses within this population. These findings warrant further studies to understand virologic, behavioral, biological, and/or environmental factors associated with this continued RV/EV circulation.Jennifer E. Schuster, MD, Merck, Sharpe, and Dohme  Involved: Self): Grant/Research Support Marian G. Michaels, MD, MPH, Viracor  John V. Williams, MD, GlaxoSmithKline Quidel  Elizabeth P. Schlaudecker, MD, MPH, Pfizer (Grant/Research Support)Sanofi Pasteur (Advisor or Review Panel member) Christopher J. Harrison, MD, GSK (Grant/Research Support)Merck (Grant/Research Support)Pfizer  Janet A. Englund, MD, AstraZeneca GlaxoSmithKline (Research Grant or Support)Meissa Vaccines (Consultant)Pfizer (Research Grant or Support)Sanofi Pasteur (Consultant)Teva Pharmaceuticals (Consultant) Claire Midgley, PhD, Nothing to disclose Natasha B. Halasa, MD, MPH, Genentech Quidel Sanofi  Natasha B. Halasa, MD, MPH, Genentech  Involved: Self): I receive an honorarium for lectures - it’s a education grant, supported by genetech, Other Financial or Material Support, Other Financial or Material Support; Sanofi  Involved: Self): Grant/Research Support, Research Grant or Support


To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression.T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity.This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors . Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (−11.7% and −9.5%) compared to non-progressors (−6.1% and −1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67.60 patients (30 progressors and 30 non-progressors) were included. In progressors, AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression.T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (−10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.This paper proposes specific clinical cut-offs for  During the first 5 years on AS, 27.5% of patients switch to radical treatment due to disease progression, with a further 12.8% leaving AS programmes for other reasons, including anxiety and concerns related to the invasive nature of repeat biopsies. Repeat biopsies do remain the cornerstone of clinical decision-making and the switch to radical treatment; however, they also present a key barrier to patient uptake of and adherence to AS. Therefore, reducing the need for unnecessary biopsies during AS presents an important clinical challenge.Active surveillance (AS) is the recommended management option for patients with low- and intermediate-favourable risk prostate cancer (PCa), who account for nearly half of the newly diagnosed cases in the US and the UK. According to two recent meta-analyses, serial MRI has a reasonable pooled negative predictive value of up to 0.81; however, the maximum pooled positive predictive value for detecting histopathological disease progression to grade group ≥2 is 0.52, highlighting the inability of serial MRI to completely replace repeat biopsies as part of AS follow-up.To address this, many centres have increased their reliance on MRI to navigate treatment decisions without a mandatory follow-up histopathological assessment. and radiologists’ experience, as well as the subjective nature of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations designed to standardise serial MRI reporting on AS. More specifically, PRECISE assessment criteria lack a clear definition of a “clinically significant” radiological progression, which is particularly critical for assigning a PRECISE category 4, defined as “significant increase in size and/or conspicuity”. Consequently, PRECISE scoring shows no superiority over non-standardised institutional criteria of disease progression, highlighting an unmet clinical need for providing objective MRI biomarkers in the AS setting. While several attempts have been made to identify specific tumour measurements and apparent diffusion coefficient (ADC) cut-offs that could offer quantitative surrogates of “clinically significant” radiological disease progression, these studies lacked systematic histopathological assessment at follow-up, which limits the reliability of their findings given the potential limitations of MRI changes vs gold-standard histopathology.There are several factors that may explain the limitations of MRI in the context of AS. These include considerable variability of MR imaging qualityIn this study, we applied several common segmentation approaches to analyse serial changes in MRI-derived tumour measurements and ADC values in AS patients with and without histopathological PCa progression. Thus, we aimed to identify objective clinically applicable cut-off values that could be used in the follow-up assessment of MR-visible lesions in patients with PCa on AS. The inclusion criteria were the presence of an MR-visible lesion at baseline, AS follow-up length of at least 2 years with at least two MRI examinations performed on the same 3 T magnet, and at least one repeat targeted biopsy within 12 months of the most recent MRI. The exclusion criteria were any prior or interim treatment for PCa or benign disease, or the presence of any pelvic metalwork. The study flowchart presented in This retrospective cohort study was approved by the local Institutional Review Board (reference number: HBREC.2020.49). The study included consecutive patients with biopsy-proven PCa enrolled on the local AS programme according to the eligibility criteria reported previously.n = 30) was defined as grade group progression on repeat targeted biopsy. The control group (n = 30) included patients harbouring both radiologically (highest PRECISE score 1–3 over the course of AS) and histopathologically stable disease confirmed as part of routine repeat biopsies mandated by the local AS protocol (see Biopsy technique section).60 patients meeting inclusion criteria and enrolled on AS in our centre between August 2013 and May 2018 were included in the final analysis and divided into two groups depending on their disease progression status. Histopathological AS progression  was administered prior to imaging unless clinically contraindicated. Multiparametric MRI protocol included axial T1 weighted imaging, multiplanar high-resolution T2 weighted two-dimensional (2D) fast recovery fast spin echo (FSE), spin-echo echoplanar imaging pulse diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging, as described previously. DWI was performed with b-values: b-150, b-750, and b-1,000; with additional small field of view DWI obtained using b-2,000s/mm2; ADC maps were automatically calculated. Follow-up studies did not include post-contrast DCE sequences, but the protocol was otherwise identical for all patients and all scans included in the analysis.Patients underwent prostate MRI on a 3 T MR750 system  using a 32-channel receiver coil. Intravenous injection of hyoscine butylbromide , or triggered earlier by clinical suspicion of progression, defined as either three consecutive elevated PSA levels above the pre-defined threshold or suspected radiological progression (PRECISE scores 4–5).Targeted biopsy was performed using MRI/ultrasound fusion by either a transrectal  or transperineal , with 2–4 target cores and 12 (TR) or 24 (TP) systematic cores background cores, as previously described.T2 weighted images and on ADC maps  were drawn on anatomical ADC maps  in conse were measured on both T2WI and ADC maps using the following techniques: (i) volumetric, with tumour segmentation by means of free-hand delineation on all slices encompassing the lesion, (ii) 2D tumour surface area derived using the largest diameter and a second perpendicular diameter ([anteroposterior diameter × transverse diameter × π]/2), and (iii) single maximum tumour diameter (The index lesionsdiameter .2) positioned in the centre of the lesion on a slice where it was most conspicuous  whole-volume ADC derived from ROIs used to measure tumour volume, (ii) mid-slice ADC derived from a free-hand ROI encompassing the largest tumour area from the axial acquisition used to measure 2D surface area and single maximum diameter, (iii) small-volume ADC obtained from a standardised circular ROI  with 5–16 years’ experience of reporting prostate MRI and considered to be experts with each having read >2000 cases.p-value ≥ 0.05). To evaluate the relationship between T2WI- and ADC-derived tumour measurements, we used the Spearman’s rank correlation test, with the agreement between the two sequences assessed using the Bland–Altman test. Intergroup differences in tumour measurements and ADC values between histopathological progressors and non-progressors were measured using the Mann–Whitney U test. A subanalysis was also performed in patients with radiological disease progression  and with radiologically stable disease . All statistical tests were two-sided, and p-values below 0.05 were considered significant. The receiver operating characteristic (ROC) analysis was performed, with the resulting areas under the ROC curve (AUCs) derived for relative changes in tumour measurements and ADC values obtained from the baseline and final MRI examinations. The DeLong test was used to compare the differences between individual AUCs.Statistical analyses were conducted using GraphPad Prism  and SPSS Statistics 17.0 . Normal distribution of the data was assessed using the D’Agostino-Pearson test ; n = 30) were followed up for significantly longer time at 48 months, compared to patients with histopathological progression (n = 30) at 36 months (p = 0.002). Baseline PSA and PSA density were significantly higher in progressors , a difference that became even more pronounced at follow-up (p < 0.001 for both). 45/60 (75%) had ISUP Grade Group 1 and 15/60 (25%) patients Group 2 disease at enrolment. 44/60 (73%) and 16/60 (27%) of index lesions were located in the PZ and TZ of the prostate, respectively. Of the 30 patients who showed histopathological progression, 16 were treated with hormone and radiotherapy, 8 underwent prostatectomy, and 6 had brachytherapy.60 patients were included in the study, with a median age of 67 years (interquartile range 59–70) and PSA of 5.6 ng mlT2WI- and ADC-derived measurements of tumour volume (rs = 0.92), 2D surface area (0.75), and maximum diameter (0.67) for all lesions . There was also acceptable agreement between T2WI- and ADC-derived tumour measurements for PZ and TZ lesions when separately obtained according to the PI-RADS dominant zonal sequence paradigm . Conversely, in non-progressors, no significant changes were noted between any of the T2WI-derived tumour measurements at follow-up MRI scans   . Box-andT2WII-derived tumour measurements, no significant baseline intergroup differences were noted between whole-volume, mid-slice, and small-volume ADC values derived from tumours in patients with and without histopathological disease progression , while progressors demonstrated a significant follow-up decrease in all tumour-derived ADC values  (p = 0.79). Conversely, the relative changes in the follow-up median whole-volume and small-volume ADC values were significantly greater in progressors compared to non-progressors  (Similar to gression . In non-09–0.03) . Interesctively) . These r) and ADC values (Supplementary Table 3) were similar compared to those reported above when the primary outcome was histopathological disease progression.When patients were regrouped based on the presence of radiological disease progression only, the trends in tumour measurements  and ADCT2WI-derived tumour measurements and ADC values are summarised in T2WI-derived tumour measurements had significantly larger AUCs compared only to mid-slice ADC values . Although T2WI-derived 2D tumour surface area had the largest AUC (0.83) of all three tumour measurements, the DeLong test showed no significant difference between the three AUCs . Simultaneously, the AUC of mid-slice ADC (0.52) was significantly lower compared to those of whole-volume ADC  and small-volume ADC , with no difference observed between the AUCs of the latter two parameters (p = 0.72). Based on these results, and mirroring clinical practice, we chose T2WI-derived maximum tumour diameter and small-volume ADC as parameters to identify individual cut-offs that could be applied clinically. The selection of individual cut-offs  and a small-volume ROI-derived ADC reduction of −8.4% . The combined model including both these values resulted in an AUC of 0.71 (p = 0.20 and p = 0.21). A separate model was also built for more clinically applicable cut-offs of 20% increase in T2WI-derived maximum tumour diameter and −10% reduction in small-volume ADC, with the resulting AUC being 0.67, which was similar to the aforementioned model (p = 0.29). Clinical examples using the proposed cut-offs are illustrated in T2WI-derived maximum tumour diameter can also be supplemented by a minimum absolute increase in diameter to improve specificity, with a minimum size threshold of 3 mm likely to be optimal in this regard.AUCs for detecting histopathological disease progression based on relative changes in each of the  of 0.71 , which dT2WI-derived tumour volume, 2D tumour surface area, and maximum tumour diameter, alongside whole-volume and small-volume ADC values were significantly different in AS patients with PCa histopathological progression compared to patients with stable disease. Specific cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (−10%) values were proposed, which could be used in routine clinical practice to supplement subjective serial MRI assessment using PRECISE recommendations. These results pave the way for future prospective multicentre studies investigating the added value of the proposed cut-offs for improving the quality of AS programmes.In this study, we demonstrate that follow-up changes in T2WI-derived tumour measurements demonstrated comparable diagnostic performance for predicting histopathological progression on AS, consistent with a previous study by Giganti et al. Clinically, evaluating serial changes in maximum tumour diameter closely mirrors the RECIST 1.1 criteria used in oncology to assess treatment outcomes. In our study, the median relative change in T2WI-derived maximum tumour diameter in progressors was 21.7%, which corresponded to the doubling of the median tumour volume in this group. These findings align with the RECIST 1.1 criteria for progressive disease, defined as a 20% increase in the sum of diameters of target lesions alongside a minimum of 5 mm absolute increase. 20% increase in T2WI diameter can be supplemented by a minimum of 3 mm absolute increase in tumour diameter, which corresponds to the 3 mm MRI slice thickness used in this study and may help mitigate the effect of interscan variability highlighted in previous studies. Moreover, this may help avoid errors in measurements due to partial volume or reproducibility at smaller lesion sizes, mirroring the incorporation of a minimum size increase of 5 mm in RECIST criteria. Using these cut-offs to supplement subjective serial MRI assessment could help improve the quality of MRI-guided AS programmes and further reduce the need for unnecessary biopsies while increasing the positive predictive value for histopathological disease progression. A previous study has shown similar relative increase in T2WI-derived maximum tumour diameter in AS patients with radiological disease progression, which is expected since the increase in size is an integral feature of PRECISE categories 4–5. In addition, our findings are consistent with the annual increases in tumour volumes (up to 23%) and diameters (up to 7%) reported by Giganti et al on different magnets using different scanning protocols, which supports the potential clinical applicability and reproducibility of our results. Interestingly, in our study, tumours in patients without histopathological and radiological disease progression also showed an increase in size over the median 4-year follow-up, which is expected given the natural tendency of tumours to grow. This further demonstrates the need of identifying specific cut-offs to differentiate “clinically significant” increases in tumour size. In line with our findings, Shoji et al showed that a 25% increase in TRUS-based maximum tumour diameter significantly increases the risk of PCa histopathological progression on AS. In addition, maximum tumour diameter has been proposed as a risk-stratification tool for patients eligible for AS, which supports the prospective use of this metric in the AS setting and lays the foundation for further multicentre investigations.Here, we show that all three T2WI-derived tumour measurements. This may reflect the known issues with repeatability and reproducibility of ADC in the prostate. In our study, all MRI examinations were performed on the same magnet with identical DWI acquisition parameters and selection of b-values for ADC map calculation in order to minimise variability, and performance of ADC is likely to reduce further if data from multiple MR systems with varying protocols are used. The observed decrease in tumour ADC values in progressors is in line with a previous report where radiological progression was the primary clinical outcome. However, it should be noted that non-progressors also demonstrated an overall reduction in tumour-derived ADC values, similar to the whole-gland ADC measurements reported previously by Morgan et al. The small-volume ROI technique used in this study is of relevance as this directly mirrors clinical practice, and has been used extensively in other studies measuring prostate tumour-derived ADC. The promise shown by ADC for differentiating indolent vs clinically significant PCa warrants further work addressing the intrinsic limitations of ADC to maximise its clinical reliability, possibly with the incorporation of ADC ratios. However, the calculation of the latter in routine clinical practice has to be justified given the increasing pressure on imaging services and the need to reduce the reporting time.ADC values derived using three different approaches showed poorer diagnostic performance compared to  and to whom the proposed cut-offs are not applicable by definition. However, as evidenced by previous studies, the presence of MR-visible lesions presents a significant risk factor for PCa progression, which warrants closer surveillance of this patient subgroup. As highlighted, all scans analysed as part of this study were performed on the same clinical MRI system with an identical acquisition protocol, which may limit the generalisability of our ADC findings given the known low intra- and intersystem reproducibility and repeatability of ADC measurements. Future work should be aimed at validating these results in multicentre studies using data obtained from different scanners and with different protocols. Moreover, gaining access to larger cohorts will help evaluate the utility of the proposed cut-offs to predict histopathological progression between risk groups rather than individual grade groups, which may improve patient survival. Finally, combining the proposed cut-offs with other quantitative MRI features such as those derived from radiomics, alongside standard clinical biomarkers of disease progression, e.g. PSA and PSA density, may further improve their diagnostic performance and help objectivise serial MRI assessment in AS. The introduction of more complex predictive models may also help increase the applicability of the proposed cut-offs to individual cases, which in the present form may be limited given considerable interquartile and total range overlaps between progressors and non-progressors reported in this study. The reported overlaps may, however, provide a quantitative explanation for the overall low reliability of using serial MRI assessment alone for excluding PCa progression on AS.This hypothesis-generating study has some limitations. The sample size is relatively small, which was dictated by stringent inclusion criteria aimed at reducing confounding factors that may compromise robust comparisons and ensuring histopathologically confirmed disease progression as a gold standard. While we also showed similar in results in tumour diameters and ADC values when comparing radiological progressors and non-progressors, histopathology offers a more reliable reference standard compared to MRI alone. Importantly, this study excluded patients with MR-invisible lesions, who comprise nearly half of the AS populationT2WI-derived tumour measurements and ADC values. Relative changes in T2WI-derived tumour diameter and small-volume ADC values can be objectively evaluated in routine clinical practice, and their cut-off values of +20% and −10%, respectively, can be validated as part of multicentre studies and used to supplement the subjective PRECISE image assessment criteria.In conclusion, we show that patients with histopathological PCa progression on AS demonstrate a significant increase in


Describing the anti-tumour immune response as a series of cellular kinetic reactions from known immunological mechanisms, we create a mathematical model that shows the CD4 It is now known that tumours evoke an immune response, which can alter the growth and makeup of a tumour. Greater understanding of the anti-tumour immune response has led to the development of immunotherapies, which have seen some degree of success in clinical trials, particularly in cancers of the blood . A TCR is specific to a particular antigen (a part of a protein recognised by a TCR). In addition to this, T-cells can be split into two main sub-types: helper and cytotoxic T-cells. Antigens are picked up by professional antigen presenting cells (APCs) and presented as a peptide on the surface molecule MHC-II. Helper T-cells can then activate by binding to MHC-II which displays the antigen that the TCR is specific to. Activated helpers can then activate cytotoxic T-cells which eliminate cells that present their conjugate antigen via MHC-I. The T-cell dependent response eliminates infected cells, but is also invoked by tumours . Such models usually fall into two extremes: either, the model is low dimensional, ‘macroscopic’, and does not fully capture the systemic behaviour of the system; or it is high dimensional, ‘microscopic’, with a large number of unknown parameters; usually this makes statistical evaluation of these models with real data difficult. Furthermore, ODE models also fail to capture the inherent stochasticity of biological processes. In this paper we consider a model that lies between these two approaches, keeping the model to a few key parameters, whilst also including inherent stochasticity of microscopic behaviour.As research has turned towards systemic modelling of the immune system there has been an increase in mathematical and computational approaches to modelling challenges in immunology. Mathematical models used are mainly The cytotoxic anti-tumour response has been modelled previously, most notably by Kuznetsov et al. . DespiteIn our work we model the interplay between the CD4e et al. , allowine et al.  and hereabsent, a case which is less relevant for our model. The role of macrophages in the tumour is a double edged sword, with macrophages eliminating tumour cells, and also forming part of the bulk tumour. This behaviour is complex, and has been modelled elsewhere . A given TCR is specific to an antigen, and if they are not specific to tumour associated antigens, they will not form part of the immune response. To model this we introduce a binary random variable As explained in Sect. T controls the noise level, such that the activation dynamics is fully stochastic for i will activate if it receives a strong enough activation signal from the environment, i.e. if the activation signal is above a certain threshold i (i.e. p(t) of antigen presenting cells with tumour associated antigens, while a cytotoxic T-cell (c(t) and For simplicity, we assume that T-cells update their activation state at regular time intervals of duration microscopic level of individual T-cells. Alternatively, one could model (We note that Eq.  models rld model  and 5)  microscoc, b and p are also subject to stochastic fluctuations.Next, we write differential equations for the concentrations of tumour and antigen presenting cells in the local environment, by modelling the cellular reactions in -8) at p at p8) ac will increase at a rate r proportionally with c, corresponding to Eq.  dir dir17) dons Eqs. –(17) canN, is sent to infinity and that the evolution of these averages is governed by the equationsi is helper or cytotoxic. Equations \documentm and a, which is equivalent to a mean-field approximation, that we show in the appendix to be exact in the limit A functions of spatial position. This would result in c, b and p becoming spatially-dependent. However, further considerations would need to be made to account for cellular drift in space. By neglecting spatial dependencies, our approach is equivalent to modelling a small, macroscopic region of a solid tumour where spatially-dependent variables can be regarded as uniform.Finally, we take Eqs.  and replWe use the system of Eqs.  to derivTo make progress analytically, we look at the long-time dynamics of the model. To this end, we separate three different timescales of immune response dynamics: the timescale of tumour cell division tions in  becomes,ution of -28)..\documenm i.e To inspect the stability of the fixed points we evaluate the derivative fined in . The fixalent to . The nonrom Eqs.  and 35)\document of Eqs.  and 27)\documentFrom the analysis of the long time dynamics of the system , and by R. To remove tumour cells a large population of cytotoxic T-cells is required and this depends on the expression of MHC-I. This is illustrated in Fig. in vivo since it also depends on the expression of MHC-I. With a low MHC-I expression, a CD4This shows that there is a critical value of MHC-I expression, above which the tumour will be eradicated, and below which it will reach some stable tumour size. Furthermore, the critical value of MHC-I depends linearly through the helper/cytotoxic ratio A and The stability of the fixed points does not depend on the T-cell activation noise level, ent}ρ in . These pThis model provides predictions for the dependence of the size of the tumour, given by Eqs.  and 27)27), on dund from , there aA common problem with ODE models of immunology is that they require temporal data for validation - which can be hard to come by for both practical and ethical reasons. Our model is based upon immune dynamics but can be used to produce predictions not based on time as in the case with the figures discussed in this section. Concurrent measurements of MHC-I, T-cell infiltration and CD4R. Here we define optimal to mean yielding the lowest stable tumour size when Eq. , fo, foR. Heall T-cells are active i.e a and m to their maximum physical values, the system of equations reduces to a single equation for the tumour cell concentration,a and m are allowed to vary below their maximum value, the tumour concentration will be higher than if locked at their maximum value. The solution of the above equation, given byIn our model we have treated the expression of MHC-I, c(t) can be found with a specific form of c(t) we find thatc(t) is also finite. The latter is due to N, fluctuations become of the same order as the mean, and can remove a small number of tumour cells in a finite time. Additionally, as discussed in the introduction, we neglect the role of NK cells to focus on the T-cell dependent response. However, at low MHC-I the NK cells play a more dominant role, adding an additional deleterious effect upon tumour growth.A tighter bound on g is the shape parameter. Furthermore, the function approaches a step function as In addition to exponential decay we have also considered a sigmoidal decay by definingThe complexities of tumour immunology require a systemic approach to better understand cancer and inform treatment. Quantitative tools are being used to a greater extent, due to the vast data produced by next generation experimental technology; mathematical modelling being among them. Mathematical modelling can suffer from two extremes: models that include detail at the molecular level tend to focus on a small set of processes to achieve analytical results at the expense of a systemic view; on the other hand, macroscopic models which achieve greater systemic resolution, tend to have a large number of parameters, which makes statistical validation impractical, especially in absence of large data sets. In our work we used statistical mechanics and dynamical systems approaches to analyse the anti-tumour response of a simplified model of the adaptive immune system, comprising antigen presenting cells, helper T-cells and cytotoxic T-cells. The end result is a system of five ODEs that highlights the role in tumour growth of different parameters linked to key cellular processes.Previous studies have shown that the prognostic value of the CD4R. However, we have shown the role that MHC-I decay can play in the evolution of tumours. The eradication of tumours will depend on the minimum expression of MHC-I - if this is too low the T-cell response will fail and other cells, most likely NK, are required for eradication, otherwise the tumour will escape. In addition to this, our work highlights the role that the profile of MHC-I decay can have on the growth of tumours. To the best of our knowledge, there is no data measuring the time evolution of MHC-I expression in tumours to be able to estimate this profile. However, availability of such data could confirm whether the variation in MHC-I expression leads to periods of equilibrium in tumour growth.Our work has focused on modelling the immune response where MHC-I expression is assumed to be constant to highlight its interplay with the helper/cytotoxic ratio One issue with mathematical models is that they often focus on the dynamics of immune cells, for which experimental data is rare. Although our model is based on dynamics, we have provided results which focus on non-temporal quantities that could be verified with standard measurements in immunology. We hope our model can serve as a motivation for experimental investigation into the combined effect of CD4The work presented here is a theoretical minimal model of the adaptive immune system, and as such has limitations. For example, the model only considers the adaptive immune response and does not explicitly take into account innate immunity such as the natural killer cells and macrophages, both of which play an important role in the anti-tumour immune response. The benefit of our approach is the rigorous mathematics which can be analysed to understand qualitative behaviour, something which is often lost when considering too many parameters.A generally applicable feature of our work is the use of non-equilibrium statistical mechanics to include an additional level of microscopic detail into the modelling of cell concentrations with ODEs. Here our model has considered the activation and sub-type of T-cells, but this framework could also be used to study other biological systems where constituent cells fluctuate stochastically between different states, while interacting with other cell concentrations in the environment. For example, there is a long history of modelling neurons as stochastic entities that fluctuate stochastically between two states, quiescent or firing an electrical signal, (for examples see Coolen and Ruijgrok ; SompoliThere are two potential extensions of our work we consider to be of particular interest. Firstly, as we have discussed, our work has neglected spatial heterogeneity by modelling a small macroscopic area of a tumour which can be regarded as uniform. Spatial heterogeneity plays an important role in the anti-tumour immune response, and the extension of the techniques from non-equilibrium statistical mechanics to systems of PDEs could provide important new results although at the expense of increased model complexity. Secondly, although we consider phenotypic heterogeneity implicitly when we allow MHC-I to vary with time, we have not explicitly modelled competition between tumour cell phenotypes. This model could be extended to account for several tumour phenotypes, evolving with different rates, with heterogeneity in the T-cell response to each phenotype. Although this framework is well adapted to such a scenario, analytical progress with multiple phenotypes may provide an interesting avenue of investigation, elucidating the evolutionary game between phenotypes. Another extension would be to adapt this model to understand how tumours begin in the first place, including the healthy tissue cells from which the tumour cells derive. Tumour immunology is rich with complexity and using refined tools from statistical mechanics may shed light on this complex system of processes.