The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host’s response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14–15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual’s recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4–5, 2019.Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as  Indeed, many cancer patients do not benefit from IO treatments even when the tumors display favorable immune characteristics [Society for Immunotherapy of Cancer (SITC) organized a Task Force on Cancer Immune Responsiveness (CIR) to stimulate interactions among multiple disciplines and outline salient open questions and define new priorities for research in tumor immunology and immunotherapy [Tumor immunotherapy has changed the therapeutic landscape for patients with cancer. While several classes of drugs are demonstrating clinical benefit, or types , 2. Why eristics  and the otherapy .The taskforce was launched by a workshop held in San Francisco on May 14–15, 2018 that convened immunologists, geneticists, cell biologists, molecular biologists, biophysicists, computational analysts and oncologists, and aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to answer the fundamental questions and uncover mechanisms of CIR.immunologic constant of rejection (ICR) [tumor microenvironment (TME) [Diverse and often divergent observational or experimental justifications for immune resistance have been described , 6. Indeon (ICR) , 8 that nt (TME) . Galon ent (TME) , 11. Thent (TME) . For insnt (TME) , 14. Furnt (TME) . In locant (TME) , 16–18.tumor inflammation signature (TIS) that largely overlaps with the ICR and importantly is predictive of clinical benefit of ICI therapy. Other signatures outlining the central role that IFN-γ signaling plays in determining the immune landscape of cancer and its responsiveness to immunotherapy agents have been described [The Cancer Genome Atlas (TCGA) dataset, a strong correlation (r > 0.9) between several other previously published transcriptional signatures reflective of the T cell-inflamed TME associated with cytolytic processes was demonstrated [The 4-category classification of tumors  based on their immune infiltrates and Immunoscore proposed in 2009  could prescribed . Of notenstrated .immunogenic cell death (ICD) [adoptive cellular therapy (ACT) suggesting that modulation of metabolic pathways may also affect immune cell function [programmed cell death protein 1 (PD-1) blockade [A set of about one thousand breast cancers from TCGA is subdivided into separate immune phenotypes called ICR-1 to ICR-4 according to the incremental level of expression of ICR genes. The expression of transcriptional signatures associated with immune regulatory properties is then considered for their presence in each immune phenotype . The trath (ICD) , TAM tyrth (ICD) , hypoxiath (ICD) , cancer-th (ICD)  and barrth (ICD) . Self-orth (ICD) . Most refunction . It has blockade , 36. Howadaptive immune resistance and propose that the cancer immunity cycle described by Chen and Mellman [primary immune resistance). In addition, immune responsive tumors may become resistant under the selective pressure induced by therapy by developing escape mechanisms (acquired immune resistance). Finally, a non-biological type of resistance to therapy (pseudo immune resistance) should be specified that it occurs when a given treatment is terminated prematurely in order to limit toxicity. Similarly, other external factors may be at the basis of lack of response that are beyond the biology of individual tumors such as variations in product quality in the case of ACT [Enrichment of immune regulatory functions within the active immune landscapes  suggests Mellman  pertainse of ACT  or in cae of ACT .trial-and-error” reshuffling of genetic traits selects for a proliferative advantage over normal cell growth. Pediatric tumors arise with primary genetic instability and most adult tumors with secondary genetic instability associated with prolonged stress and inflammation. The intrinsic biology of the cancer cell primarily orchestrates its surroundings [healing wound” model [damage associated molecular pattern (DAMP) [Immune active cancers display a distinct genetic profile characterized by a high mutational burden , 41. Thioundings  by relead” model , 48. Thed” model . Indeed,d” model , 50 chard” model . Thus, td” model  associatn (DAMP) , 54.CIR is determined by the summative effects of the genetic background of the host, somatic alterations related to the oncogenic process, and environmental modifiers . This syGrowing evidence suggests that the host immunity is influenced by inherited factors. However, the germline genetic contribution to CIR has not been systematically explored due to several hurdles. First, since highly effective immunotherapeutic approaches such as the ICIs have been only recently implemented, limited datasets are preventing conclusive association studies. Second, large datasets such as TCGA have scant information on clinical outcome, particularly on response to IO agents. In addition, TCGA collected samples from primary tumors (with the exception of melanoma) while IO is applied mostly in metastatic settings. Yet, these datasets are still useful for assessing the contribution of the genetic background to the development of anti-tumoral immunity by matching germline data to the functional orientation of TME derived from transcriptional data.Twin have shown that both heritable and non-heritable factors significantly contribute to an observed phenotype –58. ThesGenome-wide association studies (GWAS) have identified more than three hundred susceptibility loci predisposing to the development of autoimmune diseases. Moreover, studies of severe autoimmune or immunodeficiency syndromes identified several causative variants [Human Leukocyte Antigen (HLA) molecules have been associated with variable responses to infection, inflammatory conditions, autoimmune diseases, and development of virus-induced tumors and some not-known to be virus-induced such as non-small cell lung cancer [variants . Polymorg cancer  and hemag cancer . HoweverAs compared to common risk polymorphisms, which are mostly localized in non-coding regions, rare protein-coding variants are more likely to lead to a loss or altered function of the protein  that maytumor mutational burden (TMB) contributes to responsiveness to ICIs in patients with germline mutations of mismatch-repair. Would patients carrying such mutations be also more likely to develop acquired, immune resistance? The influence of germline variants on cancer-cell’s intrinsic features to modulate anti-tumor immune response needs to be addressed by germline-somatic integrative analyses through whole-exome/whole-genome sequencing [Germline genetic factors might influence CIR in myriad ways –67. Somequencing  paired wquencing . Can theC-C motif chemokine receptor (CCR)-5 and IFN regulatory factor-5 and responsiveness to chemo-immunotherapy [interleukin (IL)-2 in metastatic melanoma was reported by early studies [Few studies have investigated the germline contributions to CIR. In metastatic melanoma, the link between polymorphisms of genes implicated in the pathogenesis of autoimmune diseases, such as otherapy  or adoptotherapy , 72, wasotherapy –75. In a studies , while a studies . A recen studies . Interes studies , suggestRecently, large genetic study on melanoma and lung cancer patients treated with ICIs has shown that a low level of germline HLA-I heterozygosity is strongly associated with poor outcomes . This efThe identification of common genetic variants associated with treatment outcome might lead to the development of better patient stratification. If such variants are identified, polygenic scores might be used to define predictive classifiers. While it is unlikely that germline variants can be used as a single marker for stratification purposes in metastatic patients, they can be integrated with other biomarkers  to develop multi-factorial predictors. In the adjuvant setting, germline variants associated with toxicity can guide the selection of patients for the modest survival benefit. Additionally, germline testing may inform the assessment of therapeutic index for administering immunotherapies in patients with pre-existing autoimmune diseases.Germline investigations might also have profound implications for therapeutic interventions. For example, the observation that specific HLA-I super types are associated with increased immune responsiveness  offers tWhile genetic studies may be helpful, each approach has significant limitations. GWAS studies are limited to identifying common variants which are either directly genotyped on an array or, more commonly, imputed. Although GWAS have identified a large number of loci associated with autoimmune disorders and other complex traits, for most complex phenotypes, discovered loci accounted for a relatively small fraction of the heritability of the phenotype. The effect sizes of the vast majority of these variants are small, with the vast majority of these odds ratios being 1.2 or less , with fenext-generation sequencing (NGS) offers notable advantages, there are also limitations: WES, as cost-efficient alternative mapping germline variations in coding regions, targets only ~ 1–2% of the genome. Nevertheless, a focused application of WES and targeted panels is widely used in screening of germline mismatch repair mutations as predictive surrogates of outcomes to PD-1 inhibitors in colon cancer [GWAS in the context of immunotherapy, as mentioned earlier, may not follow the pattern of expected low-penetrant risk effects in disease risk. Thus, it is possible that IO GWAS will identify genetic loci with stronger risk effects and clinical applicability, as suggested by recent pharmacogenomics associations , 88. Impn cancer  and othen cancer . WGS proRecent findings suggest that germline variants might shape intra-tumoral immune response, and influence responsiveness and toxicity to immunotherapy.Current large cancer databases are useful resources to explore the relationship between individuals’ genetic background and intra-tumoral immune response but lack information on treatment outcome, especially on immunotherapeutic agents.The constitution of appropriate databases paired with high-powered studies are needed to define the magnitude of genetic germline contributions to CIR and to identify putative germline genetic immune biomarkers.It is critical to establish dedicated large collaborative consortia or networks collecting harmonized clinico-pathological information, which represents a major roadblock in the systematic exploration of the germline component in IO.Germline information should be integrated with phenotypic information such as somatic alterations, epigenetic and transcriptional features to increase prediction accuracy.Analytic integrative pipelines need to be implemented for deciphering causal associations and for prioritizing putative functional variants and pathways.Once identified, genetic germline biomarkers might be used to increase treatment outcome, adverse event prediction and to define novel therapeutic strategies.Which are the key molecular mechanisms involved in anti-tumor immunity that might be modulated by germline genetic variants?Are common genetic polymorphisms associated with a differential spontaneous or treatment-induced anti-tumor immune response?How can we implement the study of host genetic diversity to identify novel biomarkers of responsiveness or toxicity to cancer immunotherapy?the taskforce members agree that it is critical to establish collaborative networks dedicated to these investigations, which will allow harmonized collection of clinic-pathological information. Collaborations might occur by sharing patient samples and/or data. The cohorts could be enriched for exceptional responders, rapid “progressors”, or for patients experiencing severe adverse events. Collection of germline DNA should be included in clinical trials. Ethical and regulatory issues might represent an obstacle for sharing germline data and need to be prospectively taken into account at the time of study design. Funding agencies are often requiring to deposit in publicly accessible repositories germline and phenotypic information generated by the awarded researches, and therefore it is expected that amount of germline data for CIR exploration will increase in the next few years. National large-scale initiatives, e.g. the UK Biobank, might represent additional resources for this kind of exploration. Going forward, it would be critical to integrate germline data with phenotypic attributes, such as transcriptomic signatures, epigenetic, and somatic alterations to increase CIR prediction accuracy [Large cooperative clinical trial groups might be best poised to accrue the necessary extremely large sample size. Thus, it is difficult to implement this approach in the therapeutic setting. As large sample sizes are needed for germline studies, accuracy . It is paccuracy .To properly address the germline questions, it will be important to standardize platforms and methodological approaches. The implementation of bioinformatics pipelines, such as fine mapping strategies to prioritize putative functional variants and to identify true causal associations, will be critical , 93. DirThe accumulation of different genetic and epigenetic alterations are the origin of inter- and intra-tumor heterogeneity impacting cancer pathways, driving phenotypic variation, and posing significant challenges to personalized cancer medicine , 94, 95.The most pressing clinical question for ICI therapy is what are the predictive biomarker(s) of response and resistance to ICI therapy? Identification of such predictive biomarker(s) would improve patient selection, limit toxicity , and guide biology-based combinations thus moving the immunotherapy field towards personalized medicine . In ordeDNA damage response (DDR) genes has also been recently suggested [cell free DNA (cfDNA) or circulating tumor cells (CTC) assessing DDR genes as a surrogate to estimating TMB could result in a potential solution to limitations of tissue availability.This critical question is currently under intense investigation. Accumulating evidence supports the existence of tumor intrinsic features such as TMB, correlated to an immune active TME and predictive of response to PD-1/PD-L1 blockade, independent of PD-L1 expression , 90, 105uggested . Indeed,uggested . This unG12D mutations lead to increased production and secretion of granulocyte-macrophage colony-stimulating factor (GM-CSF) and accumulation of immunosuppressive myeloid cells, suppressing cytotoxic lymphocytes [isocitrate dehydrogenase 1 (IDH1) mutations are associated with variations in the degree of leukocyte infiltrate, macrophage content, and repression of tumor-associated immune responses [Recent observations have demonstrated that specific somatic alterations in tumor cells correlate with changes in the TME, including overall lymphocytic content, cellular composition, and intracellular signaling , 108. Inphocytes . In glioesponses . Analysiesponses , 111. Inesponses .The chromatin provides the physical substrate where epigenetic mechanisms and signaling pathways converge to coordinate transcriptional programs, playing a critical role in cellular phenotype and cellular memory. The chromatin also plays an essential role to repress transposable elements. During cancer development, the global chromatin landscape in cancer cells becomes deregulated, as a consequence of altered transcriptional profiles or mutation in genes encoding chromatin-remodeling factors, histone modifying enzymes, and DNA methylation enzymes.This deregulated chromatin landscape of cancer cells can play a critical role in the immune landscape and immune responsiveness. As one example, SWI/SNF chromatin remodeling complex genes are inactivated through mutation in ~ 20% of cancers. One subunit of this complex, PBRM1 is mutated in ~ 40% of patients with clear cell renal cell cancer (ccRCC). PBRM1 was recently shown to inhibit activation of IFN-stimulated genes in cancer cells in response to IFN-γ produced by T cells. Indeed, PBRM1 inactivation increases sensitivity of cancer cells to T cell-mediated killing and truncating loss-of-function mutations in PBRM1 is associated with increased response rates to anti-PD-1 or anti-PD-L1 in ccRCC patients , 113.Moreover, deregulated chromatin landscape in cancer cells can be targeted therapeutically to increase immune responsiveness. For example, DNA demethylating drugs were previously shown to re-activate human endogenous retrovirus (HERVs), leading to formation of dsRNA and activation of pattern recognition receptors, such as the MDA5/MAVS anti-viral pathway. This leads to a ‘viral mimicry’ state, where cancer cells activate antiviral responses, leading to immunogenic cell death, Type I and III IFN production, and increased antigen processing and presentation , 115. ReFinally, besides cancer cell intrinsic chromatin deregulation, immune cells in the TME can also show epigenetic alterations. A recent study showed that chronically stimulated T cells acquire de novo DNA methylation programs that lock them into an exhausted phenotype. Moreover, inhibition of DNA methyltransferases can avoid the onset of exhaustion and increase immune responsiveness . AltogetThe following main research questions will be the focus of the field of immunotherapy of cancer for the next several years: Can our knowledge of how somatic alterations influence the TME help us optimize immunotherapy combinations? Are there shared themes, across cancer types, subtypes, or immune response subtypes  that canThere are many tumor-intrinsic characteristics that are invariably correlated to response to ICI and composition of immune microenvironment.Multiple levels of molecular events at genomic, epi-genomic and regulatory stages can affect the CIR.The uncovering of the casual mechanistic relationships between genomic and epi-genomic events and immune composition opens the possibility to reprogramming the microenvironment and offers novel therapeutic opportunities.How do we deal with the molecular subtypes that define intrinsic resistance to ICI and expand CIR?Can our knowledge of how cancer-intrinsic features influence the tumor microenvironment help us optimize immunotherapy combinations?How do we harmonize biomarkers derived from different technologies in order to specifically tailor IO therapy for a patient and increase the likelihood of response?Will understanding the role of epigenetic re-programming downstream of molecular alterations in tumor cells reveal new opportunities to combat cancer immune-evasion strategies?Recognizing that the efforts aimed at addressing somatic genetic alterations in cancer are often overlapping with germline studies for various technical and analytical reasons, the SITC task force made a decision to combine the two working groups and align their strategy that has been described in the previous section, by creating broad consortia for the accrual, analysis, interpretation and validation of identified determinants of CIR. In the upcoming second workshop on CIR to be held in Houston in September 2019, we combine de facto the two working groups and will discuss the pathways for effective functional integration.+ T cells and sensitivity to immunotherapy [CIR can be determined by transcriptional alterations within the TME, and transcriptional patterns can therefore be used to categorize CIR. Early studies using transcriptional profiling suggested a general association between the presence of activated, tumor-specific CD8otherapy , 119–121otherapy , 123, weotherapy  that pre+ T cell activation [+ T cells, different groups have used distinct signatures. Moving forward, it will be critical to identify the specific set of genes faithfully predicting intra-tumor CD8+ T cell infiltration and to transcriptionally define key immune cell types directly involved in the development/modulation of T cell responses, including immune potentiating dendritic cells or immune suppressive innate cells . While deriving a consensus transcriptional signature for CD8+ T cells should be achievable using currently available data sets, more effort is needed to comprehensively characterize genes involved in immune regulation of dendritic cells, macrophages, NK cells, and neutrophils before specific signatures can be generated.Transcriptional profiling of immune infiltrates is certainly the most advanced of the these categories with the majority of the work focusing on signatures associated with cytotoxic CD8tivation , 126. HoAdvances in single cell genomics now allow paired analysis of T-cell receptor (TCR) repertoire and transcriptional profiles associated with specific TCR clones within the TME. Identification of TCR-α and TCR-β chain pairs in concert with elucidation of neo-antigens or tumor-associated antigens might allow us to decipher the immune-dominant T cell responses across multiple patients, or across different tumors within the same patient. Initial studies in melanoma patients and melanoma-bearing mice are revealing an association between relative expansion of certain T cell clones and responses to immunotherapy , 128. Anepidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and RAS/RAF/MEK as well as expression of mesenchymal transition genes, have been all found to contribute to immune resistance [Over the last couple of years, an increasing number of preclinical and clinical studies have provided evidence that transcriptional alterations within tumor cells can have a direct impact on the abundance and functionality of immune cells within the TME , 47, 125sistance –132. Intsistance . Alteratsistance , 134. HoAlterations in expression of genes associated with tumor-immune recognition have been primarily associated with both innate and acquired resistance to immunotherapy. These alterations mainly include deficiencies in antigen-presentation machinery and the IFN-γ response pathway , 135–137Similar to cancer cell-intrinsic alterations, several reports suggested that fibroblasts within the TME can interfere with anti-tumor immune responses , 139. AsBesides refining stroma-related signatures, there is an unmet need to pair analysis of stroma transcriptional changes with immunohistochemistry or multiplex immunofluorescent staining. This integrated analysis would empower conclusions drawn from transcriptional analyses and would further increase its ultimate predictive value. To complete this integrative approach, a major undertaking to generate databases is needed to correlate the transcriptional profiles (and other patient specific information) with clinical response to immunotherapy including immune-checkpoint blockade.Efforts to obtain a comprehensive understanding of the transcriptional profiles defining T cell-inflamed and non-inflamed TMEs and their clinical impact are further hampered by the current imprecise criteria to assess clinical responses to immunotherapy. Commonly used clinical criteria, overall survival and progression free survival, do not always capture the true nature of response at the level of the single tumor lesion. For example, while most tumor lesions in a patient might be successfully eliminated by the immunotherapeutic treatment, one individual lesion might progress. This isolated progression event would mark this patient as “resistant” to immunotherapy per response criteria but might confuse the interpretation of the underlying biology. Likely, these mixed responses represent two different biological phenomena of immune responsiveness: 1) the cancer may be intrinsically responsive to immunotherapy and 2) individual lesions may have developed escape mechanisms (acquired immune deficiency) that allows their isolated growth. Thus, mixed responses and related survival should be categorized separately as biologically closer to acquired immune resistance than other forms of lack of response that denote a non-responsiveness to the first therapy, such as primary, compensatory, and pseudo immune-resistance. Probably, it would be better to consider long-term progression-free survival and long-term overall survival, or other clinical endpoints. These parameters are less affected by conditions, which can influence the median value . If the More accurate clinical assessment could significantly improve the research efforts to reveal transcriptional profiles associated with response or resistance by tracking the evolution of the biopsied lesions following immunotherapy, rather than the overall health of the patient. Likewise, a tremendous amount of information could be gained if multiple lesions within the same patients could be analyzed in order to concurrently eliminate any local or intra-patient factors impacting on anti-tumor immunity . ContrasGeneration of transcriptional immune signature linked to functional impact of immune cells on the overall anti-tumor immunity.In order to understand the effects of tumor cell-intrinsic signaling on anti-tumor immunity, generate methods that allow increased resolution of tumor-immune interactions.Obtain longitudinal insights into how immunotherapy, and therapy in general, is impacting immune infiltration and cancer cell signaling.Can we generate transcriptional signature with high predictive value for a specific tumor-immune microenvironment?Can transcriptional profiling be developed as a biomarker for the CIR?What technological advances do we need to dissect the tumor-immune microenvironment in space and time?As per germline and somatic genetic studies, the success of this focused effort by SITC will depend on the collection and sharing of congregate information that can integrate genetic with transcriptional, clinical, and epidemiological data. In addition, other layers of potentially useful information will depend on the integration of environmental and behavioral information that may affect individual patients, such as co-morbidities, associated therapies, dietary habits, microbiome composition etc. As transcriptional information can most effectively summarize genetic with functional information, it will be the primary role of this task force to identify venues for integration and entice support from different agencies for the accrual and/or access to quality information that will be queried systematically following hypothesis-driven path. As described in this section, as much as complicated that it may appear, cancer immune biology is starting to demonstrate recognizable patterns and predictable associations with potential causative implications. Thus, we predict that more hypothesis validation, rather than discovery approaches, will help solve the enigma of CIR.Immunogenic cell death (ICD) is a regulated form of cell death accompanied by the release of DAMPs that modulate the immunogenic potential of dying cells , 142. ICNumerous forms of regulated cell death have the potential to induce ICD, and each is characterized by unique spatiotemporal sequences of DAMP release along with release of tumor-associated antigens and recruitment of antigen presenting cells , 143. InThe mechanism of cell death affects immunogenic potential by inducing varying patterns of DAMP release, and efficacy of ICD-inducing therapy is shaped by factors intrinsic to the cancer cells, the TME, and infiltrating immune cells. Chemotherapy and targeted therapy may induce variable quality and quantity of ICD depending on the underlying mechanism of cytotoxicity, dose of the agent used, and sensitivity of the local host immune cells to these agents. For many therapies, especially chemotherapy or radiation, lower doses may induce more potent immune responses than higher doses, with the added benefit of fewer side effects, by changing the temporal dynamics of DAMP release and cell death –149. OncThere are two classical models for measuring ICD in experimental settings in vivo . In the calreticulin (CALR), type I IFNs, histones, heat shock proteins (HSPs), markers of immunological fitness , and specific immune cell populations (T cell repertoire and phenotype) or cell surface receptors they express. One promising biomarker strategy is the systemic immune inflammation index, which measures ratios of specific immune cell types (e.g. neutrophils vs. lymphocytes) to monitor shifts in the balance of immune populations accumulating within the TME and peripheral circulation [To monitor the induction of ICD and its downstream effects, a robust biomarker strategy must be incorporated routinely into clinical trials. These biomarkers should measure the type of cell death, the release of DAMPs, and the abundance, identity, and location of immune cells that contribute to a functional adaptive immune response. Specifically, biomarkers should distinguish between bona fide ICD and direct immune modulatory effects of the therapeutic drugs by focusing on cell types that are immediate sensors of DAMPs. As the end stage of ICD is a protective T cell response, T cell populations also should be measured to assess success of these strategies. Proposed clinical biomarkers of ICD include direct measurements of DAMPs such as HMGB1, ATP, culation , 157. OnTumor cell death can be classified as either immunogenic (eliciting protective immunity) or tolerogenic (failure to elicit protective immunity).Cell death elicited therapeutically can induce the release of DAMPs which shape the subsequent immune response.ICD is currently measured in model systems via its functional consequences – protection from tumor challenge, but robust biomarkers for clinical utility remain undefined.Characterize upstream and downstream events that drive ICD on a genetic, molecular, and cellular level.Establish better animal models for ICD assessment that more closely replicate human tumor immune biology.Develop techniques to measure tumor cell death and released DAMPs in vivo accurately and efficiently.Improve biomarkers to measure consequences of ICD including the induction and maintenance of anti-tumor immunity.To accelerate our understanding of ICD and ability to manipulate it for clinical benefit, the field needs to:What are the key molecular events that occur during immunogenic cell death that prime a robust immune response and promote immunological memory?Which therapeutic strategies will more effectively promote ICD while minimizing off target inhibition of immune responses?How can detection of immunogenic cell death be routinely incorporated into clinical trials?A clear value of the study of ICD is its relevance to the determinism of CIR. The value of in vitro ICD studies is limited because several aspects of the immune physiology determining CIR can only be study in in vivo models. Yet, as discussed in the following section, animal (mouse) models have their own limitations. Thus, a decision was made by the SITC task force to integrate the two study groups into one to better define ways to exploit the induction of DAMPs in the context of ICD in xenograft models or other tissue modeling substitutes. This approach will be able to help us understand the role of different components of innate and adaptive immunity and how the components are sequentially involved in the determinism of CIR. Thus, the two working groups will be combined in the upcoming CIR workshop with the intent of building hypothesis-driven models that could address the respective questions and other related questions, for instance, the requirement of chemo-attraction and the mechanisms of chemo-repulsion that may affect migration of adoptively transferred immune cells. These issues will be the basis of discussion going forward.Despite advances in cancer genomics and cell biology to aid the rational design of new oncology drug targets, the rate of translating promising preclinical findings into successful human clinical trials remains extremely low. One obvious reason is that animal models of tumors do not reflect all of the features of genetic heterogeneity, complex tissue architecture, and immune microenvironment of human cancer. There is an urgent need to develop well-characterized preclinical models to improve the correlation between preclinical efficacy and clinical outcomes , 159. ToSevere Combined Immune-Deficiency (SCID) to Non-obese-diabetic SCID Gamma (NSG) murine models [hematopoietic stem cells (HSC), but success has been limited because of the gradual development of xenogeneic graft-versus-host-disease (GVHD) [patient-derived xenografts (PDX) in HSC-engrafted NSG mice has shown the similar response patterns as in patients [In past decades, there was significant advancement in the development of immune-compromised mice, from athymic nude to e models . NSG mice (GVHD) . HSC groe (GVHD) . The lace (GVHD) . To highpatients , 163. Whmajor histocompatibility (MHC) Class I and Class II could reduce the severity of GVHD [human macrophage colony-stimulating factor (hM-CSF), hIL-3/hGM-CSF, human signal regulatory protein (hSIRPA), human thrombopoietin (hTPO), Rag2-deficient, γ-chain negative (MISTRG), and NSG mice carrying the transgenes for hstem cell factor, hGM-CSF, and hIL-3 (NSG-SGM3). These new generation mice are engineered with a CSF-1-dependent and -independent compartment, enabling the study of the interaction between myeloid cells, antigen presenting cells, and T cells in the reconstituted TME [The next generation of humanized mouse models has to focus on ameliorating the deficiencies of current models. Transgenic expression of human cytokines, HLA molecules, and certain hormones would help this goal. In addition, knockdown of mouse  of GVHD . One of uted TME , 166. Mebeta-2-microglobulin (B2m) gene loss has been identified in therapy-resistant tumors [B2m in the carcinogen-induced lung squamous cell line (UN-SCC680AJ) susceptible to PD-1 blockade confers resistance to PD-1 blockade [Despite some encouraging initial responses, ICIs are not effective in many patients with lung cancer, and acquired resistance is often implicated in clinical failure . To studt tumors , 136. Knchimeric antigen receptor (CAR) T cells for solid tumor in either immune active or immune silent tumors. The IO approaches and the corresponding TME models are different. Altering the balance in the immunosuppressive TME to enhance immune activation could focus predominantly on experimental models of adaptive immune responses such as the balance between Th2 and Th1. This may include blockade of CSF1-mediated sustenance of macrophages and IL-4/IL-13-mediated Th2 cell growth [Studying the complex niche of the TME is critical for understanding key questions related to IO. For example, the lack of efficacy of adoptively transferred T cells such as l growth . Based o+ T cells. In squamous cell carcinoma, in contrast, B cells and humoral immunity enhance macrophage pro-tumorigenic programing and subsequent tumor growth, as well as inhibit antigen presentation to CD8+ T cells [Another issue in preclinical models is the tissue-specific regulatory activity displayed by different types of tumors. In mesothelioma, Th2 cells promote macrophage pro-tumorigenic programing of tumor tissue and further inhibit antigen presentation by dendritic cells to CD8 T cells , 170. ThTesting mechanisms of CIR in vivo by adopting animal models has several limitations for the reasons discussed above. Surrogate approaches have been described attempting to utilize tissue reconstructs such as three-dimensional organoids , in vitrRelevant immune cells and cytokines need to be considered for the therapy being testedIf necessary, implantation site should be matched to cancer tissue of origin (orthotopic vs. subcutaneous)Best done with immune PDXs or genetically engineered mouse modelsIf possible, the genetics of the tumor model should match the corresponding human genetic background status that it is supposed to representMouse models are important to answer mechanistic questions that are unlikely to be answered by clinical trials and to validate hypotheses generated from clinical observations. The choice of mouse model needs to be selected for the experimental purpose:Limitations in supporting robust human immune responses need to be overcomeCost remains high (tends to lower throughput)There is an unmet need for unlimited HSCs for reconstitutionTransplantable cells may not be necessarily representative of the original tumorsWhile humanized mouse models are becoming more representative, challenges remain:Transgenic models have very low mutation load and poor intra- and inter-tumor heterogeneity that may not be representative of any human tumorCarcinogen models have high mutational load and high heterogeneity that may make them unrealistically immunogenicPDX models may become skewed in their biology by the diverse immune environment encountered by growing in a different speciesFragments of PDXs maintain, at least initially, intra-tumoral immune cells but these are lost in subsequent passages making them dynamically unstableIndividual models have their own limitations:+ T cell-focusedMost of the field is currently CD8Other cell types are important in effector functionsAPCs, myeloid, B cells, and other rare cells subtypes remain understudiedAll immune cells should be analyzed when possible:What are the current limitations of humanized PDX mouse models?What approaches can be undertaken towards more faithful models of human cancer-human myeloid cells interface?How to develop models that better model to reproduce tumor mutational load?As described in the previous section, animal and surrogate tissue modeling system are critical to understand the physiology of innate and adaptive immune responses. No individual model suffices as it cannot represent the heterogeneity of cancer tissues from the same patient and, even more, among patients and distinct cancer typologies. In particular, the determinism of CIR can be dissected starting from basic concepts such as the dissection of the role of ICD as an initiator of the danger signal. Only context dependent models will be able to dissect this question satisfactorily and, therefore, the two conceptually overlapping working groups will be combined to address the role of ICD and innate immune activation in the most relevant model system based on a hypothesis-driven path.Over the course of the two-day workshop, five working groups of the SITC Cancer Immune Responsiveness Task Force (CIR), incorporating workshop attendees, discussed the different determinant of CIR, i.e. the genetic background of the host, somatic alterations related to the oncogenic process, and environmental modifiers, and the development of improved in vivo models for screening therapeutic strategies. Following the workshop, each working group identified the most relevant questions that will help advance the understanding of CIR (see Table


Staphylococcus aureus (MRSA), first described in the 1960s, has evolved rapidly to emerge as a predominant cause of OSI in children, and therefore empiric treatment for OSI should include an antibiotic effective against MRSA. Characterizing MRSA strains can be done by antimicrobial susceptibility testing, detection of Panton–Valentine leukocidin (PVL) gene, staphylococcal cassette chromosome mec (SCCmec) typing, pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST). Worldwide, community-onset methicillin-resistant staphylococcal disease is widespread and is mainly associated with a PVL-producing clone, ST8/USA300. Many studies have implied a correlation between PVL genes and more severe infection. We review MRSA OSI along with the pertinent aspects of its pathogenesis, clinical spectrum, diagnosis, and current guidelines for management.Osteoarticular infections (OSI) are a significant cause of hospitalizations and morbidity in young children. The pediatric patient with OSI presents unique challenges in diagnosis and management due to higher morbidity, effect on growth plate with associated long-lasting sequelae, and challenges in early identification and management. Methicillin-resistant  Staphylococcus aureus (S. aureus) is a major cause of bacterial infections in humans worldwide. Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of bacteremia and invasive diseases that include pulmonary, skin, and soft tissues and musculoskeletal infections in children  or resistant to vancomycin [vancomycin resistant staphylococcus aureus ] are associated with treatment failures with vancomycin, lack of clinical and microbiologic response and overall poor outcomes [According to the IDSA guidelines intravenous vancomycin is the antimicrobial agent of choice for acute hematogenous pediatric OSI due to MRSA . Vancomyoutcomes . AlthougClindamycin (40 mg/kg/day) is an alternative antimicrobial for MRSA OSI in a stable patient without bacteremia or endovascular foci of infection, if clindamycin resistance rate is <10%, followed by oral therapy, given its high oral bioavailability and good bone penetration . Clindamerm(A) or erm(C) genes in the MRSA genome encode the methylase protein responsible for methylation of the 23S rRNA-binding site. Mutations in these genes confer inducible macrolide–lincosamide–streptogramin B (MLSB) resistance, resulting in resistance to clindamycin, macrolides and streptogramins  [B is characterized by the erythromycin-clindamycin D test, i.e., clindamycin resistance induction in the presence of erythromycin [erm(C) S. aureus isolates have been shown to have a much higher mean frequency of mutation to clindamycin resistance (14-fold) than that of the erm(A) S. aureus isolates [B-inducible MRSA isolates may result in the selection of constitutive methylase-producing organisms as well as erm resistance. Therefore, the use of clindamycin clinically for patients with serious infections is discouraged in the presence of D-test-positive MRSA organisms. The other adverse effects of clindamycin include diarrhea and risk of Clostridiodes (clostridium) difficile colitis.The pristin) ,36. In tAccording to IDSA guidelines, daptomycin and linezolid are alternative antimicrobial agents for MRSA OSI. Daptomycin  is a lipopeptide antibiotic that is bactericidal for MRSA. Although approved for MRSA BSI (loodstream infections), daptomycin is not useful for treating disseminated disease involving the lungs, due to its propensity for binding surfactant and consequent inactivation [S. aureus has five copies and mutations in two or more genes are needed for the resistance to manifest [Linezolid is an oxazolidinone antibiotic that achieves good bone concentrations. It acts by binding to the bacterial 50S ribosomal subunit during formation of the initiation complex, thus inhibiting protein synthesis, and is bacteriostatic against MRSA . The recmanifest . Newer amanifest ,38. It hmanifest . Dalbavamanifest . Daptomymanifest . In children with MRSA OSI, a total of 4–6 weeks of antibiotics for MRSA osteomyelitis and 3–4 weeks for septic arthritis are recommended .S. aureus , none of the isolates were MRSA [The ideal duration of total and intravenous antibiotics still remains an area of active investigation and is yet to be determined, but clinical improvement and downward-trending CRP usually dictate the switch from intravenous antibiotics to oral antibiotics, and clinical resolution with normalization of ESR is relied on as a guide to adequate duration of therapy ,20,24,40ere MRSA . Short cere MRSA .The general approach of transition from intravenous antibiotics to an oral antibiotic regimen should be directed by clinical response to treatment and must be individualized for each patient with MRSA OSI.Early surgical intervention is often required in OSI caused by MRSA, and the need for repeat interventions is usually indicated by the clinical response to treatment.The IDSA guidelines state that prompt surgical debridement of soft tissue abscesses is essential and is recommended to be undertaken whenever feasible . SurgicaIn summary, MRSA is a major cause of pediatric morbidity and mortality in the United States and worldwide. Through evolution, MRSA has emerged as one of the predominant etiologic agents for pediatric OSI, and PVL-producing MRSA strains have been linked with severe disease manifestations. Prompt recognition and initiation of optimal antimicrobial treatment of MRSA OSI in children is vital for preventing disease dissemination as well as complications. In addition to appropriate antimicrobials, early surgical intervention is often necessary in MRSA OSI.


The metabolic plasticity of cancer cells is considered a highly advantageous phenotype that is crucial for disease progression and acquisition of treatment resistance. A better understanding of cancer metabolism and its adaptability after treatments is vital to develop more effective therapies. To screen novel therapies and combination regimens, three-dimensional (3D) culture models of cancers are attractive platforms as they recapitulate key features of cancer. By applying non-perturbative intensity-based redox imaging combined with high-throughput image analysis, we demonstrated metabolic heterogeneity in various 3D culture models of pancreatic cancer. Photodynamic therapy and oxaliplatin chemotherapy, two cancer treatments with relevance to pancreatic cancer, induced perturbations in redox state in 3D microtumor cultures of pancreatic cancer. In an orthotopic mouse model of pancreatic cancer, a similar disruption in redox homeostasis was observed on ex vivo slices following photodynamic therapy in vivo. Taken together, redox imaging on cancer tissues combined with high-throughput analysis can elucidate dynamic spatiotemporal changes in metabolism following treatment, which will benefit the design of new metabolism-targeted therapeutic approaches. Pancreatic ductal adenocarcinoma (PDAC) continues to have a particularly poor prognosis due to limited effective treatment options . Moreove2 are oxidized to NAD+ and FAD, respectively. Through detection of the fluorescence intensities of NAD(P)H (NAD(P)HI) and FAD (FADI), the optical redox ratio (ORR) can be derived by dividing FADI by the sum of NAD(P)HI and FADI -2-deoxyglucose (2-NBDG) and tetramethyl rhodamine ethyl ester (TMRE) as molecular probes for glucose uptake and mitochondrial membrane potential, respectively. This method revealed metabolic alterations in mammospheres representing different disease stages of breast cancer, showing a global decreases in glucose uptake and increases in mitochondrial membrane potential as the disease stage progressed [Recent years also saw the emerging application of redox imaging to various forms of 3D cultures, of which three examples are given below. Firstly, Walsh et al. used a custom-built multiphoton microscopy setup for fluorescence lifetime imaging of NAD(P)H and FAD on patient-derived breast cancer and pancreatic organoids ,53. By iogressed . These sCompared to these previous studies exploring redox imaging on 3D culture models, a major benefit of the approach presented in this study is that it provides a wealth of information using widely available instrumentation and established image analysis tools. Although none of these aspects are new concepts in their own respect, their integration into an easily implementable bioassay was not previously explored. Leveraging this method to obtain multiparametric readouts for numerous individual microtumors per sample enabled us to compare basal redox states and treatment-induced perturbations in a statistically robust manner. However, redox imaging by itself, regardless of the method used, is rather inconclusive, as redox perturbations can have multiple origins. Continued research should, therefore, leverage redox imaging as a multiparametric non-perturbative method that can be easily combined with, and corroborate, more specific metabolic assays H and FAD: The emission of auxiliary fluorophores will likely dominate over the low fluorescence emission of these cofactors. One possible solution, which remains to be explored, is the sectioning of individual organoids, on which the redox imaging can be done as described for the in vivo experiment. In sequential sections, one can investigate the localization of stromal cells and cancer cells using immunohistochemistry, and investigate the redox state of the tissue using the redox imaging method on subsequent slides. However, such approaches are end-point measurements, and dynamic changes in redox state will be difficult to capture.One potential application of redox imaging that remains to be further explored is the preparation of 3D reconstructions of tumor redox states to identify basal and treatment-influenced redox states. Although higher-throughput imaging methods are desirable for such applications, there are in principle no technical constraints preventing to obtain full 3D reconstructions of the redox state in spheroids, organoids, microtumors, and tumor tissues in vivo and ex vivo. For real-time imaging in vivo, the obvious limitation remains the limited optical permeability of tissue. For example, in vivo multiphoton microscopy was demonstrated to enable fluorescence lifetime imaging of NAD(P)H and FAD up to a depth of 70 µm . With twWith the advent of advanced microscopy tools such as adaptive optics and light-sheet microscopy, perhaps both single-cell and full tumor evaluations within 3D and in vivo cancer models and patient tissues will become more widely available in the near future. Combinations of the different methods presented by us and others may ultimately pave the way for these investigations. Such methods, potentially in combination with other spatially and/or temporally resolved metabolic imaging methods will be instrumental to optimize therapeutic and diagnostic strategies and advance cancer management .Tracking the adaptive metabolism and redox state in organotypic cultures and ex vivo cancer tissues represents a powerful investigational tool that can be easily combined with complementary assays to assess treatment effects in a more comprehensive manner. The results of this study revealed metabolic alterations in both adherent microtumor cultures and suspended spheroid cultures derived from three PDAC cell lines. The PDT-induced perturbations in redox state were recapitulated on an orthotopic pancreatic cancer model in vivo. Spatiotemporal tracking of metabolic states in cancer tissues will provide a deeper understanding on metabolic plasticity in malignant tissues and aid in elucidating the metabolic origins that lead to therapy insensitivity. Such discoveries will ultimately facilitate the design of new mechanistically informed cancer therapies. Such advances are particularly relevant in the clinical management of advanced pancreatic cancer, as there remains an urgent need for more effective and less harmful treatment strategies.


Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics . As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics—particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).Dynamic causal modeling (DCM)—a framework for inferring hidden neuronal states from brain activity measurements  and their context-dependent modulation—was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as  Neuroimaging analyses in humans and non-human primates (NHP) have become increasingly sophisticated. One such innovative image analysis technique is dynamic causal modeling (DCM) which has been applied to human fMRI data . As such, we discuss: (1) what factors need to be considered when applying DCM to NHP (keeping in mind that it was designed for human fMRI data); and (2) considering said factors, what strategies can one implement when modeling effective connectivity to fMRI data recorded under anesthesia. The latter is an important consideration in NHP fMRI, since most imaging experiments are done under anesthesia and anesthetics have been demonstrated to impart changes in BOLD and neural dynamics, particularly the inhibitory drive  DCM overview and motivation for NHP data application; (2) comparison of cerebrovascular architecture and functional networks between humans and NHPs; (3) effects of anesthetics on vascular physiology, BOLD contrast, and neural dynamics—focusing on effective communication within and between networks. Lastly, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM). Taking all these together, we can avoid pitfalls in DCM application in NHP data and facilitate more accurate interpretation of the observed neural dynamics as measured through BOLD fMRI.This section discusses an overview of DCM, including its assumptions, and the motivations for NHP data application. DCM, developed by Friston et al. , is a meIn DCM, one needs to specify which regions to include in the overall model. As such, DCM follows an analysis that can address which regions in the brain an experimental manipulation induces changes in BOLD response such as a general linear model (GLM)  signal  neural state equations that provide an abstraction of the summed activity of a neuronal population. The neural state equation is comprised of three parameters that embody: (1) intrinsic connectivity among regions in the absence of input (A matrix); (2) direct influence of extrinsic input on neuronal activity (C matrix); and (3) context-dependent change in connectivity induced by the input (B matrix). Neural dynamics in DCM have been characterized by single-state, two-state, and adaptive two-state equations. The single-state neural equation  is estimated from the measured BOLD data using is estimated iteratively from the measured BOLD data using Variational Bayesian (VB) algorithm  is done to compare a series of models that represent different prior hypotheses of connectivity architecture to examine which of the competing models most likely generated the observed data. This is done by comparing the evidence for each model. Model evidence—the normalization constant for the product of the likelihood of the data and prior probability of the parameters—is approximated using Laplace approximation in standard DCM  deterministic assumption on the inputs that enter the system; (2) Gaussian assumption on the posterior density; and (3) assumption of equal detection of BOLD signal changes throughout the brain. Firstly, in classical DCM, all processes in the system are considered deterministic such that it is presumed that neural dynamics in a region is entirely due to incoming afferents from other regions and/ or experimental inputs. This assumption is not always tenable in NHP fMRI studies which are mostly done under anesthesia—this restricts task-based imaging to simpler visual/ auditory experiments and many imaging experiments are task-free. However, extensions of DCM in humans have allowed its application in resting state studies: (1) using classical DCM, one can stimulate nodes with fluctuations of specific frequencies . Moreover, advances in high-field fMRI at the sub-millimeter scale, allows imaging and modeling at laminar resolutions , which is often more feasible in animal studies, including NHP. For instance, Havlicek et al.  showed tFurthermore, DCM was originally developed specifically for fMRI data , cerebral blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO2) , and anesthetics. In particular, effective connectivity changes have been demonstrated in thalamo-cortical, cortico-thalamic, and cortico-cortical (both association and sensory cortices) connections. Under propofol, ketamine, or isoflurane, selective disruption of frontal to parietal feedback has been widely replicated in human subjects and rats  fronto-parietal  that are due to endogenous (autonomous) dynamics, not exogenous experimental inputs  modeling feedforward neurovascular coupling ; and (2) incorporating a viscoelastic effect in the Balloon model. Experiments by Lindauer et al.  and PoweLastly, the BOLD signal change equation was modified to accommodate different magnetic field strengths. k1, k2, and k3 are parameters that reflect baseline physiological properties of brain tissue and acquisition parameters and have been adjusted to depend on different magnetic field strengths. Moreover, they also suggest revised values for ε  and r0  depending on acquisition sequence (gradient echo vs. spin echo) .In summary, the most important factors to consider when applying DCM in NHP under anesthesia are cerebrovascular physiology as well as anesthetic-induced changes in neural and BOLD response dynamics. Moreover, caution is necessary when interpreting results if the model includes regions or networks in which effective connectivity may be modulated by anesthetics such as: (1) fronto-parietal; (2) sensory-motor ; and (3) thalamocortical networks specifically involving somatosensory and motor function.The integrated P-DCM (Havlicek et al., This paper presents the motivations for applying DCM to NHP fMRI and potential strategies for addressing anesthetic effects on neuronal activity and BOLD response, which is pertinent in primate neuroimaging under anesthesia. Clearly, a number of challenges remain. For example, the validity of this physiologic-stochastic DCM integration needs to be established. Additionally, its applicability to resting state data is also an interesting consideration. Stochastic DCM has been validated in resting-state fMRI (Razi et al., DJ and DD conceived the review focus, reviewed the literature, finalized the manuscript, and approved the final version of the manuscript. DJ summarized the literature review and wrote the first draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


It has been known since the time of patient H. M. and Karl Lashley’s equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for “reverse-learning,” and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions. Each evening we fall into an offline state defined by a diminished responsiveness to the environment, attenuated movement, intrinsically organized brain activity and bizarre thought patterns. It is during these sleep epochs that brains stabilize important, and erase unnecessary, information gleamed from waking experience in an attempt to ensure adaptive future behavior and efficient management of neural resources.What follows is a general overview of sleep, oscillations, synaptic plasticity, neuromodulation, known interactions between these processes, memory consolidation and forgetting. Following the general overview of relevant topics, the remainder of this article is dedicated to reviewing what is known about the link between non-rapid eye movement (NREM) and rapid eye movement sleep (REMS) oscillations, memory consolidation and forgetting. As well as, advocating for a candidate cellular mechanism of sleep-dependent memory consolidation, speculating on potential sleep-dependent forgetting mechanisms and discussing a largely notional model for NREM-REMS cycling wherein sequential sleep stages serve to first lay down and edit and then stabilize and integrate engrams. Taken together, the aim of this article is three-fold: (I) to convince the reader that sleep oscillations act and interact in a coordinated manner to activate windows of synaptic plasticity; (II) to promote the notion that sleep oscillations, by activating transient windows of synaptic plasticity, mediate bi-directional memory processing; and (III) to advance a primarily notional model for NREM-REMS cycling compatible with objectives I and II.Sleep is irresistible, necessary for survival and neurobiologically complex. Sleeps heterogeneous architecture can be deconstructed into NREM and REMS stages occupying 80% and 15%–20% of the offline period, respectively. NREMS is defined by a high-voltage, low frequency, synchronous electroencephalographic (EEG) pattern, vague, disconnected, mundane mentation and parasympathetic dominance  brain waste clearance by the perivascular glymphatic system  aqueous humor distribution  and efficacy weakening long-term depression  reverberation  would quickly become saturated. Such saturation would prevent the useful application of acquired knowledge and diminish any capacity for future learning. For these reasons, information stored by the brain needs to be regularly and selectively cleared. The clearance of information, characterized by a decreased accessibility and/or fidelity of said information and termed forgetting, is therefore crucial for healthy brain function, adaptive behavior and learning.Forgetting can occur in an active or passive manner. Active forgetting is deliberate, organized and acts to selectively clear unnecessary information from the brain. Comparatively, passive forgetting is unintentional and disadvantageous, often causing adaptive information to become decreasingly available and veridical. Notably, the active forgetting described here refers to a series of adaptive, sleep-dependent processes organized for gist extraction and the clearance of unimportant memories and differs from other forms of active forgetting described elsewhere, including intrinsic and motivated forgetting  neurogenesis-dependent circuit rearrangement/overwriting , cortical SOs (VI and VIII) and hippocampal theta rhythm (VII), as discussed below. Notably, mechanisms VI and VIII are similar in that both can be realized by SOs. The distinction between the two is that homeostatic synaptic downscaling can also arise from mechanisms which are at their core biochemical  will be divided into two subsections: first, a subsection on REMS hippocampal theta, a fundamental REMS oscillation with a documented role in memory consolidation and disintegration and second, a subsection discussing several potential consolidative and forgetting functions of non-theta REMS activity.Electrophysiologically, REMS is characterized by hippocampal theta  during NREMS information flow between the hippocampus and cerebral cortex is reversed relative to wakefulness, favoring consolidative outward flow as opposed to memory encoding inward flow . E-LTP is characterized by temporary , covalent, post-translation modifications including phosphorylation of AMPA receptors and associated proteins. Comparatively, L-LTP involves protein synthesis Huang, , cytoskeAn integrative NREMS E-LTP REMS L-LTP memory consolidation model aligns well with the tendency for daytime naps to be occupied by REMS  the described NREMS E-LTP REMS L-LTP model; (II) a model in which the REMS expression of immediate early genes, triggered by NREMS oscillations Ribeiro, , finalizMemories are remembered during NREM (SPW) and REM (theta) sleep for two reasons operating in anti-parallel: first, to consolidate recently encoded information at the systems and/or synaptic level—perhaps through a mechanism where NREMS lays down and edits an unstable cortical trace which is then stabilized and integrated by REMS—and second, to gradually erase hippocampal memory traces as the information represented by these traces is consolidated to stable neocortical storage. Additional oscillations act to fine tune the brains memory bank during sleep by interacting with ripples to stabilize adaptive information  and by acting in isolation to remove irrelevant, non-adaptive data (noise); a diagrammatic summary of the discussed sleep rhythms, their origins, impact on synapse strength and memory function is included as JL wrote the article and designed the figures.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Digital interventions are proposed as one way by which effective treatments for self-harm and suicidal ideation may be improved and their scalability enhanced. Mobile devices offer a potentially powerful medium to deliver evidence-based interventions with greater specificity to the individual when the intervention is needed. The recent proliferation of publicly available mobile apps designed for suicide prevention underlines the need for robust evidence to promote safe practice.This review aimed to examine the effectiveness of currently available mobile health  technology tools in reducing suicide-specific outcomes.The following databases were searched: Cochrane Central Register of Controlled Trials (The Cochrane Library), MEDLINE, EMBASE, PsycINFO, and relevant sources of gray literature. All published and unpublished randomized controlled trials (RCTs), pseudo-RCTs, and pre-post observational studies that evaluated the effectiveness of mHealth technology in suicide prevention delivered via mobile computing and communication technology were included. Studies were included if they measured at least one suicide outcome variable . A total of 2 review authors independently extracted data and assessed study suitability, in accordance with the Cochrane Collaboration Risk of Bias Tool, on July 31, 2018. Owing to the heterogeneity of outcomes found across studies, results were not amenable for pooled synthesis, and a meta-analysis was not performed. A narrative synthesis of the available research is presented here.A total of 7 studies met criteria for inclusion . Four published articles that reported on the effectiveness of the following mobile phone apps were included: iBobbly, Virtual Hope Box, BlueIce, and Therapeutic Evaluative Conditioning. Results demonstrated some positive impacts for individuals at elevated risk of suicide or self-harm, including reductions in depression, psychological distress, and self-harm and increases in coping self-efficacy. None of the apps evaluated demonstrated the ability to significantly decrease suicidal ideation compared with a control condition. In addition, 3 unpublished and recently completed trials also met criteria for inclusion in the review.Further research is needed to evaluate the efficacy of stand-alone mHealth technology–based interventions in suicide prevention. The small number of studies reported in this review tentatively indicate that such tools may have a positive impact on suicide-specific outcomes. Future mHealth intervention evaluations would benefit from addressing the following 3 main methodological limitations : (1) heterogeneity of outcomes: a lack of standardized measurement of suicide outcomes across studies; (2) ecological validity: the tendency to exclude potential participants because of the elevated suicide risk may reduce generalizability within clinical settings; and (3) app regulation and definition: the lack of a standardized classification system for mHealth intervention type points to the need for better definition of the scope of such technologies to promote safe practice.PROSPERO CRD42017072899; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=72899RR2-10.2196/resprot.8635 More than 800,000 people die by suicide every year globally, accounting for 1.4% of all deaths worldwide , resultifactors to risk algorithms. Furthermore, in an attempt to improve the accuracy of suicide estimates, Kristoufek et al [In a meta-analysis of studies that have attempted to longitudinally predict suicidal thoughts or behavior-related outcomes, Franklin et al  found thek et al  found thIn parallel, suicidology researchers have argued that Ecological Momentary Assessment (EMA)—high-frequency data collection in an individual’s usual environment—provides the potential for the development of temporal, individualized prediction of risk states. Thompson et al  tested tmHealth is a component of electronic health . The Global Observatory for eHealth defines mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs) and other wireless devices” . AccordiAlthough effective face-to-face treatments for self-harm and suicidal ideation are available -21, acceHowever, the regulation of such technologies and the identification of boundaries in terms of their clinical utility require further attention . In the Despite the motivation to use mHealth technologies, there is a dearth of specific outcomes data on the efficacy of mHealth technology interventions on suicide outcomes. In 2014, Christensen et al  conducteAs digital technology may be particularly attractive for young people, it is essential that mHealth technology and mobile phone apps, in particular, are subject to research evaluation  and co-dThe objective of this review was to examine the effectiveness of currently available mHealth technology tools in reducing suicide-specific outcomes in individuals taking part in a suicide prevention intervention delivered via mHealth technology.Following the rise in the number of publicly available mHealth technology tools for suicide prevention, a review of the efficacy of this modality on suicide-specific outcomes is required. A review of the content and usability of available tools has been undertaken , and empThis review aimed to address the following research question: do suicide prevention mHealth technology tools effectively reduce suicide-specific outcomes?The study procedure has been developed in line with the author’s proposed protocol , the Preactive or inactive control groups were eligible for inclusion. Inactive control groups were those in which participants received no intervention during the trial period (or were placed on a waiting list). Active control groups were those that used apps (not aimed at suicide outcomes), face-to-face interventions, or other forms of patient contact to control for the time/attention given to those in the intervention condition. Studies comparing mHealth-based interventions with antidepressants were eligible for inclusion.No restrictions were placed on diagnoses or any clinical or demographic characteristics of eligible samples. Studies using The types of studies included were all published and unpublished RCTs, pseudo-RCTs, and pre-post observational studies, which evaluated the effectiveness of mHealth technology in suicide prevention. Studies were included if the full report was accessible in English. Only studies that evaluated mobile tools relating specifically to suicide prevention or where suicidality is explicitly mentioned were included.Participants were individuals who took part in a suicide prevention intervention via mHealth technology. No restriction was placed on the age or gender of participants included in the studies reviewed. mHealth technology represents a modality that is accessible across the lifespan. However, the age of participants included in each study was noted in this review, and, where this information was available, it was used to draw conclusions regarding the efficacy of this modality for specific age groups.Included studies reported on a suicide prevention intervention delivered via mHealth technology, that is, interventions aimed to reduce suicide risk by employing mobile communication or mobile computing technology. The review included studies with psychological and nonpsychological interventions . As defined by Slattery et al  in a proIncluded studies comprised at least one suicide-specific outcome. This could include suicidal behavior, nonsuicidal self-injurious behavior, suicidal ideation, and suicidal intent.Secondary outcomes were symptoms of depression or anxiety, as measured using administered or self-reported scales, where this information was available.All databases were searched from their start date. Studies were included if a full-text paper in English was made available, either through databases or through contact with the study authors. The following databases were searched: MEDLINE, EMBASE, PsycINFO, and Cochrane Central Register of Controlled Trials (The Cochrane Library). Although the same search strategy was used for each database, appropriate changes were made to accommodate the different interfaces. Details of the search strategy are provided in Clinical trial registries were searched to identify completed and in-progress trials. This included ClinicalTrials.gov, the metaRegister of controlled trials, and the WHO International Clinical Trials Registry Platform. Grey literature was searched using the OpenGrey database, which included technical or research reports, doctoral dissertations, and conference papers from the previous 5 years.Journal of Medical Internet Research and a separate search in the PubMed database were also conducted to identify any additional relevant studies.The reference lists of relevant systematic reviews and of included studies were also searched to identify additional studies that may be relevant. A handsearch of the mobile* OR mobile phone* OR cell* phone* OR mobile health OR m-health OR mhealth OR mobile app* OR mobile technolog* OR text messag* OR smartphone* OR personal digital assist* OR patient monitoring device*suicid* OR suicid* gesture* OR suicid* behavio* OR suicid* idea* OR suicid* attempt* OR self?mutilat* OR self?harm* OR self?injur* OR suicid* intent* OR deliberate self?harm* OR deliberate self?injur*trials OR randomised controlled trials OR randomized controlled trialsThe review team managed and conducted the review and had experience in systematic review methods, information retrieval, and statistics. Furthermore, 2 independent investigators (KF and EH) judged article eligibility, with any disagreements resolved through discussion with a third reviewer (RM). In addition, 3 researchers were involved so that measures to minimize bias and error were implemented at all stages of the review.In addition to the review team, a broader research group was consulted at various stages, including individuals  with expertise in the areas of computer science, clinical psychology, and suicide prevention policy.Studies that were identified by the search strategy were managed using EndNote X8 . MembersA data extraction form was created before data extraction. Data were extracted independently by 2 reviewers and verified by another reviewer using a customized form. Where the necessary outcome data were unavailable, the study authors were contacted. The authors were not blind to the study author, institution, or journal. Data were extracted relevant to the following categories: (1) study population and design, (2) intervention, and (3) outcome. Tables were created to present the characteristics of the studies included, containing the following information, where available: participant characteristics, geographic location, assessment periods, assessment/screening measures, description of intervention and comparison interventions, primary and secondary outcomes, theoretical basis, therapeutic content, mode of delivery , suicide prevention strategies, behavior change techniques, control condition, intensity and frequency of use, and treatment engagement (retention and attrition).The reviewers independently assessed the risk of bias using the recommended Cochrane Collaboration’s Risk of Bias Tool  to assesNarrative Synthesis in Systematic Reviews tool [The level of heterogeneity was taken into account when considering the suitability of the data for a meta-analysis. A small number of studies were identified with a large amount of heterogeneity present. The data were not amenable to synthesis, and a meta-analysis was not conducted. A full narrative review was undertaken using the ews tool .The narrative synthesis involved the following elements:Developing a theory—the theoretical basis of the evaluated interventions was identified.Tabulation of data—extracted data from included studies included data on participants, interventions, outcome measures, country of origin, duration, delivery of the intervention, number of participants in each group, context in which intervention was delivered, results, and author comments.Textual descriptions summarizing each study were extracted to capture additional findings not identified during earlier stages of the synthesis.Vote counting as a descriptive tool—identified studies where the effect of the intervention was positive and statistically significant.The PRISMA flowchart of the study selection process is presented in Any disagreements regarding study eligibility were resolved following consensus discussions between the 3 reviewers . The lead authors of the 3 unpublished studies were contacted for further information on program design, study design, data analysis, and methodology, as required. Overall, 4 published studies were included in this review (one of which described 3 RCTs), reporting on a total of 624 participants. A summary of the published studies included in this review is presented in The studies were conducted in the United States , Australseriously contemplating or planning a suicide attempt [A total of 4 studies met the criteria for inclusion in the review, including 2 RCTs ,49, 1 pi attempt . Of the  attempt , whereas attempt ,50.In terms of therapeutic modalities, app content was informed by acceptance and commitment therapy (ACT) , dialectSuicide-specific outcomes were reported in each study. These included suicide ideation ,50, selft58.1=2.40; P=.02), the interaction of intervention arm by time (pre- vs postintervention) was not significant . Estimated marginal means show that any difference between change in the 2 arms arose because of a slight but nonsignificant difference  in mean baseline status between the 2 arms. Bush [In terms of efficacy, 2 studies described a statistically significant positive effect of the mobile app intervention on 1 or more suicide outcomes. Franklin’s analyses of 3 studies reported that TEC produced moderate reductions for all self-injurious thoughts and behaviors except suicidal ideation. Reductions were reported for self-cutting episodes (32%-40%), suicide plans (21%-59%), and suicidal behaviors (33%-77%) . TEC effms. Bush  measuredt49=−1.77; P=.04; Cohen d=0.47). Diminished aversion toward the self was associated with less self-cutting , nonsuicidal self-injury , suicidal ideation , and suicide plans . Stallard [t31=2.11; P=.04; 95% CI 0.17-9.64) on postuse symptoms of depression (Mood and Feelings Questionnaire) and 13.53 on symptoms of anxiety , which was evident across all anxiety subscales. All studies reported significant efficacy of the app interventions on secondary outcomes. Bush et al  found thRisk of bias in the 4 published studies was evaluated using the Cochrane Collaboration Risk of Bias Tool . OverallIn all, 3 completed but unpublished trials that met the criteria for inclusion are described in further detail in prescriptible mHealth apps that are currently available, proven effective, and preferably stand-alone. The paucity of robust evidence evaluating the efficacy of mHealth interventions on suicide outcomes indicates that there is not yet sufficient research to support the prescribing of stand-alone mHealth tools for suicide prevention.However, the evaluation studies included here indicate that mHealth tools show promise for individuals at elevated risk of suicide and self-harm and can be a useful adjunct to face-to-face therapy. This review confirms and updates the findings of Witt et al  who idenOverall, the evaluated mHealth technology interventions demonstrated some positive results for individuals at elevated risk of suicide or self-harm, including reductions in depression, psychological distress, and self-harm, and increases in coping self-efficacy. Mobile apps are one mechanism by which the scalability of effective interventions for suicide prevention can be improved . The useHowever, neither the iBobbly nor Virtual Hope Box or the TEC apps demonstrated the ability to significantly decrease suicidal ideation compared with a control condition. The studies included reported a positive impact on secondary outcomes, such as measures of depression and anxiety. This confirms parallel findings within face-to-face interventions. Insufficient evidence exists to suggest that CBT focusing on mental illness reduces suicidal cognitions and behaviors. CBT specifically focusing on suicidal cognitions and behaviors has been found to be effective . Such inIn terms of clinical utility, suicide prevention mobile apps can be most beneficial for individuals at risk of suicide, particularly those with decreased help-seeking behaviors, when delivered as an adjunct to therapy, and when they deliver suicide-specific interventions. Future researchers could leverage the current and existing research findings by combining the delivery via mobile app of EMA with evidence-based psychological interventions. In-built EMA in suicide prevention apps has the potential to provide individualized time-stamped data spanning biological, social, and psychological variables, alongside behavioral measures of app usage and engagement to facilitate a greater understanding of suicide processes. The current evidence points to the need to focus on more dynamic intervention approaches, such as Just-In-Time Adaptive Interventions (JITAIs).A significant limitation found across all studies pertained to the heterogeneity of the outcomes studied. This hampered the extent to which data could be compared and generalized. Diversity in nomenclature continues to challenge research in this area. Although in the United States, distinction is frequently made between nonsuicidal self-injury and suicidal behavior , for exaSimilarly, no 2 studies used the same suicide outcome measure in this review. Studies used combinations of suicide-specific measures, single suicide-specific items, including measures assessing mental health more broadly, and self-report. Heterogeneity of outcomes evident across studies may be reflective of difficulties within research and clinical practice in identifying homogenous subgroups of self-harming patients . It is iA further methodological issue, which is likely to reduce ecological validity, is the practice of excluding individuals at increased risk of suicide from participation. Importing empirically supported treatments for depressed adolescents or suicidal adolescents may not be appropriate because the trials in which efficacy was established excluded suicidal teens . SimilarThe findings highlight broader issues in relation to regulation and policy pertaining to mHealth technology. Some have argued that it is “now critical, even if paradoxical, to draw new boundaries in the seemingly boundless world of digital health” . Indeed,This review extends the reviews undertaken by Larsen et al  and WittRisk of bias assessment indicated methodological issues across studies, which could be addressed in future research. The small sample sizes across studies and attrition during studies limited the generalization of findings. Blinding procedures were problematic across studies, and the small number of research-validated suicide prevention apps available makes comparisons with similar interventions difficult. These difficulties may signal a need for a shift in focus from traditional research methods investigating unidirectional cause-effect relationships to examining dynamic systems by applying machine learning approaches to big data.The 4 completed and published studies included in this review evaluated the iBobbly, Virtual Hope Box, BlueIce, and TEC apps. Together, results evaluating the apps show some positive results for individuals at elevated risk of suicide or self-harm, including reductions in depression, psychological distress, and self-harm and increases in coping self-efficacy. Despite the continued growth in the development and availability of suicide prevention mHealth tools, there is currently limited evaluative research on the efficacy of such tools in reducing suicide-specific outcomes.However, neither the use of iBobbly nor Virtual Hope Box or the TEC apps demonstrated the ability to significantly decrease suicidal ideation compared with a control condition. Further evaluation studies would benefit from addressing 3 main methodological issues, which arose across studies: (1) heterogeneity of outcomes, (2) exclusion of individuals at higher risk of suicide, and (3) performance biases arising because of blinding procedures. Although the evidence available indicates some progress, the pace of suicide prevention app development needs to be matched by a greater focus on empirically supported mHealth technology–based interventions. Future research endeavors would benefit from leveraging the current findings with existing EMA research to support the development and evaluation of dynamic interventions (such as JITAIs) delivered via mobile apps to provide effective intervention while simultaneously enhancing our understanding of suicide processes.