Monitoring, a regulation of cognition component of metacognition, is an essential aspect of self-regulated learning. Monitoring is recognized as learners' ability to successfully understand what they are learning, and typically involves metacognitive activities such as questioning, reflection, drawing inferences, and self-generating feedback. However, while extant research converges on the notion that monitoring is a malleable and trainable skill, no investigation to date has systematically explored differences in monitoring accuracy effects. Therefore, a meta-analysis was conducted on research that examined the effect of learning strategy instruction on monitoring compared with a control. The meta-analysis explored how weighted effect sizes varied as a function of learning strategies used, study characteristics, and other potential moderators. A systematic search of major databases ultimately produced 56 independent effect sizes involving 7,667 participants, which were subsequently extracted and analyzed. Across the 56 studies, learning strategy instruction interventions yielded a moderate unbiased grand mean effect size (g = -.565; 95% confidence interval [-.639, -.491]), indicating improved monitoring accuracy compared to a control. Moderator analysis results revealed that the weighted mean effect size was larger for studies conducted in laboratories with adult-only samples (ranging in size from 101 to 200 participants) that used deep learning strategies, prediction and postdiction confidence judgments, the difference between prediction-postdiction judgments to calculate monitoring accuracy, and multiple-choice response options for the performance test. Weighted mean effect sizes for the type of monitoring measure, research design, learning strategy instruction duration, and geographic location did not vary significantly among studies. Educational Impact and Implications StatementThe present meta-analysis included 56 independent studies examining the influence of learning strategy instruction interventions on metacognitive monitoring accuracy. This meta-analysis supports the conclusion that learning strategy instruction interventions lead to appreciable gains in metacognitive monitoring accuracy, even after accounting for key moderators such as the learning strategies trained. Thus, research on the observed effects of learning strategy interventions aimed at improving metacognitive monitoring accuracy should continue in hopes of uncovering the most effective learning strategies to improve learning outcomes.
Successful outcome of immune checkpoint blockade in patients with solid cancers is in part associated with a high tumor mutational burden (TMB) and the recognition of private neoantigens by T-cells. The quality and quantity of target recognition is determined by the repertoire of ‘neoepitope’-specific T-cell receptors (TCRs) in tumor-infiltrating lymphocytes (TIL), or peripheral T-cells. Interferon gamma (IFN-γ), produced by T-cells and other immune cells, is essential for controlling proliferation of transformed cells, induction of apoptosis and enhancing human leukocyte antigen (HLA) expression, thereby increasing immunogenicity of cancer cells. TCR αβ-dependent therapies should account for tumor heterogeneity and availability of the TCR repertoire capable of reacting to neoepitopes and functional HLA pathways. Immunogenic epitopes in the tumor-stroma may also be targeted to achieve tumor-containment by changing the immune-contexture in the tumor microenvironment (TME). Non protein-coding regions of the tumor-cell genome may also contain many aberrantly expressed, non-mutated tumor-associated antigens (TAAs) capable of eliciting productive anti-tumor immune responses. Whole-exome sequencing (WES) and/or RNA sequencing (RNA-Seq) of cancer tissue, combined with several layers of bioinformatic analysis is commonly used to predict possible neoepitopes present in clinical samples. At the ImmunoSurgery Unit of the Champalimaud Centre for the Unknown (CCU), a pipeline combining several tools is used for predicting private mutations from WES and RNA-Seq data followed by the construction of synthetic peptides tailored for immunological response assessment reflecting the patient’s tumor mutations, guided by MHC typing. Subsequent immunoassays allow the detection of differential IFN-γ production patterns associated with (intra-tumoral) spatiotemporal differences in TIL or peripheral T-cells versus TIL. These bioinformatics tools, in addition to histopathological assessment, immunological readouts from functional bioassays and deep T-cell ‘adaptome’ analyses, are expected to advance discovery and development of next-generation personalized precision medicine strategies to improve clinical outcomes in cancer in the context of i) anti-tumor vaccination strategies, ii) gauging mutation-reactive T-cell responses in biological therapies and iii) expansion of tumor-reactive T-cells for the cellular treatment of patients with cancer.
Desesperanza en jóvenes: Un análisis por sexo josé valDéz MeDina y colaboraDores
BackgroundImmunotherapy has changed the standard of care for multiple cancers; however, its efficacy is limited. Chemotherapy and radiation had little effect in pancreatic ductal adenocarcinoma (PDAC) outcome1 in patients with metastatic disease, hence the urgency for new effective courses of treatment. Increasing evidence suggests mucosal-associated invariant T-cells (MAIT) play a role in anti-cancer T-cell responses, by recognizing transformed cells or bacterial products. MAIT respond towards microbial antigens and vitamin derivatives, produce pro-inflammatory cytokines2 3 and have been found present in primary and metastatic cancer lesions.3 4 Long-term survival PDAC patients present a unique microbiome pattern. In contrast, some microbial species may promote oncogenesis.5 6The focus of this project is the characterization of MAIT as immune effector cells in PDAC specimens.MethodsWe performed a retrospective analysis of long-term survivors (LTS) and short-term survivors (STS) patients with pancreatic cancer associating clinical endpoints with the presence of MAIT infiltration in the tumor tissue using immunofluorescence staining for MR1 (MHC class I-related gene, a MAIT ligand receptor), CD3 and TCR Vα7.2 (frequently reported chain in MAIT). Tumor infiltrating lymphocytes (TILs) were expanded and tested for recognition of microbial products presented to TILs or to PBMCs defined by cytokine production (ELISA), cytotoxicity (CD107a induction assay), CD69 or 4-1BB upregulation (flow cytometry). Reactive MAIT will be molecularly defined by deep TCR (T-cell receptor) sequencing which allows to ‘back-trace’ MR1 reactive TIL in the tumor specimen. The complex interaction of microbial antigen presentation from freshly harvested tumor specimens to TILs is being optimized for Nanolive technology that allows to follow live cell interactions for several days.ResultsTIL reactivity directed against microbial products from different bacterial species was detected by IFN-γ production and CD69 upregulation in responder TILs. A broader panel of TILs is currently being tested against bacterial species. TCRs will undergo laser microdissection for subsequent TCR repertoire sequencing. A more pronounced MAIT infiltration in close vicinity to tumor cells in LTS compared to STS is being studied, further supporting the anti-tumor role of MAIT.ConclusionsMAIT cells may exhibit anti-tumor properties, based on cytokine production and cellular marker activation. TCRs directed against cancer cells can serve as viable blueprints to engage with MR1 on PDAC recognizing tumor-associated targets or microbial products that elicit IFN-γ production. This allows to explore MAIT TCRs for adoptive therapies or distinct microbial species that drive clinically relevant responses.AcknowledgementsThe authors would like to thank to Champalimaud Foundation Biobank and Vivarium Facility at Champalimaud Foundation.Ethics ApprovalThis study was approved by the Champalimaud Foundation Ethics Committee and by Ethics Research Committee of NOVA Medical School of NOVA University of Lisbon.ConsentFor each patient, written informed consent and approval by the Ethical Committee of the Champalimaud Foundation will be obtained. The study will be in compliance with the Declaration of Helsinki.ReferencesSideras, K. et al. Role of the immune system in pancreatic cancer progression and immune modulating treatment strategies. Cancer Treat. Rev 2014;40: 513–522.Toubal A, Nel I, Lotersztajn S & Lehuen A. Mucosal-associated invariant T cells and disease. Nat Rev. Immunol 2019;19:643–657.Lukasik Z, Elewaut D & Venken K. Mait cells come to the rescue in cancer immunotherapy?Cancers (Basel). 12, 1–19 ( 2020).Vacchini A, Chancellor A., Spagnuolo J., Mori L. & De Libero G. MR1-restricted t cells are unprecedented cancer fighters. Front Immunol 2020;11:1–8.Aykut B, et al. The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL. Nature 2019:574;264–267.Pushalkar S, et al. The pancreatic cancer microbiome promotes oncogenesis by induction of innate and adaptive immune suppression. Cancer Discov 2018;8:403–416.
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