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Publications
Abstract 2993: Unmutated tumor antigens are abundant and contribute to tumor control in melanoma
Recognition of MHC-I-associated tumor antigens (TAs) by CD8+ T cells is central to antitumor immunity. Owing to the elevated tumor mutationa… (voir plus)l burden (TMB) in melanoma, the marked efficacy of immune checkpoint blockade (ICB) has been attributed to the recognition of mutated TAs. However, recent reports showed that response to ICB in melanomas with low TMB is associated with CD8+ T-cell reactivity against melanocyte lineage-associated antigens (LSAs). Here, we systematically evaluated the contribution of all TA classes, i.e., mutated and unmutated, canonical and non-canonical, to the antigenic landscape of melanoma. We characterized the TAs from melanoma biopsies and patient-derived cell lines using proteogenomics. Out of 79450 MHC-I-associated peptides (MAPs) identified from 19 samples, we found 557 unmutated TAs classified as tumor-specific (TSA), tumor-associated (TAA), or LSAs. These TAs most often derived from annotated open-reading frames, followed by ncRNAs and intergenic regions. By contrast, only 6 MAPs were mutated and tumor-specific, which could be partially explained by a decreased expression of mutations within MAP-generating genomic regions. While the number of unmutated TAs with predicted presentation (TApres) in melanoma patients was similar between responders and non-responders pre-ICB, non-responders showed marks of inefficient antigen presentation. In consequence, only responders lost TApres upon treatment, in tandem with an expansion in tumor-infiltrating lymphocytes. These results reveal a previously underappreciated contribution of unmutated TAs to tumor control in melanoma and suggest that enhancing their recognition could improve the ICB efficacy in non-responders.
Citation Format: Anca Apavaloaei, Qingchuan Zhao, Leslie Hesnard, Krystel Vincent, Marie-Pierre Hardy, Chantal Durette, Joël Lanoix, Jean-Philippe Laverdure, Jean-David Larouche, Maria Virginia Ruiz Cuevas, Grégory Ehx, Sébastien Lemieux, Pierre Thibault, Claude Perreault. Unmutated tumor antigens are abundant and contribute to tumor control in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2993.
Although deep learning (DL) models have shown great success in many medical image analysis tasks, deployment of the resulting models into r… (voir plus)eal clinical contexts requires: (1) that they exhibit robustness and fairness across different sub-populations, and (2) that the confidence in DL model predictions be accurately expressed in the form of uncertainties. Unfortunately, recent studies have indeed shown significant biases in DL models across demographic subgroups (e.g., race, sex, age) in the context of medical image analysis, indicating a lack of fairness in the models. Although several methods have been proposed in the ML literature to mitigate a lack of fairness in DL models, they focus entirely on the absolute performance between groups without considering their effect on uncertainty estimation. In this work, we present the first exploration of the effect of popular fairness models on overcoming biases across subgroups in medical image analysis in terms of bottom-line performance, and their effects on uncertainty quantification. We perform extensive experiments on three different clinically relevant tasks: (i) skin lesion classification, (ii) brain tumour segmentation, and (iii) Alzheimer's disease clinical score regression. Our results indicate that popular ML methods, such as data-balancing and distributionally robust optimization, succeed in mitigating fairness issues in terms of the model performances for some of the tasks. However, this can come at the cost of poor uncertainty estimates associated with the model predictions. This tradeoff must be mitigated if fairness models are to be adopted in medical image analysis.
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and i… (voir plus)tem representations. Many state-of-the-art (SOTA) methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks (GNNs) to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information (tags) associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts which is the correct pairing between the representations obtained from the users that have interacted with this item and the tags assigned to it. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the users’ decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose a user intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.
2023-04-03
2023 IEEE 39th International Conference on Data Engineering (ICDE) (publié)
Objective While National Surgical, Obstetric and Anaesthesia Plans (NSOAPs) have emerged as a strategy to strengthen and scale up surgical h… (voir plus)ealthcare systems in low/middle-income countries (LMICs), the degree to which children’s surgery is addressed is not well-known. This study aims to assess the inclusion of children’s surgical care among existing NSOAPs, identify practice examples and provide recommendations to guide inclusion of children’s surgical care in future policies. Design We performed two qualitative content analyses to assess the inclusion of children’s surgical care among NSOAPs. We applied a conventional (inductive) content analysis approach to identify themes and patterns, and developed a framework based on the Global Initiative for Children’s Surgery’s Optimal Resources for Children’s Surgery document. We then used this framework to conduct a directed (deductive) content analysis of the NSOAPs of Ethiopia, Nigeria, Rwanda, Senegal, Tanzania and Zambia. Results Our framework for the inclusion of children’s surgical care in NSOAPs included seven domains. We evaluated six NSOAPs with all addressing at least two of the domains. All six NSOAPs addressed ‘human resources and training’ and ‘infrastructure’, four addressed ‘service delivery’, three addressed ‘governance and financing’, two included ‘research, evaluation and quality improvement’, and one NSOAP addressed ‘equipment and supplies’ and ‘advocacy and awareness’. Conclusions Additional focus must be placed on the development of surgical healthcare systems for children in LMICs. This requires a focus on children’s surgical care separate from adult surgical care in the scaling up of surgical healthcare systems, including children-focused needs assessments and the inclusion of children’s surgery providers in the process. This study proposes a framework for evaluating NSOAPs, highlights practice examples and suggests recommendations for the development of future policies.
In this work, we evaluate three popular fairness preprocessing algorithms and investigate the potential for combining all algorithms into a … (voir plus)more robust preprocessing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.