A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences

Xiong, Danyi; Zhang, Ze; Wang, Tao; Wang, Xinlei

doi:10.1016/j.csbj.2021.05.038

Cited by 21 publications

(39 citation statements)

References 54 publications

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“…The multiple instance learning standard assumption meets the biological assumptions that responders (“positive bags”) must harbor at least one true neoantigen (“positive instance”) while non-responders (“negative bags”) harbor only neoantigen candidates that cannot trigger anti-tumoral activity (“negative instances”) 17 . The MILES (Multiple-Instance Learning via Embedded Instance Selection) 34 algorithm was chosen as the algorithm of choice in this study as it performed well in a previous benchmarking study related to cancer detection based on TCR sequences 18 .…”

Section: Resultsmentioning

confidence: 99%

“…The MILES (Multiple-Instance Learning via Embedded Instance Selection) 34 algorithm was chosen as the algorithm of choice in this study as it performed well in a previous benchmarking study related to cancer detection based on TCR sequences 18 .…”

Section: Neoantigen Candidate Profiles Are Heterogeneous In Cancer Pa...mentioning

confidence: 99%

“…For our analysis, patients are referred to as bags and the clinical response to ICB is used as the bag label. Multiple instance learning has been used in the field of cancer immunology for distinguishing tumor from normal samples on their T-cell receptor (TCR) sequence profiles 18 and for predicting T-cell infiltration on neoantigen candidate profiles 19 .…”

Section: Introductionmentioning

confidence: 99%

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Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

Lang

Sorn

Schrörs

et al. 2022

Preprint

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A successful response to immune checkpoint blockade treatment (ICB) depends on the functional re-invigoration of neoantigen-specific T cells and their anti-tumoral activity. Previous studies showed that the patient's neoantigen candidate load is an imperfect predictor of the response to ICB. Further studies provided evidence that the overall response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. To our knowledge, this is the first study to predict the response to ICB therapy based on qualitative neoantigen candidate profiles in the context of the mutation type, using a multiple instance learning approach. Multiple instance learning is a special branch of machine learning which classifies labelled bags that are formed by a set of unlabeled instances. The multiple instance learning approach performed systematically better than random guessing and was independent of the neoantigen candidate load. Qualitative modeling performed better in comparison to the quantitative approach, in particular for modelling low-abundant fusion genes. Our findings suggest that multiple instance learning is an appropriate method to predict immunotherapy efficacy based on qualitative neoantigen candidate profiles without relying on direct T-cell response information and provide a foundation for future developments in the field.

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Section: Resultsmentioning

confidence: 99%

Section: Neoantigen Candidate Profiles Are Heterogeneous In Cancer Pa...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

Lang

Sorn

Schrörs

et al. 2022

Preprint

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“…Finally, bag-based approaches use distance functions that capture the similarity between patients as input for modified ML models. Weber et al ( 2019 ) and Nowicka et al ( 2019 ) proposed mapping-based approaches on single-cell data, whereas Cheplygina et al ( 2014 ) and Xiong et al ( 2021 ) compared both instance-based and bag-models on imaging data and next-generation sequencing data, respectively.…”

Section: Challenges In Computational Analysismentioning

confidence: 99%

Challenges in translational machine learning

et al. 2022

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Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

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“…In MIL, an object, called a bag, contains a set of instances. MIL was introduced in [3] to solve the problem of drug activity prediction, but many other studies have already applied this approach successfully, such as image classification [4], cancer detection via images or sequences [5,6], text categorization [7], speaker recognition [8] and web mining [9]. Amongst the characteristics of problems that are fit to be solved by MIL approaches are those in week supervision scenarios that do not work well with standard machine learning pipelines [10].…”

Section: Introductionmentioning

confidence: 99%

A Visual Mining Approach to Improved Multiple- Instance Learning

Castelo

Ponti²,

Minghim³

2021

Algorithms

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Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming an MIL problem into standard supervised learning. Visualization can be a useful tool to assess learning scenarios by incorporating the users’ knowledge into the classification process. Considering that multiple-instance learning is a paradigm that cannot be handled by current visualization techniques, we propose a multiscale tree-based visualization called MILTree to support MIL problems. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing users to understand the MIL datasets in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help users improve the model even further. Our methods can handle both binary and multiclass scenarios. In our experiments, SVM was used to build the classifiers. With support of the MILTree layout, the initial classification model was updated by changing the training set, which is composed of the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can support exploring and improving models in MIL scenarios and that our instance selection methods outperform the currently available alternatives in most cases.

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A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences

Abstract: Graphical abstract

Cited by 21 publications

References 54 publications

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

Challenges in translational machine learning

A Visual Mining Approach to Improved Multiple- Instance Learning

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