Deep Genomic Signature for early metastasis prediction in prostate cancer

Sharifi-Noghabi, Hossein; Liu, Yang; Erho, Nicholas; Shrestha, Raunak; Alshalalfa, Mohammed; Davicioni, Elai; Ester, Martin

doi:10.1101/276055

Cited by 13 publications

(11 citation statements)

References 43 publications

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“…To evaluate the performance of Velodrome on patients, we followed the experimental design of previous pharmacogenomics methods and designed an association study based on the known associated target genes for the investigated drugs (Geeleher et al 2017; Mourragui et al 2019; Hossein Sharifi-Noghabi et al 2019; Hossein Sharifi-Noghabi, Peng, et al 2020). In this analysis, we employed the TCGA Kidney cancer cohort (TCGA-KIRC) as a tissue type well represented in our cell line datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Various methods of transfer learning have been proposed in the context of drug response prediction. These methods either address these discrepancies implicitly (Hossein Sharifi-Noghabi et al 2019; Snow et al 2020; Kuenzi et al 2020), or explicitly which means they assume that the model has access to the desired labeled or unlabeled target domain during training (Hossein Sharifi-Noghabi, Peng, et al 2020; Mourragui et al 2019, 2020; Ma et al 2021; Zhu et al 2020; Salvadores, Fuster-Tormo, and Supek 2020; Najgebauer et al 2020; Peres da Silva, Suphavilai, and Nagarajan 2021; Warren et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Sharifi-Noghabi

Harjandi

Zolotareva

et al. 2021

Preprint

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Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid this assumption, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients and therefore, may guide precision oncology more accurately.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Sharifi-Noghabi

Harjandi

Zolotareva

et al. 2021

Preprint

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“…For example, in slow progressing cancers such as prostate cancer, large patient datasets with gene expression and short-term clinical data (source domain) are available; however, patient datasets with long-term clinical data (target domain) are small. AITL may be beneficial to learn a model to predict these long-term clinical labels using the source domain and its short-term clinical labels ( Sharifi-Noghabi et al , 2019a ). Finally, although we designed the multi-task subnetwork for a regression task on the source domain and a classification task on the target domain, in principle, AITL can easily be modified to incorporate different types of outputs.…”

Section: Resultsmentioning

confidence: 99%

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

et al. 2020

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Motivation The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. Availability and implementation https://github.com/hosseinshn/AITL. Supplementary information Supplementary data are available at Bioinformatics online.

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“…For example, in slow progressing cancers such as prostate cancer, large patient datasets with gene expression and short-term clinical data (source domain) are available, however, patient datasets with long-term clinical data (target domain) are small. AITL may be beneficial to learn a model to predict these long-term clinical labels using the source domain and its short-term clinical labels (Sharifi-Noghabi et al, 2019a). For future research directions, we believe that the TCGA dataset consisting of gene expression data of more than 12,000 patients (without drug response outcome) can be incorporated in an unsupervised transfer learning setting to learn better features that are domain-invariant between cell lines and cancer patients.…”

Section: Discussionmentioning

confidence: 99%

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

Noghabi

Peng

Zolotareva

et al. 2020

Preprint

Self Cite

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Motivation: the goal of pharmacogenomics is to predict drug response in patients using their singleor multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: 1) in the input space, the gene expression data due to difference in the basic biology, and 2) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. Availability of codes and supplementary material: https://github.com/hosseinshn/AITL Contact: ccollins@prostatecentre.com and ester@cs.sfu.ca © 1

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Deep Genomic Signature for early metastasis prediction in prostate cancer

Cited by 13 publications

References 43 publications

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

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