Deep transfer learning for drug response prediction

The goal of precision oncology is to make accurate predictions for cancer patients via some omics data types of individual patients. Major challenges of computational methods for drug response prediction are that labeled clinical data is very limited, not publicly available, or has drug response for one or two drugs. These challenges have been addressed by generating large-scale pre-clinical datasets such as cancer cell lines or patient-derived xenografts (PDX). These pre-clinical datasets have multi-omics characterization of samples and are often screened with hundreds of drugs which makes them viable resources for precision oncology. However, they raise new questions: how can we integrate different data types? how can we handle data discrepancy between pre-clinical and clinical datasets that exist due to basic biological differences? and how can we make the best use of unlabeled samples in drug response prediction where labeling is extra challenging? In this thesis, we propose methods based on deep neural networks to answer these questions. First, we propose a method of multi-omics integration. Second, we propose a transfer learning method to address data discrepancy between cell lines, patients, and PDX models in the input and output space. Finally, we proposed a semi-supervised method of out-of-distribution generalization to predict drug response using labeled and unlabeled samples. The proposed methods have promising performance when compared to the state-of-the-art and may guide precision oncology more accurately

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics. 2019;35(14):I501-I509.Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online

Pelvic hydatid cyst with uncommon sciatalgia manifestation: a case report

Background: Hydatid disease or echinococcosis is a common parasitic disease of human and bovine, caused by infection with larva of the cestode echinococcus. Liver is the most common organ that is involved in this disease. Pelvic involvement and neurological symptoms, due to mass effect of pelvic involvement, in lower extremities are very uncommon manifestations of the disease.Case presentation: A forty six year old man was referred to clinic of surgery at Ghaem Hospital, Medical University of Mashhad, Iran. The patient complained about weakness and motor impairment in right lower extremity accompanied by numbness and radicular pain over past two months. Physical examination demonstrated muscular atrophy and reduced muscular strength in right lower extremity. Computed tomography and ultrasonographic studies showed a cystic mass in right side of the pelvic cavity with extention to the sciatic notch and another cystic mass in right gluteal region. Surgical operation revealed a cystic mass deep in pelvic cavity with the extention to the right sciatic notch with compression of nerve roots. The cystic mass was contained of daughter cysts which confirmed the diagnosis of hydatid cyst disease. This diagnosis was confirmed by pathologic assessment.Conclusion: Although uncommon, but hydatid disease can involve the pelvic cavity and make a pelvic, usually cystic, mass; that can make compression on nerve roots and so making neurologic symptoms in lower extremities. So in endemic areas for hydatid disease, such as Iran, pelvic hydatid cysts should be considered as a possible differential diagnosis in patients presenting with the sciatic pain and neurological manifestations in whom a pelvic mass has been found too

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

Sharifi-Noghabi H, Peng S, Zolotareva O, Collins CC, Ester M. AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics. Bioinformatics (Oxford, England). 2020;36(Supplement_1):i380-i388.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. © The Author(s) 2020. Published by Oxford University Press

Consistency of in vitro drug sensitivities within pharmacological classes

Multiple comparative analyses between the common drugs and cell lines of the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP) have previously shown low consistency between the in vitro phenotypic measures of a drug in one study with the other. While several potential sources of inconsistency have been tested, the similar targets of tested compounds has yet to be tested as a contributing factor of discrepancy. This analysis includes two methods of reclassifying drugs into classes based on their targets to identify the truer set of consistent cell lines, showing an increased correlation between the two pharmacogenomic studies