A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer

Nasimian, Ahmad; Ahmed, Mehreen; Hedenfalk, Ingrid; Kazi, Julhash U.

doi:10.1016/j.csbj.2023.01.020

Cited by 8 publications

(6 citation statements)

References 66 publications

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“…Machine learning models: A brief description of machine learning models was described previously [ 77 , 78 ]. Pharmacogenomic data were collected from BeatAML, TCGA, and scDEAL studies.…”

Section: Methodsmentioning

confidence: 99%

A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia

Nasimian

Ashiri

Ahmed

et al. 2023

IJMS

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Despite incredible progress in cancer treatment, therapy resistance remains the leading limiting factor for long−term survival. During drug treatment, several genes are transcriptionally upregulated to mediate drug tolerance. Using highly variable genes and pharmacogenomic data for acute myeloid leukemia (AML), we developed a drug sensitivity prediction model for the receptor tyrosine kinase inhibitor sorafenib and achieved more than 80% prediction accuracy. Furthermore, by using Shapley additive explanations for determining leading features, we identified AXL as an important feature for drug resistance. Drug−resistant patient samples displayed enrichment of protein kinase C (PKC) signaling, which was also identified in sorafenib−treated FLT3−ITD−dependent AML cell lines by a peptide−based kinase profiling assay. Finally, we show that pharmacological inhibition of tyrosine kinase activity enhances AXL expression, phosphorylation of the PKC−substrate cyclic AMP response element binding (CREB) protein, and displays synergy with AXL and PKC inhibitors. Collectively, our data suggest an involvement of AXL in tyrosine kinase inhibitor resistance and link PKC activation as a possible signaling mediator.

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“…Machine learning models: A brief description of machine learning models was described previously [ 77 , 78 ]. Pharmacogenomic data were collected from BeatAML, TCGA, and scDEAL studies.…”

Section: Methodsmentioning

confidence: 99%

A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia

Nasimian

Ashiri

Ahmed

et al. 2023

IJMS

Self Cite

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“…The principal components from the PCA have been used as input. In the studies reported by Nasimian et al [21], they used a similar approach and trained a deep learning model with a much bigger patient cohort with 2616 samples. The best performance of their model in predicting the platinum resistance status of patients had an f1-score of 83.1.…”

Section: Prediction Of Platinum Response Statusmentioning

confidence: 99%

“…In this study, we use differential gene expression analysis to identify initial biomarkers associated with outcome and apply SHAP (Shapley additive explanations) algorithm for feature extraction, which has so far been used only once in ovarian cancer data for platinum sensitivity and to our knowledge, not on outcome prediction [20,21]. We analyze one of the largest publicly available gene expression data sets for ovarian cancer to predict the outcome of patients with ovarian cancer and find candidate biomarkers associated with differential outcomes or responses to chemotherapy.…”

Section: Introductionmentioning

confidence: 99%

A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning

Schilling,

Beyerlein,

Chien

2023

Algorithms

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The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.

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“…Recent research endeavors utilizing disease-centric and drug-specific biological attributes have exhibited notable predictive efficacy. 4 , 5 , 6 , 7 , 8 Nevertheless, these methodological approaches necessitate substantial investment in terms of time and resources and demand a synergetic collaboration between researchers in the fields of biology and data science to ensure the effectiveness and rationality of the strategy.…”

Section: Introductionmentioning

confidence: 99%

AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

Nasimian,

Younus,

Tatli

et al. 2024

Patterns

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A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer

Cited by 8 publications

References 66 publications

A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia

A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia

A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning

AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

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