Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Berginski, Matthew E.; Jenner, Madison R.; Joisa, Chinmaya U; Loeza, Silvia G. Herrera; Golitz, Brian T.; Lipner, Matthew B.; Leary, Jack R.; Rashid, Naim U.; Johnson, Gary L.; Yeh, Jen Jen; Gomez, Shawn M.

doi:10.1101/2021.07.21.451515

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The high-throughput screening data gathered in PDAC patient-derived cell lines was gathered from Lipner et al [14] with methods as described in Berginski et al 2021 [15].…”

Section: Methodsmentioning

confidence: 99%

“…We then further validated the model by predicting inhibitor effects from collected RNAseq data in tumor (two) and stroma (one) derived cell lines from PDAC patients [14,15]. Importantly, these patient-derived cell lines were profiled for baseline gene expression in-house, and represent a novel and highly heterogeneous transcriptional landscape which the model has not seen before.…”

Section: Experimental Validation Of Model Predictions Were Successful...mentioning

confidence: 99%

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Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Joisa

Chen

Berginski

et al. 2022

Preprint

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Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Correspondingly, their central role in signaling implicates kinome dysfunction as a common driver of cancer, where numerous kinases have been identified as having a causal or modulating role in cancer development and progression. Driven by their importance, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Given the growing importance of kinase-targeted therapies, linking the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9), and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. Further, we validated these models experimentally by testing predicted effects in breast cancer cell lines, and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

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“…The high-throughput screening data gathered in PDAC patient-derived cell lines was gathered from Lipner et al [14] with methods as described in Berginski et al 2021 [15].…”

Section: Methodsmentioning

confidence: 99%

Section: Experimental Validation Of Model Predictions Were Successful...mentioning

confidence: 99%

Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Joisa

Chen

Berginski

et al. 2022

Preprint

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Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies

Joisa,

Chen,

Beville

et al. 2023

Preprint

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Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2∼ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model’s ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2∼ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2∼ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

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Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors

Joisa,

Chen,

Berginski

et al. 2023

PeerJ

View full text Add to dashboard Cite

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

show abstract

Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Cited by 3 publications

References 29 publications

Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors

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