Count me in: A patient-driven research initiative to accelerate cancer research.

Wagle, Nikhil; Allen, Eliezer M. Van; Bass, Adam J.; Anastasio, Elana; Dunphy, Michael; McGillicuddy, Mary; Stoddard, Rachel; Balch, Sara; Thomas, Beena; Tomson, Brett N.; Nguyen, Colleen; Jain, Esha; Wankowicz, Stephanie A.; Palma, Jim; Maiwald, Simone; Baker, Esme O.; Zimmer, Andrew; Golub, Todd R.; Lander, Eric S.

doi:10.1200/jco.2018.36.15_suppl.e13501

Cited by 5 publications

(10 citation statements)

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“…In order to have every patient benefit from genomically-informed medicine, not just the ones who have a mutation that matches an FDA approved drug, we need to build models that predict how patients will respond to drugs, regarding both efficacy and toxicities. To get there, we need large well-curated datasets, such as those being created by efforts like Count Me In [Wagle et al, 2018], biomedical devices to economically capture relevant patient features such as liquid biopsy [Siravegna et al, 2017], and careful data handling strategies and machine learning algorithms that leverage existing biological knowledge. Here we show how best practices in machine learning may be combined to create a drug recommendation system that outperforms tissue type based recommendations.…”

Section: Discussionmentioning

confidence: 99%

A Drug Recommendation System (Dr.S) for Cancer Cell Lines

et al. 2020

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Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.

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Section: Discussionmentioning

confidence: 99%

A Drug Recommendation System (Dr.S) for Cancer Cell Lines

et al. 2020

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“…We analyzed the transcriptomics profiles from 932 CCLE cell lines, 434 patient-derived tumor xenografts, 10'550 patient tumors from TCGA, 406 metastatic tumors from MET500 29 and 203 breast tumors from Count Me In (CMI) 16 . Integrating these datasets by performing dimensionality reduction with 2D Uniform Manifold Approximation and Projection (UMAP), reveals a clear separation of samples based on their origin (Fig.…”

Section: Global Pan Cancer Alignment Of Transcriptional Profiles From...mentioning

confidence: 99%

“…In addition, gene expression profiles of >400 patient-derived tumor xenograft (PTX) models are available via the Novartis Institutes for Biomedical Research Patient-derived Tumor Xenograft Encyclopedia 9 . Comprehensive molecular characterization of primary and metastatic tumors along with clinical data from >11,000 patients are available from the The Cancer Genome Atlas (TCGA) 14 , MET500 15 and Count Me In (CMI) 16 projects. These efforts provide a powerful opportunity to unravel the systematic differences between cancer cell lines, xenograft models and patient tumors, and to identify the cancer models that sufficiently recapitulate the biology of patient tumors without relying on clinical annotations.…”

Section: Introductionmentioning

confidence: 99%

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning

Dimitrieva

Janssens

et al. 2022

Preprint

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Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals. To address these challenges, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived tumor xenografts and 11159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models to resemble the ones of clinical tumors, and therefore can be used to improve the clinical translation of insights gained from pre-clinical models. As a batch effect removal method, MOBER can be applied widely to transcriptomics datasets of different origin, allowing for integration of multiple datasets simultaneously.

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“…Additionally collaborative efforts to aggregate molecular data, most notably Project GENIE, are gaining traction and now include more than 120 000 sequenced tumors [ 96 ]. Patient driven efforts to share molecular data have also been successful and are expanding [ 97 ]. The increasing utility of tumor molecular profiling should also drive oncologists to order profiling on a greater number of patients, fueling an exponential increase in the amount of available data.…”

Section: Current Challenges and Potential Solutions For Ai In Molecul...mentioning

confidence: 99%

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

Shen

2021

Emerging Topics in Life Sciences

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A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

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Count me in: A patient-driven research initiative to accelerate cancer research.

Cited by 5 publications

References 0 publications

A Drug Recommendation System (Dr.S) for Cancer Cell Lines

A Drug Recommendation System (Dr.S) for Cancer Cell Lines

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

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