Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer

McMillan, Elizabeth A.; Ryu, Myung Jeom; Diep, Caroline H.; Mendiratta, Saurabh; Clemenceau, Jean R.; Vaden, Rachel M.; Kim, Ju Hwa; Motoyaji, Takashi; Covington, Kyle R.; Peyton, Michael; Huffman, Kenneth E.; Wu, Xiaofeng; Girard, Luc; Sung, Yeojin; Chen, Pei Hsaun; Mallipeddi, Prema L.; Lee, Joo Young; Hanson, Jordan; Voruganti, Sukesh; Yu, Yunku; Park, Sunho; Sudderth, Jessica; DeSevo, Christopher; Muzny, Donna M.; Doddapaneni, Harshavardhan; Gazdar, Adi F.; Gibbs, Richard A.; Hwang, Tae Hyun; Heymach, John V.; Wistuba, Ignacio I.; Coombes, Kevin R.; Williams, Noelle S.; Wheeler, David A.; MacMillan, John B.; DeBerardinis, Ralph J.; Roth, Michael G.; Posner, Bruce A.; Minna, John D.; Kim, Hyun‐Seok; White, Michael A.

doi:10.1016/j.cell.2018.03.028

Cited by 113 publications

(92 citation statements)

References 35 publications

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“…GDRC also correlated positively with EGFR phosphorylation at Y1173 (pY1173), a marker for EGFR signaling and Gefitinib sensitivity ( Figure 5C ). Finally, comparing metabolic features with high-throughput drug sensitivity data (McMillan et al, 2018) revealed that GDRC cell lines tend to be sensitive to the EGFR inhibitor Erlotinib; these cell lines have low areas under the Erlotinib dose-response curve (AUC) ( Figure 5C ).…”

Section: Resultsmentioning

confidence: 99%

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

Chen

Cai

Huffman

et al. 2019

Preprint

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SummaryIntermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

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Section: Resultsmentioning

confidence: 99%

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

Chen

Cai

Huffman

et al. 2019

Preprint

Self Cite

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“…As our 2D co-culture system enables easy tracking of the growth dynamics of chemosensitive and chemoresistant cells under simple epifluorescence microscopy, we can further optimize them for high throughput screening purposes to identify optimal combinatorial therapies and discovery of new treatment strategies via screening of experimental small molecule libraries (34). Our 2D co-culture system also provides us the control to build various types of spatial architecture by simply changing the location of initial inoculation (Fig 6).…”

Section: Discussionmentioning

confidence: 99%

A spatial cell culture model for predicting chemotherapy dosing strategies

Zhu

Deb

Danino

2019

Preprint

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21Predicting patient responses to chemotherapy regimens is a major challenge in 22 cancer treatment. To do this requires quantitative mathematical models to predict opti-23 mal dose and frequency for a particular drug, and experimental model systems such as 24 three-dimensional organoids that accurately recapitulate the tumor microenvironment 25 and heterogeneity. However, tracking the spatial dynamics of multiple cell types in 26 three-dimensions can be a significant challenge in terms of time and throughput. Here 27we develop a two-dimensional system that allows for simple tracking of cell populations 28 via fluorescence microscopy for modeling spatial dynamics in tumors. We first develop 29 multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, 30 and demonstrate how well mixed and spatially heterogeneous populations expand in a 31 two-dimensional colony. We subject cell populations to varied dose and frequency of 32 chemotherapy and measure colony growth radius and populations. We then build a 33 mathematical model to describe the dynamics of both chemosensitive and 34 chemoresistant populations, where we determine which number of doses can produce 35 the smallest tumor size based on parameters in the system. In the future, this system 36 can be adapted to quickly optimize dosing strategies in the setting of heterogeneous cell 37 types or patient derived cells with varied chemoresistance. 38 39 Chemotherapy dosing strategies have primarily followed a maximum tolerated dosage 41 (MTD) approach, in which patients are given high doses of chemotherapy to kill as 42 many tumor cells as possible (1)(2)(3)(4)(5). This approach is based on early assumptions that 43 tumors are composed of a homogenous, exponentially growing cell population and thus 44 maximum doses are likely to cause the highest disease eradication (6, 7). However, tu-45 mors are composed of genetically heterogeneous cells with varied chemoresistance, 46 and further diversity in chemoresistance can arise due to drug selection pressure from 47

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“…Recent work on lung cancer patient selection demonstrates that the traditional "chemistry-first" approach can be sustained as long as biomarkers, genomewide targets and genetic landscapes are included in the models. 165 Biological embeddings may help to generalize this approach, smoothing the transition from the blockbuster system of drugs to a personalized medicine one. 166…”

Section: Discussionmentioning

confidence: 99%

“…All in all, as pharmaceutical research is moving towards precision medicine, there is a need to enrich computational methods with generic knowledge of biology as well as patient‐ and cohort‐specific samples. Recent work on lung cancer patient selection demonstrates that the traditional “chemistry‐first” approach can be sustained as long as biomarkers, genome‐wide targets and genetic landscapes are included in the models . Biological embeddings may help to generalize this approach, smoothing the transition from the blockbuster system of drugs to a personalized medicine one …”

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confidence: 99%

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola

Fernández‐Torras

Bertoni

et al. 2018

WIREs Comput Mol Sci

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Biological data is accumulating at an unprecedented rate, escalating the role of data‐driven methods in computational drug discovery. This scenario is favored by recent advances in machine learning algorithms, which are optimized for huge datasets and consistently beat the predictive performance of previous art, rapidly approaching human expert reasoning. The urge to couple biological data to cutting‐edge machine learning has spurred developments in data integration and knowledge representation, especially in the form of heterogeneous, multiplex and semantically‐rich biological networks. Today, thanks to the propitious rise in knowledge embedding techniques, these large and complex biological networks can be converted to a vector format that suits the majority of machine learning implementations. Here, we explain why this can be particularly transformative for drug discovery where, for decades, customary chemoinformatics methods have employed vector descriptors of compound structures as the standard input of their prediction tasks. A common vector format to represent biology and chemistry may push biological information into most of the existing steps of the drug discovery pipeline, boosting the accuracy of predictions and uncovering connections between small molecules and other biological entities such as targets or diseases. This article is categorized under: Computer and Information Science > Databases and Expert Systems Computer and Information Science > Chemoinformatics

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Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer

Cited by 113 publications

References 35 publications

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

A spatial cell culture model for predicting chemotherapy dosing strategies

Formatting biological big data for modern machine learning in drug discovery

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