https://github.com/broadinstitute/profiling-resistance-mechanisms

Predicting pharmacodynamic responses to cancer drugs using cell morphology

https://github.com/broadinstitute/profiling-resistance-mechanisms

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Keywords

cancer carpenter-lab cell-painting machine-learning morphology pharmacodynamics resistance
Last synced: 5 months ago · JSON representation

Repository

Predicting pharmacodynamic responses to cancer drugs using cell morphology

Basic Info
  • Host: GitHub
  • Owner: broadinstitute
  • License: bsd-3-clause
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 1.57 GB
Statistics
  • Stars: 7
  • Watchers: 8
  • Forks: 5
  • Open Issues: 27
  • Releases: 2
Topics
cancer carpenter-lab cell-painting machine-learning morphology pharmacodynamics resistance
Created almost 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

DOI

Discovering Morphological Markers of Drug Resistance

In this repository we analyze Cell Painting data generated from multiple cell line clones that were resistant or sensitive to bortezomib.

Citation

Kelley ME, Berman AY, Stirling DR, Cimini BA, Han Y, Singh S, Carpenter AE, Kapoor TM, Way GP. High-content microscopy reveals a morphological signature of bortezomib resistance. (2023) eLife; 12:e91362. DOI: https://doi.org/10.7554/eLife.91362.

Data collection and processing

We cultured a colon cancer cell line (HCT116), treated with a proteosome inhibitor (Bortezomib), and selected two resistant clones. We applied Cell Painting to these cell lines (in triplicate) under four conditions (DMSO, 0.7nm, 7nm, and 70nm Bortezomib).

The Cell Painting assay captures several cellular morphology features (described in more detail here). Our hypothesis was that morphological features could distinguish wildtype from resistant clones.

We processed the cell painting data using CellProfiler. We use CellProfiler to test quality control, segment images to extract nuclei, and measure features captured by cell painting.

This repository contains all image analysis pipelines and image-based profiling pipelines (see 0.generate-profiles).

Pilot analyses

This repository ingests the processed Cell Painting data and performs several downstream analyses.

Using the triplicate measurements, and two batches, we perform the following pilot analyses:

  • Obtain similarity matrices for each batch independently and combined; perform hierarchical clustering; visualize heatmaps.
    • These analyses were performed using the Morpheus WebApp
    • An outline of the results can be viewed here.
  • Apply UMAP to the batched data to observe large differences across variables
  • Apply t-tests to determine cell morphology differences between conditions:
    • We test for differences between resistant clones at two doses of Bortezomib (0.7nm and 7nm)
    • We also test for differences between wildtype and resistant clones at a low dose of Bortezomib (0.7nm)

UMAP Batch Analysis

UMAP

T-test to Determine Morphological Differences

ttest

Reproducibility

We use conda to manage package versions. After installing conda, obtain all required packages:

```bash conda env create --force --file environment.yml

Activate environment

conda activate resistance-mechansisms ```

Clone the github repository. First, generate and enable SSH Keys if you haven't already.

```bash

Then clone and enter repo

git clone git@github.com:broadinstitute/profiling-resistance-mechanisms cd profiling-resistance-mechanisms ```

All analyses are presented in analysis.sh. To reproduce, perform the following:

bash ./analysis.sh

Bug Reporting

Please file an issue with any questions or bug reports.

Internal documents

GDrive folder

Owner

  • Name: Broad Institute
  • Login: broadinstitute
  • Kind: organization
  • Location: Cambridge, MA

Broad Institute of MIT and Harvard

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Last synced: about 1 year ago

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  • Average time to close issues: 3 months
  • Average time to close pull requests: 8 days
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  • Total pull request authors: 2
  • Average comments per issue: 1.79
  • Average comments per pull request: 0.43
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Top Authors
Issue Authors
  • gwaybio (40)
  • shntnu (3)
Pull Request Authors
  • gwaybio (56)
  • shntnu (1)
  • DavidStirling (1)
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