https://github.com/greenelab/nf1_inactivation
Using Machine Learning to Identify Glioblastoma patients with NF1 inactivation
https://github.com/greenelab/nf1_inactivation
analysis cancer gene-expression machine-learning methodology
Last synced: 12 months ago
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Using Machine Learning to Identify Glioblastoma patients with NF1 inactivation
- Host: GitHub
- URL: https://github.com/greenelab/nf1_inactivation
- Owner: greenelab
- License: bsd-3-clause
- Created: 2016-07-25T14:14:04.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-12-19T13:47:27.000Z (over 9 years ago)
- Last Synced: 2025-04-09T04:41:43.735Z (about 1 year ago)
- Topics: analysis, cancer, gene-expression, machine-learning, methodology
- Language: Python
- Homepage:
- Size: 78.1 KB
- Stars: 11
- Watchers: 5
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# NF1 Inactivation Classifier for Glioblastoma
**2016 Gregory Way, Robert Allaway,
Stephanie Bouley, Camilo Fadul,
Yolanda Sanchez, and Casey Greene**
---
[](https://zenodo.org/badge/latestdoi/18957/greenelab/nf1_inactivation)
## Summary
The repository contains instructions to replicate and build upon a classifier
trained to detect an NF1 inactivation signature in glioblastoma gene expression
data. We leverage publicly available data from the Cancer Genome Atlas (TCGA) to
train a logistic regression classifier with an elastic net penalty using
stochastic gradient descent.
NF1 is a tumor suppressor that regulates RAS (a well characterized oncogene).
When NF1 is inactivated, RAS signaling continues unabated leading to
uncontrolled cell growth. Patients with neurofibromatosis type I (caused by
heterozygous germline mutation of NF1) have a predisposition
for multiple tumor types including optic gliomas, pheochromocytomas, and
malignant peripheral nerve sheath tumors. Furthermore, NF1 is one of the most
commonly mutated genes in glioblastoma.
NF1 can be inactivated genetically or by other mechanisms including microRNAs
or targeted degradation by the proteosome
([McGillicuddy et al. 2009](http://www.ncbi.nlm.nih.gov/pubmed/19573811)).
Therefore, detecting inactivation solely by sequencing the NF1 gene can result
in false negatives. Because we have previously identified compounds that are
synthetically lethal in NF1 inactivated cells
([Wood et al. 2011](http://www.ncbi.nlm.nih.gov/pubmed/21697395)),
the ability to detect patients with NF1 inactivation signatures could inform
treatment decisions.
## Reproducibility
```bash
# All of our results and figures can be regenerated with one command:
bash run_pipeline.sh
```
We provide an `environment.yml` file for python packages and use the
[checkpoint package](https://cran.r-project.org/web/packages/checkpoint/index.html)
for managing R packages. We also provide a
[Docker image](https://hub.docker.com/r/gregway/nf1_inactivation) to reproduce
the computing environment.
## Contact
Please report all bugs and direct analysis questions by filing a
[GitHub issue](https://github.com/greenelab/nf1_inactivation/issues)
Please direct all other correspondence to: `csgreene@mail.med.upenn.edu` or
`yolanda.sanchez@dartmouth.edu`
## Data
All data is publicly available.
* TCGA data used to train the classifier was retrieved from
[UCSC Xena](https://genome-cancer.soe.ucsc.edu/proj/site/xena/datapages/).
* Our gene expression validation data was deposited under accession
[GSE85033](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85033).
## Acknowledgements
This work was supported by the Genomics and Computational Biology graduate group
at The University of Pennsylvania (G.P.W); the Gordon and Betty Moore
Foundation’s Data-Driven Discovery Initiative (grant number GBMF 4552 to
C.S.G.); and the American Cancer Society (grant number IRG 8200327 to C.S.G.).