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Nicolas Alcala edited this page Nov 28, 2023 · 25 revisions

Project 3: Compute a small-cell neuroendocrine score to predict cancer aggressiveness across lung cancers

Background

Small cell lung cancer is a very aggressive type of lung cancer with stem cell characteristics. Transdifferentiation--the process of transformation from one cancer type to another--toward a small cell phenotype is known to be a major route to acquiring resistance to therapy. A study (Balanis et al. Cancer Cell 2019; see graphical abstract below) has shown that tumors from other cancer types could undergo such transdifferentiation, and that the degree of small-cellness predicts important clinical characteristics such as survival.

The project aims to check whether some rare lung cancers also acquire such a small cell phenotype, by computing a transcriptomic small cell score to test whether tumors with high scores also exist in these tumor types, and test if it also correlates with survival and known molecular groups.

Data

Requirements

Scripting in R

Steps

  • download the datasets
  • normalize the gene expression following the steps in https://nikobshinyapps.shinyapps.io/PCAprojection/
  • create a function to compute the small cell score of a tumor transcriptome
    1. for each sample, extract the list of genes with non-zero weights from Balanis et al. Table S1
    2. multiply the gene expression by their weights and sum the total to obtain a score
  • apply the function to all neuroendocrine neoplasms
  • compare the small cell scores (visualisations and statistical tests) across clinical (histopathological groups, age, sex, ) and molecular characteristics (molecular groups)

Expected difficulties

  • combining different datasets (e.g., gene names from Balanis et al. and the neuroendocrine neoplasm transcriptomic data will probably not match perfectly)
  • data interpretation

Resources

[email protected] (Nicolas Alcala)

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