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Sequential analysis of transcript expression patterns improves survival prediction in multiple cancers.

Jordan Mandel1, Raghunandan Avula2, Edward V Prochownik3,4,5,6

  • 1The Division of Hematology/Oncology, Children's Hospital of Pittsburgh of UPMC, Rangos Research Center, Room, 5124, 4401 Penn Ave, Pittsburgh, PA, 15224, USA. JAM526@pitt.edu.

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Cancer patient survival can be predicted using t-distributed stochastic neighbor embedding (t-SNE) on gene expression data. Sequential application of t-SNE or combining it with whole transcriptome profiling improves survival prediction accuracy.

Keywords:
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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Computational Biology

Background:

  • Long-term cancer patient survival often correlates with specific whole transcriptome profiles.
  • Gene expression patterns can be superior predictors of survival compared to standard clinical methods.
  • The Cancer Genome Atlas (TCGA) provides a rich dataset for exploring cancer transcriptomics.

Purpose of the Study:

  • To investigate if sequential application of t-distributed stochastic neighbor embedding (t-SNE) improves cancer survival prediction.
  • To determine if t-SNE can refine survival prediction initially achieved through whole transcriptome profiling.
  • To explore the correlation between functionally-related transcript expression patterns and long-term survival across diverse cancer types.

Main Methods:

  • RNAseq data from 10,227 TCGA tumors were analyzed using t-SNE clustering of 362 transcripts from 15 cancer pathways.
  • Relevant survival-associated clusters were re-analyzed using sequential t-SNE with a second pathway's transcripts.
  • Alternatively, whole transcriptome profiling-identified groups were subjected to a second t-SNE analysis.

Main Results:

  • Sequential t-SNE (t-SNE➔t-SNE) and whole transcriptome profiling➔t-SNE analyses demonstrated improved survival prediction in many cases.
  • A dynamic R Shiny web application was developed for interactive exploration of t-SNE-based transcriptome clustering and survival analysis.
  • The application allows users to explore survival predictability across TCGA cancers, pathways, and clusters using individual or sequential approaches.

Conclusions:

  • Long-term patient survival is correlated with expression patterns of specific transcript sets from cancer pathways.
  • Iterative t-SNE clustering or t-SNE applied after whole transcriptome-based clustering can significantly enhance survival prediction.
  • The developed web application and associated scripts facilitate further research into cancer transcriptomics and survival analysis.