APL Bioengineering (Jun 2020)

Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival

  • Sri Priya Ponnapalli,
  • Matthew W. Bradley,
  • Karen Devine,
  • Jay Bowen,
  • Sara E. Coppens,
  • Kristen M. Leraas,
  • Brett A. Milash,
  • Fuqiang Li,
  • Huijuan Luo,
  • Shi Qiu,
  • Kui Wu,
  • Huanming Yang,
  • Carl T. Wittwer,
  • Cheryl A. Palmer,
  • Randy L. Jensen,
  • Julie M. Gastier-Foster,
  • Heidi A. Hanson,
  • Jill S. Barnholtz-Sloan,
  • Orly Alter

Journal volume & issue
Vol. 4, no. 2
pp. 026106 – 026106-14


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Modeling of genomic profiles from the Cancer Genome Atlas (TCGA) by using recently developed mathematical frameworks has associated a genome-wide pattern of DNA copy-number alterations with a shorter, roughly one-year, median survival time in glioblastoma (GBM) patients. Here, to experimentally test this relationship, we whole-genome sequenced DNA from tumor samples of patients. We show that the patients represent the U.S. adult GBM population in terms of most normal and disease phenotypes. Intratumor heterogeneity affects ≈ 11 % and profiling technology and reference human genome specifics affect 30%. With a 2.25-year Kaplan–Meier median survival difference, a 3.5 univariate Cox hazard ratio, and a 0.78 concordance index, i.e., accuracy, the pattern predicts survival better than and independent of age at diagnosis, which has been the best indicator since 1950. The prognostic classification by the pattern may, therefore, help to manage GBM pseudoprogression. The diagnostic classification may help drugs progress to regulatory approval. The therapeutic predictions, of previously unrecognized targets that are correlated with survival, may lead to new drugs. Other methods missed this relationship in the roughly 3B-nucleotide genomes of the small, order of magnitude of 100, patient cohorts, e.g., from TCGA. Previous attempts to associate GBM genotypes with patient phenotypes were unsuccessful. This is a proof of principle that the frameworks are uniquely suitable for discovering clinically actionable genotype–phenotype relationships.