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Tensor GSVD of Patient- and Platform-Matched Tumor and Normal DNA Copy-Number Profiles Uncovers Chromosome Arm-Wide Patterns of Tumor-Exclusive Platform-Consistent Alterations Encoding for Cell Transformation and Predicting Ovarian Cancer Survival

Fig 3

Survival analyses of the discovery and validation sets of patients classified by tensor GSVD, or tensor GSVD and tumor stage at diagnosis.

(a) Kaplan-Meier (KM) curves of the discovery set of 249 patients classified by the 6p+12p x-probelet coefficient, show a median survival time difference of 11 months, with the corresponding log-rank test P-value < 10−2. The univariate Cox proportional hazard ratio is 1.7. (b) Survival analyses of the 249 patients classified by the 7p x-probelet coefficient. (c) The 249 patients classified by the Xq x-probelet coefficient. (d) The 249 patients classified by both the 6p+12p tensor GSVD and tumor stage at diagnosis, show the bivariate Cox hazard ratios of 1.5 and 4.0, which do not differ significantly from the corresponding univariate hazard ratios of 1.7 and 4.4, respectively. This means that the 6p+12p tensor GSVD is independent of stage, the best predictor of OV survival to date. The 61 months KM median survival time difference is about 85% and more than two years greater than the 33 month difference between the patients classified by stage alone. This means that the tensor GSVD and stage combined make a better predictor than stage alone. (e) The 249 patients classified by both the 7p tensor GSVD and stage. (f) The 249 patients classified by both the Xq tensor GSVD and stage. (g) KM curves of the validation set of 148 stage III-IV patients classified by the 6p+12p arraylet correlation, show a median survival time difference of 22 months, with the corresponding log-rank test P-value < 10−2, and the univariate Cox proportional hazard ratio 1.9. This validates the survival analyses of the discovery set of 249 patients. (h) Survival analyses of the 148 patients classified by the 7p arraylet correlation. (i) The 148 patients classified by the Xq arraylet correlation.

Fig 3

doi: https://doi.org/10.1371/journal.pone.0121396.g003