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. 2019 Aug 20;3(3):036104.
doi: 10.1063/1.5099268. eCollection 2019 Sep.

GSVD- And Tensor GSVD-uncovered Patterns of DNA Copy-Number Alterations Predict Adenocarcinomas Survival in General and in Response to Platinum

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Free PMC article

GSVD- And Tensor GSVD-uncovered Patterns of DNA Copy-Number Alterations Predict Adenocarcinomas Survival in General and in Response to Platinum

Matthew W Bradley et al. APL Bioeng. .
Free PMC article

Abstract

More than a quarter of lung, uterine, and ovarian adenocarcinoma (LUAD, USEC, and OV) tumors are resistant to platinum drugs. Only recently and only in OV, patterns of copy-number alterations that predict survival in response to platinum were discovered, and only by using the tensor GSVD to compare Agilent microarray platform-matched profiles of patient-matched normal and primary tumor DNA. Here, we use the GSVD to compare whole-genome sequencing (WGS) and Affymetrix microarray profiles of patient-matched normal and primary LUAD, USEC, and OV tumor DNA. First, the GSVD uncovers patterns similar to one Agilent OV pattern, where a loss of most of the chromosome arm 6p combined with a gain of 12p encode for transformation. Like the Agilent OV pattern, the WGS LUAD and Affymetrix LUAD, USEC, and OV patterns are correlated with shorter survival, in general and in response to platinum. Like the tensor GSVD, the GSVD separates these tumor-exclusive genotypes from experimental inconsistencies. Second, by identifying the shorter survival phenotypes among the WGS- and Affymetrix-profiled tumors, the Agilent pattern proves to be a technology-independent predictor of survival, independent also of the best other indicator at diagnosis, i.e., stage. Third, like no other indicator, the pattern predicts the overall survival of OV patients experiencing progression-free survival, in general and in response to platinum. We conclude that comparative spectral decompositions, such as the GSVD and tensor GSVD, underlie a mathematically universal description of the relationships between a primary tumor's genotype and a patient's overall survival phenotype, which other methods miss.

Figures

FIG. 1.
FIG. 1.
The GSVD of the 6p + 12p WGS profiles of patient-matched LUAD tumor and normal DNA. The GSVD of Eq. (1) is depicted in a raster display with a relative WGS read-count, i.e., DNA copy-number amplification (red), no change (black), and deletion (green). This GSVD depiction is denoted as approximate, even though the GSVD is exact, because only the first through the 5th and the 134th through the 138th row and the corresponding tumor and normal column basis vectors and generalized singular values are explicitly shown. The angular distances of Eq. (2) are depicted in the bar chart in the inset. The red and green contrasts for the datasets Di, the dataset-specific column basis vectors Ui and generalized singular values Σi, and the shared row basis vectors VT are c =1, 150 and 7.5 × 10−4, and 10, respectively.
FIG. 2.
FIG. 2.
An adenocarcinoma genotype-phenotype relation. The (a) Agilent OV, (b) Affymetrix LUAD, and (c) WGS LUAD patterns, which correspond to column basis vectors that are significant in and exclusive to the tumor genomes, are depicted in plots of relative copy numbers, ordered and colored based upon genomic coordinates, with the medians of the segments identified in the Agilent OV pattern by CBS (black lines), including OV-specific (blue), adenocarcinoma-shared (black), and WGS technology-filled in, possibly LUAD-specific (red) CNAs. (d) The corresponding WGS LUAD row basis vector is depicted in the plot showing the classification of the 138 patients into low (red) or high (blue) superposition coefficients. (e) The WGS LUAD tumor dataset is depicted in a raster showing the genotype-phenotype relation.
FIG. 3.
FIG. 3.
The adenocarcinoma genotypes encode for transformation via the Ras pathway supported by the calcium pathway and the integrator complex. The 6p + 12p LUAD, USEC, and OV genotypes are depicted in a diagram of the WGS technology-filled in, possibly LUAD-specific, multihistocompatibility complex (yellow) in addition to the Agilent microarray-described Ras and calcium pathways and integrator complex, which include CNAs unrecognized in adenocarcinomas prior to the discovery of the Agilent OV pattern (violet). Explicitly shown are amplifications (red) and deletions (green) of genes and transcript variants (rectangles), either adenocarcinoma-shared (black) or specific (blue), and relationships that directly or indirectly lead to increased (arrows) or decreased (bars) activities of the genes and transcripts and the tumor suppressor proteins p53 and p16 (circles).
FIG. 4.
FIG. 4.
The 6p + 12p Agilent OV pattern is a technology-independent predictor of LUAD overall survival, independent also of the best other indicator at diagnosis, i.e., stage. The classifications of (a) the 488 Affymetrix LUAD patients based upon the Agilent OV pattern and, in addition, (b) stage and of (c) the 144 platinum-treated patients among the 488 based upon the Agilent OV pattern are depicted in KM curves, highlighting the median survival time differences (yellow) with the corresponding log-rank P-values and Cox hazard ratios.
FIG. 5.
FIG. 5.
The 6p + 12p Agilent OV pattern predicts the overall survival of OV patients experiencing PFS. The classifications of (a) 265 of the 479 Affymetrix OV patients who experienced PFS ≥ 0 months and (b) 177 of the 265 patients who experienced PFS ≥ 11 months, based upon the 6p + 12p Agilent OV pattern.

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