GSVD- and tensor GSVD-uncovered patterns of DNA copy-number alterations predict adenocarcinomas survival in general and in response to platinum

Matthew W Bradley,Orly Alter,Heidi A Hanson,Sri Priya Ponnapalli,Katherine A Aiello

doi:10.1063/1.5099268

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.

Highlights

LUAD, USEC, and OV tumors account for տ 12%; Շ0:5%, and տ 2% of cancer deaths in the US, respectively
We show that these relations are suitable for profiling technologies other than Agilent comparative genomic hybridization (CGH) microarrays, i.e., whole-genome sequencing (WGS),[14] and Affymetrix single nucleotide polymorphism (SNP) microarrays, which together with the Agilent CGH microarrays represent the main technologies
We find that the GSVD comparisons of patient-matched normal and primary LUAD, USEC, and OV tumor profiles measured by Affymetrix SNP microarrays and normal and primary LUAD tumor profiles measured by WGS uncover similar combinations across 6p þ 12p

Summary

INTRODUCTION

LUAD, USEC, and OV tumors account for տ 12%; Շ0:5%, and տ 2% of cancer deaths in the US, respectively. Recently and only in OV, patterns of DNA CNAs were discovered which predict the overall survival of patients, in general as well as following the platinum-based treatment of the primary tumor and throughout the course of the disease.[11,12] The patterns, across the chromosome arms 7p and, separately, Xq and across the combination of the two arms 6p þ 12p together but not separately, were discovered by using the tensor GSVD, a “comparative spectral decomposition,” to compare Agilent microarray platform-matched profiles of patientmatched normal and primary OV tumor DNA. In a comparison of technology-matched profiles of patient-matched tumor and normal genomes, the tensor GSVD can uncover the combinations of patterns that mathematically are consistent across the technologies. Vectors each, a Di-specific column-wise orthonormal Ui 2 RKiÂLM and shared invertible row-normalized VxT 2 RLÂL and VyT 2 R;MÂM

LM XL X M

Findings

DISCUSSION