Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer

Abstract

DNA copy number aberrations (CNA) are frequently observed in colorectal cancers (CRC). There is an urgent need for CNA-based biomarkers in clinics,. n For Stage III CRC, if combined with imaging or pathologic evidence, these markers promise more precise care. We conducted this Stage III specific biomarker discovery with a cohort of 134 CRCs, and with a newly developed high-efficiency CNA profiling protocol. Specifically, we developed the profiling protocol for tumor-normal matched tissue samples based on low-coverage clinical whole-genome sequencing (WGS). We demonstrated the protocol's accuracy and robustness by a systematic benchmark with microarray, high-coverage whole-exome and -genome approaches, where the low-coverage WGS-derived CNA segments were highly accordant (PCC >0.95) with those derived from microarray, and they were substantially less variable if compared to exome-derived segments. A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss, containing the TP53 tumor suppressor gene, that was significantly associated with reduced survival (P = 0.0139, HR = 1.688, 95% CI = [1.112-2.562]), which was validated by an independent cohort of 187 Stage III CRCs. In summary, this low-coverage WGS protocol has high sensitivity, high resolution and low cost and the identified 17p-loss is an effective poor prognosis marker for Stage III patients.

Figure 1

The study design and whole genome sequencing analytical workflow. (A) The whole-genome sequencing (WGS) analysis share the same sample preparation, DNA extraction and quality control steps as WES (color shaded light green). The prepared genomic DNA libraries are pooled for WGS directly, while they require additional PCR amplification and hybridization steps to generate exomic libraries for pooled WES. (B) We integrated CNVkit, Gistic2 and various R packages to perform copy number segmentation, CNA calling and biomarker discovery analyses.

Figure 2

Benchmarks to evaluate low-coverage WGS approach and bioinformatics. (A) Pearson’s correlation coefficients (PCC) between low-coverage WGS and microarray segments as stratified by segmentation tools; (B) PCC between low-coverage and high-coverage WGS as stratified by tumor purity; (C) The means of robust standard deviation (MAD) as stratified by low-coverage WGS and WES analysis platforms.

Figure 3

Copy number profiles of the discovery cohort. Copy number ratios (CNR) are shown for upper split panel: patients had chr17p loss; and lower split panel patients had no chr17p loss – all based on Gistic2 calls. Row color coding: black for shorter survival patients (the lower 50%) and grey for longer survival patients (the upper 50%).

Figure 4

Arm-level chr17p loss predicts for poorer survival in Stage III CRC. (A) Venn diagram for shared arm-level CNAs between the discovery and TCGA validation cohorts. (B) The Kaplan–Meier plots of the Stage III CRC discovery cohort as stratified by patients’ status of carrying the chr17p arm loss (SCNA_CHR_ARM_17p_del = 1 for yes, otherwise 0). Also shown are box plots for comparing (C) number of focal CNAs (D_N_Focals) and (D) number of arm-level CNAs (D_N_Arms) between patients carrying or not carrying the chr17p loss.