Personalised mapping of tumour development in synchronous colorectal cancer patients

Abstract

Synchronous colorectal cancers (syCRCs) are two or more primary tumours identified simultaneously in a patient. Previous studies report high inter-tumour heterogeneity between syCRCs, suggesting independent origin and different treatment response, making their management particularly challenging, with no specific guidelines currently in place. Here, we performed in-depth bioinformatic analyses of genomic and transcriptomic data of a total of eleven syCRCs and one metachronous CRC collected from three patients. We found mixed microsatellite status between and within patients. Overlap of mutations between synchronous tumours was consistently low (<0.5%) and heterogeneity of driver events across syCRCs was high in all patients. Microbial analysis revealed the presence of Fusobacterium nucleatum species in patients with MSI tumours, while quantification of tumour immune infiltration showed varying immune responses between syCRCs. Our results suggest high heterogeneity of syCRCs within patients but find clinically actionable biomarkers that help predict responses to currently available targeted therapies. Our study highlights the importance of personalised genome and transcriptome sequencing of all synchronous lesions to aid therapy decision and improve management of syCRC patients.

Keywords: Cancer genomics; Colorectal cancer; Genome informatics.

Fig. 1. Genomic and transcriptomic analyses for patient A.

a A Venn diagram of SNVs shows 0.49% overlap between tumours. b Variant Allele Frequencies (VAFs) of putative driver mutations show heterogeneous drivers in A1 and A2. c Mutational signature analysis. d Genomic landscape of CNAs shows CIN in both A1 (ploidy of 3.58) and A2 (ploidy of 2.2). e Log-ratio of putative driver CNAs highlights heterogeneity of tumourigenic events between A1 and A2. f Microbial analysis of DNA data shows microbial abundance at the phylum level. g Quantification of tumour immune infiltration for eight immune cell populations across A1 and A2.

Fig. 2. Genomic and transcriptomic analyses for patient B.

a Venn diagrams of SNVs for all lesions B1–B5 (left) and for MSI lesions B1–B4 (right) show low overlap between tumours. b Putative phylogenetic tree based on driver mutations. c VAFs of putative driver mutations. d Mutational signature analysis. e Transcript counts of the MLH1 detected by RNA-seq analysis. f Genomic landscape of CNAs shows low CIN for MSI samples B1 (ploidy: 2), B2 (ploidy: 1.95), B3 (ploidy: 2) and B4 (ploidy: 1.98), and higher CIN for MSS sample B5 (ploidy: 2.13). g Median log-ratio of putative driver CNAs highlights heterogeneity of tumourigenic events between all lesions. h Microbial analysis of DNA data shows microbial abundance at the phylum level. i Quantification of tumour immune infiltration reveals distinct profiles for eight immune cell populations across B1–B5.

Fig. 3. Genomic and transcriptomic analyses for patient C.

a Venn diagram of SNVs shows low overlap between tumours. b Putative phylogenetic tree based on driver mutations. c VAFs of putative driver mutations. d Mutational signature analysis. e Transcript counts of the MLH1 detected by RNA-seq analysis. f Genomic landscape of CNAs shows low CIN for all samples C1 (ploidy: 2.03), C2 (ploidy: 2.06), C3 (ploidy: 2.13), C4 (ploidy: 2.02) and C5 (ploidy: 2.13). g Median log-ratio of putative driver CNAs highlights heterogeneity of tumourigenic events between all lesions. h Microbial analysis of DNA data shows microbial abundance at the phylum level. i Quantification of tumour immune infiltration reveals varying fractions for eight immune cell populations across all samples.