PMC7335056_01
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.
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Fig1
Table 1). The majority of mutations found in patient A were unique to either A1 or A2 (Fig. 1a and Supplementary Fig. 1a, b). Additional mutations in SMAD4 and MYC occurred in A1, while none of the overlapping mutations were identified as a known driver (Fig. 1b). This analysis revealed similar signature profiles in A1 and A2, with a significant proportion of the age-related signature 1. (Fig. 1c and Supplementary Fig. 1c). Copy number alteration (CNA) analysis revealed high CIN in both tumours and tumour A1 appeared to exhibit hyperdiploidy (Fig. 1d and Supplementary Fig. 1d). Similarly, TP53 was found to be deleted in A2 but amplified in A1, further highlighting heterogeneity of these tumours (Fig. 1e). DNA analysis of gut microbial organisms in patient A revealed high abundance of Bacteroidetes and Firmicutes spp. (Fig. 1f). A1 displayed lower abundance of neutrophils but greater amounts of B cells and CD8+ T cells, whereas CD4+ T cells were only detected in A2, which showed higher neutrophil-to-lymphocyte ratio and higher CD4/CD8 ratio, both linked to poor clinical outcomes (Fig. 1g).
PMC7335056_02
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.
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Fig2
Most mutations found in patient B were unique to each primary tumour (Fig. 2a and Supplementary Fig. 2a, b). B1 and B4 showed an MSH6 insertion, with tumour B1 acquiring further mutations in PIK3CA H1047R and FBXW7 R385C and tumour B4 developing mutations in FBXW7 G587fs, PMS1 F544fs and TP53 L257P. Mutations in PIK3CA H1047R and FBXW7 G587fs were also found in B3. B5 (MSS tumour) shared one APC E1554fs mutation with B1, but no mutations with the rest of the primaries, presenting distinct ones: FBXW7 R399* and KRAS G12D (Fig. 2b, c). Mutational signature analysis showed tumours B1-B4 with a high proportion of MMR-deficiency-related signatures, and absence of these signatures in B5 (Fig. 2d and Supplementary Fig. 2c). This was corroborated by protein immunohistochemistry (IHC), which showed loss of MLH1 in tumours B1-B4 and reduction in MLH1 transcript expression (Fig. 2e), likely due to hypermethylation of the MLH1 promoter. B5 (Fig. 2f and Supplementary Fig. 2d). B5 was the only sample to show EGFR amplification, KRAS was amplified in B3 and B5 while tumour suppressor genes, such as DCC and SMAD4, were deleted in B4 and B5 (Fig. 2g). DNA analysis of gut microbial organisms associated with each tumour within patient B revealed prevalence of Bacteroidetes and Firmicutes spp., and evidence of F. nucleatum in all lesions (Fig. 2h). Together with the lowest infiltration, B1 showed a lower abundance of neutrophils, a higher fraction of CD8+ T cells and a lack of CD4+ T cells. The highest neutrophil-to-lymphocyte ratio was seen in B5, followed by B4, B2, B3 and B1 (Fig. 2i).
PMC7335056_03
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.
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Fig3
Most mutations occurred exclusively in each lesion (Fig. 3a and Supplementary Fig. 3a, b). The same BRAF V600E mutation occurred in all tumours, identifying it as a likely early event in tumour development (Supplementary Fig. 3c). C2 and C4 experienced a deletion in the MSH6 gene, with tumour C2 acquiring additional mutations in APC R876* and tumour C4 gaining mutations in APC E1554fs, PIK3CA Q546R and TP53 K382fs. C3 experienced a deletion in MSH3 and acquired a mutation in POLH R253C (Fig. 3b, c and Supplementary Fig. 3d). Mutational signature analysis unveiled high proportions of MMR-deficiency related signatures in all tumours (Fig. 3d and Supplementary Fig. 3e). This was corroborated by loss of MLH1 expression detected by IHC and reduction in MLH1 transcript expression (Fig. 3e) in all tumours. CNA analysis reported low CIN in all tumours in patient C (Fig. 3f and Supplementary Fig. 3f). We identified amplified oncogenes, such as KRAS in C1 and MYC in C3-C5, and deleted tumour suppressor genes, such as DCC in C5 (Fig. 3g). C1-C5 (Fig. 3h). Lower CD4/CD8 ratios were seen in C4 and C5 (Fig. 3i).