The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models.

b. APOBEC enrichment in all mutations.The differences between responders and non-responders are not statistically significant.NR: no responders, R: responders, triangle shape represents the complete responders among the responder group.The clonal TMB for partial responders is significantly higher compared to non-responders (p-value = 0.026).The differences in clonal TMB between non-responders and complete responders and partial responders compared to complete responders did not reach statistical significance.c.Relationship between subclonal TMB and response to ICI treatment.The differences in subclonal TMB values between the three response groups were not significant.NR: no responders, PR: partial responders, CR: complete responders.

Figure S2 .
Figure S2.Relationship between APOBEC mutations and response to treatment.a. APOBEC enrichment in clonal mutations.There is a significant positive relationship between response to treatment and the APOBEC enrichment in clonal mutations (p-value = 0.03, Wilcoxon rank sum test).

Figure S3 .
Figure S3.Relationship between TMB and the three different response groups to ICI therapy.a. Relationship between TMB and response to ICI treatment.The differences in TMB values between the three response groups were not significant.b.Relationship between clonalTMB and response to ICI treatment.

Figure S4 .
Figure S4.Relationship between ICI treatment response and the number of putative binders predicted with NetMHCpan 4.0.The number of predicted binders tends to be higher in responders than in non-responders.a-c: Number of putative binders with predicted affinity rank < 0.5%.d-f: Number of putative binders with predicted affinity IC 50 < 500nM.g-i: Number of putative binders with predicted affinity IC 50 < 50nM.P-values were calculated using the Wilcoxon rank sum test.NR: no responders, R: responders, triangle shape represents the complete responders among the responder group.

Figure S5 .
Figure S5.Relationship between ICI treatment response and the number of putative binders predicted with MHCflurry 2.0.The number of putative binders tends to be higher in responders than non-responders.a-c: Number of putative binders with predicted affinity rank < 2%.d-f: Number of putative binders with predicted affinity rank < 0.5%.g-i: Number of putative binders with predicted affinity IC 50 < 500nM.j-l: Number of putative binders with predicted affinity IC 50 < 50nM.P-values were

Figure
Figure S6.a. Relationship of differential agretopicity index (DAI) and treatment response.No significant difference in highly differential peptides (DAI>9) was seen between response groups.b.Relationship between the number of predicted stable binders and treatment response.No significant difference in the number of putative stable binders (binding stability<1.4h)was seen between response groups.P-values were calculated using Wilcoxon rank sum test.NR: no responders, R: responders, triangle shape represents the complete responders among the responder group.

Figure S7 .
Figure S7.Non-synonymous mutations affect the binding affinity of the peptide.a. Formation of new MHC I binders.Enrichment for different amino acid substitutions (above) or positions in the peptide (below) are measured as the log2 ratio of the substitution frequency in the set of new binders versus the frequency in the set of peptides that do not change binding status.Enriched amino acids using a chi-square test: tyrosine (Y), phenylalanine (F), leucine (L) and histidine (H) at p-value < 10 -5 , tryptophan (W) at p-value = 0.002872.b.Loss of MHC I binding capacity.Enrichment for different amino acid substitutions (above) or positions in the peptide (below) are measured as the log2 ratio of the substitution frequency in the set of peptides associated with loss of MHC I binding versus the frequency in the set of peptides that do not change their binding status.Enriched amino acids using a chi-square test: cysteine (C) (p-value < 10 -5 ), glycine (G) (p-value =6.55x10 -5 ).

Figure S8 .
Figure S8.Relationship of number of predicted new binders and treatment response.a. Binders derived from total mutations.Responders have a higher number of new putative binders than non-responders (Wilcoxon test, p-value = 0.05).b.Binders derived from clonal mutations.Number of putative binders originating from clonal mutations is significantly higher in responders than non-responders (Wilcoxon test, p-value = 0.016).c.Binders derived from subclonal mutations.No significant difference can be observed for the number of putative binders originating from subclonal mutations between responders and non-responders.NR: no responders, R: responders, triangle shape represents the complete responders among the responder group.

Figure S10 .
Figure S10.Pathways of gene set enrichment analysis significantly related to ICI response (adjusted P < 0.05, comparing 13 responders and 7 non-responders).Selected pathways are shown.The complete list of pathways with their adjusted p-value, normalized enrichment score and the included genes is provided in the Additional data file 3.

Figure S11 .
Figure S11.Network of significantly enriched pathways.Enrichment map of GSEA results (adjusted p-value < 0.05) comparing a. responders versus non-responders and b. complete responders versus partial-responders.Nodes represent genesets and edges represent the connectivity between genesets (combined metric Jaccard+Overlap >0.375).Red and blue represent positive and negative enrichment scores, respectively.

Figure S12 .
Figure S12.Quality control of RNASeq data.a. PCA using the top 500 most variable genes.b.Dendrogram using the top 500 most variable genes, 1-correlation distance and Ward2 linkage method.R16 was detected as an outlier.No batch effect was present.