Genomic, clinical data, and scripts for PD-1 blockade resistance in metastatic melanoma

We analyzed whole-exome-sequencing (WES) of pre-treatment tumor and matched normals from four cohorts (n=140) of previously ICB-naïve aPD-1 ICB treated patients. We found high intratumoral genomic heterogeneity and low ploidy robustly identified patients with intrinsic resistance to aPD-1 ICB. Utilizing a melanoma cohort from a period prior to targeted- and ICB-therapy (“untreated” cohort), we found that genomic heterogeneity was predictive while ploidy was prognostic. To establish clinically actionable predictions, we optimized a predictive model using ploidy and heterogeneity to identify, with high confidence (90% PPV), a subset of patients with intrinsic resistance to and worse survival on aPD1 ICB. We validated this model with independent cohorts, and further showed that a significant proportion of patients predicted to have intrinsic resistance to single agent aPD-1 ICB responded to combination ICB, suggesting these patients may benefit disproportionately from combination ICB.

Methods
Metastatic melanoma patients treated with immune checkpoint blockade were identified from published work (Liu et al. Nature Medicine 2019 & Freeman et al. Cell Reports Medicine 2022) and completed clinical trials (BMS Checkmate 038 and checkmate 064). We included only samples without prior exposure to ipilimumab, with WES data of the paired tumor and normal tissue obtained before PD1 blockade. Clinicopathological and demographic data were obtained from Liu et al Nature Medicine 2019, from BMS for the two clinical trials and for the validation cohort from Freeman et al. Data are shown in Fig. 1 and in Supplementary table 1. The best objective response (BOR) to aPD1 ICB was only available for a subgroup of the patients included in Freeman et al. and wasn’t available for the combination immunotherapy-treated (“combo”) cohort.

Samples from the BMS and Freeman et al. cohorts were re-analyzed with the Broad Institute CGA pipeline (57–67) using the TERRA platform, adopting the same quality controls filters used for the Liu et al. Nature Medicine 2019. In particular quality control cutoffs were as follows: mean target coverage > 50X (tumor) and >30X (normal), cross contamination of samples estimation (ContEst)<5%, tumor purity >= 10%, DeTiN ≤ 20% TiN. A power filter combining coverage and tumor purity was applied as described (e.g. minimum 80% power to detect clonal mutations) in Liu et al. Nature Medicine 2019. Three samples were excluded for low purity and two samples for low power.

MuTect2 was used to identify somatic single-nucleotide variants in targeted exons, with computational filtering of artifacts introduced by DNA oxidation during sequencing or FFPE-based DNA extraction using a filter-based method. Subsequently Strelka was used to identify small insertions or deletions. Lastly, Oncotator was used to annotate the Identified alterations.

Absolute was used for the estimation of ploidy, and purity and for the cancer cell fraction (CCF) estimation of individual mutations. For each sample, the optimal solution (purity, ploidy) was manually selected among the local solutions. Heterogeneity was computed as the proportion of the subclonal mutations, with a mutation defined as subclonal if the cancer cell fraction (CCF) was lower than 0.8.  To support the cutoff of 0.8 for CCF, we have performed a sensitivity analysis (supplementary fig. 4), demonstrating that the heterogeneity stratification was maintained even when different cutoff values were utilized.