PMC7450962_01
Patient's baseline characteristics. (A) Bootstrap distribution of each category ranked by median values and category. For each category, the median reference distribution and the 2.5th to 97.5th percentile range is shown. Patient expression values for each category are shown as dots. Categories in which our patient's score is above the 97.5th percentile (red dots) or below the 2.5th percentile (teal dots) are highlighted, are shown as bold numbers, and are described in the legend key. (B) Boxplots showing distribution of signature expression in the reference cohort. The box shows the interquartile range, and the whiskers represent 1.5x the interquartile range from the top and bottom quartiles. Teal dots show our patient's expression value for each signature, which is in the lowest quartile for both signatures. Finally, genes for which our patient had expression values below the 2.5th (panel C, teal squares) or above the 97.5th (panel D, red squares) bootstrap distribution percentiles are shown. IFNG, interferon gamma signature.
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When we compared our patient's baseline expression of 26 immune cell types and 100 immune function metagenes with those in the reference cohort, her neutrophil metagene expression was below the 2.5th percentile, whereas the cell cycle and immunosuppression metagenes were above the 97.5th percentile of the reference distribution (Fig 1A), indicating a highly proliferative tumor with an immunosuppressed microenvironment. The tumor also showed low expression of two previously validated immunotherapy predictive gene signatures, indicating low probability of response to ICB (Fig 1B). Single-gene level analysis revealed significantly low expression of 27 genes including PDCD1, CD8A, and KLRC2 (Fig 1C), suggesting impaired T-cell and natural killer (NK) cell activity. In addition, we found high expression of 33 genes, including TGFB, HLA-G, CD63, CCL28, and CXCL16 (Fig 1D), which are associated with an immune evasive phenotype, increased cell motility, and invasion.
Tumor clonal evolution and dynamic changes in immune microenvironment. (A) Expression scores of each category at all three different time points are shown, ranked by baseline expression value and annotation category. Legend boxes and bold numbers show categories for which we observed an increase or decrease of >= 0.5 log2 fold change between either baseline and 8 weeks (first period) and/or 8 weeks and mastectomy (second period). (B) Tumor immune dysfunction and exclusion (TIDE)-based (Data Supplement) T-cell dysfunction and exclusion shown together with correlation coefficients of the gene expression values for our patient with cancer-associated fibroblast (CAF), myeloid-derived suppressor cell (MDSC), and M2 tumor-associated macrophages (TAM M2) signatures. (C) Variant allele frequency (VAF) plot (left) and fishplot (right) illustrating the clonal architecture and evolution (based on Sciclone [Waltham, MA] and Clonevol tools; Data Supplement) of the tumor between baseline and mastectomy time points. For optimal visualization, only mutations considered to have a high functional impact (Data Supplement) are labeled. IFNG, interferon gamma signature; MATH, mutant allele tumor heterogeneity; TIL, tumor-infiltrating lymphocyte; TLR, Toll-like receptor.
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However, this was accompanied by an increase in T-cell dysfunction score, indicating an ineffective immune response (Fig 2B). Other mutations with the highest variant allele frequency at baseline included ZNF385C, CACNA1E, NXPE1, and DYNC1H1, which have poorly understood functions in cancer (Fig 2C; Data Supplement). By using Sciclone (Waltham, MA) and ClonEvol, we identified four distinct tumor clones, all present at baseline and in the mastectomy specimen, with little evidence for clonal selection during therapy (Figs 2C-D).