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Meta-Analysis
. 2014 May;24(5):743-50.
doi: 10.1101/gr.165985.113. Epub 2014 Apr 29.

Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival

Scott D Brown  1 , Rene L Warren, Ewan A Gibb, Spencer D Martin, John J Spinelli, Brad H Nelson, Robert A Holt
Affiliations
Meta-Analysis

Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival

Scott D Brown et al. Genome Res. 2014 May.

Abstract

Somatic missense mutations can initiate tumorogenesis and, conversely, anti-tumor cytotoxic T cell (CTL) responses. Tumor genome analysis has revealed extreme heterogeneity among tumor missense mutation profiles, but their relevance to tumor immunology and patient outcomes has awaited comprehensive evaluation. Here, for 515 patients from six tumor sites, we used RNA-seq data from The Cancer Genome Atlas to identify mutations that are predicted to be immunogenic in that they yielded mutational epitopes presented by the MHC proteins encoded by each patient's autologous HLA-A alleles. Mutational epitopes were associated with increased patient survival. Moreover, the corresponding tumors had higher CTL content, inferred from CD8A gene expression, and elevated expression of the CTL exhaustion markers PDCD1 and CTLA4. Mutational epitopes were very scarce in tumors without evidence of CTL infiltration. These findings suggest that the abundance of predicted immunogenic mutations may be useful for identifying patients likely to benefit from checkpoint blockade and related immunotherapies.

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Figures

Figure 1.
Figure 1.
Boxplots showing the number of mutations per patient for each cancer type. The y-axis is cut off at 250 mutations for better visualization of the majority of the data. The dark horizontal bar shows the median, whereas the box encompasses the interquartile range (middle 50% of the data). Whiskers reach the farthest data point that is within 1.5× the interquartile range from the nearest box edge (quartile). Box width is proportional to the sample size (lung: 34, ovary: 218, breast: 24, colorectal: 170, brain: 16, kidney: 53).
Figure 2.
Figure 2.
Overall survival for patients based on CD8A or HLA-A expression. Kaplan-Meier curves were constructed to look at the difference in survival of patients (n = 512) with low and high expression levels of CD8A (A) or HLA-A (B). Patients were split into two groups based on the median expression value. Patients with high expression showed increased survival compared to those with low expression of either (A) CD8A (HR = 0.71, 95% CI = 0.53 to 0.94, P = 1.7 × 10−2) or (B) HLA-A (HR = 0.59, 95% CI = 0.44 to 0.81, P = 8.6 × 10−4). Tick marks on the graph denote the last time survival status was known for living patients.
Figure 3.
Figure 3.
The total number of mutations in tumors is not associated with survival, while the number of predicted immunogenic mutations is associated with survival. (A,C) A “skew plot” was made for all patients (n = 515), ordering patients along the x-axis according to their CD8A expression. Each patient’s CD8A expression was plotted above the x-axis, and total mutation count (A) or predicted immunogenic mutation count (C) was plotted below the x-axis. 73.6% of the total mutation count belonged to patients with above median CD8A expression (P = 2.0 × 10−6), and 84.7% of the total predicted immunogenic mutation count belonged to patients with above median CD8A expression (P = 1.0 × 10−6). (B,D) Kaplan-Meier curves were constructed to look at the difference in survival between patients with low versus high numbers of mutations. Patients (n = 468) were split into two groups based on the median mutation count. There was no difference in survival between the two groups when stratifying on total mutation count (B) (HR = 0.91, 95% CI = 0.68 to 1.23, P = 5.5 × 10−1), but there was a statistically significant difference between the two groups when stratifying on predicted immunogenic mutation count (D) (HR = 0.53, 95% CI = 0.36 to 0.80, P = 2.1 × 10−3). Tick marks on the Kaplan-Meier graphs denote the last time survival status was known for living patients.
Figure 4.
Figure 4.
Hive plot showing that tumors with high immunogenic mutation counts have higher expression of CD8A, PDCD1, and CTLA4. On each axis is the log expression value (log[FPKM]) for CD8A (top), PDCD1 (left), and CTLA4 (right). Values go from small to large moving toward the center of the plot. Each ring represents one patient, and the intersection with the axis represents that patient’s value for that axis. Patients with zero predicted immunogenic mutations are colored orange, and patients with at least one predicted immunogenic mutation are colored blue. Blue rings tend to cluster around the center of the plot, indicating concordance between increased predicted immunogenic mutation count and elevated CD8A, PDCD1, and CTLA4 expression (P = 1.0 × 10−6).
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