Obtain various immune biomarkers scores, which combined together give the immunophenoscore (Charoentong et al., 2017).
Arguments
- expr_data
A normalized gene expression matrix (or data frame) with gene symbols as row names and samples as columns.
- extract
A string controlling which type of biomarker scores you want to obtain. Possible choices are:
"ips"(default), only raw and discrete IPS scores;"class", IPS scores together with the four summary class scores;"all", all possible biomarker scores.
Value
A tibble with one row for each sample in expr_data, a column sample_id
indicating sample identifiers and a number of additional columns depending
on the choice of extract.
Details
The immunophenoscore is conceived as a quantification of tumor immunogenicity. It is obtained by aggregating multiple immune biomarkers scores, which are grouped into four major classes:
MHC molecules (MHC), expression of MHC class I, class II, and non-classical molecules;
Immunomodulators (CP), expression of certain co-inhibitory and co-stimulatory molecules;
Effector cells (EC), infiltration of activated CD8+/CD4+ T cells and Tem (effector memory) CD8+/CD4+ cells;
Suppressor cells (SC), infiltration of immunosuppressive cells (Tregs and MDSCs).
The table below shows in detail the 26 immune biomarkers and cell types grouped by class together with the number of genes which represent them:
| Class | | Biomarker/cell type | | No. genes |
| MHC | B2M | 1 |
| MHC | HLA-A | 1 |
| MHC | HLA-B | 1 |
| MHC | HLA-C | 1 |
| MHC | HLA-DPA1 | 1 |
| MHC | HLA-DPB1 | 1 |
| MHC | HLA-E | 1 |
| MHC | HLA-F | 1 |
| MHC | TAP1 | 1 |
| MHC | TAP2 | 1 |
| CP | CD27 | 1 |
| CP | CTLA-4 | 1 |
| CP | ICOS | 1 |
| CP | IDO1 | 1 |
| CP | LAG3 | 1 |
| CP | PD1 | 1 |
| CP | PD-L1 | 1 |
| CP | PD-L2 | 1 |
| CP | TIGIT | 1 |
| CP | TIM3 | 1 |
| EC | Act CD4 | 24 |
| EC | Act CD8 | 26 |
| EC | Tem CD4 | 27 |
| EC | Tem CD8 | 25 |
| SC | MDSC | 20 |
| SC | Treg | 20 |
Algorithm
Samplewise gene expression z-scores are obtained for each of 26 immune cell types and biomarkers. Then, weighted averaged z-scores are computed for each class and the raw immunophenoscore (\(IPS_{raw}\)) results as the sum of the four class scores. Finally, the immunophenoscore (\(IPS\)) is given as an integer value between 0 and 10 in the following way:
\(IPS = 0\), if \(IPS_{raw} \le 0\);
\(IPS = [10 * (IPS_{raw} / 3)]\), if \(0 < IPS_{raw} < 3\);
\(IPS = 10\), if \(IPS_{raw} \ge 3\).
References
Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., Hackl, H., & Trajanoski, Z. (2017). Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell reports, 18(1), 248–262. doi:10.1016/j.celrep.2016.12.019 .
See also
hack_sig() to compute Immunophenoscore biomarkers in different
ways (e.g. use signatures = "ips" and method = "singscore").
check_sig() to check if all/most of the Immunophenoscore biomarkers are
present in your expression matrix (use signatures = "ips").
Examples
hack_immunophenoscore(test_expr)
#> # A tibble: 20 × 3
#> sample_id raw_score ips_score
#> <fct> <dbl> <dbl>
#> 1 sample1 0.942 3
#> 2 sample2 -0.348 0
#> 3 sample3 0.0939 0
#> 4 sample4 -0.335 0
#> 5 sample5 1.64 5
#> 6 sample6 1.73 6
#> 7 sample7 -0.153 0
#> 8 sample8 0.468 2
#> 9 sample9 -0.236 0
#> 10 sample10 -2.31 0
#> 11 sample11 0.114 0
#> 12 sample12 1.46 5
#> 13 sample13 -0.866 0
#> 14 sample14 0.909 3
#> 15 sample15 0.647 2
#> 16 sample16 0.749 2
#> 17 sample17 0.344 1
#> 18 sample18 -0.322 0
#> 19 sample19 -1.10 0
#> 20 sample20 0.809 3
hack_immunophenoscore(test_expr, extract = "class")
#> # A tibble: 20 × 7
#> sample_id raw_score ips_score cp_score ec_score mhc_score sc_score
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sample1 0.942 3 0.126 -0.148 0.312 0.652
#> 2 sample2 -0.348 0 0.407 0.0679 -0.796 -0.0268
#> 3 sample3 0.0939 0 0.627 0.0347 -0.483 -0.0843
#> 4 sample4 -0.335 0 -0.0161 0.292 -0.777 0.166
#> 5 sample5 1.64 5 0.778 0.0607 0.876 -0.0742
#> 6 sample6 1.73 6 0.415 0.191 1.26 -0.136
#> 7 sample7 -0.153 0 -0.0862 -0.0140 -0.0677 0.0153
#> 8 sample8 0.468 2 0.355 0.0814 -0.508 0.539
#> 9 sample9 -0.236 0 -0.0908 0.0357 -0.481 0.300
#> 10 sample10 -2.31 0 0.198 -0.258 -2.12 -0.129
#> 11 sample11 0.114 0 -0.338 0.0755 0.552 -0.176
#> 12 sample12 1.46 5 0.360 0.105 0.904 0.0910
#> 13 sample13 -0.866 0 0.195 0.165 -1.28 0.0480
#> 14 sample14 0.909 3 0.433 -0.000192 0.656 -0.179
#> 15 sample15 0.647 2 0.971 0.0167 -0.503 0.163
#> 16 sample16 0.749 2 0.905 -0.0463 -0.115 0.00550
#> 17 sample17 0.344 1 0.0661 -0.0334 0.401 -0.0898
#> 18 sample18 -0.322 0 -0.751 0.0160 0.788 -0.376
#> 19 sample19 -1.10 0 -0.466 -0.0592 -0.488 -0.0839
#> 20 sample20 0.809 3 0.269 0.353 0.462 -0.275