Journal: Nature biotechnology

Article Title: Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

doi: 10.1038/s41587-021-01021-3

Figure Lengend Snippet: a. Approaches to identify potential nuORF-derived neoantigens. b-f. Potential neoantigens from nuORFs with somatic mutations. b. Percent of ORFs with median ≥30x read coverage y-axis) by WES (n = 18 samples: primary melanoma and GBM and matched normal) and WGS (n = 2 samples: MEL11 and matched normal, hashed) for different types of ORFs (x-axis) (*p < 0.01, t-test). Error bars: 95% CI. c. Number of Ribo-seq supported, non-synonymous SNVs (y-axis) in MEL11 in annotated ORFs, nuORFs, or in both ORF types when they overlap. d. Number of high affinity (<500 nM, netMHCpan v4.0) potential neoantigens (y-axis) from annotated ORFs (grey) and nuORFs (pink) in MEL11. e. The rate of SNV-derived potential neoantigen peptides with high binding affinity (<500 nM, netMHCpan v4.0) (y-axis) from annotated ORFs (grey) and nuORFs (pink) across 1,170 netMHCpan v4.0 trained HLA alleles (means: 1.4% annotated, 1.6% nuORFs (0.1–0.3% higher, CI 95%)). f. PCAWG-TCGA analysis of somatic SNVs in nuORFs. Percent of SNVs (y-axis) overall (light pink), supported by RNA-seq (pink), and nonsynonymous, supported by RNA-seq (dark pink) in three cancer types (x-axis). Bottom: number of samples analyzed. For all boxplots (E,F): median, with 25% and 75% (box range), and 1.5 IQR (whiskers) are shown.

Article Snippet: Variant analysis, read coverage, and neoantigen predictions PCAWG-TCGA WGS VCF files for CLL, GBM and SKCM were accessed via ICGC Bionimbus ( https://icgc.bionimbus.org/ ) using the Gen3-client ( https://gen3.org/resources/user/gen3-client/ ).

Techniques: Derivative Assay, Immunopeptidomics, Binding Assay, RNA Sequencing

Journal: Nature biotechnology

Article Title: Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

doi: 10.1038/s41587-021-01021-3

Figure Lengend Snippet: a. Approaches to identify potential nuORF-derived neoantigens. b. nuORFs have low sequence coverage by WES compared to WGS. Distribution of WES read coverage (x axis) across different ORF types (y axis). Bottom: WGS read coverage across all ORFs of all types. Vertical red line marks 30x coverage. n = 86421 (annotated), 61398 (lncRNA), 61248 (Out-of-frame), 33823 (5’ uORF), 31453 (3’ dORF), 20337 (5’ overlap uORF), 18316 (3’ overlap dORF), 7941 (Pseudogene), 2371 (Other), 323846 (WGS). Median, with 25% and 75% (box range), and 1.5 IQR (whiskers) are shown. c. Somatic variants in the melanoma patient-derived cell line reflect the variants detected in the original tumor. Cancer-specific SNVs and InDels identified by WES from the primary tumor and by WGS from the tumor-derived cell line. d. Ribo-seq can be used to identify translated variants. Example of a translated SLC7A1 5’ uORF with a cancer-specific SNV. Top: histogram of Ribo-seq reads supporting the translation of the 5’ uORF. Middle: Ribo-seq reads supporting translation of the mutant (green) and wild-type alleles. Predicted neoantigen outlined in red.

Article Snippet: Variant analysis, read coverage, and neoantigen predictions PCAWG-TCGA WGS VCF files for CLL, GBM and SKCM were accessed via ICGC Bionimbus ( https://icgc.bionimbus.org/ ) using the Gen3-client ( https://gen3.org/resources/user/gen3-client/ ).

Techniques: Immunopeptidomics, Derivative Assay, Sequencing, Mutagenesis

Journal: Nature biotechnology

Article Title: Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

doi: 10.1038/s41587-021-01021-3

Figure Lengend Snippet: a. PCAWG-TCGA analysis of SNVs in annotated ORFs and nuORFs. Number of all, transcribed (RNA-seq support), and transcribed nonsynonymous SNVs (y axis) in annotated ORFs and nuORFs (x axis) in CLL, GBM, and SKCM. In CLL, 2/73 samples had no transcribed SNVs, and 3/73 patients had no transcribed nonsynonymous SNVs. n = 73 (CLL,All), 71 (CLL, Expressed), 70 (CLL, Expressed nonsynonymous), 33 (GBM), 36 (SKCM) independent samples. Median, with 25% and 75% (box range), and 1.5 IQR (whiskers) are shown. b. nuORFs with SNVs are translated in unrelated CLL samples. Number (left) and fraction (right) of transcribed nonsynonymous nuORF SNVs detected across 70 CLL samples (y axis) with Ribo-seq TPM > 0 in 0 or more unrelated CLL samples profiled by Ribo-seq (x axis). c. Transcription frequently indicates translation for annotated ORFs and nuORFs. Percent of annotated (grey) and nuORFs (pink) with RNA-seq and Ribo-seq support (y axis) in two CLL samples (x axis).

Article Snippet: Variant analysis, read coverage, and neoantigen predictions PCAWG-TCGA WGS VCF files for CLL, GBM and SKCM were accessed via ICGC Bionimbus ( https://icgc.bionimbus.org/ ) using the Gen3-client ( https://gen3.org/resources/user/gen3-client/ ).

Techniques: RNA Sequencing