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ABSTRACT Extensive tumour inflammation, which is reflected by high levels of infiltrating T cells and interferon-γ (IFNγ) signalling, improves the response of patients with melanoma to

checkpoint immunotherapy1,2. Many tumours, however, escape by activating cellular pathways that lead to immunosuppression. One such mechanism is the production of tryptophan metabolites

along the kynurenine pathway by the enzyme indoleamine 2,3-dioxygenase 1 (IDO1), which is induced by IFNγ3,4,5. However, clinical trials using inhibition of IDO1 in combination with blockade

of the PD1 pathway in patients with melanoma did not improve the efficacy of treatment compared to PD1 pathway blockade alone6,7, pointing to an incomplete understanding of the role of IDO1

and the consequent degradation of tryptophan in mRNA translation and cancer progression. Here we used ribosome profiling in melanoma cells to investigate the effects of prolonged IFNγ

treatment on mRNA translation. Notably, we observed accumulations of ribosomes downstream of tryptophan codons, along with their expected stalling at the tryptophan codon. This suggested

that ribosomes bypass tryptophan codons in the absence of tryptophan. A detailed examination of these tryptophan-associated accumulations of ribosomes—which we term ‘W-bumps’—showed that

they were characterized by ribosomal frameshifting events. Consistently, reporter assays combined with proteomic and immunopeptidomic analyses demonstrated the induction of ribosomal

frameshifting, and the generation and presentation of aberrant _trans-_frame peptides at the cell surface after treatment with IFNγ. Priming of naive T cells from healthy donors with

aberrant peptides induced peptide-specific T cells. Together, our results suggest that IDO1-mediated depletion of tryptophan, which is induced by IFNγ, has a role in the immune recognition

of melanoma cells by contributing to diversification of the peptidome landscape. Access through your institution Buy or subscribe This is a preview of subscription content, access via your

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subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS ADAR1 MASKS THE CANCER IMMUNOTHERAPEUTIC PROMISE OF ZBP1-DRIVEN NECROPTOSIS Article 25 May

2022 BLOCKADE OF THE AHR RESTRICTS A TREG-MACROPHAGE SUPPRESSIVE AXIS INDUCED BY L-KYNURENINE Article Open access 11 August 2020 PARP14 INHIBITION RESTORES PD-1 IMMUNE CHECKPOINT INHIBITOR

RESPONSE FOLLOWING IFNΓ-DRIVEN ACQUIRED RESISTANCE IN PRECLINICAL CANCER MODELS Article Open access 26 September 2023 DATA AVAILABILITY Data have been deposited in the Gene Expression

Omnibus (GEO) with accession code GSE142822. The genomic data relevant to the MD55A3 cells are found in BioProject with accession code PRJNA316754 (sample ID 1M1). Proteomics and peptidomics

data have been deposited in the PRIDE repository78 with accession code PXD020224. The codes used in the study are available on GitHub (https://github.com/apataskar/bump_finder_example2 and

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improving support for quantification data. _Nucleic Acids Res_. 47, D442–D450 (2019). CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS R.A. is supported by the Dutch Cancer

Society (KWF projects 10315, 11037 and 11574), the European Research Council (ERC–PoC 665317 and ERC-AdG 832844) and the Dutch science organization (NWO-TOP 91216002).Y.S. is supported by

the Israel Science Foundation grant no. 696/17, the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 770854), MRA (622106), Israel Science

Foundation (696/17), Rising Tide Foundation, Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, Estate of Alice Schwarz-Gardos, Estate of John Hunter, Knell Family and the

Hamburger Family. A.P. is supported by a long-term EMBO fellowship grant (EMBO ALTF 796-2018). O.B.B. and M. Alterlaar are supported by the Dutch NWO X-omics Initiative. J.O. and M.L. are

supported by the South-Eastern Regional Health Authority Norway, the Research Council of Norway, the Norwegian Cancer Society, Stiftelsen Kristian Gerhard Jebsen, the University of Oslo and

Oslo University Hospital. We thank M. Delic-Sarac, S. Meyer and T. J. Gjerdingen for HLA typing and processing of blood from in-house healthy donors; P. Kvistborg for providing selected

tetramers; and S. Reich-Zeliger, A. Nachshon, L. Eisenbach, A. Navon, S. Pinto, A. Peri, S. Cohen, A. Admon, A. Kacen and all members of the Agami laboratory for discussions. AUTHOR

INFORMATION Author notes * These authors contributed equally: Osnat Bartok, Abhijeet Pataskar, Remco Nagel AUTHORS AND AFFILIATIONS * Department of Molecular Cell Biology, Weizmann Institute

of Science, Rehovot, Israel Osnat Bartok, Eden Goldfarb, Deborah Hayoun, Ronen Levy, Michal Alon & Yardena Samuels * Division of Oncogenomics, Oncode Institute, The Netherlands Cancer

Institute, Amsterdam, The Netherlands Abhijeet Pataskar, Remco Nagel, Pierre-Rene Körner, Inger Z. M. Kreuger, Julien Champagne & Reuven Agami * Department of Cancer Immunology,

Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway Maarja Laos, Weiwen Yang, Morten M. Nielsen & Johanna Olweus * Institute of Clinical Medicine,

University of Oslo, Oslo, Norway Maarja Laos, Weiwen Yang, Morten M. Nielsen & Johanna Olweus * Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research,

Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands Esther A. Zaal, Maarten Altelaar & Celia R. Berkers *

Department of Biochemistry and Cell Biology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands Esther A. Zaal & Celia R. Berkers * Proteomics Facility, The

Netherlands Cancer Institute, Amsterdam, The Netherlands Onno B. Bleijerveld & Maarten Altelaar * Division of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer

Institute, Amsterdam, The Netherlands Xinyao Huang, Juliana Kenski & Daniel S. Peeper * Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX,

USA Jennifer Wargo * Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Jennifer Wargo * Life Sciences Core Facilities, Weizmann Institute of

Science, Rehovot, Israel Alexander Brandis * The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel Yishai Levin *

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel Orel Mizrahi & Noam Stern-Ginossar * Department of Biological Regulation, Weizmann Institute of Science,

Rehovot, Israel Sacha Lebon * Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel Tamar Geiger * Erasmus MC, Rotterdam

University, Rotterdam, The Netherlands Reuven Agami Authors * Osnat Bartok View author publications You can also search for this author inPubMed Google Scholar * Abhijeet Pataskar View

author publications You can also search for this author inPubMed Google Scholar * Remco Nagel View author publications You can also search for this author inPubMed Google Scholar * Maarja

Laos View author publications You can also search for this author inPubMed Google Scholar * Eden Goldfarb View author publications You can also search for this author inPubMed Google Scholar

* Deborah Hayoun View author publications You can also search for this author inPubMed Google Scholar * Ronen Levy View author publications You can also search for this author inPubMed 

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publications You can also search for this author inPubMed Google Scholar * Celia R. Berkers View author publications You can also search for this author inPubMed Google Scholar * Tamar

Geiger View author publications You can also search for this author inPubMed Google Scholar * Daniel S. Peeper View author publications You can also search for this author inPubMed Google

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inPubMed Google Scholar * Reuven Agami View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS O.B., A.P. and R.N., conceived the project, designed

and performed experiments, analysed data and wrote the manuscript; R.A. and Y.S. conceived the project, designed experiments, wrote the manuscript and supervised the project; J.O. and M.L.

designed experiments for aberrant peptide immunogenicity testing and wrote the manuscript; M. Alon assisted in the peptidomics analysis; M.L. performed immunogenicity testing of aberrant

peptides; W.Y. produced monomers; M.M.N. optimized combinatorial tetramer staining and contributed to HLA typing and blood processing of in-house healthy PBMC donors; R.L. generated the

frameshift sequence database for peptidomics data; M. Alon, D.H. and E.G. performed the bioinformatics analyses of the peptidomics data; T.G. and D.H. performed the correlation analysis

between the synthetic and endogenous spectra; S.L. assisted in peptide validation; P.-R.K. performed the diricore analysis of ribosome profiling data; I.Z.M.K. and S.L. performed cloning of

reporters, western blot and flow cytometry analyses; J.C. performed the tGFP western blot; E.A.Z., C.R.B. and A.B. performed the metabolomics analyses; O.B.B., M. Alterlaar and Y.L.

performed mass spectrometry; X.H., J.K. and D.S.P. provided reagents and technical assistance for the T cell co-culture assays; J.W. helped in generating melanoma cell lines; and O.M. and

N.S.-G. assisted in performing ribosome profiling. All authors read and approved the manuscript. CORRESPONDING AUTHORS Correspondence to Yardena Samuels or Reuven Agami. ETHICS DECLARATIONS

COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature_ thanks Nahum Sonenberg, Petra Van Damme, Jonathan Yewdell and the

other, anonymous, reviewer(s) for their contribution to the peer review of this work. PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps

and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 DIRICORE ANALYSIS CHARACTERIZES RIBOSOME OCCUPANCY CHANGES AFTER IFNΓ AND TRYPTOPHAN-DEPLETION

TREATMENTS OF MELANOMA CELLS. A, Western blot analysis for IDO1 expression in control and IFNγ-treated conditions in three different melanoma cell lines as indicated (_n_ = 3). B, Tryptophan

(left) and kynurenine (right) levels as determined by mass spectrometry of the indicated melanoma cell lines in control conditions or 48h after the start of IFNγ treatment. Data represent

averages of three independent experiments ± s.d. C, Schematic depicting the principle behind diricore analysis: E, P and A-site occupancy of the ribosome can be mapped on a typical RPF at

9th, 12th and 15th positions, respectively. This allows a position-specific sub-sequence analysis to be performed (bottom left) to probe for the codon enriched differentially between two

conditions at E, P or A sites (presented as bar-plots). In addition, diricore analysis depicts 5′-RPF densities across a codon of interest (bottom right, presented as line-plots). D,

Metagene density profiles depicting global shifts of RPFs to the start of the coding sequence upon IFNγ treatment (red, yellow) as compared to control (grey). The _y_ axis is intra-gene

normalized RPF density. Ribosome profiling data of two independent biological replicates for MD55A3 (top) and 108T (bottom) are represented. E, Diricore analysis line plots depicting

differential ribosome occupancy (5′-RPF) at −30 to +60 codons in MD55A3 (left) and 108T (right) cells. Plots depict 5′-RPF densities after the start ATG codon (top, in green), tryptophan

codon (middle, in red) and cysteine codon (lower, in grey). The _y_ axis is the ratio between number of reads in IFNγ versus control conditions. F, Diricore analysis bar-plots depicting

differential codon usage (at position 15 of the RPFs) in IFNγ versus the control condition for MD55A3 (top) and 108T cells (bottom). Data represent the average of two independent biological

replicates. G, Diricore line plots depicting cumulative signal of RPFs across the coding region normalized into percentiles for ATF4, CDC6 and ATP5G1 in 12T control (grey tones) and

IFNγ-treated cells (yellow, red). W-bumps (greyed areas) indicate an increased number of reads downstream of the tryptophan codons (dashed lines). H, Tryptophan levels as measured by mass

spectrometry from 12T cells subjected to the indicated treatments. Bars represent the average of three independent experiments ± s.d. I, UCSC tracks representing IDO1 mRNA reads in MD55A3

cells in the indicated conditions as a measure for the induction of transcription of this gene. J, Metagene RPF density profiles for control (black), IFNγ (red), IDOi (grey) and IFNγ + IDOi

(green)-treated 12T cells. K, Diricore analysis bar plots depicting differential codon usage (at position 15 of the RPFs) in IFNγ- versus control-treated 12T cells. The graph represents the

average of two independent biological replicates. This experiment is the control of Fig. 1f. L, Tryptophan levels as measured by mass spectrometry in MD55A3 cells following 48-h growth in

control or tryptophan-depleted medium. Values represent the average of three independent replicates ± s.d. M, Metagene density profiles for 12T control (dark grey) and tryptophan depletion

cells (red). The lines represent the average of two independent biological replicates. N, Diricore analysis line plots depicting differential (IFNγ/control) ribosome occupancy (5′-RPF) at

−30 to +30 codons of the indicated amino acid in control versus tryptophan-depleted 12T cells. O, Diricore analysis bar plots depicting differential codon usage (at position 15 of the RPFs)

in control and tryptophan-depleted 12T cells. All diricore plots represent the average of at least two biological replicates. EXTENDED DATA FIG. 2 BUMP-FINDER IDENTIFIES

TRYPTOPHAN-ASSOCIATED BUMPS. A, Computational approach for unbiased detection of bumps in comparative ribosome profiling experiments. The algorithm scans transcripts in 100 windows of equal

length for peaks in ribosome occupancy and filters for differential peaks in treated versus untreated samples. B, C, Density of codons per amino acid in the region of 60 codons upstream and

downstream of the peak of bumps identified with bump-finder in ribosome profiling data of 12T cells. Data shown iare derived from two independent biological replicates treated with either

IFNγ (B) or control (C). D, Ratio between upstream and downstream reads 30 codons from the peak of bumps identified in control conditions when treated with IFNγ in MD55A3 (top) and 108T

cells (bottom). E, RPF density in control- (grey line) and IFNγ- (red line) treated conditions in MD55A3 and 108T cells. The area marked in grey indicates the W-bump region. F, Densities

(top lines) and heat maps (bottom) of ribosomal P-sites 100 nucleotides surrounding every tryptophan codon in control and IFNγ conditions in 12T, MD55A3 and 108T cells. G, Classification of

all transcripts containing tryptophan codons, one group associated with W-bumps (‘Bumps’), and another group that is not associated with bumps (‘No bumps’). Graphs represent RPF density in

the region of 300 nucleotides surrounding the tryptophan codon. Bumps indicated in grey shading. H, Bar plot depicting the enrichment of presence of two tryptophan codons within a region of

eight codons in the bumps group over the no bumps group. W indicates a codon for tryptophan, whereas X indicates all remaining amino acids. I, Heat map depicting frequencies of codons for

each amino-acid at every position 25 codons upstream and downstream, with respect to tryptophan. J, Line plots depicting RPF density at tryptophan stratified by distance between two

tryptophan residues (black). In each graph the control (the occurrence of a single tryptophan) is presented in red. EXTENDED DATA FIG. 3 PROTEOMICS ANALYSES AFTER IFNΓ TREATMENT OF MD55A3

MELANOMA CELLS. A, Volcano plot depicting overall changes in the proteome upon IFNγ treatment as observed by analysis of quantitative mass-spectrometry data. _x_ axis: log-transformed fold

change between IFNγ versus control conditions in MD55A3 cells; _y_ axis: corresponding log-transformed adjusted _P_ value calculated from three independent biological replicates. Highlighted

in blue are proteins that are significantly differentially expressed. Both IFNγ-mediated induction of IDO1 and WARS and the immunoproteasome components are indicated. B, Left, box plots

depicting log-transformed fold change in the levels of protein (red; average of three replicates) and mRNA (brown; average of two replicates) in IFNγ-versus control-treated cells. Proteins

were grouped according to the number of asparagine (N), tyrosine (Y) or phenylalanine (F) residues in the protein sequence. Boxes depict first, second and third quartiles; whiskers depict

the range excluding the outliers. Test: Wilcoxon test (two-tailed); NS, not significant, *_P_ < 0.05. For asparagine box plots the actual _P_ values are 0.7, 0.8, 0.055 for protein

quantification (left) and 0.8,0.2, 0.013 for RNA quantifications (right) in the order shown. Middle, same as left but for number of tyrosine (Y) residues. Actual _P_ values are 0.7, 0.86 and

0.02 for protein quantification (left) and 0.98, 0.13 and 0.14 for RNA quantifications (right) in the order shown. Right, same as middle but for number of phenylalanine (F) residues. Actual

_P_ values are 0.7, 0.97 and 0.23 for protein quantification (left) and 0.3, 0.015 and 0.0038 for RNA quantifications (right) in the order shown. C, Western blot analysis of ubiquitinylated

proteins in total cell lysates of MD55A3 cells mock- or IFNγ-treated, which were additionally incubated minus or plus MG132 (_n_ = 1). D, A panel showing proteins with increased abundance

in total cell lysates of MD55A3 cells treated with MG132 versus controls. Log2-transformed fold changes were calculated on data of three independent replicates. E, Box plots depicting log

fold change in protein levels (average of three replicates) in IFNγ-treated versus control conditions in MD55A3 cells treated with proteasome inhibitor, for proteins stratified for different

numbers of aspargine (N), tyrosine (Y) and phenylalanine (F) residues in their sequence. Boxes depict first, second and third quartiles; whiskers depict the range excluding the outliers.

Test: Wilcoxon test (two-tailed); NS, not significant and *_P_ < 0.05. Actual _P_ values are 0.34, 0.44, 0.77 and 0.96, 0.84, 0.024 and 0.29, 0.84, 0.34 in the order shown. F, Box plots

depicting protein length (number of amino acids) for stratified group of genes with increasing number of tryptophan (left) and asparagine (middle), with their ratios (right). G, Density of

RPFs (average of two replicates) 300 codons across individual tryptophan codons (black line), two tryptophans that are present within a distance of 8 codons (green line) and two tryptophans

that are present at a distance greater than 8 codons (red line). H, Box plot depicting bump scores (average of two replicates) for instances of two tryptophans separated by fewer than 8

codons (green) and more than 8 codons (red). Bump scores are calculated in MD55A3 cells. Boxes depict first, second and third quartiles; whiskers depict the range excluding the outliers.

Test: Wilcoxon Ttest (two-tailed); ***_P_ < 0.0005. Actual _P_ value is < 2.2 ×10−16. I, Box plot depicting protein level changes (log-transformed fold change, average of three

replicates) between IFNγ and control conditions. The graph represents genes that have two tryptophan codons within a distance of 8 codons (green) or genes having a distance of more than 8

codons between two tryptophans (red). Test: Wilcoxon test (two-tailed); ***_P_ < 0.0005. Actual _P_ value is < 2 ×10−16 J, Same as I, but for asparagine (N), in MD55A3 cells. Test:

Wilcoxon test (two-tailed); NS, not significant. Actual _P_ value is 0.83. EXTENDED DATA FIG. 4 REPORTER ASSAYS FOR THE DETECTION OF TRYPTOPHAN-ASSOCIATED OUT-OF-FRAME EVENTS. A, A

hypothetical model suggesting a possible mechanism causative for W-bumps. In the normal scenario, ribosomes do not encounter problems when translating a tryptophan and progress translation

at regular speed (top). Tryptophan shortage, on the other hand, can lead to stalling on the tryptophan codon (bottom left), or could in theory induce frameshifting events, leading to

aberrant peptide production (bottom right). As the secondary structure of growing polypeptide chains is attained in the lower tunnel of the ribosome, the loss of an α-helical secondary

structure in this tunnel could hamper ribosomal progression. B, Box plot depicting bump-score (from two replicates) in MD55A3 cells from the group of selected peptides with ordered

out-of-frame peptides (‘Selected’ from Fig. 3b) and every tryptophan in the proteome (‘All’ from Fig. 3b). Boxes depict first, second and third quartiles; whiskers depict the range excluding

the outliers. Test: two-sample _t_-test; *_P_ = 0.056. C, Schematic representation of the final protein sequences that would form due to frameshifting events. The in-frame construct (top)

contains a His tag and would end up in the pull-down (PD) fraction. Whenever a frameshift occurs at the position of the tryptophan in ATF4, this protein would lose its His tag, and

consequently would end up in the supernatant fraction (S). The +1 and +2 out-of-frame constructs (bottom) do not contain a His tag, whereby the resulting proteins always end up in the

supernatant fraction (S) in a His-tag pull-down assay. When frameshifting events take place, however, the His tag is incorporated into the peptide, whereby the resulting protein ends up in

the pull-down fraction (PD). D, Western blot analysis of a sequential V5-tag immunoprecipitation on the supernatant samples of the Frame reporter expressing cells from Fig. 3e (_n_ = 1). The

image indicates the presence of V5-tag-containing peptides that do not contain a His tag generated from the in-frame reporter. E, Tryptophan levels in MD55A3 cells expressing the

V5-ATF4-His reporter constructs that were used in Fig. 3e and in D. Tryptophan levels were analysed by mass spectrometry after 48 h of treatment. Bars represent the average of three

independent replicates ± s.d. F, Tryptophan levels in MD55A3 cells expressing the V5-ATF4-His reporter constructs that were used in H. Tryptophan levels were analysed by mass spectrometry

after 24 and 48 h of treatment in triplicate. G, Western blot analysis showing V5-tagged peptides in pull-down samples of MD55A3 reporter cells that were mock-, IFNγ- or IFNγ + IDOi-treated

(_n_ = 2). H, Western blot analysis of V5-tagged proteins in pull-down samples of MD55A3 reporter cells that were either mock-treated or cultured in tryptophan-depleted medium (_n_ = 1). I,

Western blot analyses showing pull-down assays followed by V5 staining on western blot of MD55A3 cells expressing the original reporter constructs as depicted in Fig. 3d (Wt), or MD55A3

cells expressing the same reporters where the tryptophan codon was mutated to a codon for tyrosine (Y mut, _n_ = 1). J, Amino acid levels as determined by mass spectrometry from lysates of

Tyr-depleted cells (48 h) versus control cells. Bars represent the average of three independent replicates ± s.d. K, Western blot analysis showing V5-tagged peptides in pull-down samples of

MD55A3 reporter cells that were mock-treated or depleted of tyrosine for 48 h (_n_ = 1). L, Flow cytometry analyses showing the histograms obtained of MD55A3 cells expressing the

V5-ATF4-tGFP reporters in all 3 frames in mock- and IFNγ-treated conditions. Plots are a representative graph out of a triplicate biological experiment. M, Flow cytometry analyses showing

the quantification of histograms obtained in L. Bars represent the average of three independent replicates ± s.d., *_P_ values in order from left to right: 4.0 × 10−6 and 9.5 × 10−4 as

determined by a two-sided _t_-test. N, Anti-V5-tag and anti-tGFP western blot analysis of whole cell lysates from MD55A3 cells expressing the indicated reporters, which were subjected to

mock or IFNγ treatments. In each blot the position of the full-length in-frame protein and the shorter out-of-frame protein are marked by the arrowheads (_n_ = 2).O, Western blot analyses

with anti-V5 antibody of His-tag pull-down samples of 888-Mel and D10 cells expressing the in-frame and +1 reporters (_n_ = 2). The cells were either grown in isolation (−), or co-cultured

with MART-1 specific T cells for 16 h (+) before the pull-down was performed. P, Western blot analysis showing IDO1 upregulation in 888-Mel and D10 cells in control and T cell co-culture

conditions (_n_ = 2). The same cells were used for pull-down experiments in Fig. 3m. Q, His-tag pull-down was executed on the lysates of mock and IFNγ-treated 888-Mel and D10 cells

expressing the in-frame and _+_1 reporters (_n_ = 2). Both supernatant (S) and His-tag pull-down samples (PD) of these cells were stained with V5 antibodies. R, The lysates of cells used in

Q were used for a western blot analysis to show the level of IDO1 induction (_n_ = 1). S, Tryptophan levels as determined by mass spectrometry analysis of 888-Mel and D10 lysates in mock,

IFNγ-treated and tryptophan-depleted conditions. Bars represent the average of three independent replicates ± s.d., except for the first bar, which is an average of two independent

replicates. T, Western blot analyses with anti-V5 antibody of His-tag pull-down samples of 888-Mel and D10 that were mock-treated (+), or grown in tryptophan-less medium for 48 h (−) before

the pull-down was performed (_n_ = 2). EXTENDED DATA FIG. 5 PROTEOMICS ANALYSIS OF 2D-LC–MS/MS DATA REVEALS THE INDUCTION OF ENDOGENOUS ABERRANT PEPTIDES AFTER IFNΓ TREATMENT OF MD55A3

MELANOMA CELLS. A, PCA plot illustrating the clustering of 2D-LC-MS/MS data from MD55A3 cells that were treated with IFNγ for 48 h as compared to mock. Both conditions were treated with

MG-132 for the last 4 h. B, Gene set enrichment analysis (GSEA)-based depiction of the proteomics data showing the induction of the IFN response genes following IFNγ treatment of MD55A3

cells. C, Schematic representation of the in silico generation of +1 and −1 frameshifted protein database (Methods). D, Bar plot depicting number of hits for the detected aberrant

(frameshifted) polypeptides in the Ensemble translations database of coding and non-coding genes. Only the aberrant peptides without any matches (_n_ = 0) were retained as true hits (red

bar). E, Heat map showing frameshifted peptides identified in the full proteome of IFN-treated MD55A3 cells along with sequence information. F, Line plot depicting log-scaled fold change in

proteomic intensity values of IFNγ- versus mock-treated MD55A3 cells. G, Same as F, but for LFQ normalized intensity values of frameshifted peptides hits along with reverse peptide hits. H,

Same as F, but only for reverse peptide hits. I, Same as F, but only for frameshifted peptide hits. J, Gene set enrichment analysis (GSEA)-based depiction of the proteomics data showing the

induction of the HLA genes following IFNγ treatment of MD55A3 cells. K, GO term analysis on the differentially expressed proteins detected in J. L, Anti-V5-tag and anti-tGFP western blot

analysis of whole cell lysates from A375 cells expressing the indicated reporters, which were subjected to mock treatment or tryptophan depletion (_n_ = 2). In each blot the position of the

full-length in-frame protein and the shorter out-of-frame protein are marked by the arrowheads. M, FACS plots representing the signal for H-2Kb-bound SIINFEKL peptides in A375 cells

expressing H-2Kb in combination with in-frame (Frame) or +1 out-of-frame (+1) V5-ATF41–63-tGFP-SIINFEKL reporters. Graphs shown are a representative curve from one of three independent

experiments. EXTENDED DATA FIG. 6 QUALITY CONTROL ANALYSIS FOR THE IMMUNOPEPTIDOMICS OF MD55A3 CELLS. A, Length distribution of the identified peptides. B, HLA binding prediction using

netMHCpan shows a high percentage of eluted peptides predicted to bind MD55A3 haplotypes. C, HLA‐I peptides were clustered by Gibbs clustering to assign the peptides to the different HLA

alleles of the patient. Logos were created to identify the HLA alleles’ motif using all peptides matching to this allele in the IEDB database (top). Peptides that were identified in the

cells treated with IFNγ, tryptophan-derived (mTRP) and non–treated (NT) were clustered to 1–6 clusters, and only the best fit (highest Kullbach Leiber distance) was used for comparison with

the IEDB. The clusters show the motifs expected of the cells’ HLA alleles. The last column indicates the outlier peptides that were not clustered. EXTENDED DATA FIG. 7 IMMUNOPEPTIDOMICS

ANALYSIS IDENTIFIES TRYPTOPHAN-ASSOCIATED ABERRANT PEPTIDES. A, Scheme summarizing the filtration steps that led to the detection of HLA-bound aberrant peptides, containing W-associated

frameshift sequences (derived from 16 samples: 4 NT, 4 +IFNγ, 4 mTpr, 4 metastases from patient 55); immunopeptidomics spectra were searched against W-specific frameshifted human proteome.

MS-identified peptides were filtered according to described steps, including HLA binding prediction, spectral quality and source gene expression validation in transcriptome or translatome

datasets. Following the above steps, 28 aberrant peptides were detected either in MD55 metastasis or treated MD55A3 samples, and not in the untreated cells. B, Predicted hydrophobicity index

(HI) and observed retention time (RT) of canonical peptides (black) and aberrant peptides at two filtration steps described in a, step 3 (blue) and step 7 (red). The observed correlation

between the RT versus HI, (using the SSRCalc tool31,70) supports the identification of the aberrant peptides presented in A. C, Schematic representation of aberrant peptides KCNK6 and PCNX2

detected via immunopeptidomics, derived from a −1 and +1 frameshift at a Trp codon (marked in red), respectively. The in-frame and out of frame amino acid sequences are indicated above

(black) and below (red/green) the mRNA sequence, respectively. D, Venn diagram including the 28 aberrant peptides detected following the filtration steps presented in A (were not identified

in the untreated MD55A3 cells), grouped according to the samples they were detected in. E, Mean LFQ normalized intensities. Canonical and aberrant peptides that exhibit above threefold

change in their intensities, are marked in black and blue/red, respectively. F, G, Similarities between the aberrant (_n_ = 28) and canonical (_n_ = 6,304) bound peptides. F, Andromeda

score. G, Binding affinity prediction with NetMHCpan showing a similar binding distribution of the canonical and aberrant peptides. The aberrant peptides are highlighted in red. Dashed line

mark strong binders rank cutoff (<0.5). H, A list of eight out of the thirteen −1 and +1 out-of-frame and _trans_-frame peptides found in the metastasis samples of patient 55. The colours

of the peptide sequences represent in-frame (black), +1 frame (green) and −1 frame (red). EXTENDED DATA FIG. 8 TANDEM MASS SPECTRA OF ENDOGENOUS ABERRANT PEPTIDES IDENTIFIED IN PATIENT 55

MATCH THE SPECTRA IDENTIFIED IN METASTASIS SAMPLES FROM PATIENT 42. Left, experimental spectra of peptides identified in metastasis samples from patient 42, who shares three of six HLA-I

alleles with patient 55 (A03:01; B07:02; C07:02). Right, head-to-tail plots show similarities between overlapping ions of the two spectra, Top (blue), patient 42; bottom (red), patient 55

(MD55). EXTENDED DATA FIG. 9 IDENTIFICATION OF T CELLS REACTIVE TO TRYPTOPHAN-DERIVED ABERRANT PEPTIDES. A, Flow cytometry analysis of CD8+ T cells reactive to BV421 and PE-labelled pMHC

multimers complexed with the ZNF513-derived aberrant peptide in co-cultures of naïve CD8+ T cells and autologous monocyte-derived dendritic cells pulsed with peptide (right) or DMSO vehicle

(left). Cells were from an HLA-C*07:02pos healthy donor. B, Boolean gating strategy for analysis of combinatorial pMHC multimer staining limits false positive signals arising due to

background fluorescence when combining in one staining multiple different pMHC multimers labelled with different fluorochromes. In total, four different fluorochromes were used in pMHC

multimer preparation and combined into four dual-colour pMHC multimer pairs, each pair complexed with a different peptide. For simplicity, gating for only one dual-colour pMHC multimer

population is shown. Gating strategy: (1) Live CD8+ T cell singlets were identified with the use of FSC and SSC gates, live/dead fixable near-IR dead cell and CD8 staining. (2) Separate

gates were used to define positive events in each pMHC channel. (3) NOT gates were used to select CD8+/pMHC multimer− events for each pMHC channel. (4) AND gates for two pMHC multimer+

populations and NOT gates for each of the remaining pMHC channels were used to select for cells that are positive only in two channels. Events staining positively for one or more than two

pMHC channels were gated out. (5) AND gate was used to combine all pMHC NOT gates. (6) OR gate was made with gates from step 4 and 5. The final plot shows only CD8+ T cells that are positive

for two pMHC channels and excludes cells positive for only one or positive for more than two channels. C, For analysis of T cells upregulating the activation marker CD137, viable CD8+ T

cell singlets were identified by FSC and SSC gates, live/dead fixable near-IR dead cell staining and CD8 staining. Subsequently, activated CD8+ T cells were identified as CD137+ events, in

which cut-off was set based on staining of T cells incubated without target cells. Plots depicting CD137+ events show the KCNK6 T cell clone 1 incubated with target cells pulsed with the

KCNK6 aberrant peptide (1 μg ml−1, top), or not (bottom). SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION This file contains Supplementary Information File contains Supplementary

Methods, Supplementary Tables 1-3 and Supplementary Figures 1-4. REPORTING SUMMARY RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Bartok, O., Pataskar,

A., Nagel, R. _et al._ Anti-tumour immunity induces aberrant peptide presentation in melanoma. _Nature_ 590, 332–337 (2021). https://doi.org/10.1038/s41586-020-03054-1 Download citation *

Received: 09 January 2020 * Accepted: 30 October 2020 * Published: 16 December 2020 * Issue Date: 11 February 2021 * DOI: https://doi.org/10.1038/s41586-020-03054-1 SHARE THIS ARTICLE Anyone

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