Identification of cryptic neoepitopes generated by chimeric transcripts in ovarian cancer

doi:10.21203/rs.3.rs-1456664/v2

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Research Article

Identification of cryptic neoepitopes generated by chimeric transcripts in ovarian cancer

https://doi.org/10.21203/rs.3.rs-1456664/v2

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posted 19 May, 2022

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Peptides identified in a customized targeted database were assessed for antigenicity. Further docking of peptide – MHC as well as peptide-MHC-TCR complexes provided proof-of-concept of antigenicity. Considerable recent interest lies in the immunogenicity of non-canonical neoantigens generated by alternative ORFs emerging from non-synonymous mutations or nucleotide insertions and deletions, exonization of untranslated regions of the genome, non-coding transcripts, transposable elements, etc. Amongst these ‘cryptic’ epitopes, those derived from chimeric transcripts which harbor sequences derived from more than one gene remain undefined, since the translational potential and immunogenicity of these transcripts is relatively unexplored. The present study was hence designed to predict and assess the neoepitopes generated by chimeric transcripts in ovarian cancer. Using a customized targeted database, we identified peptides uniquely generated by such transcripts, expressed either as reading-frame or sense/anti-sense isoforms. Further assessment of the antigenicity of these peptides identified a small number of putative neoepitopes that are likely to be presented to MHC-I in an allele-specific manner, and further recognized by specific TCRs. Effectively, such generation of chimeric molecules by relaxed rigidity of canonical transcription and translation may present opportunities in immunotherapy.

The tractability of gene boundaries through gene and transcript fusions presents a wide horizon of transcript diversity, especially in aberrant cellular contexts such as cancer^1–3. Chimeric transcripts (CTs) harboring sequences derived from more than one gene represents one such class of transcript diversity. The constancy of structural complexities of CTs across tumor types, arising from differential splicing and/or inclusion of regulatory, canonically untranslated sequences suggests a non-random mechanism of their origin^4–5. While it is believed that a majority of such CTs are eliminated by cellular surveillance mechanisms including nonsense mediated decay, a few reportedly generate fusion proteins^6–7.

Current interests in cancer vaccines rely on discovery of effective tumor-specific neoepitopes^8–11, since they elicit T-cell responses that are not subject to host central tolerance, and also lower extent of autoimmune toxicities^12–13. Conventionally, tumor-specific non-synonymous mutations ‘reorganize’ the ORFs of protein-coding genes and perturbcanonical protein sequences to generate neoepitopes that may be recognized and bound by specific T-cell receptors^14–16. Personalized neoantigen discovery and validation remains a challenge since current algorithms rely on the binding affinity of putative neoantigens to HLA-I, while immunopeptidomics is complexed by the need for motif deconvolution in multi-allelic datasets^18–21. Several cancers associated with a low mutational burden including acute lymphoblastic leukemia, high-grade serous ovarian (HGSC), prostate and pancreatic cancer generate functional CD8⁺ T cell responses suggesting the presence of neoantigens^22–23. HGSC is considered as being immunologically ‘cold’ with a mutational landscape largely restricted to TP53, BRCA1 and BRCA2²⁴. Yet, a fraction of tumors presents with high tumor infiltrating lymphocytes, wherein a dominance of CD8 + cells correlate with improved prognosis²⁵. This suggests a possibility of neoantigens being generated through other mechanisms.

On this background we hypothesized that chimeric transcripts identified in the TCGA ovarian cancer RNA sequencing datasets may be translated to generate novel chimeric, tumor-specific peptides, and a few of these presented as neoepitopes to CD8 + cells. We tested this by initially detecting their translational products in mass spectrometry datasets using a customized targeted chimeric transcript derived peptide database for identification of chimeric peptides. These peptides were further assessed for potential antigenicity via prediction of proteasome/ immunoproteasome processing, ferrying of degradation products to the endoplasmic reticulum by the transporter associated with antigen processing (TAP), recognition, binding to major histocompatibility complex class I (MHC-I) molecules for presentation and recognition by specific CD8⁺ T-cell receptors (TCRs) that would mark the tumor cell for destruction. Identification of such chimeric epitopes highlights the cross-talks between relaxed transcription, translation and host immunity, which could be harnessed towards immunotherapy.

Identification of Chimeric Transcript Longest Read-derived peptides (cLRPs)

We identified several candidate chimeric transcripts (CTs) in TCGA RNA-Seq data from 160 OvCa samples applying a customized computational workflow on the Seven Bridges Cancer Genomics Cloud platform (SBgenomics; http://www.cancergenomicscloud.org). All spanning reads of each CT was consolidated to derive longest read (LR) CT sequences (ranged between 71–148 bp). To resolve the coding capabilities of CTs, we developed a customized chimeric LR peptide (cLRP) database comprising of in silico translation of all LR sequences in 6 reading frames (RF) along with ENSP protein sequences and randomly generated decoy peptides (https://www.ensembl.org). This was applied as a backend reference for probing ovarian tumor mass spectra datasets generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC; Edwards et al. 2015), using the FenyoLab xtandem pipeline on the SBGenomics Cloud (Fig.S1a). This pipeline uses the X! Tandem search engine with pre-defined search parameters (Ruggles et al.2016; Ardeljan et al. 2019). Peptide Spectral Matches (PSMs) that qualified a confidence threshold were selected from outputs of this X!Tandem-based pipeline. Corresponding iTRAQ reporter ion intensities from spectral data (3 tumor samples and 1 site-specific pooled reference (labeled either with iTRAQ [email protected]/[email protected]) were extracted for computation of relative peptide abundances from sample enrichment ratios and summation of reporter ion intensities from different scans and bRPLC fractions; selecting for FDR values < 0.01 (expect values 10^− 02). Thence the order of magnitude of relative peptide expression range was computed as log2 transformed total peptide abundance. Examination of the charge state of PSMs in terms of b- and y- ion fragmentation profiles was performed using Proteome Discoverer™ (Thermo Scientific™).

Prediction of antigenicity

Predictions of 9–11 residue degradation products of the tumor-specific cLRPs using the PCPS Proteasomal Cleavage Prediction Server (http://imed.med.ucm.es/Tools/pcps/) were followed by deploying TAPPred server for predicting binding affinity of peptides (IC50) towards the TAP transporter (http://crdd.osdd.net/raghava/tappred/) and generating TAP Scores. MHC Class-I alleles were selected for screening from two geographical regions viz. India (South Asia) and US (North America) from the HLA Allele Frequency Net Database (http://www.allelefrequencies.net; A:B:C alleles − 245:404:120) as is outlined in detail in Supplementary Figs. 1). Binding of peptides to MHC Class I was predicted using Netmhcpan 4.1 that implements Neural Network Aligning (N-N Align) and is trained on Binding Affinity (BA) Data and Eluted Ligand (EL) datasets from mass spectrometry to present antigen to MHC molecules using concurrent motif deconvolution (process of associating ligand to MHC molecule), and rank peptides based on BA as a percentile score with respect to predication of top 100 peptides; hence lower BA rank corresponds to strong MHC I binders. Haplotype-based determination of the stability and affinity of potential neoantigens was determined by NetMHCstabpan1.0 (NetMHCstabpan − 1.0 - Services - DTU Health Tech) that predicts stability and affinity of a peptide towards an allele; a threshold of 0.5% combined rank stability was set to identify peptides most likely to bind to MHC molecules with T-Half (Predicted Half Life) > 2h and IC50 values < 100nM. Derivation of Allele Harmonic Binding Rank (AHBR) and Peptide Harmonic Best Rank (PHBR) was performed as described in Supplementary Methods.

Peptide MHC Docking

Peptide structures were designed in PepFold3D model 1 structure (by convention considered most stable) was visualized in PyMol 4.2 and torsion angles edited using AutoDock 4. PDB structures of relevant and available HLA molecules were downloaded from IMGT and cleared of peptide ligands and artifacts from X-Ray crystallographic analysis using PyMol 4.2. These were submitted to CASTp for prediction of six peptide binding pockets within each molecule as described in Supplementary Methods.

Gibbs Motif analysis

Gibbs Cluster 2.0 [GibbsCluster-2.0 Server (dtu.dk)] was applied to input test peptide sequences along with a set of validated restricting peptides for respective alleles obtained from MHCBN (A comprehensive database of MHC binding and Non-binding peptides; osdd.net, VDJDb (cdr3.net) and published literature (Cole et.al 2006). Default MHC class I parameters were selected to identify clusters with highest Kullbach-Leiber distance and probability scores to predict a binding motif for each allele.

Determination of Agretopicity Index and Validation of antigenicity of parental proteins

A modified agretopicity Index was derived to compare the binding affinities of antigenic epitopes with corresponding 8-11-mer amino acid sequences in the 6-frame in silico translated outputs of its transcript sequence. Shorter peptides generated in some cases due to the presence of stop codons in the alternative frames, were disregarded and only 9-mers were processed (NetMHCstabpan 1.0) for comparison of affinities with the same allele. Similarly, 6-frame protein isoforms of the specific parental transcripts generating the cLR of PSEN2-CABC1 transcript, viz. PSEN2-001 (ENST00000366783.3) and CABC1,ADCK3-004 (ENST00000366779.1; Ensembl Genome Browser, https://www.ensembl.org, Homo_sapiens - GRCh37) were predicted in silico, and each of the 12 generated proteins were processed through established pipeline for prediction of antigenicity as above.

Docking of p:MHC:TCR complexes

CDR3 sequences of α and β chains with associated V and J genes of the available pMHC-TCR complexed with alleles HLA-A*11:01, HLA-A*24:02 and HLA-B*27:05 (Ladell, 2014) were derived from VDJ database (VDJdb : https://cdr3.net). This provided references of 2 TCR:viral (EBV) epitopes:HLA-A*11:01:2; 18 TCRs: human epitopes: HLA-A*24:02:4; and 1 TCR:viral (HIV) epitopes:HLA-B*27:05:4; (ImmuneScape VDJ Assembler, https://tcr2.cs.biu.ac.il/home; https://sysimm.ifrec.osaka-u.ac.jp/immune-scape/mhc1). 10 peptides restricting 3 alleles were modeled with respective complexed TCR, selecting TCR sequences where epitope of human origin was available; in case of epitopes of viral origin, TCR structures with minimum VDJdb acquisition Score of 1 were considered as structure of confidence. ERGOS II (https://biu.ac.il) was used to derive p:MHC:TCR binding scores for test peptide peptide:allele complex and CDR3 sequences of α and β chain sequences with associated V and J genes. Autoencoder-based model along with VDJ Database as training set were provided as parameters. ImmuneScape VDJ chain assembler derived full length chain sequences of TCR-αβ using CDR3 and VJ genes were further provided for modeling in ImmuneScape Modeler along with epitope and HLA-Alleles to obtain PDB output, which in turn was provided as input to TCR 3D repertoire database (https://umd.edu) for derivation of Incident and docking angles and TCR-CoM coordinates. Further, PyMol and Discovery Studio were used to visualize PDB structures for comparison of polar bond lengths and interacting residues with reference PDB structures obtained in VDJdb wherever available. while another complex structure was generated for HLA-A*11:01 towards evaluation of obtained TCR-p:MHC complexes.

Comparison with pre-reported neoantigens

Pre-validated neoantigens reported in Melanoma (Ott et.al,2020) and Glioblastoma (Keskin et.al, 2019) were processed through the same pipeline deployed in the present study. For the melanoma dataset, a reference peptide, DELEIKAY reported to restrict HLA-B*18:01 (PDB ID:4XXC https://www.rcsb.org/structure/4XXC) was selected for simulation of docking and comparison of interactions with predicted peptides. 2 TCRs, viz. TRAV19*01-J3*01: TRBV20-1*01-J2-7*01; TRAV1-2*01-J32*01:TRBV18*01-J1-4*01 reported to complex with HLA-B*18:01 and epitopes of human origin (EEAAGIGIL, MEVDPIGHLY; VDJ assembler) were derived to execute the second level of molecular modeling was and identify similar interactions as those in respective references. For glioblastoma, simulation of docking was performed and compared with reported reference, RRKWRRWHL for peptides predicted to restrict HLA-B*27:05 (https://www.rcsb.org/structure/5IB1) and further for secondary docking with TCR TRAV14/DV4/J21-TRBV6-5*01/J1-1*01 reportedly complexed with HLA-B*27:05 and epitopes of Viral HIV origin (KRWIILGLNK; VDJ assembler) for comparison of interactions as those of reference.

Statistical Analysis

Two groups of patients were demarcated within the cohort based on their TcTP burden (lower and higher than median values of CTs and cLRPs), viz. Group 1 (n = 51, low TcTP) and Group 2 (n = 50, high TcTP). Kaplan Meier (K-M) plots were constructed using survival package in R and tested for significance by log-rank test (p < 0.05). Differences between the K-M curves for the 2 groups were computed using survival probability as function of time and by inspecting the visual shape of plots, median overall survival (OS) and progression-free survival (PFS) in days. Between and within group analysis of variance (ANOVA) was performed for derived AHBR and PHBR values to segregate the variation of peptide distribution and allele restriction for two groups. Trends in distribution across G1 and G2 were analyzed through one-way ANOVA (Kruskal-Wallis H-test, performed using Microsoft Excel 2019 Office Analysis ToolPak), testing null hypothesis stating equal mean values of both groups which was rejected if calculated value (F-value) was observed to be greater than the critical chi-square value (F-crit) at p < 0.05. Additional attributes (number of predicted antigenic peptides through NetMHCpan, number of these peptides detected in mass spectra, summation of relative abundance of cLRPs and MS detected antigenic peptides, allele restriction by NetMHCpan predicted antigenic peptides and mass spectrometry detected antigenic peptides, total AHBR-PHBR score, patient OS in days) were extracted and scaled based on their minimum to maximum values for visualization in MeV (Multiple Experiment Viewer v4.9).

Translation of chimeric transcripts (CTs) in ovarian cancer generates tumor-specific (TS) peptides

A necessary first step was to identify peptides generated from chimeric transcript (CT) sequences around the fusionpoint identified in the TCGA ovarian cancer RNA-sequencing datasets. We consolidated all spanning reads of each CT to derive long read (LR) sequences for focused analyses rather than full-length transcript sequences since our working hypothesis was to evaluate chimeric peptides harboring residues of both parental proteins which were likely to be tumor-specific and perceived as non-self by the cellular machinery. Thus, 997 unique chimeric LR-derived peptides (cLRPs) were identified in ovarian tumor mass spectra datasets generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium²⁶ (CPTAC) using a X! Tandem search engine-based pipeline and a targeted chimeric LR-derived peptide (cLRP) database^27–28 (Fig.S1a; Methods) on the Seven Bridges Cancer Genomics Cloud²⁹. Each tumor displayed a distinct profile of 28–132 cLRPs with a relative peptide expression range between 3.5-8 orders of magnitude (Fig. 1a-i). Since false-positive rates for PSM subgroups can vary (different peptide sizes, charge state of precursor ions, missed enzymatic cleavage, etc.), we affirmed chimerism by examining the charge state of PSMs in terms of b- and y- ion fragmentation profiles (Fig.S1b). While most peptides were transcribed from a single reading-frame (RF) without preferential usage of sense / antisense strands, RF isoforms of the same CT were also identified in some cases (Figs.S1c,S1d).

Tumor and tissue specificity was further inferred by probing expression of cLRPs in ovarian controls (OvCtrls; CPTAC data analyzed in parallel with tumor data), and human lymphoblastoid cell line data³⁰ (LBL; PXD001406). OvCtrls and LBLs displayed distinct cLRP profiles at frequencies of 29–49 and 0–9 /sample respectively, with corresponding relative peptide expression between 3.5–6.7 and 2-4.08 orders of magnitude. cLRP frequency as well as expression levels were lower in OvCtrls over primary tumors and even lower in LBL cultures (Figs. 1a-i,a-ii,a-iii). Peptide profiling designated 843 cLRPs to be Ovarian Tumor-Specific (TS), while remaining were Tumor-Associated (TA). Effectively, translation potential could be assigned to only 21.12% of the identified CTs as affirmed by the specificity imposed by ion fragmentation profiling. Such lower coding capability as compared to that of parental genes despite the enhanced peptide diversity via RF isoforms of a single CT may indicate either a non-coding regulatory nature of CTs, or elimination of newly formed proteins by cell surveillance and homeostatic mechanisms.

Tumor associated Chimeric Transcript-Protein (TcTP) burden may define MHC-I allele restriction by cLRPs and patient prognosis in a personalized manner

While oncogenic roles have been recently assigned to CTs³¹, it is likely that several chimeric proteins may fail to achieve a stable conformation and thereby targeted for proteasomal degradation. Interestingly, recent findings suggest cryptic neoepitopes generated through proteasomal degradation bind to MHC class I molecules and are presented to CD8 + cells in eliciting anti-tumor responses^32–35. To evaluate a similar possible fate within the 844 tumor-specific cLRPs in our study, we first predicted their constitutive and immunoproteasome degradation products using the Proteasomal Cleavage Prediction Server followed by identifying good and moderate strong binding peptides to the transporter associated with antigen processing protein complex (TAP; binding scores 6–7, 7–8 and 8-9.5 respectively) on TAPPred server (Methods). Further presentation of these 526 9-mer peptides to MHC Class I molecules was screened using NetMHCPan 4.1 in a set of 769 high frequency restricted MHC Class-I alleles representing Indian and Caucasian populations derived from the HLA Allele Frequency Net Database³⁶ (Supplementary Information 1). This identified 369 peptides that restrict 98.9% of selected MHC alleles with a Binding Affinity (BA) Rank </=0.5%; restriction being either between single or multiple peptide-alleles (Fig.S2; Tables S1, S2, S3). We also determined the Patient Harmonic Mean Best Rank (PHBR) and Allele Harmonic Binding Rank (AHBR) scores for these 369 putatively antigenic peptides, and correlated them with cLRP frequencies and relative expression in patients along with non-antigenic cLRP-derived peptides (Figs. 1b; Methods). This winnowed out a subset of 248 peptides (PHBR and AHBR < 0.3), whose wider distribution across patients and higher binding rank within the restricted HLA alleles indicated higher probability of their antigenicity (Fig. 1c; Table S4).

We further examined the association of CTs and cLRPs in terms of a tumor-associated Chimeric Transcript-Protein (TcTP) burden, with overall patient survival (OS) in the cohort. This identified 2 distinct groups; patients in Cluster1 with lower average OS presented with a lower TcTP burden (< 60 CTs, < 40 cLRPs), while Cluster 2 patients with significantly higher average OS presented with higher TcTP burden (> 65 CTs, > 55 cLRPs; Fig. 2a). As a corollary, differences in outcomes between patient groups in the TCGA cohort demarcated on the basis of median CT-cLRP values in the cohort as either Group 1 (lower TcTP burden) or Group 2 (higher TcTP burden) also indicated a chimerism-based survival advantage (Fig. 2b). Importantly, the variance between PHBR and AHBR within both groups was significant while only AHBR between the 2 groups was significant (Fig. 2c), emphasizing possible improved presentation and specificity in binding of cLRP to MHC-I alleles. Consolidation of all such parameters accentuated differences between the 2 groups of patients demarcated based on their TcTP burden (Fig. 2d-I). Lower TcTP (Group1) tumors were associated with fewer antigenic peptides and of a lower relative abundance, hence are likely to restrict fewer MHC-I alleles than Group 2 tumors with higher TcTP (Figs. 2d-II to 2d-VI). Most importantly, cLRP derived neoantigens as a determinant of patient allele(s) are suggested to influence prognosis; this allele-specificity of cLRPs could serve as protective factor in a personalized manner and effectively impart a survival advantage in at least a subset of ovarian cancer patients (Fig. 2d-VII). Overall, a certain threshold of TcTP burden may be essential to assign a CT-derived survival advantage, especially on the background of rare outliers in Group 1 that could imply other, unknown protective mechanisms.

Positional amino acid binding preferences in cLRP-derived epitope-MHC complexes exhibit homology with binding pocket preferences of reference peptide:MHC structures

We increased the stringency of our prediction pipeline through haplotype-based determination of the stability and affinity of these potential neoantigens (NetMHCstabpan1.0; Methods), to identify a subset of 55 peptides restricting 117 HLA alleles at a high confidence and significant BA rank (p:MHC interactions; Thalf > 2; IC50 < 100nm; Table S5;Figs. 3a,3b;Fig.S3). HLA-C alleles were strikingly absent in this subset that represent cLRP derivatives most likely to harbor neoantigenic potential, including a highly stability ASCSVAWSW:HLA-B*57:26 complex (Thalf = 31.44h), while ATIRTVSSW:HLA-B*58:19 presented with highest affinity (IC50 = 3.12nM). Figure 3

Further molecular modelling of the p:MHC interactions was performed using reported PDB allele structures (http://www.IMGT/3Dstructure-DB). 12 MHC allele structures were thus available within our list of restricted alleles, which interact with 27 peptides in 39 complexes, 27 of which display significant polar, electrostatic, and hydrophobic interactions (Figs.S4a,S4b). Within these, we performed Gibbs motif analyses for 7 complexes (for which binding pocket details were available in elucidated crystal p:MHC structures;Methods). This guided the assignment of positional residue specificity within cLR-p:MHC molecule interactions (Fig. 3c). Further comparisons vis-à-vis polar, electrostatic and hydrophobic interactions within these 7 complexes with reference to reference structures (RP:MHC) available for each allele revealed some identical interactions, amongst others involving interactions between a different peptide residue and the same MHC residue at a specific position (we termed these as MHC interactive residues; Table S6, Supplementary Information 4 Video 1 ).

Thus, the high probability positional residues identified through Gibbs Cluster analysis anchor ATQGRSWRK in HLA-A*11:01 through interactions of A1 with T171 and E63 (pocket A, identical to reference), and K9 with Y99 (pocket D, MHC interactive residue); this complex is additionally stabilized through binding of T2 and Q3 with MHC interactive residues in pockets B and C respectively (Fig. 3d-i). Our observation of the KRMLASFSF:HLA-B*27:09 complex being stabilized through 3 polar and 1 electrostatic bonds between R2 and B pocket residues (E45,E63,T24, identical to reference) is supported by an earlier report indicating pocket B of HLA-B*27:09 to be sterically and electrostatically suited to bind to arginine³⁷. This complex displays additional bonds between K1 and MHC interactive residues in pocket A that create an outward directed kink in the peptide and exposes remaining residues (Fig. 3d-ii). A high probability positional Y9 residue anchors RQRQKRIAY in pocket F of HLA B*15:01 identical to the reference, along with additional bonds between R1 and MHC interactive residues in pockets A and B (Fig. 3d-iii). Pocket B of HLA-B*58:01 interacts with several residues of KQLLHSWKW including the high probability positional residues W9, S6 and K8; a predicted Y3-R97 salt bridge however may have a destabilizing effect (Fig. 3d-iv). The LAARPGPRW:HLA B*57:03 and IFWDIFCRF:HLA-A*24:02 models however did not compare favourably either with Gibbs analyses or comparative outputs with reference complexes (Figs.S4c-i,c-ii). The high probability positional R2 of RRTERAPRF:HLA-B*27:05 displayed interactions with MHC interactive residues in pockets E and F, with additional stabilization through interactions of Y3,P7 and F9 with MHC interactive residues (Fig.S4c-iii). Conclusively, commonalities identified between cLRP- and R- p:MHC structures vis-à-vis involvement and positional preferences of specific residues of peptides and MHC molecules, will contribute to their recognition as neoepitopes.

Antigenic epitopes present a low Agretopicity Index over corresponding sequence reading-frame / strand isoforms

We assessed the agretopicity index of each of the 7 predicted p:MHC complexes to compare the affinities of antigenic epitopes with their alternative (8–11)-mer reading-frame/strand isoforms (Mehods). All isoforms of the remaining peptides displayed lower affinity than the 7 test peptides (IC50 > 800-fold higher; Fig.S5a; Table S7), indicating the latter to be more robust binders to the respective alleles. We further extended this theme by evaluating the 6 reading-frame protein isoforms of full-length parental protein isoforms for antigenicity, as is exemplified in PSEN2-CABC1 that harbors the peptide ATQGRSWRK (Figs.S5b;5c;5d). This identified 6 peptides restricting HLA-A*11:01 (1 from PSEN2 and 5 from CABC1), all of which are derived from reading-frame / strand isoforms and none from the canonical parental proteins (Table S8). While all harbored the highest positional specificity binding residue K9 (predicted in Gibbs Motif analysis for HLA-A*11:01), none of these peptides except ATQGRSWRK were detected in mass spectrometry, suggesting that a single RF of this cLRP was translated. This also revealed ATQGRSWRK as not being a chimeric peptide, but derived from a frame-shift in PSEN2 transcript.

cLR-p:MHC:TCR complex stabilization involves dynamic interactions similar to reference structures

A next level of molecular modeling to examine interactions involved in recognition of cLR-p:MHC complexes by α and β chains of TCR was performed, which is the defining prelude to MHC-I mediated T-cell cytotoxicity. cLR-p:MHC:TCR complexes were generated considering the reported complementary CDR3 chain sequences available for 3 alleles (HLA-A*24:02:4, HLA-A*11:01:2, HLA-B*27:05:4). Comparison of these models with reference epitope interactions displayed relatively similar ERGO II scores across all complexes, with those for ATQGRSWRK:HLA-A*11:01 and KIINPIIRK:HLA-A*11:01 being indicated at a higher confidence (Fig.S6a; Table S9). Interactions of ATQGRSWRK:HLA-A*11:01 with 2 reported TCRs were examined and compared for stability and affinity with those of the 2 reference peptides (one each for MHC and TCR binding) viz. AIMPARFYPK:HLA-A*11:01³⁸ (RP:MHC) and HLA-A*11:01-TCR: IVTDFSVIK ³⁹ (RP-TCR). ATQGRSWRK:HLA-A*11:01:TRAV21*01/J50*01-TRBV6.6*01/J2.3*01 complex displayed a strong binding score with 4 polar interactions similar to those in the RP:TCR (R5-S98; peptide:TCR-β Figs. 4a-i,4a-ii) and RP:MHC (A1-Y7, A1-Y159 in A pocket; T2-E63 in B pocket; Fig. 4a-iii), which were further stabilized with de novo interactions including K9-D116 (MHC-F pocket; Fig. 4a-iv; Table S10). Alignment of crossing and incident angles (61.368° and 11.3246° respectively) with a smaller θ (x-axis angle of projection of the TCR:CoM–MHC:CoM vector; -33.41°) led to a diagonal placement of TCR on the cLR-p:MHC structure and facilitates a R5-S98 interaction (Fig. 4b,Fig.S6b, Supplementary Information 4 Video 2). Visualization of polar, hydrophobic and electrostatic clouds indicated evasion of steric hinderance; specifically, presentation of R5 towards the outside facing plane (surface) of the MHC groove along with a lateral shift of its amino terminal within the same pocket makes it available for interaction with the TCR-β chain (Fig.S6c-i,6c-ii, left panels). TCR recognition and R5-S98 interaction also achieves a robust anchoring of the peptide deeper into the hydrophobic MHC pocket (Fig.S6c-i,56c-ii, right panels) enhancing stabilization of entire complex.

On the other hand, no interactions between peptide and TCR residues were evident in the ATQGRSWRK:HLA-A*11:01: TRAV35*01/J49*01- TRBV11-2*01/J1-2*01 complex, although most of the polar, electrostatic and hydrophobic p:MHC interactions were retained and geometrical alignment of crossing and incident angles were similar to those within the ATQGRSWRK:HLA-A*11:01:TRAV21*01/J50*01-TRBV6.6*01/J2.3*01 complex (Table 11; Fig.S6d). This is likely due to the steric hindrance generated by an altered TCR:CoM–MHC:CoM vector (θ: -143.15°), which in turn results in a positional shift of S6 deeper into the MHC complex that collaterally decreases the possibility of R5 interacting with TCR–β. Comparative modeling by superimposing ATQGRSWRK:HLA-A*11:01 with the 2 reported TCRs further emphasized the planar shift and steric hinderance reducing the chances of p:TCR interactions within the A*11:01: TRAV35*01/J49*01- TRBV11-2*01/J1-2*01 complex (Fig. 4c). Effectively, this highlights prioritization of neoepitopes through its recognition and binding to the TCR by, (i) determining the orientation and conformation of a firmly anchored peptide in the MHC molecule with specific exposed residues, and (ii) extent of perturbations within the p:MHC-I complex due to the proximity of a TCR through steric hinderance.

Similar assessment of earlier reported immunizing mutated neoantigens affirms prediction analyses

We further compared our evaluation with reported, tumor-specific mutated immunizing neoantigens in melanoma⁴⁰ and glioblastoma⁴¹. Processing of the reported immunizing mutated peptides in these studies through our stability- and affinity-based prediction identified 6 and 24 antigenic peptides in melanoma and glioblastoma respectively (Figs. 5a-i,5b-i; Table S11;Figs.S7a;S7b;S7c). Molecular modeling of some of these, for example NEVSEVTVF-HLA-A*18:01 (melanoma) revealed polar interactions (V8-R97;F9-W147) similar to those in reference p:MHC complex (Fig. 5a-ii; Table S12). Following TCR recognition and binding, the NEVSEVTEF:HLA-B*18:01:TRAV1-2*01-J32*01:BV18*01-J1-4*01 complex displayed a polar p:MHC interaction (E2-S24) identical with the reference (DELEIKAY:HLA-B*18:01), and unique p:TCR-α (polar:S4-G92) and p:TCR-β (electrostatic:E8-K51;Fig. 5a-iii) interactions. A second complex NEVSEVTEF: HLA-B*18:01: TRAV19*01-J3*01:BV20-1*01-J2-7*01 was characterized with similar polar p:MHC interaction (E8-W147) and unique p:TCR-α (polar:S4-R96) and p:TCR-β (electrostatic:E8-R99) interactions as compared with reference complexes.(Fig. 5b-iv).

The glioblastoma peptides AAHRARYFW and VSAAHRARY restricting HLA-B*27:05 display an overlap with the reported peptide ARYFWGNLA, which presents 3 polar p:MHC interactions (A2-E63, A2-R62, A2-Y159;Fig.S7d-i), and complexes with TRAV14/DV4/J21-TRBV6-5/J1-1 through three similar polar, and single electrostatic and hydrophobic p:MHC interactions (A1-Y171, A2-E63, A2-Y159, W9-D77, A1-W167 respectively) and exclusive polar (R4-P28, R6-N98) and hydrophobic (A5-Y52) interactions with TCR-α (Fig.S7d-ii,iii; Table S12). VSAAHRARY displayed 6 similar polar p:MHC interactions (V1-Y171,V1-Y159,S2-E63,S2-R62,Y9-D77,Y9-W147) and a single hydrophobic interaction (V1-W167) as that of reported peptide, and interacts with TCR TRAV14/DV4/J21-TRBV6-5/J1-1 through five similar polar pMHC (V1-Y171,V1-Y7,V1-Y159,S2-E63,A3-Y99), and exclusive polar interactions in TCR-β (A7-G98) and TCR-α (H5-Y52), and two exclusive electrostatic interactions TCR-α (R6-E95, R8-E30; Figs. 5b-ii;5b-iii;5b-iv). A few more peptides in both tumor datasets were indicated to be antigenic through Gibbs cluster motif analysis and docking (data not shown). Taken together, our prediction of neoepitopes generated from cLRPs represents a robust approach suggesting chimeric transcript derived proteins and their reading frame isoforms to present cryptic epitopes in tumors.

Genomic sequencing followed by evaluation of immunogenicity is a recognized neoantigen prediction approach towards personalized cancer therapy, especially in tumors with a high mutation burden⁴². An underlying assumption is that while most intragenic mutations would not alter gene expression and translation capabilities, they generate a tumor-specific protein modified, which could harbour a neoepitope capable of evoking host immunity. Chimeric transcripts on the other hand, being considered a fall-out of aberrant transcriptional and/or splicing machinery in a cell, often may not be translated and have not been evaluated as a source of neoantigens. The mass spectrometry-based identification of peptides derived from chimeric reads using a customized database was hence an essential prelude in our antigenicity prediction, and assigned protein-coding potential to around a fifth of the chimeras produced, a large majority of which were tumor-specific. While we began with the premise of screening only the chimeric peptides with residues of both partners, a realization was that chimera generation may also mediate frame-shift of either / both parental partners besides using of the anti-sense strand, to generate additional neoantigens. A further study towards derivation of full-length chimeric transcripts and mass-spectrometry based identification of their translation products to explore for neoantigenecity would widen the horizons of the present study.

Our assessment of the frequency of chimeric transcripts and peptides vis-a-vis a Chimeric Transcript-Protein (TcTP) burden indicated influences on overall patient survival in a personalized manner through MHC-I allele-specific restriction by some peptides. Modelling the latter at a molecular level revealed the complex dynamics of peptide recognition and presentation, which is challenging since a few thousand human MHC-I alleles are known, and individuals can express as many as six distinct alleles, each with different epitope affinities. We hope that since our predictions were monoallelic, validation may widen the horizons of neoantigenicity to enhance the repertoire of peptide recognition. Allele – based patient stratification may thus identify individuals with an immune checkpoint blockade (ICB) benefit, as is reported in non-small-cell lung cancer and melanoma⁴³. At the other end of the personalization spectrum, patients expressing HLA-I molecules with promiscuous peptide binding capabilities are indicated to display a significantly worse prognosis after ICB⁴⁴. The specificity in recognition assigned through our modelling of the TCR interactions shifts the focus to peptide-TCR interactions as a definitive complementation to MHC-I based predictions and thereby strengthens the neoepitope prediction pipeline.

The field of chimeric neoepitopes is thus nascent as yet, and will require several further approaches as in case of other tumor-specific neoepitopes, to realize its full potential. Attrition of host responses through in situ systems influences of an immunosuppressive microenvironment (T-regs, cytokine balance, etc), inactivation of MHC-I and loss of antigen presentation, T-cell apoptosis/ exhaustion / anergy, altered immune checkpoints, etc. that define personalized features besides a patient’s alleles will continue to present challenges⁴⁵. Considered broadly, our findings including studying the conformation and dynamics of peptide-MHC-TCR interactions to qualify a bona fide neoepitope from chimeric transcript-derived peptides also contribute to understanding why only some of the several predicted neoantigens are likely to elicit immunogenicity; this will be useful in developing novel, immunogenicity based personalized therapies.

HGSC - High-grade serous ovarian tumors, ORF – Open Reading Frame, CTs - Chimeric transcripts, MHC-I - Major Histocompatibility Complex class I, TCR-T-cell receptor, TS-Tumor-Specific, LR- Long Read, cLRPs- Chimeric LR-derived Peptides, CPTAC- Clinical Proteomic Tumor Analysis Consortium, RF- Reading Frame, PSMs-Peptide Spectral Matches, OvCtrls- Ovarian Controls, LBL- Lymphoblastoid, TA- Tumor-Associated, AFND- Allele Frequency Net Database, BA Rank – Binding Affinity Rank, EL -Eluted Ligand, PHBR-Patient Harmonic Mean Best Rank, AHBR-Allele Harmonic Binding Rank, TOA- Tumor Ovarian control associated, TLA- Tumor-LBL associated, TcTP- Tumor-associated Chimeric Transcript-Protein, OS- Overall Survival, PFS- Progression Free Survival, pMHC- peptide MHC, RP:MHC- Reference Peptide MHC, cLR-p:MHC- Chimeric Longest Read peptide-MHC, cLR-p:MHC TCR- Chimeric Longest Read peptide-MHC TCR, RP-TCR Reference Peptide-TCR, TCR:CoM– TCR Centre of Mass, MHC:CoM- MHC Centre of Mass, PCPS- Proteasomal Cleavage Prediction Server, VDJdb -VDJ database, KM-Kaplan Meier, ANOVA- Analysis of Variance

Author Contributions

SAB conceptualized and planned the study design, performed and curated proteomics analysis, VMD performed, analysed and interpreted experimental data and wrote the manuscript draft AW analysed proteomics data using Proteome Discoverer™ and generated b-y ion plots. SAB supervised and interpreted experimental data and edited the manuscript draft to its final form.

Funding

This work was supported by the Tata Innovation Fellowship of the Department of Biotechnology, Government of India (BT/HRD/35/01/04/2017), Fulbright-Nehru grants of USIEF (Award No. 2055/F-NAPE/2015), ICMR-DHR lnternational Fellowship for Senior Bio-medical Scientists (INDO/FRC/452(S-60y2019-20-lHD). VMD was granted project research fellowship by Department of Biotechnology, (India BT/HRD/35/01/04/2017).

Availability of data and materials

The materials that support the conclusion of this review have been included within the article.

Acknowledgements

Research was supported by the Tata Innovation Fellowship of the epartment of Biotechnology, Government of India (BT/HRD/35/01/04/2017), Fulbright-Nehru grants of USIEF (Award No. 2055/F-NAPE/2015), ICMR-DHR lnternational Fellowship for Senior Bio-medical Scientists (INDO/FRC/452(S-60;2019-20-lHD). VMD was granted project research fellowship by Department of Biotechnology, (India BT/HRD/35/01/04/2017). Data used in this publication were generated by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH) and is acknowledged. We extend our gratitude to Prof. David Fenyö and Dr. McKerrow, for sharing and explaining workflows of the FenyoLab xtandem pipeline on the SBGenomics Cloud. The collaborative grant with Seven Bridges Genomics ($10,000 of compute and storage credits is deeply appreciated). The Seven Bridges Cancer Research Data Commons Cloud Resource has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, Contract No. HHSN261201400008C and ID/IQ Agreement No. 17X146 under Contract No. HHSN261201500003I and 75N91019D00024Technical assistance from Mr. Avinash Mali and support from the NCCS Proteomics facility are gratefully acknowledged. All figures in this manuscript were created using BioRender.com

Conflicts of Interest

The authors declare no competing interests.

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Identification of cryptic neoepitopes generated by chimeric transcripts in ovarian cancer

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