Supplementary Fig1. PGV001 feasibility and recurrence free survival a) Survival plot depicting overall survival (OS). N=12. Median survival depicted by vertical dotted line b) Survival plot depicting recurrence free survival (RFS). Median RFS is depicted by vertical dotted line. N=12. c) Swimmer plot depicting time-line of clinical events for each patient since their curative intent treatment. d) Comparing linear correlation between TMB or neoantigen load vs OS at 60 months from 1st vaccine. Pearson correlation coefficient (r) calculated for fitting of the correlation with 95% confidence interval (95%CI). TMB: tumor mutation burden, NeoAg: neoantigens, OS: overall survival,

Supplementary Fig5. TCR sequencing on neoantigen reactive T cells in patient PBMCs a) (Top) Depiction of methodology utilized to generate ex vivo and in vitro expanded and stimulated (IVS) T cell samples from screen, week 8 (W8) and week 28 (W28) timepoints for TCR sequencing. (Bottom) Gating strategy and representative dot plot for isolation by FAC sorting of IFNγ positive, neoantigen reactive T cells for TCR sequencing. b) (Top) Representative differential abundance plots from PID:012 comparing TCR clones between two conditions listed on X and Y axes. (Bottom) Logic and calculation of “relevant” clones in vivo. First using the IVS T cells, lists of TCR sequences differentially abundant in peptide stimulated (Pepstim) Week 8 (W8) and Week 28 (W28) versus screening were generated, named W8IVS>scr and W28IVS>scr, respectively. Similarly, lists of TCR sequences differentially abundant in peptide expanded W8 and W28 samples versus DMSO expanded cells was obtained, named W8ctrl>IVS and W28ctrl>IVS, respectively. Sequences in W8ctrl>IVS and W28ctrl>IVS were removed from W8IVS>scr and W28IVS>scr to obtain vaccine induced “relevant” TCR sequences in IVS T cells named, W8ΔIVS and W28ΔIVS. Ex vivo T cells were analyzed to identify lists of differentially abundant TCRs in W8 and W28 versus screening, named W8ex>scr and W28ex>scr. Finally, an intersection between relevant clones in IVS (W8ΔIVS and W28ΔIVS) and abundant clones in W8 or W28 ex vivo T cells (W8ex>scr and W28ex>scr) was performed to identify relevant neoantigen reactive TCR sequences in vivo, named W8clones and W28clones. c) Stacked bar plot depicting number and frequency of new (detected only post vaccination) and expanded (existed at baseline but frequency expanded post vaccination) W8clones and W28clones TCR clones in PID:015. Each color represents distinct TCR clones and the height of each stack represents each clone’s frequency. d) For PID:015, Venn diagram showing overlap between ex vivo and IVS clones to obtain W8clones and W28clones and their overlap to identify persisting clones in vivo. For PID:015, no persisting clones were found.

Supplementary Table3: List of mutated peptide IDs and peptide sequence in each patients’ vaccine

Vaccines comprising peptide antigens for inducing T cell immunity are being developed for a broad range of therapeutic applications including prevention and treatment of cancer, autoimmunity, and infectious diseases. However, many peptide antigens contain cysteine and/or methionine, which are prone to form oxidation products that can present challenges to manufacturing and reduce biological activity. To address this challenge, we introduced oxidation resistant (OXR) antigens wherein the cysteine and methionine residues of naturally occurring, wild type (WT) peptide antigens are substituted with isosteric residues that are structurally related but omit the oxidation-prone sulfur atom. Our results showed that vaccination with OXR antigens substituting cysteine and methionine with isosteres alpha-aminobutyric acid and norleucine, respectively, induced immune responses to the WT antigen that were equivalent or higher than those induced by vaccination with WT antigens. T cell responses were not affected by the position of the amino acid substitutions indicating that the isosteres do not negatively impact major histocompatibility complex (MHC) binding or T cell recognition. The T cells induced were high quality and associated with anti-tumor efficacy in vivo . Interestingly, substitution of cysteine with serine, which replaces the sulfur for an oxygen, did not yield cross-reactive T cell responses, highlighting the high degree of molecular discernment of peptide-MHC processing and presentation. In sum, OXR antigens provide a generalizable strategy for eliminating sulfur oxidation products and improving the manufacturability and shelf-life of peptide-based vaccines without affecting desired biologic activity.

Supplementary Fig4. CD4+ and CD8+ T cell activation by PGV001 PBMCs from patients were expanded in vitro in presence of neoantigen peptides (pools of peptides, OLPs+Mins, corresponding to each vaccine SLP) and restimulated with peptide/s followed by intracellular staining and flowcytometry. All data is normalized to MOG. a) Aligned dot plot showing CD8+ and CD4+ T cell responses at baseline induced by 96 evaluated neoantigens across 10 patients. Horizontal dotted line depicts cut-off threshold for immunogenicity. 15 and 32 peptides were found to be immunogenic at baseline against CD8+ and CD4+ T cells, respectively. b) Line plots showing longitudinal CD8+ and CD4+ T cell immune responses induced by neoantigens found to be immunogenic at baseline in (a) and that induced greater than 2-fold increase in response following PGV001 treatment. c) Each pie chart shows proportion of vaccine peptides that induced production of IFNγ, TNFα or IL-2 in CD8+ or CD4+T cells in each patient at any timepoint post vaccine initiation. Refer to Material and Methods for definition of “response” in flowcytometry assay. d) CD8+ and CD4+ T cell immune responses elicited by tetanus peptides at various time points in 10 evaluated patients.

Supplementary Fig7. Characterizing peripheral cellular immune environment in patients receiving PGV001. Multicolor flowcytometry performed to phenotype circulating immune cells in patients and healthy donors (HD). PID-017 was excluded from analysis. a) Tabulated view of age and sex of the seven healthy donors whose PBMCs are used in the study. b) Frequency of lymphoid immune cell subsets over the course of treatment. Each dot represents a subject. # p value indicates un-paired two-sided Student’s T-test comparing HD with patient cohort # <0.05, ## <0.01. c) Frequency of myeloid immune cell subsets over the course of treatment. Each dot represents a subject. d) Depicting Fold change from “Pre” in listed immune cell subsets over the course of treatment. e) Pie charts depicting CD4+ and CD8+ T cell states in patient blood and healthy donors. f) �4+ T cells expressing TIGIT and CTLA4 shown as a fold change from baseline in patient blood. *p value indicates paired two-sided Student’s T-test comparing post treatment samples with “pre”. * <0.05. Data in pie charts depicts median. Patient samples, N=12. Healthy donor samples, N=7. Data in graphs shown as mean with bar graphs showing +/- SEM.

Supplementary Table2 – Variant count per patient

Supplementary Table4: Number of Subjects who had Treatment Emergent Adverse Events by grade, system (13 subjects)

Supplementary Fig6. Gating strategy for phenotyping of immune cells by flow cytomtery. EM: Effector memory, TEMRA: Effector memory Re-expression RA, CM: Central Memory

Supplementary Fig2. PGV001-induced T cell immunity as measured by ex vivo IFNγ ELISPOT assay PBMC samples were stimulated with neoantigen peptides (pools of peptides: composed of 15mer OLPs+ 9-10mer predicted Mins corresponding to each vaccine SLP) and analyzed by IFNγ ELISPOT. All data is MOG normalized. a) Line diagrams showing longitudinal changes in IFNγ secretion upon stimulation with responder neoantigen peptides in each patient. Each line represents an individual neoantigen and each dot represents an individual time-point. For definition of “Responder” neoantigen: See Materials and Methods. b) Aligned dot plot showing IFNγ secretion at baseline (Pre) induced by all 126 neoantigens used in the study for 13 patients. Horizontal dotted line depicts cut-off threshold for immunogenicity in the assay. Peptides with immunogenicity at baseline, above the dotted line, are labeled. “Immunogenic” neoantigen: See Materials and Methods. c) Line plot showing post-vaccination immune response, as measured by IFNγ release in ex vivo ELISPOT assay, by the neoantigen peptides found to be “immunogenic” at baseline in (b). d) Plot showing number of IFNγ SFCs/million PBMCs elicited by tetanus peptide at various time points in 13 patients. e) Line plots showing immune response, as measured by IFNγ release in ex vivo ELISPOT assay, elicited by mutated neoantigen peptide pools vs their wild type (WT) counterparts at Week 28 over a range of peptide concentrations. Note: For PID:016 Week 31 sample was used. f) Kaplan Meier Curve comparing OS between subjects that responded, in ex vivo ELISPOT assay, to more or less than 40% neoantigens in their vaccines. PID-017 was lost of follow up and excluded from this analysis. g) Comparing number of SFCs/million PBMCs elicited by each neoantigen in patients Alive (N=6) or Deceased (N=4) at 60-month survival follow up. Total 96 neoantigens analyzed. h) Pie chart showing proportion of neoantigens that elicited antigen specific response in patients within Alive (N=6) vs Deceased (N=4) cohorts. SFC: spot forming cells. In f-h patients that expired with no evidence of their disease recurrence are excluded. *p value indicates two-sided Student’s T-test. *** <0.001. # p value indicates Log-rank (Matel-Cox) test ### <0.001. in patients within Alive (N=6) vs Deceased (N=4) cohorts. SFC: spot forming cells. In f-h patients that expired with no evidence of their disease recurrence are excluded. *p value indicates two-sided Student’s T-test. *** <0.001. # p value indicates Log-rank (Matel-Cox) test ### <0.001.

Supplementary Fig3. Neoantigen specific antibody responses induced by PGV001. Patient plasma was subjected to seromics by ELISA using linear SLPs in the vaccines. N=12 patients. PID-017 excluded from analysis due to high background. a) Stacked bar graph depicting number of neoantigens that induced an IgG/A or M response in each patient at Week 8 and Week 28. For seromics by ELISA a “Responder” neoantigen is defined as a peptide that induced an antibody titer of greater than 100 compared to baseline. b) Line diagrams showing changes in antibody isotypes induced by responder peptides in patients from pre-treatment (Pre), through Prime (Week 8) and Post (Week 28, Week 31 or End of treatment (EOT)). c) Line graph showing changes in titers of total IgG-subclasses induced by responder peptides in PID-006 and PID-008. d) Heat map depicting poly-ICLC specific antibody responses in each patient. e) Plot depicting linear correlation between total IgG antibody titer and IFNγ ELISPOT response at Week 8 (Prime) and Week 28/31 (Post). Pearson correlation coefficient and Spearman correlation coefficient calculated for fitting of the correlation with 95% confidence interval (95%CI). Each dot represents a neoantigen. Horizontal line depicts threshold for antibody titer response at 100. The vertical dotted line depicts threshold for ELISPOT immunogenicity at 60 SFC/million PBMCs. For Week 8, 126 neoantigens evaluated while for Week 28/31, 116 neoantigens were evaluated.

Supplementary Table5: Cellular Immunophenotyping antibody panels

Supplementary Table1. Staging at time of enrollment, adjuvant treatment following curative intent treatment until the end of vaccination and vaccination timing

SIGNIFICANCE:The PGV001 platform is feasible, safe, and immunogenic. The OpenVax pipeline predicted immunogenic neoantigens in tumors with wide-ranging mutational burdens. Data from this study prompted three additional PGV001 trials, one in newly diagnosed glioblastoma, one in urothelial cancer in combination with an ICI, and another in prostate cancer.

T cell receptors (TCR) are pivotal in mediating tumour cell cytolysis via recognition of mutation-derived tumour neoantigens (neoAgs) presented by major histocompatibility class-I (MHC-I). Understanding the factors governing the emergence of neoAg from somatic mutations is a major focus of current research. However, the structural and cellular determinants controlling TCR recognition of neoAgs remain poorly understood. This study describes the multi-level analysis of a model neoAg from the B16F10 murine melanoma, H2-D b /Hsf2 p.K72N 68-76 , as well as its cognate TCR 47BE7. Through cellular, molecular and structural studies we demonstrate that the p.K72N mutation enhances H2-D b binding, thereby improving cell surface presentation and stabilizing the TCR 47BE7 epitope. Furthermore, TCR 47BE7 exhibited high functional avidity and selectivity, attributable to a broad, stringent, binding interface enabling recognition of native B16F10 despite low antigen density. Our findings provide insight into the generation of anchor-residue modified neoAg, and emphasize the value of molecular and structural investigations of neoAg in diverse MHC-I contexts for advancing the understanding of neoAg immunogenicity.

Supplementary Figures and Tables

This file contains supplementary methods, reference, tables (3), figures (10) and the corresponding legends for tables and figures.

<p>Supplementary Table S3. GGI analysis p-values determined using GRAIL</p>

Abstract Only a minority of those exposed to human papillomavirus (HPV) develop HPV-related cervical and oropharyngeal cancer. Because host immunity affects infection and progression to cancer, we tested the hypothesis that genetic variation in immune-related genes is a determinant of susceptibility to oropharyngeal cancer and other HPV-associated cancers by performing a multitier integrative computational analysis with oropharyngeal cancer data from a head and neck cancer genome-wide association study (GWAS). Independent analyses, including single-gene, gene-interconnectivity, protein–protein interaction, gene expression, and pathway analysis, identified immune genes and pathways significantly associated with oropharyngeal cancer. TGFβR1, which intersected all tiers of analysis and thus selected for validation, replicated significantly in the head and neck cancer GWAS limited to HPV-seropositive cases and an independent cervical cancer GWAS. The TGFβR1 containing p38–MAPK pathway was significantly associated with oropharyngeal cancer and cervical cancer, and TGFβR1 was overexpressed in oropharyngeal cancer, cervical cancer, and HPV+ head and neck cancer tumors. These concordant analyses implicate TGFβR1 signaling as a process dysregulated across HPV-related cancers. This study demonstrates that genetic variation in immune-related genes is associated with susceptibility to oropharyngeal cancer and implicates TGFβR1/TGFβ signaling in the development of both oropharyngeal cancer and cervical cancer. Better understanding of the immunogenetic basis of susceptibility to HPV-associated cancers may provide insight into host/virus interactions and immune processes dysregulated in the minority of HPV-exposed individuals who progress to cancer. Cancer Res; 74(23); 6833–44. ©2014 AACR.

Supplementary Figures and Tables