7, 644–657. https://doi.org/10.1158/2326- 6066.CIR-18-0439 .https://www.ncbi.nlm.nih.gov/pubmed/30745366 .ll Article 1026 Cancer Cell 40, 1010–1026, September 12, 2022 STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Streptavidin R-PE Agilent Cat # PJRS25-1 Streptavidin PE Biolegend Cat # 405204 Sreptavidin APC Agilent Cat # PJ27S Streptavidin APC Biolegend Cat # 405207 Streptavidin BV711 BD Biosciences Cat # 563262 Streptavidin BV711 BioLegend Cat # 405241 Streptavidin BV605 BioLegend Cat # 405229 Streptavidin PE-CF594 BD Biosciences Cat # 562284 Streptavidin BV421 Biolegend Cat # 405225 Streptavidin BUV395 BD Biosciences Cat # 564176 Streptavidin BV650 BD Biosciences Cat # 563855 Streptavidin BV650 BioLegend Cat # 405232 LIVE/DEAD Fixable Near-IR Dead Cell Stain KitThermoFisher Cat # L10119 CD19 APC-H7 BD Biosciences Cat # 560177; RRID:AB_1645470 CD3 BUV805 BD Biosciences Cat # 612893; RRID:AB_2870181 CD3 APC BD Biosciences Cat # 340661 CD3 BV711 BD Biosciences Cat # 740832; RRID:AB_2740489 CD4 BV711 BD Biosciences Cat # 563028; RRID:AB_2737961 CD4 BUV395 BD Biosciences Cat # 563028 CD4 FITC Biolegend Cat # 300306 CD8 Alexa Fluor 700 BD Biosciences Cat # 561423; RRID:AB_10682894 CD8 APC-H7 BD Biosciences Cat # 563795; RRID:AB_2722501 CD8 BUV395 BD Biosciences Cat # 555636; RRID:AB_395998 CD8 PE-Cy5 BD Biosciences Cat # 563561; RRID:AB_2744288 CD14 BUV395 BD Biosciences Cat # 563785; RRID:AB_2744293 CD16 BUV395 BD Biosciences Cat # 560177; RRID:AB_1645470 CD19 APC-H7 BD Biosciences Cat # 563549; RRID:AB_2738272 CD19 BUV395 BD Biosciences Cat # 564655; RRID:AB_2744311 CD19 BUV496 BD Biosciences Cat # 562661; RRID:AB_2744343 CD25 BV605 BD Biosciences Cat # 302710; RRID:AB_10916120 CD26 APC Biolegend Cat # 557745; RRID:AB_396851 CD69 PE-Cy7 BD Biosciences Cat # 555543; RRID:AB_395927 CD62L FITC BD Biosciences Cat # 560607; RRID:AB_1727500 CD45RO BD Biosciences Cat # 560673; RRID:AB_1727496 CD45RA Alexa Fluor 700 BD Biosciences Cat # 561423; RRID:AB_10682894 CD107a BV786 BD Biosciences Cat # 563869; RRID:AB_2738458 CD137 (41BB) BV650 BD Biosciences Cat # 564092; RRID:AB_2738586 CD152 (CTLA4) BV421 BD Biosciences Cat # 562743; RRID:AB_2737762 CD152 (CTLA4) PE-Cy5 BD Biosciences Cat # 555854; RRID:AB_396177 CD223 (Lag3) BV605 BD Biosciences Cat # 745160; RRID:AB_2742761 CD223 (lag3) BV786 BD Biosciences Cat # 744727; RRID:AB_2742438 CD278 (ICOS) BUV395 BD Biosciences Cat # 564777; RRID:AB_2738946 CD278 (ICOS) BV421 BD Biosciences Cat # 562901; RRID:AB_2737878 (Continued on next page )ll Article Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 e1 Continued REAGENT or RESOURCE SOURCE IDENTIFIER CD366 (Tim3) BV510 Biolegend Cat # 345030; RRID:AB_2565831 Foxp3 PE-Cy7 Thermo Fisher Cat # 25-4776-42 HLA-DR BUV496 BD Biosciences Cat # 749866; RRID:AB_2874106 IFNgPE Thermo Fisher Cat # 61-2799-42; RRID:AB_2574598 PD-1 PE-eFluor 610 eBioscience Cat # 328649; RRID:AB_2800854 TotalSeq /C212-C0155 anti-human CD107a (LAMP-1)BioLegend Cat # 353251; RRID:AB_2800943 TotalSeq /C212-C0155 anti-human CD197 (CCR7)BioLegend Cat # 302649; RRID:AB_2800745 TotalSeq /C212-C0155 anti-human CD25 BioLegend Cat # 302853; RRID:AB_2800747 TotalSeq /C212-C0155 anti-human CD27 BioLegend Cat # 313553 TotalSeq /C212-C0155 anti-human CD278 (ICOS)BioLegend Cat # 328237; RRID:AB_2800853 TotalSeq /C212-C0155 anti-human CD39 BioLegend Cat # 304163; RRID:AB_2800764 TotalSeq /C212-C0155 anti-human CD45RA BioLegend Cat # 304259; RRID:AB_2800766 TotalSeq /C212-C0155 anti-human CD45RO BioLegend Cat # 304851; RRID:AB_2800770 TotalSeq /C212-C0155 anti-human CD62L BioLegend Cat # 351356; RRID:AB_2800937 TotalSeq /C212-C0155 anti-human CD127 (IL-7R a)BioLegend Cat # 309839; RRID:AB_2800807 TotalSeq /C212-C0155 anti-human CD137 (4-1BB)BioLegend Cat # 369621; RRID:AB_2801015 TotalSeq /C212-C0155 anti-human CD152 (CTLA-4)BioLegend Cat # 369335; RRID:AB_2814327 TotalSeq /C212-C0155 anti-human CD223 (LAG-3)BioLegend Cat # 302722; RRID:AB_2810435 TotalSeq /C212-C0155 anti-human CD26 BioLegend Cat # 329963; RRID:AB_2800862 TotalSeq /C212-C0155 anti-human CD279 (PD-1)BioLegend Cat # 345049; RRID:AB_2800925 TotalSeq /C212-C0155 anti-human CD366 (Tim-3)BioLegend Cat # 310951; RRID:AB_2800810 TotalSeq /C212-C0155 anti-human CD69 BioLegend Cat # 372729; RRID:AB_2801021 TotalSeq /C212-C0155 anti-human TIG IT (VSTM3)BioLegend Cat # 61-2799-42; RRID:AB_2574598 TotalSeq /C212-C0155 anti-human KLRG1 (MAFA)BioLegend Cat # 138433 CD3 Thermo Fisher Cat # MA1-90582 CD4 Abcam Cat # ab133616; RRID:AB_2750883 HLA DP/DQ/DR Thermo Fisher Cat # MA1-25914 Pan CK Biocare Cat # CM011A DAPI Fisher Scientiﬁc Cat # D21490 CD3 [F7.2.38] Dako Cat # M7254; RRID:AB_2631163 CD4 [EPR6855] Abcam Cat # ab181724; RRID:AB_2864377 CD8 [C8/144B] Dako Cat # M7103; RRID:AB_2075537 Cytokeratin [PCK-26] Sigma Aldrich Cat # C5992; RRID:AB_2134432 Cytokeratin [AE1] eBioscience Cat # 14-9001-82; RRID:AB_1834477 Biological samples Patient PBMCs from NT-002 trial This manuscript N/A Patient Tumor Biopsies from NT-002 trial This manuscript N/A Healthy Donor PBMCs Precision for Medicine Cat # 93000-10M Healthy Donor PBMCs StemExpress Cat # LE010F (Continued on next page )ll Article e2 Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 Continued REAGENT or RESOURCE SOURCE IDENTIFIER Chemicals, peptides, and recombinant proteins CEF viral peptide pool JPT Cat # PM-CEF-E Cell Stimulation Cocktail Life Technologies Cat # 00-4970-93 Dasatinib Sigma-Aldrich Cat # CDS023389Benzonase Millipore Sigma Cat # 70746d-biotin solution AVIDITY Cat # BIO200MHC class II tetramers This manuscript N/A Critical commercial assays5 Feature Barcode kit 10X Genomics Cat # 1000256 Chromium Next GEM ChipK single cell kit 10X Genomics Cat # 1000286 Chromium Next GEM Single Cell 5 Kit v2 10X Genomics Cat # 1000263 Chromium Single Cell Human TCR Ampliﬁcation Kit10X Genomics Cat # 1000252 Library Construction Kit 10X Genomics Cat # 1000190 Dual Index Kit TN Set A 10X Genomics Cat # 1000250 Dual Index Kit TT Set A 10X Genomics Cat # 1000215 MiSeq Reagent Kit v2 (300 cycles) Illumina Cat # MS-102-2002 Agelent HS DNA kit Agilent Technologies Cat # 5067-4626 Pan T cell isolation kit Miltenyi Biotec Cat # 130-096-535 AEC substrate-chromogen BD Biosciences Cat # 551951 Fixation/Permeabilization Solution Kit BD Biosciences Cat # 554714 Foxp3/Transcription Factor Staining Buffer SeteBioscience Cat # 00-5523-00 Intracellular Fixation and Permeabilization Buffer SeteBioscience Cat # 88-8824-00 GolgiStop BD Biosciences Cat # 554724 GolgiPlug BD Biosciences Cat # 555029 IFN gamma Human ELISPOT Kit Invitrogen Cat # 88-7386-88 U-Plex development pack, 10-assay sector plateMeso Scale Discovery Cat # K15235N Meso Scale Discovery (MSD) Read Buffer T (4x)Meso Scale Discovery Cat # R92TC Meso Scale Discovery (MSD) U-Plex SystemMeso Scale Discovery Cat # K15067L CD3 MicroBeads, human Miltenyi Biotec Cat # 130-050-101 CD4 MicroBeads, human Miltenyi Biotec Cat # 130-045-101 AllPrep DNA/RNA FFPE kit Qiagen Cat # 80234 Qubit RNA HS Assay Kit ThermoFisher Cat # Q32855 RNA 6000 Nano Kit Agilent Cat # 5067-1511 RNA 6000 Pico Kit Agilent Cat # 5067-1513 RNeasy Plus Micro Kit Qiagen Cat # 74034 RNeasy Plus Mini Kit Qiagen Cat # 74134 Prolong Gold Fisher Scientiﬁc Cat # P36930 OPAL 540 Akoya Cat # FP1494001KT OPAL 620 Akoya Cat # FP1495001KT OPAL 650 Akoya Cat # FP1496001KT OPAL 690 Akoya Cat # FP1497001KT ImmPRESS Universal HRP Vector Cat # MP-7800 Antibody diluent/block Akoya Cat # ARD1001EA Antigen unmasking solution, citrate based Vector labs Cat # H-3300 (Continued on next page )ll Article Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 e3 RESOURCE AVAILABILITY Lead contact Further information and requests for resources and reagents should be directed to and will be fulﬁlled by the Lead Contact, LakshmiSrinivasan ( lakshmi.srinivasan@external.biontech.us ). Materials availability All unique/stable reagents generated in this study are available from the Lead contact with a completed Materials Transfer Agreement. Data and code availability All vaccine peptide and immune assay peptide sequences are provided in Table S6 . Patient sequencing information will be provided upon request for patients consistent with their institutional informed consent. Epitope selection utilized previously published algo-rithms and methods as described in the STAR Methods (Ott et al., 2020 ). EXPERIMENTAL MODEL AND SUBJECT DETAILS NT-002 study design Details of the study can be found in the attached clinical study protocol NT-002 ( Data S1 ) and patient demographics can be found in Table 1 .Continued REAGENT or RESOURCE SOURCE IDENTIFIER Antigen unmasking solution, tris based Vector labs Cat # H-3301 Experimental models: Cell linesJurkat, Clone E6-1 ATCC Cat # ATCC-TIB-152 293FT Cell Line Thermo Fisher Cat # R70007 FuGENE /C210HD Transfection Reagent Promega Corporation Cat # E2311 Software and algorithms R (version 4.0.5) CRAN https://www.r-project.org/ Image J (version 1.53n) Schneider et al., 2012 https://imagej.nih.gov/ij/ FlowJo (version 10) FlowJo /C212Software (for Windows) Version 10. Becton, Dickinson andCompany; 2019.https://www.ﬂowjo.com/ IPD-IMGT/HLA N/A https://www.ebi.ac.uk/ipd/imgt/hla GraphPad Prism (version 9) GraphPad Software, La Jolla California, USAwww.graphpad.com BWA-MEM (version 0.7.13) Li and Durbin, 2009 N/A STAR-Fusion (version 2.5.1.b) Haas et al., 2017 N/A GATK 3.5 workﬂow McKenna et al., 2010 N/A ConTest Cibulskis et al., 2011 N/A VarDict (version 1.4.6) Lai et al., 2016 N/A Strelka (version 1.0.15) Saunders et al., 2012 N/A VarScan2 (version 2.3.9) Koboldt et al., 2012 N/A Atlas Indel2 (version 1.4.3) Challis et al., 2012 N/A Seurat (version 4.0.5) Christoforides et al., 2013 N/A Platypus (version 0.8.1) Rimmer et al., 2014 N/A RSEM (version 1.2.31) Li and Dewey, 2011 N/A NetMHCpan (version 3.0) Nielsen and Andreatta, 2016 N/A MiXCR (version 3.0.12) Bolotin et al., 2015 N/A DescTools package Signorell, 2016 N/A Loupe VDJ Browser (version 4.0) 10x Genomics N/A SnapGene SnapGene N/All Article e4 Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 The clinical sites that enrolled patients were Dana Farber Cancer Institute, Sarah Cannon Research Institute, University of Califor- nia Los Angeles, and Washington University. Study protocol was reviewed and approved by institutional review boards and an informed consent was obtained from the patients enrolled in the study. The primary objective of this phase 1b trial was to evaluate the safety of administering NEO-PV-01 with pembrolizumab/chemo- therapy in untreated patients with advanced or metastatic non-squamous NSCLC. Secondary objectives included determination of anti-tumor activity, assessed by RECIST objective response rate, clinical beneﬁt rate, duration of response, response conversion rate, progression free survival, and overall survival. The study employed multiple exploratory objectives to characterize immune responses to NEO-PV-01 regimen in this population. Vaccine-induced responses were evaluated by assessment of antigen-speciﬁc CD8+and CD4+T cell responses in peripheral blood during and following vaccine administration. Correlation of patient responses including prolonged PFS with biomarkers in peripheral blood and tumor was conducted as an exploratory objective; assessments included tumor programmed death ligand (PD-L1)expression and abundance and phenotypes of tumor inﬁltrating lymphocytes. Patients samples used across the various assays are listed in Table S6 . Treatment regimen NEO-PV-01 is a personalized cancer vaccine consisting of up to 20 synthesized peptides of approximately 14–35 amino acids in length that are derived from an individual patient’s mutated tumor DNA. The NEO–PV–01 drug product is administered following mix-ing with adjuvant poly-ICLC. For each patient, the peptides in poly-ICLC are divided in up to 4 pools for injection subcutaneously,rotating in extremity or ﬂank site administrations. In this phase 1b study, NEO-PV-01 therapy is administered following four 3-weekly cycles of a chemotherapy + pembrolizumab treatment regimen. Pembrolizumab was administered as a 200 mg IV infusion on cycle day 1 every 3 weeks. Carboplatin (AUC 5) andpemetrexed 500 mg/m 2were also given on day 1 of each treatment cycle. Patients were required to receive all 4 pembrolizumab + chemotherapy cycles prior to vaccination. In instances where these treatments were held, NEO-PV-01 initiation was also delayed until all 4 cycles were complete. All patients underwent radiographic evaluation at study week 8 to assess disease response to initial chemotherapy + pembrolizu- mab and had a conﬁrmatory assessment at week 12 to document disease status prior to initiation of NEO-PV-01 vaccination. NEO-PV-01 is administered on a schedule of 5 priming doses days 1 and 4 of study week 12 and then weekly in study weeks 13, 14, and 15. Two additional NEO-PV-01 administrations are provided as booster dosing on study weeks 19 and 23 to complete vaccine administration and the study primary treatment phase. Pembrolizumab treatment was maintained on its every 3 week schedule during the week 12-24 vaccination period and then from weeks 24 –103 in the absence of progressive disease, initiation of an alternate antineoplastic therapy, intolerable toxicities, with-drawal of consent, or death. Eligibility Patients eligible for study participation were those with histologically conﬁrmed, locally advanced or metastatic non-squamous NSCLC and no prior therapy for their advanced disease. Other key inclusion criteria include written informed consent for study partic- ipation, age R18 years, ECOG performance status 0 or 1, at least 1 site of RECIST measurable disease, disease accessible for on treatment tumor biopsy, and acceptable screening laboratory values within 30 days prior to treatment including hematology: abso- lute neutrophil count (ANC) R1.5310 3/mL, platelet count R1003103/mL, hemoglobin > 9 g/dL, chemistry: serum creatinine % 1.53upper limit of normal (ULN) or creatinine clearance (CrCl) R40 mL/min/1.73 m2, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) %2.53ULN or%53ULN with liver metastases, total bilirubin %1.53ULN or direct bilirubin %ULN for patients with total bilirubin levels >1.5 3ULN, and PT, PTT both < 1.5 x ULN unless on anticoagulation therapy. Patients eligible for study participation did not meet any of the following major exclusion criteria, including receipt of any prior sys- temic cancer therapy for advanced or metastatic NSCLC, receipt of any investigational agent or study therapy within 4 weeks of ﬁrststudy treatment, receipt of radiation therapy to the lung greater than 30 Gray < 6 months prior to study treatment, known CNS me- tastases unless clinically stable > 4 weeks, receipt of non-oncology vaccine therapy during and up to 8 weeks following the period of NEO-PV-01 administration, active autoimmune disease requiring systemic treatment within the last 2 years except for physiologiccorticosteroid replacement therapy, interstitial lung disease, active pneumonitis or history of pneumonitis requiring corticosteroid therapy, unstable angina or congestive heart failure, active infection requiring treatment, active hepatitis B, C, or history of HIV, or history of invasive malignancy unless disease free > 2 years, Patients with anaplastic lymphoma kinase (ALK) translocations orepidermal growth factor receptor (EGFR) mutations must have received prior treatment with ALK or EGFR inhibitors, respectively. A complete list of inclusion and exclusion criteria is provided in the NT-002 study protocol (Appendix). Clinical assessments Safety assessments conducted during the primary treatment phase included adverse event collection through patient reported symptoms, symptom-directed physical examination, and vital sign and safety laboratory assessments. Adverse events were moni- tored throughout the study from time of ﬁrst dose of pembrolizumab (Cycle 1, Day 1) through study week 103 or 30 days after the lastpembrolizumab treatment, whichever occurred ﬁrst. Serious adverse events (SAEs) were reported from the time of signing the patient informed consent through study week 103 or 90 days after the last dose of pembrolizumab, whichever occurred ﬁrst.ll Article Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 e5 Radiographic assessments to evaluate response to study treatment were conducted at weeks 8 and 12 prior to NEO-PV-01 admin- istration, at study weeks 24, 36, 51, 63, 75, 87, and 99. Anti-tumor activity was assessed for objective response rate (ORR), duration of response (DOR), progression free survival (PFS) and overall survival (OS). ORR is deﬁned as the proportion of patients who achieve CR or PR based on RECIST v1.1. DOR is deﬁned as the date of the ﬁrst documented conﬁrmed response to the date of the ﬁrst docu-mented PD or death. PFS is deﬁned as the time from the date of ﬁrst dosing to the date of ﬁrst documented PD or death. OS is deﬁned from the date of ﬁrst dosing to death. Statistical efﬁcacy analyses were performed for both the ITT set deﬁned as receiving at least one dose of any study drug, and the safety set deﬁned as receiving at least one dose of vaccine. The Kaplan–Meier method was used toestimate PFS and OS. For PFS, patients who did not have a PFS event on study or who missed 2 consecutive tumor assessmentswere censored at the time of next scheduled assessment from the last tumor assessment. For OS, patients who were alive or who were lost to follow-up were censored at the last record on database. Patient samples Patient samples collected on the study and analyzed across the various assays are listed in Table S6 . 38 patients were enrolled on the study. Peripheral blood mononuclear cells (PBMCs) samples for comprehensive immune system monitoring were obtained from leu-kaphereses performed up to 7 days prior to initiation of NEO-PV-01 vaccination, 7 days (Week 20) following the ﬁrst NEO-PV-01 booster vaccination and then at study week 52. Additional immune monitoring assessments were performed from 80 mL peripheral blood samples obtained prior to study treatment, and study weeks 6, 14, 16, 24, 36, 48, 63, 75, 87, and 99. Patient PBMCs wereprocessed and stored as described previously ( Ott et al., 2020 ). Tumor samples (surgical and core-needle biopsies) were obtained at pre-treatment, pre-vaccine (week 10-12) and post-vaccine (week 24) timepoints. Samples were processed and sequenced as described previously ( Ott et al., 2020 ). Pre-treatment biopsies were used for generation of NEO-PV-01, and advanced for sequencing as described previously ( Ott et al., 2020 ). METHOD DETAILS Generation of NEO-PV-01 Whole exome sequencing As previously described ( Ott et al., 2020 ), WES (library protocol: ACE(TM) Cancer Research Exome, Personalis, Menlo Park, CA) and RNA-Seq (library protocol: ACE(TM) Cancer Research Transcriptome, Personalis, Menlo Park, CA) libraries were sequenced using Illumina HiSeq in a CLIA/CAP accredited laboratory (Personalis, Menlo Park, CA). WES was conducted on a tumor sample and a normal blood sample per patient (depths: 163–335 reads and 80–137 reads, respectively); RNA-Seq was conducted on tumor sam-ples only (depth: 37–309 reads). The HLA-A, HLA-B, and HLA-C genotype of each patient was determined by amplifying informative exons by polymerase chain reaction (PCR) using locus-speciﬁc primers. Dye-terminator sequencing fragments from the PCR fragments were analyzed on a capillary sequencer to determine nucleotide sequences for each haplotype (BloodCenter of Wisconsin, Milwaukee, WI).Alignment and mutation calling All alignment, mutation calling and ﬁltering, and gene expressio n quantiﬁcation was done in acc ordance with methods described previously ( Ott et al., 2020 ). In the event that an adequate RNA library could not be prepared, methods described previously were implemented ( Ott et al., 2020 ). All computational analysis steps were conducte d using an automated analysis pipeline hosted on a cloud computing platform (Seven Bridges Genomics, Charle stown, MA). Tumor and germline WES and tumor RNA-Seq FASTQs were aligned to the human genome (Gencode V19) using the BWA-MEM (version 0.7.13) (H. Li and Durbin 2009 ) and STAR (version 2.5.1.b), respectively. All alignments were post-processed with the GATK 3.5 workﬂow ( McKenna et al., 2010 )i n c l u d i n gG A T KI n - del Realigner, GATK Base Recalibrator, and Pica rd Mark Duplicates (version 1.140). ConTest ( Cibulskis et al., 2011 )w a su s e dt o conﬁrm that all three samples originated from the same individu al. Somatic variants were called on the basis of tumor and normal WES using an ensemble of seven different mutation calling algorithms: VarDict (version 1.4.6) ( Lai et al., 2016 ), Strelka (version 1.0.15) ( Saunders et al., 2012 ), VarScan2 (version 2.3.9) ( Koboldt et al., 2012 ), Mutect2 (from the GATK version 3.5 bundle) ( Cibul- skis et al., 2011 ) ,A t l a sI n d e l 2( v e r s i o n1 . 4 . 3 )( Challis et al., 2012 ), Seurat (version 2.6) ( Christoforides et al., 2013 ), and Platypus (version 0.8.1) ( Rimmer et al., 2014 ). The three sequencing datasets were then realigned using HaplotypeCaller (from the GATK version 3.5 bundle), specifying the candidate mutations (union of the seven call sets) as known variants. Finally, the variants were ﬁltered according to the following features: the level of r ead support in the tumor WES data, the presence of variant reads in the normal WES data, read orientation bias, adequacy of cov erage in the normal WES sample, the presence of neighboring (+/_30nt) variants (somatic or germline), and read quality bias observed in mutation-supporting reads. RNA-Seq expression levels of all genes and transcripts were quantiﬁed in transcripts per million (TPM) using RSEM (version 1.2.31) (B. Li and Dewey 2011 ). The overall expression of each somatic variant was calculated as the product of the RSEM-derived transcript expression (summingacross all overlapping protein-coding transcripts) and the frac tion of RNA-Seq reads supporting the variant. Variants with zero supporting RNA reads were still considered as valid mutations (a nd counted toward tumor mutation burden) but were not consid- ered for inclusion in vaccine. RNA-Seq was additionally processe d using STAR-Fusion (version 2.5.1.b) to identify transcript fu- sions (requiring both junction support and spanning read pairs) . In the event that an adequate RNA library could not be prepared, fusion calling was skipped, and reference expression was obtained from TCGA samples with high tumor purity (as assessed by Absolute; ﬁve samples per tumor type) ( Carter et al. 2012 ). Since these patients lacked RNA-Seq as a ﬁlter against false positivell Article e6 Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 mutation calls, their somatic variants were required to have a h igher level of read support in the tumor WES and to have been iden- tiﬁed by more than one mutation caller. Vaccine peptide selection Patients with at least 50 non-synonymous point mutations and/or gene fusions were advanced to the vaccine design step. The vac-cine peptides, also referred to as immunizing (IM) peptides, were identiﬁed by selecting 6 peptides on the basis of HLA-I presentation scores (also referred to as epitope quality scores, see below), 2 peptides based on high expression, and 2 peptides based on highly expressed frameshifts, iteratively until the full roster of 30 peptides was completed. The latter two epitope classes were required tohave expression levels > 10 TPM and > 5 TPM, respectively (if none were available, the slots were ceded to HLA-I selections). Gener-ally, each mutation was targeted by only one vaccine peptide and excluded from subsequent selections unless the span of novel sequence generated could not be covered by a single peptide (as can happen with frameshifts) or if the likelihood of manufacturing success was questionable for the ﬁrst peptide per known synthesis constraints. The trade-off between synthesis constraints and im-mune-related scoring further dictated the relative length of the vaccine peptides (allowable range: 14-35 amino acids) and whether they were centered or shifted with respect to the site of mutation. HLA-I presentation scores were determined based on a logistic regression that considered binding predictions, allele-speciﬁc expression, and proteasomal cleavage potential. Binding predictions were calculated as the weighted average of NetMHCpan- 3.0 percent rank ( Nielsen and Andreatta 2016 ) and a neural network trained on mass spectrometry data using methods described previously ( Abelin et al. 2017 ) with respective weights of 20% and 80%. Proteasomal cleavage potential was also calculated using a neural network trained on mass spectrometry data using methods described previously ( Abelin et al. 2017 ). The score of a candi- date vaccine peptide was determined by summing all the relevant peptide-allele combinations (epitopes lengths 8-12; HLA-A HLA-B, and HLA-C alleles). Secondary characteristics were used to up-weight and down-weight candidate epitopes but were purposely tuned to have less importance than the above-mentioned factors. Thus, all other parameters being equal, the selection algorithm favored more clonal epitopes (higher mutant allele fraction) over sub-clonal epitopes, frameshifts over point mutations, oncogenes over passenger genes, and peptides with good manufacturing scores over those with poor predicted manufacturability. Our scoring system did not considerthe relative binding scores of wild-type peptides or similarity to known pathogen sequences ( Luksza et al. 2017 ). GMP peptide manufacturing The GMP grade peptides of NEO-PV-01 were manufactured as described previously ( Ott et al., 2020 ). The GMP grade peptides of NEO-PV-01 were synthesized using solid-phase peptide synthesis (SPPS) on automated parallel peptide synthesizers. 9-ﬂuorenylmethyloxycarbonyl (Fmoc) chemistry was employed. After the assembly of the peptide chains, the peptides were removed from the resins using cleavage cocktails containing triﬂuoroacetic acid and scavengers. The peptides were precipitated and washedwith ether. After drying, the crude peptides were puriﬁed on automated preparative high-performance liquid chromatography (prepHPLC) systems with UV and mass spectrometry (MS) detectors. The prep HPLC fractions were analyzed using ultra-performance liquid chromatography (UPLC)-UV/MS systems to determine molecular weights (MW) and purities. The fractions containing the target peptides and desired purities were lyophilized to solid powders with the ﬁnal purities equal to or higher than 95%. Up to 20 peptidesper vaccine were formulated in a buffered aqueous solution containing 4% DMSO in isotonic dextrose and mixed into up to four pools, each containing up to ﬁve peptides. The pooled peptide solutions were ﬁltered through 0.22 mm ﬁlters for sterilization. The vaccines were analyzed for identity (molecular weights based on UPLC-UV/MS analysis), sterility, endotoxin, and strength beforetheir releases from the GMP manufacturing site. The amino- and carboxyl-termini of the peptides are free amines and carboxylic acids respectively, without structural modiﬁcations. Peptide synthesis for immunological assays The peptides for in vitro immune response studies, immune assay peptides (IAP) were synthesized using SPPS on computer- controlled high-throughput peptide synthesizers. The peptides were assembled on resins by employing Fmoc chemistry. The chemical cleavages of peptides from the resins were performed by using TFA solution containing scavengers followed by peptideprecipitation and washes with diethyl ether. The assay peptides (ASP) were puriﬁed on automated prep HPLC systems equipped with UV and MS detectors. The prep HPLC fractions of each peptide that met the molecular weight (MW) and purity criteria based on UPLC-UV/MS analysis were pooled and dried using either parallel centrifugal evaporators or lyophilizers. The epitope peptides(EPT) were resuspended in acetonitrile and water and dried on a lyophilizer after diethyl ether washes and analyzed for required purity and MW by using UPLC-UV/MS. Assay peptides (ASP) were 13-15 amino acids and overlapped by at least 9 amino acids to cover the immunizing peptide (IM) sequence or were 8-11 amino acids and predicted to bind class I ( Ott et al., 2017 ). All peptide sequences are provided in Table S6 . Personalized ctDNA assays Design and application of personalized and tumor-informed ctDNA (multiplex-PCR, next-generation sequencing) assays was conduct-ed with blinding to clinical data by Natera. Paired tumor and peripheral blood WES data were used to identify and design the Signatera assay with 16 tumor-speciﬁc somatic single nucleotide variants (SNVs) for each patient. Additionally, another custom assay consisting of 16 tumor-speciﬁc SNVs was designed from our BioNTech’s list of 30 prioritized high-quality patient-speciﬁc neoepitopes based ondesignability and other internal bioinformatics algorithmic criteria ( Reinert et al., 2019 ;Coombes et al., 2019 ;Christensen et al., 2019 ). Given that these algorithms were developed independently, there is a potential of variants to overlap between the two assays. For thell Article Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 e7 ﬁrst Signatera 16-plex assay, SNVs present in the tumor but absent in the germline were used to select the 16 targets, with prioritization based on multiple factors, including the observed variant allele frequency in the tumor tissue and the associated background noise pro- ﬁle in the plasma. The multiplexed targeted PCR was conducted followed by amplicon deep sequencing on an Illumina platform. Variant allele frequencies were determined for each of the target mutations. The plasma sample was considered ctDNA-positive on observing atleast two or more variants. Absolute ctDNA levels were then calculated for ctDNA-positive plasma samples (MTM per mL) by normal- izing variant allele frequencies observed by the plasma volume used for each sample. As described previously, MTM per mL was calcu- lated from all tested targets, including undetected targets ( Bratman et al., 2020 ). Detection of neoantigen-speciﬁc immune responses Neoantigen-speciﬁc immune responses were detected either ex vivo or after 5-day stimulation as described previously ( Ott et al., 2020 ). For ex vivo assays, patient PBMCs were rested overnight at 2 x 10 6cells/mL in X-Vivo media (Lonza) supplemented with 1% penicillin/streptomycin (GIBCO). For neoantigen pre-exposure assays, patient PBMCs were rested overnight and cultured with individual peptides (2mM) or pooled peptides (2mM per peptide) for 5 days at a density of 5x106cells/ml in a 24-well plate. On day 3 of culture, half the well volume was replaced with fresh media. All immunizing peptides were tested in both the ex vivo as well as in the 5-day stimulation with the neoantigen peptide assay formats. Immunizing peptides are referred to as IM followed by the peptide number. IFNgELISpot assay IFN-gELISpots were performed as described previously ( Ott et al., 2020 ). IFN gELISpot assays were performed using 96-well Multi- Screen Filter Plates (Millipore) and the Ready-Set-Go! Human IFN gELISpot Kit (Invitrogen) according to manufacturer’s instructions. Plates were coated overnight at 4/C14C with anti IFN gCapture Antibody diluted in 1X Coating Buffer, washed with 1X Coating Buffer and blocked with X Vivo media (Lonza) containing 1% penicillin/streptomycin (GIBCO) for 1h. For ex vivo ELISpots, PBMCs were plated in triplicate with 1x106cells per well. For cells that were pre-exposed to peptide, cells were washed and plated in triplicate with 1x105 cells per well. Peptides were added to ELISpot wells at 2mM per peptide. Each plate included a healthy donor positive and negative control with the CEF viral peptide pool (JPT) and Cell Stimulation Cocktail (Life Technologies) to conﬁrm reagent performance. Plates were incubated overnight at 37/C14C. Plates were washed 3 times using PBS with 0.05% Tween 20 and detection antibody diluted in 1X ELISpot diluent was added to wells for 2h. After washing 3 times with PBS with 0.05% Tween 20, Avidin-HRP was diluted in 1XELISpot diluent and added to wells for 45 min. Plates were washed with both PBS with Tween 20 and PBS 3 times. AEC sub- strate-chromogen (BD Biosciences) was then added for 20 min. Plates were rinsed with deionized water 3 times and allowed to dry at room temperature overnight. Spots were imaged and enumerated using an Immunospot analyzer (Cellular TechnologyLimited). Responses were characterized as positive if spot-forming cell count detected was at least 10 spots over the DMSO controland +3 standard deviations and conﬁrmed in repeat experiments. Characterization of CD4 +/CD8+T cell responses CD4+and CD8+T cell response were characterized as described previously ( Ott et al., 2020 ). CD3+T cells were isolated from patient PBMCs by negative selection using the Pan T cell isolation kit (Miltenyi). Both the CD3+and CD3/C0populations were collected, washed, and counted after isolation. The CD3/C0population was used as APCs (antigen-presenting cells) for this assay. The CD3+population then underwent CD4+positive isolation using CD4+microbeads (Miltenyi). Both the CD4+(positive-selection) and CD8+(negative-selection) T cells were collected, washed, and counted. A co-culture containing APCs and either CD4+or CD8+cells at a ratio of 3:1, 2:1, or 1:1 were plated in a 96-well ﬂat bottom polystyrene plate for 24-48h. Peptide was added directly to wells, in triplicate, at 2mM per peptide. Ifthe T cells were pre-exposed to neoantigen peptide for 5–6 days, CD3 /C0APCs were isolated from fresh patient PBMCs using CD3 mi- crobeads (Miltenyi). Supernatants were collected at the end of the co-culture and frozen at /C080/C14C until use. The Meso Scale Discovery (MSD) U-Plex system was used to detect 10 distinct analytes from the co-culture supernatants. The analytes measured were: IL-1b, IL-2, IL-6, IL-9, IL-13, IL-15, IL-17a, IFN g, and TNF-a. MSD 10-plex plates were coated with the linker-antibody solution for 1h with shaking at room temperature. The plates were then washed 3 times with PBS with 0.05% Tween 20 on a BioTek plate washer. Super- natant and standards were diluted 1:2 and added to the plate for 1h with shaking at room temperature. Plates were washed again with PBS with 0.05% Tween 20 before the addition of the detection antibody solution for 1h. Plates were washed with PBS with 0.05%Tween 20 and 2X Read Buffer (MSD) was added to the plates for detection on the MSD SECTOR S 600 instrument. Concentration in pg/mL was calculated by the MSD Discovery Workbench software by comparing luminescent signal of the patient samples against the known standard curve. CD4 +and CD8+positive responses were determined separately based on the corresponding DMSO con- trols. A positive signal was determined by an IFN gsignal 1.5-fold higher than the DMSO control. CD107a mobilization assay Detection of CD107a mobilization on both CD4+and CD8+T cells was performed using ﬂow cytometry as described previously in ( Ott et al., 2020 ). For ex vivo detection, patient PBMCs were recalled with 2 mM peptide or DMSO for 6 or 24h. For patient PBMCs pre- exposed to neoantigen peptide, CD3/C0APCs were isolated from fresh PBMCs using CD3 microbeads (Miltenyi) and co-cultured with CD3+T cells at a T cell:APC ratio of 2:1 and recalled with 2mM peptide or DMSO for 6 or 24h. Anti-CD107a antibody was added 6h prior to the end of co-culture. GolgiStop/Plug (BD Biosciences) were added 4h prior to the end of co-culture. Subsequently, cells were stained with cell surface antibodies at 4/C14C for 30 min, followed by ﬁxation/permeabilization with Fixation and Permeabilizationll Article e8 Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 Solution (BD Biosciences), and subsequently stained with antibodies against intracellular proteins at 4/C14C for 30 min. Cells were stored in FACS buffer at 4/C14C until acquisition on a BD LSR Fortessa instrument. Gating was performed for IFN gand CD107a based on FMO stained controls. MHCII protein expression, puriﬁcation, and peptide exchange Signal peptides and ectodomains of alpha and beta MHC class II alleles were encoded as fusions to heterodimeric coiled coils in a pcDNA3.4 vector (Thermo Fisher Scientiﬁc). Allele-speciﬁc placeholder epitope peptides were covalently fused to the N-terminus ofeach beta allele. Soluble heterodimers of alpha/beta proteins were secreted directly into culture media post-transfection into Ex-pi293F cells (Thermo Fisher Scientiﬁc) and puriﬁed using HisTrap Excel media (Cytiva). Puriﬁed MHC class II alleles were subse- quently biotinylated and the placeholder peptide cleaved prior to peptide exchange. Epitope peptides (at 20-fold molar excess to allele) were loaded onto puriﬁed MHC II alleles using equimolar recombinant soluble HLA-DM overnight at 37 /C14C. Multimer generation and staining Dual-multimer approach was used where each peptide-HLA II allele was conjugated to two different streptavidin ﬂuorophores. Pep-tide exchanged allele was incubated with ﬂuorochrome-conjugated streptavidin antibodies on ice for 30 min in the dark. Biotin was added to block any unoccupied sites on streptavidin ﬂuorophore. Reaction was spun down at 3500 RPM (4 /C14C) for 10 min to remove any aggregates. The conjugated multimer supernatants were used for making a pooled multimer mix. Patient PBMCs were thawed and subjected to T cell enrichment by using a human Pan T Cell Isolation Kit (Miltenyi Biotec). Isolated T cells were counted and treated with Benzonase and 50nM Dasatinib for 20 min at 37/C14C in RPMI media containing 10% FBS. En- riched T cells were centrifuged at 1500 RPM for 5 min, washed once with FACS buffer (1X PBS +0.5% BSA) supplemented with 50nM dasatinib, and plated in a 96-well plate (3x106 cells/well). Cells were resuspended in a pooled multimer mix made in FACS buffer andincubated for 1 h at 37 /C14C. Phenotyping of multimer+CD4+T cells Phenotyping of neoantigen-speciﬁc CD4+T cells was performed as described previously ( Ott et al., 2020 ). PBMCs were thawed and treated with 0.025 U/ul benzonase and 50 nM dasatinib in X-Vivo media (Lonza) at 37/C14C for 20 min. Cells were then stained with MHC Class II tetramers at 37/C14C for 1 h, followed by staining with surface antibodies at 4/C14C for 30 min. Cells were ﬁxed at 20/C14C for 20 min using the Fixation/Permeabilization Kit (BD Biosciences). Cells were washed and stored in FACS buffer at 4/C14C until acquisition on a BD LSR Fortessa instrument. RNA extraction from FFPE tumors and PBMCs For FFPE tumor blocks, 2–4 scrolls of 20 mM thickness were deparafﬁnized using heptane and RNA was extracted using the AllPrep DNA/RNA FFPE kit according to the manufacturers’ instructions (QIAGEN). PBMCs were thawed and subjected to negative selection using the Pan T cell Isolation Kit (Miltenyi) according to manufacturer’s protocol. T cells were counted, centrifuged for 15 min at 300xgthen ﬂash frozen and stored as dry pellets at /C080 /C14C. RNA isolation was performed using the RNeasy Plus Micro Kit (QIAGEN) on the QIAcube (QIAGEN; Protocol: Puriﬁcation of total RNA using gDNA Eliminator and RNeasy MinElute spin columns) according to man- ufacturer’s protocol. RNA concentration was measured using the Qubit RNA HS Assay Kit (ThermoFischer) according to manufac-turer’s protocol. Eluted RNA was stored at /C080 /C14C. Gene expression analysis Using the transcript per million (TPM) quantiﬁcation of gene expression from the tumor biopsy RNA, we summed the TPM values forall detected HLA-DR, HLA-DP and HLA-DQ per patient. The log-transformed values were plotted against each patient PFS. TCR library preparation and sequencing TCRblibraries were prepared from isolated RNA using the Long Read iR-Proﬁle Reagent System (iRepertoire) at the iRepertoire headquarters according to manufacturer’s protocol. Libraries were sequenced using the MiSeq Reagent Kit v2 300-cycles (Illumina) at the iRepertoire headquarters according to manufacturer’s protocol. Throughout the study, the number of samples per pool wasdesigned to maintain equal sequencing depths across samples. TCR repertoire generation TCR repertoires were generated by running a licensed copy of MiXCR 3.0.12 on the paired-end raw sequencing fastq ﬁles. The pa- rameters included the species speciﬁcations (Human, hsa), starting material (RNA), 5 and -3 0primers (v and c primers, respectively) with no adapters, and searching for either TCR a(tra) or TCR bchains (trb). TCR aor TCR bCDR3 clonotypes were ﬁltered by removal of non-functional sequences (out-of-frame sequences or those containing stop codons). Clonal frequency was calculated based onthe count for each clone out of the total count. TCR repertoire diversity/clonality analysis The unique number of nucleotide/amino acid TCR aor TCR bCDR3s was calculated per sample. Global diversity/clonality coefﬁcients have been calculated as follows:ll Article Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 e9 Gini Coefﬁcient – ranges between 0 (all clones are equally frequent – repertoire diversity) and 1 (frequency dominated by one clone, repertoire clonality). Calculated using the ‘‘Gini’’ function from the ‘‘DescTools’’ R package (Signorell et al., 2016). Normalized Shannon’s Entropy – higher values represent higher inequality of the frequencies ( Jia et al., 2015 ;Hanson et al., 2020 ). Single-cell RNA sequencing of neoantigen-speciﬁc CD4+T cells using HLA class II multimer staining Cell surface protein labeling for single-cell RNA sequencing and cell sorting After HLA Class II multimer staining, Fc blocking was performed through incubation with human FcR Blocking Reagent (Miltenyi Bio-tec) for 10 min at 4 /C14C. For cell surface protein labeling for sequencing, a mix of 19 TotalSeq-C antibodies (BioLegend) was added to the multimer-stained cells. Cells were simultaneously labeled with FITC anti-human CD4 (BD), BV711 anti-human CD3 (BD), AF700 anti-human CD8 (BioLegend) antibodies, and LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (Thermo Fisher). After 30-min of incu- bation at 4/C14C, cells were washed twice in PBS with 1% BSA and sorted using a BD Aria cell sorter (BD Bioscience). For each patient sample, two different CD4+T cell populations were sorted: Tetramer/C0CD4+(Live CD3+CD4+CD8/C0) and Tetramer+CD4+(Live CD3+ CD4+CD8/C0ASP multimer+). Single-cell TCR, surface epitope and transcriptome sequencingAfter sorting, labeled cells were resuspended at /C241x10 3cells/ml in PBS supplemented with 2% FBS and submitted to subsequent sin- gle-cell RNA sequencing (scRNA-Seq) processes. Sample processing for single-cell gene expression (GEX), TCR V(D)J clonotypes (VDJ), and cell surface protein expression (CITE) libraries was performed following the manufacturer’s protocol (Chromium NextGEM Single Cell 5 0Reagent Kits v2 (Dual Index), 10X Genomics). Brieﬂy, sorted Tetramer+and Tetramer/C0CD4+T cell suspensions were partitioned into nanoliter-scale Gel Beads-in-Emulsions (GEMs) using a Chromium Controller (10X Genomics). Subsequent Gel Bead-in-Emulsion reverse transcription (GEM-RT) reaction & post clean-up and cDNA ampliﬁcation were carried out to generate cDNAs for scRNA-Seq libraries. Constructing cDNA libraries for GEX (Library Construction Kit), VDJ (Chromium Single Cell Human TCR Ampli-ﬁcation Kit) and CITE (5 0Feature Barcode kit) was performed based on the manufacturer’s protocols. All sequencing library quality con- trols were performed by using Bioanalyzer High Sensitivity DNA Chip Kits with an Agilent 2100 Bioanalyzer (Agilent Technology). Sequencing libraries were quantiﬁed using KAPA Library Quantiﬁcation Kits - Complete kit and LightCycler 480 (Roche). All libraries were incorporated with Sample Index sequences (Dual Index Kit TN Set A for CITE libraries and Dual Index Kit TT Set A for VDJ & GEX libraries) and sequenced on MiSeq (MiSeq Reagent Kit v2 (300-cycles)) or NovaSeq S1 platform (Illumina) depending on sequencing depth. The sequencing parameters were: Read 1 of 26 cycles, i7 Index of 10 cycles, i5 Index of 10 cycles, and Read 2of 90 cycles. Single-cell RNA sequencing data analysis Cell Ranger version 6.0.1 (10X Genomics) was used to align raw sequencing data with ‘‘count’’ option. Library with RNA was alignedon GRCh38-2020-A genome. TCR library was aligned on vdj_GRCh38_alts_ensembl-5.0.0 assembly. CITE library was aligned on a custom panel of antibodies. R package Seurat 4.0.4 ( Stuart et al., 2019 ) was used for downstream analysis of data acquired from cellranger count. We ﬁltered cells that (1) had more than 25% of mitochondrial gene content, (2) had less than 1024 UMI fromRNA or 3) had less than 90 UMI for CITE library. RNA library was log-normalized with a scale factor of 10 4. The 2000 most variable genes were detected by the FindVariableFeatures function. These 2000 genes were overlaid with 10X Genomics Immunological panel (1056 genes) to de-noise the list, resulting in 344 genes used in downstream analysis. Latent variables - number of UMI’sand mitochondrial content - were regressed out using a negative binomial model with function ScaleData. Principal component anal- ysis (PCA) was performed with the RunPCA function. For RNA library harmony function was called (R package Harmony, ( Korsunsky et al., 2019 )) to remove donor effect. CITE library was normalized using CLR method, scaled (by ScaleData function) using all available antibodies (except for LAG3, TIM3, 41BB, CTLA4 and KLRG1 that had low expression and off-target binding) and PCA was run on it.RNA- and CITE-processed data were used to run WNN method by FindMultiModalNeighbors function to create new modality to run dimensionality reduction and clustering that takes into account RNA and protein levels. A UMAP dimensionality reduction was per- formed on the WNN using the ﬁrst 20 PCA components for harmonized RNA and 13 for CITE to obtain a two-dimensional represen-tation of the cell states. For clustering, we used and FindClusters function with SLM algorithm and resolution 0.25. To identify marker genes, we used FindAllMarkers function to compare cluster against all other clusters, and FindMarkers to compare selected clusters. For each cluster, only genes that were expressed in more than 15% of cells with at least 0.15-fold differences were considered. Forheatmap representation we used mean expression of protein markers inside each cluster. Heatmaps were built with ComplexHeatmap R package ( Gu et al. 2016 ). To analyze TCR we ﬁltered out all TCR that did not corresponding cell barcode in RNA + CITE library post quality control. Gini coefﬁcient was computed with DescTools R package. Cloning of neoantigen-speciﬁc TCRs Lentivirus vectors were generated on 293FT producer cell lines using shuttle plasmids (GenScript) containing TCR beta and alphachains under the control of an SFFV promoter and separated by a furin cleavage site and a P2A ribosomal skip sequence. HumanTCR variable regions were fused to cysteine-modiﬁed mouse constant regions for both chains resulting in recombinant mTCR ( Co- hen et al., 2006 ;Kuball et al., 2007 ). The mTCR-transduced, puromycin-selected (Thermo Fisher) Jurkat cells were expanded in RPMI media supplemented with 10% FBS (Thermo Fisher). Neoantigen reactivity of the mTCR Jurkat cells was determined by IL-2 secre-tion measured by electrochemiluminescence (MSD) in 24-h in vitro assays where mTCR transduced Jurkat cells were co-cultured with neoantigen peptide and CD3 +T cell-depleted autologous patient PBMCs.ll Article e10 Cancer Cell 40, 1010–1026.e1–e11, September 12, 2022 MultiOmyx staining For multiplexed T cell analysis, multiplexing immunoﬂuorescence (IF) analysis on FFPE tumor blocks was performed at NeoGeonomics Laboratories (Lake Forest, CA). Each FFPE slide was presented to a NeoGenomics pathologist for tissue annotation and selection.