Black line indicates median. p value derivedfrom a two-tailed Student’s t test.See also Figure S2 . 11 HDs for which materials were available (Table S1 ). TCR- asequencing showed increased large and hyperexpanded clones in PFS-9 versus no PFS-9 patientsamples, matching the TCR- bsequencing results. Reproducible results were also observed for the various clonality/diversitymeasurements, as well as repertoire stabil-ity (Figures S2 D–S2F). This concordance between TCR- aand TCR- brepertoire sequencing supports that either can represent the clonal struc-ture in a sample. 30 Peripheral TCR Repertoire Stability and DiversityCorrelate with Effector/Memory Phenotypes We next hypothesized that the increased TCR repertoire clonal- ity and stability in patients with PFS-9 is related to the phenotypeof the T cells of these patients. We compared the cumulative fre-quency of the persistent TCR- bclones (G segment) with the fre- quencies of T cell subpopulations determined by ﬂow cytometry (Figures S3 A and S3B). We observed a strong positive correla- tion between the cumulative frequency of persistent clones and the frequency of effector-memory/memory CD8 +and CD4+T cells across patients. We also observed a strong nega- tive correlation with naive T cells, and to a lower extent with CTLA-4-expressing T cells ( Figure 3 B;Figure S3 C). A compara- ble enrichment of effector-memory/memory T cells has beenreported in patients with chronic viral infections, 23again 4Cell Reports Medicine 1, 100141, November 17, 2020Reportll OPEN ACCESS suggesting similarities in immune phenotypes of patients with PFS-9 and individuals with a chronic viral infection. Interestingly, we also detected a positive correlation between the persistent TCR- bclones and the memory B cell compartment, and a nega- tive correlation with the naive B cell compartment ( Figure 3 B). Immunophenotyping of PBMCs showed different subpopulation frequencies between patients with and without PFS-9, consis- tent with reports from independent cohorts.15,16Together, these ﬁndings suggest that TCR repertoire features can inform the B cell compartment and perhaps other immune cell phenotypes. This is further supported by the correlation between the ratio ofeffector-memory to naive CD8 T cells and the ratio of class- switched to naive B cells ( Figure 3 C). These results suggest that a broad view of a patient’s adaptive immune system is rele-vant in predicting the clinical efﬁcacy of immunotherapy. To directly test for a relationship between the frequency of a clone, its phenotype and antigen speciﬁcity, we sorted and sequenced the TCR- abof single neoantigen-speciﬁc T cells for a few patients with sample availability ( STAR Methods ). We vali- dated via transduction eight TCR- ab sequences ( STAR Methods ), which target six epitopes from across ﬁve patients (Table S2 ). Three TCRs, originating from patients M1 and M13, were previously reported in Ott et al., 2020. 7Of the validated TCRs, four were not detectable at the pre-T time pointA B 30507090 02040Percent positive0.60.70.8Gini Coefficient R = 0.5 p = 0.00044 R = -0.74 p = 5.9e-09 R = -0.38 p = 0.0091R = 0.42 p = 0.0038 R = -0.7 p = 6.2e-08R = 0.7 p = 4.8e-08 PFS-9 No PFS-9R = 0.29 p = 0.039R = 0.82 p = 1e-13 PFS-9 No PFS-9PFS-9No PFS-9C -4-202 - 2 024 log2 (memory CD8 to naïve CD8)log2 (class−switched memory B / naïve B) 0.2 0.3 0.4 0.5 0.6 Cumulative frequency of consistent clones (G) 0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 Cumulative frequency of consistent clones (G)Naïve CD8 Naïve BEffector-memory CD8 Class-switched memory B 0102030 255075100Naïve CD4Effector-memory CD4 255075 010203040Figure 3. Peripheral TCR Repertoires Stabil- ity and Diversity Are Correlated and PositivelyCorrelate with Effector/Memory Phenotypes (A) The Gini coefﬁcient of each patient versus the cumulative frequency of the G segment from eachtime points. Pearson’s correlation coefﬁcient (R) andthe associated p value are indicated.(B) The percentage of positive effector-memoryCD8 +, memory CD4+T cell, class-switched memory B cell, naive CD8+, naive CD4+T cell, and naive B cell populations versus the cumulative frequency ofthe G segment (the persistent TCR- bclones). Pearson’s correlation coefﬁcient (R) and the asso-ciated p value are indicated.(C) The ratio of class-switched memory B cells tonaive B cells versus the ratio of CD8 +effector- memory T cells to naive CD8+T cells. Pearson’s correlation coefﬁcient (R) and the associated p valueare indicated.See also Figure S3 ,Figure S4 , and Table S2 . (M1/C0TCR3,7M4/C0TCR1, M13-TCR4, and M13-TCR67) and signiﬁcantly expanded only following vaccination, reaching me-dium (n = 2) or large (n = 2) frequency cat-egories post-vaccine, suggesting vac- cine-induced expansion. Two validated TCRs (M1-TCR3 and M4-TCR1) were spe-ciﬁcally selected for validation based on their expansion following vaccine treat- ment. Excluding these two, three of thesix remaining neoantigen-speciﬁc clones (M6/C0TCR1.1, M14-TCR1, M14-TCR2) were found in the large frequency category of the pre-T peripheral TCR- brepertoire, indicating that at least some large clones at baseline are neoantigen speciﬁc ( Fig- ure S4 ). The frequency of these validated TCR- aand TCR- bin the periphery correlated to the number of IFN- gspot forming cells as reported in Ott et al., 2020. 7Immunophenotyping showed that, in all cases, the population from which the neoan- tigen-speciﬁc T cells were obtained was of memory/effector-memory phenotype 7(Figure S4 ). Together, the results from this small set of validated TCRs allow us to integrate the TCR sequence, clone size, functionality,and phenotype. These data show that there are cases in which larger clones at the pre-T time point are neoantigen speciﬁc and are putatively IFN- gproducing with a memory phenotype, while in other cases we detect clones that expanded followingvaccine to have these same features. Combined Baseline Peripheral TCR- a/bRepertoire Features and Immunophenotyping Predict PFS We sought to determine the strength of the combined pre- treatment measurements from PBMCs in predicting PFS, andwhich individual measurements are key to the prediction. For this analysis, we aggregated over 25 PFS-9-informative baseline features per patient, which allowed us to build a predic-tive model ( Table S3 ). To identify dominant biomarkers, we used Cell Reports Medicine 1, 100141, November 17, 2020 5Reportll OPEN ACCESS principal-component analysis (PCA), an unsupervised algorithm that represents the data along their axes with the largest vari- ance. Importantly, PCA was performed without providing the al- gorithm with the explicit labels of the patients’ PFS-9 status. Thealgorithm separated patients with and without PFS-9 along PC1,the axis representing the highest variance in the data ( Figure 4 A). In order to better understand the key features driving this sepa- ration, we examined the impact of the features (loadings) on PC1(Figure 4 B). Among the top features correlating with PC1 are a clonal TCR repertoire, Gini coefﬁcient, and a memory T cell phenotype, including PD-1 +T cells. Conversely, TCR repertoire diversity (as indicated by DE50) and naive T cell phenotypes negatively correlate with PC1. A class-switched memory B cell phenotype also correlated with PC1, which might be related torecent ﬁndings of the involvement of these cells in the tumormicroenvironment in patients responding to ICI. 31The relative contribution of each of these features to PC1 demonstrated by the loadings ( Figure 4 B) points to the measurements that are most important in the makeup of the PC1 score. When categorizing patients as having either a positive or a negative PC1 value, we saw a signiﬁcant difference betweenthe median PFS of the two groups (6.93 months for PC1 <0 while at 21.6 months the median has not yet been reached for PC1 >0, hazard ratio = 0.079, p = 0.0031) ( Figure 4 C), and overall a signif-effector−memory CD4+memory CD4+Normalized Shannon's entropyGini CoefficientSum of Squareseffector−memory CD8+CD3+PD1+CD4+PD1+CD8+memory CD8+CD8+PD1+class−switched memory BCD3+naive B+Transitional B+central memory CD8+CD4+CTLA4+naive−effector CD8+central memory CD4+CD8+CTLA4+# unique amino−acid CDR3sCD4+Naive CD8+DE50Naive CD4+naive−effector CD4+ −0.2 −0.1 0.0 0.1 0.2 0.3 Loadings−202 −5 0 5 PC1 (40.9%)PC2 (13.3%)FACS TCRA DB PFS-9 No PFS-9 log10 (fraction of clones shared with all HD) PC1C +++++ + + + + ++ 0.000.250.500.751.00 06 1 2 18 2484 32 1 10 10 9 61PC1 < 0 > 0 p = 0.0031 Hazard-ratio = 0.079Log-rank Time (months)PFS −2.6−2.4−2.2−2.0−1.8−1.6 −5 0 5PFS-9 No PFS-9 R = -0.5 p = 0.035Figure 4. PCA of Baseline Peripheral TCR- b Repertoire Features and Immunophenotyp-ing Separates Patients by PFS Status at 9Months (A) First 2 components from a PCA of the aggregate peripheral measurements from the TCR- breper- toire and immunophenotyping.(B) The contributions (loadings) of the measuredfeatures to PC1. Color indicates source of data.(C) Kaplan-Meier curves for PFS of patients withPC1 >0 (teal) versus patients with PC1 <0 (red).(D) The fraction of public TCR- bCDR3 sequences (shared with all 11 HDs) versus the PC1 score ofeach patient. Pearson’s correlation coefﬁcient (R)and the associated p value are indicated.See also Figure S4 andTable S1 . icant correlation between PC1 and PFS (Figure S5 A). When repeating the analysis with the same features but replacing the TCR- brepertoire values by those derived from the TCR- asequencing, both the PCA plot and the median PFS analysis reach a similar patient stratiﬁcation ( Fig- ures S5 C–S5E). These ﬁndings demon- strate a marked difference in peripheral blood samples of patients with and without PFS-9. A clonal T cell repertoire and an an-tigen-experienced PBMC phenotype were the features that correlated most with longer PFS following combined anti-PD-1and neoantigen therapy. To assess repertoire overlap between the samples from melanoma patients and HDs, we screened TCR- bCDR3 sequences from each pa- tient for their presence in the HD samples. We quantiﬁed the proportion of TCR- bsequences (out of the 1,000 most frequent clones at baseline) that were shared with the 11 HD repertoires,and the number of HDs with which they were shared. The pro- portion of highly public clones (shared with all 11 healthy donor repertoires) was decreased in patients with PFS-9 compared topatients without PFS-9 ( Figure S5 B ) .F u r t h e r m o r e ,w eo b s e r v e d a negative correlation between the overall proportion of the public clones and the PC1 score of each patient ( Figure 4 D). These data indicate that the increased PC1 scores reﬂect a decreased publicness of the TCR repertoire, suggesting that the expanded circulating T cell clones in patients with PFS-9are not commonly found in HDs, and are potentially private, tu-mor-related clones. This additional characteristic might reﬂect the patient immune state and could function as a clinically meaningful predictor of response to immunotherapy. DISCUSSION Here, we show that data integration from a comprehensive anal- ysis of PBMC samples enables an accurate response prediction. These analyses indicate that PFS in metastatic melanomapatients can be predicted with pre-treatment peripheral blood 6Cell Reports Medicine 1, 100141, November 17, 2020Reportll OPEN ACCESS samples, strongly suggesting a pre-existing immune state that is amenable to immune modulation through ICI and/or neoantigen therapy. Characteristics of this immune state include high effector-memory T and B cell frequencies, as well as a clonalTCR repertoire with low turnover and decreased levels of highly public TCRs. A diverse, non-clonal TCR baseline reper- toire detected in both the no PFS-9 patient samples and theyoung HD samples suggests a reduced tumor antigen-inducedimmune modulation in patients without PFS-9. These ﬁndings support the hypothesis that, in some patients, metastatic cancer leads to changes in the peripheral blood immune repertoire,rendering these patients more likely to respond to immuno- therapy. It should be noted that varying levels of differences be- tween a patient’s tumor TCR repertoire and peripheral bloodrepertoire could be expected, due to the unique conditions within the tumor microenvironment, T cell trafﬁcking, and antigen exposure. 32,33 We propose a minimally invasive approach that could facilitate patient selection and improve decision making regarding the appropriate course of treatment. Limitations of Study This study presents a cohort of patients treated with both NEO-PV-01 and nivolumab, with a limited set of patientsand available samples. Due to the unique treatment settings, the study currently lacks a validation set and additional studies would need to be performed to validate these resultsand potentially extend them to other cancer indications and immunotherapies. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: dKEY RESOURCES TABLE dRESOURCE AVAILABILITY BLead Contact BMaterials Availability BData and Code Availability dEXPERIMENTAL MODEL AND SUBJECT DETAILS BHuman Subjects and Study Design BClinical assessments BPeripheral Samples dMETHOD DETAILS BT Cell and RNA Isolation for TCR Sequencing BTCR Library Preparation and Sequencing BPhenotypic analysis of peripheral samples BTCR Repertoire Generation BTCR Repertoire Diversity/Clonality Analysis BTCR Repertoire Stability BPrincipal Component Analysis BPublic TCR Sequence Analysis BscTCRseq of neoantigen-speciﬁc TCRs BIFN-gELISpot assay for CEF Reactivity dQUANTIFICATION AND STATISTICAL ANALYSIS BStatistical Analysis dADDITIONAL RESOURCESSUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. xcrm.2020.100141 .