Gaynor,1 Michael S. Rooney,1and Lakshmi Srinivasan1,* 1Neon Therapeutics/BioNTech US, Cambridge, MA, USA 2Dana Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA 3Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA 4These authors contributed equally 5Lead Contact *Correspondence: asaf.poran@biontech.us (A.P.), lakshmi.srinivasan@biontech.us (L.S.) https://doi.org/10.1016/j.xcrm.2020.100141 SUMMARY T cells use highly diverse receptors (TCRs) to identify tumor cells presenting neoantigens arising from genetic mutations and establish anti-tumor activity. Immunotherapy harnessing neoantigen-speciﬁc T cells to targettumors has emerged as a promising clinical approach. To assess whether a comprehensive peripheral mono-nuclear blood cell analysis predicts responses to a personalized neoantigen cancer vaccine combined with anti-PD-1 therapy, we characterize the TCR repertoires and T and B cell frequencies in 21 patients with metastatic melanoma who received this regimen. TCR- a/b-chain sequencing reveals that prolonged progression-free survival (PFS) is strongly associated with increased clonal baseline TCR repertoires andlongitudinal repertoire stability. Furthermore, the frequencies of antigen-experienced T and B cells in theperipheral blood correlate with repertoire characteristics. Analysis of these baseline immune featuresenables prediction of PFS following treatment. This method offers a pragmatic clinical approach to assesspatients’ immune state and to direct therapeutic decision making. INTRODUCTION Evidence has been accumulating that the immune system can recognize, prevent, and control tumor growth.1–3It is now widely accepted that somatic mutations in tumor cells can lead to neo- antigen presentation, recognition by cytotoxic T cells through their T cell receptor (TCR), and tumor cell killing.4–6We recently reported the ﬁrst clinical trial combining a personalized neoanti- gen vaccine (NEO-PV-01) with PD-1 inhibition in multiple solid tumors and showed robust de novo induction of neoantigen- speciﬁc T cells.7TCR repertoires have previously been used as potential biomarkers for response to immune checkpoint inhibi- tors (ICI), but often with low predictive conﬁdence.8–13A recent study demonstrated how the peripheral blood TCR repertoireresponse to one cycle of anti-PD-1 is informative of later treat- ment effectiveness. 14In addition, phenotyping of immune popu- lations from peripheral blood mononuclear cells (PBMCs)15–20 has been previously used as ICI response predictors. However, associating PBMC TCR repertoires with immune cell pheno- types could prove to be a strong response predictor for ICI ther-apy. We developed a highly predictive model based on the com- bined analysis of peripheral blood TCR repertoire features and frequencies of T and B cell subpopulations at the pre-treatmenttime point. This model indicates an underlying susceptible im-mune state that is distinct for metastatic melanoma patients who had a favorable response to personalized neoantigen vac- cine plus anti-PD-1 treatment. RESULTS Patients with Longer PFS Exhibit Increased Peripheral TCR Clonality prior to and throughout TreatmentMetastatic melanoma patients enrolled in the neoantigen vac- cine trial NT-001 (NCT02897765) received nivolumab combined with the personalized neoantigen vaccine NEO-PV-01. 7Three leukapheresis samples were taken at week 0 (pre-treatment, pre-T), week 10 (pre-vaccination, pre-V), and week 20 (post- vaccination, post-V) ( Figure 1 A). First, RNA was extracted from CD3+pan-T cells from these leukapheresis samples and subjected to T cell receptor a-chain and b-chain (TCR- aand TCR- b) sequencing.21We analyzed a total of 57 samples from 21 melanoma patients in the trial for whom we had samplesavailable from at least one time point. Patients were classiﬁed into two groups: those who achieved progression-free survival (PFS) 9 months post-initiation of nivolumab therapy and thosewho did not (PFS-9 and no PFS-9, respectively). Of the 21 pa- tients analyzed in this study, 14 had PFS-9, and seven did not 7 (Table S1 ). Cell Reports Medicine 1, 100141, November 17, 2020 ª2020 The Author(s). 1 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).ll OPEN ACCESS p = 0.049 p = 0.0052 p = 0.00043 0.000.050.100.15DE50 PFS-9 HD No PFS-9PFS-9 No PFS-9PFS-9 No PFS-9Normalized Shannon's Entropyp = 0.0076 p = 0.0026Pre- treatmentPre- vaccine p = 0.0075Post- vaccine 0.50.60.70.81.00.9Gini Coefficient PFS-9 HD No PFS-90.03 0.000.25 0.030.500.751.00 PFS-9 HD No PFS-9PFS-9 HD No PFS-9Fraction of clonesClone size-category Rare < 10e-6 < Small < 10e-5 < Medium < 10e-4 < Large < 10e-3Hyperexpanded ≥ 10e-3 -3-2-1 log10 (fraction) -5-4-3-2p = 0.0033 Pre- treatmentp = 0.00074 Pre- vaccinep = 0.36 Post- vaccinep = 0.0034 Pre- treatmentp = 0.0035 Pre- vaccinep = 0.0025 Post- vaccine Large HyperexpandedA C DB Pre- treatmentPre- vaccinePost- vaccine NIVOLUMAB NEO-PV-01 Boosters Priming Week: 0 12 20 Leukapheresis Leukapheresi s Leukaphere SAMPLETCR Sequencing Immunophenotyping by flow cytometry p = 0.7 p = 0.0032p = 0.0021 p = 0.71p = 0.96 p = 0.00081p = 0.00043p = 0.18 Clonality Clonality0.02 0.01 0.00104 p = 0.0040 p = 0.91p = 0.0155 p = 0.0039 p = 0.0028 −0.9−0.8−0.7−0.6 Clonality Figure 1. Patients with Lack of Progression at 9 Months Have a Higher Peripheral TCR Repertoire Clonality prior to and throughout Study Treatment (A) Treatment schedule outline of the single-arm clinical trial of nivolumab plus personalized neoantigen vaccine (NEO-PV-01). Timings are indica ted for nivo- lumab (blue arrow), personalized vaccine (NEO-PV-01, green arrow), and leukaphereses (orange dots). Leukaphereses from three time points (pre-tr eatment, pre-vaccine, and post-vaccine) are used for CD3+T cell isolation for TCR- a/bsequencing and PBMCs for immunophenotyping by ﬂow cytometry. (B) The proportion of clones belonging to each clone size category, averaged across patients (or HDs), and time points. An inset focusing on larger bin s is provided (right). The legend deﬁnes frequency-based categories. (legend continued on next page) 2Cell Reports Medicine 1, 100141, November 17, 2020Reportll OPEN ACCESS We assigned each TCR- bamino acid (AA) complementary determining region 3 (CDR3) clone to a category based on its observed frequency: rare, small, medium, large, or hyperex- panded22(STAR Methods ). The relative fractions of each cate- gory in each sample were averaged across the aggregate samples of patients with and without PFS-9 ( Figure 1 B). Notable differences between the two groups were observed, particularlyin the large and hyperexpanded categories ( Figure 1 B, right). Similarly, we analyzed samples from 11 healthy donors (HDs) to compare with the TCR- brepertoires of melanoma patients. The clonal composition of the HDs resembled those of patientswithout PFS-9. We compared the fractions of clones in each category of patients with and without PFS-9 at each time point (Figure 1 C;Figure S1 A). Interestingly, at pre-T and pre-V, the large and hyperexpanded categories were increased in patients with PFS-9 compared with patients without PFS-9, at the expense of the categories of less abundant clones, particularlythe rare clones. At post-V, only the hyperexpanded categoryshowed a signiﬁcant difference, suggesting a possible vac- cine-speciﬁc effect ( Figure 1 C, right panel). These results demonstrate that samples from patients with PFS-9 have asigniﬁcantly increased proportion of high frequency clones, especially hyperexpanded clones. This resembles the increased clonality of peripheral TCR repertoires of patients who have achronic viral infection. 23These increases in high-frequency cat- egories were also detected when comparing patients with PFS-9 with HDs. To further characterize the distribution of clonal frequencies in the repertoires, we quantiﬁed the skewedness/evenness of the frequency distributions using clonality and diversity parametersat each time point. We calculated the Gini coefﬁcient, 10,24Diver- sity-Evenness (DE50),9normalized Shannon’s entropy,25,26sum of squares, Lorentz curves,27and the number of unique nucleo- tide and AA TCR- bCDR3 sequences10and tested their associa- tion with PFS-9 status ( Figure 1 D;Figures S1 B–S1D). The difference in Gini coefﬁcient and DE50 between patients with and without PFS-9 at the pre-T time point were reported in Ottet al., 2020. 7These parameters indicated an increased clonality of the T cell repertoire in PFS-9 patients at all three time points. While repertoire clonality increases with age,22there were no sig- niﬁcant age differences between patients with and withoutPFS-9. Similarly, the two groups had identical proportions of males and females ( Table S1 ). Therefore, patient age and sex are unlikely to confound the clonality differences. We detectedno difference in clonality between patients without PFS-9 and HDs, although the HD population is younger on average than the patient population. We found no differences between eitherthe average CDR3 length or the total count of aligned TCR molecules of patients with and without PFS-9 ( Figure S1 C). In addition, we determined the reactivity to the common viral anti-gens CMV, EBV, or inﬂuenza (CEF) by an interferon (IFN)- gELISpot assay to assess the impact of exposure to these viruses on the measured TCR repertoire characteristics. We detected no difference in CEF reactivity between patients with and without PFS-9 ( Figure S1 E). Taken together, these data demonstrate that peripheral TCR repertoire clonality was increased in mela- noma patients with PFS-9 even prior to initiation of treatment. These ﬁndings support that TCR clonality from PBMCs mayrepresent a minimally invasive biomarker predicting responseto the combination therapy. Peripheral TCR Repertoires in Patients with Prolonged PFS Are More Stable over Time Notably, the overall clonality measurements did not change signiﬁcantly in response to treatment. We therefore sought tounderstand the extent to which the identities of the TCR- brep- ertoires changed over time. We ﬁrst tested whether turnover rates, as measured by the Jensen-Shannon Divergence(JSD), 28,29correlated with PFS-9 status. Analyzing the most frequent clones covering the top 20% of each repertoire, we compared the JSD of pre-T to either the pre-V or post-V time points. Both comparisons demonstrated lower JSD values, indi-cating lower TCR turnover, in patients with PFS-9 ( Figure 2 A). The same was true when comparing the post-V to the pre-V time point ( Figure S2 A), indicating that the differences in turn- over do not depend on the choice of baseline. The difference in turnover between patients with and without PFS-9 remains signiﬁcant regardless of the fraction of the repertoire used forthe calculation ( Figure S2 B). Of note, comparing pre-V with post-V turnover (relative to pre-T) in patients with PFS-9 shows no difference, whereas the turnover of TCR clones in the reper-toires of patients without PFS-9 continues to increase from pre-V to post-V ( Figure S2 B). To further characterize repertoire stability, we tested TCR- b overlap across all three time points ( Figure 2 B). We calculated the cumulative frequencies of clones detected at a single time point (Venn diagram segment A, B, or C), two time points (segment D, E, or F), and persistent clones, which were foundin all three samples over a 5-month treatment period (segment G). The cumulative frequency of persistent T cell clones (in segment G) is signiﬁcantly increased in patients with PFS-9compared with patients without PFS-9 ( Figure 2 C, left). This increase comes at the expense of the frequencies of clones de- tected at a single time point (segments A, B, and C, Figure 2 C, right) but not of clones found at two time points ( Figure S2 C). No difference was detected in the absolute number of unique clones in segment G in patients with or without PFS-9 ( Figure 2 D). This indicates that the increased cumulative frequency of thissegment is driven by the clone sizes, rather than the number of individual clones. These ﬁndings demonstrate an increased lon- gitudinal stability of the TCR- brepertoire in patients with PFS-9. To test whether repertoire clonality and the long-term stability (C) The (log–) fraction of clones belonging to the large (left) or hyperexpanded (right), for patients with and without PFS-9 at each time point or HDs. Boxplots indicate 25%, 50%, and 75% percentiles, and whiskers extend to the smallest/largest value within 1.5 times the interquartile range. p values are deri ved from a two-tailed Student’s t test.(D) The skewedness of the TCR- brepertoire frequency distribution measured by the Gini coefﬁcient, DE50, and normalized Shannon’s entropy of each HD and patient across time points. The black line indicates median. p values are derived from a two-tailed Student’s t test.See also Figure S1 andTable S1 . Cell Reports Medicine 1, 100141, November 17, 2020 3Reportll OPEN ACCESS are correlated, we analyzed the association between the Gini co- efﬁcient ( Figure 1 D) and the cumulative frequency of the persis- tent clones (G segment). We found a strong correlation between these values ( Figure 3 A), indicating that repertoire clonality and stability are linked, and the expanded clones are persistentacross the three time points. Conversely, we observed a nega- tive correlation comparing TCR- bclonality with the cumulative frequency of clones that were only detected at a single time point(segments A, B, and C). TCR- a-Chain Sequencing Is Indicative of Highly Consistent Repertoire Clonality and StabilityTCR speciﬁcity is determined by the TCR- aand TCR- b-chain pairing. While the pairing of these chains is not preserved in bulk RNA sequencing, we evaluated whether the CDR3 se-quences of the TCR- achain (CDR3 a) support our ﬁndings regarding the clonality and stability of the TCR- brepertoires. To that end, we sequenced the CDR3 aof all 21 patients andp = 0.27 3.5 3.04.05.0 4.5 PFS-9 No PFS-9log10 (# Unique AA in segment G)PFS-9 No PFS-9 PFS-9No PFS-9p = 0.0041 p = 0.0013 p = 0.000085G 0.20.40.6Cumulative Frequencyp = 0.00044 p = 0.00245 0.00.20.40.60.8A B CDE FGPre-treatmentPre-vaccine Post-Vaccine A B CG PFS-9A B CG No PFS-9Pre-treatment to Post-vaccinePre-treatment to Pre-vaccine p = 0.0045 p = 0.0012 p = 0.00089A B C 0.20.30.40.50.60.7 Pre-treatment Pre-vaccine Post-vaccine Pre-treatment Pre-vaccine Post-vaccineA CB DStability Stability Jensen−Shannon Divergence (top 20%)Figure 2. Peripheral TCR Repertoires Are More Stable over Time in Patients Who DoNot Progress at 9 Months (A) Jensen-Shannon Divergence (JSD) of TCR- b CDR3 sequences accounting for the top 20% of therepertoire frequency between pre-V (left) or post-V(right) and baseline (pre-T) of patients with andwithout PFS-9. Low JSD values represent repertoirestability as indicated by the arrow (left). Black lineindicates median. p values are derived from a two-tailed Student’s t test.(B) Schematic of a 3-way repertoire comparison.Segments in the Venn diagram represent the cu-mulative frequencies of the TCR- bCDR3 sequences detected in the intersecting time points in thatsegment. (C) Comparison of the cumulative frequencies of TCR- bCDR3 sequences between patients with and without PFS-9, at each time point. High cumulativefrequency of G represents repertoire stability is asindicated by the arrow (left). Black line indicatesmedian. p values are derived from a two-tailedStudent’s t test. Venn diagram on the bottom leftillustrate repertoire stability of patients with andwithout PFS-9.(D) The number of unique TCR- bCDR3 AA se- quences detected at all three time points in patientswith and without PFS-9. The black line indicatesmedian.