Dynamics of Tumor and Immune Responses during Immune Checkpoint Blockade in Non–Small Cell Lung Cancer

Despite the durable clinical benefit observed with immune checkpoint inhibitors for patients with non–small cell lung cancer (NSCLC), the majority of patients are either refractory or eventually develop acquired resistance after an initial response (1). Similar to the targeted therapy paradigm, success of immuno-oncology seems to depend on choosing patient populations most likely to benefit. The plasticity of the immune system under immunotherapy has weakened single biomarker-driven approaches (2) and currently used predictive biomarkers have been unable to accurately identify the subset of patients who benefit from these therapies.

We hypothesized that noninvasive molecular analyses that evaluate tumor-derived cell free circulating tumor DNA (ctDNA) and tumor-extrinsic [T-cell receptor (TCR) repertoire] parameters may be useful for rapidly determining which patients would ultimately benefit from immune checkpoint blockade. Such approaches may be of particular importance for immune-targeted agents as the therapeutic responses have been challenging to evaluate using radiographic imaging due to tumor immune infiltration (3). Conventional response criteria such as the RECIST do not consistently capture the unique patterns and timing of antitumor immune responses (4, 5).

The temporal relationship between detection of ctDNA and emergence of recurrent or progressive disease has been shown in patients with early-stage NSCLC (6, 7) and as we show in our companion study in patients with advanced NSCLC receiving targeted therapies (8). During treatment with immunotherapy, our group has shown that ctDNA may be predictive of outcome in melanoma patients treated with CTLA-4 blockade (9). ctDNA changes have been associated with therapeutic outcome during immune checkpoint blockade in NSCLC (10–12), however these analyses have been limited by the low sensitivity of the approaches, permitting analyses in approximately half of the cases analyzed. Even less is known about the dynamics of the peripheral T-cell repertoire during immune checkpoint blockade in NSCLC (13) and how these changes relate to ctDNA levels and tumor response.

To overcome these issues and to allow ultrasensitive evaluation of ctDNA during therapy, we have developed targeted error-correction sequencing (TEC-Seq), a custom capture and sequencing approach that permits sensitive and specific detection of low abundance sequence alterations using next-generation sequencing (14). We have also developed new methods of evaluating TCR clonal expansion in the tumor microenvironment during immune checkpoint blockade (13). Here, we use these approaches to investigate whether ctDNA and TCR dynamics are reflective of therapeutic outcome for patients with NSCLC treated with immune checkpoint blockade.

Our study group consisted of 24 metastatic patients with NSCLC treated with immune checkpoint blockade as a standard of care (n = 19) or in the setting of a clinical trial (n = 5) between October 2014 and August 2016 at Johns Hopkins Sidney Kimmel Cancer Center. In parallel, we evaluated 14 patients with stage I to IIIA surgically resectable NSCLC that received anti-PD1 therapy in the setting of a neoadjuvant nivolumab clinical trial (15). The studies were conducted in accordance with the Declaration of Helsinki, were approved by the Institutional Review Board (IRB), and patients provided written informed consent for sample acquisition for research purposes. Clinical characteristics for all patients are summarized in Supplementary Table S1.

Therapeutic responses were evaluated by the RECIST version 1.1 (16). Baseline disease burden was determined by the sum of the longest diameters of target lesions as determined by RECIST 1.1 criteria. After baseline imaging, radiographic evaluation was performed at 5- to 10-week intervals or as clinically indicated for the metastatic cohort and 7 days prior to surgery for the early-stage cohort. The timing of radiologic assessments typically followed the early timepoints of blood sample collection. Although this approach may be subject to lead-time bias, we sought to mirror the imaging schedule used in clinical practice. Furthermore, in contrast to chemotherapy or targeted therapy, where therapeutic response may be accurately evaluated by imaging early after treatment initiation, the unique nature and timing of response to immune checkpoint blockade mandates response assessments at later timepoints including confirmation of the radiologic response. Of the 19 metastatic patients with NSCLC with detectable ctDNA, one achieved complete response (CGLU111), three patients achieved partial response (CGLU135, CGLU337, and CGLU347), and 12 achieved SD (CGLU115, CGLU117, CGLU159, CGLU160, CGLU162, CGLU168, CGLU203, CGLU211, CGLU212, CGLU340, CGLU351, and CGLU357) as best overall response. Three patients (CGLU121, CGLU243, and CGLU348) experienced disease progression. Of the three patients with partial response, two eventually developed molecular resistance. In the neoadjuvant cohort, a repeat chest CT ≤7 days prior to surgery revealed stable disease for all patients with detectable ctDNA at baseline.

Progression-free survival (PFS) and overall survival (OS) were defined as the time elapsed between the date of treatment initiation and the date of disease progression or death from disease, or the date of death, respectively (Supplementary Table S1). For the early stage cases with detectable ctDNA, two patients demonstrated a major pathologic response (pMPR defined as ≥90% decrease in tumor burden; CGLU206 and CGLU249), three patients had a partial pathologic response (at least 30% decrease in the tumor burden; CGLU205, CCGLU219, and CGLU221) and two patients had a pathologic nonresponse (CGLU222 and CGLU225).

For all patients, at least two serial blood samples (range 2–8) were collected over the course of treatment for isolation of plasma and extraction of cell-free DNA for genomic analyses. We analyzed a total of 105 serial plasma samples that were obtained prior to anti-PD1, at 4 to 8 weeks and additional time points during therapy for all metastatic patients with NSCLC except for CGLU135 and CGLU161. For these two patients, blood from early timepoints was not available and blood samples from the time of radiographic response and the time of acquired resistance were analyzed. A detailed description of the time points analyzed is shown in Supplementary Tables S2 and S3. Baseline tumors were analyzed by whole exome sequencing or targeted next-generation sequencing for patients in the metastatic cohort, with the exception of CGLU168, for which a tumor specimen from the time of resistance to immune checkpoint blockade was used. For the early-stage cohort, tumor samples prior to therapy initiation or at the time of resection in the cases where baseline tumor was not available, were analyzed by whole exome sequencing (15).

Whole blood was collected in K2 EDTA tubes; plasma and cellular components were separated by centrifugation at 800 × g for 10 minutes at 4°C. Plasma was centrifuged a second time at 18,000 × g at room temperature to remove any remaining cellular debris and stored at −80°C until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH). TEC-Seq next-generation sequencing cell-free DNA libraries were prepared from 12 to 125 ng of cfDNA. Genomic libraries were prepared as previously described and targeted capture was performed using the Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes, described in Supplementary Table S4 (14). TEC-Seq libraries were sequenced using 100 bp paired end runs on the Illumina HiSeq 2500 (Illumina). The analytical performance and validation including sensitivity and specificity and limits of detection of our ctDNA platform have been recently reported (14).

Primary processing of next-generation sequence data for cfDNA samples was performed as previously described (14) using Illumina CASAVA software (v1.8), including demultiplexing and masking of dual index adapter sequences. Sequence reads were aligned against the human reference genome (hg19) using Novoalign with additional realignment of select regions using the Needleman–Wunsch method (17). Next, candidate somatic mutations, consisting of point mutations, small insertions, and deletions were identified using VariantDx (17) across the targeted regions of interest. VariantDx examined sequence alignments of cfDNA plasma samples while applying filters to exclude alignment and sequencing artifacts as previously described (14). Specifically, an alignment filter was applied to exclude quality failed reads, unpaired reads, and poorly mapped reads in the plasma. A base quality filter was applied to limit inclusion of bases with reported Phred quality score >30. Criteria for calling alterations in cfDNA have been previously described (14). TEC-Seq characteristics are shown in Supplementary Table S5.

Genomic alterations in ctDNA were cross-referenced against each patient's tumor-specific genomic alterations to identify bona fide tumor-specific ctDNA variants. Variants identified in ctDNA as previously described (14) and in the matching tumor with a MAF of ≥2% were considered tumor specific. We focused on somatic variants that were identified both in the tumor sample and in ctDNA for each patient to exclude variants related to clonal hematopoiesis (Supplementary Table S6). Our dataset is deposited in the database of Genotypes and Phenotypes (dbGaP; study ID 32485).

To assess the cellular prevalence of plasma mutations in their corresponding tumors, tumor samples of each case were analyzed as follows. The density of reads mapping to target and off-target regions in tumor whole exome sequence data was corrected for GC content, target size, and sequence complexity and compared with a reference panel of normal samples to establish log copy ratio values as a measure of relative copy number across the genome (18). Bin-level copy ratio values were segmented using circular binary segmentation (19). Segment copy ratio values and minor allele frequency of germline heterozygous SNPs overlapping the segments were analyzed to determine the purity and ploidy of the sample, and allele-specific copy number for segments using an in-house pipeline. Next, we used SCHISM (20) to determine the cellular prevalence of mutations based on the observed variant allele frequency, estimated copy number, and sample purity by following an approach similar to that previously described (13). This approach to clonality assessment was not feasible for mutations in four cases (CGLU168, CGLU206, CGLU219, and CGLU249) where purity and ploidy could not be determined due to low tumor content. Mutation cellularity analysis is summarized in Supplementary Table S7.

TCR clones were evaluated in pretreatment tumor tissue (with the exception of CGLU117, where tumor tissue from the time of resistance was also analyzed), and 40 serial peripheral blood lymphocytes (PBL) by next-generation sequencing in the metastatic NSCLC cohort (Supplementary Table S8). DNA from pretreatment tumor samples and PBLs was isolated by using the Qiagen DNA FFPE and Qiagen DNA Blood Mini Kit, respectively (Qiagen). TCR-β CDR3 regions were amplified using the survey (tumor) or deep (PBLs) ImmunoSeq assay in a multiplex PCR method using 45 forward primers specific to TCR Vβ gene segments and 13 reverse primers specific to TCR Jβ gene segments (Adaptive Biotechnologies; refs. 21, 22). Productive TCR sequences were further analyzed. TCR sequencing data from TILs were used to identify tumor-specific TCR clonotypes in the peripheral blood. Peripheral TCR clones achieving a frequency of at least 0.005% were evaluated for differential abundance between baseline and the time of radiographic response using Fisher exact test with FDR P-value correction (corrected P ≤ 0.05). Those differentially abundant clones also found in the tumor were further selected to determine their frequencies in peripheral blood prior to treatment, at the time of response and upon emergence of resistance (Supplementary Tables S9 and S18). We calculated the average productive frequency of differentially abundant clones and used it as a metric of TCR dynamics during therapy. To cluster significantly expanded intratumoral TCR-β CDR3s based on potential recognition specificity, we used the GLIPH method (Grouping of Lymphocyte Interactions by Paratope Hotspots; ref. 23).

Subsequent to initial filtering, we further reduced noise by eliminating clones that did not have frequencies beyond a mean rate of five counts. Thus, when two points were examined (baseline and time of radiologic response), the total sum of counts were greater or equal to 10. Using these data, we examined the usage of CDR3b Variable (V) and Joining (J) regions, and their overall clonal composition by known significant clones at the two time points.

We used a multiplex bead-based immunoassay on the Luminex platform that examines cytokines involved in T-cell activation, expansion, differentiation, and long-term proliferation (IFNγ, IL1, IL2), Th1 immune response (IL12), acquisition of the Th2 phenotype (IL4) as well as immunosuppressive cytokines important for regulatory T cells (IL10) in four patients of the early stage cohort where additional serum was available. Differences in concentration of cytokines were evaluated between baseline and on treatment (week 2–6) samples.

ctDNA values were dichotomized as detectable and undetectable. Characteristics for each group were compared using chi-square or Fischer exact test for categorical variables. Pearson correlation coefficient (R) was used to assess correlations between continuous variables. Differences between molecular responders and nonresponders were assessed by the Mann–Whitney test. Tumors were classified based on their nonsynonymous sequence alteration load in high and low mutators as previously described (24). The median point estimate and 95% confidence interval for PFS and OS were estimated by the Kaplan–Meier method. Survival curves were compared by using the log-rank test. Univariate Cox proportional hazards regression analysis was used to determine the impact of ctDNA molecular response on PFS and OS. All P values were based on two-sided testing and differences were considered significant at P < 0.05. Statistical analyses were done using the SPSS software program (version 25.0.0 for Windows; IBM).

We analyzed 105 serial blood samples from 38 patients with NSCLC, including 24 patients with metastatic NSCLC during immune checkpoint blockade and 14 patients with stage I to IIIA surgically resectable NSCLC that received anti-PD1 therapy as part of a clinical trial of neoadjuvant nivolumab (Table 1; Supplementary Table S1; ref. 15). The median duration of follow-up was 12.7 months (range 3.0–37.8 months) and 16 months (range 2–30 months) for the metastatic and early-stage patients respectively, and median duration of treatment was 7 months (range 1–20 months) for the metastatic cohort. We evaluated response to immune checkpoint blockade using standard computed tomographic (CT) imaging and changes in tumor burden were assessed by RECIST 1.1. Blood samples for the metastatic patients with NSCLC were prospectively collected prior to therapy, at an early time point between 4 and 8 weeks from treatment initiation and at additional serial time points during therapy until the time of disease progression (Supplementary Tables S2 and S3). For the patients with early-stage NSCLC treated with anti-PD1 therapy in the neoadjuvant setting, blood samples were collected prior to immunotherapy, at 2 weeks, immediately prior to resection and post-resection (Supplementary Table S2). ctDNA was measured using the TEC-Seq approach (14) and the TCR repertoire was studied longitudinally by means of TCR sequencing (Fig. 1). Given the possibility of hematopoietic alterations that may be detected in the plasma (14), especially in heavily treated patients, we focused only on tumor-specific sequence alterations in cell-free DNA. Clinical characteristics, outcome, and liquid biopsy analyses are summarized in Table 1.

In the metastatic NSCLC cohort, ctDNA was detected in 19 of 24 patients either at baseline (n = 14) or at other time points when baseline samples were not available (n = 5), with a median mutant allele fraction of 1.87% (range 0.09%–34.7%). In the early-stage cohort, ctDNA was detected at baseline in 7 of 14 patients, with a median allele fraction of 0.34% (range 0.15%–2.19%). For patients with detectable ctDNA, an average of one tumor-specific alterations were detected (median 1, range 1–4) affecting one or more of 12 driver genes, including those commonly altered in lung cancer (Supplementary Tables S4–S6). The vast majority of tumor-specific variants were clonal in the corresponding tumor samples (Supplementary Table S7).

We observed three patterns of molecular response in ctDNA for patients treated with immune checkpoint inhibitors. Among the patients with a molecular response (n = 9), individuals had a dramatic reduction in ctDNA to undetectable levels on average at 9 weeks from treatment initiation (Fig. 2A–E; Supplementary Fig. S1). As an example, for patient CGLU111 with a sustained clinical response, ctDNA-based molecular analyses showed a complete molecular response at week 4, more than 5 weeks prior to a radiologic partial response and 26 weeks earlier than complete radiologic response determined by RECIST 1.1 (Fig. 2). In contrast, for patients with a pattern of molecular resistance (n = 10), ctDNA levels had limited fluctuations or displayed a rise 3 to 16 weeks after therapeutic initiation. As a representative patient, ctDNA levels in CGLU121 continued to rise from the time of initiation of immune checkpoint blockade, consistent with radiographic disease progression (Fig. 3A–E). All patients with ctDNA features of primary molecular resistance had radiologic disease progression that followed molecular resistance by 5.5 weeks (Supplementary Fig. S2).

The third observed pattern, seen in five of the molecular responders, was one consistent with molecular acquired resistance, where ctDNA dynamics reflected clonal evolution under selective pressure of anti-PD1 therapy and emergence of immune escape. In such cases, tumor-specific variants were undetectable at the time of response followed by increase in mutant allele fraction at the time of acquired resistance (Supplementary Fig. S1). Emergence of molecular resistance preceded disease progression on imaging by an average of 10.8 weeks. Overall, ctDNA-based molecular responses were detected on average 8.7 weeks earlier than conventional RECIST1.1 response assessment (6.7 weeks vs. 15.4 weeks, P = 0.004; Supplementary Fig. S3).

Early ctDNA clearance was a significant prognostic factor for PFS and OS. Patients with a reduction of ctDNA to undetectable levels demonstrated a significantly longer PFS and OS compared with patients with no evidence of ctDNA elimination (log rank P = 0.001 and 0.008, respectively; Fig. 4A and B; Supplementary Fig. S4). The duration of the molecular responses tightly correlated with PFS and OS (Supplementary Fig. S5).

Radiographic imaging at the time of first assessment was a worse predictor of outcome to anti-PD1 therapy compared with ctDNA molecular response for these patients (Fig. 4A and B; Supplementary Fig. S6). Patients with radiographically stable disease (n = 12) had differential responses to immune checkpoint blockade that were consistent with their molecular response pattern (Supplementary Fig. S7). More specifically, five patients with stable disease by imaging showed a clear molecular response pattern, with ctDNA elimination between week 4 and 13 from immune checkpoint blockade initiation (Supplementary Fig. S1). All five patients derived clinical benefit from PD-1 blockade (PFS and OS ranging from 7.3 to 13.6 and 12 to 21.3 months, respectively; Supplementary Fig. S7), suggesting that imaging failed to detect the magnitude of therapeutic response.

Interestingly, ctDNA molecular responses more accurately predicted PFS and OS compared with tumor mutation burden in our cohort (TMB; Supplementary Fig. S8). When TMB and ctDNA were combined the ctDNA-based molecular responders clustered together independent of the TMB for both PFS and OS (Fig. 4C and D). Given that clonal mutation burden may be a more accurate predictor of response to immune checkpoint blockade, we performed survival analyses incorporating clonal TMB and we again found that ctDNA dynamics predict survival independent of clonal TMB status (Supplementary Fig. S9).

Given the challenges with radiologic response assessments to immune checkpoint blockade, we sought to validate our observations in a NSCLC cohort where the therapeutic effect was measured at a pathologic level instead of using conventional imaging. We analyzed serial plasma samples from a recently reported clinical trial of neoadjuvant nivolumab for early-stage operable NSCLC (15). For these patients, the therapeutic effect was rigorously measured by pathologic assessment of residual tumor after two doses of anti-PD1 therapy (15, 25). Similar to our initial analyses, we observed that all tumors with a major or partial pathologic response to anti-PD1 therapy demonstrated a molecular response pattern of elimination of tumor-specific mutations in the circulation (Fig. 5A; Supplementary Fig. S10). In contrast, tumors without a pathologic response demonstrated a molecular resistance pattern at the time of resection of the primary tumor (Fig. 5B; Supplementary Fig. S10).

We investigated how immune checkpoint blockade affects the peripheral TCR repertoire and whether there are TCR clonotype dynamic changes reflective of a systemic antitumor immune response. We focused our analyses on TCR clones found in the tumor microenvironment using TCR sequencing and investigated their dynamics in the peripheral blood, identifying those with a statistically significant differential abundance from baseline. Twelve of the 24 metastatic patients with NSCLC had available samples from both tumor infiltrating lymphocytes and peripheral blood lymphocytes for analysis (Supplementary Tables S3 and S8), including five that had previously undergone TCR sequencing (13) but had not been analyzed using this approach.

Similar to ctDNA analyses, we observed distinct patterns in TCR clonotype dynamics among the analyzed patients. For patients with clinical responses to immune checkpoint blockade, a statistically significant oligoclonal expansion of preexisting intratumoral T-cell clones was observed in peripheral blood at the time of radiologic response to PD1 blockade (CGLU111, CGLU117, CGLU127, and CGLU212; Fig. 2; Supplementary Fig. S11; Supplementary Tables S9–S12). For patients who developed acquired resistance, productive frequencies of intratumoral clones significantly decreased in peripheral blood at the time of acquired resistance (CGLU117, CGLU127, CGLU135, and CGLU161; Supplementary Fig. S11; Supplementary Tables S10, S11, S13, S14), with a timing that was similar to ctDNA analyses for most cases.

In contrast, for patients CGLU121 and CGLU115 that had primary resistance to immunotherapy, we did not identify any differentially abundant TCR clones among serial peripheral blood samples (Fig. 3; Supplementary Fig. S12). These patients progressed radiographically within 5 to 13 weeks from initiation of therapy and, in line with the clinical course, there was no evidence of TCR clonal expansion among the intratumoral TCR repertoire. A transient oligoclonal TCR expansion was observed for nonresponding patient CGLU159 at week 11; however, productive frequencies of differentially abundant clones quickly decreased to baseline levels at week 16, which coincided with disease progression (Supplementary Fig. S12; Supplementary Table S15). Patients CGLU162, CGLU203, and CGLU243 had 1 to 26 intratumoral TCR clones with differential abundance at the time of best radiographic response compared with baseline but were classified as ctDNA molecular nonresponders (Supplementary Fig. S12; Supplementary Tables S16–S18). Patients CGLU203 and CGLU243 had unfavorable outcome to anti-PD1 therapy, suggesting that for these patients, ctDNA kinetics may more accurately predict therapeutic outcome.

We did not identify any shared TCR clones among the differentially expanded ones for all patients analyzed, consistent with the notion that the mutation-associated neoantigen repertoires are largely private. We evaluated putative shared CDR3 motifs among significantly expanded TCR clones using the grouping of lymphocyte interactions by paratope hotspots algorithm (23). Interestingly, TCR clones CSARVGVGNTIYF and CSARSGVGNTIYF, which were differentially abundant at the time of response to immune checkpoint blockade for patient CGLU127 and CGLU135, respectively, clustered together, suggesting a common specificity to a tumor- or mutation-associated antigen. We subsequently investigated potential differential sequence features focusing on Variable (V) and Joining (J) gene usage and CDR3 lengths among different timepoints for each patient. Usage of specific V and J gene segments increased at the time of response compared with baseline for a patient with sustained response (CGLU111) in contrast to a representative patient with primary resistance (CGLU121; Supplementary Fig. S13). Our findings on differential V gene usage may suggest clonotypic amplifications of specific immune subsets (CD8⁺ vs. CD4⁺) during immune checkpoint blockade (26).

The unique nature of responses to immune checkpoint blockade (27, 28) and known limitations of conventional radiologic response assessments (3) highlight the need for development of biomarker-driven approaches to interpret therapeutic responses. Success of immunotherapy approaches depends on choosing patient populations most likely to benefit. There is therefore an urgent clinical need for molecular assays of response and resistance to immune-targeted agents. To this end, we analyzed ctDNA and TCR clonal dynamics during immune checkpoint blockade in NSCLC and assessed the value of longitudinal monitoring of liquid biopsies as a surrogate for response to therapy. Our findings indicate that ctDNA dynamics after treatment initiation may allow patients with primary resistance to immune checkpoint blockade to be rapidly identified and redirected to receive alterative options.

Noninvasive detection and monitoring of acquired resistance to EGFR-targeted therapy has been evaluated by serial sampling of ctDNA (29, 30) and as we have shown in a complementary study, changes in ctDNA levels may predict response to targeted therapy in NSCLC (8). Longitudinal assessment of ctDNA in patients with metastatic melanoma receiving anti-PD1 therapy has been demonstrated to be an accurate predictor of tumor response and therapeutic outcome (31) and early ctDNA clearance may correlate with durable clinical benefit to PD-1 blockade (10, 12). ctDNA dynamics may be also informative in differentiating pseudoprogression from disease progression during immunotherapy (32). However, these approaches have been limited by low sensitivity and specificity of ctDNA methods, with many patients lacking detectable alterations and potential admixture between tumor alterations and those involved in clonal hematopoiesis (33, 34).

Moreover, interpretation of ctDNA analyses without knowledge of tumor-specific somatic alterations may be difficult in the setting of heavily pretreated patient populations such as patients with late-stage lung cancer, given the mutagenic effects of systemic chemotherapy and ionizing radiation on cells of the myeloid lineage (35). To address the possible presence of alterations in cfDNA from clonal nonmalignant hematopoietic cells, we have focused our analyses of variants in ctDNA that were also identified through next-generation sequencing of the matched tumor, allowing distinction of tumor-specific from blood cell proliferation variants.

Clonal expansion of intratumoral T cells may predict therapeutic outcome for immune checkpoint blockade (36); however, little is known about the significance of peripheral expansion of TCR clones found in the tumor microenvironment during therapy. Expansion of peripheral CD8⁺ T-cell populations has been shown to precede immune-related adverse events in patients treated with ipilimumab (37). We investigated whether the patients with NSCLC in our cohort developed immune-related adverse events at the time of TCR clonal expansion and did not identify a definitive pattern with the exception of patient CGLU243, where pneumonitis emerged shortly after treatment initiation. Although there were cases for which TCR expansion preceded the development of a grade 2 to 4 immune-related adverse event (CGLU161, CGLU117), such events were also noted significantly later from the time of TCR expansion (CGLU111, CGLU135). These observations highlight the challenges with interpretation of the evolving peripheral TCR repertoire. Assessing the quality of the immune response in conjunction with clonotypic amplifications may provide additional information on the evolving TCR repertoire; to this end, we looked at differences in cytokine levels in selected early-stage patients with available serum at baseline and 2 to 6 weeks during anti-PD1 therapy. We did not identify any significant changes in cytokine levels in peripheral blood between baseline and week 2 to 6 on anti-PD1 therapy, however these analyses were limited by small number of cases tested (Supplementary Fig. S14).

In summary, we have developed dynamic assays that capture the tumor-immune system equilibrium and assess immune editing of neoantigens during immunotherapy. We have shown that these approaches have advantages compared with conventional radiologic response assessment and static molecular analyses such as baseline TMB. We believe that these methods are especially suited for the interpretation of unique responses seen with immune-targeted agents that are not adequately captured by traditional response criteria. In addition to more accurately predicting long-term response to immunotherapy, we were able to predict therapeutic outcome on average 8.7 weeks earlier than radiographic imaging. However, our work is limited by the small sample size, cohort heterogeneity, and retrospective nature of the analyses. Validation of these findings may lead to early therapeutic decisions to ensure that an ineffective treatment is discontinued as well as allow response adaptive combination and sequencing of subsequent therapies. Additional work will be needed to address the frequency of serial monitoring and feasibility of interpreting ctDNA dynamics without prior knowledge of tumor mutations. Prospective studies will be needed to assess whether switching therapy based on ctDNA dynamics prior to radiologic progression will improve outcome and ultimately whether a liquid biopsy approach can replace conventional imaging as a gold standard for early response assessment to immune checkpoint blockade.

P.M. Forde reports receiving a commercial research grant from AstraZeneca, BMS, Corvus, Kyowa, Merck, and Novartis, and is a consultant/advisory board member of Abbvie, AstraZeneca, BMS, Boehringer, EMD Serono, Lilly, Merck, and Novartis. J.R. White is a consultant at Personal Genome Diagnostics and has ownership interest (including stock, patents, etc.) in Resphera Biosciences. J. Naidoo reports receiving a commercial research grant from AstraZeneca/MedImmune and Merck, is a consultant/advisory board member of AstraZeneca/MedImmune, Bristol-Myers Squibb, Takeda, Genentech/Roche, and has provided expert testimony for Bristol-Myers Squibb and AstraZeneca/MedImmune. V. Adleff is a consultant/advisory board member of Personal Genome Diagnostics. C.L. Hann reports receiving other commercial research support from Bristol-Myers Squibb and is a consultant/advisory board member of Bristol-Myers Squibb and Genentech. B. Levy is a consultant/advisory board member of Genentech, AstraZeneca, Eli Lilly, Celgene, and Takeda. J. Feliciano is a consultant/advisory board member of AstraZeneca, Merck, Genentech, and Eli Lilly, and has provided expert testimony for legal review. P. Illei reports receiving other commercial research support from Bristol Myers-Squib and is a consultant/advisory board member of AstraZeneca, Bayer, Roche, and Abbvie. J.M. Isbell has ownership interest (including stock, patents, etc.) in LumaCyte, Inc. J.L. Sauter has ownership interest (including stock, patents, etc.) in Merck & Company, Inc. (New), Chemed Corporation, Pfizer, Thermo Fisher Scientific, Celgene, and Allergan PLC SHS. J. Taube reports receiving a commercial research grant from Bristol Myers Squibb and is a consultant/advisory board member of Bristol Myers Squibb, Astra Zeneca, Merck, and Amgen. D.M. Pardoll reports receiving a commercial research grant from Bristol Myers Squibb, Astra Zeneca, Compugen, Merck, has ownership interest (including stock, patents, etc.) in WindMil, Trieza Therapeutics, Potenza, Ervaxx, Dracen Pharmaceuticals, DNAtrix, Aduro Biotech, Five Prime, and Tizona, and is a consultant/advisory board member of Amgen, Bayer, Camden Partners, Jansesen, FLX Bio, Immunomics, and Rock Springs Capitol. J.E. Chaft is a consultant/advisory board member of AstraZeneca, Merck, BMS, and Genentech. M.D. Hellmann reports receiving a commercial research grant from BMS, is a consultant/advisory board member of Merck, BMS, AstraZeneca/MedImmune, Genentech/Roche, Janssen, Nektar, Syndax, Mirati, and Shattuck Labs, and has provided expert testimony for a patent that has been filed by MSK related to the use of tumor mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from PGDx. J.R. Brahmer reports receiving a commercial research grant from BMS and is a consultant/advisory board member of BMS, Merck, Genentech, Amgen, Janssen, Syndax, Celgene, and AstraZeneca. V.E. Velculescu has ownership interest (including stock, patents, etc.) in Ignyta and Personal Genome Diagnostics and is a consultant/advisory board member of Ignyta and Personal Genome Diagnostics. No potential conflicts of interest were disclosed by the other authors.

Conception and design: V. Anagnostou, P.M. Forde, J.E. Chaft, J.R. Brahmer, V.E. Velculescu

Development of methodology: V. Anagnostou, P.M. Forde, N. Niknafs, J. Phallen, V. Adleff, D.M. Pardoll, V.E. Velculescu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): V. Anagnostou, P.M. Forde, C. Hruban, J. Naidoo, S. Rosner, J. Phallen, A. Leal, K.N. Smith, L. Rhymee, C.L. Hann, J. Feliciano, C. Georgiades, P. Illei, E. Gabrielson, M.V. Brock, J.M. Isbell, J.L. Sauter, J. Taube, M.D. Hellmann, J.R. Brahmer

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): V. Anagnostou, P.M. Forde, J.R. White, N. Niknafs, C. Hruban, K. Marrone, I.K.A. Sivakumar, D.C. Bruhm, J. Phallen, V. Adleff, K.N. Smith, T.R. Cottrell, D.N. Palsgrove, B. Levy, J. Feliciano, F. Verde, J.M. Isbell, J.L. Sauter, J. Taube, R.B. Scharpf, R. Karchin, D.M. Pardoll, M.D. Hellmann, J.R. Brahmer, V.E. Velculescu

Writing, review, and/or revision of the manuscript: V. Anagnostou, P.M. Forde, J.R. White, N. Niknafs, J. Naidoo, K. Marrone, I.K.A. Sivakumar, D.C. Bruhm, K.N. Smith, T.R. Cottrell, C.L. Hann, B. Levy, J. Feliciano, P. Illei, M.V. Brock, J.M. Isbell, J.L. Sauter, J. Taube, D.M. Pardoll, M.D. Hellmann, J.R. Brahmer, V.E. Velculescu

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.R. White, S. Rosner, L. Rhymee, Q.K. Li, V.E. Velculescu

Study supervision: V. Anagnostou, P.M. Forde, R.B. Scharpf, R. Karchin, V.E. Velculescu

Other (biopsy performance, biopsy consent): J. Feliciano

This work was supported in part by U.S. NIH grants CA121113 (to V. Velculescu, V. Anagnostou), CA006973 (to D. Pardoll, V. Velculescu), CA180950 (to V. Velculescu), the Commonwealth Foundation (to V. Velculescu), the Bloomberg-Kimmel Institute for Cancer Immunotherapy (to V. Anagnostou, P. Forde, J. Brahmer, D. Pardoll, V. Velculescu), the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (to V. Velculescu), the Eastern Cooperative Oncology Group- American College of Radiology Imaging Network (to V. Anagnostou), MacMillan Foundation (to V. Anagnostou), the V Foundation (to V. Anagnostou, V. Velculescu), the ICTR-ATIP UL1TR001079 (to V. Anagnostou), the Pardee Foundation (to V. Anagnostou), Swim Across America (to V. Anagnostou), the William R. Brody Faculty Scholarship (to R. Karchin), the SU2C-ACS Lung Cancer Dream Team (to P. Forde and E. Gabrielson), PRIME Oncology (to J. Naidoo), the MSK Cancer Center Support Grant/Core Grant (P30 CA008747), the SU2C DCS International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415; to V. Velculescu), the SU2C-LUNGevity-American Lung Association Lung Cancer Interception Dream Team, Translational Cancer Research Grant (SU2C-AACR-DT23-17 to J. Brahmer, V. Velculescu), the Allegheny Health Network – Johns Hopkins Research Fund (to V. Anagnostou, V. Velculescu), the LUNGevity Foundation (to V. Anagnostou and P. Forde), the Mark Foundation (to A. Leal, V. Velculescu), and Bristol Meyers Squibb (to P. Forde). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. This publication was made possible in part by the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by Grant Number UL1TR001079 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS, or NIH. We thank Dr. Suzanne Topalian and members of our laboratories for helpful discussions and critical review of the manuscript.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.