ABSTRACT
Major immunotherapy challenges include a limited number of predictive biomarkers and the unusual imaging features post-therapy, such as pseudo-progression, which denote immune infiltrate-mediated tumor enlargement. Such phenomena confound clinical decision-making, since the cancer may eventually regress, and the patient should stay on treatment. We prospectively evaluated serial, blood-derived cell-free DNA (cfDNA) (baseline and 2–3 weeks post-immune checkpoint inhibitors [ICIs]) for variant allele frequency (VAF) and blood tumor mutation burden (bTMB) changes (next-generation sequencing) (N = 84 evaluable patients, diverse cancers). Low vs. high cfDNA-derived average adjusted ΔVAF (calculated by a machine-learning model) was an independent predictor of higher clinical benefit rate (stable disease ≥6 months/complete/partial response) (69.2% vs. 22.5%), and longer median progression-free (10.1 vs. 2.25 months) and overall survival (not reached vs. 6.1 months) (all P < .001, multivariate). bTMB changes did not correlate with outcomes. Therefore, early dynamic changes in cfDNA-derived VAF were a powerful predictor of pan-cancer immunotherapy outcomes.
KEYWORDS: Immune checkpoint inhibitor, liquid biopsy, response, cancer
Brief summary
Liquid biopsy to predict immunotherapy response.
Background
As a transformative cancer treatment, immunotherapies, especially checkpoint inhibitors, such as antibodies against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) or the programmed death receptor 1 (PD-1) and its ligand (PD-L1), have achieved salutary anti-cancer effects among patients, even those suffering from advanced, metastatic disease.1–5 However, across different solid tumors, the efficacy of checkpoint inhibitors is limited to a relatively small portion of patients. Recently, the Food and Drug Administration (FDA) authorized two tumor-agnostic approvals for the checkpoint inhibitor pembrolizumab,6,7 based on deficient mismatch repair/microsatellite instability-high (dMMR/MSI-H) and tumor mutational burden (TMB) ≥10 mutations/Mb, respectively, because these two parameters predict better outcomes with immunotherapy.6–12 Still, patients with MSI-H have a response rate of only about 40% across solid cancers with pembrolizumab.7 The only other approved biomarker for predicting anti-PD-1/PD-L1 inhibitor response is PD-L1 expression by immunohistochemistry.13,14
Since most patients do not achieve durable benefit from the checkpoint inhibitors, even in the presence of approved response biomarkers, additional investigation is warranted. Liquid biopsy is a noninvasive method that captures circulating cell-free DNA (cfDNA) released into the bloodstream from tumor cells. With techniques such as next-generation sequencing (NGS), liquid biopsies can be exploited to detect somatic mutations in tumors. Mutation profile measured from cfDNA can help in selecting the right treatment for patients and assist in monitoring cancer progression and recurrence.15–23
To better predict immune response and resistance markers for anti-PD-1/PD-L1 inhibitors, especially in the early course of the treatment, we prospectively evaluated serial plasma samples for cfDNA in order to assess dynamic changes in variant allele frequency (VAF) and in blood TMB (bTMB), and the implication of these changes for checkpoint blockade responsiveness. Our results suggest that VAF changes in cfDNA provide an early forecast of outcomes after immunotherapy and therefore that implementation of early liquid biopsy may facilitate drug development in this field.
Methods
Study design and patient treatment
We prospectively enrolled 104 patients with diverse metastatic malignancies who received anti-PD-1/PD-L1-based therapies and obtained serial plasma to measure variants from tumor-derived cfDNA. Blood samples from timepoint A were obtained on the day of therapy, prior to the first treatment cycle. Subsequent blood samples prior to the second cycle of therapy (~3 weeks after the first treatment cycle) were collected as timepoint B samples. Blood samples were processed at Ambry Genetics (https://www.ambrygen.com/). The study was conducted under the UCSD IRB-approved PREDICT protocol (NCT02478931) and investigational therapies for which patients gave consent.
Next-generation sequencing
cfDNA was extracted from whole blood collected in 10 mL Streck tubes. Briefly, plasma was isolated from 10 to 20 mL of blood by centrifugation at 1,600 g for 10 minutes. cfDNA was extracted from plasma using QIAamp Circulating Nucleic Acid Kit (Qiagen) and quantified by Bioanalyzer (Agilent). Sequencing libraries were prepared using 10–30 ng of cfDNA with KAPA HyperPrep kits (Roche) according to manufacturer’s instructions. Sequencing was conducted on the HiSeq2500 (Illumina Inc.) using 100 bp paired-end sequencing according to the manufacturer’s workflow. Genomic alterations including single nuclear variations (SNVs), insertions and deletions from cfDNA samples were detected using a targeted NGS-based panel with 89 genes (Supplementary Table 1), including genes participating in antigen processing and presentation, immune response, and genes associated with cancer.12 Germline variants are detected from whole blood samples using whole-exome sequencing as described previously.24 Variants identified in cfDNA samples are used to calculate the changes in VAF and TMB.
Statistical analysis and outcome evaluation
A t-test was used for continuous data, and a logistic regression test was used for categorical data. OS was calculated from the start of treatment to last follow-up. PFS was calculated from the start of treatment until progression or death, whichever came first. Survival analyses were assessed by Kaplan–Meier analysis, and Cox’s proportional hazard model was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Patients still alive (for OS) or still progression-free (for PFS) at last follow-up were censored at that point. Response was assessed by RECIST criteria per the physician. For multivariate analysis, variables with P values < .1 in univariate analysis were included in the multivariate regression model.
Average adjusted ΔVAF
With NGS, VAF is measured as the percentage of sequencing reads observed matching the variants divided by the overall coverage at that locus. Change in VAF (ΔVAF) after immunotherapy is calculated as ΔVAF = VAFtimepointB – VAFtimepointA. To improve the prediction accuracy for patient response, we applied a machine learning algorithm to adjust ΔVAF calculation. We used an equation adjusted ΔVAF = VAFtimepointB – alpha*VAFtimepointA by adding an alpha value (weight) to VAFtimepointA. Average adjusted ΔVAF was defined as the average of each of the adjusted ΔVAFs for each alteration in any one patient (see Supplemental Methods for additional details).
ΔBlood TMB (ΔbTMB) calculation
TMB of blood (bTMB) was defined as the number of SNVs, insertions and deletions (indels) from coding regions per megabase of targeted regions. All base substitutions and indels in the coding region of targeted genes, including synonymous alterations, are counted. To calculate the TMB per megabase, the total number of mutations counted is divided by the size of the targeted regions (179 kb). ΔbTMB between timepoints A and B is calculated as ΔbTMB = bTMBB – bTMBA.
Results
Patient characteristics
In this study, blood samples were collected from 104 patients to isolate cfDNA from plasma (Materials and Methods). DNA was isolated from 198 blood samples obtained from a total of 104 patients and sequenced with NGS. Twenty patients were excluded from the analysis: (i) 10 patients did not have blood samples from timepoint B and (ii) cfDNA samples from the other 10 patients failed NGS quality control. In total, a sample cohort of 168 distinct plasma samples obtained from 84 evaluable, immunotherapy-treated patients was included in this study (Figure 1 and Supplementary Table 2).
Figure 1.
Consort diagram of cancer patients who were evaluated for progression-free survival (PFS) and overall survival (OS) (N = 84), and with response assessment (N = 79).
Abbreviations: OS, overall survival; PFS, progression-free survival.
Patient characteristics of the treated patients are described in Table 1. The most common primary tumor sites were gastrointestinal (30.8%), genitourinary (16.3%) and gynecologic (13.5%), and head and neck (11.5%) cancers. All 104 patients were treated with immunotherapy, either as monotherapy, or in combination with a targeted agent, chemotherapy, or other immunotherapy. Overall, 102 patients received an anti-PD-1/PD-L1 agent in their regimen and two patients received anti-CTLA-4 monotherapy. Of the 84 evaluable patients, the best response from 79 patients was available at the time of data cutoff (Figure 1). Patients were considered as having clinical benefit if they showed stable disease (SD) for ≥6 months, partial response (PR), or complete response (CR). Patients with SD for <6 months or progressive disease were considered as non-responders (Table 1). Patients with ongoing SD for <6 months were considered too early to evaluate for response assessment. All 84 patients were evaluable for progression-free survival (PFS) and overall survival (OS).
Table 1.
Patient characteristics (N = 104).*
| Basic characteristics | N (%) |
|---|---|
| Age, median (range) (years) | 61.5 (21.0–91.5) |
| Sex, N (%) | |
| Women | 41 (39.4%) |
| Men | 63 (60.6%) |
| Type of cancer, N (%) | |
| Gastrointestinal | 32 (30.8%) |
| Genitourinary | 17 (16.3%) |
| Gynecologic | 14 (13.5%) |
| Head and neck | 12 (11.5%) |
| Skin/melanoma | 7 (6.7%) |
| Lung | 6 (5.8%) |
| Breast | 5 (4.8%) |
| Hematologic | 4 (3.8%) |
| Central nervous system | 3 (2.9%) |
| Others** | 4 (3.8%) |
| Anti-PD-1/PD-L1-based therapy administered as, N (%) | |
| First line | 36 (34.6%) |
| Second line | 38 (36.5%) |
| Third line | 13 (12.5%) |
| ≥Fourth line | 17 (16.3%) |
| Type of immunotherapy, N (%) | |
| Anti-PD-1/PD-L1 alone | 51 (49.0%) |
| Anti-PD-1/PD-L1 with targeted agents | 25 (24.0%) |
| Anti-PD-1/PD-L1 with chemotherapy | 7 (6.7%) |
| Anti-PD-1/PD-L1 with anti-CTLA-4 | 15 (14.4%) |
| Anti-PD-1/PD-L1 with anti-CTLA-4 and targeted agents | 2 (1.9%) |
| Anti-PD-1/PD-L1 with chemotherapy and targeted agents | 2 (1.9%) |
| Anti-CTLA-4 alone | 2 (1.9%) |
| Best response, N (%) | |
| Complete response | 4 (3.8%) |
| Partial response | 22 (21.2%) |
| Stable disease ≥6 months | 12 (11.5%) |
| Stable disease <6 months | 12 (11.5%) |
| Progressive disease | 43 (41.3%) |
| Response assessment unavailable or too early to be evaluated*** | 11 (10.6%) |
*The characteristics were curated on all 104 patients. N = 84 patients had treatment follow-up and passed cfDNA NGS quality control.
**Others: includes patients with sarcoma (N = 1), myxofibrosarcoma (N = 1), thymus squamous cell carcinoma (N = 1), and adrenal cortical carcinoma (N = 1).
***N = 8 had stable disease at the time of data cutoff; however, follow-up was less than 6 months and thus not included in the response analysis. N = 3 without adequate clinical information to assess the response.
Prediction of clinical benefit (SD ≥6 months/CR/PR) after immunotherapy with average adjusted ΔVAF in cfDNA
Cancer patients with higher tumor burden or more aggressive disease also have a higher concentration of cfDNA in their blood.18–21 Drops in cfDNA level have also been shown to correlate with patient response to immune checkpoint inhibitors.22,23,25 In cancer patients, cfDNA level can be measured with techniques such as NGS and droplet digital PCR. It is quantified by determining the allelic fraction of cfDNA fragments that contains cancer-associated variants.
In this study, we proposed that change in the VAF (which itself reflect the percent circulating tumor DNA) of the genomic profile of cfDNA from tumor is associated with patient response to immunotherapy. In order to measure the change, blood samples from timepoint A were obtained on the day of therapy, prior to the first treatment cycle. Subsequent blood samples prior to the second cycle of therapy (~3 weeks after the first treatment cycle) were collected as timepoint B samples. Variants with VAF ≥0.1% including single nuclear variations (SNVs) and indels from cfDNA were detected using NGS (Methods). When comparing variants between timepoint A and timepoint B samples from each patient, we observed dynamic changes of VAF after the first treatment cycle, reflecting the change of both cfDNA level in blood and tumor burden in patients (Supplementary Tables 3–5).
Using timepoint A samples as baseline, we calculated VAF changes (ΔVAF = VAFtimepointB – VAFtimepointA) between timepoint A and timepoint B for each variant. For each patient, an average of ΔVAF values from all variants (including somatic, synonymous, and variants of unknown significance) from the patient was calculated. Based on the data, patients with complete response are likely to have average ΔVAF < 0, while patients with progressive disease are likely to have average ΔVAF > 0. Variants from patient #67 and #81 were used as examples for complete response and progressive disease, respectively (Supplementary Tables 3 and 4), with average ΔVAF of −0.32 and 3.35.
For patients who partially respond to immunotherapy, we began with the hypothesis that it is plausible that some tumor subclones respond to immunotherapy and reduce in size, while other subclones do not respond to treatment, so they do not shrink or keep growing. In this scenario, average ΔVAF may not be an optimal predictor for patient response, as ΔVAF for each variant may decrease or increase. Therefore, we developed a machine-learning model to adjust the calculation of average ΔVAF in order to improve its prediction accuracy for patient response. We applied maximum likelihood estimation (MLE) approach to find the best weighted difference to adjust ΔVAF calculation (Adj ΔVAF = VAFtimepointB – 0.37 × VAFtimepointA). For each patient, average adjusted ΔVAF was calculated by averaging Adj ΔVAF values from all variants from the patient. Further details of the average adjusted ΔVAF calculation are described in the Supplementary Methods. Average adjusted ΔVAF was examined per patient, and the median value was then calculated for all 84 patients (median = 0.11; ≤median [low] versus >median [high]). Patients with a low average adjusted ΔVAF (≤0.11) were predicted to have clinical benefit (SD ≥6 months/CR/PR) and patients with a high average adjusted ΔVAF (>0.11) were predicted to have no benefit from immunotherapy (SD <6 months/PD). As an example, there were a total 15 variants detected from patient #69 (Supplementary Table 5). Adj ΔVAF was calculated for each of the 15 variants. Average adjusted ΔVAF was 2.25 from the 15 variants. The average adjusted ΔVAF of 2.25 is higher than the median value (0.11) from 84 patients, and patient #69 was predicted to be a non-responder of immunotherapy (and indeed the patient had progressive disease).
Patients with low cfDNA-derived average adjusted ΔVAF (≤0.11) have higher rate of clinical benefit (SD ≥6 months/CR/PR) from immunotherapy
We checked average adjusted ΔVAF distribution in 79 evaluable patients for response assessment. Average adjusted ΔVAF in patients with clinical benefit showed a range between –1.98 and 0.52, with a median value of 0.031; in contrast, average adjusted ΔVAF in patients without clinical benefit showed a range between −0.019 and 6.6, with a median value of 0.38 (P < .001) (Figure 2(a)). Checking patients with low average adjusted ΔVAF (≤0.11), there were 69.2% patients with clinical benefit (SD ≥6 months/CR/PR) as compared to 22.5% with clinical benefit (SD ≥6 months/CR/PR) among patients with high average adjusted ΔVAF (>0.11) (P value from logistic regression test <0.001) (Figure 2(b,c)). In multivariable Cox regression models, after adjusting for age, gender, cancer subtypes, treatment line, changes in blood TMB (ΔbTMB), tissue TMB, tissue MSI, and tissue PD-L1 status, low average adjusted ΔVAF remained as the most significant variable associated with patient clinical benefit (SD ≥6 months/CR/PR) (odds ratio [OR] for low versus high average adjusted ΔVAF, 9.65 [95% confidence intervals [CI]: 2.74–41.71, P value < .001]) (Table 2).
Figure 2.
Patients with low average adjusted ΔVAF (≤0.11) show higher rate of clinical benefit (SD ≥6 months/CR/PR) (N = 79). (a) Boxplot showing distribution of average adjusted ΔVAF from patients with SD ≥6 months/CR/PR (N = 36) and patients with SD <6 months or PD (N = 43). Line across the box = median, upper and lower edges of box = interquartile range of the average adjusted ΔVAF, upper and lower whisker = maximum and minimum average adjusted ΔVAF, dots = average adjusted ΔVAF from each patient. P values were computed using a t-test. (b) Bar graph showing the percentage of patients with SD ≥6 months/CR/PR versus SD <6 months/PD with average adjusted ΔVAF low (N = 39) versus average adjusted ΔVAF high (N = 40). P values were computed using logistic regression test. Patients (N = 79) with response assessment available and NGS data from timepoint A and B samples who passed sequencing quality control were included in the analysis. (c) Violin plot of average adjusted ΔVAF from patients with response assessment (N = 79). Patients with CR, PR, or SD ≥6 months are shown as green color. Patients with SD <6 months or PD are shown as red color. Patients are ordered by the average adjusted ΔVAF value (red lines) from low to high and dichotomized by the median (black dash line). Black dots represent adjusted ∆VAF.
Abbreviations: CR, complete response; NGS ,next-generation sequencing; PR, partial response; SD, stable disease; PD, progressive disease.
Table 2.
Univariate and multivariate analyses for best response after immunotherapy (N = 79)
| Status | Response (SD ≥6 m/CR/PR) |
||||||
|---|---|---|---|---|---|---|---|
| N | Response (SD ≥6 m/CR/PR) |
Univariate |
Multivariate* |
||||
| OR (95% CI) | P value | OR (95% CI) | P value | ||||
| ∆bTMB | ≤0 mutation/Mb | 56 | 29 (51.8%) | 2.46 (0.90–7.25) | 0.08 | 2.83 (0.73–12.08) | 0.08 |
| >0 mutation/Mb | 23 | 7 (30.4%) | - | ||||
| Average adjusted ΔVAF | Low (≤0.11) | 39 | 27 (69.2%) | 7.75 (2.93–22.24) | <0.001 | 9.65 (2.74–41.71) | <0.001 |
| High (>0.11) | 40 | 9 (22.5%) | - | ||||
| Age (years)** | ≤60.5 | 40 | 17 (42.5%) | 1.29 (0.53–3.15) | 0.58 | ||
| >60.5 | 39 | 19 (48.7%) | - | ||||
| Gender | Female | 32 | 14 (43.8%) | 0.88 (0.35–2.18) | 0.79 | ||
| Male | 47 | 22 (46.8%) | - | ||||
| GI cancers | YES | 28 | 13 (46.4%) | 1.06 (0.42–2.67) | 0.91 | ||
| NO | 51 | 23 (45.1%) | - | ||||
| GU cancers | YES | 12 | 2 (16.7%) | 0.19 (0.03–0.81) | 0.022 | 0.23 (0.02–1.39) | 0.039 |
| NO | 67 | 34 (50.7%) | |||||
| Treatment line | <2 | 24 | 13 (54.2%) | 1.64 (0.63–4.38) | 0.31 | ||
| ≥2 | 55 | 23 (41.8%) | |||||
| Tissue TMB high*** | Yes (baseline) | 9 | 6 (66.7%) | 0.0042 | 0.025 | ||
| No | 40 | 11 (27.5%) | 0.19 (0.03–0.85) | 0.27 (0.03–2.30) | |||
| Unknown | 30 | 19 (63.3%) | 0.86 (0.16–4.00) | 0.86 (0.04–14.19) | |||
| Tissue MSI high | Yes (baseline) | 5 | 3 (60.0%) | 0.02 | 0.58 | ||
| No | 42 | 13 (31.0%) | 0.30 (0.04–2.01) | 2.84 (0.20–47.35) | |||
| Unknown | 32 | 20 (62.5%) | 1.11 (0.13–7.66) | 2.39 (0.09–78.91) | |||
| Tissue PD-L1 positive | Yes (baseline) | 17 | 7 (41.2%) | 0.099 | 0.28 | ||
| No | 28 | 9 (32.1%) | 0.68 (0.19–2.40) | 1.28 (0.27–6.24) | |||
| Unknown | 34 | 20 (58.8%) | 2.04 (0.63–6.90) | 3.83 (0.62–26.63) | |||
Patients (N = 79) with evaluable responses and NGS data from timepoint A and B samples who passed sequencing QC were included in the analysis.
All P values ≤0.05 are listed in bold.
*Variables with P value < 0.1 in the univariate analyses were entered into the multivariate analysis.
**Median age of the 79 patients is 60.5 years.
*** Tissue TMB high was defined by the laboratory; for instance, Foundation Medicine defined tissue TMB high as ≥20 mutations per megabase.
See Supplementary Table 6 for the vendor used to assess TMB, MSI, and PD-L1.
Abbreviations: ∆bTMB, serial changes in blood TMB; GI, gastrointestinal; GU, genitourinary; MSI, microsatellite instability; NGS, next-generation sequencing; QC, quality control; TMB, tumor mutational burden; VAF, variant allele frequency.
Patients with low versus high cfDNA-derived average adjusted ΔVAF have statistically better progression-free survival and overall survival after immunotherapy
Low average adjusted ΔVAF (≤0.11) was a predictor of longer PFS (Figure 3(a) and Table 3) and OS (Figure 3b and Table 3) according to the Kaplan–Meier analysis. All treated patients (N = 84) were included in the PFS and OS analyses of the low average adjusted ΔVAF (≤0.11) versus high average adjusted ΔVAF (>0.11) (median PFS, 10.1 versus 2.25 months, P value < .001; median OS, not reached after a median follow-up of 9.75 months versus 6.1 months, P value < .001) (Figure 3). In multivariable Cox regression models, including variables such as ∆bTMB, TMB from tissue, MSI and PD-L1 status, low average adjusted ΔVAF (≤0.11) remained as the most significant variable associated with a prolonged PFS (hazard ratio [HR] for low [≤0.11)] versus high [>0.11], 0.35 [95% CI 0.19–0.64, P value < .001]) and with a prolonged OS (HR for low [≤0.11] versus high [>0.11], 0.35 [95% CI 0.19–0.63, P value < .001]) (Table 3).
Figure 3.
Patients with reduced average adjusted ΔVAF show better progression-free survival (PFS) and overall survival (OS) (N = 84). Kaplan–Meier curves displaying (a) PFS and (b) OS for patients with average adjusted ΔVAF low versus average adjusted ΔVAF high. Patients (N = 84) with timepoint A and B samples who passed NGS quality control criteria were used in the analysis. P values were calculated by the two-sided log-rank test. *Median overall survival not reached after a median follow-up of 9.75 months.
Table 3.
Univariate and multivariate analyses for progression-free survival and overall survival after immunotherapy (N = 84)
| Status | N | Progression-free survival |
Overall survival |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Univariate |
Multivariate* |
Univariate |
Multivariate* |
|||||||
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| ∆bTMB | ≤0 mutation/Mb | 59 | 0.79 (0.44–1.4) | 0.42 | 0.61 (0.27–1.35) | 0.23 | ||||
| >0 mutation/Mb | 25 | |||||||||
| Average adjusted ΔVAF | Low (≤0.11) | 42 | 0.35 (0.2–0.62) | <0.001 | 0.35 (0.19–0.64) | <0.001 | 0.11 (0.04–0.33) | <0.001 | 0.35 (0.19–0.63) | <0.001 |
| High (>0.11) | 42 | |||||||||
| Age (years)** | ≤60.5 | 43 | 1.24 (0.73–2.14) | 0.43 | 1.01 (0.46–2.21) | 0.98 | ||||
| >60.5 | 41 | |||||||||
| Gender | Female | 34 | 0.9 (0.52–1.56) | 0.7 | 0.88 (0.39–1.95) | 0.75 | ||||
| Male | 50 | |||||||||
| GI cancers | YES | 29 | 0.95 (0.55–1.66) | 0.87 | 1.04 (0.46–2.36) | 0.92 | ||||
| NO | 55 | |||||||||
| GU cancers | YES | 12 | 2.79 (1.39–5.62) | 0.0096 | 2.47 (1.16–5.25) | 0.011 | 2.06 (0.77–5.51) | 0.18 | ||
| NO | 72 | |||||||||
| Treatment line | <2 | 26 | 0.85 (0.47–1.55) | 0.6 | 0.69 (0.27–1.72) | 0.41 | ||||
| ≥2 | 58 | |||||||||
| Tissue TMB high*** | Yes (baseline) | 10 | <0.001 | <0.001 | 0.0069 | <0.001 | ||||
| No | 41 | 5.30 (1.62–17.31) | 4.36 (1.17–16.16) | 6.75 (0.90–50.74) | 4.96 (1.43–17.16) | |||||
| Unknown | 33 | 1.80 (0.52–6.24) | 2.24 (0.48–10.47) | 2.19 (0.26–18.23) | 2.17 (0.48–9.85) | |||||
| Tissue MSI high | Yes (baseline) | 5 | 0.0081 | 0.49 | 0.032 | 0.64 | ||||
| No | 44 | 2.24 (0.68–7.34) | 0.71 (0.18–2.83) | 3.29 (0.44–24.83) | 0.73 (0.2–2.69) | |||||
| Unknown | 35 | 0.93 (0.27–3.19) | 0.46 (0.09–2.47) | 1.11 (0.13–9.22) | 0.5 (0.11–2.36) | |||||
| Tissue PD-L1 positive | Yes (baseline) | 17 | 0.051 | 0.97 | 0.19 | |||||
| No | 28 | 1.23 (0.61–2.47) | 0.97 (0.46–2.06) | 2.47 (0.70–8.67) | ||||||
| Unknown | 39 | 0.58 (0.28–1.22) | 0.88 (0.32–2.40) | 1.29 (0.35–4.76) | ||||||
Patients (N = 84) with timepoint A and B samples who passed NGS quality control criteria were used in the analysis.
All P values ≤ 0.05 are listed in bold.
*Variables with P value < 0.1 in the univariate analyses were entered into the multivariate analysis.
**Median age of the 84 patients is 60.5 years.
*** Tissue TMB high was defined by the laboratory; for instance, Foundation Medicine defined tissue TMB high as ≥20 mutations per megabase.See Supplementary Table 6 for the vendor used to assess TMB, MSI, and PD-L1.
Abbreviations: ∆bTMB, serial changes in blood TMB; GI, gastrointestinal; GU, genitourinary; MSI, microsatellite instability; TMB, tumor mutational burden; VAF, variant allele frequency.
Serial changes in blood TMB (∆bTMB) were not associated with clinical outcome
Since tissue and blood-based TMB have been implicated as markers to predict response to checkpoint inhibitors,10,26 we checked whether serial changes in blood TMB (ΔbTMB = bTMBtimepointB – bTMBtimepointA) after treatment are associated with clinical outcomes. As shown in Supplementary Figure 1, there were no significant difference in the PFS and OS analyses between patients with decrease/no change (ΔbTMB ≤ 0) versus increase (ΔbTMB > 0) (N = 84) (median PFS, 6.7 versus 3.4 months, P value = .42; median OS, not reached after a median follow-up of 7 months versus 9.5 months, P value = .22). ΔbTMB was not used for multivariate analysis since P values from univariate analysis were not significant (Table 3). Similarly, baseline blood TMB (cutoff of 20 mutations/megabase) did not predict the outcome from immunotherapy.
Discussion
The introduction of immune checkpoint inhibition into the treatment arena for advanced cancers has provided significant clinical benefit and improved treatment outcomes for a subset of patients with cancer. Unfortunately, though, most patients do not respond to these agents. Various markers exist and continue to be developed in efforts to predict which tumors may be more susceptible to these drugs: PD-L1, TMB high, and MSI-high.9,10,13 Despite these efforts, there are limited markers able to predict treatment responses early in the clinical time course. Striving to fill this unmet need, we investigated serial cfDNA samples among patients with diverse cancers treated with anti-PD-1/PD-L1 therapies.
Our patients who had serial cfDNA with lower average adjusted ΔVAF reflected the change in percent cfDNA in the blood post-treatment as compared to baseline and had significantly improved clinical outcomes (SD ≥6 months/PR/CR rates, PFS, and OS) (Tables 2, 3, Figures 2 and 3). These results are consistent with prior reports suggesting that serial cfDNA measurements can provide an early readout of outcome for immunotherapy which could aid the radiographic assessment.23,25,27 Radiographic evaluation of benefit from checkpoint inhibitors can occasionally be challenging, especially in the setting of pseudoprogression, since it is difficult to impossible to determine in such patients whether the tumor will eventually respond (and hence therapy should continue) or whether it is indeed growing (and therefore therapy should be stopped).28 Our results suggest that the use of a second blood sample for cfDNA at weeks 2–3 after therapy can provide early assessment of the response. Therefore, serial cfDNA assessment may guide clinical decision that may allow patient to transition to the next line of therapy early on in case of progression.23,27
Although higher tissue TMB can predict response to immune checkpoint inhibition as shown in the current report (Tables 2 and 3) as well as in previous literature,10,29,30 and there is a pan-cancer FDA-approval for the anti-PD1 antibody pembrolizumab for patients with TMB ≥10 mutations/mb,31 serial TMB dynamics in cfDNA in our study was not predictive of clinical outcome (Tables 2, 3 and Supplementary Figure 1). This might be due to the inherent mechanism of action of immune checkpoint inhibitors. These drugs are generally believed to cause tumor apoptosis via immune-mediated cell death and caspase initiation, not via DNA alkylation, methylation, or other intranuclear/intracellular mechanisms that may lead to increases in TMB.32
There are several limitations to this study. First, the patients enrolled were heterogeneous (though this could also point to the generalizability of the results across cancers) and the sample size was moderately small. Thus, larger samples size will be required for further validation. Additionally, a variable treatment regimen with immunotherapies could be a confounder. Furthermore, the pattern of tumor DNA shedding can vary among different histologies and requires further investigation. Lastly, ideal timing of serial cfDNA analysis is not established. Future evaluation with longitudinal study and different timepoints of blood sampling is required.
In conclusion, in a prospective study, we have shown that dynamic changes in VAF from cfDNA during the first 2 ~ 3 weeks of anti-PD-1/PD-L1 therapies were predictive of clinical outcomes. Therefore, these cfDNA changes may reflect a “molecular response”23 that appears well before radiologic changes are measured. Implementation of early blood-derived liquid biopsy response markers from immunotherapy may facilitate drug development in this field.
Supplementary Material
Acknowledgments
RK is funded in part by the Joan and Irwin Jacobs Fund, and by National Cancer Institute grant P30 CA023100.
Funding Statement
This work was supported by the National Cancer Institute [P30 CA023100].
Disclosure statement
SK serves as a consultant for Foundation Medicine, receives speaker fees from Roche and research grants from ACT Genomics, Sysmex, Konica Minolta, and OmniSeq. JJA and LK have no disclosures. BL, SWC, DBF, DQ, RH, JH, PG, and HML are employed by Ambry Genetics, and NK, TI, and MK are employed by Konica Minolta, Inc., when they are working on this project. RK receives research funding from Genentech, Incyte, Merck, Serono, Pfizer, Sequenom, Foundation Medicine, Grifols, and Guardant, as well as consultant fees from Loxo, X Biotech, NeoMed, and Actuate Therapeutics; speaker fees from Roche; and an ownership interest in IDbyDNA and Curematch, Inc., and is a Board member of CureMatch and CureMetrix, Inc.
Supplementary material
Supplemental data for this article can be accessed on the publisher’s website.
References
- 1.Sharabi A, Kim SS, Kato S, Sanders PD, Patel SP, Sanghvi P, Weihe E, Kurzrock R.. Exceptional response to nivolumab and Stereotactic Body Radiation Therapy (SBRT) in neuroendocrine cervical carcinoma with high tumor mutational burden: management considerations from the center for personalized cancer therapy at UC San Diego Moores Cancer Center. Oncologist. 2017;22(6):631–9. doi: 10.1634/theoncologist.2016-0517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Cohen EEW, Soulières D, Le Tourneau C, Dinis J, Licitra L, Ahn M-J, Soria A, Machiels J-P, Mach N, Mehra R, et al. Pembrolizumab versus methotrexate, docetaxel, or cetuximab for recurrent or metastatic head-and-neck squamous cell carcinoma (KEYNOTE-040): a randomised, open-label, phase 3 study. Lancet. 2019;393(10167):156–167. doi: 10.1016/S0140-6736(18)31999-8. [DOI] [PubMed] [Google Scholar]
- 3.Rini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, Pouliot F, Alekseev B, Soulières D, Melichar B, et al. Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N Engl J Med. 2019;380(12):1116–1127. doi: 10.1056/NEJMoa1816714. [DOI] [PubMed] [Google Scholar]
- 4.Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee J-L, Fong L, Vogelzang NJ, Climent MA, Petrylak DP, Choueiri TK, et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med. 2017;376(11):1015–1026. doi: 10.1056/NEJMoa1613683. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Larkin J. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med. 2019;381(16):1535–1546. doi: 10.1056/NEJMoa1910836. [DOI] [PubMed] [Google Scholar]
- 6.Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, Chung HC, Kindler HL, Lopez-Martin JA, Miller Jr WH, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21(10):1353–1365. doi: 10.1016/S1470-2045(20)30445-9. [DOI] [PubMed] [Google Scholar]
- 7.Marcus L, Lemery SJ, Keegan P, Pazdur R. FDA approval summary: pembrolizumab for the treatment of microsatellite instability-high solid tumors. Clin Cancer Res. 2019;25(13):3753–3758. doi: 10.1158/1078-0432.CCR-18-4070. [DOI] [PubMed] [Google Scholar]
- 8.Goodman AM, Castro A, Pyke RM, Okamura R, Kato S, Riviere P, Frampton G, Sokol E, Zhang X, Ball ED, et al. MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med. 2020;12(1):45. doi: 10.1186/s13073-020-00743-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Goodman AM, Sokol ES, Frampton GM, Lippman SM, Microsatellite-Stable KR. Tumors with high mutational burden benefit from immunotherapy. Cancer Immunol Res. 2019;7(10):1570–1573. doi: 10.1158/2326-6066.CIR-19-0149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, Stephens PJ, Daniels GA, Kurzrock R. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol Cancer Ther. 2017;16(11):2598–2608. doi: 10.1158/1535-7163.MCT-17-0386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Jardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 2021;39(2): 154–173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Patel SJ, Sanjana NE, Kishton RJ, Eidizadeh A, Vodnala SK, Cam M, Gartner JJ, Jia L, Steinberg SM, Yamamoto TN, et al. Identification of essential genes for cancer immunotherapy. Nature. 2017;548(7669):537–542. doi: 10.1038/nature23477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847–856. doi: 10.1158/1535-7163.MCT-14-0983. [DOI] [PubMed] [Google Scholar]
- 14.Davis AA, Patel VG. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer. 2019;7(1):278. doi: 10.1186/s40425-019-0768-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Patel H, Okamura R, Fanta P, Patel C, Lanman RB, Raymond VM, Kato S, Kurzrock R. Clinical correlates of blood-derived circulating tumor DNA in pancreatic cancer. J Hematol Oncol. 2019;12(1):130. doi: 10.1186/s13045-019-0824-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kato S, Okamura R, Mareboina M, Lee S, Goodman A, Patel SP, Fanta PT, Schwab RB, Vu P, Raymond VM, et al. Revisiting Epidermal Growth Factor Receptor (EGFR) Amplification as a target for anti-EGFR therapy: analysis of cell-free circulating tumor DNA in Patients with advanced malignancies. JCO Precis Oncol. 2019;3:1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kato S, Krishnamurthy N, Banks KC, De P, Williams K, Williams C, Leyland-Jones B, Lippman SM, Lanman RB, Kurzrock R, et al. Utility of genomic analysis in circulating tumor DNA from patients with carcinoma of unknown primary. Cancer Res. 2017;77(16):4238–4246. doi: 10.1158/0008-5472.CAN-17-0628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Vu P, Khagi Y, Riviere P, Goodman A, Kurzrock R. Total number of alterations in liquid biopsies is an independent predictor of survival in patients with advanced cancers. JCO Precis Oncol. 2020;4. doi: 10.1200/PO.19.00204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Baumgartner JM, Raymond VM, Lanman RB, Tran L, Kelly KJ, Lowy AM, Kurzrock R. Preoperative circulating tumor DNA in patients with peritoneal carcinomatosis is an independent predictor of progression-free survival. Ann Surg Oncol. 2018;25(8):2400–2408. doi: 10.1245/s10434-018-6561-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lee Y, Park S, Kim WS, Lee JC, Jang SJ, Choi J, Choi C-M. Correlation between progression-free survival, tumor burden, and circulating tumor DNA in the initial diagnosis of advanced-stage EGFR-mutated non-small cell lung cancer. Thorac Cancer. 2018;9(9):1104–1110. doi: 10.1111/1759-7714.12793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Oxnard GR, Paweletz CP, Kuang Y, Mach SL, O’Connell A, Messineo MM, Luke JJ, Butaney M, Kirschmeier P, Jackman DM, et al. Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative next-generation genotyping of cell-free Plasma DNA. Clin Cancer Res. 2014;20(6):1698–1705. doi: 10.1158/1078-0432.CCR-13-2482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Goldberg SB, Narayan A, Kole AJ, Decker RH, Teysir J, Carriero NJ, Lee A, Nemati R, Nath SK, Mane SM, et al. Early assessment of lung cancer immunotherapy response via circulating tumor DNA. Clin Cancer Res. 2018;24(8):1872–1880. doi: 10.1158/1078-0432.CCR-17-1341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jensen TJ, Goodman AM, Kato S, Ellison CK, Daniels GA, Kim L, Nakashe P, McCarthy E, Mazloom AR, McLennan G, et al. Genome-wide sequencing of cell-free DNA identifies copy-number alterations that can be used for monitoring response to immunotherapy in cancer patients. Mol Cancer Ther. 2019;18(2):448–458. doi: 10.1158/1535-7163.MCT-18-0535. [DOI] [PubMed] [Google Scholar]
- 24.Farwell Hagman KD, Shinde DN, Mroske C, Smith E, Radtke K, Shahmirzadi L, El-Khechen D, Powis Z, Chao EC, Alcaraz WA, et al. Candidate-gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases. Genet Med. 2017;19(2):224–235. doi: 10.1038/gim.2016.95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhang Q, Luo J, Wu S, Si H, Gao C, Xu W, Abdullah SE, Higgs BW, Dennis PA, van der Heijden MS, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov. 2020;10(12):1842–1853. doi: 10.1158/2159-8290.CD-20-0047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Khagi Y, Goodman AM, Daniels GA, Patel SP, Sacco AG, Randall JM, Bazhenova LA, Kurzrock1 R. Hypermutated circulating tumor DNA: correlation with response to checkpoint inhibitor-based immunotherapy. Clin Cancer Res. 2017;23(19):5729–5736. doi: 10.1158/1078-0432.CCR-17-1439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Husain H, Melnikova VO, Kosco K, Woodward B, More S, Pingle SC, Weihe E, Park BH, Tewari M, Erlander MG, et al. Monitoring daily dynamics of early tumor response to targeted therapy by detecting circulating tumor DNA in Urine. Clin Cancer Res. 2017;23(16):4716–4723. doi: 10.1158/1078-0432.CCR-17-0454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wang GX, Kurra V, Gainor JF, Sullivan RJ, Flaherty KT, Lee SI, Fintelmann FJ. Immune checkpoint inhibitor cancer therapy: spectrum of imaging findings. Radiographics. 2017;37(7):2132–2144. doi: 10.1148/rg.2017170085. [DOI] [PubMed] [Google Scholar]
- 29.Klempner SJ, Fabrizio D, Bane S, Reinhart M, Peoples T, Ali SM, Sokol ES, Frampton G, Schrock AB, Anhorn R, et al. Tumor mutational burden as a predictive biomarker for response to immune checkpoint inhibitors: a review of current evidence. Oncologist. 2020;25(1):e147–e159. doi: 10.1634/theoncologist.2019-0244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Samstein RM, Lee C-H, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(2):202–206. doi: 10.1038/s41588-018-0312-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Subbiah V, Solit DB, Chan TA, Kurzrock R. The FDA approval of pembrolizumab for adult and pediatric patients with tumor mutational burden (TMB) >/=10: a decision centered on empowering patients and their physicians. Ann Oncol. 2020;31(9):1115–1118. doi: 10.1016/j.annonc.2020.07.002. [DOI] [PubMed] [Google Scholar]
- 32.Klempner SJ, Hendifar A, Waters KM, Nissen N, Vail E, Tuli R, Mita A. Exploiting temozolomide-induced hypermutation with pembrolizumab in a refractory high-grade neuroendocrine neoplasm: a proof-of-concept case. JCO Precis Oncol. 2020;4(4):614–619. doi: 10.1200/PO.20.00034. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.