Abstract
Background
The composition of gut microbiota affects antitumor immune responses, preclinical and clinical outcome following immune checkpoint inhibitors (ICI) in cancer. Antibiotics (ATB) alter gut microbiota diversity and composition leading to dysbiosis, which may affect effectiveness of ICI.
Patients and methods
We examined patients with advanced renal cell carcinoma (RCC) and non-small-cell lung cancer (NSCLC) treated with anti-programmed cell death ligand-1 mAb monotherapy or combination at two academic institutions. Those receiving ATB within 30 days of beginning ICI were compared with those who did not. Objective response, progression-free survival (PFS) determined by RECIST1.1 and overall survival (OS) were assessed.
Results
Sixteen of 121 (13%) RCC patients and 48 of 239 (20%) NSCLC patients received ATB. The most common ATB were β-lactam or quinolones for pneumonia or urinary tract infections. In RCC patients, ATB compared with no ATB was associated with increased risk of primary progressive disease (PD) (75% versus 22%, P < 0.01), shorter PFS [median 1.9 versus 7.4 months, hazard ratio (HR) 3.1, 95% confidence interval (CI) 1.4–6.9, P < 0.01], and shorter OS (median 17.3 versus 30.6 months, HR 3.5, 95% CI 1.1–10.8, P = 0.03). In NSCLC patients, ATB was associated with similar rates of primary PD (52% versus 43%, P = 0.26) but decreased PFS (median 1.9 versus 3.8 months, HR 1.5, 95% CI 1.0–2.2, P = 0.03) and OS (median 7.9 versus 24.6 months, HR 4.4, 95% CI 2.6–7.7, P < 0.01). In multivariate analyses, the impact of ATB remained significant for PFS in RCC and for OS in NSCLC.
Conclusion
ATB were associated with reduced clinical benefit from ICI in RCC and NSCLC. Modulatation of ATB-related dysbiosis and gut microbiota composition may be a strategy to improve clinical outcomes with ICI.
Keywords: antibiotics, immune checkpoint inhibitors, non-small-cell lung cancer, renal cell carcinoma, microbiota
Key Message
Antibiotics within 30 days before the first dose of anti-PD-(L)1 mAb were associated with decreased clinical outcomes to ICB in patients with advanced renal cell carcinoma and non-small-cell lung cancer cancers, even after multivariate analyses adjusted for known risk factors in each tumor type.
Introduction
Immune checkpoint inhibitors (ICIs) targeting the programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) axis have changed the therapeutic landscape in both renal cell carcinoma (RCC) and non-small-cell lung cancer (NSCLC). In RCC, higher objective response rate and longer overall survival (OS) were seen with nivolumab (anti-PD-1 mAb) compared with everolimus [1] and represents a therapeutic option in relapsed RCC. The robust clinical effect of anti-PD-1 mAb was observed regarless of PD-L1 expression and standard prognostic factors [2]. In NSCLC, anti-PD-(L)1 mAb improved response rates and survival in a subset of NSCLC patients [3–5] in both first line or second line but few clinical features reliably predict benefit.
Primary resistance to ICI is common in patients with both RCC and NSCLC, ranging from 35% to 44% and remains unpredictable [1, 3]. Much recent effort has focused on biomarkers of response to immunotherapy (e.g. PD-L1 [6], mutation burden [5], gene expression signatures of inflammation [7, 8]) but the identification of more reliable predictors associated with resistance (primary or acquired) is also critical to instruct new strategies to improve precision and broaden responder groups.
Building on recent data [9, 10], we hypothesized that the modulation of gut microbiota by antibiotics (ATB) may be associated with resistance to anti-PD-1 mAb. The intestinal microbiota represents a complex ecosystem essential for maintaining gut homeostasis and prevent systemic inflammation [11, 12]. The interactions between the host and micro-organisms have been identified as a complex, inter-connected network where certain microbes tailor local and systemic immune system [13]. ATB are known to affect gut microbiota, including loss of distinct species (poor diversity), favoring expansion of others, consequently shifting the metabolic capacity [14]. ATB-induced dysbiosis has been associated with a variety of chronic inflammatory disorders [15–17]. In oncology, converging findings demonstrate the negative impact of ATB-induced dysbiosis in mice. We built upon this experience to expand the series of patients examined using patients with advanced RCC and NSCLC treated with anti-PD-(L)1 therapy at two different centers to evaluate the impact of ATB on resistance.
Methods
Patients
Patients with advanced RCC (n = 121) at Gustave Roussy and NSCLC (n = 239) at Memorial Sloan Kettering Cancer Center treated with ICI were identified. NSCLC patients and half of RCC patients have been previously reported [10] but have been fully reanalyzed for multivariate analysis. All patients with RCC received anti-PD-1 or anti-PD-L1 (PD-(L)1) mAb alone or in combination with anti-CTLA-4 mAb or bevacizumab on clinical trials. Patients with NSCLC received anti-PD-(L)1 mAb alone or in combination with anti-CTLA-4 mAb.
Patient records were reviewed to determine any oral or intravenous ATB use within the 30 or 60 days before the start of anti-PD-(L)1 therapy. The class of antibiotic, indication, route of administration and duration were collected. Clinicopathologic characteristics were collected for all patients. In RCC patients, additional features included cumulative size of tumor burden (<10 cm versus ≥ 10 cm) [18] and site of metastases. In NSCLC patients, smoking status and PD-L1 expression (high defined as ≥50% expression or low/no expression) were also collected if available.
For the evaluation of tumor response, CT scans were reviewed by local specialized radiologists (JAR at GR; DH, NL, AJP at MSKCC) and response was determined by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [19]. All patients were followed-up until death or data lock (September 2017 for RCC and March 2017 for NSCLC).
Statistical analysis
Patient characteristics were described according to the status of ATB (ATB versus no ATB) and compared using Fisher or chi-squared test for categorical data and t-test for continuous data. Two ATB therapeutic windows were analyzed, either ATB prescribed 30 days or up to 60 days before the first injection of ICI, termed ATB-30 and ATB-60, respectively.
Survival curves were estimated by the Kaplan–Meier method and compared with the Log-rank test (univariate analysis). Univariate hazard ratios (HRs) were calculated using log-rank method. Multivariable Cox regression model was used to determine HRs and 95% confidence intervals (CIs) for progression-free survival (PFS) and OS between ATB and no ATB, adjusting for other clinicopathologic features. Statistical tests were two-sided, and a P-value <0.05 was considered statistically significant. Statistical analyses were carried out using SPSS.
Results
One hundred and twenty-one patients with RCC and 239 patients with NSCLC were examined in independent cohorts from two academic cancer centers. The frequency of ATB use within 30 days of starting PD-(L)1 therapy was relatively similar in NSCLC (20%) and RCC (13%) patients. β-Lactams ± inhibitors were the most commonly administered ATB in both groups (82% of RCC and 32% of NSCLC) (supplementary Table S1, available at Annals of Oncology online). Quinolones and sulfonamides were also frequently used in NSCLC cohorts.
Among RCC patients, 106 of 121 (88%) received PD-(L)1 monotherapy, 10 (8%) PD-(L)1 plus CTLA-4 mAbs and 5 (4%) PD-(L)1 mAbs plus bevacizumab. All RCC patients were treated in clinical trials. Clinicopathologic characteristics were typical of patients with advanced RCC and were generally well balanced between those who received ATB or not, with the exception of tumor burden (larger tumors in the ATB group) and lines of prior therapy (2+ prior lines more common in the ATB group) (Table 1A). No patients with RCC were hospitalized within 30 days of starting PD-(L)1 therapy.
Table 1A.
Baseline characteristics of renal cell carcinoma (RCC) cohort
| Characteristics | Total (n = 121) | ATB 30-0 (n = 16) | No ATB (n = 105) | P-value | |
|---|---|---|---|---|---|
| Age, years | Median | 61 | 61 | 61 | |
| Range | 28–83 | 29–83 | 30–82 | ||
| Age, years, n (%) | <65 | 79 (65) | 8 (50) | 69 (66) | 0.22 |
| ≥65 | 42 (35) | 8 (50) | 36 (34) | ||
| Gender, n (%) | Male | 80 (66) | 9 (56) | 71 (68) | 0.37 |
| Female | 41 (34) | 7 (44) | 34 (32) | ||
| Nephrectomy, n (%) | Yes | 103 (85) | 13 (82) | 90 (86) | 0.64 |
| No | 18 (15) | 3 (18) | 15 (14) | ||
| Histology, n (%) | Clear cell | 115 (95) | 14 (88) | 101 (96) | 0.13 |
| Nonclear cell | 6 (5) | 2 (12) | 4 (4) | ||
| IMDC risk group, n (%) | Good | 25 (21) | 3 (19) | 22 (21) | 0.96 |
| Intermediate | 72 (59) | 10 (62) | 62 (59) | ||
| Poor | 24 (20) | 3 (19) | 21 (20) | ||
| Tumor burden (mm), n (%) | <100 | 94 (78) | 9 (56) | 85 (81) | 0.03 |
| ≥100 | 27 (22) | 7 (44) | 20 (19) | ||
| Site of metastasis, n (%) | Lung Liver Bone Brain | 84 (69) | 8 (50) | 76 (72) | 0.09 |
| 34 (28) | 8 (50) | 26 (23) | |||
| 33 (27) | 5 (31) | 28 (27) | |||
| 14 (12) | 0 (0) | 14 (13) | |||
| Number of prior lines, n (%) | 0–1 | 69 (57) | 4 (25) | 65 (62) | <0.01 |
| ≥2 | 52 (43) | 12 (75) | 40 (38) | ||
| Treatment, n (%) |
|
106 (88) | 14 (88) | 92 (88) | 0.86 |
| 10 (8) | 1 (6) | 9 (8) | |||
| 5 (4) | 1 (6) | 4 (4) | |||
| ATB, n (%)a | Prophylaxis Therapy | 0 | 0 | 0 | |
| 16 (13) | 16 (100) | 0 | |||
List of ATB and indication for prophylaxis and therapy are available in supplementary Table S1, available at Annals of Oncology online.
ATB, antibiotics; IMDC, International Metastatic Renal Cell Carcinoma Database Consortium (includes: Karnofsky performance status, time from diagnosis to treatment, hemoglobin, serum calcium concentration, neutrophil and platelet counts); α, anti; PD-(L)1, PD-1 or PD-L1; mAb, monoclonal antibody; Bev, bevacizumab.
Bold values reflect statistical significant; *P<0.05, **P<0.01.
Among NSCLC patients, 48 (20%) patients received ATB within 30 days of PD-(L)1 therapy. Two hundred and five (86%) received PD-(L)1 monotherapy and 34 (14%) received PD-(L)1 plus CTLA-4 mAbs. Fifty-four (23%) were enrolled in clinical trials. Clinicopathologic features were also typical of patients with advanced NSCLC, well balanced between the ATB versus no ATB groups with the exception of recent hospitalization (more common in the ATB group) and more patients included in the clinical trials in the no ATB group (Table 1B).
Table 1B.
Baseline characteristics of non-small-cell lung cancer (NSCLC) cohort
| Characteristics | Total (n = 239) | ATB 30-0 (n = 48) | No ATB (n = 191) | P-value | |
|---|---|---|---|---|---|
| Age, years | Median | 66 | 63 | 66 | |
| Range | (22–92) | (31–92) | (22–88) | ||
| Age, years, n (%) | <65 | 110 (46) | 27 (56) | 83 (43) | 0.12 |
| ≥65 | 129 (54) | 21 (44) | 108 (57) | ||
| Gender, n (%) | Male | 118 (49) | 24 (50) | 94 (49) | 0.92 |
| Female | 121 (51) | 24 (50) | 97 (51) | ||
| ECOG performance status, n (%) | 0 | 53 (22) | 5 (11) | 48 (25) | 0.05 |
| 1 | 183 (77) | 43 (89) | 140 (74) | ||
| 2 | 3 (1) | 0 | 3 (1) | ||
| Histology, n (%) | Squamous | 34 (14) | 6 (13) | 28 (15) | 0.71 |
| Nonsquamous | 205 (86) | 42 (88) | 163 (85) | ||
| Smoking status, n (%) | Smoker | 193 (81) | 37 (77) | 156 (82) | 0.47 |
| Nonsmoker | 46 (19) | 11 (23) | 35 (18) | ||
| Number of prior lines, n (%) | <3 | 178 (74) | 33 (69) | 145 (76) | 0.31 |
| ≥3 | 61 (26) | 15 (31) | 46 (24) | ||
| Hospitalization <30 days, n (%) | Yes | 20 (8) | 16 (33) | 4 (2) | <0.01 |
| No | 219 (93) | 32 (67) | 187 (98) | ||
| Treatment, n (%) | α-PD-(L)1 mAb | 205 (86) | 45 (94) | 160 (84) | 0.08 |
| α-PD-(L)1 mAb + α-CTLA-4 | 34 (14) | 3 (6) | 31 (16) | ||
| Clinical trial, n (%) | Yes | 54 (23) | 5 (11) | 49 (25) | 0.02 |
| No | 185 (77) | 43 (89) | 142 (74) | ||
| PD-L1 expression, n (%) | High (≥50%) | 21 (9) | 6 (12) | 15 (8) | 0.12 |
| Low (<50%) | 64 (27) | 9 (19) | 55 (29) | ||
| Unknown | 154 (64) | 33 (69) | 121 (63) | ||
| ATB, n (%)a | Prophylaxis | 15 (6) | 15 (31) | 0 | |
| Therapy | 33 (14) | 33 (69) | 0 | ||
List of ATB and indication for prophylaxis and therapy are available in supplemental Table S1, available at Annals of Oncology online.
ECOG, Eastern Cooperative Oncology Group.
Bold values reflect statistical significant; *P<0.05, **P<0.01.
In patients with RCC, recent ATB was associated with increased rate of primary progressive disease (PD) (75% versus 22%, P < 0.01) (Figure 1A). PFS and OS were also shorter in those patients with ATB than in those with no ATB (median PFS, 1.9 months versus 7.4 months, HR 3.1, 95% CI 1.4–6.9, P < 0.01; median OS, 17.3 months versus 30.6 months, HR 3.5, 95% CI 1.1–10.8, P = 0.03) (Figure 1B and C).
Figure 1.
Best overall response (A), progression-free survival (PFS) (B), and overall survival (OS) (C) in patients with RCC treated with ICI, stratified by use of ATB within 30 days of initiating ICI. Best overall response (D), PFS (E), and OS (F) in patients with NSCLC treated with ICI, stratified by use of ATB within 30 days of initiating ICI. P-values calculated with chi-squared and log-rank tests.
In patients with NSCLC, recent ATB was not associated with an increased rate of primary PD (52% versus 43%, P = 0.26) (Figure 1D). Similar to RCC, however, PFS and OS were significantly shorter in NSCLC patients with ATB than in those with no ATB (median PFS, 1.9 months versus 3.8 months, HR 1.5, 95% CI 1.0–2.2, P = 0.03; median OS, 7.9 months versus 24.6 months, HR 4.4, 95% CI 2.6–7.7, P < 0.01) (Figure 1E and F).
To assess the robustness of the observation that recent ATB was associated with decreased benefit with ICI, we also examined the effect of ATB within 60 days of starting therapy [ATB-60 days, n = 22 for RCC (18%); n = 68 for NSCLC (28%)] (supplementary Tables S2 and S3, available at Annals of Oncology online). Taking into account that gut dysbiosis from ATB can take 1–3 months to normalize [20], we hypothesized that a trend for similar results would be seen with the extended timeline. In RCC, ATB-60 days remained associated with increased risk of primary progression (P < 0.01) (supplementary Figure S1A, available at Annals of Oncology online), shorter PFS (median PFS, 3.1 months versus 7.4 months, HR 2.3, 95% CI 1.2–4.4, P < 0.01) (supplementary Figure S1B, available at Annals of Oncology online) and trended toward worse OS (median OS, 23.4 months versus 30 months, HR 1.9, 95% CI 0.8–4.7, P = 0.15) (supplementary Figure S1C, available at Annals of Oncology online). In NSCLC, ATB-60 days compared with no ATB group was not different in terms of objective response or PFS (supplementary Figure S1D and E, available at Annals of Oncology online), but remained significantly associated with shorter OS (median OS, 9.8 months versus 21.9 months, HR 2.0, 95% CI 1.3–3.2, P < 0.01) (supplementary Figure S1F, available at Annals of Oncology online).
We next examined the impact of ATB on PFS and OS within individual subgroups of patients. As noted, there were a few variables in both RCC and NSCLC cohorts that were more common in the respective ATB groups. In both RCC and NSCLC cohorts, although not statistically significant due to small numbers, ATB group was generally associated with worse PFS and OS within nearly every subgroup examined (Figure 2).
Figure 2.
Subgroup analyses of independent prognostic factors for PFS (A and B) and OS (C and D) stratification in RCC and NSCLC, respectively. P-value for interaction calculated with Cox proportional hazards model.
Finally, we carried out a multivariate analysis of the effect of ATB administration, taking into account classical prognostic factors relevant to RCC and NSCLC, respectively. In RCC, both ATB and tumor burden were significantly associated with worse PFS and OS as univariate (Tables 2A and 2B), and were analyzed in multivariate modeling. ATB and tumor burden remained independently associated with worse PFS in multivariate analysis (HR for ATB 2.0, P < 0.01), while neither variable remained statistically significant for OS (HR for ATB 2.1, P = 0.11). In NSCLC, ATB and several other clinicopathologic variables were associated with PFS or OS as univariate and further evaluated in multivariate model (Tables 3A and 3B). In the Cox regression analysis, ATB were not significantly associated with worse PFS (HR 1.3, 95% CI 0.9–1.8, P = 0.17) but remained significantly associated with OS (HR 2.5, 95% CI 1.6–3.7, P < 0.01).
Table 2A.
Uni- and multivariate analyses for progression-free survival (PFS) in renal cell carcinoma cohort
| Univariate analysis |
Multivariate analysis |
|||
|---|---|---|---|---|
| Prognostic factor | PFS | P-value | PFS | P-value |
| HR (95% CI) | HR (95% CI) | |||
| ATB 30-0/No ATB | 2.3 (1.3–4.0) | <0.01 | 2.2 (1.3–3.3) | <0.01 |
| Age | 1.2 (0.8–1.8) | 0.43 | – | – |
| ≥65 years/<65 years | ||||
| IMDC risk group | 1.0 (0.6–1.7) | 0.92 | – | – |
| Intermediate/good | ||||
| Poor/good | 1.7 (0.9–3.2) | 0.11 | ||
| Tumor burden | 2.1 (1.3–3.3 ) | <0.01 | 2.0 (1.2–3.2) | <0.01 |
HR, hazard ratio; CI, confidence interval.
Bold values reflect statistical significant; *P<0.05, **P<0.01.
Table 2B.
Uni- and multivariate analyses for overall survival (OS) in renal cell carcinoma cohort
| Univariate analysis |
Multivariate analysis |
|||
|---|---|---|---|---|
| Prognostic factor | OS | P-value | OS | P-value |
| HR (95% CI) | HR (95% CI) | |||
| ATB 30-0/No ATB | 2.4 (1.1–5.7) | 0.04 | 2.1 (0.9–5.0) | 0.11 |
| Age | 0.8 (0.4–1.5) | 0.43 | – | – |
| ≥ 65 years/< 65 years | ||||
| IMDC risk group | 0.7 (0.3–1.7) | 0.45 | 2.1 (0.8–5.3) | 0.12 |
| Intermediate/good | ||||
| 2.4 (1.0–6.0) | 0.06 | |||
| Poor/good | ||||
| Tumor burden | 2.4 (1.2–4.6) | <0.01 | 1.8 (0.9–3.6) | 0.09 |
Bold values reflect statistical significant; *P<0.05, **P<0.01.
Table 3A.
Uni- and multivariate analyses for PFS in non-small-cell lung cancer cohort
| Univariate analysis |
Multivariate analysis |
|||
|---|---|---|---|---|
| Prognostic factor | PFS | P-value | PFS | P-value |
| HR (95% CI) | HR (95% CI) | |||
| ATB 30-0/No ATB | 1.4 (1.0– 2.0) | 0.04 | 1.3 (0.9–1.8) | 0.17 |
| Age | 1.2 (0.9–1.6) | 0.2 | – | – |
| ≥ 65 years/<65 years | ||||
| Histology | 1.1 (0.8–1.5) | 0.92 | – | – |
| Squamous/nonsquamous | ||||
| Smoking status | 0.7 (0.5–1.0) | 0.04 | 0.7 (0.5–1.0) | 0.03 |
| Smoker/nonsmoker | ||||
| Number of prior regimens | 1.4 (1.0–1.9) | 0.05 | 1.2 (0.8–1.6) | 0.40 |
| ≥3/<3 | ||||
| ECOG performance status | 1.7 (1.2–2.4) | <0.01 | 1.5 (1.0–2.2) | 0.03 |
| 0 versus 1 | ||||
| Clinical trial | 0.7 (0.5–1.0) | 0.02 | 0.8 (0.6–1.1) | 0.19 |
| Yes versus no | ||||
| Hospitalization | 1.2 (0.8–1.7) | 0.45 | – | – |
| Yes versus no | ||||
ECOG, Eastern Cooperative Oncology Group.
Bold values reflect statistical significant; *P<0.05, **P<0.01.
Table 3B.
Uni- and multivariate analyses for OS in non-small-cell lung cancer cohort
| Univariate analysis |
Multivariate analysis |
|||
|---|---|---|---|---|
| Prognostic factor | OS | P-value | OS | P-value |
| HR (95% CI) | HR (95% CI) | |||
| ATB 30-0/No ATB | 2.9 (1.9–4.4) | <0.01 | 2.5 (1.6–3.7) | <0.01 |
| Age | 1.3 (0.9–1.9) | 0.23 | – | – |
| ≥65 years/<65 years | ||||
| Histology | 1.4 (0.8–1.6) | 0.57 | – | – |
| Squamous/nonsquamous | ||||
| Smoking status | 1.2 (0.7–1.9) | 0.55 | – | – |
| Smoker/nonsmoker | ||||
| Number of prior regimens | 1.9 (1.3–2.9) | <0.01 | 1.6 (1.1–2.4) | 0.02 |
| ≥3/<3 | ||||
| ECOG performance status | 3.6 (1.9–6.5) | <0.01 | 2.6 (1.4–4.9) | <0.01 |
| 0 versus 1 | ||||
| Clinical trial | 0.4 (0.2–0.7) | <0.01 | 0.6 (0.3–1.0) | 0.06 |
| Yes versus no | ||||
| Hospitalization | 1.1 (0.6–1.9) | 0.76 | – | – |
| Yes versus no | ||||
ECOG, Eastern Cooperative Oncology Group.
Bold values reflect statistical significant; *P<0.05, **P<0.01.
Discussion
This study reports the substantial rate of ATB use (13%–20%) proximal to initiating ICI and the potential clinical impact on benefit from ICI. In independent cohorts of RCC and NSCLC followed-up in two cancer centers, prior ATB was associated with worse outcomes with ICI.
More precisely, patients in ATB group treated with ICI had a higher rate of PD in RCC and a significant reduction in PFS and OS compared with those with no ATB in both cancer types. ATB appears to be a determinant of poor prognosis in the context of ICI independently of classical prognostic markers and within individual patient subgroups. This paper builds upon our prior publication [10] on the negative impact of ATB, which included 249 patients diagnosed with advanced NSCLC (n = 140), RCC (n = 67) and urothelial cancer (n = 42) treated with anti-PD-1/PD-L1 mAb. In that report ATB use was defined as 60 days before and 30 days after the first injection of ICI, and ATB used was associated with decreased PFS from 3.5 to 4.1 months and OS from 11.5 to 20.6 months compared with the no ATB group [10].
In this expanding field, ATB are recognized to be able to shift the microbiota composition temporally. ATB dysbiosis is associated with a decrease in microbiota diversity and impacts on taxonomic richness [21]. Another level of complexity exists in the perturbation of microbiome according to the class, the duration, the route of ATB administration and the presence of resistant commensals which play a key role in microbiota recolonization and recomposition. 16S ribosomal RNA sequencing of gene amplicons of patients’ feces revealed that microbiota returns to its baseline within 1–3 months after ATB discontinuation. However, some bacteria may take years to fully recover [14, 20].
To ascertain the difference in ATB timing, we carried out a sub-group analysis for patients receiving ATB 60 days before starting ICI. Interestingly, the impact of ATB 60 days before was not as potent as within the first 30 days before ICI. Partial microbiota recovery following ATB might explain the difference in clinical outcomes observed.
Very recent reports highlighted that the composition of gut microbiota may dictate, at least in part, the anticancer activity of ICI. In three different cancer types, patients with higher diversity or richness of the stool sample at baseline correlated with a better clinical outcome at 6 months. In metastatic melanoma, patients with ‘favorable’ microbiota composition were enriched in Ruminococcaceae family members and Bifidobacterium spp. This favorable microbiota translated into best overall response rate at 6 months [22, 23]. The third study in patients with NSCLC and RCC amenable to anti-PD-1 mAb showed that Akkermansia muciniphila (accompanied with Ruminococcacae) was significantly associated to more favorable outcomes. In this paper, the changes in the microbiota composition due to ATB uptake were not addressed. Authors have also characterized other microbiota sites including saliva in cancer patients; however, in MM [23] and head and neck squamous-cell carcinoma [24], there was no correlation with ICI activity.
In addition, the microbiota composition could account for uncoupling efficacy from toxicity in metastatic melanoma patients treated with anti-CTLA-4 [25, 26]. Baseline fecal samples with high bacterial diversity enriched with Bacteroidetes phylum and depleted with Firmicutes correlated with the absence of immune colitis. Of note, in the present study, the frequency of immune-related adverse effects was too small (data not shown) to evaluate the association between ATB and adverse events.
Our study has several limitations. The first one is the retrospective component of the data entry from two institutions, although patients’ clinical responses were standardized and reviewed objectively using the RECIST 1.1 criteria in both centers, the timing of scan acquisition was not prespecified. Furthermore, we acknowledge some notable differences in patient characteristics, including a higher rate of recent hospitalization in the NSCLC patients treated with ATB. Recent hospitalization was not associated with worse PFS or OS as a univariate. In RCC even in the current uncertainty of PD-1 efficacy in naive versus previously treated patients [1, 27–29], we reported a difference in line of therapy in both groups. To acknowledge the encountered differences in patient characteristics, we examined the impact of ATB within individual subgroups and found overall consistency of the effect of ATB. We also conducted multivariable analysis to adjust for significant prognostic factors identified in the univariate analysis, which still showed a negative impact of ATB. Secondly, the analysis did not take into consideration additional factors with a potential impact on the microbiota composition such as diet, country of origin, or other medications. Thirdly, we did not characterize the mechanism by which ATB exert a detrimental effect on clinical outcomes. We speculate that ATB-related dysbiosis decreases the diversity, shifts the microbiota and probably eradicates the most immunogenic bacteria required to engaged the immune system unleashed by PD-(L)1 blockade. Whether antibiotherapy reflects a general prognostic association or is causalitively linked with resistance to ICI remains a matter of debate. It is noteworthy that ATB-based conditionning of tumor bearing mice blunts the efficacy of PD-1 or PD-1 + CTLA-4 blockade in otherwise ‘normal’ animals, suggesting a causal link between ATB and primary resistance to ICI and immunogenic chemotherapy [9, 10, 30, 31]. Finally, the lack of PD-L1 status characterization in most of the patients (outside of some NSCLC) leaves open the possibility that tumor-intrinsic factors could influence these results.
Altogether, these results confirm that ATB-associated dysbiosis might be deleterious in patients treated with ICI, suggesting that an intact gut microbiota is needed to mobilize the immune system regardless of the tumor site. Efforts to improve antibiotic stewardship are already ongoing to prevent the emergence of multidrug resistant organisms [32], which can be particularly dangerous for cancer patients [33, 34]. The data in this report may add additional incentive to avoid unnecessary ATB. More studies are warranted to confirm the deleterious effect of ATB in large prospective trials and to develop novel diagnostic tools based on gut microbiota in cancer patients, to predict response/resistance to ICI and identified the key microbiota signatures for each tumor site. The discovery of bacteria capable of shifting an unfavorable ATB-associated dysbiosis to a favorable microbiota will help build a future therapeutic concept, whereby modulation of gut microbiota by bacteria could increase ICI clinical activity.
Funding
Gustave Roussy Fondation Philanthropia (no grant number applies) to LD and BR; RK Smiley Canadian Hematology Society 2017 (National Canadian grant) (no grant number applies).
Disclosure
The authors have declared no conflicts of interest.
Supplementary Material
References
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