LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade

Ronglai Shen; Michael A Postow; Matthew Adamow; Arshi Arora; Margaret Hannum; Colleen Maher; Phillip Wong; Michael A Curran; Travis J Hollmann; Liwei Jia; Hikmat Al-Ahmadie; Niamh Keegan; Samuel A Funt; Gopa Iyer; Jonathan E Rosenberg; Dean F Bajorin; Paul B Chapman; Alexander N Shoushtari; Allison S Betof; Parisa Momtaz; Taha Merghoub; Jedd D Wolchok; Katherine S Panageas; Margaret K Callahan

doi:10.1126/scitranslmed.abf5107

. Author manuscript; available in PMC: 2022 Aug 25.

Published in final edited form as: Sci Transl Med. 2021 Aug 25;13(608):eabf5107. doi: 10.1126/scitranslmed.abf5107

LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade.

Ronglai Shen ^1,^†, Michael A Postow ^2,^3,^†, Matthew Adamow ^4,^5,^†, Arshi Arora ^1,^†, Margaret Hannum ¹, Colleen Maher ^2,⁵, Phillip Wong ^4,⁵, Michael A Curran ⁶, Travis J Hollmann ^5,⁷, Liwei Jia ^7,⁸, Hikmat Al-Ahmadie ⁷, Niamh Keegan ², Samuel A Funt ^2,³, Gopa Iyer ^2,³, Jonathan E Rosenberg ^2,³, Dean F Bajorin ^2,³, Paul B Chapman ^2,³, Alexander N Shoushtari ^2,³, Allison S Betof ^2,³, Parisa Momtaz ^2,³, Taha Merghoub ^2,^3,^5,^9,¹⁰, Jedd D Wolchok ^2,^3,^5,^9,¹⁰, Katherine S Panageas ^1,^†, Margaret K Callahan ^2,^3,^5,^†,^*

¹Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

²Department of Medicine, Memorial Sloan Kettering Cancer Center New York, NY, USA

³Weill Cornell Medical College, New York, NY, USA

⁴Immune Monitoring Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

⁵Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

⁶Department of Immunology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA

⁷Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

⁸University of Texas at Southwestern Medical Center, Dallas, TX, USA

⁹Swim Across America/Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA

¹⁰Human Oncology Pathogenesis Program, Sloan Kettering Institute, New York, NY, USA

Author contributions:

R.S. – Development of initial concept, oversight of all data generation and analysis and all aspects of manuscript development and editing to produce a final manuscript.

M.P - Contributed to development and refinement of initial concept and to clinical data acquisition and analysis and to manuscript development.

M.A. – Data generation and analysis (flow cytometry). Reviewed and approved the final manuscript.

A.A. - Data analysis/bioinformatics. Reviewed and approved the final manuscript.

Margaret Hannum, MS- Data analysis/bioinformatics. Reviewed and approved the final manuscript.

C.M.- Data extraction and analysis (clinical data). Reviewed and approved the final manuscript.

P.W.- Data generation and analysis (flow cytometry).

M.A.C. – Contributed to development of concept. Reviewed, edited and approved the final manuscript.

T.H.- Data generation and analysis (pathology). Reviewed and approved the final manuscript.

L.J. - Data generation and analysis (pathology). Reviewed and approved the final manuscript.

H.A. - Data generation and analysis (pathology). Reviewed and approved the final manuscript.

N.K. - Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

S.F.- Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

G.I.- Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

J.R.- Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

D. B. - Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

P.C. - Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

A. S. - Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

A. B.- Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

P.M. - Contributed to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

T. M.- Contributed to development of concept. Reviewed, edited and approved the final manuscript.

J.W. - Contributed to the initial concept and to acquisition and analysis of clinical data. Reviewed, edited and approved the final manuscript.

K.P. - Development of initial concept, oversight of all data generation and analysis and all aspects of manuscript development and editing to produce a final manuscript.

M.K.C. - Development of initial concept, oversight of all data generation and analysis, wrote the first draft and edited all subsequent drafts to develop the final manuscript.

^†

These authors contributed equally

To whom correspondence should be addressed: Margaret K. Callahan, MD, PhD, callaham@mskcc.org

PMCID: PMC9254663 NIHMSID: NIHMS1801836 PMID: 34433638

Abstract

Immune checkpoint blocking (ICB) antibodies are a cornerstone in cancer treatment, however they benefit only a subset of patients and biomarkers to guide treatment choices are lacking. We designed this study to identify blood-based correlates of clinical outcome in ICB-treated patients. We performed immune profiling of 188 ICB-treated melanoma patients using multiparametric flow cytometry to characterize immune cells in patients’ pre-treatment peripheral blood. A novel supervised statistical learning approach was applied to a discovery cohort to classify phenotypes and determine their association with survival and treatment response. We identified 3 distinct immune phenotypes (immunotypes), defined in part by the presence of a LAG-3+CD8+ T-cell population. Melanoma patients with a LAG+ immunotype had poorer outcomes after ICB with a median survival of 22.2 months compared to 75.8 months for those with the LAG- immunotype (P=0.031). An independent cohort of 94 ICB-treated urothelial carcinoma patients was used for validation where LAG+ immunotype was significantly associated with response, survival and progression-free survival. Multivariate Cox regression and stratified analyses further show LAG+ immunotype is an independent marker of outcome when compared to known clinical prognostic markers and previously described biomarkers. The pre-treatment peripheral blood LAG+ immunotype defines patients who are significantly less likely to benefit from ICB and suggests a strategy for identifying actionable immune targets for further investigation.

One Sentence Summary:

Peripheral blood LAG+CD8+ T cells, are more commonly identified in immunotherapy-treated cancer patients with less favorable outcomes.

Introduction

Antibodies that block immunologic checkpoints such as programmed cell death protein 1 (PD-1), its ligand (PD-L1), or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) target the immune system, enabling it to mount a successful anti-tumor response in a subset of patients. These antibodies are standard treatment for a variety of cancers, including melanoma and urothelial carcinoma (UC). For patients who are resistant to PD-1/L1 or CTLA-4 blockade, new agents targeting co-regulators of T-cell activation are in development.(1) Clinical trials of agents targeting lymphocyte-activation gene 3 (LAG-3), T-cell immunoglobulin domain and mucin domain 3 (TIM-3), and T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domains (TIGIT) have reported anti-tumor activity, although response rates appear modest, perhaps due to a lack of biomarkers for patient selection.(2–7) Identifying patients unlikely to benefit from approved antibodies and suitable for new therapies or combinations is an important unmet need.

Molecularly targeted anti-cancer therapies have had notable success through the rational and precise selection of patients based on characterization of their tumors. For therapies that target the immune system, it has been more challenging to determine which agents are most likely to benefit individual patients. The lack of biomarkers for ICB is a significant unmet need in oncology. Intratumoral expression of PD-L1 often correlates with response to PD-1/L1 blocking antibodies; however, PD-L1 has only been predictive in some studies and not others; its clinical use is therefore limited.(8, 9) Tumor mutation burden (TMB) has also been linked to outcomes in ICB-treated patients with melanoma, urothelial cancer and other cancers.(10) Nevertheless, for most patients who are candidates for checkpoint blockade, the clinical utility of biomarkers is limited and better ways to match patients with treatments are needed. A pre-treatment, blood-based biomarker that could guide clinical decision-making would be especially attractive. While recent publications have identified some promising candidates for further exploration, none have been prospectively validated or incorporated into clinical practice. (11–13)

We conducted this study to profile the expression of targetable immune molecules expressed in the pre-treatment peripheral blood of cancer patients treated with ICB. Utilizing a large, clinically robust dataset with consistent banking and sample preparation methods, we applied innovative statistical and computational tools to multiparametric flow cytometry data to maximize the likelihood of signal detection. We designed this retrospective study with the aim of identifying blood-based biomarkers relevant for patients considering ICB and identifying promising immune targets for patients who are resistant to standard ICB therapies and may be the most appropriate candidates for novel agents or combinations.

Results

Stratification of patients by immunotype

Our discovery cohort consisted of 188 ICB-treated melanoma patients (Figure 1A). The median duration of follow up was 4.3 years [range 0.34–7.50 years] The clinical characteristics of this population are shown in Table 1; patients received either anti-PD-1 antibodies (n=76, 40%), anti-CTLA-4 antibodies (n=13, 7%), or both in combination (n=99, 53%). Pre-treatment peripheral blood samples were collected from ICB-treated patients, cryopreserved and banked, and then subsequently analyzed by flow cytometry (Figure 1B). With overall survival as an endpoint, we applied a supervised clustering analysis (survClust) to stratify patients based on the multivariate pattern of 78 flow cytometry parameters (Supplemental Table S1).(14) SurvClust analysis was applied to an initial population of 136 patients with a median duration of followup of 5.6 years; samples were collected from 7 clinical trials open at our institution between 2011–2017. A rigorous cross-validation analysis revealed 3 clusters of patients with distinct patterns of expression of immune markers which we called ‘immunotypes’ (Figure 2A). The first immunotype (LAG+) was uniquely characterized by high expression of LAG-3 on multiple T-cell populations, most representative of which was LAG-3+ CD8+ T-cells (Figure 2B). The LAG+ immunotype represented 17.0% (23/136) of patients and was defined in part by the presence of ki67-LAG-3+ CD8+ T cells. The second immunotype (LAG-) reflected 65.4% (89/136) of the population and was defined by a paucity of LAG-3+ T cells and low levels of other co-markers on T-cells. The third immunotype had a high proportion of LAG-3+ T-cells with concurrently high numbers of proliferating Ki67+ CD8+ T-cells and T-cells expressing TIM-3 and ICOS. We named this the proliferative (PRO) immunotype and it comprised 17.6% (24/136) of patients.

Table 1.

Clinical Characteristics of Patients in the Melanoma Discovery Cohort and Urothelial Carcinoma Cohort, According to Peripheral Blood Immunotype.

Characteristic	Melanoma					Urothelial Carcinoma
Characteristic	Overall, N = 188	LAG+, N = 31¹	LAG−, N = 128¹	PRO, N = 29¹	p-value²	Overall, N = 94	LAG+, N = 9¹	LAG−, N = 70¹	PRO, N = 15¹	p-value²
*Age (median, range)*	62 (19, 88)	65 (27, 88)	61 (19, 83)	64 (29, 80)	0.25	67 (31, 83)	69 (50, 76)	66 (31, 83)	66 (51, 76)	0.6

*Male*	114 (61%)	20 (65%)	77(60%)	17 (59%)	0.9	76 (81%)	8 (89%)	56 (80%)	12 (80%)	>0.9

*Stage*					0.02					>0.9
III	16 (8.5%)	5 (16%)	11 (9%)	0 (0%)		1 (1.1%)	0 (0%)	1 (1.4%)	0 (0%)
IV M1a	28 (15%)	2 (6%)	20 (16%)	6 (21%)
IV M1b	30 (16%)	5 (16%)	25 (20%)	0 (0%)
IV M1c	95 (51%)	17 (55%)	60 (47%)	18 (62%)
IV M1d	19 (10%)	2 (6%)	12 (9%)	5 (17%)
IVA						29 (31%)	3 (33%)	22 (31%)	4 (27%)
IVB ³						64 (68%)	6 (67%)	47 (67%)	11 (73%)

*Liver metastases*	67 (36%)	13 (42%)	40 (31%)	14 (48%)	0.16	26 (28%)	5 (56%)	15 (21%)	6 (40%)	0.045

*LDH (median, IQR)*	209(175, 295)	204 (175, 265)	203 (171, 251)	290 (205, 508)	0.005	201 (167, 237)	180 (153, 192)	204 (167, 236)	211 (184, 254)	0.2

*Histology*					0.077					0.3
Cutaneous/Unknown	143 (76%)	19 (61%)	99 (77%)	25 (86%)
Mucosal/Acral/Uveal	45 (24%)	12 (39%)	29 (23%)	4 (14%)
Pure transitional cell						72 (77%)	7 (78%)	56 (80%)	9 (60%)
Mixed histology						22 (23%)	2 (22%)	14 (20%)	6 (40%)

*Prior Immunotherapy*	54 (29%)	8 (26%)	35 (27%)	11 (38%)	0.5	0 (0%)	0 (0%)	0 (0%)	0 (0%)

*Prior Platinum Treatment*						86 (91%)	8 (89%)	64 (91%)	14 (93%)	0.8

*Prior Intravesical BCG*						33 (35%)	4 (44%)	25 (36%)	4 (27%)	0.6

*Bellmunt Prognostic Score*										0.038
0						33 (35%)	4 (44%)	28 (40%)	1 (6.7%)
1						48 (51%)	4 (44%)	31 (44%)	13 (87%)
2+						13 (14%)	1 (11%)	11 (16%)	1 (6.7%)

*Treatment*					0.2					0.4
Anti-PD1	76 (40%)	15 (48%)	45 (35%)	16 (55%)		67 (71%)	6 (67%)	48 (69%)	13 (87%)
Anti-CTLA4	13 (7%)	2 (6%)	9 (7%)	2 (7%)
Combination	99 (53%)	14 (45%)	74 (58%)	11 (38%)		27 (29%)	3 (33%)	22 (31%)	2 (13%)

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Statistics presented: median (minimum, maximum); n (%); median (IQR)

Statistical tests performed: Kruskal-Wallis test; Fisher’s exact test

Non-lymph node distant metastases

Figure 2. — A. survClust analysis of 78 flow cytometry parameters identified three peripheral blood immunotypes in the discovery dataset of melanoma patients treated with immune checkpoint blockade (ICB) (n=136). B. Distribution of select individual flow cytometry parameters that characterize each immunotype. C. Kaplan-Meier analysis of overall survival by immunotype in the discovery dataset (n=188). Comparisons between immunotypes were made for the total dataset (p=0.04) and for the LAG+ versus LAG- immunotype (p=0.008). D. Kaplan-Meier analysis of overall survival by immunotype in the subset of patients treated with anti-PD1 monotherapy in the discovery dataset (n=76). Comparisons between immunotypes were made for the total dataset (p=0.01) and for the LAG+ versus LAG- immunotype (p=0.001). E. Kaplan-Meier analysis of progression free survival by immunotype in the subset of patients treated with anti-PD1 monotherapy in the discovery dataset (n=76). Comparisons between immunotypes were made for the total dataset (p=0.040) and for the LAG+ versus LAG- immunotype (p=0.007). F. Multivariate analysis of immunotype, stage, LDH, and liver metastases in the full cohort of melanoma patients (n=171; 171 patients in the 188-patient dataset were evaluable for all parameters).

A four-marker classifier to define immunotype

The initial clustering analysis used an input of 78 individual flow cytometry parameters to define three immunotypes; we next assessed the data to determine if a smaller number of parameters would be sufficient for this classification. First, we examined the flow markers that most significantly contributed to the classification of patients into specific immunotypes (Supplementary Figure S2). To identify the optimal combination of markers for classifying a sample into one of the immunotypes, we applied a penalized multinomial logistic regression approach(15) and identified 4 markers (% LAG-3+ CD8+ T-cells, % Ki67+ CD8+ T-cells, %Tim-3+ CD8+ T-cells, and % ICOS+ CD8+ T-cells) that most strongly influenced the classification (Figures 2B and Supplementary Figure S3). These 4 markers were able to reproduce the clustering assignment derived from the full panel of flow markers with 89% accuracy. A multinomial logistic regression was then built and the 4 marker classifier was completely locked down and then taken forward for assigning an immunotype for additional samples. This 4-marker classifier was applied to an additional 52 ICB-treated melanoma patients not available during the initial survClust analysis (Figure 1A). This additional analysis allowed the assembly of a 188-patient melanoma discovery dataset with survival, PFS and response outcomes. Within the discovery dataset, the 3 immunotypes were distributed with 16.5%, 68.1% and 15.4% patients of classified within the LAG+, LAG-, and proliferative (PRO) immunotypes, respectively (Table 1).

Immunotype relates to clinical outcome in ICB-treated melanoma patients

We next sought to determine if immunotype was related to treatment response (defined as complete or partial response), progression free survival (PFS) or overall survival (OS) after ICB treatment in the discovery dataset, n=188. Melanoma patients with the LAG- immunotype performed best in terms of survival with median survival of 75.8 months (Ref) as compared to 22.2 months (HR =1.99, 95% CI =[1.17 – 3.39]) and 68.1months (HR =1.38, 95% CI=[0.76 – 2.51]) for those with either the LAG+ or the PRO immunotype, respectively (Figure 2C). A pairwise comparison of OS between patients with the LAG+ and LAG- immunotype shows a highly significant difference in survival outcome (p=0.008). Similar trends were observed for the LAG+ immunotype and treatment response; patients with the LAG+ immunotype compared to those in the LAG- or PRO groups (39% versus 53% or 52%), although this did not reach statistical significance (P=0.352).

Since clinical outcomes are dependent upon the ICB regimen, we examined these associations stratified by ICB treatment received. The association between immunotype and survival outcome remained strong in the subset of patients who received anti-PD-1 monotherapy (n=76) with a median survival of 75.8 months (Ref) for those with the LAG- immunotype versus 12.3 months (HR = 2.8, 95% CI = 1.4–5.5) and 20.9 months (HR = 1.6, 95% CI = 0.7–3.3) for those with the LAG+ and PRO immunotypes (Figure 2D). Amongst the patients who received anti-PD-1 monotherapy, those with the LAG+ immunotype had the lowest rate of response (20% versus 49% for the LAG- or 38% for the PRO), although this did not reach statistical significance (P=0.133). For patients who received the combination of ipilimumab and nivolumab (n=99, 53%) there was not a significant association between immunotype and OS; however, the shorter median follow up for this subgroup (1.76 years compared to 5.57 years for anti-PD-1 monotherapy) and fewer events preclude a definitive conclusion (Supplemental Figure S4A). The number of patients who received ipilimumab monotherapy (n=13, 7%) was insufficient for a similar subset analysis.

We next evaluated the relationship between immunotype and PFS. For patients who received anti-PD-1 monotherapy, immunotype was significantly associated with PFS (p=0.007) with the pairwise comparison of the LAG+ and LAG- showing a difference in median PFS of 2.7 versus 10.6 months (HR=2.23, 95%CI [1.18–4.23]) (Figure 2E). There was no significant association between immunotype and PFS in patients who received the combination of anti-PD-1 and anti-CTLA-4 therapy, however, again the relatively short followup and paucity of progression events in this subgroup preclude a definitive conclusion (Supplemental Figure S4B).

LAG+ immunotype is independent of known clinical prognostic factors

Table 1 presents the distribution of clinical characteristics of melanoma patients in the discovery dataset stratified by immunotype (for demographics of melanoma subgroups, see Supplementary Table S2). The age, gender, and prior treatments were all well balanced. Melanoma substage, histology, liver metastases, ICB treatment and levels of lactate dehydrogenase (LDH) were also evaluated. Of these factors, only LDH was significantly associated with immunotype (P=0.002). In order to assess whether immunotype was independently associated with overall survival, we fit a multivariate analysis. After adjusting for LDH, liver metastases and stage, LAG+ immunotype remained statistically significant (P=0.03, Figure 1E) whereas the PRO immunotype no longer retained a significant association (Figure 2F). While not significantly imbalanced, there were numerical differences in the histology and ICB treatment received according to immunotype. In order to address this, we analyzed survival outcomes by immunotype in the following scenarios (1) in the subpopulation of patients with cutaneous melanoma or melanoma of unknown primary, excluding patients with acral, mucosal or uveal melanoma (Supplementary Figure S5) or (2) in the subpopulation of patients treated with anti-PD-1 monotherapy (Figure 1D). In both cases, patients with the LAG+ immunotype had significantly poorer survival outcomes. We also performed a multivariate survival analysis adjusting for histology and ICB treatment and found that the LAG+ immunotype remained significant (P=0.04, Supplementary Figure S5).

Immunotype relates to clinical outcome in ICB-treated urothelial cancer patients

We applied the 4-marker classifier to flow cytometry data from pre-treatment peripheral blood samples in an independent cohort of 94 ICB-treated UC patients. Clinical characteristics of these groups are presented in Table 1. We assigned each UC patient to one of the immunotypes based on maximum predicted probability (Supplementary Figure S6). Clustering using this 4-marker classifier identified a similar distribution of the three immunotypes discovered in the melanoma cohort (Figure 3A). Within the UC cohort, 9.6% (9/94) patients were classified as the LAG+ immunotype, 74.5% (70/94) were classified as LAG- immunotype and 16.0% (15/94) were classified as proliferative (PRO) immunotype.

Figure 3. — A. Heatmap display of the 4-marker classifier (LAG-3+ CD8+ T cells, Ki67+ CD8+ T cells, Tim-3+ CD8+ T cells, and ICOS+ CD8+ T cells) in the validation cohort of urothelial cancer (UC) patients treated with immune checkpoint blockade (ICB). B. Kaplan-Meier analysis of overall survival by predicted immunotype in UC patients (n=94). C. Kaplan-Meier analysis of overall survival by predicted immunotype in the subset of UC patients treated with anti-PD1 monotherapy (n=67). D. Kaplan-Meier analysis of progression free survival by predicted immunotype in UC patients (n=94). E. Multivariate analysis of immunotype, stage, LDH, and liver metastases in UC patients (n=93; 93 patients in the 94-patient full dataset were evaluable for all parameters).

The primary clinical outcomes evaluated were treatment response (defined as complete and partial response), PFS and OS. Validating the observation in melanoma patients, similar stratification of survival distributions by immunotype were observed in the UC cohort. UC patients with the LAG- immunotype performed best in terms of survival with a median OS of 27.6 months (Ref) compared to 4.7 months (HR = 3.9, 95% CI = 1.8–8.2) and 6.5 months (HR = 2.3, 95% CI = 1.2–4.4) for the LAG+ and PRO immunotypes, respectively (Figure 3B). No treatment responses were observed among patients classified as LAG+ immunotype, compared to 49% for the LAG- and 27% for the PRO immunotypes (P=0.007). These associations remained for both survival and response outcomes in the subset of patients who received anti-PD-1 monotherapy (n=67) (Figure 3C). Lastly, we examined the relationship between immunotype and PFS in patients with UC. In UC, the association between immunotype and PFS is highly significant and a pairwise comparison between LAG+ and LAG- immunotype shows a difference in median PFS of 1.2 versus 3.6 months (HR 3.03, 95% CI =[1.45–6.35]) with poorer outcomes seen patients with the LAG+ immunotype (Figure 3D). This pattern is also significant in UC treated with anti-PD-1 monotherapy where the pairwise comparison between LAG+ and LAG- immunotype shows a significant difference (p= 0.037) (Supplemental Figure 4C). The number of UC patient who received the combination of anti-PD-1 and anti-CTLA-4 therapy (n= 27) was insufficient to evaluate independently.

We next evaluated if any of the clinical features of the UC patient population correlated with the immunotype defined by the flow cytometry analysis (Table 1). The age, gender, stage, and prior treatment were all well balanced across the three immunotypes. In a multivariate analysis including immunotype, stage, LDH, and liver metastases, both the LAG+ and PRO immunotypes had significantly poorer outcomes (P<0.001, P=0.02, respectively) (Figure 3E). We also performed a multivariate analysis of immunotype, stage, LDH, and Bellmunt score in UC patients who progressed after platinum-based chemotherapy (n=86), and both the LAG+ and PRO immunotypes remained significant (P<0.001, P=0.015, respectively) (Supplementary Figure S7).

Immunotype is independent of previously defined immune markers

Tumor characteristics such as PD-L1 expression and TMB have been correlated with clinical outcomes in melanoma and UC patients treated with ICB. The distribution of these markers did not show any disproportionate allocation across the immunotypes (Supplementary Figure S8).

Regardless of PD-L1 or TMB status, the LAG+ immunotype remains associated with poor survival. In the melanoma cohort, PD-L1+ patients had significantly more favorable outcomes compared to the PD-L1- patients (median OS 75.8 v 23.0 months). However, amongst the PD-L1+ melanoma patients, those with the LAG+ immunotype had poorer survival outcomes and more closely resembled PD-L1- patients (Figure 3A, 3C). This pattern is even more striking in those who received anti-PD-1 monotherapy (Figure 3B, 3D).

While the PD-L1 and TMB data were more limited in the melanoma cohort (n=60) due to limited tissue availability from earlier trials, we could more confidently establish the relationship between TMB and LAG+ immunotype in the UC validation cohort. In particular, the favorable survival outcome conferred by either TMB-high or PD-L1+ status was abrogated amongst patients with the LAG+ immunotype (Figure 3E). Patients in the LAG+ immunotype also showed reduced response rate in PD-L1+ and TMB-high tumors (Figure 3H). Consistent with prior literature,(16) high absolute lymphocyte count (ALC) was a favorable factor for melanoma patients. However, in patients with high ALC, the LAG+ immunotype conferred poorer outcomes (Figure 3G and 3F).

Discussion

In this analysis, we identify and validate a pattern of pre-treatment peripheral blood markers that correlates with both response and survival in patients who received ICB. Our observation has several implications that may be relevant to current and future immunotherapies. For patients receiving PD-1/L1 blockade, unique information about their likelihood of response and survival may be detected from a peripheral blood sample collected pre-treatment. There are no validated biomarkers for ICB that make use of pre-treatment peripheral blood samples. Some tumor characteristics such as expression of PD-L1 and TMB have been correlated with clinical outcomes after ICB.(17–22) However, both TMB and PD-L1 testing require a tumor biopsy sample. The peripheral blood immunotype is independent of either TMB or PD-L1 status.

The LAG-3+ CD8+ T-cells that help define the LAG+ immunotype suggest a rational mechanism underlying the poorer outcomes for this group and provide justification for clinically targeting LAG-3. LAG-3 is a unique checkpoint that is functionally distinct from and non-redundant with PD-1 and CTLA-4. (23, 24) Patients with high levels of LAG-3 expressing peripheral T-cells may be inhibiting the anti-tumor response in a way that PD-1 or CTLA-4 blockade cannot overcome. LAG-3 has shown promise as a target in preclinical models and agents targeting LAG-3 are already in early stages of clinical development, where they have shown modest activity in unselected patient populations.(2–4, 6, 25, 26) Biomarker selection could be especially impactful for further development of these agents, and indeed, early evidence supports this.. For example, the anti-LAG-3 antibody relatlimab in combination with nivolumab had a response rate of 13% in an unselected population of melanoma patients, but the subset of patients whose tumors (presumably infiltrating lymphocytes) were positive for LAG-3 were most likely to benefit.(4) We hypothesize that the LAG+ immunotype described in this study may identify patients more likely to benefit from the combination of LAG-3 and PD-1 blockade stratifying patients by immunotype may represent a step toward a more tailored, patient-specific approach to treatment with immunotherapy and testing new agents alone or in combination with existing PD-1/L1 and CTLA-4 inhibitors.

There are notable limitations to this study, which is based on a single-institution analysis of retrospectively banked samples. Validation of the 4-marker classifier in a prospective study will be needed as a next step. Additionally, our flow cytometry panel, which focused mainly on T-cell phenotype, provides a limited view of the diversity of peripheral blood immune cells, does not include some clinically relevant targets such as TIGIT, does not evaluate myeloid or NK cell subsets which have been previously been linked to outcomes after checkpoint blockade, and cannot evaluate immune cell function.(13, 27–30) Lastly, the inclusion of a cohort of patients who did not receive ICB would be a helpful comparator, but unfortunately was not available for this analysis. However, notable strengths include a relatively large, clinically annotated robust dataset, a reliable and uniform approach to PBMC banking and flow cytometry coupled with bioinformatic adjustments for variability between batches of samples, and a machine learning algorithm for discovery. Furthermore, our analysis identifies a blood-based marker that correlates significantly with both survival (melanoma and UC) and response (UC) outcomes, although the significance of the LAG+ immunotype has not been assessed in patients not receiving ICB. None of the published studies examining PBMC samples of anti-PD-1/L1 treated cancer patients using single-cell phenotypic analysis have identified the biomarker profiles identified here.(11–13, 27–37) One small study in gastric cancer (n=30) did correlate LAG+CD8+ T cells with better outcomes in patients who received anti-PD-1.(37) Differences in the patient population or in methodology or markers may explain this. Of the published studies, many are limited in their evaluation of potentially confounding clinical prognostic markers or they present composite scores incorporating both known clinical prognostic markers and peripheral blood markers which correlate with clinical outcomes. In contrast, the LAG+ immunotype significantly correlates with clinical outcome independent of known prognostic clinical markers. Other promising peripheral blood-based T cell markers that have been recently identified, including PD-1+CD56+ cells, PD-1+CD73+CD8+ cells, and CD39+CD8+ cells, could not be evaluated in this study due to limitations in the panel of markers selected for flow cytometry.(11–13) Studies that use standardized methods to integrate the analysis of multiple candidate biomarkers will be important to move the field forward. Alternative approaches to the analysis of peripheral blood samples including proteomics and detection of cytokines may provide additional data relevant for ICB-treated patients and future studies will be needed to understand how these markers relate to immunotype.(38–40) Likewise, the analysis of immune populations in the tumor provides complimentary information that should be synthesized with studies of peripheral blood immune cells.

This study offers evidence that characterization of a patient’s peripheral blood immune cells prior to treatment initiation may provide actionable data to inform clinical decision-making, including the potential to identify immunotherapy targets that may be relevant to individual cancer patients, although prospective validation is needed to advance this concept into clinical practice. Establishing a more precision medicine-like approach for patients considering ICB is clearly important as these medicines can have a salutary effect on survival but may be accompanied by potentially fatal or morbid toxicities. The financial cost of cancer therapy also points to the need for a better means of patient selection. Our data suggest a plausible role for LAG-3 in resistance to PD-1 blockade, supporting LAG-3 as a clinically relevant target and suggesting a subpopulation of cancer patients who may benefit most from LAG-3 blockade.

Materials and Methods

Patients/Specimen Collection

Patients were consented for blood collection under an approved protocol in accordance with the Institutional Review Board of Memorial Sloan Kettering Cancer Center. Patients were included if they participated in one of the following clinical studies: NCT01024231, NCT01295827, NCT01621490, NCT01844505, NCT01927419, NCT01928394, NCT02083484, NCT02553642, NCT03122522. Patients were accrued between 2009 and 2019. Patients were excluded (n=104) if they did not have banked samples (n=99) or if they did not have tumor response assessment or survival follow up (n=4). One patient was excluded after a pathologic re-evaluation changed the primary cancer diagnosis from urothelial carcinoma to cervical carcinoma. There are no systematic differences that we can detect in the demographics or clinical characteristics between the melanoma patients included (n=188) or excluded (n=84) from the analysis. Only 20 UC patients were excluded from analysis. Response was determined by RECIST criteria. Best overall response was determined (partial response [PR], complete response [CR], stable disease [SD], progression of disease [PD]), except in cases where patients had clinical progression and no radiographic assessment (PD).

Flow Cytometry

Cryopreserved peripheral blood mononuclear cells (PBMCs) were prepared and flow cytometry performed as previously described.(31) Data were analyzed using Flow-jo software by an investigator (M.A.) blinded to clinical outcome (Supplemental Figure S1).

Tumor Biospecimen Analysis

PD-L1 staining was performed according to institutional standard operating procedures using the Cell Signaling Technology antibody clone E1L3N. The proportion of PD-L1-positive tumor cells was determined by pathologists (T.H., H.A., L.J.) blinded to clinical outcome using a cutoff of 1%. TMB was estimated for tumors sequenced by the MSK-IMPACT platform as total mutation count per Mb sequenced; a cutoff of the highest 20% was used to define high TMB.(41)

Statistical Analysis

For clinical associations, Wilcoxon rank-sum test, Kruskal-Wallis test, or Fisher’s exact test were used as appropriate. The Kaplan-Meier method was used for survival estimation and the lon-rank test was used for comparisons. Cox proportional hazards model was used for association analysis with survival outcome for univariate and multivariate analysis, and for calculating the hazard ratio estimates along with 95% confidence intervals. Two-sided P values of less than .05 were considered statistically significant. For additional details please see supplemental methods.

Batch correction.

For each patient sample, we have information on the clinical trial ID, the time the sample was collected, and flow cytometry processing date. Overall, samples from the same clinical trial were collected in roughly the same time frame. We observed a moderate degree of “batch” effect by trial ID. Principal component analysis was used to visualize the pattern of the flow cytometry data across the batches. The ComBat algorithm was used to remove the “unwanted” variations due to batch based on an empirical Bayes approach.(42) The batch-adjusted data set was then used for subsequent analyses. No significant batch effect was observed in the bladder cancer data, but to be consistent with the adjustment, we applied the same ComBat analysis to remove any residual batch variation.

survClust analysis.

survClust is an outcome-weighted clustering algorithm for patient stratification building on ideas from supervised text classification(14). The algorithm learns a weighted distance matrix that down-weighs flow cytometry features that bear no relevance to the clinical outcome of interest. The immune cell phenotype in peripheral blood of cancer patients as measured by high dimensional flow cytometry is complex and influenced by a multitude of factors. Unsupervised learning does not necessarily lead to unique answers in highly complex data as many local optima may exist that pose special challenges in optimization. survClust overcomes the challenge by learning an outcome-weighted distance matrix from flow cytometry data incorporating a vector of hazard ratios estimated from Cox regression as weights. For a pair of two patient sample vector $a$ and $b$ , the weighted distance is calculated as follows:

d_{w} (a, b) = \sqrt{{(a - b)}^{T} W (a - b)},

(1)

where, $a$ and $b$ denote flow cytometry marker vectors of length $p$ capturing the peripheral T cell phenotype profile in the corresponding PBMC sample, $W$ is a $p \times p$ diagonal weight matrix with $W = {d i a g {w_{1}, \dots, w}_{p}}$ . The weights $w_{j} (j = 1, \dots, p)$ are obtained by fitting a univariate Cox proportional hazards model for each flow cytometry marker:

h (t ∣ x_{p}) = h_{o} \times \exp (x_{j}^{T} * β),

(2)

where $t$ represents the survival time, $x_{j}$ is the j^th column of matrix $X$ of length $N, h_{0}$ is the baseline hazard function, $β$ is the regression coefficient and $e x p (β)$ is the Hazard Ratio (HR). We consider the absolute value of HR on the logarithmic scale as the weight ( $w$ = abs $(β)$ ). An HR=1 corresponds to the null that the feature is not associated with survival. This is reflected in a log(1) =0 weigh in the distance matrix. The weighted distance matrix is then projected onto a lower dimensional space via multidimensional scaling (MDS). Patient samples are then clustered into subgroups via the K-means algorithm in the MDS projected space. An implementation of survClust is publically available at https://github.com/arorarshi/survClust. The number of clusters is determined through a 5-fold cross-validation (CV). Cluster identity is tracked by a centroid relabeling approach. The final class label for each sample is assigned through consensus of the prediction across all the CV rounds.

Validation analysis.

A multinomial logistic regression was fitted using LAG3, KI67, TIM3, and ICOS on CD8 and trained in the melanoma cohort. This model was then used to calculate predicted probability of each bladder cancer sample belonging to each of the three T-cell immunotypes. Each sample was then assigned to an immunotype based on maximum predicted probability. The nnet R package was used for this analysis.

Software

All statistical analysis was performed in R version 3.6.3.(42) Heatmaps were plotted using pheatmap and survival curves were drawn with the help of survminer R packages.

Supplementary Material

Supplementary data file S1

NIHMS1801836-supplement-Supplementary_data_file_S1.xlsx^{(120.6KB, xlsx)}

Supplementary data file S2

NIHMS1801836-supplement-Supplementary_data_file_S2.csv^{(45.3KB, csv)}

Supplementary materials

NIHMS1801836-supplement-Supplementary_materials.pdf^{(2.6MB, pdf)}

Figure 4. — A. Kaplan-Meier analysis of overall survival in subset of PD-L1+ melanoma patients, stratified by LAG- immunotype (solid blue line) versus LAG+ immunotype (solid red line) compared to PD-L1- subset (dashed grey line). B. Kaplan-Meier analysis of overall survival in subset of PD-L1+ melanoma patients treated with anti-PD-1 monotherapy, stratified by LAG- immunotype (solid blue line) versus LAG+ immunotype (solid red line) compared to PD-L1- subset (dashed grey line). Multivariate analysis of immunotype, PD-L1, TMB and ALC status for overall survival in all ICB-treated melanoma patients **(C)**, in PD-1 monotherapy treated melanoma patients **(D)** or in ICB-treated UC patients **(E)**. Multivariate analysis of immunotype, PD-L1, TMB and ALC status for response in all ICB-treated melanoma patients **(F)**, in PD-1 monotherapy treated melanoma patients **(G)** or in ICB-treated UC patients **(H)**.

Acknowledgments:

Funding: MSKCC Society, Parker Institute for Cancer Immunotherapy, NIH P30 CA008748

Competing interests:

R.S. reports grants from NCI/NIH, Parker Institute for Cancer Immunotherapy/MSK, MSK Society/MSK during the conduct of the study.

M.P. reports grants and personal fees from BMS, Merck, Array BioPharma, and Novartis; personal fees from Incyte, NewLink Genetics, and Aduro; grants from RGenix and Infinity.

A.A. reports grants from NCI/NIH, Parker Institute for Cancer Immunotherapy/MSK, MSK Society/MSK during the conduct of the study.

M.H. reports grants from NCI/NIH during the conduct of the study.

P. W. reports personal fees from Leap Therapeutics.

M.A.C. reports personal fees from ImmunoGenesis, ImmunOS, Agenus, Alligator, Aptevo, Mabimmune, Oncoresponse, Pieris, Xencor, and Merck. In addition, M.A.C. has a patent for antibodies which bind PD-L1 and prevent its binding to PD-1 pending and licensed to ImmunoGenesis, a patent for antibodies which bind PD-L2 and prevent its binding to PD-1 pending and licensed to ImmunoGenesis, a patent for dual specificity antibodies which bind both PD-L1 and PD-L2 and prevent their binding to PD-1 pending and licensed to ImmunoGenesis, and a patent for dual specificity antibodies sharing a common light chain which bind both PD-L1 and PD-L2 and prevent their binding to PD-1 pending and licensed to ImmunoGenesis.

H.A. reports personal fees from Bristol-Myers Squibb and AstraZeneca.

S.F. reports other support from Genetech/Roche, AstraZeneca, Kite Pharma, Allogene Therapeutics, Urogen Pharma, Kronos Bio, Neogene Therapeutics, and Vaxigene Therapeutics, outside the submitted work.

G.I. reports personal fees from Mirati Therapeutics and Janssen; grants from Novartis.

J.R. reports personal fees and other from AstraZeneca, QED Therapeutics, Astellas, Seattle Genetics, Janssen, Bioclin, and Bayer; personal fees from BMS, Boehringer Ingelheim, GSK, Lilly, Pfizer, Mirati, and Merck; and other support from Chugai.

P.C. reports personal fees from Cell Medica, Merck, Immunocore and Scancell and stocks from Rgenix as well as research support from Pfizer outside the scope of this manuscript.

A.S. reports personal fees and non-financial support from Bristol-Myers Squibb and Immunocore; personal fees from Castle Biosciences; and non-financial support from Xcovery.

A.B. reports personal fees from LG Chem Life Sciences Innovation Center, Inc., Nanobiotix, and Iovance Biotherapeutics; other support from Leap Therapeutics.

T.M. reports other support from IMVAQ therapeutics; personal fees from Immuno Therapeutics and Pfizer; grants and personal fees from Infinity Pharmaceuticals, Inc., grants from Bristol-Myers Squibb, Surface Oncology, Kyn Therapeutics, Peregrine Pharmeceuticals, Inc., Adaptive Biotechnologies, Leap Therapeutics, Inc., and Aprea. In addition, Dr. Merghoub has patents related to work on Oncolytic Viral therapy (issued), Alpha Virus Based Vaccine (issued), Neo Antigen Modeling (issued), CD40 (pending), GITR (issued), OX40 (issued), PD-1 (issued), and CTLA-4 (issued).

J.W. reports grants and personal fees from Bristol Myers Squibb and AstraZeneca; personal fees and other from Tizona Pharmaceuticals, Adaptive Biotechnologies, Imvaq Therapuetics, Serametrix, and Arsenal-Caprion; personal fees from Beigene, Linneaus, Amgen, Apricity, Ascentage, Astellas, Bayer, Celgene, Chugai, Eli Lilly, Elucida, F-Star, Kyowa Hakko Kirin, Merck, Neon Therapeutics, Polynoma, Psioxus, Recepta, Takara, Trieza, Truvax; Surface, Syndax, Syntalogic, Sellas, and Boehringer Ingelheim; grants from Sephora. In addition, Dr. Wolchok has a patent Xenogeneic DNA Vaccines with royalties paid to Merial, a patent Alphavirus with royalties paid to Serametrix, a patent NewCastle Disease Viruses for Cancer Therapy pending, a patent Genomic Signature to Identify Responders to Ipilimumab in Melanoma pending, a patent Engineered Vaccinia Viruses for Cancer Immunotherapy pending to Imvaq, a patent Anti-CD40 agonist mAb fused to Monophosphoryl Lipid A (MPL) for cancer therapy pending, a patent CAR+ T cells targeting differentiation antigens as means to treat cancer pending, a patent Anti-PD1 antibody licensed to Agenus, a patent Anti-CTLA4 antibodies licensed to Agenus, and a patent Anti-GITR antibodies and methods of use thereof licensed to Agenus/Incyte.

K.P. reports stock ownership in the following companies: Johnson&Johnson, Pfizer, Catalyst Biotech, and Viking Therapeutics.

M.K.C. reports grants from Bristol Myers Squibb for projects outside this manuscript, personal fees from Merck, InCyte, Moderna, ImmunoCore, and AstraZeneca. Additionally, she has an immediate family member who is employed at Bristol Myers Squibb and receives unvested stock as a form of compensation.

References

1.Andrews LP, Yano H, Vignali DAA, Inhibitory receptors and ligands beyond PD-1, PD-L1 and CTLA-4: breakthroughs or backups. Nat Immunol 20, 1425–1434 (2019). [DOI] [PubMed] [Google Scholar]
2.Brignone C, Escudier B, Grygar C, Marcu M, Triebel F, A phase I pharmacokinetic and biological correlative study of IMP321, a novel MHC class II agonist, in patients with advanced renal cell carcinoma. Clin Cancer Res 15, 6225–6231 (2009). [DOI] [PubMed] [Google Scholar]
3.Eastgate MA, Atkinson V, Khattak MA, Roy AC, Haydon AM, Mueller C, Dunkelmann T, Brignone C, Triebel F, in Journal of Clinical Oncology 36, no. 15_suppl. (2018). [Google Scholar]
4.Ascierto PA, Melero I, Bhatia S, Bono P, Sanborn RE, Lipson EJ, Callahan MK, Gajewski T, Gomez-Roca CA, Hodi FS, Curigliano G, Nyakas M, Preusser M, Koguchi Y, Maurer M, Clynes R, Mitra P, Suryawanshi S, Muñoz-Couselo E, Initial efficacy of anti-lymphocyte activation gene-3 (anti–LAG-3; BMS-986016) in combination with nivolumab (nivo) in pts with melanoma (MEL) previously treated with anti–PD-1/PD-L1 therapy. Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017) 9520–9520., (2017). [Google Scholar]
5.Harding JJ, Patnaik A, Moreno V, Stein M, Jankowska AM, Velez de Mendizabal N, Liu ZT, Koneru M, Calvo E, in J Clin Oncol 37, 2019 (suppl 8; abstr 12). (2019). [Google Scholar]
6.Peguero J, Felip E, Bernard D, Margarita M, Enric C, Tim C, Pawan B, Krebs MG, Triebel F, in SITC. (2019). [Google Scholar]
7.D. e. a. Rodriguez-Abreu, in ASCO. (2020). [Google Scholar]
8.Signorelli D, Giannatempo P, Grazia G, Aiello MM, Bertolini F, Mirabile A, Buti S, Vasile E, Scotti V, Pisapia P, Cona MS, Rolfo C, Malapelle U, Immune-Oncology YG, Patients Selection for Immunotherapy in Solid Tumors: Overcome the Naive Vision of a Single Biomarker. Biomed Res Int 2019, 9056417 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
9.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 7, 278 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
10.TMB Faces Validation Hurdles. Cancer Discov 9, 1334 (2019). [DOI] [PubMed] [Google Scholar]
11.Bochem J, Zelba H, Amaral T, Spreuer J, Soffel D, Eigentler T, Wagner NB, Uslu U, Terheyden P, Meier F, Garbe C, Pawelec G, Weide B, Wistuba-Hamprecht K, Peripheral PD-1+CD56+ T-cell frequencies correlate with outcome in stage IV melanoma under PD-1 blockade. PLoS One 14, e0221301 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Capone M, Fratangelo F, Giannarelli D, Sorrentino C, Turiello R, Zanotta S, Galati D, Madonna G, Tuffanelli M, Scarpato L, Grimaldi AM, Esposito A, Azzaro R, Pinto A, Cavalcanti E, Pinto A, Morello S, Ascierto PA, Frequency of circulating CD8+CD73+T cells is associated with survival in nivolumab-treated melanoma patients. J Transl Med 18, 121 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Koh J, Kim Y, Lee KY, Hur JY, Kim MS, Kim B, Cho HJ, Lee YC, Bae YH, Ku BM, Sun JM, Lee SH, Ahn JS, Park K, Ahn MJ, MDSC subtypes and CD39 expression on CD8(+) T cells predict the efficacy of anti-PD-1 immunotherapy in patients with advanced NSCLC. Eur J Immunol, (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Arshi A, Olshen AB, Seshan VE, Shen R, Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering. Genome Medicine, (2020, manuscript accepted). [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Friedman J, Hastie T, Tibshirani R, Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33, 1–22 (2010). [PMC free article] [PubMed] [Google Scholar]
16.Postow MA, Chasalow SD, Kuk D, Panageas KS, Cheng ML, Yuan J, Wolchok JD, Absolute lymphocyte count as a prognostic biomarker for overall survival in patients with advanced melanoma treated with ipilimumab. Melanoma Res 30, 71–75 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, Hollmann TJ, Bruggeman C, Kannan K, Li Y, Elipenahli C, Liu C, Harbison CT, Wang L, Ribas A, Wolchok JD, Chan TA, Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371, 2189–2199 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS, Miller ML, Rekhtman N, Moreira AL, Ibrahim F, Bruggeman C, Gasmi B, Zappasodi R, Maeda Y, Sander C, Garon EB, Merghoub T, Wolchok JD, Schumacher TN, Chan TA, Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA, Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 20, 5064–5074 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, Dawson N, O’Donnell PH, Balmanoukian A, Loriot Y, Srinivas S, Retz MM, Grivas P, Joseph RW, Galsky MD, Fleming MT, Petrylak DP, Perez-Gracia JL, Burris HA, Castellano D, Canil C, Bellmunt J, Bajorin D, Nickles D, Bourgon R, Frampton GM, Cui N, Mariathasan S, Abidoye O, Fine GD, Dreicer R, Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Daud AI, Wolchok JD, Robert C, Hwu WJ, Weber JS, Ribas A, Hodi FS, Joshua AM, Kefford R, Hersey P, Joseph R, Gangadhar TC, Dronca R, Patnaik A, Zarour H, Roach C, Toland G, Lunceford JK, Li XN, Emancipator K, Dolled-Filhart M, Kang SP, Ebbinghaus S, Hamid O, Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. J Clin Oncol 34, 4102–4109 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HE, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS, Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Grosso JF, Goldberg MV, Getnet D, Bruno TC, Yen HR, Pyle KJ, Hipkiss E, Vignali DA, Pardoll DM, Drake CG, Functionally distinct LAG-3 and PD-1 subsets on activated and chronically stimulated CD8 T cells. J Immunol 182, 6659–6669 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Woo SR, Turnis ME, Goldberg MV, Bankoti J, Selby M, Nirschl CJ, Bettini ML, Gravano DM, Vogel P, Liu CL, Tangsombatvisit S, Grosso JF, Netto G, Smeltzer MP, Chaux A, Utz PJ, Workman CJ, Pardoll DM, Korman AJ, Drake CG, A D. Vignali, Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72, 917–927 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Kraman M, Faroudi M, Allen NL, Kmiecik K, Gliddon D, Seal C, Koers A, Wydro MM, Batey S, Winnewisser J, Young L, Tuna M, Doody J, Morrow M, Brewis N, FS118, a Bispecific Antibody Targeting LAG-3 and PD-L1, Enhances T-Cell Activation Resulting in Potent Antitumor Activity. Clin. Cancer Res, (2020). [DOI] [PubMed] [Google Scholar]
26.Grosso JF, Kelleher CC, Harris TJ, Maris CH, Hipkiss EL, De Marzo A, Anders R, Netto G, Getnet D, Bruno TC, Goldberg MV, Pardoll DM, Drake CG, LAG-3 regulates CD8+ T cell accumulation and effector function in murine self- and tumor-tolerance systems. J Clin Invest 117, 3383–3392 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Youn JI, Park SM, Park S, Kim G, Lee HJ, Son J, Hong MH, Ghaderpour A, Baik B, Islam J, Choi JW, Lee EY, Kim HR, Seo SU, Paik S, Yoon HI, Jung I, Xin CF, Jin HT, Cho BC, Seong SY, Ha SJ, Kim HR, Peripheral natural killer cells and myeloid-derived suppressor cells correlate with anti-PD-1 responses in non-small cell lung cancer. Sci Rep 10, 9050 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM, Dummer R, Robinson MD, Levesque MP, Becher B, High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24, 144–153 (2018). [DOI] [PubMed] [Google Scholar]
29.Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M, Zhou J, Manos M, Eastman LM, Maecker HT, Hodi FS, Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer 6, 18 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Martens A, Wistuba-Hamprecht K, Geukes Foppen M, Yuan J, Postow MA, Wong P, Romano E, Khammari A, Dreno B, Capone M, Ascierto PA, Di Giacomo AM, Maio M, Schilling B, Sucker A, Schadendorf D, Hassel JC, Eigentler TK, Martus P, Wolchok JD, Blank C, Pawelec G, Garbe C, Weide B, Baseline Peripheral Blood Biomarkers Associated with Clinical Outcome of Advanced Melanoma Patients Treated with Ipilimumab. Clin Cancer Res 22, 2908–2918 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Huang AC, Postow MA, Orlowski RJ, Mick R, Bengsch B, Manne S, Xu W, Harmon S, Giles JR, Wenz B, Adamow M, Kuk D, Panageas KS, Carrera C, Wong P, Quagliarello F, Wubbenhorst B, D’Andrea K, Pauken KE, Herati RS, Staupe RP, Schenkel JM, McGettigan S, Kothari S, George SM, Vonderheide RH, Amaravadi RK, Karakousis GC, Schuchter LM, Xu X, Nathanson KL, Wolchok JD, Gangadhar TC, Wherry EJ, T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Wistuba-Hamprecht K, Martens A, Heubach F, Romano E, Geukes Foppen M, Yuan J, Postow M, Wong P, Mallardo D, Schilling B, Di Giacomo AM, Khammari A, Dreno B, Maio M, Schadendorf D, Ascierto PA, Wolchok JD, Blank CU, Garbe C, Pawelec G, Weide B, Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur. J. Cancer 73, 61–70 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Zappasodi R, Budhu S, Hellmann MD, Postow MA, Senbabaoglu Y, Manne S, Gasmi B, Liu C, Zhong H, Li Y, Huang AC, Hirschhorn-Cymerman D, Panageas KS, Wherry EJ, Merghoub T, Wolchok JD, Non-conventional Inhibitory CD4(+)Foxp3(−)PD-1(hi) T Cells as a Biomarker of Immune Checkpoint Blockade Activity. Cancer Cell 34, 691 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM, Gherardini PF, Prestwood TR, Chabon J, Bendall SC, Fong L, Nolan GP, Engleman EG, Systemic Immunity Is Required for Effective Cancer Immunotherapy. Cell 168, 487–502 e415 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Kagamu H, Kitano S, Yamaguchi O, Yoshimura K, Horimoto K, Kitazawa M, Fukui K, Shiono A, Mouri A, Nishihara F, Miura Y, Hashimoto K, Murayama Y, Kaira K, Kobayashi K, CD4(+) T-cell Immunity in the Peripheral Blood Correlates with Response to Anti-PD-1 Therapy. Cancer Immunol Res 8, 334–344 (2020). [DOI] [PubMed] [Google Scholar]
36.Fairfax BP, Taylor CA, Watson RA, Nassiri I, Danielli S, Fang H, Mahe EA, Cooper R, Woodcock V, Traill Z, Al-Mossawi MH, Knight JC, Klenerman P, Payne M, Middleton MR, Peripheral CD8(+) T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat Med 26, 193–199 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Ohmura H, Yamaguchi K, Hanamura F, Ito M, Makiyama A, Uchino K, Shimokawa H, Tamura S, Esaki T, Mitsugi K, Shibata Y, Oda H, Tsuchihashi K, Ariyama H, Kusaba H, Oda Y, Akashi K, Baba E, OX40 and LAG3 are associated with better prognosis in advanced gastric cancer patients treated with anti-programmed death-1 antibody. Br J Cancer 122, 1507–1517 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Weber JS, Sznol M, Sullivan RJ, Blackmon S, Boland G, Kluger HM, Halaban R, Bacchiocchi A, Ascierto PA, Capone M, Oliveira C, Meyer K, Grigorieva J, Asmellash SG, Roder J, Roder H, A Serum Protein Signature Associated with Outcome after Anti-PD-1 Therapy in Metastatic Melanoma. Cancer Immunol Res 6, 79–86 (2018). [DOI] [PubMed] [Google Scholar]
39.Yuen KC, Liu LF, Gupta V, Madireddi S, Keerthivasan S, Li C, Rishipathak D, Williams P, Kadel EE 3rd, Koeppen H, Chen YJ, Modrusan Z, Grogan JL, Banchereau R, Leng N, Thastrom A, Shen X, Hashimoto K, Tayama D, van der Heijden MS, Rosenberg JE, McDermott DF, Powles T, Hegde PS, Huseni MA, Mariathasan S, High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat Med 26, 693–698 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, Walsh AM, Baxi V, Pandya D, Baradet T, Locke D, Wu Q, Reilly TP, Phillips P, Nagineni V, Gianino N, Gu J, Zhao H, Perez-Gracia JL, Sanmamed MF, Melero I, Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat Med 26, 688–692 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, Kaley TJ, Kendall SM, Motzer RJ, Hakimi AA, Voss MH, Russo P, Rosenberg J, Iyer G, Bochner BH, Bajorin DF, Al-Ahmadie HA, Chaft JE, Rudin CM, Riely GJ, Baxi S, Ho AL, Wong RJ, Pfister DG, Wolchok JD, Barker CA, Gutin PH, Brennan CW, Tabar V, Mellinghoff IK, DeAngelis LM, Ariyan CE, Lee N, Tap WD, Gounder MM, D’Angelo SP, Saltz L, Stadler ZK, Scher HI, Baselga J, Razavi P, Klebanoff CA, Yaeger R, Segal NH, Ku GY, DeMatteo RP, Ladanyi M, Rizvi NA, Berger MF, Riaz N, Solit DB, Chan TA, Morris LGT, Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 51, 202–206 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data file S1

NIHMS1801836-supplement-Supplementary_data_file_S1.xlsx^{(120.6KB, xlsx)}

Supplementary data file S2

NIHMS1801836-supplement-Supplementary_data_file_S2.csv^{(45.3KB, csv)}

Supplementary materials

NIHMS1801836-supplement-Supplementary_materials.pdf^{(2.6MB, pdf)}

[R1] 1.Andrews LP, Yano H, Vignali DAA, Inhibitory receptors and ligands beyond PD-1, PD-L1 and CTLA-4: breakthroughs or backups. Nat Immunol 20, 1425–1434 (2019). [DOI] [PubMed] [Google Scholar]

[R2] 2.Brignone C, Escudier B, Grygar C, Marcu M, Triebel F, A phase I pharmacokinetic and biological correlative study of IMP321, a novel MHC class II agonist, in patients with advanced renal cell carcinoma. Clin Cancer Res 15, 6225–6231 (2009). [DOI] [PubMed] [Google Scholar]

[R3] 3.Eastgate MA, Atkinson V, Khattak MA, Roy AC, Haydon AM, Mueller C, Dunkelmann T, Brignone C, Triebel F, in Journal of Clinical Oncology 36, no. 15_suppl. (2018). [Google Scholar]

[R4] 4.Ascierto PA, Melero I, Bhatia S, Bono P, Sanborn RE, Lipson EJ, Callahan MK, Gajewski T, Gomez-Roca CA, Hodi FS, Curigliano G, Nyakas M, Preusser M, Koguchi Y, Maurer M, Clynes R, Mitra P, Suryawanshi S, Muñoz-Couselo E, Initial efficacy of anti-lymphocyte activation gene-3 (anti–LAG-3; BMS-986016) in combination with nivolumab (nivo) in pts with melanoma (MEL) previously treated with anti–PD-1/PD-L1 therapy. Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017) 9520–9520., (2017). [Google Scholar]

[R5] 5.Harding JJ, Patnaik A, Moreno V, Stein M, Jankowska AM, Velez de Mendizabal N, Liu ZT, Koneru M, Calvo E, in J Clin Oncol 37, 2019 (suppl 8; abstr 12). (2019). [Google Scholar]

[R6] 6.Peguero J, Felip E, Bernard D, Margarita M, Enric C, Tim C, Pawan B, Krebs MG, Triebel F, in SITC. (2019). [Google Scholar]

[R7] 7.D. e. a. Rodriguez-Abreu, in ASCO. (2020). [Google Scholar]

[R8] 8.Signorelli D, Giannatempo P, Grazia G, Aiello MM, Bertolini F, Mirabile A, Buti S, Vasile E, Scotti V, Pisapia P, Cona MS, Rolfo C, Malapelle U, Immune-Oncology YG, Patients Selection for Immunotherapy in Solid Tumors: Overcome the Naive Vision of a Single Biomarker. Biomed Res Int 2019, 9056417 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.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 7, 278 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.TMB Faces Validation Hurdles. Cancer Discov 9, 1334 (2019). [DOI] [PubMed] [Google Scholar]

[R11] 11.Bochem J, Zelba H, Amaral T, Spreuer J, Soffel D, Eigentler T, Wagner NB, Uslu U, Terheyden P, Meier F, Garbe C, Pawelec G, Weide B, Wistuba-Hamprecht K, Peripheral PD-1+CD56+ T-cell frequencies correlate with outcome in stage IV melanoma under PD-1 blockade. PLoS One 14, e0221301 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Capone M, Fratangelo F, Giannarelli D, Sorrentino C, Turiello R, Zanotta S, Galati D, Madonna G, Tuffanelli M, Scarpato L, Grimaldi AM, Esposito A, Azzaro R, Pinto A, Cavalcanti E, Pinto A, Morello S, Ascierto PA, Frequency of circulating CD8+CD73+T cells is associated with survival in nivolumab-treated melanoma patients. J Transl Med 18, 121 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Koh J, Kim Y, Lee KY, Hur JY, Kim MS, Kim B, Cho HJ, Lee YC, Bae YH, Ku BM, Sun JM, Lee SH, Ahn JS, Park K, Ahn MJ, MDSC subtypes and CD39 expression on CD8(+) T cells predict the efficacy of anti-PD-1 immunotherapy in patients with advanced NSCLC. Eur J Immunol, (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Arshi A, Olshen AB, Seshan VE, Shen R, Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering. Genome Medicine, (2020, manuscript accepted). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Friedman J, Hastie T, Tibshirani R, Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33, 1–22 (2010). [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Postow MA, Chasalow SD, Kuk D, Panageas KS, Cheng ML, Yuan J, Wolchok JD, Absolute lymphocyte count as a prognostic biomarker for overall survival in patients with advanced melanoma treated with ipilimumab. Melanoma Res 30, 71–75 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, Hollmann TJ, Bruggeman C, Kannan K, Li Y, Elipenahli C, Liu C, Harbison CT, Wang L, Ribas A, Wolchok JD, Chan TA, Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371, 2189–2199 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS, Miller ML, Rekhtman N, Moreira AL, Ibrahim F, Bruggeman C, Gasmi B, Zappasodi R, Maeda Y, Sander C, Garon EB, Merghoub T, Wolchok JD, Schumacher TN, Chan TA, Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA, Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 20, 5064–5074 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, Dawson N, O’Donnell PH, Balmanoukian A, Loriot Y, Srinivas S, Retz MM, Grivas P, Joseph RW, Galsky MD, Fleming MT, Petrylak DP, Perez-Gracia JL, Burris HA, Castellano D, Canil C, Bellmunt J, Bajorin D, Nickles D, Bourgon R, Frampton GM, Cui N, Mariathasan S, Abidoye O, Fine GD, Dreicer R, Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Daud AI, Wolchok JD, Robert C, Hwu WJ, Weber JS, Ribas A, Hodi FS, Joshua AM, Kefford R, Hersey P, Joseph R, Gangadhar TC, Dronca R, Patnaik A, Zarour H, Roach C, Toland G, Lunceford JK, Li XN, Emancipator K, Dolled-Filhart M, Kang SP, Ebbinghaus S, Hamid O, Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. J Clin Oncol 34, 4102–4109 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HE, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS, Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Grosso JF, Goldberg MV, Getnet D, Bruno TC, Yen HR, Pyle KJ, Hipkiss E, Vignali DA, Pardoll DM, Drake CG, Functionally distinct LAG-3 and PD-1 subsets on activated and chronically stimulated CD8 T cells. J Immunol 182, 6659–6669 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Woo SR, Turnis ME, Goldberg MV, Bankoti J, Selby M, Nirschl CJ, Bettini ML, Gravano DM, Vogel P, Liu CL, Tangsombatvisit S, Grosso JF, Netto G, Smeltzer MP, Chaux A, Utz PJ, Workman CJ, Pardoll DM, Korman AJ, Drake CG, A D. Vignali, Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72, 917–927 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Kraman M, Faroudi M, Allen NL, Kmiecik K, Gliddon D, Seal C, Koers A, Wydro MM, Batey S, Winnewisser J, Young L, Tuna M, Doody J, Morrow M, Brewis N, FS118, a Bispecific Antibody Targeting LAG-3 and PD-L1, Enhances T-Cell Activation Resulting in Potent Antitumor Activity. Clin. Cancer Res, (2020). [DOI] [PubMed] [Google Scholar]

[R26] 26.Grosso JF, Kelleher CC, Harris TJ, Maris CH, Hipkiss EL, De Marzo A, Anders R, Netto G, Getnet D, Bruno TC, Goldberg MV, Pardoll DM, Drake CG, LAG-3 regulates CD8+ T cell accumulation and effector function in murine self- and tumor-tolerance systems. J Clin Invest 117, 3383–3392 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Youn JI, Park SM, Park S, Kim G, Lee HJ, Son J, Hong MH, Ghaderpour A, Baik B, Islam J, Choi JW, Lee EY, Kim HR, Seo SU, Paik S, Yoon HI, Jung I, Xin CF, Jin HT, Cho BC, Seong SY, Ha SJ, Kim HR, Peripheral natural killer cells and myeloid-derived suppressor cells correlate with anti-PD-1 responses in non-small cell lung cancer. Sci Rep 10, 9050 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM, Dummer R, Robinson MD, Levesque MP, Becher B, High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24, 144–153 (2018). [DOI] [PubMed] [Google Scholar]

[R29] 29.Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M, Zhou J, Manos M, Eastman LM, Maecker HT, Hodi FS, Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer 6, 18 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Martens A, Wistuba-Hamprecht K, Geukes Foppen M, Yuan J, Postow MA, Wong P, Romano E, Khammari A, Dreno B, Capone M, Ascierto PA, Di Giacomo AM, Maio M, Schilling B, Sucker A, Schadendorf D, Hassel JC, Eigentler TK, Martus P, Wolchok JD, Blank C, Pawelec G, Garbe C, Weide B, Baseline Peripheral Blood Biomarkers Associated with Clinical Outcome of Advanced Melanoma Patients Treated with Ipilimumab. Clin Cancer Res 22, 2908–2918 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Huang AC, Postow MA, Orlowski RJ, Mick R, Bengsch B, Manne S, Xu W, Harmon S, Giles JR, Wenz B, Adamow M, Kuk D, Panageas KS, Carrera C, Wong P, Quagliarello F, Wubbenhorst B, D’Andrea K, Pauken KE, Herati RS, Staupe RP, Schenkel JM, McGettigan S, Kothari S, George SM, Vonderheide RH, Amaravadi RK, Karakousis GC, Schuchter LM, Xu X, Nathanson KL, Wolchok JD, Gangadhar TC, Wherry EJ, T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Wistuba-Hamprecht K, Martens A, Heubach F, Romano E, Geukes Foppen M, Yuan J, Postow M, Wong P, Mallardo D, Schilling B, Di Giacomo AM, Khammari A, Dreno B, Maio M, Schadendorf D, Ascierto PA, Wolchok JD, Blank CU, Garbe C, Pawelec G, Weide B, Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur. J. Cancer 73, 61–70 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Zappasodi R, Budhu S, Hellmann MD, Postow MA, Senbabaoglu Y, Manne S, Gasmi B, Liu C, Zhong H, Li Y, Huang AC, Hirschhorn-Cymerman D, Panageas KS, Wherry EJ, Merghoub T, Wolchok JD, Non-conventional Inhibitory CD4(+)Foxp3(−)PD-1(hi) T Cells as a Biomarker of Immune Checkpoint Blockade Activity. Cancer Cell 34, 691 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM, Gherardini PF, Prestwood TR, Chabon J, Bendall SC, Fong L, Nolan GP, Engleman EG, Systemic Immunity Is Required for Effective Cancer Immunotherapy. Cell 168, 487–502 e415 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Kagamu H, Kitano S, Yamaguchi O, Yoshimura K, Horimoto K, Kitazawa M, Fukui K, Shiono A, Mouri A, Nishihara F, Miura Y, Hashimoto K, Murayama Y, Kaira K, Kobayashi K, CD4(+) T-cell Immunity in the Peripheral Blood Correlates with Response to Anti-PD-1 Therapy. Cancer Immunol Res 8, 334–344 (2020). [DOI] [PubMed] [Google Scholar]

[R36] 36.Fairfax BP, Taylor CA, Watson RA, Nassiri I, Danielli S, Fang H, Mahe EA, Cooper R, Woodcock V, Traill Z, Al-Mossawi MH, Knight JC, Klenerman P, Payne M, Middleton MR, Peripheral CD8(+) T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat Med 26, 193–199 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Ohmura H, Yamaguchi K, Hanamura F, Ito M, Makiyama A, Uchino K, Shimokawa H, Tamura S, Esaki T, Mitsugi K, Shibata Y, Oda H, Tsuchihashi K, Ariyama H, Kusaba H, Oda Y, Akashi K, Baba E, OX40 and LAG3 are associated with better prognosis in advanced gastric cancer patients treated with anti-programmed death-1 antibody. Br J Cancer 122, 1507–1517 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Weber JS, Sznol M, Sullivan RJ, Blackmon S, Boland G, Kluger HM, Halaban R, Bacchiocchi A, Ascierto PA, Capone M, Oliveira C, Meyer K, Grigorieva J, Asmellash SG, Roder J, Roder H, A Serum Protein Signature Associated with Outcome after Anti-PD-1 Therapy in Metastatic Melanoma. Cancer Immunol Res 6, 79–86 (2018). [DOI] [PubMed] [Google Scholar]

[R39] 39.Yuen KC, Liu LF, Gupta V, Madireddi S, Keerthivasan S, Li C, Rishipathak D, Williams P, Kadel EE 3rd, Koeppen H, Chen YJ, Modrusan Z, Grogan JL, Banchereau R, Leng N, Thastrom A, Shen X, Hashimoto K, Tayama D, van der Heijden MS, Rosenberg JE, McDermott DF, Powles T, Hegde PS, Huseni MA, Mariathasan S, High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat Med 26, 693–698 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, Walsh AM, Baxi V, Pandya D, Baradet T, Locke D, Wu Q, Reilly TP, Phillips P, Nagineni V, Gianino N, Gu J, Zhao H, Perez-Gracia JL, Sanmamed MF, Melero I, Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat Med 26, 688–692 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, Kaley TJ, Kendall SM, Motzer RJ, Hakimi AA, Voss MH, Russo P, Rosenberg J, Iyer G, Bochner BH, Bajorin DF, Al-Ahmadie HA, Chaft JE, Rudin CM, Riely GJ, Baxi S, Ho AL, Wong RJ, Pfister DG, Wolchok JD, Barker CA, Gutin PH, Brennan CW, Tabar V, Mellinghoff IK, DeAngelis LM, Ariyan CE, Lee N, Tap WD, Gounder MM, D’Angelo SP, Saltz L, Stadler ZK, Scher HI, Baselga J, Razavi P, Klebanoff CA, Yaeger R, Segal NH, Ku GY, DeMatteo RP, Ladanyi M, Rizvi NA, Berger MF, Riaz N, Solit DB, Chan TA, Morris LGT, Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 51, 202–206 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade.

Ronglai Shen, PhD

Michael A Postow, MD

Matthew Adamow, BS

Arshi Arora, MS

Margaret Hannum, MS

Colleen Maher, BA

Phillip Wong, PhD

Michael A Curran, PhD

Travis J Hollmann, MD, PhD

Liwei Jia, MD, PhD

Hikmat Al-Ahmadie, MD

Niamh Keegan, MD

Samuel A Funt, MD

Gopa Iyer, MD

Jonathan E Rosenberg, MD

Dean F Bajorin, MD

Paul B Chapman, MD

Alexander N Shoushtari, MD

Allison S Betof, MD, PhD

Parisa Momtaz, MD

Taha Merghoub, PhD

Jedd D Wolchok, MD, PhD

Katherine S Panageas, DrPH

Margaret K Callahan, MD, PhD