Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

doi:10.1126/science.aan4236

. 2018 Jan 5;359(6371):97-103.

doi: 10.1126/science.aan4236. Epub 2017 Nov 2.

Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

V Gopalakrishnan^{1

2}, C N Spencer^{2

3}, L Nezi³, A Reuben¹, M C Andrews¹, T V Karpinets³, P A Prieto¹, D Vicente¹, K Hoffman⁴, S C Wei⁵, A P Cogdill^{1

5}, L Zhao³, C W Hudgens⁶, D S Hutchinson⁷, T Manzo³, M Petaccia de Macedo⁶, T Cotechini⁸, T Kumar³, W S Chen⁹, S M Reddy¹⁰, R Szczepaniak Sloane¹, J Galloway-Pena¹¹, H Jiang¹, P L Chen⁹, E J Shpall¹², K Rezvani¹², A M Alousi¹², R F Chemaly¹¹, S Shelburne^{3

11}, L M Vence⁵, P C Okhuysen¹¹, V B Jensen¹³, A G Swennes⁷, F McAllister¹⁴, E Marcelo Riquelme Sanchez¹⁴, Y Zhang¹⁴, E Le Chatelier¹⁵, L Zitvogel¹⁶, N Pons¹⁵, J L Austin-Breneman¹, L E Haydu¹, E M Burton¹, J M Gardner¹, E Sirmans¹⁷, J Hu¹⁸, A J Lazar^{6

9}, T Tsujikawa⁸, A Diab¹⁷, H Tawbi¹⁷, I C Glitza¹⁷, W J Hwu¹⁷, S P Patel¹⁷, S E Woodman¹⁷, R N Amaria¹⁷, M A Davies¹⁷, J E Gershenwald¹, P Hwu¹⁷, J E Lee¹, J Zhang³, L M Coussens⁸, Z A Cooper^{1

3}, P A Futreal³, C R Daniel^{4

2}, N J Ajami⁷, J F Petrosino⁷, M T Tetzlaff^{6

9}, P Sharma^{5

19}, J P Allison⁵, R R Jenq³, J A Wargo^{20

3}

Affiliations

¹ Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
² Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX 77030, USA.
³ Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁵ Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁶ Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁷ Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA.
⁸ Department of Cell, Developmental and Cell Biology, Oregon Health and Sciences University, Portland, OR 97239, USA.
⁹ Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁰ Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹¹ Department of Infectious Diseases, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹² Department of Stem Cell Transplantation, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹³ Department of Veterinary Medicine and Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁴ Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁵ Centre de Recherche de Jouy-en-Josas, Institut National de la Recherche Agronomique, 78352 Jouy-en-Josas, France.
¹⁶ Centre d'Investigation Clinique Biothérapie, Institut Gustave-Roussy, 94805 Villejuif Cedex, France.
¹⁷ Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁸ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁹ Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
²⁰ Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. jwargo@mdanderson.org.

PMID: 29097493
PMCID: PMC5827966
DOI: 10.1126/science.aan4236

Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

V Gopalakrishnan et al. Science. 2018.

. 2018 Jan 5;359(6371):97-103.

doi: 10.1126/science.aan4236. Epub 2017 Nov 2.

Authors

Affiliations

¹ Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
² Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX 77030, USA.
³ Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁵ Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁶ Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁷ Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA.
⁸ Department of Cell, Developmental and Cell Biology, Oregon Health and Sciences University, Portland, OR 97239, USA.
⁹ Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁰ Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹¹ Department of Infectious Diseases, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹² Department of Stem Cell Transplantation, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹³ Department of Veterinary Medicine and Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁴ Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁵ Centre de Recherche de Jouy-en-Josas, Institut National de la Recherche Agronomique, 78352 Jouy-en-Josas, France.
¹⁶ Centre d'Investigation Clinique Biothérapie, Institut Gustave-Roussy, 94805 Villejuif Cedex, France.
¹⁷ Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁸ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁹ Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
²⁰ Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. jwargo@mdanderson.org.

PMID: 29097493
PMCID: PMC5827966
DOI: 10.1126/science.aan4236

Abstract

Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy (n = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples (n = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity (P < 0.01) and relative abundance of bacteria of the Ruminococcaceae family (P < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.

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Figures

**Figure 1. Enhanced gut microbiome diversity is associated with improved response to anti-PD-1 immunotherapy in patients with metastatic melanoma**
(A) Schema of sample collection and analyses. (B) Stacked bar plot of phylogenetic composition of common bacterial taxa (>0.1% abundance) at the order level in oral (n=109, top) and fecal (n=53, bottom) samples by 16S rRNA sequencing. (C) Inverse Simpson diversity scores of the gut microbiome in R (n=30) and NR (n=13) to anti PD-1 immunotherapy by Mann-Whitney (MW) test. Error bars represent the distribution of diversity scores. (D) Phylogenetic composition of fecal samples (n=39) at the family level (>0.1% abundance) at baseline. High (blue) (>11.63, n=13), intermediate (gold) (7.46-11.63, n=13) and low (red) (<7.46, n=13) diversity groups were determined using tertiles of Inverse Simpson scores. (E) Kaplan-Meier (KM) plot of progression-free survival (PFS) by fecal diversity; high (median PFS undefined), intermediate (median PFS=232 days), and low (median PFS=188 days). High vs intermediate diversity (HR 3.60, 95% C.I. 1.02-12.74) and high vs low (HR 3.57, 95% C.I. 1.02-12.52) by univariate Cox model. *p<0.05, **p<0.01. (F) Principal coordinate analysis of fecal samples (n=43) by response using Weighted UniFrac distances.

**Figure 2. Compositional differences in the gut microbiome are associated with responses to anti-PD-1 immunotherapy**
(A) Heatmap of OTU abundances in R (n=30) and NR (n=13). Columns denote patients grouped by response and sorted by diversity within R and NR groups; rows denote bacterial OTUs grouped into 3 sets according to their enrichment/depletion in R versus NR: Set 1 (enriched in R), Set 2 (unenriched), and Set 3 (enriched in NR), and then sorted by mean abundance within each set. (B) Phylogenetic composition of OTUs within each set at the order level. Set 1 (enriched in R); Set 2 (unenriched); Set 3 (enriched in NR). (C) Taxonomic cladogram from LEfSe showing differences in fecal taxa. Dot size is proportional to the abundance of the taxon. Letters correspond to the following taxa: (a) *Gardnerella vaginalis*, (b) Gardnerella, (c) *Rothia*, (d) *Micrococcaceae*, (e) *Collinsella stercoris*, (f) *Bacteroides mediterraneensis*, (g) *Porphyromonas pasteri*, (h) *Prevotella histicola*, (i) *Faecalibacterium prausnitzii*, (j) *Faecalibacterium*, (k) *Clostridium hungatei*, (l) *Ruminococcus bromii*, (m) Ruminococcaceae, (n) *Phascolarctobacterium faecium*, (o) *Phascolarctobacterium*, (p) *Veilonellaceae*, (q) *Peptoniphilus*, (r) *Desulfovbrio alaskensis.* (D) LDA scores computed for differentially-abundant taxa in the fecal microbiomes of R (blue) and NR (red). Length indicates effect size associated with a taxon. p=0.05 for the Kruskal-Wallis test; LDA score > 3. (E) Differentially-abundant gut bacteria in R (blue) vs NR (red) by MW test (FDR-adjusted) within all taxonomic levels. (F) Pairwise comparisons by MW test of abundances of metagenomic species (MGS) identified by metagenomic WGS in fecal samples (n=25): R (n=14, blue), NR (n=11, red). *p<0.05, **p<0.01. Colors reflect gene abundances visualized using “barcodes” with the following order of intensity: white(0)<light blue<blue<green<yellow<orange<red for increasing abundance and each color change corresponds to a 4x fold abundance change. In these barcodes, MGS appear as vertical lines (co-abundant genes in a sample) colored according to the gene abundance.

**Figure 3. Abundance of crOTUs within the gut microbiome is predictive of response to anti-PD-1 immunotherapy**
(A) Top: Unsupervised hierarchical clustering by complete linkage of Euclidean distances of crOTU abundances in 43 fecal samples. Bottom: Stacked bar plot of relative abundances at the order level by crOTU community-type. (B) Association of crOTU community types with response to anti-PD-1 by Fisher’s exact test. crOTU community type 1 (black, n=11: R=11, NR=0); crOTU community type 2 (orange, n=32: R=19, NR=13). Blue bars indicate responders, whereas red bars indicate non-responders. (C) Comparison KM PFS curves by long-rank test in patients with high abundance (dark blue, n=19, median PFS=undefined) or low abundance (light blue, n=20, median PFS=242 days) of *Faecalibacterium* (top PFS curve). High abundance (dark red, n=20, median PFS=188 days) or low abundance (light red, n=19, median PFS=393 days) of Bacteroidales (bottom PFS curve). (D) Unsupervised hierarchical clustering of pathway class enrichment calculated as the number of MetaCyc pathways predicted in the metagenomes of fecal samples from 25 patients (R=14, NR=11). Columns represent patient samples (blue=R, red=NR) and rows represent enrichment of predicted MetaCyc pathways (blue=low enrichment, black=medium enrichment, yellow= high enrichment). Black text: biosynthetic pathways, blue text: degradative pathways. *p<0.05.

**Figure 4. A favorable gut microbiome is associated with enhanced systemic and anti-tumor immunity**
(A) Quantification by IHC of the CD8+ T cell infiltrate at pre-treatment in tumors in R (n=15, blue) and NR (n=6, red) by one-sided MW test. Error bars represent the distribution of CD8+ T cell densities. (B) Pairwise Spearman rank correlation heatmap of significantly different taxa in fecal samples (n=15) at baseline and CD3, CD8, PD-1, FoxP3, Granzyme B, PD-L1 and RORγT density by H-score in matched tumors. (C) Univariate linear regression between CD8+ counts/mm2 in the tumor versus *Faecalibacterium* (blue, r²=0.42, p<0.01) and Bacteroidales (red, r²=0.06, p=0.38) abundance in the gut. (D) Pairwise Spearman rank correlation heatmap between significantly different fecal taxa and frequency of indicated cell types by flow cytometry in peripheral blood at baseline. (E) Representative multiplex IHC images and (F) Frequency of various immune cell types in patients having high *Faecalibacterium* (n=2) or Bacteroidales (n=2) in the gut. (G) Experimental design of studies in germ-free (GF) mice. Time in days (indicated as D) relative to tumor injection (2.5-8x10⁵ tumor cells). (H) Difference in size by MW test of tumors at day 14, implanted in R-FMT (blue) and NR-FMT mice (red) expressed as fold change (FC) relative to average tumor volume of Control GF mice. Data from 2 independent FMT experiments (R-FMT, n=5, median FC=0.18; NR-FMT, n=6, median FC=1.52). (I) Representative tumor growth curves for each GF mouse from α-PD-L1 treated R-FMT (blue n=2, median tumor volume=403.7 mm³), NR-FMT (red n=3, median tumor volume=2301 mm³), and Control (black, n=2, median tumor volume=771.35 mm³) mice. Statistics are as follows: p=0.20 (R-FMT vs NR-FMT), p=0.33 (NR-FMT vs Control) by MW test. Dotted black line marks tumor size cutoff for α-PD-L1 treatment (500mm³). (J) Quantification of CD8+ density in tumor of R-FMT (n=2, median=433.5 cells/HPF across 12 regions), NR-FMT (NR-FMT n=2, median=325 cells/HPF across 12 regions) and Control mice (n=2, median=412 cells/HPF across 9 regions). MW test p=0.30 (R-FMT vs Control). (K) Quantification of CD8+ density in gut (R-FMT n=2, median=67 cells/HPF across 7 regions), NR-FMT (n=2, median=24 cells/HPF across in 5 regions), Control n=2 (median=47 cells/HPF across 10 regions). MW test p=0.17 (R-FMT vs Control). *p<0.05, **p<0.01, ****p<0.0001.

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Immunotherapy: Gut bacteria modulate responses to PD-1 blockade.
Killock D. Killock D. Nat Rev Clin Oncol. 2018 Jan;15(1):6-7. doi: 10.1038/nrclinonc.2017.182. Epub 2017 Nov 21. Nat Rev Clin Oncol. 2018. PMID: 29158588 No abstract available.
Precision medicine using microbiota.
Jobin C. Jobin C. Science. 2018 Jan 5;359(6371):32-34. doi: 10.1126/science.aar2946. Science. 2018. PMID: 29302001 No abstract available.
Anti-PD1 in the wonder-gut-land.
Vetizou M, Trinchieri G. Vetizou M, et al. Cell Res. 2018 Mar;28(3):263-264. doi: 10.1038/cr.2018.12. Epub 2018 Jan 16. Cell Res. 2018. PMID: 29336431 Free PMC article.
Microbiome: Gut microbiota sways response to cancer immunotherapy.
York A. York A. Nat Rev Microbiol. 2018 Mar;16(3):121. doi: 10.1038/nrmicro.2018.12. Epub 2018 Jan 22. Nat Rev Microbiol. 2018. PMID: 29355853 No abstract available.
Checkpoint Checkmate: Microbiota Modulation of Cancer Immunotherapy.
Keen EC, Crofts TS, Dantas G. Keen EC, et al. Clin Chem. 2018 Sep;64(9):1280-1283. doi: 10.1373/clinchem.2017.286229. Epub 2018 Apr 24. Clin Chem. 2018. PMID: 29691222 Free PMC article. No abstract available.
The microbiome influence.
Stower H. Stower H. Nat Med. 2018 Dec;24(12):1782. doi: 10.1038/s41591-018-0285-2. Nat Med. 2018. PMID: 30523317 No abstract available.
[Gut microbiome influences efficacy of immunotherapy].
de Nonneville A. de Nonneville A. Bull Cancer. 2019 Jan;106(1):10. doi: 10.1016/j.bulcan.2018.12.004. Epub 2019 Jan 11. Bull Cancer. 2019. PMID: 30642560 French. No abstract available.

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References

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[1] Schadendorf D, Hodi FS, Robert C, Weber JS, Margolin K, Hamid O, Patt D, Chen T-T, Berman DM, Wolchok JD. Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma. J. Clin. Oncol. 2015;33:1889–1894. doi: 10.1200/JCO.2014.56.2736. - DOI - PMC - PubMed

[2] Schadendorf D, Hodi FS, Robert C, Weber JS, Margolin K, Hamid O, Patt D, Chen T-T, Berman DM, Wolchok JD. Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma. J. Clin. Oncol. 2015;33:1889–1894. doi: 10.1200/JCO.2014.56.2736. - DOI - PMC - PubMed

[3] Robert C, et al. KEYNOTE-006 investigators. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 2015;372:2521–2532. doi: 10.1056/NEJMoa1503093. - DOI - PubMed

[4] Robert C, et al. KEYNOTE-006 investigators. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 2015;372:2521–2532. doi: 10.1056/NEJMoa1503093. - DOI - PubMed

[5] Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Hassel JC, Rutkowski P, McNeil C, Kalinka-Warzocha E, Savage KJ, Hernberg MM, Lebbé C, Charles J, Mihalcioiu C, Chiarion-Sileni V, Mauch C, Cognetti F, Arance A, Schmidt H, Schadendorf D, Gogas H, Lundgren-Eriksson L, Horak C, Sharkey B, Waxman IM, Atkinson V, Ascierto PA. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 2015;372:320–330. doi: 10.1056/NEJMoa1412082. - DOI - PubMed

[6] Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Hassel JC, Rutkowski P, McNeil C, Kalinka-Warzocha E, Savage KJ, Hernberg MM, Lebbé C, Charles J, Mihalcioiu C, Chiarion-Sileni V, Mauch C, Cognetti F, Arance A, Schmidt H, Schadendorf D, Gogas H, Lundgren-Eriksson L, Horak C, Sharkey B, Waxman IM, Atkinson V, Ascierto PA. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 2015;372:320–330. doi: 10.1056/NEJMoa1412082. - DOI - PubMed

[7] Vesely MD, Schreiber RD. Cancer immunoediting: Antigens, mechanisms, and implications to cancer immunotherapy. Ann. N. Y. Acad. Sci. 2013;1284:1–5. doi: 10.1111/nyas.12105. - DOI - PMC - PubMed

[8] Vesely MD, Schreiber RD. Cancer immunoediting: Antigens, mechanisms, and implications to cancer immunotherapy. Ann. N. Y. Acad. Sci. 2013;1284:1–5. doi: 10.1111/nyas.12105. - DOI - PMC - PubMed

[9] Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: A common denominator approach to cancer therapy. Cancer Cell. 2015;27:450–461. doi: 10.1016/j.ccell.2015.03.001. - DOI - PMC - PubMed

[10] Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: A common denominator approach to cancer therapy. Cancer Cell. 2015;27:450–461. doi: 10.1016/j.ccell.2015.03.001. - DOI - PMC - PubMed

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Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

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Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

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