The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index; 6 of the 13 sources its claims cite were checked for support.
The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients
Open accessAbstract
Good bacteria help fight cancer Resident gut bacteria can affect patient responses to cancer immunotherapy (see the Perspective by Jobin). Routy et al. show that antibiotic consumption is associated with poor response to immunotherapeutic PD-1 blockade. They profiled samples from patients with lung and kidney cancers and found that nonresponding patients had low levels of the bacterium Akkermansia muciniphila . Oral supplementation of the bacteria to antibiotic-treated mice restored the response to immunotherapy. Matson et al. and Gopalakrishnan et al. studied melanoma patients receiving PD-1 blockade and found a greater abundance of “good” bacteria in the guts of responding patients. Nonresponders had an imbalance in gut flora composition, which correlated with impaired immune cell activity. Thus, maintaining healthy gut flora could help patients combat cancer. Science , this issue p. 91 , p. 104 , p. 97 ; see also p. 32
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
26 Own result · 6 Own interpretation · 4 No full text of the sources · 4 Own method · 2 Supported · 2 Supported so far · 1 Not checked yet · accuracy not yet measured
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Two additional studies have identified an association of the commensal microbiome with anti–PD-1 mAb efficacy in different solid cancers (7,14).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Not checked — no full text of the sourcecites [7] source
- Supportedcites [14] sourceExperimental — accuracy not yet measured
The opening summary and patient-cohort results report an association between gut microbiome composition and response to anti-PD-1 therapy in melanoma, a solid cancer. This paper supports the claim as one of the two cited studies; it need not establish the findings of the other study. Page numbers are not supplied in our copy. (LLM, conf=0.92)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To determine whether the difference in tumor control could be attributed to host immunity, interferon-γ (IFN-γ) enzyme-linked immunosorbent spot (ELISPOT) of ex vivo SIY-stimulated splenocytes was performed and indicated an increased frequency of activated T cells from R microbiota-reconstituted mice 3 weeks after inoculation with B16.SIY melanoma cells (Fig. 3C).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A significant association was observed between commensal microbial composition and clinical response.”
- Also stated in: § Abstract · hedged
“Together, our data suggest that the composition of the commensal microbiota in patients is associated with therapeutic efficacy of anti–PD-1 monoclonal antibody (mAb).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Abstract · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After removing OTUs present in less than 10% of the samples, the 16S sequencing revealed 62 OTUs of different abundance in R versus NR [P < 0.05, unadjusted, permutation test with Quantitative Insights Into Microbial Ecology (QIIME)] (table S2).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Quantitative PCR results for these 10 species were integrated into a summation quantitative PCR score for each patient, which was significantly higher in responders (P = 0.004) (Fig. 2D).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The human microbiota-colonized mouse groups segregated into two phenotypes with respect to tumor growth rate: (i) a faster growing group and (ii) a slower growing group (Fig. 3A).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A principal component analysis (PCA) of the 63 OTUs revealed separation of R from NR (Fig. 1B).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hierarchical clustering of samples based on relative abundance of these OTUs revealed that most patients were accurately grouped according to clinical response (fig. S2).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the ability of the human microbiota to support improved tumor control in mice usually, but not always, paralleled the clinical response to anti–PD-1 seen in the donor patient.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the microbiota parameters still markedly separated responders and nonresponders.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 10
- It may state two separate claims as one. 17
Every source the paper cites for this claim was checked and supports it.
In the paper
“prior work in mice has indicated that some commensals have the potential to be immune-inhibitory—for example, through the induction of FoxP3+ regulatory T cells (11,12).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Supportedcites [11] source
The Abstract reports that indigenous Clostridium species promoted the accumulation of Foxp3+ regulatory T cells in the colons of mice. It also describes intestinal Foxp3+ Tregs as immunosuppressive, supporting the claim's qualified description of some commensals as potentially immune-inhibitory. (LLM, conf=0.93)
- Supportedcites [12] source
The Abstract reports that colonizing germ-free animals with the commensal B. fragilis induces Foxp3+ regulatory T cells and increases their suppressive capacity. The Discussion links this Treg induction to protection from experimental colitis, supporting the claim's example of immune inhibition in mice. (LLM, conf=0.94)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Tumor-intrinsic activation of the Wnt/β-catenin pathway (15) and deletion or mutation of Pten (16) have been shown to lead to deficient T cell infiltration into the tumor microenvironment and resistance to checkpoint blockade immunotherapy.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Not checked — no full text of the sourcecites [15] source
- Supportedcites [16] source
Citation (16) is attached to the PTEN part of the sentence, not the Wnt/β-catenin part. The Results report that PTEN deletions and loss-of-function mutations are enriched in non-T-cell-inflamed melanomas, that PTEN-loss tumors have less CD8+ T-cell infiltration, and that patients with PTEN-absent tumors had poorer tumor-size responses to anti-PD-1 treatment. No page numbers are available in our copy. (LLM, conf=0.92)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Every source the paper cites for this claim was checked and supports it.
In the paper
“There were 16 responders (from here on, referred to as R) and 26 nonresponders (NR), yielding a response rate of 38%, which is in line with published clinical data of anti–PD-1 therapy in metastatic melanoma patients (5, 6).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Supportedcites [5] source
The citing paper reports the 38% rate for its own patients; it cites this source for the comparison with published anti–PD-1 melanoma results. In Results > Clinical Activity, the source reports melanoma response rates of 19–41% across dose levels, a range that includes 38%. Its overall melanoma response rate was 28%, not 38%. (LLM, conf=0.90)
- Supportedcites [6] source
The Rates of Response section reports pembrolizumab response rates of 33.7% and 32.9% in patients with advanced melanoma. The citing paper's 38% is its own result; the cited trial supports the comparison to published anti-PD-1 clinical data, not an exact 38% rate. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Anti–PD-L1 was markedly efficacious in mice colonized with R microbiota yet completely ineffective in NR-derived mice (Fig. 3F), demonstrating a profound impact of the commensal microbiota on immunotherapy efficacy in vivo.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When plotted against the absolute change in tumor size as assessed with RECIST, a clean correlation was observed so that patients with a ratio over 1.5 all showed clinical response (Fig. 2E).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: OTUs more abundant in responders are beneficial and those more abundant in nonresponders are non-beneficial.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Exome sequencing followed by enumeration of nonsynonymous somatic mutations showed a trend of higher frequency in R, as did levels of PD-L1 and PD-1 mRNA (fig. S6, C to E) and enumeration of baseline CD8+ T cells by means of immunohistochemistry (fig. S6F). Although these trends not reaching statistical significance level of 0.05 were likely limited by sample size, the microbiota parameters still”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Potential species matches were identified for 43 of the original 63 OTUs (table S4).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Interrogation of fecal DNA from these mice by means of quantitative PCR recapitulated the results from our analysis of patients. Of the PCR reactions validated in patients, six were observed in re-constituted mice, with the same pattern of enrichment as was seen in patients (Fig. 2G).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two of three mouse cohorts reconstituted with R fecal material had slower baseline tumor growth, and two of the three cohorts reconstituted from NR showed faster baseline tumor growth.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Analysis of the tumor microenvironment also showed a significantly greater number of SIY–specific CD8+ T cells, but not of FoxP3+CD4+ regulatory T cells, in these mice (Fig. 3, D and E), which is consistent with increased priming of tumor antigen-specific CD8+ T cells.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A BLAST search of the 63 OTUs against the NCBI database of bacterial sequences returned for most OTUs multiple species with ≥98% identity (table S3).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we found that B16.SIY melanoma tumor growth in GF mice was similar to that in specific pathogen–free (SPF) mice (both from Taconic), and colonization of GF mice with feces from Taconic SPF mice did not affect this baseline growth rate (fig. S8).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One Bifidobacteriaceae OTU was significantly more abundant in R, and a second Bifidobacteriaceae OTU (559527) had borderline significance (P = 0.058, unadjusted) and was included in the analyses (total = 63 OTUs).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Groups C and D, which did not show the same pattern of tumor control as the therapeutic outcome in the original human donors, showed a large degree of difference of microbiota composition from the original human donors (fig. S9). In agreement, a binary Bray-Curtis dissimilarity index for each donor/recipient pair was highest, at 0.7, for cohorts C and D versus 0.5 to 0.6 for the rest of the groups”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, Akkermansia muciniphila OTU (185186), in line with the study of anti–PD-1 efficacy in epithelial cancers by Routy et al. (7), was detected by means of 16S sequencing in four patients, and all were responders, but statistical analysis of the entire cohort is limited by the number of samples above the detection threshold.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Not checked — no full text of the sourcecites [7] source
plaincorrelationaln = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We conclude that whereas reconstitution of GF mice with human fecal material often recapitulates the microbial composition and the phenotype of the human donor, in some cases there is a high degree of drift so that some bacteria expand and others contract to a degree that is sufficient to change phenotype.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No major differences in patient characteristics were observed in R versus NR, except a borderline difference in prior (but not current) smoking history (table S1).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Decreased regulatory T cells, increased Batf3 DCs, and augmented TH1responses would all be expected to improve immune-mediated tumor control.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Clinical response rate was determined in a blinded manner from biomarker results by using Response Evaluation Criteria In Solid Tumors (RECIST) version 1.1.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results suggest a reduced spontaneous immune-mediated tumor control inherent to GF mice, which makes them suitable recipients for human-derived microbiota, with an opportunity to detect improved antitumor immunity depending on microbial composition.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“An increased frequency of the Batf3-lineage dendritic cells (DCs) and greater T helper cell 1 (TH1) responses were also found with bacteria currently identified to be more abundant in responders (13).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Not checked — no full text of the sourcecites [13] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Germline polymorphisms in immune regulatory genes also have the potential to influence the magnitude of spontaneous anti-tumor T cell responses (17).”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
What the sources say
- Not checked — no full text of the sourcecites [17] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This list of species is likely an underestimate of the total number of entities showing differential abundance in R versus NR because of the stringency of this composite analysis.”
§ The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients > Abstract
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
OTUs more abundant in responders are beneficial and those more abundant in nonresponders are non-beneficial.
“the total numbers of potentially “beneficial” and “non-beneficial” OTUs were scored for each patient (fig. S5), and a ratio was calculated.”
“a ratio of “beneficial” OTUs to “nonbeneficial” OTUs was the strongest predictor of clinical response.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Intestinal dysbiosis, broadly defined as an imbalance in the composition or function of the gut microbiota, is a significant contributor to resistance against immune checkpoint inhibitor (ICI) therapies.1–16,”
From Gut dysbiosis in oncology: a risk factor for immunoresistance (2026) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Studies in mouse models have provided compelling evidence that gut microbiota modifications resulting from faecal microbiota transplantation (FMT) improve ICI response rates38,42,91; however, the magnitude of these antitumour responses depends of factors such as the mouse strain or provider, mouse sex and individual experimental conditions106.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [91]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multiple studies have reported significant differences in microbial diversity and composition between responding and non-responding melanoma, non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC) patients treated with anti-PD-1 alone or in combination with anti-CTLA-4 agents (ipilimumab).2,4-8”
From Faecalibacterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Bifidobacterium, Akkermansia, and Roseburia have been associated with improved tumor immunotherapy in clinic34–37.”
From Combining gut microbiota modulation and chemotherapy by capecitabine-loaded prebiotic nanoparticle improves colorectal cancer therapy (2023) · cites this paper as [36]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“We next tested the generalizability of the balance described in Fig. 2a by computing it for patients from six independent melanoma cohorts5,7–10,12. Despite small sample sizes and large heterogeneity in terms of DNA isolation protocols and sequencing platforms (Supplementary Table 6), this analysis showed that the balance achieves a comparable AUC in several of the independent cohorts (Extended”
From Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Besides, reconstitution of germ-free mice with fecal material from responding patients could lead to improved tumor control, augmented T cell responses, and greater efficacy of anti-PD-L1 therapy.14”
From Gut microbiota and metabolites associate with outcomes of immune checkpoint inhibitor–treated unresectable hepatocellular carcinoma (2022) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“In human melanoma patients undergoing immunotherapy, gut microbiome composition has been significantly associated with clinical response [9–12]”
From Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients (2019) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“antitumor immunity was enhanced in germ-free mice receiving fecal transfer from the responding patients [9, 10].”
From Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients (2019) · cites this paper as [10]
In the citing paper
“The three studies all demonstrated that gut microbiota composition and diversity were predictive of response to ICI immunotherapy [31–33].”
From Gut microbiota influence immunotherapy responses: mechanisms and therapeutic strategies (2022) · cites this paper as [33]
In the citing paper
“A similar finding was that fecal microbiota transplantation (FMT) from ICI responding patients to germ-free or antibiotic-treated mice could improve tumor control and ameliorate responses to ICI, whereas FMT from non-responders failed to do so [31–33].”
From Gut microbiota influence immunotherapy responses: mechanisms and therapeutic strategies (2022) · cites this paper as [33]
In the citing paper
“Moreover, specific gut microbes, such as Bifidobacterium [16, 33, 51, 141], Akkermansia [31, 38], Enterococcus [15, 33, 141], Faecalibacterium [49, 142], and Ruminococcaceae [32], play the role of immune adjuvants in ICI immunotherapy.”
From Gut microbiota influence immunotherapy responses: mechanisms and therapeutic strategies (2022) · cites this paper as [33]
In the citing paper
“Recently, several studies have indicated that FMT could augment the antitumor effect of ICI and overcome resistance to immunotherapy [31–33].”
From Gut microbiota influence immunotherapy responses: mechanisms and therapeutic strategies (2022) · cites this paper as [33]
In the citing paper
“Previous studies have suggested that the gut microbiome influences the response to checkpoint inhibitors (CPIs) in patients with cancer.”
From Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial (2022) · cites this paper as [4]
In the citing paper
“in preclinical models, transplantation of fecal material enriched with Bifidobacterium spp. alone (even without CPIs) was sufficient to delay tumor growth4,5.”
From Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial (2022) · cites this paper as [4]
In the citing paper
“The intestinal microbiota has been shown to directly impact on the efficacy of specific cancer immune therapies1–3.”
From Microbial short-chain fatty acids modulate CD8+ T cell responses and improve adoptive immunotherapy for cancer (2021) · cites this paper as [2]
In the citing paper
“Multiple landmark studies have identified a role for the gut microbiome in modifying ICI treatment response1,2”
From Gut microbiota-derived hexa-acylated lipopolysaccharides enhance cancer immunotherapy responses (2025) · cites this paper as [1]
In the citing paper
“Although multiple studies have reported that a favorable gut microbiome is associated with response to anti–PD-1 in cancer patients, its precise composition is not yet fully understood (10–12).”
From Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients (2021) · cites this paper as [11]
In the citing paper
“As previously reported1,2, broad-spectrum antibiotics significantly abrogated anti-PD-1-induced tumour reduction”
From Gut microbiota-derived hexa-acylated lipopolysaccharides enhance cancer immunotherapy responses (2025) · cites this paper as [1]
In the citing paper
“administration of certain gut commensals or fecal microbiota transplantation (FMT) promotes anti–PD-1 efficacy in melanoma-bearing mice (10–12).”
From Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients (2021) · cites this paper as [11]
In the citing paper
“Specifically in melanoma, key bacterial species belonging to various phyla, including Actinobacteria (Bifidobacteriaceae spp. and Coriobacteriaceae spp.) and Firmicutes (Ruminococcaceae spp. and Lachnospiraceae spp.), are associated with favorable response to mAbs targeting PD-1 in cancer patients, although limited concordance among the identified species has been reported in different studies (10”
From Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients (2021) · cites this paper as [11]
In the citing paper
“In mice, composition of the gut microbiome modulates therapeutic activity of anti–PD-1 and anti–programmed death-ligand 1 (PD-L1)”
From Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients (2021) · cites this paper as [11]
In the citing paper
“Recently, the gut microbiota has emerged as a potent new factor associated with the efficacy of anti–CTLA4, anti–PD-1, and anti–PD-L1 treatment (8-14).”
From Enterococcus peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy (2021) · cites this paper as [13]
In the citing paper
“Multiple analyses of the commensal microbiome from human cohorts treated with immunotherapies targeting PD-1 have revealed that the bacterial genus Enterococcus is enriched in responding patients (12, 13).”
From Enterococcus peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy (2021) · cites this paper as [13]
In the citing paper
“the detection of potential NlpC/p60 orthologs in other genera such as Lactobacillus (Fig. 2C, Table S5), which has also been identified in immunotherapy-responsive patients (13), may provide additional specific microbiota correlations to predict and improve patient outcomes.”
From Enterococcus peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy (2021) · cites this paper as [13]
In the citing paper
“consistent with enhanced priming of a tumor antigen-specific immune response (9, 13).”
From Enterococcus peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy (2021) · cites this paper as [13]
In the citing paper
“Gut bacteria modulate the response to immune checkpoint blockade (ICB) treatment in cancer”
From Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response (2021) · cites this paper as [3]
In the citing paper
“Over the past decade, following the epidemiological demonstration of the negative effect of antibiotics on the efficacy of ICIs13,42,90, specific microbial taxa have been associated with response or resistance to ICIs across distinct cancer types26,38–40,42,91–100.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [91]
In the citing paper
“single studies in patients with melanoma receiving ICIs identified an enrichment of species such as F. prausnitzii, Bifidobacterium longum and Bacteroides caccae in samples from patients with a response (‘responders’)38,44,91, although the concordance of these microbiome characteristics with treatment response across studies was low97.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [91]
In the citing paper
“Our meta-analysis of microbial sequencing data from nine independent αPD-1-treated melanoma cohorts82–88 revealed a higher abundance of pfl, a key enzyme in bacterial formate production, linked to ICI efficacy.”
From Exercise-induced microbiota metabolite enhances CD8 T cell antitumor immunity promoting immunotherapy efficacy (2025) · cites this paper as [83]
In the citing paper
“The intestinal microbiome has emerged as a tumor-extrinsic predictive biomarker to immune checkpoint blockade (ICB)1–6.”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [5]
In the citing paper
“However, there is limited concordance among species identified across studies, which included small numbers of patients and used different analytical approaches, not always adjusted for multiple testing4,5,12,13.”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [5]
In the citing paper
“Administration of certain gut commensals promotes efficacy of anti-programmed cell death protein-1 (PD-1) therapy in mice, providing evidence that microbiota can drive immunotherapy outcomes4–6.”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [5]
In the citing paper
“Several studies, small in size and using variable analytical methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“Several studies have examined the stool composition of patients on checkpoint blockade with melanoma, non-small cell lung cancer (NSCLC), and renal cell cancer (RCC) (5–10). These studies have implicated increased alpha-diversity (8), along with specific members of each of the major bacterial phyla colonizing humans”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“While individually significant, a feature of these studies is that each has largely identified different sets of microbes and, overall, there is a lack of consensus regarding the microbial signals associated with clinical response and/or whether putative microbiota signals are tumor-specific or impacted by other clinical metadata.”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“These results indicate that data from our standardized methodology for 16S rRNA amplicon data produces comparable relative abundance profiles, including for high-interest species such as Faecalibacterium prausnitzii and Akkermansia muciniphila (Fig S1).”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“In contrast, the gut microbiota of non-responding patients seemed to be less diverse, with the genera Bacteroides (phylum Bacteroidetes), Ruminococci and Roseburia (both Firmicutes) being prominent.”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“In contrast, B. thetaiotaomicron was reported as enriched in R in the Frankel and Matson cohorts (5,7) whereas, using a consistent re-analysis approach for all cohorts, this species was enriched in NR.”
From A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors (2021) · cites this paper as [5]
In the citing paper
“The gut microbiome has been shown to have multiple effects on tumor biology, such as the transformation process, tumor progression, and the response to anticancer therapies including immunotherapy (21-29).”
From The human tumor microbiome is composed of tumor type–specific intracellular bacteria (2020) · cites this paper as [24]
In the citing paper
“For example, several bacterial biomarkers have been reported for response to checkpoint inhibitors in non-GI cancer indications (Frankel et al., 2017; Gopalakrishnan et al., 2018; Matson et al., 2018; Peters et al., 2019; Routy et al., 2018).”
From Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers (2021) · cites this paper as Matson et al., 2018
In the citing paper
“Furthermore, melanoma patients treated with anti-PD-L1 had higher levels of Bifdobacterium longum, Collinsella aerofaciens, and Enterococcus faecium, highlighting the importance of the microbiome [122].”
From Immune Checkpoint Inhibitors in Cancer Therapy (2022) · cites this paper as [122]
In the citing paper
“as the gut and tumour microbiota can affect the survival of patients with cancer and their responses to therapy8,14,40–42,50”
From Identification of bacteria-derived HLA-bound peptides in melanoma (2021) · cites this paper as [41]
In the citing paper
“The role of the gut microbiome in modulating tumor responses to immunotherapy in melanoma [5–7], non-small cell lung cancer, renal cell carcinoma, and urothelial carcinoma [6, 8] has received increased attention across a series of studies in recent years.”
From Gut microbiome affects the response to anti-PD-1 immunotherapy in patients with hepatocellular carcinoma (2019) · cites this paper as [5]
In the citing paper
“Microbial taxa associated with response to ICB in cancer patients differed among different reports (Gopalakrishnan et al., 2018; Matson et al., 2018; Routy et al., 2018).”
From Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment (2021) · cites this paper as Matson et al., 2018
In the citing paper
“Specific antitumor effects of microbiota have been associated with changes in T cell activation and/or recruitment into the tumor or tumor-draining lymph nodes (Matson et al., 2018; Routy et al., 2018; Shi et al., 2020a; Sivan et al., 2015; Tanoue et al., 2019; Vetizou et al., 2015).”
From Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment (2021) · cites this paper as Matson et al., 2018
In the citing paper
“These studies suggest that gut bacteria influences the activation of the immune system, promoting cancer-associated inflammation and ultimately affecting tumor responses to therapies.”
From Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes (2019) · cites this paper as Matson et al., 2018
In the citing paper
“The key role of the gut microbiota as a host factor mediating tumor responses to chemotherapy and immunotherapy in patients with melanoma and lung cancers has been recently highlighted”
From Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes (2019) · cites this paper as Matson et al., 2018
In the citing paper
“Bifidobacterium-treated mice showed increased IFN-γ production, followed by stronger tumor-specific T cell responses and slower tumor growth [36].”
From Gut microbiota-mediated immunomodulation in tumor (2021) · cites this paper as [36]
In the citing paper
“Recolonized germ-free mice with fecal samples from responders or dominant microbiota in responders could improve antitumor effects of ICIs [36, 94].”
From Gut microbiota-mediated immunomodulation in tumor (2021) · cites this paper as [36]
In the citing paper
“Responses to ICTs have been correlated with commensal gut flora, and there appear to be substantial differences in the diversity and composition in patients who respond to ICT versus nonresponders (205,206).”
From The Next Decade of Immune Checkpoint Therapy (2021) · cites this paper as [205]
In the citing paper
“Moreover, observations in humans and mice of gut microbes that stratify therapy response, particularly immunotherapy (16, 17, 21, 26), have not uncommonly shown varying taxonomic differences that persist despite uniform bioinformatic re-analyses, although there is greater concordance when examining functional profiles (161, 162).”
From The microbiome and human cancer (2021) · cites this paper as [21]
In the citing paper
“The composition of the commensal microbiota in patients is associated with the therapeutic efficacy of anti-PD-1 monoclonal antibody.42”
In the citing paper
“Very recent reports highlighted that the composition of gut microbiota may dictate, at least in part, the anticancer activity of ICI.”
From Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer (2018) · cites this paper as [22]
In the citing paper
“The microbiome has been described to majorly impact tumor immunity and response to ICI10–17”
From Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment (2023) · cites this paper as [13]
In the citing paper
“Previously, we demonstrated that differential bacterial signatures comprise the gut microbiome of responders versus non-responders to ICI therapy”
From Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis (2018) · cites this paper as [12]
In the citing paper
“and additionally showed that modulation of the gut microbiome of germ-free mice via FMT from patients could alter anti-tumor immunity and response to ICI therapy in gnotobiotic mice11–13.”
From Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis (2018) · cites this paper as [12]
In the citing paper
“Given these considerations, it is perhaps even more striking that bacteria generally associated with health (such as Clostridiales, Ruminococcacae, Faecalibacterium spp., Akkermansia muciniphila, B. fragilis,a n dBifidobacteria) (13, 14, 18-20) or immunogenicity (Enterococci, Collinsella, and Alistipes)( 11, 13, 14, 20) were found to be abundant in responding patients.”
From The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies (2018) · cites this paper as [20]
In the citing paper
“One of the striking findings that distinguishes cancer patient responders from nonresponders after PD-1 blockade immunotherapy is the ratio of putatively favorable to unfavorable bacteria (20).”
From The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies (2018) · cites this paper as [20]
In the citing paper
“Notably, fecal microbial transplantation (FMT) o ff e c e sf r o mp a t i e n t s( w h os h o w e dc l i n i c a lr esponse to immune checkpoint blockade) transferred a”
From The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies (2018) · cites this paper as [20]
In the citing paper
“16S ribosomal RNA (rRNA)-based sequencing of gene amplicons and shotgun DNA sequencing of patient stool samples have identified subsets of bacteria more abundant in responding versus nonresponding patients.”
From The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies (2018) · cites this paper as [20]
In the citing paper
“To date, Bifidobacteria spp.(15, 20), Akkermansia muciniphilia (14), E. hirae (16), and Bacteroides spp.(13) have been shown to improve antitumor T cell responses and support better tumor control in vivo.”
From The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies (2018) · cites this paper as [20]
In the citing paper
“A meta-analysis found that high abundance of Faecalibacterium genus was the strongest signal associated with response to ICI, using either anti-PD-1 or anti-CTLA-4 antibodies.5–7,12,13”
From Faecalibacterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [7]
In the citing paper
“In patients with cancer, specific taxa, including F. prausnitzii, A. muciniphila and Bifidobacteria, have been associated with improved clinical responses to ICI.2,6,7”
From Faecalibacterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [7]
In the citing paper
“In this context, the gut microbiota has been involved in response to cancer treatments, and in particular to immunotherapy.2,6,7,32”
From Faecalibacterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [7]
In the citing paper
“The effects of the gut microbiota on the anti-tumor response has been demonstrated in preclinical models as well as in patients with melanoma, renal cell carcinoma and NSCLC”
From Hallmarks of response, resistance, and toxicity to immune checkpoint blockade (2021) · cites this paper as Matson et al., 2018
In the citing paper
“The gut and tumor microbiome have been associated with anti-tumor responses and, in some cases, response to immune checkpoint blockade”
From Hallmarks of response, resistance, and toxicity to immune checkpoint blockade (2021) · cites this paper as Matson et al., 2018
In the citing paper
“Thomas Gajewski in Chicago, Jennifer Wargo at the MD Anderson Cancer Centre, and our laboratory at the Gustave Roussy Cancer Centre in Paris later published seminal studies in the same 2018 issue of Science, demonstrating direct links between gut microbiota composition and the success of ICB in different cancer populations, including melanoma, non–small-cell lung cancer (NSCLC), and urothelial and”
From Microbiota-centered interventions to boost immune checkpoint blockade therapies (2025) · cites this paper as Matson et al., 2018
In the citing paper
“higher abundances of the commensals Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium stimulate a more favorable T cell-mediated response to anti-PD-1 therapy in both preclinical models and patients suffering from metastasized melanoma.190–192”
From Interaction between microbiota and immunity in health and disease (2020) · cites this paper as [190]
In the citing paper
“Increasing evidence suggests that the gut microbiome may modulate the efficacy of cancer immunotherapy.”
From A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy (2023) · cites this paper as [3]
In the citing paper
“Gut microbes such as Akkermansia muciniphila, Faecalibacterium prausnitzii and Bifidobacterium longum have been associated with improved responses to immune checkpoint blockade (ICB)1–3”
From A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy (2023) · cites this paper as [3]
In the citing paper
“These microbes were also previously reported to be associated with better response to ICB immunotherapy2,3,38,39.”
From A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy (2023) · cites this paper as [3]
In the citing paper
“These effects were mediated by increased densities of CD8+ T-cells and reduced FoxP3+CD4+ Tregs in the tumor microenvironment”
From The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy (2018) · cites this paper as Matson et al., 2018
In the citing paper
“mice that received responder-stool growing tumors at slower rate and having markedly improved efficacy to anti-PD- L1 immunotherapy when compared to mice that received non-responder stool”
From The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy (2018) · cites this paper as Matson et al., 2018
In the citing paper
“patients who responded to anti-PD-1 therapy had enrichment of Bifidobacterium longum, Collinsella aerofaciens and Enterococcus faecium in baseline fecal samples”
From The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy (2018) · cites this paper as Matson et al., 2018
In the citing paper
“at lower levels of taxonomy within these phyla these generalizations do not apply, as some taxa within Firmicutes have been associated with a lack of response (Roseburia, Streptococcus)”
From The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy (2018) · cites this paper as Matson et al., 2018
In the citing paper
“Bacteroidetes have also been associated with response (such as Akkermansia muciniphila, Bifidobacterium longum and Bifidobacterium adolescentis, and Colinsela aerofaciens) and non-response (such as Actinomyces viscosus, Garnderella vaginalis) (Frankel et al., 2017, Gopalakrishnan et al., 2018, Matson et al., 2018, Routy et al., 2018).”
From The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy (2018) · cites this paper as Matson et al., 2018
In the citing paper
“The microbiota is emerging as a determinant of responses to various anticancer therapies, including immunotherapy122–125.”
From Enhancing cancer immunotherapy using antiangiogenics: opportunities and challenges (2018) · cites this paper as [125]
In the citing paper
“Mice that were transplanted with stool from patients responding to anti-PD-1 antibodies had higher levels of CD8 T-cell density in tumor tissue.”
From Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors (2019) · cites this paper as [31]
In the citing paper
“Furthermore, mice transplanted with stool from responding patients also had higher levels of CD8 T-cell concentrations in the gut than those transplanted with stool from non-responders.”
From Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors (2019) · cites this paper as [31]
In the citing paper
“Significant differences in microbial diversity and composition have been noted between responding and non-responding melanoma patients treated with anti-PD-1 therapy; different studies suggest different species may be enriched in responding versus non-responding patients [30, 31].”
From Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors (2019) · cites this paper as [31]
In the citing paper
“They collected stool materials from 42 patients with metastatic melanoma before administering PD-1 blockade, and the clinical response rate was 38%, showing that there were 16 responders (R) and 26 non-responders (NR).”
From Gut Microbiome as a Potential Factor for Modulating Resistance to Cancer Immunotherapy (2020) · cites this paper as [55]
In the citing paper
“As expected, PD-1 inhibitor exerted its antitumor activity in mice colonized with R bacteria but was ineffective in NR bacteria colonized mice, thus confirming in vivo the influence of gut microbiome on human immunity.”
From Gut Microbiome as a Potential Factor for Modulating Resistance to Cancer Immunotherapy (2020) · cites this paper as [55]
In the citing paper
“The authors selected 10 bacterial species that were different between R and NR, 8 of those were found to be more abundant in R (including Bifidobacterium, also observed in preclinical studies).”
From Gut Microbiome as a Potential Factor for Modulating Resistance to Cancer Immunotherapy (2020) · cites this paper as [55]
In the citing paper
“For instance, some researchers calculated the proportion of “beneficial” and “non-beneficial” operational taxonomic units and their ratio for every patient and reached a limit conclusion—a ratio of over 1.5 indicated clinical benefit (55).”
From Gut Microbiome as a Potential Factor for Modulating Resistance to Cancer Immunotherapy (2020) · cites this paper as [55]
In the citing paper
“Along the same line, Matson et al. [172] performed a metagenomics characterization of stools samples from melanoma patients treated with immune checkpoint inhibitors, further corroborating the finding that responders showed a different microbiome compared to those not responding to therapy.”
From Gut Microbiota and Cancer: From Pathogenesis to Therapy (2019) · cites this paper as [172]
In the citing paper
“Multiple translational studies, published in 2018, further support the pivotal role of the gut microbiome in modulating the response to immune checkpoint blockade [170,171,172].”
From Gut Microbiota and Cancer: From Pathogenesis to Therapy (2019) · cites this paper as [172]
In the citing paper
“They identified and functionally proven in vivo the importance of Bifidobacterium longum, Enterococcus faecium and Collinsella aerofaciens in ameliorating anti-PD-L1 efficacy [172].”
From Gut Microbiota and Cancer: From Pathogenesis to Therapy (2019) · cites this paper as [172]
In the citing paper
“Given the multiplicity of effects that gut microbiome may play on the host’s immune system, it is not surprising that emerging studies strongly linked the patients’ microbiome composition with the intrinsic efficacy of immune checkpoint inhibitors-based immunotherapy, in the treatments of different solid tumors [170,171,172].”
From Gut Microbiota and Cancer: From Pathogenesis to Therapy (2019) · cites this paper as [172]
In the citing paper
“It was reported that the gut microbiota of immunotherapy-sensitive patients was distinct from that of immunotherapy-resistant populations [311, 312].”
From Combination strategies with PD-1/PD-L1 blockade: current advances and future directions (2022) · cites this paper as [312]
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMPreprint
Li, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.3997 · Cited by 10,846
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C., Turnbaugh, P. J., Fierer, N., & Knight, R. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,944
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Cited by
Sorted by citations
Items 2,201–2,300 of 3,140
The progress on the relationship between gut microbiota and immune checkpoint blockade in tumors
Jiang, H., Ye, Y., Wang, M., Sun, X., Sun, T., Chen, Y., Li, P., Zhang, M., & Wang, T. (2023) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2023.2212526 · Cited by 5
“…2013; J. Huang et al., 2020; Ju et al., 2020; Klunemann et al., 2021; Koyama et al., 2016; Kythreotou et al., 2018; Majidpoor & Mortezaee, 2021; Matson et al., 2018; Mazmanian et al., 2005; Nicholson et al., 2012; Okazaki et al., 2013; Oliva et al., 2018; Osuch et al., 2020; O’Donnell et al.,…”
Ferreira, A., Neves, M. T., Baleiras, A., Malheiro, M., & Martins, A. (2022) · Cureus · doi:10.7759/cureus.32586 · Cited by 6
“[30] found that the enrichment of a group of eight species driven by Bifidobacterium longum was correlated with responsiveness to PD-1 therapy.”
BackgroundYang, Z., Liang, L., Li, X., Peng, X., Qian, X., Huang, Z., Wang, X., Hu, Q., & Fu, X. (2026) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2026.117711 · Cited by 5
Neumann, S., Peyroux, E. M., Woodall, M. J., Shields, N. J., Young, S. L., & Pattison, S. T. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.88137 · Cited by 4
The microbiome and precision oncology: an emerging paradigm in anticancer therapy
Bhat, S. A., Kaur, R., Chauhan, A., & Pal, A. (2022) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2022.2035313 · Cited by 6
“Transplantation of these microbes from responder patients in mice demonstrated slow tumour growth and improved response to anti-PD-L1 therapy (Matson et al. 2018).”
BackgroundMechanisms of Anti-PD Therapy Resistance in Digestive System Neoplasms
Wu, Y., Jiang, X., Yu, Z., Xing, Z., Ma, Y., & Qing, H. (2024) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/0115748928269276231120103256 · Cited by 5
Microbiome-modulated immunotherapy in oncology: Current applications and future prospects
Liu, Y., Miao, K., & Yang, X. (2026) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2026.01.001 · Cited by 4
Microbiome and the Hallmarks of Cancer
Bleich, R. M., & Arthur, J. C. (2021) · Physiology in health and disease · doi:10.1007/978-3-030-67951-4_1 · Cited by 8
Carcinogenetic mechanisms employed by the oral microbiome: A narrative review
Menghani, S. V. (2025) · The American Journal of the Medical Sciences · doi:10.1016/j.amjms.2025.01.001 · Cited by 6
Clinical Detection and Management of Colorectal Cancer Concerning the Gut Microbiome
Farahani, H. E., Pourhajibagher, M., & Bahador, A. (2025) · Scientifica · doi:10.1155/sci5/1945321 · Cited by 5
Morel, C., Li, R., Fiuza‐Luces, C., Brougham, M. F. H., Pascual-Gázquez, J. F., Torquati, L., & Iniesta, R. R. (2025) · Journal of Nutrition · doi:10.1016/j.tjnut.2025.101300 · Cited by 5
“Recent research suggests that the gut microbiome (GM), an ecosystem of bacteria — archaea and eukarya inhabiting our gastrointestinal tract — may influence response to cancer treat-ment and mitigate treatment-associated side effects [2,3].”
Gu, W., Koh, H., Jang, H., Lee, B., & Kang, B. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.05059-22 · Cited by 6
“…trial to trace disease status over a lengthy period to survey the sequential progress of disease or recovery for different treatment groups (e.g., treatment versus placebo, new treatment versus old treatment), not restricted to the disease status at a single speci fi c time point (1, 2, 9, 12).”
BackgroundMicrobiome and Immunotherapy for Melanoma
Haddad, A., & Holder, A. M. (2024) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2024.05.010 · Cited by 5
Usyk, M., Hayes, R. B., Knight, R. D., González, A., Li, H., Osman, I., Weber, J. S., & Ahn, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.16.589761 · Cited by 4
“The results shown here in the adjuvant setting and research in the metastatic setting (Lee et al., 2022; Matson et al., 2018; Routy et al., 2018) (Peters et al., 2019) are beginning to identify bacteria that impact ICB treatment outcomes, setting the stage for future studies modifying the GMB to…”
BackgroundInfluentialOveracre-Delgoffe, A. E., Cillo, A. R., Bumgarner, H. J., Burr, A. B. P., Tometich, J. T., Bhattacharjee, A., Bruno, T. C., Vignali, D. A. A., & Hand, T. W. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.27.428417 · Cited by 4
“Alternatively, some compositions of the microbiome and specific organisms are associated with immunotherapy responses in animal models and melanoma patients (Gopalakrishnan et al., 2018; Iida et al., 2013; Matson et al., 2018; Routy et al., 2018), highlighting the therapeutic potential of modifying the microbiome to m…”
BackgroundAntibiotics, the microbiome and gastrointestinal cancers: A causal interference?
Ramai, D., Salati, M., Pomati, G., Amoroso, C., Facciorusso, A., Botticelli, A., & Ghidini, M. (2022) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2022.102315 · Cited by 4
Microbiota and immune-related adverse events in cancer immunotherapy
Schneider, S. M., Fan, C., Wang, Y., Jenq, R. R., & Watowich, S. S. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00921-3 · Cited by 6
Turchan, W. T., Pitroda, S. P., & Weichselbaum, R. R. (2022) · Neoplasia · doi:10.1016/j.neo.2022.100782 · Cited by 4
[Research Progress and New Immunotherapy Strategies of Tumor Microenvironment Metabolism].
Peng, Y., Luo, X.-M., & Chen, J.-Y. (2023) · 四川大学学报：医学版 · doi:10.12182/20230560502 · Cited by 4
Kudo‐Saito, C., Boku, N., Hirano, H., & Shoji, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1009701 · Cited by 5
“maintaining physiological conditions, including nutrient absorption and immune responses, and thus partly but significantly impacts on therapeutic efficacies induced by ICIs, such as anti-CTLA4 mAb (15), anti-PD1 mAb (16), and anti-PDL1 mAb (17).”
BackgroundThe Roles of the Virome in Cancer
(2021) · doi:10.20944/preprints202111.0087.v1 · Cited by 4
Turco, L., Monica, R. D., Giordano, P., Cuomo, M., Biazzo, M., Mateu, B., Liello, R. D., Daniele, B., Normanno, N., Luca, A. D., et al. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1458990 · Cited by 4
Barbosa, E. C., Bucar, E. E. C., JUBÉ, G. R., SILVEIRA, L. B., SILVA, N. C. D., FARIA, P. C. C., Ramos, P., Moraes, V. R. Y., & BARROS, J. O. B. (2023) · Revista do Colégio Brasileiro de Cirurgiões · doi:10.1590/0100-6991e-20233490-en · Cited by 5
Shoji, F., Kawabata, T., Kosai, K., Fujishita, T., Toyozawa, R., Shimamatsu, S., Ito, K., Taguchi, K., & Yamaguchi, M. (2025) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2025.101066 · Cited by 5
“8 9-13 More-over, the intratumoral microbiome has been recently evaluated in various cancers, including those of the breast, ovary, pancreas, bone, and lung, and in melanoma and brain tumors.”
Impact of Data and Study Characteristics on Microbiome Volatility Estimates
Park, D. J., & Plantinga, A. (2023) · Genes · doi:10.3390/genes14010218 · Cited by 4
Intestinal microbiom modulates the response to antitumor immunotherapy
Olekhnovich, E. I., Manolov, A. I., Pavlenko, A. V., Konanov, D. N., Fedorov, D. E., Tikhonova, P., Glushchenko, O. E., & Ilina, E. N. (2020) · Biomeditsinskaya Khimiya · doi:10.18097/pbmc20206601054 · Cited by 4
Current understanding of gut microbiota in tackling COVID-19
Sumathi, S. H., Suganya, K., Swathi, K., Sudha, B., & Sneha, S. (2021) · Annals of Phytomedicine An International Journal · doi:10.21276/ap.covid19.2021.10.1.15 · Cited by 3
“The majority of these bacteria are bacteraemia-associated bacteria, implying a risk of a serious disease path due to secondary viral infection (Matson et al., 2018).”
BackgroundEvaluating a potential model to analyze the function of the gut microbiota of the giant panda
Zhang, W., Xie, J., Xia, S., Fan, X., Schmitz‐Esser, S., Zeng, B., Zheng, L., Huang, H., Wang, H., Zhong, J., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1086058 · Cited by 5
“FMT of human fecal samples into GF mice is a useful tool to analyze causal relationships between the gut microbiota and human phenotypes (Matson et al., 2018).”
MethodChen, Y., Wang, Y., Hu, J.-Y., Wang, E.-C., Jia, X.-H., Wang, J., Min, G., Jin, W.-L., & Jiang, L. (2026) · Gut Microbes · doi:10.1080/19490976.2026.2652476 · Cited by 7
Jang, H., Koh, H., Gu, W., & Kang, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-25093-6 · Cited by 5
Nanotechnology and Immunomodulators in Cancer
Volovat, C. D., Volovăț, S. R., & Agop, M. (2022) · Springer eBooks · doi:10.1007/978-981-16-6379-6_5 · Cited by 4
Gut Bacteria Provide Genetic and Molecular Reporter Systems to Identify Specific Diseases
Dicks, L. M. T. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25084431 · Cited by 5
“Bifidobacterium longum , Bifidobacterium adolescentis , Collinsella aerofaciens , and Enterococcus faecium have been shown to be more abundant in patients with metastatic melanoma who responded to treatment compared to non-responding patients [111].”
ResultJenkins, S. V., Alimohammadi, M., Terry, A. S., Griffin, R. J., Tackett, A. J., Leung, J. W. C., Vang, K. B., Byrum, S. D., & Dings, R. P. M. (2021) · Angiogenesis · doi:10.1007/s10456-021-09771-z · Cited by 5
Li, Y., Guan, J., Jing, Y., et al. (2025) · International Journal of General Medicine · doi:10.2147/ijgm.s541736 · Cited by 4
Liu, K., Zang, C., Wang, J., et al. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1264082 · Cited by 4
“For exposure of interest, the GWAS for smoking were from GSCAN and UK Biobank (24, 25).”
MethodSong, W., Liu, J., Yang, J., Chen, T., Zhu, J., & Liu, X. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1547964 · Cited by 4
Tailoring bacterial taxa for immune cell modulation
Margalit, O., & Boursi, B. (2021) · HepatoBiliary Surgery and Nutrition · doi:10.21037/hbsn-21-263 · Cited by 3
“For example, Bifidobacterium longum was shown to be associated with a response to immunotherapy (15), while Bacteroidales order was associated with a lack of response (13).”
BackgroundDiet–microbiome synergy underlies obesity-associated immunotherapy efficacy
Desharnais, L., Swaby, A. M., Messaoudene, M., Doré, S., Yu, M. W., Fiset, B., Breton, V., Ponce, M., Hu, Y., Wilson, L., et al. (2026) · Nature · doi:10.1038/s41586-026-10750-x · Cited by 8
“In patients with cancer and in preclinical cancer models, Lactobacillus spp. is associated with favourable response to ICI 35–40 .”
Intratumoral microbiota in cancer: molecular mechanism and therapeutic strategies
Yu, Y., Guo, Z., Luo, Z., Dian, Y., Yang, X., Chen, X., Zeng, F., & Deng, G. (2026) · Molecular Biomedicine · doi:10.1186/s43556-026-00473-w · Cited by 4
“In melanoma, fecal material from ICI responders improved antitumor responses in recipient models, and clinical studies further showed that FMT combined with anti-PD-1 therapy could alter the gut microbiome and reprogram the TME in a subset of resistant patients [9, 387].”
The oncobiome; what, so what, now what?
Abbas, M., & Tangney, M. (2025) · Microbiome Research Reports · doi:10.20517/mrr.2024.89 · Cited by 6
Targeting the microbiota‐ miRNA ‐protease axis: A new therapeutic avenue in melanoma
Katsoulieris, E. N., Ioannou, P., & Afratis, N. A. (2026) · FEBS Journal · doi:10.1111/febs.70386 · Cited by 4
Microbiome dynamics in immune checkpoint blockade
Kim, C. W., Kim, H.-J., & Lee, H. K. (2024) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2024.04.013 · Cited by 5
The changes in the p53 protein across the animal kingdom pointing to its involvement in longevityPreprint
Bartas, M., Brázda, V., Volná, A., Červeň, J., Pečínka, P., & Zawacka‐Pankau, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.06.080200 · Cited by 4
Fu, J., & Zheng, H. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1403825 · Cited by 3
Abril, A. G., Lanzi, P. G., & Notario, V. (2019) · Springer eBooks · doi:10.1007/978-3-030-21862-1_16 · Cited by 4
Hamza, M., Wang, S., Liu, Y., Li, K., Zhu, M., & Chen, L. (2025) · Microbiological Research · doi:10.1016/j.micres.2025.128156 · Cited by 6
Wu, D., Fan, Y., Zhang, M., Wang, X., He, X., Guo, X., Liu, Y., Cao, C., & Deng, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1598436 · Cited by 7
“Transplantation of these microbiota into mice enhanced the ef fi cacy of immunotherapy (27).”
Un tour d’horizon du développement de biomarqueurs pour les anticorps PD-1 et PD-L1 en cancérologie
Chaput, L., & Jordheim, L. P. (2021) · Therapies · doi:10.1016/j.therap.2021.06.005 · Cited by 4
A Summary of the Fourth Annual Virology Education HIV Microbiome Workshop
Williams, B., Ghosh, M., Boucher, C. A., Bushman, F. D., Carrington‐Lawrence, S., Collman, R. G., Dandekar, S., Dang, Q., Malaspina, A., Paredes, R., et al. (2020) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2019.0197 · Cited by 4
Hou, Q., & Xu, H. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-3266-5_7 · Cited by 4
Kirkup, B. M., McKee, A. M., Madgwick, M., Price, C. A., Dreger, S. A., Makin, K. A., Caim, S., Gall, G. L., Paveley, J., Leclaire, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.07.982108 · Cited by 4
“Other groups have made progress in understanding how antibiotics affect immunogenic cancers (38-41), however, to date, only a small number of studies have looked at their effects on BrCa (28, 42).”
Local Delivery of Immunomodulatory Antibodies for Gastrointestinal Tumors
Silva-Pilipich, N., Covo-Vergara, Á., & Smerdou, C. (2023) · Cancers · doi:10.3390/cancers15082352 · Cited by 5
Building the Canadian Bladder Cancer Research Network (CBCRN): Progress During a Pandemic
Black, P. C., Alimohamed, N. S., Kassouf, W., Gore, J. L., McCoy, K. D., Nelson, B. H., Carvalho, D. D. D., Ouellette, R. J., Devins, F., Cornacchia, T., et al. (2022) · Canadian Urological Association Journal · doi:10.5489/cuaj.7810 · Cited by 4
Precision Medicine: Enabling Healthcare Progress in the Twenty-First Century
Carmo‐Fonseca, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-70761-3_2 · Cited by 4
Death and the Miser: microbiota regulate the outcome of checkpoint inhibition immunotherapy
Rijkers, G. T., Andriessen, S. Q., & van Overveld, F. J. (2019) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2019.1677158 · Cited by 4
A probiotic bacterium modulates antitumour γδ T-cell responses in lung cancer
Goto, Y., Dolton, G. M., Thomas, H. L., Morin, T., Tajima, Y., Imamura, K., Sakata, S., Oka, K., Hayashi, A., Takahashi, M., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1750569 · Cited by 4
Li, Y., Wang, H., Guo, X., Zhou, J., Duan, L., Si, X., Zhang, L., Liu, X., Qian, J., & Zhang, L. (2019) · PubMed Central · doi:10.3779/j.issn.1009-3419.2019.10.10 · Cited by 4
Intestinal colonization regulates systemic anti-commensal immune sensitivity and hyperreactivity
Burkhard, R., Koegler, M., Brown, K., Wilson, K., Mager, L. F., Zucoloto, A. Z., Thomson, C. A., Nanjundappa, R. H., Skalosky, I., Ahmadi, S., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1030395 · Cited by 3
Qiao, T., & Zhu, Z. (2025) · Microorganisms · doi:10.3390/microorganisms13112595 · Cited by 5
“Another study identified a group of microbial species associated with the response to ICI therapy in patients with metastatic melanoma, including Bifidobacterium longum , Collinsella aerofaciens , and Enterococcus faecium [19].”
Yiu, C. H., Winardi, K., & Lu, C. Y. (2026) · Current Oncology Reports · doi:10.1007/s11912-026-01789-0 · Cited by 3
“…demonstrated that specific commensal bacterial species— including Bifidobacterium , Akkermansia muciniphila , and members of the Ruminococcaceae family—are enriched in ICI responders and are associated with enhanced CD8 + and CD4 + T-cell effector function and dendritic cell activation [22 – 24].”
Role of the microbiota in oncogenesis
Grigorievskaya, Z. V., Николаевна, П. И., Bagirova, N. S., Агинова, В. В., & Kononets, P. V. (2023) · Siberian Journal of Oncology · doi:10.21294/1814-4861-2023-22-2-129-142 · Cited by 3
Exploring Impact of gut Microbiome on Human Health and disease: A Review
Li, Y. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2024.la19346 · Cited by 3
Fang, J., Wu, M., Shen, H., Liu, W., & Zhang, T. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1616928 · Cited by 3
Colorectal cancer biomarkers and their impact on the clinical practice
Crespo, J., Victorino, A. P., Araujo, K., Araujo, L. H., & Vieira, F. M. D. A. C. (2021) · Brazilian Journal of Oncology · doi:10.5935/2526-8732.20210025 · Cited by 4
Success of immune checkpoint blockade therapies – mechanisms and implications for hepatology
Bengsch, B., & Thimme, R. (2019) · Zeitschrift für Gastroenterologie · doi:10.1055/a-0805-6936 · Cited by 4
672 Identification of microbial-derived HLA-bound peptides in melanoma
Nagler, A., Kalaora, S., Rosenberg, D. G., Alon, M., Barnea, E., Levy, R., Vervier, K., Trabish, S., Dadosh, T., Levin‐Zaidman, S., et al. (2020) · Regular and Young Investigator Award Abstracts · doi:10.1136/jitc-2020-sitc2020.0672 · Cited by 3
Lee, H. I., Jang, B., Chang, J. H., Kim, E., Lee, T. H., Park, J. H., & Chie, E. K. (2024) · Cancer Research and Treatment · doi:10.4143/crt.2024.521 · Cited by 3
“Landmark studies have successfully enhanced the efficacy of anticancer therapies by modulating the gut microbiome in both mouse and human models [6,7].”
Zhang, L., Zhou, D., Feng, J., Liao, Z.-J., Shu, X.-L., Yang, R., Gao, Y., Zhou, H., Zhang, W., Zou, Y. F., et al. (2025) · npj Biofilms and Microbiomes · doi:10.1038/s41522-025-00827-2 · Cited by 4
Laboratory biomarkers of an effective antitumor immune response. Clinical significance.
Malkova, A. M., Sharoyko, V. V., Zhukova, N. V., Gubal, A. R., & Орлова, Р. В. (2021) · Cancer Treatment and Research Communications · doi:10.1016/j.ctarc.2021.100489 · Cited by 5
Chen, Y.-C., Su, Y.-Y., Chu, T.-Y., Wu, M.-F., Huang, C.-C., & Lin, C.-C. (2025) · npj Biofilms and Microbiomes · doi:10.1038/s41522-024-00598-2 · Cited by 3
“For Melanoma analysis, we collected two distinct cohorts from McCulloch et al. (PRJNA762360) 38 and Matson et al. (PRJNA399742) 39 , respectively.”
Ai, D., Xing, Y., Zhang, Q., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.853213 · Cited by 3
The microbiota impact: bacteria shaping immunity, disease and response to therapy
Nuti, M., Zizzari, I. G., Botticelli, A., et al. (2018) · Translational Cancer Research · doi:10.21037/tcr.2018.05.25 · Cited by 3
“…(Phylum Actinobacteria); Enterococcus faecium (Phylum Firmicutes) Abundance in metastatic melanoma responder patients treated with anti-PD-1 Human Matson et al., 2018 ICI, immune checkpoint inhibitors; NSCLC, non-small cell lung cancer; RCC, renal cell carcinoma; MoAb, monoclonal antibody.”
BackgroundEnterococcus peptidoglycan remodeling promotes immune checkpoint inhibitor therapy
(2020) · doi:10.1101/2020.08.20.256263 · Cited by 366
“3I), consistent with enhanced priming of a tumor antigen-specific 45 immune response (9, 13).”
BackgroundMethodHoman, E. J., & Bremel, R. D. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1155679 · Cited by 4
“We include the GI microbiome ecosystem in our consideration because it is a recognized contributor to T cell repertoire diversity and it has been shown to predict the T cell response to checkpoint inhibitors (7, 37, 98).”
BackgroundZhang, X., Mallick, H., & Rahnavard, A. (2026) · Communications Medicine · doi:10.1038/s43856-026-01612-8 · Cited by 8
“In ICI ‑ only cohorts, several short ‑ chain ‑ fatty ‑ acid-producing commensals were enriched in responders 8,9,14 , whereas taxa linked to dysbiosis,suchas Hungatellahathewayi and Clostridiuminnocuum ,weremore associated in non ‑ responders.”
Zhang, F., Hu, K., Sun, C., Chen, R., Ni, G., Liu, X., Wei, L., & Su, R. (2026) · Gut Microbes · doi:10.1080/19490976.2026.2662690 · Cited by 4
“19,30,40 In non-responders, the Desulfovibrionaceae and Bacteroidaceae families showed major positive IG scores, while the Coprobacillaceae family contributed negative IG scores.”
Zhao, Z., Fei, K., Wang, Z., Duan, J., Bai, H., & Wang, J. (2020) · Research Square · doi:10.21203/rs.3.rs-48380/v1 · Cited by 3
“Moreover, Enterococcus is more abundant in metastatic melanoma patients that responded to immunotherapy [42], which may lead to improved tumor control and greater e cacy of immunotherapy by augmented T cell responses.”
BackgroundAlamoudi, M. K., Alsaleh, A. A., Thyagarajan‐Sahu, A., Alkholifi, F. K., Raza, M. L., & Sahu, R. P. (2025) · Cancers · doi:10.3390/cancers17091518 · Cited by 3
Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction
Kok, C. R., Mulakken, N., Thissen, J. B., Martí, J. M., Lee, R., Trainer, J. B., Goncalves, A., Ranganathan, H., Avila-Herrera, A., Jaing, C. J., et al. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag422 · Cited by 3
“2018, Matson et al .”
Graphical-model based high dimensional generalized linear models
Li, Y., Xu, W., & Gao, X. (2021) · Electronic Journal of Statistics · doi:10.1214/21-ejs1831 · Cited by 3
“In order to understand why only a subset of patients benefit from the immunotherapy in cancer treatment, Matson et al. [22] conducted studies to find whether or not some microbiome species are predictors which can be used to classify metastatic melanoma patients’ response to the immunotherapy.”
BackgroundSkladaný, Ľ., Koller, T., Kukla, M., & Šoltýs, K. (2023) · Advances in predictive, preventive and personalised medicine · doi:10.1007/978-3-031-34884-6_9 · Cited by 3
Association of Intratumoral Microbiota with Prognosis in Patients with Lacrimal Gland Tumor
Hu, J., Yang, Y., Feng, Y., Yu, Y., Song, X., & Jia, R. (2025) · Biomedicines · doi:10.3390/biomedicines13040960 · Cited by 3
“Moreover, cytokines and immunosuppressive molecules serve as independent regulators of tumor immunity, with interventions targeting these pathways showing promise in impeding the progression of melanoma [45].”
Microbiome data integration via shared dictionary learning
Yuan, B., & Wang, S. (2025) · Nature Communications · doi:10.1038/s41467-025-63425-y · Cited by 7
Gut Microbiome as a Predictor of the Anti-PD-1 Therapy Success: Metagenomic Data Analysis
Fedorov, D. E., Pavlenko, A. V., Olekhnovich, E. I., Klimina, K. M., Покатаев, И. А., Manolov, A. I., Konanov, D. N., Veselovsky, V. A., & Ilina, E. N. (2021) · Biochemistry (Moscow) Supplement Series B Biomedical Chemistry · doi:10.1134/s1990750821020049 · Cited by 3
Wang, Q., & Ding, H. (2025) · Digestive Diseases and Sciences · doi:10.1007/s10620-025-09490-1 · Cited by 3
Antohi, A. L., Gheorghiță, A. D., Andronic, O., Pîrcălăbioru, G. G., & Treteanu, A. R. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27062538 · Cited by 4
Frontiers in Cancer Immunotherapy: Understanding the Role of Gut Microbiota
Prosperi, D. (2020) · Current Pharmaceutical Biotechnology · doi:10.2174/138920102101191209163306 · Cited by 3
“In addition, albeit apparently without a direct connection, increasing evidence supporting the function of gut microbiome in eliciting cancer onset and progression as well as the response to cancer treatments has been observed in the recent years [1-4].”
BackgroundChen, D., Wu, R., Liu, Z., Wei, Y., & Kuang, D. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004035 · Cited by 5
“[176,177] Clinically, colonization by Bifidobacterium longum , Collinsella aeraciens , and Enterococcus faecium correlates with superior con-trol of metastatic melanoma, [178] and fecal microbiota transplantation (FMT) has produced objective responses in anti-PD-1-refractory patients.”
Mattavelli, E., PRAT, V. D., Corallo, S., Tartara, A., Figini, S., Simeis, F. D., Marino, S., Uggè, A., Caccialanza, R., Pedrazzoli, P., et al. (2026) · Immunotherapy · doi:10.1080/1750743x.2026.2648431 · Cited by 6
Sugumar, K., Alabd, A., Alabd, A., Hue, J. J., Lyons, J., Fields, S., Wainberg, Z. A., Zheng, L., Coogle, B., Kasi, A., et al. (2025) · Oncotarget · doi:10.18632/oncotarget.28739 · Cited by 4
Are swabs an appropriate way to sample for skin microbiome research?
Bernigaud, C., & Chosidow, O. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18257 · Cited by 3
Ma, X., Xu, Y., Nian, Y., Luo, R., Chen, T., Su, K., Li, T., Shen, C., Xie, B., Dai, H., et al. (2026) · American Journal of Transplantation · doi:10.1016/j.ajt.2026.02.030 · Cited by 3
Hernani, R., Albert, E., Hernani‐Morales, C., Zúñiga, S., Benzaquen, A. O., González‐Castillo, L., Colomer, E., Morell, J., Català-Senent, J. F., Piñana, J. L., et al. (2026) · European Journal Of Haematology · doi:10.1111/ejh.70121 · Cited by 4
“These findings contrast with prior data in both CAR-T therapy and CPI-treated melanoma patients [13, 27, 28].”
Zuccaro, V., Lombardi, A., Asperges, E., Sacchi, P., Marone, P., Gazzola, A., Arcaini, L., & Bruno, R. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20071748 · Cited by 3
“[33,35] confirmed these data in human studies: they found that in patients with metastatic melanoma, Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium were more abundant in the stool samples (collected before the start of therapy) of chemo-free therapy responders, thus supporting the anti-tumor…”
ResultThe landscape of tissue-resident microbiota across normal, polyp, and colorectal cancer tissues
Xiang, H., Shen, B., Lao, W., Bai, B., Chen, M., Zhou, L., Yu, G., Zhang, W., Chen, H., Wang, Y., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69705-5 · Cited by 5
Fan, W., Xu, H., Wu, Y., Li, H., & Peng, Y. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1682148 · Cited by 3
The Great Debate at ‘Immunotherapy Bridge’, Naples, December 5, 2019
Ascierto, P. A., Bifulco, C. B., Galon, J., Garbe, C., Khleif, S. N., McQuade, J. L., Odunsi, K. O., Okada, H., Paulos, C. M., Quezada, S. A., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000921 · Cited by 4
Ochi, N., Ichihara, E., Yokoyama, T., Inoue, K., Tamura, T., Watanabe, H., Takata, I., Kano, H., Nakamura, K., Kawai, H., et al. (2025) · International Journal of Clinical Oncology · doi:10.1007/s10147-025-02860-9 · Cited by 4
“[6 , 7, 9, 18–22].”
Metadata
- Authors
- Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.-L., Luke, J. J., & Gajewski, T. F.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Matson, Vyara
- Fessler, Jessica
- Bao, Riyue
- Gajewski, Thomas F.
- ORCID
- Venue
- Science
- Published
- 2018-01-05
- Volume
- 359
- Issue
- 6371
- Pages
- 104-108
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6707353
- OpenAlex
- W2782333299
- Cited by
- 3,107
- References
- 55
- Citations by year
- 2026: 342 · 2025: 355 · 2024: 351 · 2023: 406 · 2022: 440 · 2021: 403 · 2020: 366 · 2019: 280 · 2018: 158
- FWCI
- 113.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (T32 CA009594)
- Melanoma Research Alliance (CA210098)
- University of Chicago Medicine Comprehensive Cancer Center
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- gut microbiota, anti-PD-1 therapy, metastatic melanoma, Akkermansia muciniphila, antibiotic use, cancer immunotherapy response, gut dysbiosis, lung cancer, kidney cancer, immune cell activity
- MeSH terms
- Gastrointestinal Microbiome, Bifidobacterium longum, Animals, Antibodies, Monoclonal, Feces, Humans, Immunotherapy, Melanoma, RNA, Ribosomal, 16S, Skin Neoplasms, T-Lymphocytes, Enterococcus faecium, Mice, Programmed Cell Death 1 Receptor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
