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Corresponding author at: Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center-James, Columbus, OH, USA.
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center-James, Columbus, OH, USADepartment of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
The gut microbiome affects many aspects of human health including aging and cancer. Recent evidence has demonstrated a causal relationship between the microbes in the gut and response to cancer treatment with immune checkpoint inhibitors (ICIs) [
]. Individuals whose cancers respond to ICIs can be distinguished from those who do not solely by the composition of their gut microbes at the start of treatment [
]. The microbiome therefore represents both a biomarker and therapeutic target for modifying and improving cancer care. As is often the case with emerging treatments, older adults are not strongly represented in the clinical trials leading to treatment approval [
]. Age is important to consider as we seek to modify the microbiome to promote treatment response. In this perspective, we summarize findings across aging and immunotherapy studies to relate the microbes found in each. We demonstrate that age-related changes tend to shift the microbiome toward a non-responder-like composition, lacking microbes demonstrated to support treatment response, which may contribute to the decreased efficacy for older adults [
]. We review the potential mechanisms by which these effects occur and posit a model to interpret the broad-level changes observed. Finally, we discuss trials currently underway to target this novel treatment modality in the understudied and growing older adult population.
1.1 The Microbiome Changes with Age
In 2007, The elderly gut metagenomics (ELDERMET) study in Ireland was the first major trial to focus on the microbiome of older adults and recruited 400 participants aged 65 years old and older. Since then, similar studies have been performed in older adult populations from other European countries as well as China and Japan [
]. Each study found differences between younger and older adults, but a universal older adult microbiome was not observed across the geographically-distinct populations. For example, the ELDERMET study found increased relative abundance of Alistipes and Oscillibacter and decreased Prevotella and Ruminococcus [
]. Most consistently, the genus Bifidobacterium is decreased, which is notable as being the microbe most enriched in infants via the pre-biotic effects of breast milk. Bifidobacterium has been associated with health in a variety of settings, including response to immunotherapy [
While Bifidobacterium is most consistently depleted in older adults, the diverse phylum Proteobacteria are consistently enriched. The Proteobacteria are more abundant in the environment than in the healthy gut, and a relatively high abundance (e.g. > ~ 10%) is associated with diverse diseases [
]. This has led to the speculation that a healthy gut is characterized by its ability to defend against constant incursions by Proteobacteria coming in from the environment.
Increased Proteobacteria in older adults could be driven by several age-related changes including (1) reduced efficacy of the immune system, (2) a lower fiber diet, and (3) decreased gut barrier function. This results in a more aerobic gut, more frequent blooms of organisms encountered in the environment (i.e. decreased microbiome stability), increased bacterial translocation across the gut barrier, and, chronic inflammation (Fig. 1). These may be causally connected via a feedback loop, whereby each aspect can exacerbate chronic inflammation.
Fig. 1Summary of age-related effects on response to immunotherapy via the microbiome. Lifestyle factors (e.g. diet, exercise, medications) affect the gut microbiota and particularly the fraction of Proteobacteria. This enters a cycle by which the microbes affect gut leakiness and systemic inflammation and thereby a variety of age-related illnesses, which then also affect the microbiome. Related diseases include cancer and particularly treatments that involve the immune system. Created with BioRender.com
A consistent feature across longitudinal studies of the microbiomes of older adults is higher intra-individual variability in older adults relative to younger. That is, the strains of microbes shift rapidly over time; in the context of common clustering approaches (e.g. principle components analysis) which demonstrates larger distances between points. The ELDERMET Study proposed diet to be the causal driver; individuals living in the community tended to have microbiomes more like healthy young controls, whereas individuals living in long-term care facilities showed reduced diversity and higher variability. Long term care was associated with lower-fiber diets, and the change in diet preceded a shift in the microbiome by roughly one year.
Regardless of the cause, the shift in microbiomes with age is a pressing concern when considering that age is a dominant risk factor for cancer and the microbiome plays a role in whether individuals will respond to ICIs. In many cancers ICIs are, or are predicted to soon be, the first-line treatment, making the link between the aging microbiome and ICI response a more pressing issue for more patients.
2. The Microbiome and Response to Immune Checkpoint Inhibitors
Several recent papers have suggested a critical role for the microbiome in response to ICIs. The first indications included retrospective analyses of patients who received microbiome-disrupting medications before the start of ICI treatment or shortly after [
Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications.
]. Patients who received antibiotics showed shorter overall survival across many cancers when controlling for covariates that might represent differences between the retrospective cohorts, including the Charlson Comorbidity Index [
Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications.
Direct measurements of the microbiome in patients receiving ICIs confirmed this epidemiological observation. Several groups demonstrated that the microbiomes of patients at the start of ICI treatment are distinct between patients who respond (R) and do not respond (NR) [
]. Mice inoculated with a sarcoma cell line and treated with ICIs showed reduced tumor size when given a fecal transplant with human R stool relative to NR stool. This suggests that the microbiome may be a biomarker for predicting response to ICIs.
In addition to a biomarker, the microbiome may be a therapeutic target. In preclinical studies, NR mice could be switched to an R state by supplementation with a single microbe that was enriched in the R microbiome: A. muciniphila. Similar findings have been reported when another microbe, an unnamed strain in the genus Ruminococcus, is enriched by feeding mice a pre-biotic [
Abstract 5730: a new polyphenol prebiotic isolated from Myrciaria dubia improves gut microbiota composition and increases anti-PD-1 efficacy in murine cancer models.
]. Later work showed a similar increase in response to ICIs by giving a community of 11 microbes, lacking A. muciniphila but containing an unnamed strain in the Ruminococcus family [
]. Finally, response to ICIs was increased by mono-colonization with a strain of Bifidobacterium, and by a molecule produced by the microbe, inosine. This demonstrates that response to ICIs could be modified by enrichment of one or a few microbes, and possibly by supplementation with small molecules such as inosine.
A consensus set of organisms that are most important, and for whom, has not been defined. A. muciniphila associated with R-patients in only one study [
]. Gopalakrishnan et al found that response correlated with higher alpha diversity and bacteria in the Ruminococcaceae family (which does not contain A. muciniphlia, nor any of the microbes found by Matson et al) [
]. Potential sources of this variation could include geographic differences in the microbiomes of patients, and convergent evolution in terms of ecological roles of the microbes or the molecules they produce, or age differences in the cohorts of each study.
There is a clear role for the microbiome in ICI response, leading to great hope for using it as a therapeutic target. However, more work is needed to define the microbes associated with the R and NR states and especially how best to modify them. It is prudent to use knowledge about healthy microbiomes to estimate which populations are likely to require microbiome modification.
3. Relating the Microbes Associated with Response to ICIs and Age
Many of the microbes that have been shown to change with age have been implicated in response to ICIs. Three of the microbes that have been shown to improve response to ICIs in preclinical models (Akkermansia [
Abstract 5730: a new polyphenol prebiotic isolated from Myrciaria dubia improves gut microbiota composition and increases anti-PD-1 efficacy in murine cancer models.
Abstract 5730: a new polyphenol prebiotic isolated from Myrciaria dubia improves gut microbiota composition and increases anti-PD-1 efficacy in murine cancer models.
]), are depleted in older adults (Fig. 2). In addition, several microbes enriched in non-responders to ICIs are also enriched in older adults. This includes the Proteobacteria like Escherichia, but also several members of the Bacteroidetes phylum. A single genus enriched in NR have been associated with younger adults (Fig. 2).
Fig. 2The gut microbes associated with aging and response to immune checkpoint inhibitors. NR = non-responders, R = responders, U/M = unknown/mixed results, Y = young, O = old.
However, the current picture is somewhat mixed. Several genera associated with response have been shown to be enriched older adults (Faecalibacterium, Enterococcus, Alistipes) (Fig. 2). Faecalibacterium, in particular, has been broadly associated with gut health and is marketed as a probiotic. While these microbes have not shown a causal relationship with response, such as for A. muciniphila described above, the possiblity remains that they will do so, perhaps in a way that is specific to older-adults Finally, the largest fraction of the genera associated with treatment response have either unknown or mixed associations with age, further highlighting the need for more study.
4. Underrepresentation of Older Adults in Cancer and Microbiome Studies
The median age of a patient diagnosed with lung cancer is 70 years, and that statistic continues to rise [
], the risk-benefit balance of ICIs may be especially profitable in older patients. However, patients enrolled in clinical trials generally tend to be younger than those treated in clinical practice [
], possibly due to selection criteria that excludes based on performance status or the presence of comorbidities. Over the past two decades, <10% of older adults age 75+ years are included in cancer clinical trials and this value has remained static [
]. The median age of a cancer diagnosis is higher than the median age of studies reporting on the association between the microbiome and response to ICIs and of trials that seek to modify the microbiome to improve cancer outcomes. As of August 2020, we found 31 trials that expressly intended to modify the microbiome to affect cancer outcomes. Four of these (13%) chose an age range to focus on older adults (Table 1).
Table 1Clinical trials to modify cancer outcomes via the microbiome.
NCT#
Age
Description
Location
Investigator
Status
Length
NCT04267874
55–77
Black raspberry diet intervention for MB modification & LC prevention
OSUCCC
D. Spakowicz
R
10/19- 12/22
NCT04229381
60+
Physical therapy & stress intervention to improve resiliency in LC patients
OSUCCC
C. Presley
R
1/20- 12/21
NCT02791737
60+
Exercise intervention to improve physical activity in cancer patients
OSUCCC
A. Rosko
N
7/16- 12/20
NCT03686202
18+
Assess efficacy of microbial ecosystem therapeutics in altering IO response
Princess Margaret CC
L. Siu A. Spreafico
N
11/18- 12/23
NCT03891979
1.5-100
Assess change in immune activation following antibiotics & IO
NYU Langone
D. Cohen
S
6/19- 5/21
NCT03772899
19+
Assess if combination FMT & IO can enhance antitumor effects in melanoma
LRCP
J. Lenehan
R
3/19- 12/23
NCT03817125
18+
Assess efficacy of oral MB intervention paired with IO in melanoma
Parker Institute (& others)
R. Ibrahim (& others)
R
1/19- 2/22
NCT04163289
18+
Assess efficacy of FMT combination treatment in reducing immune-toxicities
LRCP
R. Fernandes S. Maleki
R
1/20- 11/28
NCT04056026
All
Enhance the MB via FMT to improve the efficacy IO
ProgenaBiome
Progena Biome
C
9/18- 12/18
NCT04130763
18-70
Assess if FMT capsules improve IO response
Beijing Cancer Hospital
L. Shen
R
12/19- 10/20
NCT04116775
18+
Assess FMT from responders to IO into non-responder PCA patients
VA Portland Health Care System
J. N Graff
R
10/19- 10/23
NCT03819296
18+
FMT for medication-induced GI complications in melanoma, GU
MD Anderson CC
Y. Wang
X
2/20- 7/22
NCT03353402
18+
Assess FMT from IO responders to non-responder melanoma patients
Sheba MC
G. Markel
R
11/17- 12/21
NCT03341143
18+
Assess if FMT improves the body's ability to fight melanoma
UPMC Hillman CC
D. Davar
S
1/18- 10/20
NCT02843425
30+
Assess if beans can increase MB health & reduce obesity-related cancer risks
MD Anderson CC
C. Daniel-MacDougall
N
7/16- 7/25
NCT01929122
18+
Assess effects of bean powder or rice bran on the MB of CRC survivors
Colorado State University
E. P Ryan
C
8/10- 12/14
NCT04079270
18+
Assess effect of diet intervention on breast cancer outcomes & biomarkers
Sheba MC
E. Gal-Yam
R
7/19- 12/25
NCT03782428
18+
Assess the role of probiotics in reducing CRC related inflammatory markers
National University of Malaysia
R. Affendi R. Ali
C
8/16- 11/18
NCT03661047
18+
Assess effects of omega-3 oil on tumor immune microenvironment in CCR
Massachusetts General Hospital
M. Song
R
11/19- 9/23
NCT03781778
18+
Assess effect of resistant starch on inflammation & MB in CCR survivors
Fred Hutch/UW Cancer Consortium
M. Neuhouser
S
5/19- 9/20
NCT03448003
18 +
Assess if comprehensive lifestyle changes can prevent breast cancer
MD Anderson CC
L. Cohen
R
4/19- 9/22
NCT03358511
18 +
Assess effect of probiotics on breast cancer immune response
Mayo Clinic
S. Chumsri
C
10/17- 5/20
NCT03028831
40-65
Fiber intervention of native Alaskan diet for MB modification & CRC reduction
AK Native Tribal Health Consortium
G. Riscuta
R
12/17- 1/22
NCT03290651
All
Probiotic intervention for displacement of cancer related inflammatory bacteria
St. Joseph's Health Care
G. Reid M. Brackstone
R
7/19- 12/21
NCT03853928
18 +
Assess if MB intervention in patients with cirrhosis alters incidence of HCC
Austral University (sponsor)
F. Piñero
X
5/19- 5/23
NCT03268655
50-75
Assess if ginger can create an anti-inflammatory, CRC-protective MB
Mayo Clinic CC (& others)
A. Prizment
C
11/18- 6/20
NCT03934827
18+
Assess efficacy of BT MRx0518 as an immunomodulating agent in tumors
Imperial College London NHS Trust
J. Krell
R
4/19- 2/22
NCT03637803
18+
Assess efficacy of BT MRx0518 paired with IO in tumor patients
M.D. Anderson CC
S. Pant
R
1/19– 3/24
NCT03353402
18+
Assess FMT from IO responders to non-responder cancer patients
Sheba MC
G. Markel
R
11/17– 4/19
NCT03775850
18+
A Study of EDP1503 in Patients With Colorectal Cancer, Breast Cancer, and Checkpoint Inhibitor Relapsed Tumors
Highlands Oncology Group (& others)
J. Bendell
R
12/18– 12/20
NCT03595683
18+
Pembrolizumab and EDP1503 in Advanced Melanoma
University of Chicago MC
J. Luke
N
10/18– 11/23
Abbreviations: OSUCCC = Ohio State University Comprehensive Cancer Center; CC = Cancer center; MC = Medical center; LRCP = London Regional Cancer Program; R = Recruiting; S = Suspended; N = Active not recruiting; C = Completed; X = Not yet recruiting; FMT = Fecal microbiota transplantation; IO = Immuno-oncology or immunotherapy; CRC = Colorectal cancer; HCC = Hepatocellular carcinoma; PCA = Prostate cancer; MB = Microbiome; LC = Lung cancer; BT = Biotherapeutic.
There are several methods proposed to modify the microbiome including probiotic supplementation (35%), fecal microbiota transplantation (FMT) (25%) and interventions for the diet (26%) and lifestyle (10%). Each has potential benefits and pitfalls with regards to their safety, suspected efficacy, and speed of modification. FMTs have the strongest track record through successful clinical trials in the context of treatment for recurrent Clostridiodes difficile infections. However, they are challenged by demonstrating donor material is safe; on June 15, 2019, the FDA issued a safety alert requiring additional testing for clinical trials using FMT following a patient death [
In Brief: FDA warns about potential risk of serious infections caused by multi-drug resistant organisms related to the investigational use of Fecal Microbiota for Transplantation.
]. Probiotics hold promise as most closely mirroring the experiments in which murine models were made to start responding to ICIs. However, probiotic supplementation has recently been shown to decrease gut diversity which has had negative effects on health such as increasing recovery time after antibiotic treatment [
]. Studies on response to ICIs found that the diversity of the gut microbiome, in addition to particular microbes such as A. muciniphila, was important for response [
]. Further study is needed to determine if probiotic supplementation can improve response or decreases diversity in a way that is detrimental to cancer outcomes. Diet-based interventions may also modify response through enriching for certain microbes, though this has not yet been demonstrated in humans [
Abstract 5730: a new polyphenol prebiotic isolated from Myrciaria dubia improves gut microbiota composition and increases anti-PD-1 efficacy in murine cancer models.
]. Rational manipulation of the microbiome with diet has been complicated, with the same foods eliciting different responses in the microbiome, presumably based on the starting condition of the microbiome. Other longitudinal studies with dietary interventions have shown relatively minor changes, where individuals' microbiomes clustered more closely with themselves at other time points than other individuals. Which method, or combination of methods, will effectively change a person's microbiome to promote response to ICIs at a clinically relevant timescale may be highly individualized.
5. Conclusion
The microbiome is a promising way to monitor and modify the state of the immune system. Applying this to older adults is complicated by many factors, including age-related changes to the microbiome. Studies focused on older adults are needed to tailor interventions to this large and rapidly growing demographic with cancer.
The following are the supplementary data related to this article.
Enrichment of microbes in young and old, and responders and non-responders to ICIs with references for each determination.
Reproducibility Statement
Code to generate Fig. 2 from Supplementary Table 1 and the fraction of trials in each clinical trial type from Table 1 are available at github.io/spakowiczlab/mageio.
Declaration of Competing Interest
The authors declare no conflicts of interest.
Acknowledgements
This work was supported by a Ohio State University Comprehensive Cancer Center Pelotonia Young Investigator Award (D.S.), a Lung Cancer Foundation of America & Bristol-Meyers Squibb Foundation & International Lung Cancer Foundation Award in Immuno-Oncology (D.S.), the National Institute of Aging (C.J.P., R03AG064374), and the National Cancer Institute K12 Training Grant for Clinical Faculty Investigators (C.J.P., K12CA133250).
DS + CP conceived of the study, MM + AB+NW + DS collected the data, RH + AB+MM generated the figures and tables, RH created the code repository, DS drafted the manuscript, all authors reviewed and approved the manuscript.
References
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Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors.
Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications.
Abstract 5730: a new polyphenol prebiotic isolated from Myrciaria dubia improves gut microbiota composition and increases anti-PD-1 efficacy in murine cancer models.
In Brief: FDA warns about potential risk of serious infections caused by multi-drug resistant organisms related to the investigational use of Fecal Microbiota for Transplantation.
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