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Melanoma pp 287-302 | Cite as

Microbiome and Melanoma

  • Reetakshi Arora
  • Amanda Hermann
  • Jennifer A. WargoEmail author
Reference work entry

Abstract

Significant advances have been made in the past decade across the melanoma care continuum, with approved systemic therapy for patients with advanced disease as well as in the adjuvant setting. We are gaining an appreciation of the factors that drive response and resistance to these therapies, and there is novel evidence that the microbiome (which refers to the microbes that inhabit our bodies along with their collective genomes) may shape overall immunity and may even impact therapeutic responses (e.g., immune checkpoint blockade). This has profound implications and calls to question if the microbiome could be used as a biomarker or therapeutic target in patients going onto treatment with immune checkpoint blockade (and potentially onto other forms of therapy). Insights are also being gained into the potential influence of the microbiota on melanoma development at the level of the skin and of the gut, though there is a tremendous knowledge yet to be gained. Each of these aspects will be discussed herein, as will strategies to target and factors that influence the microbiome.

Keywords

Melanoma Microbiome Checkpoint blockade Immunity 

Introduction

The human microbiome is a complex aggregate of microorganisms (including bacteria, archaea, viruses, and fungi) as well as their associated genomes. Though once known for their pathogenic properties, these microbes are now implicated in helping to regulate the delicate balance of health and disease (Sekirov et al. 2010). Many of the initial insights into this role of the microbiome focused on gut microbes and metabolism (Turnbaugh and Gordon 2009); however microbes throughout the body are now implicated regulating a host of physicologic properties including local and systemic immunity (Lloyd-Price et al. 2016). The increased recognition of the role of the microbiome came about in part due to advances in sequencing techniques – which allowed the identification and characterization of these microbes without the need to culture them. Since initial approaches were described (Staley and Konopka 1985), there are now numerous means through which microbiota may be characterized – and may yield insight into their function as well as their phylogeny (Wade 2002; Duncan et al. 2007; Eckburg et al. 2005; Shendure and Ji 2008; Pace 1997; Venter et al. 2004). Such approaches have now been used to characterize the human microbiome in participants worldwide – through efforts such as the Human Microbiome Project (HMP) and American Gut (Human Microbiome Project Consortium 2012a, b; McDonald et al. 2018).

In addition to their role in normal physiology and maintenance of overall health, these microbes may impact disease states, particularly when imbalances of these microbes may exist in a particular body site (termed “dysbiosis”) (Frosali et al. 2015). A classic example of this is in the context of Clostridium difficile infection in the gut, which is associated with a massive dysbiosis (Khanna et al. 2016). However more subtle disruptions in the microbiota in the gut and at other sites have been associated with diseases and conditions throughout the body – ranging from autism and heart disease to cancer (Strati et al. 2017; Tang et al. 2017; Sheflin et al. 2014; Zitvogel et al. 2015; Zou et al. 2018). Disruptions of the skin microbiome have been associated with conditions such as eczema and psoriasis (Trivedi 2012; Grice 2014), among other conditions.

The link between microbiota and cancer has now been described at multiple levels – with the earliest reports focusing on the contribution of microbes to carcinogenesis (such as in the case of hepatitis viruses and hepatocellular cancer and Helicobacter pylori in gastric cancer). However there is now a growing appreciation of the complexity of the potential contribution of these microbes to carcinogenesis and also to response to therapy (Tsilimigras et al. 2017), both at the site of disease and at distant sites. This is poignantly illustrated in the recent observations that microbes may be found within human tumors and that they may either facilitate (Miller et al. 2018) or inhibit (Geller et al. 2017) therapeutic responses. Perhaps even more profound is the recent data supporting the impact of gut microbiota on responses to immunotherapy (specifically immune checkpoint blockade) in patients with melanoma and other cancers (Matson et al. 2018; Gopalakrishnan et al. 2018a; Routy et al. 2018a; Yi et al. 2018; Kroemer and Zitvogel 2018; Bhatt et al. 2017; Chaput et al. 2017).

Together, these findings have profound implications for patients with melanoma and other cancers – as the microbiome could potentially serve as a biomarker and could even be therapeutically targeted (using fecal microbiota transplant among other strategies). Each of these will be discussed herein, with the goal of providing the basis for an understanding of the role and potential of the microbiome in patients with melanoma.

The Microbiome in Health and Disease

Gut Microbiome

As noted previously, the microbiome contributes to numerous critical functions within the host. Perhaps one of the most impactful contributions is the influence of the gut microbiota on systemic immunity – which could potentially alter immune function and immunosurveillance for cancer and can also influence responses to immunotherapy for melanoma as shown in recent studies (Matson et al. 2018; Gopalakrishnan et al. 2018a; Routy et al. 2018a; Chaput et al. 2017; Frankel et al. 2017). Certainly, there is extensive interaction between microbes in the lumen of the gut and immune cells in the lamina propria along its vast length and surface area, and there are also more distantly interactions at the level of the mesenteric lymph node. These host-microbial interactions are paramount to overall health, and there is a delicate balance through which host immune cells recognize and eliminate pathogenic microbes while remaining tolerant to critical commensal microbes as well as food antigens. However despite these immune cells being tolerant to commensals, there is now clear evidence that the overall immunity is in part shaped by interactions with these gut microbes (Honda and Littman 2016), including from mouse models where germ-free mice demonstrate markedly altered immune function (Johansson et al. 2015; Spiljar et al. 2017) but also from studies in human cohorts.

Microbes interact with immune constituents (including T cells, B cells, dendritic cells, neutrophils, and others) scattered along the lamina propria of the gut as well as in the organized structures of the gut-associated lymphoid tissue (GALT) (Fig. 1a). The means through which they interact are numerous and include local interactions such as engagement of pathogen-associated molecular patterns (PAMPs) (such as lipopolysaccharide (LPS) and flagellin) with Toll-like receptors present on innate immune cells and interstitial epithelial cells (IECs). These PAMPs can also induce maturation of dendritic cells in the area, which can then traffic to mesenteric lymph nodes where they may stimulate CD4+ and CD8+ T lymphocytes (Lathrop et al. 2011). These lymphocytes and other immune cells can then act locally to secrete cytokines such as interleukin-10 and interleukin 17 or may traffic directly into the bloodstream where they can mediate distant effects. CD4+ regulatory T cells (Tregs) play an important role in promoting immunologic tolerance to commensal microbes (Furusawa et al. 2013), limiting inflammation. Microbes may also influence immunity via production of metabolites such as short-chain fatty acids (SCFAs) (Reichardt et al. 2014).
Fig. 1

Microbiota can cross-talk with immune cells: (a) Gut microbiota and immune cells. Gut microbes within the lumen of the gut interact with immune cells to elicit immune response. Microbes or microbial metabolites can activate dendritic cells (DCs) which migrate to the draining lymph node to activate naïve T cells to effector cells. These effector cells then enter into systemic circulation. These microbes and microbial by-products alter DCs which can skew the T-cell phenotype – T helper-1 (TH1), T helper-17 (TH17), or T regulatory cells (Tregs). (b) Skin microbiota and immune cells. Commensal organism over the skin surface and associated structures can metabolize the host proteins and lipids into bioactive products that can inhibit the invasion of pathogens. Immune cell types are found within the skin, including Langerhans cells, dendritic epidermal γδ T cells (DETCs), and memory αβ T cells in the epidermis, and subsets of dendritic cells, macrophages, neutrophils, mast cells, γδ T cells, innate lymphoid cells (ILCs), CD4+ and CD8+ T effector cells, and T regulatory cells (Treg) are found in dermis. Skin commensals can also induce T-cell responses, and these commensal-specific T cells express effector genes with immunoregulatory signatures

Though we do not yet have a deep understanding of the ideal constituents of a “healthy” microbiome, there is evidence that disruptions of the gut microbiome (dysbiosis) may lead to pathologic conditions including autoimmune and inflammatory diseases such as inflammatory bowel disease (IBD), type I diabetes, rheumatoid arthritis (RA), and multiple sclerosis (Tsilimigras et al. 2017; Kim et al. 2017; Mima et al. 2017; Garcia-Castillo et al. 2016) and have also been associated with cancer (Sears and Garrett 2014; Yang and Jobin 2017).

Skin Microbiome

In addition to the gut microbiota, microbiota at other sites (such as the skin) may also influence immunity and overall health (Byrd et al. 2018), with disruption potentially leading to disease states (Grice and Segre 2011). The skin harbors a lower microbial biomass compared to gut, owing to different physical and chemical properties (Chen et al. 2018). The microbial ecology of human skin is complex, and microbiota analysis from healthy donors has identified Staphylococcus, Micrococcus, Corynebacterium, Brevibacterium, Propionibacterium, and Acinetobacter species as normal residents in skin (Gao et al. 2007). The most common fungal species present on normal human skin are Malassezia (Sanford and Gallo 2013). Both environmental and host factors can have direct effect on skin microbiome such as physiology, external environment, immune system, lifestyle, body location, age, gender, and underlying medical conditions (Grice and Segre 2011).

The stratum corneum layer of the skin epidermis and epidermal tight junctions are two of the main elements in the barrier function of the skin (De Benedetto et al. 2012). Components of the skin microbiome may influence immunity and other host functions via a number of different mechanisms (Belkaid and Segre 2014) (Fig. 1b). This includes their ability to metabolize host proteins and lipids into bioactive molecules such as free fatty acids (Belkaid and Segre 2014) that in turn can stimulate keratinocyte-derived immune mediators (complement, and IL-1) and other immune cells within the dermis. Work from Work from Dr. Yasmine group showed how skin microbiota plays a key role in promoting protective immunity to dermal infections. Their group demonstrated that skin commensals can induce T cell responses restricted to MHC class I molecules and these commensal-specific T cells express immunoregulatory and tissue repair signature genes promoting accelerated skin wound closure. Therefore, suggesting the capacity of skin microbiota to induce immune responses that couples antimicrobial function with tissue repair (Linehan et al. 2018).

Disruptions of the skin microbiota are associated with a number of disease conditions – including psoriasis, atopic dermatitis, and acne vulgaris (Trivedi 2012; Grice 2014). Psoriasis is a chronic multifactorial autoimmune disorder affecting the skin, characterized by raised, scaly, well-demarcated, erythematous oval plaques (Nestle et al. 2009), and can be provoked or exacerbated by specific pathogens including bacteria (S. aureus and Streptococcus pyogenes), viruses (human papillomavirus and endogenous retroviruses), and fungi (Malassezia and Candida albicans) (Fry and Baker 2007). Fahlén et al. found Streptococcus as the most common genus in both normal and psoriasis skin, whereas Staphylococcus and Propionibacterium were significantly lower in psoriasis compared with control limb skin (Fahlen et al. 2012), while Alekseyenko et al. showed significant increase in abundances of Corynebacterium, Propionibacterium, Staphylococcus, and Streptococcus in psoriatic plaques (Alekseyenko et al. 2013). In contrast Gao et al. revealed Propionibacterium species being less abundant in psoriasis than in normal controls (Gao et al. 2008). In another study, a reduction in Firmicutes and an increase in Proteobacteria were reported in psoriatic patients (Drago et al. 2016), while Liew et al. reported that Firmicutes were significantly overrepresented and Actinobacteria and Propionibacterium were significantly underrepresented in psoriatic lesions. Although this confirms that psoriasis exhibits a distinct microbiota from healthy unaffected skin, however, conflicting reports warrant a thorough characterization of the abundance of microbes in psoriatic patients. Of note, M proteins, found on Group A, C, and G β-hemolytic streptococci, are associated with worsening of chronic plaque psoriasis by mimicking keratin determinants with subsequent psoriatic T-cell activation (McFadden et al. 1991; Valdimarsson et al. 2009). This theory is validated by the fact that the interaction between type IV collagen and αα integrin found exclusively on epidermal psoriatic T cells results in the expansion of this subset of cells and the manifestation of psoriasis (Conrad et al. 2007). T-cell activation in psoriasis is also shown to be predisposed by antigens such as streptococcal pyogenic toxin A and B as well as peptidoglycan (Boyman et al. 2007; Davison et al. 2001; Baker et al. 2006). The role of the skin microbiome in the development and progression of melanoma and other cancers is incompletely understood at this point, though active investigations are currently underway.

Profiling the Microbiome

As previously mentioned, advances in techniques to characterize the microbiome have resulted in a marked increase in our understanding of these microbes in the setting of health and disease. Several different techniques exist to profile the microbiome, and these each have unique advantages and disadvantages (Lagier et al. 2018; Cogdill et al. 2018) (Table 1).
Table 1

Methods to profile microbiome

Microbiome profiling method

Profiling description

Advantages

Disadvantages

16S rRNA sequencing

Processing relevant samples to DNA

PCR amplification of hypervariable region(s) of 16S gene

Sequencing and comparison with reference databases

Quantify ecology metrics: alpha- and beta-diversity

Characterize differential abundance of bacteria taxa or operational taxonomic units (OTUs)

Fairly rapidly performed

Low cost of analysis

Reduced accuracy of taxonomic identification due to copy number variations and PCR primer and amplification bias

Cannot inform the functional biological capacity of a given microbial community

Only accounts for bacteria; cannot quantify viruses, fungi, and protozoa in the sample

Whole metagenomic sequencing (WMS)

Non-targeted sequencing process

Involves sequencing of the entire genome of all microbes in a given sample

Annotates assembled or unassembled reads against a protein database

Allows sequencing of viruses, fungi, protozoa, and archaea

Assesses functional potential of microbial communities

Deeper resolution to characterize down to the species level

Yields relative abundances of orthologous gene families or pathways

Significantly higher cost in terms of time and money

Less tolerant of low biomass or contaminated samples

Requires more complex computational analytic approaches

Metatranscriptomic

High throughput sequencing of RNA isolated from complex microbial populations

-mRNA/cDNA sequencing for high-resolution gene expression profiling

Help identify the subset of genes within a microbial community expressed in sample

High throughput and sensitivity

Characterization of known and unknown transcripts

Detection of microbial genes is technique-sensitive due to limited stability of RNA and low proportions of mRNA in stool samples

Involve multiple purification steps

Computationally intense as they require normalization of transcripts to DNA copy numbers

Metabolomics

Non-sequencing-based, culture-independent approaches to molecular profiling of the human microbiome

Can perform on low amount of sample

Time efficient

Identify secreted and intracellular microbial products

Quantification of small-molecule metabolites generated by microorganisms

Yield functional information of the microbiome

Study the impact of microorganism in health and disease

Lack accuracy in differentiating between host-derived and microbial-derived molecules

Many unknown metabolites in databases

Strict identification of compound labor intensive

Metaproteomics

Proteins/peptides are analyzed

Protein monitoring and profiling

Quantification of protein or peptide levels that can provide a high-resolution snapshot of bacteria-host interaction and metabolites generated by microorganisms

Identify differential microbial proteins production under various physiological/environmental conditions

Heterogeneous stability

Difficult to analyze all the metabolites present in the sample

Many unknown proteins in databases

Culturomics

High throughput culture method to complement taxonomic identification by metagenomics by advances in mass spectroscopy techniques (MALDI-TOF)

Identification of bacteria that have been considered to be difficult to culture

Allows characterization of specific microbes

Rapidly and accurately identify large number of colonies

Characterizing the viability of detected microorganisms

Labor intensive and time consuming

Targeted/specific PCR

Target-specific microbial taxa

Detection of very small quantities of bacteria that often remain undetected by 16S profiling

More accurate bacterial species and strain identification than traditional qPCR

Allows detection of archaeal, fungal, and viral communities

Accurate species identification

Require harmonization of the extraction and PCR conditions between the studies

Cannot inform the biological function of a given microbial community

This includes 16 s sequencing which involves next-generation sequencing techniques to characterize the 16 s subunit of the ribosome (which is unique to prokaryotes). Regions of the 16 s subunit vary between bacterial species – thus allowing use of this technique to determine relative abundances of differential bacterial species within a given sample (Zhang et al. 2018). Additionally, one can use this approach to assess alpha diversity – which is a measure of the differences in abundance of certain bacterial taxa between samples and/or groups. Beta diversity may also be derived using this technique, which refers to the similarity/dissimilarity between groups of samples (Caporaso et al. 2010). An advantage of this technique is the relatively low cost and speed of analysis using this approach; however limitations exist as species-level determination may not be feasible nor are other components of the microbiota assessed using this approach (such as viruses, fungi, and protozoa).

Another technique that can be used to profile the microbiome is whole metagenomic sequencing – or WMS. This approach involves sequencing of the entire genomic content; thus it allows characterization of microbes beyond bacteria and allows better resolution with the ability to characterize down to the species level (and even to specific strains). Thus this approach has many advantages over 16 s sequencing; however it is currently more costly and also requires more advanced bioinformatics approaches for data analysis.

Additional approaches include culturomics and PCR-based approaches targeting specific bacterial taxa. Culturomics is appealing, in that it allows isolation and characterization of specific microbes associated with the specific phenotype of interest. Though somewhat labor intensive, this approach is gaining momentum to overcome some of the limitations of pure sequencing approaches (Seng et al. 2009). PCR-based approaches may also be used to interrogate for single or limited taxa of interest, and the cost and turnaround time of such analyses provide advantages. On top of this, metabolomic profiling and transcriptomic profiling performed in parallel may be quite useful as it may yield additional information regarding functional status of the microbiome (Lagier et al. 2012).

Role of the Microbiome in Melanoma and Other Cancers

Influence of Tumor and Gut Microbiome on Carcinogenesis and Response to Cancer Therapy

The notion that microbes could contribute to carcinogenesis and response to cancer therapy originated many years ago (Littman et al. 2004; Welton et al. 1979; Nagy et al. 1998), though the full impact of this is only now being appreciated with additional insights clearly to be gained. This is perhaps best studied in the case of luminal gastrointestinal malignancies such as gastric cancer and colorectal cancer, where bacteria have a demonstrated link to carcinogenesis (with Helicobacter pylori in the case of gastric cancer, and Fusobacterium nucleatum in the case of colorectal cancer) (Peek and Blaser 2002; Mima et al. 2015).

Beyond these examples, there is now extensive evidence linking microbes (including bacteria, viruses, protozoa, and others) to cancer – with therapeutic strategies ranging from eradication of these pathogens to facilitate cancer treatment (Rosenberg et al. 2008; Uribe-Herranz et al. 2018) to prevention of these infections through. Microbes in tumors have also been shown to impact therapeutic responses to systemic therapy such as immune checkpoint blockade, with virally driven tumors exhibiting enhanced responses to therapy likely owing to recognition of “foreign” antigens (Smola 2017; Rieckmann et al. 2013; Tashiro and Brenner 2017).

In addition to microbes at the level of the tumor impacting carcinogenesis and response to cancer therapy, microbes at the level of the gut can do this as well through their impact on immunity and potentially on immunosurveillance of cancer (Routy et al. 2018b; Zitvogel et al. 2017). There is evidence to support the concept that generalized dysbiosis of gut microbiota may contribute to carcinogenesis (Tsilimigras et al. 2017; Garrett 2015), as repeated use of antibiotics has been associated with the development of both gastrointestinal (GI) tract and non-GI tract tumors in large case-control studies (Boursi et al. 2015). Various mechanisms have been proposed by which dysbiosis might affect tumorigenesis and tumor growth, however a comprehensive understanding of the complex mechanisms through which these commensal microbes impact immunity and carinogenesis is critical and work analyzing this is currently underway.

One mechanism through which gut dysbiosis may have an impact is through the induction of an inflammatory state that can promote carcinogenesis via pro-inflammatory toxins (such as produced by Bacteroides fragilis (Purcell et al. 2017; Wu et al. 2009)), increased reactive oxygen species (Mangerich et al. 2012), and alterations in signaling pathways (Fusobacterium nucleatum) (Kostic et al. 2013). Alternatively, bacterial products/metabolites (Dalmasso et al. 2014; He et al. 2018) may also contribute to carcinogenesis. For example, components of F. nucleatum including the FadA adhesion (FadAc) can activate β-catenin/Wnt signaling pathways resulting in oncogenic transcriptional changes (Sears and Garrett 2014; Rubinstein et al. 2013). F. nucleatum has been demonstrated to play a role in the development and progression of colon adenomas and colon cancer (Castellarin et al. 2012; McCoy et al. 2013; Warren et al. 2013) and has also been identified in nodal and distant metastasis (Yu et al. 2016; Bullman et al. 2017). Another well-explored example of gut microbiota-associated malignancy is hepatocellular carcinoma (HCC) where microbial modification of primary bile acids produced by the liver to secondary bile acids such as deoxycholic acid (DCA) can cause DNA damage, hepatotoxicity, and carcinogenesis (Yoshimoto et al. 2013). The gut microbiota is also associated with the response to infectious hepatitis, obesity, and the development of nonalcoholic steatohepatitis (NASH) as well as other forms of cirrhosis, all of which are key risk factors for the development of HCC (Mima et al. 2017).

Although many other studies demonstrate a direct association of dysbiosis and other malignancies, additional preclinical, clinical, and epidemiological studies will certainly strengthen the relationship between dysbiosis and cancer. Furthermore, the harmonization of characterization techniques/pipelines is necessary to bring the parity between the studies to be able to conclude healthy vs tumorigenic microbes. With the current studies, it is undeniably conceivable that strategies to modulate the microbiota may be used to improve cancer immunosurveillance (Zitvogel et al. 2018) and it would be productive to explore gut microbiota and/or their metabolic products as potential biomarkers of cancer development.

Influence of the Gut and Tumor Microbiome on Response to Melanoma Therapy

Though the role of microbes in influencing cancer development has been studied for many years, their role in melanoma was not elucidated until recently. However seminal work by Dr. Gajewski and others has now put melanoma in the spotlight – with a clear and significant demonstrable contribution of the microbiome in response to melanoma therapy.

The earliest of this work was published in 2015, where preclinical studies demonstrated that mice with different gut microbiota demonstrated differential responses to melanoma therapy – specifically to immune checkpoint blockade (Sivan et al. 2015). Specifically, Gajewski’s group showed that identical strains of mice (C57BL6) purchased from two different vendors (Taconic Farms vs. Jackson Laboratories) had distinct gut microbiomes, and this was associated with differential response to treatment with immune checkpoint blockade (targeting the programmed death receptor 1 – PD-1) to treat established melanoma tumors (B16). Strikingly, they also found that by modulating the gut microbiota they could enhance responses to therapy in these mice (either through co-housing, as mice are naturally coprophagic, or by transfer of specific bacterial strains). Furthermore, the group provided insight into the mechanism through which these gut microbiota were enhancing antitumor immunity – demonstrating that mice with a “favorable” gut microbiome had more functional antigen-presenting cells (APCs) such as dendritic cells capable of priming antigen-specific T-cell responses (Sivan et al. 2015). Similar work was published in the same issue of Science by Zitvogel and colleagues, demonstrating a reliance on gut microbiota to treatment with immune checkpoint blockade in preclinical models (specifically to CTLA-4 blockade) across several cancer types (using sarcoma, melanoma, and colon cancer tumor models) (Vetizou et al. 2015).

These studies sparked excitement in the field though some skepticism given that findings were only demonstrated in preclinical models. This excitement turned to action when several groups then turned to human cohorts to test the relevance of these findings, and this work has now shown an association between gut microbiota in response (as well as toxicity) to immune checkpoint blockade in melanoma in numerous published studies (Matson et al. 2018; Gopalakrishnan et al. 2018a; Chaput et al. 2017) (Table 2). Several of these studies were published together in Science in 2018 strengthening the link between gut microbiota and response to immune checkpoint blockade in melanoma as well as in other cancer types (Gopalakrishnan et al. 2018a; Routy et al. 2018a; Chaput et al. 2017; Frankel et al. 2017). In these studies, distinct bacterial “signatures” were noted in the gut microbiota of responders versus nonresponders to anti-PD-1 therapy in patients with melanoma (Matson et al. 2018; Gopalakrishnan et al. 2018a; Frankel et al. 2017) and in non-small cell lung cancer and renal cell carcinoma (Routy et al. 2018a) – with higher diversity and enrichment of specific bacterial taxa (such as Bifidobacterium, Ruminococcus, Faecalibacterium, and Akkermansia) in responders to therapy. Although only modest overlap has been noted in specific bacterial taxa associated with response across these cohorts, phylogenetic commonalities do exist and functional status (what these microbes are doing to immunity) may be much more important than the names of these bacterial taxa.
Table 2

Human studies demonstrating modulatory function of gut microbiome on response to immune checkpoint blockade therapy for melanoma

 

Influence of gut microbiome in ICB therapy

Outcome

Bacteria

References

1

Enhanced efficacy of PD-1 blockade therapy

Higher abundance in responders

Elevating levels of effector T cells in peripheral blood and TILs

Increasing densities of CD8+ T cells in tumor microenvironment

Ruminococcaceae

Gopalakrishnan et al. (2018a)

2

Enhanced efficacy of PD-1 blockade therapy

Higher abundance in responders

Veillonella parvula

Matson et al. (2018)

3

Enhanced efficacy of PD-1 blockade therapy

Higher abundance in responders

Decreasing peripherally derived Tregs

Bifidobacterium adolescents

Matson et al. (2018)

4

Enhanced efficacy of PD-1 blockade therapy

Higher abundance in responders

Bifidobacterium longum

Matson et al. (2018)

5

Reduced efficacy of PD-1 blockade therapy

Higher abundance in non-responders

Ruminococcus obeum

Matson et al. (2018)

6

Enhanced CTLA-4 blockade efficacy therapy

Higher abundance in responders

Inducing activation of Treg

Promoting development of tolerogenic macrophages and dendritic cells

Prolonging progression-free survival/overall survival

Butyrate-producing bacterium

Chaput et al. (2017)

7

Enhanced efficacy of PD-1 blockade therapy

Decreasing peripherally derived Tregs

Collinsella aerofaciens

Matson et al. (2018)

8

Enhanced efficacy of PD-1 blockade therapy

Decreasing peripherally derived Tregs

Enterococcus faecium

Matson et al. (2018)

9

Enhanced CTLA-4 blockade efficacy therapy

Prolonging overall survival

Elevating colitis risk

Faecalibacterium prausnitzii

Chaput et al. (2017)

10

Enhanced efficacy of PD-1 blockade therapy

Higher abundance in nonresponders

Klebsiella pneumonia

Matson et al. (2018)

11

Enhanced efficacy of PD-1 blockade therapy

Decreasing peripherally derived Tregs

Parabacteroides merdae

Matson et al. (2018)

12

Enhanced efficacy of CTLA-4 blockade therapy

Prolonging progression-free survival/overall survival

Elevating colitis risk

Gemmiger formicilis

Chaput et al. (2017)

13

Reduced efficacy of PD-1 blockade therapy

Higher abundance in nonresponders

Roseburia intestinalis

Matson et al. (2018)

Several of these manuscripts demonstrated that these phenotypes could be recapitulated by fecal microbiota transplant (FMT) from responding and nonresponding patients into germ-free mice, with subsequent tumor implantation and treatment with immune checkpoint blockade (Matson et al. 2018; Gopalakrishnan et al. 2018a; Routy et al. 2018a). Modulation of the gut microbiota was shown to enhance therapeutic response in several of these preclinical models. Additionally, there was evidence in human cohorts that negative modulation of the gut microbiome could impact therapeutic response, as treatment of patients with antibiotics around the time of first administration of checkpoint blockade was associated with impaired survival on anti-PD-1-based therapy (Routy et al. 2017), which has now been validated in subsequent cohorts (Derosa et al. 2018). Based on findings from these studies, efforts are currently underway to positively modulate the gut microbiota in patients with melanoma going onto immune checkpoint blockade (NCT03643289, NCT03595683, NCT03341143, NCT03772899).

Importantly, the tumor microbiome may be relevant in patients with melanoma – as microbes have been identified in tumors across several different histologies including lung cancer, breast cancer, colon cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, ovarian cancer, and prostate cancer. Though the specific mechanism through which these microbes gain access to tumors is incompletely understood, systemic seeding from infection or bacterial translocation from the GI tract may occur and has even been shown to occur in healthy individuals with normal gut mucosal integrity. These microbes have been shown to influence response to chemotherapy (Geller et al. 2017) and immunotherapy (Miller et al. 2018) in other cancer types, and investigations regarding the role of intra-tumoral microbes in melanoma are currently underway.

Targeting the Microbiome to Treat Disease

Though the concept of targeting the microbiome to treat cancer is somewhat novel, this approach has been used in noncancer indications such as Clostridium difficile colitis and other conditions for years with proven efficacy in some cases (van Nood et al. 2013; Juul et al. 2018) – particularly with regard to targeting the gut microbiota. The gut microbiome may be targeted using several different approaches (Fig. 2), including through the use of fecal microbiota transplant (FMT), the administration of bacterial consortia (Hibberd et al. 2017), dietary intervention (Ramirez-Farias et al. 2009; Turnbaugh et al. 2007), and targeted approaches against specific taxa using antibiotics or phage (Wong and Santiago 2017; Pranjol and Hajitou 2015; Abedon et al. 2017; Budynek et al. 2010).
Fig. 2

Methods of microbiome modulation: The gut microbiome may be targeted using several different approaches, including through the use of fecal microbiota transplant (FMT), the administration of bacterial consortia, dietary intervention, and targeted approaches against specific taxa using antibiotics or phage

The first published report of the use of fecal microbiota transplant (FMT) was in 1958 when Eisenman reported successful treatment of C. difficile colitis using this approach in several patients (Eiseman et al. 1958). However the concept has been around for centuries, with efforts to manipulate the composition of the gut microbiota used over 1700 years ago in China for the treatment of diarrhea. Since the approach was first published and reported, it is now being widely evaluated in the treatment of numerous conditions ranging from inflammatory bowel disease and multiple sclerosis (Paramsothy et al. 2017) and now in the treatment of cancer (NCT03353402, NCT03341143, NCT03678493, NCT02928523). FMT can be administered via a number of different routes, including colonoscopy and also via oral administration (Gough et al. 2011), and great consideration needs to be taken into appropriate donor selection to minimize the risk of transmissible infectious diseases and undesirable traits such as obesity (Rao and Young 2015). Thus far, healthy donor FMT has focused primarily on these issues, though the choice of donors is likely to be more complicated when considering treatment for conditions such as cancer – as the “optimal” gut microbiota composition to facilitate antitumor immune responses is incompletely understood (Cogdill et al. 2018; Gopalakrishnan et al. 2018b). However evidence from published studies suggests that responders to immune checkpoint blockade do have distinct signatures in the gut microbiome (Gopalakrishnan et al. 2018a); thus it may be prudent to screen potential donors for this signature in addition to the routine screening tests. To date, most of the planned and ongoing trials to modulate the gut microbiota in patients with melanoma on immune checkpoint blockade incorporate FMT from complete responders to therapy (NCT03353402, NCT03341143). Certainly, use of FMT in such trials is a logical and likely necessary first step in a rational approach to target the gut microbiome in patients with cancer.

Based on published studies, there is also an ongoing effort to target the gut microbiota using a mixture of several (or even single) bacterial strains that have been associated with therapeutic response to immune checkpoint blockade (NCT03595683, NCT03637803). This approach has some potential advantages over FMT including ease of manufacturing and scalability; however, as noted we do not have a clear understanding of which bacterial taxa and strains may be beneficial when comparing across published cohorts. Additionally the number of composition of an “optimal” consortia of bacteria to enhance therapeutic responses is unknown; thus this approach is likely to be iterative and informed by early trials and studies in larger cohorts of patients. Moreover, there has been interest in testing probiotic preparations in combination with treatment with checkpoint blockade; however substantial limitations exist with this approach as published studies show that there is tremendous variability in the ability of commercial probiotic supplements to engraft in the gastrointestinal tract and these preparations are less well-regulated than other formulations, with recent evidence that such formulations may actually impair engraftment of healthy commensal bacteria (Zmora et al. 2018; Suez et al. 2018).

Approaches to specifically target detrimental microbes are also being used, either with targeted antibiotic approaches or with use of viruses that target specific bacteria (bacteriophages). The phages have the potential to infect bacteria in the gut (Lusiak-Szelachowska et al. 2017) and have been shown to contribute to the efficacy of approaches such as fecal microbiota transplant for noncancer indications (Zuo et al. 2018). However these components of the microbiota are less well-studied, and additional investigation is needed to better understand their role in melanoma and other cancers.

Another means to modulate the gut microbiota is via dietary intervention, though this has not been thoroughly investigated in the setting of treatment of cancer, and incorporation of such analyses is critically needed. However some insights may be gained from studies performed in noncancer populations where such studies have been done. Such studies have focused on diets that have been recommended and have been associated with a lower risk of developing or dying from cancer (such as the Mediterranean Diet and Healthy Eating Index); however the influence of these diets on gut microbiota has not been well-studied. Nonetheless such diets are associated with enhanced immune function and reduced levels of systemic inflammation (Oude Griep et al. 2013), and more formal studies of dietary intervention are currently underway.

Conclusions and Future Directions

Significant advances have been made in the treatment of melanoma, and there is increasing evidence that environmental and host factors may impact melanomagenesis and response to melanoma therapy. This includes the tumor and gut microbiota; however the full impact of these variables is incompletely understood. As we move forward as a field, it will be important to take these factors into consideration and to use insights gained to derive strategies to improve responses to melanoma therapy and ultimately to prevent melanoma altogether.

Notes

Author Contributions

Conception: Hermann and Wargo

Writing: Arora and Wargo

Creation of figures: Arora and Wargo

Critical review and revision of the manuscript: All authors

Conflict of Interest Disclosures

J. Wargo is an inventor on a US patent application (PCT/US17/53.717) submitted by the University of Texas MD Anderson Cancer Center that covers methods to enhance immune checkpoint blockade responses by modulating the microbiome. J. Wargo is a paid speaker for Imedex, Dava Oncology, Omniprex, Illumina, Gilead, MedImmune, and Bristol-Myers Squibb. She is a consultant/advisory board member for Roche-Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, and MicrobiomeDx. J. Wargo also receives clinical trial support from GlaxoSmithKline, Roche-Genentech, Bristol-Myers Squibb, and Novartis. J. Wargo is a clinical and scientific advisor at MicrobiomeDx and a consultant at Biothera Pharma and Merck Sharp and Dohme.

The other authors declared no conflicts of interest.

Funding/Support

J. Wargo has honoraria from speakers’ bureau of Dava Oncology, Bristol-Myers Squibb, and Illumina and is an advisory board member for GlaxoSmithKline, Novartis, and Roche-Genentech. J. Wargo is supported by the NIH (1 R01 CA219896-01A1), US-Israel Binational Science Foundation (201332), Kennedy Memorial Foundation (0727030), the Melanoma Research Alliance (4022024), American Association for Cancer Research Stand Up To Cancer (SU2C-AACR-IRG-19-17), Department of Defense (W81XWH-16-1-0121), MD Anderson Cancer Center Multidisciplinary Research Program Grant, Andrew Sabin Family Fellows Program, and MD Anderson Cancer Center’s Melanoma Moon Shots Program. J. Wargo is a member of the Parker Institute for Cancer Immunotherapy at MD Anderson Cancer Center.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Reetakshi Arora
    • 1
  • Amanda Hermann
    • 1
  • Jennifer A. Wargo
    • 1
    • 2
    Email author
  1. 1.Department of Surgical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of Genomic MedicineThe University of Texas MD Anderson Cancer CenterHoustonUSA

Section editors and affiliations

  • David E. Fisher
    • 1
  • Nick Hayward
    • 2
  • David C. Whiteman
    • 3
  • Keith T. Flaherty
    • 4
  • F. Stephen Hodi
    • 5
    • 6
  • Hensin Tsao
    • 7
    • 8
  • Glenn Merlino
    • 9
  1. 1.Department of Dermatology, Harvard/MGH Cutaneous Biology Research Center, and Melanoma Program, MGH Cancer CenterMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  2. 2.QIMR Berghofer Medical Research InstituteHerstonAustralia
  3. 3.QIMR Berghofer Medical Research InstituteHerstonAustralia
  4. 4.Henri and Belinda Termeer Center for Targeted TherapiesMGH Cancer CenterCambridgeUSA
  5. 5.FraminghamUSA
  6. 6.Department of Medicine, Brigham and Women's HospitalDana-Farber Cancer InstituteBostonUSA
  7. 7.AuburndaleUSA
  8. 8.Harvard-MIT Health Sciences and TechnologyCambridgeUSA
  9. 9.Center for Cancer ResearchNational Cancer InstituteBethesdaUSA

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