Webinar Review: Immuno-oncology and the Microbiome


     
In a recent webinar, Dr. Benjamin Cuiffo of Biomodels addressed the role of microbiome in preclinical immuno-oncology research. There is growing evidence that microbial imbalance (dysbiosis) is associated with many illnesses, including inflammation, autoimmune disease, and even cancer — but many researchers fail to account for its impact in preclinical study design.

If you missed it, here's a summary of the key developments discussed during Dr. Cuiffo's presentation.

Tumor Immunotherapy and the Microbiome

There is growing evidence that a patient's microbiome influences both cancer progression and response to therapy. Dr. Cuiffo showed several examples from the literature.

  • The anticancer efficacy of cyclophosphamide in normal mice is diminished upon treatment with broad spectrum antibiotics1.
  • Melanoma tumors grow at a different rate in C57BL/6 mice from different vendors, with more aggressive tumor growth in mice from Taconic Biosciences compared to mice from the Jackson Laboratory.
    • The immune-mediated control of tumors in Jax mice can be transferred to Taconic mice via cohousing or fecal transplantation. Bifidobacterium was identified as responsible for this effect.
    • Taconic mice administered Bifidobacterium spp. display greater immune control of tumors, and the efficacy of a checkpoint inhibitor immunotherapy is enhanced upon co-administration of Bifidobacterium spp2.
Dr. Cuiffo showed data from experiments at Biomodels using a syngeneic melanoma model in C57BL/6 mice. Compared to conventional mice, tumors grew slower in germ-free C57BL/6NTac mice from Taconic, and they did not respond to anti-PD-L1 therapy.

Modulation of the Microbiome as a Therapeutic Strategy

“Germ-free mice do represent a critical tool for investigation of the microbiome.”
– Dr. Benjamin Cuiffo,
Biomodels
Two new papers demonstrated that the patient gut microbiome does indeed affect response to cancer immunotherapy3,4. These results validated the findings of earlier preclinical studies and support the idea of rational modulation of the microbiome as a valid therapeutic strategy.

Dr. Cuiffo discussed several potential methods, including oncobiotics and vaccines.

Considerations for Preclinical Studies

Dr. Cuiffo provided several useful recommendations for immuno-oncology preclinical studies.

  • Control for microbiome effects in all preclinical studies where the mechanism of action may rely on the immune system.
  • Source animal models from the same vendor and even the same barrier for a study series.
  • Containment housing and consistent husbandry are required so as not to perturb the microbiome.
  • Germ-free mice provide a blank canvas for microbiome research. They can be colonized with any type of microbiota, then used to study immune response relevant to that microbiota.
Dr. Benjamin Cuiffo A wide range of preclinical models are relevant for immuno-oncology studies. Murine syngeneic tumor models are commonly used and are amenable to microbiome modulation. Either conventional, germ-free, or germ-free mice associated with a particular microbiome (such as a clinical sample) can be used.

Dr. Cuiffo introduced the concept for an exciting new double humanized model in which mice are humanized via the introduction of a human immune system as well as human microbiota.

For more on these and other topics, you can access a free video of Dr. Cuiffo's full presentation.

References:
1. Viaud, S.; Saccheri, F.; Mignot, G.; Yamazaki, T.; Daillere, R.; Hannani, D.; Enot, D. P.; Pfirschke, C.; Engblom, C.; Pittet, M. J.; Schlitzer, A.; Ginhoux, F.; Apetoh, L.; Chachaty, E.; Woerther, P.-L.; Eberl, G.; Berard, M.; Ecobichon, C.; Clermont, D.; Bizet, C.; Gaboriau-Routhiau, V.; Cerf-Bensussan, N.; Opolon, P.; Yessaad, N.; Vivier, E.; Ryffel, B.; Elson, C. O.; Dore, J.; Kroemer, G.; Lepage, P.; Boneca, I. G.; Ghiringhelli, F.; Zitvogel, L. Science 2013, 342 (6161), 971-976.
2. Sivan, A.; Corrales, L.; Hubert, N.; Williams, J. B.; Aquino-Michaels, K.; Earley, Z. M.; Benyamin, F. W.; Lei, Y. M.; Jabri, B.; Alegre, M.-L.; Chang, E. B.; Gajewski, T. F. Science 2015, 350 (6264), 1084-1089.
3. Gopalakrishnan V; Spencer CN; Nezi L; Reuben A; Andrews MC; Karpinets TV; Prieto PA; Vicente D; Hoffman K; Wei SC; Cogdill AP; Zhao L; Hudgens CW; Hutchinson DS; Manzo T; Petaccia de Macedo M; Cotechini T; Kumar T; Chen WS; Reddy SM; Sloane RS; Galloway-Pena J; Jiang H; Chen PL; Shpall EJ; Rezvani K; Alousi AM; Chemaly RF; Shelburne S; Vence LM; Okhuysen PC; Jensen VB; Swennes AG; McAllister F; Sanchez EMR; Zhang Y; Le Chatelier E; Zitvogel L; Pons N; Austin-Breneman JL; Haydu LE; Burton EM; Gardner JM; Sirmans E; Hu J; Lazar AJ; Tsujikawa T; Diab A; Tawbi H; Glitza IC; Hwu WJ; Patel SP; Woodman SE; Amaria RN; Davies MA; Gershenwald JE; Hwu P; Lee JE; Zhang J; Coussens LM; Cooper ZA; Futreal PA; Daniel CR; Ajami NJ; Petrosino JF; Tetzlaff MT; Sharma P; Allison JP; Jenq RR; Wargo JA. Science 2017, epub ahead of print.
4. Routy B; Le Chatelier E; Derosa L; Duong CPM; Alou MT; Daillère R; Fluckiger A; Messaoudene M; Rauber C; Roberti MP; Fidelle M; Flament C; Poirier-Colame V; Opolon P; Klein C; Iribarren K; Mondragón L; Jacquelot N; Qu B; Ferrere G; Clémenson C; Mezquita L; Masip JR; Naltet C; Brosseau S; Kaderbhai C; Richard C; Rizvi H; Levenez F; Galleron N; Quinquis B; Pons N; Ryffel B; Minard-Colin V; Gonin P; Soria JC; Deutsch E; Loriot Y; Ghiringhelli F; Zalcman G; Goldwasser F; Escudier B; Hellmann MD; Eggermont A; Raoult D; Albiges L; Kroemer G; Zitvogel L. Science 2017, epub ahead of print.

Share this Insight