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,
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.
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
Watch the Taconic Biosciences' Webinar: Read the Related Taconic Biosciences' Insights:
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.