Cancer IO Sebinar “Ex vivo models in IO Research” recording and summary are now available!

Recording and summary of the Cancer IO Sebinar session “Ex vivo models in IO research” is now available!

In the first edition of the Cancer IO Sebinar series we heard about the latest devel­opment of patient-derived ex vivo models in an immuno-oncology setting, and dis­cussed about the pos­sible future use of these models in clinical decision making.

Our invited speakers and pan­elists were David Barbie from Dana-Farber Cancer Institute & Harvard Medical School, Johana Kuncova-Kallio from UMP Bio­med­icals, Peeter Kari­htala from Hel­sinki Uni­versity Hos­pital and Jeroen Pouwels from Uni­versity of Helsinki.

Watch the recording or read the webinar notes below written by M.D. Sini Luoma. 

Intro­duction to Ex vivo models (3:20 in the video)

Jeroen Pouwels, Research Coordinator in Cancer IO, gave the intro­duction to ex vivo models. Pre­clinical drug study models are important espe­cially in cancer research, because over 90 % of the cancer drugs fail in clinical devel­opment. There are many dif­ferent approaches for ex vivo drug testing. Because of more accurate dimen­sions, 3D cell lines are better than 2D models but still they fail to capture the het­ero­geneity of the tumor envir­onment. Cancer organoids rep­resent a 3D structure of the tumor tissue.

There are a lot of immun­o­ther­apies in clinical research. Their huge advantage is the ability to cure the patient in some cases. To be able to test the immune ther­apies, one needs to have the immune system cells in the test model. Mouse models have been used but they are not ideal, and some mice are immun­ode­fi­cient. Many drugs that are very effi­cient in mice are not effective at all in humans.

Patient-derived explant cul­tures (PDECs) are derived from authentic tumor and include tumor cells and immune cells. PDECs therefore can retain a func­tional immune con­texture. However, one problem with PDEC is that there can be a lot of het­ero­geneity between the ori­ginal tumor and the meta­stases, making it dif­ficult to predict if the drug tested in PDEC derived from one tumor site will be effective in other tumors of the patient.

3D solu­tions and tissue pre­ser­vation (16:36)

Johanna Kuncova-Kallio, Dir­ector in UPM Bio­med­icals, gave a talk about 3D solu­tions and tissue pre­ser­vation. As an example, she presented a nano­cel­lulose com­ponent (GrowDex) that can be used as a cell carrier. When pre­paring organoids, an auto­mated 3D printing of the models can be used.

Ex vivo systems incor­por­ating the tumor microen­vir­onment (28:55)

Dr. David Barbie gave a thorough present­ation about ex vivo systems incor­por­ating the tumor microen­vir­onment. The tumor microen­vir­onment is important, because in addition to cancer cells also the other cells, like stromal and immune cells, act­ively con­tribute to tumor sur­vival, growth and drug res­istance. The dynamics of the tumor microen­vir­onment are not cap­tured by one moment, and most of the pub­lished research lacks the time dimension. The problem is that repeated tumor biopsies are invasive, and not pre­dictive. To try to predict if the patient will respond to immune therapy, for example patient-derived xeno­grafts and organoids can be used. 

The tumor-spe­cific expansion can be achieved within weeks to months, which is often too long a time for decision-making in patients with relapsed cancer. Dr. Barbie stated that the novel ex-vivo tech­no­logies will enable pre­dictive func­tional pre­cision medicine. Results will be available in a few days, which is fast enough for relapsing cancer.

For the inter­esting panel dis­cussion hosted by Cancer IO’s Com­mu­nic­a­tions Manager Heidi Haikala, see the video from 1:28:24.

Rel­evant pub­lic­a­tions by Dr Barbie:

Ex Vivo Pro­filing of PD-1 Blockade Using Organ­o­typic Tumor Spheroids. Cancer Discov. 2018 Feb;8(2):196-215. doi: 10.1158/2159-8290.CD-17-0833. Epub 2017 Nov 3.PMID: 29101162 

Engin­eering approaches for studying immune-tumor cell inter­ac­tions and immun­o­therapy. iScience. 2020 Dec 23;24(1):101985.doi: 10.1016/j.isci.2020.101985. 

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