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How can cancer preventing genes from animals be transferred to humans?

How can cancer preventing genes from animals be transferred to humans?


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I recently read this non-peer reviewed article that states that the prevalence of cancer in crocodiles or elephants is really low, much lower than humans. It is said below

A team of researchers in the US looked closer, and found an abundance of a gene called TP53. This gene is known for its ability to repair damaged DNA and thus halt the spread of cancer, and it's some 20 times more common in elephants than it is in human beings. It appears elephants have developed more of these genes as they've evolved, in part to protect calves born to older mothers

"These findings, if replicated, could represent an evolutionary-based approach for understanding mechanisms related to cancer suppression," says the report, published in the Journal of the American Medical Association.

Naked mole rats are even more miraculous - they never develop cancer, even when scientists try and induce it artificially. What appears to be happening, at least according to a recent study, is that the mole rats are using natural mechanisms to clamp down on the spread of cancer and fight back against the mutation.

Question

So why is it that these genes cannot be transferred from animals to humans? Maybe when the baby is at fetus stage? What are the current problems/limitations and if possible, how would scientists transfer these genes? My logic is that since mice and elephants are both mammals, it should not be too hard to achieve this.


While this research is interesting and might help curing cancer at some point, it's not a ground breaking revelation.

The gene in question here is TP53, coding for the very-well known tumor supressor protein p53. Humans do have this gene, but apparently elephants hav acquired multiple copies of it (~20) during evolution and that might explain why they don't get cancer so easily.

The problem of testing this is not a technical one. Using modern genetic engineering methods it should be fairly easy to multiply the gene in human or mice embryos, or introduce the additional variants from the elephants. Someone might already be planning to do that in mice.

For use on humans however, this is highly unethical. Without proof that these gene copies actually prevent cancer (and one observational study is not a proof), you can't just use a new medical treatment. Secondly this treatment would require genetic modification of an embryo (because its actually cancer prevention, not acute treatment) and that is illegal almost everywhere on earth (even genetic modification of somatic tissue for cancer treatment is just now becoming an experimental therapy).


Cancer in Wild Animals

Cancer seems to affect all animals, from anteaters to zebras. Much less is known about the cancers that affect wild animals, in part because it is hard to study. Animals move around and may not be easily observed for long periods of time. The cancers that have been studied are very interesting and will certainly prove useful in the study of human cancer. As an example, Tasmanian devils have a type of cancer that can be spread from animal to animal by biting!


Cancer in Cats

Cats are susceptible to a variety of cancers. Among the most common are lymphoma, squamous cell carcinoma (skin cancer), mammary cancer, mast cell tumors, oral tumors, fibrosarcoma (soft tissue cancer) , osteosarcoma (bone cancer), respiratory carcinoma, intestinal adenocarcinoma, and pancreatic/liver adenocarcinoma. The disease has become so prevalent that it is now the most common cause of death in cats.4


Certain breeds are more prone to certain cancers than others. Signs and symptoms differ depending on the type and stage of the cancer. Detection and diagnosis requires some detective work. Tumors that are visible and/or detectable by touch are most easily identified. Vets often perform additional tests to make an accurate diagnosis. Along with a physical exam, they may perform blood and urine tests, cytology, imaging and biopsies.

Treatment options vary and include surgery, chemotherapy, radiation, immunotherapy, photodynamic therapy, or a combination of these. In many cases, cancer can be successfully treated. Early detection and diagnosis is critical. Regular visits to the veterinarian can help prevent and manage cancer. The American Veterinary Medical Association recommends twice-a-year wellness exams for all cats.5 Because the causes of cancer in cats are similar to those in humans, risk can be reduced by lowering the animal's exposure to harmful carcinogens, including tobacco smoke.6


Critical Thinking Questions

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    Researchers Advance Findings on Key Gene Related to Cancer Metastasis

    BUFFALO, NY — New evidence reported by researchers at Roswell Park Comprehensive Cancer Center (Roswell Park) lends support to the hypothesis that the SSeCKS/AKAP12 gene is a key inhibitor of prostate cancer metastasis. The data are some of the first to demonstrate this dynamic in transgenic animal models, with promising implications for development of targeted therapies for prostate cancer and perhaps for other solid-tumor cancers.

    A team led by Irwin H. Gelman, PhD, noted that aggressive prostate cancers in humans typically turn off or delete two major regulatory genes, SSeCKS/AKAP12 and Rb. To explore this dynamic, the researchers developed a transgenic animal model to study the effects on prostate cancer progression of deleting these two genes. They report in Cancer Research, a peer-reviewed journal published by the American Association for Cancer Research, that the loss of these two genes and associated protein products leads to early prostate cancer. Moreover, more than 80 percent of the transgenic models in their study developed metastatic lesions in lymph nodes near the prostate.

    “This correlates with our earlier finding that SSeCKS/AKAP12 inhibits the chemotaxis of metastatic prostate tumor cells — that is, their ability to move on to another environment in response to chemical attractants,” said Dr. Gelman, the John & Santa Palisano Chair in Cancer Genetics at Roswell Park. “Thus, our data suggest that SSeCKS plays a role in preventing the early dissemination of prostate cancer cells to metastatic sites. Importantly, we show that humans whose prostate cancers have turned off or deleted the SSeCKS/AKAP12 gene have significantly higher rates of metastasis formation compared to cases where SSeCKS/AKAP12 levels are sustained.”

    While the SSeCKS/AKAP12 gene is deleted in about a third of metastatic prostate cancers, precluding benefit from targeted therapies exploiting this vulnerability, the remaining two-thirds of such tumors may be treatable with drugs that induce the reactivation of SSeCKS/AKAP12 production. Dr. Gelman and colleagues are now looking to identify the genomic signatures controlled by SSeCKS/AKAP12 in the suppression of metastasis pathways — at the level of the tumor cells themselves and in the cells that form the metastatic microenvironment.

    “At least 93 percent of cancer patients die because of complications due to metastatic cancers, yet the vast majority of pathways studied and therapies developed address the biology of primary cancers,” Dr. Gelman noted. “This current research is important in that it addresses specific mechanisms of cancer metastasis, with the result that genetic tests and therapies derived from such studies will have a higher chance of affecting cancer patient survival.”


    Tardigrade protein helps human DNA withstand radiation

    Experiments show that the tardigrade’s resilience can be transferred to cultures of human cells.

    Tardigrades, or water bears, are pudgy, microscopic animals that look like a cross between a caterpillar and a naked mole rat. These aquatic invertebrates are consummate survivors, capable of withstanding a host of extremes, including near total dehydration and the insults of space.

    Now, a paper 1 published on 20 September in Nature Communications pinpoints the source of yet another tardigrade superpower: a protective protein that provides resistance to damaging X-rays. And researchers were able to transfer that resistance to human cells.

    “Tolerance against X-ray is thought to be a side-product of [the] animal's adaption to severe dehydration,” says lead study author Takekazu Kunieda, a molecular biologist at the University of Tokyo. According to Kunieda, severe dehydration wreaks havoc on the molecules in living things. It can even tear apart DNA, much like X-rays can.

    The researchers wanted to know how tardigrades protected themselves against such harsh conditions. So Kunieda and his colleagues began by sequencing the genome of Ramazzottius varieornatus, a species that is particularly stress tolerant. It's easier to study processes within the tardigrade's cells when the animal's genome is inserted into mammalian cells, says Kunieda. So researchers manipulated cultures of human cells to produce pieces of the water bear's inner machinery to determine which parts were actually giving the animals their resistance.

    Eventually, Kunieda and his colleagues discovered that a protein known as Dsup prevented the animal's DNA from breaking under the stress of radiation and desiccation. And they also found that the tardigrade-tinged human cells were able to suppress X-ray induced damage by about 40%.

    Genomic treasure trove

    “Protection and repair of DNA is a fundamental component of all cells and a central aspect in many human diseases, including cancer and ageing,” says Ingemar Jönsson, an evolutionary ecologist who studies tardigrades at Kristianstad University in Sweden.

    This makes the new paper’s findings “highly interesting for medicine”, says Jönsson. It opens up the possibility of improving the stress resistance of human cells, which could one day benefit people undergoing radiation therapies.

    Kunieda adds that these findings may one day protect workers from radiation in nuclear facilities or possibly help us to grow crops in extreme environments, such as the ones found on Mars.

    Bob Goldstein, a biologist at the University of North Carolina at Chapel Hill who helped to sequence the genome of another tardigrade species 2 , says the research is exciting and clever. He also thinks that the study’s authors are correct in predicting that this is probably just the first of many such discoveries.

    “The tardigrade is resistant to a lot of different kinds of extremes,” says Goldstein. And this means that the animals must have many different ways of protecting themselves.

    “We are really just at the beginning of exploring the genetic treasure that the tardigrade genome represents,” says Jönsson.


    Discussion

    Integration of bacterial DNA in the human somatic genome

    Through this extensive analysis of several large human genome sequencing projects, we present evidence supporting LGT from bacteria to the human somatic genome. In terminally differentiated cells, we expect and observe that putative LGTs are detected consistently at low levels. Examination of clonally expanding tumors reveals many more transfers, as we would expect from a rapidly expanding population of cells. In all of the cases examined, the composition of the microbiome across the samples is different from the composition of bacterial DNA integrated into the human genome. When only the regions on the human genome with >4× coverage are examined, a pattern emerges of integration in the mitochondria for LAML and five genes in STAD. Remarkably, in STAD, four of those five genes have previously been shown to be implicated in cancer [54]–[57]. Together we believe this presents a compelling case that LGT occurs in the human somatic genome and that it could have an important role in human diseases associated with mutation.

    While it is possible that these LGT mutations may play a role in carcinogenesis, it is also necessary to consider that they could simply be passenger mutations. The rapidly proliferating tumor cells may be more permissive to LGT from bacteria due to mutations in tumor suppressor genes or down regulation of DNA repair pathways. As a result of clonal expansion, rare mutations may be amplified throughout the tumor. Based on our analysis, it is impossible to determine if the LGTs have a causal role in cancer, or are simply a byproduct of carcinogenesis.

    Likewise, while it is possible that the bacteria are causing mutations that benefit the bacteria, it is equally plausible that this occurs by random chance, or some combination of the two. If the mutations occur by random chance, mutations that induce carcinogenesis will be selected for over time within a local population of cells. This may explain why we observe low levels of LGT across the entire genome with increased coverage in specific genes in the STAD and LAML samples. In contrast, mutations that would benefit the bacteria would include those that create a micro-environment that promotes bacterial growth. This may explain why similar mutations, both in location and bacterial integrant, are observed in multiple individuals (Figure 8).

    Laboratory artifacts

    While the extensive coverage across these putative integrations in multiple samples is strong support for bacterial integration being present in human tumors, we recognize the concern that such bacterial/human read pairs may arise merely from chimeric DNA generated during library construction. We pursued obtaining specimens for validation or establishing collaborations to accomplish this validation with TCGA investigators. Unfortunately, the combination of patient consent and access policy precludes the possibility of experimental validation on these samples by researchers that lack an IRB tied to a grant award from NCI/TCGA. As our funding is from the NIH New Innovator Program this was not possible. Collaborating with current TCGA investigators was also pursued but was found to be explicitly forbidden. However, we anticipate the future successful validation of these results by researchers with access to samples and the proper authorization.

    However, further analyses suggest that these are not laboratory artifacts. If chimeras arise in library construction, they should increase as the prevalence of bacterial DNA/RNA increases. Therefore, we evaluated the possibility of a correlation between the number of read pairs arising from the Pseudomonas-like DNA and putative LGT read pairs for these six STAD samples. The Spearman-rank correlation between these values was not significantly different from zero (P = 0.19), indicating no correlation between the abundance of reads from the bacteria genome and reads supporting integration of Pseudomonas-like DNA in the human genome for these six samples. Overall, no relationship was observed between bacterial integrations and (a) the microbiome composition, (b) human transcript abundance, or (c) mitochondrial transcript abundance.

    Yet, to further examine laboratory artifact chimerism in these samples, the distribution of the insert sizes for paired reads was compared between representative LAML samples, STAD samples, and Neisseria meningitidis whole genome sequencing project samples (Figure S9). The mappings with N. meningitidis were used to establish that ∼0.22–0.29% of reads were outside this distribution when the reads were mapped to the assembled genome for that exact strain. For comparison, 0.94–1.12% of reads were outside this distribution when reads mapped to a divergent genome from the same species (Table S5). The same percentage values for paired reads only mapping to bacteria in the STAD samples ranged from 0.06–0.60% while those for LAML ranged from 0.65–0.87% (Table S5). Given that the database we searched against was limited to only those with complete genomes, it is highly unlikely that the bacterial DNA sequenced through the TCGA is from the same strain that has a genome deposited in RefSeq. Therefore, we anticipate values should lie between 0.22–1.12% as is seen in the N. meningitidis controls. We observed that bacterial read pairs in the TCGA data fell outside the distribution less frequently (0.06%–0.60%). While this percentage is used as a proxy for laboratory artifactual chimerism, bacterial genomes are known to be fluid with genome rearrangements happening in single growths that would result in the same outcome. When these results are taken together, there is no indication that there is a higher level of chimerism in the bacterial DNA of TCGA samples than is normally observed. Furthermore, this level of chimerism would not explain 4× or 150× coverage across the bacterial integrations that are discussed here considering that PCR duplicates were removed. Of note, high coverage chimerism in bacterial samples would lead to an inability to properly assemble the corresponding genomes, which is not observed in microbial genome sequencing projects.

    We note, however, that laboratory artifact chimeras could be detected in TCGA samples with whole genome amplification. As such these samples were eliminated from further analysis beyond what is presented in Table 1. In ovarian cancer, numerous read pairs that would normally support integrations were detected in both tumor and normal samples (Table 1). Upon further examination, all of the putative integrations involved E. coli DNA and are likely chimeras formed during the whole genome amplification used for these samples and that formed between human genomic DNA and small pieces of E. coli DNA introduced with recombinant enzymes.

    Further support that the putative integrations in LAML and STAD samples are not laboratory artifacts comes from the fact that reads supporting integrations were detected 672× and 13.2× more frequently, respectively, in these cancer samples than in representative non-cancer samples (Table 1). This is expected if such mutations were part of the clonally expanding tumor. Across all samples, there are 1,033 reads supporting integrations in the normal samples with 13,392,142,331 read pairs sequenced, yielding an estimated integration frequency of 7.7×10 −8 . In contrast, 690,528 read pairs support integrations in tumor samples out of 42,533,195,146 paired reads sequenced, yielding an integration frequency of 1.6×10 −5 , or 210× higher. Even when compared to the highest integration rate assessed in normal samples, which was 6.02×10 −6 in the Trace Archive data, the aggregate rate across all cancer samples is still >2.5-fold higher.

    While the integration rate in cancers is 210× higher than that in normal samples across the TCGA, this comparison is not directly between matched tumor and normal pairs since normal samples were only present for OV, GBM, and BRCA. However, many different types of normal samples can be used in cancer studies and therefore other comparisons besides matched pairs are quite valid. In fact, no one type of normal sample may be perfect for all experiments. For example, a small piece of adjacent breast tissue determined to be non-cancerous by a pathologist would be considered the normal specimen for breast cancer [58]. These samples are often taken from the margins of tumors when they are resected during surgery. In that case, it's possible they could have cancer characteristics not evident by histology [59], [60]. In OV, blood-derived samples were collected as normal samples from some patients, while others had normal tissue collected. In GBM, only blood-derived samples were collected as normal samples. In other cancer studies, skin tissue from patients prior to treatment may be used [61]. Some blood cancers lack a normal sample because the cancer originates in the bone marrow. Therefore, all of the patient's blood contains cancerous cells [62]. In this instance either blood from healthy individuals [63] or blood taken from the patient once in complete remission [64] may be used as a normal sample.

    Unfortunately, the STAD and LAML samples of greatest interest here for driving the dramatically increased integration rate in the tumor samples also lack normal matched samples in this data release. Given the lack of normal matched samples, and that blood samples from healthy individuals are frequently used as normal samples for studying types of leukemia [63], it is informative to compare LAML to normal samples from OV, GBM, and BRCA or 1000 Genomes data. Of note, the normal samples for OV, GBM, and BRCA have integration rates of 1.1×10 −7 , 2.3×10 −8 , and 1.2×10 −7 (Table 1) respectively, while the integration rate of samples from the 1000 Genomes project was 2.3×10 −6 . Of these, the BRCA mutation rate is most relevant to STAD and LAML as all three are RNA-based sequencing. Comparing these, LAML samples have an integration rate 672× higher than the integration rate for the BRCA normal samples (Table 1). Even if the LAML cancer samples are compared to the normal samples with the highest integration rate, those in the Trace Archive, the integration rate for LAML is still almost 14× higher. While the overall integration frequency of cancer samples is 1.6×10 −5 , or 210× higher than the normal integration rate of 7.7×10 −8 (Table 1), the LAML integration rate is the main driver of the increased frequency. Most tumor types do not have an increased integration rate relative to normal samples (Table 1).

    Another main contributor to the significant increase of integrations in cancer samples is STAD, which has an integration rate of 1.6×10 −6 and is 13.2× higher than the integration rate for the BRCA normal samples (Table 1). Considering STAD is in close proximity to the microbiome, normal stomach tissue would better reflect this exposure to the microbiome, including an increased likelihood of bacterial integration. That means it would be particularly informative if available. Unfortunately, this release of the TCGA lacks STAD normal samples or any other normal samples with constant exposure to the microbiome. This prevents us from determining the rate of integration in non-cancer cells with an abundant microbiome. Further work is needed to resolve differences in the integration rate between normal samples that have constant contact with the microbiome and those that do not.

    Bacterial integration of Acinetobacter-like DNA in mitochondrial genomes

    The majority of the bacterial integrations detected were between an Acinetobacter-like organism and the mitochondrial genome. Acinetobacter spp. are known to invade epithelial cells and induce caspase-dependent and caspase-independent apoptosis [65]. Uptake of apoptotic bodies and caspase-dependant DNA fragmentation is known to facilitate LGT between mammalian cells [66], including LGT of oncogenes [67].

    While we present this as bacterial integration into the mitochondrial genome, it is possible that mitochondrial DNA is integrating into an Acinetobacter-like genome. However, despite the numerous complete Acinetobacter genomes sequenced, mitochondrial DNA has not been detected in the genome of an Acinetobacter isolate.

    Human mitochondria have an essential role in many key cellular processes such as the generation of cellular energy, production of reactive oxygen species, and initiation of apoptosis. The accumulation of somatic mutations in the mitochondrial genome has been implicated in carcinogenesis [68]. For instance, mutations in the mitochondrial cytochrome oxidase subunit I (COI) gene contribute to the tumorigenicity of prostate cancer through an increased production of reactive oxygen species [69]. The LGT from bacteria, such as Acinetobacter, to the mitochondria may be generating novel mutations in the mitochondrial genome and therefore influencing tumor progression.

    Bacterial integration in proto-oncogenes in stomach adenocarcinomas

    The integrations we identified in STAD frequently appear to be in, or near, the untranslated region (UTR) of known proto-oncogenes. In this case, these proto-oncogenes are genes known to be up-regulated in cancers. Despite occurring in or near UTRs, this does not reflect similarity in these sequences. The mappings are specific as observed by both the BWA matches and BLAST searches against NT. While CEACAM5 and CEACAM6 are paralogs, they are sufficiently diverged to be resolved. We postulate that these putative integrations have mutated a repressor binding site and have induced over-expression leading to carcinogenesis. While this is a tempting speculation, it needs to be experimentally verified.

    In chromosome 2, one STAD sample had an integration site in the second exon of thymosin β10 (TMSB10 Figure 8A) while another integration site was found in IGKV4-1. The TMSB10 gene has been shown by SAGE to be up-regulated in gastric tumors and confirmed with Northern blots [54].

    On chromosome 19, integrations were identified in CEACAM5 and CEACAM6 of STAD tumors (Figure 8BC). The same integration site in CEACAM5 was detected in two separate samples while a third sample had a similar integration in CEACAM6. CEACAM proteins were initially identified as prominent tumor-associated antigens in human colon cancer [55]. Approximately 50% of human tumors show over-expression of CEA family proteins [56]. CEACAM5 and CEACAM6 mediate cell adhesion by binding to themselves and other CEACAM family members [55]. Over-expression disturbs ordered tissue formation in 3D tissue culture and leads to increased tumor formation in mice [55].

    On chromosome 5, integration sites were identified in STAD tumors in different portions of CD74 with three samples having an integration in the 5′-end of the gene and one of those samples having a second integration in the 3′-UTR of CD74 (Figure 8D). CD74 initiates antigen presentation as well as signaling cascades that result in cell survival. Therefore it is not surprising that while its regulation is tightly controlled in normal tissues, it has increased expression in many cancers including gastrointestinal carcinomas and precancerous pancreatic lesions [57].

    Importantly, and significantly, we only identified integrations meeting our criteria in these 4 tumor-associated genes and one other immune-related gene. We did not first look at all known oncogenes and try to find bacterial integration with these criteria, nor did we look at oncogenes and try to explain why they are up-regulated. These four oncogenes merely emerged as those having such integrations.

    While there is an association between bacterial DNA integration and up-regulation of these genes, it is important to note that LGT is not associated with the most abundant bacterial transcripts. Such a result would be expected if the read pairs were merely laboratory-based artifactual chimeras generated during library construction. While these human transcripts are up-regulated in the tumors when compared to other tumors, in at least two cases they are not the most abundant transcripts. In fact, in the 143 STAD samples, if we examine the most abundant transcript, it is most frequently annexin A2 (Table 3), which was not identified as having a bacterial integration. Using our search criteria, we find no evidence of human-bacteria chimeras in any of the most abundant transcripts (Table 3) that would suggest such sequences arise from laboratory artifacts. If we, instead, focus only on the abundance of the four up-regulated genes above and on the ten samples where we identified bacterial integration in these genes, we see no clear pattern that would correlate LGT with transcript abundance. In CEACAM5, which has the most bacterial integrations, and CEACAM6, they are >75% less abundant than the most abundant transcript in that sample (Table S6). In addition, there are between 35 and 95 transcripts that are more abundant depending on the sample examined (Table 4).


    Regulation During Development

    The regulation of gene expression is extremely important during the early development of an organism. Regulatory proteins must turn on certain genes in particular cells at just the right time so the individual develops normal organs and organ systems. Homeobox genes are a large group of genes that regulate development during the embryonic stage. In humans, there are an estimated 235 functional homeobox genes. They are present on every chromosome and generally grouped in clusters. Homeobox genes contain instructions for making chains of 60 amino acids called homeodomains. Proteins containing homeodomains are transcription factors that bind to and control the activities of other genes. The homeodomain is the part of the protein that binds to the target gene and controls its expression.


    References

    Rossi, G., Manfrin, A. & Lutolf, M. P. Progress and potential in organoid research. Nat. Rev. Genet. 19, 671–687 (2018).

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009). Sato et al. provide the first example of organoids derived from AdSCs isolated from mouse gut.

    Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).

    Fujii, M. et al. Human intestinal organoids maintain self-renewal capacity and cellular diversity in niche-inspired culture condition. Cell Stem Cell 23, 787–793 (2018).

    Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013). Lancaster et al. report that the complexity of human brain development can be modelled by human PSC-derived organoids.

    Takasato, M. et al. Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564–568 (2015).

    Hu, H. et al. Long-term expansion of functional mouse and human hepatocytes as 3D organoids. Cell 175, 1591–1606 (2018).

    Turco, M. Y. et al. Long-term, hormone-responsive organoid cultures of human endometrium in a chemically defined medium. Nat. Cell Biol. 19, 568–577 (2017).

    Huch, M. & Koo, B. K. Modeling mouse and human development using organoid cultures. Development 142, 3113–3125 (2015). In this review, Huch and Koo summarize the development of various organoid culture systems and compare mouse and human systems.

    Simian, M. & Bissell, M. J. Organoids: a historical perspective of thinking in three dimensions. J. Cell Biol. 216, 31–40 (2017).

    Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).

    Kelava, I. & Lancaster, M. A. Dishing out mini-brains: current progress and future prospects in brain organoid research. Dev. Biol. 420, 199–209 (2016).

    Kretzschmar, K. & Clevers, H. Organoids: modeling development and the stem cell niche in a dish. Dev. Cell 38, 590–600 (2016).

    Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    Tiriac, H., Plenker, D., Baker, L. A. & Tuveson, D. A. Organoid models for translational pancreatic cancer research. Curr. Opin. Genet. Dev. 54, 7–11 (2019).

    Fatehullah, A., Tan, S. H. & Barker, N. Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 18, 246–254 (2016).

    Kelava, I. & Lancaster, M. A. Stem cell models of human brain development. Cell Stem Cell 18, 736–748 (2016).

    Sulston, J. E., Schierenberg, E., White, J. G. & Thomson, J. N. The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev. Biol. 100, 64–119 (1983).

    Mullins, M. C., Hammerschmidt, M., Haffter, P. & Nusslein-Volhard, C. Large-scale mutagenesis in the zebrafish: in search of genes controlling development in a vertebrate. Curr. Biol. 4, 189–202 (1994).

    Haffter, P. & Nusslein-Volhard, C. Large scale genetics in a small vertebrate, the zebrafish. Int. J. Dev. Biol. 40, 221–227 (1996).

    Nusslein-Volhard, C. The zebrafish issue of development. Development 139, 4099–4103 (2012).

    Brenner, S. The genetics of Caenorhabditis elegans. Genetics 77, 71–94 (1974).

    Takebe, T. et al. Vascularized and functional human liver from an iPSC-derived organ bud transplant. Nature 499, 481–484 (2013). This is the first report of organ bud formation through self-condensation of cells from different lineages.

    Spence, J. R. et al. Directed differentiation of human pluripotent stem cells into intestinal tissue in vitro. Nature 470, 105–109 (2011). Spence et al. identify a step-by-step procedure to generate human intestinal organoids derived from PSCs.

    Harris, T. W. et al. WormBase: a multi-species resource for nematode biology and genomics. Nucleic Acids Res. 32, D411–D417 (2004).

    Baumeister, R. & Ge, L. The worm in us — Caenorhabditis elegans as a model of human disease. Trends Biotechnol. 20, 147–148 (2002).

    Poulin, G., Nandakumar, R. & Ahringer, J. Genome-wide RNAi screens in Caenorhabditis elegans: impact on cancer research. Oncogene 23, 8340–8345 (2004).

    Lui, J. H., Hansen, D. V. & Kriegstein, A. R. Development and evolution of the human neocortex. Cell 146, 18–36 (2011).

    Kuzawa, C. W. et al. Metabolic costs and evolutionary implications of human brain development. Proc. Natl Acad. Sci. USA 111, 13010–13015 (2014).

    Sanoh, S. et al. Predictability of metabolism of ibuprofen and naproxen using chimeric mice with human hepatocytes. Drug. Metab. Dispos. 40, 2267–2272 (2012).

    Inoue, T. et al. CYP2C9-catalyzed metabolism of S-warfarin to 7-hydroxywarfarin in vivo and in vitro in chimeric mice with humanized liver. Drug. Metab. Dispos. 36, 2429–2433 (2008).

    McCauley, H. A. & Wells, J. M. Pluripotent stem cell-derived organoids: using principles of developmental biology to grow human tissues in a dish. Development 144, 958–962 (2017).

    Takahashi, K. et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 (2007).

    Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

    Fowler, J. L., Ang, L. T. & Loh, K. M. A critical look: Challenges in differentiating human pluripotent stem cells into desired cell types and organoids. Wiley Interdiscip. Rev. Dev. Biol. 9, e368 (2019).

    Dutta, D., Heo, I. & Clevers, H. Disease modeling in stem cell-derived 3D organoid systems. Trends Mol. Med. 23, 393–410 (2017).

    Marton, R. M. & Paşca, S. P. Organoid and assembloid technologies for investigating cellular crosstalk in human brain development and disease. Trends Cell Biol. 15, 133–143 (2020).

    Nishinakamura, R. Human kidney organoids: progress and remaining challenges. Nat. Rev. Nephrol. 15, 613–624 (2019).

    Prior, N., Inacio, P. & Huch, M. Liver organoids: from basic research to therapeutic applications. Gut 68, 2228–2237 (2019).

    Lancaster, M. A. & Huch, M. Disease modelling in human organoids. Dis. Model Mech. 12, dmm039347 (2019).

    Sachs, N. et al. Long-term expanding human airway organoids for disease modeling. EMBO J. 38, e100300 (2019).

    Osakada, F., Ikeda, H., Sasai, Y. & Takahashi, M. Stepwise differentiation of pluripotent stem cells into retinal cells. Nat. Protoc. 4, 811–824 (2009).

    McCracken, K. W. et al. Modelling human development and disease in pluripotent stem-cell-derived gastric organoids. Nature 516, 400–404 (2014).

    Thomson, J. A. et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998).

    Evans, M. J. & Kaufman, M. H. Establishment in culture of pluripotential cells from mouse embryos. Nature 292, 154–156 (1981).

    Martin, G. R. Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc. Natl Acad. Sci. USA 78, 7634–7638 (1981).

    Wert, G. & Mummery, C. Human embryonic stem cells: research, ethics and policy. Hum. Reprod. 18, 672–682 (2003).

    Huang, C. Y. et al. Human iPSC banking: barriers and opportunities. J. Biomed. Sci. 26, 87 (2019).

    Liu, G., David, B. T., Trawczynski, M. & Fessler, R. G. Advances in pluripotent stem cells: history, mechanisms, technologies, and applications. Stem Cell Rev. Rep. 16, 3–32 (2020).

    Soldner, F. & Jaenisch, R. Stem cells, genome editing, and the path to translational medicine. Cell 175, 615–632 (2018).

    Avior, Y., Sagi, I. & Benvenisty, N. Pluripotent stem cells in disease modelling and drug discovery. Nat. Rev. Mol. Cell Biol. 17, 170–182 (2016).

    Lancaster, M. A. et al. Guided self-organization and cortical plate formation in human brain organoids. Nat. Biotechnol. 35, 659–666 (2017).

    Takebe, T. et al. Generation of a vascularized and functional human liver from an iPSC-derived organ bud transplant. Nat. Protoc. 9, 396–409 (2014).

    Zhang, Y. et al. 3D modeling of esophageal development using human PSC-derived basal progenitors reveals a critical role for notch signaling. Cell Stem Cell 23, 516–529 (2018).

    Trisno, S. L. et al. Esophageal organoids from human pluripotent stem cells delineate Sox2 functions during esophageal specification. Cell Stem Cell 23, 501–515 (2018).

    McCracken, K. W. et al. Wnt/beta-catenin promotes gastric fundus specification in mice and humans. Nature 541, 182–187 (2017).

    Dye, B. R. et al. In vitro generation of human pluripotent stem cell derived lung organoids. eLife 4, e05098 (2015).

    Barker, N., van de Wetering, M. & Clevers, H. The intestinal stem cell. Genes Dev. 22, 1856–1864 (2008).

    Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).

    Stange, D. E. et al. Differentiated Troy+chief cells act as reserve stem cells to generate all lineages of the stomach epithelium. Cell 155, 357–368 (2013).

    Barker, N. et al. Lgr5 +ve stem cells drive self-renewal in the stomach and build long-lived gastric units in vitro. Cell Stem Cell 6, 25–36 (2010).

    Huch, M. et al. In vitro expansion of single Lgr5 + liver stem cells induced by Wnt-driven regeneration. Nature 494, 247–250 (2013).

    Huch, M. et al. Unlimited in vitro expansion of adult bi-potent pancreas progenitors through the Lgr5/R-spondin axis. EMBO J. 32, 2708–2721 (2013).

    Bartfeld, S. et al. In vitro expansion of human gastric epithelial stem cells and their responses to bacterial infection. Gastroenterology 148, 126–136 (2015).

    Schlaermann, P. et al. A novel human gastric primary cell culture system for modelling Helicobacter pylori infection in vitro. Gut 65, 202–213 (2016).

    Huch, M. et al. Long-term culture of genome-stable bipotent stem cells from adult human liver. Cell 160, 299–312 (2015).

    Loomans, C. J. M. et al. Expansion of adult human pancreatic tissue yields organoids harboring progenitor cells with endocrine differentiation potential. Stem Cell Rep. 10, 712–724 (2018).

    Lee, S. H. et al. Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell 173, 515–528 (2018).

    Rock, J. R. et al. Basal cells as stem cells of the mouse trachea and human airway epithelium. Proc. Natl Acad. Sci. USA 106, 12771–12775 (2009).

    Sampaziotis, F. et al. Reconstruction of the mouse extrahepatic biliary tree using primary human extrahepatic cholangiocyte organoids. Nat. Med. 23, 954–963 (2017).

    Boretto, M. et al. Development of organoids from mouse and human endometrium showing endometrial epithelium physiology and long-term expandability. Development 144, 1775–1786 (2017).

    Linnemann, J. R. et al. Quantification of regenerative potential in primary human mammary epithelial cells. Development 142, 3239–3251 (2015).

    Karthaus, W. R. et al. Identification of multipotent luminal progenitor cells in human prostate organoid cultures. Cell 159, 163–175 (2014).

    Chua, C. W. et al. Single luminal epithelial progenitors can generate prostate organoids in culture. Nat. Cell Biol. 16, 951–961 (2014).

    Kessler, M. et al. The Notch and Wnt pathways regulate stemness and differentiation in human fallopian tube organoids. Nat. Commun. 6, 8989 (2015).

    Barkovich, A. J., Guerrini, R., Kuzniecky, R. I., Jackson, G. D. & Dobyns, W. B. A developmental and genetic classification for malformations of cortical development: update 2012. Brain 135, 1348–1369 (2012).

    Heymann, D. L. et al. Zika virus and microcephaly: why is this situation a PHEIC? Lancet 387, 719–721 (2016).

    Calvet, G. et al. Detection and sequencing of Zika virus from amniotic fluid of fetuses with microcephaly in Brazil: a case study. Lancet Infect. Dis. 16, 653–660 (2016).

    Mlakar, J. et al. Zika virus associated with microcephaly. N. Engl. J. Med. 374, 951–958 (2016).

    Dang, J. et al. Zika virus depletes neural progenitors in human cerebral organoids through activation of the innate immune receptor TLR3. Cell Stem Cell 19, 258–265 (2016).

    Garcez, P. P. et al. Zika virus impairs growth in human neurospheres and brain organoids. Science 352, 816–818 (2016). Garcez et al. show the utility of complex brain organoids for translational Zika virus research.

    Cugola, F. R. et al. The Brazilian Zika virus strain causes birth defects in experimental models. Nature 534, 267–271 (2016).

    Yoon, K. J. et al. Zika-virus-encoded NS2A disrupts mammalian cortical neurogenesis by degrading adherens junction proteins. Cell Stem Cell 21, 349–358 (2017).

    Xu, M. et al. Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen. Nat. Med. 22, 1101–1107 (2016).

    Ramani, S., Atmar, R. L. & Estes, M. K. Epidemiology of human noroviruses and updates on vaccine development. Curr. Opin. Gastroenterol. 30, 25–33 (2014).

    Ettayebi, K. et al. Replication of human noroviruses in stem cell-derived human enteroids. Science 353, 1387–1393 (2016). Ettayebi et al. demonstrate that organoid culture systems can support research on difficult pathogens that previously could not be cultivated.

    Rotavirus vaccines. WHO position paper—January 2013. Wkly. Epidemiol. Rec. 88, 49–64 (2013).

    Saxena, K. et al. Human intestinal enteroids: a new model to study human Rotavirus infection, host restriction, and pathophysiology. J. Virol. 90, 43–56 (2016).

    Yin, Y. et al. Modeling rotavirus infection and antiviral therapy using primary intestinal organoids. Antivir. Res. 123, 120–131 (2015).

    To, K. K., Chan, J. F., Chen, H., Li, L. & Yuen, K. Y. The emergence of influenza A H7N9 in human beings 16 years after influenza A H5N1: a tale of two cities. Lancet Infect. Dis. 13, 809–821 (2013).

    Zhou, J. et al. Differentiated human airway organoids to assess infectivity of emerging influenza virus. Proc. Natl Acad. Sci. USA 115, 6822–6827 (2018).

    Klenk, H. D. Influenza viruses en route from birds to man. Cell Host Microbe 15, 653–654 (2014).

    McAuley, J. L., Gilbertson, B. P., Trifkovic, S., Brown, L. E. & McKimm-Breschkin, J. L. Influenza virus neuraminidase structure and functions. Front. Microbiol. 10, 39 (2019).

    Bartfeld, S. Modeling infectious diseases and host–microbe interactions in gastrointestinal organoids. Dev. Biol. 420, 262–270 (2016).

    Leslie, J. L. et al. Persistence and toxin production by Clostridium difficile within human intestinal organoids result in disruption of epithelial paracellular barrier function. Infect. Immun. 83, 138–145 (2015).

    Heo, I. et al. Modelling Cryptosporidium infection in human small intestinal and lung organoids. Nat. Microbiol. 3, 814–823 (2018).

    Rusnati, M. et al. Recent strategic advances in CFTR drug discovery: an overview. Int. J. Mol. Sci. 21, 2407 (2020).

    Dekkers, J. F. et al. A functional CFTR assay using primary cystic fibrosis intestinal organoids. Nat. Med. 19, 939–945 (2013). Dekkers et al. report the use of organoids in precision medicine for patients with cystic fibrosis.

    Dekkers, J. F. et al. Characterizing responses to CFTR-modulating drugs using rectal organoids derived from subjects with cystic fibrosis. Sci. Transl. Med. 8, 344ra384 (2016).

    Berkers, G. et al. Rectal organoids enable personalized treatment of cystic fibrosis. Cell Rep. 26, 1701–1708 (2019).

    van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015). This report describes the first cancer biobank based on an organoid system.

    Fujii, M. et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor requirements during tumorigenesis. Cell Stem Cell 18, 827–838 (2016).

    Weeber, F. et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc. Natl Acad. Sci. USA 112, 13308–13311 (2015).

    Engel, R. M. et al. Patient-derived colorectal cancer organoids upregulate revival stem cell marker genes following chemotherapeutic treatment. J. Clin. Med. 9, 128 (2020).

    Ooft, S. N. et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci. Transl. Med. 11, eaay2574 (2019).

    Jacob, F. et al. A patient-derived glioblastoma organoid model and biobank recapitulates inter- and intra-tumoral heterogeneity. Cell 180, 188–204 (2020).

    Fusco, P. et al. Patient-derived organoids (PDOs) as a novel in vitro model for neuroblastoma tumours. BMC Cancer 19, 970 (2019).

    Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

    Driehuis, E. et al. Pancreatic cancer organoids recapitulate disease and allow personalized drug screening. Proc. Natl. Acad. Sci. USA 116, 26580–26590 (2019).

    Seino, T. et al. Human pancreatic tumor organoids reveal loss of stem cell niche factor dependence during disease progression. Cell Stem Cell 22, 454–467 (2018).

    Broutier, L. et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 23, 1424–1435 (2017).

    Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 (2018).

    Seidlitz, T. et al. Human gastric cancer modelling using organoids. Gut 68, 207–217 (2019).

    Yan, H. H. N. et al. A comprehensive human gastric cancer organoid biobank captures tumor subtype heterogeneity and enables therapeutic screening. Cell Stem Cell 23, 882–897 (2018).

    Nanki, K. et al. Divergent routes toward Wnt and R-spondin niche independency during human gastric carcinogenesis. Cell 174, 856–869 (2018).

    Li, X. et al. Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics. Nat. Commun. 9, 2983 (2018).

    Boretto, M. et al. Patient-derived organoids from endometrial disease capture clinical heterogeneity and are amenable to drug screening. Nat. Cell Biol. 21, 1041–1051 (2019).

    Kim, M. et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nat. Commun. 10, 3991 (2019).

    Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat. Med. 25, 1607–1614 (2019).

    Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26 (2020).

    Thomas, K. R. & Capecchi, M. R. Site-directed mutagenesis by gene targeting in mouse embryo-derived stem cells. Cell 51, 503–512 (1987).

    Hockemeyer, D. & Jaenisch, R. Gene targeting in human pluripotent cells. Cold Spring Harb. Symp. Quant. Biol. 75, 201–209 (2010).

    Porteus, M. H. & Baltimore, D. Chimeric nucleases stimulate gene targeting in human cells. Science 300, 763 (2003).

    Bibikova, M., Beumer, K., Trautman, J. K. & Carroll, D. Enhancing gene targeting with designed zinc finger nucleases. Science 300, 764 (2003).

    Miller, J. C. et al. A TALE nuclease architecture for efficient genome editing. Nat. Biotechnol. 29, 143–148 (2011).

    Wiedenheft, B., Sternberg, S. H. & Doudna, J. A. RNA-guided genetic silencing systems in bacteria and archaea. Nature 482, 331–338 (2012).

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Cho, S. W., Kim, S., Kim, J. M. & Kim, J. S. Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease. Nat. Biotechnol. 31, 230–232 (2013).

    Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).

    Schwank, G. et al. Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients. Cell Stem Cell 13, 653–658 (2013). Schwank et al. report the first study to apply CRISPR–Cas9-based gene correction in an organoid system.

    Drost, J. et al. Sequential cancer mutations in cultured human intestinal stem cells. Nature 521, 43–47 (2015).

    Matano, M. et al. Modeling colorectal cancer using CRISPR–Cas9-mediated engineering of human intestinal organoids. Nat. Med. 21, 256–262 (2015).

    Andersson-Rolf, A. et al. One-step generation of conditional and reversible gene knockouts. Nat. Methods 14, 287–289 (2017).

    Merenda, A. et al. A protocol for multiple gene knockout in mouse small intestinal organoids using a CRISPR-concatemer. J. Vis. Exp. 125, e55916 (2017).

    Andersson-Rolf, A. et al. Simultaneous paralogue knockout using a CRISPR–concatemer in mouse small intestinal organoids. Dev. Biol. 420, 271–277 (2016).

    Michels, B. E. et al. Pooled in vitro and in vivo CRISPR–Cas9 screening identifies tumor suppressors in human colon organoids. Cell Stem Cell 26, 782–792 (2020).

    Ringel, T. et al. Genome-scale CRISPR screening in human intestinal organoids identifies drivers of TGF-beta resistance. Cell Stem Cell 26, e438 (2020).

    Dotti, I. et al. Alterations in the epithelial stem cell compartment could contribute to permanent changes in the mucosa of patients with ulcerative colitis. Gut 66, 2069–2079 (2017).

    Kraiczy, J. et al. DNA methylation defines regional identity of human intestinal epithelial organoids and undergoes dynamic changes during development. Gut 68, 49–61 (2019).

    Suzuki, K. et al. Single cell analysis of Crohn’s disease patient-derived small intestinal organoids reveals disease activity-dependent modification of stem cell properties. J. Gastroenterol. 53, 1035–1047 (2018).

    Howell, K. J. et al. DNA methylation and transcription patterns in intestinal epithelial cells from pediatric patients with inflammatory bowel diseases differentiate disease subtypes and associate with outcome. Gastroenterology 154, 585–598 (2018).

    Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    Nanki, K. et al. Somatic inflammatory gene mutations in human ulcerative colitis epithelium. Nature 577, 254–259 (2020).

    Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

    Yang, H., Wang, H. & Jaenisch, R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 9, 1956–1968 (2014).

    Yang, H. et al. One-step generation of mice carrying reporter and conditional alleles by CRISPR/Cas-mediated genome engineering. Cell 154, 1370–1379 (2013).

    Kostic, A. D., Howitt, M. R. & Garrett, W. S. Exploring host-microbiota interactions in animal models and humans. Genes. Dev 27, 701–718 (2013).

    Takebe, T. et al. Vascularized and complex organ buds from diverse tissues via mesenchymal cell-driven condensation. Cell Stem Cell 16, 556–565 (2015).

    Wimmer, R. A. et al. Human blood vessel organoids as a model of diabetic vasculopathy. Nature 565, 505–510 (2019).

    Bar-Ephraim, Y. E., Kretzschmar, K. & Clevers, H. Organoids in immunological research. Nat. Rev. Immunol. (2019).

    Kim, J., Koo, B. K. & Yoon, K. J. Modeling host-virus interactions in viral infectious diseases using stem-cell-derived systems and CRISPR/Cas9 technology. Viruses 11, 124 (2019).

    Schreurs, R. et al. Human fetal TNF-alpha-cytokine-producing CD4 + effector memory T cells promote intestinal development and mediate inflammation early in life. Immunity 50, 462–476 (2019).

    Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988 (2018).

    Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598 (2018).

    Schnalzger, T. E. et al. 3D model for CAR-mediated cytotoxicity using patient-derived colorectal cancer organoids. EMBO J. 38, e100928 (2019).

    Nozaki, K. et al. Co-culture with intestinal epithelial organoids allows efficient expansion and motility analysis of intraepithelial lymphocytes. J. Gastroenterol. 51, 206–213 (2016).

    Noel, G. et al. A primary human macrophage-enteroid co-culture model to investigate mucosal gut physiology and host-pathogen interactions. Sci. Rep. 7, 45270 (2017).

    Leeman, K. T., Pessina, P., Lee, J. H. & Kim, C. F. Mesenchymal stem cells increase alveolar differentiation in lung progenitor organoid cultures. Sci. Rep. 9, 6479 (2019).

    Lee, J. H. et al. Anatomically and functionally distinct lung mesenchymal populations marked by Lgr5 and Lgr6. Cell 170, 1149–1163 (2017).

    Koike, H. et al. Modelling human hepato-biliary-pancreatic organogenesis from the foregut-midgut boundary. Nature 574, 112–116 (2019).

    Bagley, J. A., Reumann, D., Bian, S., Levi-Strauss, J. & Knoblich, J. A. Fused cerebral organoids model interactions between brain regions. Nat. Methods 14, 743–751 (2017).

    Xiang, Y. et al. Fusion of regionally specified hPSC-derived organoids models human brain development and interneuron migration. Cell Stem Cell 21, 383–398 (2017).

    Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    Adhya, D. et al. Understanding the role of steroids in typical and atypical brain development: advantages of using a “brain in a dish” approach. J. Neuroendocrinol. 30, e12547 (2018).

    Zhang, C., Zhao, Z., Abdul Rahim, N. A., van Noort, D. & Yu, H. Towards a human-on-chip: culturing multiple cell types on a chip with compartmentalized microenvironments. Lab. Chip 9, 3185–3192 (2009).

    Zhang, Y. S. et al. Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. Proc. Natl Acad. Sci. USA 114, E2293–E2302 (2017).

    Giobbe, G. G. et al. Extracellular matrix hydrogel derived from decellularized tissues enables endodermal organoid culture. Nat. Commun. 10, 5658 (2019).

    Jee, J. H. et al. Development of collagen-based 3D matrix for gastrointestinal tract-derived organoid culture. Stem Cell Int. 2019, 8472712 (2019).

    Cruz-Acuna, R. et al. PEG-4MAL hydrogels for human organoid generation, culture, and in vivo delivery. Nat. Protoc. 13, 2102–2119 (2018).

    Ng, S., Tan, W. J., Pek, M. M. X., Tan, M. H. & Kurisawa, M. Mechanically and chemically defined hydrogel matrices for patient-derived colorectal tumor organoid culture. Biomaterials 219, 119400 (2019).

    Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560–564 (2016).

    Broguiere, N. et al. Growth of epithelial organoids in a defined hydrogel. Adv. Mater. 30, e1801621 (2018).

    Smith, A. G. et al. Inhibition of pluripotential embryonic stem cell differentiation by purified polypeptides. Nature 336, 688–690 (1988).

    Williams, R. L. et al. Myeloid leukaemia inhibitory factor maintains the developmental potential of embryonic stem cells. Nature 336, 684–687 (1988).

    Ying, Q. L. et al. The ground state of embryonic stem cell self-renewal. Nature 453, 519–523 (2008).

    Nichols, J. et al. Validated germline-competent embryonic stem cell lines from nonobese diabetic mice. Nat. Med. 15, 814–818 (2009).

    Tesar, P. J. et al. New cell lines from mouse epiblast share defining features with human embryonic stem cells. Nature 448, 196–199 (2007).

    Brons, I. G. et al. Derivation of pluripotent epiblast stem cells from mammalian embryos. Nature 448, 191–195 (2007).

    Gafni, O. et al. Derivation of novel human ground state naive pluripotent stem cells. Nature 504, 282–286 (2013).

    Chan, Y. S. et al. Induction of a human pluripotent state with distinct regulatory circuitry that resembles preimplantation epiblast. Cell Stem Cell 13, 663–675 (2013).

    Ware, C. B. et al. Derivation of naive human embryonic stem cells. Proc. Natl Acad. Sci. USA 111, 4484–4489 (2014).

    Theunissen, T. W. et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 15, 524–526 (2014).

    Guo, G. et al. Epigenetic resetting of human pluripotency. Development 144, 2748–2763 (2017).

    Guo, G. et al. Naive pluripotent stem cells derived directly from isolated cells of the human inner cell mass. Stem Cell Rep. 6, 437–446 (2016).

    Takashima, Y. et al. Resetting transcription factor control circuitry toward ground-state pluripotency in human. Cell 158, 1254–1269 (2014).

    Theunissen, T. W. et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 15, 471–487 (2014).

    Van der Jeught, M. et al. Application of small molecules favoring naive pluripotency during human embryonic stem cell derivation. Cell Reprogram. 17, 170–180 (2015).

    Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).

    Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 5, 536–544 (2020).

    Zhao, B. et al. Recapitulation of SARS-CoV-2 infection and cholangiocyte damage with human liver ductal organoids. Protein Cell https://doi.org/10.1007/s13238-020-00718-6 (2020).

    Monteil, V. et al. Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell 181, 905–913 (2020).

    Lamers, M. M. et al. SARS-CoV-2 productively infects human gut enterocytes. Science https://doi.org/10.1126/science.abc1669 (2020).

    Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).

    Sungnak, W. et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat. Med. 26, 681–687 (2020).

    Li, W. et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 426, 450–454 (2003).

    Hoffmann, M. et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181, 271–280 (2020).

    Walls, A. C. et al. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 181, 281–292 (2020).

    Yan, R. et al. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 367, 1444–1448 (2020).

    Shang, J. et al. Structural basis of receptor recognition by SARS-CoV-2. Nature 581, 221–224 (2020).


    A better future thanks to animal research

    These are only a few examples of the countless benefits animal research has brought to people with cancer, but there are thousands of other drugs and treatment techniques that are built on knowledge from tests in animals.

    And it’s not just cancer patients that benefit from animal research. As the Royal Society’s position statement on the use of animals in research points out, ‘virtually every medical achievement in the past century has depended directly or indirectly on research on animals.’

    At Cancer Research UK, animal research is never undertaken lightly – we seek to use alternatives wherever it’s possible, and fund research into alternative methods as well. But this fact remains – millions of people all over the world are alive today thanks to animal research. Much of this knowledge has also been used to tackle diseases that affect animals themselves, including cancer.

    Many people working for and supporting us know first-hand how devastating cancer can be, and all of us are deeply committed to beating the disease. Animal research is, at present, a necessary means to an end: helping people with cancer to survive.

    Dr David Scott, Director of Discovery Research and Research Funding