Data repositories for data on the Tumor Microenvironment?

Data repositories for data on the Tumor Microenvironment?

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Does anyone know of any data repositories for data on the Tumor Microenvironment? I know of the The Cancer Gene Atlas and the Gene Expression Omnibus databases but I'm more curious about data on the (molecular) composition of the TME. (I'm also curious about any databases with data less specific.)

Have you seen the St Jude Children's Research Hospital Pediatric Cancer data repository PeCan?

They have a bunch of data on a wide range of different types of pediatric cancers. These are all in the form of genomes and RNA-seq. However, they also have a bunch of visualizations set up so that you can see how these profiles work from a perspective of mutations and RNA in tumours of all sorts.

DCB Research Resources

DCB supports a variety of resources that are available to scientists studying cancer to aid them in their research. Descriptions of these resources and information on how to access them are included below. Along with research tools, DCB shares information about the NIH Citizen Science Working Group.

The NCI Resources for Researchers is a directory of NCI-supported tools and services for cancer researchers. Most resources are free of cost and available to anyone.

The NCI Mouse Repository is an NCI-funded resource for mouse cancer models and associated strains. The repository makes strains available to all members of the scientific community (academic, non-profit, and commercial). NCI Mouse Repository strains are cryoarchived and distributed as frozen germoplasm (embryos and/or sperm).

MicroRNAs (miRNAs) are small non-coding RNA molecules known to play an important role in fundamental cellular processes through negative post-transcriptional regulation of gene expression. In an effort to address the role that microRNAs play in human cancer, their use as diagnostic tools, and their potential function as new targets for therapeutic intervention in the treatment of cancer, the Division of Cancer Biology (DCB) of the National Cancer Institute (NCI) has supported the generation of mouse embryonic stem cells (mESCs) harboring most known mouse miRNAs. In order to enhance novel information obtained on their role in cancer, 1501 genetically engineered mESC lines were produced, harboring conditional microRNA transgenes.

For more information, please visit the NCI Mouse Repository website.

NCI partnered with the National Institute of General Medical Sciences (NIGMS) to fund the construction and management of a state-of-the-art macromolecular crystallography synchrotron beamline for determining structures of biologically important macromolecules. The beamline, GM/CA CAT, is located at the Advanced Photon Source on the grounds of Argonne National Lab just outside of Chicago.

NCI has a significant amount of beamtime dedicated for the use of its grantees. Investigators interested in taking experiments to this facility should contact the beamline directly or email Dr. Anowarul Amin for additional information.

Chernobyl Tissue Bank (CTB)

DCB supports and manages biospecimen resources that collect, store, process, and disseminate human biological specimens (biospecimens) and associated data set for research on human cancer biology. The Chernobyl Tissue Bank is an international collaborative project that is supported by NCI and another global partner, with active participation from Russia and Ukraine, two countries heavily affected by the 1986 Chernobyl accident. The objective of the CTB is to establish and maintain a research resource that supports studies on the biology of thyroid cancer, the major health consequence of the Chernobyl accident.

For more information on this Tissue Bank, please visit the Chernobyl Tissue Bank website.

The International Registry of Werner Syndrome

The International Registry of Werner Syndrome is the primary repository of samples and data from patients with Werner Syndrome (WS), and was established in 1988 as part of an objective to positionally clone the WS gene.

The registry ascertains and genotypes new pedigree cases from around the world, using lymphoblastoids and/or fibroblasts from human research participants, and provides genetic confirmation of classical WS. It also establishes and cryopreserves cell lines and other material from these pedigrees (both affected patients and their clinically unaffected siblings), including Epstein-Barr transformed peripheral blood B lymphocytes, primary skin fibroblasts, immortalized skin fibroblasts, WRN cDNA constructs, and others. All these materials are available for research.

For more information on this Registry, please visit the Werner Syndrome Registry website.

This core facility provides custom synthesis and distribution of soluble MHC-peptide tetramer reagents that can be used to stain antigen-specific T cells. The facility is supported by a contract from the National Institute of Allergy and Infectious Diseases, with steering committee participation from NCI through DCB.

For more information about this program, please visit the Emory University tetramer website.

Reagents Available to NCI-funded Researchers

The Tumor Microenvironment Network (TMEN) generated a number of resources that are now available to NCI-funded cancer researchers. These resources include:

  • EHS (Engelbreth-Holm-Swarm) sarcoma-derived laminin rich matrix
    • A pool of EHS sarcoma cell-derived extracellular matrix available for those who work on three-dimensional models to study tumor-host interactions.
    • CD10-PE/Cy5, CD140b-Biotin, CD16-PE/Cy5, CD18-PE/Cy5, CD20-PE/Cy5, CD24-FITC, CD24-PE, CD2-PE/Cy5, CD31-Biotin, CD3-PE/Cy5, CD44-APC, CD44-PE, CD45-PE/Cy5, CD45-PE/Cy7, CD64-Biotin, EGFR-FITC, EGFR-PE, EpCAM-APC, EpCAM-FITC, EpCAM-PE, H2Kd-Biotin, mCD45-PE/Cy5, SAV-PE/Cy5, SAV-PE/Cy7
    • A tumor bank of characterized human breast and colon solid tumors containing xenografts. Vials of frozen tumor cells from each tumor type have been stored for use. Recipients would be responsible for amplifying the stem cells and serving as a central bank for their institution as well as other investigators outside their institution.
    • This bank contains bone marrow cells from C57BL/6J and C57BL/6-Tg-(UBC-GFP) 30 Scha/J mouse lines in cryopreserved aliquots that can be re-infused as needed.
    • RCAS(A)-GFP – An avian retroviral vector for GFP expression in TVa mice. The vector needs to be introduced in DF1 cells for virus generation. Normally the DF1 cells themselves are introduced into mice directly.
    • RCAS(B)-DsRed - An avian retroviral vector for DsRed expression in TVb mice. The vector needs to be introduced in DF1 cells for virus generation. Normally the DF1 cells themselves are introduced into mice directly.

    Researchers interested in obtaining these reagents should contact Dr. Jeff Hildesheim, Division of Cancer Biology, NCI, for additional information and a reagent request form.

    The Physical Sciences - Oncology Network Bioresource Core Facility (PBCF)

    The Physical Sciences - Oncology Network Bioresource Core Facility (PBCF) is an NCI-funded resource at ATCC housing an authenticated set of non-malignant and cancerous cell lines, cell culture reagents, and related standard operating protocols available to all NIH-funded cancer researchers.

    Features of the PBCF include:
    • Free cost of each cell line and reagents (shipping & handling fees apply)
    • Each batch of cell lines originates from the same lot number and passage number, making their use ideal for collaborative studies
    • 41 distinct cell lines are available from 8 cancer types and non-malignant counterparts, all with detailed culture SOPs
    • First shipment of each cell line includes a media/reagent starter kit (if requested)
    • Genomic, proteomic, and physical characterization data for many of the cell lines are available.

    Datasets of published work using the PBCF cell lines should be reported to the PBCF and the NCI. For information on the list of cell lines available and order forms, please visit the NCI Physical Sciences-Oncology Network Bioresource webpage or email Dr. Nastaran Zahir with questions.

    Cancer Complexity Knowledge Portal

    This Community Research resource provides information about and access to data, research, tools, and publications generated through the Cancer Systems Biology Consortium (CSBC), the Physical Sciences - Oncology Network (PS-ON), and the Cancer Tissue Engineering Collaborative (TEC).

    For more information, please visit the Cancer Complexity Knowledge Portal.

    Biomedical Citizen Science and Crowdsourcing: The NIH Citizen Science Working Group

    This trans-NIH working group is investigating the usefulness and possible incorporation of citizen science methodologies into biomedical research in a way that maintains NIH’s high scientific and ethical standards.

    Citizen science is a collaborative approach to research involving the public as direct collaborators and partners in the research process itself — not just as subjects of the research or advisors to the research. Citizen science takes many forms, and involves a variety of approaches benefiting from the creativity and problem-solving skills of the public and from citizen-collected data and insights not obtainable through conventional approaches.

    This working group investigates, shares best practices, and engages in discussion with other agencies and groups promoting citizen science in other fields. The working group is composed of program officers, scientific review officers, and others from across NIH interested in furthering the adoption and incorporation of citizen science methodology into biomedical research.

    MondoA-Thioredoxin-Interacting Protein Axis Maintains Regulatory T-Cell Identity and Function in Colorectal Cancer Microenvironment

    Background & aims: The metabolic features and function of intratumoral regulatory T cells (Tregs) are ambiguous in colorectal cancer. Tumor-infiltrating Tregs are reprogrammed to exhibit high glucose-depleting properties and adapt to the glucose-restricted microenvironment. The glucose-responsive transcription factor MondoA is highly expressed in Tregs. However, the role of MondoA in colorectal cancer-infiltrating Tregs in response to glucose limitation remains to be elucidated.

    Methods: We performed studies using mice, in which MondoA was conditionally deleted in Tregs, and human colorectal cancer tissues. Seahorse and other metabolic assays were used to assess Treg metabolism. To study the role of Tregs in antitumor immunity, we used a subcutaneous MC38 colorectal cancer model and induced colitis-associated colorectal cancer in mice by azoxymethane and dextran sodium sulfate.

    Results: Our analysis of single-cell RNA sequencing data of patients with colorectal cancer revealed that intratumoral Tregs featured low activity of the MondoA-thioredoxin-interacting protein (TXNIP) axis and increased glucose uptake. Although MondoA-deficient Tregs were less immune suppressive and selectively promoted T-helper (Th) cell type 1 (Th1) responses in a subcutaneous MC38 tumor model, Treg-specific MondoA knockout mice were more susceptible to azoxymethane-DSS-induced colorectal cancer. Mechanistically, suppression of the MondoA-TXNIP axis promoted glucose uptake and glycolysis, induced hyperglycolytic Th17-like Tregs, which facilitated Th17 inflammation, promoted interleukin 17A-induced of CD8 + T-cell exhaustion, and drove colorectal carcinogenesis. Blockade of interleukin 17A reduced tumor progression and minimized the susceptibility of MondoA-deficient mice to colorectal carcinogenesis.

    Conclusions: The MondoA-TXNIP axis is a critical metabolic regulator of Treg identity and function in the colorectal cancer microenvironment and a promising target for cancer therapy.

    Keywords: Carcinogenesis Glucose Immunosuppression Inflammation Transcription factor.

    Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved.

    Washington University Open Scholarship

    In the last twenty years both computational biology and cancer biology have made great strides and in the last 5 years the merger of the two has helped to revolutionize our knowledge of personalized targeted therapy and the diversity of cancer. In cancer, cell-to-cell interactions between tumor cells and their microenvironment are critical determinants of tumor tissue biology and therapeutic responses. Interactions between glioblastoma (GBM) cells and endothelial cells (ECs) establish a purported stem cell niche. We hypothesized that genes that mediate these interactions would be important, particularly as therapeutic targets. Using a novel computational approach to deconvoluting expression data from mixed physical coculture of GBM cells and ECs, we identified a previously undescribed upregulation of the cAMP specific phosphodiesterase PDE7B in GBM cells in response to ECs. We further found that elevated PDE7B expression occurs in most GBM cases and has a negative effect on survival. PDE7B overexpression resulted in the expansion of a stem-like cell subpopulation, increased tumor aggressiveness, and increased growth in an intracranial GBM model. This deconvolution algorithm provides a new tool for cancer biology, particularly when looking at cell-to-cell interactions, and these results identify PDE7B as a therapeutic target in GBM.


    Tissue-infiltrating immune and non-immune stromal cells contribute to the measured signal in gene expression experiments. Retrieving this information can yield estimates of the abundance of tissue-infiltrating cells [19], illustrated here in cancer samples. To harness this information, we developed the MCP-counter method, implemented in an easy-to-use R package.

    It produces a score for each of ten distinct MCP. We validated that these scores are accurate abundance estimates in three different settings: a) transcriptomic profiles of 4804 validation MCP samples, in which the MCP-counter score separated positive and negative samples (relative to each of the ten cell populations) with high specificity and sensitivity b) in an in vitro RNA mixture setting, where we showed that MCP-counter scores corresponding to the cell populations from which RNAs were extracted highly correlated (Pearson’s correlation coefficients ranging from 0.93 to 0.99) with the RNA fraction of the corresponding cell population in the mixture and c) in an ex vivo setting where we showed that MCP-counter estimates correlated with IHC measurements of the corresponding cell densities. Using the in vitro setting, we showed that MCP-counter’s lower limit of detection for a population was below 2 % of the sample’s total RNA proportion when using Affymetrix Human Genome U133 Plus 2.0 microarrays. This limit of detection might be lowered by using more sensitive gene expression techniques, Nanostring, or RNA-sequencing assays. We consistently observed lower limits of detection using qPCR data for two cell populations (Fig. 3c).

    Other techniques to quantitatively characterize the cellular composition of a heterogeneous tissue notably include flow cytometry and enzymatic IHC. MCP-counter estimates are conceptually close to IHC-estimated cell densities (number of cells per surface unit on a tissue section), as the produced estimates can be used to compare the abundance of the corresponding cell populations across samples. Unlike IHC, however, MCP-counter enables the simultaneous quantification of ten cell populations with a single gene-expression experiment, while IHC quantifications are usually limited to a couple of markers. Information of the cells’ spatial localization, which is available in IHC experiments, is lost, however, when using transcriptomic technologies. Histological confirmation of MCP-counter estimates may thus be necessary in cases where contamination of samples by surrounding tissues is unavoidable. DNA-sequencing data could also be leveraged to estimate the proportion of cells with rearranged T-cell receptor or B-cell receptor loci, providing information about both the abundance and repertoire of these two populations. The eight other populations for which MCP-counter provides estimates are, however, unquantifiable using DNA-sequencing data. Studying the clonality of T and B cells is, however, an interesting covariate to complement abundance estimates of these cell populations and is accessible from RNA-seq data, as recently demonstrated in tumor samples [20–22].

    MCP-counter is more sensitive and specific in the interpretation of its scores than other previously published TM-based methods [11, 12] as a result of the rigorous, unbiased, and conservative approach to define the TM sets on which it is based (Fig. 4b) and, importantly, has been quantitatively validated experimentally (Fig. 3). It conceptually differs from flow cytometry experiments or flow cytometry-inspired computational methods such as CIBERSORT [9], which aim at describing the relative proportions of various cell populations within a single sample (Fig. 4a). In contrast, MCP-counter is specifically designed to compare the absolute abundance of a given cell population across multiple samples.

    MCP-counter scores linearly correlate with the corresponding cell population abundances across samples, but they are expressed in arbitrary units. These arbitrary units are dependent on the gene expression platform used to produce the data and one can only compare samples produced with the same gene expression platforms. Nonetheless, we showed that the relative cellular abundance across three large tumor datasets, totaling more than 19,000 tumors and obtained with three different gene expression platforms, are largely consistent (Additional file 2: Figure S10), validating the use of MCP-counter to assess which samples are most or least infiltrated by each characterized cell population. Nonetheless, since MCP-counter scores are based on summarized gene expression features (such as reads per kilobase per millions), its accuracy may suffer if the quality of this summary is low. The cell populations whose abundance is estimated by MCP-counter are usually at relatively low frequencies in tissue samples. Thus, sequencing samples at high depth (>80 million reads per sample) [23], which has been reported to improve the quantification of rare transcripts, may improve the accuracy of MCP-counter estimates from RNA-sequencing samples.

    We illustrated the use of MCP-counter on non-diseased human tissues and observed abundance estimates consistent with the known immunological status of the samples. We applied MCP-counter to describe the average MCP cellular abundances in 32 non-hematopoietic human malignancies. This analysis confirmed the very high vascularization of clear-cell renal cell carcinoma and showed that cervical squamous cell carcinoma tumors, which are often virally induced, are highly infiltrated by T lymphocytes and, notably, cytotoxic T cells but only moderately by other immune subsets. Other results appeared more surprising, such as the high abundance of fibroblastic cells in the microenvironment of stromal tumors—which may originate from a subset of dedifferentiated tumor cells or the relatively low vascularization of hepatocellular carcinoma samples—which is possibly due to the unique phenotype of endothelial cells in the liver. MCP-counter should in these cases be compared with histopathological knowledge and data within a given cancer.

    MCP-counter is most relevant to stratify a cohort of similar samples based on the composition of their immune and stromal microenvironments, or to follow the composition of the microenvironment over time. The use of MCP-counter confirmed that significant univariate associations between OS and tumor infiltration by cytotoxic lymphocytes were mostly positive [4]. In contrast, significant associations between prognosis and extensive abundance of non-immune stromal cell populations and, notably, fibroblasts were shown to be mostly negative using MCP-counter. These observations, largely consistent with the published literature [1, 4, 6, 7], validate the use of MCP-counter to assess the prognostic value of MCP in other cohorts of patients.

    MCP-counter complements IHC approaches in that it enables the analysis of ten cell populations using a single gene expression experiment, thus enabling the rapid generation of research hypotheses than can then be confirmed and spatially studied using histological data. We notably illustrated its use to separately classify lung adenocarcinoma, colorectal, and breast tumors into microenvironment-defined subgroups. In this setting, we were able to confirm the prognostic impact of three previously published microenvironment-based tumor classifications. These results suggest that MCP-counter may enable the identification of new multi-marker microenvironmental stratifications.

    MCP-counter relies on TM which have been identified in a dataset containing gene expression profiles of cancer cell lines from 21 different anatomic locations among its negative controls, ensuring applicability in a wide range of samples. This large diversity of control samples may, however, discard TM which would be relevant in a specific setting: for instance, the screening procedure discarded NCAM1 (CD56), a widely used marker of NK cells, as it is also expressed by nervous malignant cells and is thus unsuitable to quantify NK cells in brain samples. The general framework that we have developed could thus be tailored to identify additional TM for investigation in a more restricted set of organs.

    MCP-counter can potentially be incorporated in clinical routines to characterize immune infiltration in samples where IHC-based quantifications are impossible, such as for fine-needle aspiration biopsies. In this setting, samples are typically collected one at a time. To complement the current multi-sample use of MCP-counter, designed for exploratory analyses, one could notably settle on a desired gene expression platform and use a set of calibrating samples. For instance, the in vitro RNA mixtures described here could help to map MCP-counter abundance scores to non-arbitrary units, such as the percentage of the corresponding cells within a sample. Setting-specific tuning may, however, be required to reach the reliability necessary for clinical protocols.


    Reinbach, G. Ueber das Verhalten der Leukocyten bei malignen Tumoren. Arch. f. Klin. Chir 46, 486–562 (1983).

    Lee, J. J., Jacobsen, E. A., McGarry, M. P., Schleimer, R. P. & Lee, N. A. Eosinophils in health and disease: the LIAR hypothesis. Clin. Exp. Allergy 40, 563–575 (2010).

    Rosenberg, H. F., Dyer, K. D. & Foster, P. S. Eosinophils: changing perspectives in health and disease. Nat. Rev. Immunol. 13, 9–22 (2013).

    Rothenberg, M. E. & Hogan, S. P. The eosinophil. Annu. Rev. Immunol. 24, 147–174 (2006).

    Andersen, C. L. et al. Association of the blood eosinophil count with hematological malignancies and mortality. Am. J. Hematol. 90, 225–229 (2015).

    Fulkerson, P. C. Transcription factors in eosinophil development and as therapeutic targets. Front. Med. 4, 115 (2017).

    Sanderson, C. J. Interleukin-5, eosinophils, and disease. Blood 79, 3101–3109 (1992).

    Collins, P. D., Marleau, S., Griffiths Johnson, D. A., Jose, P. J. & Williams, T. J. Cooperation between interleukin-5 and the chemokine eotaxin to induce eosinophil accumulation in vivo. J. Exp. Med. 182, 1169–1174 (1995).

    Rothenberg, M. E. et al. IL-5-dependent conversion of normodense human eosinophils to the hypodense phenotype uses 3T3 fibroblasts for enhanced viability, accelerated hypodensity, and sustained antibody-dependent cytotoxicity. J. Immunol. 143, 2311–2316 (1989).

    Weller, P. F. & Spencer, L. A. Functions of tissue-resident eosinophils. Nat. Rev. Immunol. 17, 746–760 (2017).

    Lee, J. J. et al. Human versus mouse eosinophils: “that which we call an eosinophil, by any other name would stain as red”. J. Allergy Clin. Immunol. 130, 572–584 (2012).

    Lewis, D. M., Lewis, J. C., Loegering, D. A. & Gleich, G. J. Localization of the guinea pig eosinophil major basic protein to the core of the granule. J. Cell Biol. 77, 702–713 (1978).

    Egesten, A., Alumets, J., von Mecklenburg, C., Palmegren, M. & Olsson, I. Localization of eosinophil cationic protein, major basic protein, and eosinophil peroxidase in human eosinophils by immunoelectron microscopic technique. J. Histochem. Cytochem. 34, 1399–1403 (1986).

    Spencer, L. A. et al. Human eosinophils constitutively express multiple Th1, Th2, and immunoregulatory cytokines that are secreted rapidly and differentially. J. Leukoc. Biol. 85, 117–123 (2009).

    Ayars, G. H., Altman, L. C., Gleich, G. J., Loegering, D. A. & Baker, C. B. Eosinophil- and eosinophil granule-mediated pneumocyte injury. J. Allergy Clin. Immunol. 76, 595–604 (1985).

    Hamann, K. J. et al. In vitro killing of microfilariae of Brugia pahangi and Brugia malayi by eosinophil granule proteins. J. Immunol. 144, 3166–3173 (1990).

    Hisamatsu, K. et al. Cytotoxicity of human eosinophil granule major basic protein to human nasal sinus mucosa in vitro. J. Allergy Clin. Immunol. 86, 52–63 (1990).

    Kubo, H., Loegering, D. A., Adolphson, C. R. & Gleich, G. J. Cytotoxic properties of eosinophil granule major basic protein for tumor cells. Int. Arch. Allergy Immunol. 118, 426–428 (1999).

    Arlandson, M. et al. Eosinophil peroxidase oxidation of thiocyanate. Characterization of major reaction products and a potential sulfhydryl-targeted cytotoxicity system. J. Biol. Chem. 276, 215–224 (2001).

    MacPherson, J. C. et al. Eosinophils are a major source of nitric oxide-derived oxidants in severe asthma: characterization of pathways available to eosinophils for generating reactive nitrogen species. J. Immunol. 166, 5763–5772 (2001).

    Rosenberg, H. F. RNase a ribonucleases and host defense: an evolving story. J. Leukoc. Biol. 83, 1079–1087 (2008).

    Ueki, S. et al. Eosinophil extracellular DNA trap cell death mediates lytic release of free secretion-competent eosinophil granules in humans. Blood 121, 2074–2083 (2013).

    Melo, R. C. & Weller, P. F. Unraveling the complexity of lipid body organelles in human eosinophils. J. Leukoc. Biol. 96, 703–712 (2014).

    Patel, V. P. et al. Molecular and functional characterization of two novel human C-C chemokines as inhibitors of two distinct classes of myeloid progenitors. J. Exp. Med. 185, 1163–1172 (1997).

    Forssmann, U. et al. Eotaxin-2, a novel CC chemokine that is selective for the chemokine receptor CCR3, and acts like eotaxin on human eosinophil and basophil leukocytes. J. Exp. Med. 185, 2171–2176 (1997).

    Shinkai, A. et al. A novel human CC chemokine, eotaxin-3, which is expressed in IL-4-stimulated vascular endothelial cells, exhibits potent activity toward eosinophils. J. Immunol. 163, 1602–1610 (1999).

    Zimmermann, N., Hershey, G. K., Foster, P. S. & Rothenberg, M. E. Chemokines in asthma: cooperative interaction between chemokines and IL-13. J. Allergy Clin. Immunol. 111, 227–242 quiz 243 (2003).

    Combadiere, C., Ahuja, S. K. & Murphy, P. M. Cloning and functional expression of a human eosinophil CC chemokine receptor. J. Biol. Chem. 270, 16491–16494 (1995).

    Ponath, P. D. et al. Molecular cloning and characterization of a human eotaxin receptor expressed selectively on eosinophils. J. Exp. Med. 183, 2437–2448 (1996).

    Daugherty, B. L. et al. Cloning, expression, and characterization of the human eosinophil eotaxin receptor. J. Exp. Med. 183, 2349–2354 (1996).

    Bertrand, C. P. & Ponath, P. D. CCR3 blockade as a new therapy for asthma. Expert. Opin. Investig. Drugs 9, 43–52 (2000).

    Yang, M. et al. Eotaxin-2 and IL-5 cooperate in the lung to regulate IL-13 production and airway eosinophilia and hyperreactivity. J. Allergy Clin. Immunol. 112, 935–943 (2003).

    Pope, S. M. et al. IL-13 induces eosinophil recruitment into the lung by an IL-5- and eotaxin-dependent mechanism. J. Allergy Clin. Immunol. 108, 594–601 (2001).

    Ellyard, J. I. et al. Eotaxin selectively binds heparin. An interaction that protects eotaxin from proteolysis and potentiates chemotactic activity in vivo. J. Biol. Chem. 282, 15238–15247 (2007).

    Simson, L. et al. Regulation of carcinogenesis by IL-5 and CCL11: a potential role for eosinophils in tumor immune surveillance. J. Immunol. 178, 4222–4229 (2007).

    Hung, K. et al. The central role of CD4 + T cells in the antitumor immune response. J. Exp. Med. 188, 2357–2368 (1998).

    Zaynagetdinov, R. et al. Interleukin-5 facilitates lung metastasis by modulating the immune microenvironment. Cancer Res. 75, 1624–1634 (2015).

    Ikutani, M. et al. Identification of innate IL-5-producing cells and their role in lung eosinophil regulation and antitumor immunity. J. Immunol. 188, 703–713 (2012).

    Cho, H. et al. Eosinophils in colorectal neoplasms associated with expression of CCL11 and CCL24. J. Pathol. Transl. Med. 50, 45–51 (2016).

    Teruya-Feldstein, J. et al. Differential chemokine expression in tissues involved by Hodgkin’s disease: direct correlation of eotaxin expression and tissue eosinophilia. Blood 93, 2463–2470 (1999).

    Lorena, S. C., Oliveira, D. T., Dorta, R. G., Landman, G. & Kowalski, L. P. Eotaxin expression in oral squamous cell carcinomas with and without tumour associated tissue eosinophilia. Oral. Dis. 9, 279–283 (2003).

    Reichman, H. et al. Activated eosinophils exert antitumorigenic activities in colorectal cancer. Cancer Immunol. Res. 7, 388–400 (2019). This study is the first to subject intratumoural eosinophils to bulk RNA sequencing, which revealed a potent IFNγ-associated signature for eosinophils that likely mediates their antitumorigenic activities in CRC.

    Cheadle, E. J. et al. Eotaxin-2 and colorectal cancer: a potential target for immune therapy. Clin. Cancer Res. 13, 5719–5728 (2007).

    Jundt, F. et al. Hodgkin/Reed–Sternberg cells induce fibroblasts to secrete eotaxin, a potent chemoattractant for T cells and eosinophils. Blood 94, 2065–2071 (1999).

    Lotfi, R., Lee, J. J. & Lotze, M. T. Eosinophilic granulocytes and damage-associated molecular pattern molecules (DAMPs): role in the inflammatory response within tumors. J. Immunother. 30, 16–28 (2007).

    Shik, D., Moshkovits, I., Karo-Atar, D., Reichman, H. & Munitz, A. Interleukin-33 requires CMRF35-like molecule-1 expression for induction of myeloid cell activation. Allergy 69, 719–729 (2014).

    Dyer, K. D. & Rosenberg, H. F. Physiologic concentrations of HMGB1 have no impact on cytokine-mediated eosinophil survival or chemotaxis in response to eotaxin-2 (CCL24). PLoS One 10, e0118887 (2015).

    Lin, F., Xue, D., Xie, T. & Pan, Z. HMGB1 promotes cellular chemokine synthesis and potentiates mesenchymal stromal cell migration via Rap1 activation. Mol. Med. Rep. 14, 1283–1289 (2016).

    Liew, F. Y., Girard, J. P. & Turnquist, H. R. Interleukin-33 in health and disease. Nat. Rev. Immunol. 16, 676–689 (2016).

    Liew, F. Y., Pitman, N. I. & McInnes, I. B. Disease-associated functions of IL-33: the new kid in the IL-1 family. Nat. Rev. Immunol. 10, 103–110 (2010).

    Pecaric-Petkovic, T., Didichenko, S. A., Kaempfer, S., Spiegl, N. & Dahinden, C. A. Human basophils and eosinophils are the direct target leukocytes of the novel IL-1 family member IL-33. Blood 113, 1526–1534 (2009).

    Curran, C. S. & Bertics, P. J. Human eosinophils express RAGE, produce RAGE ligands, exhibit PKC-δ phosphorylation and enhanced viability in response to the RAGE ligand, S100B. Int. Immunol. 23, 713–728 (2011).

    Chu, V. T. et al. Eosinophils promote generation and maintenance of immunoglobulin-A-expressing plasma cells and contribute to gut immune homeostasis. Immunity 40, 582–593 (2014).

    Cormier, S. A. et al. Pivotal advance: eosinophil infiltration of solid tumors is an early and persistent inflammatory host response. J. Leukoc. Biol. 79, 1131–1139 (2006).

    Lee, J. J. & Lee, N. A. Eosinophil degranulation: an evolutionary vestige or a universally destructive effector function? Clin. Exp. Allergy 35, 986–994 (2005).

    Dennis, K. L. et al. Adenomatous polyps are driven by microbe-instigated focal inflammation and are controlled by IL-10-producing T cells. Cancer Res. 73, 5905–5913 (2013).

    Svensson, L. & Wenneras, C. Human eosinophils selectively recognize and become activated by bacteria belonging to different taxonomic groups. Microbes Infect. 7, 720–728 (2005).

    Bruijnzeel, P. L. et al. Eosinophil migration in atopic dermatitis. I: increased migratory responses to N-formyl-methionyl-leucyl-phenylalanine, neutrophil-activating factor, platelet-activating factor, and platelet factor 4. J. Invest. Dermatol. 100, 137–142 (1993).

    Svensson, L. et al. House dust mite allergen activates human eosinophils via formyl peptide receptor and formyl peptide receptor-like 1. Eur. J. Immunol. 37, 1966–1977 (2007).

    da Silva, J. M. et al. Relevance of CCL3/CCR5 axis in oral carcinogenesis. Oncotarget 8, 51024–51036 (2017).

    Hirai, H. et al. CCR1-mediated accumulation of myeloid cells in the liver microenvironment promoting mouse colon cancer metastasis. Clin. Exp. Metastasis 31, 977–989 (2014).

    Gatault, S., Legrand, F., Delbeke, M., Loiseau, S. & Capron, M. Involvement of eosinophils in the anti-tumor response. Cancer Immunol. Immunother. 61, 1527–1534 (2012).

    Davis, B. P. & Rothenberg, M. E. Eosinophils and cancer. Cancer Immunol. Res. 2, 1–8 (2014).

    Qian, B. Z. & Pollard, J. W. Macrophage diversity enhances tumor progression and metastasis. Cell 141, 39–51 (2010).

    Biswas, S. K. & Mantovani, A. Macrophage plasticity and interaction with lymphocyte subsets: cancer as a paradigm. Nat. Immunol. 11, 889–896 (2010).

    Fridlender, Z. G. et al. Polarization of tumor-associated neutrophil phenotype by TGF-β: “N1” versus “N2” TAN. Cancer Cell 16, 183–194 (2009).

    Mesnil, C. et al. Lung-resident eosinophils represent a distinct regulatory eosinophil subset. J. Clin. Invest. 126, 3279–3295 (2016). This study reveals two distinct eosinophil populations displaying opposing activities in the lungs of allergen-challenged mice.

    Furbert-Harris, P. M. et al. Activated eosinophils upregulate the metastasis suppressor molecule E-cadherin on prostate tumor cells. Cell Mol. Biol. 49, 1009–1016 (2003).

    Munitz, A. et al. 2B4 (CD244) is expressed and functional on human eosinophils. J. Immunol. 174, 110–118 (2005).

    Kataoka, S., Konishi, Y., Nishio, Y., Fujikawa-Adachi, K. & Tominaga, A. Antitumor activity of eosinophils activated by IL-5 and eotaxin against hepatocellular carcinoma. DNA Cell Biol. 23, 549–560 (2004).

    Lucarini, V. et al. IL-33 restricts tumor growth and inhibits pulmonary metastasis in melanoma-bearing mice through eosinophils. Oncoimmunology 6, e1317420 (2017).

    Gatault, S. et al. IL-18 is involved in eosinophil-mediated tumoricidal activity against a colon carcinoma cell line by upregulating LFA-1 and ICAM-1. J. Immunol. 195, 2483–2492 (2015).

    Legrand, F. et al. Human eosinophils exert TNF-α and granzyme A-mediated tumoricidal activity toward colon carcinoma cells. J. Immunol. 185, 7443–7451 (2010).

    Andreone, S. et al. IL-33 promotes CD11b/CD18-mediated adhesion of eosinophils to cancer cells and synapse-polarized degranulation leading to tumor cell killing. Cancers 11, 1664 (2019).

    Taylor, R., Lee, T. D. & Hoskin, D. W. Adhesion of tumoricidal eosinophils to MCA-38 colon adenocarcinoma cells involves protein tyrosine kinase activation and is diminished by elevated cyclic AMP in the effector cell. Int. J. Oncol. 13, 1305–1311 (1998).

    Graziano, R. F., Looney, R. J., Shen, L. & Fanger, M. W. FcγR-mediated killing by eosinophils. J. Immunol. 142, 230–235 (1989).

    Furbert-Harris, P. M. et al. Activated eosinophils infiltrate MCF-7 breast multicellular tumor spheroids. Anticancer Res. 23, 71–78 (2003).

    Mattes, J. et al. Immunotherapy of cytotoxic T cell-resistant tumors by T helper 2 cells: an eotaxin and STAT6-dependent process. J. Exp. Med. 197, 387–393 (2003).

    Xing, Y. et al. CCL11-induced eosinophils inhibit the formation of blood vessels and cause tumor necrosis. Genes Cell 21, 624–638 (2016).

    Yaffe, L. J. & Finkelman, F. D. Induction of a B-lymphocyte receptor for a T cell-replacing factor by the crosslinking of surface IgD. Proc. Natl Acad. Sci. USA 80, 293–297 (1983).

    Carretero, R. et al. Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8 + T cells. Nat. Immunol. 16, 609–617 (2015). This elegant study shows that eosinophils shape the TME by regulating the vasculature, and further demonstrates that IFNγ-activated and TNF-activated eosinophils promote tumour rejection by enhancing CD8 + T cell recruitment and skewing macrophage polarization.

    Xie, F. et al. The infiltration and functional regulation of eosinophils induced by TSLP promote the proliferation of cervical cancer cell. Cancer Lett. 364, 106–117 (2015).

    Zhang, B. et al. TSLP promotes angiogenesis of human umbilical vein endothelial cells by strengthening the crosstalk between cervical cancer cells and eosinophils. Oncol. Lett. 14, 7483–7488 (2017).

    Stathopoulos, G. T. et al. Host-derived interleukin-5 promotes adenocarcinoma-induced malignant pleural effusion. Am. J. Respir. Crit. Care Med. 182, 1273–1281 (2010).

    Eiro, N. et al. Relationship between the inflammatory molecular profile of breast carcinomas and distant metastasis development. PLoS One 7, e49047 (2012).

    Martinelli-Klay, C. P., Mendis, B. R. & Lombardi, T. Eosinophils and oral squamous cell carcinoma: a short review. J. Oncol. 2009, 310132 (2009).

    Wong, D. T., Bowen, S. M., Elovic, A., Gallagher, G. T. & Weller, P. F. Eosinophil ablation and tumor development. Oral. Oncol. 35, 496–501 (1999).

    Puxeddu, I. et al. Human peripheral blood eosinophils induce angiogenesis. Int. J. Biochem. Cell Biol. 37, 628–636 (2005).

    Panagopoulos, V. et al. Inflammatory peroxidases promote breast cancer progression in mice via regulation of the tumour microenvironment. Int. J. Oncol. 50, 1191–1200 (2017).

    Walsh, M. T., Connell, K., Sheahan, A. M., Gleich, G. J. & Costello, R. W. Eosinophil peroxidase signals via epidermal growth factor-2 to induce cell proliferation. Am. J. Respir. Cell Mol. Biol. 45, 946–952 (2011).

    Hennigan, K. et al. Eosinophil peroxidase activates cells by HER2 receptor engagement and β1-integrin clustering with downstream MAPK cell signaling. Clin. Immunol. 171, 1–11 (2016).

    Curran, C. S., Evans, M. D. & Bertics, P. J. GM-CSF production by glioblastoma cells has a functional role in eosinophil survival, activation, and growth factor production for enhanced tumor cell proliferation. J. Immunol. 187, 1254–1263 (2011).

    Yasukawa, A. et al. Eosinophils promote epithelial to mesenchymal transition of bronchial epithelial cells. PLoS One 8, e64281 (2013).

    Kratochvill, F. et al. TNF counterbalances the emergence of M2 tumor macrophages. Cell Rep. 12, 1902–1914 (2015).

    Odemuyiwa, S. O. et al. Cutting edge: human eosinophils regulate T cell subset selection through indoleamine 2,3-dioxygenase. J. Immunol. 173, 5909–5913 (2004).

    Astigiano, S. et al. Eosinophil granulocytes account for indoleamine 2,3-dioxygenase-mediated immune escape in human non-small cell lung cancer. Neoplasia 7, 390–396 (2005).

    Simon, H. U. et al. Interleukin-2 primes eosinophil degranulation in hypereosinophilia and Wells’ syndrome. Eur. J. Immunol. 33, 834–839 (2003).

    Sosman, J. A. et al. Evidence for eosinophil activation in cancer patients receiving recombinant interleukin-4: effects of interleukin-4 alone and following interleukin-2 administration. Clin. Cancer Res. 1, 805–812 (1995).

    Ellem, K. A. et al. A case report: immune responses and clinical course of the first human use of granulocyte/macrophage-colony-stimulating-factor-transduced autologous melanoma cells for immunotherapy. Cancer Immunol. Immunother. 44, 10–20 (1997).

    Gebhardt, C. et al. Myeloid cells and related chronic inflammatory factors as novel predictive markers in melanoma treatment with ipilimumab. Clin. Cancer Res. 21, 5453–5459 (2015).

    Webster, R. M. The immune checkpoint inhibitors: where are we now? Nat. Rev. Drug. Discov. 13, 883–884 (2014).

    Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Martens, A. et al. Baseline peripheral blood biomarkers associated with clinical outcome of advanced melanoma patients treated with ipilimumab. Clin. Cancer Res. 22, 2908–2918 (2016).

    Lang, B. M. et al. Long-term survival with modern therapeutic agents against metastatic melanoma — vemurafenib and ipilimumab in a daily life setting. Med. Oncol. 35, 24 (2018).

    Moreira, A., Leisgang, W., Schuler, G. & Heinzerling, L. Eosinophilic count as a biomarker for prognosis of melanoma patients and its importance in the response to immunotherapy. Immunotherapy 9, 115–121 (2017).

    Hude, I. et al. Leucocyte and eosinophil counts predict progression-free survival in relapsed or refractory classical Hodgkin lymphoma patients treated with PD1 inhibition. Br. J. Haematol. 181, 837–840 (2018).

    Weide, B. et al. Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin. Cancer Res. 22, 5487–5496 (2016).

    Rafei-Shamsabadi, D., Lehr, S., von Bubnoff, D. & Meiss, F. Successful combination therapy of systemic checkpoint inhibitors and intralesional interleukin-2 in patients with metastatic melanoma with primary therapeutic resistance to checkpoint inhibitors alone. Cancer Immunol. Immunother. 68, 1417–1428 (2019).

    Alenmyr, L. et al. Blockade of CTLA-4 promotes airway inflammation in naive mice exposed to aerosolized allergen but fails to prevent inhalation tolerance. Scand. J. Immunol. 62, 437–444 (2005).

    Zheng, X. et al. CTLA4 blockade promotes vessel normalization in breast tumors via the accumulation of eosinophils. Int. J. Cancer 146, 1730–1740 (2020). This study provides the first mechanistic evidence that eosinophils are important effector cells in the antitumour response that is elicited by blockade of CTLA4.

    House, I. G. et al. Macrophage-derived CXCL9 and CXCL10 are required for antitumor immune responses following immune checkpoint blockade. Clin. Cancer. Res. 26, 487–504 (2019).

    Wolf, M. T. et al. A biologic scaffold-associated type 2 immune microenvironment inhibits tumor formation and synergizes with checkpoint immunotherapy. Sci. Transl. Med. 11, eaat7973 (2019).

    Spolski, R., Li, P. & Leonard, W. J. Biology and regulation of IL-2: from molecular mechanisms to human therapy. Nat. Rev. Immunol. 18, 648–659 (2018).

    Rosenberg, S. A. IL-2: the first effective immunotherapy for human cancer. J. Immunol. 192, 5451–5458 (2014).

    Van Gool, F. et al. Interleukin-5-producing group 2 innate lymphoid cells control eosinophilia induced by interleukin-2 therapy. Blood 124, 3572–3576 (2014).

    Huland, E. & Huland, H. Tumor-associated eosinophilia in interleukin-2-treated patients: evidence of toxic eosinophil degranulation on bladder cancer cells. J. Cancer Res. Clin. Oncol. 118, 463–467 (1992).

    Abdel-Wahab, Z. et al. Transduction of human melanoma cells with interleukin-2 gene reduces tumorigenicity and enhances host antitumor immunity: a nude mouse model. Cell Immunol. 159, 26–39 (1994).

    Rivoltini, L. et al. In vitro anti-tumor activity of eosinophils from cancer patients treated with subcutaneous administration of interleukin 2. Role of interleukin 5. Int. J. Cancer 54, 8–15 (1993).

    Vlad, A. M. et al. A phase II trial of intraperitoneal interleukin-2 in patients with platinum-resistant or platinum-refractory ovarian cancer. Cancer Immunol. Immunother. 59, 293–301 (2010).

    Ruffell, B., DeNardo, D. G., Affara, N. I. & Coussens, L. M. Lymphocytes in cancer development: polarization towards pro-tumor immunity. Cytokine Growth Factor Rev. 21, 3–10 (2010).

    Reichman, H., Karo-Atar, D. & Munitz, A. Emerging roles for eosinophils in the tumor microenvironment. Trends Cancer 2, 664–675 (2016).

    Widmer, M. B., Acres, R. B., Sassenfeld, H. M. & Grabstein, K. H. Regulation of cytolytic cell populations from human peripheral blood by B cell stimulatory factor 1 (interleukin 4). J. Exp. Med. 166, 1447–1455 (1987).

    Rothenberg, M. E., Luster, A. D. & Leder, P. Murine eotaxin: an eosinophil chemoattractant inducible in endothelial cells and in interleukin 4-induced tumor suppression. Proc. Natl Acad. Sci. USA 92, 8960–8964 (1995).

    Tepper, R. I., Pattengale, P. K. & Leder, P. Murine interleukin-4 displays potent anti-tumor activity in vivo. Cell 57, 503–512 (1989).

    Khazaie, K. et al. Persistence of dormant tumor cells in the bone marrow of tumor cell-vaccinated mice correlates with long-term immunological protection. Proc. Natl Acad. Sci. USA 91, 7430–7434 (1994).

    Tepper, R. I., Coffman, R. L. & Leder, P. An eosinophil-dependent mechanism for the antitumor effect of interleukin-4. Science 257, 548–551 (1992). This study demonstrates that constitutive expression of IL-4 in athymic nude mice elicits a tumour-associated eosinophil infiltrate associated with decreased tumour growth, stimulating further investigations into the role of eosinophils in cancer.

    Saleh, M. The role of tumour-derived mIL-4 on rat C6 glioma regression. Int. J. Oncol. 10, 1223–1227 (1997).

    Addison, C., Gauldie, J., Muller, W. & Graham, F. An adenoviral vector expressing interleukin-4 modulates tumorigenicity and induces regression in a murine breast-cancer model. Int. J. Oncol. 7, 1253–1260 (1995).

    Gao, K. et al. Transgenic expression of IL-33 activates CD8 + T cells and NK cells and inhibits tumor growth and metastasis in mice. Cancer Lett. 335, 463–471 (2013).

    Perales-Puchalt, A. et al. IL-33 delays metastatic peritoneal cancer progression inducing an allergic microenvironment. Oncoimmunology 8, e1515058 (2019).

    Hollande, C. et al. Inhibition of the dipeptidyl peptidase DPP4 (CD26) reveals IL-33-dependent eosinophil-mediated control of tumor growth. Nat. Immunol. 20, 257–264 (2019).

    Lu, B., Yang, M. & Wang, Q. Interleukin-33 in tumorigenesis, tumor immune evasion, and cancer immunotherapy. J. Mol. Med. 94, 535–543 (2016).

    Dougan, M., Dranoff, G. & Dougan, S. K. GM-CSF, IL-3, and IL-5 family of cytokines: regulators of inflammation. Immunity 50, 796–811 (2019).

    Hoeller, C., Michielin, O., Ascierto, P. A., Szabo, Z. & Blank, C. U. Systematic review of the use of granulocyte–macrophage colony-stimulating factor in patients with advanced melanoma. Cancer Immunol. Immunother. 65, 1015–1034 (2016).

    Dranoff, G. et al. Vaccination with irradiated tumor cells engineered to secrete murine granulocyte–macrophage colony-stimulating factor stimulates potent, specific, and long-lasting anti-tumor immunity. Proc. Natl Acad. Sci. USA 90, 3539–3543 (1993).

    McNeel, D. G. et al. A transient increase in eosinophils is associated with prolonged survival in men with metastatic castration-resistant prostate cancer who receive sipuleucel-T. Cancer Immunol. Res. 2, 988–999 (2014).

    Correale, P. et al. Second-line treatment of non small cell lung cancer by biweekly gemcitabine and docetaxel ± granulocyte–macrophage colony stimulating factor and low dose aldesleukine. Cancer Biol. Ther. 8, 497–502 (2009).

    Yang, D. et al. Eosinophil-derived neurotoxin acts as an alarmin to activate the TLR2–MyD88 signal pathway in dendritic cells and enhances Th2 immune responses. J. Exp. Med. 205, 79–90 (2008).

    Klion, A. D., Ackerman, S. J. & Bochner, B. S. Contributions of eosinophils to human health and disease. Annu. Rev. Pathol. 15, 179–209 (2020).

    Kuang, F. L. et al. Long-term clinical outcomes of high-dose mepolizumab treatment for hypereosinophilic syndrome. J. Allergy Clin. Immunol. Pract. 6, 1518–1527.e5 (2018).

    Murphy, K. et al. Long-term safety and efficacy of reslizumab in patients with eosinophilic asthma. J. Allergy Clin. Immunol. Pract. 5, 1572–1581.e3 (2017).

    Andersen, C. L. et al. Eosinophilia in routine blood samples as a biomarker for solid tumor development — a study based on the Copenhagen Primary Care Differential Count (CopDiff) database. Acta Oncol. 53, 1245–1250 (2014).

    Popov, H., Donev, I. S. & Ghenev, P. Quantitative analysis of tumor-associated tissue eosinophilia in recurring bladder cancer. Cureus 10, e3279 (2018).

    Lowe, D., Fletcher, C. D. & Gower, R. L. Tumour-associated eosinophilia in the bladder. J. Clin. Pathol. 37, 500–502 (1984).

    Onesti, C. E. et al. Predictive and prognostic role of peripheral blood eosinophil count in triple-negative and hormone receptor-negative/HER2-positive breast cancer patients undergoing neoadjuvant treatment. Oncotarget 9, 33719–33733 (2018).

    Ownby, H. E., Roi, L. D., Isenberg, R. R. & Brennan, M. J. Peripheral lymphocyte and eosinophil counts as indicators of prognosis in primary breast cancer. Cancer 52, 126–130 (1983).

    Spiegel, G. W., Ashraf, M. & Brooks, J. J. Eosinophils as a marker for invasion in cervical squamous neoplastic lesions. Int. J. Gynecol. Pathol. 21, 117–124 (2002).

    van Driel, W. J. et al. Tumor-associated eosinophilic infiltrate of cervical cancer is indicative for a less effective immune response. Hum. Pathol. 27, 904–911 (1996).

    Prizment, A. E. et al. Tumor eosinophil infiltration and improved survival of colorectal cancer patients: Iowa Women’s Health Study. Mod. Pathol. 29, 516–527 (2016).

    Fernandez-Acenero, M. J., Galindo-Gallego, M., Sanz, J. & Aljama, A. Prognostic influence of tumor-associated eosinophilic infiltrate in colorectal carcinoma. Cancer 88, 1544–1548 (2000).

    Pretlow, T. P. et al. Eosinophil infiltration of human colonic carcinomas as a prognostic indicator. Cancer Res. 43, 2997–3000 (1983).

    Ishibashi, S. et al. Tumor-associated tissue eosinophilia in human esophageal squamous cell carcinoma. Anticancer. Res. 26, 1419–1424 (2006).

    Zhang, Y. et al. Clinical impact of tumor-infiltrating inflammatory cells in primary small cell esophageal carcinoma. Int. J. Mol. Sci. 15, 9718–9734 (2014).

    Songun, I. et al. Expression of oncoproteins and the amount of eosinophilic and lymphocytic infiltrates can be used as prognostic factors in gastric cancer. Dutch Gastric Cancer Group (DGCG). Br. J. Cancer 74, 1783–1788 (1996).

    Iwasaki, K., Torisu, M. & Fujimura, T. Malignant tumor and eosinophils. I. Prognostic significance in gastric cancer. Cancer 58, 1321–1327 (1986).

    von Wasielewski, R. et al. Tissue eosinophilia correlates strongly with poor prognosis in nodular sclerosing Hodgkin’s disease, allowing for known prognostic factors. Blood 95, 1207–1213 (2000).

    Enblad, G., Sundstrom, C. & Glimelius, B. Infiltration of eosinophils in Hodgkin’s disease involved lymph nodes predicts prognosis. Hematol. Oncol. 11, 187–193 (1993).

    Said, M. et al. Tissue eosinophilia: a morphologic marker for assessing stromal invasion in laryngeal squamous neoplasms. BMC Clin. Pathol. 5, 1 (2005).

    Thompson, A. C., Bradley, P. J. & Griffin, N. R. Tumor-associated tissue eosinophilia and long-term prognosis for carcinoma of the larynx. Am. J. Surg. 168, 469–471 (1994).

    Dorta, R. G. et al. Tumour-associated tissue eosinophilia as a prognostic factor in oral squamous cell carcinomas. Histopathology 41, 152–157 (2002).

    Jain, M. et al. Assessment of tissue eosinophilia as a prognosticator in oral epithelial dysplasia and oral squamous cell carcinoma—an image analysis study. Pathol. Res. Int. 2014, 507512 (2014).

    Rakesh, N., Devi, Y., Majumdar, K., Reddy, S. S. & Agarwal, K. Tumour associated tissue eosinophilia as a predictor of locoregional recurrence in oral squamous cell carcinoma. J. Clin. Exp. Dent. 7, e1–e6 (2015).

    Bishara, S. et al. Pre-treatment white blood cell subtypes as prognostic indicators in ovarian cancer. Eur. J. Obstet. Gynecol. Reprod. Biol. 138, 71–75 (2008).

    Steel, J. L. et al. Cancer-related symptom clusters, eosinophils, and survival in hepatobiliary cancer: an exploratory study. J. Pain Symptom Manage. 39, 859–871 (2010).

    Shinke, G. et al. The postoperative peak number of leukocytes after hepatectomy is a significant prognostic factor for cholangiocarcinoma. Mol. Clin. Oncol. 10, 531–540 (2019).

    Meyerholz, D. K., Griffin, M. A., Castilow, E. M. & Varga, S. M. Comparison of histochemical methods for murine eosinophil detection in an RSV vaccine-enhanced inflammation model. Toxicol. Pathol. 37, 249–255 (2009).

    Yamaguchi, Y. et al. Models of lineage switching in hematopoietic development: a new myeloid-committed eosinophil cell line (YJ) demonstrates trilineage potential. Leukemia 12, 1430–1439 (1998).

    Macias, M. P. et al. Identification of a new murine eosinophil major basic protein (mMBP) gene: cloning and characterization of mMBP-2. J. Leukoc. Biol. 67, 567–576 (2000).

    Willetts, L. et al. Immunodetection of occult eosinophils in lung tissue biopsies may help predict survival in acute lung injury. Respir. Res. 12, 116 (2011).

    Erjefalt, J. S. et al. Degranulation patterns of eosinophil granulocytes as determinants of eosinophil driven disease. Thorax 56, 341–344 (2001).

    Doyle, A. D. et al. Homologous recombination into the eosinophil peroxidase locus generates a strain of mice expressing Cre recombinase exclusively in eosinophils. J. Leukoc. Biol. 94, 17–24 (2013).

    Grace, J. O. et al. Reuse of public, genome-wide, murine eosinophil expression data for hypotheses development. J. Leukoc. Biol. 104, 185–193 (2018).

    Zhu, F., Liu, P., Li, J. & Zhang, Y. Eotaxin-1 promotes prostate cancer cell invasion via activation of the CCR3–ERK pathway and upregulation of MMP-3 expression. Oncol. Rep. 31, 2049–2054 (2014).

    Levina, V. et al. Role of eotaxin-1 signaling in ovarian cancer. Clin. Cancer Res. 15, 2647–2656 (2009).

    Hitoshi, Y. et al. Distribution of IL-5 receptor-positive B cells. Expression of IL-5 receptor on Ly-1(CD5) + B cells. J. Immunol. 144, 4218–4225 (1990).

    Varricchi, G. et al. Eosinophils: the unsung heroes in cancer? Oncoimmunology 7, e1393134 (2018).

    Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259 (2018).


    Cancer immunotherapy has brought a paradigm shift to cancer treatment in recent years. Numerous scRNA-seq datasets have been generated to decipher the complex cell-type compositions and expression heterogeneity in the TME. However, a well-curated, uniformly processed and annotated data portal for TME scRNA-seq data reuse is still not available. In this context, we present TISCH as a comprehensive single-cell web portal for cancer biologists to investigate and visualize single-cell gene expression in the TME. TISCH shows several advantages compared to the existing single-cell tumor resources. First, TISCH is the most comprehensive TME single-cell data portal to our knowledge, including single-cell transcriptome atlas of around 2 million cells from 27 cancer types. The diverse cell types and cancer types present in TISCH enable users to systematically and holistically investigate the TME heterogeneity. Second, all the TISCH datasets were uniformly processed, annotated, and manually curated, which removes the barriers for cross-study comparisons and benefits the data-reuse. Finally, with the meta-information provided, TISCH allows comparisons between different patients, immunotherapy treatment groups and response groups, showing potential clinical indications for cancer therapy.

    In summary, TISCH is a useful repository for TME single-cell transcriptomic data. It provides a user-friendly web resource for interactive gene expression visualization of cellular differences across multiple datasets at the single-cell resolution. TISCH will be a valuable resource for cancer biologists and immuno-oncologists to study gene regulation and immune signaling in the TME, identify novel drug targets and provide insights on therapy response. In the future, we will continue to pay efforts to improve TISCH. We will maintain the web resources regularly to integrate new datasets. We will also provide novel functions in TISCH, such as inferring gene–gene co-expression and cell–cell interactions based on expression correlations at the single-cell level. As the increasing numbers of public TME scRNA-seq data are available, we anticipate continued development and maintenance of the TISCH web resource will benefit the broader cancer research community.


    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019 (US statistics). CA Cancer J Clin. 201969(1):7–34.

    Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, et al., editors. SEER cancer statistics review, 1975-2017, National Cancer Institute. Bethesda 2020., based on November 2019 SEER data submission.

    Yachida S, Lacobuzio-Donahue CA. The pathology and genetics of metastatic pancreatic cancer. Arch Pathol Lab Med. 2009:133(3):413–22.

    Kruger S, Haas M, Burger PJ, Ormanns S, Modest DP, Westphalen CB, et al. Isolated pulmonary metastases define a favorable subgroup in metastatic pancreatic cancer. Pancreatology. 201616:593–8 Elsevier B.V.

    Deeb A, Haque SU, Olowokure O. Pulmonary metastases in pancreatic cancer, is there a survival influence? J Gastrointest Oncol. 20156:E48–51 Pioneer Bioscience Publishing.

    Yamashita K, Miyamoto A, Hama N, Asaoka T, Maeda S, Omiya H, et al. Survival Impact of pulmonary metastasis as recurrence of pancreatic ductal adenocarcinoma. Dig Surg. 201532(6):464–71.

    Tumeh PC, Hellmann MD, Hamid O, Tsai KK, Loo KL, Gubens MA, et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol Res. 20175:417–24 American Association for Cancer Research Inc.

    Tazdait M, Mezquita L, Lahmar J, Ferrara R, Bidault F, Ammari S, et al. Patterns of responses in metastatic NSCLC during PD-1 or PDL-1 inhibitor therapy: comparison of RECIST 1.1, irRECIST and iRECIST criteria. Eur J Cancer. 201888:38–47 Elsevier Ltd.

    Oliver AJ, Lau PKH, Unsworth AS, Loi S, Darcy PK, Kershaw MH, et al. Tissue-dependent tumor microenvironments and their impact on immunotherapy responses. Front Immunol. 20189:70 Frontiers Media S.A.

    Sharma G, Colantuoni C, Goff LA, Fertig EJ, Stein-O’Brien G. projectR: an R/Bioconductor package for transfer learning via PCA, NMF, correlation and clustering. Valencia A, editor. Bioinformatics. 202036:3592–3 Oxford University Press.

    Jin X, Demere Z, Nair K, Ali A, Ferraro GB, Natoli T, et al. A metastasis map of human cancer cell lines. Nature. 2020588(7837):331–6. Nature Research.

    Foley K, Rucki AA, Xiao Q, Zhou D, Leubner A, Mo G, et al. Semaphorin 3D autocrine signaling mediates the metastatic role of annexin A2 in pancreatic cancer. Sci Signal. Am Assoc Adv Sci. 20158(388):ra77.

    Hingorani SR, Wang L, Multani AS, Combs C, Deramaudt TB, Hruban RH, et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell. 20057(5):469–83.

    Li B, Li T, Pignon JC, Wang B, Wang J, Shukla SA, et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat Genet. 201648(7):725–32. Nature Publishing Group.

    Kinkead HL, Hopkins A, Lutz E, Wu AA, Yarchoan M, Cruz K, et al. Combining STING-based neoantigen-targeted vaccine with checkpoint modulators enhances antitumor immunity in murine pancreatic cancer. JCI Insight NLM (Medline). 20183(20):e122857.

    Yoshimura K, Soares KC, Jaffee E, Schulick RD, Olino K, Jain A, et al. A Preclinical murine model of hepatic metastases. J Vis Exp. 2014(91):e51677. MyJoVE Corporation.

    Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 201038(suppl_2):W214–20.

    Wang J, Sanmamed MF, Datar I, Su TT, Ji L, Sun J, et al. Fibrinogen-like protein 1 is a major immune inhibitory ligand of LAG-3. Cell. 2019176:334–347.e12 Cell Press.

    Nagarsheth N, Wicha MS, Zou W. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol. 2017:17(9):559–72 Nature Publishing Group.

    O’Donnell JS, Long GV, Scolyer RA, Teng MWL, Smyth MJ. Resistance to PD1/PDL1 checkpoint inhibition. Cancer Treat Rev. 2017:9:71–81 W.B. Saunders Ltd.

    Simonetti O, Goteri G, Lucarini G, Filosa A, Pieramici T, Rubini C, et al. Potential role of CCL27 and CCR10 expression in melanoma progression and immune escape. Eur J Cancer. 200642(8):1181–7.

    Tselepis A, Grant AJ, Walker LSK, Bertus Eksteen DH, Miles A, Curbishley SM. Regulatory T cells expressing CCR10 CCL28 production and the recruitment of epithelial inflammation is associated with 2021.

    Obermajer N, Muthuswamy R, Odunsi K, Edwards RP, Kalinski P. PGE 2-induced CXCL 12 production and CXCR4 expression controls the accumulation of human MDSCs in ovarian cancer environment. Cancer Res. 201171(24):7463–70. NIH Public Access.

    Chen Y, Ramjiawan RR, Reiberger T, Ng MR, Hato T, Huang Y, et al. CXCR4 inhibition in tumor microenvironment facilitates anti-programmed death receptor-1 immunotherapy in sorafenib-treated hepatocellular carcinoma in mice. Hepatology. 201561:1591–602 John Wiley and Sons Inc.

    Li J, Byrne KT, Yan F, Yamazoe T, Chen Z, Baslan T, et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity. 201849:178–193.e7.

    Stein-O’Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, et al. Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species. Cell Syst. 20198:395–411.e8 Cell Press.

    Fernandez E, Mahmoud YD, Veigas F, Rocha D, Balzarini M, Lujan HD, et al. MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data. bioRxiv. 2019:726562. Cold Spring Harbor Laboratory.

    Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The immune landscape of cancer. Immunity. 201848:812–830.e14 Cell Press.

    Feig C, Jones JO, Kraman M, Wells RJB, Deonarine A, Chan DS, et al. Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci U S A. 2013110(50):20212–7.

    Greene GF, Kitadai Y, Pettaway CA, Von Eschenbach AC, Bucana CD, Fidler IJ. Correlation of metastasis-related gene expression with metastatic potential in human prostate carcinoma cells implanted in nude mice using an in situ messenger RNA hybridization technique. Am J Pathol. 1997150(5):1571–82.

    Singh RK, Bucana CD, Gutman M, Fan D, Wilson MR, Fidler IJ. Organ site-dependent expression of basic fibroblast growth factor in human renal cell carcinoma cells. Am J Pathol. 1994145(2):365–74.

    Hoover M, Adamian Y, Brown M, Maawy A, Chang A, Lee J, et al. A novel method for RNA extraction from FFPE samples reveals significant differences in biomarker expression between orthotopic and subcutaneous pancreatic cancer patient-derived xenografts. Oncotarget. 20178:5885–94 Impact Journals LLC.

    Nakamura T, Fidler IJ, Coombes KR. Gene expression profile of metastatic human pancreatic cancer cells depends on the organ microenvironment. Cancer Res. 200767(1):139–48.

    Choudhury A, Moniaux N, Ulrich AB, Schmied BM, Standop J, Pour PM, et al. MUC4 mucin expression in human pancreatic tumours is affected by organ environment: the possible role of TGFβ2. Br J Cancer. 200490(3):657–64.

    Zhan B, Wen S, Lu J, Shen G, Lin X, Feng J, et al. Identification and causes of metabonomic difference between orthotopic and subcutaneous xenograft of pancreatic cancer. Oncotarget. 20178:61264–81 Impact Journals LLC.

    Gutman M, Singh RK, Xie K, Bucana CD, Fidler IJ. Regulation of interleukin-8 expression in human melanoma cells by the organ environment. Cancer Res. 199555(11):2470–5.

    Hensel JA, Khattar V, Ashton R, Lee C, Siegal GP, Ponnazhagan S. Location of tumor affects local and distant immune cell type and number. Immun Inflamm Dis. 20175(1):85–94.

    Zhao X, Li L, Starr TK, Subramanian S. Tumor location impacts immune response in mouse models of colon cancer. Oncotarget Impact J LLC. 20178:54775–87.

    Devaud C, Westwood JA, John LB, Flynn JK, Paquet-Fifield S, Duong CPM, et al. Tissues in different anatomical sites can sculpt and vary the tumor microenvironment to affect responses to therapy. Mol Ther. 201422:18–27 Nature Publishing Group.

    Lu Y, Shiau F, Yi W, Lu S, Wu Q, Pearson JD, et al. Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development. Dev Cell. 202053:473–491.e9 Cell Press.

    Stein-O’Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, et al. Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. Cell Syst. 20198:395-411.e8 Cell Press.

    Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat Protoc. 202015(4):1484–506. Nature Research.

    Cherry C, Maestas DR, Han J, Andorko JI, Cahan P, Fertig EJ, et al. Intercellular signaling dynamics from a single cell atlas of the biomaterials response. bioRxiv. 2020:2020.07.24.218537. Cold Spring Harbor Laboratory.

    Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods. 202017(2):159–62. Nature Research.

    Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 201411(4):417–22. Nature Publishing Group.

    Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M, Vazquez G, et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell. 2018174:968–981.e15 Cell Press.

    Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods. 202118(1):9–14. Nature Research.

    Lipson EJ, Lilo MT, Ogurtsova A, Esandrio J, Xu H, Brothers P, et al. Basal cell carcinoma: PD-L1/PD-1 checkpoint expression and tumor regression after PD-1 blockade. J Immunother Cancer. 20175:23 BioMed Central Ltd.

    Yanik EL, Kaunitz GJ, Cottrell TR, Succaria F, McMiller TL, Ascierto ML, et al. Association of HIV status with local immune response to anal squamous cell carcinoma: implications for immunotherapy. JAMA Oncol. 20173(7):974–8. American Medical Association.

    Ho WJ, Yarchoan M, Charmsaz S, Munday RM, Danilova L, Sztein MB, et al. Multipanel mass cytometry reveals anti–PD-1 therapy–mediated B and T cell compartment remodeling in tumor-draining lymph nodes. JCI Insight. 20205(2):e132286 American Society for Clinical Investigation.

    Hartmann FJ, Simonds EF, Bendall SC. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci Rep. 20188:10770 Nature Publishing Group.

    Chevrier S, Crowell HL, Zanotelli VRT, Engler S, Robinson MD, Bodenmiller B. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 20186:612–620.e5 Cell Press.

    Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 201714(4):417–9. Nature Publishing Group.

    Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 201644(8):e71.

    Robins HS, Campregher PV, Srivastava SK, Wacher A, Turtle CJ, Kahsai O, et al. Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells. Blood. 2009114:4099–107.

    Hopkins AC, Yarchoan M, Durham JN, Yusko EC, Rytlewski JA, Robins HS, et al. T cell receptor repertoire features associated with survival in immunotherapy-treated pancreatic ductal adenocarcinoma. JCI Insight NLM (Medline). 20183(13):e122092

    Weber LM, Nowicka M, Soneson C, Robinson MD. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. Commun Biol. 2019:2:183 Springer Science and Business Media LLC.

    Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 201587(7):636–45.

    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 201937(1):38–47. Nature Publishing Group.

    Robinson MD, Mccarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinforma Appl Note. 201026(1):139–40.

    Smyth GK, Smyth GK. Limma: linear models for microarray data. In: Gentlem R, Carey V, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics and computational biology solutions using R and Bioconductor, vol. 420.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 201415:550 BioMed Central.

    Ho W. Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune-regulatory pathways [Internet]. Github. 2021 Available from:

    Ho W. KPCMets_Immune [Internet]. Flow Repository. 2021 Available from:

    Ho W. Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune-regulatory features [Internet]. Gene Expr. Omnibus. 2021. Available from:


    Modern machine learning techniques provide the foundation to solve these issues, allowing us to simplify complex datasets and to identify biologically meaningful hypotheses about the TME’s response kinetics and its relationship to disease progression (Fig. 1). Unsupervised machine learning and network analysis are powerful tools for reducing the dimensionality and complexity of these datasets so that researchers can explore them to generate new hypotheses. Supervised techniques can then be used to convert high-dimensional measurements into accurate predictions of outcomes or sharp tests of particular mechanistic hypotheses. However, the ultimate utility of these techniques depends critically on the quality and integrity of their data and on the clinical validation of the patterns and predictions they produce.

    The recently announced Tumor Profiler Study (1) is an exciting new effort in this direction, with substantial potential to advance our understanding of the TME’s role in disease. Three aspects of its design make it a model study for the TME: (i) integrating machine learning methods with detailed multiomic TME and kinetic data for a large and diverse population (ii) developing a sustainable model of the translatability of costs, technical expertise, assay limitations, data platform integration, and required infrastructure and (iii) using profiling and machine learning results to change clinical practice by identifying patient- and tumor-specific vulnerabilities, i.e., precision medicine, through complex datasets.



    Graduation Year

    Document Type


    Degree Name

    Degree Granting Department

    Biology (Cell Biology, Microbiology, Molecular Biology)

    Major Professor

    Committee Member

    Committee Member

    Committee Member


    3D, acid, carbonic anhydrase IX, glycolysis, pH, protons


    Cancer is a complex and heterogeneous disease. Not only is there considerable variability between different cancer types, but there is enormous variability between and within patients who have the same type of cancer. Within tumors, there are multiple cell types, including cancer cells, stromal cells, and immune cells. The tumor microenvironment often induces the healthy cells to become pro-tumorigenic. Cell metabolism is exquisitely sensitive to changes in the tumor microenvironment and can be measured to infer the aggressiveness of cancer and predict response to therapy. In this dissertation, we aim to understand how the microenvironment, specifically low pH, affects the phenotype, metabolism, and function of cell types within tumors and ultimately, how this relates to metastasis and therapeutic outcome.

    Chapter 2: Unfortunately, measuring metabolism is challenging as metabolism is dynamic and can change depending on the types of models we use. Current studies utilize 2D models of cancer, i.e., cancer cell lines grown in flat, plastic flasks or dishes. 2D cultures are exposed to higher concentrations of nutrients, drugs, and have a reduced cell surface area to volume due to adhering to the plastic this also causes altered expression of adhesion proteins. The 2D models do not accurately reflect the tumor in vivo, and this can result in discrepancies between in vitro and in vivo results. Growing cancer cell lines in 3D better recapitulates what occurs in vivo. We developed an in vitro live-cell tooling and methodology to metabolically profile 3D cell cultures and directly compare them to 2D cell cultures. Using this, we observed differences in the basal metabolism of 2D and 3D cell cultures in response to metabolic inhibitors and chemotherapeutics. We further expanded this methodology to profile 3D microtissues generated from mouse organs and tumors. The metabolic profiles of microtissues derived from normal organs (heart, kidney) were consistent when comparing microtissues derived from the same organ. Treatment of heart and kidney microtissues with cardio- or nephrotoxins had early and marked effects on tissue metabolism.

    In contrast, microtissues derived from different regions of the same tumors exhibited significant metabolic heterogeneity, which correlated to histology. Hence, metabolic profiling of complex microtissues is necessary to understand the effects of morphology and structure on metabolism. This method has the potential to be used as a reproducible, early and sensitive measure of drug toxicity and a potential drug screening methodology. Moreover, this methodology provides an important stepping stone to improve experimental design between in vitro 2D studies and in vivo animal studies.

    Chapter 3: Development of novel methodologies and tooling development can drive progress in scientific research. Our method highlights the metabolic heterogeneity within the tumor which is often missed by other means. One under-explored component of tumor heterogeneity is the substantial distinction between the edge and the core. Ongoing intratumoral evolution is apparent in molecular variations among cancer cells from different regions of the same tumor. However, genetic data alone provide little insight into environmental selection forces and cellular phenotypic adaptations that govern the underlying Darwinian dynamics. We have observed in three spontaneous murine cancers (prostate cancers in TRAMP and PTEN mice, pancreatic cancer in KPC mice), that there are two subpopulations with distinct niche-construction adaptive strategies that remained stable in culture. One population is what we call a “pioneer” phenotype, which is found at the invasive edge of tumors. Pioneers are invasive cells that produce an acidic environment through upregulated aerobic glycolysis and upregulation of proton exporting machinery, such as carbonic anhydrase -9 (CA-IX). The other metabolic population observed is what we call an “engineer” phenotype. Engineers are found at the core of tumors they are non-invasive and have upregulated angiogenesis. These engineer cells are metabolically near-normal with an increased reliance on oxidative metabolism. Intratumoral evolution occurs generating cells with different fitness advantages in different regions of the tumors. These cells may purposefully migrate to their preferred area i.e. edge vs core, or end up there serendipitously, and once there, their phenotype may change with exposure to changing microenvironmental conditions or may be fixed. In our cases, these cells were cultured for many generations and their metabolic profiles were steady, indicating that the selected phenotypes were fixed.

    Darwinian interactions of these subpopulations were investigated in TRAMP prostate cancers. Computer simulations predicted and experiments confirmed that invasive, acid-producing, proliferative (C2) “pioneer” cells maintained a fitness advantage over non-invasive, angiogenic, quiescent (C3) “engineer” cells. This is most likely managed by promoting invasion and hampering the immune response. Immunohistochemical analysis of untreated tumors confirmed that C2 cells invariably outgrew and were more abundant than C3 cells in TRAMP prostate tumors. However, the C2 adaptive strategy phenotype incurs a high cost due to inefficient energy production (i.e., aerobic glycolysis). Mathematical model simulations predicted that small perturbations of the microenvironmental extracellular pH (pHe) could invert the cost/benefit ratio of the C2 strategy and select for C3 cells. Altering pH using buffer therapy, NaHCO3, increased pH in a TRAMP mouse model, and promoted the growth of C3 engineer cells over C2, enabling the tumor to remain low grade and reducing metastasis when treating established tumors. Mathematical models of the intratumoral Darwinian interactions of environmental selection forces and cancer cell adaptive strategies indicated that the tumor trajectory was steered into a less invasive pathway through the application of small but selective biological forces, such as altering pH.

    Pioneer cells found at the invasive edges of tumors, tend to have a high glycolytic metabolism and generate an acidic microenvironment. These cells are necessary for local invasion and cancer progression. Therefore, we were interested to see the impact of this acidic microenvironment on other components of the tumor. We studied how glycolytic metabolism and low pH affects three components of the tumor: the stroma, the immune compartment, and the cancer cells themselves.

    Glycolytic metabolism of cancer co-opts vital nutrients needed for normal cell growth and this forces the surrounding cell types to use other metabolites for fuel. One area where this occurs is in bone metastases. We have identified that the metabolic interplay between bone stroma and cancer cells enhances metastases. In our study, we demonstrated that highly glycolytic triple-negative MDA-MB-231 cancer cells, compared to non-metastatic MCF7 cells, release more lactate and form osteolytic bone metastasis in vivo. In vitro, lactate generated by cancer cells was shown to be consumed by osteoclasts as a fuel for oxidative metabolism, ultimately enhancing Type I collagen resorption in the bone. The transport of lactate into osteoclasts was mediated by MCT1, as shown by the significantly upregulated expression during osteoclast differentiation, and using an MCT-1 inhibitor, 7-(N-benzyl-N-methylamino)-2-oxo-2H-chromene-3-carboxylic acid, which impaired Type I collagen resorption in the osteoclasts. Together, these data indicate that lactate released by glycolytic breast carcinoma cells in the bone microenvironment promotes the formation of osteolytic lesions and provide the rationale for using MCT1 inhibitors as a novel therapeutic approach in patients with bone metastases.

    Additionally, one common symptom of bone metastasis in patients is pain (cancer-induced bone pain -CIBP). We have shown bone metastatic cancer cells are highly glycolytic, which generates high lactate levels and an acidic microenvironment. We postulated the acid in the tumor microenvironment may be a cause of CIBP which warranted further investigation. We found breast carcinoma cells that prefer bone as a metastatic site have very high extracellular proton efflux and express pumps/ion transporters associated with acid-base balance (MCT4, CA9, and V-ATPase). Acidosis can stimulate and sensitize the nociceptors in bone and result in hyperalgesia. Exposing cancer-associated fibroblasts, mesenchymal stem cells, and osteoblasts to acidic pH promoted the expression of inflammatory and nociceptive mediators (NGF, BDNF, IL6, IL8, IL1b, and CCL5). Further, the impairment of intratumoral acidification via V-ATPase targeting in bone metastases models significantly reduced CIBP. Current treatments for patients with CIBP are often ineffective, but our in vivo results correlate with patients clinically providing a potential new treatment avenue. Pain in patients, as measured by a questionnaire, correlated with higher levels of inflammatory and nociceptive mediators’ production, e.g. IL6 and IL8 by the stromal cells, suggesting tumor microenvironments generate pro-tumor stroma. Notably, a clinical trial (NCT01350583) treated 9 patients in a phase I trial of Na-bicarbonate (to raise pH) as an adjuvant to reduce bone pain. While the study failed to escalate, there was a significant reduction in patient reported CIBP. In summary, intratumoral acidification in the bone marrow may activate the tumor-associated stroma promoting CIBP and this finding offers a new target for palliative treatments in advanced cancer.

    Chapter 4: Immune cells are another component of the tumor that are exposed to the stressful microenvironmental conditions generated by tumor cells. In our studies, we have focused on effector T cells of the immune system i.e. CD8+ lymphocytes that have the potential to kill tumor cells through release of granzyme B. CD8+ T cells are regulated under normal conditions through expression of checkpoint inhibitor proteins that reduce effective lifetime or prevent T cell activity. Immunotherapy, such as checkpoint blockade inhibitors, increases T cell persistence, and has induced durable responses in many patients. However, many tumor types and many patients have shown minimal or no response, and it is unknown why. As we have discussed, aggressive tumors are often acidic, and we have seen effects of low pH on both cancer cells and the stroma. However, the impact of acid on T cells is unclear. Therefore, we aimed to understand the effect of acidity on T cell function. We found that activated murine T cells primarily rely on glycolytic metabolism, have reduced effector function and reduced glycolysis in low pH conditions. Specifically, acid inhibits glycolysis by preventing lactate transport out of the cell, which increases intracellular lactate and reduces intracellular pH resulting in downregulation of glycolysis. Altering the pH of solid tumors using buffer therapy improved the efficacy of immunotherapies. These findings have shown that acidity in solid tumors inhibits T cell effector function, which is vital for response to immunotherapy. By reducing intratumoral acidosis, we may be able to improve immunotherapy response in tumors and patients which have so far seen little benefit.

    Chapter 5: In our studies, we assume low pH is generated by the highly glycolytic metabolism of cancer cells. However, expression of proteins involved in cellular acid export, such as carbonic anhydrase IX, are often concurrently expressed in cells which exhibit high levels of glycolysis such as in pioneer cells at the invasive edges. What if acid export drove the uptake and metabolism of glucose? Prior work strongly correlates tumor acidity with increased invasion and metastasis, and that neutralization of tumor acidity can prevent the formation of metastases in many systems. However, correlation is not causation. We tested the hypothesis that acidosis can systemically cause metastasis by induction of acid production through the expression of plasma membrane proton exporters. We designed two “gain-of-function” systems to generate acid production independent of metabolic acid production. We engineered acid-producing cells by overexpressing a yeast proton pump, H-ATPase (PMA1), or a proton-exporting exofacial human carbonic anhydrase 9 (CA-IX). In vitro studies showed expression of either proton exporter into non-metastatic human cancer cell lines enhanced aerobic glycolysis, migration, and invasion. In vivo, the acid-producing cells formed higher-grade tumors, which were associated with increased spontaneous and experimental metastases. Neutralizing acidity with buffer therapy reduced metastasis. Therefore, increased rates of H+ export can drive increased aerobic glycolysis (the Warburg Effect) and convert non-metastatic cells into those that form extensive metastasis. Thus, we propose that variables associated with metastasis may be activated and enhanced by increased acid production, and thus acidosis is systemically causal of metastasis.

    Overall, we have been able to understand the impact of low pH on multiple different cell types in tumors, how this impacts their function and the resultant implications for cancer aggressiveness and response to therapy.