Patent Application
20220202811
This application contains a Sequence Listing, which was submitted in ASCII format via USPTO EFS-Web, and is hereby incorporated by reference in its entirety. The ASCII copy, created on Feb. 20, 2020, is named Sequence-Listing_009062-8398WO_ST25 and is 13 kilobytes in size.
This application relates to the treatment of various types of retinoic acid receptor-related orphan receptor gamma (RORγ)-dependent cancer.
Many types of cancer are highly resistant to current treatments and thus remain a lethal disease. Development of more effective therapeutic strategies is critically dependent on identification of factors that contribute to tumor growth and maintenance. Some types of cancer share molecular dependency on cancer stem cells and have similar molecular signaling pathways. Therefore, new and effective therapeutic approaches for targeting common molecular signaling pathways lead to additional cancer therapies.
In one aspect, provided herein is a method of treating an RORγ-dependent cancer. The method entails administrating to a subject in need a therapeutically effective amount of a composition comprising one or more RORγ inhibitors. In certain embodiments, the subject suffers from a RORγ-dependent cancer such as pancreatic cancer, leukemia, and lung cancer including small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In certain embodiments, the subject suffers from a metastatic cancer. In certain embodiments, the RORγ inhibitor includes SR2211, JTE-151, JTE-151A, and AZD-0284, or an analog or derivative thereof represented by any one of formulae I, II, III, IIIA, and IV. In certain embodiments, the method further entails administering to the subject one or more chemotherapeutic agents. The composition comprising one or more RORγ inhibitors may be administered before or after administration of the one or more chemotherapeutic agents. Alternatively, the composition comprising one or more RORγ inhibitors and the one or more chemotherapeutic agents may be administered simultaneously. In certain embodiments, the method further entails administering to the subject one or more radiotherapies before, after, or during administration of the composition comprising one or more RORγ inhibitors.
In another aspect, disclosed herein is a pharmaceutical composition for treating a RORγ-dependent cancer. The pharmaceutical composition comprises a therapeutically effective amount of one or more RORγ inhibitors. In certain embodiments, the RORγ-dependent cancer includes pancreatic cancer, leukemia, and lung cancer including small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In certain embodiments, the cancer is a metastatic cancer. In certain embodiments, the RORγ inhibitor includes SR2211, JTE-151, JTE-151A, and AZD-0284, or an analog or derivative thereof represented by any one of formulae I, II, III, IIIA, and IV. In certain embodiments, the pharmaceutical composition further comprises a therapeutically effective amount of one or more chemotherapeutic agents. In certain embodiments, the pharmaceutical composition further comprises one or more pharmaceutically acceptable carriers, excipients, preservatives, diluent, buffer, or a combination thereof.
In yet another aspect, provided herein is a combinational therapy for a RORγ-dependent cancer. The combinational therapy comprises performing surgery, administering one or more chemotherapeutic agents, administering one or more radiotherapies, and/or administering one or more of immunotherapies to a subject in need thereof before, during, or after administering a composition comprising one or more RORγ inhibitors. In certain embodiments, the RORγ-dependent cancer includes pancreatic cancer, leukemia, and lung cancer including small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In certain embodiments, the cancer is a metastatic cancer. In certain embodiments, the RORγ inhibitor includes SR2211, JTE-151, JTE-151A, and AZD-0284, or an analog or derivative thereof represented by any one of formulae I, II, III, IIIA, and IV. In certain embodiments, the surgery, chemotherapy, radiotherapy, and/or immunotherapy is performed or administered to the subject before, during, after administering the composition comprising one or more RORγ inhibitor.
In yet another aspect, disclosed herein is a method of inhibiting cancer cell growth comprising contacting one or more cancer cells with an effective amount of one or more RORγ inhibitors in vivo, in vitro, or ex vivo. In certain embodiments, the RORγ-dependent cancer cell includes cells of pancreatic cancer, leukemia, and lung cancer including small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In certain embodiments, the cancer cell is a metastatic cancer cell. In certain embodiments, the RORγ inhibitor includes SR2211, JTE-151, JTE-151A, and AZD-0284, or an analog or derivative thereof represented by any one of formulae I, II, III, IIIA, and IV.
In yet another aspect, disclosed herein is a method of detecting a cancer, progression of cancer, or cancer metastasis in a subject comprising comparing the level of RORγ in a biological sample such as blood circulating tumor cells, a biopsy sample, or urine from the subject with the average level of RORγ of a population of healthy subjects, wherein an elevated level of RORγ indicates that the subject suffers from the cancer or cancer metastasis.
In yet another aspect, disclosed herein is a method of determining the prognosis of a subject receiving a cancer treatment comprising comparing the level of RORγ in a biological sample such as blood circulating tumor cells, a biopsy sample, or urine from the subject before and after receiving the cancer treatment, wherein a reduced level of RORγ indicates that the cancer treatment is effective for the subject.
This application contains at least one drawing executed in color. Copies of this application with color drawing(s) will be provided by the Office upon request and payment of the necessary fees.
Disclosed herein in various embodiments are techniques of identifying a cancer target common for several types of cancer, such as RORγ, therapeutic uses, diagnostic uses, and prognostic uses of the small molecule compounds inhibiting the cancer target, combinational therapy using the RORγ inhibitors in combination with one or more other cancer therapies, as well as pharmaceutical compositions comprising the RORγ inhibitors.
Drug resistance and resultant relapse remain key challenges in pancreatic cancer and are in part driven by the inherent heterogeneity of the tumor that prevents effective targeting of all malignant cells. To better understand the pathways that confer an aggressive phenotype and drug resistance, a combination of RNA-seq, ChIP-seq and genome-wide CRISPR screening was utilized to systematically map molecular dependencies of pancreatic cancer stem cells, which are highly drug resistant cells that are also enriched in the capacity to drive tumor progression. Integration of these data revealed an unexpected role for immuno-regulatory pathways in stem cell self-renewal and maintenance in autochthonous tumors. In particular, RORγ, a nuclear hormone receptor known for its role in inflammatory cytokine responses and T cell differentiation, emerged as a key regulator of stem cells. RORγ transcriptional levels increased during pancreatic cancer progression, and the locus was amplified in a subset of pancreatic cancer patients. Functionally RORγ inhibition, whether achieved via genetic or pharmacologic approaches, led to a striking defect in pancreatic cancer growth in vitro and in vivo, and improved survival in genetically engineered models. Finally, a large-scale retrospective analysis of patient samples revealed that RORγ expression in PanIn lesions was positively correlated with advanced disease, lymphatic vessel invasion and lymph node metastasis, suggesting that RORγ expression could be a useful marker to predict pancreatic cancer aggressiveness. Collectively, these data reveal an unexpected co-option of immuno-regulatory signals by pancreatic cancer stem cells and suggest that therapeutics currently being used for autoimmune indications should be evaluated as a novel treatment strategy for pancreatic cancer patients.
While cytotoxic agents remain the standard of care for most cancers, their use is often associated with initial efficacy, followed by disease progression. This is particularly true for pancreatic cancer, a highly aggressive disease, where current multidrug chemotherapy regimens result in tumor regression in 30% of patients, quickly followed by disease progression in the vast majority of cases. This progression is largely due to the inability of chemotherapy to successfully eradicate all tumor cells, leaving behind subpopulations that can trigger tumor re-growth. Thus, identifying the cells that are preferentially drug resistant, and understanding their vulnerabilities, is critical to improving patient outcome and response to current therapies.
Previous work has focused on identifying the most tumorigenic populations within pancreatic cancer. Through this, subpopulations of cells marked by expression of CD24+/CD44+/ESA+, cMet, CD133, Nestin, ALDH, and more recently DCLK1 and Musashi, have been shown to harbor “stem cell” characteristics, in being enriched for the capacity to drive tumorigenesis and recreate the heterogeneity of the original tumor. Importantly, these tumor propagating cells or “cancer stem cells” have been shown to be highly resistant to cytotoxic therapies, such as gemcitabine, consistent with the finding that cancer patients with a high cancer stem cell signature have poorer prognosis relative to those with a low stem cell signature. Although pancreatic cancer stem cells are epithelial in origin, these cells frequently express EMT-associated programs, which may in part explain their over-representation in circulation and propensity to seed metastatic sites. Because these studies define stem cells as a population that present a particularly high risk for disease progression, defining the molecular signals that sustain them remains an essential goal for achieving complete and durable responses.
A combination of RNA-seq, ChIP-seq and genome-wide CRISPR screening was used to define the molecular framework that sustains the aggressive nature of pancreatic cancer stem cells. These data identified a network of key nodes regulating pancreatic cancer stem cells, and revealed an unanticipated role for immuno-regulatory genes in pancreatic cancer stem cell self-renewal and maintenance. Among these, RORγ, a nuclear hormone receptor known for its role in Th17 cell specification and regulation of inflammatory cytokine production, emerged as a key regulator of stem cells. RORγ expression increased with progression and blockade of RORγ signaling via genetic or pharmacological approaches depleted the cancer stem cell pool and profoundly inhibited human and mouse tumor propagation, in part by triggering the collapse of a super-enhancer-associated oncogenic network. Finally, sustained treatment with RORγ inhibitor led to a significant improvement in autochthonous models of pancreatic cancer. Together, these data offered a unique comprehensive map of pancreatic cancer stem cells and identified critical vulnerabilities that may be exploited to improve therapeutic targeting of aggressive, drug resistant pancreatic cells.
As disclosed herein, the molecular dependencies of pancreatic cancer stem cells have been systematically mapped out, including highly drug resistant cells that are also enriched in the capacity to drive progression. A sub-population of cells within pancreatic cancer that harbor stem cell characteristics and display preferential capacity to drive lethality and therapy resistance was identified. Because this work showed that these cancer stem cells were preferentially drug resistant and drove lethality, networks and cellular programs critical for the maintenance and function of these aggressive pancreatic cancer cells were identified. A combination of RNA-Seq, ChIP Seq and genome-wide CRISPR screening was used to develop a network map of core programs regulating pancreatic cancer and a unique multiscale map of programs that represent the core dependencies of pancreatic cancer stem cells. This analysis revealed an unexpected role for immunoregulatory genes in stem cell function and pancreatic cancer growth. In particular, retinoic acid receptor-related orphan receptor gamma (RORγ) emerged as a key regulator of pancreatic cancer stem cells.
As demonstrated in the working examples, RORγ expression was shown to be low in normal pancreatic cells but significantly increased in epithelial tumor cells with disease progression. ShRNA-mediated knockdown confirmed the role of RORγ identified by the genetic CRISPR-based screen as it led to a decrease in sphere formation of pancreatic cancer cells in vitro, and dramatically suppressed tumor initiation and propagation in vivo. Consistent with this, inhibition of RORγ resulted in a dose-dependent reduction in the number of pancreatic cancer spheroids in vitro, and combined delivery of RORγ inhibitor and gemcitabine in KPC mice with advanced pancreatic cancer led to depletion of the stem cell pool and lowered the tumor burden by half. Further, RORγ expression was low in normal human pancreas and in pancreatitis and rose with human pancreatic cancer progression. Blocking RORγ in human pancreatic cancer reduced growth in vitro and in vivo, suggesting that it plays an important role in human disease as well.
Leukemia and pancreatic cancer stem cells have some common features and shared molecular dependencies. As demonstrated in the working examples, KLS cells were isolated from WT and RORγ knockout (RORc−/−) mice, retrovirally transduced with BCR-ABL and Nup98-HOXA9, and cultured in primary and secondary colony assays in vitro. A significant decrease in both colony number and overall colony area in primary and secondary colony assays was observed, indicating that growth and propagation of blast crisis CML is critically dependent on RORγ. In addition, an impact on acute myelogenous leukemia (AML) growth as well as RORγ expression in lymphoid tumors was observed, suggesting a role for RORγ signaling in these cancers as well.
The RORγ pathway also emerged as a key regulator of stem cells, as its expression was low in non-stem cells both at the RNA and protein levels but enriched in stem cell populations. RORγ was found to regulate potent oncogenes marked by super enhancers in stem cells and was shown to correlate to the aggressive nature of pancreatic cancer stem cells. Blockade of RORγ signaling via genetic or pharmacological approaches depleted the cancer stem cell pool and profoundly inhibited pancreatic tumor progression. Therapeutic, genetic, or CRISPR-based inhibition of RORγ has also proven to be effective in reducing cancer cell growth in leukemia and lung cancer. Moreover, given that the above identified roles of RORγ in cancer stem cell functions may not be particularly limited to one type of cancer, there is reason to believe that the RORγ pathway can be broadly utilized to epithelial and other types of cancers that share similar molecular dependencies of cancer stem cells. Taken together, it suggests that RORγ signaling play an important in cancer stem cells, and that targeting the RORγ pathway would be effective at inhibiting stem cell-driven cancers where RORγ expression level is high.
Various RORγ inhibitors, as well as their analogs and derivatives, may be used in treating an RORγ-dependent cancer. For example, SR2211 is a selective synthetic RORγ modulator and an inverse agonist, represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is an analog and/or derivative of SR2211. For example, the RORγ inhibitor may have a structure of Formula I:
including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor has a structure of Formula I, including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
Another example of an RORγ inhibitor is AZD-0284, another inverse agonist, represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is an analog and/or derivative of AZD-0284. For example, the RORγ inhibitor may have a structure of Formula II:
including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor has a structure of Formula II, including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor is a racemic mixture of AZD-0284 (rac-AZD-0284) represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is a racemic mixture of an inverse amide derivative of AZD-0284 represented by the following chemical structure:
Yet another example of an RORγ inhibitor is JTE-151, disclosed as Compound A-58 in U.S. Pat. No. 8,604,069, and its chemical name is (4S)-6-[(2-chloro-4-methylphenyl)amino]-4-{4-cyclopropyl-5-[cis-3-(2,2-dimethylpropyl)cyclobutyl]isoxazol-3-yl}-6-oxohexanoic acid, represented by the following chemical structure:
Another example of an RORγ inhibitor is JTE-151A, represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is an analog and/or derivative of JTE-151 or JTE-151A. For example, the RORγ inhibitor may have a structure of Formula III:
including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor has a structure of Formula III, including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor has a structure of Formula III, including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor is a racemic mixture of JTE-151 (rac-JTE-151) represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is a racemic mixture of an inverse amide derivative of JTE-151 represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is an analog and/or derivative of JTE-151 having a structure of Formula IV:
including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor is an analog and/or derivative of JTE-151A. For example, the RORγ inhibitor may have a structure of Formula IIIA:
including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor has a structure of Formula IIIA, including pharmaceutically acceptable salts thereof, pharmaceutically acceptable isomers thereof, and pharmaceutically acceptable derivatives thereof, wherein:
In certain embodiments, the RORγ inhibitor is a racemic mixture of JTE-151A (rac-JTE-151A) represented by the following chemical structure:
In certain embodiments, the RORγ inhibitor is a racemic mixture of an inverse amide derivative of JTE-151A represented by the following chemical structure:
The term “alkyl” refers to a straight or branched or cyclic chain hydrocarbon radical or combinations thereof, which can be completely saturated, mono- or polyunsaturated and can include di- and multivalent radicals. Examples of hydrocarbon radicals include, but are not limited to, groups such as methyl, ethyl, n-propyl, isopropyl, n-butyl, t-butyl, isobutyl, sec-butyl, n-pentyl, neopentyl, n-hexyl, n-heptyl, n-octyl, cyclopropyl, cyclobutyl, cyclopentyl, cyclohexyl, (cyclohexyl) methyl, cyclopropylmethyl, and the like.
The term “haloalkyl” refers to an alkyl group with 1, 2, 3, 4, 5, or 6 hydrogens substituted with the same or different halogen, preferably a halogen selected from the group consisting of F, Cl, Br, and I. Examples of haloalkyl groups include, without limitation, halomethyl (e.g., CF3), haloethyl, halopropyl, halobutyl, halopentyl, and halohexyl. Examples of halomethyl groups may have a structure of —C(X2)(X3)-X1 wherein X1 is selected from the group consisting of F, Cl, Br, and I; and X2 and X3 can be the same or different and are independently selected from the group consisting of H, F, Cl, Br, and I.
The term “hydroxyalkyl” refers to an alkyl group with 1, 2, 3, 4, 5, or 6 hydrogens substituted with hydroxyl groups. Examples of hydroxyalkyl groups include, without limitation, hydroxymethyl, hydroxyethyl, hydroxypropyl, hydroxybutyl, hydroxypentyl, and hydroxyhexyl. Examples of hydroxymethyl groups may have a structure of —C(X12)(X13)-X11 wherein X11 is OH; and X12 and X13 can be the same or different and are independently selected from the group consisting of H and OH.
The term “aminoalkyl” refers to an alkyl group with 1, 2, 3, 4, 5, or 6 hydrogens substituted with amino groups. Examples of aminoalkyl groups include, without limitation, aminomethyl, aminoethyl, aminopropyl, aminobutyl, aminopentyl, and aminohexyl. Examples of aminomethyl groups may have a structure of —C(X22)(X23)-X21 wherein X21 is amino; and X22 and X23 can be the same or different and are independently selected from the group consisting of H and amino.
The term “thiolalkyl” refers to an alkyl group with 1, 2, 3, 4, 5, or 6 hydrogens substituted with thiol groups. Examples of thiolalkyl groups include, without limitation, thiolmethyl, thiolethyl, thiolpropyl, thiolbutyl, thiolpentyl, and thiolhexyl. Examples of thiolmethyl groups may have a structure of —C(X32)(X33)-X31 wherein X31 is thio; and X32, and X33 can be the same or different and are independently selected from the group consisting of H and thiol.
The term “alkylcarbonyl” refers to —C(═O)—X41 wherein X41 is an alkyl group as defined herein. Examples of alkylcarbonyl groups include, without limitation, acetyl, propionyl, butyrionyl, pentanonyl, and hexanonyl.
The term “alkylimino” refers to —C(═N—X51)-X52 wherein X51 is H or OH; and X52 is an alkyl group as defined herein. Examples of alkylimino groups include, without limitation, —C(═NH)CH3, and —C(═N—OH)CH3.
The term “aryl” refers to aromatic groups that have only carbon ring atoms, optionally substituted with one or more substitution groups selected from the group consisting of halo, alkyl, amino, and hydroxyl. Examples of aryl groups include, without limitation, phenyl and naphthyl.
The term “heteroaryl” refers to aromatic groups having 1, 2, 3, or 4 heteroatoms as ring atoms, optionally substituted with one or more substitution groups selected from the group consisting of halo, alkyl, amino, and hydroxyl. Suitable heteroatoms include, without limitation, O, S, and N. Examples of heteroaryl groups include, without limitation, pyridyl, pyridazyl, pyrimidyl, pyrazinyl, thienyl, pyrrolyl, and imidazolyl.
The analogs and derivatives of the small molecule compounds disclosed herein have improved activities or retain at least partial activities in inhibiting RORγ and have other improved properties such as less toxicity for a subject receiving the compounds, analogs and derivatives thereof.
Examples of pharmaceutically acceptable salts include, without limitation, non-toxic inorganic and organic acid addition salts such as hydrochloride derived from hydrochloric acid, hydrobromide derived from hydrobromic acid, nitrate derived from nitric acid, perchlorate derived from perchloric acid, phosphate derived from phosphoric acid, sulphate derived from sulphuric acid, formate derived from formic acid, acetate derived from acetic acid, aconate derived from aconitic acid, ascorbate derived from ascorbic acid, benzenesulphonate derived from benzensulphonic acid, benzoate derived from benzoic acid, cinnamate derived from cinnamic acid, citrate derived from citric acid, embonate derived from embonic acid, enantate derived from enanthic acid, fumarate derived from fumaric acid, glutamate derived from glutamic acid, glycolate derived from glycolic acid, lactate derived from lactic acid, maleate derived from maleic acid, malonate derived from malonic acid, mandelate derived from mandelic acid, methanesulphonate derived from methane sulphonic acid, naphthalene-2-sulphonate derived from naphtalene-2-sulphonic acid, phthalate derived from phthalic acid, salicylate derived from salicylic acid, sorbate derived from sorbic acid, stearate derived from stearic acid, succinate derived from succinic acid, tartrate derived from tartaric acid, toluene-p-sulphonate derived from p-toluene sulphonic acid, and the like. Such salts may be formed by procedures well known and described in the art. Other acids such as oxalic acid, which may not be considered pharmaceutically acceptable, may be useful in the preparation of salts useful as intermediates in obtaining a chemical compound of the invention and its pharmaceutically acceptable acid addition salt.
Examples of pharmaceutically acceptable salts also include, without limitation, non-toxic inorganic and organic cationic salts such as the sodium salts, potassium salts, calcium salts, magnesium salts, zinc salts, aluminium salts, lithium salts, choline salts, lysine salts, and ammonium salts, and the like, of a chemical compound disclosed herein containing an anionic group. Such cationic salts may be formed by suitable procedures in the art.
Examples of pharmaceutically acceptable derivatives include, without limitation, ester derivatives, amide derivatives, ether derivatives, thioether derivatives, carbonate derivatives, carbamate derivatives, phosphate derivatives, etc.
Also disclosed herein are methods of treating cancer using one or more RORγ inhibitors or a composition comprising one or more RORγ inhibitors disclosed herein in combination with one or more other cancer therapies targeting a specific type of the cancer. The RORγ inhibitors or a composition comprising one or more RORγ inhibitors can be administered sequentially or simultaneously with one or more other cancer therapies over an extended period of time. Such methods may be used to treat any RORγ-dependent cancer or tumor cell type, including but not limited to primary, recurrent, and metastatic pancreatic cancer, lung cancer, and leukemia.
The RORγ inhibitors and compositions comprising the RORγ inhibitors disclosed herein can be used in combination with other conventional cancer therapies such as surgery, immunotherapy, radiotherapy, and/or chemotherapy to obtain improved or synergistic therapeutic effects. For example, surgery, chemotherapy, radiotherapy, and/or immunotherapy can be performed or administered before, during, or after the administration of the RORγ inhibitors or compositions comprising the RORγ inhibitors. As one of ordinary skill in the art would understand, the chemotherapy, immunotherapy, radiotherapy, and/or the RORγ inhibitors or compositions comprising the RORγ inhibitors can be administered to a subject in need thereof one or more times at the same or different doses, depending on the diagnosis and prognosis of the cancer. One skilled in the art would be able to combine one or more of these therapies in different orders to achieve the desired therapeutic results. In certain embodiments, the combinational therapy achieves synergist effects in comparison to any of the treatments administered alone.
Depending on the cancer type, various chemotherapeutic agents can be selected for use in combination with one or more RORγ inhibitors or a composition comprising one or more RORγ inhibitors disclosed herein. In certain embodiments, the chemotherapeutic agents for pancreatic cancer include but are not limited to gemcitabine (Gemzar), 5-fluorouracil (5-FU), irinotecan (Camptosar), oxaliplatin (Eloxatin), albumin-bound paclitaxel (Abraxane), capecitabine (Xeloda), cisplatin, paclitaxel (Taxol), docetaxel (Taxotere), and irinotecan liposome (Onivyde). In certain embodiments, the chemotherapeutic agents for leukemia include but are not limited to vincristine or liposomal vincristine (Marc daunorubicin or daunomycin (Cerubidine), doxorubicin (Adriamycin), cytarabine or cytosine arabinoside (ara-C) (Cytosar-U), L-asparaginase or PEG-L-asparaginase or pegaspargase (Oncaspar), 6-mercaptopurine (6-MP) (Purinethol), methotrexate (Xatmep, Trexall, Otrexup, Rasuvo), cyclophosphamide (Cytoxan, Neosar), prednisone (Deltasone, Prednisone Intensol, Rayos), imatinib mesylate (Gleevec), and nelarabine (Arranon). In certain embodiments, the chemotherapeutic agents for lung cancer include but are not limited to cisplatin (Platinol), carboplatin (Paraplatin), docetaxel (Taxotere), gemcitabine (Gemzar), paclitaxel (Taxol), vinorelbine (Navelbine), pemetrexed (Alimta), albumin-bound paclitaxel (Abraxane), etoposide (VePesid or Etopophos), doxorubicin (Adriamycin), ifosfamide (Ifex), irinotecan (Camptosar), paclitaxel (Taxol), topotecan (Hycamtin), vinblastine (Oncovir), and vincristine (Oncovin).
In certain embodiments, the combinational therapy leads to improved clinical outcome and/or higher survival rate for cancer patients, especially for metastatic cancer patients. In certain embodiments, the combinational therapy achieves the same therapeutic effect, a better therapeutic effect, or even a synergistic effect when administered at a lower dose and/or for a short period of time than any of the treatments administered alone. For example, when an RORγ inhibitor and a chemotherapeutic agent are used in a combinational therapeutic, either or both may be administered at a lower dose than the RORγ inhibitor or the chemotherapeutic agent administered alone. In another example, when an RORγ inhibitor and a radiotherapy are used in a combinational therapeutic, either or both may be administered at a lower dose or the radiotherapy may be administered for a shorter period than the RORγ inhibitor or the chemotherapeutic agent administered alone. This advantage of the combinational therapy has a significant impact on the clinical outcome because the toxicity, drug resistance, and/or other undesirable side effects caused by the treatment are reduced due to the reduced dose and/or reduced treatment period. One hurdle of cancer therapy is that many cancer patients have to discontinue the treatment due to the severity of the side effects, which sometimes even cause complications.
In certain embodiments, multiple doses of one or more RORγ inhibitors or compositions comprising one or more RORγ inhibitors are administered in combination with multiple doses or multiple cycles of other cancer therapies. In these embodiments, the RORγ inhibitors and other cancer therapies can be administered simultaneously or sequentially at any desirable intervals. In certain embodiments, the RORγ inhibitors and other cancer therapies can be administered in alternate cycles, e.g., administration of one or more doses of the RORγ inhibitor disclosed herein followed by administration of one or more doses of a chemotherapeutic agent.
Provided herein is a method of treating and/or preventing a RORγ-dependent cancer in a subject. The method entails administering a therapeutically effective amount of one or more RORγ inhibitors or a composition comprising one or more RORγ inhibitors provided herein to the subject. In certain embodiments, the method further entails administering one or more other cancer therapies such as surgery, immunotherapy, radiotherapy, and/or chemotherapy to the subject sequentially or simultaneously.
Also provided herein is a method of preventing or delaying progression of an RORγ-dependent benign tumor to a malignant tumor in a subject. The method entails administering an effective amount of one or more RORγ inhibitors or a composition comprising one or more RORγ inhibitors provided herein to the subject. In certain embodiments, the method further entails administering one or more other therapies such as such as surgery, immunotherapy, radiotherapy, and/or chemotherapy to the subject sequentially or simultaneously.
As used herein, the term “subject” refers to a mammalian subject, preferably a human. A “subject in need thereof” refers to a subject who has been diagnosed with cancer, or is at an elevated risk of developing cancer. The phrases “subject” and “patient” are used interchangeably herein.
The terms “treat,” “treating,” and “treatment” as used herein with regard to cancer refers to alleviating the cancer partially or entirely, preventing the cancer, decreasing the likelihood of occurrence or recurrence of the cancer, slowing the progression or development of the cancer, or eliminating, reducing, or slowing the development of one or more symptoms associated with the cancer. For example, “treating” may refer to preventing or slowing the existing tumor from growing larger, preventing or slowing the formation or metastasis of cancer, and/or slowing the development of certain symptoms of the cancer. In some embodiments, the term “treat,” “treating,” or “treatment” means that the subject has a reduced number or size of tumor comparing to a subject without being administered with the treatment. In some embodiments, the term “treat,” “treating,” or “treatment” means that one or more symptoms of the cancer are alleviated in a subject receiving the RORγ inhibitors or pharmaceutical compositions comprising the RORγ inhibitors as disclosed herein and/or other cancer therapies comparing to a subject who does not receive such treatment.
A “therapeutically effective amount” of one or more RORγ inhibitors or the pharmaceutical composition comprising one or more RORγ inhibitors as used herein is an amount of the RORγ inhibitor or pharmaceutical composition that produces a desired effect in a subject for treating and/or preventing cancer. In certain embodiments, the therapeutically effective amount is an amount of the RORγ inhibitor or pharmaceutical composition that yields maximum therapeutic effect. In other embodiments, the therapeutically effective amount yields a therapeutic effect that is less than the maximum therapeutic effect. For example, a therapeutically effective amount may be an amount that produces a therapeutic effect while avoiding one or more side effects associated with a dosage that yields maximum therapeutic effect. A therapeutically effective amount for a particular composition will vary based on a variety of factors, including but not limited to the characteristics of the therapeutic composition (e.g., activity, pharmacokinetics, pharmacodynamics, and bioavailability), the physiological condition of the subject (e.g., age, body weight, sex, disease type and stage, medical history, general physical condition, responsiveness to a given dosage, and other present medications), the nature of any pharmaceutically acceptable carriers, excipients, and preservatives in the composition, and the route of administration. One skilled in the clinical and pharmacological arts will be able to determine a therapeutically effective amount through routine experimentation, namely by monitoring a subject's response to administration of the RORγ inhibitor or the pharmaceutical composition and adjusting the dosage accordingly. For additional guidance, see, e.g., Remington: The Science and Practice of Pharmacy, 22nd Edition, Pharmaceutical Press, London, 2012, and Goodman & Gilman's The Pharmacological Basis of Therapeutics, 12th Edition, McGraw-Hill, New York, N.Y., 2011, the entire disclosures of which are incorporated by reference herein.
In some embodiments, a therapeutically effective amount of an RORγ inhibitor disclosed herein is in the range from about 10 mg/kg to about 150 mg/kg, from 30 mg/kg to about 120 mg/kg, from 60 mg/kg to about 90 mg/kg. In some embodiments, a therapeutically effective amount of an RORγ inhibitor disclosed herein is about 15 mg/kg, about 30 mg/kg, about 45 mg/kg, about 60 mg/kg, about 75 mg/kg, about 90 mg/kg, about 105 mg/kg, about 120 mg/kg, about 135 mg/kg, or about 150 mg/kg. A single dose or multiple doses of an RORγ inhibitor may be administered to a subject. In some embodiments, the RORγ inhibitor is administered twice a day.
It is within the purview of one of ordinary skill in the art to select a suitable administration route, such as oral administration, subcutaneous administration, intravenous administration, intramuscular administration, intradermal administration, intrathecal administration, or intraperitoneal administration. For treating a subject in need thereof, the RORγ inhibitor or pharmaceutical composition can be administered continuously or intermittently, for an immediate release, controlled release or sustained release. Additionally, the RORγ inhibitor or pharmaceutical composition can be administered three times a day, twice a day, or once a day for a period of 3 days, 5 days, 7 days, 10 days, 2 weeks, 3 weeks, or 4 weeks. In certain embodiments, the RORγ inhibitor or pharmaceutical composition can be administered every day, every other day, or every three days. The RORγ inhibitor or pharmaceutical composition may be administered over a pre-determined time period. Alternatively, the RORγ inhibitor or pharmaceutical composition may be administered until a particular therapeutic benchmark is reached. In certain embodiments, the methods provided herein include a step of evaluating one or more therapeutic benchmarks such as the level of RORγ in a biological sample such as blood circulating tumor cells, a biopsy sample, or urine to determine whether to continue administration of the RORγ inhibitor or pharmaceutical composition.
One or more RORγ inhibitors disclosed herein can be formulated into pharmaceutical compositions. In some embodiments, the pharmaceutical composition comprises only one RORγ inhibitor. In some embodiments, the pharmaceutical composition comprises two or more RORγ inhibitors. The pharmaceutical compositions may further comprise one or more pharmaceutically acceptable carriers, excipients, preservatives, or a combination thereof. A “pharmaceutically acceptable carrier or excipient” refers to a pharmaceutically acceptable material, composition, or vehicle that is involved in carrying or transporting a compound of interest from one tissue, organ, or portion of the body to another tissue, organ, or portion of the body. For example, the carrier or excipient may be a liquid or solid filler, diluent, excipient, solvent, or encapsulating material, or some combination thereof. Each component of the carrier or excipient must be “pharmaceutically acceptable” in that it must be compatible with the other ingredients of the formulation. It also must be suitable for contact with any tissue, organ, or portion of the body that it may encounter, meaning that it must not carry a risk of toxicity, irritation, allergic response, immunogenicity, or any other complication that excessively outweighs its therapeutic benefits.
The pharmaceutical compositions can have various formulations, e.g., injectable formulations, lyophilized formulations, liquid formulations, oral formulations, etc. depending on the administration routes disclosed in the foregoing paragraphs.
In certain embodiments, the pharmaceutical composition may further comprise one or more additional therapeutic agents such as one or more chemotherapeutic agents or one or more radiation therapeutic agents. The one or more additional therapeutic agents may be formulated into the same pharmaceutical composition comprising the RORγ inhibitor disclosed herein or into separate pharmaceutical compositions for combinational therapy. Depending on the cancer type, various chemotherapeutic agents can be selected for use in combination with one or more RORγ inhibitors or a composition comprising one or more RORγ inhibitors disclosed herein. In certain embodiments, the chemotherapeutic agents for pancreatic cancer include but are not limited to gemcitabine (Gemzar), 5-fluorouracil (5-FU), irinotecan (Camptosar), oxaliplatin (Eloxatin), albumin-bound paclitaxel (Abraxane), capecitabine (Xeloda), cisplatin, paclitaxel (Taxol), docetaxel (Taxotere), and irinotecan liposome (Onivyde). In certain embodiments, the chemotherapeutic agents for leukemia include but are not limited to vincristine or liposomal vincristine (Marqibo), daunorubicin or daunomycin (Cerubidine), doxorubicin (Adriamycin), cytarabine or cytosine arabinoside (ara-C) (Cytosar-U), L-asparaginase or PEG-L-asparaginase or pegaspargase (Oncaspar), 6-mercaptopurine (6-MP) (Purinethol), methotrexate (Xatmep, Trexall, Otrexup, Rasuvo), cyclophosphamide (Cytoxan, Neosar), prednisone (Deltasone, Prednisone Intensol, Rayos), imatinib mesylate (Gleevec), and nelarabine (Arranon). In certain embodiments, the chemotherapeutic agents for lung cancer include but are not limited to cisplatin (Platinol), carboplatin (Paraplatin), docetaxel (Taxotere), gemcitabine (Gemzar), paclitaxel (Taxol), vinorelbine (Navelbine), pemetrexed (Alimta), albumin-bound paclitaxel (Abraxane), etoposide (VePesid or Etopophos), doxorubicin (Adriamycin), ifosfamide (Ifex), irinotecan (Camptosar), paclitaxel (Taxol), topotecan (Hycamtin), vinblastine (Oncovir), and vincristine (Oncovin).
The following examples are intended to illustrate various embodiments of the invention. As such, the specific embodiments discussed or any specific materials and methods disclosed are not to be construed as limitations on the scope of the invention. It will be apparent to one skilled in the art that various equivalents, changes, and modifications may be made without departing from the scope of invention, and it is understood that such equivalent embodiments are to be included herein. Further, all references cited in the disclosure are hereby incorporated by reference in their entirety, as if fully set forth herein.
This working example demonstrates the novel identification and characterization of pathways involving RORγ in pancreatic cancer. This working example further demonstrates that pharmacologic blockade of RORγ using SR2211, an inhibitor of RORγ, can effectively inhibit pancreatic cancer growth both in vitro and in vivo. Collectively, the data demonstrate that the RORγ pathway presents novel molecular targets for the treatment of cancer and may lead to the development of new classes of therapeutics that can be used in cancer treatment.
A. Transcriptomic and Epigenetic Map of Pancreatic Cancer Cells Reveals a Unique Stem Cell State
The KPf/fC mouse model of pancreatic ductal adenocarcinoma (PDAC) was used to show that a reporter mouse designed to mirror expression of the stem cell signal Musashi (Msi) could effectively identify tumor cells that preferentially harbor capacity for drug resistance and tumor re-growth. Further, Msi2+ tumor cells were 209-fold enriched in the ability to give rise to organoids in limiting dilution assays (
To map the functional landscape of the stem cell state, a combination of RNA-seq, ChIP-seq and genome-wide CRISPR screening was utilized. Pancreatic cancer cells were isolated from Msi2-reporter (REM2) KPf/fC mice based on GFP and EpCAM expression and analyzed by RNA-seq (
Consistent with the significant molecular differences found in stem cells by transcriptomic analysis, the distribution of H3 lysine-27 acetylation (H3K27ac,
B. Genome-Scale CRISPR Screen Identifies Core Functional Programs in Pancreatic Cancer
In some embodiments, a genome-wide CRISPR screen was carried out to define which of the programs uncovered by the transcriptional and epigenetic analyses represented true functional dependencies of stem cells. Primary cell cultures highly enriched for stem cells (
Computational integration of the transcriptomic and CRISPR-based functional genomic data was carried out using a network propagation method similar to one developed previously. First, the network was seeded with genes that were preferentially enriched in stem cells RNAseq log FC>2 and also identified as essential for stem cell growth FDR<0.5 in 3-dimensional sphere cultures in the CRISPR assay (
C. Hijacked Immunorequlatory Programs as Direct Regulators of Pancreatic Cancer Cells
Ultimately the power of such a map is the ability to provide a systems level view of new dependencies. Thus, in some embodiments, the network map was used as a framework to select an integrated gene set based on the transcriptomic, epigenomic and the CRISPR functional genomic analysis (Table 1). Selected genes were subsequently inhibited via viral shRNA delivery into KPf/fC cells, and the impact on pancreatic cancer propagation assessed by stem cell sphere assays in vitro or by tracking tumor growth in vivo. For example, while many genes within the pluripotency and developmental core program were known to be important in pancreatic cancer (e.g., elements of the Wnt, Hedgehog and Hippo pathways), others had not yet been explored, and presented new opportunities for discovery (
An unexpected discovery from this map was the identification of immune pathways/cytokine signaling as a core program. In line with this, retrospective analysis of the RNA-seq and ChIP-seq analysis revealed that multiple immuno-regulatory cytokine receptors and their associated ligands were expressed in tumor epithelial cells, both in stem and non-stem cells (
D. RORγ, a Mediator of T Cell Fate, is a Critical Dependency in Pancreatic Cancer
In some embodiments, to understand how the gene networks defined above are controlled, transcription factors were focused on because of their powerful role in regulating broad hierarchical programs key to cell fate and identity. Of the 53 transcription factors identified within the map, 12 were found to be enriched in stem cells by transcriptomic and epigenetic parameters (
RORγ was an unanticipated dependency as it is a nuclear hormone receptor that has been predominantly studied in the context of Th17 cell differentiation as well as lipid and glucose metabolism in the context of circadian rhythm. Consistent with this, it mapped to both the hijacked cytokine signaling/immune subnetwork and the nuclear receptor/metabolism subnetwork (
To define the transcriptional programs RORγ controls in pancreatic cancer cells, a combination of ChIP-seq and RNA-seq was used to map the molecular changes triggered by RORγ loss. Loss of RORγ led to extensive modifications in transcriptional programs key to driving cancer growth, including stem cell signals such as Wnt, BMP, and Fox (
The finding that RORγ is a key dependency in pancreatic cancer was important, as multiple inhibitors have been developed to target this pathway in autoimmune disease. Pharmacologic blockade of RORγ using the inverse agonist SR2211 decreased sphere and organoid formation in both KPf/fC and KPR172H/+C cells (
To visualize whether RORγ blockade impacts tumor progression by targeting stem cells, SR2211 was delivered in REM2-KPf/fC mice with late-stage autochthonous tumors and responses were subsequently tracked via live imaging. In vehicle-treated mice, large stem cell clusters could be readily identified throughout the tumor based on GFP expression driven by the Msi reporter (
Since treatment with the inhibitor in immunocompetent mice or in patients in vivo could have an impact on both cancer cells and immune cells, such as Th17 cells, the effect of SR2211 was tested in immunocompromised mice. As shown in
To further explore the functional relevance of RORγ to human pancreatic cancer, RORγ was inhibited both genetically and through pharmacologic inhibitors in human PDAC cells. CRISPR based disruption of RORγ using 5 independent guides led to a ˜3 to 9-fold loss of colony formation (
Finally, to determine whether expression of RORγ could serve as a prognostic for specific clinicopathologic features, RORγ immunohistochemistry was performed on tissue microarrays from a clinically annotated retrospective cohort of 116 PDAC patients (Table 3). For 69 patients, matched pancreatic intraepithelial neoplasia (PanIN) lesions were available. RORγ protein was detectable (cytoplasmic expression only/low or cytoplasmic and nuclear expression/high,
The most common outcome for pancreatic cancer patients following a response to cytotoxic therapy is not cure, but eventual disease progression and death driven by drug resistant stem cell-enriched populations. The presently disclosed technology has allowed one to develop a comprehensive molecular map of the core dependencies of pancreatic cancer stem cells by integrating their epigenetic, transcriptomic and functional genomic landscape. The data thus provide a novel resource for understanding therapeutic resistance and relapse, and for discovering new vulnerabilities in pancreatic cancer. As an example, the MEGF family of orphan receptors represent a potentially actionable family of adhesion GPCRs, as this class of signaling receptors have been considered druggable in cancer and other diseases. Importantly, the presently disclosed epigenetic analyses revealed a significant relationship between super-enhancer-associated genes and functional dependencies in stem cell conditions; stem cell-unique super-enhancer associated genes were more likely to drop out in the CRISPR screen in stem cell conditions compared to super-enhancer associated genes in non-stem cells (
The presently disclosed screens identified an unexpected dependence of KPf/fC stem cells on inflammatory and immune mediators, such as the CSF1R/IL-34 axis and IL-10R signaling. While these have been previously thought to act primarily on immune cells in the microenvironment, the data presented here suggest that stem cells may have evolved to co-opt this cytokine-rich milieu, allowing them to resist effective immune-based elimination. These findings also suggest that agents targeting CSF1R, which are under investigation for pancreatic cancer, may act not only on the tumor microenvironment but also directly on pancreatic epithelial cells themselves. These data also raise the possibility that therapies designed to activate the immune system to attack tumors may have effects on tumor cells directly: just as chemotherapy can kill tumor cells but may also impair the immune system, therapies designed to activate the immune system such as IL-10 may also promote the growth of tumor cells. This dichotomy of action will need to be considered in order to better optimize immunomodulatory treatment strategies.
A major new discovery driven by the network map was the identification of RORγ as a key immuno-regulatory pathway hijacked in pancreatic cancer. This together with the implication of RORγ in prostate cancer models suggests that this pathway may not be restricted to pancreatic cancer but may be more broadly utilized in other epithelial cancers. Interestingly, while cytokines such as IL17, IL21, IL22, and CSF2 are known targets of RORγ in Th17 cells, none of these were downregulated in RORc-deficient pancreatic tumor cells. The fact that RORγ regulated potent oncogenes marked by super-enhancers in stem cells, suggest it may be critical for defining the stem cell state in pancreatic cancer. In addition, the network of genes impacted by RORγ inhibition included other immune-modulators such as CD47, raising the possibility that it may also mediate interaction with the surrounding niche and immune system cells. Finally, one particularly exciting aspect of this work is the possibility that RORγ represents a potential therapeutic target for pancreatic cancer. Given that inhibitors of RORγ are currently in Phase II trials for autoimmune diseases, repositioning these agents as pancreatic cancer therapies warrants further investigation.
E. Experimental Model, Subject, and Method Details
Mice
REM2 (Msi2eGFP/+) reporter mice were generated as previously described (Fox et al., 2016); all of the reporter mice used in experiments were heterozygous for the Msi2 allele. The LSL-KrasG12D mouse, B6.129S4-Krastm4Tyj/J (Stock No: 008179), the p53flox/flox mouse, B6.129P2-Trp53tm1Brn/J (Stock No: 008462), and the RORγ-knockout mouse (Stock No: 007571), were purchased from The Jackson Laboratory. Dr. Chris Wright provided Ptf1a-Cre mice as previously described (Kawaguchi et al., 2002). LSL-R172H mutant p53, Trp53R172H mice were provided by Dr. Tyler Jacks as previously described (Olive et al., 2004) (JAX Stock No: 008183). The mice listed above are immunocompetent, with the exception of RORγ-knockout mice which are known to lack TH17 T-cells as described previously (Ivanov et al., 2006); these mice were maintained on antibiotic water (sulfamethoxazole and trimethoprim) when enrolled in flank transplantation and drug studies as outlined below. Immune compromised NOD/SCID (NOD.CB17-Prkdcscid/J, Stock No: 001303) and NSG (NOD.Cg-PrkdcscidIL2rgtm1Wji/SzJ, Stock No: 005557) mice purchased from The Jackson Laboratory. All mice were specific-pathogen free and bred and maintained in the animal care facilities at the University of California San Diego. Animals had access to food and water ad libitum and were housed in ventilated cages under controlled temperature and humidity with a 12-hour light-dark cycle. All animal experiments were performed according to protocols approved by the University of California San Diego Institutional Animal Care and Use Committee. No sexual dimorphism was noted in all mouse models. Therefore, males and females of each strain were equally used for experimental purposes and both sexes are represented in all data sets. All mice enrolled in experimental studies were treatment-naïve and not previously enrolled in any other experimental study.
Both REM2-KPf/fC and WT-KPf/fC mice (REM2; LSL-KraGG12D/+; Trp53f/f; Ptf1a-Cre and LSL-KrasG12D/+; Trp53f/f; Ptf1a-Cre respectively) were used for isolation of tumor cells, establishment of primary mouse tumor cell and organoid lines, and autochthonous drug studies as described below. REM2-KPf/fC and KPf/fC mice were enrolled in drug studies between 8 to 11 weeks of age and were used for tumor cell sorting and establishment of cell lines when they reached end-stage disease between 10 and 12 weeks of age. REM2-KPf/fC mice were used for in vivo imaging studies between 9.5-10.5 weeks of age. KPR172HC (LSL-KrasG12D/+; Trp53R172h/+; Ptf1a-Cre) mice were used for cell sorting and establishment of tumor cell lines when they reached end-stage disease between 16-20 weeks of age. In some studies, KPf/fC-derived tumor cells were transplanted into the flanks of immunocompetent littermates between 5-8 weeks of age. Littermate recipients (WT or REM2-LSL-KrasG12D/+; Trp53f/f or Trp53f/f mice) do not develop disease or express Cre. NOD/SCID and NSG mice were enrolled in flank transplantation studies between 5 to 8 weeks of age; KPf/fC derived cell lines and human FG cells were transplanted subcutaneously for tumor propagation studies in NOD/SCID recipients and patient-derived xenografts and KPf/fC derived cell lines were transplanted subcutaneously in NSG recipients as described in detail below.
Human and Mouse Pancreatic Cancer Cell Lines
Mouse primary pancreatic cancer cell lines and organoids were established from end-stage, treatment-naïve KPR172HC and WT- and REM2-KPf/fC mice as follows: tumors from endpoint mice (10-12 weeks of age for KPf/fC or 16-20 weeks of age for KPR172HC mice) were isolated and dissociated into single cell suspension as described below. Cells were then either plated in 3D sphere or organoid culture conditions detailed below or plated in 2D in 1× DMEM containing 10% FBS, 1× pen/strep, and 1× non-essential amino acids. At the first passage in 2D, cells were collected and resuspended in HBSS (Gibco, Life Technologies) containing 2.5% FBS and 2 mM EDTA, then stained with FC block followed by 0.2 μg/106 cells anti-EpCAM APC (eBioscience). EpCAM+ tumor cells were sorted then re-plated for at least one additional passage. To evaluate any cellular contamination and validate the epithelial nature of these lines, cells were analyzed by flow cytometry again at the second passage for markers of blood cells (CD45-PeCy7, eBioscience), endothelial cells (CD31-PE, eBioscience), and fibroblasts (PDGFR-PacBlue, Biolegend). Cell lines were derived from both female and male KPR172HC and WT- and REM2-KPf/fC mice equivalently; both sexes are equally represented in the cell-based studies outlined below. Functional studies were performed using cell lines between passage 2 and passage 6. Human FG cells were originally derived from a PDAC metastasis and have been previously validated and described (Morgan et al., 1980). Patient-derived xenograft cells and organoids were derived from originally-consented (now deceased) PDAC patients and use was approved by UCSD's IRB; cells were de-identified and therefore no further information on patient status, treatment or otherwise, is available. FG cell lines were cultured in 2D conditions in lx DMEM (Gibco, Life Technologies) containing 10% FBS, 1× pen/strep (Gibco, Life Technologies), and 1× non-essential amino acids (Gibco, Life Technologies). 3D in vitro culture conditions for all cells and organoids are detailed below.
Patient Cohort for PDAC Tissue Microarray
The PDAC patient cohort and corresponding TMAs used for RORγ immunohistochemical staining and analysis have been reported previously (Wartenberg et al., 2018). Patient characteristics are detailed in Table 3. Briefly, a total of 4 TMAs with 0.6 mm core size was constructed: three TMAs for PDACs, with samples from the tumor center and invasive front (mean number of spots per patient: 10.5, range: 2-27) and one TMA for matching PanINs (mean number of spots per patient: 3.7, range: 1-6). Tumor samples from 116 patients (53 females and 63 males; mean age: 64.1 years, range: 34-84 years) with a diagnosis of PDAC were included. Matched PanIN samples were available for 69 patients. 99 of these patients received some form of chemotherapy; 14 received radiotherapy. No sexual dimorphism was observed in any of the parameters assessed, including overall survival (p=0.227), disease-free interval (p=0.3489) or RORγ expression in PDAC (p=0.9284) or PanINs (p=0.3579). The creation and use of the TMAs were reviewed and approved by the Ethics Committee at the University of Athens, Greece, and the University of Bern, Switzerland, and included written informed consent from the patients or their living relatives.
Tissue Dissociation, Cell Isolation, and FACS Analysis
Mouse pancreatic tumors were washed in MEM (Gibco, Life Technologies) and cut into 1-2 mm pieces immediately following resection. Tumor pieces were collected into a 50 ml Falcon tube containing 10 ml Gey's balanced salt solution (Sigma), 5 mg Collagenase P (Roche), 2 mg Pronase (Roche), and 0.2 μg DNAse I (Roche). Samples were incubated for 20 minutes at 37° C., then pipetted up and down 10 times and returned to 37° C. After 15 more minutes, samples were pipetted up and down 5 times, then passaged through a 100 μm nylon mesh (Corning). Red blood cells were lysed using RBC Lysis Buffer (eBioscience) and the remaining tumor cells were washed, then resuspended in HBSS (Gibco, Life Technologies) containing 2.5% FBS and 2 mM EDTA for staining, FACS analysis, and cell sorting. Analysis and cell sorting were carried out on a FACSAria III machine (Becton Dickinson), and data were analyzed with FlowJo software (Tree Star). For analysis of cell surface markers by flow cytometry, 5×105 cells were resuspended in HBSS containing 2.5% FBS and 2 mM EDTA, then stained with FC block followed by 0.5 μl of each antibody. For intracellular staining, cells were fixed and permeabilized using the BrdU flow cytometry kit (BD Biosciences); Annexin V apoptosis kit was used for analysis of apoptotic cells (eBioscience). The following rat antibodies were used: anti-mouse EpCAM-APC (eBioscience), anti-mouse CD133-PE (eBioscience), anti-mouse CD45-PE and PE/Cy7 (eBioscience), anti-mouse CD31-PE (BD Bioscience), anti-mouse Gr-1-FITC (eBioscience), anti-mouse F4/80-PE (Invitrogen), anti-mouse CD11b-APC (Affymetrix), anti-mouse CD11c-BV421 (Biolegend), anti-mouse CD4-FITC (eBioscience) and CD4-Pacific blue (Bioglegend), anti-mouse CD8-PE (eBioscience), anti-mouse IL-17-APC (Biolegend), anti-mouse BrdU-APC (BD Biosciences), and anti-mouse Annexin-V-APC (eBioscience). Propidium-iodide (Life Technologies) was used to stain for dead cells.
In Vitro Growth Assays
Described below are the distinct growth assays used for pancreatic cancer cells. Colony formation is an assay in Matrigel (thus adherent/semi-adherent conditions), while tumorsphere formation is an assay in non-adherent conditions. Cell types from different sources grow better in different conditions. For example, the murine KPR172H/+C and the human FG cell lines grow much better in Matrigel, while KPf/fC cell lines often grow well in non-adherent, sphere conditions (though they can also grow in Matrigel).
Pancreatic Tumorsphere Formation Assay
Pancreatic tumorsphere formation assays were performed and modified from (Rovira et al., 2010). Briefly, low-passage (<6 passages) WT or REM2-KPf/fC cell lines were infected with lentiviral particles containing shRNAs; positively infected (red) cells were sorted 72 hours after transduction. 100-300 infected cells were suspended in tumorsphere media: 100 μl DMEM F-12 (Gibco, Life Technologies) containing 1× B-27 supplement (Gibco, Life Technologies), 3% FBS, 100 μM B-mercaptoethanol (Gibco, Life Technologies), 1× non-essential amino acids (Gibco, Life Technologies), 1× N2 supplement (Gibco, Life Technologies), 20 ng/ml EGF (Gibco, Life Technologies), 20 ng/ml bFGF2 (Gibco, Life Technologies), and 10 ng/ml ESGRO mLIF (Thermo Fisher). Cells in media were plated in 96-well ultra-low adhesion culture plates (Costar) and incubated at 37° C. for 7 days. KPf/fC in vitro tumorsphere formation studies were conducted at a minimum of n=3 independent wells per cell line across two independent shRNA of n=3 wells; however, the majority of these experiments were additionally completed in >1 independently-derived cell lines n=3, at n=3 wells per shRNA.
Matrigel Colony Assay
For FG and KPR172H/+C cells, 300-500 cells were resuspended in 50 μl tumorsphere media as described below, then mixed with Matrigel (BD Biosciences, 354230) at a 1:1 ratio and plated in 96-well ultra-low adhesion culture plates (Costar). After incubation at 37° C. for 5 min, 50 μl tumorsphere media was placed over the Matrigel layer. Colonies were counted 7 days later. For RORγ inhibitor studies, SR2211 or vehicle was added to cells in tumorsphere media, then mixed 1:1 with Matrigel and plated. SR2211 or vehicle was also added to the media that was placed over the solidified Matrigel layer. For FG colony formation, n=5 independent wells across 5 independent CRISPR sgRNA and two independent non-targeting gRNA. KPR172H/+C cells were plated at n=3 wells per shRNA from one cell line.
Organoid Culture Assays
Tumors from 10-12 week old end stage REM2-KPf/fC mice were harvested and dissociated into a single cell suspension as described above. Tumor cells were stained with FC block then 0.2 μg/106 cells anti-EpCAM APC (eBioscience). Msi2+/EpCAM+ (stem) and Msi2−/EpCAM+ (non-stem) cells were sorted, resuspended in 20 μl Matrigel (BD Biosciences, 354230). For limiting dilution assay, single cells were resuspended in matrigel at the indicated numbers from 20,000 to 10 cells/20 μL and were plated as a dome in a pre-warmed 48 well plate. After incubation at 37° C. for 5 min, domes were covered with 300 μl PancreaCult Organoid Growth Media (Stemcell Technologies). Organoids were imaged and quantified 6 days later. Limiting dilution analysis for stemness assessment was performed using web based-extreme limiting dilution analysis (ELDA) software (Hu and Smyth, 2009). Msi2+/EpCAM+ (stem) and Msi2−/EpCAM+ (non-stem) organoids were derived from n=3 independent mice and plated at the indicated cell numbers.
Organoids from REM2-KPf/fC were passaged at ˜1:2 as previously described (Boj et al., 2015). Briefly, organoids were isolated using Cell Recovery Solution (Corning 354253), then dissociated using Accumax Cell Dissociation Solution (Innovative Cell Technologies AM105), and plated in 20 μl matrigel (BD Biosciences, 354230) domes on a pre-warmed 48-well plate. After incubation at 37° C. for 5 min, domes were covered with 300 μl PancreaCult Organoid Growth Media (Stemcell Technologies). SR2211 (Cayman Chemicals 11972) was resuspended in DMSO at 20 mg/ml, diluted 1:10 in 0.9% NaCl containing 0.2% acetic acid, and further diluted in PancreaCult Organoid Media (Stemcell Technologies) to the indicated dilutions. Organoids were grown in the presence of vehicle or SR2211 for 4 days, then imaged and quantified, n=3 independent wells plated per dose per treatment group.
Primary patient organoids were established and provided by Dr. Andrew Lowy. Briefly, patient-derived xenografts were digested for 1 hour at 37° C. in RPMI containing 2.5% FBS, 5 mg/ml Collagenase II, and 1.25 mg/ml Dispase II, then passaged through a 70 μM mesh filter. Cells were plated at a density of 1.5×105 cells per 50 μl Matrigel. After domes were solidified, growth medium was added as follows: RPMI containing 50% Wnt3a conditioned media, 10% R-Spondinl-conditioned media, 2.5% FBS, 50 ng/ml EGF, 5 mg/ml Insulin, 12.5 ng/ml hydrocortisone, and 14 μM Rho Kinase Inhibitor. After establishment, organoids were passaged and maintained as previously described (Boj et al., 2015). Briefly, organoids were isolated using Cell Recovery Solution (Corning 354253), then dissociated into single cell suspensions with TrypLE Express (ThermoFisher 12604) supplemented with 25 μg/ml DNase I (Roche) and 14 μM Rho Kinase Inhibitor (Y-27632, Sigma). Cells were split 1:2 into 20 μl domes plated on pre-warmed 48 well plates. Domes were incubated at 37° C. for 5 min, then covered with human complete organoid feeding media (Boj et al., 2015) without Wnt3a-conditioned media. SR2211 was prepared as described above, added at the indicated doses, and refreshed every 3 days. Organoids were grown in the presence of vehicle or SR2211 for 7 days, then imaged and quantified, n=3 independent wells plated per dose per treatment group. All images were acquired on a Zeiss Axiovert 40 CFL. Organoids were counted and measured using ImageJ 1.51s software.
Flank Tumor Transplantation Studies
For the flank transplantation studies outlined below, investigators blinded themselves when possible to the assigned treatment group of each tumor for analysis; mice were de-identified after completion of flow cytometry analysis. The number of tumors transplanted for each study is based on past experience with studies of this nature, where a group size of 10 is sufficient to determine if pancreatic cancer growth is significantly affected when a regulatory signal is perturbed (see Fox et al., 2016).
For shRNA-infected pancreatic tumor cell propagation in vivo, cells were infected with lentiviral particles containing shRNAs and positively infected (red) cells were sorted 72 hours after transduction. 1000 low passage, shRNA-infected KPf/fC, or 2×105 shRNA-infected FG cells were resuspended in 50 μl culture media, then mixed 1:1 with matrigel (BD Biosciences). Cells were injected subcutaneously into the left or right flank of 5-8 week-old NOD/SCID recipient mice. Subcutaneous tumor dimensions were measured with calipers 1-2× weekly for 6-8 weeks, and two independent transplant experiments were conducted for each shRNA at n=4 independent tumors per group.
For drug-treated KPf/fC flank tumors, 2×104 low passage REM2-KPf/fC tumor cells were resuspended in 50 μl culture media, then mixed 1:1 with matrigel (BD Biosciences). Cells were injected subcutaneously into the left or right flank of 5-8 week-old non-tumor bearing, immunocompetent littermates or NSG mice. Tumor growth was monitored twice weekly; when tumors reached 0.1-0.3 cm3, mice were randomly enrolled in treatment groups and were treated for 3 weeks as described below. After 3 weeks of therapy, tumors were removed, weighed, dissociated, and analyzed by flow cytometry. Tumor volume was calculated using the standard modified ellipsoid formula ½ (Length×Width2); n=2-4 tumors per treatment group in immunocompetent littermate recipients and n=4-6 tumors per treatment group in NSG recipients.
For chimeric transplantation studies, 2×104 low passage REM2-KPf/fC tumor cells were resuspended in 50 μl culture media, then mixed 1:1 with matrigel (BD Biosciences). Cells were injected subcutaneously into the left or right flank of 5-8 week-old RORγ-knockout or wild-type recipients; recipient mice were maintained on antibiotic water (sulfamethoxazole and trimethoprim). Tumor growth was monitored twice weekly; when tumors reached 0.1-0.3 cm3, mice were randomly enrolled in treatment groups and were treated for 3 weeks as described below. After 3 weeks of therapy, tumors were removed, weighed, dissociated, and analyzed by flow cytometry. Tumor volume was calculated using the standard modified ellipsoid formula ½ (Length×Width2); n=5-7 tumors per treatment group.
For drug-treated human pancreatic tumors 2×104 human pancreatic FG cancer cells or 2×106 patient-derived xenograft cells were resuspended in 50 μl culture media, then mixed 1:1 with matrigel (BD Biosciences). Cells were injected subcutaneously into the left or right flank of 5-8 week-old NSG recipient mice. Mice were randomly enrolled in treatment groups and were treated for 3 weeks as described below. After 3 weeks of therapy, tumors were removed, weighed, and dissociated. Subcutaneous tumor dimensions were measured with calipers 1-2× weekly. Tumor volume was calculated using the standard modified ellipsoid formula ½ (Length×Width2); at minimum n=4 tumors per treatment group.
In Vivo and In Vitro Drug Therapy
The RORγ inverse agonists SR2211 (Cayman Chemicals, 11972, or Tocris, 4869) was resuspended in DMSO at 20 mg/ml or 50 mg/ml, respectively, then mixed 1:20 in 8% Tween80-PBS prior to use. Gemcitabine (Sigma, G6423) was resuspended in H2O at 20 mg/ml. For in vitro drug studies, low passage (<6 passage) WT- or REM2-KPf/fC cells, (<10 passage) KPR172H/+C cells, or FG cells were plated in non-adherent tumorsphere conditions or Matrigel colony conditions for 1 week in the presence of SR2211 or vehicle. For KPf/fC littermate, NSG mice, and RORγ-knockout mice bearing KPf/fC-derived flank tumors and for NSG mice bearing flank patient-derived xenograft tumors, mice were treated with either vehicle (PBS) or gemcitabine (25 mg/kg i.p., 1× weekly) alone or in combination with vehicle (5% DMSO, 8% Tween80-PBS) or SR2211 (10 mg/kg i.p., daily) for 3 weeks. RORγ-knockout mice and paired wild-type littermates were maintained on antibiotic water (sulfamethoxazole and trimethoprim). For NOD/SCID mice bearing flank FG tumors, mice were treated with either vehicle (5% DMSO in corn oil) or SR2211 (10 mg/kg i.p., daily) for 2.5 weeks. All flank tumors were measured 2× weekly and mice were sacrificed if tumors were >2 cm3, in accordance with IACUC protocol. For KPf/fC autochthonous survival studies, 8 week old tumor-bearing KPf/fC mice were enrolled in either vehicle (10% DMSO, 0.9% NaCl with 0.2% acetic acid) or SR2211 (20 mg/kg i.p., daily) treatment groups, and treated until moribund, where n=4 separate mice per treatment group. For all drug studies, tumor-bearing mice were randomly assigned into drug treatment groups; treatment group size was determined based on previous studies (Fox et al., 2016).
Immunofluorescence Staining
Pancreatic cancer tissue from KPf/fC mice was fixed in Z-fix (Anatech Ltd, Fisher Scientific) and paraffin embedded at the UCSD Histology and Immunohistochemistry Core at The Sanford Consortium for Regenerative Medicine according to standard protocols. 5 μm sections were obtained and deparaffinized in xylene. The human pancreas paraffin embedded tissue array was acquired from US Biomax, Inc (BIC14011a). For paraffin embedded mouse and human pancreas tissues, antigen retrieval was performed for 40 minutes in 95-100° C. 1× Citrate Buffer, pH 6.0 (eBioscience). Sections were blocked in PBS containing 0.1% Triton X100 (Sigma-Aldrich), 10% Goat Serum (Fisher Scientific), and 5% bovine serum albumin (Invitrogen).
KPf/fC cells and human pancreatic cancer cell lines were suspended in DMEM (Gibco, Life Technologies) supplemented with 50% FBS and adhered to slides by centrifugation at 500 rpm. 24 hours later, cells were fixed with Z-fix (Anatech Ltd, Fisher Scientific), washed in PBS, and blocked with PBS containing 0.1% Triton X-100 (Sigma-Aldrich), 10% Goat serum (Fisher Scientific), and 5% bovine serum albumin (Invitrogen). All incubations with primary antibodies were carried out overnight at 4° C. Incubation with Alexafluor-conjugated secondary antibodies (Molecular Probes) was performed for 1 hour at room temperature. DAPI (Molecular Probes) was used to detect DNA and images were obtained with a Confocal Leica TCS SP5 II (Leica Microsystems). The following primary antibodies were used: chicken anti-GFP (Abcam, ab13970) 1:500, rabbit anti-RORγ (Thermo Fisher, PA5-23148) 1:500, mouse anti-E-Cadherin (BD Biosciences, 610181) 1:500, anti-Keratin (Abcam, ab8068) 1:15, anti-Hmga2 (Abcam. Ab52039) 1:100, anti-Celsr1 (EMD Millipore abt119) 1:1000, anti-Celsr2 (BosterBio A06880) 1:250.
Tumor Imaging
9.5-10.5 week old REM2-KPf/fC mice were treated either vehicle or SR2211 (10 mg/kg i.p., daily) for 8 days. For imaging, mice were anesthetized by intraperitoneal injection of ketamine and xylazine (100/20 mg/kg). In order to visualize blood vessels and nuclei, mice were injected retro-orbitally with AlexaFluor 647 anti-mouse CD144 (VE-cadherin) antibody and Hoechst 33342 immediately following anesthesia induction. After 25 minutes, pancreatic tumors were removed and placed in HBSS containing 5% FBS and 2 mM EDTA. 80-150 μm images in 1024×1024 format were acquired with an HCX APO L20× objective on an upright Leica SP5 confocal system using Leica LAS AF 1.8.2 software. GFP cluster sizes were measure using ImageJ 1.51s software. 2 mice per treatment group were analyzed in this study; 6-10 frames were analyzed per mouse.
Analysis of Tissue Microarrays, Immunohistochemistry (IHC) and Staining Analysis
TMAs were sectioned to 2.5 μm thickness. IHC staining was performed on a Leica BOND RX automated immunostainer using BOND primary antibody diluent and BOND Polymer Refine DAB Detection kit according to the manufacturer's instructions (Leica Biosystems). Pre-treatment was performed using citrate buffer at 100° C. for 30 min, and tissue was stained using rabbit anti-human RORγ(t) (polyclonal, #PA5-23148, Thermo Fisher Scientific) at a dilution of 1:4000. Stained slides were scanned using a Pannoramic P250 digital slide scanner (3DHistech). RORγ(t) staining of individual TMA spots was analyzed in an independent and randomized manner by two board-certified surgical pathologists (C.M.S and M.W.) using Scorenado, a custom-made online digital TMA analysis tool. Interpretation of staining results was in accordance with the “reporting recommendations for tumor marker prognostic studies” (REMARK) guidelines. Equivocal and discordant cases were re-analyzed jointly to reach a consensus. RORγ(t) staining in tumor cells was classified microscopically as 0 (absence of any cytoplasmic or nuclear staining), 1+ (cytoplasmic staining only), and 2+ (cytoplasmic and nuclear staining). For patients in whom multiple different scores were reported, only the highest score was used for further analysis. Spots/patients with no interpretable tissue (less than 10 intact, unequivocally identifiable tumor cells) or other artifacts were excluded.
Statistical Analysis of TMA Data
Descriptive statistics were performed for patients' characteristics. Frequencies, means, and range values are given. Association of RORγ(t) expression with categorical variables was performed using the Chi-square or Fisher's Exact test, where appropriate, while correlation with continuous values was tested using the non-parametric Kruskal-Wallis or Wilcoxon test. Univariate survival time differences were analyzed using the Kaplan-Meier method and log-rank test. All p-values were two-sided and considered significant if <0.05.
shRNA Lentiviral Constructs and Production
Short hairpin RNA (shRNA) constructs were designed and cloned into pLV-hU6-mPGK-red vector by Biosettia. Virus was produced in 293T cells transfected with 4 μg shRNA constructs along with 2 μg pRSV/REV, 2 μg pMDLg/pRRE, and 2 μg pHCMVG constructs (Dull et al., 1998; Sena-Esteves et al., 2004). Viral supernatants were collected for two days then concentrated by ultracentrifugation at 20,000 rpm for 2 hours at 4° C. Knockdown efficiency for the shRNA constructs used in this study varied from 45-95%.
RT-qPCR Analysis
RNA was isolated using RNeasy Micro and Mini kits (Qiagen) and converted to cDNA using Superscript III (Invitrogen). Quantitative real-time PCR was performed using an iCycler (BioRad) by mixing cDNAs, iQ SYBR Green Supermix (BioRad) and gene specific primers. Primer sequences are available in Table 4. All real time data was normalized to B2M or Gapdh.
Genome-Wide Profiling and Bioinformatic Analysis, Primary Msi2+ and Msi2− KPf/fC RNA-seq, Data Analysis, and Visualization, Stem and Non-Stem Tumor Cell Isolation Followed by RNA-Sequencing
Tumors from three independent 10-12 week old REM2-KPf/fC mice were harvested and dissociated into a single cell suspension as described above. Tumor cells were stained with FC block then 0.2 μg/106 cells anti-EpCAM APC (eBioscience). 70,00-100,00 Msi2+/EpCAM+ (stem) and Msi2−/EpCAM+ (non-stem) cells were sorted and total RNA was isolated using RNeasy Micro kit (Qiagen). Total RNA was assessed for quality using an Agilent Tapestation, and all samples had RIN≥7.9. RNA libraries were generated from 65 ng of RNA using Illumina's TruSeq Stranded mRNA Sample Prep Kit following manufacturer's instructions, modifying the shear time to 5 minutes. RNA libraries were multiplexed and sequenced with 50 basepair (bp) single end reads (SR50) to a depth of approximately 30 million reads per sample on an Illumina HiSeq2500 using V4 sequencing chemistry.
RNA-seq Analysis
RNA-seq fastq files were processed into transcript-level summaries using kallisto (Bray et al., 2016), an ultrafast pseudo-alignment algorithm with expectation maximization. Transcript-level summaries were processed into gene-level summaries by adding all transcript counts from the same gene. Gene counts were normalized across samples using DESeq normalization (Anders and Huber 2010) and the gene list was filtered based on mean abundance, which left 13,787 genes for further analysis. Differential expression was assessed with an R package limma (Ritchie et al., 2015) applied to log2-transformed counts. Statistical significance of each test was expressed in terms of local false discovery rate lfdr (Efron and Tibshirani, 2002) using the limma function eBayes (Lönnstedt, I., and Speed, T. 2002). lfdr, also called posterior error probability, is the probability that a particular gene is not differentially expressed, given the data.
Cell State Analysis
For cell state analysis, Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) was performed with the Bioconductor GSVA (Hänzelmann et al., 2013) and the Bioconductor GSVAdata c2BroadSets gene set collection, which is the C2 collection of canonical gene sets from MsigDB3.0 (Subramanian et al., 2005). Briefly, GSEA evaluates a ranked gene expression data-set against previously defined gene sets. GSEA was performed with the following parameters: mx.diff=TRUE, verbose=TRUE, parallel.sz=1, min.sz=5, max.sz=500, rnaseq=F.
Primary Msi2+ and Msi2− KPf/fC ChIP-seq for Histone H3K27ac, Stem and Non-Stem Tumor Cell Isolation Followed by H3K27ac ChIP-Sequencing
70,000 Msi2+/EpCAM+ (stem) and Msi2−/EpCAM+ (non-stem) cells were freshly isolated from a single mouse as described above. ChIP was performed as described previously (Deshpande et al., 2014); cells were pelleted by centrifugation and crosslinked with 1% formalin in culture medium using the protocol described previously (Deshpande et al., 2014). Fixed cells were then lysed in SDS buffer and sonicated on a Covaris S2 ultrasonicator. The following settings were used: Duty factor: 20%, Intensity: 4 and 200 Cycles/burst, Duration: 60 seconds for a total of 10 cycles to shear chromatin with an average fragment size of 200-400 bp. ChIP for H3K27Acetyl was performed using the antibody ab4729 (Abcam, Cambridge, UK) specific to the H3K27Ac modification. Library preparation of eluted chromatin immunoprecipitated DNA fragments was performed using the NEBNext Ultra II DNA library prep kit (E7645S and E7600S-NEB) for Illumina as per the manufacturer's protocol. Library prepped DNA was then subjected to single-end, 75-nucleotide reads sequencing on the Illumina NexSeq500 sequencer at a sequencing depth of 20 million reads per sample.
H3K27ac Signal Quantification from ChIP-seq Data
Pre-processed H3K27ac ChIP sequencing data was aligned to the UCSC mm10 mouse genome using the Bowtie2 aligner (version 2.1.0 (Langmead and Salzberg, 2012), removing reads with quality scores of <15. Non-unique and duplicate reads were removed using samtools (version 0.1.16, Li et al., 2009) and Picard tools (version 1.98), respectively. Replicates were then combined using BEDTools (version 2.17.0). Absolute H3K27ac occupancy in stem cells and non-stem cells was determined using the SICER-df algorithm without an input control (version 1.1; (Zang et al., 2009), using a redundancy threshold of 1, a window size of 200 bp, a fragment size of 150, an effective genome fraction of 0.75, a gap size of 200 bp and an E-value of 1000. Relative H3K27ac occupancy in stem cells vs non-stem cells was determined as above, with the exception that the SICER-df-rb algorithm was used.
Determining the Overlap Between Peaks and Genomic Features
Genomic coordinates for features such as coding genes in the mouse mm10 build were obtained from the Ensembl 84 build (Ensembl BioMart). The observed vs expected number of overlapping features and bases between the experimental peaks and these genomic features (datasets A and B) was then determined computationally using a custom python script, as described in (Cole et al., 2017). Briefly, the number of base pairs within each region of A that overlapped with each region of B was computed. An expected background level of expected overlap was determined using permutation tests to randomly generate >1000 sets of regions with equivalent lengths and chromosomal distributions to dataset B, ensuring that only sequenced genomic regions were considered. The overlaps between the random datasets and experimental datasets were then determined, and p values and fold changes were estimated by comparing the overlap occurring by chance (expected) with that observed empirically (observed). This same process was used to determine the observed vs expected overlap of different experimental datasets.
RNA-Seq/ChIP-Seq Correlation, Overlap Between Gene Expression and H3K27ac Modification
Genes that were up- or down-regulated in stem cells were determined using the Cuffdiff algorithm, and H3K27ac peaks that were enriched or disfavoured in stem cells were determined using the SICER-df-rb algorithm. The H3K27ac peaks were then annotated at the gene level using the ‘ChippeakAnno’ (Zhu et al., 2010) and ‘org.Mm.eg.db’ packages in R, and genes with peaks that were either exclusively up-regulated or exclusively down-regulated (termed ‘unique up’ or ‘unique down’) were isolated. The correlation between up-regulated gene expression and up-regulated H3K27ac occupancy, or down-regulated gene expression and down-regulated H3K27ac occupancy, was then determined using the Spearman method in R.
Creation of Composite Plots
Composite plots showing RNA expression and H3K27ac signal across the length of the gene were created. Up- and down-regulated RNA peaks were determined using the FPKM output values from Tophat2 (Kim et al., 2013), and up- and down-regulated H3K27ac peaks were determined using the SICER algorithm. Peaks were annotated with nearest gene information, and their location relative to the TSS was calculated. Data were then pooled into bins covering gene length intervals of 5%. Overlapping up/up and down/down sets, containing either up- or down-regulated RNA and H3K27ac, respectively, were created, and the stem and non-stem peaks within these sets were plotted in Excel.
Super-Enhancer Identification
Enhancers in stem and non-stem cells were defined as regions with H3K27ac occupancy, as described in Hnisz et al. 2013. Peaks were obtained using the SICER-df algorithm before being indexed and converted to .gff format. H3K27ac Bowtie2 alignments for stem and non-stem cells were used to rank enhancers by signal density. Super-enhancers were then defined using the ROSE algorithm, with a stitching distance of 12.5 kb and a TSS exclusion zone of 2.5 kb. The resulting super-enhancers for stem or non-stem cells were then annotated at the gene level using the R packages ‘ChippeakAnno’ (Zhu et al., 2010) and ‘org.Mm.eg.db’, and overlapping peaks between the two sets were determined using ‘ChippeakAnno’. Super-enhancers that are unique to stem or non-stem cells were annotated to known biological pathways using the Gene Ontology (GO) over-representation analysis functionality of the tool WebGestalt (Wang et al., 2017).
Genome-Wide CRISPR Screen, CRISPR Library Amplification and Viral Preparation
The mouse GeCKO CRISPRv2 knockout pooled library (Sanjana et al., 2014) was acquired from Addgene (catalog #1000000052) as two half-libraries (A and B). Each library was amplified according to the Zhang lab library amplification protocol (Sanjana et al., 2014) and plasmid DNA was purified using NucleoBond Xtra Maxi DNA purification kit (Macherey-Nagel). For lentiviral production, 24×T225 flasks were plated with 21×106 293T each in 1× DMEM containing 10% FBS. 24 hours later, cells were transfected with pooled GeCKOv2 library and viral constructs. Briefly, media was removed and replaced with 12.5 ml warm OptiMEM (Gibco). Per plate, 200 μl PLUS reagent (Life Technologies), 10 μg library A, and 10 μg library B was mixed in 4 ml OptiMEM along with 10 μg pRSV/REV (Addgene), 10 μg pMDLg/pRRE (Addgene), and 10 μg pHCMVG (Addgene) constructs. Separately, 200 μl Lipofectamine (Life Technologies) was mixed with 4 ml OptiMEM. After 5 minutes, the plasmid mix was combined with Lipofectamine and left to incubate at room temperature for 20 minutes, then added dropwise to each flask. Transfection media was removed 22 hours later and replaced with DMEM containing 10% FBS, 5 mM MgCl2, 1 U/ml DNase (Thermo Scientific), and 20 mM HEPES pH 7.4. Viral supernatants were collected at 24 and 48 hours, passaged through 0.45 μm filter (corning), and concentrated by ultracentrifugation at 20,000 rpm for 2 hours at 4° C. Viral particles were resuspended in DMEM containing 10% FBS, 5 mM MgCl2, and 20 mM HEPES pH 7.4, and stored at −80° C.
CRISPR Screen in Primary KPf/fC Cells
3 independent primary REM2-KPf/fC cell lines were established as described above and maintained in DMEM containing 10% FBS, 1× non-essential amino acids, and 1× pen/strep. At passage 3, each cell line was tested for puromycin sensitivity and GeCKOv2 lentiviral titer was determined. At passage 5, 1.6×108 cells from each cell line were transduced with GeCKOv2 lentivirus at an MOI of 0.3. 48 hours after transduction, 1×108 cells were harvested for sequencing (“T0”) and 1.6×108 were re-plated in the presence of puromycin according to previously tested puromycin sensitivity. Cells were passaged every 3-4 days for 3 weeks; at every passage, 5×107 cells were re-plated to maintain library coverage. At 2 weeks post-transduction, cell lines were tested for sphere forming capacity. At 3 weeks, 3×107 cells were harvested for sequencing (“2D; cell essential genes”), and 2.6×107 cells were plated in sphere conditions as described above (“3D; stem cell essential genes”). After 1 week in sphere conditions, tumorspheres were harvested for sequencing.
Analysis of the 2D data sets revealed that while some genes were required for growth in 2D, other genes that were not (detectably) required for growth in 2D were still required for growth in 3D (for example, Rorc Sox4, Foxo1, Wnt1 and ROBO3). These findings suggested that growth in 3D is dependent on a distinct or additional set of pathways. Since only stem cells give rise to 3D spheres, targets within the 3D datasets were prioritized for subsequent analyses. Of the genes that significantly dropped out in 3D, some also dropped out in 2D either significantly or as a trend.
DNA Isolation, Library Preparation, and Sequencing
Cells pellets were stored at −20° C. until DNA isolation using Qiagen Blood and Cell Culture DNA Midi Kit (13343). Briefly, per 1.5×107 cells, cell pellets were resuspended in 2 ml cold PBS, then mixed with 2 ml cold buffer C1 and 6 ml cold H2O, and incubated on ice for 10 minutes. Samples were pelleted 1300×g for 15 minutes at 4° C., then resuspended in 1 ml cold buffer C1 with 3 ml cold H2O, and centrifuged again. Pellets were then resuspended in 5 ml buffer G2 and treated with 100 μl RNAse A (Qiagen 1007885) for 2 minutes at room temperature followed by 95 μl Proteinase K for 1 hour at 50° C. DNA was extracted using Genomic-tip 100/G columns, eluted in 50° C. buffer QF, and spooled into 300 μl TE buffer pH 8.0. Genomic DNA was stored at 4° C. For sequencing, gRNAs were first amplified from total genomic DNA isolated from each replicate at T0, 2D, and 3D (PCR1). Per 50 μl reaction, 4 μg gDNA was mixed with 25 μl KAPA HiFi HotStart ReadyMIX (KAPA Biosystems), 1 μM reverse primer1, and 1 μM forward primer1 mix (including staggers). Primer sequences are available upon request. After amplification (98° C. 20 seconds, 66° C. 20 seconds, 72° C. 30 seconds, ×22 cycles), 50 μl of PCR1 products were cleaned up using QIAquick PCR Purification Kit (Qiagen). The resulting ˜200 bp products were then barcoded with IIlumina Adaptors by PCR2. 5 μl of each cleaned PCR1 product was mixed with 25 μl KAPA HiFi HotStart ReadyMIX (KAPA Biostystems), 10 μl H2O, 1 μM reverse primer2, and 1 μM forward primer2. After amplification (98° C. 20 seconds, 72° C. 45 seconds, ×8 cycles), PCR2 products were gel purified, and eluted in 30 μl buffer EB. Final concentrations of the desired products were determined and equimolar amounts from each sample was pooled for Next Generation Sequencing.
Processing of the CRISPR Screen Data
Sequence read quality was assessed using fastqc (www.bioinformatics.babraham.ac.uk/proiects/fastqc/). Prior to alignment, 5′ and 3′ adapters flanking the sgRNA sequences were trimmed off using cutadapt v1.11 (Martin, 2011) with the 5′-adapter TCTTGTGGAAAGGACGAAACACCG (SEQ ID NO: 1) and the 3′ adapter GTTTTAGAGCTAGAAATAGCAAGTT (SEQ ID NO: 2), which came from the cloning protocols of the respective libraries deposited on Addgene (www.addgene.org/pooled-library/). Error tolerance for adapter identification was set to 0.25, and minimal required read length after trimming was set to 10 bp. Trimmed reads were aligned to the GeCKO mouse library using Bowtie2 in the—local mode with a seed length of 11, an allowed seed mismatch of 1 and the interval function set to ‘S,1,0.75’. After completion, alignments are classified as either unique, failed, tolerated or ambiguous based on the primary (‘AS’) and secondary (‘XS’) alignment scores reported by Bowtie2. Reads with the primary alignment score not exceeding the secondary score by at least 5 points were discarded as ambiguous matches. Read counts were normalized by using the “size-factor” method. All of this was done using implementations in the PinAPL-Py webtool, with detailed code available at github.com/LewisLabUCSD/PinAPL-Py.
gRNA Growth and Decay Analysis
A parametric method is used in which the cell population with damaged gene i grows as Ni(t)=Ni(0)e(α
Since we are interested in genes essential for growth, we performed a single-tailed test for xi. We collect the three values of xi, one from each biological replicate, into a vector xi. A statistically significant effect will have all three values large (>1) and consistent. If xi were to denote position of a point in a three-dimensional space, we would be interested in points that lie close to the body diagonal and far away from the origin. A suitable statistic is s=(x·n)2−[x−(x·n)n]2, where n=(1,1,1)/√{square root over (3)} is the unit vector in the direction of the body diagonal and · denotes scalar product. A q-value (false discovery rate) for each gene is estimated as the number of s-statistics not smaller than si expected in the null model divided by the observed number of S-statistics not smaller than si in the data. The null model is simulated numerically by permuting gene labels in xi for every experimental replicate, independently of each other, repeated 103 times.
STRING Interactome Network Analysis
The results from the CRISPR 3DV experiment were integrated with the RNA-seq results using a network approach. Likely CRISPR-essential genes were identified by filtering to include genes which had a false-discovery rate corrected p-value of less than 0.5, resulting in 94 genes. A relaxed filter was chosen here because the following filtering steps will help eliminate false positives, and the network analysis method helps to amplify weak signals. These genes were further filtered in two ways: first, we included only genes which were expressed in the RNA-seq data (this resulted in 57 genes), and second, we further restricted by genes which had enriched expression in stem cells by >2 log fold change in the RNA-seq (this resulted in 10 genes). These results are used to seed the network neighborhood exploration. We used the STRING mouse interactome as our background network, including only high confidence interactions (edge weight>700). The STRING interactome contains known and predicted functional protein-protein interactions. The interactions are assembled from a variety of sources, including genomic context predictions, high throughput lab experiments, and co-expression databases. Interaction confidence is a weighted combination of all lines of evidence, with higher quality experiments contributing more. The high confidence STRING interactome contains 13,863 genes, and 411,296 edges. Because not all genes are found in the interactome, our seed gene sets are further filtered when integrated with the network. This results in 39 CRISPR-essential, RNA-expressed seed genes, and 5 CRISPR-essential, RNA differentially-expressed seed genes. After integrating the seed genes with the background interactome, we employed a network propagation algorithm to explore the network neighborhood around these seed genes. Network propagation is a powerful method for amplifying weak signals by taking advantage of the fact that genes related to the same phenotype tend to interact. We implement the network propagation method that simulates how heat would diffuse, with loss, through the network by traversing the edges, starting from an initially hot set of ‘seed’ nodes. At each step, one unit of heat is added to the seed nodes, and is then spread to the neighbor nodes. A constant fraction of heat is then removed from each node, so that heat is conserved in the system. After a number of iterations, the heat on the nodes converges to a stable value. This final heat vector is a proxy for how close each node is to the seed set. For example, if a node was between two initially hot nodes, it would have an extremely high final heat value, and if a node was quite far from the initially hot seed nodes, it would have a very low final heat value. This process is described by the following as in (Vanunu et al., 2010):
F
t
=W′F
t−1+(1−α)Y
Where Ft is the heat vector at time t, Y is the initial value of the heat vector, W′ is the normalized adjacency matrix, and α ∈ (0,1) represents the fraction of total heat which is dissipated at every timestep. We examine the results of the subnetwork composed of the 500 genes nearest to the seed genes after network propagation. This will be referred to as the ‘hot subnetwork’. In order to identify pathways and biological mechanisms related to the seed genes, we apply a clustering algorithm to the hot subnetwork, which partitions the network into groups of genes which are highly interconnected within the group, and sparsely connected to genes in other groups. We use a modularity maximization algorithm for clustering, which has proven effective in detecting modules, or clusters, in protein-protein interaction networks. These clusters are annotated to known biological pathways using the over-representation analysis functionality of the tool WebGestalt. We use the 500 genes in the hot subnetwork as the background reference gene set. To display the networks, we use a spring-embedded layout, which is modified by cluster membership (along with some manual adjustment to ensure non-overlapping labels). Genes belonging to each cluster are laid out radially along a circle, to emphasize the within cluster and between cluster connections. VisJS2jupyter was used for network propagation and visualization. Node color is mapped to the RNAseq log fold change, with down-regulated genes displayed in blue, upregulated genes displayed in red, and genes with small fold changes displayed in gray. Labels are shown for genes which have a log fold change with absolute value greater than 3.0. Seed genes are shown as triangles with white outlines, while all other genes in the hot subnetwork are circles. The clusters have been annotated by selecting representative pathways from the enrichment analysis.
KPR172HC Single Cell Analysis
Freshly harvested tumors from two independent KPR172hC mice were subjected to mechanical and enzymatic dissociation using a Miltenyi gentleMACS Tissue Dissociator to obtain single cells. The 10× Genomics Chromium Single Cell Solution was employed for capture, amplification and labeling of mRNA from single cells and for scRNA-Seq library preparation. Sequencing of libraries was performed on a Illumina HiSeq 2500 system. Sequencing data was input into the Cell Ranger analysis pipeline to align reads and generate gene-cell expression matrices. Finally, Custom R packages were used to perform gene-expression analyses and cell clustering projected using the t-SNE (t-Distributed Stochastic Neighbor Embedding) clustering algorithm. scRNA-seq datasets from the two independent KPR127hC tumor tissues generated on 10× Genomics platform were merged and utilized to explore and validate the molecular signatures of the tumor cells under dynamic development. The tumor cells that were used to illustrate the signal of Il10rb, Il34 and Csf1r etc. were characterized from the heterogeneous cellular constituents using SuperCT method developed by Dr. Wei Lin and confirmed by the Seurat FindClusters with the enriched signal of Epcam, Krt19 and Prom1 etc. (Xie et al., 2018). The tSNE layout of the tumor cells was calculated by Seurat pipeline using the single-cell digital expression profiles.
KPf/fC Single Cell Analysis
Three age-matched KPf/fC pancreatic tumors were collected and freshly dissociated, as described above. Tumor cells were stained with rat anti-mouse CD45-PE/Cy7 (eBioscience), rat anti-mouse CD31-PE (eBioscience), and rat anti-mouse PDGFRα-PacBlue (eBioscience) and tumor cells negative for these three markers were sorted for analysis. Individual cells were isolated, barcoded, and libraries were constructed using the 10× genomics platform using the Chromium Single Cell 3′ GEM library and gel bead kit v2 per manufacturer's protocol. Libraries were sequenced on an Illumina HiSeq4000. The Cell Ranger software was used for alignment, filtering and barcode and UMI counting. The Seurat R package was used for further secondary analysis using default settings for unsupervised clustering and cell type discovery.
shRorc vs. shCtrl KPf/fC RNA-seq
Primary WT-KPf/fC cell lines were established as described above. WT-KPf/fC cells derived from an individual low passage cell line (<6 passage) were plated and transduced in triplicate with lentiviral particles containing shCtrl or shRorc. Positively infected (red) cells were sorted 5 days after transduction. Total RNA was isolated using the RNeasy Micro Plus kit (Qiagen). RNA libraries were generated from 200 ng of RNA using Illumina's TruSeq Stranded mRNA Sample Prep Kit (Illumina) following manufacturer's instructions. Libraries were pooled and single end sequenced (1×75) on the Illumina NextSeq 500 using the High output V2 kit (Illumina Inc., San Diego Calif.).
Read data was processed in BaseSpace (basespace.illumina.com). Reads were aligned to Mus musculus genome (mm10) using STAR aligner (code.google.com/p/rna-star/) with default settings. Differential transcript expression was determined using the Cufflinks Cuffdiff package (Trapnell et al., 2012) (github.com/cole-trapnell-lab/cufflinks). Differential expression data was then filtered to represent only significantly differentially expressed genes (q value<0.05). This list was used for pathway analysis and heatmaps of specific significantly differentially regulated pathways.
shRorc vs. shCtrl KPf/fC ChIP-seq for Histone H3K27ac
Primary WT-KPf/fC cell lines were established as described above. Low passage (<6 passages) WT-KPf/fC cells from two independent cell lines were plated and transduced in triplicate with lentiviral particles containing shCtrl or shRorc. Positively infected (red) cells were sorted 5 days after transduction. ChIP-seq for histone H3K27-ac, signal quantification, and determination of the overlap between peaks and genomic features was conducted as described above.
Super-enhancers in control and shRorc-treated KPf/fC cell lines as well as Musashi stem cells were determined from H3K27ac ChlPseq data using the ROSE algorithm (younglab.wi.mit.edu/super enhancer code.html). The Musashi stem cell super-enhancer peaks were then further refined to include only those unique to the stem cell state (defined as present in stem cells but not non-stem cells) and/or those with RORγ binding sites within the peaks. Peak sequences were extracted using the ‘getSeq’ function from the ‘BSGenome.MMusculus.UCSC.mm10’ R package. RORγ binding sites were then mapped using the matrix RORG_MOUSE.H10MO.C.pcm (HOCOMOCO database) as a reference, along with the ‘matchPWM’ function in R at 90% stringency. Baseline peaks were then defined for each KPf/fC cell line as those overlapping each of the four Musashi stem cell peaklists with each KPC control SE list, giving eight in total. The R packages ‘GenomicRanges’ and ‘ChIPpeakAnno’ were used to assess peak overlap with a minimum overlap of 1 bp used. To estimate the proportion of super-enhancers that are closed on RORC knockdown, divergence between each baseline condition and the corresponding KPf/fC shRorc super-enhancer list was assessed by quantifying the peak overlap and then expressing this as a proportion of the baseline list (‘shared %’). The proportion of unique peaks in each condition was then calculated as 100%-shared % and plotted.
sgRORC vs sgNT Human RNA-seq
Human FG cells were plated and transduced in triplicate with lentiviral particles containing Cas9 and non-targeting guide RNA or guide RNA against Rorc. Positively infected (green) cells were sorted 5 days after transduction. Total RNA was isolated using the RNeasy Micro Plus kit (Qiagen). RNA libraries were generated from 200 ng of RNA using Illumina's TruSeq Stranded mRNA Sample Prep Kit (Illumina) following manufacturer's instructions. Libraries were pooled and single end sequenced (1×75) on the Illumina NextSeq 500 using the High output V2 kit (Illumina Inc., San Diego Calif.).
Comparative RNA-seq and Cell State Analysis
RORC knockdown and control RNA-seq fastq files in mouse KPf/fC and human FG cells were processed into transcript-level summaries using kallisto (Bray et al., 2016). Transcript-level summaries were processed into gene-level summaries and differential gene expression was performed using sleuth with the Wald test (Pimentel et al., 2017). GSEA was performed as detailed above (Subramanian et al., 2005). Gene ontology analysis was performed using Metascape using a custom analysis with GO biological processes and default settings with genes with a FDR<5% and a beta value>0.5.
cBioportal
RORC genomic amplification data from cancer patients was collected from the Memorial Sloan Kettering Cancer Center cBioPortal for Cancer Genomics (www.cbioportal.org).
Quantification and Statistical Analysis
Statistical analyses were carried out using GraphPad Prism software version 7.0d (GraphPad Software Inc.). Sample sizes for in vivo drug studies were determined based on the variability of pancreatic tumor models used. For flank transplant and autochthonous drug studies, tumor bearing animals within each group were randomly assigned to treatment groups. Treatment sizes were determined based on previous studies (Fox et al., 2016). Data are shown as the mean±SEM. Two-tailed unpaired Student's t-tests with Welch's correction or One-way analysis of variance (ANOVA) for multiple comparisons when appropriate were used to determine statistical significance (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001).
The level of replication for each in vitro and in vivo study is noted in the figure legends for each figure and described in detail in the Method Details section above. However to summarize briefly, in vitro tumorsphere or colony formation studies were conducted with n=3 independent wells per cell line across two independent shRNA of n=3 wells; however, the majority of these experiments were additionally completed in >1 independently derived cell line, n=3 wells per shRNA. For limiting dilution assays, organoids were derived from 3 independent mice; drug-treated mouse and human organoids were plated at n=3 wells per dose per treatment condition. Flank shRNA studies were conducted twice independently, with n=4 tumors per group in each experiment. Flank drug studies were conducted at n=2-7 tumors per treatment group; autochthonous KPf/fC survival studies were conducted with a minimum of 4 mice enrolled in each treatment group. Live imaging studies were carried out with two mice per treatment group.
Statistical considerations and bioinformatic analysis of large data-sets generated are explained in great detail above. In brief, primary KPf/fC RNA-seq was performed using Msi2+ and Msi2− cells sorted independently from three different end-stage KPf/fC mice. Primary KPf/fC ChIP-seq was performed using Msi2+ and Msi2− cells sorted from an individual end-stage KPf/fC mouse. The genome-wide CRISPR screen was conducted using three biologically independent cell lines (derived from three different KPf/fC tumors). Single-cell analysis of tumors represents merged data from ˜10,000 cells across two KPR172HC and three KPf/fC mice. RNA-seq for shRorc and shCtrl KPf/fC cells was conducted in triplicate, while ChIP-seq was conducted in single replicates from two biologically independent KPf/fC cell lines.
This working example demonstrates that the RORγ pathway plays important roles in more aggressive subtypes of pancreatic cancer and can prevent cancer progression from benign to malignant state.
RORγ inhibition has been demonstrated to block growth of adenosquamous carcinoma of the pancreas (ASCP), the most aggressive subtype of pancreatic cancer. A new Msi2-CreER mouse model of aggressive pancreatic cancer was created, in which Cre is driven off of the Msi2 promoter and can be conditionally triggered by tamoxifen delivery. This Msi2-CreER driver can be crossed into mice bearing distinct mutations such as Ras (leading to myeloproliferative neoplasia), p53, or Myc. When the Msi2-CreER driver was crossed into an LSL-MyCT58A model developed by Dr. Robert Wechsler-Reya at SBP/Rady, La Jolla, Calif. (Mollaoglu et al., 2017) (
Using this model, high expression of RORγ was observed in ASCP and ACC tumors (
Moreover, RORγ inhibitor SR2211 can block the growth of benign pancreatic intraepithelial neoplasia (PanIN) lesions. The effect of SR2211 was tested on dissociated primary murine PanIN derived organoids. SR2211 reduced both organoid number and organoid volume, suggesting that RORγ inhibition may prevent cancer progression from benign to malignant state.
This working example demonstrates that RORγ also plays an important role in leukemia and presents a promising target in the treatment of leukemia potentially due to the similarities between leukemia and pancreatic cancer stem cells. The data suggests that inhibition of RORγ is effective at reducing leukemia cell growth and projects RORγ inhibitors as promising therapeutic agents for treating leukemia.
Given the common features and shared molecular dependencies between leukemia and pancreatic cancer stem cells, it was examined whether RORγ was also required for growth of aggressive leukemia, using blast crisis chronic myeloid leukemia (CML) as a model. As shown in
This working example demonstrates that RORγ also plays an important role in lung cancer, as pharmacological inhibition of RORγ by SR2211 inhibited tumor sphere formation of lung cancer cells, suggesting that therapeutic approaches targeting RORγ can be effective at treating lung cancer.
As shown in
This working example demonstrates that AZD-0284, an inhibitor of RORγ, is effective in impairing the growth of mammalian pancreatic cancer and leukemia. The results suggest that AZD-0284 can be an effective therapeutic agent for cancer treatment.
Pharmacologic blockade of RORγ using AZD-0284 in combination with gemcitabine decreased KPf/fC organoid growth (
The derived KPf/fC organoid were maintained and passaged at ˜1:2. Briefly, organoids were isolated using Cell Recovery Solution (Corning 354253), then dissociated using Accumax Cell Dissociation Solution (Innovative Cell Technologies AM105), and plated in 20 μl Matrigel (BD Biosciences, 354230) domes on a pre-warmed 48-well plate. After incubation at 37° C. for 5 min, domes were covered with 300 μl PancreaCult Organoid Growth Media (Stemcell Technologies).
The organoid forming capacity of KPf/fC cells grown in the presence of vehicle, 3 μM AZD-0284, 0.02 nM gemcitabine, or both was assessed by imaging and measurements of organoid volume (
The effect of AZD-0284 at a higher dose on KPf/fC organoid growth was also examined (
Similarly, the effects of AZD-0284 at different doses were examined on KPf/fC organoids (
Next, the impact of AZD-0284 was tested on tumor-bearing KPf/fC mice in vivo (
Moreover, the effect of AZD-0284 was assessed on primary patient-derived PDX1535 organoids (
The primary patient-derived PDX1535 organoids were grown in the presence of vehicle, 3 μM AZD-0284, 0.04 nM gemcitabine, or both (
The effect of AZD-0284 at a higher dose was also tested on primary patient-derived PDX1535 organoids (
Similarly, the effects of AZD-0284 at different doses were examined on primary patient-derived PDX1535 organoids (
Furthermore, the effect of AZD-0284 was assessed on another primary pancreatic cancer patient-derived cells, PDX1356, using the organoid assay described above (
The effect of AZD-0284 at a higher dose was also tested on primary patient-derived PDX1356 organoids (
Finally, the impact of AZD-0284 was tested on immunodeficient mice transplanted with primary patient-derived cancer cells in vivo (
Given the common features and shared molecular dependencies between leukemia and pancreatic cancer stem cells, the effect of AZD-0284 was tested on leukemia cells (
Taken together, these data show AZD-0284, an RORγ inhibitor, as a promising drug to be used in anti-cancer therapies and/or used in combination with chemotherapy medication for more effective cancer treatment in a variety of types of caners, including pancreatic cancer and leukemia.
This working example demonstrates that JTE-151, another inhibitor of RORγ, is effective in impairing the growth of mammalian pancreatic cancer in vitro and in vivo. The results show that JTE-151 can be used as an effective therapeutic agent for cancer treatment.
First, pharmacologic blockade of RORγ using JTE-151 was tested on pancreatic cell organoids as described above. Pancreatic cancer cells derived from two genetically engineered mouse models (GEMMS) were used for the organoid studies (
About 4,000 organoids from each of the non-germline and germline mouse models were plated as single cells in multi-well plates, as described above, and treated with JTE-151 for 4 days (
Next, the impact of JTE-151 was tested on tumor-bearing KPf/fC mice in vivo.
Similarly, the anti-cancer effect of JTE-151 was tested on tumor-bearing KPf/fC mice in vivo at a higher dose of 120 mg/kg (
Moreover, the anti-cancer effect of JTE-151 was determined in an organoid assay using pancreatic cancer cells derived from mice bearing primary patient-derived xenografts. A schematic of the experimental design is shown in
As shown in
As shown in
As shown in
Similarly, the effects of JTE-151 at different doses were examined on human pancreatic cancer Fast Growing (FG) cells using the organoid assay (
Finally, the impact of JTE-151 was examined in vivo on mice bearing primary patient-derived pancreatic cancer xenografts (
Two other primary patient-derived xenografts, PDX1535 (
Taken together, these data show that JTE-151 treatment blocked the growth of primary mammalian pancreatic cancer cells (human and mouse) both in vitro in organoid cultures and in vivo. Collectively, these studies demonstrate that targeting RORγ with JTE-151 is effective at blocking pancreatic cancer growth in vitro and in vivo and can potentially lead to effective new treatments for pancreatic cancer. Considering that inhibition of RORγ has been shown to reduce other types of cancer growth, including leukemia and lung cancer, JTE-151 has great potential to be used generally in anti-cancer therapies either alone or in combination with chemotherapy medication.
Table 1 shows selected genes from stem cell networks identified by enriched gene expression in stem cells (RNA seq), preferentially open (H3K27ac ChIP-seq), or essential for growth (CRISPR screens). RNA-seq: fold change indicate expression in stem/non-stem. H3K27ac ChIP-seq: up indicates H3K27ac peaks enriched in stem cells; Stem cell SE, super enhancer unique to stem cells; Shared SE, super-enhancer in both stem and non-stem cells; N.D., H3K27ac not detectecd CRISPR screens; 2D, conventional growth conditions; 3D, stem cell conditions; ✓✓✓, p<0.005; ✓, gene ranks in top 10% of depleted guides (p<0.049 for 2D, p<0.092 for 3D); -, gene not in top 10% of depleted.
Table 2 includes select novel drug targets in pancreatic cancer, and indicates the impact of target inhibition by the indicated antagonist on in vitro and in vivo pancreatic cancer cell growth. Check marks indicate the extent of growth suppression observed in the indicated assay; -, no detectable response; ND, not determined.
The references, patents and published patent applications listed below, and all references cited in the specification above are hereby incorporated by reference in their entirety, as if fully set forth herein.
Spahn, P. N., Bath, T., Weiss, R. J., Kim, J., Esko, J. D., Lewis, N. E., and Harismendy, O. (2017). PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens. Sci Rep 7, 15854.
This application claims the benefit of U.S. Provisional Patent Application No. 62/808,231 filed on Feb. 20, 2019, 62/881,890 filed on Aug. 1, 2019, 62/897,202 filed on Sep. 6, 2019, 62/903,595 filed on Sep. 20, 2019, and 62/959,607 filed on Jan. 10, 2020. The contents of these provisional applications are incorporated by reference in their entirety.
This invention was made with government support under Grant Numbers R01 CA186043 and R01 CA197699, awarded by the National Institutes of Health. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US20/19118 | 2/20/2020 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62808231 | Feb 2019 | US | |
| 62881890 | Aug 2019 | US | |
| 62897202 | Sep 2019 | US | |
| 62903595 | Sep 2019 | US | |
| 62959607 | Jan 2020 | US |
