IMMUNE RESPONSE PROFILING OF TUMOR-DERIVED EXOSOMES FOR CANCER DIAGNOSIS

Abstract

Disclosed herein are methods of (i) detecting cancer or cancer type in a subject or (ii) simultaneously testing for, or distinguishing between, multiple types of cancer in a subject; methods of screening subjects for a prevalence of cancer type or cancer types; methods of managing a subject with a cancer type; and methods of identifying whether a subject having a cancer type is responding to management of that cancer type; and methods of generating a response profile specific for a cancer type. Disclosed herein also include tumor-derived exosome-induced immune response or cancer-specific response profile created based on the measurement of functional impacts of tumor-derived exosomes on immune cells in vitro for use or when used for detecting or diagnosing cancer or cancer type in a subject; tumor-derived exosome-induced immune response for use in or when used for creating a cancer-specific response profile measuring functional impacts of tumor-derived exosomes on immune cells in vitro; and use of a tumor-derived exosome-induced immune response for generating a cancer-specific response profile measuring functional impacts of tumor-derived exosomes on immune cells in vitro. Disclosed herein also include tests, assays, kits, apparatus or devises for use or when used for the method, the response, the profile or the use as disclosed herein.

Claims

1.-46. (canceled)

47. A method of (i) detecting cancer or cancer type in at least one subject or (ii) simultaneously testing for, or distinguishing between, multiple types of cancer in at least one subject, said method comprising the step of using a cancer-specific response profile, created based on the measurement of functional impacts of tumor-derived exosomes on immune cells in vitro, to identify the cancer or the cancer type in the at least one subject, wherein said tumor-derived exosomes are isolated from the at least one subject.

48. The method of claim 47, comprising the step of screening a plurality of said at least one subject for a prevalence of cancer type or cancer types, said method comprising the step of using a cancer-specific response profile created for each said subject, created based on the measurement of functional impacts of tumor-derived exosomes on immune cells in vitro, to identify the cancer or the cancer types in each said subject, wherein said tumor-derived exosomes are isolated from the subjects.

49. A method of managing a subject with a cancer type, said method comprising the steps of: (1) using a cancer-specific response profile, created based on the measurement of functional impacts of tumor-derived exosomes on immune cells in vitro, to identify the cancer type in the subject, wherein said tumor-derived exosomes are isolated from the subject; an (2) managing the subject if the subject has been found to have the cancer type.

50. A method of identifying whether a subject having a cancer type is responding to management of that cancer type, said method comprising the steps of: (1) using a cancer-specific response profile, created based on the measurement of functional impacts of tumor-derived exosomes on immune cells in vitro, to identify the cancer type in the subject, wherein said tumor-derived exosomes are isolated from the subject; and (2) comparing the respective cancer-specific response profile created before and during and/or after management of the cancer type, wherein a change in the cancer-specific response profile identifies the subject as having responded to the management of the cancer type.

51. A method of generating a response profile specific for a cancer type, said method comprising the steps of: (1) measuring functional impacts of tumor-derived exosomes on immune cells in vitro, wherein said tumor-derived exosomes are isolated from a subject having a specific cancer type; and (2) creating a cancer-specific response profile based on the functional impact specific for the cancer type.

52. The method of claim 51, wherein said cancer-specific response profile is used in a method selected from the group consisting of: (i) detecting cancer or cancer type in at least one subject or (ii) simultaneously testing for, or distinguishing between, multiple types of cancer in at least one subject; (iii) screening a plurality of subjects for a prevalence of cancer type or cancer types; (iv) managing a subject with a cancer type, said method comprising the steps of: (1) using the cancer-specific response profile to identify the cancer type in the subject; and (2) managing the subject if the subject has been found to have the cancer type; and (v) identifying whether a subject having a cancer type is responding to management of that cancer type.

53. The method of claim 52, wherein the immune cells comprise one or more immune cells selected from the group consisting of T-cells, natural killer (NK cells), and B cells.

54. The method of claim 53, wherein the immune cells are T-cells.

55. The method of claim 52, wherein creating the cancer-specific response profile comprises measuring one or more of the following selected from the group consisting of: suppression of the function of immune cells; impairment of immune cell responses to stimulants; promotion of expansion of regulatory immune cells; induction of apoptosis of cytotoxic immune cells; and immunostimulation.

56. The method of claim 52, wherein creating the cancer-specific response profile comprises measuring immunosuppression due to one or more of the following immunoregulatory molecules selected from the group consisting of: IL-10, TGF-β, PD-1, PDL-1, TRAIL, FasL, CD39 and CD73.

57. The method of claim 52, wherein creating the cancer-specific response profile comprises measuring immunostimulatory effect due to one or more of the following molecules selected from the group consisting of: tumor antigens and heat shock proteins.

58. The method of claim 52, wherein creating the cancer-specific response profile comprises measuring at least one expression level of a marker on and/or in an immune cell.

59. The method of claim 58, wherein the marker is selected from the group consisting of: an immune cell activation marker; an immune cell proliferation marker; an immune cell exhaustion marker; an immune cell cytotoxicity marker; an immune cell cytotoxicity and apoptosis marker; and an immune cell inhibitory marker.

60. The method of claim 52, wherein the cancer is selected from the group consisting of renal carcinoma, colorectal carcinoma, skin cancer, leukemia, lymphoma, tumors of the central nervous system, breast cancer, prostate cancer, cervical cancer, uterine cancer, lung cancer, ovarian cancer, testicular cancer, thyroid cancer, astrocytoma, glioma, pancreatic cancer, mesotheliomas, gastric cancer, liver cancer, renal cancer including nephroblastoma, bladder cancer, oesophageal cancer, cancer of the larynx, cancer of the parotid, cancer of the biliary tract, endometrial cancer, adenocarcinomas, small cell carcinomas, neuroblastomas, adrenocortical carcinomas, epithelial carcinomas, desmoid tumors, desmoplastic small round cell tumors, endocrine tumors, Ewing sarcoma family tumors, germ cell tumors, hepatoblastomas, hepatocellular carcinomas, non-rhabdomyosarcome soft tissue sarcomas, osteosarcomas, peripheral primitive neuroectodermal tumors, retinoblastomas, and rhabdomyosarcomas.

61. The method of claim 52, comprising the step of comparing the created cancer-specific response profile of the subject with one or more previously created reference cancer-specific response profiles, wherein each said reference profile was created based on a subject diagnosed with a particular type of cancer.

62. The method of claim 52, comprising the step of comparing the created cancer-specific response profile of the subject with a prebuilt database of reference cancer-specific response profiles, wherein matching or near matching subject and reference profiles indicate the type of cancer that the subject has.

63. The method of claim 52, wherein a plurality of different functional impact types are used to create a cancer-specific response profile.

64. The method of claim 52, wherein the subject is a human.

65. The method of claim 52, wherein the tumor-derived exosomes are isolated from a liquid biopsy taken from the subject.

66. The method of claim 47, wherein the immune cells are T-cells.

Description

BRIEF DESCRIPTION OF FIGURES

[0162] Various embodiments of the invention will be described with reference to the following Figures.

[0163] FIG. 1: Characterizations and quantitative detection of TEXs produced in cancer cells culture. (A) Particle size of exosomes harvested from culture medium of different cancer cells was measured by Zeta View. Data represent the mean±standard deviation (SD) (n=10/group). (B) A sample histogram of particle size distribution of B16F10 TEXs. (C) Exosomes were linked to aldehyde/sulfate latex beads, followed by staining with anti-mouse CD63 and anti-mouse CD9. Flow cytometry analysis of fluorescence intensity of CD63 and CD9 on B16F10 TEXs coated beads and blank beads are shown. (D) Sample histogram of CD25 expression on activated CD4.sup.+ T-cells after treatment with 40×10.sup.8, 20×10.sup.8, 10×10.sup.8 and 0 EG7-OVA TEXs for two days. (E-G) T-cells were co-incubated with varying doses of TEXs from different cancer cells in the presence of supporting signals for 2 days, followed by markers staining and flow cytometry analysis. Parameter Score was calculated for each marker and Exo Score was computed with or without parameter selection. (E) Dose titration curves of Exo Scores for B16F10 TEXs and EG7 are presented with (solid lines) or without (dotted lines) parameter selection. (F) Dose titration curves of Exo Scores for A498 and HCT116 TEXs are shown after parameter selection. (G) Distinct patterns of Parameter Scores for B16F10, EG7-OVA, A498 and HCT116 TEXs. Pooled results are shown from at least three independent experiments for each cancer type. Act=activated.

[0164] FIG. 2: T-TEX diagnoses TEXs with interference from HEXs in blood. Blood obtained from C57Bl/6 mice was pooled before aliquoting. PBS or varying doses of TEXs from B16F10 and EG7-OVA cancer cells were spiked in to aliquots of blood. Spiked-in TEXs were re-harvested together with HEXs in the blood before co-culture with T-cells for 2 days. T-cell markers were stained and analyzed via flow cytometry. Parameter Score was calculated for each marker and Exo Score was computed after parameter selection. Data represent the mean±SD. Pooled results are shown from at least two independent experiments for each cancer type. (A) Dose titration curves of Exo Scores for B16F10 TEXs/HEXs mixture and EG7 TEXs/HEXs mixture. (B) Distinct patterns of Parameter Scores for B16F10 and EG7-OVA TEXs in the background of HEXs in blood.

[0165] FIG. 3: T-TEX diagnoses tumor-bearing mice against three types of tumor at the same time and identifies their cancer type. B16F10 melanoma cells (1×10.sup.6) were injected intravenously (i.v.) to induce lung metastases in C57Bl/6 mice for 10 days (n=14). EG7-OVA cells (1×10.sup.6) were injected s.c. into C57Bl/6 mice, and tumor was allowed to establish for 10 days (n=7). A498 renal carcinoma cells (4×10.sup.6) together with Matrigel® were inoculated s.c. into NCr nude mice for 10 weeks (n=27). Tumor-bearing mice and healthy control mice were then bled after the respective inoculation period, and exosomes in blood were harvested for T-TEX assay. (A) Exo Scores for healthy controls and mice with B16F10 lung metastasis after parameter selection. (B) Exo Scores for healthy mice and mice with EG7-OVA s.c. tumor after parameter selection. (C) Exo Scores for healthy mice and mice with A498 xenograft after parameter selection. (D) Distinct patterns of Parameter Scores for exosomes harvested from B16F10 lung metastasis, EG7-OVA s.c. tumor and A498 xenograft. (E) Exo Scores of A498 xenograft mice when diagnosed against B16F10 and EG7-OVA tumor pattern. (F) Normalized deviation of A498 xenograft mice from A498 Parameter Score pattern in each marker. (G) Normalized deviation of A498 xenograft mice from EG7-OVA Parameter Score pattern in each marker. N8=naïve CD8.sup.+ T-cells. N4=naïve CD4.sup.+ T-cells. A8=activated CD8.sup.+ T-cells. A4=activated CD4.sup.+ T-cells. (H) Deviation Scores of tumor-bearing mice when tested against B16F10, EG7-OVA and A498 tumor patterns. **, p<0.01; ***, p<0.001; ****, p<0.0001, by student t-test.

[0166] Herein the inventors describe, amongst other things, for the first time an approach to simultaneously diagnose multiple types of cancer by measuring/profiling functional impacts of their TEXs on T-cells, to create cancer-specific response profiles. The inventors have developed a diagnostic assay, T-TEX (named after the two key components in the assay), to capture the TEX-induced immune responses, designed algorithms to quantify the responses and have generated a cancer-specific data base of immune response profiles (reference cancer-specific profiles). The inventors have also created Exo Score to give an overall yes or no answer to cancer diagnosis, and Deviation Score to reflect the closeness of test samples to barcode patterns in the data base, thus scrutinizing the type of cancers. The inventors have detected, differentiated and quantified TEXs generated from four different cancer cell cultures. The inventors have also diagnosed tumor-bearing mice against three types of tumor at the same time with more than 89% sensitivity for each.

[0167] As T-TEX leverages on the functional impact of tumor signatures in the blood, it may circumvent the limitations in the current cancer biomarker development. It may also detect multiple types of cancer at the same time with a pre-built database, and serve as a first-line complimentary test to existing technology or standalone test to save potential patients/subjects from repetitive tests.

Materials and Methods

Materials

[0168] Heat inactivated fetal bovine serum (FBS) and Live/Dead fixable Aqua dead cell stain kit were obtained from Life Technologies (CA, USA). Concanavalin A Type VI (Con A) was obtained from Sigma-Aldrich (St. Louis, Mo.). Recombinant mouse interleukin-2 (IL-2) and interleukin-7 (IL-7) were obtained from eBioscience (MA, USA). Ficoll-Pague Plus was from GE Health Care (Waukesha, Wis.). Human peripheral blood mononuclear cells (PBMC), human interleukin-2 (IL-2), human interleukin-7 (IL-7), EasySep™ CD4.sup.+ or CD8.sup.+ T-cell Enrichment Kit for both mouse and human were bought from STEMCELL Technologies (Vancouver, Canada). Mouse and human anti-CD3/CD28 dynabeads and aldehyde/sulfate latex beads were purchased from Thermo Fisher Scientific (MA, USA). Matrigel® was obtained from BD Biosciences (CA, USA).

[0169] AccuCount rainbow fluorescent count beads (10.1 μm) were bought from Spherotech (Lake forest, IL). Anti-human ki67 Percp-Vio700 was from Miltenyi Biotec (BG, Germany). Anti-mouse CD16/32, anti-mouse CD8a APC, anti-mouse PD-1 APC-eFluor 780, anti-mouse Tim3 PE-Cy7, anti-mouse CD25-FITC anti-mouse GranzymeB-PE, anti-mouse CD4-eFluor 780, anti-mouse CTLA4 PE, anti-mouse FasL-Percp-eFluor 710, anti-mouse CD69 FITC, anti-mouse ki67 PE-Cy7, anti-mouse IFNγ, APC, anti-human CD4 APC-eFluor 780, anti-human CD8a APC-eFluor 780, anti-human CD69 APC, anti-human PD-1 PE-Cy7, anti-human CD25 FITC, anti-human Granzyme B PE, anti-human CTLA4 PE, anti-human Tim3 APC, anti-human IFNγ, FITC, anti-mouse CD16/32, human Fc Receptor binding inhibitor monoclonal antibody and Intracellular Fixation & Permeabilization Buffer Set were purchased from eBiosceince (San Diego, Calif.). All reagents were used as received unless otherwise noted.

Animals and Cell Lines

[0170] The experimental protocol was approved by the Institutional Animal Care and Use Committee of Biological Resource Centre, Agency for Science, Technology and Research (A*STAR), Singapore. Six to eight week-old female C57Bl/6 mice and NCr nude mice were from the Singapore InVivos.

[0171] B16F10 mouse melanoma cells, EG7-OVA mouse lymphoma cells, A498 human renal carcinoma cells, HCT116 human colorectal carcinoma cells and S. aureus were acquired from American Type Culture Collection (Manassas, Va., USA).

T-Cell Isolation and Activation

[0172] Spleens from C57Bl/6 mice were ground through a 70-μm cell strainer and red blood cells were removed by incubating with ACK lysis buffer (1 mL per spleen) for 3 min at 25° C. Naïve CD4.sup.+ or CD8.sup.+ T-cells were isolated from splenocytes directly via magnetic negative selection using an EasySep™ Mouse CD4.sup.+ or CD8.sup.+ T-cell Enrichment Kit, respectively. For activated CD8.sup.+ and CD4.sup.+ T-cells, splenocytes after ACK lysis were washed with ice cold PBS, and then cultured in T-cell medium with Con A at a final concentration of 2 μg/mL and murine IL-7 at 1 ng/mL at 37° C. for activation. After 2-day incubation, dead cells were removed by Ficoll-Pague Plus gradient separation, and CD8.sup.+ or CD4.sup.+ T-cells were isolated by EasySep™ Mouse CD8.sup.+ or CD4.sup.+ T-cell Enrichment Kit, respectively. Purified CD8.sup.+ or CD4.sup.+ T-cells were re-suspended at 0.75×10.sup.6/mL in T-cell medium containing 10 ng/mL recombinant murine IL-2. After 48 h, cells were washed in PBS and re-suspended in T-cell media for assays.

[0173] Human PBMCs were activated by Con A (2 μg/mL) and human IL-7 (1 ng/mL) at 37° C. for 2 days in T-cell medium. After removing dead cells by Ficoll-Pague Plus gradient separation, human CD8.sup.+ and CD4.sup.+ T-cells were isolated via EasySep™ human CD8.sup.+ or CD4.sup.+ T-cell Enrichment Kit, respectively. Purified CD8.sup.+ or CD4.sup.+ human T-cells were re-suspended at 1×10.sup.6/mL in T-cell medium containing 20 ng/mL of recombinant human IL-2. After 10 days, cells were washed in PBS and re-suspended in T-cell medium for assays.

Production of TEXs from cancer cell culture

[0174] FBS was spun at 110000 g for 3 hat 4° C. to remove exosomes. B16F10, A498 and HCT116 cancer cells were cultured in tumor medium (RPMI 1640 medium supplemented with 10% exosome-free FBS and 50 U/mL of Penicillin-Streptomycin), while EG7-OVA lymphoma cells were cultured in T-cells medium (tumor medium supplemented with Non-Essential Amino Acids, β-mercaptoethanol and pyruvate). After tumor cells grew confluent, tumor cell culture medium was harvested and spun down at 1000 g for 5 min at 4° C. Supernatant was collected and spun down at 10000×g for 30 min at 4° C. After the supernatant was collected and spun down by ultracentrifugation (Beckman Coulter, CA, USA) at 110,000 g for 70 min at 4° C., exosome pellets were re-suspended in 200 μI of PBS, quantified by Zeta View® (Particle Metrix GmbHAm, Meerbusch, Germany) and stored in −80° C. freezer.

Generation and Harvest of TEXs in Blood

[0175] TEXs Spiked into Blood

[0176] Blood from 6 to 8 week-old healthy female C57Bl/6 mice was obtained via cardiac puncture. Different amounts of TEXs produced by B16F10 or EG7-OVA cells were spiked into the blood, and re-harvested together with HEXs via sequential centrifugations. The amount of TEXs in the mixture of TEXs and HEXs was assumed to be the same as those spiked into blood without loss. HEXs alone were also harvested from healthy mice blood without TEXs spiked in to serve as controls.

TEXs from Tumor-Bearing Mice

[0177] B16F10 melanoma cells were suspended at 1×10.sup.6 cells per 200 μL of PBS, and injected i.v. to induce lung metastases in C57Bl/6 mice for 10 days. For s.c. tumor models, EG7-OVA cells (1×10.sup.6) in 100 μL of PBS were injected s.c. into C57Bl/6 mice and tumor was allowed to establish for 10 days (100±45 cm.sup.2). In human tumor xenograft model, A498 renal carcinoma cells (4×10.sup.6) in 100 μL of PBS together with 100 μL Matrigel® were inoculated s.c. into NCr nude mice. After 10 weeks, tumor size was ˜114±67 cm.sup.2. Tumor size was monitored before bleeding and tumor area was calculated as the product of 2 measured orthogonal diameters (D.sub.1×D.sub.2). Both healthy and tumor-bearing mice were bled (800-1000 μL) via cardiac puncture at respective time points to harvest HEXs and TEXs in the presence of background HEXs.

TEXs Harvest from Blood

[0178] Murine or human blood was spun at 3000 g for 5 min at 4° C. to obtain plasma that was further spun at 10000 g for 30 min at 4° C. Supernatant was then centrifuged at 110,000 g for 70 min at 4° C. Exosome pellets were re-suspended in 100 μI of PBS and stored in −80° C. freezer.

Immune Response Assays

[0179] Murine naïve CD8.sup.+ T-cells (5×10.sup.4), naïve CD4.sup.+ T-cells (5×10.sup.4), activated CD8.sup.+ T-cells (5×10.sup.4) and activated CD4.sup.+ T-cells (5×10.sup.4) were each treated with PBS or an equivalent volume of varying doses of TEXs (in PBS) produced by B16F10 and EG7-OVA cancer cells in vitro. HEXs and TEXs/HEXs mixture harvested from the same volume of mouse blood were used in place of PBS and TEXs in PBS for assays to detect spiked-in TEXs, B16F10 lung metastasis, B16F10 and EG7 s.c. tumor. Naïve CD8.sup.+ and naïve CD4.sup.+ T-cells were supplemented with 1 μL of anti-mouse CD3/CD28 dynabeads while activated CD8.sup.+ and CD4.sup.+ T-cells were supplied with murine IL-2 with a final concentration of 8 ng/mL. Total volume per well was topped up to 120 μL with T-cell medium. T-cells were co-cultured with exosomes in the presence of supporting signals at 37° C. for 2 days before flow cytometry analysis.

[0180] For assays with exosomes from A498 and HCT116 cell lines, blood of A498 xenograft tumor-bearing mice and lung cancer patients, human T-cells, human IL-2 (16 ng/mL) and anti-human CD3/CD28 dynabeads were used while the rest of the setup remained the same.

Flow Cytometry Analysis

[0181] After co-incubation with exosomes for 2 day, T-cells were added with counting beads, spun down and washed 2× with ice cold PBS before Aqua Live/Dead staining. T-cells were then washed 1× in FACS buffer and blocked by anti-mouse CD16/CD32 or anti-human FcR binding inhibitor monoclonal antibody before splitting into two halves for surface-staining of CD8, CD4, CD25, Tim3, CTLA4, PD-1, FasL, CD69 and pSTAT5. After washing 2× in FACS buffer, samples were fixed and permeabilized in eBioscience Intracellular Fixation & Permeabilization Buffer Set, followed by staining for ki67, Granzyme B and IFNγ. After intracellular staining, cells were washed 1× in FACS buffer and re-suspended in FACS buffer before analyzing on a BD LSR II or Celesta flow cytometer. All data were processed using FlowJo software.

Data Analyses

[0182] Parameter Score

[0183] Flow cytometry data of every sample was processed to compute geometric Mean Fluorescence Intensity (gMFI) for each stained marker. All gMFI values were normalized to the average of PBS controls if TEXs were from in vitro cancer cell culturing or HEXs controls if TEXs were harvested from blood. Normalized gMFI value was then log-2 transformed to obtain parameter score (M) for that marker.

[00014]$\mathrm{Parameter}.Math..Math.\mathrm{Score}.Math..Math.\left(M_i\right)={\log }_2.Math.\frac{{\mathrm{gMFI}}_i.Math..Math.\mathrm{of}.Math..Math.\mathrm{sample}}{\mathrm{Average}.Math..Math.{\mathrm{gMFI}}_i.Math..Math.\mathrm{of}.Math..Math.\mathrm{PBS}.Math..Math.\mathrm{or}.Math..Math.\mathrm{HEX}.Math..Math.\mathrm{controls}}$

Parameter Selection

[0184] For dose titration and spiked-in experiments, Spearman's rank-order correlation and linear regression were performed on dose and parameter score data. Parameters were selected if their correlation coefficient ρ and coefficient of determination R2 fulfilled one of the following conditions and passed visual checking:

[0185] 1. |ρ|>0.3 and R.sup.2>0.2

[0186] 2. |ρ|>0.4 and R.sup.2>0.1

[0187] 3. |ρ|>0.2 and R.sup.2>0.3

[0188] For In assays for murine tumor models and human cancer patients, student t-test was conducted and the magnitude of the differences between the mean of healthy and tumor groups was calculated. Parameters were selected if

[00015]$p<0.05.Math..Math.\mathrm{or}.Math..Math..Math.\stackrel{\_}{M_{t.Math.u.Math.m.Math.o.Math.r}}-\stackrel{\_}{M_{H.Math.e.Math.a.Math.l.Math.t.Math.h.Math.y}}.Math.>0.2$

Exo Score

[0189] Exo Score was the mean absolute values of n parameter scores.

[00016]$\mathrm{Exo}.Math..Math.\mathrm{Score}=\frac{{\Sigma }_{i=1}^n.Math..Math.M_i.Math.}{n}$

Deviation Score

[0190] Normalized parameter deviation is defined as following where x is the parameter score of a test sample for marker i, while M is the identified parameter score for that marker.

[00017]$\mathrm{Normalized}.Math..Math.\mathrm{parameter}.Math..Math.\mathrm{deviation}.Math..Math.\left(\mathrm{NPD}\right).Math.=\frac{x_i-M_i}{.Math.M_i.Math.}$

[0191] Deviation Score is the mean of the absolute values of average NPD,

[00018]$\mathrm{Deviation}.Math..Math.\mathrm{Score}.Math.=\frac{{\Sigma }_{i=1}^n.Math..Math.\stackrel{\_}{{\mathrm{NPD}}_{\mathrm{.Math.}}}.Math.}{n}$

[0192] FlowJo was used to compute all gMFI values. Data processing and statistical analyses were performed using RStudio (Version 1.0.153) and GraphPad Prism software. All values and error bars are mean±SD except where indicated differently.

Results and Discussion

Design of Diagnostic Assay T-TEX to Detect TEX-Induced Immune Responses

[0193] B16F10 mouse skin melanoma cells, A498 human renal carcinoma cells and HCT116 human colorectal carcinoma cells were cultured to generate representative TEXs from different tumor types and species. Since the inventors' diagnostic assay relied on the TEX-induced immune responses, EG7-OVA mouse lymphoma cells, a type of cancer cells originating from immune system itself was also included to evaluate whether T-TEX would also be applicable to immune system cancer.

[0194] For immune responses screening, the inventors used naïve CD8.sup.+ T-cells, naïve CD4.sup.+ T-cells, activated (Act) CD8.sup.+ T-cells or Act CD4.sup.+ T-cells to co-culture with TEXs in the presence of T-cell supporting molecules. For TEXs from B16F10 and EG7-OVA cells, T-cells from mouse spleens were used while for TEXs from A498 and HCT116 cells, T-cells from human peripheral blood mononuclear cells (PBMC) were employed. After 2 day of co-culture, various T-cell surface and intracellular markers were stained and analyzed via flow cytometry to provide insights about the TEXs. The markers screened include activation markers (CD69, CD25, pSTAT5), proliferation marker (ki67), exhaustion marker (Tim3), cytotoxicity marker (Granzyme B), protein crucial for cytotoxicity and immune cell apoptosis (FasL).sup.33 and those involved in immune checkpoint inhibitory signaling pathways (PD-1, CTLA4).

T-TEX Detects Dose-Dependent Immune Responses to TEXs Generated in Cancer Cell Culture

[0195] Extracellular vesicles (EVs) secreted by tumor cells cultured in exosome-free medium were harvested from culture medium via sequential centrifugations. The yielded vesicles had a mean size ranging from 110±6 nm to 120±6 nm for different types of cancer cells (FIG. 1A), falling into the size range for exosomes. A typical histogram of the size distribution of EVs from B16F10 is shown in FIG. 1B. In addition, harvested B16F10 EVs were tested positive for tetraspanins CD63 and CD9 (FIG. 10), which are exosome biomarkers associated with the exosomal membrane.sup.34. These combined indicated that the EVs produced from cancer cell culture could be used as TEXs for the diagnostic assay.

[0196] At the end of T-TEX, the inventors obtained fluorescence intensity of markers in the designed panel as output. Sample histograms of fluorescence intensity of CD25 on T-cells after treatment with varying doses of TEXs were shown (FIG. 1D). CD25 expression was quantified by computing its geometric mean fluorescence intensity (gMFI), and normalized to the average gMFI of PBS controls so that CD25 expression could be compared fairly to other markers regardless of their default expression levels. The normalized CD25 expression was then log-2 transformed to give the Parameter Score of CD25. After computing Parameter Scores for all markers at different doses of TEXs, the inventors selected markers by conducting linear regressions and Spearman's rank-order correlation tests of Parameter Score against doses. For the diagnostic assay to be quantitative, markers demonstrating stronger linear dose-dependent responses will be favored (large R.sup.2 value in linear regression). However, some of the marker responses might plateau after a certain dose, thus yielding a poorer linear fit. These parameters might still enhance the sensitivity of the assay at low concentration of TEXs, which would be useful for early stage cancer detection. These parameters can be recruited due to their high correlation coefficient in Spearman's rank-order test.

[0197] The inventors then calculated Exo Score, the mean of absolute values of Parameter Score for selected markers, to demonstrate the average magnitude of deviation per parameter of treated samples away from the controls. Without parameter selection, dose titration curve of Exo Score exhibited poor linear fits as R.sup.2 was 0.2353 and 0.8117 for B16F10 and EG7-OVA TEXs, respectively (FIG. 1E dotted lines). Parameter selection significantly improved the R.sup.2 value to 0.9067 and 0.9069 and increased the sensitivity of the assay by doubling the magnitude of change (steeper slope) (FIG. 1E). The inventors also managed to obtain unidirectional dose-dependent Exo Scores for TEXs from HCT116 (R.sup.2=0.9650 in linear fitting) and A498 cells (R.sup.2=0.9108 in Michaelis-Menten fitting) (FIG. 1F). Thus, not only could Exo Score detect the presence of TEXs generated from different types of cancer cells, it was also a quantitative assessment of the amount of TEXs present. Furthermore, the patterns of selected markers and their corresponding Parameter Scores were distinct among all four types of TEXs (FIG. 1G), demonstrating the possibility of using Parameter Score pattern to differentiate the types of cancer.

T-TEX Identifies TEXs in the Background of Healthy Cell Derived Exosomes in Blood

[0198] Exosomes secreted by healthy cells are present abundantly in blood.sup.22, 35, 36, and they might affect the function of immune cells in T-TEX. To better mimic the real clinical setting, the inventors sought to evaluate whether Exo Score and Parameter Score could detect TEXs in the background of heathy cell derived exosomes (HEXs) from blood. Varying doses of B16F10 and EG7-OVA TEXs were spiked into healthy mice blood. The added TEXs were re-harvested together with HEXs originally in the blood via sequential centrifugation, and the mixture of TEXs and HEXs was tested by the inventors' assay. HEXs harvested from an equivalent volume of blood without TEX spiked in were used as controls to be normalized to. The Exo Score of EG7-OVA TEXs still exhibited a linear relationship with doses (R.sup.2=0.9772), while that of B16F10 TEXs was better fitted by Michaelis-Menten model (R.sup.2=0.8758) as Exo Score plateaued after 30×10.sup.8 dose (FIG. 2A). Due to the loss of exosomes during sequential centrifugation steps, the actual amount of TEXs used in the assays should be smaller than the indicated spiked-in amount, and the Exo Score curves might represent the responses in a lower range of doses. Nevertheless, Exo Score still detected TEXs with interference from HEXs in blood. It showed that T-TEX could diagnose cancer by using exosomes directly harvested from blood without the need to isolate TEXs. As expected, the patterns of selected markers and their corresponding Parameter Scores varied substantially from the results obtained in the last section (FIG. 1G, FIG. 2B) due to the interference of HEXs on T-cells in the assays. However, the patterns were still significantly different between B16F10 and EG7-OVA TEXs (FIG. 2B). Therefore, Parameter Scores could still be used to differentiate TEXs secreted by the two types of cancer cells.

T-TEX Diagnoses Tumor-Bearing Mice and Identifies their Respective Cancer Type

[0199] The inventors next evaluated T-TEX in the diagnosis of tumor-bearing mice. The inventors tested their assay in three tumor models, B16F10 murine lung metastasis model, EG7-OVA murine subcutaneous (s.c.) tumor model and A498 human tumor xenograft in immunodeficient mice to represent tumors from different origins, locations and species. Blood from healthy mice was used as controls.

[0200] As it was difficult to quantify the amount of TEXs in mice, the inventors changed their parameter selection criteria to the following: 1) mean Parameter Score differed more than 0.2 between healthy and tumor-bearing mice to improve sensitivity of the assay; 2) p-value in student t-test was smaller than 0.05 to increase the probability that the differences between healthy and tumor groups were not due to chance.

[0201] Compared to healthy mice, the Exo Scores of tumor-bearing mice were all significantly higher (FIG. 3A-C). The sensitivity of T-TEX was 93% and 100% for B6F10 and EG7-OVA tumor, respectively, with cut-off at 3 SDs above the mean of healthy controls. The sensitivity increased to 100% for both types of tumor with cut-off at 2 SDs above healthy control mean. The sensitivity of their assay to human cancer cell A498 in xenograft model was 93% (1 SD), 89% (2 SD), or 78% (3 SD) (FIG. 3C). The lower sensitivity in xenograft model might be due to the larger variation in tumor sizes by the time of bleeding. In addition, three tumor models all have their own distinctive patterns of eligible parameters and Parameter Scores (FIG. 3D).

[0202] Despite Exo Score was crucial in determining parameter patterns for different cancer type and could give an overall yes or no answer to diagnosis, it might not be able to differentiate types of cancers during the actual diagnosis stage. For example, when mice with A498 xenograft were diagnosed against B16F10 and EG7-OVA, more than 70% of mice were tested positive as their Exo Scores computed according to patterns for B16F10 and EG7-OVA were higher than the respective cut-off of 3 SDs (FIG. 3E). Thus, the inventors need another indicator to inform them about the specific type of cancer. A close look at the data revealed that the normalized parameter deviation of A498 tumor bearing mice from A498 pattern was random (FIG. 3F). On the other hand, test data of mice with A498 tumor exhibited strong directional changes in comparison to EG7-OVA pattern (FIG. 3G). Deviation Score, mean of the absolute values of average parameter deviation, was designed to capture the deviation of test samples from any known cancer patterns. Mice with A498 tumor showed Deviation Score larger than 1 to B16F10 and EG7 patterns, while only 0.1 to A498 pattern, indicating the tumors are A498 (FIG. 3H). Similarly, B16F10 and EG7-OVA tumor-bearing mice have high Deviation Scores when tested against other types of tumor, but not to the tumor they possessed (FIG. 3H). These results illustrated that Exo Score and Deviation Score could work together to identify the tumor-bearing mice, as well as specifying the type of cancer.

CONCLUSIONS

[0203] The inventors have demonstrated a cancer diagnostic test, T-TEX, which can simultaneously detect multiple types of cancer by profiling functional impacts of their TEXs on T-cells. The inventors created Exo Score to give an overall yes or no answer to diagnosis, and Deviation Score to reflect the consistency of test samples to response patterns in the database, thus scrutinizing the type of cancer. T-TEX detects and quantifies TEXs from four different cancer cell lines and diagnoses mice against three types of tumor at the same time with more than 89% sensitivity for each. In the future, the assay can be expanded to use other types of immune cells such as Natural Killer (NK) cells and B cells for cancer.

[0204] Overall, as T-TEX leverages on the functional impacts instead of content of tumor signatures in blood, it will circumvent the limitations involved in current cancer biomarker development. With a pre-built database, it can also detect multiple types of cancer at the same time, thus serving as a first-line complimentary test to existing technology or a standalone test to minimize the burden of repeated testing.

REFERENCES

[0205] 1 Siravegna, G., Marsoni, S., Siena, S. & Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 14, 531-548 (2017). [0206] 2 Crowley, E., Di Nicolantonio, F., Loupakis, F. & Bardelli, A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol 10, 472-484 (2013). [0207] 3 Karachaliou, N., Mayo-de-Las-Casas, C., Molina-Vila, M. A. & Rosell, R. Real-time liquid biopsies become a reality in cancer treatment. Ann Transl Med 3, 36 (2015). [0208] 4 Haber, D. A. & Velculescu, V. E. Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA. Cancer Discovery 4, 650-661 (2014). [0209] 5 Pantel, K. Blood-based analysis of circulating cell-free DNA and tumor cells for early cancer detection. PLoS Med 13, e1002205 (2016). [0210] 6 Ilie, M. et al. Current challenges for detection of circulating tumor cells and cell-free circulating nucleic acids, and their characterization in non-small cell lung carcinoma patients. What is the best blood substrate for personalized medicine? Ann Transl Med 2, 107 (2014). [0211] 7 Hong, B. & Zu, Y. Detecting circulating tumor cells: Current challenges and new trends. Theranostics 3, 377-394 (2013). [0212] 8 Simons, M. & Raposo, G. Exosomes—Vesicular carriers for intercellular communication. Curr Opin Cell Biol 21, 575-581 (2009). [0213] 9 El-Andaloussi, S., Mager, I., Breakefield, X. O. & Wood, M. J. Extracellular vesicles: Biology and emerging therapeutic opportunities. Nat Rev Drug Discov 12, 347-357 (2013). [0214] 10 Raposo, G. & Stoorvogel, W. Extracellular vesicles: Exosomes, microvesicles, and friends. J Cell Biol 200, 373-383 (2013). [0215] 11 Kahlert, C. et al. Identification of double stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J Biol Chem 289, 3869-3875 (2014). [0216] 12 Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol 10, 1470-1476 (2008). [0217] 13 Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nat Rev Genet 17, 257-271 (2016). [0218] 14 Taylor, D. D. & Gercel-Taylor, C. MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol 110, 13-21 (2008). [0219] 15 Yoshioka, Y. et al. Ultra-sensitive liquid biopsy of circulating extracellular vesicles using ExoScreen. Nat Commun 5, 3591 (2014). [0220] 16 Melo, S. A. et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523, 177-182 (2015). [0221] 17 Cancer Genome Atlas Research Network, Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113-1120 (2013). [0222] 18 Aravanis, A. M., Lee, M. & Klausner, R. D. Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell 168, 571-574 (2017). [0223] 19 Meacham, C. E. & Morrison, S. J. Tumor heterogeneity and cancer cell plasticity. Nature 501, 328-337 (2013). [0224] 20 Wan, J. C. M. et al. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat Rev Cancer 17, 223-238 (2017). [0225] 21 Heitzer, E., Ulz, P. & Geigl, J. B. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem 61, 112-123 (2015). [0226] 22 Li, M. et al. Analysis of the RNA content of the exosomes derived from blood serum and urine and its potential as biomarkers. Philos Trans R Soc Lond B Biol Sci 369, 20130502 (2014). [0227] 23 Goossens, N., Nakagawa, S., Sun, X. & Hoshida, Y. Cancer biomarker discovery and validation. Transl Cancer Res 4, 256-269 (2015). [0228] 24 Munson, P. & Shukla, A. Exosomes: potential in cancer diagnosis and therapy. Medicines (Basel) 2, 310-327 (2015). [0229] 25 Kunigelis, K. E. & Graner, M. W. The dichotomy of tumor exosomes (TEX) in cancer immunity: Is it all in the conTEXt? Vaccines (Basel) 3, 1019-1051 (2015). [0230] 26 Robbins, P. D. & Morelli, A. E. Regulation of immune responses by extracellular vesicles. Nat Rev Immunol 14, 195-208 (2014). [0231] 27 Whiteside, T. L. Tumor-derived exosomes and their role in tumor-induced immune suppression. Vaccines 4, 35 (2016). [0232] 28 Valenti, R. et al. Tumor-released microvesicles as vehicles of immunosuppression. Cancer Res 67, 2912-2915 (2007). [0233] 29 Abusamra, A. J. et al. Tumor exosomes expressing Fas ligand mediate CD8.sup.+ T-cell apoptosis. Blood Cells Mol Dis 35, 169-173 (2005). [0234] 30 Clayton, A., Mitchell, J. P., Court, J., Mason, M. D. & Tabi, Z. Human tumor-derived exosomes selectively impair lymphocyte responses to interleukin-2. Cancer Res 67, 7458-7466 (2007). [0235] 31 Clayton, A., Al-Taei, S., Webber, J., Mason, M. D. & Tabi, Z. Cancer exosomes express CD39 and CD73, which suppress T cells through adenosine production. J Immunol 187, 676-683 (2011). [0236] 32 Muller, L., Mitsuhashi, M., Simms, P., Gooding, W. E. & Whiteside, T. L. Tumor-derived exosomes regulate expression of immune function-related genes in human T cell subsets. Sci Rep 6, 20254 (2016). [0237] 33 Nguyen, T. & Russell, J. The regulation of FasL expression during activation-induced cell death (AICD). Immunology 103, 426-434 (2001). [0238] 34 Andreu, Z. & Yanez-Mo, M. Tetraspanins in extracellular vesicle formation and function. Front Immunol 5, 442 (2014). [0239] 35 Vlassov, A. V., Magdaleno, S., Setterquist, R. & Conrad, R. Exosomes: Current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochim Biophys Acta 1820, 940-948 (2012). [0240] 36 Caby, M. P., Lankar, D., Vincendeau-Scherrer, C., Raposo, G. & Bonnerot, C. Exosomal-like vesicles are present in human blood plasma. Intl Immunol 17, 879-887 (2005).