METHODS AND MATERIALS FOR THE DETECTION OF LATENT TUBERCULOSIS INFECTION

Abstract

There are provided methods of determining the latent tuberculosis (TB) infection status in an individual comprising: (i) providing a sample comprising T-cells; (ii) exposing, the sample of (i) to one or more TB antigens; (iii) identifying T-cells in the sample that are CD4 positive and secrete IFN-γ in response to TB antigens; (iv) identifying those cells of (iii) which are also HLA-DR positive; and optionally (v) calculating the cells identified in (iv) as a percentage of those identified in (iii); wherein the identification of cells in (iv) and/or the percentage of cells calculated in (v) correlates to latent TB infection status of the individual, and wherein steps (iii) and (iv) can be carried out either sequentially or simultaneously. There are also provided compositions and kits for use in such methods.

Claims

1. A method of determining the latent tuberculosis (TB) infection status in an individual comprising: (I) providing a sample comprising T-cells: (ii) exposing the sample of (i) to one or more TB antigens; (iii) identifying T-cells in the sample that are CD4 positive and secrete IFN-γ in response to TB antigens; (iv) identifying those cells of (iii) which are also HLA-DR positive, and optionally (v) calculating the cells identified in (iv) as a percentage of hose identified in (iii); wherein the identification of cells in (iv) and/or the percentage of cells calculated in (v) correlates to latent TB infection status of the individual, and wherein steps (iii) and (iv) can be carried out either sequentially or simultaneously.

2. The method of claim 1 wherein the latent TB infection status determined by the method corresponds to the risk of the latent TB infection progressing to an active TB infection.

3. The method of claim 2 wherein the risk of the latent TB infection progressing to an active TB infection is proportional to the percentage value calculated in step (v) of the method.

4. The method of any one of claims 1-3 wherein there is a high risk of the latent TB infection progressing to an active TB infection when the percentage calculated in step (v) is greater than a cut off value.

5. The method of claim 4 wherein the cut off value is a value between 10% and 50%, between 20% and 40%, between 25% and 40%, between 20% and 35%, between 25% and 35%, or between 25% and 30%.

6. The method of any previous claim wherein the latent TB infection status determined by the method corresponds to the amount of time elapsed since the individual was originally infected with TB.

7. The method of claim 6 wherein the time elapsed since the individual was originally infected with TB is inversely proportional to the percentage value calculated in step (v).

8. The method of any previous claim wherein the cells identified in step (iii) are additionally CD3 positive.

9. The method of any previous claim wherein the cells identified in step (iii) are additionally CD8 negative.

10. The method of any previous claim wherein the T-cells identified in step (iii) are identified as live T-cells, preferably by use of a dead cell marker.

11. The method of any previous claim wherein an additional step (ii-a) is performed between steps (ii) and (iii) to block the release of cytokines, preferably by adding a golgi-inhibitor.

12. The method of any previous claim wherein the sample is a blood sample (preferably a PBMC sample), a bronochoalveolar lavage (BAL) sample or a cerebral spinal (CBF) sample.

13. The method of any previous claim wherein steps (iii) and/or (iv) are performed by multi-parameter flow cytometry.

14. The method of any previous claim wherein an additional step is first performed in order to determine that the sample is obtained from an individual infected with latent TB.

15. The method of claim 14 wherein the additional step is performed using ELISpot platform, an interferon gamma release assay (IGRA) and/or a tuberculin skin test.

16. The method of claim 14 or 15 wherein there is an absence of clinical and/or radiological features of active TB in the individual.

17. A composition comprising a plurality of antibodies or antigen-binding fragments thereof that binds to each of CD4, IFN-γ and HLA-DR and wherein the plurality comprises antibodies or antigen-binding fragments thereof that are individually specific for each of CD4, IFN-γ and HLA-DR.

18. The composition of claim 17 wherein the plurality of antibodies or antigen-binding fragments thereof additionally binds to CD3 and additionally comprises antibodies or antigen-binding fragments thereof that are individually specific for CD3.

19. The composition of claim 17 or 18 wherein the plurality of antibodies or antigen-binding fragments thereof additionally binds to CDB and additionally comprises antibodies or antigen-binding fragments thereof that are individually specific for CD8.

20. The composition of any one of claims 17-19 wherein the plurality of antibodies or antigen-binding fragments thereof additionally binds to a live or dead cell marker and additionally comprises antibodies or antigen-binding fragments thereof that are individually specific for a live or dead cell marker.

21. The composition of any of claims 17-20 wherein the antibody or antigen-binding fragments thereof is selected from antibody or antigen-binding fragment thereof is selected from the group consisting of Fv fragments, soFv fragments, Fab, single variable domains and domain antibodies.

22. The composition of any one of claims 17 to 21 wherein the antibodies or antigen-binding fragments thereof with a particular specificity are separately detectable to those with a different specificity.

23. The composition of any one of claims 17 to 22 wherein the antibodies or antigen-binding fragments thereof are visually detectable.

24. The composition of any one of claims 17 to 23 wherein the antibodies or antigen-binding fragments thereof are labelled (e.g. with a fluorescent label).

25. The composition of claims 17 to 24 for use in determining the risk of a latent TB infection progressing to an active TB infection.

26. The composition of any one of claims 17 to 24 for use in treating an active TB infection wherein the use comprises identifying if a subject is at risk of developing an active TB infection and subsequently administrating the most appropriate preventative treatment for that infection.

27. A method of treating a subject determined to be at risk of developing an active TB infection comprising: (a) conducting the method of any of claims 1 to 16; and (b) administrating the most appropriate preventative treatment to the subject depending on the outcome of the step (a).

28. A method of stratifying an individual for preventative treatment of active TB infection comprising: (a) conducting the method of any of claims 1 to 16; and (b) stratifying Individuals determined to be at risk of developing active TB infection for the most appropriate preventative treatment depending on the outcome of the step (a).

29. A kit for determining tuberculosis (TB) infection status in an individual comprising: (i) a composition comprising a plurality of antibodies or antigen-binding fragments thereof that binds to each of CD4, IFN-γ and HLA-DR and wherein the plurality comprises antibodies or antigen-binding fragments thereof that are individually specific for each of CD4, IFN-γ and HLA-DR; (ii) instructions for use.

30. A kit as claimed in claim 27 wherein the plurality of antibodies or antigen-binding fragments thereof of the composition of (i) additionally binds one or more of CD3 and CD8 and additionally comprises antibodies or antigen-binding fragments thereof that are individually specific for each of one or more of CD3 and CD8.

31. A kit as claimed in any one of claims 29 to 30 additionally comprising one or more TB antigens, a live and/or dead cell discriminator, a fixing and/or permeabilization kit; a golgi inhibitor; and/or a positive control.

32. The method of any of claims 1-16 or the kit of claim 31 wherein the TB antigens comprise PPD and/or RD-1 antigens.

33. The method of any of claims 1-16 or 32 or the kit of claim 31 or 32 wherein the TB antigens comprise one or more of ESAT-6, CFP-10, Rv3615c, and Rv3879c.

34. Use of a kit as claimed in any one of claims 29 to 33 the method of any of claims 1 to 16.

35. A method, composition, kit or use substantially as described herein with reference to the Examples and Figures.

Description

[0120] Examples embodying an aspect of the invention will now be described with reference to the following figures in which:

[0121] FIG. 1 shows example flow plots showing HLA-DR vs IFNγ expression (A) in PFD-stimulated CD4+ T cells, and (B) histograms showing distribution of HLA-DR expression in PPD-specific CD4+IFNγ+ T cells (gate indicates % positive), from 2 LTBI individuals with TB contact (left panels) and 2 individuals without TB contacts (right panels).

[0122] FIG. 2 shows dot plot (A) and ROC curve (B) showing the difference in % HLA-DR expression in PPD-specific CD4+IFNγ+ T cells in LTBI individuals with or without TB contacts.

[0123] FIG. 3 shows measurement of the CD27 MFI (TAM-TB; A), CD45RA-CD27− (B) and CD45RA-CD27+ (C) signatures in LTBI subjects with or without TB contact.

[0124] FIG. 4 shows the results from stimulated PBMCs from 46 IGRA-positive individuals from a cohort with PPD and analysed using the signature of the invention.

[0125] FIG. 5 shows a gating strategy for identification of Mtb-specific CD4+ IFN+ T cells which are positive for HLA-DR.

[0126] FIG. 6 shows percentage of HLA-DR+ cells in the CD3+ CD4+ IFNγ+ cell population for IGRA+ve subjects who progressed to active TB within 2 years of IGRA testing (progressors) and IGRA+ subjects who did not progress to TB within that time-frame (non-progressors).

EXAMPLES

Example 1

[0127] This method works as an immune based test which is able to identify individuals with LTBI who have a greater risk of development to ATB. Thus, the test can be used to risk stratify patients with LTBI for preventative treatment.

Eligibility for the Test:

[0128] Individuals who have been diagnosed as having latent TB, i.e. already have had a positive IGRA/TST test for TB infection, and an absence of clinical/radiological features of TB.

Samples:

[0129] A blood sample may be used (5 ml collected in heparin tube), from which the PBMC are extracted. Whole blood can also be used. If the assay is to be conducted at a later date, the PBMC must be cryopreserved.

Methods

Assay Steps:

[0130] 1. Fresh blood/PBMC or PBMCs recovered from cryo-storage are first rested for 8 hours in a 48 well culture plate, at 37° C., 5% CO2 for 16 hours [0131] 2. After resting, the PBMC are stimulated with three different conditions: [0132] Mycobaterium tuberculosis (Mtb) antigens (PPD at 16.67 ug/ml or RD-1 antigens such as: ESAT-6, CFP-10, Rv3615c, Rv38790 each at 10 ug/ml); [0133] unstimulated; [0134] PMA/ionomycin (1 ug/ml and 100 ug/ml respectively) [0135] 3. After 2 hours, the release of cytokines by the immune cells is blocked by adding a Golgi-inhibitors: a combination of Brefeldin A and Monensin at concentrations of 5 ug/ml and 2 uM respectively. [0136] 4. At the end of the stimulation, the cells are recovered on ice, washed in PBS and stained with specific antibodies and viability stains [0137] 5. The cells are first stained with a viability die, then antibodies specific for the following extra-cellular markers; CO3, CD4, HLA-DR, (additional markers may also be used). [0138] 6. The cells are then fixed, permeabilised and stained with an antibody for intracellular IFNγ [0139] 7. The samples are analysed using a flow cytometry analyser as quickly as possible, then analysed using FlowJo software [0140] 8. The proportion of Mtb-specific CD4+ T cells is calculated by comparing the antigen stimulated samples to the unstimulated. Those which are cytokine positive in the antigen stimulated samples are deemed Mtb-specific, and can be analysed using the immune signature % HLA-DR+ in IFNγ+ population. [0141] the percentage cut-off values for using immune signatures such as this varies between labs and cohorts. So far, the optimal cut-off for high risk vs low risk LTBI is >28% but may be higher (up to 50%) in future cohorts.

Results

[0142] Adekambi et al previously demonstrated that the proportion of activated Mtb-specific CD4+IFNγ+ cells can discriminate between active TB and latent TB with high accuracy, with an equivalent performance seen when either CD38, HLA-DR or Ki-67 was used as the marker of activation [1]. Since then, others have shown that the optimal differentiation between groups in HIV infected individuals is achieved by using HLA-DR as the marker of activation [2].

[0143] To test whether the proportion of activated Mtb-specific CD4+IFNγ+ cells is associated with a known risk factor for progression within a cohort of subjects with latent TB infection (LTB1), we evaluated the proportion of HLA-DR+ Mtb-specific T cells in individuals with an identified recent contact with a TB case, and in those with no recent contact. We calculated the proportion of HLA-DR+ Mtb-specific CD4+IFNγ+ cells, as detailed in [1]; example dot plots from the two groups are displayed in FIG. 1.

[0144] By comparing the two groups, we identified that the proportion of CD4+IFNγ+ cells which were HLA-DR+ was statistically higher in those with an identified contact with a TB case, and that this signature could discriminate between these two groups with reasonable accuracy (AUC=0.813) (FIG. 2).

[0145] This result is surprising and unexpected, because in principle one wouldn't necessarily expect a signature which distinguishes LTBI and active TB to identify risk of progression in LTBI.

[0146] In order to derive the optimal cut-off value to be used for this signature in this particular cohort, a ROC-curve was generated using the data (in this case using Prism), which provides details of the sensitivity and specificity for each data point. Using this analysis, a cut-off of 28% was found to give 89% sensitivity and 68% specificity, while a cut-off of 32.5% gave 67% sensitivity and 84% specificity for identifying those with a TB contact (FIG. 2). Therefore, the cut-off could be adjusted accordingly, depending on how the user would wish to use this test in this target population.

[0147] We also measured 3 other cellular immune signatures which were proposed by a German research group (the TAM-TB assay [3] and an Italian research group (the proportion of CD45RA-CD27−/+ cells within the Mtb-specific CD4+IFNγ+ population [4]) in our cohort of LTBI patients. We found that these other signatures which have also shown promise in distinguishing between active TB and LTBI were not different between the LTBI with contact and without contacts group (FIG. 3), which supports our previous finding that these signatures are not different between recent and remote exposure [5]. Therefore, it is not the case that ail signatures which discriminate between active TB and LTBI are associated with risk of progression from LTBI to ATB, further supporting the surprising nature of the invention described herein.

Example 2

[0148] Quantification of the forward risk associated with the immune signature can be achieved by using a long-term prospective longitudinal cohort study of individuals with LTBI followed up to assess for progression to tuberculosis and correlation with baseline immune signatures. The performance of the HLA-DR signature in a cohort of individuals who were either contacts of TB cases, or recent arrivals to the UK from areas of high endemicity for TB, can be assessed, An ongoing study “PREDICT” is the largest of its kind, and over 10,000 individuals were recruited, from which a proportion (about 90 cases, ˜1% of overall cohort) developed active disease within the study period (2 years follow up). Using a nested case-control cohort approach, the progressors who were IGRA positive and for whom we have samples, were matched with 2× controls per case. So far we have stimulated PBMCs from 46 IGRA-positive individuals from this cohort with PPD and analysed them using this signature (FIG. 4), This study is currently blind to the patient groups. However, we know that the cohort is split with 2:1 controls:progressors, and that the results from the signature identify two distinct populations, with 26% (of the cohort with a relatively high % of HLA-DR+, and 74% of the cohort with a relatively low % of HLA-DR+ (FIG. 4), It therefore seems highly likely that those with the higher responses are those who later progress to active TB, therefore validating the use of this signature in a large cohort to produce highly specific results.

Example 3

[0149] The nested case-control study described in Example 2 and the corresponding data in FIG. 4 were subsequently unblinded. The results are displayed in FIG. 6 and Table 1, below. These prospective clinical outcome data provide confirmatory evidence (in addition to the cross-sectional data presented in FIG. 2) of the efficacy of the invention.

TABLE-US-00001 TABLE 1 Progressors Non Progressors Total samples 13 33 tested (n = 46) Indeterminate  0  3 results* Total samples 13 30 yielding evaluable results (n = 43) Total positive 12  6 (cut-off >44) Proportion scoring 12/13 (92%)  6/30 (20%) positive Diagnostic Sensitivity 92% Diagnostic specificity 80%

[0150] The subjects tested in this nested case-control study were all IGRA+ and are as described in Example 2. The case-control study derives from the larger longitudinal prospective cohort study, PREDICT, also described in Example 2. The cryopreserved PBMC samples from the 46 subjects in the nested case-control study of Example 2 were thawed and stimulated with PPD under the same conditions and incubation period as described in Methods of Example 1, including addition of Golgi-inhibitors and fluorescently labelled antibodies to CD3, CD4, HLA-DR and, following fixation and permeabilisation, an additional labelled antibody for intracellular IFNγ.

[0151] Using the same gating strategy as described in FIG. 5, the proportion of PPD (Mtb)-specific Cd4+IFNγ+ T cells which are positive for HLA-DR was calculated and the results are displayed in FIG. 6 below. 43 of 46 samples showed IFNγ-responding cells in response to PPD stimulation; 3 samples that gave no IFNγ response to PPD and could therefore not be further analysed to identify HLA-DR+ ceils within an IFNγ-positive population. Accordingly, these samples are deemed indeterminate in Table 1 and omitted from FIG. 6. A cut-off of 44% HLA-DR+ cells in the CD3+CD4+IFNγ+ cell population was applied to the data displayed in FIG. 6 to generate the summary results in Table 1.

[0152] The results in Table 1 confirm that this assay predicts which IGRA+ subjects are at risk of progression to active TB within 2 years since 12 of 13 progressors scored positive in the assay whilst only 6 of 30 non-progressors were positive. This assay can thus risk-stratify IGRA+ persons by risk of progression to active TB thereby targeting those more likely to benefit from preventative therapy.

REFERENCES

[0153] 1. Adekambi, T., et al., Biomarkers on patient T cells diagnose active tuberculosis and monitor treatment response. The Journal of Clinical Investigation, 2015. 125(5): p. 1827-1838. [0154] 2. Wilkinson, K A., et al., Activation Profile of Mycobacterium tuberculosis-Specific CD4+ T Cells Reflects Disease Activity Irrespective of HIV Status. American Journal of Respiratory and Critical Care Medicine, 2016. 193(11): p. 1307-1310. [0155] 3. Portevin, D., et al., Assessment of the novel T-cell activation marker– tuberculosis assay for diagnosis of active tuberculosis in children: a prospective proof-of-concept study. The Lancet Infectious Diseases. 14(10): p. 931-938, [0156] 4. Petruccioli, E., et al., Assessment of CD27 expression as a tool for active and latent tuberculosis diagnosis. Journal of Infection, 2015. 71(5): p. 526-533. [0157] 5. Halliday, A., et al., Stratification of Latent Mycobacterium tuberculosis Infection by Cellular Immune Profiling. The Journal of Infectious Diseases, 2017. 215(9): p. 1480-1487.