Patent Application
20240027459
This invention relates to the field of immuno-oncology serum-based biomarkers to predict long-term tumour progression in general (prognostic) or in response to various classes of anticancer therapy (predictive).
Tumour stage, residual disease after initial surgery, histological type and tumour grade are the most important clinico-pathological biomarkers related to the clinical outcome of cancer patients [Gadducci et al. (2009) Crit. Rev. Oncol. Hematol. 69, 12-27; Vošmik et al. (2018) Pathol Oncol Res. 24, 373-383] Beyond these, specific biomarkers are often utilised either for long-term estimation of cancer patient survival (i.e. prognostic biomarkers) or to guide the application of, or monitor the responses to, different anticancer therapies (i.e. predictive biomarkers) [Nalejska et al. (2014) Mol Diagn Ther. 18, 273-284; Califf (2018) Exp. Biol. Med. 243, 213-221]. The use of such prognostic or predictive biomarkers gained a huge momentum recently with the emergence of cancer immunotherapy [Yuan et al. (2016) J. Immunother. Cancer 4, 3; Shindo et al. (2019) Cancers (Basel) 11, 1223]. This was because several highly efficacious immunotherapies were found to work better in patients possessing a combination of broad tumour-level biomarkers like high tumour mutational/neoantigen burden (TMB), high PD-L1 levels and high tumour-infiltrating lymphocytes (TILs) [Cristescu et al. (2018) Science. 362 eaar3593; Fumet et al. (2020) Eur. J. Cancer 131, 40-50]. In general, broad biomarkers (like the ones mentioned above) have been found to predict patient responses to immunotherapy better than very specific biomarkers (e.g. TCR or BCR clonality) [Topalian et al. (2020) Science 367 eaax0182; Pardoll (2012) Nat. Rev. Cancer. 12, 252-264; Havel et al. (2019) Nat. Rev. Cancer 19, 133-150]. However, a major drawback of these tumour-level biomarkers is: (1) their detection is highly invasive thereby making repeated detections problematic; and (2) tumour-material may not be always available at every phase of patient monitoring and follow-up to inform clinicians thereby hampering long-term usage of these biomarkers for immuno-oncology. To overcome these issues, the field of immuno-oncology is starting to intensively consider less invasive sources of good biomarkers like those that can be detected in patient serum [Ferreira et al. (2019) Curr Drug Targets 20, 81-86; Mehnert et al. (2017) Clin. Cancer Res. 23, 4970-4979]. In the last years, several investigations have assessed different biological variables in sera from cancer patients in order to detect biomarkers able to reflect either the response to chemotherapy, targeted therapy or immunotherapy and/or survival. Predictive, monitoring or prognostic relevance of various serum-associated cytokines, chemokines, metabolites and specific immune cell phenotypes has been assessed and, in some case, validated for clinical use in immuno-oncology context e.g. serum levels of IL6 or CRP [Chalabi et al. (2020) Nat. Med. 26, 566-576; Versluis et al. (2020) Nat. Med. 26, 475-484]. However, this progress is far too limited, as several of these biomarkers are not always reliable and their contextual activities are not completely elucidated. For example, the interaction between tumour and immune system/stromal cells, the production of cytokines/chemokines by the tumour itself and the peripheral immune interactions can result in different local and systemic levels of cytokines/chemokines in cancer patients. Several studies have shown that most cytokines/chemokines are not independent prognostic biomarkers, and individual cytokines are not specific/reliable enough for screening purposes. And although several new multiplex techniques like, the cytokine bead array (CBA), Luminex or MSD platform, have been developed for simultaneous analysis of multiple cytokines/chemokines in serum samples; yet these technical advancements have not entirely overcome the biological and clinical limitations of immunological serum biomarkers.
All these multiplexed serum immunological biomarker assays tend to have a largely quantitative approach toward biomarker identification. This creates a big hurdle in estimating the patient serum's “phenome” (a sum of all phenotypic traits) since correct phenome-mapping requires both quantitative as well as qualitative (i.e. functional or effector) estimation of serum factors. The outright caveat of quantitative biomarker-estimation approach for immunological factors is that, quantity for a given cytokine/chemokine may not be linearly associated with their actual biological functions. Cytokines/chemokines linearly exert their specific functions only up to a threshold of concentration (which is specific for each cytokine/factor, its biological context as well as diseased tissue), beyond which their effects tend to exert a self-regulating function thereby causing functional “plateau” or even suppression. This situation is further complicated by the fact that, in full serum derived from cancer patients, immuno-stimulatory, immuno-suppressive and, immuno-homeostatic cytokines, haematopoietic factors or chemokines are simultaneously present. Thus, factors with contradictory or complementary immunological functions co-exist in the serum at the same time. This situation is further complicated by post-translational modifications of cytokines/chemokines that can drastically alter their immunological functions. Finally, there are several known or unknown biomolecular entities that are not covered (or cannot be reliably covered) by routinely applicable multiplexed technologies. Accordingly, multi-parametric detection of all (or some of) these factors via multiplexed technologies, frequently ends up creating a problematic situation with regard to serum phenome-mapping i.e., detection of contradictory (functional or prognostic/response/predictive) trends for these cytokines, chemokines or factors, which makes it hard to draw concise immunological conclusions about the therapy-responsiveness and immunological status of the patient. Although efforts are being made to overcome these limitations via application of advanced computational or bioinformatics approaches, yet these tend to fail in predicting non-linear characteristics of immunological pathways e.g. oscillations, chaos and concentrations-dependent non-linearity. Thus, deciphering the functionally “integrated immunological activity” of these cytokines/chemokines/factors remains unknown based only on the quantitative measurement of these individual proteins (as is currently the standard practice in the field everywhere). This in turn makes it almost impossible to reliably map a patients' “Immunological Serum Biomarkers” (ISB)-based phenotype. In the field of serum biomarkers in oncology currently, actionable analysis of functional immunological status of cancer patients' sera is entirely absent on the level of soluble factors.
The invention relates to functional immunological biomarker assay to reliably estimate the ISB-phenotype in cancer patients. Specifically, this biomarker modality (i.e. serum-based functional immunological assay or sFIS assay) consists of two of the most major serum-relevant immunological pathways (i.e. Nuclear factor kappa-light-chain-enhancer of activated B cells or NFkB and, interferon or IFNs responses) detected on the level of genetic reporter monocytes of human origin, exposed to the serum derived from cancer patients. This sFIS assay is particularly efficient at simultaneously estimating differential long-term survival of cancer patients. It simultaneously integrates both negative and positive prognostic/predictive biomarker modalities in the same assay.
The present invention is summarised in the following statements.
1. An in vitro method for determining the health status of a cancer patient, the method comprising the steps of:
Herein the blood sample is typically a processed blood sample such as plasma, or preferably serum. Alternatively, the processed blood sample is a purified protein or lipid fraction obtained from the blood sample. Or a physiologically generated blood-filtered fluid e.g., ascites fluid, which is blood filtered fluid that builds-up in the patient's peritoneum.
The method can be performed prior, after or during therapy as well as at first or recurrence diagnosis stage.
2. The method according to statement 1, wherein the health status predicts the life expectancy of the cancer patient after cancer therapy.
3. The method according to statement 1 or 2, wherein the reporter gene construct for NFkB signalling, and the reporter gene construct system for IFN signalling, are in the same cell.
4. The method according to statement 1 or 2, wherein the reporter gene construct for NFkB signalling, and the reporter gene construct system for IFN signalling, are in different cells.
5. The method according to any one of statements 1 to 4, wherein the cells are primate cells, preferably human cells.
For example, rodent cells can be used when they express human immune receptors or sensors upstream of the reporter constructs.
6. The method according to any one of statements 1 to 5, wherein the cells in step 1) are monocytes.
Other suitable cells are for example macrophages, dendritic cells, granulocytes, T cells, B cells, NK cells, lymphocytes, myeloid cells, cancer cells, malignant cells, adenoma cells, carcinoma cells, neoplastic cells, virally or chemically transformed cells, epithelial cells, fibroblasts and embryonic cells.
. The method according to any one of statements 1 to 6,
In an alternative method a ≥75th percentile of overall data point distribution in a patient screening data set is used to make the assessment.
8. The method according to any one of statements 1 to 6, wherein a ≥1.3 fold-change of expression level of the reporter gene and/or activity of the expressed reporter gene for NFKb signalling compared to the reference value is a positive indicator of the health status of the cancer patient. (the cancer patient is likely to show a prolonged overall survival).
In an alternative method a ≥75th percentile of overall data point distribution in a patient screening data set is used to make the assessment.
The method according to any one of statements 1 to 6, where the reporter gene is a luciferase, a fluorescent or bioluminescent protein or an alkaline phosphatase
9. The method according to any one of statements 1 to 8, wherein the expressed reporter gene is a secreted protein
10. The method according to any one of statements 1 to 9, wherein the IFN reporter protein is under the control of an interferon-induced transcription factors-responsive promoter linked to one or more (e.g. 5) copies of IFN-stimulated response element (ISRE) sequences.
Interferon-induced transcription factors are for example members of the STAT family proteins and members of the IRF family.
11. The method according to any one of statements 1 to 9, wherein the NFKB reporter protein is under the control of a NFkB signalling complex-responsive promoter linked to multiple (e.g. three) copies of c-REL binding site and multiple (e.g. five) copies of the NFKB consensus transcriptional response element.
NFkB signalling complex-responsive protein are for example NFkB p50/p65 or members of the REL family.
12. The method according to any one of statements 1 to 11, wherein the patient has ovarian cancer, malignant ovarian cancer or high-grade serous ovarian carcinoma.
14. The method according to any one of statements 1 to 12, wherein the patient underwent an anticancer therapy selected from the group consisting of chemotherapy, radiotherapy, small-molecule inhibitors, targeted therapy, palliative therapy, alternative therapies and immunotherapy.
15. Use of engineered human cells comprising a reporter gene construct for NFkB signalling, and/or a reporter gene construct system for IFN signalling, in determining the health status of a cancer patient.
16. An in vitro method for predicting tumour progression or response to anticancer therapy of a cancer patient, wherein the cancer is selected from the group consisting of ovarian cancer, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC) and liver hepatocellular carcinoma (LHC), the method comprising the steps of:
17. The method according to statement 16, wherein the response to anticancer therapy predicts the life expectancy of the cancer patient after cancer therapy.
18. The method according to statement 16, wherein the prediction of tumour progression predicts the medium-to-long term survival of a cancer patient.
19. The method according to any one of statements 16 to 18, wherein the immune cells are human cells.
20. The method according to any one of statements 16 to 19, wherein the immune cells in step 1) are monocytes.
21. The method according to any one of statements 16 to 20, wherein a 1.5 fold-change of expression and/or activity of the first reporter protein for NFkB signalling, compared to the reference value, is a negative indicator of tumour progression or response to anticancer therapy.
22. The method according to any one of statements 16 to 20, wherein a 1.3 fold-change of expression level and/or activity of the second reporter protein for IFN signalling, compared to the reference value, is a positive indicator of the tumour progression or response to anticancer therapy.
23. The method according to any one of statements 16 to 22, where the reporter protein is selected from the group consisting a luciferase, a fluorescent or bioluminescent protein and an alkaline phosphatase.
24. The method according to any one of statements 16 to 23, wherein the expressed reporter gene is a secreted protein.
25. The method according to an one of statements 16 to 24, wherein the ovarian cancer is malignant ovarian cancer or high-grade serous ovarian carcinoma.
26. The method according to any one of statements 16 to 25, wherein the patient underwent an anticancer therapy selected from the group consisting of chemotherapy, radiotherapy, small-molecule inhibitors, targeted therapy, palliative therapy, alternative therapies and immunotherapy.
27. Use of an engineered mammalian cells comprising a NFkB reporter gene construct, and an IFN reporter gene construct,
in predicting tumour progression or response to anticancer therapy of a cancer patient, wherein the cancer is selected from the group consisting of ovarian cancer, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC) and liver hepatocellular carcinoma (LHC).
Control measurements are performed using serum from a healthy person (i.e. without cancer), typically using a pool of serum of several persons. The pooling of control sera avoids bias by an undiagnosed cancer, by a condition other than cancer which would effect NFkB or IFN signalling, and averages for age and sex, and other variations in the population.
The expression of reporter genes is determined directly by detecting the presence of mRNA (rtPCR) of or the expressed protein via e.g. the fluorescence of a Green Fluorescent Protein, or by an antibody assays,
Alternatively, when the reporter gene encodes an enzyme, the activity of the enzyme is a used as measure of the expressed protein.
In certain embodiments the expressed protein is an secreted protein, such that the expression or activity is determined in the medium of the cell culture.
In other embodiments, the expressed protein is an intracellular protein, and the product of the enzymatic reaction is secreted.
In other embodiments, the expressed protein is an intracellular protein and the protein and/or reaction products of an enzymatic reaction are detected by microscopic methods, or by cell sorting or, are determined in the cell lysate.
A non-limiting list of reporter genes includes fluorescent proteins, enzymes capable of generating colorimetric products, or enzymes capable of generating fluorescent products, such as luciferase or alkaline phosphatase. When both reporter genes are in the same cell, reporter genes are selected such that they can be individually detected.
The methods of the present invention are suitable on various types of cancer such as ovarian cancer, lung cancer, cervical cancer, leukaemia, lymphoma, melanoma, sarcoma, bladder cancer, renal cell cancer, breast cancer, colorectal cancer, brain cancer, urothelial cancer, kidney cancer, cancers of epithelial origin and cancers with circulating metastatic cancer cells.
The methods of the present invention allow to evaluate the health status of a patient undergoing or having completed a therapy such as chemotherapy, radiotherapy, immunotherapy, targeted therapy, surgery, palliative therapy, herbal therapy, therapy with anticancer vaccines, oncolytic viruses, cytokines/chemokines, recombinant proteins or physico-chemical therapeutics.
Figure Legends:
The present invention shows that the major ISB-based phenotype-defining pathways largely come down to innate inflammatory (NFkB inflammatory response) or effector innate/adaptive immune pathways (IFNα-β/IFNγ response), mainly operating via either NF-kB response modules (NFkB or REL) or an IFN response module (ISRE-binding TFs, IRF, IRF1/8). Thus, the “core” ISB-based phenotype in cancer patients seems to consist of various serum-associated cytokines or immunological factors largely engaging the NFkB signalling or IFNs signalling immune-pathways.
Cancer serum biomarkers can in fact proficiently induce NFkB and/or IFN signalling, however there is no minimal set of genes that can be used as a diagnostic modality for a broad range of cancer types. This problem is addressed in the present invention, by using the intracellular downstream signalling, which represents the overall response of stimulation/inhibition as a prognostic/predictive tool rather than using the individual signalling proteins.
The distinctive predictive impact on OV-tumours associated with NFkB and IFNs signalling (genetic) signatures was investigated in OV patients. The ability to predict OV-tumours' short-term (i.e., pathological response or pR) and medium-term (i.e., ≥1 year long, relapse-free survival or RFS) responsiveness to standard-of-care (SOC) anti-OV chemotherapy was determined (e.g., chemotherapies based on platinum/taxane-agents, gemcitabine and topotecan). Pre-treatment tumour associated IFNs signalling signature levels failed to differentiate subsequent chemotherapy responders from non-responders in terms of both pR (
The present invention illustrates that a “core” ISB-based phenotype in OV patients is best captured by a human myeloid cells-based functional biomarker screening “platform” that can simultaneously capture the induction of NFkB as well as IFNs responses in myeloid cells exposed to patient's serum-associated peripheral inflammatory factors thereby giving the best integrated serum-functional immunological status (sFIS) for OV patients.
THP1 binary-reporter cells such as used in example 1 were suitable for the present sFIS assay. These selection/testing criteria and such assessment workflow can be eventually applied to test any such reporter system, whether procured commercially or generated in-house, for suitability in running the present sFIS assay. As long as such criteria are met, any reporter system can be utilized for running this sFIS assay. The present invention shows such as disclosed in the examples can faithfully capture the functional immunological disbalance in the serum immunobiology of OV patients and thereby more directly emphasize the inflammatory (NFkB response>IFNs response) vs. immunogenic (IFNs response≥NFkB response) ISB-based phenotype characteristics.
The present invention shows that ISB-based phenotype estimation has better utility in predicting medium-to-long term survival of OV-patients (i.e. progression-free survival or PFS/overall survival or OS) rather than short-term responses (pR). This provides an advantage since the current serum biomarker analyses state-of-the-art already has a well-established biomarker for predicting short-term pR (i.e. serum-associated OV antigen, CA125). The present invention provides a reliable biomarker modality for predicting long-term patient survival on the basis of serum profiling. The present invention also show that negative prognostic/predictive biomarkers tend to have a more consistent biomarker performance in OV-patients irrespective of therapy subgroups whereas positive prognostic/predictive biomarkers may show therapy-dependent variability and importantly, both these complicated trends can be simultaneously as well as sufficiently captured by the present sFIS assay, much better than CA125 analyses (for PFS/OS estimation), thereby further outlining the versatility of the present assay.
A 96-well plates-based throughput sFIS assay is based on human myeloid (monocytic) THP1 cell line stably expressing two inducible reporter constructs encoding for: (1) a luciferase gene (coding for a secreted form of luciferase enzyme) under the direct control of a dedicated promoter sequence linked to IFN-stimulated response element (ISRE) sequences; and (2) a secreted embryonic alkaline phosphatase (SEAP) reporter gene (coding for the widely utilized SEAP enzyme, a truncated form of GPI-anchored placental alkaline phosphatase) under the control of a dedicated promoter linked to c-REL binding site as well as NFKB consensus transcriptional response element. Of note, these inducible reporter constructs are interchangeable or even replaceable for other reporter outputs (e.g. fluorescence reporters, various luciferases and other colorimetric substrate-generating enzymes), as long as two distinct (inducible) reporter systems are separately linked to NFkB or IFN signalling pathway thereby allowing their distinct assessment, when both reporter systems are in the same cell line. Also, such constructs do not need to be expressed by the same THP1 cell line and can in fact be expressed by separate THP1 sub-cell lines. These THP1 reporter cells are supposed to sense functional cytokines, inflammatory factors, pattern-recognition receptor (PRR) agonists or IFN cytokines, thereby engaging the NFkB and/or IFN responses, resulting in secretion of SEAP/luciferase into the extracellular medium (in parallel to transcription of canonical NTGs or ISGs programs) thereby faithfully reporting commencement of NFkB-driven and/or ISRE-based transcriptional programs. By providing the proper substrate solutions for these enzymes, simultaneous detection of these two functional immunological readouts can be achieved per sample (colorimetric for SEAP and bioluminescence for luciferase enzyme; or other desired readouts as feasible or suitable so long as they are completely distinct i.e. fluorescence vs. bioluminescence vs. colorimetric).
Accordingly, exposing these THP1 binary-reporter cells in a control experiment to standard PRR agonists like lipopolysaccharide (LPS: a TLR4 agonist), 5′ppp-dsRNA bound to a transfection reagent LysoVec (a RIG-I agonist) and 2′3′-cGAMP (a STING agonist) differentially (and sometimes distinctively) stimulated the NFkB (
In general, TRAIL, IL18, IL6 and IL10 did not sufficiently activate either of the transcriptional programs; although interestingly TGFβ a well-known pleiotropic cytokine induced threshold levels of both NFkB and IFNs responses (
The biomarker efficiency of serum-induced NFkB or IFNs response through a “prospective retrospective” biomarker analyses strategy [Henry & Hayes (2012) Mol. Oncol. 6, 140-146; Simon et al. (2009) J. Natl. Cancer Inst. 101, 1446-1452] was tested on 98 archived serum specimens derived from randomly selected 32 OV-patients throughout their disease course at UZ Leuven, Belgium (hereafter referred to as the “UZL-CSI OV cohort”). For validating ISB-based phenotype-defining biomarker trends via sFIS assay, the UZL-CSI cohort was highly suitable since it provided the necessary immunological “dynamism” in terms of overall serum context. This is because it was composed of a diversity of pre-treatment (at primary/recurrence diagnosis or untreated recurrence stages) as well as on/post-treatment (with standard-of-care chemotherapies or specific clinical trials-related targeted or immune-therapies) serum specimens.
Remarkably, serum derived from these OV-patients indeed induced significantly higher NFkB responses in the THP1 binary-reporter cells as compared to IFNs responses (
An immune signalling-amplitude analysis for NFkB/IFN responses generated via the present sFIS assay showed that OV-patients that have prolonged survival (PFS or OS; but especially OS) (
The above observations evidently outlined the superiority of the present sFIS assay in reliably mapping ISB-based phenotype, especially on the level of serum-associated functional immunobiology and immune-signalling amplitudes. Next, the biomarker efficacy of NFkB and IFN responses derived from the present sFIS assay were tested. The OV-patients with shorter PFS/OS had serum that induced significantly higher NFkB responses in the present sFIS assay whereas OV-patients with longer-PFS/OS had serum that induced higher IFN responses (although statistical significance was only reached for longer OS) (
As mentioned previously the UZL-CSI cohort was composed of serum specimens derived from heterogeneous clinical settings for OV-patients including those derived from patients on- or post-anti-OV therapies. To this end, it was investigated how anticancer therapy was affecting NFkB or IFN responses generated by the sFIS assay. The present sFIS assay showed more differential inflammatory trends between different treatment or non-treatment ovarian cancer patient subgroups such that, most untreated subgroups had more IFN responses than NFkB responses (especially, at recurrence diagnosis and pre-treatment recurrence stages) (
Day 1.
Step 1. Scrape the semi-adherent cultures of the NFkB (SEAP reporter enzyme) and IFNs (LUCIA reporter enzyme) response (binary) genetic reporter THP1 human myeloid cells (in this case commercially procured from Invivogen; but similary cells are available from several other manufacturer's or can also be generated in-house) off the regular/routine cell culture flask(s) (while still in their selection media) with a standard cell scraper (Sarstedt).
Selection medium is THP1 cell culture medium with selection antibiotics: RPMI 1640, 2 mM L-glutamine, 25 mM HEPES, 10% heat-inactivated fetal bovine serum, 100 μg/ml Normocin, Pen-Strep (100 μg/ml), 10 μg/ml Blasticidin and 100 μg/ml of Zeocin.
Step 2. Count the amount of live THP1 cells through standard cell counting methodology.
Step 3. Plate 35000 THP1 cells in each well of a standard 96-well flat-bottom plate (in 100 μL cell culture medium*). Plate enough wells, considering the number of patient serum samples to be tested as well as the following mandatory sFIS assay controls:
The THP1 cells for the sFIS assay itself should be plated in the normal cell culture medium without their specific selection antibiotics.*
Day 3.
Step 4. Add 100 μL of (freshly defrosted) patient serum per well (in the 96-well plate from Day 1) on top of the THP1 cells thereby making the total volume in each well, 200 μL (i.e., 100 μL patient serum+100 μL THP1 normal culture medium). Composition of the normal THP1 cell culture medium: RPMI 1640 media, 2 mM L-glutamine, 25 mM HEPES, 10% heat-inactivated fetal bovine serum (FBS), 100 μg/ml Normocin and Pen-Strep (100 μg/ml).
Step 5. To setup the sFIS assay controls add, to the corresponding wells (as plated on Day 1), 100 μl of the above THP1 normal culture medium (background control), THP1 normal culture medium containing 1000 μg/mL of LPS (positive control) or 100 μl of normal human serum (baseline control) (see Step 3 above).
Day 4.
Step 6. Take the 200 μL of media/serum mixtures off the respective wells from the corresponding 96-well plates after 24 h of incubation as initiated on Day 3. In parallel, thaw the Quanti-Blue and Quanti-Luc/Quanti-Luc GOLD reagents (stored at −20° C.) and let them normalize to room-temperature. Herein, Quanti-Blue (Invivogen) is a substrate for the SEAP reporter enzyme while Quanti-Luc or Quanti-Luc GOLD (Invivogen) are the substrates for the LUCIA reporter enzyme.
Step 7. Pipette 100 μL of this media/serum (into a new standard cell culture-grade clear bottom 96-well plate) and, add 100 μL of Quanti-Blue reagent (the substrate for the SEAP enzyme, reporting for activation of the NFkB activity; Invivogen), and incubate at 37° C. in standard cell culture incubator until the LPS positive control wells become medium-dark blue due to production of a colorimetric byproduct (takes approximately 4-6 h).
Step 7A. Read the SEAP activity via absorbance capture (655 nm wavelength) with a standard 96-well plate reader with default (bottom-reading mode) settings for colorimetric/absorbance reading (e.g. FlexStation, Molecular Devices).
Step 8. Pipette the other 100 μL of the media/serum (into a separate, white, opaque-bottom 96-well plate) and add 50 μL of Quanti-Luc or Quanti-Luc GOLD reagents (the substrates for the LUCIA enzyme, reporting for activation of the ISRE/IRF activity; Invivogen).
Step 8A. Read the LUCIA luciferase activity via bioluminescence (500 ms of integrated signal) immediately (very important if using Quanti-Luc rather than Quanti-Luc GOLD!) after adding the Quanti-Luc/Quanti-Luc GOLD reagents (with the above plate reader; default settings for bioluminescence detection and top-reading mode) and repeat this 2 times with a 5 min interval and select the readings with most stable values as judged on the basis of background/baseline/positive controls.
The raw data are now ready for further analyses.
Further analysis.
The sFIS Assay Biomarker Cut-Offs:
The above examples demonstrate the biomarker efficacy of the present sFIS assay with respect to well-established clinico-pathological estimators of patient survival or prognosis (pR, PFS or OS). Beyond these, there also exists an immunological parameter that predicts patient prognosis i.e., neutrophil-to-lymphocytes ratio (NLR), calculated by taking a ratio between absolute counts of neutrophils and lymphocytes estimated in the blood of the cancer patients. It is universally established that a very high NLR score predicts shorter survival in cancer patients thereby making it an important immunological negative prognostic parameter. To this end, it was evaluated how the present sFIS assay outputs align with NLR scores relative to the standard CA125 biomarker. In line with clinical experience and expectations, the serum CA125 antigen levels (indicative of high degree of tumour burden) in OV-patients positively correlated with higher NLR scores (
Several studies have shown that origins of many (if not all) peripheral immuno-biomarkers in cancer patients can be traced to tumours. To appreciate if this also highlights a systemic inflammatory circuit on the level of NFkB/IFN responses, an existing scRNAseq map was probed that profiled tumour-infiltrating and (matched) blood-derived immune cells (procured from, renal cell or large cell neuroendocrine, carcinoma patients), with validated NFkB signalling or (type I/II) IFNs response-associated genetic signatures Within tumour-infiltrating immune cells, NFkB/IFN response signatures were mainly expressed by myeloid cells, which could also be traced to at least a small subset of peripheral myeloid cells, thereby implying a myeloid-level NFkB/IFN response circuit between the tumour and the periphery. To this end 12 TCGA-datasets were selected (spanning>5000 cancer patients) with diverse solid-tumours typically showing either immunotherapy-responsiveness (i.e., lung cancer, LUAD/LUSC; head & neck cancer, HNSC; bladder cancer, BLCA; renal cell cancer, KIRC; liver cancer, LIHC), or immunotherapy-resistance (ovarian cancer, OV; endometrial cancer, UCEC; sarcoma, SARC; breast cancer, BRCA; pancreatic cancer, PAAD; cervical cancer, CESC). Likewise, these immuno-biomarkers exhibited a highly variable cancer-type dependent prognostic impact on patient overall survival (OS). These observations exposed the considerable expressional (quantitative) and functional (effect on OS) heterogeneity or contradictions amongst major immuno-biomarkers. Next, it was investigated whether the more simplified ‘dynamics’ of the binary NFkB/IFN responses may better summarize a general inflammatory status than these heterogenous patterns of individual immuno-biomarkers. To address this, the median expression and median hazard ratios (HR) were derived for each of the highly expressed immuno-biomarkers across all 12 cancer-types and applied dimensionality reduction (PCA). This consolidated two distinct clusters i.e., a cluster of immuno-biomarkers with negative prognostic impact (i.e., high gene expression=OS↓), and those with positive (i.e., high gene expression=OS↑). Subsequently, the literature-driven annotation of the immuno-biomarker genes within these clusters for major NFkB target genes (NTGs) or IFNs stimulated genes (ISGs) showed that, negative prognostic immuno-biomarkers ‘enumerated’ a disbalance in these signalling pathways in favour of NFkB signalling (NTGs>ISGs), whereas positive prognostic immuno-biomarkers tended to have a relatively better balance (NTGs≈ISGs) between these signalling modalities. Thus, a disbalance in tumoural immuno-dynamic signalling that favours NFkB responses predicted shortened patient survival whereas more balanced signalling between IFN-NFkB responses predicted prolonged patient survival.
The prognostic impact of tumoral NFkB/IFN response signatures on patient OS was investigated in the above 12 cancer-types, to delineate which cancer-types might best capture the contrasting interplay between NFkB (negative prognostic) and IFN (positive prognostic) responses (
OV-patients exhibiting a bad prognostic, si-NFkB responseHIGHsi-IFN responseLOW/NULL status, is consistent with OV's immuno-resistant nature. To this end, it was investigated whether blunting si-NFkB response while potentiating si-IFN response can be utilized as a guiding strategy to design precision combinatorial immunotherapy regime against OV. To address therapeutic inhibition of si-NFkB response, we pursued an in-silico drug-prediction relying on a computational algorithm exploiting biomedical literature-associated drug-gene relationships to predict drugs or drug-target's associations to the NFkB response-signature. Interestingly, this analyses recurrently enriched for anti-cytokine immuno-therapies or inflammatory therapeutic-targets, especially TNF inhibitory/blocking therapeutics like TNF-alpha inhibitors or infliximab (an anti-TNF antibody), to broadly target the NFkB response. This was concurrent with the observation that, amongst various cytokines that were screened, TNF was the most robust NFkB response-inducer.
The notion of applying anti-cytokine immunotherapies like anti-TNF antibodies is not new. In fact such anti-cytokine immunotherapies have shown recurrent success in preclinical studies whilst failing to reach similar success in clinical trials, across various cancers including OV. Thus, it has been proposed that the success of anti-cytokine immunotherapy might be contingent on biomarker-driven application, however precision biomarkers guiding its application are elusive. It was investigated whether si-NFkB response can better guide application of anti-TNF immunotherapy in OV-patients. Re-analyses of a small (only existing) human OV clinical trial, administering infliximab, wherein pre/on-treatment transcriptomic profiles for ovarian cancer-ascites were available, showed that: TNF levels in ovarian cancer-ascites couldn't sufficiently differentiate infliximab-responding vs. non-responding patients, whereas heightened NFkB response signature relatively better differentiated infliximab-responders from non-responders.
To validate these observations on the level of sFIS assay, the murine (preclinical) ID8 cells-based orthotopic model of metastatic OV was exploited, since these metastatic ID8-tumour bearing mice experience a latent (significant) spike in serum TNF-levels, post-tumour implantation. Herein, various (pre-)clinical studies have demonstrated anti-tumour efficacy as well as immunostimulatory or IFNs-inducing impact of (standard) anti-OV therapies like, combination of paclitaxel+carboplatin (PTX+CBP) chemotherapies, and/or PARP inhibitors (PARPi); yet it is not entirely clear which of these conventional therapies might best potentiate the anti-tumour efficacy of anti-TNF antibodies. It was further investigated whether sFIS assay may allow us to predict the anti-tumour efficacy of above therapeutics (alone or in combination with anti-TNF immunotherapy). ID8-tumour bearing mice were treated with PTX+CBP, PARPi, or anti-TNF immunotherapy alone, or in combinatorial regimens (i.e., PTX+CBP+anti-TNF antibody, or PARPi+anti-TNF antibody) (
| Number | Date | Country | Kind |
|---|---|---|---|
| 20192170.7 | Aug 2020 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/073091 | 8/19/2021 | WO |
