Method for predicting the develepment of resistance to immunotherapy

Abstract

The present invention relates to a method for predicting the development of resistance to immunotherapy, in particular to immune checkpoint inhibitors (ICI), wherein said method comprises measuring the expression levels of several miRNAs in a biological sample collected from a patient with cancer before the start of an anti- tumour treatment.

Description

[0001] METHOD FOR PREDICTING THE DEVELEPMENT OF RESISTANCE TO IMMUNOTHERAPY

[0002] The present invention relates to a method for predicting the development of resistance to immunotherapy. In particular, the invention relates to a method for predicting the development of resistance to immunotherapy, in particular to immune checkpoint inhibitors (ICIs), wherein said method comprises measuring the levels of expression of several miRNAs in a biological sample collected from a patient with cancer before the start of an anti-tumour treatment.

[0003] Furthermore, the present invention also relates to the use of antagonists of several miRNAs for the treatment of solid tumours.

[0004] It is well known that immunotherapy is by now the standard of care for numerous solid tumours, including melanoma.

[0005] In particular, metastatic melanoma is one the most aggressive and lethal skin cancers, characterised by a constantly increasing incidence at a global level (M. Liu et al., 2023). The prognosis for patients affected by melanoma is highly variable and depends mainly on the stage of the disease at the time of the diagnosis. For patients with localised melanoma, classified as stages I and II, the prognosis is generally positive, with five-year survival rates of over 90% (Saginala et al., 2021). However, for patients with metastatic disease, corresponding to stage IV, the situation is dramatically different: the five-year survival rates fall below 15-20% (Enninga et al., 2017). This decrease is attributable to the spread of the disease into distant organs, which complicates the treatment options and often renders traditional therapies, such as chemotherapy, ineffective.

[0006] Thanks to the introduction of targeted therapies and immunotherapy, the treatment of metastatic melanoma has seen radical progress, significantly improving management of the disease.

[0007] However, the appearance of resistance to treatments still represents one of the main therapeutic challenges, pushing research towards continuous innovation and the optimisation of existing therapeutic strategies.

[0008] The most effective treatments include targeted therapies, which focus on specific genetic mutations, such as BRAF mutations, which are found in 50% of the cases of melanoma. BRAF inhibitors, on their own or in combination with MEK inhibitors, represent a standard therapeutic strategy for patients with BRAF-mutatedmelanoma (Chapman et al., 2011; Hauschild et al., 2012). These drugs act by inhibiting the molecular signalling pathways altered by the tumour, thereby blocking cell proliferation and slowing the progression of the disease.

[0009] Despite the initial successes, drug resistance emerges frequently over time. Tumour cells, in fact, activate alternative signalling pathways, such as those mediated by MEK, PI3K or AKT, which enable the tumour’s survival also in the presence of inhibitors.

[0010] Drug resistance is a complex phenomenon which can manifest itself through genetic mutations in the tumour, molecular adaptations or clonal evolution of tumour subpopulations (LoRusso et al., 2020; Sosman et al., 2012).

[0011] This situation has driven research to explore new therapeutic solutions, such as the introduction of immunotherapy, which aims to enhance the immune response against the tumour. Immunotherapeutic approaches, in particular immune checkpoint inhibitors (ICIs), have revolutionised the treatment of metastatic melanoma, a disease which, until a short while ago, had few effective therapeutic options. The drugs that inhibit the PD-1 or CTLA-4 receptors act by stimulating the immune system to recognise and destroy tumour cells, thus strengthening the immune response against melanoma (Knight et al., 2023). In the past, patients with advanced melanoma were mainly treated with chemotherapy and traditional immunological therapies, such as interferon alpha or interleukin-2, which showed modest results. The response to treatments was generally poor and the prognosis for patients with distant metastasis remained unfavourable. However, the introduction of immune checkpoint inhibitors, such as nivolumab, pembrolizumab and ipilimumab, has radically changed this situation.

[0012] Unlike targeted therapies, which act directly on tumour mutations, ICIs stimulate an immune system response against the tumour, enabling a broader and more effective therapeutic response in a larger number of patients. In particular, these drugs act by blocking the proteins that limit the immune system’s activity against the tumour, thus enabling T cells to more effectively attack tumour cells. As a result, the overall survival of patients has increased considerably, with response rates that now exceed 50-60%. In particular, combined therapies, such as the association of nivolumab and ipilimumab, have led to lasting responses in a significant number of patients.

[0013] Although immunotherapy has significantly improved the therapeuticapproach to metastatic melanoma, not all patients respond favourably to therapy (Hamid et al., 2019; Schatton et al., 2010). It is estimated, in fact, that about 40-50% of patients do not obtain a significant response to ICIs, and many of those who initially respond develop a relapse of the disease within the first two years (Larkin et al., 2019).

[0014] Resistance to ICIs is tied to a series of factors, including activation of immune evasion mechanisms or the existence of tumour populations intrinsically resistant to therapy or else the development of a tumour microenvironment that inhibits the activity of an efficient immune tumour rejection response (Lei et al., 2020; Lim et al., 2023).

[0015] Therefore, despite the therapeutic potentialities of ICIs, resistance represents a significant obstacle, highlighting the need for predictive methods that may guide the selection of the most suitable patients and monitor the response to therapy in real time and in the least invasive manner possible.

[0016] For this purpose, the analysis of circulating tumour DNA (ctDNA) and circulating microRNAs (miRNAs) is emerging as one of the most promising methods (Fattore et al., 2021). The advent of liquid biopsy represents, in fact, an enormous opportunity to overcome the limits of traditional tissue biopsies. Strategies based on liquid biopsy envisage the analysis of various analytes originating from many body fluids, including blood, plasma, serum, ascites, saliva, urine, and pleural, pericardial and cerebrospinal fluids. Circulating biomarkers are able to provide crucial information about the molecular dynamics of the tumour, allowing its evolution and the response to treatments to be monitored in a non-invasive manner (Johnson et al., 2022). These biomarkers can be extracted from easily accessible biological fluids, such as, for example, blood, plasma or serum, without the need for repeated biopsies, thus representing a practical, safe approach for longitudinal monitoring of patients. CtDNA, which is the DNA released by tumour cells into the bloodstream, is one of the most widely studied biomarkers for monitoring metastatic melanoma. CtDNA is particularly useful for detecting minimal residual disease (MRD), a condition wherein the tumour, despite not being clinically visible, is still present at a molecular level. The existing studies are mainly focused on the ability of microRNAs to distinguish between healthy subjects and patients affected by melanoma, with some evidence describing their role also in other types of tumours. In fact, the presence of ctDNA in the blood makes it possible to monitor diseaserelapse and detect early signs of progression, even before they become evident through imaging or clinical symptoms. Furthermore, the measurement of ctDNA can be used to monitor treatment effectiveness in real time by detecting reductions or increases in the levels of ctDNA in response to therapy (Eckhoff et al., 2024). Recently, the FDA approved the Signatera™ MRD test for monitoring ctDNA in patients with colorectal cancer, a result which suggests the effectiveness of this biomarker also for other solid tumours (Fattore et al., 2021 ).

[0017] In melanoma, although the application of ctDNA is still at the development stage, various studies indicate that it could play a crucial role in the personalisation of treatments and improvement of the prognosis (Ricciardi et al., 2023; Tian et al., 2024).

[0018] In this context, circulating miRNAs are gaining increasing attention as biomarkers for metastatic melanoma.

[0019] MiRNAs are small non-coding RNA molecules that regulate gene expression and are involved in numerous cellular processes, including tumour cell proliferation, differentiation and metastasis. These miRNAs can be released into the blood by tumour cells in response to modifications in the tumour microenvironment or interaction with the immune system. The analysis of circulating miRNAs offers the advantage of requiring very reduced amounts of biological material as compared to ctDNA, making the analysis even more practical and less invasive.

[0020] Although microRNAs have shown a considerable potential, their use as circulating biomarkers for monitoring the response to immunotherapy is still little explored.

[0021] In recent years, several studies have explored the potential of circulating microRNAs as biomarkers predictive of the response to treatment with ICIs in melanoma. The objective of these studies is to analyse the levels of miRNAs present in patients’ blood, with the aim of understanding how variations in the levels of these miRNAs may reflect the response to therapy and improve the selection of the patients who could benefit most from immunological treatments, such as ICIs. An emblematic example of such research is a study conducted in 2020 by Nakahara et al., who examined miRNA levels in a sample of 33 patients with melanoma before the start of treatment with nivolumab or pembrolizumab and divided the patients into responders and non-responders based on the clinical response. Among the miRNAs analysed, miR-16-5p, miR-17-5p and miR-20a-5p showed to be significantly higherin responders, suggesting that these miRNAs may be used as reliable indicators of a positive response to therapy (Nakahara et al., 2020). In a study of the same year, Bustos et al. identified, in plasma samples of patients treated with ICIs (ipilimumab, nivolumab, pembrolizumab or a combination of ipilimumab and nivolumab), a significant increase in miR-1234-3p, miR-4649-3p and miR-615-3p in unresponsive stage IV patients. In patients who had obtained a complete response, by contrast, the levels of these miRNAs showed to have decreased. The authors further showed that, in the analysis of longitudinal samples, miR-615-3p and miR-4649-3p could represent useful biomarkers for monitoring the response to this treatment in patients with melanoma (Bustos et al., 2020). Huber et al. instead conducted a retrospective analysis on 87 patients with metastatic melanoma treated with ICIs or targeted therapies (tyrosine kinase inhibitors, TKIs). Their results indicated that the levels of miR-146a, let-7e, miR-125a and miR-146b are significantly correlated with overall survival (OS) and progression-free survival (PFS), suggesting that these miRNAs may be used as biomarkers predictive of the response to ICIs in patients with advanced melanoma (Huber et al., 2018). Another innovative diagnostic approach was developed by Tengda et al., who designed a diagnostic panel based on exo-miRNA, in particular exo-miRNA-532-5p and exo-miR-106b, for the early diagnosis of melanoma. These miRNAs were found to be significantly different between 30 patients with melanoma and 30 healthy subjects. The panel was validated in 95 serum samples, showing a sensitivity of 82.1% and specificity of 91.6%, with better results compared to traditional biomarkers such as LDH, MIA and SWOB. Furthermore, the exo-miRNA panel enabled an effective distinction to be made between patients with and without metastasis, between patients in the early and advanced stages of the disease, and between those treated with pembrolizumab and untreated patients, suggesting that this method may contribute significantly to improving the early diagnosis of melanoma and, consequently, to the clinical management of patients (Tengda et al., 2018).

[0022] It is important to highlight that all these studies analysed circulating miRNA expression levels by canonical quantitative PCR. This method poses numerous disadvantages in terms of sensitivity, accuracy and normalisation of the measurement of the analytes of interest.

[0023] In the light of the above, it appears evident that there is a need to provide new diagnostic methods to predict resistance to immunotherapy in patients withcancer, in particular patients with melanoma or advanced melanoma, which are capable of overcoming the disadvantages of the known methods.

[0024] The solution according to the present invention fits into this context; it aims to provide novel biomarkers capable of predicting the response to immune checkpoint inhibitors (ICIs), in particular in melanoma, with the possibility of developing them as complementary diagnostic tools in a clinical setting.

[0025] Based on what was said above, the identification of reliable biomarkers that can guide clinical decisions before the start of therapy with ICIs is fundamental in order to avoid ineffective, costly treatments and reduce patient exposure to non-optimal therapies. This would represent a significant clinical advantage for patients and a reduction in costs for the national health system. Furthermore, since immunotherapy is by now the standard of care for numerous solid tumours, it is plausible that the method of the present invention is also applicable to other types of cancers besides melanoma.

[0026] In particular, according to the present invention, a new molecular signature was identified, based on seven miRNAs that, for the first time, have revealed to be capable of predicting the response to a treatment with immunotherapy in a robust manner, with high specificity and sensitivity.

[0027] As reported further below in the experiments conducted to support the present invention, through a miRNA-seq analysis, 19 deregulated microRNAs were initially identified and, subsequently, seven miRNAs (miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p) capable of significantly distinguishing patients who respond to immunotherapy from those who do not were identified, opening up new possibilities for the use of circulating biomarkers in the management of metastatic melanoma. In particular, the aforesaid miRNAs showed a significant modulation between responders and non-responders to the treatment with PD-1 inhibitors (nivolumab). The difference in the detected expression makes these seven microRNAs particularly relevant, suggesting their direct involvement in the response to immunotherapy treatment in melanoma.

[0028] Among the seven microRNAs studied as potential biomarkers predictive of the therapeutic response in melanoma, miR-1246 has already been identified as an oncomiRNA in various types of tumours, including breast cancer, colorectal cancer, lung cancer, ovarian cancer, hepatocellular carcinoma and oral squamous carcinoma (Ghafouri-Fard et al., 2021). Its expression is frequently high in tumourscompared to normal tissues (Dai et al., 2021; Ghafouri-Fard et al., 2021), and high plasma or serum levels have been associated with an unfavourable prognosis and resistance to therapy in numerous cancers (Jin et al., 2019; Zhang et al., 2020). However, in some tumours, like prostate cancer and renal cell carcinoma, miR-1246 acts as a tumour suppressor (Bhagirath et al., 2018; H.-T. Liu & Fan, 2020). A study conducted by Levati et al. highlighted that baseline plasma levels of miR-1246 are correlated with the clinical response and prognosis of patients with metastatic melanoma treated with BRAF inhibitors (BRAFi) and MEK (MEKi). In particular, it was demonstrated that the ratio between miR-1246 (oncomiR) and miR-485-3p (tumour suppressor miRNA) is able to identify patients with a low probability of response to targeted therapy, as well as those at risk of a shorter progression-free survival (PFS) and an increase in mortality. This ratio could thus be combined with already well-established prognostic factors, thus improving the clinical management of patients with BRAF-mutated metastatic melanoma (Levati et al., 2022).

[0029] Furthermore, the association between miR-211-5p and melanoma is already known. MiR-211-5p is overexpressed in the plasma of patients with advanced melanoma, with particularly high levels in stage IV patients, where values 68 times higher than in healthy controls are found. Though an inhibitory role of miR-211-5p against cell invasion was initially hypothesised, recent studies have confuted that function, suggesting that it may be a marker of the melanocyte lineage. The high level of miR-211-5p in the serum exosomes of patients suggests that this miRNA is secreted by tumour cells, but the transfer of miRNA via exosomes does not seem to significantly influence the expression of its target genes, raising questions about its functional role as a paracrine messenger (Margue et al., 2015). MiR-211-5p thus emerges as one of the most promising biomarkers for the prognosis of metastatic melanoma. However, its involvement as a predictor of the response to immunotherapy in melanoma (as described according to the present invention) has never been described. In particular, miR-211-5p has been identified as a biomarker for melanoma aggressiveness and relapse in various studies (Margue et al., 2015; Stark et al., 2015). For example, miR-211-5p is included in the MELmiR-7 panel (which also includes miR-509-3p, for which, however, there exists no further evidence regarding its use as a biomarker for melanoma), which demonstrated high sensitivity in discriminating between the various stages of melanoma (stage l-ll, III, IV) (Polini et al., 2019).Therefore, although the association between miR-211-5p and melanoma has been amply documented, at present there do not exist any studies that have assessed its use as a circulating biomarker for the response to therapeutic treatments in melanoma.

[0030] Unlike in the case of miR-211-5p, studies on its homologue miR-211-3p are more limited and have never been carried out with liquid biopsy evaluations.

[0031] MiR-181b-5p is a miRNA present in extracellular vesicles (EVs), is expressed in an anomalous manner in various tumours and is frequently associated with a negative prognosis. This miRNA plays a significant role in modulating the tumour microenvironment (TME), a dynamic system that is fundamental in tumour progression and metastasis, by mediating intercellular communication through EVs. In esophageal squamous cells carcinoma (ESCC), for example, high levels of miR-181b-5p in tissues and in serum EVs are correlated with an unfavourable clinical outcome. Bioinformatic analyses suggest that miR-181b-5p may act directly on PTEN and PHLPP2, two important tumour suppressors that negatively regulate the Akt pathway in tumour cells. Furthermore, EVs containing miR-181b-5p promote tumour angiogenesis through the modulation of Ang1, Ang2 and eNOS signalling, activating the Akt pathway and thus contributing to the formation of blood vessels which support tumour growth and metastasis (Wang et al., 2020). In melanoma, although miR-181b-5p, together with miR-181b-5p, does not directly target TNFa transcripts, it has been observed that it reduces TNFa secretion, probably because of TCR signal downregulation. The presence of miR-181b-5p in melanoma-derived exosomes suggests an indirect involvement in the modulation of the immune response, facilitating the immune evasion of the tumour. This effect could reduce the activation of CD8+ T cells, thus compromising the immune response against the tumour. In short, miR-181b-5p present in EVs plays a crucial role in regulating the tumour microenvironment, promoting melanoma progression and evasion of immune surveillance (Vignard et al., 2020).

[0032] Research on miR-873-3p is still very limited, and the studies that explore its role are few. Unlike miR-873-5p, which has received much attention, miR-873-3p could in any case have a significant impact in the development and treatment of cancer, but further studies are necessary in order to clarify its function. Furthermore, little is still known regarding the presence and role of miR-873-3p in the bloodstream, an aspect that is important for its possible use as a diagnostic or therapeuticbiomarker.

[0033] To date, no associations have been identified between miR-320b and melanoma in the scientific literature. One study highlighted that miR-320b is capable of distinguishing patients infected by SARS-CoV-2 from the control subjects, suggesting that high levels of this microRNA in patients with COVID-19 could modulate the inflammatory process. In particular, miR-320b has been identified as a potential indicator of disease severity, thus contributing to identifying patients at a risk of a negative outcome, with an important application in the clinical management of vulnerable patients (de Souza Nicoletti et al., 2024).

[0034] Therefore, according to the present invention, the seven miRNAs described above as biomarkers predictive of patient response to therapy with ICIs have been identified for the first time.

[0035] The data collected from pre-treatment liquid biopsy samples indicate that the expression levels of the miRNAs of the invention can act as predictive biomarkers, anticipating the response to treatments with ICIs, also several months in advance. This innovative diagnostic approach not only has the potential to identify patients who will respond positively to therapy but also offers the advantage of significantly reducing patient exposure to drugs that could prove ineffective, thus avoiding useless and potentially harmful treatments. By reducing the use of ineffective therapies, the prediction obtained by means of the method of the invention contributes to minimising the toxic side effects tied to ICIs, thereby improving patients’ quality of life. Furthermore, this type of predictive biomarkers allows an optimisation in the allocation of therapeutic resources, making the management of treatments more efficient and improving clinical outcomes.

[0036] Furthermore, the biomarkers subject-matter of the method of the invention are characterised by the following advantages:

[0037] a) non-invasive detection, since they can be detected in liquid biopsies; b) repeatability, to enable real-time longitudinal monitoring during the disease and / or treatment; and

[0038] c) easy measurability, with the possibility of being standardised.

[0039] Furthermore, as reported further below, according to the present invention, use was made of one of the most advanced and promising techniques available in the field of liquid biopsy, namely digital PCR (dPCR). This technology offers greater precision compared to traditional techniques, such as real-time PCR (qPCR), as itenables an absolute quantification of analytes, offering a highly sensitive and precise analysis, also at low expression levels. Thanks to digital PCR it is possible to monitor, in a multiparametric manner, various miRNAs within a single sample, thus providing a powerful tool for assessing the therapeutic response. Therefore, if applied to the detection of the miRNAs according to the present invention, this approach could represent a significant breakthrough in the personalisation of treatments, enabling a more accurate, timely prediction of the response to immune checkpoint inhibitors, and thus improving the clinical management of patients with metastatic melanoma.

[0040] In addition, the present invention aims to propose targeted therapies based on the mechanism of action of several of the abovementioned microRNAs. In particular, according to the present invention, the use of inhibitors of specific miRNAs that act as oncogenic miRNAs (oncomiRs) would enhance patients’ immune response, thereby improving the effectiveness of ICIs or acting as independent treatments in the case of resistance to them. Therefore, in addition to serving as predictive biomarkers, the miRNAs of the present invention can also be exploited for the development of new therapies for melanoma and other cancers, enabling personalised treatments that act directly on the molecular mechanisms of the tumour and opening the way to personalised, more effective treatments.

[0041] Ultimately the adoption of the innovative methods according to the present invention would lead to a significant reduction in health costs, not only for the individual patient, but also at the level of the national health system, by optimising resources and improving the efficiency of care. The miRNA-based integrated approach of diagnostics and targeted therapy could revolutionise the management of solid tumours and, in particular, of metastatic melanoma, with positive impacts on patient health and on the overall healthcare economy.

[0042] Therefore, a specific object of the present invention is a method for identifying a cancer patient unresponsive to an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor, said method comprising or consisting of measuring or obtaining a measure of the expression of at least three miRNAs selected from miR-211-5p (MIMAT0000268), miR-320b (MIMAT0005792), miR-1246 (MI0006381), miR-181b-5p (MIMAT0000257), miR-509-3p (MIMAT0002881), miR-873-3p (MIMAT0022717) and miR-211-3p (MIMAT0022694) in a biological sample of a cancer patient before the start of said anti-tumour treatment,wherein said patient is an unresponsive cancer patient when the expression level of said at least three miRNAs is greater than the expression level of the same miRNAs measured in a biological sample of a cancer patient responsive to the same anti-tumour treatment before the start of said anti-tumour treatment.

[0043] According to the present invention, said at least three miRNAs used in the aforesaid method can thus be at least three, at least four, at least five or at least six miRNAs selected from miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p or can be all seven miRNAs, miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p.

[0044] Unresponsive cancer patient means a cancer patient who experiences progressive disease (PD) within 6 months after the start of treatment with an immune checkpoint inhibitor. Responsive cancer patient, by contrast, means a cancer patient who is experiencing a partial or complete response (PR or CR) or stable disease (SD), as per RECIST criteria, for at least six months after the start of a treatment with an immune checkpoint inhibitor.

[0045] According to the present invention, the identification of an unresponsive cancer patient is understood as the identification of a cancer patient who has a high probability of developing progressive disease following therapy with an immune checkpoint inhibitor.

[0046] Expression of said at least three microRNAs means the quantity of miRNA expressed.

[0047] According to the present invention, when said cancer patient is an unresponsive cancer patient, the expression level of each of said at least three miRNAs can be greater than an expression level cut-off value obtained by means of the following steps:

[0048] - measuring or obtaining a measurement of the expression of said at least three miRNAs in biological samples of a population of cancer patients before the start of an immunotherapy-based anti-tumour treatment, wherein said population of cancer patients comprises a group of unresponsive cancer patients and a group of responsive cancer patients;

[0049] - for each miRNA, obtaining a cut-off value by means of an ROC curve to distinguish unresponsive cancer patients from responsive cancer patients.

[0050] According to the method of the invention, the measured expression of each of said at least three miRNAs is preferably determined as the absolute value (orabsolute level).

[0051] Preferably, the method of the invention comprises or consists in measuring or obtaining a measurement of the expression of at least three miRNAs, wherein said three miRNAs comprise miR-211-5p, which has shown the best diagnostic performance among the microRNAs of the signature (AUC = 0.731884058). Therefore, the method of the invention can comprise or consist in measuring at least three, four, five, six or seven of said miRNAs, wherein one of them is miR-211-5p.

[0052] In particular, said method can comprise or consist in measuring the expression or obtaining a measurement of the expression of the following combinations of miRNAs:

[0053] miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;

[0054] miR-181b-5p, miR-1246, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;

[0055] miR-181b-5p, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p;

[0056] miR-181b-5p, miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p;

[0057] miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0058] miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0059] miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-873-3p; miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-873-3p;

[0060] miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p;

[0061] miR-211-3p, miR-211 -5p, miR-509-3p, miR-873-3p;

[0062] miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p; miR-181b-5p, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0063] miR-181b-5p, miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p;

[0064] miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p;

[0065] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p;

[0066] miR-181b-5p, miR-1246, miR-320, miR-211-5p;

[0067] miR-181b-5p, miR-1246, miR-211 -3p, miR-211-5p;

[0068] miR-1246, miR-320, miR-211 -3p, miR-211-5p;

[0069] miR-1246, miR-320, miR-211-5p, miR-509-3p;

[0070] miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p;miR-1246, miR-320, miR-211-5p, miR-873-3p;

[0071] miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0072] miR-181b-5p, miR-1246, miR-211-5p;

[0073] miR-211-5p, miR-509-3p, miR-873-3p;

[0074] miR-181b-5p, miR-1246, miR-211-5p, miR-873-3p;

[0075] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-873-3p;

[0076] miR-181b-5p, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0077] miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0078] miR-1246, miR-211-3p, miR-211-5p;

[0079] miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;

[0080] miR-211-3p, miR-211-5p, miR-873-3p;

[0081] miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p;

[0082] miR-320, miR-211-3p, miR-211-5p, miR-873-3p;

[0083] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211-5p, miR-509-3p;

[0084] miR-320, miR-211-3p, miR-211-5p;

[0085] miR-181b-5p, miR-211-5p, miR-509-3p, miR-873-3p;

[0086] miR-320, miR-211-3p, miR-211-5p, miR-509-3p;

[0087] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-509-3p;

[0088] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-873-3p;

[0089] miR-1246, miR-320, miR-211-5p;

[0090] miR-181b-5p, miR-211-3p, miR-211-5p, miR-873-3p;

[0091] miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;

[0092] miR-181b-5p, miR-320, miR-211-5p, miR-509-3p;

[0093] miR-1246, miR-211-5p, miR-873-3p;

[0094] miR-320, miR-211-5p, miR-509-3p;

[0095] miR-181b-5p, miR-320, miR-211-5p, miR-873-3p;

[0096] miR-320, miR-211-5p, miR-873-3p;

[0097] miR-181b-5p, miR-211-5p, miR-873-3p;

[0098] miR-181b-5p, miR-320, miR-211-5p;

[0099] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p;

[0100] miR-181b-5p, miR-211-3p, miR-211-5p;

[0101] miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p;

[0102] miR-181b-5p, miR-211-5p, miR-509-3p;miR-211-3p, miR-211-5p, miR-509-3p.

[0103] According to some embodiments of the method of the present invention, said at least three miRNAs can comprise or consist of

[0104] miR-1246, miR-320 and miR-509-3p; or

[0105] miR-211-5p, miR-509-3p and miR-873-3p; or

[0106] miR-181b-5p, miR-1246 and miR-211-5p; or

[0107] miR-1246, miR-211-3p and mir-509-3p; or

[0108] miR-211 -3p, miR-509-3p and miR-873-3p.

[0109] Therefore, the method according to the present invention can comprise the above-described combinations of three miRNAs on their own or together with one or more of the remaining miRNAs of the present invention, that is, in combinations of four, five, six or all the miRNAs described above.

[0110] According to the method of the invention, the expression of the miRNAs can be measured by digital PCR, real-time PCR, microarray analysis, RNA hybridisation methods, such as northern blot or dot blot, or next-generation RNA sequencing, preferably by digital PCR.

[0111] Furthermore, according to the present invention, the immune checkpoint inhibitor used for the immunotherapy-based anti-tumour treatment can be selected from an anti-PD1 antibody, e.g. nivolumab, anti-CTLA4 antibody, e.g. ipilimumab, anti PDL-1 antibody, anti-LAG3 antibody and combinations thereof. For example, possible combinations can be anti-PD1, such as nivolumab, and anti-CTLA4, such as ipilimumab or anti-PD1, such as nivolumab, and anti-LAG3, such as relatlimab.

[0112] In addition, according to the present invention, the cancer patient can be a patient affected by a solid tumour. In particular, said solid tumour can be selected from the group consisting of melanoma, lung cancers or colon cancers.

[0113] According to the method of the present invention, the biological sample in which the expression of said at least three miRNAs is measured is preferably a liquid biological sample, or liquid biopsy sample, e.g. blood, serum and plasma.

[0114] The present invention also relates to a method for obtaining an expression level cut-off value for distinguishing cancer patients unresponsive to an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor from cancer patients responsive to the same treatment, said method comprising - measuring the expression of at least three miRNAs selected from miR-211-5p (MIMAT0000268), miR-320b (MIMAT0005792), miR-1246 (M 10006381), miR-181b-5p (MIMAT0000257), miR-509-3p (MIMAT0002881), miR-873-3p (MIMAT0022717) and miR-211-3p (MIMAT0022694) in biological samples of a population of cancer patients before the start of an immunotherapy-based antitumour treatment with an immune checkpoint inhibitor, wherein said population of cancer patients comprises a group of unresponsive cancer patients and a group of responsive cancer patients;

[0115] - for each miRNA, obtaining a cut-off value by means of an ROC curve. As mentioned above, the method of the invention preferably comprises or consists in measuring or obtaining a measurement of the expression of at least three miRNAs, wherein said three miRNAs comprise miR-211-5p.

[0116] In particular, said method can comprise or consist in measuring the expression or obtaining a measurement of the expression of the following combinations of miRNAs:

[0117] miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0118] miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0119] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211-3p, miR-211-5p, miR-873-3p;

[0120] miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-873-3p;

[0121] miR-1246, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0122] miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0123] miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-873-3p; miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-873-3p;

[0124] miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p;

[0125] miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0126] miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p; miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;

[0127] miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-509-3p;

[0128] miR-1246, miR-211-3p, miR-211-5p, miR-509-3p;

[0129] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p;

[0130] miR-181b-5p, miR-1246, miR-320, miR-211-5p;

[0131] miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p;miR-1246, miR-320, miR-211-3p, miR-211-5p;

[0132] miR-1246, miR-320, miR-211-5p, miR-509-3p;

[0133] miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p;

[0134] miR-1246, miR-320, miR-211-5p, miR-873-3p;

[0135] miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0136] miR-181b-5p, miR-1246, miR-211-5p;

[0137] miR-211-5p, miR-509-3p, miR-873-3p;

[0138] miR-181b-5p, miR-1246, miR-211-5p, miR-873-3p;

[0139] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-873-3p;

[0140] miR-181b-5p, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0141] miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;

[0142] miR-1246, miR-211-3p, miR-211-5p;

[0143] miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;

[0144] miR-211-3p, miR-211-5p, miR-873-3p;

[0145] miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p;

[0146] miR-320, miR-211-3p, miR-211-5p, miR-873-3p;

[0147] miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211-5p, miR-509-3p;

[0148] miR-320, miR-211-3p, miR-211-5p;

[0149] miR-181b-5p, miR-211-5p, miR-509-3p, miR-873-3p;

[0150] miR-320, miR-211-3p, miR-211-5p, miR-509-3p;

[0151] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-509-3p;

[0152] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-873-3p;

[0153] miR-1246, miR-320, miR-211-5p;

[0154] miR-181b-5p, miR-211-3p, miR-211-5p, miR-873-3p;

[0155] miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;

[0156] miR-181b-5p, miR-320, miR-211-5p, miR-509-3p;

[0157] miR-1246, miR-211-5p, miR-873-3p;

[0158] miR-320, miR-211-5p, miR-509-3p;

[0159] miR-181b-5p, miR-320, miR-211-5p, miR-873-3p;

[0160] miR-320, miR-211-5p, miR-873-3p;

[0161] miR-181b-5p, miR-211-5p, miR-873-3p;

[0162] miR-181b-5p, miR-320, miR-211-5p;

[0163] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p;miR-181 b-5p, miR-211-3p, miR-211-5p;

[0164] miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p;

[0165] miR-181b-5p, miR-211-5p, miR-509-3p;

[0166] miR-211-3p, miR-211-5p, miR-509-3p.

[0167] Furthermore, said at least three miRNAs can comprise or consist of miR-1246, miR-320 and miR-509-3p; or

[0168] miR-211-5p, miR-509-3p and miR-873-3p; or

[0169] miR-181b-5p, miR-1246 and miR-211 -5p; or

[0170] miR-1246, miR-211-3p and mir-509-3p; or

[0171] miR-211 -3p, miR-509-3p and miR-873-3p.

[0172] Therefore, the method described above can comprise the combinations of three miRNAs described above on their own or together with one or more of the remaining miRNAs of the present invention, that is, in combinations of four, five, six or all the miRNAs described above.

[0173] The present invention further relates to a method for diagnosing and treating a cancer patient unresponsive to an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor, said method comprising

[0174] a) obtaining a measurement of the expression of at least three miRNAs selected from miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p in a biological sample of a cancer patient before the start of said anti-tumour treatment;

[0175] b) identifying a cancer patient unresponsive to an immunotherapy-based antitumour treatment with an immune checkpoint inhibitor, wherein in said unresponsive cancer patient the expression level of said at least three miRNAs is greater than the expression level of the same miRNAs measured in a biological sample of a cancer patient responsive to the same anti-tumour treatment before the start of said antitumour treatment; and

[0176] c) treating the unresponsive patient with one or more antagonists of one or more miRNAs selected from miR-181b-5p, miR-4516, miR-3180 and miR-4488. In particular, the antagonists of the aforesaid miRNAs, can be anti-miRNA oligonucleotides, such as, for example, locked nucleic acids (LNA).

[0177] Moreover, a further object of the present invention is an antagonist of one or more miRNAs selected from miR-181b-5p, miR-4516, miR-3180 and miR-4488, or a pharmaceutical composition comprising said antagonist together with one or morepharmaceutically acceptable excipients and / or adjuvants, for use in the treatment of solid tumours. In particular, said solid tumour can be selected in the group consisting of melanoma, lung cancers or colon cancers.

[0178] According to the present invention, said antagonist can be an anti-miRNA oligonucleotide, such as, for example, an LNA, or locked nucleic acid.

[0179] The present invention will now be described by way of non-limiting illustration according to a preferred embodiment thereof, with particular reference to the examples and the figures of the appended drawings, wherein:

[0180] - Figure 1 shows the experimental design. In particular, Figure 1 illustrates the experimental flow followed for enrolling and dividing the 48 patients with metastatic melanoma, who were classified into two main groups: patients responding (LP) and not responding (FP) to the treatment with nivolumab, based on iRECIST criteria. Serum samples were collected from each patient before the start of the therapy with nivolumab. Subsequently, RNA was isolated from each serum sample and analysed by means of miRNA sequencing (miRNA-seq). The analysis made it possible to identify a differential expression profile, revealing 19 miRNAs that are more greatly expressed in non-responding patients compared to those responding to the treatment. Pval<0.01 & FDR<0.01 & logFC>0.5 & AU00.70.

[0181] - Figure 2 shows a heatmap of the miRNAs expressed in the responding and non-responding patients.

[0182] In particular, Figure 2 presents a heatmap that displays the differential expression of the 19 miRNAs identified in the miRNA-seq analysis (described in Figure 1) in the patients responding and not responding to the treatment with nivolumab. Every row in the heatmap represents a miRNA, whereas the columns correspond to the individual patients divided on the basis of their therapeutic response. The colour of the cells reflects the levels of expression of each miRNA, with more intense colours indicating a higher expression. The miRNAs showing a significantly higher expression in non-responding patients compared to responding ones are clearly highlighted, suggesting a potential link between these biomarkers and the lack of response to the treatment.

[0183] - Figures 3-9 show box plots of the miRNA expression levels. In particular, figures 3-9 show box plots that represent the expression levels (in copies per microlitre) of the miRNAs analysed, highlighting the ones that were significantly capable of distinguishing non-responding patients (FP) from responding ones (LP).Every box plot shows the distribution of the expression levels for each miRNA in the two groups of patients; these results suggest that specific miRNAs could be useful for monitoring the therapeutic response and predicting disease progression in patients treated with nivolumab. A T-test with values of p < 0.05 is considered significant.

[0184] - Figures 10-18 show box plots of the miRNA expression levels. In particular, figures 10-18 show box plots that represent the expression levels (in copies per microlitre) of the miRNAs analysed, which proved not to be capable of significantly distinguishing non-responding patients (FP) from those responding (LP) to the treatment. No significant differences were observed between the two groups of patients for the miRNAs shown in these figures, indicating that these miRNAs are not potentially useful as biomarkers for monitoring the response to treatment or predicting disease progression. The box plots highlight the distribution of the expression values for each miRNA in the two groups, but without showing any significant separation between patients with FP and LP. T-test ns = not significant.

[0185] Figure 19 shows a box plot of the signature of the significant miRNAs.

[0186] In particular, Figure 19 shows a box plot that represents the signature composed of the significant miRNAs identified in the previous analyses. In this figure, the selected miRNAs were combined in order to assess their overall discriminating power, not as individual markers, but as a set of miRNAs (signature). The box plot shows the overall expression levels of the signature in patients with disease progression (FP) compared to responding patients (LP). A t-test with values of p < 0.05 is considered significant.

[0187] - Figure 20 shows the ROC curve of the miRNA signature. In particular, Figure 20 shows the ROC (receiver operating characteristic) curve of the signature composed of the 7 significant miRNAs. This curve is used to evaluate the discriminating capacity of the miRNA signature in distinguishing patients with disease progression (FP) from those responding (LP) to the treatment in terms of area under the curve (AUC).

[0188] - Figure 21 shows a Kaplan-Meier analysis of the miRNA signature. In particular, Figure 21 shows a Kaplan-Meier plot illustrating the progression-free survival (PFS) of patients with metastatic melanoma, divided into two groups based on the expression of the miRNA signature. The patients were classified into two groups: patients with a high expression of the signature (signature high) and thosewith a low expression of the signature (signature low) based on the hazard ratio, Cox models and log-rank p-values.

[0189] - Figures 22-26 show ROC curves. In particular, figures 22-26 show ROC curves generated as previously described with the combinations of 3 miRNAs capable of achieving AUC values of at least 0.75.

[0190] - Figure 27 shows a graph of the interaction of the miRNAs with their target genes. MiRNet 2.0 software (https: / / www.mirnet.ca / miRNet / home.xhtml) was used to analyse the network of interaction of the miRNAs miR-181b-5pb-5p, miR-4516, miR-3180 and miR-4488 with their target genes, such as CXCL9, CXCL10, CXCR3, GZMB and PRF1.

[0191] - Figure 28 shows the serum-plasma correlation of miRNA levels. In particular, the figure shows the correlation between the expression levels of the seven microRNAs belonging to the signature measured in serum and plasma samples obtained from the same 10 patients. Overall, the analysis included 70 pairs of values (7 microRNAs x 10 patients). The correlation between serum and plasma was determined by means of the Spearman correlation test. The analysis showed a strong positive correlation between the expression levels of the microRNAs of the signature in the two biological matrices (Spearman’s r = 0.917; IC 95%: 0.8673-0.9483; p < 0.0001).

[0192] EXAMPLE 1. Identification of a circulating microRNA-based molecular signature capable of predicting the response to immunotherapy in melanoma.

[0193] MATERIALS AND METHODS

[0194] The biological samples drawn from patients were collected and stored in the cancer biobank at the Istituto Nazionale per la Cura dei Tumori “Fondazione G. Pascale” in Naples. The collection of samples took place following strict ethical guidelines and standardised procedures in order to ensure sample quality and integrity. The use of the biological samples was approved with Protocol no. 33 / 17 oss (PI Dr Paolo A. Ascierto, Current Research M2-2 - “Valutazione di Biomarcatori indicativi di risposta al trattamento con Immunoterapia e Farmaci Innovativi"-“Evaluation of Biomarkers Indicative of Response to Treatment with Immunotherapy and Innovative Drugs”) approved by the Pascale IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Ethics Committee on 10 / 01 / 2018, and Protocol no. 37 / 22 oss (PI Dr Paolo A. Ascierto, Current Research L2-1 -“ Valutazione dei meccanismi di resistenza e dei biomarcatori indicativi di risposta altrattamento con Immunoterapia e / o Farmaci Innovativi”- “Evaluation of Mechanisms of Resistance and Biomarkers Indicative of Response to Treatment with Immunotherapy and / or Innovative Drugs”) approved by the Ethics Committee of the Pascale IRCCS-Santobono-Pausilipon Hospital on 09 / 11 / 2022. All patients signed a general informed consent form which authorised the use of their samples for scientific research purposes, with a guarantee of anonymity and the protection of personal data. The samples were then analysed in anonymous form at the above-named facilities, in observance of ethical principles and legislation currently in force.

[0195] Experimental design

[0196] The retrospective study involved a total of 48 patients with a diagnosis of metastatic melanoma, treated with first-line immunotherapy by administration of nivolumab, a PD-1 inhibitor. The principal aim of the study was to analyse the profile of circulating miRNAs in the patients’ serum before the start of treatment with nivolumab in order to identify biomarkers predictive of response or lack of response to the treatment.

[0197] The study inclusion criteria included that patients were eligible for treatment with nivolumab, a PD-1 inhibitor. All patients were of an age > 18 years, capable of understanding the information and of signing the informed consent form before the start of the procedures. The diagnosis of metastatic melanoma was confirmed based on the criteria of the American Joint Committee on Cancer (AJCC), with locally advanced stages or the presence of metastatic lesions. The clinical characteristics of the patients, including variables such as sex, genetic mutations, the therapy received, and the serum levels of lactate dehydrogenase (LDH), were determined and documented for every patient enrolled in the study at the Istituto Nazionale per la Cura dei Tumori “Fondazione G. Pascale” in Naples.

[0198] Isolation and analysis of circulating miRNAs

[0199] The miRNeasy Serum / Plasma Kit (Qiagen), which enables an efficient purification of miRNAs from serum or plasma, was used to isolate the circulating miRNAs, following the manufacturer’s directions. The identification of the miRNAs of interest was carried out by means of miRNA-seq using the QIAseq miRNA Library Kit (Qiagen) and following the datasheet instructions. Reverse transcription of the extracted RNA was performed using the TaqMan™ Advanced miRNA cDNA Synthesis Kit (Applied Biosystems), which enables an optimal reverse transcriptionof miRNAs and an efficient conversion into cDNA for the subsequent analyses. Since the total RNA concentration was not detectable either with the Nanodrop system or with the Qubit test, for every sample a volume of 2 pL of RNA was used as input for reverse transcription, following the manufacturer’s specific recommendations. After the reverse transcription, an analysis of the miRNA expression levels was performed using the QuantStudio Absolute Q Digital PCR system (Applied Biosystems), a platform enabling an absolute quantification of molecular targets without the need for normalisation to exogenous controls, whose circulating data might not yet be completely identified. Digital PCR technology makes it possible to obtain a highly sensitive absolute measurement of miRNAs, also at low concentrations, by virtue of its ability to detect every single cDNA molecule in a single amplified product. For quantification of the miRNAs, specific TaqMan probes were used for each miRNA analysed, which enabled highly precise data to be obtained. The concentration of each miRNA was expressed as copies per microlitre (copies / pL), using a digital quantification that ensured an extremely accurate determination of the expression levels without the risk of normalisation errors. This method allowed highly reliable data on the circulating miRNAs to be obtained, as digital PCR is capable of precisely and accurately quantifying miRNAs even when there is low abundance, while avoiding biases associated with traditional PCR methods.

[0200] Statistical and bioinformatic analyses

[0201] The following statistical tests were applied to identify the miRNAs that were significantly deregulated in FP vs LP: Pval<0.01 & FDR<0.01 & logFC>0.5 & AUC>0.70. ROC (receiver operating characteristic) curves were used to determine the diagnostic accuracy of our biomarkers. The sensitivity, specificity and accuracy parameters were calculated together to obtain the area under the curve (AUC) of the different miRNA signatures identified. The ability of the miRNA signatures to predict the response to immunotherapy in terms of progression-free survival (PFS) was assessed with Kaplan-Meier curves, constructed with an evaluation of the hazard ratio, Cox models and log-rank p-values. The network of the miRNAs and their target genes was generated by means of miRNet 2.0 software (https: / / www.mirnet.ca / miRNet / home.xhtml). The between-group differences were analysed with a two-tailed T-test and were considered statistically significant with values of p < 0.05.RESULTS

[0202] In order to investigate the diagnostic potential of the circulating miRNAs, profiling was carried out by means of miRNA-seq analysis on serum samples taken from patients with metastatic melanoma. In particular, 48 serum samples provided by the “Pascale” IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) in Naples were analysed, all collected before the start of therapy with nivolumab, an anti-PD-1 antibody. The patients were divided into two cohorts of equal size: 24 responding to the treatment (LP) and 24 not responding (FP), according to iRECIST criteria (Figure 1).

[0203] The bioinformatic analyses conducted on the miRNA-seq data highlighted a significant upregulation of 19 miRNAs in patients not responding to the treatment compared to responding patients. The microRNAs identified are: miR-320d, miR-320b, miR-320c, miR-181b-5p, miR-1246, miR-671-5p, miR-4488, miR-34b-3p, miR-760, miR-296-3p, miR-873-5p, miR-211-3p, miR-873-3p, miR-4516, miR-4492, miR-92b-3p, miR-509-3p, miR-3180 and miR-211-5p. The statistical results show a substantial difference between the two groups, with values of p < 0.01, indicating a high statistical significance. Furthermore, the logFC values greater than 0.5 suggest a significant increase in the expression of these miRNAs in non-responders (Figure 2).

[0204] These results were subsequently validated using one of the most advanced, precise technologies for the analysis of circulating biomarkers, digital PCR (dPCR), which enables a highly sensitive quantitative measurement of the levels of different analytes in biological fluids, such as blood or plasma. Unlike traditional techniques such as quantitative PCR (qPCR), dPCR is capable of providing an absolute quantification of the number of target copies, significantly reducing the errors tied to variability among samples and improving sensitivity, especially where low-concentration biomarkers are involved. In this manner, the expression levels of 16 miRNAs out of the 19 identified were analysed; the expression of 3 was not possible to analyse because there are no commercially available probes, also due to total sequence overlap with other miRNAs of the same family. Significant deregulation of 7 of the 16 miRNAs analysed was confirmed thanks to the dPCR analyses, as these miRNAs are capable of distinguishing, to a statistically significant degree, patients responding to immunotherapy from non-responders. The significantly deregulated miRNAs are: miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p, and miR-211-3p (Figures 3-9). In contrast, the other 9 previously identified miRNAs did not show a presence in patients’ serum in concentrations differing to a statistically significant degree between LP and FP (Figures 10-18).

[0205] In addition, the 7 circulating miRNAs were combined as a single molecular signature and, in this case as well, a particularly significant p-value (0.0006) was obtained, suggesting a strong association with the therapeutic response and a considerable capacity to discriminate between responding and non-responding patients (Figure 19). These data further support the inventive idea at basis of this filing that the selected miRNAs are effectively involved in the mechanism of response to the treatment and may have a crucial role in predicting its therapeutic effectiveness.

[0206] In order to confirm this assumption, the predictive value of the molecular signature of the 7 miRNAs was assessed with an ROC (receiver operating characteristic) curve analysis. ROC curves are an essential tool for determining the diagnostic accuracy of a biomarker, since they allow for exploring the trade-off between sensitivity (ability to correctly identify responding patients) and specificity (ability to correctly identify non-responding patients) as the decision thresholds vary. The sensitivity, specificity and accuracy parameters were calculated together to obtain the area under the curve (AUC), which reached a value of 0.81 (specificity 91%; sensitivity 71%) (Figure 20). This AUC value suggests that the signature thus has good diagnostic potential, with an ability to predict the response to treatment in a robust manner. In particular, an AUC greater than 0.7 is considered a good indicator of discrimination, suggesting that the molecular signature of 7 miRNAs could be used for precise patient stratification, thereby improving personalised therapeutic decisions. ROC analysis, therefore, confirms the usefulness of these miRNAs as predictive biomarkers for optimising the therapeutic approach in patients undergoing immunotherapy treatments. Finally, the molecular signature of 7 miRNAs was used to construct Kaplan-Meier curves, which confirmed that the patients who have higher expression levels of these circulating biomarkers have a greater probability of not responding to the treatment with immunotherapy (measured as progression-free survival - PFS) (hazard ratio 4.13, p-value= 0.000363) (Figure 21).

[0207] In addition, with the aim of potentially reducing the number of miRNAs to be measured while maintaining significant AUC values, it was analysed whethercombinations of at least 3 of the 7 miRNAs identified were capable of achieving AUC values of at least 0.75.

[0208] The results shown in figures 22-26 revealed 5 combinations capable of satisfying the previous requirements:

[0209] 1. miR-1246 / miR-320 / miR-509-3p (AUC: 0.77);

[0210] 2. miR-211-5p / miR-509-3p / miR-873-3p (AUC: 0.77);

[0211] 3. miR-181b-5p / miR-1246 / miR-211-5p (AUC: 0.77);

[0212] 4. miR-1246 / miR-211-3p / mir-509-3p (AUC: 0.78);

[0213] 5. miR-211-3p / miR-509-3p / miR-873-3p (AUC 0.78).

[0214] Furthermore, the predictive value of all the possible combinations of microRNAs derived from original signature composed of seven microRNAs was assessed considering exclusively combinations composed of at least three microRNAs and always including miR-211-5p. This microRNA was maintained fixed in all combinations since, in the analysis thereof as a single marker, it showed the best diagnostic performance among the microRNAs of the signature (AUC = 0.731884058). The results are shown below in Table 1.

[0215] Table 1

[0216] n. miRNAs AUC

[0217] 7 miR-181b-5p, miR-1246, miR-320, miR-211 -3p, 0.813405797

[0218] miR-211-5p, miR-509-3p, miR-873-3p

[0219] 6 miR-181b-5p, miR-1246, miR-211 -3p, miR-211 -5p, 0.802536232

[0220] miR-509-3p, miR-873-3p

[0221] 6 miR-181b-5p, miR-320, miR-211 -3p, miR-211 -5p, 0.797101449

[0222] miR-509-3p, miR-873-3p

[0223] 4 miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p 0.795289855

[0224] 5 miR-181b-5p, miR-1246, miR-211 -3p, miR-211 -5p, 0.795289855

[0225] miR-873-3p

[0226] 5 miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p, 0.791666667

[0227] miR-873-3p

[0228] 6 miR-1246, miR-320, miR-211 -3p, miR-211-5p, 0.791666667

[0229] miR-509-3p, miR-873-3p

[0230] 5 miR-320, miR-211 -3p, miR-211 -5p, miR-509-3p, 0.791666667

[0231] miR-873-3p

[0232]

[0233] miR-181 b-5p, miR-1246, miR-320, miR-211-3p, 0.789855072 miR-211-5p, miR-873-3p

[0234] miR-1246, miR-320, miR-211-3p, miR-211-5p, 0.786231884 miR-873-3p

[0235] miR-1246, miR-320, miR-211-3p, miR-211-5p, 0.786231884 miR-509-3p

[0236] miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p 0.785326087 miR-181b-5p, miR-1246, miR-320, miR-211-3p, 0.78442029 miR-211-5p, miR-509-3p

[0237] miR-181b-5p, miR-211-3p, miR-211-5p, miR-509- 0.78442029

[0238] 3p, miR-873-3p

[0239] miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, 0.780797101 miR-509-3p

[0240] miR-1246, miR-211-3p, miR-211-5p, miR-509-3p 0.778985507 miR-181b-5p, miR-1246, miR-320, miR-211-5p, 0.777173913 miR-509-3p

[0241] miR-181b-5p, miR-1246, miR-320, miR-211-5p 0.777173913 miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p 0.775362319 miR-1246, miR-320, miR-211-3p, miR-211-5p 0.775362319 miR-1246, miR-320, miR-211-5p, miR-509-3p 0.775362319 miR-181b-5p, miR-1246, miR-320, miR-211-3p, 0.775362319 miR-211-5p

[0242] miR-1246, miR-320, miR-211-5p, miR-873-3p 0.773550725 miR-320, miR-211-5p, miR-509-3p, miR-873-3p 0.77173913 miR-181b-5p, miR-1246, miR-211-5p 0.77173913 miR-211-5p, miR-509-3p, miR-873-3p 0.77173913 miR-181b-5p, miR-1246, miR-211-5p, miR-873-3p 0.77173913 miR-181b-5p, miR-1246, miR-320, miR-211 -5p, 0.77173913 miR-873-3p

[0243] miR-181b-5p, miR-320, miR-211-5p, miR-509-3p, 0.77173913 miR-873-3p

[0244] miR-1246, miR-320, miR-211-5p, miR-509-3p, 0.77173913 miR-873-3p

[0245]

[0246] miR-1246, miR-211-3p, miR-211-5p 0.769927536 miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p, 0.769927536 miR-873-3p

[0247] miR-211-3p, miR-211-5p, miR-873-3p 0.769021739 miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p 0.768115942 miR-320, miR-211-3p, miR-211-5p, miR-873-3p 0.768115942 miR-181b-5p, miR-1246, miR-320, miR-211-5p, 0.768115942 miR-509-3p, miR-873-3p

[0248] miR-1246, miR-211-5p, miR-509-3p 0.766304348 miR-320, miR-211-3p, miR-211-5p 0.766304348 miR-181b-5p, miR-211-5p, miR-509-3p, miR-873- 0.766304348

[0249] 3p

[0250] miR-320, miR-211-3p, miR-211-5p, miR-509-3p 0.766304348 miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, 0.766304348 miR-509-3p

[0251] miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, 0.766304348 miR-873-3p

[0252] miR-1246, miR-320, miR-211-5p 0.766304348 miR-181b-5p, miR-211-3p, miR-211-5p, miR-873- 0.764492754

[0253] 3p

[0254] miR-1246, miR-211-5p, miR-509-3p, miR-873-3p 0.764492754 miR-181b-5p, miR-320, miR-211-5p, miR-509-3p 0.762681159 miR-1246, miR-211-5p, miR-873-3p 0.760869565 miR-320, miR-211-5p, miR-509-3p 0.759057971 miR-181b-5p, miR-320, miR-211-5p, miR-873-3p 0.753623188 miR-320, miR-211-5p, miR-873-3p 0.751811594 miR-181b-5p, miR-211-5p, miR-873-3p 0.746376812 miR-181b-5p, miR-320, miR-211-5p 0.746376812 miR-181b-5p, miR-320, miR-211-3p, miR-211-5p 0.744565217 miR-181b-5p, miR-211-3p, miR-211-5p 0.742753623 miR-181b-5p, miR-211-3p, miR-211-5p, miR-509- 0.740942029

[0255] 3p

[0256]

[0257] 3 miR-181b-5p, miR-211-5p, miR-509-3p 0.726449275

[0258] 3 miR-211-3p, miR-211-5p, miR-509-3p 0.72192029

[0259]

[0260] In particular, Table 1 shows the 57 possible combinations of microRNAs derived from the original signature composed of seven microRNAs, exclusively considering combinations of at least three microRNAs and always including miR-211-5p. The combinations were generated progressively, starting from the complete signature of seven microRNAs until arriving at smaller signatures composed of three microRNAs, for a total of 57 combinations.

[0261] Per each combination, the discriminating capacity was assessed by ROC analysis, and the signatures were arranged in descending order, based on the AUC value. The first row of the table corresponds to the complete signature with seven microRNAs, which showed the best overall performance (AUC = 0.813405797). Moving down the table, the reduction in the number of microRNAs included in the signature is associated with a gradual decline in diagnostic performance, until arriving at the three-microRNA combinations, whose minimum AUC is equal to 0.72192029. However, all the AUC values indicated in the table are greater than 0.7, a threshold which, as mentioned above, is considered a good indicator of discrimination, suggesting that each one of the combinations containing miR-211-5p can be used for precise patient stratification. The n column indicates the number of microRNAs included in each combination (ranging from 7 to 3), whereas the miRNAs column specifies the microRNAs composing the individual signature. The last column shows the AUC value of the ROC curve associated with each combination. Overall, this analysis highlights that integrating a number of microRNAs improves the discriminating performance compared to using single markers and supports the robustness of the complete signature, despite showing that some reduced combinations maintain a good diagnostic capacity.

[0262] It is important to mention that, in addition to the diagnostic potential as described above, another important implication of the present invention is the possible therapeutic impact.

[0263] In this regard, bioinformatic analyses were conducted, which revealed that 4 of the 19 microRNAs identified, miR-181b-5p, miR-4516, miR-3180 and miR-4488, are capable of regulating the gene pathways responsible for the development, inmelanoma, of an immunosuppressive microenvironment characterised by a reduced presence and activation of effector T cells. This data is of particular interest, since the low presence and activation of CD8 T lymphocytes (called cold microenvironment) is one of the largest determinants of resistance to immunotherapy (Galassi et al., 2024). Among the main genes that were identified as potential targets of all 4 of these miRNAs we have the cytokines CXCL9 and CXCL10 and their receptor CXCR3, which are responsible for the recall of CD8 cells in the tumour microenvironment. Moreover, also identified were the genes perforin (PRF1) and granzyme B (GZMB), which are among the most responsible for the cytotoxic action of CD8 T cells against tumour cells.

[0264] The bioinformatic results shown in Figure 27 and generated by means of miRNet 2.0 software (https: / / www.mirnet.ca / miRNet / home.xhtml) clearly demonstrate that the 4 miRNAs are involved in the formation of a complex network of molecular interactions which involve numerous target genes, among which it is worth highlighting the presence of CXCL9, CXCL10, CXCR3, GZMB and PRF1. These data generated by means of in silico analysis support the possible therapeutic implications of the miRNAs described above.

[0265] Finally, the correlation between the expression levels of the seven microRNAs belonging to the signature in serum and plasma samples obtained from the same patients was analysed. The results indicate that the overall pattern of the microRNA signature is highly in agreement between serum and plasma, suggesting that the signature may be reliably detected irrespective of the type of biological fluid used (Figure 28).

[0266] The present invention has been described by way of non-limiting illustration according to the preferred embodiments thereof, but it is to be understood that variations and / or modifications may be introduced by the person skilled in the art without going outside the scope of protection thereof, as defined by the appended claims.

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Claims

CLAIMS1) A method for identifying a cancer patient unresponsive to an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor, said method comprising measuring the expression of at least three miRNAs selected from miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p in a biological sample of a cancer patient before the start of said anti-tumour treatment,wherein said patient is an unresponsive cancer patient when the expression level of said at least three miRNAs is greater than the expression level of the same miRNAs measured in a biological sample of a cancer patient responsive to the same anti-tumour treatment before the start of said anti-tumour treatment.2) The method according to the preceding claim, whereinwhen said cancer patient is an unresponsive cancer patient, the expression level of each of said at least three miRNAs is greater than an expression level cutoff value obtained by means of the following steps:- measuring the expression of said at least three miRNAs in biological samples of a population of cancer patients before the start of an immunotherapybased anti-tumour treatment, wherein said population of cancer patients comprises a group of unresponsive cancer patients and a group of responsive cancer patients;- for each miRNA, obtaining a cut-off value by means of an ROC curve to distinguish unresponsive cancer patients from responsive cancer patients.3) The method according to any one of the preceding claims, wherein the measured expression of each of said at least three miRNAs is determined as an absolute value.4) The method according to any one of the preceding claims, wherein said at least three miRNAs comprise miR-211-5p.5) The method according to the preceding claim, wherein said at least three miRNAs comprise or consist ofmiR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p;miR-181 b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-873-3p;miR-1246, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-320, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-873-3p; miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-873-3p;miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p;miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p, miR-509-3p; miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-509-3p;miR-1246, miR-211-3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p;miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p;miR-1246, miR-320, miR-211-3p, miR-211-5p;miR-1246, miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211-3p, miR-211-5p;miR-1246, miR-320, miR-211-5p, miR-873-3p;miR-320, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211-5p;miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211-5p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-873-3p;miR-181b-5p, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-211-3p, miR-211-5p;miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;miR-211-3p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-1246, miR-211-5p, miR-509-3p;miR-320, miR-211-3p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211-5p, miR-509-3p;miR-320, miR-211-3p, miR-211-5p;miR-181 b-5p, miR-211-5p, miR-509-3p, miR-873-3p;miR-320, miR-211-3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211-3p, miR-211-5p, miR-873-3p;miR-1246, miR-320, miR-211-5p;miR-181b-5p, miR-211-3p, miR-211-5p, miR-873-3p;miR-1246, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-320, miR-211-5p, miR-509-3p;miR-1246, miR-211-5p, miR-873-3p;miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211-5p, miR-873-3p;miR-320, miR-211-5p, miR-873-3p;miR-181b-5p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-320, miR-211-5p;miR-181b-5p, miR-320, miR-211-3p, miR-211-5p;miR-181b-5p, miR-211-3p, miR-211-5p;miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-211-5p, miR-509-3p;miR-211-3p, miR-211-5p, miR-509-3p.6) The method according to any one of the preceding claims, wherein said at least three miRNAs comprise or consist ofmiR-1246, miR-320 and miR-509-3p; ormiR-211-5p, miR-509-3p and miR-873-3p; ormiR-181b-5p, miR-1246 and miR-211-5p; ormiR-1246, miR-211-3p and mir-509-3p; ormiR-211 -3p, miR-509-3p and miR-873-3p.7) The method according to any one of the preceding claims, wherein the expression of the miRNAs is measured by digital PCR, real-time PCR, microarray analysis, RNA hybridisation methods, such as northern blot or dot blot, or nextgeneration RNA sequencing, preferably digital PCR.8) The method according to any one of the preceding claims, wherein said immune checkpoint inhibitor is selected from an anti-PD1 antibody, e.g. nivolumab, anti-CTLA4 antibody, e.g. ipilimumab, anti-PDL-1 antibody, anti-LAG3 antibody and combinations thereof.9) The method according to any one of the preceding claims, wherein the cancer patient is a patient affected by a solid tumour.10) The method according to the preceding claim, wherein said solid tumour is selected from the group consisting of melanoma, lung cancers or colon cancers.11) The method according to any one of the preceding claims, wherein the biological sample is a liquid biological sample such as blood, serum or plasma.12) A method for obtaining an expression level cut-off value for distinguishing cancer patients unresponsive to an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor from cancer patients responsive to the same treatment, said method comprising- measuring the expression of at least three miRNAs selected from miR-211-5p, miR-320b, miR-1246, miR-181b-5p, miR-509-3p, miR-873-3p and miR-211-3p in biological samples of a population of cancer patients before the start of an immunotherapy-based anti-tumour treatment with an immune checkpoint inhibitor, wherein said population of cancer patients comprises a group of unresponsive cancer patients and a group of responsive cancer patients;- for each miRNA, obtaining a cut-off value by means of an ROC curve. 13) The method according to the preceding claim, wherein said at least three miRNAs comprise miR-211-5p.14) The method according to the preceding claim, wherein said at least three miRNAs comprise or consist ofmiR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211 -3p, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-1246, miR-211 -3p, miR-211-5p, miR-873-3p;miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-320, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-873-3p; miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-873-3p;miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p;miR-211-3p, miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p; miR-181b-5p, miR-211 -3p, miR-211-5p, miR-509-3p, miR-873-3p; miR-181b-5p, miR-1246, miR-211-3p, miR-211-5p, miR-509-3p;miR-1246, miR-211 -3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p;miR-181b-5p, miR-1246, miR-211 -3p, miR-211-5p;miR-1246, miR-320, miR-211 -3p, miR-211-5p;miR-1246, miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-1246, miR-320, miR-211 -3p, miR-211-5p;miR-1246, miR-320, miR-211-5p, miR-873-3p;miR-320, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211-5p;miR-211-5p, miR-509-3p, miR-873-3p;miR-181b-5p, miR-1246, miR-211 -5p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-873-3p;miR-181b-5p, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p;miR-1246, miR-211 -3p, miR-211-5p;miR-181b-5p, miR-1246, miR-211 -5p, miR-509-3p, miR-873-3p;miR-211-3p, miR-211 -5p, miR-873-3p;miR-181b-5p, miR-1246, miR-211 -5p, miR-509-3p;miR-320, miR-211 -3p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-1246, miR-320, miR-211-5p, miR-509-3p, miR-873-3p; miR-1246, miR-211 -5p, miR-509-3p;miR-320, miR-211 -3p, miR-211-5p;miR-181b-5p, miR-211 -5p, miR-509-3p, miR-873-3p;miR-320, miR-211 -3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211 -3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211 -3p, miR-211-5p, miR-873-3p;miR-1246, miR-320, miR-211-5p;miR-181b-5p, miR-211 -3p, miR-211-5p, miR-873-3p;miR-1246, miR-211 -5p, miR-509-3p, miR-873-3p;miR-181 b-5p, miR-320, miR-211-5p, miR-509-3p;miR-1246, miR-211-5p, miR-873-3p;miR-320, miR-211-5p, miR-509-3p;miR-181b-5p, miR-320, miR-211-5p, miR-873-3p;miR-320, miR-211-5p, miR-873-3p;miR-181b-5p, miR-211-5p, miR-873-3p;miR-181b-5p, miR-320, miR-211-5p;miR-181b-5p, miR-320, miR-211-3p, miR-211-5p;miR-181b-5p, miR-211-3p, miR-211-5p;miR-181b-5p, miR-211-3p, miR-211-5p, miR-509-3p;miR-181b-5p, miR-211-5p, miR-509-3p;miR-211-3p, miR-211-5p, miR-509-3p.15) An antagonist of one or more miRNAs selected from miR-181b-5p, miR-4516, miR-3180 and miR-4488, or a pharmaceutical composition comprising said antagonist together with one or more excipients and / or adjuvants, for use in the treatment of solid tumours.16) The antagonist or pharmaceutical composition according to claim 15, for use according to claim 15, wherein said solid tumour is selected in the group consisting of melanoma, lung cancers or colon cancers.17) The antagonist or pharmaceutical composition according to any one of claims 15-16, for use according to any one of claims 15-16, wherein said antagonist is an anti-miRNA oligonucleotide, such as, for example, an LNA.

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