Patent Description:
Acute myeloid leukaemia (AML), also known as acute myelogenous leukaemia, acute myeloblastic leukaemia, acute granulocytic leukemia or acute nonlymphocytic leukemia, is an aggressive cancer of the blood and bone marrow. AML is characterised by excessive production of immature white blood cells, known as myeloblasts, by bone marrow. In healthy individuals, blasts normally develop into mature white blood cells. In AML, however, the blasts do not differentiate normally but remain at a premature arrested state of development.

In AML, the bone marrow may also make abnormal red blood cells and platelets. The number of these abnormal cells increases rapidly, and the abnormal cells begin to crowd out the normal white blood cells, red blood cells and platelets that the body needs. If left untreated, acute myeloid leukaemia is rapidly fatal.

Various classification systems have been devised for classifying AML into disease subtypes, with the aim of enabling more accurate prognosis of disease progression and identification of the optimal form of treatment. The earliest system was the French-American-British (FAB) classification, first devised in the <NUM> by a group of French, American and British leukaemia experts. This system divides AML into subtypes according to the type of cell from which the leukaemia has developed, and the stage of maturity reached by the myeloblast cells at the point of arrest. Subtypes M0 to M5 originate from precursors of white blood cells and range from undifferentiated myeloblastic leukaemia (M0) to monocytic leukaemia (M5). Subtype M6 originates in very early forms of red blood cells (erythroid leukaemia), whilst subtype M7 AML starts in early forms of cells that form platelets (megakaryoblastic leukaemia).

Under the FAB system, AML is categorised by visual inspection of cytomorphological features under the microscope, and by identification of various chromosomal abnormalities. An updated version of the FAB categorisation was published in <NUM> - see <NPL>.

Since the FAB system was first devised in the <NUM>, the level of knowledge in the field has moved on considerably. Whilst the system has been updated to incorporate some of this knowledge, it was felt to be necessary to create a new classification system, taking into account additional factors now known to affect prognosis and to be determinative in optimising effective treatment.

The World Health Organization (WHO) classification system accordingly divides AML into several broad groups. These include:-.

In addition to these two main classification systems, AML is further categorised and subtyped by reference to specific molecular markers which are found to correlate with certain phenotypes and outcomes. For example, patients with mutations in the NPM1 gene or CEBPA genes are known to have a better long term outcome, whilst patients with certain mutations in FLT3 have been found to have a worse prognosis - see<NPL>.

Conventional treatment for AML includes chemotherapy and radiation therapy, as well as stem cell and bone marrow transplants. Most patients respond well at first to such therapy, but there is a high rate of relapse and patients typically become refractory to primary treatments. Overall, the <NUM> year survival rate for AML patients undergoing conventional therapy is around <NUM>%.

Historically, with the exception of acute promyelocytic leukaemia, therapy for AML has not been targeted to the disease subtype. Rather, classification of AML according to the above-mentioned systems has principally served to inform clinical decisions as to the appropriate intensity of treatment. More recently, however, efforts have been made to identify targeted forms of treatment suitable for specific disease types and patient subgroups. Kinase pathway inhibitors have been the subject of particular interest as possible new personalised therapeutics in AML. Recently, the FLT3 inhibitor midostaurin has been approved by the FDA for treatment of adult patients having newly-diagnosed AML with certain activating mutations in the FLT3 gene. The pre-clinical efficacy of MEK inhibitors for treating AML with oncogenic NRAS mutations has also been investigated (<NPL>). However, kinase pathway inhibitors are not yet used routinely for treatment of AML, and the sensitivity of these screening protocols for reliably identifying patients who are susceptible to kinase pathway inhibitor treatment has not been fully tested.

There remains therefore a need for improved methods for targeted treatment of AML, and in particular for identifying patients who will be responsive to treatment with kinase pathway inhibitors, and those who will not be responsive to such treatment. Accurate and sensitive stratification is needed not only to ensure that patients who will respond to a particular treatment can be identified as such and treated appropriately, but also to ensure that patients who will not respond are not treated unnecessarily.

The present inventors have integrated proteomic, kinomic and genomic profiling to investigate the mechanisms that sensitise primary AML cells to kinase pathway inhibitors, thus permitting identification of biomarker panels which accurately identify sensitive cells. The inventors have found that leukaemia cells with an advanced differentiation status show higher sensitivity to kinase pathway inhibitors than less differentiated leukaemia cells. The differentiation status of leukaemia cells, which may be assessed according to protocols and criteria described herein, thus provides an effective biomarker for identification of leukaemia cells and patients that are sensitive to kinase pathway inhibitors. This enables new and effective stratification of patients for kinase inhibitor therapy. The inventors have further identified correlations between specific gene mutations and kinase pathway inhibitor sensitivity. These have the potential, inter alia, for providing effective companion diagnostic tests for use in conjunction with kinase pathway inhibitor therapy.

In a first aspect, the present invention provides a method for predicting the efficacy of midostaurin for treatment of acute myeloid leukaemia in an individual patient, comprising the steps of:.

In a second aspect, the present invention provides a method for screening a plurality of patients with acute myeloid leukaemia to determine whether the acute myeloid leukaemia of any one or more of said plurality of patients may be effectively treated with midostaurin, comprising the steps of:.

In a third aspect, the present invention provides midostaurin for use in a method of treating acute myeloid leukaemia in a patient, wherein the patient has leukaemia with an advanced differentiation status, defined by:.

Described herein is a method for predicting the efficacy of a kinase pathway inhibitor for treatment of acute myeloid leukaemia in an individual patient, which kinase pathway inhibitor inhibits a kinase signalling pathway that is involved in cell proliferation or cell survival, comprising the steps of:.

Described herein, but not claimed, is a method of treating acute myeloid leukaemia in an individual patient, comprising the steps of:.

Described herein is a method of screening a plurality of patients with acute myeloid leukaemia to determine whether the acute myeloid leukaemia of any one or more of said plurality of patients may be effectively treated with a kinase pathway inhibitor, which kinase pathway inhibitor inhibits a kinase signalling pathway that is involved in cell proliferation or cell survival, comprising the steps of:.

Described herein is a kinase pathway inhibitor, which kinase pathway inhibitor inhibits a kinase pathway that is involved in cell proliferation or cell survival, for use in a method of treating acute myeloid leukaemia in an individual patient, wherein the treatment comprises :.

The present disclosure further provides for computer implementation of the method of screening according to the third aspect of the invention and the method of predicting efficacy of kinase pathway inhibitor therapy according to the first aspect of the invention. The present invention also provides software for performing either or both of these computer-implemented methods.

The first two aspects of the present invention each comprise a step (a) of determining the differentiation status of a patient's leukaemia. As described above, AML involves proliferation of aberrant, partially-differentiated myeloblasts. The term "differentiation status of a patient's leukaemia" thus refers to the differentiation status of the patient's leukaemia cells. Suitably, therefore, step (a) may involve determining the differentiation status of leukaemia cells which have previously been obtained from the patient. Alternatively, step (a) may further involve obtaining leukaemia cells from the patient, prior to determining the differentiation status of said leukaemia cells. Said leukaemia cells may, for example, be obtained from peripheral blood samples or from bone marrow samples. This invention is applicable to all AML patients, including newly-diagnosed (untreated) AML patients, AML patients who have undergone or are undergoing other forms of treatment, and relapsed AML patients.

Suitably, the differentiation status of the leukaemia cells may be determined by analysing data relating to the leukaemia cells as described hereinbelow. In some embodiments, said data has previously been gathered and recorded and step (a) comprises obtaining or receiving said data for analysis. In other embodiments, step (a) further comprises gathering and recording said data for analysis, as described hereinbelow. In some embodiments, said step of determining the differentiation status of the patient's leukaemia may consist of determining whether or not the patient's leukaemia is advanced (a binary (yes/no) determination).

The differentiation status of a patient's leukaemia may be determined by analysing data relating to morphological and/or cytochemical features of the leukaemia cells, and/or by analysing data relating to expression, activation and/or phosphorylation in the leukaemia cells of one or more differentiation markers such as cell surface differentiation markers and/or functional differentiation markers including kinase pathway activity markers, and/or by reference to data recording the classification of the leukaemia cells under the French-American-British (FAB) classification as described in <NPL>. The data may include any type of information concerning the cells, including without limit information regarding the appearance, properties, characteristics, genotype, phenotype, activity, classification and function of the cells, and including without limit images of the cells, written descriptions of the cells, and measurements of all types obtained from the cells.

Said data relating to morphological features of the leukaemia cells may include data recording the visual appearance of the cells under a light microscope, optionally using a stain such as Romanowsky's stain. Said data may, for example, include visual images of the cells and/or written descriptions of the cells. Step (a) may comprise analysing the data to determine if the cells satisfy the FAB criteria for identification of M4 cells as defined in Bennett et al, op. In particular, an advanced differentiation status may be determined if the data indicates that at least <NUM>% of the leukaemia cells have an appearance characteristic of granulocytic-monocytic cells, and/or if the data indicates that amongst the leukaemia cells, myeloblasts, monoblasts and promonocytes constitute <NUM>% or more of nonerythroid cells, and myeloblasts and granulocytes constitute <NUM>% or less of nonerythroid cells. An advanced differentiation status may for example be determined if the data indicates that at least <NUM>% of the cells have lightly granulated, greyish cytoplasm and folded nuclei, characteristic of granulocytic-monocytic cells (M4 FAB). An advanced differentiation status may be determined if in a sample obtained from bone marrow, the blast cells (myeloblasts, promyelocytes, myelocytes and later granulocytes) constitute more than <NUM>% but less than <NUM>% of the non-erythroid cells; and, preferably but not essentially, if in a sample obtained from peripheral blood, the monocyte count (monoblasts, promonocytes and monocytes) is <NUM> x <NUM><NUM>/L or more. See Bennett, op. cit, at page <NUM>.

In some embodiments, said data relating to morphological features of the leukaemia cells has previously been recorded and step (a) comprises obtaining said data for analysis. In other embodiments, step (a) further comprises gathering and recording said data relating to morphological features of the leukaemia cells for analysis. Methods for collecting and recording said data relating to morphological features of the cells are conventional and well-known in the art, being described in Bennett et al (op cit) and elsewhere.

Said data relating to cytochemical features of the leukaemia cells may include data recording the response of the cells to reagents such as sudan black B and/or peroxidase and/or specific or non-specific esterases. Said data may, for example, include visual images of the cells, written descriptions of the cells, flow cytometry data and other types of cytochemical data. Step (a) may comprise analysing the data to determine if the cells satisfy the FAB criteria for identification of M4 cells as defined in Bennett et al, op. In particular, an advanced differentiation status may be determined if the data indicates that at least <NUM>% of the cells are responsive to sudan black B and/or peroxidase and/or specific or non-specific esterase (M4 FAB).

In some embodiments, said data relating to cytochemical features of the leukaemia cells has previously been recorded and step (a) comprises obtaining said data for analysis. In other embodiments, step (a) further comprises gathering and recording said data relating to cytochemical features of the leukaemia cells for analysis. Methods for collecting and recording data relating to cytochemical features of cells are conventional and well-known in the art, being described in Bennett et al (op cit) and elsewhere.

Said data relating to expression, activation and/or phosphorylation in the leukaemia cells of one or more differentiation markers such as cell surface differentiation markers and/or functional differentiation markers may include data recording the presence or absence or the level of expression on the surface of the leukaemia cells of one or more cell surface differentiation markers, such as signalling molecules, which cell surface differentiation markers are typically expressed or over-expressed in healthy myelomonocytic cells and which cell surface differentiation markers are not typically expressed or over-expressed in undifferentiated myeloblasts; wherein the presence of said one or more cell surface differentiation markers on the leukaemia cells, or the expression of said one or more cell surface differentiation markers at a high level on the leukaemia cells indicates an advanced differentiation status. Said data may, for example, include a written description of the cells, or any type of data obtained from an assay measuring cell surface protein expression, such as by mass cytometry or any other technique or assay that is known in the art. In some embodiments, said data has previously been recorded and step (a) comprises obtaining said data for analysis. In other embodiments, step (a) further comprises collecting and recording said data for analysis, according to standard conventional methods and protocols known in the art, for example by mass cytometry.

Said cell surface differentiation markers may comprise a panel of cell surface marker proteins including one or more of CD3, CD7, CD11b, CD11c (integrin α-X, ITAX), CD14, CD15, CD16, CD18 (integrin β, ITB2), CD19, CD33, CD34, CD35 (CR1), CD38, CD44, CD45, CD64, CD97, CD117, CD123, CD180, CD184, HLA-C (1C02), APOBR, the platelet membrane receptor Gi24 (VSIR) and HLA-DR; and/or any cell surface proteins which are expressed in conjunction with said one or more cell surface marker proteins. CD markers, also known as cluster of differentiation markers, are a well-defined subset of cellular surface receptors (epitopes) that are specific as to cell type and stage of differentiation, and which are recognized by antibodies. The cell surface marker proteins listed above are all known in the art and are well characterised - see, for example, <NPL>. These cell surface marker proteins have been found to be typically expressed typically at a high level on the surface of leukaemia cells with an advanced differentiation status which are sensitive to kinase pathway inhibitors, but are not typically expressed in undifferentiated myeloblasts. Step (a) may therefore involve analysing the data to determine if the panel of cell surface marker proteins is expressed or is expressed at a high level by said leukaemia cells, where an advanced differentiation status is determined if the panel of cell surface marker proteins is expressed or is expressed at a high level. Preferably, the panel of cell surface marker proteins includes any two, or three, or four, or five, or six, or seven, or eight, or nine, or ten, or eleven, or twelve, or thirteen, or fourteen, or fifteen, or sixteen, or seventeen, or eighteen, or all of CD11b, CD11c (integrin α-X, ITAX), CD14, CD15, CD16, CD18 (integrin β, ITB2), CD33, CD35 (CR1), CD38, CD44, CD45, CD64, CD97, CD123, CD180, HLA-C (1C02), APOBR, the platelet membrane receptor Gi24 (VSIR) and HLA-DR.

References to expression of one or more cell surface proteins, such as cell surface marker proteins, at a "high level", as used here and elsewhere in the specification, denote a level of expression which is higher than the average level of expression of the relevant cell surface proteins. References to a "low level" of expression similarly denote a level of expression which is the same as or less than the average level of expression of the cell surface proteins. The average level of expression of the cell surface proteins is a standardised value which may be determined by reference to an average calculated across a plurality of samples, or by reference to the level of expression of the cell surface proteins in undifferentiated myeloblasts or other healthy cell types, which may be established either by laboratory analysis according to methods well known in the art (including LC-MS/MS), or by reference to information available in the art. Thus, for example, the average level of expression of the cell surface proteins may be determined by establishing the range of expression levels of the cell surface proteins in cell samples obtained from a large number of AML patients, and calculating the mean level of expression across the samples. A "high level" of expression of the cell surface proteins is a level of expression which is higher than the calculated mean.

Optionally, the panel of cell surface marker proteins may further include one or more of CD19, CD <NUM>, CD7, CD34, CD3, and CD <NUM>. The panel of cell surface marker proteins may advantageously include CD45, and/or CD11b, and/or CD44, and/or CD14, and/or CD16, and/or CD64 and/or CD15. In particular, the panel of cell surface marker proteins may include any one of CD45, CD11b, CD44, CD14, CD16, CD64 and CD15, or any two, three, four, five or six of CD45, CD11b, CD44, CD14, CD16, CD64 and CD15. Suitably, the panel of cell surface marker proteins may consist of CD45, CD11b, CD44, CD14, CD16, CD64 and CD15. In some preferred embodiments, the panel of cell surface markers consists of any one, two, three, four, five, six, seven, eight, nine, ten or all of CD11b, CD14, CD15, CD16, CD33, CD38, CD44, CD45, CD64, CD123 and HLA-DR. In other preferred embodiments, the panel of cell surface marker proteins consists of any one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen or all of CD3, CD7, CD11b, CD14, CD15, CD16, CD19, CD33, CD34, CD38, CD44, CD45, CD64, CD117, CD123, CD184, and HLA-DR.

Said data relating to expression, activation and/or phosphorylation in the leukaemia cells of one or more differentiation markers such as cell surface differentiation markers and/or functional differentiation markers may, additionally or alternatively, comprise data recording the expression and/or activation and/or phosphorylation of one or more functional differentiation markers, which functional differentiation markers are typically expressed, over-expressed, activated and/or phosphorylated in healthy monomyelocytic cells, and which functional differentiation markers are not typically expressed, over-expressed, activated and/or phosphorylated in undifferentiated myeloblasts; wherein the expression, activation and/or phosphorylation of said one or more functional differentiation markers indicates an advanced differentiation status. Said data may, for example, include a written description of the cells, or any type of data obtained from an assay measuring expression, activation or phosphorylation of cellular proteins, according to any technique known in the art, such as LC-MS/MS analysis or immunochemical techniques including Western blotting, ELISA, and reversed phase protein assays. In some embodiments, said data has previously been recorded and step (a) comprises obtaining said data for analysis. In other embodiments, step (a) further comprises collecting and recording said data for analysis, according to standard conventional methods known in the art, such as by LC-MS/MS.

Said one or more functional differentiation markers may comprise a panel of protein markers including one or more enzymes, integrins, kinases, phosphatases, signal transduction regulators, cytoplasmic proteins and phosphoproteins, membrane proteins and phosphoproteins, including cytoplasmic and membrane phosphoproteins that are involved in GTPase or other forms of cell signalling, which protein markers are typically expressed, over-expressed and/or activated in healthy monomyelocytic cells, and are not typically expressed, over-expressed and/or activated in undifferentiated myeloblasts.

The panel of protein markers may include any one, two, three, four, five, six, seven, eight, nine, ten or more of lysozyme C (LYZ), neutrophil cytosol factor <NUM> (NCF2), myeloid cell nuclear differentiation antigen (MNDA), AK1C4, ERG, Nesprin <NUM>, Voltage-gated hydrogen channel <NUM>, Fructose-<NUM>,<NUM>-bisphosphatase <NUM>, Monocyte differentiation antigen CD14, Thymidine phosphorylase, CD180 antigen, Putative annexin A2-like protein, Retinoid-inducible serine carboxypeptidase, Annexin A2, Golgi-associated plant pathogenesis-related protein <NUM>, Integrin beta-<NUM>, BTB/POZ domain-containing protein KCTD12, Cytoskeleton-associated protein <NUM>, Integrin alpha-X, Complement receptor type <NUM>, Annexin A5, Uncharacterized protein FLJ45252, Galectin-<NUM>, Adenylate kinase isoenzyme <NUM>, Protein S100-A10, Thiamine-triphosphatase, Deoxynucleoside triphosphate triphosphohydrolase SAMHD1, Mitochondrial amidoxime-reducing component <NUM>, Coronin-1B, Arf-GAP with Rho-GAP domain, ANK repeat and PH domain-containing protein <NUM>, Granulins, Ribonuclease inhibitor, Long-chain-fatty-acid--CoA ligase <NUM>, Protein S100-A11, Pro-cathepsin H, Cathepsin S, Galectin-<NUM>, Transport and Golgi organization protein <NUM> homolog, Arf-GAP domain and FG repeat-containing protein <NUM>, Long-chain-fatty-acid--CoA ligase <NUM>, Ras GTPase-activating-like protein IQGAP1, Allograft inflammatory factor <NUM>, Transcription intermediary factor <NUM>-beta, Beta-arrestin-<NUM>, Dihydropyrimidine dehydrogenase [NADP(+)], Alpha-N-acetylgalactosaminidase, Cathepsin B, Aminopeptidase B, Lysosomal protective protein, Phosphoglycerate mutase <NUM>, Polypeptide N-acetylgalactosaminyltransferase <NUM>, Cytokine receptor-like factor <NUM>, Calpastatin, EF-hand domain-containing protein D2, Dual specificity mitogen-activated protein kinase kinase <NUM>, Major vault protein, Alpha-galactosidase A, Tyrosine-protein kinase SYK, Sister chromatid cohesion protein PDS5 homolog B, Calpain-<NUM> catalytic subunit, FK506-binding protein <NUM>, Protein disulfide-isomerase, Tensin-<NUM>, Apolipoprotein B receptor, Transforming protein RhoA, Plastin-<NUM>, Actin-related protein <NUM>/<NUM> complex subunit <NUM>, CD97 antigen, Cathepsin Z, Neuroblast differentiation-associated protein AHNAK, Unconventional myosin-If, Pyruvate kinase PKM, Protein THEMIS2, Plastin-<NUM>, Tyrosine-protein phosphatase non-receptor type <NUM>, Ezrin, Leucine-rich repeat-containing protein <NUM>, Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-<NUM>, Coronin-1A, Radixin, Transketolase, Growth factor receptor-bound protein <NUM>, V-type proton ATPase subunit B (kidney isoform), Coatomer subunit epsilon, Alpha-soluble NSF attachment protein, Rho GDP-dissociation inhibitor <NUM>, and/or Guanine nucleotide-binding protein subunit beta-<NUM>, and/or any proteins which are selectively expressed and/or activated therewith. These protein markers have been found to be typically expressed and/or activated in leukaemia cells with an advanced differentiation status which are sensitive to kinase pathway inhibitors, but are not typically expressed and/or activated in undifferentiated myeloblasts. Step (a) may therefore comprise analysing the data to determine if the panel of protein markers is expressed and/or activated in said leukaemia cells, where an advanced differentiation status is determined if the panel of protein markers is expressed and/or activated in the cells.

Advantageously, the panel of protein markers may include any one, two, three, four or five of lysozyme C (LYZ), neutrophil cytosol factor <NUM> (NCF2), myeloid cell nuclear differentiation antigen (MNDA), AK1C4, and ERG; and step (a) may comprise analysing the data to determine if this panel of protein markers is expressed in the leukaemia cells, where an advanced differentiation status is determined if the panel of protein markers is expressed in the cells.

Said one or more functional differentiation markers may additionally or alternatively comprise a panel of kinase pathway activity markers including one or more kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors, phosphopeptides and/or other kinase signalling molecules that are typically expressed and/or activated and/or phosphorylated in a kinase signalling pathway in healthy monomyelocytic cells but are not typically expressed and/or activated and/or phosphorylated in undifferentiated myeloblasts. Advantageously, the kinase signalling pathway may be a pathway that is inhibited by the kinase pathway inhibitor. Thus, for example, where the kinase pathway inhibitor is a RAS-RAF-MEK-ERK pathway inhibitor such as trametinib, the panel of kinase pathway activity markers may comprise markers of the RAS-RAF-MEK-ERK signalling pathway.

In particular, the panel of kinase pathway activity markers may include one or more kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors and/or other kinase signalling molecules that are expressed and/or activated in a kinase signalling pathway that is involved in cell proliferation or cell survival. In some embodiments, one or more of said kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors and/or other kinase signalling molecules may be expressed and/or activated in a kinase signalling pathway that is inhibited by said kinase pathway inhibitor. In some embodiments, one or more of said kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors and/or other kinase signalling molecules may be inhibited by said kinase pathway inhibitor.

The panel of kinase pathway activity markers may include any one, two, three, four, five, six, seven, eight, nine, ten or more of FES, PKC and protein kinase C isoforms including PKCδ (KPCD), PRKCA, PRKCB, and PRKCD, PKA, PAK including PAK1 and PAK2, STK10, GSK3A, RSK2, RAS, RAF, MEK including MEK1 (MAP2K1), ERK including MAPK3 (ERK1) and MAPK1 (ERK2), PI3K, AKT including AKT1, MTOR, S6 kinase, STATS, CAMKK, SYK (KSYK), LYN, P38A, CDK1, CK2A1, PKACA, IRAK4, PKCB iso2, Cot, PKCD, PKCA, PKCB, PKCG, PKCH, BRAF, MEK2, PDK1, CDK2, PTN6, D3 (PLD3), IQGAP1, GRB2, RHOA, RHOG and S10AB, and any kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors and/or other kinase signalling molecules that are selectively expressed or activated therewith. These kinase pathway activity markers have been found to be typically expressed and/or activated and/or phosphorylated in leukaemia cells with an advanced differentiation status which are sensitive to kinase pathway inhibitors, but are not typically expressed and/or activated and/or phosphorylated in undifferentiated myeloblasts. Step (a) may therefore comprise analysing the data to determine if the panel of kinase pathway activity markers is expressed and/or activated and/or phosphorylated in said leukaemia cells, where an advanced differentiation status is determined if the panel of kinase pathway activity markers is expressed and/or activated and/or phosphorylated in the cells.

Advantageously, the panel of kinase pathway activity markers may include any one, two, three, or four of PKC, ERK, PAK1 and P38α and step (a) may include analysing the data to determine if this panel of kinase pathway activity markers is expressed and activated in the leukaemia cells, where an advanced differentiation status is determined if the panel of kinase pathway activity markers is expressed and activated in the cells.

Suitably, the panel of kinase pathway activity markers may include any one, two, three, four or five of PKCD, PKCA, PKACA, IRAK4 and CK2A1, and step (a) may include analysing the data to determine if this panel of kinase pathway activity markers is expressed and activated in the leukaemia cells, where an advanced differentiation status is determined if the panel of kinase pathway activity markers is expressed and activated in the cells. Alternatively, the panel of kinase pathway activity markers may include any one, two, three, four, five or six of MAPK1, MAPK2, AKT, AKT1S1, MAP2K1 and MAP2K2, and step (a) may include analysing the data to determine if this panel of kinase pathway activity markers is expressed and activated in the leukaemia cells, where an advanced differentiation status is determined if the panel of kinase pathway activity markers is expressed and activated in the cells.

The panel of kinase pathway activity markers may additionally or alternatively comprise a panel of one or more phosphorylation sites which are typically phosphorylated or are typically phosphorylated at a high level in a kinase signalling pathway in healthy monomyelocytic cells but are not typically phosphorylated or not typically phosphorylated at a high level in undifferentiated myeloblasts Step (a) may comprise analysing the data to determine if the panel of phosphorylation sites is phosphorylated at a high level in said leukaemia cells, where an advanced differentiation status is determined if the panel of phosphorylation sites is phosphorylated at a high level in the leukaemia cells.

References to phosphorylation at a "high level", as used here and elsewhere in the specification, denote a level of phosphorylation which is higher than the average phosphorylation of the reference protein or at the reference phosphorylation site. References to a "low level" of phosphorylation similarly denote a level of phosphorylation which is the same as or less than the average phosphorylation of the reference protein or at the reference phosphorylation site. The average phosphorylation of the reference protein or the reference phosphorylation site is a standardised value which may be determined by reference to an average calculated across a plurality of samples, or by reference to the phosphorylation state of the reference protein or the reference phosphorylation site in undifferentiated myeloblasts or other healthy cell types, which may be established either by laboratory analysis according to methods well known in the art (including LC-MS/MS), or by reference to information available in the art. Thus, for example, the average level of phosphorylation at a particular phosphorylation site may be determined by establishing the range of phosphorylation at that site in cell samples obtained from a large number of AML patients, and calculating the mean phosphorylation across the samples. A "high level" of phosphorylation at that site is a level of phosphorylation which is higher than the calculated mean.

In particular, the panel of phosphorylation sites may include one or more phosphorylation sites that are phosphorylated at a high level in a kinase signalling pathway which is involved in cell proliferation or cell survival. In some embodiments, one or more of said phosphorylation sites may be phosphorylated at a high level in a kinase signalling pathway which is inhibited by said kinase pathway inhibitor.

The panel of phosphorylation sites may include any one, two, three, four, five, six, seven, eight, nine, ten or more than ten of the phosphorylation sites set out in Table <NUM> below.

Suitably, the panel of phosphorylation sites may include any one, two, three, four, five, six, seven, eight, nine, ten or more than ten of the phosphorylation sites set out in Table <NUM> below.

Advantageously, the panel of phosphorylation sites may comprise one or more phosphorylation sites in kinases, including one, two, three, four, five, six, seven, eight, nine, ten or more than ten of PAK1 at S144, PAK2 at S141, MAPK1 at Y187, MAPK1 at T185, RPS6KA1 at S380, MAPK3 at T202, MAPK3 at Y204, MAP3K3 at S166, SYK at S295 and S297, IRAK3 at S110, PKN1 (<NUM>-<NUM> + phospho ST), STK10 (<NUM>-<NUM> + phospho ST), RIPK3 at S410, PRKCD at T218, PRKCD at T295, PRKCD at Y313, PRKCD at T507, PRKCD at T645, PRKCD at S664, PRKCD atT2638, MARK2 at S535, MAP3K2 at S535, PRKD2 (<NUM>-<NUM> + phospho Y), NRK at s805, PRKAR2A at S58, ZAK (<NUM>-<NUM> + phospho ST), MAP4K4 at S900, CDK9 at S347, RPS6KA4 (<NUM>-<NUM> + <NUM> phospho ST), MAST3 (<NUM>-<NUM> + phospho ST), NEK9 (<NUM>-<NUM> + phospho ST), GSK3A (<NUM>-<NUM> + phospho ST), RPS6KA3 at S369, RIPK2 at S531, AAK1 at T606, TYK2 at Y292, PDPK2 at S214, PRKAA1 (<NUM>-<NUM> + phospho ST), STK11P at S772, BAZ1B at S1468, CLK1 at S140, MAP4K2 at S328, WNK1 (<NUM>-<NUM> + phospho ST), CDK11A at S271, FES at Y713, and/or TNIK at S769, and step (a) may comprise analysing the data to determine if the panel of phosphopeptides is phosphorylated at a high level in the leukaemia cells, where an advanced differentiation status is determined if the panel of phosphopeptides is phosphorylated at a high level in the cells.

Preferably, the panel of phosphorylation sites may comprise regulatory phosphorylation sites in kinases, such as PAK1 at S144, PAK2 at S141, MAPK1 at Y187 and/or T185, and RPS6KA1 at S380, and step (a) may comprise analysing the data to determine if the panel of phosphopeptides is phosphorylated at a high level in the leukaemia cells, where an advanced differentiation status is determined if the panel of phosphopeptides is phosphorylated at a high level in the cells.

Suitably, the panel of phosphorylation sites may include MAPK1 at Y187, PAK2 at S141 and PRKCD at Y313, and step (a) may comprise analysing the data to determine if the panel of phosphopeptides is phosphorylated in the leukaemia cells, where an advanced differentiation status is determined if the panel of phosphopeptides is phosphorylated in the cells.

Optionally, the panel of phosphorylation sites may include FES at Y713, MAPK3 at T202/Y204, MAPK1 at T185/Y187, PAK1 at S144, MEK1 at S222, PAK2 at S141 and PRKCD at S645, and step (a) may comprise analysing the data to determine if the panel of phosphopeptides is phosphorylated in the leukaemia cells, where an advanced differentiation status is determined if the panel of phosphopeptides is phosphorylated in the cells.

The panel of phosphorylation sites may, for example, include one or more phosphorylation sites on one or more PKC isoforms including PRKCA, PRKCB and/or PRKCD), and/or one or more phosphorylation sites on one or more of STK10, GSK3A, PAK1, PAK2 and Gi24 (VSIR), as indicated in Table <NUM>. For example, the panel of phosphorylation sites may include S21 of GSK3A and/or T507, T295, T218, Y313, T507, and/or S664 of PRKCD, and/or S20 of STK10, and/or S13 of STK10, and/or S144 of PAK <NUM>, and/or S141 of PAK2.

Suitably, the panel of phosphorylation sites may consist of phosphorylation sites on MAPK1, including at Y187 and/or T185 of MAPK1, and/or at T202 or Y204 of MAPK3, and/or at S21 of GSK3A, and step (a) may comprise analysing the data to determine if the panel of phosphopeptides is phosphorylated in the leukaemia cells, where an advanced differentiation status is determined if the panel of phosphopeptides is phosphorylated in the cells.

Said data recording the classification of the leukaemia cells under the French-American-British (FAB) classification system may comprise data of any kind which indicates the FAB classification of the leukaemia cells. An advanced differentiation status may be determined if the leukaemia cells are classified as M4, M4 eos or M5, preferably if the leukaemia cells are classified as M4.

The methods of the invention enable the effective identification of AML patients who are suitable for kinase inhibitor therapy, based on the differentiation status of the patients' leukaemia. Whilst kinase inhibitors of various types have previously been suggested as candidates for use in AML therapy, there has been no previous disclosure or suggestion that the differentiation status of a patient's leukaemia may in any way indicate suitability for kinase inhibitor therapy. The present invention therefore provides a new and non-obvious grouping of AML patients who are suitable for kinase inhibitor therapy. As proved by the specific examples, this grouping is highly selective for AML patients who will respond to kinase inhibitor therapy.

The present disclosure provides a kinase pathway inhibitor, which kinase pathway inhibitor inhibits a kinase signalling pathway that is involved in cell proliferation or cell survival, for use in a method of treating acute myeloid leukaemia in a patient, wherein the patient has leukaemia cells with an advanced differentiation status. Preferably, the differentiation status of the leukaemia cells may be determined according to the methods of the invention as described herein.

According to the invention, the kinase pathway inhibitor is midostaurin.

A kinase pathway inhibitor is an agent such as a small molecule or antibody which blocks the activity of a kinase pathway. Kinase pathway inhibitors may include inhibitors of enzyme and kinase pathway signalling molecules, including kinases, phosphatases, and G proteins. Suitably, the kinase pathway inhibitor may be a kinase inhibitor. A kinase inhibitor is an agent which blocks the kinase activity of a protein kinase. Such agents are well known and are widely available in the art. The inhibitory capability of a kinase inhibitor can be assessed by determining the activity of a kinase before and after incubation with the candidate compound. Kinase profiling methods for identifying kinase inhibitors are also widely available in the art, thus putting a large range of kinase inhibitors for use in the present invention at the disposal of the skilled person. One assay which may be used for the identification of agents capable of inhibiting specific kinases is a radioactive filter binding assay using 33P ATP, described in <NPL>; <NPL>. This method is sensitive, accurate and provides a direct measure of activity. Thus results are directly comparable between samples.

Preferably, the kinase pathway inhibitor may inhibit any one or more of the FLT3 pathway, the PKC pathway, the RAS-RAF-MEK-ERK pathway, the PI3K-AKT-MTOR-S6K pathway, the PAK pathway, the JAK-STAT pathway, the CAMKK pathway, or any kinase signalling pathway parallel thereto. The kinase pathway inhibitor may, for example, be a kinase inhibitor which inhibits one or more of PKC, PAK, RAF, MEK, ERK, P13K, AKT, MTOR, S6K, STATS, CAMKK, SYK, LYN, JAK, RTK, ALK, CDK, and BTK.

The kinase pathway inhibitor may be a kinase inhibitor which is :.

The kinase pathway inhibitor may, for example, be one of afatinib, alecitinib, alpelisib, axitinib, bosutinib, brigatinib, buparlisib, cabozantinib, ceritinib, cobimetinib, copanlisib, crizotinib, dabrafenib, dasatinib, dequalinium chloride, duvelisib, erlotinib, everolimus, gefitinib, ibrutinib, idelalisib, imatinib, lapatinib, lenvatinib, miltefosin, nilotinib, nintedanib, osimertinib, pacritinib, palbociclib, pazopanib, ponatinib, quizartinib, radaforolimus, rapamycin, regorafenib, ribociclib, ruxolitinib, selumetinib, sorafenib, sunitinib, temsirolimus, tofacitinib, vandetanib, vemurafenib and zotarolimus.

The kinase pathway inhibitor may be a MEK inhibitorsuch as APS-<NUM>-<NUM>, AZ <NUM>, AZD8330, BI-<NUM>, Binimetinib, BIX <NUM>, CEP-<NUM>, Cobimetinib, Dabrafenib, DEL-<NUM>, ERK5-IN-<NUM>, FR <NUM>, GDC-<NUM>, GDC-<NUM>, HA15, Honokiol, PD0325901, PD184352, PD318088, PD98059, Pimasertib, PLX7904, Refametinib, RO5126766, SC1, SCH772984, SCH772984, Selumetinib, SGX-<NUM>, SL-<NUM>, Sorafenib, TAK733, Trametinib, U0126, U0126, Ulixertinib, Vandetanib, Vemurafenib, VX-11e, or XMD8-<NUM>; or a FLT3/PKC inhibitor such as midostaurin; or a PAK inhibitor such as FRAX1013, FRAX486, FRAX597, IPA-<NUM>, and PF3758309; or a PI3K/AKT/MTOR inhibitor selected from <NUM>-Methyladenine, A66, A-<NUM>, Afuresertib, Akti-<NUM>/<NUM>, Alpelisib, AMG319, Apitolisib , AS-<NUM>, AS-<NUM>, AS-<NUM>, AT13148, AT7867, AZD1208, AZD5363, AZD6482, AZD8055, AZD8186, AZD8835, BGT226, BI-78D3, Buparlisib, CAY10505, CC-<NUM>, CCT128930, CH5132799, Copanlisib, CP-<NUM>, CPI-<NUM>, CUDC-<NUM>, CX-<NUM> HCl, CZ415, CZC24832, Dactolisib, Duvelisib, ETP-<NUM>, Everolimus, GDC-<NUM>, GDC-<NUM>, Gedatolisib, GNE-<NUM>, GSK1059615, GSK2269557, GSK2292767, GSK2636771, GSK690693, HS-<NUM>, IC-<NUM>, Idelalisib, INK <NUM>, Ipatasertib, KU-<NUM>, KU-<NUM>, KU-<NUM>, LTURM34, LY294002, LY3023414, MHY1485, Miltefosine, Miransertib, MK-<NUM>, NU7026, NU7441, Omipalisib, OSI-<NUM>, Palomid <NUM>, Perifosine, PF-<NUM>, PF-<NUM>, PHT-<NUM>, PI-<NUM>, PI-<NUM>, Pictilisib, PIK-<NUM>, PIK-<NUM>, PIK-<NUM>, PIK-<NUM>, Pilaralisib, PIM447, Piperlongumine, PKI-<NUM>, PP121, Rapamycin, Ridaforolimus, SAR405, SC79, SGI-<NUM>, SIS3, SKI II, SRPIN340, Tacrolimus, Taselisib, Temsirolimus, TG100-<NUM>, TG100713, TGR-<NUM>, TIC10, TIC10 Analogue, Torin <NUM>, Torin <NUM>, Torkinib, Triciribine, Uprosertib, VE-<NUM>, Vistusertib, Voxtalisib, VPS34-IN1, VS-<NUM>, WAY-<NUM>, Wortmannin, WYE-<NUM>, WYE-<NUM>, WYE-<NUM>, XL147 analogue, XL388, Zotarolimus or ZSTK474. Suitably, the kinase pathway inhibitor may be trametinib, or midostaurin, or PF <NUM>.

As demonstrated by the experimental data provided herein, the inventors have found that the present invention provides an accurate test for identifying AML patients who will be responsive to treatment with FLT3/PKC pathway inhibitors such as midostaurin.

The kinase pathway inhibitor is a FLT3/PKC pathway inhibitor such as midostaurin, and step (a) involves determining the differentiation status of the patient's leukaemia by :.

wherein either : a high level of phosphorylation in the leukaemia cells of GSK3A, PRKCA, PRKCB, PRKCD, STK10, PAK1, PAK2MAPK1 and/or MAPK3; or expression by the leukaemia cells of said group of CD markers; indicates an advanced differentiation status.

In these embodiments, said data relating to the phosphorylation of one or more phosphorylation sites in GSK3A, PRKCA, PRKCB, PRKCD, STK10, PAK1, PAK2MAPK1 and/or MAPK3 may comprise data relating to : the phosphorylation of GSK3A at pS21 and/or the phosphorylation of PRKCD at Y313, pT507, pT295, pT218, and/or pS664 and/or the phosphorylation of STK10 at pS20 and/or pS13, and/or the phosphorylation of PAK1 at pS144 and/or the phosphorylation of PAK2 at pS141, and/or the phosphorylation of MAPK1 at Y187 and/or T185, and/or the phosphorylation of MAPK3 at T202 and/or Y204.

An activating mutation of NRAS, KRAS, HRAS or BRAF is a mutation which has the effect of constitutively switching the protein "on". Such mutations may, for example, include :.

Said data relating to the genotype of the leukaemia cells may comprise any information from which a skilled person could deduce the presence or absence of an activating mutation in NRAS, KRAS, HRAS and/or BRAF. The data may include, without limitation, the sequence of the NRAS, KRAS, HRAS and/or BRAF genes in the leukaemia cells, the sequence of the or each encoded protein expressed by the leukaemia cells, or data recording the presence or absence of an activating mutation in NRAS, KRAS, HRAS and/or BRAF in the leukaemia cells. In some embodiments, said data has previously been gathered and recorded and step (a) comprises obtaining said data for analysis. In other embodiments, step (a) further comprises gathering and recording said data for analysis. Said data may be gathered and recorded without difficulty according to techniques and protocols well known in the art and as exemplified herein.

The present inventors have also found that leukaemia cells possessing an activating mutation in FLT3, or displaying activation of a FLT3-driven pro-survival kinase signalling pathway operating in parallel to the RAS-RAF-MEK-ERK pathway, or having a high level of phosphorylation on certain phosphomarkers as identified below, can show resistance to treatment with MEK pathway inhibitors. It is thought that mutation of FLT3 and/or activation of parallel FLT3-driven pro-survival signalling pathways may provide the cells with alternative survival mechanisms notwithstanding the inhibition of the RAS-RAF-MEK-ERK pathway.

An activating mutation of FLT3 is a mutation which has the effect of constitutively switching the FLT3 protein "on". Such mutations may, for example, include internal tandem duplications (ITD) of the juxtamembrane domain or point mutations usually involving the tyrosine kinase domain, such as at D835. Said data relating to the genotype of the leukaemia cells may comprise any information from which a skilled person could deduce the presence or absence of an activating mutation in FLT3. The data may include, without limitation, the sequence of the FLT3 gene in the leukaemia cells, the sequence of the FLT3 protein expressed by the leukaemia cells, or data recording the presence or absence of an activating mutation in FLT3 in the leukaemia cells. Said data may be gathered and interpreted by the skilled person without difficulty according to techniques and protocols well known in the art.

Said step of determining the activation in the leukaemia cells of a FLT3-driven kinase signalling pathway that is involved in cell proliferation or cell survival other than the RAS-RAF-MEK-ERK pathway may comprise determining the activation of more than one FLT3-driven kinase signalling pathway. The or each FLT3-driven kinase signalling pathway may preferably be selected from the PKC pathway, the PI3K-AKT-MTOR-S6K pathway, the PAK pathway, the JAK-STAT pathway, or the CAMKK pathway. Preferably, the FLT3-driven kinase signalling pathway may be the JAK-STAT pathway, the PI3K-AKT-MTOR-S6K pathway or the CAMKK pathway. Suitably, the FLT3-driven kinase signalling pathway may be the JAK-STAT (STATS) pathway.

Said activity markers of the FLT3-driven kinase signalling pathway may include any markers which can be used to identify the activation of the FLT3-driven kinase signalling pathway. These may include any kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors, phosphopeptides and/or other kinase signalling molecules which are selectively activated in the FLT3-driven kinase signalling pathway, or any molecules which are selectively phosphorylated in the FLT3-driven kinase signalling pathway (phosphomarkers). Conveniently, the activity markers of the FLT3-driven kinase signalling pathway may include one or more phosphomarkers, and the data relating to the activity markers may comprise data relating to the phosphorylation of the one or more phosphomarkers, where a high level of phosphorylation of the one or more phosphomarkers indicates that the FLT3-driven kinase signalling pathway is activated.

Said one or more phosphomarkers may, for example, include phosphorylation sites in one or more of the proteins STAT5A and/or CAMKK1, for example phosphorylation sites at S780 and/or S128 of STAT5A, and phosphorylation sites at S548 of CAMKK1. These phosphorylation sites are selectively phosphorylated in FLT3-driven kinase signalling pathways. The data relating to the activity markers may thus comprise data relating to the level of phosphorylation of either or both of STAT5A and CAMKK1, such as the phosphorylation of STAT5A at S780 and/or S128, and/or the level of phosphorylation of CAMKK1 at S548, where a high level of phosphorylation indicates activation of the FLT3-driven kinase signalling pathway.

Said data relating to the phosphorylation of TOP2A and/or KDM5C in the leukaemia cells may comprise data relating to the phosphorylation of TOP2A and/or KDM5C, such as data relating to the phosphorylation of TOP2A at S1213 and/or the phosphorylation of KDM5C at S317.

The present disclosure also envisages the use of alternative phosphomarkers of the FLT3-driven kinase signalling pathway, including the STATS pathway and/or the CAMKK pathway, which may equally be used for determining the activation of the FLT3-driven kinase signalling pathway.

The data relating to the activity markers may comprise any information from which a skilled person could deduce the activation of the activity markers, such as the expression or activation of the activity markers, such as the level of phosphorylation of the activity markers. Such data may include, for example, LC-MS/MS data. In some embodiments, said data has previously been gathered and recorded and step (a)(ii) comprises obtaining said data for analysis. In other embodiments, step (a)(ii) further comprises gathering and recording said data for analysis. Said data may be gathered and recorded without difficulty according to techniques and protocols well known in the art and as exemplified herein, for example by LC-MS/MS or by immunochemical techniques.

The differentiation status of the leukaemia cells may be determined according to any of the method steps described herein. Preferably, however, said step of determining the differentiation status of the leukaemia cells may comprise :.

wherein a high level of phosphorylation of the one or more phosphorylation sites and/or the presence of said group of CD markers on said leukaemia cells indicates an advanced differentiation status.

The present disclosure is of particular interest in respect of kinase pathway inhibitors which have been approved for treatment of AML or may shortly be approved for treatment of AML. These include the FLT3/PKC pathway inhibitor midostaurin, and the RAS-RAF-MEK-ERK pathway inhibitor trametinib. As demonstrated herein, the present invention provides a significantly improved protocol for identifying patients who will respond to treatment with these kinase pathway inhibitors. The availability of an accurate companion diagnostic test for identifying potentially responsive patients is of significant therapeutic and clinical benefit, as it will aid in ensuring that patients who will respond to treatment are identified as such and can benefit from this treatment, whilst patients who will not respond are not unnecessarily subjected to treatment.

The present invention accordingly provides midostaurin for use in a method of treating acute myeloid leukaemia in a patient, wherein the patient has leukaemia with an advanced differentiation status, defined by :.

In particular, the present invention provides midostaurin for use in a method of treating acute myeloid leukaemia in a patient, wherein the patient has leukaemia with an advanced differentiation status, defined by :.

The disclosure further provides a method of treating acute myeloid leukaemia in an individual patient suffering from acute myeloid leukaemia, comprising the steps of:.

The biological sample may be a peripheral blood sample or a bone marrow sample. The kinase pathway inhibitor may be selected from a MEK pathway inhibitor, a FLT3/PKC pathway inhibitor and a PAK pathway inhibitor.

In this aspect of the invention, step (b) may comprise detecting morphological and/or cytochemical features of the leukaemia cells in the sample obtained from the patient, where an M4 classification under the French-American-British (FAB) classification system indicates an advanced differentiation status.

Said step of detecting morphological and/or cytochemical features of the leukaemia cells may include preparing the cells for microscopical analysis and visually observing the cells under a light microscope to detect morphological signs of differentiation; and/or assaying the behaviour, activity or response of the cells to specific conditions or test reagents such as such as sudan black B and/or peroxidase and/or specific or non-specific esterases.

Optionally, step (b) may comprise performing an in vitro assay to detect the expression level of one or more cell surface differentiation markers on the surface of the leukaemia cells in the sample obtained from the patient, which cell surface differentiation markers are typically expressed or over-expressed in healthy myelomonocytic cells and which cell surface differentiation markers are not typically expressed or over-expressed in undifferentiated myeloblasts, where the expression of said one or more cell surface differentiation markers at a high level on the surface of the leukaemia cells indicates an advanced differentiation status. Said assay may be an LC-MS/MS assay or an immunochemical assay such as a Western blot assay, an ELISA assay or a reversed phase protein assay.

The cell surface differentiation markers may comprise a panel of cell surface marker proteins including one or more of CD3, CD7, CD11b, CD11c (integrin α-X, ITAX), CD14, CD15, CD16, CD18 (integrin β, ITB2), CD19, CD33, CD34, CD35 (CR1), CD38, CD44, CD45, CD64, CD97, CD117, CD123, CD180, CD184, HLA-C (1C02), APOBR, the platelet membrane receptor Gi24 (VSIR) and HLA-DR; and/or any cell surface proteins which are expressed in conjunction with said one or more cell surface marker proteins.

Suitably, the panel of cell surface marker proteins may comprise :.

In methods of treatment disclosed herein, but not claimed, step (b) may comprise performing an in vitro assay to detect the expression and/or activation and/or phosphorylation of one or more functional differentiation markers in the leukaemia cells in the sample obtained from the patient, which functional differentiation markers are typically expressed, over-expressed, activated and/or phosphorylated in healthy monomyelocytic cells, and which functional differentiation markers are not typically expressed, over-expressed, activated and/or phosphorylated in undifferentiated myeloblasts; wherein the expression, activation and/or phosphorylation of said one or more functional differentiation markers in the leukaemia cells indicates an advanced differentiation status. Said assay may be an LC-MS/MS assay or an immunochemical assay such as a Western blot assay, an ELISA assay or a reversed phase protein assay. The one or more functional differentiation markers may comprise a panel of protein markers including one or more enzymes, integrins, kinases, phosphatases, signal transduction regulators, cytoplasmic proteins and phosphoproteins, membrane proteins and phosphoproteins, including cytoplasmic and membrane phosphoproteins that are involved in GTPase or other forms of cell signalling, which protein markers are typically expressed, over-expressed and/or activated in healthy monomyelocytic cells, and are not typically expressed, over-expressed and/or activated in undifferentiated myeloblasts; and wherein the expression and/or activation of said panel of protein markers in the leukaemia cells indicates an advanced differentiation status.

Optionally, the panel of protein markers may include any one, two, three, four or five of lysozyme C (LYZ), neutrophil cytosol factor <NUM> (NCF2), myeloid cell nuclear differentiation antigen (MNDA), AK1C4, and ERG.

Suitably, the one or more functional differentiation markers may comprise a panel of kinase pathway activity markers including one or more kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors, phosphopeptides and/or other kinase signalling molecules that are typically expressed and/or activated and/or phosphorylated in a kinase signalling pathway in healthy monomyelocytic cells but are not typically expressed and/or activated and/or phosphorylated in undifferentiated myeloblasts; and wherein the expression and/or activation and/or phosphorylation of the panel of kinase pathway activity markers in the leukaemia cells indicates an advanced differentiation status. In particular, the panel of kinase pathway activity markers may comprise markers of a kinase signalling pathway that is inhibited by the kinase pathway inhibitor.

The panel of kinase pathway activity markers may, for example, comprise any one, two, three, four, five, six, seven, eight, nine, ten or more of FES, PKC and protein kinase C isoforms including PKCδ (KPCD), PRKCA, PRKCB, and PRKCD, PKA, PAK including PAK1 and PAK2, STK10, GSK3A, RSK2, RAS, RAF, MEK including MEK1 (MAP2K1), ERK including MAPK3 (ERK1) and MAPK1 (ERK2), PI3K, AKT including AKT1, MTOR, S6 kinase, STATS, CAMKK, SYK (KSYK), LYN, P38A, CDK1, CK2A1, PKACA, IRAK4, PKCB iso2, Cot, PKCD, PKCA, PKCB, PKCG, PKCH, BRAF, MEK2, PDK1, CDK2, PTN6, D3 (PLD3), IQGAP1, GRB2, RHOA, RHOG and S10AB, and any kinases, phosphatases, phospholipoases, integrins, signal transduction regulators, G proteins, transmembrane receptors and/or other kinase signalling molecules that are selectively expressed or activated therewith.

The panel of kinase pathway activity markers may comprise :.

The panel of kinase pathway activity markers may comprise a panel of one or more phosphorylation sites which are typically phosphorylated or are typically phosphorylated at a high level in a kinase signalling pathway in healthy monomyelocytic cells but are not typically phosphorylated or are not typically phosphorylated at a high level in undifferentiated myeloblasts; and wherein phosphorylation or a high level of phosphorylation at the panel of phosphorylation sites in the leukaemia cells indicates an advanced differentiation status.

The panel of phosphorylation sites may comprise :.

In methods of treating acute myeloid leukaemia described herein, but not claimed, the kinase pathway inhibitor may inhibit any one or more of the FLT3 pathway, the PKC pathway, the RAS-RAF-MEK-ERK pathway, the PI3K-AKT-MTOR-S6K pathway, the PAK pathway, the JAK-STAT pathway, the CAMKK pathway, or any kinase signalling pathway parallel thereto. Suitably, the kinase pathway inhibitor may be a MEK inhibitor, or a FLT3/PKC inhibitor, or a PAK inhibitor.

In such methods, step (b) may comprise :.

wherein phosphorylation or phosphorylation at a high level of the one or more phosphorylation sites and/or the expression of said group of CD markers at a high level on said leukaemia cells indicates an advanced differentiation status.

In methods of treating acute myeloid leukaemia described herein, but not claimed, wherein the kinase pathway inhibitor is a FLT3/PKC pathway inhibitor such as midostaurin, step (b) may comprise :.

wherein either : phosphorylation or a high level of phosphorylation in the leukaemia cells of any one or more of GSK3A, PRKCA, PRKCB, PRKCD, STK10, PAK1, PAK2, MAPK1 and/or MAPK3; or expression at a high level by the leukaemia cells of said group of CD markers; indicates an advanced differentiation status.

In methods of treating acute myeloid leukaemia described herein, but not claimed, wherein the kinase pathway inhibitor is a MEK inhibitor such as trametinib, step (b) may further comprise performing an in vitro assay to detect the genotype of the leukaemia cells obtained from the patient and determining that any one of NRAS, KRAS, HRAS or BRAF in the leukaemia cells in the sample obtained from the patient has an activating mutation. Suitably, said assay may involve sequencing NRAS, KRAS, HRAS or BRAF in the leukaemia cells in the sample obtained from the patient, and identifying an activating mutation in the sequence data thereby obtained.

In these methods, step (b) may comprise :.

wherein phosphorylation or a high level of phosphorylation of the one or more phosphorylation sites and/or the expression of said group of CD markers at a high level on said leukaemia cells indicates an advanced differentiation status.

In methods of treating acute myeloid leukaemia described herein, but not claimed, wherein the kinase pathway inhibitor is a MEK inhibitor such as trametinib, step (b) may further comprise :.

In methods of treating acute myeloid leukaemia described herein, but not claimed, wherein the kinase pathway inhibitor is a MEK inhibitor such as trametinib, step (b) may comprise :.

Suitably, said activity markers of the FLT3-driven kinase signalling pathway may include one or more phosphorylation sites which are selectively phosphorylated by the FLT3-driven kinase signalling pathway. In these embodiments, phosphorylation or a high level of phosphorylation of the one or more phosphorylation sites indicates that the FLT3-driven kinase signalling pathway is activated. Said one or more phosphorylation sites may include phosphorylation sites in one or both of STAT5A and CAMKK1, such as STAT5A at S780 and/or S128, and/or CAMKK1 at S548.

Optionally, said step of detecting the level of phosphorylation of one or both of TOP2A and/or KCM5C in the leukaemia cells may comprise detecting the phosphorylation of TOP2A at S1213 and/or the phosphorylation of KDM5C at S317.

The present disclosure provides, but does not claim, a method of treating acute myeloid leukaemia in an individual patient suffering from acute myeloid leukaemia, comprising the steps of:.

The present disclosure further provides, but does not claim, a method of treating acute myeloid leukaemia in an individual patient suffering from acute myeloid leukaemia, comprising the steps of:.

(B) said marker site is not phosphorylated or is not phosphorylated at a high level in the patient's leukaemia cells, and/or there is no activating mutation in FLT3 in the patient's leukaemia cells,
administering trametinib to the patient for treatment of acute myeloid leukaemia.

The present invention is illustrated with reference to the specific examples provided below, and to the figures, in which :.

The study was performed in <NUM> randomly selected primary samples of mononuclear cells extracted from the peripheral blood of AML patients at diagnosis. Experiments were performed as described below to determine the in vitro viability of the cells in response to treatment with inhibitors of the kinases FLT3/PKC (midostaurin, FLT3/PKCi), PAK (PF-<NUM> PAKi), CK2 (silmitasertib CK2i) and MEK (trametinib, MEKi) The P38 inhibitor (P38i) TAK-<NUM> was included as a negative control. Cells obtained from of <NUM> AML patients with well annotated clinical data were treated with these compounds for <NUM>.

Dose response curves showed that, as expected, the <NUM> tested samples presented heterogeneous responses to all compounds (<FIG> and <FIG>). As drug response curves are difficult to interpret when treatments do not reduce viability by ≥<NUM>%, we used the <NUM> dose (which is expected to inhibit the intended kinase based on the compounds' reported in vitro IC50s) to define sensitivity to treatment. At the <NUM> dose, PAKi was the most potent of all the compounds tested, as it reduced the viability of <NUM>/<NUM> (<NUM>%) AML cases by ><NUM>% relative to DMSO control, followed by FLT3/PKCi (<NUM>/<NUM>, <NUM>%), MEKi (<NUM>/<NUM>, <NUM>%) and CK2i (<NUM>/<NUM>, <NUM>%). At the same threshold, P38i treatment only reduced the viability of <NUM> AML cases (<NUM>%).

Clustering analysis of the cell sensitivity data showed a tendency of PAKi sensitive cells to also be sensitive to MEKi and FLT3/PKCi (<FIG>). In contrast, the response rates to MEKi, PAKi or FLT3/PKCi were very different to those to CK2i and P38i, suggesting that PAKi, MEKi and FLT3/PKCi have very similar, albeit non-identical, modes of action, which are different from those of CK2i and P38i.

We found that M4 cells responded significantly better than M1 cells to MEKi (Figure 1D), suggesting that AML cases of the M4 subtype were more sensitive to MEKi than those categorized as M1. This indicates that the differentiation status of the leukaemia cells may be a marker for sensitivity to kinase inhibitor therapy.

We investigated differences in kinase signaling between these AML subtypes. Using a mass spectrometry method as described below we identified and quantified <NUM>,<NUM> phosphopeptide ions in these experiments. Of these, we selected the <NUM> phosphorylation sites showing the most significant differences (based on Student's t-test p-values) across groups as a phosphoproteomics signature that discriminated M4 from M1 AML subtypes (<FIG>). Since M4 cells are more differentiated than M1, we hypothesized that this signature may be linked to the differentiation stage of the analyzed blasts. In a hierarchical clustering analysis, this phosphoproteomics signature subdivided our cohort of <NUM> patients into two defined groups (<FIG>). We termed "M1-Like" the group that included <NUM> of the <NUM> cases of the M1 subtype, and "M4-Like" the group that comprised all M4 cases (<FIG>). The M1-Like and M4-Like groups consisted of <NUM> and <NUM> cases respectively.

We used ontology enrichment analysis and kinase substrate enrichment analysis (KSEA) to investigate the biological processes and signaling pathways enriched in the different groups. Analysis of phosphoproteomic differences between cases (<FIG>), showed that M4-Like cases had an increase in cytoplasm and membrane phosphoproteins involved in GTPase signaling, while M1-Like increased nuclear phosphoproteins with DNA and RNA binding properties. KSEA, a computational procedure that estimates individual kinase activity based on the phosphorylation of their known substrates, showed that the activities of PKC, ERK, PAK1 and P38α were enriched in the cells of the M4-Like group, whereas the activities of CDK7, CK1A and AurB were enriched in M1-Like cells (Figure 2C). Some increased phosphorylation sites in the M4-Like group were in kinases at regulatory sites including PAK1 at S144, PAK2 at S141, MAPK1 at Y187 and RPS6KA1 at S380 (<FIG>). These data indicate that M4-Like cells activate kinase signaling pathways, such as PKCs, MAPK and PAK kinases, which are known to act downstream of cell surface receptors, to a greater extent than M1-Like cells.

To measure differentiation status with precision, we used mass cytometry to immunophenotype <NUM> cases of the <NUM> AML cohort (for which we had available material) by measuring the surface expression of <NUM> differentiation markers (<FIG>). We found that M4-Like cases had a greater expression of myelomonocytic differentiation markers than M1-Like cases (<FIG> and Figure 10C). We next investigated if the presence of specific differentiation markers was linked to the activation of kinase signaling pathways. We found that the surface expression of CD45, CD11b, CD44, CD14, CD16, CD64 and CD15 was statistically associated (r > <NUM>, p < <NUM>) with the phosphorylation patterns of <NUM> to <NUM> sites per marker (<FIG>). Examples include the phosphorylation of ERK2 (MAPK1 gene) at Y187, PAK2 at S141 and PKCδ (gene PRKCD) at Y313, which were statistically associated with the expression of several differentiation markers (scatter plots for MAPK1, PAK2 and PKCδ phosphorylation sites are shown in <FIG> and p-values of association in <FIG>). We found the CD markers to be co-expressed (<FIG>). Hierarchical clustering subdivided our cohort of <NUM> patients in two groups, which we named CDs+ and CDs- (<FIG>) and which comprised of <NUM> and <NUM> cases, respectively, and which overlapped with M4-Like and M1-Like groups. In this example and in the examples below, the CDs+ cells were characterized by the surface expression of a panel of cell surface markers consisting of CD33, CD123, HLA-DR, CD44, CD38, CD15, CD45, CD16, CD64, CD11b, and CD14.

To investigate the biochemical differences of AML blast as a function of cell differentiation status in more detail, we compared differences in the proteomes, phosphoproteomes and kinase activities of CDs+ and CDs- AML cases. The proteomic analysis identified <NUM>,<NUM> proteins (<FIG>) and uncovered a set of proteins, previously linked to differentiation, showing greater expression in the CDs+ group relative to CDs-; including integrins, lysozyme C and other proteins linked to myeloid differentiation (<FIG>). Of interest, several kinases, phosphatases and signal transduction regulators were also expressed at higher levels in the CDs+ relative to CDs- cases (<FIG>).

As for the results of the phosphoproteomic analysis, CDs+ cases had an increase in the phosphorylation of ~<NUM> times more sites than CDs- cases (<FIG>). Ontology enrichment analysis highlighted the expression of phosphoproteins linked to immune, GTPase and kinase signaling in CDs+, with CDs- cases showing an increase in the amounts of nuclear phosphoproteins and those linked to the regulation of transcription (<FIG>). Kinases with increased phosphorylation in the CDs+ group relative to the CDs- cases included FES at Y713, ERK1 (MAPK3) at T202/Y204, ERK2 (MAPK1) at T185/Y187, PAK1 at S144, MEK1(MAP2K1) at S222, PAK2 at S141 and PKC-δ (PRKCD) at S645 (<FIG>). In line with these observations, the CDs+ group enriched the activities of several kinases relative to CDs- cells, including PKA, several isoforms of PKC, BRAF, MEK and ERK (<FIG>). The increased expression of integrins, survival kinases and other signaling regulators in CDs+ cells relative to CDs- cells (<FIG>) suggests that an increase in kinase pathway activation in more differentiated cells (<FIG>) is due, at least in part, to a higher expression of these signaling molecules.

Since M4-Like and CDs+ cases activated kinase survival pathways to a greater extent than M1-Like and CDs- cases, respectively, we hypothesized that there may be a difference in how the cells from these patient groups may respond to kinase inhibitors. Consistently, cell viability analysis as a function of treatment with kinase inhibitors showed that M4-Like and CDs+ cases were more sensitive than M1-Like and CDs- to <NUM> PAKi, <NUM> MEKi, and <NUM> FLT3/PKCi (<FIG>), which is a concentration that can inhibit PKCδ, a kinase found to be highly active in our assays (Figure 2C, <FIG>, <FIG>). The same trends of responses were observed after treatment with other compound concentrations (<FIG>). There were no differences between the responses to the CK2i or P38i across groups (<FIG>). Together, phosphoproteomics and differentiation marker expression stratified AML patients into groups with markedly different patterns of kinase activities and sensitivities to FLT3/PKC, PAK and MEK inhibitors. These results therefore suggest the existence of a link between differentiation, kinase-driven survival pathway activity, and the sensitivity of AML cells to kinase inhibitors.

To investigate the mechanisms that could contribute to the pharmacological and biochemical differences observed in AML of dissimilar differentiation phenotypes, we sequenced in our sample cohort the <NUM> most frequently mutated genes in AML. We found that <NUM> of these genes were mutated in at least <NUM> of the <NUM> cases included in the analysis (<FIG>). Interestingly, genes with roles in kinase signaling, including NRAS, BRAF and FLT3, were more frequently mutated in cells of the CDs+ group (<FIG>, p = <NUM> by hypergeometric test).

We found that cells with mutated NRAS or BRAF increased the phosphorylation of MAPK1 (ERK2) at T185 and Y187, an activity marker for the RAS/MEK/ERK pathway (<FIG>). Consistent with published studies, we found that mutations on those genes were also significantly associated with the sensitivity of the cells to MEKi (<FIG>). Cells with FLT3 mutations (in a NRAS/BRAF WT background) also showed relatively high RAS/MEK/ERK pathway activity (as assessed by MAPK1 phosphorylation - <FIG>), although these were not more sensitive to MEKi than cells WT for FTL3, NRAS and BRAF (<FIG>), suggesting that the genetic background associated to pathway activation influences responses to pathway inhibition. Also of interest, CDs+ cases negative for NRAS/BRAF mutations showed high RAS/MEK/ERK pathway activation relative to CDs- samples (<FIG>) and were more sensitive to MEKi than the undifferentiated cases of the same NRAS/BRAF genotype (<FIG>).

In order to rationalize responses further, we performed a systematic analysis integrating the cells' mutational profiles with the mass spectrometry and mass cytometry data. Cells with mutated NRAS, high MAPK1 phosphorylation or positive for CDs+ were more sensitive to MEKi than cells WT for NRAS, low for MAPK1 phosphorylation or negative for the CDs phenotype, respectively (<FIG>(i-iv)). Cells with the NRAS/BRAF/FLT3-ITD genotypes were not more sensitive to MEKi than cells with just either NRAS or BRAF mutations (<FIG>(v)). In contrast, the <NUM> cases positive for either NRAS/BRAF/CDs+ were on average more sensitive to MEKi than cells without this molecular signature (<FIG>(vi-ix)).

To assess the significance of the differences in MEKi sensitivities as a function of the different molecular markers, we plotted the p-values of the comparisons illustrated in <FIG>. Combining the NRAS/BRAF/CDs+ signature produced the most significant difference with a log<NUM> p-value of -<NUM>, followed by the NRASBRAF/MAPK1hi/CDs+ signature whose log<NUM> p-value was -<NUM> (<FIG>). These results suggest that AML cells can activate the MEK/ERK pathway by either mutations on NRAS/BRAF or by the surface expression of CD markers, consequently rendering them more sensitive to MEKi treatment than cells with WT genes or low differentiation status.

Although cases with either NRAS/BRAF mutations or the CDs+ phenotype (NRAS/BRAF/CDs+ cases) were highly sensitive to MEKi, <NUM> out of <NUM> cases with this signature demonstrated a viability ><NUM>% after treatment (<FIG>(viii)). To investigate the reasons for these differences in responses within the NRAS/BRAF/CDs+ cases, we compared mutation status and the phosphoproteome in the <NUM> cases positive for NRAS/BRAF/CDs+. Within these <NUM> cases, we found that FLT3-ITD positive cells were significantly more resistant to MEKi than cells without this mutation (p = <NUM>, <FIG>). Several phosphorylation markers were also found to be associated with responses to MEKi within the NRAS/BRAF/CDs+ cases, including those at STAT5A S780, STAT5A S128, TOP2A S1213, KDM5C S317 and CAMKK1 S458 (<FIG>), suggesting that cells with the NRAS/BRAF/CDs+ signature but relatively resistant to MEKi use FLT3-driven pathways to proliferate, which include STATS <NUM>. Accordingly, samples that were positive for NRAS/BRAF/CDs+ and negative for FLT3-ITD or with low STAT5A or KDM5C phosphorylation were more sensitive to <NUM> MEKi than the other cells (<FIG>), with essentially all NRAS/BRAF/CDs+ cases that presented low KDM5C phosphorylation being sensitive (viability < <NUM>%) to MEKi treatment (<FIG>, middle panel). In addition, we found that NRAS/BRAF/CDs+ cases that were negative for FLT3-ITD or low for KDM5C phosphorylation were also more sensitive to other concentrations of MEKi, and this difference was greater than when considering NRAS/BRAF of CDs status alone (<FIG>).

To identify determinants of sensitivity to inhibitors other than MEKi, we compared sensitivity to the compounds as a function of mutational status, phosphorylation marker expression, or a combination of the two (<FIG>). We found that NRAS mutation was the only strong genomic determinant of sensitivity to MEKi with IDH2 mutations showing a small (p ~ <NUM>) effect on responses to CK2i (<FIG> left panel). Surprisingly, FLT3-ITD status did not have an effect on the responses of cells to the FLT3/PKCi (<FIG> and <FIG>). In contrast, several phosphorylation markers including, those on protein kinases C isoforms (gene names PRKCA, PRKCB and PRKCD), STK10, GSK3A and PAK1/<NUM> and on the platelet membrane receptor Gi24 (C10orf54), were found to be associated with responses to PAKi, FLT3/PKCi and MEKi (<FIG> middle panel). Integration of genomic or CDs markers with phosphorylation data increased the significance (decreased p-values) of the associations (<FIG> right panel). For example, samples positive for either CDs or phosphorylation on GSK3A or PCKδ were more sensitive to FLT3/PKCi than other cases (<FIG> and <FIG>). As for CK2i, there was a small association between CD34 expression or IDH2 mutation and sensitivity to this compound, although the effect was small (<FIG>). Taken together, our results suggest that integration of differentiation status (as defined by CD marker expression) with genomic and phosphoproteomics signatures produces groups of AML cases characterized by their degree of sensitivity to MEKi and FLT3/PKCi.

Peripheral blood or bone marrow samples were obtained from patients suffering from acute myeloid leukaemia. Mononuclear leukaemia cells were extracted from these samples and assays were performed on the cells as described herein in order to detect :.

In step (ii), a high level of phosphorylation was identified where the phosphorylation at the reference site was higher than the average phosphorylation at that site, calculated across a plurality of patient samples.

Patients whose cells were positive for either (i) or (ii) were identified as suitable for treatment with midostaurin.

In steps (a)(ii) and (b)(i), a high level of phosphorylation was identified where the phosphorylation at the reference site was higher than the average phosphorylation at that site, calculated across a plurality of patient samples.

Patients whose cells were positive for both (a) and (b) were identified as suitable for treatment with trametinib.

A central goal of targeted therapy is to identify actionable patient-specific pathways that can direct effective personalized treatments. In this study, we found that differentiation status determined the extent and/or nature of kinase pathway activation across AML samples. Some of the surface differentiation markers (e.g., CD45, and CD123) are membrane receptors or have roles in the recognition of extracellular signals, which are transduced and propagated intracellularly by protein kinase cascades. Cells positive for these CD markers had higher expression of proteins associated with myelomonocytic differentiation and kinase signaling relative to CDs- cells, and consequently presented an increase in the phosphorylation and activation of pro-survival kinases (<FIG>), which was translated into an increased sensitivity in how these cells responded to treatments with PAKi, midostaurin and trametinib (<FIG>).

The integration of mass spectrometry and cytometry data with recurrent mutations present AML showed that, consistent with other studies <NUM>, activating mutations in NRAS were linked to a higher ERK (MAPK) activity and conferred sensitivity MEKi (<FIG>). In our patient cohort, NRAS mutations seemed to be the only clear genomic determinant of responses when considered in isolation, and surprisingly, neither FLT3-ITD nor FLT3-TKD mutations were associated with the responses to midostaurin (<FIG>). Our data suggest that the RAS/MEK/ERK pathway may be activated in AML by either the presence of NRAS/BRAF activating mutations or by signals emanating from upstream cell surface CD markers or associated receptors. Thus MEKi treatment was more likely to reduce AML cell viability in cases positive for at least one of these markers (<FIG>).

However, despite the clear contribution of RAS/MEK/ERK activation to the extent of responses to MEKi, only ~<NUM>% (<NUM>/<NUM>) of cases positive for RAS/MEK/ERK activation showed high responses to MEKi. We found that cases that were relatively resistant to MEKi, despite activating the RAS/MEK/ERK pathway, possessed the FLT3-ITD genotype and had high levels of phosphorylated regulatory proteins, including STAT5A, KDM5C and the topoisomerase 2A at S1213, a site that regulates the activity of the enzyme (<FIG>). Thus, AML cell populations that responded well to MEKi showed a high activity of the target pathway (RAS/MEK/ERK) together with a low activity of the FLT3/STAT pathway (<FIG>), which is known to sustain viability and proliferation of primary AML cells by acting in parallel to RAS/MEK/ERK signaling.

Our results therefore suggest two distinct mechanisms of intrinsic resistance to MEK inhibition. The first one occurs in cells that are not addicted to the pro-survival actions of MEK because these have low RAS/MEK/ERK pathway activity. The second mechanism occurs in cells which, albeit having a highly active RAS/MEK/ERK, bypass MEK inhibition because the FLT3/STAT5 axis acts as a compensatory mechanism.

Pemovska et al. observed that <NUM>% of AML patient samples were more sensitive to trametinib than mononuclear cells from healthy donors, and we found that <NUM>% of our cases showed ><NUM>% reduction in viability as a result of treatment with this drug (<FIG>). Thus, trametinib, a drug already approved by the FDA for the treatment of melanoma, is worth consideration for repurposing to treat the <NUM>-<NUM>% of AML cases predicted to respond to such treatment.

As for the PAKi and FLT3/PKCi, we did not find an association between genetic alterations and responses to these compounds (<FIG>). We observed, however, that cells with a more differentiated phenotype and those with high phosphorylation of GSK3A and PKCδ responded better to midostaurin than cells with low phosphorylation on these markers (note that PKCδ is upstream of GSK3A [Ref <NUM>]). Our results therefore suggest that the mode of action of midostaurin (the FLT3/PKCi used in this work), which is in later stages of clinical development <NUM>,<NUM>, involves the inhibition of PKCδ, a known target of this drug.

In conclusion, we found that AML cells activate the receptor tyrosine kinase signaling network during differentiation, resulting in a marked increase in the activity of pro-survival pathways regulated by MEK and PKC isoforms. The combination of target and parallel kinase pathway activation (caused by genetic and non-genetic events) determined the extent by which AML cells respond to treatments with trametinib or midostaurin.

The study was performed in <NUM> primary samples of mononuclear cells extracted from the peripheral blood of AML patients at diagnosis. Samples were randomly selected from the BCI tissue bank collection. Initially, <NUM> samples were included in the study but nine were later excluded because these were not viable in the ex-vivo experiments. Material availability allowed proteomics and mass cytometry analysis of <NUM> samples and DNA sequencing of <NUM> samples. Ex-vivo drug testing was performed in quadruplicate sampling replicates and viability values averaged and expressed relative to vehicle control.

Patients gave informed consent for the storage and use of their blood cells for research purposes. Experiments were performed in accordance with the Local Research Ethics Committee.

Cells were lysed and proteins digested using trypsin as previously described in <NPL>.

LC-MS/MS identification and quantification of peptides and phosphopeptides was performed in an orbitrap mass spectrometer (Q-Exactive Plus). Normalized quantitative data were used to calculate fold changes between groups and statistical significance (assessed by Student's t-test). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD005978 and DOI <NUM>/PXD005978.

Inference of kinase activities from the phosphoproteomics data was performed using Kinase substrate enrichment analysis (KSEA) as described in <NPL>.

DAVID software (https://david. gov/) was used to determine the enrichment of gene ontologies (GO), which were considered enriched when the Bonferroni's corrected p-values were < <NUM>. Hierarchical clusters were constructed within the R statistical computing environment (<NUM>. <NUM>) using the Euclidean distance metric in the heatmap2 function.

Primary cells were coated with metal conjugated antibodies, as indicated by the manufacturer, and analyzed on a CyTOF2 mass cytometer (Fluidigm). Data were normalized using the normalizer within the DVS Sciences CyTOF Instrument Control Software (v <NUM>.

Ex-vivo drug testing of AML primary cells was as previously described in <NPL>. Briefly, cells were re-suspended in MS-<NUM> conditioned IMDM medium, seeded in <NUM> well plates and treated with vehicle or <NUM> to <NUM> of the indicated inhibitors for <NUM>. Cells were stained with Guava ViaCount reagent and cell number and viability was measured. Flow cytometry data were analyzed using CytoSoft (v2.

Target enrichment of a <NUM> gene myeloid panel was achieved using an in-house True SeqCustom Amplicon (TSCA) design (Illumina, San Diego, USA).

Statistical anlaysis was performed in R (version <NUM>. <NUM>), Micorsoft Excel <NUM> or Prism (version <NUM>). The p-values returned from Mann Witney, Anova or Student's t-test, as indicated in the figures, were adjusted for multiple testing using the Tukey or Benjamini-Hochberg procedures as required.

Cell were harvested by centrifugation at 500xg at <NUM> for <NUM>, washed twice with cold PBS supplemented with <NUM> Na<NUM>VO<NUM> and <NUM> NaF, snap frozen and stored at -80C until further processing. Cell pellets were lysed in urea buffer (<NUM> urea in <NUM> in HEPES pH <NUM> supplemented with <NUM> Na<NUM>VO<NUM>, <NUM> NaF, <NUM> Na<NUM>P<NUM>O<NUM> and <NUM> sodium β-glycerophosphate) for <NUM> and further homogenized by sonication (<NUM> cycles of <NUM> on <NUM> off; Diagenode Bioruptor® Plus, Liege, Belgium). Insoluble material was removed by centrifugation at <NUM> x g for <NUM> at <NUM> and protein in the cell extracts was quantified by bicinchoninic acid (BCA) analysis.

For phosphoproteome analyses, we used published methods with some modifications.

Briefly, <NUM>µg of protein were reduced and alkylated by sequential incubation with <NUM> DTT and <NUM> iodoacetamyde for <NUM>. The urea concentration was diluted to <NUM> with <NUM> HEPES (pH <NUM>) and <NUM>µL of conditioned trypsin beads [(<NUM>% slurry of TLCK-trypsin (Thermo-Fisher Scientific; Cat. #<NUM>)] conditioned with <NUM> washes of <NUM> HEPES (pH <NUM>)) were added and the samples incubated for <NUM> at <NUM> with agitation. Trypsin beads were removed by centrifugation at <NUM>,<NUM> x g for <NUM> at <NUM>. For phosphoproteomics analyses, <NUM>µg of protein were used.

Following trypsin digestion, peptide solutions were desalted using <NUM> OASIS-HLB cartridges (Waters, Manchester, UK). Briefly, OASIS cartridges were accommodated in a vacuum manifold (-<NUM> mmHg), activated with <NUM> ACN and equilibrated with <NUM> washing solution (<NUM>% ACN, <NUM>% TFA). After loading the samples, cartridges were washed with <NUM> of washing solution. For phosphoproteomics analyses, peptides were eluted with <NUM>µL of glycolic acid buffer <NUM> (<NUM> glycolic acid, <NUM>% ACN, <NUM>% TFA) and subjected to phosphoenrichment. For proteomics analyses peptides were eluted with <NUM>µL of ACN solution (<NUM>% ACN, <NUM>% TFA), dried in a speed vac (RVC <NUM>-<NUM>, Martin Christ Gefriertrocknungsanlagen GmbH, Osterode am Harz, Germany) and stored at -<NUM>.

Phosphopeptides were enriched using TiO<NUM> (GL Sciences) as previously described with some modifications (<NUM>). Sample volumes were normalized to <NUM> using glycolic acid buffer <NUM> (<NUM> glycolic acid, <NUM>% ACN, <NUM>% TFA), <NUM>µL of TiO<NUM> beads (<NUM>% slurry in <NUM>% TFA) were added to the peptide mixture, incubated for <NUM> at room temperature with agitation and centrifuged for <NUM> at 1500xg. For each sample, <NUM>% of the supernatant was transfer to fresh tubes and stored in ice and the remaining <NUM>% used to resuspend the bead pellets that were loaded into an empty prewashed PE-filtered spin-tips (Glygen, MD, USA) and packed by centrifugation at <NUM> x g for <NUM>. After loading the remaining volume of the supernatant by centrifugation at 1500xg for <NUM> mim, spin tips were sequentially washed with <NUM>µL of glycolic acid buffer <NUM>, ammonium acetate buffer (<NUM> ammonium acetate in <NUM>% ACN) and <NUM>% ACN by RT centrifugation for <NUM> at 1500xg. For phosphopeptide recovery, the addition <NUM>µL of <NUM>% ammonium water followed by centrifugation for <NUM> at <NUM> x g was repeated <NUM> times. Eluents were snap frozen in dry ice, dried in a speed vac and peptide pellets stored at -<NUM>.

For phosphoproteomics, peptide pellets were resuspended in <NUM>µL of reconstitution buffer (<NUM> fmol/µL enolase in <NUM>% ACN, <NUM>% TFA) and <NUM>µL were loaded onto an LC-MS/MS system consisting of a Dionex UltiMate <NUM> RSLC directly coupled to an Orbitrap Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific). For proteomics, pellets were resuspended in reconstitution buffer (<NUM>µg/µL) and <NUM>µL were injected. The LC system used mobile phases A (<NUM>% ACN: <NUM>% FA) and B (<NUM>% ACN; <NUM>% FA). Peptides were trap in a µ-pre-column (catalog no <NUM>) and separated in an analytical column (Acclaim PepMap <NUM> ;catalog no <NUM>). The following parameters were used: <NUM>% to <NUM>% B gradient for <NUM> and a flow rate of <NUM>µL/min.

As they eluted from the nano-LC system, peptides were infused into the online connected Q-Exactive Plus system operating with a <NUM> duty cycle. Acquisition of full scan survey spectra (m/z <NUM>-<NUM>,<NUM>) with a <NUM>,<NUM> FWHM resolution was followed by, data-dependent acquisition in which the <NUM> most intense ions were selected for HCD (higher energy collisional dissociation) and MS/MS scanning (<NUM>-<NUM>,<NUM>/z) with a resolution of <NUM>,<NUM> FWHM. A <NUM> dynamic exclusion period was enabled with an exclusion list with <NUM> ppm mass window. Overall duty cycle generated chromatographic peaks of approximately <NUM> at the base, which allowed the construction of extracted ion chromatograms (XICs) with at least <NUM> data points. The raw files for the extra samples were also uploaded into PRIDE.

Mascot Daemon <NUM>. <NUM> was used to automate peptide identification from MS data. Peak list files (MGFs) from RAW data were generated with Mascot Distiller v2. <NUM> and loaded into the Mascot search engine (v2. <NUM>) in order to match MS/MS data to peptides (<NPL>). The searches were performed against the SwissProt Database (SwissProt_2012Oct. fasta for proteomics or uniprot_sprot_2014_08. fasta for phosphoproteomics analysis) with a FDR of ~<NUM>% and the following parameters: <NUM> trypsin missed cleavages, mass tolerance of ±<NUM> ppm for the MS scans and ±<NUM> mmu for the MS/MS scans, carbamidomethyl Cys as a fixed modification, PyroGlu on N-terminal Gln and oxidation of Met as variable modifications. For phosphoproteomics experiments Phosphorylation on Ser, Thr, and Tyr was also included as variable modifications. The in-house developed Pescal software was used for label-free peptide quantification (<NUM>), XICs for all the peptides identified across all samples were constructed with ±<NUM> ppm and ±<NUM> mass and retention time windows, respectively. Peak areas from all XICs were calculated. Undetectable peptides were given an intensity value of <NUM>. Values of <NUM> technical replicates per sample were averaged and intensity values for each peptide were normalized to total sample intensity.

Mass cytometry was used to characterize CD markers in AML cells (<NPL>). Cells (<NUM>×<NUM><NUM>) were transfered to fresh tubes, washed twice with PBS and incubated with 1x Cell-ID™ Cisplatin solution (Fluidigm; Cat. <NUM>) for <NUM> at RT. Cells were washed with Maxpar Cell Staining buffer and pellets were resuspended and incubated with <NUM>µL of <NUM>µg/mL HAG (human γ-Globulins, Sigma-Aldrich; Cat. G4386-<NUM>) for <NUM> at RT. After adding <NUM>µL of antibody mix (<NUM>/<NUM> dilution of each antibody; Suplemental Tablet), samples were incubated for <NUM> at RT. The cells were then washed twice with Maxpar Cell Staining buffer, pellets were resuspended in Fix and Perm Buffer and left overnight at <NUM>. Next day, Ir intercalator was added to a final concentration of 1x and samples were incubated for <NUM> at RT. Permeabilized cells were washed twice with Maxpar Cell Staining buffer and twice with Maxpar water.

The following antibodies were used in mass cytometry assays as described below :.

Primers for BRAF V600 PCR were forward <NUM>'-TCTTCATGAAGACCTCACAGT-<NUM>' and reverse <NUM>'-CCAGACAACTGTTCAAACTGA-<NUM>'. <NUM>-<NUM> ng of DNA was used as template and the thermal conditions were as follows: initial heating period for <NUM> at <NUM>, <NUM> cycles at <NUM> for <NUM>, <NUM> for <NUM> and <NUM> for <NUM>, and finally <NUM> at <NUM>. Amplicones were sequenced by GATC Biotech (Constanza, Germany) using the forward primer. Positive cases were validated using the reverse primer.

Claim 1:
A computer-implemented method for predicting the efficacy of midostaurin for treatment of acute myeloid leukaemia in an individual patient, comprising the steps of:
(a)
(i) analysing data relating to the expression on the surface of the patient's leukaemia cells of a group of CD markers consisting of CD11b, CD14, CD15, CD <NUM>, CD33, CD38, CD44, CD45, CD64, CD <NUM> and HLA-DR to determine whether said group of CD markers is expressed at a high level on the surface of the patient's leukaemia cells; and
(ii) analysing data relating to phosphorylation in the leukaemia cells at one or more phosphorylation sites selected from : GSK3A at pS21 ; PRKCD at Y313, pT507, pT295, pT218, and/or pS664 of PRKCD; STK10 at pS20 and/or pS13 of STK10; PAK1 at pS144 ofPAK1 ; PAK2 at pS141 ofPAK2; MAPK1 at Y187 and/or T185; and MAPK3 at T202 and/or Y204 to determine whether one or more of said phosphorylation sites is phosphorylated or is phosphorylated at a high level in the patient's leukaemia cells; and
(b) where said group of CD markers is expressed at a high level on the surface of the patient's leukaemia cells, and one or more of said phosphorylation sites in the leukaemia cells is phosphorylated or is phosphorylated at a high level, predicting that the acute myeloid leukaemia in the patient may be effectively treated with midostaurin