Method for epithelial cancer prognosis

Abstract

A method for providing a prognosis for an epithelial cancer in a patient is disclosed. The method comprises: Detecting, in a biological sample obtained from the patient, a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker. Determining the prognosis for the epithelial cancer based on the detecting the plurality of markers.

Description

[0001] METHOD FOR EPITHELIAL CANCER PROGNOSIS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to the field of biomedicine. Some example embodiments relate to biomarkers and their use in diagnostic and prognostic cancer medicine.

[0004] BACKGROUND

[0005] Epithelial cancers, including head-and-neck squamous cell carcinoma (HNSCC), constitute a diverse group of malignancies originating from epithelial tissues lining various organs and structures in a body. HNSCC specifically arises in mucosal linings of head and neck region, encompassing areas such as oral cavity, pharynx, and larynx. Overall five-year survival rate of HNSCC patients is approximately 50%, and recurrence rate is about 50% during first two years after diagnosis, and patients with failure after first-line therapy have a median overall survival of less than one year. Clinical-pathological parameters such as tumor primary site, nodal involvement, tumor thickness, and the status of the surgical margins have been shown to relate to prognosis, recurrence, and survival only weakly.

[0006] HNSCC is known for its aggressive nature, potential for metastasis, disease complexity and difficulty of accurate treatment. Therefore, early detection and accurate prognosis are crucial for optimizing patient outcomes. Biomarkers (or “markers”) may be understood as molecular or biochemical indicators associated with a presence or progression of a disease. These markers offer insights into a molecular landscape of the disease, aiding clinicians in tailoring treatment strategies and monitoring disease progression. This disclosure generally relates to markers that may be used to provide new insights into diagnosis and prognosis of epithelial cancers, such as HNSCC. These insights may improve accuracy of diagnosis or prognosis and may offer aid in optimizing the treatment of patients. A method that may provide an increased accuracy of clinical prognosis in epithelial cancers is disclosed. SUMMARY

[0007] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0008] Example embodiments of the present disclosure enable a method and / or an apparatus for providing a prognosis for epithelial cancer based on detecting specific markers in a sample from a patient. This method may be achieved by the features of the independent claims. Further example embodiments are provided in the dependent claims, the detailed description, and the drawings.

[0009] According to a first aspect, a method for providing a prognosis for an epithelial cancer in a patient is disclosed. The method may comprise: Detecting, in a biological sample obtained from the patient, a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker. Determining the prognosis for the epithelial cancer based on the detecting the plurality of markers.

[0010] With such a method, a prognosis for an epithelial cancer is obtained for the patient. This prognosis may be more accurate than one obtained through other methods. This prognosis may assist a clinician in deciding further diagnostic, treatment, or surgical options for the patient.

[0011] According to an example embodiment of the first aspect: The at least one epithelial marker may comprise at least one of Keratin 8, Keratin, 14, Keratin 18, B-catenin, EpCam and E-cadherin. The at least one mesenchymal marker may comprise at least one of: Vimentin and Slug. The at least one stromal cell type marker may comprise at least one of: Vimentin, PDGFRB-B, CD34, FAP, a-SMA, CD3, and CD1 lb. The at least one morphological marker may comprise at least one of: nuclear size, nuclear shape, distance to nearest neighbor, distance to nearest stromal or epithelial cell, number of neighbors, and cell density.

[0012] With such a method, a prognosis for an epithelial cancer is obtained for the patient. This prognosis may be more accurate than one obtained through other methods. This prognosis may assist a clinician in deciding further diagnostic, treatment, or surgical options for the patient.

[0013] According to an example embodiment of the first aspect, the method for providing a prognosis for an epithelial cancer in a patient may further comprise: Detecting, in a biological sample obtained from the patient, a plurality of markers, wherein the plurality of markers may further comprise at least one epithelial stem cell-like marker, at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix protein marker.

[0014] According to an example embodiment of the first aspect: The at least one stem-cell like epithelial marker may comprise at least one of: Sox2, K14, and BMI1. The at least one epidermal growth factor signaling marker may comprise Amphiregulin. The at least one actomyosin contractility marker may comprise Myh9. The at least one extracellular matrix protein marker may comprise Tenascin- C.

[0015] With such a method, a prognosis for an epithelial cancer is obtained for the patient. This prognosis may be more accurate than one obtained through other methods. This prognosis may assist a clinician in deciding further diagnostic, treatment, or surgical options for the patient.

[0016] According to an example embodiment of the first aspect, the epithelial cancer may be a head-and-neck squamous cell carcinoma.

[0017] According to an example embodiment of the first aspect, the biological sample may be a tumor biopsy.

[0018] According to an example embodiment of the first aspect, the determining the prognosis may comprise at least one of: Determining a survival probability of the patient on the basis of the detecting the plurality of markers. Determining a prediction of treatment response on the basis of the detecting the plurality of markers.

[0019] According to an example embodiment of the first aspect, the detecting the plurality of markers may comprise: Contacting, per marker within at least a first subset of markers within the plurality of markers, the biological sample with a reagent specific for the marker. The reagent may be one of: a probe, a dye, an antibody, and an antibody fragment, and the marker may be a protein, or the reagent may be a primer and the marker may be a nucleotide sequence. Identifying, per marker within at least the first subset of markers, whether the reagent specific for the marker has detected the presence of the marker in the biological sample.

[0020] According to an example embodiment of the first aspect, the detecting the plurality of markers may comprise: Determining, per marker within at least a second subset of markers within the plurality of markers, a quantitative value or a semiquantitative value of an amount of the marker. The marker may be a nucleotide sequence or a protein.

[0021] According to an example embodiment of the first aspect, the detecting of the plurality of the markers may further comprise: Imaging the biological sample to obtain image data. Performing image analysis on the image data obtained.

[0022] According to a second aspect, an apparatus is disclosed. The apparatus may comprise: At least one processor. At least one memory including computer program code. The at least one memory and the computer code may be configured to, with the at least one processor, cause the apparatus at least to perform: Receiving data comprising information on a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least on stromal cell type marker, and at least one morphological marker detected in a biological sample obtained from a patient. Determining a prognosis for an epithelial cancer in the patient based on the data. Providing output indicating the prognosis.

[0023] According to an example embodiment of the second aspect, the at least one memory and the computer program code may be configured to, with the at least one processor, cause the apparatus to perform: Receiving data comprising information on a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least on stromal cell type marker, and at least one morphological marker detected in a biological sample obtained from a patient. Determining a prognosis for an epithelial cancer in the patient based on the data.

[0024] According to a third aspect, a computer program product embodied on a computer readable medium may comprise computer-readable program code configured to, when read and executed by a computer system, cause the computer system at least to perform: Receiving data comprising information on a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker detected in a biological sample obtained from a patient. Determining a prognosis for an epithelial cancer in the patient based on the data. Providing output indicating the prognosis.

[0025] According to an example embodiment of the third aspect, the computer program product may be embodied on a computer-readable medium readable by the computer system.

[0026] According to a fourth aspect, a kit for providing a prognosis for an epithelial cancer in a biological sample from a patient is disclosed. The kit may comprise: A reagent specific for at least one epithelial marker, a reagent specific for at least one mesenchymal marker, and a reagent specific for at least one stromal cell type marker. An apparatus capable of receiving data comprising information on a plurality of markers and determining a prognosis for an epithelial cancer in the patient based on the data. A computer program product capable of receiving data comprising information on a plurality of markers and determining a prognosis for an epithelial cancer in the patient based on the data. Instructions for use.

[0027] According to an example embodiment of the fourth aspect, the kit may further comprise: A reagent specific for at least one epithelial stem cell-like marker. A reagent specific for at least one epidermal growth factor signaling marker. A reagent specific for at least one actomyosin contractility marker. A reagent for at least one extracellular matrix protein marker.

[0028] Any example embodiment may be combined with one or more other example embodiments. Many of the attendant features will be more readily appreciated as they become better understood by reference to the following detailed description considered in connection with the accompanying drawings.

[0029] DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are included to provide a further understanding of the example embodiments and constitute a part of this specification, illustrate example embodiments and together with the description help to understand the example embodiments. In the drawings: FIG. 1 illustrates a schematic block diagram of an example environment suitable for a method for providing a prognosis for an epithelial cancer in a patient;

[0031] FIG. 2 illustrates a schematic block diagram of an apparatus configured to practice one or more example embodiments;

[0032] FIG. 3 illustrates a schematic flow chart of a method according to an example embodiment;

[0033] FIG. 4 illustrates a schematic flow chart of a method according to another example embodiment;

[0034] FIG. 5 shows experimental results related to Example 1; and FIG. 6 shows experimental results related to Example 2.

[0035] DETAILED DESCRIPTION

[0036] Reference will now be made in detail to example embodiments, examples of which are illustrated in the accompanying drawings. The detailed description provided below in connection with the appended drawings is intended as a description of the present examples and is not intended to represent the only forms in which the present example may be constructed or utilized. The description sets forth the functions of the example and the sequence of steps for constructing and operating the example. However, the same or equivalent functions and sequences may be accomplished by different examples.

[0037] Although the specification may refer to “an”, “one”, or “some” embodiment(s) in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that the feature may not apply to other embodiments. Single features of different embodiments may also be combined to provide other embodiments. Furthermore, words “comprising” and “including” should be understood as not limiting the described embodiments / example, and the embodiments / examples may contain also features / structures that have not been specifically mentioned.

[0038] Furthermore, although the numerative terminology, such as “first”, “second”, etc., may be used herein to describe various embodiments, elements, or features, it should be understood that these embodiments, elements, or features should not be limited by this numerative terminology. This numerative terminology is used herein only to distinguish one embodiment, element, or feature from another embodiment, element, or feature. For example, a first cell discussed below could be called a second cell, and vice versa, without departing from the teachings of the present disclosure.

[0039] In this disclosure, a method is disclosed for offering a prognosis for a type of cancer affecting epithelial tissues of a patient. The method involves examining a biological sample from the individual, identifying various markers, including an epithelial marker, a mesenchymal marker, a stromal cell type marker, and a morphological marker. The prognosis for the epithelial cancer is then determined based on the analysis of these identified markers.

[0040] In the context of this specification, the term “patient” refers to a human subject that preferably has an epithelial cancer diagnosis.

[0041] In the context of this specification, the term "prognosis" refers to an anticipated course and outcome of a medical condition, especially a disease or disorder, as predicted based on factors such as the patient's health status, a nature of the disease, and an effectiveness of available treatments. Prognosis may provide an estimate of a likelihood of recovery, recurrence, or progression of the medical condition over time.

[0042] In the context of this specification, the term "epithelial" may be understood as a descriptive reference to tissues or cells characterized by their closely packed arrangement, forming layers that line the surfaces or cavities of various structures within the body. Epithelial tissues serve as protective barriers, facilitating selective permeability and absorption, and form a covering of all body surfaces, line body cavities and hollow organs, and are the major tissue in glands. The use of the term "epithelial" within this specification implies a focus on characteristics associated with epithelial tissues, including their structural organization and functional properties relevant to the specified context. In the context of this specification, the term "mesenchyme" may be understood as a reference to a type of connective tissue characterized by loosely arranged cells with a considerable amount of extracellular matrix. Mesenchymal cells comprise all types of fibroblasts and / or cells that have a potential to differentiate into various cell types, including those forming bone, cartilage, muscle, and other connective tissues. The use of the term "mesenchyme" within this specification indicates a consideration of developmental or regenerative aspects related to these versatile cells and their potential contributions to a formation and maintenance of diverse tissues or structures within the specified context, behavioral aspects including but not limited to decreased cell-cell adhesion, and increased migration and invasion capacities as well as expression of a protein or proteins that are characteristic to this population of cells.

[0043] In the context of this specification, the term "stroma" may be understood as a reference to any type of non-epithelial tissue or cells that are in proximity to the tumor. The stroma typically consists of connective tissue, blood vessels, and other supportive components that provide the necessary framework for a functioning of the tissue. In various biological contexts, the term "stroma" can be applied to describe supportive elements that surround and support functional cells within a particular tissue or organ or tumor.

[0044] In the context of this specification, the term "epithelial cancer" may be understood as referring to a malignant neoplasm originating from epithelial tissues, characterized by uncontrolled growth and potential invasion into surrounding tissues. Epithelial cancers are typically carcinomas and develop when cells of epithelial tissues undergo uncontrolled and abnormal growth, forming tumors.

[0045] In the context of this specification, the term "epithelial-to-mesenchymal transition" (EMT) refers to a biological process in which epithelial cells undergo a phenotypic change, acquiring characteristics associated with mesenchymal cells. This transition involves a loss of cell-cell adhesion and polarity typical of epithelial cells and an acquisition of migratory and invasive properties reminiscent of mesenchymal cells.

[0046] In the context of this specification, the term "partial epithelial-to- mesenchymal transition" (pEMT) refers to a state where cells exhibit some, but not all, characteristics of a complete EMT. This intermediate state is characterized by a partial loss of epithelial features and a partial acquisition of mesenchymal traits. The concept of pEMT recognizes a heterogeneity and plasticity of transitioning cell populations, which may coexist with both epithelial and mesenchymal attributes. In the context of this specification, the term "biological sample" may be understood as a biological specimen extracted or having been extracted from a patient. It may be a biological specimen derived from a localized or metastatic mass of abnormal tissue characterized by uncontrolled and potentially malignant cell growth. It may be a biopsy, which may be understood as a medical procedure involving the extraction of a small sample of tissue or cells from a living organism, typically from a suspected abnormal or diseased area. Biopsies can be performed through various methods, including needle biopsy, incisional biopsy, or excisional biopsy.

[0047] In the context of this specification, the term “marker” may be understood as a measurable indicator, such as a molecular, genetic, or biochemical feature, used to assess biological or pathological processes, disease presence, or treatment response. The term marker may refer to for example a gene, that may be found to be associated with a specific tissue as compared to other tissues. It does not necessarily refer to a marker that would be statistically fully validated as having a specific effectiveness in a clinical setting. The marker may be a gene, a moiety, a combination of two or more genes, a (measurable or measured) quantity thereof, a differential expression of a gene or two or more genes, an over expression of a gene or two or more genes, an under expression of a gene or two or more genes, a protein, a combination of two or more proteins, a measurable quantity of a protein or two or more proteins, an over expression of a protein, an under expression of a protein, a non-quantitative or semi-quantitative detection of a protein, a ratio or other value derived thereof, a detection of a specific cell type, a shape of a cell, a density of cells, a size of a cell, a size of a nucleus, a shape of a nucleus, distance between cells, distance to specific tissues, number of adjacent cells, or other characteristic of a tissue. The markers and any combinations thereof, optionally in combination with further analyses and / or measures, may be used to define a prognosis for the patient.

[0048] In the context of this specification, the term "epithelial marker" may be understood as a specific molecular or biochemical indicator associated with the presence, activity, or characteristics of epithelial cells, often utilized for diagnostic or prognostic purposes. An example of such an epithelial marker may be Keratin 8, Keratin, 14, Keratin 18, B-catenin, EpCam, or E-cadherin, but it may be any marker associated with epithelial cells, preferably in the context of epithelial cancer. For example, an epithelial marker may be any of the family of structural fibrous proteins (keratins).

[0049] In the context of this specification, the term "mesenchymal marker" may be understood as a molecular or cellular feature indicative of mesenchymal cell characteristics, often relevant in the context of epithelial-to-mesenchymal transition (EMT) processes. An example of such a mesenchymal marker may be Vimentin or Slug, but it may be any marker associated with mesenchymal cells or EMT or partial EMT, preferably in the context of epithelial cancer. Such other markers may be, for example, N-cadherin, fibroblast activation protein alpha (FAP), alpha smooth muscle actin (a-SMA), Fibronectin, CD44 or others. The title of this category is descriptive and not intended to limit the group of markers.

[0050] In the context of this specification, the term “stromal cell type marker” may be understood as a molecular or biochemical characteristic that can be used to identify or distinguish a specific type of stromal cell. A "stromal cell type marker" could be a protein, gene, or other molecular or morphological feature that is associated with a particular subtype or function of stromal cells. An example of such a stromal cell type marker may be Vimentin, PDGFRB-B, CD34, FAP, a- SMA, CD3, and CDl lb, but it may be any marker associated with stromal cell types, preferably in the context of epithelial cancer. Such other markers may be, for example, CD45, CD90, CD29, or CD 105. The title of this category is descriptive and not intended to limit the group of markers. It may be true for example, that a marker is a “mesenchymal stromal cell marker” and can be construed as both a mesenchymal and a stromal cell type marker.

[0051] In the context of this specification, the term "morphological marker" may be understood as a visual or microscopic characteristic, such as cell shape or tissue structure, serving as an observable feature for diagnostic or prognostic purposes. The term “morphological marker” may be understood as quantitative measurements that describe a shape, a size, and / or structural features of an object or specimen. An example of such a morphological marker may be nuclear size, nuclear shape, distance to stroma or another cell type, number of neighbors, and cell density, but it may be any visual or microscopic characteristic, preferably in the context of epithelial cancer. The title of this category is descriptive and not intended to limit the group of markers, for example, changes in cell shape are associated with EMT, but are listed as “morphological markers” and not “mesenchymal markers” in this specification.

[0052] A method as described above comprising the detection of at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological maker in a biological sample obtained from the patient, may provide a prognosis for the patient. In such a method, the markers may alone or in combination with each other be indicative of specific prognostic features. In an example embodiment, a combination of detecting an epithelial marker and a mesenchymal marker in the same cell compartment to be a signal of mesenchymal transition in cancer cells (e.g., state of partial EMT) close to the stroma, which may be indicative of a specific prognosis for the patient. Patients may be classified based on the markers detected in the corresponding biological samples. With such a method, novel cancer subtypes differing in their prognosis may be identified. The method may enable classifying patients into patient groups that are responders / non-responders to certain treatments. The method may enable identifying diagnostic biomarkers for determining which patient should be given which treatments. The method may additionally be used to monitor a disease or condition, for example, to track prognosis, remission, recurrence, and / or effectiveness of a treatment. The method may additionally be used as part of a treatment to decide further clinical tests or procedures, wherein the further diagnostic test is selected from a group consisting of ultrasound, diagnostic x-ray, magnetic resonance imaging, or biopsy, and wherein the treatment is selected from a group consisting of surgery, chemotherapy, hormonal therapy, radiation therapy, biological therapy such as immunotherapy, small molecule therapy, or antibody therapy, or a combination thereof.

[0053] In an example embodiment, the method may further comprise the detection of at least one epithelial stem cell-like marker, at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix protein marker. With such a method, a prognostic value may be improved for some epithelial cancer subtypes and / or patients. The improvement of the prognostic value may mean that addition of one or all further markers may improve patient specific prognosis by making it more accurate (e.g., narrow down the prognosis) or alternatively broader (e.g., it may provide more specific information of other features of the epithelial cancer). It may be the case that certain epithelial cancer patients benefit only from a subset of the makers, e.g., only a subset of markers is needed for an accurate prognosis. It may also be the case that certain epithelial cancer patients benefit from use of all markers, e.g., all markers may be needed for the accurate prognosis.

[0054] In the context of this specification, the term "epithelial stem-cell-like marker" refers to a molecular or biochemical indicator associated with cells exhibiting characteristics reminiscent of epithelial or embryonic stem cells. These markers may include specific proteins or genetic factors that are expressed in cells with self-renewal capacity and the potential to differentiate into various epithelial cell types. An example of such an epithelial stem-cell-like marker may be SOX2, KI 4, or BMI1, but it may be any marker associated with epithelial or embryonic stem-cells, preferably in context of epithelial cancer.

[0055] In the context of this specification, the term "epidermal growth factor signaling marker" refers to a molecular or cellular feature indicative of the activation or modulation of the epidermal growth factor (EGF) signaling pathway. EGF signaling is a critical pathway involved in cell proliferation, differentiation, and survival. Markers associated with EGF signaling may include components of the pathway, such as an epidermal growth factor receptor (EGFR), or downstream effectors that play a role in cellular responses to EGF stimulation. An example of such an epidermal growth factor signaling marker may be Amphiregulin, but it may be any marker associated with the EGF signaling pathway, preferably in context of epithelial cancer.

[0056] In the context of this specification, the term "actomyosin contractility marker" refers to a molecular or cellular indicator associated with the contractile activity of the actomyosin cytoskeleton within cells. Actomyosin contractility plays a crucial role in cell shape, motility, and tissue morphogenesis. Markers associated with actomyosin contractility may include proteins involved in the regulation of actin and myosin, as well as functional readouts such as changes in cell shape and contractile force. An example of such an actomyosin contractility marker may be Myh9, but it may be any marker associated with actomyosin contractility, preferably in context of epithelial cancer. The marker may be, for example, from the family of myosin II (e.g., Myh9, MyhlO, Myhl4).

[0057] In the context of this specification, the term “extracellular matrix protein marker” refers to a molecular or cellular indicator associated with the connective tissue, basement membranes or other types of extracellular matrix. Extracellular matrix plays a crucial role in cell adhesion, cell survival, proliferation, motility, and tissue morphogenesis. An example of such an extracellular matrix marker may be Tenascin-C, but it may be any marker associated with extracellular matrix, preferably in context of epithelial cancer. The marker may be for example from the family of laminins (e.g., LN511, LN332), family of collagens or fibronectin.

[0058] FIG. 1 illustrates an example embodiment of a general exemplary environment that is suitable for analyzing marker data. FIG. 1 presents a simplified environment showing some devices, apparatuses, and functional entities, all being logical units whose implementation and / or number may differ from what is shown. Any suitable protocols, elements, equipment, functions, and / or structures may be used to implement the environment. It is apparent to a person skilled in the art that the environment comprises any number of shown elements, other equipment, other functions, and structures that are not illustrated. They, as well as the protocols used, are well-known by persons skilled in the art, and are irrelevant to the actual invention. Therefore, they need not to be discussed in more detail here.

[0059] In the example embodiment illustrated in FIG. 1, the environment 100 comprises at least one user device 110 connectable over one or more networks 120 to at least one detection equipment 130 comprising the means to detect markers and at least one prognosis equipment 140 comprising the means to provide prognosis.

[0060] A user device 110 refers to a computing device (equipment, apparatus) that may be a portable device or a desktop device such as personal computer, and it may also be referred to as a user terminal or a user apparatus. Portable computing devices (apparatuses) include wireless mobile communication devices operating with or without a subscriber identification module (SIM) in hardware or in software, including, but not limited to, the following types of devices: laptop computer, touch screen computer and tablet (tablet computer). The user device 110 may comprise one or more user interfaces. The one or more user interfaces may be any kind of a user interface, e.g., a screen, a keypad, a loudspeaker, a microphone, a touch user interface, an integrated display device, and / or external display device. The user device 110 is configured to support obtaining and analyzing imaging data, e.g., by carrying out methods described in more detail below. For that purpose, the user device 110 may be capable of installing applications. The user device may comprise marker data or a part of the marker data 132a, permanently or temporarily. The user device may comprise prognosis data or a part of the prognosis data 142a permanently or temporarily.

[0061] A network 120 may be any wired or wireless network, or a combination thereof, enabling transmission of information between different apparatuses / devices over the network. These include, but are not limited to, local area networks (LAN), cellular networks, and wireless local area networks (WLAN).

[0062] The detection equipment 130 is configured to detect markers and may send marker data or part of the marker data 132 to the user device 110 and / or receive the marker data or part of the marker data 132 from the user device 110. For that purpose, the detection equipment 130 comprises a memory and a processor coupled to the memory. The memory may be any kind of conventional or future data repository, including distributed and centralized storing of data, managed by any suitable management system forming part of the detection equipment 130. An example of distributed storing includes a cloud-based storage in a cloud environment (which may be, e.g., a public cloud, a community cloud, a private cloud, or a hybrid cloud). Cloud storage services may be accessed through a colocated cloud computer service, a web service application programming interface (API) or by applications that utilize API, such as cloud desktop storage, a cloud storage gateway or Web-based content management systems. Further, the detection equipment 130 may comprise several servers with databased, which may be integrated to be visible to a user device as one database and one database server. However, the manner in which data structures are stored and retrieved, and the location where different pieces of data to be obtained for the marker analysis pipeline are irrelevant to the invention. The processor may comprise one or more processing cores containing circuitry configured to execute instructions. The memory stores processor executable instructions to be executed by the processor to analyze the marker data 132 as will be described in detail below. The marker data 132 may be encoded in the detection equipment 130 to a format suitable for the user device 110, the detection equipment may obtain the data from another device or server in a format suitable for the user device 110, or the data may be encoded to a suitable format in the user device 110.

[0063] The prognosis equipment 140 is configured to provide prognosis and may send prognosis data or part of the prognosis data 142 to the user device 110 and / or receive the prognosis data or part of the prognosis data 142 from the user device 110. For that purpose, the prognosis equipment 140 comprises a memory and a processor coupled to the memory. The memory may be any kind of conventional or future data repository, including distributed and centralized storing of data, managed by any suitable management system forming part of the prognosis equipment 140. An example of distributed storing includes a cloud-based storage in a cloud environment (which may be, e.g., a public cloud, a community cloud, a private cloud, or a hybrid cloud). Cloud storage services may be accessed through a co-located cloud computer service, a web service application programming interface (API) or by applications that utilize API, such as cloud desktop storage, a cloud storage gateway or Web-based content management systems. Further, the prognosis equipment 140 may comprise several servers with databased, which may be integrated to be visible to a user device as one database and one database server. However, the manner in which data structures are stored and retrieved, and the location where different pieces of data to be obtained for the prognosis analysis pipeline are irrelevant to the invention. The processor may comprise one or more processing cores containing circuitry configured to execute instructions. The memory stores processor executable instructions to be executed by the processor to analyze the prognosis data 142 as will be described in detail below. The prognosis data 142 may be encoded in the prognosis equipment 140 to a format suitable for the user device 110, the prognosis equipment 140 may obtain the data from another device or server in a format suitable for the user device 110, or the data may be encoded to a suitable format in the user device 110.

[0064] FIG. 2 illustrates an example embodiment of an apparatus 200 configured to perform operations of one or more example embodiments, e.g., functionalities described below with reference to FIG. 3 and 4. The apparatus 200 may be for example used to implement the prognosis apparatus 140. The apparatus 200 may comprise at least one processor 202. The at least one processor 202 may comprise, for example, one or more of various processing devices or processor circuitry, such as for example a co-processor, a microprocessor, a controller, a digital signal processor (DSP), a processing circuitry with or without an accompanying DSP, or various other processing devices including integrated circuits such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like.

[0065] The apparatus 200 may further comprise at least one memory 204. The at least one memory 204 may be configured to store, for example, computer program code or the like, for example operating system software and application software. The at least one memory 204 may comprise one or more volatile memory devices, one or more non-volatile memory devices, and / or a combination thereof. For example, the at least one memory 204 may be embodied as magnetic storage devices (such as hard disk drives, floppy disks, magnetic tapes, etc.), optical magnetic storage devices, or semiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM (random access memory), etc.).

[0066] The apparatus 200 may further comprise a communication interface 208 configured to enable apparatus 200 to transmit and / or receive information to / from other devices, functions, or entities. In one example, the apparatus 200 may use communication interface 208 to transmit or receive information over a servicebased interface (SBI) message bus of the core network 130, for example to the core network 130 and / or the RAN 120. The communication interface 208 may therefore comprise a data communication interface and be configured for communication between devices, for example according to one or more data communication protocols. The apparatus 200 may be for example configured to transmit indication(s) of verified or non-verified performance, for example to an automated service ticket system, or to provide network configuration instructions to the RAN 120 to cause reconfiguration of network object(s). The apparatus 200 may further comprise a user interface 210, for example for providing user output by the apparatus, such as for example visual and / or audible signal(s), for example by speaker(s), display(s), light(s), or the like. User interface 210 may be used for example for outputting indication(s) of verified or non-verified performance to a human user.

[0067] When the apparatus 200 is configured to implement some functionality, some component and / or components of the apparatus 200, such as for example the at least one processor 202 and / or the at least one memory 204, may be configured to implement this functionality. Furthermore, when the at least one processor 202 is configured to implement some functionality, this functionality may be implemented using program code 206 comprised, for example, in the at least one memory 204.

[0068] The functionality described herein may be performed, at least in part, by one or more computer program product components such as for example software components. According to an example embodiment, the apparatus 200 comprises a processor or processor circuitry, such as for example a microcontroller, configured by the program code when executed to execute the embodiments of the operations and functionality described. A computer program or a computer program product may therefore comprise instructions for causing, when executed, the apparatus 200 to perform the method(s) described herein. Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field- programmable Gate Arrays (FPGAs), application-specific Integrated Circuits (ASICs), application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), Graphics Processing Units (GPUs). The apparatus 200 comprises means for performing at least one method described herein. In one example, the means comprises the at least one processor 202, the at least one memory 204 including the program code 206 configured to, when executed by the at least one processor, cause the apparatus 200 to perform the method.

[0069] The apparatus 200 may comprise a computing device such as for example an access point, an access node, a base station, a server, a network device, a network function device, or the like. Although the apparatus 200 is illustrated as a single device it is appreciated that, wherever applicable, functions of the apparatus 200 may be distributed to a plurality of devices, for example to implement example embodiments as a cloud computing service.

[0070] FIG. 3 illustrates a flow chart according to an example embodiment of a method for providing a prognosis for an epithelial cancer in a patient. The apparatus 200 illustrated with FIG. 2 is configured to perform the operation of the method in the example environment 100 illustrated with FIG. 1.

[0071] Referring to FIG. 3, a plurality of markers is detected in operation 301 in a biological sample obtained from the patient. The plurality of markers may comprise at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker. The plurality of markers may further comprise, at least one epithelial stem cell-like marker, at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix protein marker. The markers may be indicative of specific prognostic features of the disease. Whether they are detected or not detected may be indicative of specific prognostic features of the disease. Markers may be indicative of specific prognostic features alone or in combination with other markers (e.g., detection of one marker in one region / tissue / cell may be indicative of a prognostic feature or detection of two in a same region / tissue / cell may be indicative of a prognostic feature). The biological sample from the patient may comprise a biopsy such as, e.g., a tumor biopsy.

[0072] In an example embodiment, the detection of the plurality of markers 301 may comprise that, per marker within at least a first subset of markers within the plurality of markers, the biological sample is contacted with a reagent specific to the marker. It is then identified whether the reagent specific for the marker has detected the presence of the marker in the biological sample. The reagent specific for the marker may, for example, be detected (marker is present) or not detected (marker is not present).

[0073] In the context of this specification, the term “reagent” refers to a substance or compound employed in experimental or diagnostic procedures to detect, measure, or manipulate biological components. The reagent may be for example: a probe, a dye, an antibody, and an antibody fragment, or a primer.

[0074] In the context of this specification, a “probe” may be understood as a specialized molecule, often a labeled or tagged biomolecule, used to selectively bind to and detect specific targets within biological samples.

[0075] In the context of this specification, the term “dye” refers to a colored or fluorescent substance that is used to stain or label biological specimens for visualization under a microscope or other imaging modalities.

[0076] In the context of this specification, an “antibody” may be understood as a specialized protein that recognizes and binds to specific target molecules or proteins of interest. The antibody may be conjugated with a fluorescent tag, enabling the visualization and localization of the target protein in cellular or tissue samples through fluorescence microscopy. The immunofluorescent tag emits light when exposed to specific wavelengths, allowing for precise and sensitive detection of the molecule or protein of interest.

[0077] In the context of this specification, an “antibody fragment” refers to a partial or modified form of an antibody that retains its ability to bind to a specific molecule or protein of interest.

[0078] In the context of this specification, a “nucleotide sequence” may refer to a linear arrangement of nucleotides, such as in DNA or RNA. It may be a singlestranded mRNA molecule synthesized during the transcription process from a DNA template (e.g., gene transcript).

[0079] In an example embodiment, the detection of a plurality of markers may comprise determining a quantitative or semi-quantitative value of an amount of the marker, per marker within at least a second subset of markers within the plurality of markers. The marker may be a nucleotide sequence or a protein. The second subset of markers may be different from the first subset of markers, or it may be wholly or partially a subset of the first subset of markers. The determining may involve the use of specific methods, such as, but not limited to, RNA-sequencing, genome sequencing (e.g., whole genome sequencing, exome sequencing), or mass spectrometry requiring that the used method is capable of cell typing (e.g. the method may be used to tell single cells apart from other cells or tissues apart from other tissues), for example, it may be a single-cell method (e.g. single-cell RNA- seq, single-cell mass spectrometry). The markers may be detected as a semi- quantitative values (“low”, “average”, “high”) or quantitative values (continuous value), for example, compared to a control condition(s) or control marker(s). The markers may be, for example, “a high expression” of a marker, “an upregulation” of a marker, “a low expression” of a marker, or “a down regulation” of a marker. The identifying the presence of the markers may involve method specific data analysis steps to produce data (wherein format of data may be method specific) known to a person skilled in the art.

[0080] In the context of this specification, the term “single-cell” may be understood as an individual, isolated biological unit or entity, typically referring to a singular prokaryotic or eukaryotic cell, distinguished from multi-cellular aggregates or entities.

[0081] In the context of this specification, the term “single-cell RNA-seq” may be understood as a study, analysis, or quantification of a plurality of messenger ribonucleic acid (mRNA) transcripts in an individual cell at a time of mRNA extraction. Varied methods for single-cell RNA-seq exist, however some form of cell isolation and unique molecular identifiers to identify a cell of origin for each mRNA after sequencing is required. Single-cell RNA-seq data analysis involves detecting cell-to-cell variation, within a typically large cell population, for the expression level of each transcript or gene. Single-cell methods are characterized by the individual cell being an only sample, without a possibility of replicates, which presents unique challenges. Data may have a large number of missing values and can include more technical noise (uneven amplification or batch effects) which must be corrected for, and typical analysis requires clustering to other cells for a comprehensive analysis of a cell withing a cell cluster. Referring to FIG. 3, the prognosis for the epithelial cancer is determined in operation 302 based on the detection of the plurality of markers in the biological sample obtained from the patient. The prognosis may be indicative of the likely course or outcome of the epithelial cancer in the patient. In an example embodiment, the prognosis may be presented as a likelihood or probability of certain outcomes associated with the epithelial cancer. In an example embodiment, the prognosis may include outcome information. In an example embodiment, the outcome information may comprise clinical patient information on at least one of: survival, remission, and recurrence.

[0082] Further, a quantitative or a semi-quantitative value for the presence of a further marker may be determined. The determination may be done on a first subset of markers that is the same as the subset of markers that detection was done on, or it may be a second subset or a subset of the first subset.

[0083] In an example embodiment, the method comprises that a biological sample is contacted with reagents specific for a plurality of markers, the presence of markers is identified and then further quantitative values or semiquantitative values are determined for the same or other set of markers.

[0084] In an example embodiment, the method comprises that quantitative or semi-quantitative values are determined for a first subset of markers and the further quantitative or semi-quantitative values are determined for a second subset of markers. The determination of the quantitative or semi-quantitative value for the second subset of markers may involve the use of specific methods, such as, but not limited to, RNA-sequencing, real time quantitative PCR, mass spectrometry, immunofluorescence microscopy, histochemical imaging, immunohistochemistry, or other applicable methods. The determination involves the standard use of the applicable method known to a person skilled in the art. The data produced is in a standardized format for the method known to a person skilled in the art.

[0085] In an example embodiment, the detection of a plurality of markers may comprise imaging of the biological sample to obtain image data. The image data may be obtained by, for example, microscopy of the sample, for example, immunofluorescence microscopy, histochemical imaging, or immunohistochemistry. In the context of this specification, "immunofluorescence microscopy” may be understood as a microscopic imaging technique that utilizes fluorescently labeled antibodies to visualize and study the distribution of specific proteins or antigens within cells or tissues. In this method, antibodies are designed to bind selectively to the target proteins, and the attached fluorescent tags emit light when illuminated with specific wavelengths. This emitted fluorescence allows for the precise localization and visualization of the targeted proteins under a fluorescence microscope.

[0086] In the context of this specification, the term “histochemical imaging” may be understood as a process of creating visual representations or images of tissue samples based on their chemical properties as revealed through histochemical techniques. Histochemical imaging may encompass colorimetric or fluorescencebased techniques. Histochemical imaging may be a label dependent or label-free method.

[0087] In the context of this specification, "immunohistochemistry" may be understood as a specialized biological technique utilized for the visualization and analysis of specific proteins within tissues. The method may involve the application of a primary antibody, targeted to a particular protein of interest, onto a tissue section. Subsequently, a secondary antibody, conjugated with a detectable marker, is applied to amplify the signal.

[0088] In an example embodiment, the at least one epithelial marker may be Keratin 14, the at least one mesenchymal marker may be Vimentin, the at least one stromal cell type marker may be PDGFRB-B or CD3, and the at least one morphological marker may comprise at least one of: distance to nearest neighbor, distance to stroma and cell density.

[0089] In an example embodiment, the at least one epithelial marker may be Keratin 14, the at least one mesenchymal marker may be Vimentin, the at least one stromal cell type marker may be CD3, and the at least one morphological marker may be distance to stroma.

[0090] In an example embodiment, the at least one epithelial marker may be Keratin 14, and the at least one mesenchymal marker may be Vimentin, and the at least one morphological marker may be distance to stroma. In an example embodiment, the at least one epithelial marker may be Keratin 14, the at least one mesenchymal marker may be Vimentin, the at least one stromal cell type marker may be CD3, and the at least one morphological marker may be distance to nearest neighbor.

[0091] In an example embodiment, the at least one epithelial marker may be Keratin 14, the at least one mesenchymal marker may be Vimentin, and the at least one morphological marker may be distance to nearest neighbor.

[0092] In an example embodiment, the plurality of markers further comprises at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix marker.

[0093] In an example embodiment, the epidermal growth factor signaling marker may be Amphiregulin, the at least one actomyosin contractility marker may be Myh9, and the at least one extracellular matrix marker may be Tenascin-C.

[0094] With such a method, including further markers to the plurality of markers may improve prognostic power of the method.

[0095] In an example embodiment, the at least one epithelial marker may be Keratin 14 and the at least one mesenchymal marker may be Vimentin. The detection of Vimentin in a cell compartment where Keratin 14 is also detected, may be indicative of pEMT and be considered as a prognostic feature that contributes to the prognosis for the patient. Further markers, or combinations of markers, may further improve the prognosis determined for the patient.

[0096] In an example embodiment, the epithelial cancer is a head-and-neck squamous cell carcinoma.

[0097] In the context of this specification, the term "head and neck squamous cell carcinoma (HNSCC)" may refer to a type of cancer originating in the squamous cells lining the mucous membranes of the head and neck region. HNSCC may be derived from the mucosal epithelium in the oral cavity, pharynx and larynx. This cancer may be associated with oncogenic strains of human papillomavirus (HPV).

[0098] In an example embodiment, the detection may be done using one or more methods of detection or reagents. The method should not be thought to be limited to any one method of detection. In an example embodiment, the determination of the quantitative value of a plurality of markers may comprise RNA-sequencing a biological sample, wherein the quantitative value is a normalized read count of the marker gene transcript in the biological sample. RNA-sequencing and subsequent data analysis may be performed with standard methods known to a person skilled in the art.

[0099] In an example embodiment, the determination of the quantitative value of a plurality of markers may comprise mass spectrometry of a biological sample, wherein the quantitative value is a normalized intensity of the marker protein and / or associated peptides in the biological sample. Mass spectrometry and subsequent data analysis may be performed with standard methods known to a person skilled in the art.

[0100] In an example embodiment, the determination of the semi-quantitative value of a plurality of markers may comprise immunofluorescence microscopy, wherein the semi-quantitative value is an intensity of a fluorescent antibody specific to the marker protein in the biological sample.

[0101] In an example embodiment, the determination of the semi-quantitative value of a plurality of markers may comprise immunohistochemistry, wherein the semi-quantitative value is an intensity of a fluorescent second antibody, specific for a first antibody specific for the marker protein in the biological sample.

[0102] In an example embodiment, the detection may further comprise imaging the biological sample to obtain image data and performing image analysis on the image data obtained.

[0103] In a specific example of this one embodiment the imaging may be done with immunofluorescence microscopy, wherein then the image analysis refers to the application of computational and analytical methods specifically tailored for processing and interpreting images obtained through immunofluorescence microscopy. Performing image analysis may involve the obtaining of semi- quantitative or quantitative values of fluorescence intensity by use of antibodies that bind the protein of interest. Obtaining said values may involve use of controls to normalize said values. Normalization of intensity data may be done for example by using DAPI (4',6-diamidino-2-phenylindole). DAPI is a nuclear stain that binds to DNA, providing a marker for cell nuclei. Normalizing immunofluorescent signals to DAPI staining helps account for variations in cell number and nuclear size, allowing for more accurate comparisons between samples. Further data analysis may involve the use of algorithms, software tools, and techniques designed to extract quantitative information from immunofluorescently labeled samples known to a person skilled in the art.

[0104] As enclosed, variable ways of performing the method exist. In some cases, a prognosis may be obtained using a minimum set of markers, whereas in some cases prognosis may be improved with additional markers.

[0105] FIG. 4 illustrates a flowchart according to an example embodiment of a method for providing a prognosis. The apparatus 200 illustrated with FIG. 2 is configured to perform the operation of the method in the example environment 100 illustrated with FIG. 1.

[0106] Referring to FIG. 4, data on a plurality of markers is received in operation 401 and data analysis is performed on the data. The data may comprise information on the detection on the plurality of markers. The data comprising the detection of the markers may be binary (e.g., “on” or “off’) or in some way other way categorical (“no detection”, “low detection”, “high detection”), or it may be quantitative, or it may be image data, or it may contain types of values specific to the method of detection know to a person skilled in the art. It is to be understood that quantitative values may be handled differently from semi-quantitative values, as quantitative values may be numerical / continuous, whereas semi-quantitative values may be numerical / categorical (e.g. discrete). Quantitative values may reflect continuous measurements that reflect an absolute amount of the marker in the sample, whereas semi-quantitative may reflect an amount in categories, e.g., “low”, “medium”, “high”. One or more clinically significant parameters may be identified based on data analysis of the received data. The one or more clinically significant parameters may be understood as parameters that contribute most to, e.g., the prognosis. Data analysis may be performed in a method appropriate way and standard data analysis steps are known to a person skilled in the art. Depending on the type of data, determining clinically significant parameters may involve the use of data wrangling methods, quality control methods, at least one statistical method, clustering, and filtering. In the context of this specification, the term “clustering” may be understood as the process or method by which entities, which can be cells, data points, or other related units, are grouped (clustered) or classified together based on shared characteristics, attributes, or patterns. Clustering may be done with varied statistical clustering algorithms. Most appropriate clustering algorithm may be chosen experimentally for each type of data in each experiment.

[0107] Referring to FIG.4, at least one type of data is received in operation 401 (e.g., it may be one type of data, two types of data or three types of data). When only one type of data is received the prognosis is based on only the clinically significant parameters identified through that received data. When two types of data are received the prognosis is determined in operation 402 based on clinically significant parameters in both types of data. When three types of data are received the prognosis is determined in operation 402 based on one or more clinically significant parameters in three types of data. Clinically significant parameters may be, for example, a detection of a specific marker, a non-detection of a specific marker, or an amount of a specific marker, that contribute most to, e.g., the prognosis. In an example embodiment, all clinically significant parameters in the data are considered when determining the prognosis. In an example embodiment, it may be that some clinically significant parameters are ignored, for example if they provide no increase in accuracy of the prognosis. They may be, for example, indicative of the same feature of the prognosis and are removed to reduce the complexity of the analysis.

[0108] The determined prognosis may comprise one or more prognostic features which may be determined for the patient. The one or more prognostic features may comprise a survival probability and / or a prediction of treatment response. The one or more prognostic features may comprise, for example, a specific indication of disease subtype, stage of disease, severity of disease, other characteristic of the disease, a relation of the characteristic to a patient outcome, or an indication likely future characteristic of the disease.

[0109] In the context of this specification, “survival probability” may be understood as a statistical measure indicating the likelihood or probability that individuals in a particular group will survive for a defined period. This measure is typically expressed as a percentage or a ratio.

[0110] In the context of this specification, “a prediction of treatment response” may be understood as a prediction based on patient characteristics (e.g., age, gender, prognosis) or prior clinical data involving patients that have similar prognostic features and for whom the treatment / survival outcomes are known. A prediction of treatment response may be, for example, an estimation of which form of treatment (such as surgery, chemotherapy, hormonal therapy, radiation therapy, biological therapy such as immunotherapy, small molecule therapy, or antibody therapy, or a combination thereof) would be beneficial for the treatment of the patient. The prediction of treatment is a probability and does not involve performing the treatment on the patient.

[0111] In an example embodiment, the prognosis may comprise a survival probability and / or a prediction of treatment response for the patient.

[0112] In an example embodiment, the prognosis may comprise a survival probability and / or a prediction of treatment response and further prognostic features, such as, epithelial cancer subtype or severity of the cancer.

[0113] Referring to FIG. 4, n output may be provided in operation 403. The output may be indicative of the prognosis. Output may further indicate clinically significant parameters. In an example embodiment, the output may be visualized on a display. The visualization may enable the user to compare differences across, e.g., tumors or patients. With such a method, the output may be provided, e.g., in full, as a summary, as images, graphs, text, parameters, or figures, which may include clinically significant parameters, which may aid the person skilled in the art to understand and / or utilize the output.

[0114] An example embodiment may be a kit for providing a prognosis for an epithelial cancer in a biological sample from a patient. Such a kit may comprise: a reagent specific for at least one epithelial marker, a reagent specific for at least one mesenchymal marker, and a reagent specific for at least one stromal cell type marker; optionally a reagent specific for at least one epithelial stem cell-like marker, a reagent specific for at least one epidermal growth factor signaling marker, a reagent specific for at least one actomyosin contractility marker, and a reagent specific for at least one extracellular matrix protein marker; an apparatus or computer program product capable of receiving data comprising information on a plurality of markers and determining a prognosis for an epithelial cancer in the patient based on the data; and instructions for use.

[0115] The use of the kit may involve the use of an apparatus or a computer program that is comprised in the kit. The use of a kit may require standard laboratory equipment and / or a computing device, not provided in the kit.

[0116] Although the subject matter has been described in language specific to structural features and / or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example embodiments of implementing the claims and other equivalent features and acts are intended to be within the scope of the claims.

[0117] It will be understood that the benefits and advantages described above may relate to one example embodiment or may relate to several example embodiments. The example embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages. It will further be understood that reference to 'an' item may refer to one or more of those items.

[0118] The steps or operations of the methods described herein may be carried out in any suitable order, or simultaneously where appropriate. Additionally, individual blocks may be deleted from any of the methods without departing from the scope of the subject matter described herein. Aspects of any of the example embodiments described above may be combined with aspects of any of the other example embodiments described to form further example embodiments without losing the effect sought.

[0119] It will be understood that the above description is given by way of example embodiments only and that various modifications may be made by those skilled in the art. The above specification, example embodiments and data provide a complete description of the structure and use of exemplary embodiments. Although various example embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed example embodiments without departing from scope of this specification.

[0120] EXAMPLES

[0121] Reference will now be made in detail to various embodiments, an example of which is illustrated in the accompanying drawings. The description below discloses some embodiments in such a detail that a person skilled in the art is able to utilize the embodiments based on the disclosure. Not all steps or features of the embodiments are discussed in detail, as many of the steps or features will be obvious to a person skilled in the art based on this specification.

[0122] FIG. 5. Multidimensional single-cell analysis reveals distinct tumor phenotypes in HNSCC. A: Dot plot showing relative expression levels of each quantified features across the five most common phenotypic descriptors. Phenotypic descriptors refer to 0: Slug, Vimentin high, 1 : Sox2-high-14 low, high cellular density, 2: K14 high, uniform, central, 3: High K14, Sox2, Bmil, 4: Mixed. B: Average distributions of epithelial phenotypic descriptors across the six identified epithelial phenotypic signatures. Note that group “A: Mixed” includes ten liver cores that served as internal controls. C: Distribution of epithelial phenotypic signatures across HPV+ oropharynx and all other primary samples. D: Distributions of stromal phenotypic signatures across different sample types; Preop RT : samples that received pre-operative radiotherapy prior to their resection, LN met: lymph node metastases. E: Distribution of stromal phenotypic signatures within epithelial phenotypic signatures. F: Disease-specific survival across the stromal phenotypic signatures; plot shows patients grouped based on their primary biopsy, with HPV+ oropharynx samples removed, p= Log-rank test. G: Diseasespecific survival across stromal phenotypic signatures split by pEMT or non-pEMT epithelial phenotypic signature. Plots show patients grouped based on their primary biopsy, with HPV+ oropharynx samples removed, p= Log-rank test. H: Distribution of primary tumor sites across different epithelial and stromal phenotypic signatures, HPV+ oropharynx sample removed. I: Overall survival in patients treated only with local operative treatment without additional radiotherapy; patients grouped based on the epithelial and stromal phenotypic signatures of their primary diagnostic biopsy, HPV+ oropharynx samples removed.

[0123] Example 1: pEMT status and stromal signature combine into a highly predictive compound biomarker in HNSCC.

[0124] To identify clinically relevant cancer cell states, subpopulations, and architecture, pathologist-curated tumor microarrays containing samples from a total of 212 HNSCC patients and their full clinical information were obtained. Samples consisted of primary (diagnostic) biopsies, and for a subset of patients, additional corresponding biopsies from post-radiotherapy biopsies, lymph node metastases and recurrent tumors were included. In short, the dataset comprised ten TMA blocks containing formalin-fixed paraffin-embedded (FFPE) samples from patients treated for new HNSCC in the Turku University Hospital region between 2005-2010 with known TNM staging and survival endpoints. A total of 212 patients were included in the dataset. While most tissue samples contained in the TMA came from primary (diagnostic) biopsies (176), a minority of samples also came from secondary tumors, including lymph node metastases (22), tumors resected following preoperative radiotherapy (32), and recurrent tumors resected months or years following primary diagnosis and treatment (55). Each slide additionally contained a number of liver cores used by pathologists to orient the block; these samples were also analyzed and were used to control for slide-to-slide variation in the dataset.

[0125] The analysis of the dataset comprises in short, the multiplexed fluorescent immunohistochemical staining and imaging that was performed in three staining and imaging rounds for the antibodies of markers listed in Table 1 and the nuclear marker DAPI stained on two serial TMA sections (Table 2). After the first- round staining and whole-slide imaging of the TMAs, the fluorescence signal was bleached, and the antibodies from the first-round staining were denatured, after which the second-round staining was performed (Table 2). The process was repeated for the third round of staining. Imaging was performed using a Zeiss Axio Scan.Zl slide scanner, with each round of staining recorded as an independent. CZI image file containing up to five fluorescent channels. Images of individual TMA cores were extracted from the whole-slide and images from the three staining rounds were registered using an affine image registration method aligning the DAPI channels of the three staining rounds. Auto fluorescent signal from red blood cells and other histology artefacts (e.g. wrinkled or folded tissue section areas) were removed. Nuclei were segmented from the DAPI channel. The nuclear regions of interest (ROIs) were expanded by 6 pixels to generate cytoplasmic ROIs. A custom python script was then used to calculate fluorescence intensity in all channels, nuclear morphometric features, and local neighborhood descriptors.

[0126] Table 1. Parameter set used to calculate phenotypic descriptors for each cell: Table 2. Antibodies used in the study:

[0127] After imaging, nuclei and cytoplasms were segmented and mean intensities of each marker were quantified in the relevant subcellular compartments, along with parameters to quantitatively describe nuclear shape and size (nuclear area, roundness, solidity, and EFC ratio) and descriptors of the local cellular neighborhood (local cell density / number of neighbors, distance from tumor-stroma interface, and alignment of cells relative to their neighbors) (Table 1). The analyses generated a multiparameter cell state descriptor for each single cell in the dataset. All cells across patients where then pooled, clustered using Louvain clustering and visualized using dimensional reduction with uniform manifold approximation and projection (UMAP).

[0128] The above-described cellular and local neighborhood features (Table 1) were treated as individual parameters that together define each cell’s “phenotypic descriptor”. A Pearson’s correlation analysis was used to compare the correlation of each parameter to all other parameters, and highly correlated (and therefore redundant) parameters were removed from the analysis. The remaining parameters were formatted as a “count matrix” and analyzed as a single-cell dataset using the Seurat package in R (Hao et al. 2023, DOI: https: / / doi.org / 10.1038 / s41587-023- 01767-y). All parameter values were normalized to remove sampling effect and scaled to obtain relative parameter expression between cells. To identify cells with similar phenotypic descriptors, principal component analysis (PCA) was performed, followed by UMAP dimensionality reduction to visualize the similarities and differences between all cells in the dataset based on patterns of phenotypic descriptor expression.

[0129] Using the bioinformatics resources from the Seurat package, the expression patterns of different markers and parameters can be explored and visualized. By checking the expression of key epithelial and stromal markers, it was found that single-cell clustering was a robust method for differentiating epithelial tumor cells from stromal cells of the microenvironment. Based on their cluster assignment, each cell was given a label denoting its epithelial (tumor) or stromal identity. Using these labels, the distance (in pixels) of each epithelial cell to the nearest stromal cell (and vice versa) was calculated using a custom python script, and this parameter was added to the global parameter set used to define each cell’s phenotypic descriptor.

[0130] With this complete parameter set, the single-cell clustering was repeated separately on the stromal and epithelial compartments. Next, the relative proportions of each cluster in any given biopsy were quantified, constituting the patient’s “phenotypic signature”. For biopsies represented by multiple TMA cores, all cells were pooled together to gain the most representative view of the whole tumor. Samples containing fewer than 100 epithelial or stromal cells were excluded from the respective analysis. Patients were then stratified into subgroups based on the similarities of their phenotypic signatures using a Pearson’s correlation approach.

[0131] Epithelial phenotypic signatures.

[0132] The above-described method identified four major cellular phenotype clusters within the tumor epithelium across the full HNSCC dataset: a Sox2-high- K14 low population characterized by high cellular density (cluster 1), a KI 4- positive population of relatively large cells negative for both pEMT and stem cell markers, which most commonly found in the tumor core (cluster 2); a stem celllike cluster with high K14, Sox2, Bmil (cluster 3), and a cluster with co-expression of K14, Slug and vimentin with also some Sox2 expression (cluster 0)(FIG. 5A-B). Together, these four phenotypes captured 89% of all the cells in the dataset; further clusters were also identified, but they corresponded to very rare cells present in only a few tumors (clusters 4+).

[0133] To explore the clinical relevance of these phenotypic clusters, the relative abundance of all of the cell populations within each biopsy and grouped patients with similar cell composition using unsupervised Pearson’s correlation-based hierarchical clustering were examined. Using this approach, six phenotypic tumor signatures, four of which could be defined by the dominance of a single phenotypic descriptor (Groups B-E), which we describe as “Dense Sox2-High K14-Low”, “Stem cell-enriched”, “Well differentiated” and “pEMT uniform” were identified (FIG. 5B). The sixth group (Group F) was made up of a combination of three descriptors, while the samples in Group A contained the control liver tissues and other tumors that differed greatly from other samples in the dataset and from each other and were thus designated a “mixed” population (FIG. 5B). As the analysis preserves the specific positions of cells, each cell could be projected back into XY space to study spatial distribution patterns of different phenotypic clusters. In the case of Group F tumors, spatial analysis revealed that the pEMT and Sox2-enriched cell populations were found at the tumor-stroma interface, while the tumor core was dominated by cells with the well-differentiated epithelial descriptor. Group F signature was designated as the “pEMT spatial” signature.

[0134] Next, further analysis was performed to find how the six epithelial signatures correlate with the tumor histopathological and clinical features as well as with disease outcomes. The 172 primary tumors represented a balanced mix of all six signatures.

[0135] One of the major clinically relevant disease subtypes within HNSCC is HPV-positive oropharyngeal cancer, which is associated with different treatment strategies and better survival outcomes than other HNSCCs. It was found that 95% of all HPV+ oropharyngeal cancers fell into the two Sox2-high phenotypic signatures (FIG. 5D), indicating evolution a specific cell phenotype in this disease. Since HPV+ oropharyngeal cancers represent a clinically distinct disease subtype, these samples were removed from further analyses.

[0136] Kaplan-Meier analysis was used to compare survival outcomes between patient groups with different phenotypic signatures, with Log-rank test used to measure statistical significance. A Cox proportional hazard model was used to quantify the impact of different clinical variables, including a patient’s phenotypic signature, on survival probability. For both tests, p < 0.05 was used as a cut-off for statistical significance.

[0137] Kaplan-Meier analysis found no significant difference in disease specific survival (DSS) between the six epithelial signatures. The six epithelial signatures correlated with histopathology findings, were indicative of disease progression (metastasis and recurrence) and were indicative of disease type (HPV+ oropharynx) but did not strongly predict overall disease outcome.

[0138] Based on the epithelial phenotypic signature alone, HPV+ oropharyngeal cancers may be fit to specific phenotypic groups that may be of prognostic value.

[0139] Stromal phenotypic signatures.

[0140] In addition to the tumor epithelium itself, the HNSCC tumor microenvironment also harbors a vast amount of heterogeneity owing to its diverse cell types, including fibroblasts, various immune cells, and vasculature. For quantitative analysis of stromal phenotypes, a marker panel to identify the predominant stromal cell types in the tumor microenvironment, including T-cells and myeloid immune cells, vascular endothelium, and different classes of cancer associated and healthy fibroblasts was used. The marker panel comprised of antibodies for markers CD34, CD3, CD1 lb, aSMA, FAP and PDGFRB.

[0141] As expected, tumor stroma was found to contain a diversity of different cell types across the analysed biopsies. Phenotypic descriptors in the stroma had high correspondence with individual markers, allowing direct assignments of cell identities to the phenotypic descriptors. As before, biopsies were grouped based on similarity between their phenotypic descriptors. Three major signatures based on the cell type most enriched in the tumor microenvironment: immune-enriched stroma (enriched for CD3+ cells and other small, densely packed cells), fibrotic stroma (sparse, enriched for elongated, co-aligned cells expressing fibroblast markers), and myeloid-high stroma (enriched for cells expressing the myeloid marker CD1 lb) were identified (FIG. 5D).

[0142] When primary and secondary tumor types were compared, the three stromal signatures were present across sample types, although immune signatures were more enriched in lymph node metastases while recurrent tumors tended to present a fibrotic signature (FIG. 5D). Interestingly, there was no cross-correlation between the epithelial and stromal patient signatures, and the six epithelial signatures were evenly distributed across the different stromal groups (FIG 5E).

[0143] In Kaplan-Meier analysis, stromal signatures were found to be predictive of patient survival, with patients in the immune-enriched group having a five-year DSS of 64%, compared to 34% for those with a fibrotic stromal signature and 52% for those with a myeloid-enriched signature (FIG 5F). Collectively these data indicated that in contrast to the epithelial phenotypes, which correlated with clinical and histopathological features but did not predict outcome, stromal phenotypic signatures are a strong predictor of disease outcome independent of clinical and histopathological phenotypes.

[0144] Based on the stromal panel alone prognostic information can be obtained for some patients. Patients with immune-enriched stroma have favorable prognosis compared to other patients. Patients with pEMT tumors and immune-enriched stroma have an extremely favorable prognosis compared to myeloid-enriched and fibrotic signatures (FIG. 5F). pEMT status and stromal signature combine into a highly predictive compound biomarker.

[0145] Further investigation was done on whether combining the two types of signatures could increase the predictive power of survival analysis and provide additional information on disease mechanisms. When the samples from each epithelial group were plotted independently on Kaplan-Meier curves separated by their stromal signature, a striking pattern emerged: in four out of six epithelial groups, survival was comparable across all stromal signatures, while in the two pEMT epithelial groups, stromal signatures revealed significant differences in survival outcomes. When samples with the same responses were pooled together into larger patient groups, there was no significant survival difference in patients with non-pEMT epithelial status regardless of stromal signature. By contrast, for patients with a pEMT epithelial signature, five-year DSS stood at 94% for those with an immune-enriched stroma compared to only 21% for those with a fibrotic stroma (FIG. 5F-G). We termed the combined epithelial and stromal signatures the “Tumor fingerprint” of each patient. Cox proportional hazard modelling showed that out of all clinical parameters tested (sex, tobacco and alcohol use, tumor site, tumor staging and grading), the pEMT / Immune-high signature was the most significant predictor of positive outcomes in this patient population.

[0146] To further ensure that the observed differences in survival did not result from confounding effects caused by samples originating from different anatomical sites, the tumor site was plotted against the pEMT status of the epithelium and the phenotypic signature of the stroma (FIG. 5H). While some differences exist in the proportion of different tumor sites in each group, we observed a balanced representation of different tumor types across the different signatures, highlighting that the classification did not classify patients based on gross tissue anatomy.

[0147] Further investigation into whether, in addition to their prognostic value, the tumor fingerprints could be used as a predictive biomarker, i.e. whether they could identify patient groups with good or poor responses to a specific treatment. For this purpose, the analysis was focused on patients in the dataset who had received only local surgery (without additional radiotherapy before or after surgery). In this patient group, neither stromal nor epithelial pEMT status alone were predictive of outcome. By contrast, combining the two signatures into the tumor fingerprint was highly predictive: patients with a pEMT epithelial and immune-enriched stromal profile had significantly higher overall survival (100% DSS) compared to patients with the same epithelial status but a fibrotic stromal profile (FIG. 51). On the other hand, in non-pEMT tumors, this trend was reversed, indicating that profiling the stroma alone may be misleading for treatment selection, and suggesting complex biological interdependencies between epithelial and stromal phenotypes that influence patient responses to operative treatment.

[0148] Taken together, these results show that the individual markers contribute to distinct phenotypes in the epithelium and stroma, and that the combination of epithelial pEMT status and the immune / fibrotic balance of the stroma into a single tumor fingerprint forms an exceptionally strong biomarker of both treatment response and patient outcome in HNSCC tumors.

[0149] Example 2: pEMT state with pro-invasive signature associates with poor patient outcomes.

[0150] The results of the TMA analysis indicated that the pEMT program may direct tumor cells to become more responsive to their microenvironment, with an immune-rich or fibrotic environment having a generally anti- or pro-tumorigenic effect, respectively.

[0151] FIG. 6: pEMT state with pro-invasive signature associates with worst patient outcomes. A: Markers and parameters used in the epithelial multiplexed immunofluorescence panel of an independent HNSCC patient cohort containing 438 primary tumors. B: Disease-specific survival (DSS) across the epithelial phenotypic signatures; with HPV+ oropharynx samples removed. p= Log-rank test. C: Representative composite immunofluorescence images of representative TMA cores of the four largest epithelial signature groups showing six merged channels; scalebar 100pm. Dashed box denotes area of higher magnification. Small images show selected single channels at higher magnification; scalebar 50um; dashed lines denote tumor / stroma border D: Mean expression of selected markers in the four largest epithelial signature groups between tumor border (Br) and tumor center (Cn). Each dot represents a single patient, p = paired T-test. E: DSS across the four largest epithelial signature groups stratified by stromal composition, p = Log-rank test. F: Dot plot showing relative expression levels of each quantified features across the five most common epithelial phenotypic descriptors. Epithelial phenotypic descriptors refer to 0: Slug, Vimentin high, 1 : Sox2 -high-14 low, high cellular density, 2: K14 high, uniform, central, 3: High KI 4, Sox2, Bmil, 4: Mixed.

[0152] CAFs promote cell state-specific reprogramming into a pro-invasive phenotype.

[0153] To characterize the precise nature and functional consequences of the increased signaling a 3D spheroid co-culture model where patient-derived tongue cancer cells were seeded into suspension cultures with or without patient-derived cancer-associated fibroblasts (CAFs) was established. The experiment was conducted in parallel on two different patient lines obtained from the University of Turku. For 3D co-culture spheroid studies, eGFP and mCherry-tagged cells were plated as single-cell suspensions into U-bottom ultra-low attachment 96-well plates (Coming 4520) in the medium described above. Each well contained approximately 1000 cells; for co-culture studies, approximately 750 CAFs and 250 patient cells were plated per well. After plating, the plate was centrifuged at 300 x g for one minute to collect all cells at the bottom in close proximity to one another. Cocultures were cultured at 37°C, 5% CO2 for up to 48h, after which single cell suspensions were generated by incubation of spheroids with 0.5% Trypsin / 0.5mM EDTA (Gibco). After washing with ice-cold DMEM, single cells were counted using Luna-II automated cell counter (Logos Biosystems) and loaded on a microwell cartridge of the BD Rhapsody Express system (BD) following the manufacturer’s instruction. Single cell whole transcriptome analysis libraries were prepared according to the manufacturer’s instructions using BD Rhapsody WTA Reagent kit (BD, 633802) and sequenced on the Illumina NextSeq 500 using High Output Kit v2.5 (150 cycles, Illumina) for 2 x 75 bp paired-end reads with 8 bp single index aiming sequencing depth of >20,000 reads per cell for each sample.

[0154] Within 24 h of seeding, the cells formed tight spheroids both in mono- and co-culture conditions and after 48 h cells were analyzed by single-cell RNA sequencing. Single-cell clustering showed that the three cell types separated well from each other. To this end we noted that the second most significantly upregulated gene in patient derived tongue cancer cells that showed higher transcriptional upregulation levels in response to co-culture with CAFs was AREG, which encodes the protein amphiregulin, a ligand of the epidermal growth factor receptor (EGFR) and that expression of genes associated with EGFR signaling were similarly upregulated. pEMT state and CAF transcriptional reprogramming in HNSCC outcome.

[0155] To test if the CAF-pEMT transcriptional reprogramming state is relevant for HNSCC outcome, we proceeded to test this in further clinical samples, an independent patient cohort consisting of 438 primary tumors (FIG. 6 A). Samples consisted of primary (diagnostic) biopsies. In short, the dataset comprised ten TMA blocks containing formalin-fixed paraffin-embedded (FFPE) samples from patients treated for new HNSCC in the Turku University Hospital region between 2005- 2015 with known TNM staging and survival endpoints. A total of 448 patients were included in the dataset. The analysis of the dataset comprises in short, the multiplexed fluorescent immunohistochemical staining and imaging that was performed in three staining and imaging rounds for the antibodies of markers listed in Table 1 and the nuclear marker DAPI stained on two serial TMA sections (Table 2) with the same protocol as in Example 1.

[0156] The analysis revealed previously unreported subgroups of patients and specifically the combination of pEMT status of the tumor and a cancer associated fibroblast (CAF)-enriched stroma as an indicator of poor survival. Previously applied tumor epithelium and stromal panels, including components of the cancer related extracellular matrix (Onco-ECM) and associated mechanosignaling (Tenascin-C and Myh9) as well as AREG were analyzed (FIG. 6A). Interestingly, including these additional markers to the epithelial panel improved the prognostic value of this panel with the mixed and EMT phenotypes showing poor prognosis (FIG. 6B). Closer examination of these above-mentioned markers of CAF-HNSCC invasive reprogramming showed that these components were enriched at the tumor stroma boundary (FIG. 6C). As predicted by the co-culture analyses, pEMT cells showed the strongest expression of the CAF-crosstalk markers (FIG. 6D).

[0157] Importantly, the combined analysis of the stromal and epithelial panel further validated the strong prognostic value of the pEMT state - stromal identity combination, particularly when enriched at the tumor-stroma interface (pEMT spatial; FIG. 6E). Interestingly, the stem cell -enriched phenotypic group now showed the prognostic split between CAF-enriched stroma predicting poor outcome and immune-enriched stroma associated with favorable outcome (FIG. 6E), and this phenotype also was associated with enrichment of the crosstalk-markers at the tumor-stroma interface (FIG. 6D). The markers contribute to epithelial phenotypic descriptors and may be used to expand the epithelial panel (FIG.6F). Including Tenascin-C and / or Amphiregulin and / or Myh9 improves prognostic value of the epithelial panel alone (FIG 6B).

[0158] Taken together, the results suggest that the pEMT status of a tumor is predictive of its responsiveness to the surrounding stroma, with fibrotic microenvironments combined with pEMT tumors resulting in the most aggressive cancer phenotypes. This disclosure provides sufficient biomarkers and / or compound biomarkers to detect pEMT status and fibrotic microenviroments and provide a more accurate prognosis for patients. Furthermore, in some phenotypes that require combinatory signature of epithelium and stroma, prognostic value is significantly improved (FIG. 6E). Morphological analysis of the tumor provides new prognostic categories, such as the pEMT spatial wherein pEMT cells are close to the stroma (FIG. 6E).

Claims

CLAIMS1. A method for providing a prognosis for an epithelial cancer in a patient, comprising: detecting, in a biological sample obtained from the patient, a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker; and determining the prognosis for the epithelial cancer based on the detecting the plurality of markers.

2. A method according to claim 1, wherein the at least one epithelial marker comprises at least one of: Keratin 8, Keratin, 14, Keratin 18, B-catenin, and EpCam; wherein the at least one mesenchymal marker comprises at least one of: Vimentin and Slug; wherein the at least one stromal cell type marker comprises at least one of: Vimentin, PDGFRB-B, CD34, FAP, a-SMA, CD3, and CD1 lb; and wherein the at least one morphological marker comprises comprising at least one of: nuclear size, nuclear shape, distance to nearest neighbor, distance to stroma, number of neighbors, and cell density.

3. A method according to any of the preceding claims, wherein the plurality of markers further comprises at least one epithelial stem cell-like marker, at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix marker.

4. A method according to claim 3, wherein the at least one stem-cell like epithelial marker comprises at least one of: Sox2, KI 4, and BMI1; wherein the at least one epidermal growth factor signaling marker comprises at least one of: Amphiregulin; wherein the at least one actomyosin contractility marker comprises Myh9; and wherein the at least one extracellular matrix marker comprises Tenascin- C.

5. A method according to any of the preceding claims, wherein the epithelial cancer is a head-and-neck squamous cell carcinoma.

6. A method according to any of the preceding claims, wherein the biological sample is a tumor biopsy.

7. A method according to any of the preceding claims, wherein the determining the prognosis comprises: determining a survival probability of the patient on the basis of the detecting the plurality of markers; and / or determining a prediction of treatment response on the basis of the detecting the plurality of markers.

8. A method according to any of the preceding claims, wherein the detecting the plurality of markers comprises: contacting, per marker within at least a first subset of markers within the plurality of markers, the biological sample with a reagent specific for the marker, wherein the reagent is one of: a probe, a dye, an antibody, and an antibody fragment, and the marker is a protein, or wherein the reagent is a primer and the marker is a nucleotide sequence; and identifying, per marker within at least the first subset of markers, whether the reagent specific for the marker has detected the presence of the marker in the biological sample.

9. A method according to any of the preceding claims, wherein the detecting the plurality of markers comprises: determining, per marker within at least a second subset of markers within the plurality of markers, a quantitative value or a semiquantitative value of an amount of the marker, wherein the marker is a nucleotide sequence or a protein.

10. A method according to any of the preceding claims, wherein the detecting the plurality of markers comprises: imaging the biological sample to obtain image data; and performing image analysis on the image data obtained.

11. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and computer program code being configured to, with the at least one processor, cause the apparatus at least to perform: receiving data comprising information on a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker detected in a biological sample obtained from a patient; determining a prognosis for an epithelial cancer in the patient based on the data; and providing output indicating the prognosis12. An apparatus according to claim 11, wherein the plurality of markers further comprises at least one stem cell-like epithelial marker, at least one epidermal growth factor signaling marker, at least one actomyosin contractility marker, and / or at least one extracellular matrix protein marker.

13. A computer program product embodied on a computer-readable medium comprising a computer-readable program code configured to, when read and executed by a computer system, cause the computer system to perform at least: receiving data comprising information on a plurality of markers comprising at least one epithelial marker, at least one mesenchymal marker, at least one stromal cell type marker, and at least one morphological marker detected in a biological sample obtained from a patient; determining a prognosis for an epithelial cancer in the patient based on the data; andproviding output indicating the prognosis.

14. A kit for providing a prognosis for an epithelial cancer in a biological sample from a patient comprising: a reagent specific for at least one epithelial marker, a reagent specific for at least one mesenchymal marker, and a reagent specific for at least one stromal cell type marker; an apparatus or computer program product capable of receiving data comprising information on a plurality of markers and determining a prognosis for an epithelial cancer in the patient based on the data; and instructions for use.

15. A kit according to claim 14, wherein the kit further comprises: a reagent specific for at least one epithelial stem cell-like marker, a reagent specific for at least one epidermal growth factor signaling marker, a reagent specific for at least one actomyosin contractility marker, and / or a reagent specific for at least one extracellular matrix protein marker.

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