WO2024147762A2 - Biomarkers, methods and kits for detecting and/or subtyping small b-cell lymphomas - Google Patents

Biomarkers, methods and kits for detecting and/or subtyping small b-cell lymphomas Download PDF

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WO2024147762A2
WO2024147762A2 PCT/SG2024/050011 SG2024050011W WO2024147762A2 WO 2024147762 A2 WO2024147762 A2 WO 2024147762A2 SG 2024050011 W SG2024050011 W SG 2024050011W WO 2024147762 A2 WO2024147762 A2 WO 2024147762A2
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hsa
mir
small
cell lymphoma
lymphoma
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WO2024147762A3 (en
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Soo Yong Tan
Swee-Shan Susan HUE
He Cheng
Yu Jin
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National University Of Singapore
MiRXES Lab Pte. Ltd.
National University Hospital (Singapore) Pte Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present invention relates generally to the field of molecular biology.
  • the present invention relates to biomarkers associated with small B-cell lymphoma and methods of using the biomarkers to determine whether a subject suffers from or is at risk of developing small B-cell lymphoma, and/or to classify B-cell lymphoma.
  • Lymphoma diagnosis is well recognized as one of the most difficult areas of diagnostic pathology.
  • the pathological diagnosis of lymphoma hinges largely upon recognition of morphological abnormalities on a well-represented tissue section.
  • identification of normal and pathological changes in different lymphoid compartments and recognition of neoplastic lymphoid entities may be highly challenging. Lymphoid tissue often appears to be a morass of small and large lymphoid cells that defies recognition of cell types and functional compartmentalization.
  • lymphoma classification often necessitating the incorporation of additional testing with a plethora of immunostains and molecular genetic investigations for definitive diagnosis, making lymphoma diagnosis one of the most complicated tasks encountered by pathologists worldwide.
  • Small B-cell lymphomas comprise a heterogeneous admixture of small and occasionally larger lymphoid cells with only mild cytologic atypia, and some cases may even retain the tissue architecture to some degree, therefore resembling reactive lymphoid proliferations to the lesser trained eye. There is also significant morphological overlap between the different subtypes of these small B-cell lymphomas, making immunohistochemistry (IHC) and/or molecular genetic testing an integral component in the proper workup of these neoplasms.
  • IHC immunohistochemistry
  • the probe is selected from the group comprising an aptamer, an antibody, an affibody, a peptide, and/or a nucleic acid.
  • MZL marginal zone lymphoma
  • MALT lymphomas usually arise in organs that are devoid of lymphoid tissue.
  • MiRNAs are evolutionary conserved, single-stranded non-coding RNAs of 19 to 25 nucleotides which primarily function in mediating the degradation or translational repression of mRNA targets. Under normal physiological conditions, miRNAs are key components of feedback mechanisms for a wide range of biological pathways such as cell proliferation, differentiation, and apoptosis. Conversely, dysregulated miRNAs have been implicated in the hallmarks of cancer including supporting tumour growth by inhibiting growth suppression, sustaining proliferative signalling and resisting cell death, activating invasion and metastasis, and promoting angiogenesis. It is now known that miRNAs regulate oncogenesis through their tumour suppressor or oncogenic activities, with increasing evidence of aberrant miRNA expression in a variety of malignancies.
  • the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of hsa-miR-224-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342
  • the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of hsa-miR-125a-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-342
  • the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p and hsa-miR-10b-5p. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p and hsa-miR-126-3p.
  • the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p and hsa-miR-145-5p.
  • the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
  • the invention also relates to use of at least one reagent suitable for detecting one or more biomarkers in the manufacture or preparation of a diagnostic agent/kit for use in any of the above method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma.
  • control may include but is not limited to, a healthy subject, a non-diseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like.
  • the controls may include but is not limited to one or more positive controls from a diseased subject or a subject suffering from, or at risk of developing small B-cell lymphoma and/or any of the subtypes such as small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
  • the control may include but is not limited to, one or more biological samples obtained from the above subjects.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs in a biological sample obtained from the subject, wherein the one or more miRNAs may include, but is not limited to, the miRNAs listed in Table 1 .
  • the one or more subtypes of B-cell lymphoma may include but is not limited to small lymphocytic lymphoma / chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
  • the small B-cell lymphoma may include but is not limited to one or more of small lymphocytic lymphoma / chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
  • the method comprises detecting/determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p in the biological sample.
  • the method comprises detecting/determining the expression level of all 14 miRNAs such as hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363- 3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126- 3p and hsa-miR-196a-5p in the biological sample.
  • miRNAs such as hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 181 a-2-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 20b-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs, that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, h
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 106b-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 363-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-25-3p, hs
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 25-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hs
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 625-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, h
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 224-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa
  • the method of determining whether a subject suffers from, or is at risk of developing one more subtype of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p and hsa-miR-340-5p.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p and hsa-miR-181 a-2-3p.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p and hsa-miR-151 a-3p.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p and hsa-miR-363-3p.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classing small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs, wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363- 3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-18
  • the expression level of the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa- miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like, wherein the one or more miRNA is compared to the expression level of the one or more miRNAs in a control, wherein an
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
  • the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa- miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR
  • control may include but is not limited to a healthy subject, a non-diseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like.
  • control may include, but is not limited to a subject suffering from, or at risk of developing, one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma I chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
  • the first control may include, but is not limited to, a healthy subject, a nondiseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like; and/or the second control may include, but is not limited to, a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
  • the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject wherein the second control may include, but is not limited to, a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma.
  • the control when classifying a subject suffering from small lymphocytic lymphoma/chronic lymphocytic leukaemia, may include, but is not limited to, subjects suffering from small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
  • the controls may be selected from subjects suffering from one or more subtypes of B-cell lymphoma that are different and/or same as the subtype to be classified.
  • the small B-cell lymphoma comprises from one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
  • the method may further comprise performing one or more procedures to determine: (a) the morphology and cell distribution of the biological sample; and/or (b) the expression level, presence or absence of one or more additional biomarkers in the biological sample. For example, this may include determining the information of a tissue sample (e.g.
  • lymphoid tissue by one or more procedures may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping, clonality test, and the like; and/or determining the expression level, presence or absence of one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6, any other biomarkers relevant to small B-cell lymphoma and the associated subtypes, and the like.
  • the additional biomarkers may be antigens, proteins and/or molecules found in or on surfaces of leukocytes and other cells relevant for the immune system, which include neutrophils, eosinophils, basophils, mast cells, monocytes, macrophages, dendritic cells, natural killer cells, and lymphocytes (B cells and T cells).
  • leukocytes and other cells relevant for the immune system which include neutrophils, eosinophils, basophils, mast cells, monocytes, macrophages, dendritic cells, natural killer cells, and lymphocytes (B cells and T cells).
  • lymphoid tissue with either or both altered architecture and monotonous proliferation of small lymphoid cells, as well as positive/negative CD20 immunophenotyping may provide information for determining whether a subject is suffering from, or at risk of developing small B-cell lymphoma or the associated subtypes.
  • the tissue information of the biological sample may be included together with the expression level of one or more miRNAs of the current invention in determining
  • the one or more procedures may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping and/or clonality test, and the like; and/or wherein the one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2, Bcl6, and the like.
  • the method may further comprise performing one or more procedures that may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping and/or clonal ity test to determine:
  • the method for treating a subject suffering from small B-cell lymphoma comprises:
  • kits for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprising (a) an isolated set of probes and/or reagents capable of detecting/determining the expression level of one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR
  • the kit comprises an isolated set of probes capable of detecting/determining one or more miRNAs by one or more methods, such as, but is not limited to, sequencing, nucleic acid hybridisation, microarray, nucleic acid amplification such as a quantitative reverse transcription- polymerase chain reaction (qRT-PCR), reverse transcription-polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), a locked nucleic acid PCR, a clustered regularly interspaced short palindromic repeat (CRISPR)-based assay, isothermal amplification assay, and the like.
  • qRT-PCR quantitative reverse transcription- polymerase chain reaction
  • RT-PCR reverse transcription-polymerase chain reaction
  • qPCR quantitative polymerase chain reaction
  • CRISPR clustered regularly interspaced short palindromic repeat
  • a risk score may be calculated using the classification algorithm (such as support vector machine with radial or linear kernel, or random forest) for a one-vs-one subtype comparison based on the expression level of the miRNAs determined in the sample.
  • the algorithm model may then determine the most probable subtype based on the calculated score.
  • a further example of such an algorithm further incorporates the use of a reference sample with known levels of assayed miRNAs to normalize the score for each assay run to account for run-to-run variations (which may be referred to as the Quantitative Reference (QR)), and in some embodiments, the mean value for the QR scores may be used to calculate the expected QR scores.
  • QR Quantitative Reference
  • the mean value for the QR scores may be used to calculate the expected QR scores.
  • the above linear model may also be applied to determining the subtypes of small B-cell lymphoma which a subject is suffering from, or at risk of suffering.
  • the relevant subtype risk scores, QR scores and cut-offs of each subtype may be determined accordingly for differentiating or identifying the subtypes that the subject is suffering from, or at risk of suffering.
  • Extranodal sites include oropharyngeal mucosa, respiratory tract, gastrointestinal tract, bladder, salivary gland, eye, thyroid, and skin tissues.
  • RNA Isolation Reverse-Transcription, cDNA Amplification, and Real-time gPCR
  • Total RNAs were isolated from FFPE tissues using the miRNeasy FFPE miRNA isolation kit (Qiagen, Germany) according to the manufacturer's protocol. Three synthetic short RNA species (spikeins) with sequences distinct from endogenous human miRNAs were added into the lysis buffer as controls to monitor and normalize for workflow variations. The miRNA was eluted using 50 pL nuclease- free water. Total RNA quantity and quality were measured by NanoDrop 2000 (Thermo-Fisher Scientific, USA). For each sample, 900ng total RNA was used for subsequent reverse-transcription and PCR reactions.
  • MiRNA profiling was performed using a multiplexed RT-qPCR platform following an established protocol (Zou R, et al. Cancers 2021 , 13, 2130). Isolated miRNAs underwent reverse transcription using the in-house reverse transcription system and modified stem-loop RT primer pools (MIRXES, Singapore) on a VeritiTM Thermal Cycler (Applied Biosystem, USA) according to the manufacturer's instructions. For each RT reaction, a standard panel comprising a series of six 10-fold dilutions of synthetic miRNA and two no-template controls (NTCs) were included on the same plate.
  • NTCs no-template controls
  • cDNA was then pre-amplified using a 14-cycle PCR reaction with Augmentation Primer Pools (MIRXES, Singapore) on the VeritiTM Thermal Cycler (Thermo-Fisher Scientific, USA). Single qPCR was performed on the amplified cDNA samples using a miRNA-specific qPCR assay and ID3EAL miRNA qPCR Master Mix according to the manufacturer’s instruction (MiRXES, Singapore). The qPCR reaction for each sample was performed with technical duplicates on the QuantStudio 5 Real-Time PCR System (Applied Biosystem, USA). Raw threshold cycle (Ct) values were calculated using the QuantStudioTM Design and analysis software with an automatic baseline setting and a threshold of 0.4.
  • Ct threshold cycle
  • RNA from additional 282 FFPE tissues were profiled using the 100-miRNA customized panel and all expression levels were obtained as Iog2 copies/sample.
  • the mean expression level of the 10 housekeeping miRNA was used to normalize both the discovery and validation cohort to ensure comparability.
  • the batch correction was performed using the ComBat approach (Biostat Oxf Engl. 2007, 8, 118-27), setting the collection site as the batch variable and including the tissue site (nodal/extra-nodal) and histology subtypes (RL, FL, MZL, MCL, SLL) as covariates.
  • Housekeeping gene normalization was applied to the raw expression levels in both discovery and validation cohorts, followed by ComBat batch correction for different collection sites in the validation dataset as described above. Batch effects between the discovery and validation cohorts were also corrected.
  • the expression and tissue site (nodal/extra-nodal) data from the two cohorts were combined to develop a multi-marker panel for accurate classification of different subtypes. Categorical data such as tissue site was converted to numerical integers (0 and 1 ) for ease of analysis.
  • Example 1 Consistency of miRNAs expression across FFPE samples in the discovery and validation cohorts
  • PCA Principal component analysis
  • a 90-miRNA and tissue information classification model was established by training and testing on a combined cohort of samples, resulting in a mean area under the ROC curve (AUC) of 0.959 (95% Cl: 0.922 to 0.988) (Fig. 3A).
  • Other performance metrics include a mean recall of 0.944, precision of 0.923 and F1 score of 0.933 (Fig. 3B).
  • the resulting classification model has a sensitivity of 94% for lymphoma and 80.4% for reactive lymphoid proliferation, and overall accuracy of 90.4% (Fig. 3C).
  • a smaller panel comprising the top 14 miRNA features can achieve an accuracy of 85.5%, while the addition of more miRNAs did not substantially improve the accuracy (Fig.
  • a 90-miRNA and tissue in-formation classification model was built by training and testing using samples from both cohorts, resulting in a sensitivity of 86.8% for FL, 87.8% for MZL, 85.2% for MCL and 84.0% for SLL and overall accuracy of 86.3% (Fig. 4A).
  • SVM with radial kernel algorithm was selected to build the classification model as it showed the best performance as compared to the random forest and SVM with linear kernel algorithms (Fig. 4B).
  • Table 6 Exemplary panel of miRNA biomarkers and the use of tissue information for classifying subtypes of small B-cell lymphoma, and/or for determining whether a subject is suffering from or is at risk of suffering from one or more subtypes of small B-cell lymphoma.
  • Example 4 MiRNA expression could infer meaningful biological differences between reactive and neoplastic lymphoid proliferation
  • miRSEA miRNA gene set enrichment
  • Example 5 Proposed two-stage diagnostic algorithm for miRNA-based classification of small B-cell lymphomas
  • telomeres may refer to lymphoid tissue with one or more of the following: altered architecture, monotonous proliferation of small lymphoid cells and +/- CD20 immunophenotype.
  • lymphoma diagnosis often requires the availability of hematopathologists with deep knowledge and experience in evaluating lymphoid lesions, high-quality laboratory infra-structure, as well as easy accessibility to a wide panel of immunohistochemical stains and additional molecular genetic testing such as fluorescence in-situ hybridization (FISH) and clonality studies, all of which may not be available in resource constrained nations.
  • FISH fluorescence in-situ hybridization
  • MiRNAs have previously been reported to be aberrantly expressed in almost all human cancers, including B-cell lymphomas. As active players in tumor pathogenetic pathways, miRNAs should have a significant influence on cancer diagnosis and prognosis. In fact, miRNA expression profiles have been reported by many investigators to be useful in tumor classification and subtyping, particularly in the setting of poorly differentiated malignancies and small biopsy samples where traditional morphological and antigenic evaluation have proven to be difficult if not impossible; while others have identified miRNA signatures associated with disease prognosis and response to treatments.
  • miRNAs can be robustly detected in FFPE tissue samples because they are small and less susceptible to degradative processes, and have been reported to be stable in FFPE archival tissue specimens that have been stored for close to 30 years (Bovell L, ef al. Front Biosci Elite Ed. 2012, 4, 1937-40). Remarkably, other investigators have reported the superiority of miRNAs as analytes compared with mRNAs for the molecular characterization of compromised archived clinical specimens and in the accurate classification of metastatic cancers of unknown primary origins.
  • miRNAs have unique attributes that render them suitable biomarkers in clinical practice, their accurate detection and quantification can be challenging because of their small size and sequence similarity among various members.
  • biomarker discovery and genome-wide expression analyses most investigators deployed high-throughput hybridization-based methods, such as microarray technology for global gene expression profiling.
  • microarray technology is a powerful approach that enables simultaneous screening of large numbers of miRNAs, its performance is most robust when frozen tissue or freshly fixed FFPE tissue are used, as prolonged storage of FFPE tissue blocks (up to 11 years) leads to a significant drop in miRNA detection.
  • Other miRNA detection methods, including in- situ hybridization and next-generation sequencing are technically more challenging. Barriers to clinical adoption include higher costs, need for sophisticated instrumentation, and complicated data interpretation.
  • qPCR quantitative PCR
  • qPCR-based miRNA profiling platforms require much lower RNA input compared with other quantification methods, which is clearly a key advantage when dealing with limited and often compromised clinical specimens.
  • one key advantage of qPCR is that it can be easily and conveniently performed in most clinical diagnostic laboratories (especially after the COVID pandemic), and it produces data that are easy to analyse. Therefore, validation of a PCR-based laboratory- developed test (LDT) for accreditation purposes is likely to be far less complex compared to other more sophisticated platforms.
  • PCR-based miRNA biomarkers discovery work lies in the design of individual primers required for specific amplification of each miRNA gene included in large-scale analyses. Due to the short length of miRNAs (roughly the size of a PCR primer), primer design for specific PCR amplification poses significant difficulty. As such, most commercially available high throughput qPCR platforms employ only one or two miRNA-specific primers with selective incorporation of universal primers. In the current study, we performed multiplex comparative analyses of 360 miRNAs based on a unique method that uses three miRNA-specific primers (i.e. stem-loop RT, forward and reverse primers), obviating the use of universal primers altogether.
  • miRNA-specific primers i.e. stem-loop RT, forward and reverse primers
  • miRNAs could potentially play diverse regulatory roles in many processes, including tissue and cancer development like lymphomagenesis.
  • KEGG pathways - cytosolic DNA sensing pathway, RIG-l-like receptor signalling pathway, and NOD-like receptor signalling pathway - are functionally related. These pathways underlie the sensing of foreign matter that may be introduced during infections, in the form of single or double-stranded DNA, from viruses and other pathogens. Hence, these pathways are particularly relevant in lymphomagenesis where infection by oncogenic viruses, such as EBV and KSHV, and other pathogens like bacteria can transform B cells into lymphomas in certain cases.
  • Gastric MALT a type of MZL
  • MZL a type of MZL
  • chronic gastritis caused by Helicobacter pylori and regress upon antibiotics treatment suggesting an infection-driven tumorigenic event.
  • another type of MZL ocular adrenal MALT, has been linked to Chlamydophila psittaci infection.
  • the G12 subfamily consist of G proteins, which are G-alpha subunits of heterotrimeric GTP-binding proteins. G proteins serve as the intermediary between GPCRs on the cell membrane and downstream signalling, and they work by binding to guanine nucleotides. G12 proteins, together with other G protein sub-families, form the most diverse group of receptors, playing a wide range of important roles in normal physiology.
  • Ga12 and Go13 have been demonstrated to regulate the maturation of B cells in the marginal zone in a murine model.
  • the overexpression or enhanced activation of Go12 and Ga13 has been linked to several cancers.
  • G12 proteins still remain one of the least understudied subfamilies in cancer biology, especially in haematological malignancies.
  • the few studies done in lymphomas do point towards significant roles that Ga12/13 signalling play in lymphomas in general.
  • NF-KB signalling is known as an important hallmark of lymphomas and much work has been done on pathways that drive its activity. Enhanced NF-KB activity has been associated with increased hedgehog signalling. Smoothened (SMO), yet another GPCR and also an essential signal transducer of the hedgehog signalling pathway, has been shown to recruit and activate Gai and Ga12, and not other G proteins. The resulting signalling complex then initiates a cascade of events involving non-canonical signalling complexes, ultimately leading to the activation of NF-KB signalling. This study suggests that Ga12 could play an important enabling role in lymphomagenesis by mediating the activation of NF-KB signalling.
  • Ga13 along with associated receptors S1 PR2 and P2RY8, appear to pro-mote the confinement of B cells to ensure a physiologically normal germinal centre. Ga13 deficiency has been shown to give rise to germinal centre B-cell-derived lymphoma in mice. Similarly, mutations in GNA13 (the gene encoding Go13), S1 PR2, or P2RY8 - found in GCB-DLBCL patients - have been demonstrated to cause the dissemination of germinal centre B-cells (and in the case of P2RY8 mutations, also enhancing cell growth), hence also leading to germinal centre B-cell-derived lymphoma. Unlike Ga12, Ga13 plays a tumour suppressive role in orchestrating the proper development of the germinal centre. Taking together the limited knowledge gathered on Go12/13 signalling events in lymphomas, we hypothesize that Ga12 signalling could be a significant player in small B-cell lymphomagenesis.
  • miRSEA analysis also identified pathways that are regularly implicated in lymphomas, hence validating the relevance of biomarker miRNAs that differentiate small B-cell lymphomas from RL.
  • Significantly upregulated pathways include the B-cell receptor signalling pathway, mTOR signalling pathway, and PI3K-Akt activation (Tables 8A and 8B).
  • Table 8A miRSEA pathway analysis for lymphoma vs RLN (KEGG).
  • miRNA expression profiling may serve as a promising biomarker and practical tool to aid the diagnosis of common lymphoid lesions.

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Abstract

Disclosed herein are biomarkers relating to small B-cell and the associated subtypes, kits and methods of determining whether a subject suffers from or is at risk of developing small B-cell lymphoma or the associated subtypes, wherein the method comprises detecting the differential expression level of at least one or more miRNAs from a biological sample of the subject.

Description

BIOMARKERS, METHODS AND KITS FOR DETECTING AND/OR SUBTYPING SMALL B-CELL LYMPHOMAS
FIELD OF INVENTION
The present invention relates generally to the field of molecular biology. In particular, the present invention relates to biomarkers associated with small B-cell lymphoma and methods of using the biomarkers to determine whether a subject suffers from or is at risk of developing small B-cell lymphoma, and/or to classify B-cell lymphoma.
BACKGROUND
Lymphoma diagnosis is well recognized as one of the most difficult areas of diagnostic pathology. The pathological diagnosis of lymphoma hinges largely upon recognition of morphological abnormalities on a well-represented tissue section. However, to many pathologists who have limited experience in examining lymphoid tissues, identification of normal and pathological changes in different lymphoid compartments and recognition of neoplastic lymphoid entities may be highly challenging. Lymphoid tissue often appears to be a morass of small and large lymphoid cells that defies recognition of cell types and functional compartmentalization. Compounding this is the complexity of lymphoma classification, often necessitating the incorporation of additional testing with a plethora of immunostains and molecular genetic investigations for definitive diagnosis, making lymphoma diagnosis one of the most complicated tasks encountered by pathologists worldwide.
Therefore, it is not surprising that errors in lymphoid tissue diagnosis are prevalent. Misdiagnoses of reactive lymphoid proliferation from neoplastic ones (and vice versa) and misclassification of neoplastic lymphoid entities can have serious consequences related to inappropriate treatments being administered to the patients. In this regard, differentiating reactive lymphoid proliferations from their mature, small-sized, or low-grade B-cell neoplastic counterparts (henceforth collectively termed small B-cell lymphomas) is particularly problematic. Small B-cell lymphomas comprise a heterogeneous admixture of small and occasionally larger lymphoid cells with only mild cytologic atypia, and some cases may even retain the tissue architecture to some degree, therefore resembling reactive lymphoid proliferations to the lesser trained eye. There is also significant morphological overlap between the different subtypes of these small B-cell lymphomas, making immunohistochemistry (IHC) and/or molecular genetic testing an integral component in the proper workup of these neoplasms.
Given the increasing gravitation towards small needle core biopsy that renders limited tissue samples, the lack of access to an adequate range of IHC in smaller hospitals as well as the lack of familiarity with ancillary molecular testing, many pathologists frequently encounter tremendous difficulties in making a confident diagnosis of such lymphoid proliferations. Tools and methods that require fewer tissue sections, low-cost and yet able to provide objective data are preferred. As such, there is a need for an alternative method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma. There is also a need for additional method of subtyping of small B-cell lymphomas. Further, there is a need for additional biomarkers or ancillary tools for accurate diagnosis and subtyping of small B-cell lymphomas as better diagnosis can lead to better treatments and clinical outcomes for patients.
SUMMARY OF INVENTION
In one aspect, the present invention relates to a method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the method comprising detecting/determining the expression level of one or more microRNAs (miRNAs) in a biological sample obtained from the subject, wherein the one or more miRNAs are selected from the group consisting of hsa-miR-139-5p, hsa-miR- 10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa- miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p; wherein an altered expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma.
In some embodiments, the method comprises determining the expression level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen or at least fourteen miRNAs.
In some embodiments, the method comprises determining the expression level of hsa-miR-139-5p, hsa- miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, the control is selected from the group consisting of a healthy subject, a nondiseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, and/or a subject not suffering from, or not at risk of developing small B- cell lymphoma.
In some embodiments, wherein when the subject is determined to suffer from, or at risk of developing small B-cell lymphoma, the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs selected from the group consisting of the hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR- 20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR- 224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of the subject suffering from one or more subtypes of small B-cell lymphoma selected from the group consisting of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low- grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments, the method comprises determining the expression level of hsa-miR-139-5p, hsa- miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, the second control is selected from a subject suffering from, or at risk of developing, one or more subtypes of small B-cell lymphoma selected from the group consisting of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma. The controls may be selected from subjects suffering from one or more subtypes of B-cell lymphoma that are different and/or same as the subtype to be classified. In some embodiments, the control may comprise one or more biological samples obtained from the above subjects. In some embodiments, the control sample may not be obtained at same time as the biological sample from the subject to be tested and may be represented by a control sample provided with the kit or in some respect, a threshold for the expression of a miRNA or expression level of the one or more miRNAs/biomarkers in a control population determined in an earlier clinical study.
In some embodiments, the method further comprises using the excision site of the biological sample (e.g., tissue sample) to determine if the subject is suffering from, or at risk of developing, one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma, optionally wherein the excision site is nodal or extranodal.
In some embodiments, the biological sample is a tissue sample (e.g. biopsy tissue sample) or a non- cellular bodily fluid sample (e.g. plasma or serum).
In some embodiments, the small B-cell lymphoma comprises one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments of the above aspects, the method further comprises performing one or more procedures to determine: (a) the morphology and cell distribution of the biological sample; and/or (b) the expression level, presence or absence of one or more additional biomarkers in the biological sample.
In some embodiments, the method further comprises performing one or more procedures selected from histopathology, immunohistochemistry, immunophenotyping and/or clonality test to determine:
(a) the morphology and cell distribution of the biological sample; and/or (b) the expression level, presence or absence of one or more additional biomarkers in the biological sample, optionally wherein the biomarker is selected from the group consisting of CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6.
In some embodiments, the one or more miRNAs are detected by one or more method selected from sequencing, nucleic acid hybridisation, microarray and nucleic acid amplification.
In another aspect, there is provided a kit for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the kit comprising:
(a) an isolated set of probes and/or reagents capable of detecting the expression level of one or more miRNAs selected from the group consisting of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR- 126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa- miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p; and
(b) instructions for use.
In some embodiments, the isolated set of probes and/or reagents are capable of detecting the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, the kit further comprises:
(a) an isolated set of probes and/or reagents capable of detecting the expression level of one or more miRNAs selected from the group consisting of the mihsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR- 25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa- miR-196a-5p; and
(b) instructions for use.
In some embodiments, the isolated set of probes and/or reagents are capable of detecting the expression level of hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa- miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the small B-cell lymphoma comprises one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments, the kit further comprises an isolated set of probes and/or reagents that are capable of detecting the expression level of one or more additional biomarkers in the biological sample, optionally wherein the one or more additional biomarkers are selected from the group consisting of CD20, CD79a, PAX-5, CD3, KI67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6.
In some embodiments, the probe is selected from the group comprising an aptamer, an antibody, an affibody, a peptide, and/or a nucleic acid.
In some embodiments, the one or more miRNAs are detected by one or more method selected from sequencing, nucleic acid hybridisation, microarray and nucleic acid amplification.
BRIEF DESCRIPTION OF DRAWINGS
Fig. 1 depicts the challenges in diagnosing small B-cell lymphoma. (A) The morphologic overlap between reactive lymphoid hyperplasia and the four histological subtypes of small B-cell lymphomas (SLL, MCL, FL and MZL) presents considerable challenge to traditional morphological diagnosis and subtyping. Classification of lymphoma from reactive lymphoid hyperplasia often relies heavily on ancillary techniques such as immunohistochemistry. (B) A wide panel of immunohistochemical stains is often used by pathologists to diagnose and subtype small B-cell lymphomas. Abbreviations: RLH, reactive lymphoid hyperplasia; SLL, small lymphocytic lymphoma/chronic lymphocytic leukaemia; CLL, chronic lymphocytic leukaemia; MCL, mantle cell lymphoma; FL, low-grade follicular lymphoma; MZL, marginal zone lymphoma.
Fig. 2 depicts the distinct lymphoma subtypes can be distinguished by miRNA expression profiles in both discovery and validation cohorts. Heatmap showing hierarchical clustering of FFPE samples of 5 different subtypes in the discovery cohort (A) and validation cohort (B) based on the expression of 90 candidate miRNAs. Principal component analysis of miRNA expression profiles of FFPE samples in the discovery (C) and validation (D) cohort. (E) Comparison of the expression changes of the 90 candidate miRNAs between the discovery and validation cohorts. (F) Numbers of miRNAs showing consistent or inconsistent changes between discovery and validation cohorts. Abbreviations: SLL, small lymphocytic lymphoma/chronic lymphocytic leukaemia; CLL, chronic lymphocytic leukaemia; FL, low-grade follicular lymphoma; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma; RL, reactive lymphoid proliferations
Fig 3 depicts using miRNA expression as a classifier to distinguish lymphoma from reactive lymphoid proliferation (RL). (A) ROC curves from the test dataset of 100 times of four-fold cross-validation using the 90-miRNA classification model to differentiate lymphoma samples and RL. The black line indicates the average ROC curve. (B) Performance metrics of the classification model to distinguish lymphoma from RL. (C) Confusion matrix of the classification model from 100 times of four-fold cross-validation. (D) Classification accuracy for distinguishing lymphoma and RL with respect to the increasing number of miRNA features included in the classification model. Abbreviations: ROC, receiver operating characteristic; AUG, area under the curve. Fig. 4 depicts using miRNA expression as a classifier to distinguish 4 subtypes of small B-cel I lymphoma. (A) Confusion matrix based on the average classification performance of four lymphoma subtypes in the test datasets of 100 iterations of four-fold cross-validation. (B) Performance comparison between three algorithms (random forest, SVM with linear kernel and SVM with radial kernel) in training the classification model. (C) Classification accuracy for distinguishing between the four subtypes with respect to the increasing number of miRNA features included in the classification model. Abbreviations: SLL, small lymphocytic lymphoma/chronic lymphocytic leukaemia; CLL, chronic lymphocytic leukaemia; FL, low-grade follicular lymphoma; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma: RL, reactive lymphoid proliferations.
Fig. 5 depicts differentially regulated pathways between lymphomas and reactive lymphoid proliferation based on deregulated miRNAs using MIRSEA analysis. (A) KEGG pathways significantly upregulated in lymphoma compared to reactive lymphoid proliferation via miRNA regulation. (B) Cytosolic DNA sensing pathway as the most significantly enriched KEGG pathway for miRNAs de-regulated between lymphoma and RL groups. (C) Reactome pathways are significantly upregulated in lymphoma compared to RL via miRNA deregulation. (D) Gaia/i 3 signalling events as the most significantly enriched reactome pathway for miRNAs de-regulated between lymphoma and RL groups. Abbreviations: KEGG, Kyoto encyclopaedia of genes and genomes; FDR, false discovery rate.
Fig. 6 depicts a proposed diagnostic algorithm incorporating miRNA-based classifiers for the workup of lymphoid proliferation morphologically suspicious of small B-cell lymphoma. Abbreviations: miRNA, microRNA; SLL, small lymphocytic lymphoma/chronic lymphocytic leukaemia; CLL, chronic lymphocytic leukaemia; FL, low-grade follicular lymphoma; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma
DEFINITIONS
As used herein, the term “miRNA” refers to microRNA, small non-coding RNA molecules, which in some examples contain about 19 to 25 nucleotides, and are found in plants, animals and some viruses. miRNAs are known to have functions in RNA silencing and post-transcriptional regulation of gene expression. These highly conserved RNAs regulate the expression of genes by binding to the 3'- untranslated regions (3'-UTR) of specific mRNAs. For example, each miRNA is thought to regulate multiple genes, and since hundreds of miRNA genes are predicted to be present in higher eukaryotes, miRNAs tend to be transcribed from several different loci in the genome. These genes encode for long RNAs with a hairpin structure that when processed by a series of RNase III enzymes (including Drosha and Dicer) form a miRNA duplex of usually about 19 to 25 nucleotides long with 2nt overhangs on the 3’ end. As would be appreciated by the skilled person in the art, miRNA is a type of polynucleotide that has sequences comprising letters such as “AUGC." It will be understood that the nucleotides are in 5’ > 3’ order from left to right and that “A” denotes adenosine, “U” denotes uracil, “G” denotes guanosine, and “C” denotes cytosine, unless otherwise noted. The letters A, U, G, and C can be used to refer to the base themselves.
As used herein, “small B-cell lymphoma” is a morphological designation to a group of B-cell lymphomas comprising mainly of small lymphocytes or a clonal population of small lymphoid cells. They are also commonly classified as “low-grade" (i.e. slow growing), and therefore, can be referred to as “low-grade B-cell lymphoma”, “low-grade non-Hodgkin lymphoma” and “indolent B-cell lymphoma”. Different subtypes and classifications of small B-cell lymphomas are known and discussed in the 5th Edition of the World Health Organization Classification of Haematolymphoid Tumours (Leukaemia, 2022, 36, pgs 1720-1748). The World Health Organization (WHO) classification of lymphomas is based on a combination of clinical, morphologic, immunophenotypic, and molecular genetic features. In the WHO classification, three major categories are recognised for the B-cell group: tumour-like lesions with B-cell predominance, precursor and mature B-cell neoplasms. Among mature B-cell lymphoma neoplasms, those composed of small lymphoid cells are regarded as “small B-cell lymphomas”. B-cell lymphomas can also be diagnostically classified into Hodgkin and non-Hodgkin lymphomas, though most are nonHodgkin lymphomas, which include Burkitt lymphoma, chronic lymphocytic leukaemia/small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, etc.
As used herein, “small lymphocytic lymphoma”, “chronic lymphocytic leukaemia" and abbreviations “SLL”, “CLL” and “CLL/SLL”, refer to forms of indolent low grade (slow growing) non-Hodgkin lymphoma that arise from white blood cells called lymphocytes. CLL and SLL are generally regarded as the same disease, with the only difference being the location where the cancer primarily occurs. When most of the cancer cells are located in the bloodstream and the bone marrow, the disease is referred to as CLL. When the cancer cells are located mostly in the lymph nodes and are rare in the blood, the disease is called SLL.
As used herein, “low-grade follicular lymphoma”, “follicular lymphoma” and abbreviation “FL” refer to a type of non-Hodgkin lymphoma (usually slow-growing, i.e. low-grade) that develops from B-cells. FL originates from germinal/follicular centre B-cells, and is usually characterised by the abnormal B-cells developing in clumps called ‘follicles’ inside lymph nodes.
As used herein, “mantle cell lymphoma” and abbreviation “MCL” refer to a lymphoma of B-cell lymphocytes, which forms in B-cells from the mantle zones of lymph nodes. The mantle zone is the outer ring of small lymphocytes surrounding the center of a lymphatic nodule. MCL is a rare B-cell non- Hodgkin lymphoma, which usually starts out in a more indolent (slow-growing) manner but can become more aggressive (fast-growing) over time. MCL is usually diagnosed as a late-stage disease and is often present in the gastrointestinal tract, bone marrow, bloodstream, and other non-lymph node sites. As used herein, “marginal zone lymphoma” and abbreviation “MZL” refer to a group of indolent (slow- growing) B-cell non-Hodgkin lymphomas beginning in a part of lymph tissue called the marginal zone. There are various types of MZL, of which mucosa-associated lymphoid tissue (MALT) lymphoma or extranodal MZL is the most common. MALT lymphomas usually arise in organs that are devoid of lymphoid tissue. MALT lymphomas are most commonly found in the stomach (called gastric MALT) but can also occur in other organs (called non-gastric MALT) like the small intestine, salivary glands, thyroid, breast, around the eye (ocular adnexa lymphoma [OAL]), lung and skin. Very frequently, MALT lymphomas arise secondary to chronic inflammation caused by infection (with bacteria or viruses) or autoimmune conditions (such as Hashimoto’s thyroiditis or Sjogren’s syndrome). Other types of MZL includes nodal MZL and splenic MZL. Nodal MZL is a rare type of MZL (30% of all MZL cases) that occurs within the lymph nodes. Splenic MZL is the rarest form of MZL (9% of all cases) and occurs most often in the spleen, blood, and bone marrow, and has been associated with hepatitis C virus (HCV) infection.
As used herein, “reactive lymphoid proliferation”, “reactive lymphoid hyperplasia”, “reactive lymphoid”, and abbreviation “RL” refer to benign conditions that involve nodular lesion characterized by marked proliferation of non-neoplastic, polyclonal lymphocytes forming follicles. These are also regarded as benign lymphoproliferative lesions which may be found in various organs such as skin, orbit, lung, gastrointestinal tract, and liver. Cause of reactive lymphoid may be due to antigenic stimulation which can result in proliferation of B-, T-cells or other specialized cells with the expansion of their corresponding anatomical compartments leading to subsequent nodal enlargement.
As used herein, “classifying small B-cell lymphoma” refers to determining the subtype(s) of small B-cell lymphoma, and/or determining whether a subject is suffering from or at risk of developing one or more subtypes of small B-cell lymphoma. In certain embodiments, the subject may have been pre-determined to be suffering from, or at risk of developing small B-cell lymphoma. In some embodiments, the subject may not have been determined to be suffering from, or at risk of developing small B-cell lymphoma. The four main subtypes of small B-cell lymphoma as described in the present invention includes, but not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma.
As used herein, “biomarker” or “marker” can refer to a gene, protein, or miRNA whose level of expression or concentration in a sample is altered compared to that of a control. As used herein, a control refers to a level of expression or concentration of a biomarker that is indicative or correlated with a different outcome compared to the outcome of interest. For example, a biomarker can be a miRNA whose level of expression or concentration is altered (e.g., increased or decreased) compared to that of a control in a sample of a subject with a condition (i.e., small B-cell lymphoma, associated subtypes or reactive lymphoid proliferation). The control can also be the average or mean level of expression or concentration of the miRNA in samples of a group of control subjects (i.e. negative control) who do not have a condition (i.e. small B-cell lymphoma, SLL/CLL, FL, MCL, MZL or other small B-cell lymphoma subtypes) or non-cancer condition such as reactive lymphoid proliferation. The control subject (i.e. positive control) may include a subject diagnosed with one or more conditions, such as but not limited to, small B-cell lymphoma, SLL/CLL, FL, MCL, MZL or other small B-cell lymphoma subtypes.
It is understood by anyone with the relevant skill in the art that comparing the level of expression in the control does not necessarily entail obtaining a sample from a subject without small B-cell lymphoma and testing said sample at the same time as the test subject. In some embodiments, said control could be a control sample incorporated in the kit or a threshold set to represent the range of expression of the biomarker where expression levels falling into this range would identify a subject as suffering from, or is at risk of developing small B-cell lymphoma.
As used herein, “biological sample” or “sample” is intended to include any sampling of cells, cell extracts, tissues, organs, or bodily fluids isolated from a subject in which expression of a biomarker can be detected. Examples of such biological samples include, but are not limited to, biopsies, smears, blood, lymph, urine, saliva, or any other bodily secretion or derivative thereof. Accordingly, a sample can include without limitation a cryosection of a fresh frozen biopsy, a formalin-fixed paraffin embedded (FFPE) tissue biopsy, a cryopreserved diagnostic cell suspension, peripheral blood, a plasma or serum sample. In some embodiments, a biological sample may be isolated from a subject having small B-cell lymphoma, or a sub-group or sub-type of a B-cell lymphoma, such as, but not limited to CLL/SLL, FL, MCL and MZL etc. A biological sample may be from a cell or tissue known to be cancerous, non- cancerous or suspected of being cancerous. In some embodiments, the sample may be from a subject suffering from a non-cancerous disease, such as reactive lymphoid proliferation. Blood can, for example, include whole blood, plasma, serum, or any derivative of blood. In some embodiments, the biological sample is a liquid biological sample. In some embodiments, the biological sample is a non-cellular biological fluid. In some embodiments, the non-cellular bodily fluid may comprise serum and/or plasma. Samples can be obtained from a subject by a variety of techniques, which are known to those skilled in the art.
As used herein, a “subject" may be a human, non-human primate, rat, mouse, cow, horse, pig, sheep, goat, dog, cat, etc. The subject may be a clinical patient, a clinical trial volunteer, an experimental animal, etc. The subject may be suspected of having or at risk for having small B-cell lymphoma or reactive lymphoid proliferation, or be diagnosed with a small B-cell lymphoma or reactive lymphoid proliferation.
As used herein, the term “expression level” or “level” of a biomarker refers to the presence/absence, amount or concentration of the biomarker in a biological sample, and may be represented in any suitable forms or units as determined by a person skilled in the art. For example, the expression level of nucleic acid (e.g. DNA or RNA) may be represented as, but not limited to, copy number (copy/mL), Ct (cycle threshold), Cq (quantification cycle), Ct/Cq, Iog2 scale expression levels or relative to other expression levels. In addition, the expression level may be expressed as a score constructed using any form of mathematical model or algorithm. In addition, the expression level of a biomarker may be derived from hierarchical clustering of various level biomarkers with respect to the disease indications and/or subtypes (visualised through heatmaps as shown, for example in Fig. 2A and B).
As used herein, the term ‘differential expression” or “altered expression” may be used interchangeably and refers to the measurement of a cellular component in comparison to a control or another sample, and thereby determining the difference in, for example, concentration, presence or intensity of said cellular component. The result of such a comparison can be given in the absolute, that is a component is present in the samples and not in the control, or in the relative, that is the expression or concentration of component is increased or decreased compared to the control. The terms “increased” and “decreased” in this case can be interchanged with the terms “upregulated” and “downregulated” which are also used in the present disclosure. In addition, the differential or altered expression of a biomarker may be derived from hierarchical clustering of various level biomarkers with respect to the disease indications and/or subtypes (visualised through heatmaps as shown, for example in Fig. 2A and B).
As used herein, “probe” refers to any molecule or agent that is capable of selectively detecting an intended target biomolecule, for example, by binding directly or indirectly to the target biomolecule. The target molecule can be a biomarker, for example, a nucleotide transcript or a protein encoded by or corresponding to a biomarker. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations, in view of the present disclosure. Probes can be specifically designed to be labelled. Examples of molecules that can be utilized as probes include, but are not limited to, oligonucleotides, RNA, DNA (e.g., primers), proteins, peptides, antibodies, aptamers, affibodies, and organic molecules. In the case of a probe designed for the detection of a nucleic acid biomarker, such a probe may be directed to the target region, the complementary nucleic acid sequence on the reverse strand, or copies of the same generated via an amplification process.
As used herein, the term “imaging test” relates to various non-invasive methods for visualising the inside of a subject’s body to diagnose a disease or determine the extent/progression of a disease and may include, but not limited to, ultrasound imaging, computerised tomography (CT) (including low dose CT scans such as low dose spiral CT or low dose helical CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and X-ray.
As used herein, the term “biopsy” relates to the removal of a sample of cells or tissues for examination, for example by a pathologist, to determine the presence or extent of a disease. Biopsy may include, but not limited to, an excisional or incisional biopsy, core tissue biopsy, needle biopsy (e.g. fine needle aspiration and core needle biopsies), bone marrow aspiration and biopsy, lumbar puncture (spinal tap) and pleural or peritoneal fluid sampling.
As used herein, the term “(statistical) classification” refers to the problem of identifying which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example is assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). In the terminology of machine learning, classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering and involves grouping data into categories based on some measure of inherent similarity or distance. Often, the individual observations are analysed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g., "A", "B", "AB" or "O", for blood type), ordinal (e.g., "large", "medium" or "small"), integer-valued (e.g., the number of occurrences of a part word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, which maps input data to a category (e.g. classifying a disease into the various subtypes).
As used herein, the term “pre-trained” or “supervised (machine) learning” refers to a machine learning task of inferring a function from labelled training data. The training data can consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm, that is the algorithm to be trained, analyses the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way.
As used herein, the term “score” refers to an integer or number, that can be determined mathematically, for example by using computational models a known in the art, which can include but are not limited to, SVM, as an example, and that is calculated using any one of a multitude of mathematical equations and/or algorithms known in the art for the purpose of statistical classification. Such a score is used to enumerate one outcome on a spectrum of possible outcomes. The relevance and statistical significance of such a score depends on the size and the quality of the underlying data set used to establish the results spectrum. For example, a blind sample may be input into an algorithm, which in turn calculates a score based on the information provided by the analysis of the blind sample. This results in the generation of a score for said blind sample. Based on this score, a decision can be made, for example, how likely the patient, from which the blind sample was obtained, has cancer or not. The ends of the spectrum may be defined logically based on the data provided, or arbitrarily according to the requirement of the experimenter. In both cases the spectrum needs to be defined before a blind sample is tested. As a result, the score generated by such a blind sample, for example the number “45” may indicate that the corresponding patient has cancer, based on a spectrum defined as a scale from 1 to 50, with “1 ” being defined as being cancer-free and “50” being defined as having cancer. DETAILED DESCRIPTION
MiRNAs are evolutionary conserved, single-stranded non-coding RNAs of 19 to 25 nucleotides which primarily function in mediating the degradation or translational repression of mRNA targets. Under normal physiological conditions, miRNAs are key components of feedback mechanisms for a wide range of biological pathways such as cell proliferation, differentiation, and apoptosis. Conversely, dysregulated miRNAs have been implicated in the hallmarks of cancer including supporting tumour growth by inhibiting growth suppression, sustaining proliferative signalling and resisting cell death, activating invasion and metastasis, and promoting angiogenesis. It is now known that miRNAs regulate oncogenesis through their tumour suppressor or oncogenic activities, with increasing evidence of aberrant miRNA expression in a variety of malignancies.
Diagnosing small B-cell lymphoma accurately is challenging. Current gold standard for lymphoma diagnosis is based on histopathological evaluation where tissue morphology is the foundation. However, morphological similarities between common subtypes of small B-cell lymphomas, which predominantly consist of small lymphoid cells with condensed chromatin (Fig. 1 A), mandate the integration of immunohistochemistry (IHC) to reach a diagnosis. However, the limited specificity and sensitivity of individual IHC marker invariably necessitate a large panel of immunostains to be used (Fig. 1 B), which in turn increases the diagnostic cost and amount of tissue sections required.
MiRNAs are deemed suitable as biomarkers because of altered miRNA expression profiles in cancer that reflect disease development, as well as the stability and the accessibility of miRNAs in tissues and in a myriad of body fluids including blood, urine and saliva. As such, miRNA-based biopsies, can potentially overcome some of these disadvantages and improve overall detection accuracy.
Inventions are described herein for differentiating small B-cell lymphoma and reactive lymphoid proliferation, classifying subtypes of small B-cell lymphoma, as well as using the results of classification for diagnosis and/or therapy selection. Example classifiers allow differentiation of small B-cell lymphoma from reactive lymphoid proliferation and to determine a subtype of small B-cell lymphoma based on expression levels of one or more miRNAs in a biological sample obtained from a subject with small B-cell lymphoma. By effectively determining subtype of small B-cell lymphoma, the classifiers provide meaningful output for the benefits of medical practices and small B-cell lymphoma patients.
The present invention relates to methods of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the methods comprises detecting and/or measuring and/or determining the expression level of one or more biomarkers I microRNAs (miRNAs) present in a biological sample obtained from the subject, specifically the miRNAs listed in Table 1 , wherein an altered / differential expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma. Said methods disclosed herein may be used to differentiate a subject suffering from, or is at risk of developing small B-cell lymphoma or reactive lymphoid proliferation.
Also disclosed herein are methods of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, the method comprises detecting and/or measuring and/or determining the expression level of one or more biomarkers I microRNAs (miRNAs) present in a biological sample obtained from the subject, specifically the miRNAs listed in Table 1 , wherein an altered I differential expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma. Said methods disclosed herein are used to classify small B-cell lymphoma in a subject into one or more subtypes of B-cell lymphoma, which include, but not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma. Advantageously, the current method allows efficient subtyping of small B-cell lymphoma into four different subtypes (for example, using algorithm or classification models) which would be far more efficient than a typical binary classification (that requires at least six binary comparisons).
As will be appreciated, the expression levels of the one or more miRNAs or the altered/differential expression levels of the one or more biomarkers/miRNAs may be determined from hierarchical clustering of various level biomarkers/miRNAs with respect to the disease indications and/or subtypes (visualised through heatmaps as shown, for example in Fig. 2A and B). In other words, the hierarchical clustering of the miRNAs and/or heatmaps (which provide information on the expression levels of each miRNA) may be used in determining whether a subject suffers from, or is at risk of developing small B- cell lymphoma. Further, such clustering and/or heatmaps may be used to classify small B-cell lymphoma in a subject into one or more subtypes of B-cell lymphoma, which include, but not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
As will be appreciated, the biomarkers, methods and kits described herein for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma may be practised alone, together or sequentially, with those described for classifying subtypes of B-cell lymphoma in a subject. By way of a non-limiting example, Fig. 6 shows a proposed diagnostic algorithm utilizing the two sets of miRNA biomarkers in a sequential workflow for diagnosing and classifying subtypes of B-cell lymphoma in a subject.
Table 1. Sequences of the miRNA biomarkers described herein in developing the classification model.
Accession numbers and sequences were obtained from miRbase.
Figure imgf000015_0001
Figure imgf000016_0001
In one aspect, the present invention relates to methods of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the methods comprises detecting and/or measuring and/or determining the expression level of one or more biomarkers I microRNAs (miRNAs) present in a biological sample obtained from the subject, wherein the one or more miRNAs may include, but is not limited to hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and wherein an altered / differential expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma.
In some embodiments, the expression level of the one or more miRNAs may include but is not limited to hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like, is compared to the expression level of the one or more miRNAs in a control, wherein an altered expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from small B-cell lymphoma.
In some embodiments, the method may be used for differentiating whether a subject suffers from, or is at risk of developing small B-cell lymphoma or reactive lymphoid proliferation.
In some embodiments, the method comprises detecting/determining the expression level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen or at least fourteen miRNAs.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen miRNAs. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs that may include, but is not limited to, hsa- miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa- miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-10b-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-126-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-29b-2-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-126-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-145-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-143-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-224-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-224-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-9- 3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-9-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa- miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-125a-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-342-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to,hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-29a-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to,hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-223-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-223-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to,hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-335-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-335-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to,hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p and hsa-miR-10b-5p. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p and hsa-miR-126-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p and hsa-miR-29b-2-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p and hsa- miR-126-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p and hsa-miR-145-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p and hsa-miR-143-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p and hsa-miR-224-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p and hsa-miR-9-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p and hsa-miR-125a-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p and hsa- miR-342-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p and hsa-miR-29a-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p and hsa-miR-223-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa- miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p in a biological sample obtained from the subject, and comparing the expression of these one or more miRNAs with that in a control, wherein an altered / differential expression levels of the one or more miRNA, as compared to the control, is indicative of the subject having or is at risk of developing small B-cell lymphoma.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma and/or one or more subtypes of small B-cell lymphoma, and classifying small B-cell lymphoma, comprises detecting and/or measuring and/or determining the expression level of hsa-miR-126-3p, hsa-miR-224-5p and hsa-miR-9-3p. In some embodiments, the miRNAs may include hsa-miR-126-3p, hsa-miR-224-5p and/or hsa-miR-9-3p.
In some embodiments, the invention relates to a biomarker combination suitable for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, wherein the combination comprises at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen or fourteen biomarkers may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR- 126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa- miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like. In some embodiments, the biomarker combination comprises hsa-miR-139-5p, hsa-miR-1 Ob- 5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR- 224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa- miR-335-5p.
In some embodiments, the invention also relates to use of at least one reagent suitable for detecting one or more biomarkers in the manufacture or preparation of a diagnostic agent/kit for use in any of the above method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma. In some embodiments, the at least one reagent is for use in measuring/determining in a biological sample obtained from a subject the expression level of at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen or fourteen biomarkers may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like. In some embodiments, the reagent is for use in measuring/determining the expression level of biomarkers comprising hsa-miR-139-5p, hsa-miR- 10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa- miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises:
(a) obtaining a biological sample from a subject;
(b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs that may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs are may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like;
(c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control;
(d) determining if said subject suffers from, or is at risk of developing small B-cell lymphoma by the differential expression of the one of more miRNAs compared to the control.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma comprises:
(a) obtaining a biological sample from a subject; (b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs may include, but is not limited to,hsa- miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like;
(c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control;
(d) determining if said subject is suffers from, or is at risk of developing small B-cell lymphoma by the differential expression of the one of more miRNAs compared to the control.
In some embodiments, the control may include but is not limited to, a healthy subject, a non-diseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like. In some embodiments, the controls may include but is not limited to one or more positive controls from a diseased subject or a subject suffering from, or at risk of developing small B-cell lymphoma and/or any of the subtypes such as small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like. In some embodiments, the control may include but is not limited to, one or more biological samples obtained from the above subjects. In some embodiments, the control sample may not be obtained at same time as the biological sample from the subject to be tested and may be represented by a control sample provided with the kit or in some respect, a threshold for the expression of a miRNA or expression level of the one or more miRNAs/biomarkers in a control population determined in an earlier clinical study.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs in a biological sample obtained from the subject, wherein the one or more miRNAs may include, but is not limited to, the miRNAs listed in Table 1 .
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs in a biological sample obtained from the subject, wherein the one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , and wherein an altered / differentiated expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma.
In some embodiments, the one or more subtypes of B-cell lymphoma may include but is not limited to small lymphocytic lymphoma / chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like. In some embodiments, the small B-cell lymphoma may include but is not limited to one or more of small lymphocytic lymphoma / chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs, wherein the miRNA comprises hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625- 5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a- 2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa- miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen miRNAs, that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the invention relates to a biomarker combination suitable for determining whether a subject suffers from, or is at risk of developing subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma, wherein the combination comprises at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen or fourteen biomarkers, that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p, and the like. In some embodiments, the biomarker combination may include, but is not limited to hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR- 181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like. In some embodiments, the invention also relates to use of at least one reagent suitable for detecting one or more biomarkers in the manufacture or preparation of a diagnostic agent/kit for use in any of the above method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject. In some embodiments, the at least one reagent is for use in measuring/determining in a biological sample obtained from a subject the expression level of at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen or fourteen biomarkers, may include, but is not limited to, hsa-miR-151 a- 5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa- miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa- miR-126-3p and hsa-miR-196a-5p, and the like. In some embodiments, the reagent is for use in measuring/determining the expression level of biomarkers comprising hsa-miR-151 a-5p, hsa-miR-340- 5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa- miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR- 363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR- 126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method comprises detecting/determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p in the biological sample. In some embodiments, the method comprises detecting/determining the expression level of all 14 miRNAs such as hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363- 3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126- 3p and hsa-miR-196a-5p in the biological sample.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625- 5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 340-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to,hsa-miR-151 a-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR- 20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR- 224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 181 a-2-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 20b-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs, that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 106b-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 363-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 25-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 625-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 9-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 224-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-9-3p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 183-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-9-3p, hsa-miR-224-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 126-3p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-196a-5p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 196a-5p and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625- 5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one more subtype of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p and hsa-miR-340-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p and hsa-miR-181 a-2-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p and hsa-miR-151 a-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p and hsa-miR-20b-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p and hsa-miR-106b- 5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p and hsa-miR-363-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p and hsa-miR-25-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p and hsa-miR-625-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p and hsa-miR-9-3p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p and hsa-miR-224-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p and hsa-miR-183- 5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR- 363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p and hsa- miR-126-3p
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of hsa-miR- 151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classing small B-cell lymphoma in a subject comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs, wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363- 3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126- 3p and hsa-miR-196a-5p in a biological sample obtained from the subject, and comparing the expression of these one or more miRNAs with that in a control, wherein differential expression levels of the one or more miRNA, as compared to the control, is indicative of the subject having or is at risk of developing small B-cell lymphoma.
In some embodiments, the expression level of the one or more miRNAs, wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa- miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like, wherein the one or more miRNA is compared to the expression level of the one or more miRNAs in a control, wherein an altered expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering, or is at risk of developing one or more subtypes of small B-cell lymphoma, wherein the small B-cell lymphoma may include but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
(a) obtaining a biological sample from a subject;
(b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs that may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like;
(c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control;
(d) determining if said subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like, by the differential expression of the one or more miRNAs compared to the control.
In some embodiments, provided herein are methods of determining whether a subject is suffering from, or at risk of developing small lymphocytic lymphoma/chronic lymphocytic leukaemia (SLL/CLL) compared to other subtypes of small B-cell lymphoma in a subject in need thereof. The methods can, for example, comprise: (a) obtaining a biological sample from a subject; (b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs that may include but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR- 181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; (c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control; (d) determining if said subject suffers from, or is at risk of developing SLL/CLL compared to other subtypes of small B-cell lymphoma. In some embodiments, the subject suffering from or is at risk of developing SLL/CLL may have been determined to be suffering from, or at risk of developing small B-cell lymphoma.
In some embodiments, provided herein are methods of determining whether a subject is suffering from, or at risk of developing low-grade follicular lymphoma (FL) compared to other subtypes of small B-cell lymphoma in a subject in need thereof. The methods can, for example, comprise: (a) obtaining a biological sample from a subject; (b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs that may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa- miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; (c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control; (d) determining if said subject suffers from, or is at risk of developing FL compared to other subtypes of small B-cell lymphoma. In some embodiments, the subject suffering from or at risk of developing FL may have been determined to be suffering from or at risk of developing small B-cell lymphoma.
In some embodiments, provided herein are methods of determining whether a subject is suffering from, or at risk of developing mantle cell lymphoma (MCL) compared to other subtypes of small B-cell lymphoma in a subject in need thereof. The methods can, for example, comprise: (a) obtaining a biological sample from a subject; (b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR- 20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR- 224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; (c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control; (d) determining if said subject is suffering from, or is at risk of developing MCL compared to other subtypes of small B-cell lymphomas In some embodiments, the subject suffering from or at risk of developing MCL may have been determined to be suffering from, or at risk of developing small B- cell lymphoma. In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
(a) obtaining a biological sample from a subject;
(b) contacting the biological sample with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs, wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR- 151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa- miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like;
(c) comparing the expression level of the one or more miRNAs in the biological sample with the level of the same miRNA in a control;
(d) determining if said subject is suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like, by the differential expression of the one of more miRNAs compared to the control.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
(a) detecting I determining the expression level of a first set of miRNAs in a biological sample obtained from a subject, wherein the first set of miRNAs comprises one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the or more miRNAs may include, but is not limited to, hsa-miR-139-5p, hsa- miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and wherein an altered expression level of the first set of miRNAs, as compared to a first control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma;
(b) wherein when the subject is determined to suffer from, or at risk of developing small B- cell lymphoma, the method further comprises detecting / determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa- miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363- 3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126- 3p, hsa-miR-196a-5p, and the like; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of the subject suffering from one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises:
(a) determining the expression level of a first set of miRNAs in a biological sample obtained from a subject, wherein the first set of miRNAs comprises one or more miRNAs that may include, but is not limited to, hsa-miRNA- 139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and wherein an altered expression level of the first set of miRNAs, as compared to a first control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma;
(b) wherein when the subject is determined to suffer from, or at risk of developing small B- cell lymphoma, the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa- miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of the subject suffering from one or more subtypes of small B-cell lymphoma, wherein the one more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein when the subject is determined to suffer from, or at risk of developing small B-cell lymphoma, the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR- 20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR- 224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-196a-5p, and the like; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of a subject suffering from one or more subtypes of small B-cell-lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include but is not limited to small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein when the subject is determined to suffer from, or at risk of developing small B-cell lymphoma, the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs that may include, but is not limited to hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of the subject suffering from one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein the first set of miRNAs comprises one or more miRNAs that may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and/or wherein the second set of miRNAs comprises one or more miRNAs that may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like.
In some embodiments, the first set of miRNAs comprises hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR- 126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa- miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p; and/or the second set of miRNAs comprises hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625- 5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein the method comprises determining the expression level of hsa-miR-139-5p, hsa- miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p
In some embodiments, the method further comprises using the excision site of the biological sample to determine if the subject is suffering from, or at risk of developing, one or more of small lymphocytic lymphoma / chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma, optionally wherein the excision site is either nodal or extranodal.
In some embodiments, the control may include but is not limited to a healthy subject, a non-diseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like.
In some embodiments, the control may include, but is not limited to a subject suffering from, or at risk of developing, one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma I chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the second control may include, but is not limited to, a subject suffering from, or at risk of developing, one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the first control may include, but is not limited to, a healthy subject, a nondiseased subject, a cancer-free subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing small B-cell lymphoma, and the like; and/or the second control may include, but is not limited to, a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like. The second controls may include, but is not limited to, subjects suffering from one or more subtypes of B-cell lymphoma that are different and/or same as the subtype to be classified. In some embodiments, where applicable, the controls may include but is not limited to, one or more positive controls from a diseased subject or a subject suffering from, or at risk of developing small B-cell lymphoma and/or any of the subtypes, wherein the small B-cell lymphoma and/or any of the subtypes may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma, and the like In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein the first control may include, but is not limited to, a healthy subject, a non-diseased subject, a small B-cell lymphoma-free subject, a subject suffering from reactive lymphoid proliferation, a subject not suffering from, or not at risk of developing B-cell lymphoma, and the like; and/or wherein the second control may include, but is not limited to, a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include, but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like.
In some embodiments, the method of determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject, wherein the second control may include, but is not limited to, a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma, wherein the one or more subtypes of small B-cell lymphoma may include but is not limited to, small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma.
In some embodiments, the control may include a subject suffering from, or at risk of developing, one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma. The control may include, but is not limited to, subjects suffering from one or more subtypes of B-cell lymphoma. For example, when classifying a subject suffering from small lymphocytic lymphoma/chronic lymphocytic leukaemia, the control may include, but is not limited to, subjects suffering from small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and the like. The controls may be selected from subjects suffering from one or more subtypes of B-cell lymphoma that are different and/or same as the subtype to be classified. In some embodiments, the control sample may not be obtained at same time as the biological sample from the subject to be tested and may be represented by a control sample provided with the kit or in some respect, a threshold for the expression of a miRNA in a control population determined in an earlier clinical study.
In some embodiments, the biological sample may include, but is not limited to, a tissue sample (e.g. biopsy tissue sample), a non-cellular bodily fluid sample (e.g. plasma or serum), and the like. As will be appreciated, when the biological sample is a tissue sample, the excision site of the tissue sample of interest would be determined during biopsy, tissue removal procedures etc. Information on the tissue excision site (i.e. nodal or extranodal) may be used in any of the above-mentioned methods to improve the accuracy of detecting/determining small B-cell lymphoma and/or classifying one or more subtypes of small B-cell lymphoma in a subject. For example, information on tissue excision site (i.e. nodal or extranodal) may be used in the method of classifying small B-cell lymphoma to determine if the subject is suffering from, or at risk of developing one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma.
As used herein, nodal excision sites may refer to locations of excised tissues at lymph nodes, spleen, thymus and Waldeyer’s ring (pharyngeal lymphoid ring). On the other hand, extranodal excision sites refer to locations other than lymph nodes, spleen, thymus and the pharyngeal lymphatic ring, which therefore include organs such as, but not limited to, oropharyngeal mucosa, respiratory tract, gastrointestinal tract, bladder, salivary gland, eye, thyroid, and skin tissues.
In some embodiments of the above aspects, the small B-cell lymphoma comprises from one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments, the method may further comprise performing one or more procedures to determine: (a) the morphology and cell distribution of the biological sample; and/or (b) the expression level, presence or absence of one or more additional biomarkers in the biological sample. For example, this may include determining the information of a tissue sample (e.g. lymphoid tissue) by one or more procedures may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping, clonality test, and the like; and/or determining the expression level, presence or absence of one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6, any other biomarkers relevant to small B-cell lymphoma and the associated subtypes, and the like. The additional biomarkers may be antigens, proteins and/or molecules found in or on surfaces of leukocytes and other cells relevant for the immune system, which include neutrophils, eosinophils, basophils, mast cells, monocytes, macrophages, dendritic cells, natural killer cells, and lymphocytes (B cells and T cells). For example, as shown in Fig. 6 in Example 5, lymphoid tissue with either or both altered architecture and monotonous proliferation of small lymphoid cells, as well as positive/negative CD20 immunophenotyping may provide information for determining whether a subject is suffering from, or at risk of developing small B-cell lymphoma or the associated subtypes. As will be appreciated, the tissue information of the biological sample may be included together with the expression level of one or more miRNAs of the current invention in determining whether a subject is suffering from, or at risk of developing small B-cell lymphoma or the associated subtypes.
In some embodiments, the one or more procedures may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping and/or clonality test, and the like; and/or wherein the one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2, Bcl6, and the like. In some embodiments, the method may further comprise performing one or more procedures that may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping and/or clonal ity test to determine:
(a) the morphology and cell distribution of the biological sample; and/or
(b) the expression level, presence or absence of one or more additional biomarkers in the biological sample, optionally wherein the biomarker may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2, Bcl6, and the like.
In some embodiments, the method includes one or more procedures that may include, but is not limited to, histopathology, immunohistochemistry, immunophenotyping and/or clonal ity test, and the like.
In some embodiments, the method includes one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2, Bcl6, and the like.
In some embodiments, a subject determined to be suffering from, or is at risk of developing small B-cell lymphoma or the associated subtypes, may be further tested using biopsy (e.g. an excisional or incisional biopsy, needle biopsy (e.g. fine needle aspiration and core needle biopsies), bone marrow aspiration and biopsy, lumbar puncture (spinal tap) and pleural or peritoneal fluid sampling); diagnostic imaging tests including but not limited to ultrasound imaging, X-ray, computed tomography (CT) scan, low dose CT scans, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan; and/or one or more blood tests. Methods for screening of small B-cell lymphomas are known in the art and include the latest version of the NCCN Clinical Practice Guidelines in Oncology for B-cell Lymphomas (covers Follicular Lymphoma, Mantle Cell Lymphoma, Marginal Zone Lymphoma) and Chronic Lymphocytic Leukaemia/Small Lymphocytic Lymphoma which are incorporated by reference herein in its entirety.
In some embodiments, the method comprises performing: imaging tests including but not limited to ultrasound imaging, X-ray, computerised tomography (CT) scan, low dose CT scans, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan; and/or one or more blood tests.
Also provided are methods for treating a subject determined to be suffering from B-cell lymphoma or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma using the methods disclosed herein.
In some embodiments, the methods may further include administering a therapeutically effective amount of an appropriate therapy or treatment, depending on the small B-cell lymphoma classification. In some embodiments, the present invention includes a method of treating a subject suffering from small B-cell lymphoma, or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma comprising:
(a) determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, or one or more of small lymphocytic lymphoma I chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma using the method of the present invention; and
(b) treating the subject determined to suffer from small B-cell lymphoma, or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma with one or more treatments may include, but is not limited to, an administration of an anti-cancer compound, surgery, immunotherapy, targeted therapy, radiation therapy, stem cell transplantation, and the like.
Such a subject would be referred to a medical practitioner and, where determined to be appropriate, treated with a suitable anti-cancer compound, surgery, immunotherapy, targeted therapy, stem cell transplantation or radiation therapy or combinations of these. Where a subject is diagnosed with small B-cell lymphoma, treatment options may include surgery, radiation therapy (including but not limited to external radiation therapy, intensity-modulated radiation therapy, proton therapy), or administering one or more anti-cancer compound that may include but is not limited to chemotherapy (including but not limited to cisplatin, carboplatin, paclitaxel, docetaxel, gemcitabine, vinorelbine, etoposide or pemetrexed whether alone or in combination), targeted therapy (such as drugs or monoclonal antibodies directed against specific aspects such as angiogenesis, EGFR, KRAS, ALK, NTRK, BRAF, ROS1 , etc and may include but is not limited to osimertinib, erlotinib, gefitinib, afatinib, dacotinib, crizotinib, ceritinib, dabrafenib, trametinib, bevacizumab, bevacizumab, amivantamab, cetuximab), immunotherapy (e.g. PD-1 and PDL-1 inhibitors such as pembrolizumab or nivolumab) or cell therapy (including chimeric antigen receptor T-cells). Methods for the treatment of small B-cell lymphoma are known in the art and include the latest versions of the NCCN Clinical Practice Guidelines in Oncology for B-cell Lymphomas (covers Follicular Lymphoma, Mantle Cell Lymphoma, Marginal Zone Lymphoma) and Chronic Lymphocytic Leukaemia/Small Lymphocytic Lymphoma which are incorporated by reference herein in its entirety.
In some embodiments, the method for treating a subject suffering from small B-cell lymphoma, or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma comprises:
(a) contacting a biological sample obtained from the subject with an isolated set of probes suitable for detecting/determining the expression level of one or more miRNAs that may include but is not limited to the miRNAs listed in Table 1 , Table 3 and Table 5;
(b) comparing the expression level of the one or more miRNAs in the biological sample with the level of the miRNA in a control; (c) determining if said subject is suffering from or is at risk of developing small B-cell lymphoma, or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma by the differential expression of the one of more miRNAs compared to the control;
(d) treating the subject determined to suffer from small B-cell lymphoma, or one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma with one or more treatments that may include, but is not limited to, an administration of an anti-cancer compound, surgery, immunotherapy, targeted therapy, radiation therapy, stem cell transplantation, and the like.
In some embodiments, the treatment may include, but is not limited to administration of an anti-cancer compound, surgery, immunotherapy, targeted therapy, radiation therapy, stem cell transplantation, and the like.
Also provided herein are compositions/agents/reagents for use in the methods disclosed herein. In some embodiments, such compositions/agents/reagents may include, but are not limited to, probes, antibodies, affibodies, nucleic acids, and/or aptamers. In some embodiments, the compositions can detect (or detects) the level of expression (e.g., miRNA) of a panel of biomarkers from a biological sample.
Any of the compositions can be provided in the form of a kit or a reagent mixture. By way of an example, labelled probes can be provided in a kit for the detection of a panel of biomarkers. Kits can include all components necessary or sufficient for assays, which can include, but is not limited to, target enrichment reagents, detection reagents (e.g., probes and/or fluorescent dyes), buffers, control reagents (e.g., positive and negative controls), amplification reagents, solid supports, labels, instruction manuals, calibrators, and reference materials, etc. In certain embodiments, the kit comprises a set of probes for the panel of biomarkers and a solid support to immobilize the set of probes. In certain embodiments, the kit comprises a set of probes for the panel of biomarkers, a solid support, and reagents for processing the sample to be tested (e.g., reagents to isolate the protein or nucleic acids from the sample).
In some embodiments, detection reagents (e.g., probes) suitable for use in the kit include, but are not limited to, oligonucleotides, RNA, DNA (e.g., primers), proteins, peptides, antibodies, aptamers, affibodies, and organic molecules. In the case of a probe designed for the detection of a nucleic acid biomarker, such a probe may be directed to the target region, the complementary nucleic acid sequence on the reverse strand, or copies of the same generated via an amplification process.
In some embodiments, provided herein are DNA-, RNA-, and protein-based detection methods that either directly or indirectly detect the biomarkers described herein. The present invention also provides compositions, reagents, and kits for such diagnostic purposes. The diagnostic methods described herein may be qualitative or quantitative. Quantitative diagnostic methods may be used, for example, to compare a detected biomarker level to a cut-off or threshold level. Where applicable, qualitative or quantitative diagnostic methods can also include amplification of target, signal, or intermediary.
In some embodiments, biomarkers are detected at the nucleic acid (e.g., DNA or RNA) level. For example, the amount of biomarker RNA (e.g., miRNA) present in a sample is determined (e.g., to determine the level of biomarker expression).
In some embodiments, the one or more biomarker nucleic acid (e.g., miRNA, amplified cDNA, etc.) can be detected/quantified/determined using a variety of nucleic acid techniques known to those of ordinary skill in the art, including but not limited to, sequencing, nucleic acid hybridisation (e.g., northern blot), microarray and nucleic acid amplification (e.g., quantitative reverse transcription polymerase chain reaction (qRT-PCR), reverse transcription polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), a locked nucleic acid (LNA) real-time PCR, a CRISPR-based assay, or isothermal amplification assay, and the like. An isothermal amplification assay can, for example, include, but is not limited to, a nicking endonuclease amplification reaction (NEAR) assay, a transcription mediated amplification (TMA) assay, a loop-mediated isothermal amplification (LAMP) assay, a helicase-dependent amplification (HDA) assay, a clustered regularly interspaced short palindromic repeat (CRISPR) assay, or a strand displacement amplification (SDA) assay. In some embodiments, the method used to detect miRNA biomarkers may comprise the use of the assay methodologies disclosed in WO201 1159256A1 and a kit for the detection of miRNAs may comprise the stem-loop oligonucleotides designed based on the teachings of WO2011159256A1 , the disclosure of which is incorporated herein by reference.
In some embodiments, the one or more miRNAs of the method are detected by one or more methods that may include but is not limited to sequencing, nucleic acid hybridisation, microarray, nucleic amplification, and the like.
Also provided herein are kits for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the kit comprising (a) an isolated set of probes and/or reagents capable of detecting/determining the expression level of one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and (b) instructions for use.
In some embodiments, there is provided herein a kit for for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the kit comprising an isolated set of probes and/or reagents capable of detecting/determining the expression level of one or more miRNAs may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and (b) instructions for use.
In some embodiments, the kit comprises an isolated set of probes and/or reagents capable of detecting/determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa- miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
Also provided are kits for determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject in need thereof, the kit comprising: (a) an isolated set of probes and/or reagents capable of detecting/determining the expression level of one or more miRNAs may include, but is not limited to the miRNAs listed in Table 1 , optionally wherein the one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR- 106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR- 183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; and (b) instructions for use.
In another aspect, there is provided a kit for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the kit comprising:
(a) an isolated set of probes and/or reagents capable of detecting the expression level of one or more miRNAs may include, but is not limited to, hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR- 126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa- miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p, hsa-miR-335-5p, and the like; and
(b) instructions for use.
In some embodiments, the isolated set of probes and/or reagents are capable of detecting the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR- 126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa- miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
In some embodiments, there is provided kits for classifying small B-cell lymphoma in a subject in need thereof, the kit comprising: (a) an isolated set of probes and/or reagents capable of detecting/determining the expression level of one or more miRNAs may include, but is not limited to, hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR- 106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR- 183-5p, hsa-miR-126-3p, hsa-miR-196a-5p, and the like; and (b) instructions for use. In some embodiments, the kit for classifying small B-cell lymphoma in a subject may include, but is not limited to Isolated set of probes and/or reagents capable of detecting the expression level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen miRNAs, and the like.
In some embodiments, the kit for determining whether a subject suffers from, or is at risk of developing one or more subtypes of small B-cell lymphoma, and/or classifying small B-cell lymphoma in a subject comprises isolated set of probes and/or reagents are capable of detecting/determining the expression level of hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
In some embodiments, the kits are used for determining small B-cell lymphoma or the subtypes, and/or classifying small B-cell lymphoma, wherein small B-cell lymphoma comprises one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
In some embodiments, the kit may be used for differentiating whether a subject suffers from, or is at risk of developing small B-cell lymphoma or reactive lymphoid proliferation.
In some embodiments, the kit further comprises an isolated set of probes and/or reagents that are capable of detecting/determining the expression level of one or more additional biomarkers in the biological sample. In some embodiments, the one or more additional biomarkers may include, but is not limited to, CD20, CD79a, PAX-5, CD3, KI67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2, Bcl6, and the like.
In some embodiments, the kit may include a probe that may include, but is not limited to, an aptamer, an antibody, an affibody, a peptide, a nucleic acid, and the like.
In some embodiments, the kit comprises an isolated set of probes capable of detecting/determining one or more miRNAs by one or more methods, such as, but is not limited to, sequencing, nucleic acid hybridisation, microarray, nucleic acid amplification such as a quantitative reverse transcription- polymerase chain reaction (qRT-PCR), reverse transcription-polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), a locked nucleic acid PCR, a clustered regularly interspaced short palindromic repeat (CRISPR)-based assay, isothermal amplification assay, and the like.
In some embodiments, the kit may include one or more miRNAs detected by one or more methods that may include, but is not limited to, sequencing, nucleic acid hybridisation, microarray, nucleic acid amplification, and the like. Also disclosed herein are kits when used in any of the methods as disclosed herein.
The invention illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms ''comprising", "including", "containing", etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.
The invention has been described broadly and generically herein. Each of the narrower species and subgeneric groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.
Throughout this disclosure, certain embodiments may be disclosed in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
Other embodiments are within the following claims and non-limiting examples. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.
Exemplary embodiments of the present invention are provided in the following examples. While the exemplary embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, it should be understood that the present invention is not limited to these examples.
Sample calculation of a prediction score or risk score for the risk of small B-cell lymphoma and associated subtypes It is known in the art that, biomarkers (e.g., miRNAs) can be combined to form a biomarker panel to calculate the disease risk score, for example using a linear model. An example would be to calculate such a risk score using logistic regression, a form of linear model. The prediction score may also be calculated using a classification algorithm selected from the group comprising support vector machine (SVM) algorithm, logistic regression algorithm, multinomial logistic regression algorithm, Fisher’s linear discriminant algorithm, quadratic classifier algorithm, perceptron algorithm, k-nearest neighbours algorithm, artificial neural network algorithm, random forests algorithm, decision tree algorithm, naive Bayes algorithm, adaptive Bayes network algorithm, and ensemble learning method combining multiple learning algorithms.
The challenge in the field pertains to identifying relevant biomarkers, such as circulatory miRNAs, that could be applied to identify an individual at risk of a disease such as small B-cell lymphoma. Where relevant miRNAs could be identified via exhaustive and well-designed studies, it would be within the skill of someone aware of the state of the art to apply the measured level of the relevant miRNAs in such statistical models to generate a score for the prediction of the risk of a subject having small B-cell lymphoma and/or one or more of the associated subtypes, such as small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma.
Examples of such mathematical methods used to perform the calculations are disclosed herein, for example, the calculation of a prediction score, can be, but are not limited to, support vector machine algorithm, logistic regression algorithm, multinomial logistic regression algorithm, Fisher’s linear discriminant algorithm, quadratic classifier algorithm, perceptron algorithm, k-nearest neighbours algorithm, artificial neural network algorithm, random forests algorithm, decision tree algorithm, naive Bayes algorithm, adaptive Bayes network algorithm, and ensemble learning method combining multiple learning algorithms. In one example, the calculation of the prediction score is calculated using linear models and support vector machine algorithms.
As an illustrative example, the control and subjects with small B-cell lymphoma and/or one or more of the associated subtypes have different disease risk score values calculated. Fitted probability distributions of the disease risk scores for the control and subjects with small B-cell lymphoma show a separation between the two groups can be found. Based on this prior probability and the fitted probability distributions previously determined, the probability (risk) of an unknown subject having small B-cell lymphoma (and/or one or more of the associated subtypes) can be calculated based on their disease risk score values. With higher score, the subject has higher risk of having small B-cell lymphoma (and/or one or more of the associated subtypes). Furthermore, the disease risk score can, for example, tell the fold change of the probability (risk) of an unknown subject having small B-cell lymphoma (and/or one or more of the associated subtypes) compared to, for example, the small B-cell lymphoma (and/or one or more of the associated subtypes) rate in high-risk population. In another illustrative example for determining whether a subject is suffering from, or at risk of suffering from one or more of subtypes of small B-cell lymphoma, such as small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma, a risk score may be calculated using the classification algorithm (such as support vector machine with radial or linear kernel, or random forest) for a one-vs-one subtype comparison based on the expression level of the miRNAs determined in the sample. The algorithm model may then determine the most probable subtype based on the calculated score. In a non-limiting example, in determining the one of the four subtypes of small B-cell lymphoma, all one-vs-one comparisons (i.e. binary classifiers) will generate a total of six scores, in which the most probable subtypes will then be predicted or determined by the model.
A requirement for the success of such process is the availability of high-quality data. The quantitative data of all the detected miRNAs in a large number of well-defined clinical samples including appropriate controls from symptomatic non-small B-cell lymphoma patients (disease controls), not only improves the accuracy, as well as precision, of the result, but also ensures the consistency of the identified biomarker panels for further clinical application using, for example, quantitative polymerase chain reaction (qPCR).
As an example, Formula 1 below exemplifies the use of a linear model for small B-cell lymphoma risk prediction, where the disease risk score (unique for each subject) indicates the likelihood of a subject having small B-cell. This is calculated by the summing the weighted measurements for, for example, 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, or 14 miRNAs.
Formula 1 - disease risk score = C + Sfti Ki x log2 copyjniRNAi
Whereby logz copyjniRNAi is the log transformed copy numbers in the volume of test sample of the n individual miRNAs (e.g., for the example of 14 miRNAs, n=14). Whereby, Ki - the coefficients used to weight multiple miRNA targets and C - constant, can be derived through the application of a linear model. Subjects with disease risk score lower than 0 will be considered as 0 and subjects with disease risk score higher than 100 will be considered as 100. It would be within the understanding of someone skilled in the art, knowing the identity of the relevant miRNA biomarkers, to derive the relevant disease risk scores and cut-offs to identify a subject at risk of having small B-cell lymphoma. For the avoidance of doubt, the above linear model may also be applied to determining the subtypes of small B-cell lymphoma which a subject is suffering from, or at risk of suffering. For example, the coefficient of each miRNA of the panel use for subtyping small B-cell lymphoma may be derived, with the constant derived accordingly. Each subtype will give a different risk score, and with a suitable cut-off determined accordingly, a person skilled in the art would be able to differentiate or identify the subtypes that the subject is suffering from, or at risk of suffering. A further example of such an algorithm for risk score calculation includes the use of logistic regression models:
Figure imgf000049_0001
Whereby, n is the number of individual miRNAs (e.g., for the example of 14 miRNAs, n = 14), Ki - the coefficients used to weight multiple miRNA targets, Ct - the cycle threshold value (generally defined as the number of cycles in a real-time PCR reaction required for the fluorescent signal to cross the threshold), and the constant can be derived through the application of a linear model.
A further example of such an algorithm further incorporates the use of a reference sample with known levels of assayed miRNAs to normalize the score for each assay run to account for run-to-run variations (which may be referred to as the Quantitative Reference (QR)), and in some embodiments, the mean value for the QR scores may be used to calculate the expected QR scores. Using the example provided above, an example of how such normalization may be performed is provided below. It would be within the understanding of someone skilled in the art, knowing the identity of the relevant miRNA biomarkers, to derive the relevant disease risk scores, QR scores and cut-offs to identify a subject at risk of having small B-cell lymphoma. Similarly, the above linear model may also be applied to determining the subtypes of small B-cell lymphoma which a subject is suffering from, or at risk of suffering. For example, the relevant subtype risk scores, QR scores and cut-offs of each subtype may be determined accordingly for differentiating or identifying the subtypes that the subject is suffering from, or at risk of suffering.
Figure imgf000049_0002
Whereby, n is the number of individual miRNAs (e.g., for the example of 14 miRNAs, n = 14), K - the coefficients used to weight multiple miRNA targets, Ct - the cycle threshold value (generally defined as the number of cycles in a real-time PCR reaction required for the fluorescent signal to cross the threshold and sometimes also known as Cq), and the constant can be derived through the application of a linear model, QRrun - the QR score obtained from the specific run and the QRexpected is the mean value of QR scores from multiple validation runs.
In some embodiments, the test may be used to stratify the patient into low-risk or high-risk categories based on the results derived from Ct of the target miRNAs. A low-risk result indicated that the patient is at low risk of suffering from or developing small B-cell lymphoma. A high-risk result indicated the presence of miRNAs associated with high risk of small B-cell lymphoma, and these patients are at risk of developing small B-cell lymphoma and hence should be considered for further diagnostic workup in accordance with clinical guidelines.
EXAMPLES Materials and Methods
Experimental Design
Formalin-fixed paraffin-embedded (FFPE) tissue samples from RL and the 4 histological subtypes of small B-cell lymphomas were included: SLL, FL, MCL, and MZL. The diagnosis was based on the criteria established in the World Health Organization (WHO) classification. Samples recruited comprise both excisional and core tissue biopsies from nodal and extranodal sites (Table 1 ). All cases were reviewed by pathologists with experience in haematolymphoid pathology to verify the diagnosis. Institutional Review Board (IRB) approval was obtained for all samples in accordance with IRB guidelines of the National Health Group (NHG), Tan Tock Seng Hospital (TTSH) Singapore and University Malaya Medical Centre (UMMC).
A total of 382 subjects were included in this study. A discovery set of 100 FFPE tissue samples was obtained from the Department of Pathology, National University Hospital (NUH), Singapore: SLL (n=23), FL (n=21 ), MCL (n=20), MZL (n=19), and RL (n=17). Whole tissue sections of the recruited samples were used, and the percentage of tumour cells was estimated to be 50% or more for each sample. A validation set of 282 FFPE tissue samples comprising SLL (n=20), FL (n=74), MCL (n=22), MZL (n=74), and RL (n— 92) was further collected from three different institutions: NUH, TTSH and UMMC. Samples were classified and analyzed using miRNA expression profiling and the results were compared to the reference diagnosis.
Table 2. Tissue sites of recruited samples in discovery and validation cohorts
Figure imgf000050_0001
’Extranodal sites include oropharyngeal mucosa, respiratory tract, gastrointestinal tract, bladder, salivary gland, eye, thyroid, and skin tissues. SLL= small lymphocytic lymphoma/chronic lymphocytic leukaemia; MCL= mantle cell lymphoma; FL= low-grade follicular lymphoma; MZL= marginal zone lymphoma
RNA Isolation, Reverse-Transcription, cDNA Amplification, and Real-time gPCR Total RNAs were isolated from FFPE tissues using the miRNeasy FFPE miRNA isolation kit (Qiagen, Germany) according to the manufacturer's protocol. Three synthetic short RNA species (spikeins) with sequences distinct from endogenous human miRNAs were added into the lysis buffer as controls to monitor and normalize for workflow variations. The miRNA was eluted using 50 pL nuclease- free water. Total RNA quantity and quality were measured by NanoDrop 2000 (Thermo-Fisher Scientific, USA). For each sample, 900ng total RNA was used for subsequent reverse-transcription and PCR reactions.
MiRNA profiling was performed using a multiplexed RT-qPCR platform following an established protocol (Zou R, et al. Cancers 2021 , 13, 2130). Isolated miRNAs underwent reverse transcription using the in-house reverse transcription system and modified stem-loop RT primer pools (MIRXES, Singapore) on a VeritiTM Thermal Cycler (Applied Biosystem, USA) according to the manufacturer's instructions. For each RT reaction, a standard panel comprising a series of six 10-fold dilutions of synthetic miRNA and two no-template controls (NTCs) were included on the same plate. cDNA was then pre-amplified using a 14-cycle PCR reaction with Augmentation Primer Pools (MIRXES, Singapore) on the VeritiTM Thermal Cycler (Thermo-Fisher Scientific, USA). Single qPCR was performed on the amplified cDNA samples using a miRNA-specific qPCR assay and ID3EAL miRNA qPCR Master Mix according to the manufacturer’s instruction (MiRXES, Singapore). The qPCR reaction for each sample was performed with technical duplicates on the QuantStudio 5 Real-Time PCR System (Applied Biosystem, USA). Raw threshold cycle (Ct) values were calculated using the QuantStudioTM Design and analysis software with an automatic baseline setting and a threshold of 0.4.
The synthetic spike-ins added at various stages were used to correct for variations in RNA isolation and RT-qPCR efficiency. The Ct values generated from the panel of 6 serially diluted synthetic miRNAs were then used to generate a standard curve. The absolute expression of each miRNA (number of copies present) was calculated by interpolation of the standard curve. MiRNA with a Ct value higher than NTCs (no template controls) was deemed undetectable and removed from the analysis.
Data processing and statistical analysis
MiRNA expression levels were calculated as Iog2 copy numbers in each sample. In the discovery cohort, miRNAs that were not detected in >10% of samples were removed, resulting in 306 miRNAs in 100 samples. Biological normalization was performed by the global normalization approach demonstrated by Mestagh et al. (Nucleic Acids Res. 2008, 36, 143), followed by Z-score standardization of each miRNA’s expression. For each subtype, Student's t-test was used to compare the miRNA expression between the subtype of interest and other subtypes (one-vs-rest). The top 3 up- and 3 down- regulated miRNAs were selected based on the most significant P-value, resulting in a total of 30 subtype biomarkers for five subtypes. For each pair of the five subtypes, the same Student’s t-test was performed and the top 3 up- and 3 down-regulated miRNAs between the two subtypes (one-vs-one) were identified, adding up to 60 miRNA markers that differentiated the two subtypes. Finally, 10 miRNAs that displayed the lowest variance across samples were selected as the housekeeping miRNAs. A 100- miRNA customized panel including 30 one-vs-rest biomarkers, 60 one-vs-one markers, and 10 housekeeping miRNAs was constructed and used for the FFPE samples in the validation cohort.
RNA from additional 282 FFPE tissues were profiled using the 100-miRNA customized panel and all expression levels were obtained as Iog2 copies/sample. The mean expression level of the 10 housekeeping miRNA was used to normalize both the discovery and validation cohort to ensure comparability. As the FFPE samples in the validation cohort were collected from three different sites (NUH, TTSH, and UMMC), the batch correction was performed using the ComBat approach (Biostat Oxf Engl. 2007, 8, 118-27), setting the collection site as the batch variable and including the tissue site (nodal/extra-nodal) and histology subtypes (RL, FL, MZL, MCL, SLL) as covariates. After normalization and batch correction, Student’s t-test was used to perform a one-vs-rest and pairwise comparison for each subtype in the validation cohort. For all the 90 previously identified markers, the expression differences between the two groups of interest in the validation cohort were compared with the findings in the discovery cohort. miRNA markers that showed the same trend of expression changes were considered successfully validated.
Subtype classification and machine learning
Housekeeping gene normalization was applied to the raw expression levels in both discovery and validation cohorts, followed by ComBat batch correction for different collection sites in the validation dataset as described above. Batch effects between the discovery and validation cohorts were also corrected. The expression and tissue site (nodal/extra-nodal) data from the two cohorts were combined to develop a multi-marker panel for accurate classification of different subtypes. Categorical data such as tissue site was converted to numerical integers (0 and 1 ) for ease of analysis.
Using the combined dataset, we first developed a classification model to differentiate reactive control (RL) and lymphoma (FL, MZL, MCL, and SLL) samples. The combined dataset was subjected to 100 iterations of 4-fold cross-validation in which 3 folds were used for training and the last fold was used for testing. Support vector machine (SVM) with radial kernel was used for model training in the training datasets, and 3-fold cross-validation was performed to tune the cost parameter from 1 to 10. We applied the best-tuned model to the testing dataset, and a confusion matrix was created based on the predicted types (reactive or cancer) vs. actual sample’s types. The model's performance was assessed by the prediction accuracy on the testing set (Kuhn, M. Journal of Statistical Software 2008, 28(5), 1 -26).
Using the miRNA expression and tissue site data from the lymphoma tissue samples, we further trained classification models to differentiate the four lymphoma subtypes FL, MCL, MZL, and SLL. Similarly, 100 iterations of 4-fold cross-validation were applied to the combined dataset. One-vs-one classification strategy was used for the multi-class classification, by employing SVM model with radial kernel to the training set for model building, and another 3-fold cross-validation was performed for the determination of the optimal cost parameter from 1 to 10 based on the highest cross-validated accuracy (Meyer D, et al. (2022). e1071 : Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071 ), TU Wien. R package version 1 .7-11 , 2002. https://CRAN.R- project.org/package=e1071 ). The best-tuned model was applied to the testing data and the model performance was evaluated by the accuracy of subtype prediction. Similarly, a confusion matrix was created based on the predicted and the actual subtypes in the testing set.
Pathway analysis of differentially expressed miRNAs
Pathway analysis was performed with all samples pooled together through miRSEA method. Briefly, miRNA and mRNA linkages were curated with miRTarBase Release 7.0 based solely on strong experimental evidence support. Pathway database was curated from Broad Institute C2 pathways sets including Kegg, Reactome, Pathway Interaction Database and Biocarta. miRNA and pathway were correlated together by identifying the specific strength of the miRNA targeting the pathway. The P-value for hypergeometric distribution was used to calculate the enrichment of miRNA targets in any given pathway in the universe of targets. MiRNA fold change together with the P-value for pathway targeting was used to calculate the regulation of pathway with weighted Kolmogorov-Smirnov-like statistics. Pathways targeted by less than 10 miRNAs, or more than 500 miRNAs were ignored. The P-value of any pathway regulation was calculated by randomly permutating miRNAs 10,000 times. False discovery rate correction was carried out by using the null distribution of all pathways and the enrichment of unpermutated dataset.
Example 1. Consistency of miRNAs expression across FFPE samples in the discovery and validation cohorts
To assess miRNAs for their potential utility as diagnostic biomarkers for diagnosis and subtyping of small B-cell lymphomas using routine FFPE samples, we first profiled the expression of 306 miRNAs in a discovery cohort of 100 samples. Out of the 306 miRNAs quantified, a 100-miRNA profiling panel comprising 10 housekeeping miRNAs and 90 candidate miRNAs was assembled for validation in the second cohort of 282 samples. Hierarchical clustering performed using the expression of the 90 candidate miRNAs showed distinct clusters representing the four subtypes of small B-cell lymphomas and RL in both the discovery and validation cohorts (Fig. 2A, B). Principal component analysis (PCA) captured 46% (PC1/2) and 28% (PC1/3) of the total variance in miRNA expression profiles in the discovery and validation cohorts respectively (Fig. 2C). In the discovery cohort, the SLL and MCL subtypes can be well discriminated from other subtypes (Fig. 2C), whereas in the validation cohort, MCL and MZL are the more distinct subtypes. Most of the candidate miRNAs showed robustness and consistency across the discovery and validation cohorts, with 83 out of 90 candidate miRNAs showing the same trend in pairwise comparisons (Fig. 2E, F).
Example 2. MiRNAs signature can differentiate lymphoma from reactive lymphoid proliferation
To differentiate lymphoma from reactive lymphoid proliferation, a 90-miRNA and tissue information classification model was established by training and testing on a combined cohort of samples, resulting in a mean area under the ROC curve (AUC) of 0.959 (95% Cl: 0.922 to 0.988) (Fig. 3A). Other performance metrics include a mean recall of 0.944, precision of 0.923 and F1 score of 0.933 (Fig. 3B). The resulting classification model has a sensitivity of 94% for lymphoma and 80.4% for reactive lymphoid proliferation, and overall accuracy of 90.4% (Fig. 3C). A smaller panel comprising the top 14 miRNA features can achieve an accuracy of 85.5%, while the addition of more miRNAs did not substantially improve the accuracy (Fig. 3D, Tables 3 and 4). Among the top 14 miRNA biomarkers, three were upregulated and the rest were down regulated. For the avoidance of doubt, it would be appreciated that a skilled person (based on Fig 3D) would be able to select any one or more of the miRNAs from the 14 miRNAs (in any order), or to build any combination based on the 14 miRNAs with good accuracy for determining small B-cell lymphoma.
Table 3. Top 14 miRNAs that achieve 85.5% classification accuracy in distinguishing between lymphoma and RL
Figure imgf000054_0001
Table 4. Exemplary panel of miRNA biomarkers for determining whether a subject is suffering from or is at risk of suffering from small B-cell lymphoma.
Figure imgf000054_0002
Figure imgf000055_0001
Example 3. MiRNAs signature can subtype small B-cell lymphomas
In addition to distinguishing neoplastic from reactive lymphoid proliferation, we explored if miRNA expression profile could also be used for subtyping lymphomas. To further differentiate the four subtypes of small B-cell lymphoma, a 90-miRNA and tissue in-formation classification model was built by training and testing using samples from both cohorts, resulting in a sensitivity of 86.8% for FL, 87.8% for MZL, 85.2% for MCL and 84.0% for SLL and overall accuracy of 86.3% (Fig. 4A). We selected the SVM with radial kernel algorithm to build the classification model as it showed the best performance as compared to the random forest and SVM with linear kernel algorithms (Fig. 4B). A smaller panel comprising the top 15 features (14 miRNAs and tissue information) can achieve an accuracy of 87.5%, while the addition of more features did not substantially improve the accuracy (Fig. 4C, Tables 5 and 6). For the avoidance of doubt, it would be appreciated that a skilled person (based on Fig. 4C) would be able to select any one or more of the features from the 14 miRNAs and tissue information (in any order), or to build any combination based on the 14 miRNAs and tissue information with good accuracy for subtyping small B-cell lymphoma.
Table 5. Top 14 miRNAs and tissue information that achieves 87.5% classification accuracy for four lymphoma subtypes
Figure imgf000056_0001
Table 6. Exemplary panel of miRNA biomarkers and the use of tissue information for classifying subtypes of small B-cell lymphoma, and/or for determining whether a subject is suffering from or is at risk of suffering from one or more subtypes of small B-cell lymphoma.
Figure imgf000056_0002
Figure imgf000057_0001
Example 4. MiRNA expression could infer meaningful biological differences between reactive and neoplastic lymphoid proliferation To gain insights into B-cell lymphomagenesis and uncover significant signalling pathways, pathway analysis via miRNA gene set enrichment (miRSEA) was performed to identify differentially regulated pathways between lymphoma and reactive lymphoid tissues. Using a cutoff q-value of 0.01 , 13 KEGG pathways (Fig. 5A) and 20 Reactome pathways (Fig. 5C) were found to be up-regulated in lymphoma as compared to RL. The topmost significant pathways include the cytosolic DNA sensing pathway (Fig. 5B) and the Ga12/13 signalling pathway (Fig. 5D).
Example 5. Proposed two-stage diagnostic algorithm for miRNA-based classification of small B-cell lymphomas
Given that distinguishing small B-cell lymphoma from reactive lymphoid proliferation represents a frequent diagnostic dilemma confronting many practicing pathologists, herein we propose a two-staged algorithm where miRNA-based classifiers instead of a wide panel of IHC markers is used to diagnose and subtype lymphoid proliferations that are morphologically suspicious of small B-cell lymphoma (Fig. 6). As used herein, such morphologically suspicious proliferation or atypical lymphoid proliferation may refer to lymphoid tissue with one or more of the following: altered architecture, monotonous proliferation of small lymphoid cells and +/- CD20 immunophenotype.
Discussion
MiRNAs as Potential Diagnostic Biomarkers
In this era of individualized medicine, precise diagnosis and subtype classification of lymphomas have become increasingly important with the availability of disease-specific protocols and new targeted agents that inhibit specific pathways for different lymphoma subtypes. However, practicing lymphoma pathology is highly challenging. Accurate lymphoma diagnosis often requires the availability of hematopathologists with deep knowledge and experience in evaluating lymphoid lesions, high-quality laboratory infra-structure, as well as easy accessibility to a wide panel of immunohistochemical stains and additional molecular genetic testing such as fluorescence in-situ hybridization (FISH) and clonality studies, all of which may not be available in resource constrained nations. In addition, pathologists have to make do with increasingly smaller samples, which do not permit the application of a large number of immunostains. Given that small B-cell proliferation is one of the most common lymphoid lesions encountered by general pathologists, we investigated the biomarker potential of miRNA expression signatures as a diagnostic adjunct to improve the identification and subtyping of small B-cell lymphoma. We used a novel high throughput qPCR platform to develop miRNA-based classifiers to distinguish neoplastic from reactive lymphoid proliferation and to subtype the four most common histological subtypes of small B-cell lymphomas. We then proposed a two-staged diagnostic process where the miRNA classifiers are incorporated to complement morphological evaluation (Fig. 6). MiRNAs have previously been reported to be aberrantly expressed in almost all human cancers, including B-cell lymphomas. As active players in tumor pathogenetic pathways, miRNAs should have a significant influence on cancer diagnosis and prognosis. In fact, miRNA expression profiles have been reported by many investigators to be useful in tumor classification and subtyping, particularly in the setting of poorly differentiated malignancies and small biopsy samples where traditional morphological and antigenic evaluation have proven to be difficult if not impossible; while others have identified miRNA signatures associated with disease prognosis and response to treatments.
The application of miRNA expression profiling in the field of molecular cancer diagnostics requires a practical and reliable method that works on routinely available clinical materials. miRNAs can be robustly detected in FFPE tissue samples because they are small and less susceptible to degradative processes, and have been reported to be stable in FFPE archival tissue specimens that have been stored for close to 30 years (Bovell L, ef al. Front Biosci Elite Ed. 2012, 4, 1937-40). Remarkably, other investigators have reported the superiority of miRNAs as analytes compared with mRNAs for the molecular characterization of compromised archived clinical specimens and in the accurate classification of metastatic cancers of unknown primary origins. In addition, it has been shown that the miRNA composition in frozen tissues is largely preserved and comparable to that of routinely fixed (6- 24 hours) FFPE tissue specimens (Szafranska AE, et al. JMolDiagn. 2008, 10, 415-23). These studies highlight the adequacy, feasibility, and exciting potential of using miRNAs in archival FFPE tissue samples as novel clinical biomarkers.
Although miRNAs have unique attributes that render them suitable biomarkers in clinical practice, their accurate detection and quantification can be challenging because of their small size and sequence similarity among various members. For biomarker discovery and genome-wide expression analyses, most investigators deployed high-throughput hybridization-based methods, such as microarray technology for global gene expression profiling. Although microarray technology is a powerful approach that enables simultaneous screening of large numbers of miRNAs, its performance is most robust when frozen tissue or freshly fixed FFPE tissue are used, as prolonged storage of FFPE tissue blocks (up to 11 years) leads to a significant drop in miRNA detection. Other miRNA detection methods, including in- situ hybridization and next-generation sequencing, are technically more challenging. Barriers to clinical adoption include higher costs, need for sophisticated instrumentation, and complicated data interpretation.
The current gold standard for quantitative gene expression measurement is quantitative PCR. qPCR is a robust and reproducible technology that can detect low levels of miRNAs with high sensitivity and specificity, and it is widely used by investigators to validate genome-wide miRNA expression data derived from other techniques, especially for the analysis of a small panel of miRNAs. The efficiency of this technique in archived FFPE specimens has also been adequately demonstrated. PCR-based miRNA profiling platforms require much lower RNA input compared with other quantification methods, which is clearly a key advantage when dealing with limited and often compromised clinical specimens. Moreover, being a well-established technology, one key advantage of qPCR is that it can be easily and conveniently performed in most clinical diagnostic laboratories (especially after the COVID pandemic), and it produces data that are easy to analyse. Therefore, validation of a PCR-based laboratory- developed test (LDT) for accreditation purposes is likely to be far less complex compared to other more sophisticated platforms.
The main challenge of PCR-based miRNA biomarkers discovery work lies in the design of individual primers required for specific amplification of each miRNA gene included in large-scale analyses. Due to the short length of miRNAs (roughly the size of a PCR primer), primer design for specific PCR amplification poses significant difficulty. As such, most commercially available high throughput qPCR platforms employ only one or two miRNA-specific primers with selective incorporation of universal primers. In the current study, we performed multiplex comparative analyses of 360 miRNAs based on a unique method that uses three miRNA-specific primers (i.e. stem-loop RT, forward and reverse primers), obviating the use of universal primers altogether. All the primers of each miRNA analysed have been carefully designed to detect single nucleotide differences. We believe that this platform offers a unique advantage to detect both low- and high-abundance miRNAs with unparalleled specificity. The combination of high sensitivity and specificity, broad dynamic range and multiplexing capability of this assay hold great promise in delivering highly reliable, reproducible, and representative disease-specific miRNA profiles.
With this novel miRNA RT-qPCR profiling platform, we found two unique miRNA-based classifiers, each comprising a small set of 14 miRNAs, that can help to diagnose and subtype the four most common entities of small B-cell lymphomas with reasonably high accuracy. We believe that the proposed two- staged diagnostic workflow incorporating miRNA-based classifiers can potentially serve as a cost- effective and practical tool to complement traditional morphological diagnosis, especially in the resource-constrained nations. Typically, when confronted with the differential diagnoses of reactive lymphoid hyperplasia versus one of the low-grade B-cell lymphomas, pathologists will order a panel of 7-8 immunostains, sometimes with additional fluorescence in situ hybridization (FISH) and B-cell clonality analyses. Using a curated panel of miRNA targets, the cost of our RT-qPCR assay is economical, especially when it is reactive in nature and only classifier 1 is needed (Table 5). The turnaround time of within a day also compares favourably to immunohistochemistry, FISH and clonality analysis.
Conventional wisdom may hold that IHC may be easier to perform and accessible compared to molecular techniques. In fact, optimization and validation of IHC is technically challenging and the range of antibodies available is limited due to cost constraints in developing nations. On the other hand, due to the need for COVID testing during the global COVID-19 pandemic, RT-qPCR machines have become widely available even in countries with limited resources, hence rendering our assay feasible in such countries. Table 7. Comparison between the proposed RT-qPCR assay and traditional IHC/FISH for small B-cell lymphoma classification
Figure imgf000061_0001
Possible pathways implicated by miRNAs
Cytosolic DNA sensing pathway, RIG-l-like receptor signalling pathway and NOD-like receptor signalling pathway
With the ability to bind to thousands of target mRNAs by complementary base pairing, miRNAs could potentially play diverse regulatory roles in many processes, including tissue and cancer development like lymphomagenesis.
Interestingly, three highly significant and upregulated KEGG pathways - cytosolic DNA sensing pathway, RIG-l-like receptor signalling pathway, and NOD-like receptor signalling pathway - are functionally related. These pathways underlie the sensing of foreign matter that may be introduced during infections, in the form of single or double-stranded DNA, from viruses and other pathogens. Hence, these pathways are particularly relevant in lymphomagenesis where infection by oncogenic viruses, such as EBV and KSHV, and other pathogens like bacteria can transform B cells into lymphomas in certain cases. Gastric MALT, a type of MZL, has been observed to arise along with chronic gastritis caused by Helicobacter pylori and regress upon antibiotics treatment, suggesting an infection-driven tumorigenic event. Another example, another type of MZL, ocular adrenal MALT, has been linked to Chlamydophila psittaci infection.
While molecular mechanisms linking infection and these pathways are still unclear in the context of small B-cell lymphoma, these pathways have been known to eventually lead downstream to NF-KB signalling, which is heavily implicated in lymphomagenesis. Constitutive NF-KB activation is a hallmark of B-cell lymphomagenesis, thus genetic alterations and pathways that drive its activation are of high clinical and therapeutic value. Hence, these pathways may constitute novel, clinically relevant upstream players in B-cell lymphomagenesis.
Additionally, inactivating somatic mutations or deletions have been reported in the tumour suppressor gene, ubiquitin-editing protein A20 (or TNFAIP3) across many types of lymphomas including MALT and FL. Overexpression of A20 has been shown to attenuate RIG-1 signalling. Restoration of wild-type A20 in A20-inactivated lymphoma cell lines also led to the repression of NF-KB signalling, suggesting that A20 is a regulator of the RIG-1 and NF-KB signalling axis. Hence, these findings appear to further support a potential link between the upregulation of the RIG-l-like signalling pathway to small B cell lymphomas.
Interestingly, a recent study reported on the absence of the expression of STING (a part of the cGAS- STING, which are major components of the cytosolic DNA sensing pathway) specifically in B-cell nonHodgkin lymphomas, including various small B-cell lymphomas like FL, MCL, MZL and SLL, but not in T- and NK-cell lymphomas. This finding may point to the involvement of other components in the pathway that may have not yet been studied in small B-cell lymphomas.
Ga12/13 signalling events
The G12 subfamily, of which Go12 and Go13 are members, consist of G proteins, which are G-alpha subunits of heterotrimeric GTP-binding proteins. G proteins serve as the intermediary between GPCRs on the cell membrane and downstream signalling, and they work by binding to guanine nucleotides. G12 proteins, together with other G protein sub-families, form the most diverse group of receptors, playing a wide range of important roles in normal physiology. In the context of this study, Ga12 and Go13 have been demonstrated to regulate the maturation of B cells in the marginal zone in a murine model. Unsurprisingly, the overexpression or enhanced activation of Go12 and Ga13 has been linked to several cancers. However, G12 proteins still remain one of the least understudied subfamilies in cancer biology, especially in haematological malignancies. However, the few studies done in lymphomas do point towards significant roles that Ga12/13 signalling play in lymphomas in general.
Constitutive NF-KB signalling is known as an important hallmark of lymphomas and much work has been done on pathways that drive its activity. Enhanced NF-KB activity has been associated with increased hedgehog signalling. Smoothened (SMO), yet another GPCR and also an essential signal transducer of the hedgehog signalling pathway, has been shown to recruit and activate Gai and Ga12, and not other G proteins. The resulting signalling complex then initiates a cascade of events involving non-canonical signalling complexes, ultimately leading to the activation of NF-KB signalling. This study suggests that Ga12 could play an important enabling role in lymphomagenesis by mediating the activation of NF-KB signalling.
Conversely, Ga13, along with associated receptors S1 PR2 and P2RY8, appear to pro-mote the confinement of B cells to ensure a physiologically normal germinal centre. Ga13 deficiency has been shown to give rise to germinal centre B-cell-derived lymphoma in mice. Similarly, mutations in GNA13 (the gene encoding Go13), S1 PR2, or P2RY8 - found in GCB-DLBCL patients - have been demonstrated to cause the dissemination of germinal centre B-cells (and in the case of P2RY8 mutations, also enhancing cell growth), hence also leading to germinal centre B-cell-derived lymphoma. Unlike Ga12, Ga13 plays a tumour suppressive role in orchestrating the proper development of the germinal centre. Taking together the limited knowledge gathered on Go12/13 signalling events in lymphomas, we hypothesize that Ga12 signalling could be a significant player in small B-cell lymphomagenesis.
Other notable signalling pathways miRSEA analysis also identified pathways that are regularly implicated in lymphomas, hence validating the relevance of biomarker miRNAs that differentiate small B-cell lymphomas from RL. Significantly upregulated pathways (with q-value of less than 0.05) include the B-cell receptor signalling pathway, mTOR signalling pathway, and PI3K-Akt activation (Tables 8A and 8B).
Table 8A. miRSEA pathway analysis for lymphoma vs RLN (KEGG).
Figure imgf000063_0001
Figure imgf000064_0001
Figure imgf000065_0001
Table 8A. miRSEA pathway analysis for lymphoma vs RLN (Reactome).
Figure imgf000065_0002
Figure imgf000066_0001
Figure imgf000067_0001
Figure imgf000068_0001
57
Figure imgf000069_0001
Figure imgf000070_0001
Conclusions
Overall, our results demonstrate that miRNA expression profiling may serve as a promising biomarker and practical tool to aid the diagnosis of common lymphoid lesions. Specifically, we identified and validated two unique miRNA-based classifiers that can help to diagnose and subtype the four most common diagnostic entities of small B-cell lymphomas.

Claims

1 . A method of determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the method comprising: determining the expression level of one or more microRNAs (miRNAs) in a biological sample obtained from the subject, wherein the one or more miRNAs are selected from the group consisting of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR- 143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa- miR-223-3p and hsa-miR-335-5p; and wherein an altered expression level of the one or more miRNAs, as compared to a control, is indicative of the subject suffering from, or at risk of developing small B-cell lymphoma.
2. The method of Claim 1 , wherein the method comprises determining the expression level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen or at least fourteen miRNAs.
3. The method of Claim 1 or 2, wherein the method comprises determining the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR- 145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa- miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
4. The method of any one of the preceding claims, wherein the control is selected from the group consisting of a healthy subject, a non-diseased subject, a cancer-free subject, a small B-cell lymphoma- free subject, a subject suffering from reactive lymphoid proliferation, and/or a subject not suffering from, or not at risk of developing small B-cell lymphoma.
5. The method of any one of Claims 1 to 4, wherein when the subject is determined to suffer from, or at risk of developing small B-cell lymphoma, the method further comprises determining the expression level of a second set of miRNAs in the biological sample, wherein the second set of miRNAs comprises one or more miRNAs selected from the group consisting of hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR-181a-2-3p, hsa-miR-151a-3p, hsa-miR-20b- 5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224- 5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p; and wherein an altered expression level of the second set of miRNAs, as compared to a second control, is indicative of the subject suffering from one or more subtypes of small B-cell lymphoma selected from the group consisting of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low- grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
6. The method of Claim 5, wherein the method comprises determining the expression level of hsa- miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
7. The method of Claim 5 or 6, wherein the second control is selected from a subject suffering from, or at risk of developing one or more subtypes of small B-cell lymphoma selected from the group consisting of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
8. The method of one of Claims 5 to 7, wherein the method further comprises using the excision site of the biological sample to determine if the subject is suffering from, or at risk of developing one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma and marginal zone lymphoma, optionally wherein the excision site is nodal or extranodal.
9. The method of any one of the preceding claims, wherein the biological sample is a tissue sample (e.g. biopsy tissue sample) or a non-cellular bodily fluid sample (e.g. plasma or serum).
10. The method of any one of the preceding claims, wherein the small B-cell lymphoma comprises from one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
11. The method of any one of the preceding claims, wherein the method further comprises performing one or more procedures selected from histopathology, immunohistochemistry, immunophenotyping and/or clonality test to determine:
(a) the morphology and cell distribution of the biological sample; and/or
(b) the expression level, presence or absence of one or more additional biomarkers in the biological sample, optionally wherein the biomarker is selected from the group consisting of CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6.
12. The method of any one of the preceding claims, wherein the one or more miRNAs are detected by one or more method selected from the group consisting of sequencing, nucleic acid hybridisation, microarray and nucleic acid amplification.
13. A kit for determining whether a subject suffers from, or is at risk of developing small B-cell lymphoma, the kit comprising: an isolated set of probes and/or reagents capable of detecting the expression level of one or more miRNAs selected from the group consisting of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126- 3p, hsa-miR-29b-2-5p, hsa-miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR- 9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
14. The kit of Claim 13, wherein the isolated set of probes and/or reagents are capable of detecting the expression level of hsa-miR-139-5p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-29b-2-5p, hsa- miR-126-5p, hsa-miR-145-5p, hsa-miR-143-5p, hsa-miR-224-5p, hsa-miR-9-3p, hsa-miR-125a-5p, hsa-miR-342-5p, hsa-miR-29a-3p, hsa-miR-223-3p and hsa-miR-335-5p.
15. The kit of Claim 13 or 14, wherein the kit comprising further comprises: an isolated set of probes and/or reagents capable of detecting the expression level of one or more miRNAs selected from the group consisting of the hsa-miR-151 a-5p, hsa-miR-340-5p, hsa-miR- 181 a-2-3p, hsa-miR-151 a-3p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa-miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR- 196a-5p.
16. The kit of Claim 15, wherein the isolated set of probes and/or reagents are capable of detecting the expression level of hsa-miR-151a-5p, hsa-miR-340-5p, hsa-miR-181a-2-3p, hsa-miR-151a-3p, hsa- miR-20b-5p, hsa-miR-106b-5p, hsa-miR-363-3p, hsa-miR-25-3p, hsa-miR-625-5p, hsa-miR-9-3p, hsa- miR-224-5p, hsa-miR-183-5p, hsa-miR-126-3p and hsa-miR-196a-5p.
17. The kit of any one of Claims 13 to 16, wherein the small B-cell lymphoma comprises one or more of small lymphocytic lymphoma/chronic lymphocytic leukaemia, low-grade follicular lymphoma, mantle cell lymphoma or marginal zone lymphoma.
18. The kit of any one of Claims 13 to 17, wherein the kit further comprises an isolated set of probes and/or reagents that are capable of detecting the expression level of one or more additional biomarkers in the biological sample, optionally wherein the one or more additional biomarkers are selected from the group consisting of CD20, CD79a, PAX-5, CD3, Ki67, CD5, CD23, CD10, CD43, LEF1 , CyclinDI , Bcl2 and Bcl6.
19. The kit of any one of Claims 13 to 18, wherein the probe is selected from the group consisting of an aptamer, an antibody, an affibody, a peptide, and/or a nucleic acid.
20. The kit of any one of statements 13 to 19, wherein the one or more miRNAs are detected by one or more method selected from sequencing, nucleic acid hybridisation, microarray and nucleic acid amplification.
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* Cited by examiner, † Cited by third party
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Non-Patent Citations (9)

* Cited by examiner, † Cited by third party
Title
"Leukaemia", vol. 36, 2022, WORLD HEALTH ORGANIZATION CLASSIFICATION OF HAEMATOLYMPHOID TUMOURS, article "Different subtypes and classifications of small B-cell lymphomas are known and discussed", pages: 1720 - 1748
"National Health Group (NHG), Tan Tock Seng Hospital (TTSH", UNIVERSITY MALAYA MEDICAL CENTRE (UMMC, article "Institutional Review Board (IRB) approval was obtained for all samples in accordance with IRB guidelines"
BIOSTAT OXF ENGL, vol. 8, 2007, pages 118 - 27
BOVELL L ET AL., FRONT BIOSCI ELITE ED, vol. 4, 2012, pages 1937 - 40
KUHN, M, JOURNAL OF STATISTICAL SOFTWARE, vol. 28, no. 5, 2008, pages 1 - 26
MESTAGH ET AL., NUCLEIC ACIDS RES, vol. 36, 2008, pages 143
MEYER D ET AL., MISC FUNCTIONS OF THE DEPARTMENT OF STATISTICS, PROBABILITY THEORY GROUP, 2022, pages e1071
SZAFRANSKA AE ET AL., J MOL DIAGN, vol. 10, 2008, pages 415 - 23
ZOU R ET AL., CANCERS, vol. 13, 2021, pages 2130

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