WO2010018563A2 - Compositions and methods for the prognosis of lymphoma - Google Patents

Compositions and methods for the prognosis of lymphoma Download PDF

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WO2010018563A2
WO2010018563A2 PCT/IL2009/000764 IL2009000764W WO2010018563A2 WO 2010018563 A2 WO2010018563 A2 WO 2010018563A2 IL 2009000764 W IL2009000764 W IL 2009000764W WO 2010018563 A2 WO2010018563 A2 WO 2010018563A2
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seq
sequence
nucleic acid
mir
prognosis
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PCT/IL2009/000764
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French (fr)
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WO2010018563A3 (en
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Moshe Hoshen
Gila Lithwick Yanai
Meir Lahav
Ranit Aharonov
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Rosetta Genomics Ltd.
Mor Research Applications Ltd.
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Publication of WO2010018563A3 publication Critical patent/WO2010018563A3/en

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    • 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
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    • 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
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    • 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
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    • 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
    • C12Q2600/136—Screening for pharmacological compounds
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    • 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 to compositions and methods for determining the prognosis of malignant lymphoma. Specifically the invention relates to microRNA molecules associated with the prognosis of lymphoma, as well as various nucleic acid molecules relating thereto or derived thereof.
  • microRNAs have emerged as an important novel class of regulatory RNA, which have a profound impact on a wide array of biological processes.
  • RNA molecules can modulate protein expression patterns by promoting RNA degradation, inhibiting mRNA translation, and also affecting gene transcription.
  • miRs play pivotal roles in diverse processes such as development and differentiation, control of cell proliferation, stress response and metabolism. The expression of many miRs was found to be altered in numerous types of human cancer, and in some cases strong evidence has been put forward in support of the conjecture that such alterations may play a causative role in tumor progression. There are currently about 800 known human miRs.
  • Diffuse large B-cell lymphoma accounts for 30-40% of all adult non- Hodgkin's lymphomas and is heterogeneous in terms of its morphology and clinical features (Harris et al., 1994, Blood, 84: 1361-1392). Approximately 50% of patients relapse after treatment and their tumors frequently become resistant to therapy. The genetic abnormalities underlying DLBCL remain poorly understood. Gene expression profiling (mRNA microarrays) has been shown to stratify patient with DLBCL into three prognostic groups. So far, these findings have not been translated to clinical practice. Thus, there exists a need for identification of new biomarkers that can be used as prognostic indicators for lymphoma.
  • the expression profile of specific miRs (SEQ ED NOS: 1-11, 26-28, 33, 39) in biological samples obtained from lymphoma patients are indicative of the cancer prognosis: the life expectancy of the patient, the expected recurrence- free survival, response to treatment and risk of recurrence.
  • a method for determining a prognosis for lymphoma in a subject comprising: (a) obtaining a biological sample from the subject;
  • said altered expression level is a change in a score based on a combination of expression levels of said nucleic acid sequences.
  • said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 1-5, 12-17, 26, 29, 33, 39 and 52; and sequences at least about 80% identical thereto and said expression levels above said reference value is indicative of poor prognosis in said subject.
  • said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 6-11, 18-25 and 27-32; and sequences at least about 80% identical thereto, and said expression levels below said reference value is indicative of poor prognosis in said subject.
  • said lymphoma is a B cell lymphoma. In certain embodiments, said B-cell lymphoma is diffuse large B cell lymphoma.
  • the subject is a human.
  • the method is used to determine a course of treatment of the subject.
  • the biological sample obtained from the subject is selected from the group consisting of bodily fluid, a cell line and a tissue sample.
  • the tissue is a fresh, frozen, fixed, wax-embedded or formalin fixed paraffin- embedded (FFPE) tissue.
  • FFPE formalin fixed paraffin- embedded
  • said tissue is a lymphoid tissue.
  • said tissue is a lymph node.
  • the expression levels are determined by a method selected from the group consisting of nucleic acid hybridization, nucleic acid amplification, and a combination thereof.
  • the nucleic acid hybridization is performed using a solid-phase nucleic acid biochip array or in situ hybridization.
  • the nucleic acid amplification method is quantitative real-time PCR.
  • the PCR method comprises forward and reverse primers.
  • the forward primer comprises a sequence selected from the group consisting of SEQ ID NOS: 60-83, a fragment thereof and a sequence having at least about 80% identity thereto.
  • the reverse primer comprises SEQ ID NO: 108, a fragment thereof and a sequence at least about 80% identical thereto.
  • the real-time PCR method further comprises a probe.
  • the probe comprising a nucleic acid sequence that is complementary to a sequence selected from SEQ ID NOS: 1-33, 39 and 52; to a fragment thereof and to a sequence at least about 80% identical thereto.
  • the probe comprises a sequence selected from the group consisting of any one of SEQ ID NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto.
  • kits for determining the prognosis of a subject with lymphoma may comprise a probe comprising a nucleic acid sequence that is complementary to a sequence selected from SEQ ID NOS: 1-33, 39 and 52; to a fragment thereof and to a sequence at least about 80% identical thereto.
  • said probe comprising a nucleic acid sequence selected from the group consisting of SEQ ED NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto.
  • the kit further comprises forward and reverse primers.
  • the forward primer comprising a sequence selected from the group consisting of SEQ ID NOS: 60-83, a fragment thereof and a sequence having at least about 80% identity thereto.
  • the kit further comprises a reverse primer comprising SEQ ID NO: 108, a fragment thereof and sequences having at least about 80% identity thereto.
  • the kit comprises reagents for performing in situ hybridization analysis.
  • prognostic for lymphoma comprises providing the forecast or prediction of (prognostic for) any one or more of the following: duration of survival of a patient susceptible to or diagnosed with lymphoma, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with lymphoma, response rate in a group of patients susceptible to or diagnosed with lymphoma, and duration of response in a patient or a group of patients susceptible to or diagnosed with lymphoma.
  • duration of survival is forecast or predicted to be increased.
  • duration of survival is forecast or predicted to be decreased.
  • duration of recurrence- free survival is forecast or predicted to be increased. In some embodiments, duration of recurrence-free survival is forecast or predicted to be decreased, hi some embodiments, response rate is forecast or predicted to be increased. In some embodiments, response rate is forecast or predicted to be decreased. In some embodiments, duration of response is predicted or forecast to be increased, hi some embodiments, duration of response is predicted or forecast to be decreased.
  • Figure 1 is a graph showing differential expression of miRs (in fluorescence units as measured by a microarray) in samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from
  • DLBCL patients with bad prognosis no remission following treatment, or relapse within 9 months
  • the Y axis represents patients with bad prognosis (46 patients)
  • the X axis represents patients with good prognosis (43 patients).
  • the parallel lines describe a fold change between groups of 1.5 in either direction.
  • hsa-miR-19b SEQ ID NO: 1
  • hsa-miR-20a SEQ ID NO: 2
  • hsa- miR-886-5p SEQ ED NO: 3
  • hsa-miR-106a SEQ ID NO: 4
  • hsa-miR-17 SEQ ID NO: 5
  • hsa-miR-150 SEQ ID NO: 6
  • hsa-miR-342-3p SEQ ID NO: 7
  • hsa-miR-100 SEQ ID NO: 8
  • hsa-miR-768-3p SEQ ID NO: 9
  • hsa-miR-125b SEQ ID NO: 10
  • hsa-miR- 181a SEQ ID NO: 11
  • P-values are calculated by Mann-Whitney test, and significance is adjusted using F
  • Figures 2A-2D are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-19b (SEQ ID NO: 1) (2A), hsa-miR-20a (SEQ ID NO: 2) (2B), hsa-miR-886-5p (SEQ ID NO: 3) (2C), and hsa-miR- 106a (SEQ ID NO: 4) (2D) in tumor samples obtained from DLBCL patients with bad or good prognosis (as defined in figure 1). For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis. The line in the box indicates the median value. The box covers the interquartile range and the horizontal lines and crosses (outliers) show the full range of signals in this group.
  • Figures 3A-3D are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-17 (SEQ ID NO: 5) (3A), hsa-miR-150 (SEQ ID NO: 6) (3B), hsa-miR-342-3p (SEQ ID NO: 7) (3C) and hsa-miR-100 (SEQ ID NO: 8) (3D) in tumor samples obtained from lymphoma patients with bad or good prognosis. For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis. The line in the box indicates the median value.
  • Figures 4A-4C are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-768-3p (SEQ ID NO: 9) (4A), hsa-miR-125b (SEQ ID NO: 10) (4B), and hsa-miR-181a (SEQ ID NO: 11) (4C) in tumor samples obtained from lymphoma patients with bad or good prognosis. For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis.
  • Figure 5 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients split by expression of hsa-miR-181a (SEQ ID NO: 11) (p-value 0.00206).
  • the highlighted background corresponds to the first 9 months (zoomed-in in figure 6).
  • Figure 6 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients split by expression of hsa-miR-181a (SEQ ID NO: 11) (p-value
  • Figures 7A-7C demonstrate the detection of DLBCL patients with bad prognosis (circles) and lymphoma patients with good prognosis (squares) using a combination of two microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5) and hsa-miR-342-3p (SEQ ID NO: 7).
  • the expression scores are shown sorted by their rank.
  • Fig. 7A The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis), separately for the two groups.
  • Fig 7B same but split by group.
  • the sensitivity of the detection of bad prognosis is 83% and the specificity of the detection is 63%.
  • Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.7543 (Fig. 7C).
  • Figures 8A-8C demonstrate the detection of lymphoma patients with bad prognosis (circles) and lymphoma patients with good prognosis (squares) using a combination of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768- 3p (SEQ ID NO: 9) and hsa-miR-181a (SEQ ID NO: 11).
  • Fig 8A The expression scores are shown sorted by their rank. The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the three miRs (probability value shown on the vertical axis), separately for the two groups.
  • Fig 8B same but split by group.
  • Receiver operating characteristic (ROC) for the metric defined by the combination of these three microRNAs has an area under the curve (AUC) of 0.78059 (Fig. 8C).
  • Figure 9 is a Kaplan Meier plot for persistence (survival) of recurrence-free status of DLBCL patients using a combination of the log 2 expression of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768-3p (SEQ ID NO: 9) and hsa-miR-
  • the highlighted background corresponds to the first 9 months (zoomed-in in figure 10).
  • Figure 10 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients up to 9 months using a combination of the log 2 expression of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768-3p (SEQ ID NO: 9) and hsa-miR-181a (SEQ ID NO: 11) with the coefficients 0.714, 0.727 and -0.49 respectively.
  • Figure 11 is a graph showing differential expression of miRs (in fluorescence units) as measured by a microarray) in lymph node samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from DLBCL patients with bad prognosis (no remission, following treatment or relapse within 9 months), comparing the median values of each miR in all patients in a group with the corresponding median for members of the other group.
  • the Y axis represents patients with bad prognosis (41 patients), and the X axis represents patients with good prognosis (32 patients).
  • the parallel lines describe a fold change between groups of 1.5 in either direction.
  • hsa-miR-19b SEQ ID NO: 1
  • hsa-miR-20a SEQ ID NO: 2
  • hsa-miR-106a SEQ ID NO: 4
  • hsa-miR- 17 SEQ ID NO: 5
  • hsa-miR-146b-5p SEQ ID NO: 26
  • hsa-miR-140-3p SEQ ID NO: 27
  • hsa-miR-138 SEQ ID NO: 28.
  • P-values are calculated by Mann- Whitney test, and all miRs were significant using FDR (false discovery rate) of 0.1 . Not tested- control probes or median signal ⁇ 300 in both groups.
  • Figure 12 is a graph showing differential expression of miRs (50-Ct) as measured by quantitative real-time PCR (qRT-PCR) in samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from DLBCL patients with bad prognosis (no remission following treatment, or relapse within 9 months), comparing the median values of each miR in all patients in one group with the corresponding median for members of the other group.
  • the Y axis represents patients with good prognosis (11 patients), and the X axis represents patients with bad prognosis (11 patients).
  • the parallel lines describe a fold change between groups of 1.5 in either direction.
  • hsa-miR-17* (SEQ ID NO: 33), hsa-miR-150 (SEQ ID NO: 6), hsa-miR-106a (SEQ ID NO: 4), hsa-miR-181a (SEQ ID NO: 11), hsa-miR-17 (SEQ ID NO: 5), hsa-miR-20a (SEQ ID NO: 2), hsa-miR-92a (SEQ ID NO: 39), hsa-miR-19b (SEQ ID NO: 1), hsa-miR-342- 3p (SEQ ID NO: 7) and hsa-miR-100 (SEQ ID NO: 8).
  • Figures 13 A-13 J are boxplot presentations comparing the expression as measured by qRT-PCR of the differential miRs: hsa-miR-342-3p (SEQ ID NO: 7) (13A), hsa-miR- 150 (SEQ ID NO: 6) (13B), hsa-miR-100 (SEQ ID NO: 8) (13C), hsa-miR-181a (SEQ ID NO: 11) (13D), hsa-miR-17* (SEQ ID NO: 33) (13E), hsa-miR-20a (SEQ ID NO: 2) (13F), hsa-miR-19b (SEQ ID NO: 1) (13G), hsa-miR-92a (SEQ ID NO: 39) (13H), hsa- miR-17 (SEQ ID NO: 5) (131) and hsa-miR-106a (SEQ ID NO: 4) (13J) in a
  • Figures 14A-14D demonstrate the separation of DLBCL patients with bad prognosis from lymphoma patients with good prognosis in the test set of 13 samples using one microRNA biomarker: hsa-miR-17* (SEQ ID NO: 33).
  • Figure 14A is a graph showing the signal of hsa-miR-17*, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and DLBCL patients with good prognosis (squares).
  • Figure 14B shows the expression levels of hsa-miR-17* in six tumor samples originating from DLBCL patients with bad prognosis (circles) and seven tumor samples originating from DLBCL patients with good prognosis (squares). The patients are sorted by increasing values of hsa-miR-17* (value shown on the vertical axis), but separately for the two groups.
  • Figure 14C depicts the histograms of frequency of expression for the two groups. The dotted line shows bad prognosis cases and the solid line good prognosis cases.
  • Figure 14D is the Response Operator Curve showing that the sensitivity (vertical axis) and specificity (1 -Specificity, horizontal axis) of the detection.
  • Figures 15A-15D demonstrate separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis using a combination of two microRNA biomarkers: hsa-miR-181a (SEQ ID NO: 11) and hsa-miR-92a (SEQ ID NO: 39).
  • Figure 15A is a graph showing a linear combination of the normalized signal of both miRs, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and tumor samples originating from DLBCL patients with good prognosis (squares).
  • the samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis).
  • Figure 15B shows the expression levels of both miRs in 11 tumor samples originating from DLBCL patients with bad prognosis (circles) and 11 tumor samples originating from DLBCL patients with good prognosis (squares).
  • the samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis), separately for the two groups.
  • Figure 15C depicts the samples expression levels (in 50-CT) for two miRs (x-axis: hsa-miR-181a, y-axis: hsa-miR-92a). Solid diagonal line depicts the best separation according to logistic regression and the dashed lines the 10 and 90% limits of this separation. In this example, one member of each group was misclassified.
  • Figure 15D is the Receiver Operating Characteristic (ROC) curve showing the sensitivity (vertical axis) and specificity (1 -Specificity, horizontal axis) of the detection for various values of the combined score of the two miRs, using the linear combination as defined before, for all samples.
  • ROC Receiver Operating Characteristic
  • Figures 16A-16D demonstrate separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis using a combination of two microRNA biomarkers: hsa-miR-342-3p (SEQ ID NO: 7) and hsa-miR-150 (SEQ ID NO: 6).
  • Figure 16A is a graph showing a linear combination of the normalized signal of both miRs, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and tumor samples originating from lymphoma patients with good prognosis (squares).
  • Figure 16B shows the expression levels of both miRs in six tumor samples originating from DLBCL patients with bad prognosis (circles) and seven tumor samples originating from DLBCL patients with good prognosis (squares).
  • Figure 16C depicts the samples expression levels (in 50-CT) for two miRs (x-axis: hsa-miR-342-3p, y- axis: hsa-miR-150). Solid diagonal line depicts the best separation according to logistic regression and the dashed lines the 10 and 90% limits of this separation.
  • Figure 16D is the Receiver Operating Characteristic (ROC) curve showing the sensitivity (vertical axis) and specificity (1- Specificity, horizontal axis) of the detection for various values of the combined score of the two miRs, using the linear combination as defined before, for all samples.
  • ROC Receiver Operating Characteristic
  • miRNA expression can serve as a novel tool for determining the prognosis of lymphoma. More particularly, it may serve for determining the prognosis of long survival versus short survival, long recurrence- free survival vs. short recurrence-free survival, and the response to treatment. Methods and compositions are provided for determining the prognosis of lymphoma. Other aspects of the invention will become apparent to the skilled artisan by the following description of the invention.
  • the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0- 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.
  • “Attached” or “immobilized” as used herein to refer to a probe and a solid support may mean that the binding between the probe and the solid support is sufficient to be stable under conditions of binding, washing, analysis, and removal.
  • the binding may be covalent or non-covalent. Covalent bonds may be formed directly between the probe and the solid support or may be formed by a cross linker or by inclusion of a specific reactive group on either the solid support or the probe or both molecules.
  • Non-covalent binding may be one or more of electrostatic, hydrophilic, and hydrophobic interactions. Included in non-covalent binding is the covalent attachment of a molecule, such as streptavidin, to the support and the non-covalent binding of a biotinylated probe to the streptavidin.
  • Bio sample as used herein may mean a sample of biological tissue or fluid that comprises nucleic acids. Such samples include, but are not limited to, tissue isolated from animals. Biological samples may also include sections of tissues such as biopsy and autopsy samples, frozen sections taken for histological purposes, blood, plasma, serum, sputum, stool, tears, mucus, urine, effusions, amniotic fluid, ascitic fluid, hair, and skin. Biological samples also include explants and primary and/or transformed cell cultures derived from patient tissues.
  • a biological sample may be provided by removing a sample of cells from an animal, but can also be accomplished by using previously isolated cells (e.g., isolated by another person, at another time, and/or for another purpose), or by performing the methods described herein in vivo.
  • Archival tissues such as those having treatment or outcome history, may also be used. cancer prognosis
  • cancer prognosis includes the forecast or prediction of any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer.
  • prognostic for cancer means providing a forecast or prediction of the probable course or outcome of the cancer.
  • prognostic for cancer comprises providing the forecast or prediction of (prognostic for) any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, and duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer.
  • “Complement” or “complementary” as used herein to refer to a nucleic acid may mean Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen base pairing between nucleotides or nucleotide analogs of nucleic acid molecules.
  • a full complement or fully complementary may mean 100% complementary base pairing between nucleotides or nucleotide analogs of nucleic acid molecules.
  • differential expression may mean qualitative or quantitative differences in the temporal and/or cellular gene expression patterns within and among cells and tissue.
  • a differentially expressed gene can qualitatively have its expression altered, including an activation or inactivation, in, e.g., normal versus disease tissue.
  • Genes may be turned on or turned off in a particular state, relative to another state thus permitting comparison of two or more states.
  • a qualitatively regulated gene will exhibit an expression pattern within a state or cell type that may be detectable by standard techniques. Some genes will be expressed in one state or cell type, but not in both.
  • the difference in expression may be quantitative, e.g., in that expression is modulated, up-regulated, resulting in an increased amount of transcript, or down-regulated, resulting in a decreased amount of transcript.
  • the degree to which expression differs need only be large enough to quantify via standard characterization techniques such as expression arrays, quantitative reverse transcriptase PCR, Northern analysis, and RNase protection.
  • “Expression profile” as used herein may mean a genomic expression profile, e.g., an expression profile of microRNAs. Profiles may be generated by any convenient means for determining a level of a nucleic acid sequence e.g. quantitative hybridization of microRNA, labeled microRNA, amplified microRNA, cRNA, etc., quantitative PCR, ELISA for quantification, and the like, and allow the analysis of differential gene expression between two samples.
  • a subject or patient tumor sample e.g., cells or collections thereof, e.g., tissues, is assayed. Samples are collected by any convenient method, as known in the art.
  • Nucleic acid sequences of interest are nucleic acid sequences that are found to be predictive, including the nucleic acid sequences provided above, where the expression profile may include expression data for 5, 10, 20, 25, 50, 100 or more of, including all of the listed nucleic acid sequences.
  • expression profile may also mean measuring the abundance of the nucleic acid sequences in the measured samples. expression ratio
  • “Expression ratio” as used herein refers to relative expression levels of two or more nucleic acids as determined by detecting the relative expression levels of the corresponding nucleic acids in a biological sample.
  • Fram is used herein to indicate a non-full length part of a nucleic acid or polypeptide.
  • a fragment is itself also a nucleic acid or polypeptide, respectively.
  • Gene used herein may be a natural (e.g., genomic) or synthetic gene comprising transcriptional and/or translational regulatory sequences and/or a coding region and/or non-translated sequences (e.g., introns, 5'- and 3 '-untranslated sequences).
  • the coding region of a gene may be a nucleotide sequence coding for an amino acid sequence or a functional RNA, such as tRNA, rRNA, catalytic RNA, siRNA, miRNA or antisense RNA.
  • a gene may also be a mRNA or cDNA corresponding to the coding regions (e.g., exons and miRNA) optionally comprising 5'- or 3 '-untranslated sequences linked thereto.
  • a gene may also be an amplified nucleic acid molecule produced in vitro comprising all or a part of the coding region and/or 5'- or 3 '-untranslated sequences linked thereto. identity
  • Identity as used herein in the context of two or more nucleic acids or polypeptide sequences may mean that the sequences have a specified percentage of residues that are the same over a specified region. The percentage may be calculated by optimally aligning the two sequences, comparing the two sequences over the specified region, determining the number of positions at which the identical residue occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the specified region, and multiplying the result by 100 to yield the percentage of sequence identity, hi cases where the two sequences are of different lengths or the alignment produces one or more staggered ends and the specified region of comparison includes only a single sequence, the residues of single sequence are included in the denominator but not the numerator of the calculation. When comparing DNA and RNA, thymine (T) and uracil (U) may be considered equivalent. Identity may be performed manually or by using a computer sequence algorithm such as BLAST or BLAST 2.0. label
  • Label as used herein may mean a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, chemical, or other physical means.
  • useful labels include 32 P, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin, digoxigenin, or haptens and other entities which can be made detectable.
  • a label may be incorporated into nucleic acids and proteins at any position.
  • logistic regression Logistic regression is part of a category of statistical models called generalized linear models. Logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these.
  • the dependent or response variable is dichotomous, for example, one of two possible types of cancer.
  • Logistic regression models the natural log of the odds ratio, i.e. the ratio of the probability of belonging to the first group (P) over the probability of belonging to the second group (1-P), as a linear combination of the different expression levels (in log-space) and of other explaining variables.
  • the logistic regression output can be used as a classifier by prescribing that a case or sample will be classified into the first type if P is greater than 0.5 or 50%.
  • the calculated probability P can be used as a variable in other contexts such as a ID or 2D threshold classifier.
  • 1D/2D threshold classifier used herein may mean an algorithm for classifying a case or sample such as a cancer sample into one of two possible types such as two types of cancer or two types of prognosis (e.g. good and bad).
  • ID threshold classifier the decision is based on one variable and one predetermined threshold value; the sample is assigned to one class if the variable exceeds the threshold and to the other class if the variable is less than the threshold.
  • a 2D threshold classifier is an algorithm for classifying into one of two types based on the values of two variables. A score may be calculated as a function (usually a continuous function) of the two variables; the decision is then reached by comparing the score to the predetermined threshold, similar to the ID threshold classifier. mismatch
  • mismatch means a nucleobase of a first nucleic acid that is not capable of pairing with a nucleobase at a corresponding position of a second nucleic acid.
  • Nucleic acid or “oligonucleotide” or “polynucleotide” used herein may mean at least two nucleotides covalently linked together.
  • the depiction of a single strand also defines the sequence of the complementary strand.
  • a nucleic acid also encompasses the complementary strand of a depicted single strand.
  • Many variants of a nucleic acid may be used for the same purpose as a given nucleic acid.
  • a nucleic acid also encompasses substantially identical nucleic acids and complements thereof.
  • a single strand provides a probe that may hybridize to a target sequence under stringent hybridization conditions.
  • a nucleic acid also encompasses a probe that hybridizes under stringent hybridization conditions.
  • Nucleic acids may be single stranded or double stranded, or may contain portions of both double stranded and single stranded sequence.
  • the nucleic acid may be DNA, both genomic and cDNA, RNA, or a hybrid, where the nucleic acid may contain combinations of deoxyribo- and ribo-nucleotides, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosine and isoguanine.
  • Nucleic acids may be obtained by chemical synthesis methods or by recombinant methods.
  • a nucleic acid will generally contain phosphodiester bonds, although nucleic acid analogs may be included that may have at least one different linkage, e.g., phosphoramidate, phosphorothioate, phosphorodithioate, or O-methylphosphoroamidite linkages and peptide nucleic acid backbones and linkages.
  • Other analog nucleic acids include those with positive backbones; non-ionic backbones, and non-ribose backbones, including those described in U.S. Pat. Nos. 5,235,033 and 5,034,506, which are incorporated by reference.
  • Nucleic acids containing one or more non-naturally occurring or modified nucleotides are also included within one definition of nucleic acids.
  • the modified nucleotide analog may be located for example at the 5 '-end and/or the 3 '-end of the nucleic acid molecule.
  • Representative examples of nucleotide analogs may be selected from sugar- or backbone-modified ribonucleotides. It should be noted, however, that also nucleobase-modified ribonucleotides, i.e. ribonucleotides, containing a non-naturally occurring nucleobase instead of a naturally occurring nucleobase such as uridines or cytidines modified at the 5-position, e.g.
  • the 2'-OH-group may be replaced by a group selected from H, OR, R, halo, SH, SR, NH 2 , NHR, NR 2 or CN, wherein R is C 1 -C 6 alkyl, alkenyl or alkynyl and halo is F, Cl, Br or I.
  • Modified nucleotides also include nucleotides conjugated with cholesterol through, e.g., a hydroxyprolinol linkage as described in Krutzfeldt et al., Nature 438:685-689 (2005), Soutschek et al., Nature 432:173-178 (2004), and U.S. Patent Publication No. 20050107325, which are incorporated herein by reference.
  • Modifications of the ribose- phosphate backbone may be done for a variety of reasons, e.g., to increase the stability and half-life of such molecules in physiological environments, to enhance diffusion across cell membranes, or as probes on a biochip.
  • the backbone modification may also enhance resistance to degradation, such as in the harsh endocytic environment of cells.
  • the backbone modification may also reduce nucleic acid clearance by hepatocytes, such as in the liver and kidney. Mixtures of naturally occurring nucleic acids and analogs may be made; alternatively, mixtures of different nucleic acid analogs, and mixtures of naturally occurring nucleic acids and analogs may be made.
  • Probe as used herein may mean an oligonucleotide capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation.
  • Probes may bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions. There may be any number of base pair mismatches which will interfere with hybridization between the target sequence and the single stranded nucleic acids described herein. However, if the number of mutations is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence.
  • a probe may be single stranded or partially single and partially double stranded.
  • Probes may be directly labeled or indirectly labeled such as with biotin to which a streptavidin complex may later bind. sensitivity
  • sensitivity used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The sensitivity for class
  • A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A", as determined by some absolute or gold standard.
  • Specificity used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The specificity for class
  • Stringent hybridization conditions used herein may mean conditions under which a first nucleic acid sequence (e.g., probe) will hybridize to a second nucleic acid sequence (e.g., target), such as in a complex mixture of nucleic acids. Stringent conditions are sequence-dependent and will be different in different circumstances. Stringent conditions may be selected to be about 5-10°C lower than the thermal melting point (T m ) for the specific sequence at a defined ionic strength pH.
  • the T m may be the temperature (under defined ionic strength, pH, and nucleic concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at T m , 50% of the probes are occupied at equilibrium).
  • Stringent conditions may be those in which the salt concentration is less than about LO M sodium ion, such as about 0.01-1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30°C for short probes (e.g., about 10-50 nucleotides) and at least about 60°C for long probes (e.g., greater than about 50 nucleotides).
  • Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide.
  • destabilizing agents such as formamide.
  • a positive signal may be at least 2 to 10 times background hybridization.
  • Exemplary stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C. substantially complementary
  • substantially complementary used herein may mean that a first sequence is at least 60%-99% identical to the complement of a second sequence over a region of 8-50 or more nucleotides, or that the two sequences hybridize under stringent hybridization conditions. substantially identical
  • substantially identical used herein may mean that a first and second sequence are at least 60%-99% identical over a region of 8-50 or more nucleotides or amino acids, or with respect to nucleic acids, if the first sequence is substantially complementary to the complement of the second sequence.
  • the term "subject” refers to a mammal, including both human and other mammals.
  • the methods of the present invention are preferably applied to human subjects.
  • therapeutically effective amount As used herein the term “therapeutically effective amount” or “therapeutically efficient” as to a drug dosage, refer to dosage that provides the specific pharmacological response for which the drug is administered in a significant number of subjects in need of such treatment.
  • the “therapeutically effective amount” may vary according, for example, the physical condition of the patient, the age of the patient and the severity of the disease. treat
  • Treat” or “treating” used herein when referring to protection of a subject from a condition may mean preventing, suppressing, repressing, or eliminating the condition.
  • Preventing the condition involves administering a composition described herein to a subject prior to onset of the condition.
  • Suppressing the condition involves administering the composition to a subject after induction of the condition but before its clinical appearance.
  • Repressing the condition involves administering the composition to a subject after clinical appearance of the condition such that the condition is reduced or prevented from worsening.
  • Elimination of the condition involves administering the composition to a subject after clinical appearance of the condition such that the subject no longer suffers from the condition.
  • reference expression profile refers to a criterion expression profile to which measured values are compared in order to determine the prognosis of a subject with lymphoma.
  • the reference expression profile may be based on the abundance of the nucleic acids, or may be based on a combined metric score thereof. reference value
  • the term "reference value” means a value that statistically correlates to a particular outcome when compared to an assay result. In preferred embodiments the reference value is determined from statistical analysis of studies that compare microRNA expression with known clinical outcomes.
  • the reference value may be a threshold score value or a cutoff score value. Typically a reference value will be a threshold above which one outcome is more probable and below which an alternative threshold is more probable.
  • sensitivity "sensitivity” used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The sensitivity for class A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A”, as determined by some absolute or gold standard. specificity
  • Specificity used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types.
  • the specificity for class A is the proportion of cases that are determined to belong to class "not A” by the test out of the cases that are in class "not A”, as determined by some absolute or gold standard. stage of cancer
  • stage of cancer refers to a numerical measurement of the level of advancement of a cancer. Criteria used to determine the stage of a cancer include, but are not limited to, the size of the tumor, whether the tumor has spread to other parts of the body and where the cancer has spread (e.g., within the same organ or region of the body or to another organ).
  • tissue sample As used herein, a tissue sample is tissue obtained from a tissue biopsy using methods well known to those of ordinary skill in the related medical arts.
  • the phrase "suspected of being cancerous” as used herein means a cancer tissue sample believed by one of ordinary skill in the medical arts to contain cancerous cells. Methods for obtaining the sample from the biopsy include gross apportioning of a mass, microdissection, laser- based microdissection, or other art-known cell-separation methods.
  • Tumor refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
  • variant "Variant” used herein to refer to a nucleic acid may mean (i) a portion of a referenced nucleotide sequence; (ii) the complement of a referenced nucleotide sequence or portion thereof; (iii) a nucleic acid that is substantially identical to a referenced nucleic acid or the complement thereof; or (iv) a nucleic acid that hybridizes under stringent conditions to the referenced nucleic acid, complement thereof, or a sequences substantially identical thereto.
  • MicroRNA and its processing may mean (i) a portion of a referenced nucleotide sequence; (ii) the complement of a referenced nucleotide sequence or portion thereof; (iii) a nucleic acid that is substantially identical to a referenced nucleic acid or the complement thereof; or (iv) a nucleic
  • a gene coding for a miRNA may be transcribed leading to production of a miRNA precursor known as the pri-miRNA.
  • the pri-miRNA may be part of a polycistronic RNA comprising multiple pri-miRNAs.
  • the pri-miRNA may form a hairpin with a stem and loop.
  • the stem may comprise mismatched bases.
  • the hairpin structure of the pri-miRNA may be recognized by Drosha, which is an RNase III endonuclease. Drosha may recognize terminal loops in the pri-miRNA and cleave approximately two helical turns into the stem to produce a 30-200 nt precursor known as the pre-miRNA. Drosha may cleave the pri-miRNA with a staggered cut typical of RNase III endonucleases yielding a pre-miRNA stem loop with a 5' phosphate and ⁇ 2 nucleotide 3' overhang. Approximately one helical turn of stem (-10 nucleotides) extending beyond the Drosha cleavage site may be essential for efficient processing. The pre-miRNA may then be actively transported from the nucleus to the cytoplasm by Ran- GTP and the export receptor Ex-portin-5.
  • the pre-miRNA may be recognized by Dicer, which is also an RNase III endonuclease. Dicer may recognize the double-stranded stem of the pre-miRNA. Dicer may also recognize the 5 1 phosphate and 3' overhang at the base of the stem loop. Dicer may cleave off the terminal loop two helical turns away from the base of the stem loop leaving an additional 5' phosphate and ⁇ 2 nucleotide 3' overhang. The resulting siRNA- like duplex, which may comprise mismatches, comprises the mature miRNA and a similar-sized fragment known as the miRNA*. The miRNA and miRNA* may be derived from opposing arms of the pri-miRNA and pre-miRNA.
  • MiRNA* sequences may be found in libraries of cloned miRNAs but typically at lower frequency than the miRNAs. Although initially present as a double-stranded species with miRNA*, the miRNA may eventually become incorporated as a single-stranded RNA into a ribonucleoprotein complex known as the RNA-induced silencing complex (RISC).
  • RISC RNA-induced silencing complex
  • Various proteins can form the RISC, which can lead to variability in specifity for miRNA/miRNA* duplexes, binding site of the target gene, activity of miRNA (repress or activate), and which strand of the miRNA/miRNA* duplex is loaded in to the RISC.
  • the miRNA* When the miRNA strand of the miRNA:miRNA* duplex is loaded into the RISC, the miRNA* may be removed and degraded.
  • the strand of the miRNA:miRNA* duplex that is loaded into the RISC may be the strand whose 5' end is less tightly paired, hi cases where both ends of the miRNA:miRNA* have roughly equivalent 5' pairing, both miRNA and miRNA* may have gene silencing activity.
  • the RISC may identify target nucleic acids based on high levels of complementarity between the miRNA and the mRNA, especially by nucleotides 2-8 of the miRNA. Only one case has been reported in animals where the interaction between the miRNA and its target was along the entire length of the miRNA. This was shown for mir- 196 and Hox B8 and it was further shown that mir-196 mediates the cleavage of the Hox B8 mRNA (Yekta et al 2004, Science 304-594). Otherwise, such interactions are known only in plants (Bartel & Bartel 2003, Plant Physiol 132-709).
  • miRNAs may direct the RISC to downregulate gene expression by either of two mechanisms: mRNA cleavage or translational repression.
  • the miRNA may specify cleavage of the mRNA if the mRNA has a certain degree of complementarity to the miRNA. When a miRNA guides cleavage, the cut may be between the nucleotides pairing to residues 10 and 11 of the miRNA. Alternatively, the miRNA may repress translation if the miRNA does not have the requisite degree of complementarity to the miRNA. Translational repression may be more prevalent in animals since animals may have a lower degree of complementarity between the miRNA and binding site.
  • any pair of miRNA and miRNA* there may be variability in the 5' and 3' ends of any pair of miRNA and miRNA*. This variability may be due to variability in the enzymatic processing of Drosha and Dicer with respect to the site of cleavage. Variability at the 5' and 3' ends of miRNA and miRNA* may also be due to mismatches in the stem structures of the pri-miRNA and pre-rm ' RNA. The mismatches of the stem strands may lead to a population of different hairpin structures. Variability in the stem structures may also lead to variability in the products of cleavage by Drosha and Dicer. c. Nucleic Acids
  • nucleic acids are provided herein.
  • the nucleic acid may comprise the sequence of SEQ ID NOS: 1-108 or variants thereof.
  • the variant may be a complement of the referenced nucleotide sequence.
  • the variant may also be a nucleotide sequence that is substantially identical to the referenced nucleotide sequence or the complement thereof.
  • the variant may also be a nucleotide sequence which hybridizes under stringent conditions to the referenced nucleotide sequence, complements thereof, or nucleotide sequences substantially identical thereto.
  • the nucleic acid may have a length of from 10 to 250 nucleotides.
  • the nucleic acid may have a length of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200 or 250 nucleotides.
  • the nucleic acid may be synthesized or expressed in a cell (in vitro or in vivo) using a synthetic gene described herein.
  • the nucleic acid may be synthesized as a single strand molecule and hybridized to a substantially complementary nucleic acid to form a duplex.
  • the nucleic acid may be introduced to a cell, tissue or organ in a single- or double- stranded form or capable of being expressed by a synthetic gene using methods well known to those skilled in the art, including as described in U.S. Patent No. 6,506,559 which is incorporated by reference.
  • Nucleic acid complex The nucleic acid may further comprise one or more of the following: a peptide, a protein, a RNA-DNA hybrid, an antibody, an antibody fragment, a Fab fragment, and an aptamer.
  • the nucleic acid may also comprise a protamine-antibody fusion protein as described in Song et al (Nature Biotechnology 2005 ;23: 709- 17) and Rossi (Nature Biotechnology 2005:23;682-4), the contents of which are incorporated herein by reference.
  • the protamine-fusion protein may comprise the abundant and highly basic cellular protein protamine.
  • the protamine may readily interact with the nucleic acid.
  • the protamine may comprise the entire 51 amino acid protamine peptide or a fragment thereof.
  • the protamine may be covalently attached to another protein, which may be a Fab.
  • the Fab may bind to a receptor expressed on a cell surface.
  • the nucleic acid may comprise a sequence of a pri-miRNA or a variant thereof.
  • the pri-miRNA sequence may comprise from 45-30,000, 50-25,000, 100-20,000, 1,000- 1,500 or 80-100 nucleotides.
  • the sequence of the pri-miRNA may comprise a pre- miRNA, miRNA and miRNA*, as set forth herein, and variants thereof.
  • the sequence of the pri-miRNA may comprise the sequence of SEQ ID NOS: 1-59 or variants thereof.
  • the pri-miRNA may form a hairpin structure.
  • the hairpin may comprise first and second nucleic acid sequence that are substantially complimentary.
  • the first and second nucleic acid sequence may be from 37-50 nucleotides.
  • the first and second nucleic acid sequence may be separated by a third sequence of from 8-12 nucleotides.
  • the hairpin structure may have a free energy less than -25 Kcal/mole as calculated by the Vienna algorithm with default parameters, as described in Hofacker et al., Monatshefte f. Chemie 125: 167-188 (1994), the contents of which are incorporated herein.
  • the hairpin may comprise a terminal loop of 4-20, 8-12 or 10 nucleotides.
  • the pri-miRNA may comprise at least 19% adenosine nucleotides, at least 16% cytosine nucleotides, at least 23% thymine nucleotides and at least 19% guanine nucleotides.
  • the nucleic acid may also comprise a sequence of a pre-miRNA or a variant thereof.
  • the pre-miRNA sequence may comprise from 45-200, 60-80 or 60-70 nucleotides.
  • the sequence of the pre-miRNA may comprise a miRNA and a miRNA* as set forth herein.
  • the sequence of the pre-miRNA may also be that of a pri-miRNA excluding from 0-160 nucleotides from the 5' and 3' ends of the pri-miRNA.
  • the sequence of the pre-miRNA may comprise the sequence of SEQ ED NOS: 1-59 or variants thereof. iv. MiRNA
  • the nucleic acid may also comprise a sequence of a miRNA (including miRNA*) or a variant thereof.
  • the miRNA sequence may comprise from 13-33, 18-24 or 21-23 nucleotides.
  • the miRNA may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides.
  • the sequence of the miRNA may be the first 13-33 nucleotides of the pre-miRNA.
  • the sequence of the miRNA may also be the last 13-33 nucleotides of the pre-miRNA.
  • the sequence of the miRNA may comprise the sequence of SEQ ID NOS: 1-11, 26-28 and 33-46; or variants thereof.
  • the nucleic acid may also comprise a sequence of an anti-miRNA that is capable of blocking the activity of a miRNA or miRNA*, such as by binding to the pri-miRNA, pre-miRNA, miRNA or miRNA* (e.g. antisense or RNA silencing), or by binding to the target binding site.
  • the anti-miRNA may comprise a total of 5-100 or 10-60 nucleotides.
  • the anti-miRNA may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides.
  • the sequence of the anti-miRNA may comprise (a) at least 5 nucleotides that are substantially identical or complimentary to the 5' of a miRNA and at least 5-12 nucleotides that are substantially complimentary to the flanking regions of the target site from the 5' end of the miRNA, or (b) at least 5-12 nucleotides that are substantially identical or complimentary to the 3' of a miRNA and at least 5 nucleotide that are substantially complimentary to the flanking region of the target site from the 3' end of the miRNA.
  • the sequence of the anti-miRNA may comprise the compliment of SEQ ID NOS: 1-11, 26-28 and 33-46; or variants thereof.
  • the nucleic acid may also comprise a sequence of a target miRNA binding site, or a variant thereof.
  • the target site sequence may comprise a total of 5-100 or 10-60 nucleotides.
  • the target site sequence may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 or 63 nucleotides.
  • the target site sequence may comprise at least 5 nucleotides of the complementarity sequence of SEQ ID NOS: 1-11, 26-28 and 33-46.
  • a synthetic gene is also provided comprising a nucleic acid described herein operably linked to a transcriptional and/or translational regulatory sequence.
  • the synthetic gene may be capable of modifying the expression of a target gene with a binding site for a nucleic acid described herein. Expression of the target gene may be modified in a cell, tissue or organ.
  • the synthetic gene may be synthesized or derived from naturally- occurring genes by standard recombinant techniques.
  • the synthetic gene may also comprise terminators at the 3'-end of the transcriptional unit of the synthetic gene sequence.
  • the synthetic gene may also comprise a selectable marker.
  • Probes A probe is also provided comprising a nucleic acid described herein. Probes may be used for screening and diagnostic methods, as outlined below. The probe may be attached or immobilized to a solid substrate, such as a biochip.
  • the probe may have a length of from 8 to 500, 10 to 100 or 20 to 60 nucleotides.
  • the probe may also have a length of at least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160,
  • the probe may further comprise a linker sequence of from 10-60 nucleotides.
  • a biochip is also provided.
  • the biochip may comprise a solid substrate comprising an attached probe or plurality of probes described herein.
  • the probes may be capable of hybridizing to a target sequence under stringent hybridization conditions.
  • the probes may be attached at spatially defined address on the substrate. More than one probe per target sequence may be used, with either overlapping probes or probes to different sections of a particular target sequence.
  • the probes may be capable of hybridizing to target sequences associated with a single disorder appreciated by those in the art.
  • the probes may either be synthesized first, with subsequent attachment to the biochip, or may be directly synthesized on the biochip.
  • the solid substrate may be a material that may be modified to contain discrete individual sites appropriate for the attachment or association of the probes and is amenable to at least one detection method.
  • substrates include glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, TeflonJ, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses and plastics.
  • the substrates may allow optical detection without appreciably fluorescing.
  • the substrate may be planar, although other configurations of substrates may be used as well. For example, probes may be placed on the inside surface of a tube, for flow- through sample analysis to minimize sample volume.
  • the substrate may be flexible, such as a flexible foam, including closed cell foams made of particular plastics.
  • the biochip and the probe may be derivatized with chemical functional groups for subsequent attachment of the two.
  • the biochip may be derivatized with a chemical functional group including, but not limited to, amino groups, carboxyl groups, oxo groups or thiol groups.
  • the probes may be attached using functional groups on the probes either directly or indirectly using a linker.
  • the probes may be attached to the solid support by either the 5' terminus, 3' terminus, or via an internal nucleotide.
  • the probe may also be attached to the solid support non-covalently.
  • biotinylated oligonucleotides can be made, which may bind to surfaces covalently coated with streptavidin, resulting in attachment.
  • probes may be synthesized on the surface using techniques such as photopolymerization and photolithography.
  • a method of diagnosis comprises detecting a differential expression level of lymphoma-associated nucleic acid in a biological sample.
  • the sample may be derived from a patient. Diagnosis of a disease state in a patient may allow for prognosis and selection of therapeutic and follow-up strategy. Furthermore, the developmental stage of cells may be classified by determining temporarily expressed lymphoma-associated nucleic acids.
  • In situ hybridization of labeled probes to tissue sections may be performed.
  • the skilled artisan can make a diagnosis, a prognosis, or a prediction based on the findings. It is further understood that the nucleic acids which indicate the diagnosis may differ from those which indicate the prognosis and molecular profiling of the condition of the cells may lead to distinctions between responsive or refractory conditions or may be predictive of outcomes.
  • Biomarkers are also provided.
  • One type of cancer screening test involves the detection of a biomarker, such as a tumor marker, in a fluid or tissue obtained from a patient.
  • Tumor markers are substances produced by cancer cells that are not typically produced by normal cells. These substances generally can be detected in the body fluids or tissues of patients with cancer.
  • Another important use for tumor markers is for monitoring patients being treated for advanced cancer. Measuring tumor markers for this purpose can be less invasive, less time-consuming, as well as less expensive, than other complicated tests, to determine if a therapy is reducing the cancer.
  • a further important use for tumor markers is for determining a prognosis of survival of a cancer patient.
  • Such prognostic methods can be used to identify surgically treated patients likely to experience cancer recurrence so that they can be offered additional therapeutic options.
  • kits may comprise a nucleic acid described herein together with any or all of the following: assay reagents, buffers, probes and/or primers, and sterile saline or another pharmaceutically acceptable emulsion and suspension base.
  • the kits may include instructional materials containing directions (e.g., protocols) for the practice of the methods described herein.
  • the kit may be a kit for the amplification, detection, identification or quantification of a target nucleic acid sequence.
  • the kit may comprise a poly(T) primer, a forward primer, a reverse primer, and a probe.
  • FFPE fluorescence-activated protein FFPE
  • RNA enriched in microRNA was isolated from the FFPE tumor specimens, and all RNAs extracted were hybridized onto microarrays according to the RNA extraction and array platform protocols described below.
  • Microarrav platform Custom microarrays were produced by printing DNA oligonucleotide probes representing 688 human microRNAs. Each probe, printed in triplicate, carries up to 22-nt linker at the 3' end of the microRNA's complement sequence in addition to an amine group used to couple the probes to coated glass slides. 20 ⁇ M of each probe were dissolved in 2X SSC + 0.0035% SDS and spotted in triplicate on Schott Nexterion® Slide E coated microarray slides using a Genomic Solutions® BioRobotics MicroGrid II according the MicroGrid manufacturer's directions. 54 negative control probes were designed using the sense sequences of different microRNAs.
  • RNA samples Two groups of positive control probes were designed to hybridize to microarray (i) synthetic small RNA were spiked to the RNA before labeling to verify the labeling efficiency and (ii) probes for abundant small RNA (e.g. small nuclear RNAs (U43, U49, U24, Z30, U6, U48, U44), 5.8s and 5s ribosomal RNA) are spotted on the array to verify RNA quality.
  • the slides were blocked in a solution containing 50 mM ethanolamine, IM Tris (pH9.0) and 0.1% SDS for 20 min at 50 0 C, then thoroughly rinsed with water and spun dry.
  • RNA-linker p-rCrU-Cy/dye (Dharmacon)
  • the labeling reaction contained total RNA, spikes (0.1-20 fmoles), 300ng RNA- linker-dye, 15% DMSO, Ix ligase buffer and 20 units of T4 RNA ligase (NEB) and proceeded at 4 0 C for lhr followed by lhr at 37 0 C.
  • the labeled RNA was mixed with 3x hybridization buffer (Ambion), heated to 95 0 C for 3 min and then added on top of the miR array. Slides were hybridized 12-16hr in 42 0 C, followed by two washes in room temperature with IxSSC and 0.2% SDS and a final wash with O.lxSSC.
  • Arrays were scanned using an Agilent Microarray Scanner Bundle G2565BA (resolution of 10 ⁇ m at 100% power). Array images were analyzed using SpotReader software (Niles Scientific).
  • Measurements of the expression of miRs were log-transformed before all further analysis. Normalization of samples was performed by calculating a median reference vector. For each sample, the best fit to this reference vector was calculated using a 2 nd degree polynomial. For analyses comparing the expression of miRs in two distinct groups ("good" vs.
  • Kaplan- Meier survival analysis was performed. The first step was the calculation of a logistic regression model, fitting the normalized expression to the two prognosis groups ("bad” was defined as 1, "good” was defined as 0). The cutoff used for plotting the two curves in the Kaplan Meier plot was that which gave the best fit of the predicted logistic regression values to the bad/good classification. Statistical significance was assessed using log rank. P-value ⁇ 0.05 was considered significant. f. qRT-PCR
  • RNA was incubated in the presence of poly (A) polymerase (PoIy(A) Polymerase NEB- M0276L), MnCl 2, and ATP for 1 hour at 37°C. Then, using an oligodT primer harboring a consensus sequence, reverse transcription was performed on total RNA using Superscript II RT (Invitrogen, Carlsbad, CA). Next, the cDNA was amplified by RT-PCR; this reaction contained a microRNA-specific forward primer, a TaqMan (MGB) probe complementary to the 3' of the specific microRNA sequence as well as to part of the polyA adaptor sequence, and a universal reverse primer complementary to the consensus 3' sequence of the oligodT tail.
  • A polymerase
  • MnCl 2 MnCl 2
  • the cycle threshold (Ct, the PCR cycle at which probe signal reaches the threshold) was determined for each microRNA. To allow comparison with results from the microarray, each value received was subtracted from 50. This 50-Ct (50 ⁇ ) expression for each microRNA for each patient was compared with the signal obtained by the microarray method. Linear regression for the microRNA readings over all patients was used to model
  • microRNAs are able to predict the prognosis of DLBCL patients
  • hsa-miR-19b SEQ ID NO: 1
  • hsa-miR-20a SEQ ID NO: 2
  • hsa-miR-886-5p SEQ ID NO: 3
  • hsa-miR-106a SEQ ID NO: 4
  • hsa-miR-17 SEQ ID NO: 5
  • hsa-miR-150 SEQ ID NO: 6
  • hsa-miR-342-3p SEQ ID NO: 7
  • hsa-miR-100 SEQ ID NO: 8
  • hsa- miR-768-3p SEQ ID NO: 9
  • hsa-miR-125b SEQ ID NO: 10
  • hsa-miR-181a SEQ ID NO: 11
  • the microRNA name is the miRBase registry name (release 10).
  • the median expression of hsa-miR-181a (SEQ ID NO: 11) in the group of patients with bad prognosis is significantly lower than the corresponding expression in the group of patients with good prognosis, with a p-value of 0.0015 and a fold change of 1.5.
  • expression values of hsa-miR-181a is indicative of the prognosis of DLBCL.
  • Example 4 The separation between DLBCL patients with bad prognosis and DLBCL patients with good prognosis using a combination of two or three microRNA biomarkers
  • hsa-miR-17 SEQ ED NO: 5
  • hsa-miR-342-3p SEQ ID NO: 7
  • the sensitivity of the detection is 83% and the specificity of the detection is 63%.
  • Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.7543.
  • hsa-miR-17 SEQ ID NO: 5
  • hsa-miR-768-3p SEQ ID NO: 7
  • ROC Receiver operating characteristic
  • microRNAs were selected for quantitative real-time PCR (qRT-PCR) analysis. 18 of these microRNAs (SEQ ID NOS: 1, 2, 4-8, 10, 11, 27, 33-40) were selected as differential probes for prognosis and six non-differential microRNAs (SEQ ID NOS: 41- 46) were chosen for signal normalization.
  • one microRNA biomarker hsa-miR-17* (SEQ ID NO: 33) can be used for the separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis.
  • the sensitivity of the detection is 83% and the specificity of the detection is 100%.
  • Receiver operating characteristic (ROC) has an area under the curve (AUC) of 0.92857.
  • hsa-miR-181a SEQ ID NO: 11
  • hsa-miR-92a SEQ ID NO: 39
  • the sensitivity of the detection is 100% and the specificity of the detection is 82%.
  • Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.97521.
  • hsa-miR-342-3p SEQ ID NO:7
  • ROC Receiver operating characteristic

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Abstract

Described herein are compositions and methods for prognosis of malignant lymphoma. The compositions are microRNA molecules associated with the prognosis of lymphoma, as well as various nucleic acid molecules relating thereto or derived thereof.

Description

COMPOSITIONS AND METHODS FOR THE PROGNOSIS OF
LYMPHOMA
CROSS REFERENCE TO RELATED APPLICATIONS The present application claims priority under 35 U.S. C. § 119(e) to U.S.
Provisional Application No. 61/088,015, filed August 12, 2008; which is herein incorporated by reference in its entirety.
FIELD OF THE INVENTION
The present invention relates to compositions and methods for determining the prognosis of malignant lymphoma. Specifically the invention relates to microRNA molecules associated with the prognosis of lymphoma, as well as various nucleic acid molecules relating thereto or derived thereof.
BACKGROUND OF THE INVENTION
In recent years, microRNAs (miRs) have emerged as an important novel class of regulatory RNA, which have a profound impact on a wide array of biological processes.
These small (typically 18-24 nucleotides long) non-coding RNA molecules can modulate protein expression patterns by promoting RNA degradation, inhibiting mRNA translation, and also affecting gene transcription. miRs play pivotal roles in diverse processes such as development and differentiation, control of cell proliferation, stress response and metabolism. The expression of many miRs was found to be altered in numerous types of human cancer, and in some cases strong evidence has been put forward in support of the conjecture that such alterations may play a causative role in tumor progression. There are currently about 800 known human miRs.
Diffuse large B-cell lymphoma (DLBCL) accounts for 30-40% of all adult non- Hodgkin's lymphomas and is heterogeneous in terms of its morphology and clinical features (Harris et al., 1994, Blood, 84: 1361-1392). Approximately 50% of patients relapse after treatment and their tumors frequently become resistant to therapy. The genetic abnormalities underlying DLBCL remain poorly understood. Gene expression profiling (mRNA microarrays) has been shown to stratify patient with DLBCL into three prognostic groups. So far, these findings have not been translated to clinical practice. Thus, there exists a need for identification of new biomarkers that can be used as prognostic indicators for lymphoma.
SUMMARY OF THE INVENTION
According to the present invention the expression profile of specific miRs (SEQ ED NOS: 1-11, 26-28, 33, 39) in biological samples obtained from lymphoma patients are indicative of the cancer prognosis: the life expectancy of the patient, the expected recurrence- free survival, response to treatment and risk of recurrence.
According to one aspect of the invention, a method for determining a prognosis for lymphoma in a subject is provided, the method comprising: (a) obtaining a biological sample from the subject;
(b) determining an expression profile in said sample of nucleic acid sequences selected from the group consisting of SEQ ID NOS: 1-33, 39 and 52; and a sequence having at least about 80% identity thereto; and
(c) comparing said expression profile to a reference value, whereby altered expression levels of the nucleic acid sequences is indicative of the prognosis of said subject.
According to some embodiments, said altered expression level is a change in a score based on a combination of expression levels of said nucleic acid sequences.
According to one embodiment, said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 1-5, 12-17, 26, 29, 33, 39 and 52; and sequences at least about 80% identical thereto and said expression levels above said reference value is indicative of poor prognosis in said subject.
According to another embodiment, said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 6-11, 18-25 and 27-32; and sequences at least about 80% identical thereto, and said expression levels below said reference value is indicative of poor prognosis in said subject.
In certain embodiments, said lymphoma is a B cell lymphoma. In certain embodiments, said B-cell lymphoma is diffuse large B cell lymphoma.
In certain embodiments, the subject is a human. In certain embodiments, the method is used to determine a course of treatment of the subject. In certain embodiments, the biological sample obtained from the subject is selected from the group consisting of bodily fluid, a cell line and a tissue sample. In certain embodiments the tissue is a fresh, frozen, fixed, wax-embedded or formalin fixed paraffin- embedded (FFPE) tissue. In certain embodiments said tissue is a lymphoid tissue. In certain embodiments said tissue is a lymph node.
According to some embodiments, the expression levels are determined by a method selected from the group consisting of nucleic acid hybridization, nucleic acid amplification, and a combination thereof. According to some embodiments, the nucleic acid hybridization is performed using a solid-phase nucleic acid biochip array or in situ hybridization.
According to other embodiments, the nucleic acid amplification method is quantitative real-time PCR. According to some embodiments, the PCR method comprises forward and reverse primers. According to other embodiments, the forward primer comprises a sequence selected from the group consisting of SEQ ID NOS: 60-83, a fragment thereof and a sequence having at least about 80% identity thereto. According to other embodiments, the reverse primer comprises SEQ ID NO: 108, a fragment thereof and a sequence at least about 80% identical thereto.
According to some embodiments, the real-time PCR method further comprises a probe.
According to some embodiments, the probe comprising a nucleic acid sequence that is complementary to a sequence selected from SEQ ID NOS: 1-33, 39 and 52; to a fragment thereof and to a sequence at least about 80% identical thereto.
According to other embodiments, the probe comprises a sequence selected from the group consisting of any one of SEQ ID NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto.
A kit for determining the prognosis of a subject with lymphoma is also provided. The kit may comprise a probe comprising a nucleic acid sequence that is complementary to a sequence selected from SEQ ID NOS: 1-33, 39 and 52; to a fragment thereof and to a sequence at least about 80% identical thereto. According to some embodiments, said probe comprising a nucleic acid sequence selected from the group consisting of SEQ ED NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto. According to some embodiments, the kit further comprises forward and reverse primers. According to some embodiments, the forward primer comprising a sequence selected from the group consisting of SEQ ID NOS: 60-83, a fragment thereof and a sequence having at least about 80% identity thereto. According to other embodiments, the kit further comprises a reverse primer comprising SEQ ID NO: 108, a fragment thereof and sequences having at least about 80% identity thereto.
According to other embodiments, the kit comprises reagents for performing in situ hybridization analysis. hi some embodiments, prognostic for lymphoma comprises providing the forecast or prediction of (prognostic for) any one or more of the following: duration of survival of a patient susceptible to or diagnosed with lymphoma, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with lymphoma, response rate in a group of patients susceptible to or diagnosed with lymphoma, and duration of response in a patient or a group of patients susceptible to or diagnosed with lymphoma. In some embodiments, duration of survival is forecast or predicted to be increased. In some embodiment, duration of survival is forecast or predicted to be decreased. In some embodiments, duration of recurrence- free survival is forecast or predicted to be increased. In some embodiments, duration of recurrence-free survival is forecast or predicted to be decreased, hi some embodiments, response rate is forecast or predicted to be increased. In some embodiments, response rate is forecast or predicted to be decreased. In some embodiments, duration of response is predicted or forecast to be increased, hi some embodiments, duration of response is predicted or forecast to be decreased. These and other embodiments of the present invention will become apparent in conjunction with the figures, description and claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a graph showing differential expression of miRs (in fluorescence units as measured by a microarray) in samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from
DLBCL patients with bad prognosis (no remission following treatment, or relapse within 9 months), comparing the median values of each miR in all patients in one group with the corresponding median for members of the other group. The Y axis represents patients with bad prognosis (46 patients), and the X axis represents patients with good prognosis (43 patients). The parallel lines describe a fold change between groups of 1.5 in either direction. Statistically significant miRs (P-value<0.05 and adjusting for FDR, see below) are marked with circles: hsa-miR-19b (SEQ ID NO: 1), hsa-miR-20a (SEQ ID NO: 2), hsa- miR-886-5p (SEQ ED NO: 3); hsa-miR-106a (SEQ ID NO: 4); hsa-miR-17 (SEQ ID NO: 5); hsa-miR-150 (SEQ ID NO: 6); hsa-miR-342-3p (SEQ ID NO: 7); hsa-miR-100 (SEQ ID NO: 8); hsa-miR-768-3p (SEQ ID NO: 9); hsa-miR-125b (SEQ ID NO: 10); hsa-miR- 181a (SEQ ID NO: 11). P-values are calculated by Mann-Whitney test, and significance is adjusted using FDR (false discovery rate) of 0.1. Not tested- control probes or median signal<300 in both groups.
Figures 2A-2D are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-19b (SEQ ID NO: 1) (2A), hsa-miR-20a (SEQ ID NO: 2) (2B), hsa-miR-886-5p (SEQ ID NO: 3) (2C), and hsa-miR- 106a (SEQ ID NO: 4) (2D) in tumor samples obtained from DLBCL patients with bad or good prognosis (as defined in figure 1). For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis. The line in the box indicates the median value. The box covers the interquartile range and the horizontal lines and crosses (outliers) show the full range of signals in this group.
Figures 3A-3D are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-17 (SEQ ID NO: 5) (3A), hsa-miR-150 (SEQ ID NO: 6) (3B), hsa-miR-342-3p (SEQ ID NO: 7) (3C) and hsa-miR-100 (SEQ ID NO: 8) (3D) in tumor samples obtained from lymphoma patients with bad or good prognosis. For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis. The line in the box indicates the median value. The box covers the interquartile range and the horizontal lines and crosses (outliers) show the full range of signals in this group. Figures 4A-4C are boxplot presentations comparing distributions of the expression as measured by a microarray of the statistically significant miRs: hsa-miR-768-3p (SEQ ID NO: 9) (4A), hsa-miR-125b (SEQ ID NO: 10) (4B), and hsa-miR-181a (SEQ ID NO: 11) (4C) in tumor samples obtained from lymphoma patients with bad or good prognosis. For each miR two boxes are shown, the left box is for the group of patients with bad prognosis while the right box is for the group of patients with good prognosis. The line in the box indicates the median value. The box covers the interquartile range and the horizontal lines and crosses (outliers) show the full range of signals in this group. Figure 5 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients split by expression of hsa-miR-181a (SEQ ID NO: 11) (p-value 0.00206). The y-axis depicts fraction of survival and the X-axis depicts months of survival, with the solid line representing the highest scoring (the predicted probability resulting from the logistic regression which fit the logged normalized expression value to the bad or good prognosis) (n=56, < 0.57445, Median time (the time when half of the patients are still recurrence-free): 95.082) and the dashed line depicting the lowest scoring (n=34, > 0.57445, Median time: 0). The highlighted background corresponds to the first 9 months (zoomed-in in figure 6).
Figure 6 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients split by expression of hsa-miR-181a (SEQ ID NO: 11) (p-value
0.00083) up to 9 months. The y-axis depicts fraction of survival and the X-axis depicts months of survival, with the solid line representing the highest scoring (the predicted probability resulting from the logistic regression which fit the logged normalized expression value to the bad or good prognosis) (n=56, < 0.57445) and the dashed line depicting the lowest scoring (n=34, > 0.57445).
Figures 7A-7C demonstrate the detection of DLBCL patients with bad prognosis (circles) and lymphoma patients with good prognosis (squares) using a combination of two microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5) and hsa-miR-342-3p (SEQ ID NO: 7). The expression scores are shown sorted by their rank. Fig. 7A: The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis), separately for the two groups. Fig 7B: same but split by group. The sensitivity of the detection of bad prognosis is 83% and the specificity of the detection is 63%. Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.7543 (Fig. 7C).
Figures 8A-8C demonstrate the detection of lymphoma patients with bad prognosis (circles) and lymphoma patients with good prognosis (squares) using a combination of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768- 3p (SEQ ID NO: 9) and hsa-miR-181a (SEQ ID NO: 11). Fig 8A: The expression scores are shown sorted by their rank. The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the three miRs (probability value shown on the vertical axis), separately for the two groups. Fig 8B: same but split by group. The sensitivity of the detection of bad prognosis is 91% and the specificity of the detection is 57%. Receiver operating characteristic (ROC) for the metric defined by the combination of these three microRNAs has an area under the curve (AUC) of 0.78059 (Fig. 8C).
Figure 9 is a Kaplan Meier plot for persistence (survival) of recurrence-free status of DLBCL patients using a combination of the log2 expression of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768-3p (SEQ ID NO: 9) and hsa-miR-
181a (SEQ ID NO: 11) with the coefficients 0.714, 0.727 and -0.49 respectively. The y- axis depicts fraction of survival and the X-axis depicts months of survival, with the dashed line representing the highest scoring (n=59, > 0.35563, Median time: 134) and the solid line depicting the lowest scoring (n=31, < 0.35563, Median time=0). The highlighted background corresponds to the first 9 months (zoomed-in in figure 10).
Figure 10 is a Kaplan Meier plot for persistence (survival) of recurrence- free status of DLBCL patients up to 9 months using a combination of the log2 expression of three microRNA biomarkers: hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768-3p (SEQ ID NO: 9) and hsa-miR-181a (SEQ ID NO: 11) with the coefficients 0.714, 0.727 and -0.49 respectively. The y-axis depicts fraction of survival and the X-axis depicts months of survival, with the dashed line representing the highest scoring (the predicted probability resulting from the logistic regression which fits the logged normalized expression value to the bad or good prognosis) (n=59, > 0.35563) and the solid line depicting the lowest scoring (n=31, < 0.35563).
Figure 11 is a graph showing differential expression of miRs (in fluorescence units) as measured by a microarray) in lymph node samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from DLBCL patients with bad prognosis (no remission, following treatment or relapse within 9 months), comparing the median values of each miR in all patients in a group with the corresponding median for members of the other group. The Y axis represents patients with bad prognosis (41 patients), and the X axis represents patients with good prognosis (32 patients). The parallel lines describe a fold change between groups of 1.5 in either direction. Statistically significant miRs are marked with circles: hsa-miR-19b (SEQ ID NO: 1), hsa-miR-20a (SEQ ID NO: 2); hsa-miR-106a (SEQ ID NO: 4); hsa-miR- 17 (SEQ ID NO: 5), hsa-miR-146b-5p (SEQ ID NO: 26), hsa-miR-140-3p (SEQ ID NO: 27), hsa-miR-138 (SEQ ID NO: 28). P-values are calculated by Mann- Whitney test, and all miRs were significant using FDR (false discovery rate) of 0.1 . Not tested- control probes or median signal<300 in both groups.
Figure 12 is a graph showing differential expression of miRs (50-Ct) as measured by quantitative real-time PCR (qRT-PCR) in samples obtained from DLBCL patients with good prognosis (complete remission following treatment and no relapse within 5 years) and from DLBCL patients with bad prognosis (no remission following treatment, or relapse within 9 months), comparing the median values of each miR in all patients in one group with the corresponding median for members of the other group. The Y axis represents patients with good prognosis (11 patients), and the X axis represents patients with bad prognosis (11 patients). The parallel lines describe a fold change between groups of 1.5 in either direction. Differential miRs (fold change > 1.5) are marked with circles: hsa-miR-17* (SEQ ID NO: 33), hsa-miR-150 (SEQ ID NO: 6), hsa-miR-106a (SEQ ID NO: 4), hsa-miR-181a (SEQ ID NO: 11), hsa-miR-17 (SEQ ID NO: 5), hsa-miR-20a (SEQ ID NO: 2), hsa-miR-92a (SEQ ID NO: 39), hsa-miR-19b (SEQ ID NO: 1), hsa-miR-342- 3p (SEQ ID NO: 7) and hsa-miR-100 (SEQ ID NO: 8). Figures 13 A-13 J are boxplot presentations comparing the expression as measured by qRT-PCR of the differential miRs: hsa-miR-342-3p (SEQ ID NO: 7) (13A), hsa-miR- 150 (SEQ ID NO: 6) (13B), hsa-miR-100 (SEQ ID NO: 8) (13C), hsa-miR-181a (SEQ ID NO: 11) (13D), hsa-miR-17* (SEQ ID NO: 33) (13E), hsa-miR-20a (SEQ ID NO: 2) (13F), hsa-miR-19b (SEQ ID NO: 1) (13G), hsa-miR-92a (SEQ ID NO: 39) (13H), hsa- miR-17 (SEQ ID NO: 5) (131) and hsa-miR-106a (SEQ ID NO: 4) (13J) in a test set of tumor samples obtained from DLBCL patients with bad (n=l l) or good prognosis (n=l l). For each miR the two groups are shown, the left group is of patients with bad prognosis while the right group is of patients with good prognosis. The middle lines indicate the median values. Figures 14A-14D demonstrate the separation of DLBCL patients with bad prognosis from lymphoma patients with good prognosis in the test set of 13 samples using one microRNA biomarker: hsa-miR-17* (SEQ ID NO: 33). Figure 14A is a graph showing the signal of hsa-miR-17*, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and DLBCL patients with good prognosis (squares). The samples are sorted (along the horizontal axis) according to increasing values of hsa-miR-17* (value shown on the vertical axis). Figure 14B shows the expression levels of hsa-miR-17* in six tumor samples originating from DLBCL patients with bad prognosis (circles) and seven tumor samples originating from DLBCL patients with good prognosis (squares). The patients are sorted by increasing values of hsa-miR-17* (value shown on the vertical axis), but separately for the two groups. Figure 14C depicts the histograms of frequency of expression for the two groups. The dotted line shows bad prognosis cases and the solid line good prognosis cases. Figure 14D is the Response Operator Curve showing that the sensitivity (vertical axis) and specificity (1 -Specificity, horizontal axis) of the detection.
Figures 15A-15D demonstrate separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis using a combination of two microRNA biomarkers: hsa-miR-181a (SEQ ID NO: 11) and hsa-miR-92a (SEQ ID NO: 39). Figure 15A is a graph showing a linear combination of the normalized signal of both miRs, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and tumor samples originating from DLBCL patients with good prognosis (squares). The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis). Figure 15B shows the expression levels of both miRs in 11 tumor samples originating from DLBCL patients with bad prognosis (circles) and 11 tumor samples originating from DLBCL patients with good prognosis (squares). The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis), separately for the two groups. Figure 15C depicts the samples expression levels (in 50-CT) for two miRs (x-axis: hsa-miR-181a, y-axis: hsa-miR-92a). Solid diagonal line depicts the best separation according to logistic regression and the dashed lines the 10 and 90% limits of this separation. In this example, one member of each group was misclassified. Figure 15D is the Receiver Operating Characteristic (ROC) curve showing the sensitivity (vertical axis) and specificity (1 -Specificity, horizontal axis) of the detection for various values of the combined score of the two miRs, using the linear combination as defined before, for all samples. Figures 16A-16D demonstrate separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis using a combination of two microRNA biomarkers: hsa-miR-342-3p (SEQ ID NO: 7) and hsa-miR-150 (SEQ ID NO: 6). Figure 16A is a graph showing a linear combination of the normalized signal of both miRs, based on real time PCR analysis, in tumor samples originating from DLBCL patients with bad prognosis (circles) and tumor samples originating from lymphoma patients with good prognosis (squares). The samples are sorted (along the horizontal axis) according to increasing values of the linear combination of the two miRs (probability value shown on the vertical axis). Figure 16B shows the expression levels of both miRs in six tumor samples originating from DLBCL patients with bad prognosis (circles) and seven tumor samples originating from DLBCL patients with good prognosis (squares). Figure 16C depicts the samples expression levels (in 50-CT) for two miRs (x-axis: hsa-miR-342-3p, y- axis: hsa-miR-150). Solid diagonal line depicts the best separation according to logistic regression and the dashed lines the 10 and 90% limits of this separation. In this example, one member of each group was misclassifϊed. Figure 16D is the Receiver Operating Characteristic (ROC) curve showing the sensitivity (vertical axis) and specificity (1- Specificity, horizontal axis) of the detection for various values of the combined score of the two miRs, using the linear combination as defined before, for all samples.
DETAILED DESCRIPTION
According to the present invention miRNA expression can serve as a novel tool for determining the prognosis of lymphoma. More particularly, it may serve for determining the prognosis of long survival versus short survival, long recurrence- free survival vs. short recurrence-free survival, and the response to treatment. Methods and compositions are provided for determining the prognosis of lymphoma. Other aspects of the invention will become apparent to the skilled artisan by the following description of the invention.
Before the present compositions and methods are disclosed and described, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. It must be noted that, as used in the specification and the appended claims, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise. For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0- 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.
a. Definitions attached
"Attached" or "immobilized" as used herein to refer to a probe and a solid support may mean that the binding between the probe and the solid support is sufficient to be stable under conditions of binding, washing, analysis, and removal. The binding may be covalent or non-covalent. Covalent bonds may be formed directly between the probe and the solid support or may be formed by a cross linker or by inclusion of a specific reactive group on either the solid support or the probe or both molecules. Non-covalent binding may be one or more of electrostatic, hydrophilic, and hydrophobic interactions. Included in non-covalent binding is the covalent attachment of a molecule, such as streptavidin, to the support and the non-covalent binding of a biotinylated probe to the streptavidin. Immobilization may also involve a combination of covalent and non-covalent interactions. biological sample "Biological sample" as used herein may mean a sample of biological tissue or fluid that comprises nucleic acids. Such samples include, but are not limited to, tissue isolated from animals. Biological samples may also include sections of tissues such as biopsy and autopsy samples, frozen sections taken for histological purposes, blood, plasma, serum, sputum, stool, tears, mucus, urine, effusions, amniotic fluid, ascitic fluid, hair, and skin. Biological samples also include explants and primary and/or transformed cell cultures derived from patient tissues. A biological sample may be provided by removing a sample of cells from an animal, but can also be accomplished by using previously isolated cells (e.g., isolated by another person, at another time, and/or for another purpose), or by performing the methods described herein in vivo. Archival tissues, such as those having treatment or outcome history, may also be used. cancer prognosis
A forecast or prediction of the probable course or outcome of the cancer. As used herein, cancer prognosis includes the forecast or prediction of any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer. As used herein, "prognostic for cancer" means providing a forecast or prediction of the probable course or outcome of the cancer. In some embodiments, "prognostic for cancer" comprises providing the forecast or prediction of (prognostic for) any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, and duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer. complement
"Complement" or "complementary" as used herein to refer to a nucleic acid may mean Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen base pairing between nucleotides or nucleotide analogs of nucleic acid molecules. A full complement or fully complementary may mean 100% complementary base pairing between nucleotides or nucleotide analogs of nucleic acid molecules. differential expression
"Differential expression" may mean qualitative or quantitative differences in the temporal and/or cellular gene expression patterns within and among cells and tissue. Thus, a differentially expressed gene can qualitatively have its expression altered, including an activation or inactivation, in, e.g., normal versus disease tissue. Genes may be turned on or turned off in a particular state, relative to another state thus permitting comparison of two or more states. A qualitatively regulated gene will exhibit an expression pattern within a state or cell type that may be detectable by standard techniques. Some genes will be expressed in one state or cell type, but not in both. Alternatively, the difference in expression may be quantitative, e.g., in that expression is modulated, up-regulated, resulting in an increased amount of transcript, or down-regulated, resulting in a decreased amount of transcript. The degree to which expression differs need only be large enough to quantify via standard characterization techniques such as expression arrays, quantitative reverse transcriptase PCR, Northern analysis, and RNase protection. expression profile
"Expression profile" as used herein may mean a genomic expression profile, e.g., an expression profile of microRNAs. Profiles may be generated by any convenient means for determining a level of a nucleic acid sequence e.g. quantitative hybridization of microRNA, labeled microRNA, amplified microRNA, cRNA, etc., quantitative PCR, ELISA for quantification, and the like, and allow the analysis of differential gene expression between two samples. A subject or patient tumor sample, e.g., cells or collections thereof, e.g., tissues, is assayed. Samples are collected by any convenient method, as known in the art. Nucleic acid sequences of interest are nucleic acid sequences that are found to be predictive, including the nucleic acid sequences provided above, where the expression profile may include expression data for 5, 10, 20, 25, 50, 100 or more of, including all of the listed nucleic acid sequences. The term "expression profile" may also mean measuring the abundance of the nucleic acid sequences in the measured samples. expression ratio
"Expression ratio" as used herein refers to relative expression levels of two or more nucleic acids as determined by detecting the relative expression levels of the corresponding nucleic acids in a biological sample.
FDR
When performing multiple statistical tests, for example in comparing the signal between two groups in multiple data features, there is an increasingly high probability of obtaining false positive results, by random differences between the groups that can reach levels that would otherwise be considered as statistically significant. In order to limit the proportion of such false discoveries, statistical significance is defined only for data features in which the differences reached a p-value (such as by a two-sided t-test) below a threshold, which is dependent on the number of tests performed and the distribution of p- values obtained in these tests. FDR or false discovery rate is the probability that one of the "significant" results was actually false. FDR adjustment is performed here by the Bejamini-Hochberg method, which compares the p-value with the p-values rank multiplied by the FDR. fragment
"Fragment" is used herein to indicate a non-full length part of a nucleic acid or polypeptide. Thus, a fragment is itself also a nucleic acid or polypeptide, respectively. gene
"Gene" used herein may be a natural (e.g., genomic) or synthetic gene comprising transcriptional and/or translational regulatory sequences and/or a coding region and/or non-translated sequences (e.g., introns, 5'- and 3 '-untranslated sequences). The coding region of a gene may be a nucleotide sequence coding for an amino acid sequence or a functional RNA, such as tRNA, rRNA, catalytic RNA, siRNA, miRNA or antisense RNA. A gene may also be a mRNA or cDNA corresponding to the coding regions (e.g., exons and miRNA) optionally comprising 5'- or 3 '-untranslated sequences linked thereto. A gene may also be an amplified nucleic acid molecule produced in vitro comprising all or a part of the coding region and/or 5'- or 3 '-untranslated sequences linked thereto. identity
"Identical" or "identity" as used herein in the context of two or more nucleic acids or polypeptide sequences may mean that the sequences have a specified percentage of residues that are the same over a specified region. The percentage may be calculated by optimally aligning the two sequences, comparing the two sequences over the specified region, determining the number of positions at which the identical residue occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the specified region, and multiplying the result by 100 to yield the percentage of sequence identity, hi cases where the two sequences are of different lengths or the alignment produces one or more staggered ends and the specified region of comparison includes only a single sequence, the residues of single sequence are included in the denominator but not the numerator of the calculation. When comparing DNA and RNA, thymine (T) and uracil (U) may be considered equivalent. Identity may be performed manually or by using a computer sequence algorithm such as BLAST or BLAST 2.0. label
"Label" as used herein may mean a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, chemical, or other physical means. For example, useful labels include 32P, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin, digoxigenin, or haptens and other entities which can be made detectable. A label may be incorporated into nucleic acids and proteins at any position. logistic regression Logistic regression is part of a category of statistical models called generalized linear models. Logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. The dependent or response variable is dichotomous, for example, one of two possible types of cancer. Logistic regression models the natural log of the odds ratio, i.e. the ratio of the probability of belonging to the first group (P) over the probability of belonging to the second group (1-P), as a linear combination of the different expression levels (in log-space) and of other explaining variables. The logistic regression output can be used as a classifier by prescribing that a case or sample will be classified into the first type if P is greater than 0.5 or 50%. Alternatively, the calculated probability P can be used as a variable in other contexts such as a ID or 2D threshold classifier.
1D/2D threshold classifier
"1D/2D threshold classifier" used herein may mean an algorithm for classifying a case or sample such as a cancer sample into one of two possible types such as two types of cancer or two types of prognosis (e.g. good and bad). For a ID threshold classifier, the decision is based on one variable and one predetermined threshold value; the sample is assigned to one class if the variable exceeds the threshold and to the other class if the variable is less than the threshold. A 2D threshold classifier is an algorithm for classifying into one of two types based on the values of two variables. A score may be calculated as a function (usually a continuous function) of the two variables; the decision is then reached by comparing the score to the predetermined threshold, similar to the ID threshold classifier. mismatch
"Mismatch" means a nucleobase of a first nucleic acid that is not capable of pairing with a nucleobase at a corresponding position of a second nucleic acid. nucleic acid
"Nucleic acid" or "oligonucleotide" or "polynucleotide" used herein may mean at least two nucleotides covalently linked together. The depiction of a single strand also defines the sequence of the complementary strand. Thus, a nucleic acid also encompasses the complementary strand of a depicted single strand. Many variants of a nucleic acid may be used for the same purpose as a given nucleic acid. Thus, a nucleic acid also encompasses substantially identical nucleic acids and complements thereof. A single strand provides a probe that may hybridize to a target sequence under stringent hybridization conditions. Thus, a nucleic acid also encompasses a probe that hybridizes under stringent hybridization conditions.
Nucleic acids may be single stranded or double stranded, or may contain portions of both double stranded and single stranded sequence. The nucleic acid may be DNA, both genomic and cDNA, RNA, or a hybrid, where the nucleic acid may contain combinations of deoxyribo- and ribo-nucleotides, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosine and isoguanine. Nucleic acids may be obtained by chemical synthesis methods or by recombinant methods.
A nucleic acid will generally contain phosphodiester bonds, although nucleic acid analogs may be included that may have at least one different linkage, e.g., phosphoramidate, phosphorothioate, phosphorodithioate, or O-methylphosphoroamidite linkages and peptide nucleic acid backbones and linkages. Other analog nucleic acids include those with positive backbones; non-ionic backbones, and non-ribose backbones, including those described in U.S. Pat. Nos. 5,235,033 and 5,034,506, which are incorporated by reference. Nucleic acids containing one or more non-naturally occurring or modified nucleotides are also included within one definition of nucleic acids. The modified nucleotide analog may be located for example at the 5 '-end and/or the 3 '-end of the nucleic acid molecule. Representative examples of nucleotide analogs may be selected from sugar- or backbone-modified ribonucleotides. It should be noted, however, that also nucleobase-modified ribonucleotides, i.e. ribonucleotides, containing a non-naturally occurring nucleobase instead of a naturally occurring nucleobase such as uridines or cytidines modified at the 5-position, e.g. 5-(2-amino)propyl uridine, 5-bromo uridine; adenosines and guanosines modified at the 8-position, e.g. 8-bromo guanosine; deaza nucleotides, e.g. 7-deaza-adenosine; O- and N-alkylated nucleotides, e.g. N6-methyl adenosine are suitable. The 2'-OH-group may be replaced by a group selected from H, OR, R, halo, SH, SR, NH2, NHR, NR2 or CN, wherein R is C1-C6 alkyl, alkenyl or alkynyl and halo is F, Cl, Br or I. Modified nucleotides also include nucleotides conjugated with cholesterol through, e.g., a hydroxyprolinol linkage as described in Krutzfeldt et al., Nature 438:685-689 (2005), Soutschek et al., Nature 432:173-178 (2004), and U.S. Patent Publication No. 20050107325, which are incorporated herein by reference. Additional modified nucleotides and nucleic acids are described in U.S. Patent Publication No. 20050182005, which is incorporated herein by reference. Modifications of the ribose- phosphate backbone may be done for a variety of reasons, e.g., to increase the stability and half-life of such molecules in physiological environments, to enhance diffusion across cell membranes, or as probes on a biochip. The backbone modification may also enhance resistance to degradation, such as in the harsh endocytic environment of cells. The backbone modification may also reduce nucleic acid clearance by hepatocytes, such as in the liver and kidney. Mixtures of naturally occurring nucleic acids and analogs may be made; alternatively, mixtures of different nucleic acid analogs, and mixtures of naturally occurring nucleic acids and analogs may be made. probe
"Probe" as used herein may mean an oligonucleotide capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation.
Probes may bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions. There may be any number of base pair mismatches which will interfere with hybridization between the target sequence and the single stranded nucleic acids described herein. However, if the number of mutations is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. A probe may be single stranded or partially single and partially double stranded.
The strandedness of the probe is dictated by the structure, composition, and properties of the target sequence. Probes may be directly labeled or indirectly labeled such as with biotin to which a streptavidin complex may later bind. sensitivity
"sensitivity" used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The sensitivity for class
A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A", as determined by some absolute or gold standard. specificity
"Specificity" used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The specificity for class
A is the proportion of cases that are determined to belong to class "not A" by the test out of the cases that are in class "not A", as determined by some absolute or gold standard. stringent hybridization conditions "Stringent hybridization conditions" used herein may mean conditions under which a first nucleic acid sequence (e.g., probe) will hybridize to a second nucleic acid sequence (e.g., target), such as in a complex mixture of nucleic acids. Stringent conditions are sequence-dependent and will be different in different circumstances. Stringent conditions may be selected to be about 5-10°C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. The Tm may be the temperature (under defined ionic strength, pH, and nucleic concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Stringent conditions may be those in which the salt concentration is less than about LO M sodium ion, such as about 0.01-1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30°C for short probes (e.g., about 10-50 nucleotides) and at least about 60°C for long probes (e.g., greater than about 50 nucleotides). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. For selective or specific hybridization, a positive signal may be at least 2 to 10 times background hybridization. Exemplary stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C. substantially complementary
"Substantially complementary" used herein may mean that a first sequence is at least 60%-99% identical to the complement of a second sequence over a region of 8-50 or more nucleotides, or that the two sequences hybridize under stringent hybridization conditions. substantially identical
"Substantially identical" used herein may mean that a first and second sequence are at least 60%-99% identical over a region of 8-50 or more nucleotides or amino acids, or with respect to nucleic acids, if the first sequence is substantially complementary to the complement of the second sequence. subject
As used herein, the term "subject" refers to a mammal, including both human and other mammals. The methods of the present invention are preferably applied to human subjects. therapeutically effective amount As used herein the term "therapeutically effective amount" or "therapeutically efficient" as to a drug dosage, refer to dosage that provides the specific pharmacological response for which the drug is administered in a significant number of subjects in need of such treatment. The "therapeutically effective amount" may vary according, for example, the physical condition of the patient, the age of the patient and the severity of the disease. treat
"Treat" or "treating" used herein when referring to protection of a subject from a condition may mean preventing, suppressing, repressing, or eliminating the condition.
Preventing the condition involves administering a composition described herein to a subject prior to onset of the condition. Suppressing the condition involves administering the composition to a subject after induction of the condition but before its clinical appearance. Repressing the condition involves administering the composition to a subject after clinical appearance of the condition such that the condition is reduced or prevented from worsening. Elimination of the condition involves administering the composition to a subject after clinical appearance of the condition such that the subject no longer suffers from the condition. reference expression proΩle
As used herein, the phrase "reference expression profile" refers to a criterion expression profile to which measured values are compared in order to determine the prognosis of a subject with lymphoma. The reference expression profile may be based on the abundance of the nucleic acids, or may be based on a combined metric score thereof. reference value
As used herein the term "reference value" means a value that statistically correlates to a particular outcome when compared to an assay result. In preferred embodiments the reference value is determined from statistical analysis of studies that compare microRNA expression with known clinical outcomes. The reference value may be a threshold score value or a cutoff score value. Typically a reference value will be a threshold above which one outcome is more probable and below which an alternative threshold is more probable. sensitivity "sensitivity" used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The sensitivity for class A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A", as determined by some absolute or gold standard. specificity
"Specificity" used herein may mean a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies a cancer into the correct type out of two possible types. The specificity for class A is the proportion of cases that are determined to belong to class "not A" by the test out of the cases that are in class "not A", as determined by some absolute or gold standard. stage of cancer
As used herein, the term "stage of cancer" refers to a numerical measurement of the level of advancement of a cancer. Criteria used to determine the stage of a cancer include, but are not limited to, the size of the tumor, whether the tumor has spread to other parts of the body and where the cancer has spread (e.g., within the same organ or region of the body or to another organ). tissue sample As used herein, a tissue sample is tissue obtained from a tissue biopsy using methods well known to those of ordinary skill in the related medical arts. The phrase "suspected of being cancerous" as used herein means a cancer tissue sample believed by one of ordinary skill in the medical arts to contain cancerous cells. Methods for obtaining the sample from the biopsy include gross apportioning of a mass, microdissection, laser- based microdissection, or other art-known cell-separation methods. tumor
"Tumor" as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. variant "Variant" used herein to refer to a nucleic acid may mean (i) a portion of a referenced nucleotide sequence; (ii) the complement of a referenced nucleotide sequence or portion thereof; (iii) a nucleic acid that is substantially identical to a referenced nucleic acid or the complement thereof; or (iv) a nucleic acid that hybridizes under stringent conditions to the referenced nucleic acid, complement thereof, or a sequences substantially identical thereto. b. MicroRNA and its processing
A gene coding for a miRNA may be transcribed leading to production of a miRNA precursor known as the pri-miRNA. The pri-miRNA may be part of a polycistronic RNA comprising multiple pri-miRNAs. The pri-miRNA may form a hairpin with a stem and loop. The stem may comprise mismatched bases.
The hairpin structure of the pri-miRNA may be recognized by Drosha, which is an RNase III endonuclease. Drosha may recognize terminal loops in the pri-miRNA and cleave approximately two helical turns into the stem to produce a 30-200 nt precursor known as the pre-miRNA. Drosha may cleave the pri-miRNA with a staggered cut typical of RNase III endonucleases yielding a pre-miRNA stem loop with a 5' phosphate and ~2 nucleotide 3' overhang. Approximately one helical turn of stem (-10 nucleotides) extending beyond the Drosha cleavage site may be essential for efficient processing. The pre-miRNA may then be actively transported from the nucleus to the cytoplasm by Ran- GTP and the export receptor Ex-portin-5.
The pre-miRNA may be recognized by Dicer, which is also an RNase III endonuclease. Dicer may recognize the double-stranded stem of the pre-miRNA. Dicer may also recognize the 51 phosphate and 3' overhang at the base of the stem loop. Dicer may cleave off the terminal loop two helical turns away from the base of the stem loop leaving an additional 5' phosphate and ~2 nucleotide 3' overhang. The resulting siRNA- like duplex, which may comprise mismatches, comprises the mature miRNA and a similar-sized fragment known as the miRNA*. The miRNA and miRNA* may be derived from opposing arms of the pri-miRNA and pre-miRNA. MiRNA* sequences may be found in libraries of cloned miRNAs but typically at lower frequency than the miRNAs. Although initially present as a double-stranded species with miRNA*, the miRNA may eventually become incorporated as a single-stranded RNA into a ribonucleoprotein complex known as the RNA-induced silencing complex (RISC). Various proteins can form the RISC, which can lead to variability in specifity for miRNA/miRNA* duplexes, binding site of the target gene, activity of miRNA (repress or activate), and which strand of the miRNA/miRNA* duplex is loaded in to the RISC.
When the miRNA strand of the miRNA:miRNA* duplex is loaded into the RISC, the miRNA* may be removed and degraded. The strand of the miRNA:miRNA* duplex that is loaded into the RISC may be the strand whose 5' end is less tightly paired, hi cases where both ends of the miRNA:miRNA* have roughly equivalent 5' pairing, both miRNA and miRNA* may have gene silencing activity.
The RISC may identify target nucleic acids based on high levels of complementarity between the miRNA and the mRNA, especially by nucleotides 2-8 of the miRNA. Only one case has been reported in animals where the interaction between the miRNA and its target was along the entire length of the miRNA. This was shown for mir- 196 and Hox B8 and it was further shown that mir-196 mediates the cleavage of the Hox B8 mRNA (Yekta et al 2004, Science 304-594). Otherwise, such interactions are known only in plants (Bartel & Bartel 2003, Plant Physiol 132-709).
A number of studies have looked at the base-pairing requirement between miRNA and its mRNA target for achieving efficient inhibition of translation (reviewed by Bartel 2004, Cell 116-281). In mammalian cells, the first 8 nucleotides of the miRNA may be important (Doench & Sharp 2004 GenesDev 2004-504). However, other parts of the microRNA may also participate in mRNA binding. Moreover, sufficient base pairing at the 3' can compensate for insufficient pairing at the 5' (Brennecke et al, 2005 PLoS 3- e85). Computation studies, analyzing miRNA binding on whole genomes have suggested a specific role for bases 2-7 at the 5' of the miRNA in target binding but the role of the first nucleotide, found usually to be "A" was also recognized (Lewis et at 2005 Cell 120- 15). Similarly, nucleotides 1-7 or 2-8 were used to identify and validate targets by Krek et al (2005, Nat Genet 37-495). The target sites in the mRNA may be in the 5' UTR, the 31 UTR or in the coding region. Interestingly, multiple miRNAs may regulate the same mRNA target by recognizing the same or multiple sites. The presence of multiple miRNA binding sites in most genetically identified targets may indicate that the cooperative action of multiple RISCs provides the most efficient translational inhibition. miRNAs may direct the RISC to downregulate gene expression by either of two mechanisms: mRNA cleavage or translational repression. The miRNA may specify cleavage of the mRNA if the mRNA has a certain degree of complementarity to the miRNA. When a miRNA guides cleavage, the cut may be between the nucleotides pairing to residues 10 and 11 of the miRNA. Alternatively, the miRNA may repress translation if the miRNA does not have the requisite degree of complementarity to the miRNA. Translational repression may be more prevalent in animals since animals may have a lower degree of complementarity between the miRNA and binding site.
It should be noted that there may be variability in the 5' and 3' ends of any pair of miRNA and miRNA*. This variability may be due to variability in the enzymatic processing of Drosha and Dicer with respect to the site of cleavage. Variability at the 5' and 3' ends of miRNA and miRNA* may also be due to mismatches in the stem structures of the pri-miRNA and pre-rm'RNA. The mismatches of the stem strands may lead to a population of different hairpin structures. Variability in the stem structures may also lead to variability in the products of cleavage by Drosha and Dicer. c. Nucleic Acids
Nucleic acids are provided herein. The nucleic acid may comprise the sequence of SEQ ID NOS: 1-108 or variants thereof. The variant may be a complement of the referenced nucleotide sequence. The variant may also be a nucleotide sequence that is substantially identical to the referenced nucleotide sequence or the complement thereof. The variant may also be a nucleotide sequence which hybridizes under stringent conditions to the referenced nucleotide sequence, complements thereof, or nucleotide sequences substantially identical thereto.
The nucleic acid may have a length of from 10 to 250 nucleotides. The nucleic acid may have a length of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200 or 250 nucleotides. The nucleic acid may be synthesized or expressed in a cell (in vitro or in vivo) using a synthetic gene described herein. The nucleic acid may be synthesized as a single strand molecule and hybridized to a substantially complementary nucleic acid to form a duplex. The nucleic acid may be introduced to a cell, tissue or organ in a single- or double- stranded form or capable of being expressed by a synthetic gene using methods well known to those skilled in the art, including as described in U.S. Patent No. 6,506,559 which is incorporated by reference. i. Nucleic acid complex The nucleic acid may further comprise one or more of the following: a peptide, a protein, a RNA-DNA hybrid, an antibody, an antibody fragment, a Fab fragment, and an aptamer. The nucleic acid may also comprise a protamine-antibody fusion protein as described in Song et al (Nature Biotechnology 2005 ;23: 709- 17) and Rossi (Nature Biotechnology 2005:23;682-4), the contents of which are incorporated herein by reference. The protamine-fusion protein may comprise the abundant and highly basic cellular protein protamine. The protamine may readily interact with the nucleic acid. The protamine may comprise the entire 51 amino acid protamine peptide or a fragment thereof. The protamine may be covalently attached to another protein, which may be a Fab. The Fab may bind to a receptor expressed on a cell surface. ii. Pri-miRNA
The nucleic acid may comprise a sequence of a pri-miRNA or a variant thereof. The pri-miRNA sequence may comprise from 45-30,000, 50-25,000, 100-20,000, 1,000- 1,500 or 80-100 nucleotides. The sequence of the pri-miRNA may comprise a pre- miRNA, miRNA and miRNA*, as set forth herein, and variants thereof. The sequence of the pri-miRNA may comprise the sequence of SEQ ID NOS: 1-59 or variants thereof.
The pri-miRNA may form a hairpin structure. The hairpin may comprise first and second nucleic acid sequence that are substantially complimentary. The first and second nucleic acid sequence may be from 37-50 nucleotides. The first and second nucleic acid sequence may be separated by a third sequence of from 8-12 nucleotides. The hairpin structure may have a free energy less than -25 Kcal/mole as calculated by the Vienna algorithm with default parameters, as described in Hofacker et al., Monatshefte f. Chemie 125: 167-188 (1994), the contents of which are incorporated herein. The hairpin may comprise a terminal loop of 4-20, 8-12 or 10 nucleotides. The pri-miRNA may comprise at least 19% adenosine nucleotides, at least 16% cytosine nucleotides, at least 23% thymine nucleotides and at least 19% guanine nucleotides. iii. Pre-miRNA
The nucleic acid may also comprise a sequence of a pre-miRNA or a variant thereof. The pre-miRNA sequence may comprise from 45-200, 60-80 or 60-70 nucleotides. The sequence of the pre-miRNA may comprise a miRNA and a miRNA* as set forth herein. The sequence of the pre-miRNA may also be that of a pri-miRNA excluding from 0-160 nucleotides from the 5' and 3' ends of the pri-miRNA. The sequence of the pre-miRNA may comprise the sequence of SEQ ED NOS: 1-59 or variants thereof. iv. MiRNA
The nucleic acid may also comprise a sequence of a miRNA (including miRNA*) or a variant thereof. The miRNA sequence may comprise from 13-33, 18-24 or 21-23 nucleotides. The miRNA may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides. The sequence of the miRNA may be the first 13-33 nucleotides of the pre-miRNA. The sequence of the miRNA may also be the last 13-33 nucleotides of the pre-miRNA. The sequence of the miRNA may comprise the sequence of SEQ ID NOS: 1-11, 26-28 and 33-46; or variants thereof. v. Anti-miRNA The nucleic acid may also comprise a sequence of an anti-miRNA that is capable of blocking the activity of a miRNA or miRNA*, such as by binding to the pri-miRNA, pre-miRNA, miRNA or miRNA* (e.g. antisense or RNA silencing), or by binding to the target binding site. The anti-miRNA may comprise a total of 5-100 or 10-60 nucleotides. The anti-miRNA may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides. The sequence of the anti-miRNA may comprise (a) at least 5 nucleotides that are substantially identical or complimentary to the 5' of a miRNA and at least 5-12 nucleotides that are substantially complimentary to the flanking regions of the target site from the 5' end of the miRNA, or (b) at least 5-12 nucleotides that are substantially identical or complimentary to the 3' of a miRNA and at least 5 nucleotide that are substantially complimentary to the flanking region of the target site from the 3' end of the miRNA. The sequence of the anti-miRNA may comprise the compliment of SEQ ID NOS: 1-11, 26-28 and 33-46; or variants thereof. vi. Binding Site of Target
The nucleic acid may also comprise a sequence of a target miRNA binding site, or a variant thereof. The target site sequence may comprise a total of 5-100 or 10-60 nucleotides. The target site sequence may also comprise a total of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 or 63 nucleotides. The target site sequence may comprise at least 5 nucleotides of the complementarity sequence of SEQ ID NOS: 1-11, 26-28 and 33-46.
d. Synthetic Gene A synthetic gene is also provided comprising a nucleic acid described herein operably linked to a transcriptional and/or translational regulatory sequence. The synthetic gene may be capable of modifying the expression of a target gene with a binding site for a nucleic acid described herein. Expression of the target gene may be modified in a cell, tissue or organ. The synthetic gene may be synthesized or derived from naturally- occurring genes by standard recombinant techniques. The synthetic gene may also comprise terminators at the 3'-end of the transcriptional unit of the synthetic gene sequence. The synthetic gene may also comprise a selectable marker.
e. Probes A probe is also provided comprising a nucleic acid described herein. Probes may be used for screening and diagnostic methods, as outlined below. The probe may be attached or immobilized to a solid substrate, such as a biochip.
The probe may have a length of from 8 to 500, 10 to 100 or 20 to 60 nucleotides.
The probe may also have a length of at least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160,
180, 200, 220, 240, 260, 280 or 300 nucleotides. The probe may further comprise a linker sequence of from 10-60 nucleotides. f. Biochip
A biochip is also provided. The biochip may comprise a solid substrate comprising an attached probe or plurality of probes described herein. The probes may be capable of hybridizing to a target sequence under stringent hybridization conditions. The probes may be attached at spatially defined address on the substrate. More than one probe per target sequence may be used, with either overlapping probes or probes to different sections of a particular target sequence. The probes may be capable of hybridizing to target sequences associated with a single disorder appreciated by those in the art. The probes may either be synthesized first, with subsequent attachment to the biochip, or may be directly synthesized on the biochip.
The solid substrate may be a material that may be modified to contain discrete individual sites appropriate for the attachment or association of the probes and is amenable to at least one detection method. Representative examples of substrates include glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, TeflonJ, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses and plastics. The substrates may allow optical detection without appreciably fluorescing.
The substrate may be planar, although other configurations of substrates may be used as well. For example, probes may be placed on the inside surface of a tube, for flow- through sample analysis to minimize sample volume. Similarly, the substrate may be flexible, such as a flexible foam, including closed cell foams made of particular plastics.
The biochip and the probe may be derivatized with chemical functional groups for subsequent attachment of the two. For example, the biochip may be derivatized with a chemical functional group including, but not limited to, amino groups, carboxyl groups, oxo groups or thiol groups. Using these functional groups, the probes may be attached using functional groups on the probes either directly or indirectly using a linker. The probes may be attached to the solid support by either the 5' terminus, 3' terminus, or via an internal nucleotide. The probe may also be attached to the solid support non-covalently. For example, biotinylated oligonucleotides can be made, which may bind to surfaces covalently coated with streptavidin, resulting in attachment. Alternatively, probes may be synthesized on the surface using techniques such as photopolymerization and photolithography. g. Diagnosis
A method of diagnosis is also provided. The method comprises detecting a differential expression level of lymphoma-associated nucleic acid in a biological sample. The sample may be derived from a patient. Diagnosis of a disease state in a patient may allow for prognosis and selection of therapeutic and follow-up strategy. Furthermore, the developmental stage of cells may be classified by determining temporarily expressed lymphoma-associated nucleic acids.
In situ hybridization of labeled probes to tissue sections may be performed. When comparing the fingerprints between an individual and a standard, the skilled artisan can make a diagnosis, a prognosis, or a prediction based on the findings. It is further understood that the nucleic acids which indicate the diagnosis may differ from those which indicate the prognosis and molecular profiling of the condition of the cells may lead to distinctions between responsive or refractory conditions or may be predictive of outcomes.
h. Biomarkers
Biomarkers are also provided. One type of cancer screening test involves the detection of a biomarker, such as a tumor marker, in a fluid or tissue obtained from a patient. Tumor markers are substances produced by cancer cells that are not typically produced by normal cells. These substances generally can be detected in the body fluids or tissues of patients with cancer. Another important use for tumor markers is for monitoring patients being treated for advanced cancer. Measuring tumor markers for this purpose can be less invasive, less time-consuming, as well as less expensive, than other complicated tests, to determine if a therapy is reducing the cancer.
A further important use for tumor markers is for determining a prognosis of survival of a cancer patient. Such prognostic methods can be used to identify surgically treated patients likely to experience cancer recurrence so that they can be offered additional therapeutic options.
i. Kits
A kit is also provided and may comprise a nucleic acid described herein together with any or all of the following: assay reagents, buffers, probes and/or primers, and sterile saline or another pharmaceutically acceptable emulsion and suspension base. In addition, the kits may include instructional materials containing directions (e.g., protocols) for the practice of the methods described herein. For example, the kit may be a kit for the amplification, detection, identification or quantification of a target nucleic acid sequence. The kit may comprise a poly(T) primer, a forward primer, a reverse primer, and a probe.
Having now generally described the invention, the same will be more readily understood through reference to the following examples, which are provided by way of illustration and are not intended to be limiting of the present invention.
EXAMPLES
Example 1 Materials and Methods a. Biological Samples
89 nodal or extra nodal tumor specimens (formalin fixed, paraffin-embedded, FFPE) obtained from diffuse large B-cell lymphoma (DLBCL) patients were used for this research. Following the biopsy, the patients were treated with CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisolone) chemotherapy.
Total RNA enriched in microRNA was isolated from the FFPE tumor specimens, and all RNAs extracted were hybridized onto microarrays according to the RNA extraction and array platform protocols described below.
Good prognosis was defined as complete remission and no relapse within 5 years (n=43). Bad prognosis was defined as no remission, or relapse within 9 months (n=46).
b. RNA extraction
Total RNA was isolated from seven to ten 10-μm-thick FFPE tissue sections using the miR extraction protocol developed at Rosetta Genomics. Briefly, the sample is incubated a few times in Xylene at 57° to remove paraffin excess, followed by Ethanol washes. Proteins are degraded by proteinase K solution at 450C for few hours. The RNA is extracted with acid phenolxhloroform followed by ethanol precipitation and DNAse digestion. Total RNA quantity and quality is checked by spectrophotometer (Nanodrop ND- 1000).
c. Microarrav platform Custom microarrays were produced by printing DNA oligonucleotide probes representing 688 human microRNAs. Each probe, printed in triplicate, carries up to 22-nt linker at the 3' end of the microRNA's complement sequence in addition to an amine group used to couple the probes to coated glass slides. 20μM of each probe were dissolved in 2X SSC + 0.0035% SDS and spotted in triplicate on Schott Nexterion® Slide E coated microarray slides using a Genomic Solutions® BioRobotics MicroGrid II according the MicroGrid manufacturer's directions. 54 negative control probes were designed using the sense sequences of different microRNAs. Two groups of positive control probes were designed to hybridize to microarray (i) synthetic small RNA were spiked to the RNA before labeling to verify the labeling efficiency and (ii) probes for abundant small RNA (e.g. small nuclear RNAs (U43, U49, U24, Z30, U6, U48, U44), 5.8s and 5s ribosomal RNA) are spotted on the array to verify RNA quality. The slides were blocked in a solution containing 50 mM ethanolamine, IM Tris (pH9.0) and 0.1% SDS for 20 min at 500C, then thoroughly rinsed with water and spun dry.
d. Cv-dye labeling of microRNA for microarray
Five μg of total RNA were labeled by ligation (Thomson et al., Nature Methods 2004, 1 :47-53) of an RNA-linker, p-rCrU-Cy/dye (Dharmacon), to the 3 ' -end with Cy3 or
Cy5. The labeling reaction contained total RNA, spikes (0.1-20 fmoles), 300ng RNA- linker-dye, 15% DMSO, Ix ligase buffer and 20 units of T4 RNA ligase (NEB) and proceeded at 40C for lhr followed by lhr at 370C. The labeled RNA was mixed with 3x hybridization buffer (Ambion), heated to 950C for 3 min and then added on top of the miR array. Slides were hybridized 12-16hr in 420C, followed by two washes in room temperature with IxSSC and 0.2% SDS and a final wash with O.lxSSC.
Arrays were scanned using an Agilent Microarray Scanner Bundle G2565BA (resolution of 10 μm at 100% power). Array images were analyzed using SpotReader software (Niles Scientific).
e. Statistical analyses
Measurements of the expression of miRs were log-transformed before all further analysis. Normalization of samples was performed by calculating a median reference vector. For each sample, the best fit to this reference vector was calculated using a 2nd degree polynomial. For analyses comparing the expression of miRs in two distinct groups ("good" vs.
"bad" prognosis), the normalized expression values in the two groups was compared using a Mann- Whitney test, with a P- value cutoff of 0.05. FDR=0.1 was used to correct for multiple hypothesis testing, when appropriate. Only miRs whose normalized median expression was >300 in at least one of the groups compared were considered.
For evaluation of the predictive value of differentially expressed miRs, Kaplan- Meier survival analysis was performed. The first step was the calculation of a logistic regression model, fitting the normalized expression to the two prognosis groups ("bad" was defined as 1, "good" was defined as 0). The cutoff used for plotting the two curves in the Kaplan Meier plot was that which gave the best fit of the predicted logistic regression values to the bad/good classification. Statistical significance was assessed using log rank. P-value<0.05 was considered significant. f. qRT-PCR
RNA was incubated in the presence of poly (A) polymerase (PoIy(A) Polymerase NEB- M0276L), MnCl2, and ATP for 1 hour at 37°C. Then, using an oligodT primer harboring a consensus sequence, reverse transcription was performed on total RNA using Superscript II RT (Invitrogen, Carlsbad, CA). Next, the cDNA was amplified by RT-PCR; this reaction contained a microRNA-specific forward primer, a TaqMan (MGB) probe complementary to the 3' of the specific microRNA sequence as well as to part of the polyA adaptor sequence, and a universal reverse primer complementary to the consensus 3' sequence of the oligodT tail.
The cycle threshold (Ct, the PCR cycle at which probe signal reaches the threshold) was determined for each microRNA. To allow comparison with results from the microarray, each value received was subtracted from 50. This 50-Ct (50α) expression for each microRNA for each patient was compared with the signal obtained by the microarray method. Linear regression for the microRNA readings over all patients was used to model
50ct by microarray values. Using this model the threshold for the separation between high and low expression samples was transferred from microarray to qRT-PCR readings and used for Kaplan-Meier analysis.
Example 2
Specific microRNAs are able to predict the prognosis of DLBCL patients The statistical analysis of the microarray results and comparison of the median values of miR expression in tumor samples obtained from DLBCL patients having good prognosis (43 patients) with the median values of miR expression in tumor samples obtained from patients with bad prognosis (46 patients) revealed significant differences in the expression pattern of specific miRs as specified in Table 1 and in figures 1-4. In the group of patients with bad prognosis the median expression values of hsa-miR-19b (SEQ ID NO: 1), hsa-miR-20a (SEQ ID NO: 2), hsa-miR-886-5p (SEQ ID NO: 3); hsa-miR- 106a (SEQ ID NO: 4) and hsa-miR-17 (SEQ ID NO: 5); were found to be above the median expression of the patients with good prognosis, whereas the expression values of hsa-miR-150 (SEQ ID NO: 6); hsa-miR-342-3p (SEQ ID NO: 7); hsa-miR-100 (SEQ ID NO: 8); hsa-miR-768-3p (SEQ ID NO: 9); hsa-miR-125b (SEQ ID NO:10) and hsa-miR- 181a (SEQ ID NO: 11) were found to be below the median expression of the patients with bad prognosis. Accordingly, relatively high expression values of hsa-miR-19b (SEQ ID NO: 1), hsa-miR-20a (SEQ ID NO: 2), hsa-miR-886-5p (SEQ ID NO: 3); hsa-miR-106a (SEQ ID NO: 4) and hsa-miR-17 (SEQ ID NO: 5); are demonstrated to be indicative of poor prognosis of lymphoma. Wherein relatively high expression values of hsa-miR-150 (SEQ ID NO: 6); hsa-miR-342-3p (SEQ ID NO: 7); hsa-miR-100 (SEQ ID NO: 8); hsa- miR-768-3p (SEQ ID NO: 9); hsa-miR-125b (SEQ ID NO: 10) and hsa-miR-181a (SEQ ID NO: 11) are demonstrated to be indicative of good prognosis.
Table 1 : Microarray results
Figure imgf000033_0001
The microRNA name is the miRBase registry name (release 10).
Example 3
Expression level of hsa-miR-181a is able to predict the prognosis of DLBCL patients
As indicated in figure 4C, the median expression of hsa-miR-181a (SEQ ID NO: 11) in the group of patients with bad prognosis is significantly lower than the corresponding expression in the group of patients with good prognosis, with a p-value of 0.0015 and a fold change of 1.5. This is further indicated in figures 5-6, which demonstrate that the fraction of surviving patients over 150 months (figure 5, p=0.00206) and 9 months (figure 6, p=O.OOO83) is higher in patients with relatively high expression level of hsa-miR-181a (SEQ K) NO: 11) than their counterparts with relatively low expression levels of this miR. Accordingly, expression values of hsa-miR-181a (SEQ ID NO: 11) is indicative of the prognosis of DLBCL. The score used is the probability calculated by using logistic regression to fit the log-normalized expression values to the prognosis status (l=bad prognosis, Osgood prognosis).
Example 4 The separation between DLBCL patients with bad prognosis and DLBCL patients with good prognosis using a combination of two or three microRNA biomarkers
Using several combinations of two or three microRNA biomarkers allowed the separation between DLBCL patients with bad prognosis (circles) and DLBCL patients with good prognosis (squares). As shown in figure 7, hsa-miR-17 (SEQ ED NO: 5) and hsa-miR-342-3p (SEQ ID NO: 7) can be used to distinguish between these two groups. The sensitivity of the detection is 83% and the specificity of the detection is 63%. Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.7543. As shown in figures 8A-8C, hsa-miR-17 (SEQ ID NO: 5), hsa-miR-768-3p (SEQ
ID NO: 9) and hsa-miR-181a (SEQ ID NO: 11) can be also used to distinguish between these two groups. The sensitivity of the detection is 91% and the specificity of the detection is 57%. Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.78059. Example 5
Assay validation by quantitative real-time PCR
24 microRNAs were selected for quantitative real-time PCR (qRT-PCR) analysis. 18 of these microRNAs (SEQ ID NOS: 1, 2, 4-8, 10, 11, 27, 33-40) were selected as differential probes for prognosis and six non-differential microRNAs (SEQ ID NOS: 41- 46) were chosen for signal normalization.
22 samples from the same cohort originally tested by the microarrays, 11 with good prognosis (stage 4) and 11 with poor prognosis (stage 4) were selected for technical validation by qRT-PCR. Seven additional tumor samples obtained from DLBCL patients having good prognosis and six additional tumor samples obtained from DLBCL patients having bad prognosis which were not tested by the microarrays, were added to the RT- PCR validation. The RT-PCR validation results are presented in figure 12 and Table 2. Figures 13 A-13 J are boxplot presentations comparing distributions of the expression as measured by RT-PCR of ten differential miRs in the validation set of 13 samples. As shown in figures 14A-14D, one microRNA biomarker: hsa-miR-17* (SEQ ID NO: 33) can be used for the separation of DLBCL patients with bad prognosis from DLBCL patients with good prognosis. The sensitivity of the detection is 83% and the specificity of the detection is 100%. Receiver operating characteristic (ROC) has an area under the curve (AUC) of 0.92857.
Using several combinations of two microRNA biomarkers allowed the separation between DLBCL patients with bad prognosis (circles) and lymphoma patients with good prognosis (squares). As shown in figures 15A-D, hsa-miR-181a (SEQ ID NO: 11) and hsa-miR-92a (SEQ ID NO: 39) can be used to distinguish between these two groups. The sensitivity of the detection is 100% and the specificity of the detection is 82%. Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.97521.
As shown in figures 16A-16D, hsa-miR-342-3p (SEQ ID NO:7) and hsa-miR-150
(SEQ ID NO:6) can be also used to distinguish between these two groups. The sensitivity of the detection is 83% and the specificity of the detection is 100%. Receiver operating characteristic (ROC) for the metric defined by the combination of these two microRNAs has an area under the curve (AUC) of 0.92857.
Table 2: RT-PCR validation results
Figure imgf000036_0001
Down re ulated in bad Pro nosis vs. ood Pro nosis:
Figure imgf000036_0002
Table 3: Sequences used in RT-PCR validation
Figure imgf000036_0003
Figure imgf000037_0001

Claims

1. A method of determining the prognosis of lymphoma in a subject comprising:
(a) obtaining a biological sample from the subject;
(b) determining an expression profile in said sample of nucleic acid sequences selected from the group consisting of SEQ ID NOS: 1-33, 39 and 52; and a sequence having at least about 80% identity thereto; and
(c) comparing said expression profile to a reference value, whereby an altered expression levels of the nucleic acid sequence is indicative of the prognosis of said subject.
2. The method of claim 1, wherein said altered expression level is a change in a score based on a combination of expression levels of said nucleic acid sequences.
3. The method of claim 1, said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 1-5, 12-17, 26, 29, 33, 39 and 52; and sequences at least about 80% identical thereto and said expression levels above said reference value is indicative of poor prognosis in said subject.
4. The method of claim 1, wherein said nucleic acid sequence is selected from the group consisting of SEQ ID NOS: 6-11, 18-25, 27-32 and sequences at least about 80% identical thereto, and said expression levels below said reference value is indicative of poor prognosis in said subject.
5. The method of claim 1, wherein the subject is a human.
6. The method of claim 1, wherein said lymphoma is a B cell lymphoma.
7. The method of claim 6, wherein said B-cell lymphoma is diffuse large B cell lymphoma.
8. The method of claim 1, wherein said biological sample is selected from the group consisting of bodily fluid, a cell line and a tissue sample.
9. The method of claim 8, wherein said tissue is a fresh, frozen, fixed, wax- embedded or formalin fixed paraffin-embedded (FFPE) tissue.
10. The method of claim 9, wherein said tissue is a lymphoid tissue.
11. The method of claim 10, wherein said lymphoid tissue is a lymph node.
12. The method of claim 1, wherein the expression level is determined by a method selected from the group consisting of nucleic acid hybridization, nucleic acid amplification, and a combination thereof.
13. The method of claim 12, wherein the nucleic acid hybridization is performed using a solid-phase nucleic acid biochip array or in situ hybridization.
14. The method of claim 12, wherein the nucleic acid amplification is performed using real-time PCR.
15. The method of claim 14, wherein the real-time PCR method comprises forward and reverse primers.
16. The method of claim 15, wherein the forward primer comprises a sequence selected from SEQ ID NOS: 60-83, a fragment thereof and a sequence at least about 80% identical thereto.
17. The method of claim 15, wherein the reverse primer comprises SEQ ID NO: 108, a fragment thereof and a sequence at least about 80% identical thereto.
18. The method of claim 15, wherein the real-time PCR method further comprises a probe.
19. The method of claim 18, wherein the probe comprising a nucleic acid sequence that is complementary to a sequence selected from the group consisting of SEQ ID NOS: 1-33, 39 and 52; a fragment thereof and a sequence at least about 80% identical thereto.
20. The method of claim 19, wherein the probe comprises a sequence selected from the group consisting of SEQ ID NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto.
21. A kit for determining a prognosis of a subject with lymphoma, said kit comprising a probe comprising a nucleic acid sequence that is complementary to a sequence selected from the group consisting of SEQ ID NOS: 1-33, 39 and 52; a fragment thereof and a sequence at least about 80% identical thereto.
22. The kit of claim 21, wherein said probe comprising a nucleic acid sequence selected from the group consisting of SEQ ID NOS: 84-107, a fragment thereof and a sequence at least about 80% identical thereto.
23. The kit of claim 21, wherein the kit further comprises forward and reverse primers.
24. The kit of claim 23, wherein said forward primer comprising a sequence selected from the group consisting of SEQ ID NOS: 60-83, a fragment thereof and a sequence at least about 80% identical thereto.
25. The kit of claim 23, wherein said reverse primer comprising SEQ ID NO: 108, a fragment thereof and a sequence at least about 80% identical thereto.
26. The kit of claim 21, wherein the kit comprises reagents for performing in situ hybridization analysis.
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