EP2350917A1 - Biomarqueurs du cancer des ovaires et leurs utilisations - Google Patents

Biomarqueurs du cancer des ovaires et leurs utilisations

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Publication number
EP2350917A1
EP2350917A1 EP09819761A EP09819761A EP2350917A1 EP 2350917 A1 EP2350917 A1 EP 2350917A1 EP 09819761 A EP09819761 A EP 09819761A EP 09819761 A EP09819761 A EP 09819761A EP 2350917 A1 EP2350917 A1 EP 2350917A1
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EP
European Patent Office
Prior art keywords
biomarker
ovarian cancer
individual
biomarkers
biological sample
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP09819761A
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German (de)
English (en)
Other versions
EP2350917A4 (fr
Inventor
Larry Gold
Marty Stanton
Edward N. Brody
Rachel M. Ostroff
Dominic Zichi
Alex A.E. Stewart
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Somalogic Inc
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Somalogic Inc
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Publication date
Application filed by Somalogic Inc filed Critical Somalogic Inc
Publication of EP2350917A1 publication Critical patent/EP2350917A1/fr
Publication of EP2350917A4 publication Critical patent/EP2350917A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57449—Specifically defined cancers of ovaries
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present application relates generally to the detection of biomarkers and the diagnosis of cancer in an individual and, more specifically, to one or more biomarkers, methods, devices, reagents, systems, and kits for diagnosing cancer, more particularly ovarian cancer, in an individual.
  • Ovarian cancer is the eighth most common cancer in women and the fifth leading cause of cancer-related deaths in women in the United States. Of all females born in the United States, one of every 70 will develop ovarian cancer and one of every 100 will die from this disease.
  • the American Cancer Society estimates that approximately 21,550 women will be diagnosed with ovarian cancer in 2009 (American Cancer Society. Cancer Facts & Figures 2009. Atlanta: American Cancer Society; 2009). It is estimated that 14,600 women will die from this disease in 2009.
  • CA- 125 cancer antigen 125
  • serum biomarker for ovarian cancer.
  • Serum concentrations of CA- 125 are elevated (>35 U/ml) in 75-80% of patients with advanced-stage disease and this marker is routinely used to follow response to treatment and disease progression in patients from whom CA- 125 -secreting tumors have been resected.
  • levels of CA- 125 are correlated with tumor volume, only 50% of patients with early-stage disease have elevated levels, indicating that the sensitivity of CA- 125 as a screening tool for early stage disease is limited.
  • CA- 125 screening is further limited by the high frequency of false-positive results associated with a variety of benign conditions, including endometriosis, pregnancy, menstruation, pelvic inflammatory disease, peritonitis, pancreatitis, and liver disease.
  • Classification of cancers determines appropriate treatment and helps determine the prognosis of the patient.
  • Ovarian cancers are classified according to histology (i.e., "grading") and extent of the disease (i.e., "staging”) using recognized grade and stage systems. In grade I, the tumor tissue is well differentiated. In grade II, tumor tissue is moderately well differentiated. In grade III, the tumor tissue is poorly differentiated. Grade III correlates with a less favorable prognosis than either grade I or II.
  • Stage I is generally confined within the capsule surrounding one (stage IA) or both (stage IB) ovaries, although in some stage I (i.e. stage IC) cancers, malignant cells may be detected in ascites, in peritoneal rinse fluid, or on the surface of the ovaries.
  • Stage II involves extension or metastasis of the tumor from one or both ovaries to other pelvic structures.
  • stage HA the tumor extends or has metastasized to the uterus, the fallopian tubes, or both.
  • Stage HB involves metastasis of the tumor to the pelvis.
  • Stage HC is stage HA or HB with the added requirement that malignant cells may be detected in ascites, in peritoneal rinse fluid, or on the surface of the ovaries.
  • the tumor comprises at least one malignant extension to the small bowel or the omentum, has formed extra-pelvic peritoneal implants of microscopic (stage IIIA) or macroscopic ( ⁇ 2 centimeter diameter, stage MB; >2 centimeter diameter, stage IIIC) size, or has metastasized to a retroperitoneal or inguinal lymph node (an alternate indicator of stage IIIC).
  • stage IV distant (i.e. non-peritoneal) metastases of the tumor can be detected.
  • Treatment options include surgery, chemotherapy, and occasionally radiation therapy. Surgery usually involves removal of one or both ovaries, fallopian tubes (salpingoophorectomy), and the uterus (hysterectomy).
  • the 1- and 5-year relative survival of ovarian cancer patients is 75% and 46%, respectively. If diagnosed at the localized stage, the 5-year survival rate is 93%; however, only 19% of all cases are detected at this stage, usually fortuitously during another medical procedure. The majority of cases (67%) are diagnosed at distant stage. For women with regional and distant disease, 5-year survival rates are 71% and 31%, respectively; the chance of recurrence in these women is 20-85%. The 10-year relative survival rate for all stages combined is 39%. Therefore, ovarian cancer tends to be diagnosed too late to save women's lives. Detecting recurrence and predicting and monitoring response to therapy is important for making informed decisions on appropriate treatment throughout the care of these patients.
  • a blood screening test that can reliably detect early stage ovarian cancer will save thousands of lives each year. Where methods of early diagnosis in cancer exist, the benefits are generally accepted by the medical community. Cancers for which widely utilized screening protocols exist have the highest 5-year survival rates, such as breast cancer (88%) and colon cancer (65%) versus 46% for ovarian cancer. However, fortuitous detection of early stage ovarian cancer is associated with a substantial increase in 5-year survival (>95%). Therefore, early detection could significantly impact long-term survival. This demonstrates the clear need for diagnostic methods that can reliably detect early-stage ovarian cancer.
  • Biomarker selection for a specific disease state involves first the identification of markers that have a measurable and statistically significant difference in a disease population compared to a control population for a specific medical application.
  • Biomarkers can include secreted or shed molecules that parallel disease development or progression and readily diffuse into the blood stream from ovarian tissue or from surrounding tissues and circulating cells in response to a tumor. The biomarker or set of biomarkers identified are generally clinically validated or shown to be a reliable indicator for the original intended use for which it was selected.
  • Biomarkers can include small molecules, peptides, proteins, and nucleic acids.
  • biomarker discovery and detection methods using these technologies have serious limitations for the identification of diagnostic biomarkers. These limitations include an inability to detect low-abundance biomarkers, an inability to consistently cover the entire dynamic range of the proteome, ⁇ reproducibility in sample processing and fractionation, and overall ⁇ reproducibility and lack of robustness of the method. Further, these studies have introduced biases into the data and not adequately addressed the complexity of the sample populations, including appropriate controls, in terms of the distribution and randomization required to identify and validate biomarkers within a target disease population.
  • biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology, for example growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to ward off the immune system, and so on. While many of these secreted proteins work in a paracrine fashion, some operate distally in the body.
  • One skilled in the art with a basic understanding of biochemical pathways would understand that many pathology-specific proteins ought to exist in blood at concentrations below (even far below) the detection limits of 2D gels and mass spectrometry. What must precede the identification of this relatively abundant number of disease biomarkers is a proteomic platform that can analyze proteins at concentrations below those detectable by 2D gels or mass spectrometry.
  • biomarkers, methods, devices, reagents, systems, and kits that enable (a) the differentiation of benign pelvic masses from ovarian cancer; (b) referral to a gynecologic oncology surgeon rather than a general gynecologic surgeon to surgically treat cases of ovarian cancer; (c) the detection of ovarian cancer biomarkers; and (d) the diagnosis of ovarian cancer.
  • the present application includes biomarkers, methods, reagents, devices, systems, and kits for the detection and diagnosis of cancer and more particularly, ovarian cancer.
  • the biomarkers of the present application were identified using a multiplex aptamer- based assay, which is described in detail in Example 1.
  • this application describes a surprisingly large number of ovarian cancer biomarkers that are useful for the detection and diagnosis of ovarian cancer.
  • identifying these biomarkers over 800 proteins from hundreds of individual samples were measured, some of which were at concentrations in the low femtomolar range. This is about four orders of magnitude lower than biomarker discovery experiments done with 2D gels or mass spectrometry.
  • ovarian cancer biomarkers are useful alone for detecting and diagnosing ovarian cancer
  • methods are described herein for the grouping of multiple subsets of the ovarian cancer biomarkers that are useful as a panel of biomarkers. Once an individual biomarker or subset of biomarkers has been identified, the detection or diagnosis of ovarian cancer in an individual can be accomplished using any assay platform or format that is capable of measuring differences in the levels of the selected biomarker or biomarkers in a biological sample.
  • one or more biomarkers are provided for use either alone or in various combinations to diagnose ovarian cancer or permit the differential diagnosis of pelvic masses as benign or malignant.
  • Exemplary embodiments include the biomarkers provided in Table 1, which as noted above, were identified using a multiplex aptamer-based assay, as described in Examples 1 and 2. The markers provided in Table 1 are useful in distinguishing benign pelvic masses from ovarian cancer.
  • certain of the described ovarian cancer biomarkers are useful alone for detecting and diagnosing ovarian cancer, methods are also described herein for the grouping of multiple subsets of the ovarian cancer biomarkers that are each useful as a panel of three or more biomarkers.
  • N biomarkers wherein N is at least two biomarkers.
  • N is selected to be any number from 2-42 biomarkers.
  • N is selected to be any number from 2-7, 2-10, 2-
  • N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, or 3-42. In other embodiments, N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, or 4-42. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5- 35, 5-40, or 5-42. In other embodiments, N is selected to be any number from 6-10, 6-15, 6- 20, 6-25, 6-30, 6-35, 6-40, or 6-42.
  • N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, or 7-42. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, or 8-42. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, or 9-42. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, or 10-42. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.
  • a method for diagnosing ovarian cancer in an individual including detecting, in a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers provided in Table 1, wherein the individual is classified as having ovarian cancer based on the at least one biomarker value.
  • a method for diagnosing ovarian cancer in an individual including detecting, in a biological sample from an individual, biomarker values that each correspond to one of at least N biomarkers selected from the group of biomarkers set forth in Table 1, wherein the likelihood of the individual having ovarian cancer is determined based on the biomarker values.
  • a method for differentiating an individual having a benign pelvic mass from an individual having ovarian cancer including detecting, in a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers set forth in Table 1, wherein the individual is classified as having ovarian cancer, or the likelihood of the individual having ovarian cancer is determined, based on the at least one biomarker value.
  • a method for diagnosing that an individual does not have ovarian cancer including detecting, in a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers set forth in Table 1, wherein the individual is classified as not having ovarian cancer based on the at least one biomarker value.
  • a method for diagnosing ovarian cancer including detecting, in a biological sample from an individual, biomarker values that each correspond to a biomarker on a panel of biomarkers selected from the group of panels set forth in Tables 2-14, wherein a classification of the biomarker values indicates that the individual has ovarian cancer.
  • a method for diagnosing an absence of ovarian cancer including detecting, in a biological sample from an individual, biomarker values that each correspond to a biomarker on a panel of biomarkers selected from the group of panels provided in Tables 2-14, wherein a classification of the biomarker values indicates an absence of ovarian cancer in the individual.
  • a computer-implemented method for indicating a likelihood of ovarian cancer.
  • the method comprises: retrieving on a computer biomarker information for an individual, wherein the biomarker information comprises biomarker values that each correspond to one of at least N biomarkers, wherein N is as defined above, selected from the group of biomarkers set forth in Table 1 ; performing with the computer a classification of each of the biomarker values; and indicating a likelihood that the individual has ovarian cancer based upon a plurality of classifications.
  • a computer-implemented method is provided for classifying an individual as either having or not having ovarian cancer.
  • the method comprises: retrieving on a computer biomarker information for an individual, wherein the biomarker information comprises biomarker values that each correspond to one of at least N biomarkers selected from the group of biomarkers provided in Table 1 ; performing with the computer a classification of each of the biomarker values; and indicating whether the individual has ovarian cancer based upon a plurality of classifications.
  • a computer program product for indicating a likelihood of ovarian cancer.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker values that each correspond to one of at least N biomarkers, wherein N is as defined above, in the biological sample selected from the group of biomarkers set forth in Table 1 ; and code that executes a classification method that indicates a likelihood that the individual has ovarian cancer as a function of the biomarker values.
  • a computer program product for indicating an ovarian cancer status of an individual.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker values that each correspond to one of at least N biomarkers in the biological sample selected from the group of biomarkers provided in Table 1; and code that executes a classification method that indicates an ovarian cancer status of the individual as a function of the biomarker values.
  • a computer-implemented method is provided for indicating a likelihood of ovarian cancer.
  • the method comprises retrieving on a computer biomarker information for an individual, wherein the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers set forth in Table 1; performing with the computer a classification of the biomarker value; and indicating a likelihood that the individual has ovarian cancer based upon the classification.
  • a computer-implemented method is provided for classifying an individual as either having or not having ovarian cancer.
  • the method comprises retrieving, from a computer, biomarker information for an individual, wherein the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers provided in Table 1; performing with the computer a classification of the biomarker value; and indicating whether the individual has ovarian cancer based upon the classification.
  • a computer program product for indicating a likelihood of ovarian cancer.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers set forth in Table 1; and code that executes a classification method that indicates a likelihood that the individual has ovarian cancer as a function of the biomarker value.
  • a computer program product for indicating an ovarian cancer status of an individual.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 1; and code that executes a classification method that indicates an ovarian cancer status of the individual as a function of the biomarker value.
  • Figure IA is a flowchart for an exemplary method for detecting ovarian cancer in a biological sample.
  • Figure IB is a flowchart for an exemplary method for detecting ovarian cancer in a biological sample using a na ⁇ ve Bayes classification method.
  • Figure 2 shows a ROC curve for a single biomarker, BAFF Receptor, using a na ⁇ ve Bayes classifier for a test that detects ovarian cancer in women with pelvis masses.
  • Figure 3 shows ROC curves for biomarker panels of from one to ten biomarkers using na ⁇ ve Bayes classifiers for a test that detects ovarian cancer in women with pelvis masses.
  • Figure 4 illustrates the increase in the classification score (specificity + sensitivity) as the number of biomarkers is increased from one to ten using na ⁇ ve Bayes classification for an ovarian cancer panel.
  • Figure 5 shows the measured biomarker distributions for BAFF Receptor as a cumulative distribution function (cdf) in RFU for the benign control group (solid line) and the ovarian cancer disease group (dotted line) along with their curve fits to a normal cdf (dashed lines) used to train the na ⁇ ve Bayes classifiers.
  • cdf cumulative distribution function
  • Figure 6 illustrates an exemplary computer system for use with various computer- implemented methods described herein.
  • Figure 7 is a flowchart for a method of indicating the likelihood that an individual has ovarian cancer in accordance with one embodiment.
  • Figure 8 is a flowchart for a method of indicating the likelihood that an individual has ovarian cancer in accordance with one embodiment.
  • Figure 9 illustrates an exemplary aptamer assay that can be used to detect one or more ovarian cancer biomarkers in a biological sample.
  • Figure 10 shows a histogram of frequencies for which biomarkers were used in building classifiers to distinguish between ovarian cancer and benign pelvic masses from an aggregated set of potential biomarkers.
  • Figure 11 shows a histogram of frequencies for which biomarkers were used in building classifiers to distinguish between ovarian cancer and benign pelvic masses from a site-consistent set of potential biomarkers.
  • Figure 12 shows a histogram of frequencies for which biomarkers were used in building classifiers to distinguish between ovarian cancer and benign pelvic masses from a set of potential biomarkers resulting from a combination of aggregated and site-consistent markers.
  • Figure 13 shows gel images resulting from pull-down experiments that illustrate the specificity of aptamers as capture reagents for the proteins LBP, C9 and IgM.
  • lane 1 is the eluate from the Streptavidin-agarose beads
  • lane 2 is the final eluate
  • lane is a MW marker lane (major bands are at 110, 50, 30, 15, and 3.5 kDa from top to bottom).
  • Figure 14A shows a pair of histograms summarizing all possible single protein na ⁇ ve Bayes classifier scores (sensitivity + specificity) using the biomarkers set forth in Table
  • Figure 14B shows a pair of histograms summarizing all possible two-protein protein na ⁇ ve Bayes classifier scores (sensitivity + specificity) using the biomarkers set forth in Table 1 (solid) and a set of random non-markers (dotted).
  • Figure 14C shows a pair of histograms summarizing all possible three-protein na ⁇ ve Bayes classifier scores (sensitivity + specificity) using the biomarkers set forth in Table
  • Figure 15 shows the sensitivity + specificity score for na ⁇ ve Bayes classifiers using from 2-10 markers selected from the full panel (•) and the scores obtained by dropping the best 5 ( ⁇ ), 10 (A) and 15 ( ⁇ ) markers during classifier generation.
  • Figure 16A shows a set of ROC curves modeled from the data in Table 18 for panels of from one to five markers.
  • Figure 16B shows a set of ROC curves computed from the training data for panels of from one to five markers as in Figure 16A.
  • the term "about” represents an insignificant modification or variation of the numerical value such that the basic function of the item to which the numerical value relates is unchanged.
  • the present application includes biomarkers, methods, devices, reagents, systems, and kits for the detection and diagnosis of ovarian cancer.
  • one or more biomarkers are provided for use either alone or in various combinations to diagnose ovarian cancer, permit the differential diagnosis of pelvic masses as benign or malignant, monitor ovarian cancer recurrence, or address other clinical indications.
  • exemplary embodiments include the biomarkers provided in Table 1, which were identified using a multiplex aptamer-based assay, as described generally in Example 1 and more specifically in Example 2.
  • Table 1 sets forth the findings obtained from analyzing blood samples from
  • Table 1 lists the biomarkers found to be useful in distinguishing samples obtained from individuals with ovarian cancer from "control" samples obtained from individuals with benign pelvic masses. Using a multiplex aptamer assay, forty-two biomarkers were discovered that distinguished samples obtained from individuals with ovarian cancer from samples obtained from people who had benign pelvic masses (see Table
  • ovarian cancer biomarkers are useful alone for detecting and diagnosing ovarian cancer
  • methods are also described herein for the grouping of multiple subsets of the ovarian cancer biomarkers, where each grouping or subset selection is useful as a panel of three or more biomarkers, interchangeably referred to herein as a "biomarker panel" and a panel.
  • biomarker panel a panel of three or more biomarkers
  • various embodiments of the instant application provide combinations comprising N biomarkers, wherein N is at least two biomarkers. In other embodiments, N is selected from 2-42 biomarkers.
  • N is selected to be any number from 2-7, 2-10, 2-
  • N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, or 3-42. In other embodiments, N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, or 4-42. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5- 35, 5-40, or 5-42. In other embodiments, N is selected to be any number from 6-10, 6-15, 6- 20, 6-25, 6-30, 6-35, 6-40, or 6-42.
  • N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, or 7-42. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, or 8-42. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, or 9-42. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, or 10-42. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.
  • the number of biomarkers useful for a biomarker subset or panel is based on the sensitivity and specificity value for the particular combination of biomarker values.
  • sensitivity and “specificity” are used herein with respect to the ability to correctly classify an individual, based on one or more biomarker values detected in their biological sample, as having ovarian cancer or not having ovarian cancer.
  • Sensitivity indicates the performance of the biomarker(s) with respect to correctly classifying individuals that have ovarian cancer.
  • Specificity indicates the performance of the biomarker(s) with respect to correctly classifying individuals who do not have ovarian cancer.
  • 85% specificity and 90% sensitivity for a panel of markers used to test a set of control samples and ovarian cancer samples indicates that 85% of the control samples were correctly classified as control samples by the panel, and 90% of the ovarian cancer samples were correctly classified as ovarian cancer samples by the panel.
  • the desired or preferred minimum value can be determined as described in Example 3.
  • Representative panels are set forth in Tables 2-14, which set forth a series of 100 different panels of 3-15 biomarkers, which have the indicated levels of specificity and sensitivity for each panel. The total number of occurrences of each marker in each of these panels is indicated at the bottom of each Table.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to at least one of the biomarkers SLPI, C9, HGF and RGM-C and at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to the biomarkers SLPI, C9, HGF and RGM-C and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to the biomarker SLPI and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to the biomarker C9and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to the biomarker HGF and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
  • ovarian cancer is detected or diagnosed in an individual by conducting an assay on a biological sample from the individual and detecting biomarker values that each correspond to the biomarker RGM-C and one of at least N additional biomarkers selected from the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
  • the ovarian cancer biomarkers identified herein represent a relatively large number of choices for subsets or panels of biomarkers that can be used to effectively detect or diagnose ovarian cancer. Selection of the desired number of such biomarkers depends on the specific combination of biomarkers chosen. It is important to remember that panels of biomarkers for detecting or diagnosing ovarian cancer may also include biomarkers not found in Table 1, and that the inclusion of additional biomarkers not found in Table 1 may reduce the number of biomarkers in the particular subset or panel that is selected from Table 1. The number of biomarkers from Table 1 used in a subset or panel may also be reduced if additional biomedical information is used in conjunction with the biomarker values to establish acceptable sensitivity and specificity values for a given assay.
  • biomarkers to be used in a subset or panel of biomarkers Another factor that can affect the number of biomarkers to be used in a subset or panel of biomarkers is the procedures used to obtain biological samples from individuals who are being evaluated for ovarian cancer. In a carefully controlled sample procurement environment, the number of biomarkers necessary to meet desired sensitivity and specificity values will be lower than in a situation where there can be more variation in sample collection, handling and storage. In developing the list of biomarkers set forth in Table 1, two sample collection sites were utilized to collect data for classifier training. [0092] One aspect of the instant application can be described generally with reference to Figures IA and B. A biological sample is obtained from an individual or individuals of interest.
  • the biological sample is then assayed to detect the presence of one or more (N) biomarkers of interest and to determine a biomarker value for each of said N biomarkers (referred to in Figure IB as marker RFU (relative fluorescence units)).
  • N one or more biomarkers of interest
  • RFU relative fluorescence units
  • Bio sample refers to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual.
  • a blood sample can be fractionated into serum or into fractions containing particular types of blood cells, such as red blood cells or white blood cells (leukocytes).
  • a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample.
  • biological sample also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example.
  • biological sample also includes materials derived from a tissue culture or a cell culture.
  • any suitable methods for obtaining a biological sample can be employed; exemplary methods include, e.g., phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure. Samples can also be collected, e.g., by micro dissection (e.g., laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
  • a "biological sample” obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.
  • a biological sample can be derived by taking biological samples from a number of individuals and pooling them or pooling an aliquot of each individual's biological sample.
  • the pooled sample can be treated as a sample from a single individual and if the presence of cancer is established in the pooled sample, then each individual biological sample can be re-tested to determine which individuals have ovarian cancer.
  • the phrase "data attributed to a biological sample from an individual” is intended to mean that the data in some form derived from, or were generated using, the biological sample of the individual.
  • the data may have been reformatted, revised, or mathematically altered to some degree after having been generated, such as by conversion from units in one measurement system to units in another measurement system; but, the data are understood to have been derived from, or were generated using, the biological sample.
  • Target refers to any molecule of interest that may be present in a biological sample.
  • a "molecule of interest” includes any minor variation of a particular molecule, such as, in the case of a protein, for example, minor variations in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, which does not substantially alter the identity of the molecule.
  • a “target molecule”, “target”, or “analyte” is a set of copies of one type or species of molecule or multi-molecular structure.
  • Target molecules refer to more than one such set of molecules.
  • target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affybodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing.
  • polypeptide As used herein, “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to polymers of amino acids of any length.
  • the polymer may be linear or branched, it may comprise modified amino acids, and it may be interrupted by non-amino acids.
  • the terms also encompass an amino acid polymer that has been modified naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component.
  • polypeptides containing one or more analogs of an amino acid including, for example, unnatural amino acids, etc.
  • Polypeptides can be single chains or associated chains. Also included within the definition are preproteins and intact mature proteins; peptides or polypeptides derived from a mature protein; fragments of a protein; splice variants; recombinant forms of a protein; protein variants with amino acid modifications, deletions, or substitutions; digests; and post-translational modifications, such as glycosylation, acetylation, phosphorylation, and the like.
  • thrombin refers to thrombin, prothrombin, or both thrombin and prothrombin.
  • marker and “biomarker” are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in an individual or of a disease or other condition in an individual. More specifically, a “marker” or “biomarker” is an anatomic, physiologic, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whether normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods including laboratory assays and medical imaging.
  • a biomarker is a protein
  • biomarker value As used herein, “biomarker value”, “value”, “biomarker level”, and “level” are used interchangeably to refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample.
  • the exact nature of the "value” or “level” depends on the specific design and components of the particular analytical method employed to detect the biomarker.
  • a biomarker indicates or is a sign of an abnormal process or a disease or other condition in an individual
  • that biomarker is generally described as being either over- expressed or under-expressed as compared to an expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual.
  • Up-regulation “up-regulated”, “over-expression”, “over-expressed”, and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • Down-regulation Down-regulated
  • under-expression under-expressed
  • any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • a biomarker that is either over-expressed or under-expressed can also be referred to as being “differentially expressed” or as having a “differential level” or “differential value” as compared to a "normal” expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual.
  • "differential expression” of a biomarker can also be referred to as a variation from a "normal” expression level of the biomarker.
  • differential gene expression and “differential expression” are used interchangeably to refer to a gene (or its corresponding protein expression product) whose expression is activated to a higher or lower level in a subject suffering from a specific disease, relative to its expression in a normal or control subject.
  • the terms also include genes (or the corresponding protein expression products) whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product.
  • Differential gene expression may include a comparison of expression between two or more genes or their gene products; or a comparison of the ratios of the expression between two or more genes or their gene products; or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease; or between various stages of the same disease.
  • Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages.
  • individual refers to a test subject or patient.
  • the individual can be a mammal or a non-mammal.
  • the individual is a mammal.
  • a mammalian individual can be a human or non-human.
  • the individual is a human.
  • a healthy or normal individual is an individual in which the disease or condition of interest (including, for example, ovarian diseases, ovarian -associated diseases, or other ovarian conditions) is not detectable by conventional diagnostic methods.
  • Diagnose refers to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual.
  • the health status of an individual can be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a disease or condition) or diagnosed as ill / abnormal (i.e., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition).
  • diagnosis encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the administration of a treatment or therapy to the individual.
  • diagnosis of ovarian cancer includes distinguishing individuals who have cancer from individuals who do not. It further includes distinguishing benign pelvic masses from ovarian cancer.
  • Prognose refers to the prediction of a future course of a disease or condition in an individual who has the disease or condition (e.g., predicting patient survival), and such terms encompass the evaluation of disease response after the administration of a treatment or therapy to the individual.
  • "Evaluate”, “evaluating”, “evaluation”, and variations thereof encompass both
  • diagnose and "prognose” and also encompass determinations or predictions about the future course of a disease or condition in an individual who does not have the disease as well as determinations or predictions regarding the likelihood that a disease or condition will recur in an individual who apparently has been cured of the disease.
  • the term "evaluate” also encompasses assessing an individual's response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or will experience toxic or other undesirable side effects, for example), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual's response to a therapy that has been administered to the individual.
  • "evaluating" ovarian cancer can include, for example, any of the following: prognosing the future course of ovarian cancer in an individual; predicting the recurrence of ovarian cancer in an individual who apparently has been cured of ovarian cancer; or determining or predicting an individual's response to an ovarian cancer treatment or selecting an ovarian cancer treatment to administer to an individual based upon a determination of the biomarker values derived from the individual's biological sample.
  • "evaluating" ovarian cancer initially detecting the presence or absence of ovarian cancer; determining a specific stage, type or sub-type, or other classification or characteristic of ovarian cancer; determining whether a pelvic mass is benign or malignant; or detecting or monitoring ovarian cancer progression (e.g., monitoring ovarian tumor growth or metastatic spread), remission, or recurrence.
  • additional biomedical information refers to one or more evaluations of an individual, other than using any of the biomarkers described herein, that are associated with ovarian cancer risk.
  • Additional biomedical information includes any of the following: physical descriptors of an individual; physical descriptors of a pelvic mass observed by MRI, abdominal ultrasound, or CT imaging; the height and/or weight of an individual; change in weight; the ethnicity of an individual; occupational history; family history of ovarian cancer (or other cancer); the presence of a genetic marker(s) correlating with a higher risk of ovarian cancer in the individual or a family member; the presence of a pelvic mass; size of mass; location of mass; morphology of mass and associated pelvic region (e.g., as observed through imaging); clinical symptoms such as bloating, abdominal pain, or sudden weight gain or loss; and the like.
  • Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual themselves by use of a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc.
  • additional biomedical information can be obtained from routine imaging techniques, including abdominal or transvaginal ultrasound, MRI, CT imaging, and PET-CT.
  • Testing of biomarker levels in combination with an evaluation of any additional biomedical information, including other laboratory tests (e.g., CA- 125 testing) may, for example, improve sensitivity, specificity, and/or AUC for detecting ovarian cancer (or other ovarian cancer-related uses) as compared to biomarker testing alone or evaluating any particular item of additional biomedical information alone (e.g., ultrasound imaging alone).
  • AUC area under the curve
  • ROC receiver operating characteristic
  • the feature data across the entire population e.g., the cases and controls
  • the true positive and false positive rates for the data are calculated.
  • the true positive rate is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases.
  • the false positive rate is determined by counting the number of controls above the value for that feature and then dividing by the total number of controls.
  • ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test.
  • the ROC curve is the plot of the true positive rate (sensitivity) of a test against the false positive rate (1 -specificity) of the test.
  • detecting or “determining” with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material/s required to generate that signal.
  • the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.
  • Solid support refers herein to any substrate having a surface to which molecules may be attached, directly or indirectly, through either covalent or non-covalent bonds.
  • a “solid support” can have a variety of physical formats, which can include, for example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass slide or coverslip); a column; a hollow, solid, semi-solid, pore- or cavity- containing particle, such as, for example, a bead; a gel; a fiber, including a fiber optic material; a matrix; and a sample receptacle.
  • Exemplary sample receptacles include sample wells, tubes, capillaries, vials, and any other vessel, groove or indentation capable of holding a sample.
  • a sample receptacle can be contained on a multi-sample platform, such as a microtiter plate, slide, microfluidics device, and the like.
  • a support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support on which capture reagents are attached generally depends on the method of attachment (e.g., covalent attachment).
  • Other exemplary receptacles include microdroplets and microfluidic controlled or bulk oil/aqueous emulsions within which assays and related manipulations can occur.
  • Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool and cotton), polymers, and the like.
  • the material composing the solid support can include reactive groups such as, for example, carboxy, amino, or hydroxyl groups, which are used for attachment of the capture reagents.
  • Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate, and polymethylpentene.
  • Suitable solid support particles that can be used include, e.g., encoded particles, such as Luminex ® -type encoded particles, magnetic particles, and glass particles.
  • methods are provided for diagnosing ovarian cancer in an individual by detecting one or more biomarker values corresponding to one or more biomarkers that are present in the circulation of an individual, such as in serum or plasma, by any number of analytical methods, including any of the analytical methods described herein.
  • biomarkers are, for example, differentially expressed in individuals with ovarian cancer as compared to individuals without ovarian cancer.
  • Detection of the differential expression of a biomarker in an individual can be used, for example, to permit the early diagnosis of ovarian cancer, to distinguish between a benign pelvic mass and ovarian cancer (such as, for example, a mass observed on an abdominal ultrasound or computed tomography (CT) scan), to monitor ovarian cancer recurrence, or for other clinical indications.
  • a benign pelvic mass and ovarian cancer such as, for example, a mass observed on an abdominal ultrasound or computed tomography (CT) scan
  • CT computed tomography
  • any of the biomarkers described herein may be used in a variety of clinical indications for ovarian cancer, including any of the following: detection of ovarian cancer (such as in a high-risk individual or population); characterizing ovarian cancer (e.g., determining ovarian cancer type, sub-type, or stage), such as by determining whether a pelvic mass is benign or malignant; determining ovarian cancer prognosis; monitoring ovarian cancer progression or remission; monitoring for ovarian cancer recurrence; monitoring metastasis; treatment selection (e.g., pre- or post-operative chemotherapy selection); monitoring response to a therapeutic agent or other treatment; combining biomarker testing with additional biomedical information, such as CA- 125 level, the presence of a genetic marker(s) indicating a higher risk for ovarian cancer, etc., or with mass size, morphology, presence of ascites, etc.
  • additional biomedical information such as CA- 125 level, the presence of a genetic marker(s) indicating
  • Biomarker testing may improve positive predictive value (PPV) over CA- 125 testing and imaging alone.
  • the described biomarkers may also be useful in permitting certain of these uses before indications of ovarian cancer are detected by imaging modalities or other clinical correlates, or before symptoms appear.
  • differential expression of one or more of the described biomarkers in an individual who is not known to have ovarian cancer may indicate that the individual has ovarian cancer, thereby enabling detection of ovarian cancer at an early stage of the disease when treatment is most effective, perhaps before the ovarian cancer is detected by other means or before symptoms appear.
  • Increased differential expression from "normal” (since some biomarkers may be down-regulated with disease) of one or more of the biomarkers during the course of ovarian cancer may be indicative of ovarian cancer progression, e.g., an ovarian tumor is growing and/or metastasizing (and thus indicate a poor prognosis), whereas a decrease in the degree to which one or more of the biomarkers is differentially expressed (i.e., in subsequent biomarker tests, the expression level in the individual is moving toward or approaching a "normal” expression level) may be indicative of ovarian cancer remission, e.g., an ovarian tumor is shrinking (and thus indicate a good or better prognosis).
  • an increase in the degree to which one or more of the biomarkers is differentially expressed may indicate that the ovarian cancer is progressing and therefore indicate that the treatment is ineffective
  • a decrease in differential expression of one or more of the biomarkers during the course of ovarian cancer treatment may be indicative of ovarian cancer remission and therefore indicate that the treatment is working successfully.
  • an increase or decrease in the differential expression of one or more of the biomarkers after an individual has apparently been cured of ovarian cancer may be indicative of ovarian cancer recurrence.
  • the individual can be re- started on therapy (or the therapeutic regimen modified such as to increase dosage amount and/or frequency, if the individual has maintained therapy) at an earlier stage than if the recurrence of ovarian cancer was not detected until later.
  • a differential expression level of one or more of the biomarkers in an individual may be predictive of the individual's response to a particular therapeutic agent.
  • changes in the biomarker expression levels may indicate the need for repeat imaging, such as to determine ovarian cancer activity or to determine the need for changes in treatment.
  • Detection of any of the biomarkers described herein may be particularly useful following, or in conjunction with, ovarian cancer treatment, such as to evaluate the success of the treatment or to monitor ovarian cancer remission, recurrence, and/or progression (including metastasis) following treatment.
  • Ovarian cancer treatment may include, for example, administration of a therapeutic agent to the individual, performance of surgery (e.g., surgical resection of at least a portion of a pelvic mass), administration of radiation therapy, or any other type of ovarian cancer treatment used in the art, and any combination of these treatments.
  • any of the biomarkers may be detected at least once after treatment or may be detected multiple times after treatment (such as at periodic intervals), or may be detected both before and after treatment.
  • Differential expression levels of any of the biomarkers in an individual over time may be indicative of ovarian cancer progression, remission, or recurrence, examples of which include any of the following: an increase or decrease in the expression level of the biomarkers after treatment compared with the expression level of the biomarker before treatment; an increase or decrease in the expression level of the biomarker at a later time point after treatment compared with the expression level of the biomarker at an earlier time point after treatment; and a differential expression level of the biomarker at a single time point after treatment compared with normal levels of the biomarker.
  • the biomarker levels for any of the biomarkers described herein can be determined in pre-surgery and post-surgery (e.g., 2-8 weeks after surgery) serum or plasma samples.
  • An increase in the biomarker expression level(s) in the post-surgery sample compared with the pre-surgery sample can indicate progression of ovarian cancer (e.g., unsuccessful surgery), whereas a decrease in the biomarker expression level(s) in the post-surgery sample compared with the pre-surgery sample can indicate regression of ovarian cancer (e.g., the surgery successfully removed the ovarian tumor).
  • Similar analyses of the biomarker levels can be carried out before and after other forms of treatment, such as before and after radiation therapy or administration of a therapeutic agent or cancer vaccine.
  • biomarker levels can also be done in conjunction with determination of SNPs or other genetic lesions or variability that are indicative of increased risk of susceptibility of disease. (See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).
  • biomarker levels can also be done in conjunction with relevant symptoms or abdominal ultrasound and CT imaging.
  • Detection of any of the biomarkers described herein may be useful after a pelvic mass has been observed through imaging to aid in the diagnosis of ovarian cancer and guide appropriate clinical care of the individual, including care by an appropriate surgical specialist.
  • biomarkers In addition to testing biomarker levels in conjunction with relevant symptoms or abdominal ultrasound or CT imaging, information regarding the biomarkers can also be evaluated in conjunction with other types of data, particularly data that indicates an individual's risk for ovarian cancer (e.g., patient clinical history, symptoms, family history of cancer, risk factors such as the presence of a genetic marker(s), and/or status of other biomarkers, etc.). These various data can be assessed by automated methods, such as a computer program/software, which can be embodied in a computer or other apparatus/device. [00123] Any of the described biomarkers may also be used in imaging tests.
  • an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in ovarian cancer diagnosis, to monitor disease progression/remission or metastasis, to monitor for disease recurrence, or to monitor response to therapy, among other uses.
  • a biomarker value for the biomarkers described herein can be detected using any of a variety of known analytical methods.
  • a biomarker value is detected using a capture reagent.
  • a capture agent or “capture reagent” refers to a molecule that is capable of binding specifically to a biomarker.
  • the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker while the capture reagent is immobilized on a solid support.
  • the capture reagent contains a feature that is reactive with a secondary feature on a solid support.
  • the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in conjunction with the secondary feature on the solid support to immobilize the biomarker on the solid support.
  • the capture reagent is selected based on the type of analysis to be conducted.
  • Capture reagents include but are not limited to aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, an F(ab') 2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, imprinted polymers, avimers, peptidomimetics, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.
  • a biomarker value is detected using a biomarker/capture reagent complex.
  • the biomarker value is derived from the biomarker/capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the biomarker/capture reagent interaction, but is dependent on the formation of the biomarker/capture reagent complex.
  • the biomarker value is detected directly from the biomarker in a biological sample.
  • the biomarkers are detected using a multiplexed format that allows for the simultaneous detection of two or more biomarkers in a biological sample.
  • capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support.
  • a multiplexed format uses discrete solid supports where each solid support has a unique capture reagent associated with that solid support, such as, for example quantum dots.
  • an individual device is used for the detection of each one of multiple biomarkers to be detected in a biological sample.
  • Individual devices can be configured to permit each biomarker in the biological sample to be processed simultaneously.
  • a microtiter plate can be used such that each well in the plate is used to uniquely analyze one of multiple biomarkers to be detected in a biological sample.
  • a fluorescent tag can be used to label a component of the biomarker/capture complex to enable the detection of the biomarker value.
  • the fluorescent label can be conjugated to a capture reagent specific to any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker value.
  • Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas
  • the fluorescent label is a fluorescent dye molecule.
  • the fluorescent dye molecule includes at least one substituted indolium ring system in which the substituent on the 3-carbon of the indolium ring contains a chemically reactive group or a conjugated substance.
  • the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700.
  • the dye molecule includes a first type and a second type of dye molecule, such as, e.g., two different AlexaFluor molecules.
  • the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.
  • Fluorescence can be measured with a variety of instrumentation compatible with a wide range of assay formats. For example, spectrofluorimeters have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J. R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.
  • a chemiluminescence tag can optionally be used to label a component of the biomarker/capture complex to enable the detection of a biomarker value.
  • Suitable chemiluminescent materials include any of oxalyl chloride, Rodamin 6G, Ru(bipy) 3 2+ , TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl oxalates, Acridinium esters, dioxetanes, and others.
  • the detection method includes an enzyme/substrate combination that generates a detectable signal that corresponds to the biomarker value.
  • the enzyme catalyzes a chemical alteration of the chromogenic substrate which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence.
  • Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta- galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6- phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.
  • HRPO horseradish peroxidase
  • alkaline phosphatase beta- galactosidase
  • glucoamylase lysozyme
  • glucose oxidase galactose oxidase
  • glucose-6- phosphate dehydrogenase uricase
  • xanthine oxidase lactoperoxidase
  • microperoxidase and the like
  • the detection method can be a combination of fluorescence, chemiluminescence, radionuclide or enzyme/substrate combinations that generate a measurable signal.
  • Multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.
  • biomarker values for the biomarkers described herein can be detected using known analytical methods including, singleplex aptamer assays, multiplexed aptamer assays, singleplex or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometric analysis, histological/cytological methods, etc. as detailed below.
  • Assays directed to the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and in the health care field.
  • One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support.
  • the aptamers are each capable of binding to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands"; see also, e.g., U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent No.
  • an "aptamer” refers to a nucleic acid that has a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree; however, in this context, the "specific binding affinity" of an aptamer for its target means that the aptamer binds to its target generally with a much higher degree of affinity than it binds to other components in a test sample.
  • An “aptamer” is a set of copies of one type or species of nucleic acid molecule that has a particular nucleotide sequence.
  • An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. "Aptamers" refers to more than one such set of molecules.
  • aptamers can have either the same or different numbers of nucleotides.
  • Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures.
  • An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind the same target molecule. As further described below, an aptamer may include a tag.
  • an aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods.
  • SELEX and “SELEX process” are used interchangeably herein to refer generally to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example binding with high affinity to a protein, with (2) the amplification of those selected nucleic acids.
  • the SELEX process can be used to identify aptamers with high affinity to a specific target or biomarker.
  • SELEX generally includes preparing a candidate mixture of nucleic acids, binding of the candidate mixture to the desired target molecule to form an affinity complex, separating the affinity complexes from the unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence.
  • the process may include multiple rounds to further refine the affinity of the selected aptamer.
  • the process can include amplification steps at one or more points in the process. See, e.g., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands".
  • the SELEX process can be used to generate an aptamer that covalently binds its target as well as an aptamer that non-covalently binds its target. See, e.g., U.S. Patent No. 5,705,337 entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX.”
  • the SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and/or phosphate and/or base positions. SELEX process -identified aptamers containing modified nucleotides are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5'- and 2'-positions of pyrimidines. U.S. Patent No.
  • SELEX can also be used to identify aptamers that have desirable off -rate characteristics. See U.S. Patent Application Publication 20090004667, entitled “Method for Generating Aptamers with Improved Off-Rates", which describes improved SELEX methods for generating aptamers that can bind to target molecules. Methods for producing aptamers and photoaptamers having slower rates of dissociation from their respective target molecules are described.
  • the methods involve contacting the candidate mixture with the target molecule, allowing the formation of nucleic acid-target complexes to occur, and performing a slow off -rate enrichment process wherein nucleic acid- target complexes with fast dissociation rates will dissociate and not reform, while complexes with slow dissociation rates will remain intact. Additionally, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to generate aptamers with improved off -rate performance. [00143] A variation of this assay employs aptamers that include photoreactive functional groups that enable the aptamers to covalently bind or "photocrosslink" their target molecules. See, e.g., U.S. Patent No.
  • the photoaptamers are photoactivated, and the solid support is washed to remove any non-specifically bound molecules. Harsh wash conditions may be used, since target molecules that are bound to the photoaptamers are generally not removed, due to the covalent bonds created by the photoactivated functional group(s) on the photoaptamers. In this manner, the assay enables the detection of a biomarker value corresponding to a biomarker in the test sample.
  • the aptamers are immobilized on the solid support prior to being contacted with the sample. Under certain circumstances, however, immobilization of the aptamers prior to contact with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamers may result in inefficient mixing of the aptamers with the target molecules on the surface of the solid support, perhaps leading to lengthy reaction times and, therefore, extended incubation periods to permit efficient binding of the aptamers to their target molecules. Further, when photoaptamers are employed in the assay and depending upon the material utilized as a solid support, the solid support may tend to scatter or absorb the light used to effect the formation of covalent bonds between the photoaptamers and their target molecules.
  • immobilization of the aptamers on the solid support generally involves an aptamer-preparation step (i.e., the immobilization) prior to exposure of the aptamers to the sample, and this preparation step may affect the activity or functionality of the aptamers.
  • Patent Application Publication 20090042206 entitled “Multiplexed Analyses of Test Samples”).
  • the described aptamer assay methods enable the detection and quantification of a non- nucleic acid target (e.g., a protein target) in a test sample by detecting and quantifying a nucleic acid (i.e., an aptamer).
  • the described methods create a nucleic acid surrogate (i.e, the aptamer) for detecting and quantifying a non-nucleic acid target, thus allowing the wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.
  • Aptamers can be constructed to facilitate the separation of the assay components from an aptamer biomarker complex (or photoaptamer biomarker covalent complex) and permit isolation of the aptamer for detection and/or quantification.
  • these constructs can include a cleavable or releasable element within the aptamer sequence.
  • additional functionality can be introduced into the aptamer, for example, a labeled or detectable component, a spacer component, or a specific binding tag or immobilization element.
  • the aptamer can include a tag connected to the aptamer via a cleavable moiety, a label, a spacer component separating the label, and the cleavable moiety.
  • a cleavable element is a photocleavable linker.
  • the photocleavable linker can be attached to a biotin moiety and a spacer section, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group to an aptamer, thereby allowing for the release of the aptamer later in an assay method.
  • a method for signal generation takes advantage of anisotropy signal change due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target.
  • binding events may be used to quantitatively measure the biomarkers in solutions.
  • Other methods include fluorescence polarization assays, molecular beacon methods, time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.
  • An exemplary solution-based aptamer assay that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has a specific affinity for the biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support including a first capture element, and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture not associated with the first solid support; (d) attaching a second tag to the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and allowing the second tag to associate with the second capture element; (g) removing any non-complexed aptamer
  • Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format.
  • monoclonal antibodies are often used because of their specific epitope recognition.
  • Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies.
  • Immunoassays have been designed for use with a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semiquantitative, and quantitative results.
  • Quantitative results are generated through the use of a standard curve created with known concentrations of the specific analyte to be detected.
  • the response or signal from an unknown sample is plotted onto the standard curve, and a quantity or value corresponding to the target in the unknown sample is established.
  • ELISA or EIA can be quantitative for the detection of an analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (I 125 ) or fluorescence.
  • Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).
  • Exemplary assay formats include enzyme-linked immunosorbent assay
  • ELISA radioimmunoassay
  • FRET fluorescence resonance energy transfer
  • TR-FRET time resolved- FRET
  • biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.
  • Methods of detecting and/or quantifying a detectable label or signal generating material depend on the nature of the label.
  • the products of reactions catalyzed by appropriate enzymes can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light.
  • detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.
  • Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.
  • Measuring mRNA in a biological sample may be used as a surrogate for detection of the level of the corresponding protein in the biological sample.
  • any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA.
  • mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR).
  • RT-PCR is used to create a cDNA from the mRNA.
  • the cDNA may be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell.
  • Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.
  • miRNA molecules are small RNAs that are non-coding but may regulate gene expression. Any of the methods suited to the measurement of mRNA expression levels can also be used for the corresponding miRNA. Recently many laboratories have investigated the use of miRNAs as biomarkers for disease. Many diseases involve wide-spread transcriptional regulation, and it is not surprising that miRNAs might find a role as biomarkers. The connection between miRNA concentrations and disease is often even less clear than the connections between protein levels and disease, yet the value of miRNA biomarkers might be substantial.
  • RNA biomarkers have similar requirements, although many potential protein biomarkers are secreted intentionally at the site of pathology and function, during disease, in a paracrine fashion. Many potential protein biomarkers are designed to function outside the cells within which those proteins are synthesized.
  • any of the described biomarkers may also be used in molecular imaging tests.
  • an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in ovarian cancer diagnosis, to monitor disease progression/remission or metastasis, to monitor for disease recurrence, or to monitor response to therapy, among other uses.
  • In vivo imaging technologies provide non-invasive methods for determining the state of a particular disease in the body of an individual. For example, entire portions of the body, or even the entire body, may be viewed as a three dimensional image, thereby providing valuable information concerning morphology and structures in the body. Such technologies may be combined with the detection of the biomarkers described herein to provide information concerning the cancer status, in particular the ovarian cancer status, of an individual.
  • the contrast agent may be bound to or associated with a capture reagent, such as an aptamer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macro molecules and/or other particulate forms.
  • a capture reagent such as an aptamer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macro molecules and/or other particulate forms.
  • the contrast agent may also feature a radioactive atom that is useful in imaging. Suitable radioactive atoms include technetium-99m or iodine- 123 for scintigraphic studies.
  • MRI magnetic resonance imaging
  • iodine- 123 again, iodine-131, indium-I l l, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron.
  • MRI magnetic resonance imaging
  • Standard imaging techniques include but are not limited to magnetic resonance imaging, contrast-enhanced abdominal or transvaginal ultrasound, computed tomography (CT) scanning, positron emission tomography (PET), single photon emission computed tomography (SPECT), and the like.
  • CT computed tomography
  • PET positron emission tomography
  • SPECT single photon emission computed tomography
  • the type of detection instrument available is a major factor in selecting a given contrast agent, such as a given radionuclide and the particular biomarker that it is used to target (protein, mRNA, and the like).
  • the radionuclide chosen typically has a type of decay that is detectable by a given type of instrument.
  • its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue but short enough that deleterious radiation of the host is minimized.
  • Exemplary imaging techniques include but are not limited to PET and SPECT, which are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.
  • PET and SPECT are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.
  • Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18.
  • Isotopes that decay by electron capture and/or gamma-emission are used in SPECT and include, for example iodine- 123 and technetium-99m.
  • An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ion in the presence of a chelating precursor to form the labile technetium-99m-precursor complex, which, in turn, reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.
  • Antibodies are frequently used for such in vivo imaging diagnostic methods.
  • Labeled antibodies which specifically bind any of the biomarkers in Table 1 can be injected into an individual suspected of having a certain type of cancer (e.g., ovarian cancer), detectable according to the particular biomarker used, for the purpose of diagnosing or evaluating the disease status of the individual.
  • the label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the spread of the cancer. The amount of label within an organ or tissue also allows determination of the presence or absence of cancer in that organ or tissue.
  • aptamers may be used for such in vivo imaging diagnostic methods.
  • an aptamer that was used to identify a particular biomarker described in Table 1 may be appropriately labeled and injected into an individual suspected of having ovarian cancer, detectable according to the particular biomarker, for the purpose of diagnosing or evaluating the ovarian cancer status of the individual.
  • the label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the spread of the cancer.
  • the amount of label within an organ or tissue also allows determination of the presence or absence of cancer in that organ or tissue.
  • Aptamer-directed imaging agents could have unique and advantageous characteristics relating to tissue penetration, tissue distribution, kinetics, elimination, potency, and selectivity as compared to other imaging agents.
  • Such techniques may also optionally be performed with labeled oligonucleotides, for example, for detection of gene expression through imaging with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as the label. Other methods for detection of gene expression include, for example, detection of the activity of a reporter gene.
  • optical imaging Another general type of imaging technology is optical imaging, in which fluorescent signals within the subject are detected by an optical device that is external to the subject. These signals may be due to actual fluorescence and/or to bioluminescence.
  • Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.
  • in vivo molecular biomarker imaging is increasing, including for clinical trials, for example, to more rapidly measure clinical efficacy in trials for new cancer therapies and/or to avoid prolonged treatment with a placebo for those diseases, such as multiple sclerosis, in which such prolonged treatment may be considered to be ethically questionable.
  • tissue samples may be used in histological or cytological methods. Sample selection depends on the primary tumor location and sites of metastases. For example, fine needle aspirates, cutting needles, and core biopsies can be used for histology. Ascites can be used for cyotology. While cytological analysis is still used in the diagnosis of ovarian cancer, histological methods are known to provide better sensitivity for the detection of cancer. Any of the biomarkers identified herein that were shown to be up-regulated (see Table 15) in the individuals with ovarian cancer can be used to stain a histological specimen as an indication of disease.
  • one or more capture reagents specific to the corresponding biomarker is used in a cytological evaluation of an ovarian cell sample and may include one or more of the following: collecting a cell sample, fixing the cell sample, dehydrating, clearing, immobilizing the cell sample on a microscope slide, permeabilizing the cell sample, treating for analyte retrieval, staining, destaining, washing, blocking, and reacting with one or more capture reagent/s in a buffered solution.
  • the cell sample is produced from a cell block.
  • one or more capture reagents specific to the corresponding biomarker is used in a histological evaluation of an ovarian tissue sample and may include one or more of the following: collecting a tissue specimen, fixing the tissue sample, dehydrating, clearing, immobilizing the tissue sample on a microscope slide, permeabilizing the tissue sample, treating for analyte retrieval, staining, destaining, washing, blocking, rehydrating, and reacting with capture reagent/s in a buffered solution.
  • fixing and dehydrating are replaced with freezing.
  • the one or more aptamers specific to the corresponding biomarker is reacted with the histological or cytological sample and can serve as the nucleic acid target in a nucleic acid amplification method.
  • Suitable nucleic acid amplification methods include, for example, PCR, q-beta replicase, rolling circle amplification, strand displacement, helicase dependent amplification, loop mediated isothermal amplification, ligase chain reaction, and restriction and circularization aided rolling circle amplification.
  • the one or more capture reagent/s specific to the corresponding biomarkers for use in the histological or cytological evaluation are mixed in a buffered solution that can include any of the following: blocking materials, competitors, detergents, stabilizers, carrier nucleic acid, polyanionic materials, etc.
  • a "cytology protocol” generally includes sample collection, sample fixation, sample immobilization, and staining.
  • Cell preparation can include several processing steps after sample collection, including the use of one or more slow off -rate aptamers for the staining of the prepared cells.
  • Sample collection can include directly placing the sample in an untreated transport container, placing the sample in a transport container containing some type of media, or placing the sample directly onto a slide (immobilization) without any treatment or fixation.
  • Sample immobilization can be improved by applying a portion of the collected specimen to a glass slide that is treated with polylysine, gelatin, or a silane. Slides can be prepared by smearing a thin and even layer of cells across the slide. Care is generally taken to minimize mechanical distortion and drying artifacts.
  • Liquid specimens can be processed in a cell block method. Or, alternatively, liquid specimens can be mixed 1 : 1 with the fixative solution for about 10 minutes at room temperature.
  • Cell blocks can be prepared from residual effusions, sputum, urine sediments, gastrointestinal fluids, cell scraping, ascites, or fine needle aspirates. Cells are concentrated or packed by centrifugation or membrane filtration. A number of methods for cell block preparation have been developed. Representative procedures include the fixed sediment, bacterial agar, or membrane filtration methods. In the fixed sediment method, the cell sediment is mixed with a fixative like Bouins, picric acid, or buffered formalin and then the mixture is centrifuged to pellet the fixed cells. The supernatant is removed, drying the cell pellet as completely as possible. The pellet is collected and wrapped in lens paper and then placed in a tissue cassette.
  • a fixative like Bouins, picric acid, or buffered formalin
  • the tissue cassette is placed in ajar with additional fixative and processed as a tissue sample.
  • Agar method is very similar but the pellet is removed and dried on paper towel and then cut in half. The cut side is placed in a drop of melted agar on a glass slide and then the pellet is covered with agar making sure that no bubbles form in the agar. The agar is allowed to harden and then any excess agar is trimmed away. This is placed in a tissue cassette and the tissue process completed.
  • the pellet may be directly suspended in 2% liquid agar at 65°C and the sample centrifuged. The agar cell pellet is allowed to solidify for an hour at 4°C. The solid agar may be removed from the centrifuge tube and sliced in half. The agar is wrapped in filter paper and then the tissue cassette. Processing from this point forward is as described above. Centrifugation can be replaced in any these procedures with membrane filtration. Any of these processes may be used to generate a "cell block sample”.
  • Cell blocks can be prepared using specialized resin including Lowicryl resins,
  • LR White, LR Gold, Unicryl, and MonoStep These resins have low viscosity and can be polymerized at low temperatures and with ultra violet (UV) light.
  • UV ultra violet
  • the embedding process relies on progressively cooling the sample during dehydration, transferring the sample to the resin, and polymerizing a block at the final low temperature at the appropriate UV wavelength.
  • Cell block sections can be stained with hematoxylin-eosin for cytomorphological examination while additional sections are used for examination for specific markers.
  • the sample may be fixed prior to additional processing to prevent sample degradation.
  • This process is called "fixation" and describes a wide range of materials and procedures that may be used interchangeably.
  • the sample fixation protocol and reagents are best selected empirically based on the targets to be detected and the specific cell/tissue type to be analyzed.
  • Sample fixation relies on reagents such as ethanol, polyethylene glycol, methanol, formalin, or isopropanol.
  • the samples should be fixed as soon after collection and affixation to the slide as possible.
  • the fixative selected can introduce structural changes into various molecular targets making their subsequent detection more difficult.
  • fixation and immobilization processes and their sequence can modify the appearance of the cell and these changes must be anticipated and recognized by the cytotechnologist.
  • Fixatives can cause shrinkage of certain cell types and cause the cytoplasm to appear granular or reticular.
  • Many fixatives function by crosslinking cellular components. This can damage or modify specific epitopes, generate new epitopes, cause molecular associations, and reduce membrane permeability.
  • Formalin fixation is one of the most common cytological and histological approaches. Formalin forms methyl bridges between neighboring proteins or within proteins. Precipitation or coagulation is also used for fixation and ethanol is frequently used in this type of fixation.
  • a combination of crosslinking and precipitation can also be used for fixation.
  • a strong fixation process is best at preserving morphological information while a weaker fixation process is best for the preservation of molecular targets.
  • a representative fixative is 50% absolute ethanol, 2 mM polyethylene glycol
  • PEG 1.85% formaldehyde. Variations on this formulation include ethanol (50% to 95%), methanol (20% - 50%), and formalin (formaldehyde) only.
  • Another common fixative is 2% PEG 1500, 50% ethanol, and 3% methanol. Slides are place in the fixative for about 10 to 15 minutes at room temperature and then removed and allowed to dry. Once slides are fixed they can be rinsed with a buffered solution like PBS.
  • a wide range of dyes can be used to differentially highlight and contrast or
  • stain cellular, sub-cellular, and tissue features or morphological structures.
  • Hematoylin is used to stain nuclei a blue or black color.
  • Orange G-6 and Eosin Azure both stain the cell's cytoplasm.
  • Orange G stains keratin and glycogen containing cells yellow.
  • Eosin Y is used to stain nucleoli, cilia, red blood cells, and superficial epithelial squamous cells.
  • Romanowsky stains are used for air dried slides and are useful in enhancing pleomorphism and distinguishing extracellular from intracytoplasmic material.
  • the staining process can include a treatment to increase the permeability of the cells to the stain.
  • Treatment of the cells with a detergent can be used to increase permeability.
  • fixed samples can be further treated with solvents, saponins, or non-ionic detergents. Enzymatic digestion can also improve the accessibility of specific targets in a tissue sample.
  • the sample is dehydrated using a succession of alcohol rinses with increasing alcohol concentration.
  • the final wash is done with xylene or a xylene substitute, such as a citrus terpene, that has a refractive index close to that of the coverslip to be applied to the slide. This final step is referred to as clearing.
  • a mounting medium is applied. The mounting medium is selected to have a refractive index close to the glass and is capable of bonding the coverslip to the slide.
  • the final evaluation of the ovarian cytological specimen is made by some type of microscopy to permit a visual inspection of the morphology and a determination of the marker's presence or absence.
  • exemplary microscopic methods include brightfield, phase contrast, fluorescence, and differential interference contrast.
  • the coverslip may be removed and the slide destained. Destaining involves using the original solvent systems used in staining the slide originally without the added dye and in a reverse order to the original staining procedure. Destaining may also be completed by soaking the slide in an acid alcohol until the cells are colorless. Once colorless the slides are rinsed well in a water bath and the second staining procedure applied.
  • specific molecular differentiation may be possible in conjunction with the cellular morphological analysis through the use of specific molecular reagents such as antibodies or nucleic acid probes or aptamers. This improves the accuracy of diagnostic cytology.
  • Micro-dissection can be used to isolate a subset of cells for additional evaluation, in particular, for genetic evaluation of abnormal chromosomes, gene expression, or mutations.
  • Preparation of a tissue sample for histological evaluation involves fixation, dehydration, infiltration, embedding, and sectioning.
  • the fixation reagents used in histology are very similar or identical to those used in cytology and have the same issues of preserving morphological features at the expense of molecular ones such as individual proteins.
  • Time can be saved if the tissue sample is not fixed and dehydrated but instead is frozen and then sectioned while frozen. This is a more gentle processing procedure and can preserve more individual markers.
  • freezing is not acceptable for long term storage of a tissue sample as subcellular information is lost due to the introduction of ice crystals.
  • Ice in the frozen tissue sample also prevents the sectioning process from producing a very thin slice and thus some microscopic resolution and imaging of subcellular structures can be lost.
  • osmium tetroxide is used to fix and stain phospholipids (membranes).
  • Dehydration of tissues is accomplished with successive washes of increasing alcohol concentration. Clearing employs a material that is miscible with alcohol and the embedding material and involves a stepwise process starting at 50:50 alcohol:clearing reagent and then 100% clearing agent (xylene or xylene substitute). Infiltration involves incubating the tissue with a liquid form of the embedding agent (warm wax, nitrocellulose solution) first at 50:50 embedding agent: clearing agent and the 100% embedding agent. Embedding is completed by placing the tissue in a mold or cassette and filling with melted embedding agent such as wax, agar, or gelatin. The embedding agent is allowed to harden.
  • the hardened tissue sample may then be sliced into thin section for staining and subsequent examination.
  • the tissue section Prior to staining, the tissue section is dewaxed and rehydrated. Xylene is used to dewax the section, one or more changes of xylene may be used, and the tissue is rehydrated by successive washes in alcohol of decreasing concentration. Prior to dewax, the tissue section may be heat immobilized to a glass slide at about 80°C for about 20 minutes.
  • Laser capture micro-dissection allows the isolation of a subset of cells for further analysis from a tissue section.
  • tissue section or slice can be stained with a variety of stains.
  • a large menu of commercially available stains can be used to enhance or identify specific features.
  • analyte retrieval uses high temperature heating of a fixed sample. This method is also referred to as heat-induced epitope retrieval or HIER. A variety of heating techniques have been used, including steam heating, microwaving, autoclaving, water baths, and pressure cooking or a combination of these methods of heating.
  • Analyte retrieval solutions include, for example, water, citrate, and normal saline buffers. The key to analyte retrieval is the time at high temperature but lower temperatures for longer times have also been successfully used. Another key to analyte retrieval is the pH of the heating solution.
  • the section is first dewaxed and hydrated.
  • the slide is then placed in 1OmM sodium citrate buffer pH 6.0 in a dish or jar.
  • a representative procedure uses an 110OW microwave and microwaves the slide at 100% power for 2 minutes followed by microwaving the slides using 20% power for 18 minutes after checking to be sure the slide remains covered in liquid.
  • the slide is then allowed to cool in the uncovered container and then rinsed with distilled water.
  • HIER may be used in combination with an enzymatic digestion to improve the reactivity of the target to immunochemical reagents.
  • One such enzymatic digestion protocol uses proteinase K.
  • a 20 ⁇ g/ml concentration of proteinase K is prepared in 50 mM Tris Base, ImM EDTA, 0.5% Triton X- 100, pH 8.0 buffer. The process first involves dewaxing sections in 2 changes of xylene, 5 minutes each. Then the sample is hydrated in 2 changes of 100% ethanol for 3 minutes each, 95% and 80% ethanol for 1 minute each, and then rinsed in distilled water. Sections are covered with Proteinase K working solution and incubated 10-20 minutes at 37°C in humidified chamber (optimal incubation time may vary depending on tissue type and degree of fixation).
  • the sections are cooled at room temperature for 10 minutes and then rinsed in PBS Tween 20 for 2x2 min. If desired, sections can be blocked to eliminate potential interference from endogenous compounds and enzymes.
  • the section is then incubated with primary antibody at appropriate dilution in primary antibody dilution buffer for 1 hour at room temperature or overnight at 4°C.
  • the section is then rinsed with PBS Tween 20 for 2x2 min. Additional blocking can be performed, if required for the specific application, followed by additional rinsing with PBS Tween 20 for 3x2 min and then finally the immuno staining protocol completed.
  • Blocking reactions may include the need to do any of the following, either alone or in combination: reduce the level of endogenous biotin; eliminate endogenous charge effects; inactivate endogenous nucleases; and inactivate endogenous enzymes like peroxidase and alkaline phosphatase.
  • Endogenous nucleases may be inactivated by degradation with proteinase K, by heat treatment, use of a chelating agent such as EDTA or EGTA, the introduction of carrier DNA or RNA, treatment with a chaotrope such as urea, thiourea, guanidine hydrochloride, guanidine thiocyanate, lithium perchlorate, etc, or diethyl pyrocarbonate.
  • Alkaline phosphatase may be inactivated by treated with 0. IN HCl for 5 minutes at room temperature or treatment with 1 mM levamisole. Peroxidase activity may be eliminated by treatment with 0.03% hydrogen peroxide.
  • Endogenous biotin may be blocked by soaking the slide or section in an avidin (streptavidin, neutravidin may be substituted) solution for at least 15 minutes at room temperature. The slide or section is then washed for at least 10 minutes in buffer. This may be repeated at least three times. Then the slide or section is soaked in a biotin solution for 10 minutes. This may be repeated at least three times with a fresh biotin solution each time. The buffer wash procedure is repeated.
  • Blocking protocols should be minimized to prevent damaging either the cell or tissue structure or the target or targets of interest but one or more of these protocols could be combined to "block" a slide or section prior to reaction with one or more slow off-rate aptamers. See Basic Medical Histology: the Biology of Cells, Tissues and Organs, authored by Richard G. Kessel, Oxford University Press, 1998.
  • mass spectrometers can be used to detect biomarker values.
  • Several types of mass spectrometers are available or can be produced with various configurations.
  • a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument- control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities.
  • an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption.
  • Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption.
  • Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time- of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).
  • Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI- MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS) N , atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS),
  • Sample preparation strategies are used to label and enrich samples before mass spectroscopic characterization of protein biomarkers and determination biomarker values.
  • Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC).
  • Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to aptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab') 2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g.
  • the foregoing assays enable the detection of biomarker values that are useful in methods for diagnosing ovarian cancer, where the methods comprise detecting, in a biological sample from an individual, at least N biomarker values that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, wherein a classification, as described in detail below, using the biomarker values indicates whether the individual has ovarian cancer. While certain of the described ovarian cancer biomarkers are useful alone for detecting and diagnosing ovarian cancer, methods are also described herein for the grouping of multiple subsets of the ovarian cancer biomarkers that are each useful as a panel of three or more biomarkers.
  • N is at least three biomarkers.
  • N is selected to be any number from 2-42 biomarkers. It will be appreciated that N can be selected to be any number from any of the above described ranges, as well as similar, but higher order, ranges.
  • biomarker values can be detected and classified individually or they can be detected and classified collectively, as for example in a multiplex assay format.
  • methods for detecting an absence of ovarian cancer, the methods comprising detecting, in a biological sample from an individual, at least N biomarker values that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, wherein a classification, as described in detail below, of the biomarker values indicates an absence of ovarian cancer in the individual. While certain of the described ovarian cancer biomarkers are useful alone for detecting and diagnosing the absence of ovarian cancer, methods are also described herein for the grouping of multiple subsets of the ovarian cancer biomarkers that are each useful as a panel of three or more biomarkers.
  • N is at least three biomarkers.
  • N is selected to be any number from 2-42 biomarkers. It will be appreciated that N can be selected to be any number from any of the above described ranges, as well as similar, but higher order, ranges.
  • biomarker values can be detected and classified individually or they can be detected and classified collectively, as for example in a multiplex assay format.
  • a biomarker "signature" for a given diagnostic test contains a set of markers, each marker having different levels in the populations of interest. Different levels, in this context, may refer to different means of the marker levels for the individuals in two or more groups, or different variances in the two or more groups, or a combination of both. For the simplest form of a diagnostic test, these markers can be used to assign an unknown sample from an individual into one of two groups, either diseased or not diseased. The assignment of a sample into one of two or more groups is known as classification, and the procedure used to accomplish this assignment is known as a classifier or a classification method. Classification methods may also be referred to as scoring methods.
  • classification methods There are many classification methods that can be used to construct a diagnostic classifier from a set of biomarker values.
  • classification methods are most easily performed using supervised learning techniques where a data set is collected using samples obtained from individuals within two (or more, for multiple classification states) distinct groups one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to give the desired classification response. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.
  • training data includes samples from the distinct groups (classes) to which unknown samples will later be assigned.
  • samples collected from individuals in a control population and individuals in a particular disease population can constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from whom the samples were obtained) as either having the disease or being free from the disease.
  • the development of the classifier from the training data is known as training the classifier. Specific details on classifier training depend on the nature of the supervised learning technique.
  • An illustrative example of the development of a diagnostic test using a set of biomarkers includes the application of a na ⁇ ve Bayes classifier, a simple probabilistic classifier based on Bayes theorem with strict independent treatment of the biomarkers.
  • Each biomarker is described by a class-dependent probability density function (pdf) for the measured RFU values or log RFU (relative fluorescence units) values in each class.
  • n j l where the individual JCJ s are the measured biomarker levels in RFU or log RFU.
  • the classification assignment for an unknown is facilitated by calculating the probability of being diseased p(d I x) having measured x compared to the probability of being disease free (control) p(c I x) for the same measured values.
  • the ratio of these probabilities is computed from the class -dependent pdfs by application of Bayes theorem, i.e., p(c ⁇ x) p(x I c)(l - P(d)) p(d ⁇ x) p(x ⁇ d)P(d)
  • log likelihood ratio This form is known as the log likelihood ratio and simply states that the log likelihood of being free of the particular disease versus having the disease and is primarily composed of the sum of individual log likelihood ratios of the n individual biomarkers.
  • an unknown sample or, more particularly, the individual from whom the sample was obtained
  • the class -dependent biomarker pdfs p(x [ I c) and p(x[ I d) are assumed to be normal or log-normal distributions in the measured RFU values x[ , i.e.
  • the Bayes classifier is fully determined and may be used to classify unknown samples with measured values x .
  • the performance of the na ⁇ ve Bayes classifier is dependent upon the number and quality of the biomarkers used to construct and train the classifier.
  • a single biomarker will perform in accordance with its KS-distance (Kolmogorov-Smirnov), as defined in Example 3, below. If a classifier performance metric is defined as the sum of the sensitivity (fraction of true positives, fjp ) and specificity (one minus the fraction of false positives, 1 - fpp ), a perfect classifier will have a score of two and a random classifier, on average, will have a score of one.
  • a greedy algorithm is any algorithm that follows the problem solving metaheuristic of making the locally optimal choice at each stage with the hope of finding the global optimum.
  • Example 4 The algorithm approach used here is described in detail in Example 4. Briefly, all single analyte classifiers are generated from a table of potential biomarkers and added to a list. Next, all possible additions of a second analyte to each of the stored single analyte classifiers is then performed, saving a predetermined number of the best scoring pairs, say, for example, a thousand, on a new list. All possible three-marker classifiers are explored using this new list of the best two-marker classifiers, again saving the best thousand of these. This process continues until the score either plateaus or begins to deteriorate as additional markers are added. Those high scoring classifiers that remain after convergence can be evaluated for the desired performance for an intended use.
  • classifiers with a high sensitivity and modest specificity may be more desirable than modest sensitivity and high specificity.
  • classifiers with a high specificity and a modest sensitivity may be more desirable.
  • the desired level of performance is generally selected based upon a trade-off that must be made between the number of false positives and false negatives that can each be tolerated for the particular diagnostic application. Such trade-offs generally depend on the medical consequences of an error, either false positive or false negative.
  • Various other techniques are known in the art and may be employed to generate many potential classifiers from a list of biomarkers using a na ⁇ ve Bayes classifier.
  • a genetic algorithm can be used to combine different markers using the fitness score as defined above. Genetic algorithms are particularly well suited to exploring a large diverse population of potential classifiers.
  • so-called ant colony optimization can be used to generate sets of classifiers.
  • Other strategies that are known in the art can also be employed, including, for example, other evolutionary strategies as well as simulated annealing and other stochastic search methods. Metaheuristic methods, such as, for example, harmony search may also be employed.
  • Exemplary embodiments use any number of the ovarian cancer biomarkers listed in Table 1 in various combinations to produce diagnostic tests for detecting ovarian cancer (see Example 2 for a detailed description of how these biomarkers were identified).
  • a method for diagnosing ovarian cancer uses a na ⁇ ve Bayes classification method in conjunction with any number of the ovarian cancer biomarkers listed in Table 1.
  • the simplest test for detecting ovarian cancer from a population of women with pelvic masses can be constructed using a single biomarker, for example, BAFF Receptor which is down-regulated in ovarian cancer with a KS -distance of
  • Adding additional biomarkers such as, for example, SLPI, C9, ⁇ 2- Antiplasmin, SAP, MMP-7, MCP-3, and HSP90 ⁇ , produces a series of ovarian cancer tests summarized in Table 17 and displayed as a series of ROC curves in Figure 3.
  • the score of the classifiers as a function of the number of analytes used in classifier construction is shown in Figure 4.
  • This exemplary ten-marker classifier has a sensitivity of 97% and a specificity of 88% with an AUC of 0.94.
  • markers listed in Table 1 can be combined in many ways to produce classifiers for diagnosing ovarian cancer.
  • panels of biomarkers are comprised of different numbers of analytes depending on a specific diagnostic performance criterion that is selected. For example, certain combinations of biomarkers will produce tests that are more sensitive (or more specific) than other combinations.
  • a panel is defined to include a particular set of biomarkers from Table 1 and a classifier is constructed from a set of training data
  • the definition of the diagnostic test is complete.
  • the procedure used to classify an unknown sample is outlined in Figure IA.
  • the procedure used to classify an unknown sample is outlined in Figure IB.
  • the biological sample is appropriately diluted and then run in one or more assays to produce the relevant quantitative biomarker levels used for classification.
  • the measured biomarker levels are used as input for the classification method that outputs a classification and an optional score for the sample that reflects the confidence of the class assignment.
  • Table 1 identifies 42 biomarkers that are useful for diagnosing ovarian cancer.
  • classifiers can be constructed such that particular biomarkers may be substituted for other biomarkers in a manner that reflects the redundancies that undoubtedly pervade the complexities of the underlying disease processes. That is to say, the information about the disease contributed by any individual biomarker identified in Table 1 overlaps with the information contributed by other biomarkers, such that it may be that no particular biomarker or small group of biomarkers in Table 1 must be included in any classifier.
  • Exemplary embodiments use na ⁇ ve Bayes classifiers constructed from the data in Table 18 to classify an unknown sample.
  • the procedure is outlined in Figures IA and B.
  • the biological sample is optionally diluted and run in a multiplexed aptamer assay.
  • the data from the assay are normalized and calibrated as outlined in Example 3, and the resulting biomarker levels are used as input to a Bayes classification scheme.
  • the log-likelihood ratio is computed for each measured biomarker individually and then summed to produce a final classification score, which is also referred to as a diagnostic score.
  • the resulting assignment as well as the overall classification score can be reported.
  • the individual log-likelihood risk factors computed for each biomarker level can be reported as well.
  • the details of the classification score calculation are presented in Example 3.
  • any combination of the biomarkers of Table 1 can be detected using a suitable kit, such as for use in performing the methods disclosed herein.
  • a suitable kit such as for use in performing the methods disclosed herein.
  • any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety, etc.
  • a kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, wherein the biomarkers include any of the biomarkers set forth in Table 1, and optionally (b) one or more software or computer program products for classifying the individual from whom the biological sample was obtained as either having or not having ovarian cancer or for determining the likelihood that the individual has ovarian cancer, as further described herein.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • kits The combination of a solid support with a corresponding capture reagent and a signal generating material is referred to herein as a "detection device" or “kit”.
  • the kit can also include instructions for using the devices and reagents, handling the sample, and analyzing the data. Further the kit may be used with a computer system or software to analyze and report the result of the analysis of the biological sample.
  • the kits can also contain one or more reagents (e.g., solubilization buffers, detergents, washes, or buffers) for processing a biological sample. Any of the kits described herein can also include, e.g., buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software and information such as protocols, guidance and reference data.
  • kits for the analysis of ovarian cancer status include PCR primers for one or more biomarkers selected from Table 1.
  • the kit may further include instructions for use and correlation of the biomarkers with ovarian cancer.
  • the kit may also include any of the following, either alone or in combination: a DNA array containing the complement of one or more of the biomarkers selected from Table 1, reagents, and enzymes for amplifying or isolating sample DNA.
  • the kits may include reagents for real-time PCR, such as, for example, TaqMan probes and/or primers, and enzymes.
  • a kit can comprise (a) reagents comprising at least capture reagent for quantifying one or more biomarkers in a test sample, wherein said biomarkers comprise the biomarkers set forth in Table 1, or any other biomarkers or biomarkers panels described herein, and optionally (b) one or more algorithms or computer programs for performing the steps of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoffs and assigning a score for each biomarker quantified based on said comparison, combining the assigned scores for each biomarker quantified to obtain a total score, comparing the total score with a predetermined score, and using said comparison to determine whether an individual has ovarian cancer.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • a method for diagnosing an individual can comprise the following: 1) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; 3) perform any data normalization or standardization required for the method used to collect biomarker values; 4) calculate the marker score; 5) combine the marker scores to obtain a total diagnostic score; and 6) report the individual's diagnostic score.
  • the diagnostic score may be a single number determined from the sum of all the marker calculations that is compared to a preset threshold value that is an indication of the presence or absence of disease.
  • the diagnostic score may be a series of bars that each represent a biomarker value and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease.
  • FIG. 6 An example of a computer system 100 is shown in Figure 6. With reference to Figure 6, system 100 is shown comprised of hardware elements that are electrically coupled via bus 108, including a processor 101, input device 102, output device 103, storage device 104, computer-readable storage media reader 105a, communications system 106 processing acceleration (e.g., DSP or special-purpose processors) 107 and memory 109.
  • processing acceleration e.g., DSP or special-purpose processors
  • Computer-readable storage media reader 105a is further coupled to computer-readable storage media 105b, the combination comprehensively representing remote, local, fixed and/or removable storage devices plus storage media, memory, etc. for temporarily and/or more permanently containing computer-readable information, which can include storage device 104, memory 109 and/or any other such accessible system 100 resource.
  • System 100 also comprises software elements (shown as being currently located within working memory 191) including an operating system 192 and other code 193, such as programs, data and the like.
  • system 100 has extensive flexibility and configurability.
  • a single architecture might be utilized to implement one or more servers that can be further configured in accordance with currently desirable protocols, protocol variations, extensions, etc.
  • embodiments may well be utilized in accordance with more specific application requirements.
  • one or more system elements might be implemented as sub- elements within a system 100 component (e.g., within communications system 106).
  • Customized hardware might also be utilized and/or particular elements might be implemented in hardware, software or both.
  • the system can comprise a database containing features of biomarkers characteristic of ovarian cancer.
  • the biomarker data (or biomarker information) can be utilized as an input to the computer for use as part of a computer implemented method.
  • the biomarker data can include the data as described herein.
  • system further comprises one or more devices for providing input data to the one or more processors.
  • the system further comprises a memory for storing a data set of ranked data elements.
  • the device for providing input data comprises a detector for detecting the characteristic of the data element, e.g., such as a mass spectrometer or gene chip reader.
  • the system additionally may comprise a database management system.
  • User requests or queries can be formatted in an appropriate language understood by the database management system that processes the query to extract the relevant information from the database of training sets.
  • the system may be connectable to a network to which a network server and one or more clients are connected.
  • the network may be a local area network (LAN) or a wide area network (WAN), as is known in the art.
  • the server includes the hardware necessary for running computer program products (e.g., software) to access database data for processing user requests.
  • the system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system.
  • the operating system can operate on a global communications network, such as the internet, and utilize a global communications network server to connect to such a network.
  • the system may include one or more devices that comprise a graphical display interface comprising interface elements such as buttons, pull down menus, scroll bars, fields for entering text, and the like as are routinely found in graphical user interfaces known in the art.
  • Requests entered on a user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more of the system databases.
  • Requests or queries entered by a user may be constructed in any suitable database language.
  • the graphical user interface may be generated by a graphical user interface code as part of the operating system and can be used to input data and/or to display inputted data.
  • the result of processed data can be displayed in the interface, printed on a printer in communication with the system, saved in a memory device, and/or transmitted over the network or can be provided in the form of the computer readable medium.
  • the system can be in communication with an input device for providing data regarding data elements to the system (e.g., expression values).
  • the input device can include a gene expression profiling system including, e.g., a mass spectrometer, gene chip or array reader, and the like.
  • the methods and apparatus for analyzing ovarian cancer biomarker information may be implemented in any suitable manner, for example, using a computer program operating on a computer system.
  • a conventional computer system comprising a processor and a random access memory, such as a remotely- accessible application server, network server, personal computer or workstation may be used.
  • Additional computer system components may include memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device.
  • the computer system may be a stand-alone system or part of a network of computers including a server and one or more databases.
  • the ovarian cancer biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and/or diagnosis.
  • the computer system can execute the computer program that may receive, store, search, analyze, and report information relating to the ovarian cancer biomarkers.
  • the computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to generate an ovarian cancer status and/or diagnosis.
  • Diagnosing ovarian cancer status may comprise generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.
  • biomarker information can be retrieved for an individual.
  • the biomarker information can be retrieved from a computer database, for example, after testing of the individual's biological sample is performed.
  • a computer can be utilized to classify each of the biomarker values.
  • a determination can be made as to the likelihood that an individual has ovarian cancer based upon a plurality of classifications.
  • the indication can be output to a display or other indicating device so that it is viewable by a person. Thus, for example, it can be displayed on a display screen of a computer or other output device.
  • a computer can be utilized to retrieve biomarker information for an individual.
  • the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers provided in Table 1.
  • a classification of the biomarker value can be performed with the computer.
  • an indication can be made as to the likelihood that the individual has ovarian cancer based upon the classification.
  • the indication can be output to a display or other indicating device so that it is viewable by a person. Thus, for example, it can be displayed on a display screen of a computer or other output device.
  • a computer program product may include a computer readable medium having computer readable program code embodied in the medium for causing an application program to execute on a computer with a database.
  • a "computer program product” refers to an organized set of instructions in the form of natural or programming language statements that are contained on a physical media of any nature (e.g., written, electronic, magnetic, optical or otherwise) and that may be used with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to act in accordance with the particular content of the statements.
  • Computer program products include without limitation: programs in source and object code and/or test or data libraries embedded in a computer readable medium.
  • the computer program product that enables a computer system or data processing equipment device to act in pre-selected ways may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing and any and all equivalents.
  • a computer program product for indicating a likelihood of ovarian cancer.
  • a computer program product for indicating a likelihood of ovarian cancer.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 1; and code that executes a classification method that indicates an ovarian disease status of the individual as a function of the biomarker value.
  • the embodiments may be embodied as code stored in a computer-readable memory of virtually any kind including, without limitation, RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments could be implemented in software, or in hardware, or any combination thereof including, but not limited to, software running on a general purpose processor, microcode, PLAs, or ASICs.
  • embodiments could be accomplished as computer signals embodied in a carrier wave, as well as signals (e.g., electrical and optical) propagated through a transmission medium.
  • signals e.g., electrical and optical
  • the various types of information discussed above could be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored on a computer readable medium.
  • many of the structures, materials, and acts recited herein can be recited as means for performing a function or step for performing a function. Therefore, it should be understood that such language is entitled to cover all such structures, materials, or acts disclosed within this specification and their equivalents, including the matter incorporated by reference.
  • the multiplexed analysis utilized 811 aptamers, each unique to a specific target.
  • aptamers without a photo-cleavable biotin linker custom stock aptamer solutions for 10%, 1% and 0.03% plasma were prepared at 8x concentration in Ix SB17, 0.05% Tween-20 with appropriate photo-cleavable, biotinylated primers, where the resultant primer concentration was 3 times the relevant aptamer concentration.
  • the primers hybridized to all or part of the corresponding aptamer.
  • Tween-20 (1500 ⁇ L of 8x stock into 4500 ⁇ L of lxSB17, 0.05% Tween-20) to achieve a 2x concentration.
  • Each diluted aptamer master mix was then split, 1500 ⁇ L each, into 4, 2 mL screw cap tubes and brought to 95°C for 5 minutes, followed by a 37°C incubation for 15 minutes. After incubation, the 4, 2 mL tubes corresponding to a particular aptamer master mix were combined into a reagent trough, and 55 ⁇ L of a 2x aptamer mix (for all three mixes) was manually pipetted into a 96-well Hybaid plate and the plate foil sealed.
  • the final result was 3, 96-well, foil-sealed Hybaid plates.
  • the individual aptamer concentration was 0.5 nM.
  • a 20% sample solution was prepared by transferring 16 ⁇ L of sample using a
  • sample and aptamer solutions were mixed on the robot by pipetting up and down.
  • MAHVN4550 were equilibrated with 100 ⁇ L of Ix SB 17, 0.05% Tween-20 for at least 10 minutes.
  • the equilibration buffer was then filtered through the plate and 133.3 ⁇ L of a 7.5% Streptavidin-agarose bead slurry (in Ix SB 17, 0.05% Tween-20) was added into each well.
  • the bead solution was manually mixed with a 200 ⁇ L, 12-channel pipettor, 15 times. After the beads were distributed across the 3 filter plates, a vacuum was applied to remove the bead supernatant.
  • Plates were blotted to remove droplets using an on-deck blot station and then incubated with orbital shakers at 800 rpm for 10 minutes at 25°C.
  • the robot removed this wash via vacuum filtration and blotted the bottom of the filter plate to remove droplets using the on-deck blot station.
  • the NHS-PEO4- biotin reagent was dissolved at 100 mM concentration in anhydrous DMSO and had been stored frozen at -20 0 C.
  • the diluted NHS-PEO4-biotin reagent was manually added to an on-deck trough and the robot program was manually re-initiated to dispense 100 ⁇ L of the NHS-PEO4-biotin into each well of each Catch 1 filter plate. This solution was allowed to incubate with Catch 1 beads shaking at 800 rpm for 5 minutes on the obital shakers.
  • the tagging reaction was quenched by the addition of 150 ⁇ L of 20 mM glycine in Ix SB 17, 0.05% Tween-20 to the Catch 1 plates while still containing the NHS tag. The plates were then incubated for 1 minute on orbital shakers at 800 rpm. The NHS- tag/glycine solution was removed via vacuum filtration. Next, 190 ⁇ L 20 mM glycine (Ix
  • Thermoshaker (Thermo Fisher Scientific, Inc., Waltham, MA ) under the BlackRay (Ted Pella, Inc., Redding, CA) light sources, and irradiated for 10 minutes while shaking at 800 rpm.
  • the robot transferred all of the photo-cleaved eluate from the 1 mL deep-well plate onto the Hybaid plate containing the previously prepared catch 2 MyOne magnetic beads (after removal of the MyOne buffer via magnetic separation).
  • the robot transferred the plate to the on deck magnetic separator station.
  • the plate was incubated on the magnet for 90 seconds before removal and discarding of the supernatant.
  • the catch 2 plate was then placed onto the magnetic separator for 90 seconds prior to transferring 90 ⁇ L of the eluate to a new 96-well plate containing 10 ⁇ L of 500 mM
  • a gasket slide was placed into an Agilent hybridization chamber and 40 ⁇ L of each of the samples containing hybridization and blocking solution was manually pipetted into each gasket.
  • An 8 -channel variable spanning pipettor was used in a manner intended to minimize bubble formation.
  • Custom Agilent microarray slides (Agilent Technologies, Inc.,
  • the assembled hybridization chambers were incubated in an Agilent hybridization oven for 19 hours at 60 0 C rotating at 20 rpm.
  • Agilent Wash Buffer 1 Approximately 400 mL Agilent Wash Buffer 1 was placed into each of two separate glass staining dishes. One of the staining dishes was placed on a magnetic stir plate and a slide rack and stir bar were placed into the buffer. [00310] A staining dish for Agilent Wash 2 was prepared by placing a stir bar into an empty glass staining dish.
  • a fourth glass staining dish was set aside for the final acetonitrile wash.
  • Each of six hybridization chambers was disassembled. One-by-one, the slide/backing sandwich was removed from its hybridization chamber and submerged into the staining dish containing Wash 1. The slide/backing sandwich was pried apart using a pair of tweezers, while still submerging the microarray slide. The slide was quickly transferred into the slide rack in the Wash 1 staining dish on the magnetic stir plate.
  • the slide rack was gently raised and lowered 5 times.
  • the magnetic stirrer was turned on at a low setting and the slides incubated for 5 minutes.
  • microarray slides were placed into Agilent scanner slide holders and loaded into the Agilent Microarray scanner according to the manufacturer's instructions.
  • Example 2 Biomarker Identification
  • the identification of potential ovarian cancer biomarkers was performed for diagnosis of ovarian cancer in women with pelvic masses. Enrollment criteria for this study were women scheduled for laparotomy or pelvic surgery for suspicion of ovarian cancer. The primary criteria for exclusion were women suffering from chronic infectious (e.g. hepatitis B, Hepatitis C or HIV), autoimmune, or inflammatory conditions or women being treated for malignancy (other than basal or squamous cell carcinomas of the skin) within the last two years. Plasma samples were collected from two different clinical sites and included 142 cases and 195 benign controls. Table 19 summarizes the site sample information.
  • the multiplexed ap tamer affinity assay was used to measure and report the RFU value for 811 analytes in each of these 337 samples. Since the plasma samples were obtained from two independent sites under similar protocols, an examination of site differences prior to the analysis for biomarkers discovery was performed. Each of the two populations, benign pelvic mass and ovarian cancer, was separately compared between sites by generating within- site, class-dependent cumulative distribution functions (cdfs) for each of the 811 analytes. The KS -test was then applied to each analyte between both site pairs within a common class to identify those analytes that differed not by class but rather by site. In both site comparisons among the two classes, statistically significant site-dependent differences were observed.
  • cdfs class-dependent cumulative distribution functions
  • biomarkers can be used to build classifiers that assign samples to either a control or disease group.
  • many such classifiers were produced from these sets of biomarkers and the frequency with which any biomarker was used in good scoring classifiers determined. Those biomarkers that occurred most frequently among the top scoring classifiers were the most useful for creating a diagnostic test.
  • Bayesian classifiers were used to explore the classification space but many other supervised learning techniques may be employed for this purpose.
  • the scoring fitness of any individual classifier was gauged by summing the sensitivity and specificity of the classifier at the Bayesian surface assuming a disease prevalence of 0.5. This scoring metric varies from zero to two, with two being an error-free classifier.
  • Example 3 The details of constructing a Bayesian classifier from biomarker population measurements are described in Example 3.
  • KS -distances were computed for all analytes using the class -dependent cdfs aggregated across all sites. Using a KS-distance threshold of 0.4, fifty-nine potential biomarkers for diagnosing malignant ovarian cancer from benign pelvic masses were identified.
  • Method (2) focused on consistency of potential biomarker changes between the control and case groups among the individual sites.
  • the class-dependent cdfs were constructed for all analytes within each site separately and from these cdfs the KS -distances were computed to identify potential biomarkers.
  • Sixty- three analytes were found to have a KS-distance greater than 0.4 in all the sites.
  • Using these Sixty-three analytes to build potential 10-analyte Bayesian classifiers there were 2031 classifiers that had a score of 1.75 or better. Twenty- four analytes occurred with a frequency greater than 5% among these classifiers and are presented in Table 21 and shown in Figure 11.
  • a set of potential biomarkers were produced by requiring an analyte to have a KS distance of 0.4 or better in the aggregated set as well as the two site comparisons. Forty-five analytes satisfy these requirements and are referred to as a blended set of potential biomarkers. For a classification score of 1.75 or better, a total of 1563 Bayesian classifiers were built from this set of potential biomarkers and twenty-seven biomarkers were identified from this set of classifiers using a frequency cut-off of 5%. These analytes are displayed in Table 22 and Figure 12 is a frequency plot for the identified biomarkers.
  • a final list of biomarkers is obtained by combining the three sets of biomarkers identified above with frequencies greater than 5% in high scoring classifiers, Tables 20-22. From these sets of twenty- five, twenty-four, and twenty-seven biomarkers, forty- two unique biomarkers were identified and are shown in Table 1.
  • Table 15 includes a dissociation constant for the aptamer used to identify the biomarker, the limit of quantification for the marker in the multiplex aptamer assay, and whether the marker was up- regulated or down-regulated in the disease population relative to the control population.
  • n 10 here.
  • Each of the terms in the summation is a log-likelihood ratio for an individual marker and the total log-likelihood ratio of a sample x being free from the disease of interest versus having the disease (i.e. in this case, ovarian cancer) is simply the sum of these individual terms plus a term that accounts for the prevalence of the disease.
  • This example describes the selection of biomarkers from Table 1 to form panels that can be used as classifiers in any of the methods described herein. Subsets of the biomarkers in Table 1 were selected to construct classifiers with good performance. This method was also used to determine which potential markers were included as biomarkers in Example 2.
  • the measure of classifier performance used here is the sum of the sensitivity and specificity; a performance of 1.0 is the baseline expectation for a random (coin toss) classifier, a classifier worse than random would score between 0.0 and 1.0, a classifier with better than random performance would score between 1.0 and 2.0. A perfect classifier with no errors would have a sensitivity of 1.0 and a specificity of 1.0, therefore a performance of 2.0 (1.0+1.0).
  • One can apply other common measures of performance such as area under the ROC curve, the F-measure, or the product of sensitivity and specificity.
  • any weighting scheme which results in a single performance measure can be used. Different applications will have different benefits for true positive and true negative findings, and will have different costs associated with false positive findings from false negative findings. For example, screening and the differential diagnosis of benign pelvic masses will not in general have the same optimal trade-off between specificity and sensitivity. The different demands of the two tests will in general require setting different weighting to positive and negative misclassifications, which will be reflected in the performance measure. Changing the performance measure will in general change the exact subset of markers selected from Table 1 for a given set of data.
  • the classifier was completely parameterized by the distributions of biomarkers in the disease and non-disease training samples, and the list of biomarkers was chosen from Table 1; that is to say, the subset of markers chosen for inclusion determined a classifier in a one-to-one manner given a set of training data.
  • the greedy method employed here was used to search for the optimal subset of markers from Table 1. For small numbers of markers or classifiers with relatively few markers, every possible subset of markers was enumerated and evaluated in terms of the performance of the classifier constructed with that particular set of markers (see Example 4, Part T).
  • Each marker subset currently on the list was extended by adding any marker from Table 1 not already part of that classifier, and which would not, on its addition to the subset, duplicate an existing subset (these are termed "permissible markers"). Every existing marker subset was extended by every permissible marker from the list. Clearly, such a process would eventually generate every possible subset, and the list would run out of space. Therefore, all the generated classifiers were kept only while the list was less than some predetermined size (often enough to hold all three marker subsets). Once the list reached the predetermined size limit, it became elitist; that is, only those classifiers which showed a certain level of performance were kept on the list, and the others fell off the end of the list and were lost.
  • biomarkers selected in Table 1 gave rise to classifiers that perform better than classifiers built with "non-markers" (i.e., proteins having signals that did not meet the criteria for inclusion in Table 1 (as described in Example 2)).
  • Figure 14 shows histograms of the performance of all possible one, two, and three-marker classifiers built from the biomarker parameters in Table 18 for biomarkers that can discriminate between benign pelvic masses and ovarian cancer and compares these classifiers with all possible one, two, and three-marker classifiers built using the 42 "non- marker" aptamer RFU signals.
  • Figure 14A shows the histograms of single marker classifier performance
  • Figure 14B shows the histogram of two-marker classifier performance
  • Figure 14C shows the histogram of three-marker classifier performance.
  • the solid lines represent the histograms of the classifier performance of all one, two, and three-marker classifiers using the biomarker data for benign pelvic masses and ovarian cancer in Table 18.
  • the dotted lines are the histograms of the classifier performance of all one, two, and three-marker classifiers using the data for benign pelvic masses and ovarian cancer but using the set of random non-marker signals.
  • the classifiers built from the markers listed in Table 1 form a distinct histogram, well separated from the classifiers built with signals from the "non-markers" for all one-marker, two-marker, and three-marker comparisons.
  • the performance and AUC score of the classifiers built from the biomarkers in Table 1 also increase at a higher rate as markers are added than do the classifiers built from the non-markers.
  • the separation of performance increases between the marker and non-marker classifiers as the number of markers per classifier increases. All classifiers built using the biomarkers listed in Table 1 perform distinctly better than classifiers built using the "non-markers".
  • Figure 15 demonstrates that classifiers constructed without the best markers perform well, implying that the performance of the classifiers was not due to some small core group of markers and that the changes in the underlying processes associated with disease are reflected in the activities of many proteins.
  • Many subsets of the biomarkers in Table 1 performed close to optimally, even after removing the top 15 of the 42 markers from Table 1. After dropping the 15 top-ranked markers (ranked by KS-distance) from Table 1, the classifier performance increased with the number of markers selected from the table to reach almost 1.80 (sensitivity + specificity), close to the performance of the optimal classifier score of 1.87 selected from the full list of biomarkers.
  • Figure 16 shows how the ROC performance of typical classifiers constructed from the list of parameters in Table 18 according to Example 3.
  • a five analyte classifier was constructed with TIMP-2, MCP-3, Cadherin-5, SLPI, and C9.
  • Figure 16A shows the performance of the model, assuming independence of these markers, as in Example 3, and
  • Figure 16B shows the empirical ROC curves generated from the study data set used to define the parameters in Table 18. It can be seen that the performance for a given number of selected markers was qualitatively in agreement, and that quantitative agreement was generally quite good, as evidenced by the AUCs, although the model calculation tends to overestimate classifier performance.
  • Example 5 Aptamer Specificity Demonstration in a Pull-down Assay
  • the final readout on the multiplex assay is based on the amount of aptamer recovered after the successive capture steps in the assay.
  • the multiplex assay is based on the premise that the amount of aptamer recovered at the end of the assay is proportional to the amount of protein in the original complex mixture (e.g., plasma).
  • the original complex mixture e.g., plasma
  • This assay can be used to visually demonstrate that a desired protein is in fact pulled out from plasma after equilibration with an aptamer as well as to demonstrate that aptamers bound to their intended protein targets can survive as a complex through the kinetic challenge steps in the assay.
  • recovery of protein at the end of this pull-down assay requires that the protein remain non-covalently bound to the aptamer for nearly two hours after equilibration.
  • non-specifically bound proteins dissociate during these steps and do not contribute significantly to the final signal. It should be noted that the pull-down procedure described in this example includes all of the key steps in the multiplex assay described above.
  • Plasma samples were prepared by diluting 50 ⁇ L EDTA-plasma to 100 ⁇ L in
  • This step will capture proteins bound to aptamers as well as proteins that may have dissociated from aptamers since the initial equilibration.
  • the beads were washed as described in Example 1. Proteins were eluted from the MyOne Streptavidin beads by incubating with 50 mM DTT in SB17T for 25 minutes at 37 0 C with shaking. The eluate was then transferred to MyOne beads coated with a sequence complimentary to the 3' fixed region of the aptamer and incubated for 25 minutes at 37 0 C with shaking. This step captures all of the remaining aptamer. The beads were washed 2x with 100 ⁇ L SB17T for 1 minute and Ix with 100 ⁇ L SB19T for 1 minute.
  • Aptamer was eluted from these final beads by incubating with 45 ⁇ L 20 mM NaOH for 2 minutes with shaking to disrupt the hybridized strands. 40 ⁇ L of this eluate was neutralized with 10 ⁇ L 80 mM HCl containing 0.05% Tween-20. Aliquots representing 5% of the eluate from the first set of beads (representing all plasma proteins bound to the aptamer) and 20% of the eluate from the final set of beads (representing all plasma proteins remaining bound at the end of our clinical assay) were run on a NuPAGE 4-12% Bis-Tris gel (Invitrogen) under reducing and denaturing conditions. Gels were imaged on an Alpha Innotech FluorChem Q scanner in the Cy5 channel to image the proteins.
  • B. Pull-down gels for aptamers were selected against LBP ( ⁇ lxl ⁇ ⁇ 7 M in plasma, polypeptide MW -60 kDa), C9 ( ⁇ lxl ⁇ ⁇ 6 M in plasma, polypeptide MW -60 kDa), and IgM ( ⁇ 9xlO ⁇ 6 M in plasma, MW -70 kDa and 23 kDa), respectively. (See Figure 13).
  • lane 1 is the eluate from the Streptavidin-agarose beads
  • lane 2 is the final eluate
  • lane 3 is a MW marker lane (major bands are at 110, 50, 30, 15, and 3.5 kDa from top to bottom).
  • biomarkers of Table 1 can be specifically excluded either as an individual biomarker or as a biomarker from any panel.
  • Table 18 Parameters derived from training set for na ⁇ ve Bayes classifier.

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Abstract

La présente invention porte sur des biomarqueurs, des procédés, des dispositifs, des réactifs, des systèmes et des trousses pour la détection et le diagnostic d'un cancer des ovaires. Sous un aspect, l'invention porte sur des biomarqueurs qui peuvent être utilisés seuls ou dans diverses combinaisons pour diagnostiquer un cancer des ovaires ou permettre le diagnostic différentiel d'une masse pelvienne comme étant bénigne ou maligne. Sous un autre aspect, l'invention porte sur des procédés pour diagnostiquer un cancer des ovaires chez un individu, les procédés comprenant la détection, dans un échantillon biologique provenant d'un individu, d'au moins une valeur de biomarqueur correspondant à au moins un biomarqueur choisi dans le groupe des biomarqueurs fournis dans le tableau 1, l'individu étant classé comme ayant un cancer des ovaires, ou la probabilité que l'individu ait un cancer des ovaires étant déterminée, sur la base de la au moins une valeur de biomarqueur.
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CN106796212A (zh) 2014-08-12 2017-05-31 新生代吉恩公司 用于基于收集的体液而监测健康的系统和方法
WO2017180909A1 (fr) 2016-04-13 2017-10-19 Nextgen Jane, Inc. Dispositifs, systèmes et procédés de collecte et de conservation d'échantillon
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