US20160222457A1 - Methods and materials for identifying malignant skin lesions - Google Patents

Methods and materials for identifying malignant skin lesions Download PDF

Info

Publication number
US20160222457A1
US20160222457A1 US14/442,673 US201314442673A US2016222457A1 US 20160222457 A1 US20160222457 A1 US 20160222457A1 US 201314442673 A US201314442673 A US 201314442673A US 2016222457 A1 US2016222457 A1 US 2016222457A1
Authority
US
United States
Prior art keywords
seq
test sample
marker gene
avg
expression level
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.)
Abandoned
Application number
US14/442,673
Inventor
Alexander MEVES
Ekaterina M. NIKOLOVA
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.)
Mayo Foundation for Medical Education and Research
Original Assignee
Mayo Foundation for Medical Education and Research
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Mayo Foundation for Medical Education and Research filed Critical Mayo Foundation for Medical Education and Research
Priority to US14/442,673 priority Critical patent/US20160222457A1/en
Assigned to MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH reassignment MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MEVES, Alexander, NIKOLOVA, Ekaterina M.
Publication of US20160222457A1 publication Critical patent/US20160222457A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/16—Primer sets for multiplex assays

Definitions

  • sequence_listing.txt is 101,321 bytes in size.
  • This document relates to methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document relates to methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • malignant skin lesions e.g., malignant pigmented skin lesions.
  • this document relates to methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • Malignant skin lesions are typically identified by obtaining a skin biopsy and morphologically assessing the biopsy's melanocytes under a microscope. Such a procedure can be difficult to standardize and can lead to overcalling of melanomas.
  • the risk of metastasis is typically determined by the invasion depth of malignant cells into the skin (i.e., the Breslow depth).
  • the Breslow depth can dictate further work-up such as a need for an invasive sentinel lymph node (SLN) procedure.
  • SSN sentinel lymph node
  • This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • quantitative PCR can be performed using a routine skin biopsy sample (e.g., a paraffin-embedded tissue biopsy) to obtain expression data (e.g., gene copy numbers) for one or more marker genes.
  • Correction protocols can be used to reduce the impact of basal keratinocyte contamination on the analysis of the expression data from the test sample.
  • the contribution of gene expression from basal keratinocytes present within the test skin sample can be determined and removed from the overall gene expression values to determine the final gene expression value for a particular gene as expressed from cells other than basal keratinocytes (e.g., melanocytes).
  • An assessment of the final gene expression values, which include minimal, if any, contribution from basal keratinocytes, for a collection of marker genes can be used to determine the benign or malignant biological behavior of the tested skin lesion.
  • one aspect of this document features a method for identifying a malignant skin lesion.
  • the method comprises, or consists essentially of, (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, and CXCL1 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a
  • the keratinocyte marker gene can be K14.
  • the marker gene can be SPP1.
  • the step (c) can comprise (i) multiplying the measured expression level of the keratinocyte marker gene for the test sample by the keratinocyte correction factor to obtain a correction value and (ii) subtracting the correction value from the measured expression level of the marker gene for the test sample to obtain the corrected value of marker gene expression for the test sample.
  • FIG. 1 is a flow chart of an exemplary process for determining the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).
  • FIG. 2 is a flow chart of an exemplary process for determining a keratinocyte correction factor for a marker gene of interest.
  • FIG. 3 is a flow chart of an exemplary process for removing copy number contamination from basal keratinocytes from a copy number value for a marker gene to determine the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for that marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).
  • a tested sample e.g., a tested skin biopsy sample
  • FIG. 4 is a diagram of an example of a generic computer device and a generic mobile computer device that can be used as described herein.
  • FIG. 5 is a flow chart of an exemplary process for using FN1 and SPP1 expression levels to determine the benign or malignant nature of a skin lesion.
  • FIG. 6 is a flow chart of an exemplary process for using FN1 and ITGB3 expression levels to determine the benign or malignant nature of a skin lesion.
  • FIG. 7 is a network diagram.
  • This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • FIG. 1 shows an exemplary process 100 for determining a gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).
  • the process begins at box 102 , where quantitative PCR using a collection of primer sets and a test sample is used to obtain a Ct value for the target of each primer set.
  • Each gene of interest can be assessed using a single primer set or multiple different primer sets (e.g., two, three, four, five, six, seven, or more different primer sets).
  • quantitative PCR is performed using each primer set and control nucleic acid of the target of each primer set (e.g., linearized cDNA fragments) to obtain a standard curve for each primer set as set forth in box 104 .
  • quantitative PCR is performed using each primer set and a known sample as an internal control (e.g., a stock biological sample) to obtain an internal control value for each primer set as set forth in box 106 . This internal control can be used to set values for each primer set across different assays.
  • the quantitative PCR performed according to boxes 102 , 104 , and 106 can be performed in parallel. For example, the quantitative PCR performed according to boxes 102 , 104 , and 106 can be performed in a single 96 well format.
  • a gene of interest included in the assay format can be a melanocyte marker (e.g., levels of MLANA and/or MITF expression) to confirm the presence of melanocytes in the test sample.
  • melanocyte markers e.g., levels of MLANA and/or MITF expression
  • Other examples of melanocyte markers that can be used as described herein include, without limitation, TYR, TYRP1, DCT, PMEL, OCA2, MLPH, and MC1R.
  • the raw copy number of each target present in the test sample is determined using the Ct values and the standard curve for each target.
  • the averaged, corrected copy number for each gene is calculated using the raw copy number of each target of a particular gene and the internal control value for each primer set (box 112 ).
  • This averaged, corrected copy number value for each gene can be normalized to a set number of one or more housekeeping genes as set forth in box 114 .
  • each averaged, corrected copy number value for each gene can be normalized to 100,000 copies of the combination of ACTB, RPL8, RPLP0, and B2M.
  • housekeeping genes that can be used as described herein include, without limitation, RRN18S, GAPD, PGK1, PPIA, RPL13A, YWHAZ, SDHA, TFRC, ALAS1, GUSB, HMBS, HPRT1, TBP, and TUPP.
  • the averaged, corrected, normalized copy number for each gene can be adjusted to remove the copy number contamination from basal keratinocytes present in the test sample.
  • copy number contamination from basal keratinocytes can be removed by (a) determining a keratinocyte correction factor for the gene of interest using one or more keratinocyte markers (e.g., keratin 14 (K14)) and one or more normal skin samples (e.g., FFPE-embedded normal skin samples), (b) determining the averaged, corrected, normalized copy number value for the one or more keratinocyte markers of the test sample and multiplying that value by the keratinocyte correction factor to obtain a correction value for the gene of interest, and (c) subtracting that correction value from the averaged, corrected, normalized copy number value of the gene of interest to obtain the final copy number for the gene of interest.
  • keratinocyte markers that can be used as described herein include, without limitation, KRTS, KRT1,
  • process 200 can be used to obtain a keratinocyte correction factor for a gene of interest.
  • the averaged, corrected, normalized copy number for one or more genes of interest e.g., Gene X
  • one or more basal keratinocyte marker genes e.g., K14
  • the keratinocyte correction factor for each gene of interest (e.g., Gene X) is determined by dividing the averaged, corrected, normalized copy number for each gene of interest present in a normal skin sample by the averaged, corrected, normalized copy number of a basal keratinocyte marker gene present in a normal skin sample.
  • Examples of keratinocyte correction factors for particular genes of interest are set forth in Table E under column “AVG per copy K14.”
  • a keratinocyte correction factor in determined for a particular gene of interest e.g., Gene X
  • the averaged, corrected, normalized copy number for the basal keratinocyte marker gene present in the test sample can be multiplied by the keratinocyte correction factor for the gene of interest (e.g., Gene X) to obtain a correction value for the gene of interest (e.g., Gene X). See, e.g., box 302 .
  • the correction value for the gene of interest (e.g., Gene X) is subtracted from the averaged, corrected, normalized copy number for the gene of interest (e.g., Gene X) present in the test sample to obtain a final copy number value of the gene of interest (e.g., Gene X) present in the test sample.
  • FIG. 4 is a diagram of an example of a generic computer device 1400 and a generic mobile computer device 1450 , which may be used with the techniques described herein.
  • Computing device 1400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • Computing device 1450 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices.
  • the components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.
  • Computing device 1400 includes a processor 1402 , memory 1404 , a storage device 1406 , a high-speed interface 1408 connecting to memory 1404 and high-speed expansion ports 1410 , and a low speed interface 1415 connecting to low speed bus 1414 and storage device 1406 .
  • Each of the components 1402 , 1404 , 1406 , 1408 , 1410 , and 1415 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 1402 can process instructions for execution within the computing device 1400 , including instructions stored in the memory 1404 or on the storage device 1406 to display graphical information for a GUI on an external input/output device, such as display 1416 coupled to high speed interface 1408 .
  • multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices 1400 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
  • the memory 1404 stores information within the computing device 1400 .
  • the memory 1404 is a volatile memory unit or units.
  • the memory 1404 is a non-volatile memory unit or units.
  • the memory 1404 may also be another form of computer-readable medium, such as a magnetic or optical disk.
  • the storage device 1406 is capable of providing mass storage for the computing device 1400 .
  • the storage device 1406 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
  • a computer program product can be tangibly embodied in an information carrier.
  • the computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein.
  • the information carrier is a computer- or machine-readable medium, such as the memory 1404 , the storage device 1406 , memory on processor 1402 , or a propagated signal.
  • the high speed controller 1408 manages bandwidth-intensive operations for the computing device 1400 , while the low speed controller 1415 manages lower bandwidth-intensive operations.
  • the high-speed controller 1408 is coupled to memory 1404 , display 1416 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 1410 , which may accept various expansion cards (not shown).
  • low-speed controller 1415 is coupled to storage device 1406 and low-speed expansion port 1414 .
  • the low-speed expansion port which may include various communication ports (e.g., USB, Bluetooth, Ethernet, or wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, an optical reader, a fluorescent signal detector, or a networking device such as a switch or router, e.g., through a network adapter.
  • input/output devices such as a keyboard, a pointing device, a scanner, an optical reader, a fluorescent signal detector, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 1400 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1420 , or multiple times in a group of such servers. It may also be implemented as part of a rack server system 1424 . In addition, it may be implemented in a personal computer such as a laptop computer 1422 . In some cases, components from computing device 1400 may be combined with other components in a mobile device (not shown), such as device 1450 . Each of such devices may contain one or more of computing device 1400 , 1450 , and an entire system may be made up of multiple computing devices 1400 , 1450 communicating with each other.
  • Computing device 1450 includes a processor 1452 , memory 1464 , an input/output device such as a display 1454 , a communication interface 1466 , and a transceiver 1468 , among other components (e.g., a scanner, an optical reader, a fluorescent signal detector).
  • the device 1450 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage.
  • a storage device such as a microdrive or other device, to provide additional storage.
  • Each of the components 1450 , 1452 , 1464 , 1454 , 1466 , and 1468 are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 1452 can execute instructions within the computing device 1450 , including instructions stored in the memory 1464 .
  • the processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors.
  • the processor may provide, for example, for coordination of the other components of the device 1450 , such as control of user interfaces, applications run by device 1450 , and wireless communication by device 1450 .
  • Processor 1452 may communicate with a user through control interface 1458 and display interface 1456 coupled to a display 1454 .
  • the display 1454 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology.
  • the display interface 1456 may comprise appropriate circuitry for driving the display 1454 to present graphical and other information to a user.
  • the control interface 1458 may receive commands from a user and convert them for submission to the processor 1452 .
  • an external interface 1462 may be provide in communication with processor 1452 , so as to enable near area communication of device 1450 with other devices. External interface 1462 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
  • the memory 1464 stores information within the computing device 1450 .
  • the memory 1464 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
  • Expansion memory 1474 may also be provided and connected to device 1450 through expansion interface 1472 , which may include, for example, a SIMM (Single In Line Memory Module) card interface.
  • SIMM Single In Line Memory Module
  • expansion memory 1474 may provide extra storage space for device 1450 , or may also store applications or other information for device 1450 .
  • expansion memory 1474 may include instructions to carry out or supplement the processes described herein, and may include secure information also.
  • expansion memory 1474 may be provide as a security module for device 1450 , and may be programmed with instructions that permit secure use of device 1450 .
  • secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • the memory may include, for example, flash memory and/or NVRAM memory, as discussed below.
  • a computer program product is tangibly embodied in an information carrier.
  • the computer program product contains instructions that, when executed, perform one or more methods, such as those described herein.
  • the information carrier is a computer- or machine-readable medium, such as the memory 1464 , expansion memory 1474 , memory on processor 1452 , or a propagated signal that may be received, for example, over transceiver 1468 or external interface 1462 .
  • Device 1450 may communicate wirelessly through communication interface 1466 , which may include digital signal processing circuitry where necessary. Communication interface 1466 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 1468 . In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 1470 may provide additional navigation- and location-related wireless data to device 1450 , which may be used as appropriate by applications running on device 1450 .
  • GPS Global Positioning System
  • Device 1450 may also communicate audibly using audio codec 1460 , which may receive spoken information from a user and convert it to usable digital information. Audio codec 1460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1450 . Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1450 .
  • Audio codec 1460 may receive spoken information from a user and convert it to usable digital information. Audio codec 1460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1450 . Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1450 .
  • the computing device 1450 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1480 . It may also be implemented as part of a smartphone 1482 , personal digital assistant, or other similar mobile device.
  • implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • ASICs application specific integrated circuits
  • These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • the systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.
  • LAN local area network
  • WAN wide area network
  • the Internet the global information network
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • Marker genes were ordered by their ability to differentiate benign from malignant tissue (Table A). This was based on the analysis of 73 benign and 53 malignant tissues, and the hypothesis that changes in expression of fibronectin-associated gene networks are indicative of malignant cell behavior. Values of the test statistic were for the Wilcoxon rank sum test. The values of the test statistic for a Winsorized two-sample test (trimmed outliers were replaced with actual values) and for the chi-square test for the zero vs. >zero versions of each variable were included. The top 5 discriminatory genes based on each statistical test were highlighted in bold.
  • the candidate gene list from Example 1 was modified to include other FN1 network genes as well as four housekeeping genes (ACTB, RPLP0, RPL8, and B2M), two keratinocyte markers (K10 and K14) to assess keratinocyte contamination, and four melanocyte markers (MITF, TYR, MLANA and PMEL) to assess melanocyte content in the skin sections.
  • housekeeping genes ACTB, RPLP0, RPL8, and B2M
  • K10 and K14 to assess keratinocyte contamination
  • melanocyte markers TYR, MLANA and PMEL
  • FN1 was identified as a component of the melanoma phenotype that is at the core of a gene network that discriminates between benign and malignant melanocytic skin lesions ( FIG. 7 ).
  • the modeling was based on the STRING 9.0 database (string-db.org).
  • mRNA extraction from paraffin-embedded biospecimen was performed using an extraction protocol (Qiagen RNA FFPE extraction kit) and an extraction robot (Qiacube from Qiagen). mRNA was transcribed into cDNA using a commercially available kit (iScript kit from BioRad), and Fluidigm technology was used for PCR cycling.
  • the primer design was performed using web-based open access software.
  • the primers were HPLC purified to minimize background and were optimized for formalin-fixed, paraffin-embedded (FFPE) tissue (i.e., highly degraded tissue).
  • FFPE formalin-fixed, paraffin-embedded
  • the primers were designed to detect a maximum number of gene transcripts and were designed to be cDNA specific (i.e., not affected by genomic DNA contamination of the total, tissue-derived cDNA).
  • the housekeeping genes, keratin genes, melanocyte-specific genes, and selected high interest genes were detected using four separate and individually designed primer pairs. The primer pairs are set forth in Table 2.
  • a standard curve for the target of each primer pair was generated with a defined number of amplicons per volume for each primer pair.
  • a standard (S7) was designed to contain about 5 million copies of amplicon-containing cDNA in a bacterial expression vector backbone (pJET1.2 obtained from Fermentas) per one microliter volume for each primer pair. From this, six 1:10 dilutions were generated such that seven standards S1 to S7 were obtained ranging from 5 to 5 million copies of amplicon.
  • total RNA was extracted from the human HaCaT, A431, and A375 cell lines, and the RNA was reverse transcribed into cDNA.
  • Cell line-derived cDNA was used as a template to amplify fragments of cDNA that contained the desired amplicons for the real time-PCR primer pairs.
  • a list of primers used to generate the desired cDNA fragments is listed in Table 3.
  • PCR reactions were performed using a high-fidelity polymerase (product name: Phusion′, obtained from New England Biolabs). PCR amplification products were checked for correct size and subsequently gel purified using the Qiagen Gel Extraction kit. Purified PCR fragments were subcloned into the bacterial expression vector pJET1.2 using a commercially available kit (Fermentas). The subcloned fragments were subsequently checked by restriction digest and DNA sequencing. Bacterial clones harboring the pJET1.2 expression vector with the correct PCR insert (containing the desired amplicon for real time PCR primer pairs) were frozen and stored at ⁇ 80° C. This was done to regenerate the same real time PCR standards over time.
  • Bacteria harboring the pJET1.2 expression vector with PCR inserts were cultured to generate sufficient amounts of vector.
  • a small aliquot of the total retrieved expression vector with insert was linearized using the PvuI-HF restriction enzyme (from New England Biolabs).
  • the digest was then purified using the Qiagen PCR purification kit.
  • Linearized cDNA was diluted to a concentration of 20 ng/ ⁇ L.
  • One ⁇ L of each of a total of 71 linearized cDNA fragments (each at a 20 ng/ ⁇ L concentration) were mixed and brought to a final volume of 1 mL to obtain standard S7.
  • Standard S7 was then diluted six times at a 1:10 ratio to obtained standards S1 to S6. Dilution was performed using ultrapure water obtained from Promega (Cat. No. P1193).
  • RNA extraction was performed using the Qiagen RNA FFPE retrieval kit and a Qiagen QiaCube extraction robot. 0.5 to 1 ⁇ g of RNA with a 260/280 ratio of greater than 1.8 were transcribed into cDNA using the BioRad iScript cDNA Synthesis kit. All biospecimens were annotated with clinical data from Mayo Clinic databases. H&E stained sections were obtained for each block analyzed and digitalized using a high-resolution slide scanner.
  • Fluidigm RT-PCR was performed using a 96 ⁇ 96 format for high throughput analysis (i.e., 96 cDNAs were analyzed for 96 markers; 9216 data points).
  • the primer pairs and cDNAs were prepared in a 96 well format. Standard curves were calculated for each primer pair. Copy numbers per 100,000 housekeeping genes were calculated for each primer pair and averaged per gene. This was initially done for cDNAs derived from FFPE-embedded skin. To correct for epidermal cell-derived cross-contamination, background signal per one copy of K14 (a basal keratinocyte marker) was calculated from FFPE-embedded normal skin samples for each primer pair and averaged.
  • Tables C and D summarize the comparisons of the gene expressions between the 73 benign and 54 metastatic.
  • Table A compares the ranked values using the Wilcoxon rank sum test, and
  • Table E compares the dichotomized values (zero vs. >0) using the chi-square test.
  • the rule was evaluated using 25 additional malignant patients who did not have mets (from the “Breslow” file). For 19 of these 25 patients, the rule was ‘negative’ (Table 9).
  • the rule also was evaluated using 33 thin melanomas (Table 10). For 25 of these 33 patients, the rule was ‘negative’.
  • results provided herein demonstrate the development of a method for determining absolute levels (copy numbers) of genes of interest (e.g., FN-associated genes) from paraffin-embedded tissue by generating a highly defined internal standard that can be regenerated indefinitely.
  • This standardization approach can allow for the comparison of results from independent experiments and thus, allows for extensive validation.
  • the RT-PCR not only produced strong signals from highly degraded RNA due to FFPE embedding, but also was amendable to high-throughput analysis and was highly cost effective. While the methods provided herein were validated for melanoma, these methods are likely applicable to other human cancers.
  • the results provided herein also demonstrate the discrimination between benign and malignant pigmented lesions based on multiple markers.
  • a test kit panel was designed to include primers for assessing expression levels of eight marker genes (ITGB3, TNC, SPP1, SPARC, PLAT, COL4A1, PLOD3, and PTK2) as well as three housekeeping genes (ACTB, RPLP0, and RPL8), one keratinocyte markers (K14) to assess keratinocyte contamination, and two melanocyte markers (MLANA and MITF) to assess melanocyte content in the skin sections.
  • the primers designed for this collection are set forth in Table 11.
  • kits One purpose of the kit was to differentiate between melanoma with high and low risk of regional metastasis, and to appropriately select patients for surgical procedures such as sentinel lymph node biopsy (SLNB) or total lymphadenectomy. Another purpose of this kit was to estimate disease-free survival, disease relapse, or likelihood of death from melanoma. To study the ability of these methods to discriminate between melanoma with high and low risk of metastasis and to establish superiority to established methods, a cohort of 158 patients between October 1998 and June 2013 were identified as having been diagnosed with high-risk melanoma and as having underwent SLNB with the intention to assess metastatic potential of the tumor.
  • SLNB sentinel lymph node biopsy
  • high-risk melanoma by current criteria are defined as melanoma with an invasion depth (Breslow depth) of ⁇ 1 mm; or melanoma with an invasion depth of 0.75 to 0.99 mm plus the presence of either one of the following three risk factors: >0 mitotic figures/mm 2 ; tumor ulceration present; patient age ⁇ 40 years.
  • Logic regression is a machine learning technique that uses Boolean explanatory variables. There was not a typical technique to create good cut points for logic regression.
  • recursive partitioning followed by standardization of cut point levels was used. These were arbitrarily set at 0, 50, 250, and 500. Cut points derived by logic regression were adjusted to the next highest standard level. The cut point for ITGB3 was maintained at 0.
  • the selected model for predicting metastasis was the following:
  • the risk of melanoma metastasis was high if ITGB3, PLAT, PTK2 or PLOD3 levels are increased and CDKN2A is low.
  • This model predicted regional metastasis (defined as a positive SLN biopsy at the time of primary cancer diagnosis) with a specificity of 80.3% and sensitivity of 97.3%.

Abstract

This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions are provided.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application claims the benefit of U.S. Provisional Application Ser. No. 61/726,217, filed Nov. 14, 2012. The disclosure of the prior application is considered part of (and is incorporated by reference in) the disclosure of this application.
    • SEQUENCE LISTING
  • The instant application contains a Sequence Listing which has been submitted in ASCII format via electronic filing and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Aug. 7, 2013, is named sequence_listing.txt and is 101,321 bytes in size.
    • BACKGROUND
  • 1. Technical Field
  • This document relates to methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document relates to methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • 2. Background Information
  • Malignant skin lesions are typically identified by obtaining a skin biopsy and morphologically assessing the biopsy's melanocytes under a microscope. Such a procedure can be difficult to standardize and can lead to overcalling of melanomas.
  • Once a diagnosis of melanoma is made by morphological assessment, the risk of metastasis is typically determined by the invasion depth of malignant cells into the skin (i.e., the Breslow depth). The Breslow depth can dictate further work-up such as a need for an invasive sentinel lymph node (SLN) procedure. Such procedures, however, can lead to inaccurate determinations of the true malignant potential of a pigmented lesion.
    • SUMMARY
  • This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • As described herein, quantitative PCR can be performed using a routine skin biopsy sample (e.g., a paraffin-embedded tissue biopsy) to obtain expression data (e.g., gene copy numbers) for one or more marker genes. Correction protocols can be used to reduce the impact of basal keratinocyte contamination on the analysis of the expression data from the test sample. For example, the contribution of gene expression from basal keratinocytes present within the test skin sample can be determined and removed from the overall gene expression values to determine the final gene expression value for a particular gene as expressed from cells other than basal keratinocytes (e.g., melanocytes). An assessment of the final gene expression values, which include minimal, if any, contribution from basal keratinocytes, for a collection of marker genes can be used to determine the benign or malignant biological behavior of the tested skin lesion.
  • In general, one aspect of this document features a method for identifying a malignant skin lesion. The method comprises, or consists essentially of, (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, and CXCL1 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for the test sample, and (d) identifying the test sample as containing a malignant skin lesion based, at least in part, on the corrected value of marker gene expression for the test sample. The keratinocyte marker gene can be K14. The marker gene can be SPP1. The step (c) can comprise (i) multiplying the measured expression level of the keratinocyte marker gene for the test sample by the keratinocyte correction factor to obtain a correction value and (ii) subtracting the correction value from the measured expression level of the marker gene for the test sample to obtain the corrected value of marker gene expression for the test sample. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
  • Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.
    • DESCRIPTION OF DRAWINGS
  • FIG. 1 is a flow chart of an exemplary process for determining the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).
  • FIG. 2 is a flow chart of an exemplary process for determining a keratinocyte correction factor for a marker gene of interest.
  • FIG. 3 is a flow chart of an exemplary process for removing copy number contamination from basal keratinocytes from a copy number value for a marker gene to determine the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for that marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).
  • FIG. 4 is a diagram of an example of a generic computer device and a generic mobile computer device that can be used as described herein.
  • FIG. 5 is a flow chart of an exemplary process for using FN1 and SPP1 expression levels to determine the benign or malignant nature of a skin lesion.
  • FIG. 6 is a flow chart of an exemplary process for using FN1 and ITGB3 expression levels to determine the benign or malignant nature of a skin lesion.
  • FIG. 7 is a network diagram.
    •  DETAILED DESCRIPTION
  • This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.
  • FIG. 1 shows an exemplary process 100 for determining a gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample). The process begins at box 102, where quantitative PCR using a collection of primer sets and a test sample is used to obtain a Ct value for the target of each primer set. Each gene of interest can be assessed using a single primer set or multiple different primer sets (e.g., two, three, four, five, six, seven, or more different primer sets). In some cases, quantitative PCR is performed using each primer set and control nucleic acid of the target of each primer set (e.g., linearized cDNA fragments) to obtain a standard curve for each primer set as set forth in box 104. In some cases, quantitative PCR is performed using each primer set and a known sample as an internal control (e.g., a stock biological sample) to obtain an internal control value for each primer set as set forth in box 106. This internal control can be used to set values for each primer set across different assays. In some cases, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in parallel. For example, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in a single 96 well format.
  • At box 108, the quality of the obtained standard curves can be confirmed. In some cases, a gene of interest included in the assay format can be a melanocyte marker (e.g., levels of MLANA and/or MITF expression) to confirm the presence of melanocytes in the test sample. Other examples of melanocyte markers that can be used as described herein include, without limitation, TYR, TYRP1, DCT, PMEL, OCA2, MLPH, and MC1R.
  • At box 110, the raw copy number of each target present in the test sample is determined using the Ct values and the standard curve for each target. In some cases, the averaged, corrected copy number for each gene is calculated using the raw copy number of each target of a particular gene and the internal control value for each primer set (box 112). This averaged, corrected copy number value for each gene can be normalized to a set number of one or more housekeeping genes as set forth in box 114. For example, each averaged, corrected copy number value for each gene can be normalized to 100,000 copies of the combination of ACTB, RPL8, RPLP0, and B2M. Other examples of housekeeping genes that can be used as described herein include, without limitation, RRN18S, GAPD, PGK1, PPIA, RPL13A, YWHAZ, SDHA, TFRC, ALAS1, GUSB, HMBS, HPRT1, TBP, and TUPP. Once normalized, the copy number values for each gene can be referred to as the averaged, corrected, normalized copy number for that gene as present in the test sample.
  • At box 116, the averaged, corrected, normalized copy number for each gene can be adjusted to remove the copy number contamination from basal keratinocytes present in the test sample. In general, copy number contamination from basal keratinocytes can be removed by (a) determining a keratinocyte correction factor for the gene of interest using one or more keratinocyte markers (e.g., keratin 14 (K14)) and one or more normal skin samples (e.g., FFPE-embedded normal skin samples), (b) determining the averaged, corrected, normalized copy number value for the one or more keratinocyte markers of the test sample and multiplying that value by the keratinocyte correction factor to obtain a correction value for the gene of interest, and (c) subtracting that correction value from the averaged, corrected, normalized copy number value of the gene of interest to obtain the final copy number for the gene of interest. Examples of keratinocyte markers that can be used as described herein include, without limitation, KRTS, KRT1, KRT10, KRT17, ITGB4, ITGA6, PLEC, DST, and COL17A1.
  • With reference to FIG. 2, process 200 can be used to obtain a keratinocyte correction factor for a gene of interest. At box 202, the averaged, corrected, normalized copy number for one or more genes of interest (e.g., Gene X) and one or more basal keratinocyte marker genes (e.g., K14) are determined using one or more normal skin samples and procedures similar to those described in FIG. 1. As box 204, the keratinocyte correction factor for each gene of interest (e.g., Gene X) is determined by dividing the averaged, corrected, normalized copy number for each gene of interest present in a normal skin sample by the averaged, corrected, normalized copy number of a basal keratinocyte marker gene present in a normal skin sample. Examples of keratinocyte correction factors for particular genes of interest are set forth in Table E under column “AVG per copy K14.”
  • With reference to FIG. 3, once a keratinocyte correction factor in determined for a particular gene of interest (e.g., Gene X), then the averaged, corrected, normalized copy number for the basal keratinocyte marker gene present in the test sample can be multiplied by the keratinocyte correction factor for the gene of interest (e.g., Gene X) to obtain a correction value for the gene of interest (e.g., Gene X). See, e.g., box 302. At box 304, the correction value for the gene of interest (e.g., Gene X) is subtracted from the averaged, corrected, normalized copy number for the gene of interest (e.g., Gene X) present in the test sample to obtain a final copy number value of the gene of interest (e.g., Gene X) present in the test sample.
  • FIG. 4 is a diagram of an example of a generic computer device 1400 and a generic mobile computer device 1450, which may be used with the techniques described herein. Computing device 1400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 1450 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.
  • Computing device 1400 includes a processor 1402, memory 1404, a storage device 1406, a high-speed interface 1408 connecting to memory 1404 and high-speed expansion ports 1410, and a low speed interface 1415 connecting to low speed bus 1414 and storage device 1406. Each of the components 1402, 1404, 1406, 1408, 1410, and 1415, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 1402 can process instructions for execution within the computing device 1400, including instructions stored in the memory 1404 or on the storage device 1406 to display graphical information for a GUI on an external input/output device, such as display 1416 coupled to high speed interface 1408. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 1400 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
  • The memory 1404 stores information within the computing device 1400. In one implementation, the memory 1404 is a volatile memory unit or units. In another implementation, the memory 1404 is a non-volatile memory unit or units. The memory 1404 may also be another form of computer-readable medium, such as a magnetic or optical disk.
  • The storage device 1406 is capable of providing mass storage for the computing device 1400. In one implementation, the storage device 1406 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1404, the storage device 1406, memory on processor 1402, or a propagated signal.
  • The high speed controller 1408 manages bandwidth-intensive operations for the computing device 1400, while the low speed controller 1415 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 1408 is coupled to memory 1404, display 1416 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 1410, which may accept various expansion cards (not shown). In the implementation, low-speed controller 1415 is coupled to storage device 1406 and low-speed expansion port 1414. The low-speed expansion port, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, or wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, an optical reader, a fluorescent signal detector, or a networking device such as a switch or router, e.g., through a network adapter.
  • The computing device 1400 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1420, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 1424. In addition, it may be implemented in a personal computer such as a laptop computer 1422. In some cases, components from computing device 1400 may be combined with other components in a mobile device (not shown), such as device 1450. Each of such devices may contain one or more of computing device 1400, 1450, and an entire system may be made up of multiple computing devices 1400, 1450 communicating with each other.
  • Computing device 1450 includes a processor 1452, memory 1464, an input/output device such as a display 1454, a communication interface 1466, and a transceiver 1468, among other components (e.g., a scanner, an optical reader, a fluorescent signal detector). The device 1450 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components 1450, 1452, 1464, 1454, 1466, and 1468, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.
  • The processor 1452 can execute instructions within the computing device 1450, including instructions stored in the memory 1464. The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor may provide, for example, for coordination of the other components of the device 1450, such as control of user interfaces, applications run by device 1450, and wireless communication by device 1450.
  • Processor 1452 may communicate with a user through control interface 1458 and display interface 1456 coupled to a display 1454. The display 1454 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1456 may comprise appropriate circuitry for driving the display 1454 to present graphical and other information to a user. The control interface 1458 may receive commands from a user and convert them for submission to the processor 1452. In addition, an external interface 1462 may be provide in communication with processor 1452, so as to enable near area communication of device 1450 with other devices. External interface 1462 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
  • The memory 1464 stores information within the computing device 1450. The memory 1464 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 1474 may also be provided and connected to device 1450 through expansion interface 1472, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 1474 may provide extra storage space for device 1450, or may also store applications or other information for device 1450. For example, expansion memory 1474 may include instructions to carry out or supplement the processes described herein, and may include secure information also. Thus, for example, expansion memory 1474 may be provide as a security module for device 1450, and may be programmed with instructions that permit secure use of device 1450. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1464, expansion memory 1474, memory on processor 1452, or a propagated signal that may be received, for example, over transceiver 1468 or external interface 1462.
  • Device 1450 may communicate wirelessly through communication interface 1466, which may include digital signal processing circuitry where necessary. Communication interface 1466 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 1468. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 1470 may provide additional navigation- and location-related wireless data to device 1450, which may be used as appropriate by applications running on device 1450.
  • Device 1450 may also communicate audibly using audio codec 1460, which may receive spoken information from a user and convert it to usable digital information. Audio codec 1460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1450. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1450.
  • The computing device 1450 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1480. It may also be implemented as part of a smartphone 1482, personal digital assistant, or other similar mobile device.
  • Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
  • To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • The systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.
  • The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.
    • EXAMPLES Example 1 Marker Genes that Discriminate Between Benign and Malignant Tissue
  • Marker genes were ordered by their ability to differentiate benign from malignant tissue (Table A). This was based on the analysis of 73 benign and 53 malignant tissues, and the hypothesis that changes in expression of fibronectin-associated gene networks are indicative of malignant cell behavior. Values of the test statistic were for the Wilcoxon rank sum test. The values of the test statistic for a Winsorized two-sample test (trimmed outliers were replaced with actual values) and for the chi-square test for the zero vs. >zero versions of each variable were included. The top 5 discriminatory genes based on each statistical test were highlighted in bold.
  • TABLE A
    Test statistic value
    Wilcoxon Winsorized
    rank sum two-sample Chi-
    gene test t-test square test
    FN1 −10.2312 −8.04081 106.714
    SPP1 −9.0279 −4.9374 86.774
    COL4A1 −8.8807 −7.27171 83.711
    TNC −8.7511 −8.31049 75.549
    ITGA3 −8.6008 −5.86334 79.788
    LOXL3 −8.1978 −6.75327 75.144
    AGRN −8.1243 −7.91238 62.611
    VCAN −8.0812 −6.24088 67.388
    PLOD3 −8.0384 −6.89248 62.691
    ITGB1 −8.0021 −7.38143 59.973
    PTK2 −7.5279 −7.19889 54.446
    CTGF −7.4997 −5.581 57.79
    PLOD1 −7.332 −7.36126 44.87
    LAMC1 −7.2425 −6.1057 54.233
    THBS1 −7.2425 −5.60331 54.233
    LOXL2 −7.2241 −6.33208 55.909
    IL6 −7.1777 −6.41883 56.966
    LOXL1 −7.1279 −6.34431 52.878
    IL8 −7.1194 −5.76042 57.296
    CYR61 −6.741 −6.97388 43.866
    ITGAV −6.5947 −6.27571 47.021
    YAP −6.4848 −6.36431 42.417
    BGN −6.3419 −6.01066 25.387
    LAMB1 −6.3293 −5.68826 37.061
    ITGB3 −6.3142 −5.13158 40.835
    CXCL1 −6.1077 −5.66564 40.137
    THBS2 −6.0427 −5.02003 37.413
    COL18A1 −6.0379 −4.9125 41.339
    SPARC −6.0272 −6.39324 38.098
    TP53 −6.0182 −6.18554 34.945
    PLOD2 −5.9082 −3.50272 47.576
    CCL2 −5.8844 −5.38758 30.69
    FBLN2 −5.5848 −4.59826 31.913
    LAMA1 −5.4876 −4.2817 31.071
    THBS4 −5.3971 −3.88786 35.27
    COL1A1 −5.325 −4.37617 34.693
    ITGA5 −4.9847 −3.56695 25.243
    TAZ −4.036 −3.26011 18.313
    POSTN −3.8054 −2.78378 19.813
    LOX −3.728 −2.8677 17.157
    CSRC −3.7078 −3.71759 13.983
    LAMA3 −3.5805 −2.99652 13.391
    CDKN1A −3.5766 −3.20447 17.228
    CDKN2A −3.5491 −2.90903 15.938
    ITGA2 −3.4083 −2.72495 11.766
    LAMC2 −3.4083 −2.53784 11.766
    PCOLCE2 −3.3469 −3.53676 14.449
    LOXL4 −3.2079 −2.76128 10.943
    PCOLCE −2.2172 −1.13805 7.993
    LAMB3 −1.2822 0.89459 7.028
    CSF2 2.175 1.93095 4.522
    • Example 2 Marker Panel Revision after Statistical Analysis
  • The candidate gene list from Example 1 was modified to include other FN1 network genes as well as four housekeeping genes (ACTB, RPLP0, RPL8, and B2M), two keratinocyte markers (K10 and K14) to assess keratinocyte contamination, and four melanocyte markers (MITF, TYR, MLANA and PMEL) to assess melanocyte content in the skin sections. Genes from Example 1 with low discriminatory value and a more distant neighborhood to FN1 were excluded from the test setup (LAMC1, LOXL2, CYR61, YAP, BGN, LAMB1, THBS2, COL18A1, SPARC, TP53, PLOD2, CCL2, FBLN2, LAMA1, THBS4, COL1A1, TAZ, POSTN, LOX, CSRC, LAMA3, CDKN1A, CDKN2A, LAMC2, PCOLCE2, LOXL4, PCOLCE, LAMB3, and CSF2). Instead, the discriminatory ability of other FN1 network genes was determined (PLAT, CSK, GDF15, FARP1, ARPC1B, NES, NTRK3, SNX17, L1CAM, and CD44). The following results were based on the analysis of 26 benign nevi and 52 primary cutaneous melanomas with documented subsequent metastasis or skin lesions of melanoma metastasis (Table B). The top 5 genes were highlighted.
  • TABLE B
    Test Statistic value
    Wilcoxon Winsorized
    rank sum two-sample Chi-square
    gene test t-test test
    COL4A1 −5.85975 −5.42545 46.3273
    FN1 −5.50862 −3.63639 35.1951
    PLAT −4.82670 −3.13568 25.7234
    IL8 −4.61443 −4.41668 28.6000
    SPP1 −4.60153 −3.08137 23.0816
    PLOD3 −4.37001 −3.91553 18.8036
    TNC −4.26431 −3.14128 19.5000
    CXCL1 −4.24452 −3.76681 20.6471
    CSK −4.15178 −2.96444 18.3962
    GDF15 −4.01364 −2.99752 13.7083
    ITGB3 −3.92608 −2.80068 16.3091
    CCL2 −3.61870 −3.45423 17.5176
    VCAN −3.46906 −2.26781 12.5593
    ITGB1 −3.40897 −3.63399 5.0221
    PLOD1 −3.40380 −3.20309 9.2625
    CTGF −3.11725 −2.20507 10.0645
    THBS1 −3.11721 −2.01257 10.0645
    ITGA3 −3.04915 −2.65398 7.5341
    FARP1 −2.99724 −2.28024 9.2857
    AGRN −2.92104 −3.30679 1.8838
    IL6 −2.85960 −3.05600 10.6257
    LOXL3 −2.84999 −2.70498 5.1096
    LOXL1 −2.69957 −2.11477 8.1250
    ARPC1B −2.57571 −2.82320 All but 1
    value >0
    NES −2.45264 −2.70056 2.4375
    PTK2 −2.22328 −2.26180 4.4057
    ITGA2 −2.08353 −1.50078 4.4571
    ITGA5 −1.93478 −1.39663 3.8451
    ITGAV −1.29341 −0.81964 3.5615
    NTRK3 −1.22485 75 of the 78 values are = 0
    MITF 0.58305 0.73916 0.4274
    SNX17 0.74754 0.90733 0.0785
    L1CAM 1.61125 0.27151 2.1081
    MLANA 2.96258 2.92548 All values
    >0
    CD44 5.23089 7.17590 All but 1
    value >0
  • Based on the results of Example 1 and above, FN1 was identified as a component of the melanoma phenotype that is at the core of a gene network that discriminates between benign and malignant melanocytic skin lesions (FIG. 7). The modeling was based on the STRING 9.0 database (string-db.org).
  • The list of all 71 genes tested is provided in Table 1.
  • TABLE 1
    List of genes used to discriminate benign skin tissue
    lesions from malignant skin tissue lesions.
    GenBank ® GenBank ®
    Gene Name Accession No. GI No.
    FN1 NM_212482 47132556
    NM_002026 47132558
    NM_212474 47132548
    NM_212476 47132552
    NM_212478 47132554
    NM_054034 47132546
    SPP1 NM_001040058 91206461
    NM_001040060 91598938
    NM_000582 38146097
    COL4A1 NM_001845 148536824
    TNC NM_002160 340745336
    ITGA3 NM_005501 171846264
    NM_002204 171846266
    LOXL3 NM_032603 22095373
    AGRN NM_198576 344179122
    VCAN NM_004385 255918074
    NM_001164098 255918078
    NM_001164097 255918076
    PLOD3 NM_001084 62739167
    ITGB1 NM_002211 182519230
    NM_133376 182507162
    NM_033668 182507160
    PTK2 NM_001199649 313851043
    NM_005607 313851042
    NM_153831 313851041
    CTGF NM_001901 98986335
    PLOD1 NM_000302 324710986
    LAMC1 NM_002293 145309325
    THBS1 NM_003246 40317625
    LOXL2 NM_002318 67782347
    IL6 NM_000600 224831235
    LOXL1 NM_005576 67782345
    IL8 NM_000584 324073503
    CYR61 NM_001554 197313774
    ITGAV NM_001144999 223468594
    NM_001145000 223468596
    NM_002210 223468593
    YAP NM_001130145 303523503
    NM_001195045 303523626
    NM_006106 303523510
    NM_001195044 303523609
    BGN NM_001711 268607602
    LAMB1 NM_002291 167614503
    ITGB3 NM_000212 47078291
    CXCL1 NM_001511 373432598
    THBS2 NM_003247 40317627
    COL18A1 NM_030582 110611234
    NM_130445 110611232
    SPARC NM_003118 365777426
    TP53 NM_000546 371502114
    NM_001126112 371502115
    NM_001126114 371502117
    NM_001126113 371502116
    PLOD2 NM_182943 62739164
    NM_000935 62739165
    CCL2 NM_002982 56119169
    FBLN2 NM_001998 51873054
    NM_001004019 51873052
    NM_001165035 259013546
    LAMA1 NM_005559 329112585
    THBS4 NM_003248 291167798
    COL1A1 NM_000088 110349771
    ITGA5 NM_002205 56237028
    TAZ NM_000116 195232764
    NM_181311 195232766
    NM_181312 195232765
    NM_181313 195232767
    POSTN NM_001135934 209862910
    NM_006475 209862906
    NM_001135935 209863010
    LOX NM_001178102 296010939
    NM_002317 296010938
    CSRC NM_005417 38202215
    NM_198291 38202216
    LAMA3 NM_198129 38045909
    NM_001127717 189217424
    CDKN1A NM_000389 310832422
    NM_001220777 334085239
    NM_078467 310832423
    NM_001220778 334085241
    CDKN2A NM_000077 300863097
    NM_058195 300863095
    NM_001195132 304376271
    ITGA2 NM_002203 116295257
    LAMC2 NM_005562 157419137
    NM_018891 157419139
    PCOLCE2 NM_013363 296317252
    LOXL4 NM_032211 67782348
    PCOLCE NM_002593 157653328
    LAMB3 NM_000228 62868214
    NM_001017402 62868216
    NM_001127641 189083718
    CSF2 NM_000758 371502128
    ACTB NM_001101 168480144
    RPLP0 NM_053275 49087137
    NM_001002 49087144
    RPL8 NM_000973 72377361
    NM_033301 15431305
    B2M NM_004048 37704380
    K10 NM_000421 195972865
    K14 NM_000526 197313720
    MITF NM_198158 296841082
    NM_198177 296841080
    NM_006722 296841079
    NM_198159 296841078
    NM_000248 296841081
    NM_001184967 296841084
    NM_198178 296923803
    TYR NM_000372 113722118
    MLANA NM_005511 5031912
    PMEL NM_001200054 318037594
    NM_001200053 318037592
    NM_006928 318068057
    NES NM_006617 38176299
    L1CAM NM_024003 221316758
    NM_001143963 221316759
    NM_000425 221316755
    GDF15 NM_004864 153792494
    ARPC1B NM_005720 325197176
    FARP1 NM_005766 48928036
    NM_001001715 159032536
    NTRK3 NM_001007156 340745351
    NM_001012338 340745349
    NM_001243101 340745352
    NM_002530 340745350
    CSK NM_001127190 187475372
    NM_004383 187475371
    CD44 NM_001001391 48255940
    NM_001001392 48255942
    NM_001202556 321400139
    NM_001001389 48255936
    NM_000610 48255934
    NM_001001390 48255938
    NM_001202555 321400137
    NM_001202557 321400141
    SNX17 NM_014748 388596703
    PLAT NM_000930 132626665
    NM_033011 132626641
  • Gene expression of target genes was assessed by SYBR/EVA-Green based RT-PCR. All tested genes were accompanied by a standard curve for quantification of absolute copy number per a defined number of housekeeping genes. mRNA extraction from paraffin-embedded biospecimen was performed using an extraction protocol (Qiagen RNA FFPE extraction kit) and an extraction robot (Qiacube from Qiagen). mRNA was transcribed into cDNA using a commercially available kit (iScript kit from BioRad), and Fluidigm technology was used for PCR cycling.
  • The primer design was performed using web-based open access software. The primers were HPLC purified to minimize background and were optimized for formalin-fixed, paraffin-embedded (FFPE) tissue (i.e., highly degraded tissue). The primers were designed to detect a maximum number of gene transcripts and were designed to be cDNA specific (i.e., not affected by genomic DNA contamination of the total, tissue-derived cDNA). The housekeeping genes, keratin genes, melanocyte-specific genes, and selected high interest genes were detected using four separate and individually designed primer pairs. The primer pairs are set forth in Table 2.
  • TABLE 2
    Primer sets for indicated genes.
    Gene
    Name Forward primer Reverse primer
    ACTB 5′-GCCAACCGCGAGAAGATG-3′;   5′-GGCTGGGGTGTTGAAGGT-3′; 
    SEQ ID NO: 1 SEQ ID NO: 2
    5′-CGCGAGAAGATGACCCAGAT-3′;  5′-GGGGTGTTGAAGGTCTCAAA-3′;
    SEQ ID NO: 3 SEQ ID NO: 4
    5′-TGACCCAGATCATGTTTGAGA-3′; 5′-GTACATGGCTGGGGTGTTG-3′; 
    SEQ ID NO: 5 SEQ ID NO: 6
    5′-CTGAACCCCAAGGCCAAC-3′;  5′-TGATCTGGGTCATCTTCTCG-3′;
    SEQ ID NO: 7 SEQ ID NO: 8
    RPLP0 5′-AACTCTGCATTCTCGCTTCC-3′;  5′-GCAGACAGACACTGGCAACA-3′;
    SEQ ID NO: 9 SEQ ID NO: 10
    5′-GCACCATTGAAATCCTGAGTG-3′; 5′-GCTCCCACTTTGTCTCCAGT-3′;
    SEQ ID NO: 11 SEQ ID NO: 12
    5′-TCACAGAGGAAACTCTGCATTC-3′; 5′-GGACACCCTCCAGGAAGC-3′; 
    SEQ ID NO: 13 SEQ ID NO: 14
    5′-ATCTCCAGGGGCACCATT-3′;  5′-AGCTGCACATCACTCAGGATT-3′;
    SEQ ID NO: 15 SEQ ID NO: 16
    RPL8 5′-ACTGCTGGCCACGAGTACG-3′;  5′-ATGCTCCACAGGATTCATGG-3′;
    SEQ ID NO: 17 SEQ ID NO: 18
    5′-ACAGAGCTGTGGTTGGTGTG-3′;  5′-TTGTCAATTCGGCCACCT-3′; 
    SEQ ID NO: 19 SEQ ID NO: 20
    5′-TATCTCCTCAGCCAACAGAGC-3′; 5′-AGCCACCACACCAACCAC-3′; 
    SEQ ID NO: 21 SEQ ID NO: 22
    5′-GTGTGGCCATGAATCCTGT-3′;  5′-CCACCTCCAAAAGGATGCTC-3′;
    SEQ ID NO: 23 SEQ ID NO: 24
    B2M 5′-TCTCTCTTTCTGGCCTGGAG-3′;  5′-GAATCTTTGGAGTACGCTGGA-3′;
    SEQ ID NO: 25 SEQ ID NO: 26
    5′-TGGAGGCTATCCAGCGTACT-3′;  5′-CGTGAGTAAACCTGAATCTTTGG-3′;
    SEQ ID NO: 27 SEQ ID NO: 28
    5′-CCAGCGTACTCCAAAGATTCA-3′; 5′-TCTCTGCTGGATGACGTGAG-3′;
    SEQ ID NO: 29 SEQ ID NO: 30
    5′-GGCTATCCAGCGTACTCCAA-3′;  5′-GCTGGATGACGTGAGTAAACC-3′;
    SEQ ID NO: 31 SEQ ID NO: 32
    KRT14 5′-ACCATTGAGGACCTGAGGAA-3′;  5′-GTCCACTGTGGCTGTGAGAA-3′;
    SEQ ID NO: 33 SEQ ID NO: 34
    5′-CATTGAGGACCTGAGGAACA-3′;  5′-AATCTGCAGAAGGACATTGG-3′;
    SEQ ID NO: 35 SEQ ID NO: 36
    5′-GATGACTTCCGCACCAAGTA-3′;  5′-CGCAGGTTCAACTCTGTCTC-3′;
    SEQ ID NO: 37 SEQ ID NO: 38
    5′-TCCGCACCAAGTATGAGACA-3′;  5′-ACTCATGCGCAGGTTCAACT-3′;
    SEQ ID NO: 39 SEQ ID NO: 40
    KRT10 5′-GAGCCTCGTGACTACAGCAA-3′;  5′-GCAGGATGTTGGCATTATCAGT-3′;
    SEQ ID NO: 41 SEQ ID NO: 42
    5′-AAAACCATCGATGACCTTAAAAA-3′; 5′-GATCTGAAGCAGGATGTTGG-3′;
    SEQ ID NO: 43 SEQ ID NO: 44
    MITF 5′-TTCCCAAGTCAAATGATCCAG-3′; 5′-AAGATGGTTCCCTTGTTCCA-3′;
    SEQ ID NO: 45 SEQ ID NO: 46
    5′-CGGCATTTGTTGCTCAGAAT-3′;  5′-GAGCCTGCATTTCAAGTTCC-3′;
    SEQ ID NO: 47 SEQ ID NO: 48
    TYR 5′-TTCCTTCTTCACCATGCATTT-3′;  5′-GGAGCCACTGCTCAAAAATA-3′;
    SEQ ID NO: 49 SEQ ID NO: 50
    5′-TCCAAAGATCTGGGCTATGA-3′;  5′-TTGAAAAGAGTCTGGGTCTGAA-3′;
    SEQ ID NO: 51 SEQ ID NO: 52
    MLANA 5′-GAGAAAAACTGTGAACCTGTGG-3′; 5′-ATAAGCAGGTGGAGCATTGG-3′;
    SEQ ID NO: 53 SEQ ID NO: 54
    5′-GAAGACGAAATGGATACAGAGC-3′; 5′-GTGCCAACATGAAGACTTTTATC-3′;
    SEQ ID NO: 55 SEQ ID NO: 56
    PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′;  5′-CCAAGGCCTGCTTCTTGAC-3′; 
    SEQ ID NO: 57 SEQ ID NO: 58
    5′-GCTGTGGTCCTTGCATCTCT-3′;  5′-GCTTCATAAGTCTGCGCCTA-3′;
    SEQ ID NO: 59 SEQ ID NO: 60
    FN1 5′-CTCCTGCACATGCTTTGGA-3′;  5′-AGGTCTGCGGCAGTTGTC-3′; 
    SEQ ID NO: 61 SEQ ID NO: 62
    5′-AGGCTTTGGAAGTGGTCATT-3′;  5′-CCATTGTCATGGCACCATCT-3′;
    SEQ ID NO: 63 SEQ ID NO: 64
    5′-GAAGTGGTCATTTCAGATGTGATT-3′; 5′-CCATTGTCATGGCACCATCT-3′;
    SEQ ID NO: 65 SEQ ID NO: 66
    5′-TGGTCATTTCAGATGTGATTCAT-3′; 5′-CATTGTCATGGCACCATCTA-3′;
    SEQ ID NO: 67 SEQ ID NO: 68
    SPP1 5′-GTTTCGCAGACCTGACATCC-3′;  5′-TCCTCGTCTGTAGCATCAGG-3′;
    SEQ ID NO: 69 SEQ ID NO: 70
    5′-CCTGACATCCAGTACCCTGA-3′;  5′-TGAGGTGATGTCCTCGTCTG-3′;
    SEQ ID NO: 71 SEQ ID NO: 72
    5′-GAATCTCCTAGCCCCACAGA-3′;  5′-GGTTTCTTCAGAGGACACAGC-3′;
    SEQ ID NO: 73 SEQ ID NO: 74
    5′-CCCATCTCAGAAGCAGAATCTC-3′; 5′-ACAGCATTCTGTGGGGCTA-3′; 
    SEQ ID NO: 75 SEQ ID NO: 76
    COL4A 1 5′-GGAAAACCAGGACCCAGAG-3′;  5′-CTTTTTCCCCTTTGTCACCA-3′; 
    SEQ ID NO: 77 SEQ ID NO: 78
    5′-AGAAAGGTGAACCCGGAAAA-3′; 5′-GGTTTGCCTCTGGGTCCT-3′; 
    SEQ ID NO: 79 SEQ ID NO: 80
    5′-GAGAAAAGGGCCAAAAAGGT-3′; 5′-CATCCCCTGAAATCCAGGTT-3′;
    SEQ ID NO: 81 SEQ ID NO: 82
    5′-AAAGGGCCAAAAAGGTGAAC-3′; 5′-CCTGGCATCCCCTGAAAT-3′; 
    SEQ ID NO: 83 SEQ ID NO: 84
    TNC 5′-GTGTCAACCTGATGGGGAGA-3′;  5′-GTTAACGCCCTGACTGTGGT-3′;
    SEQ ID NO: 85 SEQ ID NO: 86
    5′-GGTACAGTGGGACAGCAGGT-3′;  5′-GATCTGCCATTGTGGTAGGC-3′;
    SEQ ID NO: 87 SEQ ID NO: 88
    5′-AACCACAGTCAGGGCGTTA-3′;  5′-GTTCGTGGCCCTTCCAGT-3′; 
    SEQ ID NO: 89 SEQ ID NO: 90
    5′-AAGCTGAAGGTGGAGGGGTA-3′;  5′-GAGTCACCTGCTGTCCCACT-3′;
    SEQ ID NO: 91 SEQ ID NO: 92
    ITGA3 5′-TATTCCTCCGAACCAGCATC-3′;  5′-CACCAGCTCCGAGTCAATGT-3′;
    SEQ ID NO: 93 SEQ ID NO: 94
    5′-CCACCATCAACATGGAGAAC-3′;  5′-AGTCAATGTCCACAGAGAACCA-3′;
    SEQ ID NO: 95 SEQ ID NO: 96
    LOXL3 5′-CAACTGCCACATTGGTGATG-3′;  5′-AAACCTCCTGTTGGCCTCTT-3′;
    SEQ ID NO: 97 SEQ ID NO: 98
    5′-TGACATCACGGATGTGAAGC-3′;  5′-GGGTTGATGACAACCTGGAG-3′;
    SEQ ID NO: 99 SEQ ID NO: 100
    AGRN 5′-TGTGACCGAGAGCGAGAAG-3′;  5′-CAGGCTCAGTTCAAAGTGGTT-3′;
    SEQ ID NO: 101 SEQ ID NO: 102
    5′-CGGACCTTTGTCGAGTACCT-3′;  5′-GTTGCTCTGCAGTGCCTTCT-3′;
    SEQ ID NO: 103 SEQ ID NO: 104
    VCAN 5′-GACTTCCGTTGGACTGATGG-3′;  5′-TGGTTGGGTCTCCAATTCTC-3′;
    SEQ ID NO: 105 SEQ ID NO: 106
    5′-ACGTGCAAGAAAGGAACAGT-3′;  5′-TCCAAAGGTCTTGGCATTTT-3′;
    SEQ ID NO: 107 SEQ ID NO: 108
    PLOD3 5′-GCAGAGATGGAGCACTACGG-3′;  5′-CAGCCTTGAATCCTCATGC-3′; 
    SEQ ID NO: 109 SEQ ID NO: 110
    5′-GGAAGGAATCGTGGAGCAG-3′;  5′-CAGCAGTGGGAACCAGTACA-3′;
    SEQ ID NO: 111 SEQ ID NO: 112
    ITGB1 5′-CTGATGAATGAAATGAGGAGGA-3′; 5′-CACAAATGAGCCAAATCCAA-3′;
    SEQ ID NO: 113 SEQ ID NO: 114
    5′-CAGTTTGCTGTGTGTTTGCTC-3′;  5′-CATGATTTGGCATTTGCTTTT-3′;
    SEQ ID NO: 115 SEQ ID NO: 116
    PTK2 5′-GCCCCACCAGAGGAGTATGT-3′;  5′-AAGCCGACTTCCTTCACCA-3′; 
    SEQ ID NO: 117 SEQ ID NO: 118
    5′-GAGACCATTCCCCTCCTACC-3′;  5′-GCTTCTGTGCCATCTCAATCT-3′;
    SEQ ID NO: 119 SEQ ID NO: 120
    CTGF 5′-CGAAGCTGACCTGGAAGAGA-3′;  5′-TGGGAGTACGGATGCACTTT-3′;
    SEQ ID NO: 121 SEQ ID NO: 122
    5′-GTGTGCACCGCCAAAGAT-3′;  5′-CGTACCACCGAAGATGCAG-3′;
    SEQ ID NO: 123 SEQ ID NO: 124
    PLOD1 5′-CTACCCCGGCTACTACACCA-3′;  5′-GACAAAGGCCAGGTCAAACT-3′;
    SEQ ID NO: 125 SEQ ID NO: 126
    5′-AGTCGGGGTGGATTACGAG-3′;  5′-ACAGTTGTAGCGCAGGAACC-3′;
    SEQ ID NO: 127 SEQ ID NO: 128
    LAMC1 5′-ATGATGATGGCAGGGATGG-3′;  5′-GCATTGATCTCGGCTTCTTG-3′;
    SEQ ID NO: 129 SEQ ID NO: 130
    THBS1 5′-CTGTGGCACACAGGAAACAC-3′;  5′-ACGAGGGTCATGCCACAG-3′; 
    SEQ ID NO: 131 SEQ ID NO: 132
    5′-GCCAAAGACGGGTTTCATTA-3′;  5′-GCCATGATTTTCTTCCCTTC-3′;
    SEQ ID NO: 133 SEQ ID NO: 134
    LOXL2 5 ′-CTCCTCCTACGGCAAGGGA-3′;  5′-TGGAGATTGTCTAACCAGATGGG-3′;
    SEQ ID NO: 135 SEQ ID NO: 136
    5′-CTCCTACGGCAAGGGAGAAG-3′;  5′-TTGCCAGTACAGTGGAGATTG-3′;
    SEQ ID NO: 137 SEQ ID NO: 138
    1L6 5′-CCAGAGCTGTGCAGATGAGT-3′;  5′-TGCATCTAGATTCTTTGCCTTTT-3′;
    SEQ ID NO: 139 SEQ ID NO: 140
    LOXL1 5′-AGGGCACAGCAGACTTCCT-3′;  5′-TCGTCCATGCTGTGGTAATG-3′;
    SEQ ID NO: 141 SEQ ID NO: 142
    5′-GCATGCACCTCTCATACCC-3′;  5′-CGCATTGTAGGTGTCATAGCA-3′;
    SEQ ID NO: 143 SEQ ID NO: 144
    1L8 5′-CTTGGCAGCCTTCCTGATT-3′;  5′-GCAAAACTGCACCTTCACAC-3′;
    SEQ ID NO: 145 SEQ ID NO: 146
    CYR61 5′-CGCTCTGAAGGGGATCTG-3′;  5′-ACAGGGTCTGCCCTCTGACT-3′;
    SEQ ID NO: 147 SEQ ID NO: 148
    5′-GAGCTCAGTCAGAGGGCAGA-3′;  5′-AACTTTCCCCGTTTTGGTAGA-3′;
    SEQ ID NO: 149 SEQ ID NO: 150
    ITGAV 5′-GACCTTGGAAACCCAATGAA-3′;  5′-TCCATCTCTGACTGCTGGTG-3′;
    SEQ ID NO: 431 SEQ ID NO: 432
    5′-GGTGGTATGTGACCTTGGAAA-3′; 5′-GCACACTGAAACGAAGACCA-3′;
    SEQ ID NO: 439 SEQ ID NO: 440
    YAP 5′-TGAACAGTGTGGATGAGATGG-3′; 5′-GCAGGGTGCTTTGGTTGATA-3′;
    SEQ ID NO: 151 SEQ ID NO: 152
    BGN 5′-AAGGGTCTCCAGCACCTCTAC-3′; 5′-AAGGCCTTCTCATGGATCTT-3′;
    SEQ ID NO: 153 SEQ ID NO: 154
    5′-GAGCTCCGCAAGGATGACT-3′;  5′-AGGACGAGGGCGTAGAGGT-3′;
    SEQ ID NO: 155 SEQ ID NO: 156
    LAMB1 5′-CATTCAAGGAACCCAGAACC-3′;  5′-GCGTTGAACAAGGTTTCCTC-3′;
    SEQ ID NO: 157 SEQ ID NO: 158
    ITGB3 5′-AAGAGCCAGAGTGTCCCAAG-3′;  5′-ACTGAGAGCAGGACCACCA-3′;
    SEQ ID NO: 159 SEQ ID NO: 160
    5′-CTTCTCCTGTGTCCGCTACAA-3′;  5′-CATGGCCTGAGCACATCTC-3′; 
    SEQ ID NO: 161 SEQ ID NO: 162
    5′-TGCCTGCACCTTTAAGAAAGA-3′; 5′-CCGGTCAAACTTCTTACACTCC-3′;
    SEQ ID NO: 163 SEQ ID NO: 164
    5′-AAGGGGGAGATGTGCTCAG-3′;  5′-CAGTCCCCACAGCTGCAC-3′; 
    SEQ ID NO: 165 SEQ ID NO: 166
    CXCL1 5′-AAACCGAAGTCATAGCCACAC-3′; 5′-AAGCTTTCCGCCCATTCTT-3′; 
    SEQ ID NO: 167 SEQ ID NO: 168
    THBS2 5′-AGGCCCAAGACTGGCTACAT-3′;  5′-CTGCCATGACCTGTTTTCCT-3′;
    SEQ ID NO: 169 SEQ ID NO: 170
    5′-GGCAGGTGCGAACCTTATG-3′;  5′-CCTTCCAGCCAATGTTCCT-3′; 
    SEQ ID NO: 171 SEQ ID NO: 172
    COL18A1 5′-GATCGCTGAGCTGAAGGTG-3′;  5′-CGGATGCCCCATCTGAGT-3′; 
    SEQ ID NO: 173 SEQ ID NO: 174
    SPARC 5′-CCCATTGGCGAGTTTGAGAAG-3′; 5′-AGGAAGAGTCGAAGGTCTTGTT-3′;
    SEQ ID NO: 175 SEQ ID NO: 176
    5′-GGAAGAAACTGTGGCAGAGG-3′;  5′-GGACAGGATTAGCTCCCACA-3′;
    SEQ ID NO: 177 SEQ ID NO: 178
    TP53 5′-ACAACGTTCTGTCCCCCTTG-3′;  5′-GGGGACAGCATCAAATCATC-3′;
    SEQ ID NO: 179 SEQ ID NO: 180
    PLOD2 5′-TGGATGCAGATGTTGTTTTGA-3′;  5′-CACAGCTTTCCATGACGAGTT-3′;
    SEQ ID NO: 181 SEQ ID NO: 182
    5′-TTGATTGAACAAAACAGAAAGATCA-3′; 5′-TGACGAGTTACAAGAGGAGCAA-3′;
    SEQ ID NO: 183 SEQ ID NO: 184
    CCL2 5′-CTGCTCATAGCAGCCACCTT-3′;  5′-AGGTGACTGGGGCATTGATT-3′;
    SEQ ID NO: 185 SEQ ID NO: 186
    FBLN2 5′-ACGTGGAGGAGGACACAGAC-3′;  5′-GGAGCCTTCAGGGCTACTTC-3′;
    SEQ ID NO: 187 SEQ ID NO: 188
    LAMA1 5′-AGCACTGCCAAAGTGGATG-3′;  5′-TTGTTGACATGGAACAAGACC-3′;
    SEQ ID NO: 189 SEQ ID NO: 190
    THBS4 5′-GTGGGCTACATCAGGGTACG-3′;  5′-CAGAGTCAGCCACCAACTCA-3′;
    SEQ ID NO: 191 SEQ ID NO: 192
    5′-CATCATCTGGTCCAACCTCA-3′;  5′-GTCCTCAGGGATGGTGTCAT-3′;
    SEQ ID NO: 193 SEQ ID NO: 194
    COL1A1 5′-TGACCTCAAGATGTGCCACT-3′; 5′-TGGTTGGGGTCAATCCAGTA-3′;
    SEQ ID NO: 195 SEQ ID NO: 196
    5′-GATGGATTCCAGTTCGAGTATG-3′; 5′-ATCAGGCGCAGGAAGGTC-3′; 
    SEQ ID NO: 197 SEQ ID NO: 198
    ITGA5 5′-CCCAAAAAGAGCGTCAGGT-3′;  5′-TTGTTGACATGGAACAAGACC-3′;
    SEQ ID NO: 199 SEQ ID NO: 200
    TAZ 5′-CTTCCTAACAGTCCGCCCTA-3′;  5′-CCCGATCAGCACAGTGATTT-3′;
    SEQ ID NO: 201 SEQ ID NO: 202
    POSTN 5′-CTGCTTCAGGGAGACACACC-3′;  5′-TGGCTTGCAACTTCCTCAC-3′; 
    SEQ ID NO: 203 SEQ ID NO: 204
    5′-AGGAAGTTGCAAGCCAACAA-3′;  5′-CGACCTTCCCTTAATCGTCTT-3′;
    SEQ ID NO: 205 SEQ ID NO: 206
    LOX 5′-GCGGAGGAAAACTGTCTGG-3′;  5′-AAATCTGAGCAGCACCCTGT-3′;
    SEQ ID NO: 207 SEQ ID NO: 208
    5′-ATATTCCTGGGAATGGCACA-3′;  5′-CCATACTGTGGTAATGTTGATGA-3′;
    SEQ ID NO: 209 SEQ ID NO: 210
    CSRC 5′-TGTCAACAACACAGAGGGAGA-3′; 5′-CACGTAGTTGCTGGGGATGT-3′;
    SEQ ID NO: 211 SEQ ID NO: 212
    5′-TGGCAAGATCACCAGACGG-3′;  5′-GGCACCTTTCGTGGTCTCAC-3′;
    SEQ ID NO: 213 SEQ ID NO: 214
    LAMA3 5′-CATGTCGTCTTGGCTCACTC-3′;  5′-AAATTCTGGCCCCAACAATAC-3′;
    SEQ ID NO: 215 SEQ ID NO: 216
    CDKN1A 5′-CATGTCGTCTTGGCTCACTC-3′;  5′-AAATTCTGGCCCCAACAATAC-3′;
    SEQ ID NO: 217 SEQ ID NO: 218
    CDKN2A 5′-AGGAGCCAGCGTCTAGGG-3′;  5′-CTGCCCATCATCATGACCT-3′; 
    SEQ ID NO: 219 SEQ ID NO: 220
    5′-AACGCACCGAATAGTTACGG-3′;  5′-CATCATCATGACCTGGATCG-3′;
    SEQ ID NO: 221 SEQ ID NO: 222
    ITGA2 5′-CACTGTTACGATTCCCCTGA-3′;  5′-CGGCTTTCTCATCAGGTTTC-3′;
    SEQ ID NO: 223 SEQ ID NO: 224
    LAMC2 5′-ATTAGACGGCCTCCTGCATC-3′;  5′-AGACCAGCCCCTCTTCATCT-3′;
    SEQ ID NO: 225 SEQ ID NO: 226
    PCOLCE2 5′-TACTTGGAAAATCACAGTTCCCG-3′; 5′-TGAATCGGAAATTGAGAACGACT-3′;
    SEQ ID NO: 443 SEQ ID NO: 444
    LOXL4 5′-GGCCCCGGGAATTATATCT-3′;  5′-CCACTTCATAGTGGGGGTTC-3′;
    SEQ ID NO: 227 SEQ ID NO: 228
    5′-CTGCACAACTGCCACACAG-3′;  5′-GTTCTGCATTGGCTGGGTAT-3′;
    SEQ ID NO: 229 SEQ ID NO: 230
    PCOLCE 5′-CGTGGCAAGTGAGGGGTTC-3′;  5′-CGAAGACTCGGAATGAGAGGG-3′;
    SEQ ID NO: 231 SEQ ID NO: 232
    5′-GAGGCTTCCTGCTCTGGT-3′;  5′-CGCAAAATTGGTGCTCAGT-3′; 
    SEQ ID NO: 233 SEQ ID NO: 234
    LAMB3 5′-GTCCGGGACTTCCTAACAGA-3′;  5′-GCTGACCTCCTGGATAGTGG-3′;
    SEQ ID NO: 235 SEQ ID NO: 236
    PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′;  5′-CCAAGGCCTGCTTCTTGAC-3′; 
    SEQ ID NO: 237 SEQ ID NO: 238
    5′-GCTGTGGTCCTTGCATCTCT-3′;  5′-GCTTCATAAGTCTGCGCCTA-3′;
    SEQ ID NO: 239 SEQ ID NO: 240
    NES 5′-CTTCCCTCAGCTTTCAGGAC-3′;  5′-TCTGGGGTCCTAGGGAATTG-3′;
    SEQ ID NO: 241 SEQ ID NO: 242
    5′-ACCTCAAGATGTCCCTCAGC-3′;  5′-CAGGAGGGTCCTGTACGTG-3′;
    SEQ ID NO: 243 SEQ ID NO: 244
    LICAM 5′-GAGACCTTCGGCGAGTACAG-3′;  5′-AAAGGCCTTCTCCTCGTTGT-3′;
    SEQ ID NO: 245 SEQ ID NO: 246
    5′-GGCGGCAAATACTCAGTGAA-3′;  5′-CCTGGGTGTCCTCCTTATCC-3′;
    SEQ ID NO: 247 SEQ ID NO: 248
    GDF15 5′-CGGATACTCACGCCAGAAGT-3′;  5′-AGAGATACGCAGGTGCAGGT-3′;
    SEQ ID NO: 249 SEQ ID NO: 250
    5′-AAGATTCGAACACCGACCTC-3′;  5′-GCACTTCTGGCGTGAGTATC-3′;
    SEQ ID NO: 251 SEQ ID NO: 252
    ARPC1B 5′-CACGCCTGGAACAAGGAC-3′;  5′-ATGCACCTCATGGTTGTTGG-3′;
    SEQ ID NO: 253 SEQ ID NO: 254
    5′-CAGGTGACAGGCATCGACT-3′;  5′-CGCAGGTCACAATACGGTTA-3′;
    SEQ ID NO: 255 SEQ ID NO: 256
    FARP1 5′-TGAGGCCCTGAGAGAGAAGA-3′;  5′-ATTCCGAAACTCCACACGTC-3′;
    SEQ ID NO: 257 SEQ ID NO: 258
    5′-TCAAGGAAATTGAGCAACGA-3′;  5′-TCTGATTTGGGCATTTGAGC-3′;
    SEQ ID NO: 259 SEQ ID NO: 260
    NTRK3 5′-TATGGTCGACGGTCCAAAT-3′;  5′-TCCTCACCACTGATGACAGC-3′;
    SEQ ID NO: 261 SEQ ID NO: 262
    5′-CACTGTGACCCACAAACCAG-3′;  5′-GCAAGTCCAACTGCTATGGA-3′;
    SEQ ID NO: 263 SEQ ID NO: 264
    CSK 5′-TGAGGCCCTGAGAGAGAAGA-3′;  5′-ATTCCGAAACTCCACACGTC-3′;
    SEQ ID NO: 265 SEQ ID NO: 266
    5′-TCTACTCCTTTGGGCGAGTG-3′;  5′-CGTCCTTCAGGGGAATTCTT-3′;
    SEQ ID NO: 267 SEQ ID NO: 268
    CD44 5′-TAAGGACACCCCAAATTCCA-3′;  5′-GCCAAGATGATCAGCCATTC-3′;
    SEQ ID NO: 269 SEQ ID NO: 270
    5′-GCAGTCAACAGTCGAAGAAGG-3′; 5′-AGCTTTTTCTTCTGCCCACA-3′;
    SEQ ID NO: 271 SEQ ID NO: 272
    SNX17 5′-AGCCAGCAAGCAGTGAAGTC-3′;  5′-TCAGGTGACTCAAGCAGTGG-3′;
    SEQ ID NO: 273 SEQ ID NO: 274
    5′-CCGGGAGTCTATGGTCAAAC-3′;  5′-CACGGCACTCAGCTTACTTG-3′;
    SEQ ID NO: 275 SEQ ID NO: 276
    PLAT 5′-TGGAGCAGTCTTCGTTTCG-3′;  5′-CTGGCTCCTCTTCTGAATCG-3′;
    SEQ ID NO: 277 SEQ ID NO: 278
    5′-GCCCGATTCAGAAGAGGAG-3′;  5′-TCATCTCTGCAGATCACTTGG-3′;
    SEQ ID NO: 279 SEQ ID NO: 280
  • The following was performed to generate a standard curve for the target of each primer pair. The standard was generated with a defined number of amplicons per volume for each primer pair. In particular, a standard (S7) was designed to contain about 5 million copies of amplicon-containing cDNA in a bacterial expression vector backbone (pJET1.2 obtained from Fermentas) per one microliter volume for each primer pair. From this, six 1:10 dilutions were generated such that seven standards S1 to S7 were obtained ranging from 5 to 5 million copies of amplicon. To obtain fragments of cDNA, total RNA was extracted from the human HaCaT, A431, and A375 cell lines, and the RNA was reverse transcribed into cDNA. Cell line-derived cDNA was used as a template to amplify fragments of cDNA that contained the desired amplicons for the real time-PCR primer pairs. A list of primers used to generate the desired cDNA fragments is listed in Table 3.
  • TABLE 3
    Primer sets for generating cDNA fragments of the indicated genes.
    Gene Name Forward primer Reverse primer
    FN1 5′-CCAGCAGAGGCATAAGGTTC-3′;  5′-AGTAGTGCCTTCGGGACTGG-3′; 
    SEQ ID NO: 281 SEQ ID NO: 282
    SPP1 5′-AGGCTGATTCTGGAAGTTCTGAGG-3′;  5′-AATCTGGACTGCTTGTGGCTG-3′; 
    SEQ ID NO: 283 SEQ ID NO: 284
    COL4A1 5′-GTTGGGCCTCCAGGATTTA-3′;  5′-GCCTGGTAGTCCTGGGAAAC-3′; 
    SEQ ID NO: 285 SEQ ID NO: 286
    TNC 5′-TGGATGGATTGTGTTCCTGA-3′;  5′-GCCTGCCTTCAAGATTTCTG-3′; 
    SEQ ID NO: 287 SEQ ID NO: 288
    ITGA3 5′-CTGAGACTGTGCTGACCTGTG-3′;  5′-CTCTTCATCTCCGCCTTCTG-3′; 
    SEQ ID NO: 289 SEQ ID NO: 290
    LOXL3 5′-GAGACCGCCTACATCGAAGA-3′;  5′-GGTAGCGTTCAAACCTCCTG-3′; 
    SEQ ID NO: 291 SEQ ID NO: 292
    AGRN 5′-ACACCGTCCTCAACCTGAAG-3′;  5′-AATGGCCAGTGCCACATAGT-3′; 
    SEQ ID NO: 293 SEQ ID NO: 294
    VCAN 5′-GGTGCACTTTGTGAGCAAGA-3′;  5′-TTGGTATGCAGATGGGTTCA-3′; 
    SEQ ID NO: 295 SEQ ID NO: 296
    PLOD3 5′-AGCTGTGGTCCAACTTCTGG-3′;  5′-GTGTGGTAACCGGGAAACAG-3′; 
    SEQ ID NO: 297 SEQ ID NO: 298
    ITGB1 5′-TTCAGTTTGCTGTGTGTTTGC-3′;  5′-CCACCTTCTGGAGAATCCAA-3′; 
    SEQ ID NO: 299 SEQ ID NO: 300
    PTK2 5′-GGCAGTATTGACAGGGAGGA-3′;  5′-TACTCTTGCTGGAGGCTGGT-3′; 
    SEQ ID NO: 301 SEQ ID NO: 302
    CTGF 5′-GCCTATTCTGTCACTTCGGCTC-3′;  5′-GCAGGCACAGGTCTTGATGAAC-3′; 
    SEQ ID NO: 303 SEQ ID NO: 304
    PLOD1 5′-GACCTCTGGGAGGTGTTCAG-3′;  5′-TTAGGGATCGACGAAGGAGA-3′; 
    SEQ ID NO: 305 SEQ ID NO: 306
    LAMC1 5′-ATTCCTGCCATCAACCAGAC-3′;  5′-CCTGCTTCTTGGCTTCATTC-3′; 
    SEQ ID NO: 307 SEQ ID NO: 308
    THBS1 5′-CAAAGGGACATCCCAAAATG-3′;  5′-GAGTCAGCCATGATTTTCTTCC-3′; 
    SEQ ID NO: 309 SEQ ID NO: 310
    LOXL2 5′-TACCCCGAGTACTTCCAGCA-3′;  5′-GATCTGCTTCCAGGTCTTGC-3′; 
    SEQ ID NO: 311 SEQ ID NO: 312
    IL6 5′-CACACAGACAGCCACTCACC-3′;  5′-CAGGGGTGGTTATTGCATCT-3′; 
    SEQ ID NO: 313 SEQ ID NO: 314
    LOXL1 5′-CAGACCCCAACTATGTGCAA-3′;  5′-CGCATTGTAGGTGTCATAGCA-3′; 
    SEQ ID NO: 315 SEQ ID NO: 316
    IL8 5′-CTCTCTTGGCAGCCTTCCT-3′;  5′-TGAATTCTCAGCCCTCTTCAA-3′; 
    SEQ ID NO: 317 SEQ ID NO: 318
    CYR61 5′-TCGCCTTAGTCGTCACCCTT-3′;  5′-TGTTTCTCGTCAACTCCACCTCG-3′; 
    SEQ ID NO: 319 SEQ ID NO: 320
    ITGAV 5′-CTGATTTCATCGGGGTTGTC-3′;  5′-TGCCTTGCTGAATGAACTTG-3′; 
    SEQ ID NO: 321 SEQ ID NO: 322
    YAP 5′-CCAGTGAAACAGCCACCAC-3′;  5′-CTCCTTCCAGTGTTCCAAGG-3′; 
    SEQ ID NO: 323 SEQ ID NO: 324
    BGN 5′-GGACTCTGTCACACCCACCT-3′;  5′-CAGGGTCTCAGGGAGGTCTT-3′; 
    SEQ ID NO: 325 SEQ ID NO: 326
    LAMB1 5′-TGCCAGAGCTGAGATGTTGTT-3′;  5′-TGTAGCATTTCGGCTTTCCT-3′; 
    SEQ ID NO: 327 SEQ ID NO: 328
    ITGB3 5′-GGCAAGTACTGCGAGTGTGA-3′;  5′-ATTCTTTTCGGTCGTGGATG-3′; 
    SEQ ID NO: 329 SEQ ID NO: 330
    CXCL1 5′-CACTGCTGCTCCTGCTCCT-3′;  5′-TGTTCAGCATCTTTTCGATGA-3′; 
    SEQ ID NO: 331 SEQ ID NO: 332
    THBS2 5′-TGACAATGACAACATCCCAGA-3′;  5′-TGAGTCTGCCATGACCTGTT-3′; 
    SEQ ID NO: 333 SEQ ID NO: 334
    COL18A1 5′-CCCTGCTCTACACAGAACCAG-3′;  5′-ACACCTGGCTCCCCTTTCT-3′; 
    SEQ ID NO: 335 SEQ ID NO: 336
    SPARC 5′-GCCTGGATCTTCTTTCTCCTTTGC-3′;  5′-CATCCAGGGCGATGTACTTGTC-3′; 
    SEQ ID NO: 337 SEQ ID NO: 338
    TP53 5′-CCCCCTCTGAGTCAGGAAAC-3′;  5′-TCATGTGCTGTGACTGCTTG-3′; 
    SEQ ID NO: 339 SEQ ID NO: 340
    PLOD2 5′-TGGACCCACCAAGATTCTCCTG-3′;  5′-GACCACAGCTTTCCATGACGAG-3′; 
    SEQ ID NO: 341 SEQ ID NO: 342
    CCL2 5′-TCTGTGCCTGCTGCTCATAG-3′;  5′-GAGTTTGGGTTTGCTTGTCC-3′; 
    SEQ ID NO: 343 SEQ ID NO: 344
    FBLN2 5′-CGAGAAGTGCCCAGGAAG-3′;  5′-AGTGAGAAGCCAGGAAAGCA-3′; 
    SEQ ID NO: 345 SEQ ID NO: 346
    LAMA1 5′-TGGAAATATCACCCACAGCA-3′;  5′-AGGCATTTTTGCTTCACACC-3′; 
    SEQ ID NO: 347 SEQ ID NO: 348
    THBS4 5′-GCTCCAGCTTCTACGTGGTC-3′;  5′-TTAATTATCGAAGCGGTCGAA-3′; 
    SEQ ID NO: 349 SEQ ID NO: 350
    COL1A1 5′-AGCCAGCAGATCGAGAACAT-3′;  5′-CCTTCTTGAGGTTGCCAGTC-3′; 
    SEQ ID NO: 351 SEQ ID NO: 352
    ITGA5 5′-CACCAATCACCCCATTAACC-3′;  5′-GCTTGAGCTGAGCTTTTTCC-3′; 
    SEQ ID NO: 353 SEQ ID NO: 354
    TAZ 5′-CCAGGTGCTGGAAAAAGAAG-3′;  5′-GAGCTGCTCTGCCTGAGTCT-3′; 
    SEQ ID NO: 355 SEQ ID NO: 356
    POSTN 5′-GCAGACACACCTGTTGGAAA-3′;  5′-GAACGACCTTCCCTTAATCG-3′; 
    SEQ ID NO: 357 SEQ ID NO: 358
    LOX 5′-CCTACTACATCCAGGCGTCCAC-3′;  5′-ATGCAAATCGCCTGTGGTAGC-3′; 
    SEQ ID NO: 359 SEQ ID NO: 360
    CSRC 5′-CTGTTCGGAGGCTTCAACTC-3′;  5′-AGGGATCTCCCAGGCATC-3′; 
    SEQ ID NO: 361 SEQ ID NO: 362
    LAMAS 5′-TACCTGGGATCACCTCCATC-3′;  5′-ACAGGGATCCTCAGTGTCGT-3′; 
    SEQ ID NO: 363 SEQ ID NO: 364
    CDKN1A 5′-CGGGATGAGTTGGGAGGAG-3′;  5′-TTAGGGCTTCCTCTTGGAGA-3′; 
    SEQ ID NO: 365 SEQ ID NO: 366
    CDKN2A- 5′-ATGGTGCGCAGGTTCTTG-3′;  5′-ACCAGCGTGTCCAGGAAG-3′; 
    004 2A-201 SEQ ID NO: 367 SEQ ID NO: 368
    CDKN2A- 5′-GAGCAGCATGGAGCCTTC-3′;  5′-GCATGGTTACTGCCTCTGGT-3′; 
    001 2A-202 SEQ ID NO: 369 SEQ ID NO: 370
    ITGA2 5′-CAAACAGACAAGGCTGGTGA-3′;  5′-TCAATCTCATCTGGATTTTTGG-3′; 
    SEQ ID NO: 371 SEQ ID NO: 372
    LAMC2 5′-CTGCAGGTGGACAACAGAAA-3′;  5′-CATCAGCCAGAATCCCATCT-3′; 
    SEQ ID NO: 373 SEQ ID NO: 374
    PCOLCE2 5′-GTCCCCAGAGAGACCTGTTT-3′;  5′-AGACACAATTGGCGCAGGT-3′; 
    SEQ ID NO: 375 SEQ ID NO: 376
    LOXL4 5′-AAGACTGGACGCGATAGCTG-3′;  5′-GGTTGTTCCTGAGACGCTGT-3′; 
    SEQ ID NO: 377 SEQ ID NO: 378
    PCOLCE 5′-TACACCAGACCCGTGTTCCT-3′;  5′-TCCAGGTCAAACTTCTCGAAGG-3′; 
    SEQ ID NO: 379 SEQ ID NO: 380
    LAMBS 5′-CTTCAATGCCCAGCTCCA-3′;  5′-TTCCCAACCACATCTTCCAC-3′; 
    SEQ ID NO: 381 SEQ ID NO: 382
    CSF2 5′-CTGCTGCTCTTGGGCACT-3′;  5′-CAGCAGTCAAAGGGGATGAC-3′; 
    SEQ ID NO: 383 SEQ ID NO: 384
    ACTB 5′-AGGATTCCTATGTGGGCGACG-3′;  5′-TCAGGCAGCTCGTAGCTCTTC-3′; 
    SEQ ID NO: 385 SEQ ID NO: 386
    RPLP0 5′-GGAATGTGGGCTTTGTGTTCACC-3′;  5′-AGGCCAGGACTCGTTTGTACC-3′; 
    SEQ ID NO: 387 SEQ ID NO: 388
    RPL8 5′-ACATCAAGGGCATCGTCAAGG-3′;  5′-TCTCTTTCTCCTGCACAGTCTTGG-3′;
    SEQ ID NO: 389 SEQ ID NO: 390
    B2M 5′-TGCTCGCGCTACTCTCTCTTTC-3′;  5′-TCACATGGTTCACACGGCAG-3′; 
    SEQ ID NO: 391 SEQ ID NO: 392
    K10 5′-TGGCCTTCTCTCTGGAAATG-3′;  5′-TCATTTCCTCCTCGTGGTTC-3′; 
    SEQ ID NO: 393 SEQ ID NO: 394
    K14 5′-AGGTGACCATGCAGAACCTC-3′;  5′-CCTCGTGGTTCTTCTTCAGG-3′; 
    SEQ ID NO: 395 SEQ ID NO: 396
    MITF 5′-GAAATCTTGGGCTTGATGGA-3′;  5′-CCGAGGTTGTTGTTGAAGGT-3′; 
    SEQ ID NO: 397 SEQ ID NO: 398
    TYR 5′-CCATGGATAAAGCTGCCAAT-3′;  5′-GACACAGCAAGCTCACAAGC-3′; 
    SEQ ID NO: 399 SEQ ID NO: 400
    MLANA 5′-CACTCTTACACCACGGCTGA-3′;  5′-CATAAGCAGGTGGAGCATTG-3′; 
    SEQ ID NO: 401 SEQ ID NO: 402
    PMEL 5′-TTGTCCAGGGTATTGAAAGTGC-3′;  5′-GACAAGAGCAGAAGATGCGGG-3′; 
    SEQ ID NO: 403 SEQ ID NO: 404
    NES 5′-GCGTTGGAACAGAGGTTGGAG-3′;  5′-CAGGTGTCTCAAGGGTAGCAGG-3′; 
    SEQ ID NO: 405 SEQ ID NO: 406
    L1CAM 5′-CTTCCCTTTCGCCACAGTATG-3′;  5′-CCTCCTTCTCCTTCTTGCCACT-3′; 
    SEQ ID NO: 407 SEQ ID NO: 408
    GDF15 5′-AATGGCTCTCAGATGCTCCTGG-3′;  5′-GATTCTGCCAGCAGTTGGTCC-3′; 
    SEQ ID NO: 409 SEQ ID NO: 410
    ARPC1B 5′-ACCACAGCTTCCTGGTGGAG-3′;  5′-GAGCGGATGGGCTTCTTGATG-3′; 
    SEQ ID NO: 411 SEQ ID NO: 412
    FARP1 5′-AACGTGACCTTGTCTCCCAAC-3′;  5′-GCATGACATCGCCGATTCTT-3′; 
    SEQ ID NO: 413 SEQ ID NO: 414
    NTRK3 5′-TTCAACAAGCCCACCCACTAC-3′;  5′-GTTCTCAATGACAGGGATGCG-3′; 
    SEQ ID NO: 415 SEQ ID NO: 416
    CSK 5′-CATGGAATACCTGGAGGGCAAC-3′;  5′-CAGGTGCCAGCAGTTCTTCAT-3′; 
    SEQ ID NO: 417 SEQ ID NO: 418
    CD44 5′-TCTCAGAGCTTCTCTACATCAC-3′;  5′-CTGACGACTCCTTGTTCACCA-3′; 
    SEQ ID NO: 419 SEQ ID NO: 420
    SNX17 5′-TCACCTCCTCTGTACCATTGC-3′;  5′-CTCATCTCCAATGCCCTCGA-3′; 
    SEQ ID NO: 421 SEQ ID NO: 422
    PLAT 5′-TGCAATGAAGAGAGGGCTCTG-3′;  5′-CGTGGCCCTGGTATCTATTTCA-3′; 
    SEQ ID NO: 423 SEQ ID NO: 424
  • The PCR reactions were performed using a high-fidelity polymerase (product name: Phusion′, obtained from New England Biolabs). PCR amplification products were checked for correct size and subsequently gel purified using the Qiagen Gel Extraction kit. Purified PCR fragments were subcloned into the bacterial expression vector pJET1.2 using a commercially available kit (Fermentas). The subcloned fragments were subsequently checked by restriction digest and DNA sequencing. Bacterial clones harboring the pJET1.2 expression vector with the correct PCR insert (containing the desired amplicon for real time PCR primer pairs) were frozen and stored at −80° C. This was done to regenerate the same real time PCR standards over time.
  • Bacteria harboring the pJET1.2 expression vector with PCR inserts were cultured to generate sufficient amounts of vector. A small aliquot of the total retrieved expression vector with insert was linearized using the PvuI-HF restriction enzyme (from New England Biolabs). The digest was then purified using the Qiagen PCR purification kit. Linearized cDNA was diluted to a concentration of 20 ng/μL. One μL of each of a total of 71 linearized cDNA fragments (each at a 20 ng/μL concentration) were mixed and brought to a final volume of 1 mL to obtain standard S7.
  • Standard S7 was then diluted six times at a 1:10 ratio to obtained standards S1 to S6. Dilution was performed using ultrapure water obtained from Promega (Cat. No. P1193).
  • The following was performed to generate cDNA from FFPE samples. FFPE blocks were cut at 20 μm sections using a standard Leica microtome. For large pieces of tissue, 2×20 μm full sections were used for RNA retrieval. For smaller tissues, up to 5×20 μm sections were combined for RNA retrieval. RNA extraction was performed using the Qiagen RNA FFPE retrieval kit and a Qiagen QiaCube extraction robot. 0.5 to 1 μg of RNA with a 260/280 ratio of greater than 1.8 were transcribed into cDNA using the BioRad iScript cDNA Synthesis kit. All biospecimens were annotated with clinical data from Mayo Clinic databases. H&E stained sections were obtained for each block analyzed and digitalized using a high-resolution slide scanner.
  • Fluidigm RT-PCR was performed using a 96×96 format for high throughput analysis (i.e., 96 cDNAs were analyzed for 96 markers; 9216 data points). The primer pairs and cDNAs were prepared in a 96 well format. Standard curves were calculated for each primer pair. Copy numbers per 100,000 housekeeping genes were calculated for each primer pair and averaged per gene. This was initially done for cDNAs derived from FFPE-embedded skin. To correct for epidermal cell-derived cross-contamination, background signal per one copy of K14 (a basal keratinocyte marker) was calculated from FFPE-embedded normal skin samples for each primer pair and averaged. Experimental samples were then normalized first to 100,000 housekeeping genes and then background-corrected for epidermal cross-contamination based on K14 copy number. In particular, the keratinocyte correction factor used for each gene is set forth in Table E under the column titled “AVG per copy K14.”
  • The study design (Example 1) involved a comparison of the expression profile of ‘true’ benign pigmented skin lesions (nevi, n=73) with ‘true’ malignant melanomas of the skin. The latter comprised i) primary skin melanomas that were documented to metastasize, either to regional lymph nodes, to other areas of skin (in-transit), or to other organs; and ii) in-transit or comparison of nevi to in-transit melanoma metastases (n=54).
  • Tables C and D summarize the comparisons of the gene expressions between the 73 benign and 54 metastatic. Table A compares the ranked values using the Wilcoxon rank sum test, and Table E compares the dichotomized values (zero vs. >0) using the chi-square test.
  • A recursive partitioning approach was used to identify cut-points for the genes that would discriminate between these two groups. After partitioning the data at a cut-point of 45 for FN1, no further additional splits in the data based on the other genes were identified by this method.
  • Using a cutoff of 45 for FN1, the sensitivity was 92.6%, and the specificity was 98.6%. These results are provided in Tables 4 and 5 along with the next possible cutoff for FN1 at 124
  • TABLE 4
    Frequency
    Percent
    Row Pct
    Col Pct Malignant Benign Total
    FN1 4 72 76
    <45 3.15 56.69 59.84
    5.26 94.74
    7.41 98.63
    FN1 50 1 51
    >=45 39.37 0.79 40.16
    98.04 1.96
    92.59 1.37
    Total 54 73 127
    42.52 57.48 100.00
  • TABLE 5
    Frequency
    Percent
    Row Pct
    Col Pct Malignant Benign Total
    FN1 8 73 81
    <124 6.30 57.48 63.78
    9.88 90.12
    14.81 100.00
    FN1 >=124 46 0 46
    36.22 0.00 36.22
    100.00 0.00
    85.19 0.00
    Total 54 73 127
    42.52 57.48 100.00
  • The ability to further discriminate between the groups was assessed by considering SPP1 or ITGB3 in addition to FN1.
  • Benign Vs. Malignant—Option 1 Using FN1 and SPP1 (FIG. 5)
  • The results are set forth in Table 6.
  • TABLE 6
    RULE for FIG. 5 Malignant Benign
    FN1 <45 and SPP1 = 0 2 72
    FN1 >=45 52 1
    or
    (FN1 <45 and SPP1 >0)
    Total 54 73

    Benign Vs. Malignant—Option 2 Using FN1 and ITGB3 (FIG. 6)
  • The results are set forth in Table 7.
  • TABLE 7
    RULE for FIG. 6 Malignant Benign
    FN1 <45 and ITGB3 = 0 3 72
    FN1 >=45 51 1
    or
    (FN1 <45 and ITGB3 >0)
    Total 54 73
  • If all three genes are included, the rule was as follows:
  • FN1<45 and SPP1=0 and ITGB3=0 denotes a negative test
      • vs.
  • all other combinations denotes a positive test.
  • This rule resulted in a specificity of 72/73 (98.6%), and a sensitivity of 53/54 (98.2%) (Table 8). Compared to a rule using FN1 alone, the specificity stayed the same but the sensitivity increased from 92.6% to 98.2% using this new rule.
  • TABLE 8
    FN1 SPP1 ITGB3 malignant Frequency
    <45 Zero Zero No 72
    <45 Zero Zero Yes 1 False Neg
    ID MM150
    (case added from
    the Breslow file)
    >=45 Zero Zero No 1 False Pos
    ID N29
    >=45 Zero Zero Yes 9
    >=45 Zero >0 Yes 1
    >=45 >0 Zero Yes 18
    >=45 >0 >0 Yes 22
    <45 Zero >0 Yes 1
    <45 >0 Zero Yes 2
  • The rule was evaluated using 25 additional malignant patients who did not have mets (from the “Breslow” file). For 19 of these 25 patients, the rule was ‘negative’ (Table 9).
  • TABLE 9
    FN1 SPP1 ITGB3 Frequency
    <45 Zero Zero 19
    <45 >0 Zero 1
    >=45 Zero Zero 2
    >=45 >0 Zero 3
    <45 1
  • The rule also was evaluated using 33 thin melanomas (Table 10). For 25 of these 33 patients, the rule was ‘negative’.
  • TABLE 10
    FN1 SPP1 ITGB3 Frequency
    <45 Zero Zero 25
    <45 Zero >0 1
    >=45 Zero Zero 5
    >=45 >0 Zero 2
  • TABLE C
    Comparison of gene expression between benign and malignant
    Benign (N = 73) Malignant (N = 54) p value
    CXCL1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  4.8 (18.4) 20.0 (26.1)
    Median   0.0   10.3
    Q1, Q3 0.0, 0.0  0.3, 31.1
    Range (0.0-141.7) (0.0-120.4)
    CSF2_AVG_NORM 0.0482
    N 73 54
    Mean (SD) 10.5 (44.1) 4.3 (8.4)
    Median   2.5   1.0
    Q1, Q3 0.6, 7.0 0.0, 4.0
    Range (0.0-375.0) (0.0-41.0) 
    CCL2_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 37.0 (99.4) 244.2 (360.9)
    Median   0.0  112.8
    Q1, Q3 0.0, 9.1  7.2, 342.2
    Range (0.0-572.0)  (0.0-1777.1)
    IL8_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 125.5 (671.3)  53.2 (160.8)
    Median   0.0   13.0
    Q1, Q3 0.0, 0.0  2.1, 52.5
    Range  (0.0-5058.7)  (0.0-1171.7)
    IL6_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  9.9 (69.1) 21.6 (35.0)
    Median   0.0   8.8
    Q1, Q3 0.0, 0.0  0.3, 25.2
    Range (0.0-589.1) (0.0-152.3)
    ITGA5_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0)  9.8 (26.8)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0 0.0, 7.0
    Range (0.0-0.0)  (0.0-168.0)
    ITGA3_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  3.2 (27.5) 168.2 (313.4)
    Median   0.0   50.2
    Q1, Q3 0.0, 0.0  2.0, 160.5
    Range (0.0-235.4)  (0.0-1506.0)
    ITGA2_AVG_NORM 0.0007
    N 73 54
    Mean (SD) 0.0 (0.0)  2.6 (10.0)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0 0.0, 0.0
    Range (0.0-0.0)  (0.0-69.7) 
    ITGAV_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  3.3 (23.9) 22.0 (32.9)
    Median   0.0   8.0
    Q1, Q3 0.0, 0.0  0.0, 31.0
    Range (0.0-199.9) (0.0-176.8)
    ITGB3_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0) 43.6 (90.3)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0  0.0, 52.5
    Range (0.0-0.0)  (0.0-495.3)
    ITGB1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 29.9 (95.1) 616.2 (742.2)
    Median   0.0  400.2
    Q1, Q3 0.0, 0.0  84.7, 869.0
    Range (0.0-487.9)  (0.0-3877.9)
    FN1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  2.9 (15.6) 1570.9 (1949.8)
    Median   0.0  898.4
    Q1, Q3 0.0, 0.0  299.5, 2186.1
    Range (0.0-123.2)  (0.0-11073.5)
    THBS1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0)  85.1 (136.1)
    Median   0.0   16.8
    Q1, Q3 0.0, 0.0  0.0, 153.8
    Range (0.0-0.0)  (0.0-786.2)
    THBS2_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  25.9 (113.4) 280.0 (513.5)
    Median   0.0   44.1
    Q1, Q3 0.0, 0.0  0.0, 340.1
    Range (0.0-729.2)  (0.0-3030.5)
    THBS4_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  38.5 (151.2) 228.2 (663.7)
    Median   0.0   22.5
    Q1, Q3 0.0, 0.0  0.0, 97.9
    Range  (0.0-1130.3)  (0.0-3977.7)
    VCAN_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  3.0 (21.7) 202.4 (262.8)
    Median   0.0  103.4
    Q1, Q3 0.0, 0.0  0.0, 283.5
    Range (0.0-181.3)  (0.0-1113.2)
    BGAN_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  69.3 (121.0) 422.4 (573.1)
    Median   0.0  248.5
    Q1, Q3  0.0, 97.9 113.5, 462.9
    Range (0.0-496.3)  (0.0-3348.1)
    SPP1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0) 1490.2 (3397.4)
    Median   0.0  338.1
    Q1, Q3 0.0, 0.0   4.9, 1577.7
    Range (0.0-0.0)   (0.0-22427.0)
    TNC_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  66.4 (240.1) 800.1 (808.7)
    Median   0.0  495.8
    Q1, Q3 0.0, 0.0  174.5, 1322.9
    Range  (0.0-1393.3)  (0.0-3162.2)
    SPARC_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  843.7 (2222.8) 3208.4 (3182.6)
    Median   0.0  2895.8 
    Q1, Q3 0.0, 0.0  407.2, 5216.3
    Range  (0.0-11175.6)  (0.0-13631.9)
    AGRN_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  4.7 (18.1) 51.2 (53.8)
    Median   0.0   42.1
    Q1, Q3 0.0, 0.0 10.7, 69.7
    Range (0.0-121.7) (0.0-242.0)
    CTGF_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.4 (3.6)  90.9 (231.6)
    Median   0.0   22.1
    Q1, Q3 0.0, 0.0  0.0, 125.9
    Range (0.0-30.6)   (0.0-1631.4)
    CYR61_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  4.8 (13.0) 27.2 (39.2)
    Median   0.0   18.7
    Q1, Q3 0.0, 0.0  4.9, 32.2
    Range (0.0-70.4)  (0.0-267.2)
    LAMA3_AVG_NORM 0.0004
    N 73 54
    Mean (SD) 1.1 (9.0) 1.2 (2.9)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0 0.0, 0.0
    Range (0.0-76.8)  (0.0-11.3) 
    LAMC1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0)  70.6 (159.4)
    Median   0.0   28.4
    Q1, Q3 0.0, 0.0  0.0, 99.3
    Range (0.0-0.0)   (0.0-1136.2)
    LAMB1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  9.2 (38.4) 221.1 (354.3)
    Median   0.0   73.1
    Q1, Q3 0.0, 0.0  0.0, 339.8
    Range (0.0-248.8)  (0.0-1877.6)
    LAMA1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  5.7 (14.5)  65.4 (149.0)
    Median   0.0   10.6
    Q1, Q3 0.0, 0.0  0.0, 49.0
    Range (0.0-76.5)  (0.0-754.3)
    LAMC2_AVG_NORM 0.0003
    N 73 54
    Mean (SD) 0.0 (0.0)  4.0 (15.3)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0 0.0, 0.0
    Range (0.0-0.0)  (0.0-91.1) 
    LAMB3_AVG_NORM 0.1473
    N 73 54
    Mean (SD) 33.5 (60.3) 32.2 (54.5)
    Median   0.0   12.1
    Q1, Q3  0.0, 44.6  0.0, 37.0
    Range (0.0-323.9) (0.0-246.0)
    COL1A1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 1534.4 (4365.3) 4191.6 (5865.9)
    Median   0.0  1704.4 
    Q1, Q3 0.0, 0.0   0.0, 6850.9
    Range  (0.0-22510.2)  (0.0-31867.0)
    COL4A1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.0 (0.0) 211.8 (344.1)
    Median   0.0  118.4
    Q1, Q3 0.0, 0.0  2.3, 261.2
    Range (0.0-0.0)   (0.0-1774.4)
    COL18A1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  94.2 (783.4) 22.8 (38.8)
    Median   0.0   4.1
    Q1, Q3 0.0, 0.0  0.0, 34.4
    Range  (0.0-6695.7) (0.0-208.8)
    LOX_AVG_NORM 0.0003
    N 73 54
    Mean (SD)  37.7 (132.8)  65.0 (113.9)
    Median   0.0   3.5
    Q1, Q3 0.0, 0.0  0.0, 58.0
    Range (0.0-991.2) (0.0-443.3)
    LOXL1_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 0.8 (7.1) 39.6 (60.3)
    Median   0.0   18.5
    Q1, Q3 0.0, 0.0  0.0, 65.0
    Range (0.0-60.4)  (0.0-349.0)
    LOXL2_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  43.3 (356.8)  68.5 (129.9)
    Median   0.0   22.1
    Q1, Q3 0.0, 0.0  0.0, 89.1
    Range  (0.0-3048.4) (0.0-821.4)
    LOXL3_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  2.2 (12.3) 28.4 (71.1)
    Median   0.0   9.2
    Q1, Q3 0.0, 0.0  2.5, 29.4
    Range (0.0-89.7)  (0.0-507.5)
    LOXL4_AVG_NORM 0.0010
    N 73 54
    Mean (SD) 33.8 (91.0) 129.1 (300.4)
    Median   0.0   9.1
    Q1, Q3  0.0, 10.2  0.0, 67.0
    Range (0.0-529.2)  (0.0-1230.0)
    PLOD1_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  33.7 (116.5) 420.3 (532.2)
    Median   0.0  242.3
    Q1, Q3 0.0, 0.0  90.2, 659.3
    Range (0.0-878.2)  (0.0-3336.8)
    PLOD2_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  44.5 (151.7)  314.8 (1284.4)
    Median   0.0   53.7
    Q1, Q3 0.0, 0.0  2.3, 103.3
    Range  (0.0-1124.0)  (0.0-9110.5)
    PLOD3_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  2.7 (11.9) 68.0 (81.2)
    Median   0.0   38.3
    Q1, Q3 0.0, 0.0  4.2, 101.9
    Range (0.0-87.4)  (0.0-330.2)
    PCOLCE2_AVG_NORM 0.0010
    N 73 54
    Mean (SD)  7.7 (25.8)  6.4 (14.9)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0 0.0, 3.1
    Range (0.0-104.8) (0.0-68.4) 
    PCOLCE_AVG_NORM 0.0232
    N 73 54
    Mean (SD)  92.1 (159.7) 170.4 (339.4)
    Median   0.0   40.9
    Q1, Q3  0.0, 122.2  0.0, 175.1
    Range (0.0-699.2)  (0.0-1945.2)
    PTK2_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  2.8 (14.4) 76.6 (81.8)
    Median   0.0   70.0
    Q1, Q3 0.0, 0.0  0.0, 127.7
    Range (0.0-116.5) (0.0-323.3)
    CSRC_AVG_NORM 0.0001
    N 73 54
    Mean (SD) 19.0 (40.9) 45.1 (65.9)
    Median   0.3   19.6
    Q1, Q3  0.0, 24.8  4.2, 46.6
    Range (0.0-266.6) (0.0-290.2)
    CDKN1A_AVG_NORM 0.0005
    N 73 54
    Mean (SD)  78.5 (150.9) 181.0 (271.7)
    Median   0.0   84.2
    Q1, Q3  0.0, 118.9  0.0, 253.3
    Range (0.0-788.2)  (0.0-1083.2)
    CDKN2A_AVG_NORM 0.0002
    N 73 54
    Mean (SD)  6.1 (19.6)  9.7 (25.8)
    Median   0.0   1.0
    Q1, Q3 0.0, 0.0 0.0, 6.9
    Range (0.0-113.2) (0.0-175.1)
    TP53_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 40.6 (98.6) 231.2 (289.8)
    Median   0.0  166.9
    Q1, Q3 0.0, 0.0  0.0, 359.9
    Range (0.0-410.8)  (0.0-1722.4)
    YAP_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  7.8 (36.6) 112.4 (161.4)
    Median   0.0   63.1
    Q1, Q3 0.0, 0.0  0.0, 173.5
    Range (0.0-246.3) (0.0-769.0)
    TAZ_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 12.2 (27.9) 32.8 (44.3)
    Median   0.0   15.0
    Q1, Q3 0.0, 0.7  0.0, 49.0
    Range (0.0-122.7) (0.0-186.4)
    MITF_AVG_NORM <0.0001
    N 73 54
    Mean (SD) 251.0 (399.5) 569.8 (494.8)
    Median   45.5  467.3
    Q1, Q3  0.0, 331.5 184.9, 777.8
    Range  (0.0-2143.3)  (0.0-2200.0)
    MLANA_AVG_NORM 0.1823
    N 73 54
    Mean (SD) 3596.0 (3671.3) 4865.4 (4966.1)
    Median  2446.8   2803.5 
    Q1, Q3  950.9, 5019.4 1210.7, 6773.0
    Range  (14.0-17180.3)  (62.8-19672.1)
    TYR_AVG_NORM 0.0040
    N 73 54
    Mean (SD) 349.7 (301.8) 839.8 (996.3)
    Median  254.3  515.1
    Q1, Q3 119.5, 527.5  161.0, 1244.9
    Range  (0.0-1169.8)  (2.0-5500.0)
    POSTN_AVG_NORM 0.0001
    N 73 54
    Mean (SD) 1138.7 (2155.7) 1933.9 (2318.1)
    Median  191.6  1252.0 
    Q1, Q3   0.0, 1449.9  397.4, 2457.4
    Range  (0.0-11078.1)  (0.0-11193.2)
    FBLN2_AVG_NORM <0.0001
    N 73 54
    Mean (SD)  2.1 (17.3) 26.5 (42.2)
    Median   0.0   0.0
    Q1, Q3 0.0, 0.0  0.0, 48.8
    Range (0.0-148.2) (0.0-150.9)
  • TABLE D
    Comparison of gene expression between benign and malignant
    Benign Malignant
    (N = 73) (N = 54) p value
    CXCL1_AVG_NORM01 <0.0001
    Zero 58 (79.5%) 12 (22.2%)
    >0 15 (20.5%) 42 (77.8%)
    CSF2_AVG_NORM01 0.0398
    Zero 15 (20.5%) 20 (37.0%)
    >0 58 (79.5%) 34 (63.0%)
    CCL2_AVG_NORM01 <0.0001
    Zero 53 (72.6%) 12 (22.2%)
    >0 20 (27.4%) 42 (77.8%)
    IL8_AVG_NORM01 <0.0001
    Zero 63 (86.3%) 10 (18.5%)
    >0 10 (13.7%) 44 (81.5%)
    IL6_AVG_NORM01 <0.0001
    Zero 65 (89.0%) 13 (24.1%)
    >0  8 (11.0%) 41 (75.9%)
    ITGA5_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 38 (70.4%)
    >0 0 (0.0%) 16 (29.6%)
    ITGA3_AVG_NORM01 <0.0001
    Zero 72 (98.6%) 13 (24.1%)
    >0 1 (1.4%) 41 (75.9%)
    ITGA2_AVG_NORM01 0.0007
    Zero  73 (100.0%) 46 (85.2%)
    >0 0 (0.0%)  8 (14.8%)
    ITGAV_AVG_NORM01 <0.0001
    Zero 71 (97.3%) 24 (44.4%)
    >0 2 (2.7%) 30 (55.6%)
    ITGB3_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 30 (55.6%)
    >0 0 (0.0%) 24 (44.4%)
    ITGB1_AVG_NORM01 <0.0001
    Zero 64 (87.7%) 11 (20.4%)
    >0  9 (12.3%) 43 (79.6%)
    FN1_AVG_NORM01 <0.0001
    Zero 69 (94.5%) 2 (3.7%)
    >0 4 (5.5%) 52 (96.3%)
    THBS1_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 24 (44.4%)
    >0 0 (0.0%) 30 (55.6%)
    THBS2_AVG_NORM01 <0.0001
    Zero 67 (91.8%) 23 (42.6%)
    >0 6 (8.2%) 31 (57.4%)
    THBS4_AVG_NORM01 <0.0001
    Zero 58 (79.5%) 15 (27.8%)
    >0 15 (20.5%) 39 (72.2%)
    VCAN_AVG_NORM01 <0.0001
    Zero 71 (97.3%) 16 (29.6%)
    >0 2 (2.7%) 38 (70.4%)
    BGAN_AVG_NORM01 <0.0001
    Zero 42 (57.5%)  7 (13.0%)
    >0 31 (42.5%) 47 (87.0%)
    SPP1_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 12 (22.2%)
    >0 0 (0.0%) 42 (77.8%)
    TNC_AVG_NORM01 <0.0001
    Zero 60 (82.2%) 3 (5.6%)
    >0 13 (17.8%) 51 (94.4%)
    SPARC_AVG_NORM01 <0.0001
    Zero 57 (78.1%) 13 (24.1%)
    >0 16 (21.9%) 41 (75.9%)
    AGRN_AVG_NORM01 <0.0001
    Zero 59 (80.8%) 5 (9.3%)
    >0 14 (19.2%) 49 (90.7%)
    CTGF_AVG_NORM01 <0.0001
    Zero 72 (98.6%) 21 (38.9%)
    >0 1 (1.4%) 33 (61.1%)
    CYR61_AVG_NORM01 <0.0001
    Zero 56 (76.7%)  9 (16.7%)
    >0 17 (23.3%) 45 (83.3%)
    LAMA3_AVG_NORM01 0.0003
    Zero 72 (98.6%) 43 (79.6%)
    >0 1 (1.4%) 11 (20.4%)
    LAMC1_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 24 (44.4%)
    >0 0 (0.0%) 30 (55.6%)
    LAMB1_AVG_NORM01 <0.0001
    Zero 66 (90.4%) 22 (40.7%)
    >0 7 (9.6%) 32 (59.3%)
    LAMA1_AVG_NORM01 <0.0001
    Zero 57 (78.1%) 16 (29.6%)
    >0 16 (21.9%) 38 (70.4%)
    LAMC2_AVG_NORM01 0.0003
    Zero  73 (100.0%) 45 (83.3%)
    >0 0 (0.0%)  9 (16.7%)
    LAMB3_AVG_NORM01 0.0061
    Zero 45 (61.6%) 20 (37.0%)
    >0 28 (38.4%) 34 (63.0%)
    COL1A1_AVG_NORM01 <0.0001
    Zero 60 (82.2%) 17 (31.5%)
    >0 13 (17.8%) 37 (68.5%)
    COL4A1_AVG_NORM01 <0.0001
    Zero  73 (100.0%) 13 (24.1%)
    >0 0 (0.0%) 41 (75.9%)
    COL18A1_AVG_NORM01 <0.0001
    Zero 64 (87.7%) 18 (33.3%)
    >0  9 (12.3%) 36 (66.7%)
    LOX_AVG_NORM01 <0.0001
    Zero 60 (82.2%) 26 (48.1%)
    >0 13 (17.8%) 28 (51.9%)
    LOXL1_AVG_NORM01 <0.0001
    Zero 72 (98.6%) 23 (42.6%)
    >0 1 (1.4%) 31 (57.4%)
    LOXL2_AVG_NORM01 <0.0001
    Zero 70 (95.9%) 19 (35.2%)
    >0 3 (4.1%) 35 (64.8%)
    LOXL3_AVG_NORM01 <0.0001
    Zero 69 (94.5%) 10 (18.5%)
    >0 4 (5.5%) 44 (81.5%)
    LOXL4_AVG_NORM01 0.0006
    Zero 53 (72.6%) 23 (42.6%)
    >0 20 (27.4%) 31 (57.4%)
    PLOD1_AVG_NORM01 <0.0001
    Zero 59 (80.8%) 12 (22.2%)
    >0 14 (19.2%) 42 (77.8%)
    PLOD2_AVG_NORM01 <0.0001
    Zero 59 (80.8%) 10 (18.5%)
    >0 14 (19.2%) 44 (81.5%)
    PLOD3_AVG_NORM01 <0.0001
    Zero 66 (90.4%) 11 (20.4%)
    >0 7 (9.6%) 43 (79.6%)
    PCOLCE2_AVG_NORM01 0.0002
    Zero 66 (90.4%) 34 (63.0%)
    >0 7 (9.6%) 20 (37.0%)
    PCOLCE_AVG_NORM01 0.0036
    Zero 42 (57.5%) 17 (31.5%)
    >0 31 (42.5%) 37 (68.5%)
    PTK2_AVG_NORM01 <0.0001
    Zero 67 (91.8%) 16 (29.6%)
    >0 6 (8.2%) 38 (70.4%)
    CSRC_AVG_NORM01 0.0001
    Zero 36 (49.3%)  9 (16.7%)
    >0 37 (50.7%) 45 (83.3%)
    CDKN1A_AVG_NORM01 0.0001
    Zero 48 (65.8%) 16 (29.6%)
    >0 25 (34.2%) 38 (70.4%)
    CDKN2A_AVG_NORM01 <0.0001
    Zero 57 (78.1%) 23 (42.6%)
    >0 16 (21.9%) 31 (57.4%)
    TP53_AVG_NORM01 <0.0001
    Zero 59 (80.8%) 16 (29.6%)
    >0 14 (19.2%) 38 (70.4%)
    YAP_AVG_NORM01 <0.0001
    Zero 68 (93.2%) 22 (40.7%)
    >0 5 (6.8%) 32 (59.3%)
    TAZ_AVG_NORM01 <0.0001
    Zero 54 (74.0%) 19 (35.2%)
    >0 19 (26.0%) 35 (64.8%)
    MITF_AVG_NORM01 <0.0001
    Zero 26 (35.6%) 2 (3.7%)
    >0 47 (64.4%) 52 (96.3%)
    MLANA_AVG_NORM01
    >0  73 (100.0%)  54 (100.0%)
    TYR_AVG_NORM01 0.2202
    Zero 2 (2.7%) 0 (0.0%)
    >0 71 (97.3%)  54 (100.0%)
    POSTN_AVG_NORM01 <0.0001
    Zero 32 (43.8%) 4 (7.4%)
    >0 41 (56.2%) 50 (92.6%)
    FBLN2_AVG_NORM01 <0.0001
    Zero 71 (97.3%) 31 (57.4%)
    >0 2 (2.7%) 23 (42.6%)
  • TABLE E
    MM79_CN MM80_CN MM81_CN MM82_CN
    AVG AVG AVG AVG AVG
    per copy per copy per copy per copy per copy
    K14 K14 K14 K14 K14 STDEV % STDE
    Figure US20160222457A1-20160804-P00899
    KRT14_AVG_NORM 1 1 1 1 1 0.000
    KRT10_AVG_NORM 2.209 2.229 2.92 3.015 2.593 0.434 17
    Figure US20160222457A1-20160804-P00899
    MITF_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15
    Figure US20160222457A1-20160804-P00899
    MLANA_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15
    Figure US20160222457A1-20160804-P00899
    TYR_AVG_NORM 0.004 0.002 0.002 0.001 0.002 0.001 56
    Figure US20160222457A1-20160804-P00899
    PMEL_AVG_NORM 0.025 0.027 0.03 0.018 0.025 0.005 20%
    FN1_AVG_NORM 0.077 0.065 0.035 0.042 0.055 0.020 36
    Figure US20160222457A1-20160804-P00899
    SPARC_AVG_NORM 1.294 1.143 0.568 1.707 1.178 0.471 40
    Figure US20160222457A1-20160804-P00899
    AGRN_AVG_NORM 0.004 0.006 0.003 0.002 0.004 0.002 46
    Figure US20160222457A1-20160804-P00899
    THBS1_AVG_NORM 0.064 0.015 0.018 0.005 0.026 0.026 103
    Figure US20160222457A1-20160804-P00899
    THBS2_AVG_NORM 0.366 0.061 0.104 0.057 0.147 0.148 100
    Figure US20160222457A1-20160804-P00899
    THBS4_AVG_NORM 0.018 0.006 0.005 0.001 0.008 0.007 98
    Figure US20160222457A1-20160804-P00899
    VCAN_AVG_NORM 0.095 0.034 0.04 0.027 0.049 0.031 64
    Figure US20160222457A1-20160804-P00899
    BGAN_AVG_NORM 0.015 0.027 0.014 0.015 0.018 0.006 35
    Figure US20160222457A1-20160804-P00899
    COL1A1_AVG_NORM 1.695 3.44 0.689 6.695 3.130 2.635 84
    Figure US20160222457A1-20160804-P00899
    COL4A1_AVG_NORM 0.069 0.026 0.03 0.016 0.035 0.023 66
    Figure US20160222457A1-20160804-P00899
    COL4A2_AVG_NORM 0.115 0.042 0.041 0.004 0.051 0.046 92
    Figure US20160222457A1-20160804-P00899
    COL18A1_AVG_NORM 0.015 0.009 0.005 0.002 0.008 0.006 73
    Figure US20160222457A1-20160804-P00899
    CTGF_AVG_NORM 0.012 0.008 0.016 0.004 0.010 0.005 52
    Figure US20160222457A1-20160804-P00899
    LOX_AVG_NORM 0.029 0.021 0.028 0.021 0.025 0.004 18
    Figure US20160222457A1-20160804-P00899
    LOXL1_AVG_NORM 0.015 0.009 0.016 0.015 0.014 0.003 23
    Figure US20160222457A1-20160804-P00899
    LOXL2_AVG_NORM 0.016 0.011 0.008 0.006 0.010 0.004 42
    Figure US20160222457A1-20160804-P00899
    LOXL3_AVG_NORM 0.003 0.002 0.002 0.001 0.002 0.001 41
    Figure US20160222457A1-20160804-P00899
    LOXL4_AVG_NORM 0.02 0.004 0.003 0.001 0.007 0.009 125
    Figure US20160222457A1-20160804-P00899
    PLOD2_AVG_NORM 0.018 0.014 0.007 0.001 0.010 0.008 75
    Figure US20160222457A1-20160804-P00899
    PLOD1_AVG_NORM 0.069 0.053 0.026 0.017 0.041 0.024 58
    Figure US20160222457A1-20160804-P00899
    SPP1_AVG_NORM 0.092 0.002 0.007 0 0.025 0.045 177
    Figure US20160222457A1-20160804-P00899
    TNC_AVG_NORM 0.025 0.02 0.027 0.013 0.021 0.006 29
    Figure US20160222457A1-20160804-P00899
    PCOLCE2_AVG_NORM 0.011 0.001 0.006 0 0.005 0.005 113
    Figure US20160222457A1-20160804-P00899
    PCOLCE_AVG_NORM 0.028 0.049 0.032 0.04 0.037 0.009 25
    Figure US20160222457A1-20160804-P00899
    PLOD3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113
    Figure US20160222457A1-20160804-P00899
    ITGB3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113
    Figure US20160222457A1-20160804-P00899
    ITGB1_AVG_NORM 0.164 0.054 0.074 0.038 0.083 0.056 68
    Figure US20160222457A1-20160804-P00899
    FBLN2_AVG_NORM 0.049 0.022 0.02 0.016 0.027 0.015 56
    Figure US20160222457A1-20160804-P00899
    CYR61_AVG_NORM 0.006 0.002 0.003 0 0.003 0.003 91
    Figure US20160222457A1-20160804-P00899
    ITGA5_AVG_NORM 0.011 0.005 0.007 0.003 0.007 0.003 53
    Figure US20160222457A1-20160804-P00899
    ITGA3_AVG_NORM 0.016 0.008 0.006 0.008 0.010 0.004 47
    Figure US20160222457A1-20160804-P00899
    ITGA2_AVG_NORM 0.08 0.034 0.019 0.084 0.054 0.033 60
    Figure US20160222457A1-20160804-P00899
    ITGAV_AVG_NORM 0.013 0.005 0.003 0.003 0.006 0.005 79
    Figure US20160222457A1-20160804-P00899
    CSRC_AVG_NORM 0.006 0.003 0.005 0.001 0.004 0.002 59
    Figure US20160222457A1-20160804-P00899
    PTK2_AVG_NORM 0.035 0.02 0.011 0.009 0.019 0.012 63
    Figure US20160222457A1-20160804-P00899
    POSTN_AVG_NORM 0.077 0.092 0.117 0.193 0.120 0.052 43
    Figure US20160222457A1-20160804-P00899
    YAP_AVG_NORM 0.079 0.029 0.033 0.031 0.043 0.024 56
    Figure US20160222457A1-20160804-P00899
    CXCL1_AVG_NORM 0.002 0 0 0 0.001 0.001 200
    Figure US20160222457A1-20160804-P00899
    CSF2_AVG_NORM 0.002 0 0 0 0.001 0.001 200
    Figure US20160222457A1-20160804-P00899
    CCL2_AVG_NORM 0.039 0.018 0.013 0.008 0.020 0.014 70
    Figure US20160222457A1-20160804-P00899
    IL8_AVG_NORM 0.003 0 0.001 0 0.001 0.001 141
    Figure US20160222457A1-20160804-P00899
    IL6_AVG_NORM 0.001 0 0 0 0.000 0.001 200
    Figure US20160222457A1-20160804-P00899
    LAMA3_AVG_NORM 0.038 0.012 0.021 0.011 0.021 0.013 61
    Figure US20160222457A1-20160804-P00899
    TP53_AVG_NORM 0.08 0.04 0.039 0.052 0.053 0.019 36
    Figure US20160222457A1-20160804-P00899
    CDKN1A_AVG_NORM 0.057 0.029 0.037 0.014 0.034 0.018 52
    Figure US20160222457A1-20160804-P00899
    CDKN2A_AVG_NORM 0.003 0.001 0.001 0 0.001 0.001 101
    Figure US20160222457A1-20160804-P00899
    TAZ_AVG_NORM 0.026 0.008 0.008 0.003 0.011 0.010 90
    Figure US20160222457A1-20160804-P00899
    LAMC1_AVG_NORM 0.062 0.013 0.016 0.008 0.025 0.025 101
    Figure US20160222457A1-20160804-P00899
    LAMB1_AVG_NORM 0.046 0.019 0.026 0.008 0.025 0.016 65
    Figure US20160222457A1-20160804-P00899
    LAMA1_AVG_NORM 0.007 0 0.001 0 0.002 0.003 168
    Figure US20160222457A1-20160804-P00899
    LAMC2_AVG_NORM 0.034 0.009 0.012 0.016 0.018 0.011 63
    Figure US20160222457A1-20160804-P00899
    LAMB3_AVG_NORM 0.042 0.016 0.026 0.017 0.025 0.012 48
    Figure US20160222457A1-20160804-P00899
    PLAT_AVG_NORM 0.032 0.02 0.034 0.04 0.032 0.001 27
    Figure US20160222457A1-20160804-P00899
    CSK_AVG_NORM 0.027 0.034 0.021 0.041 0.031 0.001 28
    Figure US20160222457A1-20160804-P00899
    GDF15_AVG_NORM 0.029 0.019 0.033 0.019 0.025 0.001 28
    Figure US20160222457A1-20160804-P00899
    FARP1_AVG_NORM 0.019 0.029 0.022 0.031 0.025 0.001 22
    Figure US20160222457A1-20160804-P00899
    ARPC1B_AVG_NORM 0.015 0.03 0.042 0.018 0.026 0.012 47
    Figure US20160222457A1-20160804-P00899
    NES_AVG_NORM 0.114 0.125 0.112 0.084 0.109 0.017 16
    Figure US20160222457A1-20160804-P00899
    NTRK3_AVG_NORM 0.021 0.025 0.022 0.033 0.025 0.001 25
    Figure US20160222457A1-20160804-P00899
    SNX17_AVG_NORM 0.112 0.099 0.089 0.123 0.106 0.015 14
    Figure US20160222457A1-20160804-P00899
    L1CAM_AVG_NORM 0.017 0.04 0.01 0.024 0.023 0.013 56
    Figure US20160222457A1-20160804-P00899
    CD44_AVG_NORM 0.112 0.089 0.09 0.123 0.104 0.017 16
    Figure US20160222457A1-20160804-P00899
    Figure US20160222457A1-20160804-P00899
    indicates data missing or illegible when filed
  • The results provided herein demonstrate the development of a method for determining absolute levels (copy numbers) of genes of interest (e.g., FN-associated genes) from paraffin-embedded tissue by generating a highly defined internal standard that can be regenerated indefinitely. This standardization approach can allow for the comparison of results from independent experiments and thus, allows for extensive validation. The RT-PCR not only produced strong signals from highly degraded RNA due to FFPE embedding, but also was amendable to high-throughput analysis and was highly cost effective. While the methods provided herein were validated for melanoma, these methods are likely applicable to other human cancers. The results provided herein also demonstrate the discrimination between benign and malignant pigmented lesions based on multiple markers.
    • Example 3 Additional Marker Panel
  • A test kit panel was designed to include primers for assessing expression levels of eight marker genes (ITGB3, TNC, SPP1, SPARC, PLAT, COL4A1, PLOD3, and PTK2) as well as three housekeeping genes (ACTB, RPLP0, and RPL8), one keratinocyte markers (K14) to assess keratinocyte contamination, and two melanocyte markers (MLANA and MITF) to assess melanocyte content in the skin sections. The primers designed for this collection are set forth in Table 11.
  • TABLE 11
    Primers for marker panel kit.
    SEQ
    Primer pair  ID
    Gene name Direction Sequence NO:
    ACTB ACTB-G -F TGCTATCCCTGTACGCCTCT 433
    ACTB-G -R GAGTCCATCACGATGCCAGT 434
    ACTB ACTB-H -F GGACTTCGAGCAAGAGATGG 435
    ACTB-H -R CTTCTCCAGGGAGGAGCTG 436
    ACTB ACTB-I -F GGCTACAGCTTCACCACCAC 425
    ACTB-I -R TAATGTCACGCACGATTTCC 426
    RPLP0 RPLP0-B -F AACTCTGCATTCTCGCTTCC   9
    RPLP0-B -R GCAGACAGACACTGGCAACA  10
    RPLP0 RPLP0-C -F GCACCATTGAAATCCTGAGTG  11
    RPLP0-C -R GCTCCCACTTTGTCTCCAGT  12
    RPL8 RPL8-B -F ACAGAGCTGTGGTTGGTGTG  19
    RPL8-B -R TTGTCAATTCGGCCACCT  20
    RPL8 RPL8-E -F ACTGCTGGCCACGAGTACG  17
    RPL8-E -R ATGCTCCACAGGATTCATGG  18
    KRT14 KRT14-D -F TCCGCACCAAGTATGAGACA  39
    KRT14-D -R ACTCATGCGCAGGTTCAACT  40
    KRT14 KRT14-F -F GATGCAGATTGAGAGCCTGA 437
    KRT14-F -R TTCTTCAGGTAGGCCAGCTC 438
    MLANA MLANA-C -F GAGAAAAACTGTGAACCTGTGG  53
    MLANA-C -R ATAAGCAGGTGGAGCATTGG  54
    MITF MITF-B -F CGGCATTTGTTGCTCAGAAT  47
    MITF-B -R GAGCCTGCATTTCAAGTTCC  48
    ITGB3 ITGB3-A -F AAGAGCCAGAGTGTCCCAAG 159
    ITGB3-A -R ACTGAGAGCAGGACCACCA 160
    ITGB3 ITGB3-B -F CTTCTCCTGTGTCCGCTACAA 161
    ITGB3-B -R CATGGCCTGAGCACATCTC 162
    PLAT PLAT-C -F CCCAGCCAGGAAATCCAT 427
    PLAT-C -R CTGGCTCCTCTTCTGAATCG 428
    PLAT PLAT-D -F CAGTGCCTGTCAAAAGTTGC 429
    PLAT-D -R CCCCGTTGAAACACCTTG 430
    PLAT PLAT-E -F GAAGGATTTGCTGGGAAGTG 441
    PLAT-E -R CGTGGCCCTGGTATCTATTT 442
    PLOD3 PLOD3-D -F GGAAGGAATCGTGGAGCAG 111
    PLOD3-D -R CAGCAGTGGGAACCAGTACA 112
    PTK2 PTK2-D -F GAGACCATTCCCCTCCTACC 119
    PTK2-D -R GCTTCTGTGCCATCTCAATCT 120
    CDKN2A CDKN2A1-C -F AGGAGCCAGCGTCTAGGG 219
    CDKN2A1-C -R CTGCCCATCATCATGACCT 220
    CDKN2A CDKN2A2-C -F AACGCACCGAATAGTTACGG 221
    CDKN2A2-C -R CATCATCATGACCTGGATCG 222
  • One purpose of the kit was to differentiate between melanoma with high and low risk of regional metastasis, and to appropriately select patients for surgical procedures such as sentinel lymph node biopsy (SLNB) or total lymphadenectomy. Another purpose of this kit was to estimate disease-free survival, disease relapse, or likelihood of death from melanoma. To study the ability of these methods to discriminate between melanoma with high and low risk of metastasis and to establish superiority to established methods, a cohort of 158 patients between October 1998 and June 2013 were identified as having been diagnosed with high-risk melanoma and as having underwent SLNB with the intention to assess metastatic potential of the tumor. Of note, high-risk melanoma by current criteria are defined as melanoma with an invasion depth (Breslow depth) of ≧1 mm; or melanoma with an invasion depth of 0.75 to 0.99 mm plus the presence of either one of the following three risk factors: >0 mitotic figures/mm2; tumor ulceration present; patient age <40 years.
  • All 158 patients met the criteria for high risk. 136 patients had a Breslow Depth ≧1 mm. 22 patients had a Breslow Depth between 0.75 and 0.99 and had at least 1 of the aforementioned 3 risk factors (ulceration, mitotic rate >0, age <40). Of the 158 patients, 36 (22.8%) had a melanoma-positive SLNB.
  • To select genes for a test kit from a pool of genes, the expression level of 52 genes (variables) was initially determined and dichotomized as zero vs. >zero and evaluated for an association with positive SLNB using the chi-square test for a 2×2 contingency level. The genes are ordered based on the value of the chi-square test statistic (Table 12).
  • TABLE 12
    Value of the chi-square test
    statistic variable
    ITGB3 68.3522
    SPP1 25.8460
    LOXL3 16.7683
    PLAT 16.5721
    LAMB1 15.7544
    YAP 13.4049
    PLOD3 12.6062
    TP53 12.3662
    COL4A1 11.8336
    TNC 11.3862
    IL8 10.4697
    ITGA5 10.3561
    COL1A1 10.0006
    VCAN 9.3250
    PLOD1 8.6959
    FN1 8.4857
    PTK2 7.9874
    ITGAV 7.7181
    LOXL1 7.2109
    LOXL2 6.6348
    ITGB1 6.3556
    CDKN1A 6.3117
    CTGF 6.2588
    GDF15 5.96939
    CSRC 5.4435
    ITGA2 5.0326
    ITGA3 4.0603
    LOX 3.8697
    COL18A1 3.3392
    IL6 3.0435
    DSPP 2.7822
    NTRK3 2.7822
    LOXL4 2.7279
    THBS2 2.5110
    SPARC 1.9884
    PCOLCE2 1.6499
    AGRN 1.6118
    CXCL1 1.3483
    TAZ 1.3458
    THBS4 1.1281
    PCOLCE 0.9198
    FBLN2 0.9198
    LAMC2 0.9157
    CCL2 0.8701
    CDKN2A 0.6047
    CSF2 0.5408
    CYR61 0.4713
    BGAN 0.4364
    LAMA3 0.3455
    POSTN 0.1902
    LAMB3 0.1058
    PLOD2 0.0152
  • As can be deduced from the chi-square test statistic, ITGB3 was highly discriminatory between melanoma with and without regional lymph node metastasis. The n (%) with a positive SLNB for those with no expression vs. expression level >0 was summarized (Table 13).
  • TABLE 13
    Overall Positive
    No. (% of 158) No. (% of each row)
    FN1_01
    Zero 110 (69.6%)  18 (16.4%)
    >0 48 (30.4%) 18 (37.5%)
    SPP1_01
    Zero 93 (58.9%) 8 (8.6%)
    >0 65 (41.1%) 28 (43.1%)
    ITGB3_01
    Zero 107 (67.7%)  4 (3.7%)
    >0 51 (32.3%) 32 (62.7%)
    TNC_01
    Zero 114 (72.2%)  18 (15.8%)
    >0 44 (27.8%) 18 (40.9%)
    PLAT_01
    Missing 18  0
    Zero 83 (59.3%) 11 (13.3%)
    >0 57 (40.7%) 25 (43.9%)
    COL4A1_01
    Zero 111 (70.3%)  17 (15.3%)
    >0 47 (29.7%) 19 (40.4%)
    SPARC_01
    Missing 4 0
    Zero 138 (89.6%) 30 (21.7%)
    >0 16 (10.4%)  6 (37.5%)
    AGRN_01
    Missing 4 0
    Zero 23 (14.9%)  3 (13.0%)
    >0 131 (85.1%)  33 (25.2%)
    THBS1_01
    Missing 135  33 
    Zero 18 (78.3%) 0 (0.0%)
    >0  5 (21.7%)  3 (60.0%)
    THBS2_01
    Missing 4 0
    Zero 114 (74.0%)  23 (20.2%)
    >0 40 (26.0%) 13 (32.5%)
    THBS4_01
    Missing 4 0
    Zero 136 (88.3%)  30 (22.1%)
    >0 18 (11.7%)  6 (33.3%)
    VCAN_01
    Missing 4 0
    Zero 137 (89.0%)  27 (19.7%)
    >0 17 (11.0%)  9 (52.9%)
    BGAN_01
    Missing 4 0
    Zero 97 (63.0%) 21 (21.6%)
    >0 57 (37.0%) 15 (26.3%)
    COL1A1_01
    Missing 4 0
    Zero 145 (94.2%)  30 (20.7%)
    >0 9 (5.8%)  6 (66.7%)
    COL18A1_01
    Missing 4 0
    Zero 146 (94.8%)  32 (21.9%)
    >0 8 (5.2%)  4 (50.0%)
    CTGF_01
    Missing 4 0
    Zero 128 (83.1%)  25 (19.5%)
    >0 26 (16.9%) 11 (42.3%)
    LOX_01
    Missing 4 0
    Zero 149 (96.8%)  33 (22.1%)
    >0 5 (3.2%)  3 (60.0%)
    LOXL1_01
    Missing 4 0
    Zero 146 (94.8%)  31 (21.2%)
    >0 8 (5.2%)  5 (62.5%)
    LOXL2_01
    Missing 4 0
    Zero 115 (74.7%)  21 (18.3%)
    >0 39 (25.3%) 15 (38.5%)
    LOXL3_01
    Missing 4 0
    Zero 67 (43.5%) 5 (7.5%)
    >0 87 (56.5%) 31 (35.6%)
    LOXL4_01
    Missing 4 0
    Zero 122 (79.2%)  25 (20.5%)
    >0 32 (20.8%) 11 (34.4%)
    PLOD2_01
    Missing 4 0
    Zero 136 (88.3%)  32 (23.5%)
    >0 18 (11.7%)  4 (22.2%)
    PLOD1_01
    Missing 4 0
    Zero 111 (72.1%)  19 (17.1%)
    >0 43 (27.9%) 17 (39.5%)
    PCOLCE2_01
    Missing 4 0
    Zero 144 (93.5%)  32 (22.2%)
    >0 10 (6.5%)   4 (40.0%)
    PCOLCE_01
    Missing 4 0
    Zero 139 (90.3%)  31 (22.3%)
    >0 15 (9.7%)   5 (33.3%)
    PLOD3_01
    Missing 4 0
    Zero 109 (70.8%)  17 (15.6%)
    >0 45 (29.2%) 19 (42.2%)
    ITGB1_01
    Missing 4 0
    Zero 62 (40.3%)  8 (12.9%)
    >0 92 (59.7%) 28 (30.4%)
    FBLN2_01
    Missing 4 0
    Zero 139 (90.3%)  31 (22.3%)
    >0 15 (9.7%)   5 (33.3%)
    CYR61_01
    Missing 4 0
    Zero 50 (32.5%) 10 (20.0%)
    >0 104 (67.5%)  26 (25.0%)
    ITGA5_01
    Missing 4 0
    Zero 135 (87.7%)  26 (19.3%)
    >0 19 (12.3%) 10 (52.6%)
    ITGA3_01
    Missing 4 0
    Zero 56 (36.4%)  8 (14.3%)
    >0 98 (63.6%) 28 (28.6%)
    ITGA2_01
    Missing 4 0
    Zero 139 (90.3%)  29 (20.9%)
    >0 15 (9.7%)   7 (46.7%)
    ITGAV_01
    Missing 4 0
    Zero 120 (77.9%)  22 (18.3%)
    >0 34 (22.1%) 14 (41.2%)
    CSRC_01
    Missing 4 0
    Zero 90 (58.4%) 15 (16.7%)
    >0 64 (41.6%) 21 (32.8%)
    PTK2_01
    Missing 4 0
    Zero 61 (39.6%)  7 (11.5%)
    >0 93 (60.4%) 29 (31.2%)
    POSTN_01
    Missing 4 0
    Zero 103 (66.9%)  23 (22.3%)
    >0 51 (33.1%) 13 (25.5%)
    YAP_01
    Missing 4 0
    Zero 137 (89.0%) 26 (19.0%)
    >0 17 (11.0%) 10 (58.8%)
    CXCL1_01
    Missing 4 0
    Zero 94 (61.0%) 19 (20.2%)
    >0 60 (39.0%) 17 (28.3%)
    CSF2_01
    Missing 4 0
    Zero 131 (85.1%)  32 (24.4%)
    >0 23 (14.9%)  4 (17.4%)
    CCL2_01
    Missing 4 0
    Zero 112 (72.7%)  24 (21.4%)
    >0 42 (27.3%) 12 (28.6%)
    IL8_01
    Missing 4 0
    Zero 99 (64.3%) 15 (15.2%)
    >0 55 (35.7%) 21 (38.2%)
    IL6_01
    Missing 4 0
    Zero 62 (40.3%) 10 (16.1%)
    >0 92 (59.7%) 26 (28.3%)
    LAMA3_01
    Missing 4 0
    Zero 148 (96.1%)  34 (23.0%)
    >0 6 (3.9%)  2 (33.3%)
    TP53_01
    Missing 4 0
    Zero 125 (81.2%)  22 (17.6%)
    >0 29 (18.8%) 14 (48.3%)
    CDKN1A_01
    Missing 4 0
    Zero 118 (76.6%)  22 (18.6%)
    >0 36 (23.4%) 14 (38.9%)
    CDKN2A_01
    Missing 4 0
    Zero 103 (66.9%)  26 (25.2%)
    >0 51 (33.1%) 10 (19.6%)
    TAZ_01
    Missing 4 0
    Zero 133 (86.4%)  29 (21.8%)
    >0 21 (13.6%)  7 (33.3%)
    LAMC1_01
    Missing 136  33 
    Zero 19 (86.4%) 0 (0.0%)
    >0  3 (13.6%)  3 (100.0%)
    LAMB1_01
    Missing 4 0
    Zero 109 (70.8%)  16 (14.7%)
    >0 45 (29.2%) 20 (44.4%)
    LAMA1_01
    Missing 4 0
    Zero 128 (83.1%)  30 (23.4%)
    >0 26 (16.9%)  6 (23.1%)
    LAMC2_01
    Missing 5 0
    Zero 145 (94.8%)  33 (22.8%)
    >0 8 (5.2%)  3 (37.5%)
    LAMB3_01
    Missing 4 0
    Zero 139 (90.3%)  33 (23.7%)
    >0 15 (9.7%)   3 (20.0%)
    GDF15_01
    Missing 28  4
    Zero 65 (50.0%) 10 (15.4%)
    >0 65 (50.0%) 22 (33.8%)
    DSPP_01
    Missing 73 13
    Zero 16 (18.8%)  7 (43.8%)
    >0 69 (81.2%) 16 (23.2%)
    NTRK3_01
    Missing 28  4
    Zero 130 (100.0%) 32 (24.6%)
  • To formulate a model that distinguishes melanoma that presents with regional metastasis at the time of diagnosis vs. no metastasis, logic regression was used. Logic regression is a machine learning technique that uses Boolean explanatory variables. There was not a typical technique to create good cut points for logic regression. To assign cut points in the variables, recursive partitioning followed by standardization of cut point levels was used. These were arbitrarily set at 0, 50, 250, and 500. Cut points derived by logic regression were adjusted to the next highest standard level. The cut point for ITGB3 was maintained at 0. The selected model for predicting metastasis was the following:
  • IF(OR(ITGB3>0,(AND(OR(PTK2>250,PLAT>500,PLOD3>250),CDKN2A<50)))=TRUE then predict metastasis
    Cut point ITGB3=0
    Cut point PLAT=500
    Cut point PTK2=250
    Cut point PLOD3=250
    Cut point CDKN2A=50
  • As can be seen from the formula, the risk of melanoma metastasis was high if ITGB3, PLAT, PTK2 or PLOD3 levels are increased and CDKN2A is low.
  • This model predicted regional metastasis (defined as a positive SLN biopsy at the time of primary cancer diagnosis) with a specificity of 80.3% and sensitivity of 97.3%.
    • OTHER EMBODIMENTS
  • It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims (23)

1. A method for identifying a malignant skin lesion, wherein said method comprises:
(a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain a measured expression level of said marker gene for said test sample,
(b) determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample,
(c) removing, from said measured expression level of said marker gene for said test sample, a level of expression attributable to keratinocytes present in said test sample using said measured expression level of said keratinocyte marker gene for said test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for said test sample, and
(d) identifying said test sample as containing a malignant skin lesion based, at least in part, on said corrected value of marker gene expression for said test sample.
2. The method of claim 1, wherein said keratinocyte marker gene is K14.
3. The method of claim 1, wherein said marker gene is SPP1 or ITGB3.
4. The method of claim 1, wherein step (c) comprises (i) multiplying said measured expression level of said keratinocyte marker gene for said test sample by said keratinocyte correction factor to obtain a correction value and (ii) subtracting said correction value from said measured expression level of said marker gene for said test sample to obtain said corrected value of marker gene expression for said test sample.
5. The method of claim 1, wherein said marker gene selected from the group consisting of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
6. The method of claim 1, wherein said marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, and SPARC.
7-8. (canceled)
9. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of at least seven marker genes selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain measured expression levels of said at least five marker genes for said test sample.
10. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
11. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, and SPARC.
12. A kit for identifying a malignant skin lesion, wherein said kit comprises:
(a) a primer pair for determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain a measured expression level of said marker gene for said test sample, and
(b) a primer pair for determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample.
13. The kit of claim 12, wherein said keratinocyte marker gene is K14.
14. The kit of claim 12, wherein said marker gene is SPP1 or ITGB3.
15. The kit of claim 12, wherein said marker gene selected from the group consisting of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
16. The kit of claim 12, wherein said marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, and SPARC.
17-18. (canceled)
19. The kit of claim 12, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of at least seven marker genes selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain measured expression levels of said at least five marker genes for said test sample.
20. The kit of claim 12, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
21. The kit of claim 12, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, and SPARC.
22. A method for identifying a malignant skin lesion, wherein said method comprises:
(a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, and CXCL1 to obtain a measured expression level of said marker gene for said test sample,
(b) determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample,
(c) removing, from said measured expression level of said marker gene for said test sample, a level of expression attributable to keratinocytes present in said test sample using said measured expression level of said keratinocyte marker gene for said test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for said test sample, and
(d) identifying said test sample as containing a malignant skin lesion based, at least in part, on said corrected value of marker gene expression for said test sample.
23. The method of claim 22, wherein said keratinocyte marker gene is K14.
24. The method of claim 22, wherein said marker gene is SPP1.
25. The method of claim 22, wherein step (c) comprises (i) multiplying said measured expression level of said keratinocyte marker gene for said test sample by said keratinocyte correction factor to obtain a correction value and (ii) subtracting said correction value from said measured expression level of said marker gene for said test sample to obtain said corrected value of marker gene expression for said test sample.
US14/442,673 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions Abandoned US20160222457A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US14/442,673 US20160222457A1 (en) 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201261726217P 2012-11-14 2012-11-14
US14/442,673 US20160222457A1 (en) 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions
PCT/US2013/053982 WO2014077915A1 (en) 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2013/053982 A-371-Of-International WO2014077915A1 (en) 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/518,783 Continuation US11840735B2 (en) 2012-11-14 2019-07-22 Methods and materials for identifying malignant skin lesions

Publications (1)

Publication Number Publication Date
US20160222457A1 true US20160222457A1 (en) 2016-08-04

Family

ID=50731585

Family Applications (2)

Application Number Title Priority Date Filing Date
US14/442,673 Abandoned US20160222457A1 (en) 2012-11-14 2013-08-07 Methods and materials for identifying malignant skin lesions
US16/518,783 Active US11840735B2 (en) 2012-11-14 2019-07-22 Methods and materials for identifying malignant skin lesions

Family Applications After (1)

Application Number Title Priority Date Filing Date
US16/518,783 Active US11840735B2 (en) 2012-11-14 2019-07-22 Methods and materials for identifying malignant skin lesions

Country Status (2)

Country Link
US (2) US20160222457A1 (en)
WO (1) WO2014077915A1 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190169694A1 (en) * 2016-05-10 2019-06-06 Mayo Foundation For Medical Education And Research Methods and materials for staging and treating skin cancer
WO2020022895A3 (en) * 2018-07-25 2020-04-02 Skylinedx B.V. Gene signatures for predicting metastasis of melanoma and patient prognosis
US11840735B2 (en) 2012-11-14 2023-12-12 Mayo Foundation For Medical Education And Research Methods and materials for identifying malignant skin lesions
US11851710B2 (en) 2014-08-14 2023-12-26 Mayo Foundation For Medical Education And Research Methods and materials for identifying metastatic malignant skin lesions and treating skin cancer

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090125247A1 (en) * 2007-08-16 2009-05-14 Joffre Baker Gene expression markers of recurrence risk in cancer patients after chemotherapy
US20100028876A1 (en) * 2006-08-14 2010-02-04 The Brigham And Women's Hospital , Inc. Diagnostic tests using gene expression ratios
US20140045915A1 (en) * 2010-08-31 2014-02-13 The General Hospital Corporation Cancer-related biological materials in microvesicles

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040010045A1 (en) 2001-09-07 2004-01-15 Taolin Yi Therapeutic compositions comprised of pentamidine and methods of using same to treat cancer
MXPA05005283A (en) * 2002-11-21 2005-07-25 Wyeth Corp Methods for diagnosing rcc and other solid tumors.
US20070154889A1 (en) 2004-06-25 2007-07-05 Veridex, Llc Methods and reagents for the detection of melanoma
TW200719903A (en) 2005-04-19 2007-06-01 Combinatorx Inc Compositions for the treatment of neoplasms
AU2007204826B2 (en) * 2006-01-11 2013-01-10 Genomic Health, Inc. Gene expression markers for colorectal cancer prognosis
NZ575090A (en) 2006-09-06 2012-02-24 Univ California Molecular diagnosis and classification of malignant melanoma using RGS1, ARPC2, FN1, SPP1 and WNT-2 polypeptide markers
EP2152911A4 (en) 2007-05-04 2010-05-19 Dermtech Int Diagnosis of melanoma by nucleic acid analysis
EP2257810B1 (en) 2008-03-05 2013-06-05 The Regents of the University of California Molecular diagnosis and classification of malignant melanoma
NZ596365A (en) 2009-05-01 2013-11-29 Oncozyme Pharma Inc Pentamidine combinations for treating cancer
AU2011305696A1 (en) 2010-09-20 2013-05-02 Advanced Cell Diagnostics, Inc. Biomarkers for differentiating melanoma from benign nevus in the skin
EP3195869B1 (en) 2012-07-27 2021-09-29 Agency for Science, Technology and Research (A*STAR) Method of promoting wound healing
US20160222457A1 (en) 2012-11-14 2016-08-04 Mayo Foundation For Medical Education And Research Methods and materials for identifying malignant skin lesions
US20170275700A1 (en) 2014-08-14 2017-09-28 Mayo Foundation For Medical Education And Research Methods and materials for identifying metastatic malignant skin lesions and treating skin cancer
MX2018013815A (en) 2016-05-10 2019-07-04 Mayo Found Medical Education & Res Methods and materials for staging and treating skin cancer.

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100028876A1 (en) * 2006-08-14 2010-02-04 The Brigham And Women's Hospital , Inc. Diagnostic tests using gene expression ratios
US20090125247A1 (en) * 2007-08-16 2009-05-14 Joffre Baker Gene expression markers of recurrence risk in cancer patients after chemotherapy
US20140045915A1 (en) * 2010-08-31 2014-02-13 The General Hospital Corporation Cancer-related biological materials in microvesicles

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Simon et al., Euripean Journal of Cancer, 1996, vol 32A, pages 1394-1400 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11840735B2 (en) 2012-11-14 2023-12-12 Mayo Foundation For Medical Education And Research Methods and materials for identifying malignant skin lesions
US11851710B2 (en) 2014-08-14 2023-12-26 Mayo Foundation For Medical Education And Research Methods and materials for identifying metastatic malignant skin lesions and treating skin cancer
US20190169694A1 (en) * 2016-05-10 2019-06-06 Mayo Foundation For Medical Education And Research Methods and materials for staging and treating skin cancer
US11060151B2 (en) * 2016-05-10 2021-07-13 Mayo Foundation For Medical Education And Research Methods and materials for staging and treating skin cancer
WO2020022895A3 (en) * 2018-07-25 2020-04-02 Skylinedx B.V. Gene signatures for predicting metastasis of melanoma and patient prognosis

Also Published As

Publication number Publication date
US20190338372A1 (en) 2019-11-07
US11840735B2 (en) 2023-12-12
WO2014077915A1 (en) 2014-05-22

Similar Documents

Publication Publication Date Title
US11840735B2 (en) Methods and materials for identifying malignant skin lesions
US20220365067A1 (en) Analysis of cell-free dna in urine and other samples
JP2024009859A (en) Variant based disease diagnostics and tracking
US11473148B2 (en) Methods of diagnosing bladder cancer
US11225685B2 (en) Methods and materials for assessing allelic imbalance
JP6317354B2 (en) Non-invasive determination of fetal or tumor methylomes by plasma
JP7443436B2 (en) Detection of advanced pancreatic dysplasia
He et al. Identification of carboxypeptidase E and γ-glutamyl hydrolase as biomarkers for pulmonary neuroendocrine tumors by cDNA microarray
CN116064795A (en) Methods and kits for determining methylation status of differentially methylated regions
US11851710B2 (en) Methods and materials for identifying metastatic malignant skin lesions and treating skin cancer
US20130316931A1 (en) Markers of melanoma and uses thereof
Smyth et al. DNA methylation associated with diabetic kidney disease in blood-derived DNA
Xiao et al. Non‐invasive diagnosis and surveillance of bladder cancer with driver and passenger DNA methylation in a prospective cohort study
US11060151B2 (en) Methods and materials for staging and treating skin cancer
JP2023516633A (en) Systems and methods for calling variants using methylation sequencing data
CN116261601A (en) Methods for detecting and predicting cancer
JP2021530214A (en) Urine DNA methylation markers for bladder cancer
Wu Circulating cell-free DNA methylation analysis of metastatic prostate cancer
US20130203623A1 (en) Method and kit for classifying a patient
Zhang et al. A high-through technique to measure DNA methylation

Legal Events

Date Code Title Description
AS Assignment

Owner name: MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MEVES, ALEXANDER;NIKOLOVA, EKATERINA M.;SIGNING DATES FROM 20150928 TO 20151001;REEL/FRAME:037499/0637

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: ADVISORY ACTION MAILED

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION