METHODS AND KITS FOR PREDICTING PROGNOSIS OF MULTIPLE SCLEROSIS

Information

  • Patent Application
  • 20100074864
  • Publication Number
    20100074864
  • Date Filed
    December 27, 2007
    17 years ago
  • Date Published
    March 25, 2010
    14 years ago
Abstract
Provided are methods and kits for predicting the prognosis of a subject diagnosed with multiple sclerosis by determining the expression level of polynucleotides which are differentially expressed between subjects diagnosed with multiple sclerosis and having good or poor clinical outcome. Also provided are methods and kits for selecting a treatment regimen of a subject diagnosed with multiple sclerosis.
Description
FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to genetic markers which are differentially expressed between multiple sclerosis patients having good or poor clinical outcome, and, more particularly, but not exclusively, to methods and kits using same for predicting the prognosis and selecting treatment regimen for multiple sclerosis.


Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system (CNS) affecting young adults (disease onset between 20 to 40 years of age) and is the third leading cause for disability after trauma and rheumatic diseases. MS disease prevalence in USA is 120/100,000 (250,000 to 350,000 cases) and in Israel about 30/100,000. The main pathologic finding in MS is the presence of infiltrating mononuclear cells, predominantly T lymphocytes and macrophages, that surpass the blood brain barrier and induce an active inflammation within the brain and spinal cord, attacking the myelin and resulting in gliotic scars and axonal loss. Thus, the multiple inflammatory foci, plaques of demyelination, gliosis and axonal pathology within the brain and spinal cord contribute to the clinical manifestations of neurological disability. The acute and chronic inflammatory processes can be visualized by brain and spinal cord MRI as hyperintense T2 or hypointense T1 lesions.


The etiology of MS is not fully understood. The disease develops in genetically predisposed subjects exposed to yet undefined environmental factors and the pathogenesis involves autoimmune mechanisms associated with autoreactive T cells against myelin antigens. It is well established that not one dominant gene determines genetic susceptibility to develop MS, but rather many genes, each with different influence, are involved. The initial pathogenic process that triggers the disease might be caused by one group of genes, while other groups are probably involved in disease activity and progression (5, 6).


MS is subdivided into several clinical subtypes; when it first presents by new onset of neurological symptoms affecting the CNS and accompanied by demyelinating lesions on brain magnetic resonance imaging (MRI), it is defined as probable MS. A diagnosis of relapsing-remitting (RRMS) definite MS is made when a subject defined as probable MS experiences a second neurological attack. The course of RRMS, which occurs in 85% of patients, is characterized by attacks during which new neurological symptoms and signs appear, or existing neurological symptoms and signs worsen. Usually an attack develops within a period of several days, lasts for 6-8 weeks, and then gradually resolves. During an acute attack, scattered inflammatory and demyelinating CNS lesions produce varying combinations of motor, sensory, coordination, visual, and cognitive impairments, as well as symptoms of fatigue and urinary tract dysfunction. The outcome of an attack is unpredictable in terms of neurological squeal, but it is well established that with each attack, the probability of complete clinical remission decreases, and neurological disability and handicap are liable to develop. In about 15% of patients the disease has a primary progressive course, characterized by gradual onset of neurological symptoms that progress over time, without any attacks. This course appears mostly in patients with disease onset above the age of 40 years and more often in males. The only course of MS in which treatment was effectively established is RRMS. Various immunomodulatory drugs have been shown to reduce the number and severity of acute attacks, and thereby to decrease the accumulation of neurological disability.


Prediction of clinical outcome in MS was reported to relate to different clinical variables such as age at disease onset, gender, and the type of neurological symptomatology presented at onset. Thus, it was suggested that onset age below 35 years, rapid development and regression of initial symptoms, a single symptom at onset, and visual loss as the initial symptom, predicts a good prognosis. On the other hand, the major clinical determinants of more severe disease are male sex, relatively older age at onset, motor or cerebellar symptoms at onset and high annual relapse rate. Brain MRI parameters have also been implicated as important in the evaluation of MS course by measuring disease load over time. Brain atrophy was reported to account for more variance than lesion burden in predicting cognitive impairment. However, all these clinical and radiological variables are limited in the ability to predict disease outcome especially during early stages of the disease. This uncertainty in forecasting disease outcome means that some MS patients who need aggressive treatment do not receive it, while others are unnecessarily treated and as a result are exposed to the risk of side effects without a sound rationale. While peripheral blood genome scale analyses were used to diagnose MS and characterize MS patients in acute relapse or remission (PCT Pub. No. WO03081201A2, EP1532268A2, AU3214604AH, US20060003327A1 to the present inventors; Achiron A, et al., 2004), to date, there are no available genetic markers which can predict the clinical outcome of multiple sclerosis.


SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present invention there is provided a method of predicting a prognosis of a subject diagnosed with multiple sclerosis, the method comprising determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103,


wherein an alteration above a predetermined threshold in the level of expression of the at least one polynucleotide sequence in the cell of the subject relative to a level of expression of the at least one polynucleotide sequence in a reference cell is indicative of the prognosis of the subject diagnosed with multiple sclerosis.


According to an aspect of some embodiments of the present invention there is provided a method of treating of a subject diagnosed with multiple sclerosis, the method comprising: (a) determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103,


wherein an alteration above a predetermined threshold in the level of expression of the at least one polynucleotide sequence in the cell of the subject relative to a level of expression of the at least one polynucleotide sequence in a reference cell is indicative of a prognosis of the subject diagnosed with multiple sclerosis; (b) selecting a treatment regimen based on the prognosis, thereby treating the subject diagnosed with multiple sclerosis.


According to an aspect of some embodiments of the present invention there is provided a kit for predicting a prognosis of a subject diagnosed with multiple sclerosis, comprising no more than 700 isolated nucleic acid sequences, wherein each of the isolated nucleic acid sequences is capable of specifically recognizing at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103.


According to an aspect of some embodiments of the present invention there is provided a probeset comprising a plurality of oligonucleotides and no more than 700 oligonucleotides wherein each of the plurality of oligonucleotides is capable of specifically recognizing at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103.


According to some embodiments of the invention, the kit further comprises a reference cell.


According to some embodiments of the invention, each of the isolated nucleic acid sequences or the plurality of oligonucleotides is bound to a solid support.


According to some embodiments of the invention, the plurality of oligonucleotides is bound to the solid support in an addressable location.


According to some embodiments of the invention, the reference cell is of a subject diagnosed with multiple sclerosis which displayed within a period of two years an increase of at least 0.5 point in an Expanded Disability Status Scale (EDSS).


According to some embodiments of the invention, the reference cell is of a subject diagnosed with multiple sclerosis which displayed within a period of two years no change in an Expanded Disability Status Scale (EDSS).


According to some embodiments of the invention, the alteration is upregulation of the expression level of the at least one polynucleotide sequence in the cell of the subject relative to the reference cell, whereas the at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:1-193.


According to some embodiments of the invention, the prognosis comprises no change in an Expanded Disability Status Scale (EDSS) of the subject within a period of two years.


According to some embodiments of the invention, the prognosis further comprises no relapses within the period of the two years.


According to some embodiments of the invention, the alteration is upregulation of the expression level of the at least one polynucleotide sequence in the cell of the subject relative to the reference cell, whereas the at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:194-431.


According to some embodiments of the invention, the prognosis comprises an increase of at least 0.5 point in an Expanded Disability Status Scale (EDSS) of the subject within a period of at least two years.


According to some embodiments of the invention, detecting the level of expression is effected using an RNA detection method.


According to some embodiments of the invention, the kit further comprising at least one reagent suitable for detecting hybridization of the isolated nucleic acid sequences and at least one RNA transcript corresponding to the at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103.


According to some embodiments of the invention, the kit comprising packaging materials packaging the at least one reagent and instructions for use in determining the prognosis of the subject diagnosed with multiple sclerosis.


According to some embodiments of the invention, the multiple sclerosis is relapsing-remitting multiple sclerosis (RRMS).


According to some embodiments of the invention, the at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:156, 143, 127, 46, 311, 140, 74, 276, 180, 182, 191, 61, 306, 115, 97, 303, 272, 50, 16, 63, 117, 406, 423, 128, 277, 47, 17, 424, 418, 190, 139, 102, 103 and 325.


According to some embodiments of the invention, the at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:127, 423, 16, 17, 424, 190 and 325.


According to some embodiments of the invention, the at least one polynucleotide comprises the 7 polynucleotides set forth by SEQ ID NOs:127, 423, 16, 17, 424, 190 and 325.


According to some embodiments of the invention, the cell of the subject is a blood cell.


According to some embodiments of the invention, the at least one polynucleotide sequence is set forth by SEQ ID NO:158.


According to some embodiments of the invention, the at least one polynucleotide comprises the polynucleotide sequences set forth by SEQ ID NOs:158, 68, 5, 58, 329 and 120.


According to some embodiments of the invention, detecting the level of expression is effected using a protein detection method.


Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.





BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.


In the drawings:



FIG. 1 is a flow chart of the study design. Overview of the strategy used for the identification and validation of predictive clinical outcome gene-expression signature in RRMS using the signature support vector machine (SVM) in combination with Forward feature selection algorithm were applied (http://ro.utia.cz/fs/fs_algorithms.html), (12, 13).



FIG. 2 depicts a heatmap of 431 differentiating genes between poor and good clinical outcome of RRMS patients. Each row of the heatmap represents a gene and each column represents a patient's sample. Genes with increased expression (upregulation) are shown in progressively brighter shades of red, and genes with decreased expression (downregulation) are shown in progressively darker shades of green. The bottom matrix shows corresponding clinical outcome attributes marked in black when applicable. EDSS (Expanded Disability Status Scale) scores were determined in RRMS patients at the recruitment to study and during a two-years follow-up; EDSS 0—no change in EDSS score; Delta EDSS neg (negative)—improvement; Delta EDSS pos (positive)—deterioration; Relapse—attack;



FIG. 3 depicts a functional annotation histogram of some of the differentiating genes between poor and good clinical outcome of RRMS. Distribution of differentiating gene expression signature according to biologically relevant functional groups. Numbers represent the number of genes from the differentiating signature which belong to each functional annotation;



FIG. 4 is a graph depicting an overabundance analysis of the differentiating genes between poor and good clinical outcome of RRMS. Actual number of genes (blue line) is significantly more abundant than expected (red line) for TNoM statistical test. X-axis denotes p-value; y-axis denotes number of genes;



FIG. 5 is a graph depicting the Leave-One-Out-Cross-Validation (LOOCV) classification. Division of errors between patients with good and poor clinical outcome of RRMS using TNoM, Info and t-test demonstrated high classification rate of 90% at p<0.0001. X-axis denotes p value; y-axis denotes error rate in %.



FIG. 6 is a graph depicting the predictive classification chart of the differentiating genes between poor and good clinical outcome of RRMS. The classification rate of 29 predictive genes is demonstrated. Highest classification rate is achieved using only 7 genes, yet according to the feature selection algorithm, genes are added to the subset as long as the classification rate is not decreased. Y axis denotes classification rate; x axis denotes the number of genes;



FIG. 7 depicts gene enrichment of the differentiating genes between poor and good clinical outcome of RRMS. Direction of an over-expressed (1) or down-expressed (−1) gene is demonstrated in the enriched groups within the poor vs. good outcome signature;



FIGS. 8
a-c are infograms depicting the representation of genes related to specific biological processes in the 431 probesets of the present invention (shown in FIGS. 2a-b; SEQ ID NOs:1-431) which are differentially expressed between MS subjects with good or poor clinical outcome. FIG. 8a—A matrix of gene sets vs. arrays (each array represents an MS subject), where a colored entry indicates that the genes in the gene set had significantly changed in a coordinated fashion in the respective array (red—increased, green—decreased, black—not changed) as compared to the expected number of genes in each biological process as calculated using the Genomica software (http://genomica.weizmann.ac.il). The names of the biological processes are shown on the top index and the MS subject reference numbers are shown on the right index of FIG. 8b. FIG. 8b shows individual clinical outcome attributes that each array belongs to. The clinical outcome attributes include: EDSS 0 (no change in EDSS score), delta EDSS neg (negative; improvement), delta EDSS pos (positive; deterioration), poor outcome (poor clinical outcome as determined during two years), and relapse (attack). The color index is a follows: pink=presence of parameter; white—absence of parameter. FIG. 8c—a Module map demonstrating overall clinical outcome attributes in which gene sets were significantly enriched. Red—the number of genes in the specific biological process is higher than expected; green—the number of genes in the specific biological process is lower than expected; and black—the number of genes in the specific biological process is as expected. Note the enrichment of zinc-ion binding gene set for patients with relapses (MS subjects Nos. 88, 93, 99, 109, 110, 173, 210, 213, 215) and cytokine activity gene set for patients with stable disease (no change in neurological disability, EDSS=0; MS subjects Nos. 23, 25, 31, 34, 89, 119, 158).



FIG. 9 is a schematic model depicting the reconstructed zinc-ion binding pathway. Pathway analysis performed using genes from the predictive signature (yellow circles) and genes brought into the pathway based on literature known relationships according to PathwayArchitect software (green circles). Arrows indicate regulatory interactions confirmed by literature database, dashed arrows indicate suggested gene interactions;



FIG. 10 is a schematic model depicting the reconstructed cytokine activity pathway. Pathway analysis performed using genes from the predictive signature (gray circles) and genes brought into the pathway based on literature known relationships according to PathwayArchitect software (blue circles). Arrows indicate regulatory interactions confirmed by literature database, dashed arrows indicate suggested gene interactions;



FIG. 11 depicts the gene expression regulatory network module. The single gene expression module from the gene expression regulatory network of 431 differentiating genes is demonstrated. Each node in the regulation tree represents a regulating gene. The expression of the regulating genes themselves is shown below their node. Cluster of gene expression profiles (rows represent genes, columns—patients arrays) arranged according to the regulation tree. Note that zinc-ion binding related genes KLF4 (regulating gene, arrow on the left) and S100B (regulated gene, arrow on the right) belong to same regulatory module.



FIG. 12 is a graph depicting the average error of the predictive ability of combination of 431 differentiating genes.





DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to genetic markers which are differentially expressed between subjects diagnosed with multiple sclerosis and having good or poor clinical outcome which can be use to predict the prognosis of a subject diagnosed with multiple sclerosis. Specifically, but not exclusively, the present invention can be used to treat multiple sclerosis by selecting a suitable treatment regimen based on the predicted clinical outcome of the subject.


Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details set forth in the following description or exemplified by the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.


While reducing the invention to practice, the present inventors have uncovered differentially expressed genes which are associated with poor or good clinical outcome of multiple sclerosis and which can be used to predict the prognosis of a subject diagnosed with multiple sclerosis.


As is shown in the Examples section which follows, the present inventors have identified 431 genetic markers which are differentially expressed between relapsing-remitting MS (RRMS) patients with good or poor clinical outcome as established after a 2-year follow-up (FIGS. 2a-b, 3, 4, 5 and Table 2 and Example 1 of the Examples section which follows). Moreover, when supervised learning and feature selection algorithms were applied and validated in an independent set of 27 samples from a prospective cohort of RRMS patients, an optimal set of 34 gene transcripts was depicted as a clinical outcome predictive gene expression signature with a classification accuracy of 88.9% (FIGS. 1, 6, Table 3, Example 2 of the Examples section which follows). This predictive signature was enriched in genes biologically related to zinc-ion binding and cytokine activity regulation pathways (FIGS. 7, 8a-c, 9, 10, 11, Example 3 of the Examples section which follows). In addition, when the SVM software based on RBF kernel were applied on a training set of 26 subjects optimal sets of genes which can predict the prognosis of RRMS patients with 100% accuracy (average error of “0”) were depicted (FIG. 12, Table 4, Example 4 of the Examples section which follows). Altogether, these results demonstrate for the first time that genetic markers can discriminate between MS patients with good and poor clinical outcome, and suggest the use of such differentially expressed genes in predicting the prognosis of multiple sclerosis.


Thus, according to one aspect of the invention there is provided a method of predicting a prognosis of a subject diagnosed with multiple sclerosis. The method is effected by determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431, wherein an alteration above a predetermined threshold in the level of expression of the at least one polynucleotide sequence in the cell of the subject relative to a level of expression of the at least one polynucleotide sequence in a reference cell is indicative of the prognosis of the subject diagnosed with multiple sclerosis.


As used herein, the phrase “a subject diagnosed with multiple sclerosis” refers to a mammal, preferably a human being, who is diagnosed with definite multiple sclerosis, e.g., a subject who experienced at least two neurological attacks affecting the CNS and accompanied by demyelinating lesions on brain magnetic resonance imaging (MRI). It will be appreciated that the disease course of patients diagnosed with multiple sclerosis can be a relapsing-remitting multiple sclerosis (RRMS) (occurring in 85% of the patients) or a progressive multiple sclerosis (occurring in 15% of the patients). According to an embodiment of the invention, the subject is diagnosed with RRMS.


As used herein, the phrase “predicting a prognosis” refers to determining the clinical outcome of the subject diagnosed with multiple sclerosis, e.g., determining the risk of deterioration in terms of neurological disability and/or the total number of relapses. For example, a good clinical outcome (good prognosis) of a subject diagnosed with multiple sclerosis is no deterioration in the neurological disability [no change in the Expanded Disability Status Scale (EDSS) score] and no relapses for a period of at least 24 months; a poor clinical outcome (poor prognosis) is a deterioration in the neurological disability (the EDSS score is increased by at least 0.5 point) within a period of at least 24 months, either with or without relapses; an intermediate clinical outcome (intermediate prognosis) is no deterioration in the neurological disability (no change in the EDSS score) and yet at least one relapse during a period of at least 24 months.


As mentioned, the method according to this aspect of the invention is effected by determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431.


According to an embodiment of the invention, the method is effected by determining in a cell of the subject a level of expression of at least two, at least three, at least four, at least five, at least six (e.g., six), at least seven (e.g., seven), at least eight, at least nine, at least 10 polynucleotide sequences, at least 20, at least 30, at least 40, at least 50 polynucleotide sequences selected from the group consisting of SEQ ID NOs:1-431, wherein an alteration above a predetermined threshold in the level of expression of each of the polynucleotide sequences in the cell of the subject relative to a level of expression of the same polynucleotide sequences in a reference cell is indicative of the prognosis of the subject diagnosed with multiple sclerosis.


As used herein, the phrase “level of expression” refers to the degree of gene expression and/or gene product activity in a specific cell. For example, up-regulation or down-regulation of various genes can affect the level of the gene product (i.e., RNA and/or protein) in a specific cell.


As used herein the phrase “a cell of the subject” refers to any cell, cell content and/or cell secreted content which contains RNA and/or proteins of the subject. Examples include a blood cell, a bone marrow cell, a cell obtained from any tissue biopsy [e.g., cerebrospinal fluid, (CSF), brain biopsy], body fluids such as plasma, serum, saliva, spinal fluid, lymph fluid, the external sections of the skin, respiratory, intestinal, and genitourinary tracts, tears, saliva, sputum and milk. According to an embodiment of the invention, the cell is a blood cell (e.g., white blood cells, macrophages, B- and T-lymphocytes, monocytes, neutrophiles, eosinophiles, and basophiles) which can be obtained using a syringe needle from a vein of the subject. It should be noted that a “cell of the subject” may also optionally comprise a cell that has not been physically removed from the subject (e.g., in vivo detection).


According to an embodiment of the invention, the white blood cell comprises peripheral blood mononuclear cells (PBMC). The phrase, “peripheral blood mononuclear cells (PBMCs)” as used herein, refers to a mixture of monocytes and lymphocytes. Several methods for isolating white blood cells are known in the art. For example, PBMCs can be isolated from whole blood samples using density gradient centrifugation procedures. Typically, anticoagulated whole blood is layered over the separating medium. At the end of the centrifugation step, the following layers are visually observed from top to bottom: plasma/platelets, PBMCs, separating medium and erythrocytes/granulocytes. The PBMC layer is then removed and washed to remove contaminants (e.g., red blood cells) prior to determining the expression level of the polynucleotide(s) therein.


It will be appreciated that the cell of the subject can be obtained at any time, e.g., immediately after an attack or during remission.


According to an embodiment of the invention, detecting the level of expression of the polynucleotide sequences of the invention is effected using RNA or protein molecules which are extracted from the cell of the subject.


Methods of extracting RNA or protein molecules from cells of a subject are well known in the art.


Once obtained, the RNA or protein molecules can be characterized for the expression and/or activity level of various RNA and/or protein molecules using methods known in the arts.


Non-limiting examples of methods of detecting RNA molecules in a cell sample include Northern blot analysis, RT-PCR, RNA in situ hybridization (using e.g., DNA or RNA probes to hybridize RNA molecules present in the cells or tissue sections), in situ RT-PCR (e.g., as described in Nuovo G J, et al. Am J Surg Pathol. 1993, 17: 683-90; Komminoth P, et al. Pathol Res Pract. 1994, 190: 1017-25), and oligonucleotide microarray (e.g., by hybridization of polynucleotide sequences derived from a sample to oligonucleotides attached to a solid surface [e.g., a glass wafer) with addressable location, such as Affymetrix microarray (Affymetrix®, Santa Clara, Calif.)].


Non-limiting examples of methods of detecting the level and/or activity of specific protein molecules in a cell sample include Enzyme linked immunosorbent assay (ELISA), Western blot analysis, radio-immunoassay (RIA), Fluorescence activated cell sorting (FACS), immunohistochemical analysis, in situ activity assay (using e.g., a chromogenic substrate applied on the cells containing an active enzyme), in vitro activity assays (in which the activity of a particular enzyme is measured in a protein mixture extracted from the cells).


For example, in case the detection of the expression level of a secreted protein is desired, ELISA assay may be performed on a sample of fluid obtained from the subject (e.g., serum), which contains cell-secreted content.


As used herein the phrase “reference cell” refers to any cell as described hereinabove of a subject diagnosed with multiple sclerosis and having a known clinical outcome (e.g., poor, good or intermediate clinical outcome) as determined during a predetermined period of time, such as 2 years. Such a reference cell can be a blood cell, a bone marrow cell, a cell obtained from any tissue biopsy (e.g., CSF, brain biopsy), body fluids such as plasma, serum, saliva, spinal fluid, lymph fluid, the external sections of the skin, respiratory, intestinal, and genitourinary tracts, tears, saliva, sputum and milk. It will be appreciated that the level of expression of the above referenced polynucleotides/polypeptides may be obtained from scientific literature.


According to an embodiment of the invention, the reference cell comprises a cell of a subject diagnosed with multiple sclerosis and having a good clinical outcome. For example, such a reference cell can be a blood cell of a subject which exhibited no deterioration in the neurological disability (no change in the EDSS score) and no relapses during a period of at least 24 months.


Since as is shown in Table 2 and is described in Example 1 of the Examples section which follows, 238 polynucleotide sequences displayed elevated expression in the MS patients having poor clinical outcome relative to the MS patients having good clinical outcome, in order to predict the prognosis of a subject diagnosed with multiple sclerosis, the level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:194-431 is determined and compared to the level of expression of the same polynucleotide sequences in a reference cell derived from a subject diagnosed with MS and having good clinical outcome, wherein an upregulation (increase) in the expression level of the at least one polynucleotide sequence above a predetermined threshold relative to the reference cell is indicative of a poor prognosis (poor clinical outcome).


Additionally or alternatively, since as is further shown in Table 2 and is described in Example 1 of the Examples section which follows, the level of expression of 193 polynucleotide sequences was downregulated in the MS patients having poor clinical outcome relative to the MS patients having good clinical outcome, in order to predict the prognosis of a subject diagnosed with multiple sclerosis, the level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-193 is determined and compared to the level of expression of the same polynucleotide sequences in a reference cell derived from a subject diagnosed with MS and having good clinical outcome, wherein downregulation (decrease) in the expression level of the at least one polynucleotide sequence above a predetermined threshold relative to the reference cell is indicative of a poor prognosis (poor clinical outcome).


According to an embodiment of the invention, the reference cell comprises a cell of a subject diagnosed with multiple sclerosis and having a poor clinical outcome. For example, such a reference cell can be a blood cell of a subject which exhibited deterioration in the neurological disability (at least 0.5 point in the EDSS score) during a period of at least 24 months, either with or without relapses.


Since as is shown in Table 2 and is described in Example 1 of the Examples section which follows, the expression level of 238 polynucleotide sequences was downregulated in MS patients having good clinical outcome relative to the level of expression in MS patients having poor clinical outcome, in order to predict the prognosis of a subject diagnosed with MS, the level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:194-431 is determined and compared to the level of expression of the same polynucleotide sequences in a reference cell derived from an MS patient with poor clinical outcome, wherein downregulation (decrease) in the expression level of the at least one polynucleotide sequence above a predetermined threshold relative to the reference cell is indicative of a good prognosis (good clinical outcome).


Additionally or alternatively, since as is shown in Table 2 and is described in Example 1 of the Examples section which follows, the level of expression of 193 polynucleotide sequences was upregulated in the MS patients having good clinical outcome relative to the level of expression in MS patients having poor clinical outcome, in order to predict the prognosis of a subject diagnosed with MS, the level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-193 is determined and compared to the level of expression of the same polynucleotide sequences in a reference cell derived from an MS patient with poor clinical outcome, wherein upregulation (increase) in the expression level of the at least one polynucleotide sequence above a predetermined threshold relative to the reference cell is indicative of a good prognosis (good clinical outcome).


It will be appreciated that the reference cell can be also a cell of a subject diagnosed with multiple sclerosis and having an intermediate clinical outcome. For example, such a reference cell can be a blood cell of a subject which exhibited no deterioration in the neurological disability (no change in the EDSS score), yet experienced at least one relapse during a period of at least 24 months.


As is further shown in FIG. 6 and Table 3 and is described in Example 2 of the Examples section which follows the present inventors have uncovered that 34 out of the 431 differentiating genetic markers are capable of classifying MS patients to those having good or poor clinical outcome with a classification accuracy of at least 89%.


Thus, according to an embodiment of the invention, the at least one polynucleotide which expression level is determined in the cell of the subject diagnosed with MS is selected from the polynucleotides set forth in SEQ ID NOs:156, 143, 127, 46, 311, 140, 74, 276, 180, 182, 191, 61, 306, 115, 97, 303, 272, 50, 16, 63, 117, 406, 423, 128, 277, 47, 17, 424, 418, 190, 139, 102, 103 and 325.


According to an embodiment of the invention, downregulation of the expression level of at least one polynucleotide sequence of the polynucleotides set forth by SEQ ID NOs:156, 143, 127, 46, 140, 74, 180, 182, 191, 61, 115, 97, 50, 16, 63, 117, 128, 47, 17, 190, 139, 102 and 103, and/or upregulation of at least one polynucleotide sequence of the polynucleotides set forth by SEQ ID NOs:311, 276, 306, 303, 272, 406, 423, 277, 424, 418, and 325, relative to a reference cell of a subject diagnosed with MS and having good clinical outcome is indicative of poor prognosis of the subject diagnosed with MS.


On the other hand, upregulation of the expression level of at least one polynucleotide sequence of the polynucleotides set forth by SEQ ID NOs:156, 143, 127, 46, 140, 74, 180, 182, 191, 61, 115, 97, 50, 16, 63, 117, 128, 47, 17, 190, 139, 102 and 103, and/or downregulation of at least one polynucleotide sequence of the polynucleotides set forth by SEQ ID NOs:311, 276, 306, 303, 272, 406, 423, 277, 424, 418, and 325 relative to a reference cell of a subject diagnosed with MS and having poor clinical outcome is indicative of good prognosis of the subject diagnosed with MS.


As is further shown in FIG. 6 and Table 3 and is described in Example 2 of the Examples section which follows, classification rate of 85.2% was achieved using markers of the following 6 genes: TPSB2 (SEQ ID NO:127), IGLJ3 (SEQ ID NO:423), HAB1 (SEQ ID NOs:16 and/or 17), RRN3 (SEQ ID NO:424), COL11A2 (SEQ ID NO:190) and KLF4 (SEQ ID NO:325).


According to an embodiment of the invention, upregulation of the expression level of IGLJ3 (SEQ ID NO:423), RRN3 (SEQ ID NO:424) and KLF4 (SEQ ID NO:325) and downregulation of TPSB2 (SEQ ID NO:127), HAB1 (SEQ ID NOs:16 and/or 17) and COL11A2 (SEQ ID NO:190) relative to a reference cell of a subject diagnosed with MS and having good clinical outcome is indicative of poor prognosis of the subject diagnosed with MS.


On the other hand, downregulation of the expression level of IGLJ3 (SEQ ID NO:423), RRN3 (SEQ ID NO:424) and KLF4 (SEQ ID NO:325) and upregulation of TPSB2 (SEQ ID NO:127), HAB1 (SEQ ID NOs:16 and/or 17) and COL (SEQ ID NO:190) relative to a reference cell of a subject diagnosed with MS and having poor clinical outcome is indicative of good prognosis of the subject diagnosed with MS.


As is further shown in FIG. 6 and Table 3 and is described in Example 2 of the Examples section which follows, classification rate of 70.4% was achieved using only one gene (RRN3; SEQ ID NO:424). Thus, according to an embodiment of the invention upregulation of the expression level of RRN3 (SEQ ID NO:424) relative to a reference cell of a subject diagnosed with MS and having good clinical outcome is indicative of a poor prognosis of a subject diagnosed with MS. On the other hand, downregulation of the expression level of RRN3 (SEQ ID NO:424) relative to a reference cell of a subject diagnosed with MS and having poor clinical outcome is indicative of a good prognosis of a subject diagnosed with MS.


As is further shown in FIG. 12 and Table 4 (Example 4) and mentioned hereinabove, when the SVM based on RBF kernel were applied on 26 subjects optimal sets of genes which can predict the prognosis of RRMS patients with 100% accuracy (average error of “0”) were depicted.


Thus, according to an embodiment of the invention the at least one polynucleotide which expression level is determined in the cell of the subject diagnosed with MS is set forth by SEQ ID NO:158.


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-2; rows 1-3; rows 1-4; rows 1-5; rows 1-6; rows 1-7; rows 1-8; rows 1-9; rows 1-10; rows 1-11; rows 1-12; rows 1-13; rows 1-14; rows 1-15; rows 1-16; rows 1-17; rows 1-18; rows 1-19; rows 1-20; rows 1-21; rows 1-22; rows 1-23; rows 1-24; rows 1-25; rows 1-26; rows 1-27; rows 1-28; rows 1-29; rows 1-30; rows 1-31; rows 1-32; rows 1-33; rows 1-34; 1-35; rows 1-36; rows 1-37; rows 1-38; rows 1-39; rows 1-40; rows 1-41; rows 1-42; rows 1-43; rows 1-44; rows 1-45; rows 1-46; rows 1-47; rows 1-48; rows 1-49; rows 1-50; rows 1-51; rows 1-52; rows 1-53; rows 1-54; 1-55; rows 1-56; rows 1-57; rows 1-58; rows 1-59; rows 1-60; rows 1-61; rows 1-62; rows 1-63; rows 1-64; rows 1-65; rows 1-66; rows 1-67; rows 1-68; rows 1-69; rows 1-70; rows 1-71; rows 1-72; rows 1-73; rows 1-74; 1-75; rows 1-76; rows 1-77; rows 1-78; rows 1-79; rows 1-80; rows 1-81; rows 1-82; rows 1-83; rows 1-84; rows 1-85; rows 1-86; rows 1-87; rows 1-88; rows 1-89; rows 1-90; rows 1-91; rows 1-92; rows 1-93; rows 1-94; 1-95; rows 1-96; rows 1-97; rows 1-98; rows 1-99; rows 1-100; rows 1-101; rows 1-102; rows 1-103; rows 1-104; rows 1-105; rows 1-106; rows 1-107; rows 1-109; rows 1-110; rows 1-112; rows 1-113; rows 1-114; rows 1-116; rows 1-122; 1-124; rows 1-125; rows 1-126; rows 1-129; rows 1-146; rows 1-157.


As is further shown in Table 4 (Example 4) other groups of genes can predict the prognosis of RRMS patients with 99.5% accuracy (average error of “0.005”).


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-108; rows 1-111; rows 1-115; rows 1-117; rows 1-118; rows 1-119; rows 1-120; rows 1-121; rows 1-123; rows 1-127; rows 1-128; rows 1-131; rows 1-132; rows 1-133; 1-135; rows 1-137; rows 1-138; rows 1-139; rows 1-141; rows 1-144; rows 1-148; rows 1-150; rows 1-152; rows 1-153; rows 1-154; rows 1-158; rows 1-160; rows 1-167.


As is further shown in Table 4 (Example 4) other groups of genes can predict the prognosis of RRMS patients with 99% accuracy (average error of “0.01”).


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-130; rows 1-134; rows 1-136; rows 1-140; rows 1-145; rows 1-147; 1-149; rows 1-151; rows 1-155; rows 1-156; rows 1-159; rows 1-162; rows 1-168; rows 1-170.


As is further shown in Table 4 (Example 4) other groups of genes can predict the prognosis of RRMS patients with 98-98.5% accuracy (average error of “0.015-0.02”).


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-142; rows 1-143; rows 1-161; rows 1-163; rows 1-164; rows 1-165; rows 1-166; rows 1-169; rows 1-172; rows 1-173; rows 1-174; rows 1-177; rows 1-178; rows 1-179; rows 1-181; rows 1-187.


As is further shown in Table 4 (Example 4) other groups of genes can predict the prognosis of RRMS patients with 95-97.5% accuracy (average error of “0.025-0.05”).


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-171; rows 1-176; rows 1-180; rows 1-183; rows 1-184; rows 1-185; rows 1-186; rows 1-188; rows 1-189; rows 1-190; rows 1-191; rows 1-192; rows 1-193; rows 1-194; rows 1-195; rows 1-196; rows 1-197; rows 1-198; rows 1-199; 1-200; rows 1-201; rows 1-202; rows 1-203; rows 1-204; rows 1-205; rows 1-206; rows 1-207; rows 1-208; rows 1-209; rows 1-210; rows 1-211; rows 1-212; rows 1-213; rows 1-214; rows 1-215; rows 1-216; rows 1-217; rows 1-218; rows 1-219; rows 1-220; rows 1-221; rows 1-222; rows 1-223; rows 1-224; rows 1-225; rows 1-226; rows 1-227; rows 1-228; rows 1-229; rows 1-230; rows 1-231; rows 1-232; rows 1-233; rows 1-234; rows 1-235; rows 1-236; rows 1-237; rows 1-238; rows 1-239; rows 1-241; rows 1-242; rows 1-243; rows 1-244; rows 1-245; rows 1-247; rows 1-248; rows 1-249; rows 1-250; rows 1-252; rows 1-255; rows 1-256; rows 1-257; rows 1-258; rows 1-259; rows 1-264.


As is further shown in Table 4 (Example 4) other groups of genes can predict the prognosis of RRMS patients with 90-94.5% accuracy (average error of “0.1-0.055”).


According to an embodiment of the invention, the polynucleotide sequences which expression level are determined in the cell of the subject diagnosed with MS are those depicted in any of the following groups of row numbers of Table 4 in Example 4 of the Examples section which follows: rows 1-240; rows 1-246; rows 1-251; rows 1-263; rows 1-254; rows 1-260; rows 1-261; rows 1-262; rows 1-263; rows 1-265; rows 1-266; rows 1-267; rows 1-268; rows 1-269; rows 1-270; rows 1-271; rows 1-272; rows 1-273; rows 1-274; rows 1-275; rows 1-276; rows 1-277; rows 1-278; rows 1-279; rows 1-280; rows 1-281; rows 1-282; rows 1-283; rows 1-284; rows 1-285; rows 1-286; rows 1-287; rows 1-288; rows 1-289; rows 1-290; rows 1-291; rows 1-292; rows 1-293; rows 1-294; rows 1-295; rows 1-296; rows 1-297; rows 1-298; rows 1-299; rows 1-300; rows 1-301; rows 1-302; rows 1-3030; rows 1-304; rows 1-305; rows 1-306; rows 1-307; rows 1-308; rows 1-309; rows 1-312; rows 1-313; rows 1-314; rows 1-315; rows 1-316; rows 1-317; rows 1-318; rows 1-324; rows 1-325; rows 1-327; rows 1-328; rows 1-335; rows 1-344;


As used herein the phrase “an alteration above a predetermined threshold” refers to a fold increase or decrease (i.e., degree of upregulation or downregulation, respectively) which is higher than a predetermined threshold such as at least about 1.004, at least about twice, at least about three times, at least about four time, at least about five times, at least about six times, at least about seven times, at least about eight times, at least about nine times, at least about 20 times, at least about 50 times, at least about 100 times, at least about 200 times, at least about 350, at least about 500 times, at least about 1000 times, at least about 2000 times, at least about 3000 times relative to the reference cell.


For example, as is shown in Table 2, while the level of expression of the polynucleotide sequences set forth by SEQ ID NOs:43-136, is at least twice higher in MS patients having good clinical outcome as compared to MS patients having poor clinical outcome, the level of expression of the polynucleotide sequences set forth by SEQ ID NOs:137-161, the polynucleotide sequences set forth by SEQ ID NOs:162-185, the polynucleotide sequences set forth by SEQ ID NOs:186-191 or the polynucleotides set forth by SEQ ID NOs:192-193 is at least 5, 10, 50 or 350 or 150 times, respectively, higher in cells of MS patients having good clinical outcome as compared to cells of MS patients having poor clinical outcome.


In addition, as is further shown in Table 2, while the level of expression of the polynucleotide sequences set forth by SEQ ID NOs:271-366, is at least twice higher in cells of MS patients having poor clinical outcome as compared to cells of MS patients having good clinical outcome, the level of expression of the polynucleotide sequences set forth by SEQ ID NOs:367-399, the polynucleotides set forth by SEQ ID NOs:400-426, the polynucleotides set forth by SEQ ID NOs:427-430 or the polynucleotide set forth by SEQ ID NO:431 is at least 5, 10, 50 or 350 times, respectively, higher in cells of MS patients having poor clinical outcome as compared to cells of MS patients having good clinical outcome.


Thus, the method of predicting the prognosis of a subject diagnosed with MS according to the invention enables the classification of MS patients to those having good prognosis (good clinical outcome, e.g., that will not deteriorate in their neurological disability and that will not experience any relapse for at least 2 years) and those having poor prognosis [poor clinical outcome, e.g., that will deteriorate in their neurological disability (e.g., at least 0.5 point in the EDSS score), with or without relapses)].


It will be appreciated that prediction of the prognosis of a subject diagnosed with MS can be used to select the treatment regimen of a subject and thereby treat the subject diagnosed with MS.


Thus, according to yet another aspect of the invention there is provided a method of treating of a subject diagnosed with multiple sclerosis. The method is effected by: (a) determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431, wherein an alteration above a predetermined threshold in the level of expression of the at least one polynucleotide sequence in the cell of the subject relative to a level of expression of the at least one polynucleotide sequence in a reference cell is indicative of a prognosis of the subject diagnosed with multiple sclerosis, and (b) selecting a treatment regimen based on the prognosis, thereby treating the subject diagnosed with multiple sclerosis.


As used herein the phrase “treating” refers to inhibiting or arresting the development of a pathology (multiple sclerosis, e.g., RRMS) and/or causing the reduction, remission, or regression of a pathology and/or optimally curing the pathology. Those of skill in the art will understand that various methodologies and assays can be used to assess the development of the pathology, and similarly, various methodologies and assays may be used to assess the reduction, remission or regression of the pathology.


As used herein the phrase “treatment regimen” refers to a treatment plan that specifies the type of treatment, dosage, schedule and/or duration of a treatment provided to a subject in need thereof (i.e., a subject diagnosed with multiple sclerosis). The selected treatment regimen can be an aggressive one which is expected to result in the best clinical outcome (e.g., complete cure of the pathology), yet may be associated with some discomfort to the subject or adverse side effects (e.g., a damage to healthy cells or tissue); or a more moderate one which may relief symptoms of the pathology yet may results in incomplete cure of the pathology. The type of treatment, dosage, schedule and duration of treatment can vary, depending on the severity of pathology and the predicted outcome (prognosis) of the subject, and those of skills in the art are capable of adjusting the type of treatment with the dosage, schedule and duration of treatment.


According to an embodiment of the invention, when the predicted prognosis of the subject diagnosed with MS is poor prognosis, i.e., there is a high probability that the subject will display an increase of at least 0.5 point in the EDSS score within a period of two years, the treatment regimen selected for treating such a subject according to the method of this aspect of the invention comprises an aggressive therapy using a medicament such as high dosage of interferon beta 1a [Rebif, which can be administered subcutaneously, at a dosage of e.g., 44 μg, three times a week].


According to an embodiment of the invention, when the predicted prognosis of the subject diagnosed with MS is good prognosis, i.e., there is a high probability that the subject will display no change in the EDSS score and no relapses within a period of two years, the treatment regimen selected for treating such a subject according to the method of this aspect of the invention comprises a moderate therapy using a medicament such as moderate dosage of interferon beta 1a [Rebif, which can be administered subcutaneously, at a dosage of e.g., 22 μg, three times a week].


Thus, the teachings of the invention can be used to adapt a treatment regimen to the subject diagnosed with MS according to its predicted clinical outcome as determined with high accuracy (over 89%) by the method of the invention. It will be appreciated that selection of suitable treatment regimens is crucial for achieving cure and remission of symptoms in the affected subjects without exposing them to un-necessary medicaments and on the other hand, is highly beneficial in terms of saving un-necessary costs to the health system.


It will be appreciated that the reagents utilized by any of the methods of the invention which are described hereinabove can form a part of a diagnostic kit/article of manufacture.


The kit of the invention comprises at least 2 and no more than 700 isolated nucleic acid sequences, preferably, at least 4 and no more than 700 isolated nucleic acid sequences, preferably, at least 4 and no more than 600 isolated nucleic acid sequences, preferably, at least 6 and no more than 500 isolated nucleic acid sequences, preferably, at least 6 and no more than 431 isolated nucleic acid sequences, preferably, at least 6 and no more than 34 isolated nucleic acid sequences, wherein each of the at least 2 and no more than 700 isolated nucleic acid sequences is capable of specifically recognizing at least one specific polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431.


The isolated nucleic acid sequences included in the kit of the invention can be single-stranded or double-stranded, naturally occurring or synthetic nucleic acid sequences such as oligonucleotides, RNA molecules, genomic DNA molecules, cDNA molecules and/or cRNA molecules. The isolated nucleic acid sequences of the kit can be composed of naturally occurring bases, sugars, and covalent internucleoside linkages (e.g., backbone), as well as non-naturally occurring portions, which function similarly to respective naturally occurring portions.


Synthesis of the isolated nucleic acid sequences of the kit can be performed using enzymatic synthesis or solid-phase synthesis. Equipment and reagents for executing solid-phase synthesis are commercially available from, for example, Applied Biosystems. Any other means for such synthesis may also be employed; the actual synthesis of the oligonucleotides is well within the capabilities of one skilled in the art and can be accomplished via established methodologies as detailed in, for example: Sambrook, J. and Russell, D. W. (2001), “Molecular Cloning: A Laboratory Manual”; Ausubel, R. M. et al., eds. (1994, 1989), “Current Protocols in Molecular Biology,” Volumes I-III, John Wiley & Sons, Baltimore, Md.; Perbal, B. (1988), “A Practical Guide to Molecular Cloning,” John Wiley & Sons, New York; and Gait, M. J., ed. (1984), “Oligonucleotide Synthesis”; utilizing solid-phase chemistry, e.g. cyanoethyl phosphoramidite followed by deprotection, desalting, and purification by, for example, an automated trityl-on method or HPLC.


According to an embodiment of the invention, each of the isolated nucleic acid sequences included in the kit of invention comprises at least 10 and no more than 50 nucleic acids, more preferably, at least 15 and no more than 45, more preferably, between 15-40, more preferably, between 20-35, more preferably, between 20-30, even more preferably, between 20-25 nucleic acids.


The kit may include at least one reagent as described hereinabove which is suitable for recognizing the at least one specific polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431. Examples include reagents suitable for hybridization or annealing of a specific polynucleotide of the kit to a specific target polynucleotide sequence (e.g., RNA transcript derived from the cell of the subject or a cDNA derived therefrom) such as formamide, sodium chloride, and sodium citrate), reagents which can be used to labele polynucleotides (e.g., radiolabeled nucleotides, biotinylated nucleotides, digoxigenin-conjugated nucleotides, fluorescent-conjugated nucleotides) as well as reagents suitable for detecting the labeled polynucleotides (e.g., antibodies conjugated to fluorescent dyes, antibodies conjugated to enzymes, radiolabeled antibodies and the like).


Additionally or alternatively, the kit of the invention comprises at least one reagent suitable for detecting the expression level and/or activity of at least one polypeptide encoded by at least one polynucleotides selected from the group consisting of SEQ ID NOs:1-431. Such a reagent can be, for example, an antibody capable of specifically binding to at least one epitope of the polypeptide. Additionally or alternatively, the reagent included in the kit can be a specific substrate capable of binding to an active site of the polypeptide. In addition, the kit may also include reagents such as fluorescent conjugates, secondary antibodies and the like which are suitable for detecting the binding of a specific antibody and/or a specific substrate to the polypeptide.


The kit preferably includes a reference cell which comprises a cell of a subject diagnosed with MS and with a known clinical outcome for at least 24 months as described hereinabove.


The kit of the invention preferably includes packaging material packaging the at least one reagent and a notification in or on the packaging material. Such a notification identifies the kit for use in predicting the prognosis of a subject diagnosed with MS and selecting a treatment regimen of a subject and thereby treating the subject diagnosed with MS. The kit may also include instructions for use in predicting the prognosis of a subject diagnosed with MS and/or selecting a treatment regimen of a subject and/or treating the subject diagnosed with MS. The kit may also include appropriate buffers and preservatives for improving the shelf-life of the kit.


It will be appreciated that the isolated nucleic acid sequences described hereinabove (e.g., oligonucleotides) can form a part of a probeset. The probeset comprises a plurality of oligonucleotides and no more than 700 oligonucleotides wherein each of the plurality of oligonucleotides is capable of specifically recognizing at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431.


It will be appreciated that the isolated nucleic acid sequences included in the kit or the probeset of the invention can be bound to a solid support e.g., a glass wafer in a specific order, i.e., in the form of an addressable microarray. Alternatively, isolated nucleic acid sequences can be synthesized directly on the solid support using well known prior art approaches (Seo T S, et al., 2004, Proc. Natl. Acad. Sci. USA, 101: 5488-93.). In any case, the isolated nucleic acid sequences are attached to the support in a location specific manner such that each specific isolated nucleic acid sequence has a specific address on the support (i.e., an addressable location) which denotes the identity (i.e., the sequence) of that specific isolated nucleic acid sequence.


According to an embodiment of the invention the microarray comprises no more than 700 isolated nucleic acid sequences, wherein each of the isolated nucleic acid sequences is capable of specifically recognizing at least one specific polynucleotide sequence selected from the group consisting of SEQ ID NOs:1-431.


As used herein the term “about” refers to ±10%.


Additional objects, advantages, and novel features of the invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. Additionally, each of the various embodiments and aspects of the invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples.


EXAMPLES

Reference is now made to the following examples, which together with the above descriptions, illustrate the invention in a non-limiting fashion.


Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, “Molecular Cloning: A laboratory Manual” Sambrook et al., (1989); “Current Protocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., “Current Protocols in Molecular Biology”, John Wiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide to Molecular Cloning”, John Wiley & Sons, New York (1988); Watson et al., “Recombinant DNA”, Scientific American Books, New York; Birren et al. (eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis, J. E., ed. (1994); “Current Protocols in Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), “Basic and Clinical Immunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi (eds), “Selected Methods in Cellular Immunology”, W. H. Freeman and Co., New York (1980); available immunoassays are extensively described in the patent and scientific literature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521; “Oligonucleotide Synthesis” Gait, M. J., ed. (1984); “Nucleic Acid Hybridization” Hames, B. D., and Higgins S. J., eds. (1985); “Transcription and Translation” Hames, B. D., and Higgins S. J., Eds. (1984); “Animal Cell Culture” Freshney, R. I., ed. (1986); “Immobilized Cells and Enzymes” IRL Press, (1986); “A Practical Guide to Molecular Cloning” Perbal, B., (1984) and “Methods in Enzymology” Vol. 1-317, Academic Press; “PCR Protocols: A Guide To Methods And Applications”, Academic Press, San Diego, Calif. (1990); Marshak et al., “Strategies for Protein Purification and Characterization—A Laboratory Course Manual” CSHL Press (1996); all of which are incorporated by reference as if fully set forth herein. Other general references are provided throughout this document. The procedures therein are believed to be well known in the art and are provided for the convenience of the reader. All the information contained therein is incorporated herein by reference.


General Materials, Experimental and Statistical Methods

Study subjects—Fifty-three patients with definite relapsing-remitting multiple sclerosis (RRMS) (37 females, 16 males), age 40.2+5.8 years, disease duration 9.9+4.2 years, annual relapse rate 1.3±0.7 and neurological disability evaluated by the Expanded Disability Status Scale (EDSS) (7) 2.0±1.0, were included in the study; 26 patients participated in the differentiating clinical outcome analysis and 27 patients in the validation process of prediction. The clinical and demographic variables were similar between groups and are presented in Table 1, hereinbelow. In the differentiating clinical outcome group 13 patients were on immunomodulatory treatments for at least three months prior to the gene expression study, and 13 patients were naïve to immunomodulatory treatment. In the validation group 11 patients were on immunomodulatory treatments for at least three months prior to the gene expression study, and 16 patients were naïve to immunomodulatory treatments. Within up to one month from blood withdrawing all patients were treated with interferon beta 1-a. None of the patients had ever received cytotoxic treatments and all were free of steroid treatment for at least 30 days before blood was withdrawn. All patients had peripheral blood counts within the normal range. The study was approved by the Sheba Medical Center Institutional Review Board, and all patients gave a written informed consent for participation.









TABLE 1







Clinical characteristics of patients with


relapsing-remitting multiple sclerosis (RRMS)










Differentiating clinical
Validation Group


Characteristic
outcome Group; N = 26
N = 27





Age (yr)
40.2 ± 5.8 
40.5 ± 1.6 


F (M)
21 (5)
16 (11)


Disease duration (yr)
9.9 ± 4.2
10.3 ± 1.6 


Relapse rate
1.3 ± 0.7
0.9 ± 0.2


EDSS
2.0 ± 1.0
2.5 ± 0.2


Treated
13
11





Table 1 depicts the clinical characteristics of patients with relapsing-remitting multiple sclerosis: patients participated in the differentiating clinical outcome group or in the validation group. Yr—year; F—female; M—male.






Clinical follow-up—Patients were prospectively followed-up for a period of two years. Neurological examination was performed once every three months and at the time of a suspected relapse, and EDSS assessment was completed accordingly. Relapse was defined as the onset of new objective neurological symptoms/signs or worsening of existing neurological disability, not accompanied by metabolic changes, fever or other signs of infection, lasting for a period of at least 48 hours accompanied by objective change of at least 0.5 point in the EDSS score. For EDSS evaluations, only stable EDSS scores that were confirmed at three months follow-up examinations were used. Confirmed relapses and EDSS scores were consecutively recorded.


Definition of clinical outcome—Clinical outcome was defined according to neurological disability as the primary criterion and total number of relapses as the secondary criterion.


Good outcome: patients that had not deteriorated in their neurological disability and had not experienced any relapse during the 24 months of follow-up.


Poor outcome: Patients that deteriorated in their neurological disability (EDSS increased by at least 0.5 points) within the 24 months of follow-up, either with or without relapses.


Intermediate outcome: Patients that did not deteriorate in their neurological disability yet experienced at least one relapse during the 24 months follow-up.


RNA isolation and microarray expression profiling—Peripheral blood mononuclear cells (PBMC) were separated on Ficoll hypaque gradient, total RNA was purified, labeled, hybridized to a Genechip array (U95Av2 and HU-133A) and scanned (Hewlett Packard, GeneArray-TM scanner G2500A) according to the manufacturer's protocol (Affymetrix Inc, Santa Clara, Calif.), as previously described (6).


Clinical outcome differentiating genes analysis—RMAExpress software was used to analyze the scanned arrays (8). In order to be consistent with the ontology and array type, all the transcripts in U95Av2 microarray were converted to the corresponding transcripts in HU-133A using NetAffex comparison table. Probesets that did not have a present signal in at least 90% of the samples were filtered. Noise effect was reduced by fitting a multiple effect model for each gene modeling the log-ratio measurement as a sum of contributions for age, gender, batch, subject state (naive or treated), and time from last steroid treatment.


Statistical methods—Statistical analysis was performed using the ScoreGenes software tools (http://compbio.cs.huji.ac.il/scoregenes/). Data was analyzed by t-test, threshold number of misclassifications (TNoM) method and the Info-test score. Differentiating genes were defined as genes whose expression was significantly higher or lower with p<0.05 in all three statistical tests. Overabundance analysis was used to compare between the number of observed and expected genes that differentiated between the good and poor clinical outcome under the null hypothesis that the classification of the samples was random (9, 10). To further verify the accuracy of the classification the leave-one-out-cross-validation (LOOCV) statistical method (11) was used. LOOCV simulates removal of a single sample for every trial and trains on the rest. The procedure is repeated until each sample is left out once and the number of correct and incorrect predictions is counted.


Predictive genes analysis—To depict the predictive genes from the differentiating clinical outcome signature support vector machine (SVM) in combination with Forward feature selection algorithm were applied (http://ro.utia.cz/fs/fs_algorithms.html), (12, 13). SVM generates a classifier based on a known labeled training set (19/26 RRMS patients with good or poor clinical outcome from the differentiating clinical outcome group). Then, the classification power of the generated classifier is evaluated by applying it to an independent test set (9/27 RRMS patients from the validation group). The feature selection algorithm finds a subset of predictive genes that enables the generated classifier to achieve the highest classification rate (14, 15). To validate the power of the predictive genes, the classifier was applied to an additional independent set (18/27 RRMS patients from the validation group). The study design is depicted in FIG. 1.


Biological functional analysis—Functional annotation of the clinical outcome differentiating and predictive gene signatures was done using Functional Classification Tools (FCT, David Bioinformatics Resources, http://david.abcc.ncifcr.gov/home.jsp). Gene enrichment was defined as group of genes highly associated with a specific biological function and statistically measured by one-tail Fisher Exact Probability Value in the David system. Biological regulatory pathways reconstruction for the predictive gene signature was performed by the (1) PathwayArchitect software http://www.stratagene.com based on literature published data, and the (2) Genomica software http://genomica.weizmann.ac.il that is based on Bayesian networks methods taken from the field of machine learning and was applied to the results of the differentiating gene microarray expression signature. This evaluation was aimed to identify potentially target genes that share a common regulatory mechanism.


Computation of the average error in predicting clinical outcome (good or poor prognosis) for each of the differentiating genes—For each of the 431 differentiating genes (SEQ ID NOs:1-431) the sample was randomly divided into 80% as a “training set” and 20% as a “test set”. The SVM used RBF (radial basic function) kernel to build a model based on the “training set”, which was further tested on the “test set” while saving the error rate. This procedure was repeated 50 times for each gene and the average error for each gene was calculated. Genes with the lowest average error were selected. Then, for each selected gene, the remaining genes were added one after the other, by selecting the next gene such that the average error after 50 repeats of the group of genes including the new gene has the lowest average error as compared to the addition of another gene. This process was repeated 430 times for each additional genes added to the previous group of genes. The results are shown in Table 4, hereinbelow and in FIG. 12.


Example 1
Identification of Genetic Markers which are Differentially Expressed Between Patients with Good or Poor Clinical Outcome of RRMS

Experimental and Statistical Results


Clinical classification of study patients—Patients were classified into three groups based on their clinical disease outcome. Patients with good outcome (N=9, mean age 39.3±3.3 years, disease duration 10.7±3.4 years), patients with intermediate outcome (N=7, mean age 35.8±5.4 years, disease duration 2.6±0.7 years) and patients with poor outcome (N=10, mean age 46.3±4.2 years, disease duration 10.3±0.9). Comparison between outcome variables demonstrated significant difference between patients with good and poor clinical outcome. Change in neurological disability assessed by the EDSS was −0.33±0.24 (good outcome) and 1.6±0.35 (poor outcome), p=0.0002, total number of relapses was 0 (good outcome) and 1.80±0.35 (poor outcome), p=0.00009, respectively.


Differentiating clinical outcome gene expression signature—The distinctive clinical outcome gene expression pattern between patients with good and poor clinical outcome included 431 differentiating genes which passed the three statistical tests with p<0.05 (FIGS. 2a-b). Functional analysis disclosed genes associated with signal transduction, catalytic activity, adhesion and inflammation (FIG. 3). Overabundance analysis of the observed compared with the expected number of genes that significantly distinguished between patients with good or poor clinical outcome was higher than expected (431 vs 200 genes at p=0.03) (FIG. 4). LOOCV resulted in a high classification rate of 90% p<0.0001 (FIG. 5), suggesting that the differentiating genes signature is reliable and not related to spurious differences due to multiple testing.









TABLE 2







Clinical outcome differentiating genes in RRMS

















SEQ




F/C




GenBank
ID
TNOM
Info
t-Test

(Poor/
Gene


Probeset ID
Acc. No.
NO:
PValue
PValue
PValue
Dir
Good)
Symbol


















207160_at
NM_000882
1
2.10E−02
3.03E−02
3.51E−03
−1
1.004
IL12A


205034_at
NM_004702
2
2.10E−02
2.60E−02
2.71E−03
−1
1.027
CCNE2


215935_at
AL080148
3
4.11E−04
2.17E−04
3.36E−03
−1
1.055
C9orf36


204919_at
NM_007244
4
2.10E−02
3.03E−02
3.45E−02
−1
1.108
PRR4


201783_s_at
NM_021975
5
4.11E−04
2.17E−04
6.77E−05
−1
1.161
RELA


213457_at
BF739959
6
3.70E−03
3.70E−03
1.79E−03
−1
1.368
MFHAS1


203145_at
NM_006461
7
3.70E−03
2.49E−03
2.82E−03
−1
1.388
SPAG5


210822_at
U72513
8
3.70E−03
1.36E−03
4.97E−04
−1
1.399
LOC283345


206376_at
NM_018057
9
2.10E−02
3.03E−02
4.24E−02
−1
1.412
SLC6A15


203426_s_at
M65062
10
2.10E−02
2.17E−02
2.08E−03
−1
1.439
IGFBP5


203205_at
NM_014663
11
3.70E−03
1.36E−03
2.65E−03
−1
1.513
JMJD2A


203324_s_at
NM_001233
12
2.10E−02
2.60E−02
1.39E−02
−1
1.516
CAV2


207509_s_at
NM_002288
13
2.10E−02
2.60E−02
2.90E−02
−1
1.544
LAIR2


217165_x_at
M10943
14
2.10E−02
3.03E−02
8.33E−03
−1
1.564
MT1F


215175_at
AB023212
15
2.10E−02
3.03E−02
6.57E−03
−1
1.583
PCNX


215778_x_at
AJ006206
16
2.10E−02
2.60E−02
2.29E−02
−1
1.591
HAB1


216875_x_at
X83412
17
2.10E−02
2.60E−02
2.29E−02
−1
1.591
HAB1


205962_at
NM_002577
18
2.10E−02
2.60E−02
3.35E−02
−1
1.606
PAK2


209685_s_at
M13975
19
2.10E−02
7.04E−03
3.04E−03
−1
1.659
PRKCB1


207504_at
NM_005182
20
2.10E−02
3.03E−02
2.69E−02
−1
1.670
CA7


213106_at
AI769688
21
2.10E−02
2.60E−02
3.44E−02
−1
1.701
ATP8A1


201889_at
NM_014888
22
2.10E−02
1.14E−02
4.56E−03
−1
1.722
FAM3C


209530_at
U07139
23
3.70E−03
3.70E−03
2.01E−03
−1
1.727
CACNB3


206910_x_at
NM_005666
24
2.10E−02
1.14E−02
2.46E−02
−1
1.736
CFHL2


213262_at
AI932370
25
2.10E−02
1.14E−02
3.86E−02
−1
1.748
SACS


204316_at
W19676
26
3.70E−03
1.36E−03
2.61E−03
−1
1.769
RGS10


209031_at
AL519710
27
2.10E−02
2.60E−02
1.08E−02
−1
1.783
IGSF4


209453_at
M81768
28
3.70E−03
3.70E−03
2.89E−02
−1
1.793
SLC9A1


212912_at
AI992251
29
2.10E−02
2.17E−02
4.27E−03
−1
1.841
RPS6KA2


204066_s_at
NM_014914
30
2.10E−02
7.04E−03
6.18E−03
−1
1.846
CENTG2


206846_s_at
NM_006044
31
2.10E−02
2.17E−02
3.55E−03
−1
1.848
HDAC6


216224_s_at
AK024083
32
2.10E−02
2.17E−02
3.55E−03
−1
1.848
HDAC6


205272_s_at
NM_006250
33
2.10E−02
2.60E−02
1.88E−02
−1
1.851
PRH1, PRH2


214534_at
NM_005322
34
2.10E−02
3.03E−02
1.80E−02
−1
1.856
HIST1H1B


205498_at
NM_000163
35
2.10E−02
2.60E−02
2.75E−02
−1
1.903
GHR


209156_s_at
AY029208
36
2.10E−02
7.04E−03
9.25E−03
−1
1.934
COL6A2


212534_at
AU144066
37
2.10E−02
7.04E−03
4.94E−03
−1
1.936
ZNF24


203022_at
NM_006397
38
2.10E−02
7.04E−03
1.89E−03
−1
1.972
RNASEH2A


203071_at
NM_004636
39
2.10E−02
2.60E−02
2.61E−02
−1
1.974
SEMA3B


212242_at
AL565074
40
3.70E−03
2.49E−03
2.51E−04
−1
1.974
TUBA1


202732_at
NM_007066
41
2.10E−02
1.14E−02
3.93E−03
−1
1.990
PKIG


205013_s_at
NM_000675
42
2.10E−02
2.60E−02
3.20E−02
−1
1.993
ADORA2A


221792_at
AW118072
43
2.10E−02
7.04E−03
1.04E−02
−1
2.037
RAB6B


208135_at
NM_006481
44
2.10E−02
3.03E−02
3.31E−02
−1
2.043
TCF2


208059_at
NM_005201
45
3.70E−03
3.70E−03
4.63E−02
−1
2.050
CCR8


207532_at
NM_006891
46
3.70E−03
3.70E−03
2.66E−02
−1
2.063
CRYGD


216699_s_at
L10038
47
2.10E−02
7.04E−03
5.23E−03
−1
2.069
KLK1


202555_s_at
NM_005965
48
2.10E−02
2.60E−02
1.97E−03
−1
2.077
MYLK


203193_at
NM_004451
49
2.10E−02
7.04E−03
9.18E−03
−1
2.119
ESRRA


215766_at
AL096729
50
2.10E−02
7.04E−03
2.58E−03
−1
2.122
GSTA1


201023_at
NM_005642
51
3.70E−03
2.49E−03
1.34E−03
−1
2.126
TAF7


204316_at
W19676
52
2.10E−02
2.17E−02
1.11E−02
−1
2.136
RGS10


204319_s_at
NM_002925
53
2.10E−02
2.17E−02
1.11E−02
−1
2.136
RGS10


209477_at
BC000738
54
2.10E−02
1.14E−02
2.29E−03
−1
2.166
EMD


216283_s_at
X64116
55
2.10E−02
3.03E−02
3.14E−02
−1
2.169
PVR


202799_at
NM_006012
56
3.70E−03
2.49E−03
8.46E−04
−1
2.171
CLPP


202127_at
AB011108
57
2.10E−02
7.04E−03
4.54E−02
−1
2.195
PRPF4B


200647_x_at
NM_003752
58
2.10E−02
7.04E−03
2.87E−02
−1
2.196
EIF3S8


210949_s_at
BC000533
59
2.10E−02
7.04E−03
2.87E−02
−1
2.196
EIF3S8


215230_x_at
AA679705
60
2.10E−02
7.04E−03
2.87E−02
−1
2.196
EIF3S8


210328_at
AF101477
61
2.10E−02
2.60E−02
3.52E−02
−1
2.260
GNMT


211988_at
BG289800
62
4.11E−04
2.17E−04
2.74E−04
−1
2.265
SMARCE1


215781_s_at
D87012
63
2.10E−02
2.60E−02
2.98E−02
−1
2.273
TOP3B


204163_at
NM_007046
64
2.10E−02
3.03E−02
2.63E−02
−1
2.319
EMILIN1


207954_at
NM_002050
65
3.70E−03
3.70E−03
1.50E−03
−1
2.323
GATA2


210358_x_at
BC002557
66
3.70E−03
3.70E−03
1.50E−03
−1
2.323
GATA2


222206_s_at
AA781143
67
2.10E−02
7.04E−03
8.50E−03
−1
2.357
NICALIN


200949_x_at
NM_001023
68
2.10E−02
1.14E−02
4.42E−02
−1
2.368
RPS20


214003_x_at
BF184532
69
2.10E−02
1.14E−02
4.42E−02
−1
2.368
RPS20


203701_s_at
NM_017722
70
2.10E−02
1.14E−02
8.29E−03
−1
2.375
FLJ20244


210463_x_at
BC002492
71
2.10E−02
1.14E−02
8.29E−03
−1
2.375
FLJ20244


204703_at
NM_006531
72
2.10E−02
7.04E−03
1.46E−02
−1
2.398
TTC10


216582_at
AL021808
73
2.10E−02
2.60E−02
6.09E−03
−1
2.451
AL021808


208687_x_at
AF352832
74
3.70E−03
3.70E−03
3.18E−02
−1
2.472
HSPA8


202314_at
NM_000786
75
2.10E−02
2.60E−02
1.22E−02
−1
2.509
CYP51A1


214095_at
AW190316
76
2.10E−02
2.60E−02
3.91E−03
−1
2.535
SHMT2


214096_s_at
AW190316
77
2.10E−02
2.60E−02
3.91E−03
−1
2.535
SHMT2


214948_s_at
AL050136
78
2.10E−02
2.17E−02
4.68E−03
−1
2.568
TMF1


206879_s_at
NM_013982
79
2.10E−02
7.04E−03
1.57E−02
−1
2.618
NRG2


205463_s_at
NM_002607
80
3.70E−03
3.70E−03
7.59E−04
−1
2.639
PDGFA


205022_s_at
NM_005197
81
3.70E−03
2.49E−03
8.78E−03
−1
2.655
CHES1


203801_at
BG254653
82
2.10E−02
7.04E−03
8.11E−03
−1
2.671
MRPS14


215746_at
L34409
83
2.10E−02
3.03E−02
1.80E−02
−1
2.676
TETRAN


201418_s_at
NM_003107
84
3.70E−03
1.36E−03
1.33E−03
−1
2.721
SOX4


202410_x_at
NM_000612
85
3.70E−03
1.36E−03
1.21E−02
−1
2.795
IGF2


204400_at
NM_005864
86
3.70E−03
1.36E−03
6.24E−04
−1
2.815
EFS


210880_s_at
AB001467
87
3.70E−03
1.36E−03
6.24E−04
−1
2.815
EFS


200790_at
NM_002539
88
2.10E−02
2.60E−02
6.27E−03
−1
2.817
ODC1


203339_at
AI887457
89
2.10E−02
7.04E−03
1.11E−02
−1
2.853
SLC25A12


203340_s_at
AI887457
90
2.10E−02
7.04E−03
1.11E−02
−1
2.853
SLC25A12


201513_at
NM_004622
91
2.10E−02
3.03E−02
1.07E−02
−1
2.915
TSN


204159_at
NM_001262
92
2.10E−02
7.04E−03
2.76E−03
−1
2.920
CDKN2C


211792_s_at
U17074
93
2.10E−02
7.04E−03
2.76E−03
−1
2.920
CDKN2C


209320_at
AF033861
94
2.10E−02
1.14E−02
2.07E−03
−1
2.975
ADCY3


209480_at
M16276
95
4.11E−04
2.17E−04
2.15E−05
−1
2.983
MHC II, DQ










beta 1


212999_x_at
AW276186
96
4.11E−04
2.17E−04
2.15E−05
−1
2.983
MHC II, DQ










beta 1


214613_at
AW024085
97
2.10E−02
7.04E−03
1.72E−02
−1
3.021
GPR3


201269_s_at
AB028991
98
2.10E−02
2.17E−02
3.88E−02
−1
3.142
KIAA1068


209361_s_at
BC004153
99
3.70E−03
1.36E−03
1.60E−02
−1
3.183
PCBP4


213840_s_at
R68573
100
2.10E−02
3.03E−02
1.34E−02
−1
3.196



204895_x_at
NM_004532
101
3.70E−03
3.70E−03
2.34E−02
−1
3.206
MUC4


217109_at
AJ242547
102
3.70E−03
3.70E−03
2.34E−02
−1
3.206
MUC4


217110_s_at
AJ242547
103
3.70E−03
3.70E−03
2.34E−02
−1
3.206
MUC4


212852_s_at
AL538601
104
3.70E−03
1.36E−03
1.29E−03
−1
3.225
SSA2


206759_at
NM_002002
105
2.10E−02
1.14E−02
5.20E−03
−1
3.227
FCER2


206760_s_at
NM_002002
106
2.10E−02
1.14E−02
5.20E−03
−1
3.227
FCER2


203422_at
NM_002691
107
2.10E−02
1.14E−02
1.86E−02
−1
3.245
POLD1


202766_s_at
NM_000138
108
3.70E−03
3.70E−03
2.33E−02
−1
3.303
FBN1


216065_at
AL031228
109
2.10E−02
3.03E−02
2.88E−02
−1
3.528
BING5


202140_s_at
NM_003992
110
2.10E−02
1.14E−02
1.85E−03
−1
3.579
CLK3


209107_x_at
U19179
111
3.70E−03
1.36E−03
8.37E−04
−1
3.612
NCOA1


210249_s_at
U59302
112
3.70E−03
1.36E−03
8.37E−04
−1
3.612
NCOA1


208041_at
NM_002929
113
3.70E−03
1.36E−03
5.34E−03
−1
3.659
GRK1


202554_s_at
AL527430
114
2.10E−02
2.60E−02
1.26E−02
−1
3.775
GSTM3


213815_x_at
AI913329
115
2.10E−02
2.60E−02
3.04E−03
−1
3.820
NY-REN 24


214892_x_at
BC004262
116
2.10E−02
2.60E−02
3.04E−03
−1
3.820
C19orf29


215954_s_at
AI200896
117
2.10E−02
2.60E−02
3.04E−03
−1
3.820
NY-REN 24


204144_s_at
NM_004204
118
3.70E−03
1.36E−03
9.11E−04
−1
3.873
PIGQ


206592_s_at
NM_003938
119
3.70E−03
3.70E−03
3.71E−03
−1
3.881
AP3D1


200936_at
NM_000973
120
3.70E−03
2.49E−03
1.59E−03
−1
3.915
RPL8


204065_at
NM_004854
121
3.70E−03
1.36E−03
6.52E−03
−1
4.113
CHST10


206327_s_at
NM_004933
122
2.10E−02
2.17E−02
1.74E−02
−1
4.164
CDH15


206328_at
NM_004933
123
2.10E−02
2.17E−02
1.74E−02
−1
4.164
CDH15


205608_s_at
U83508
124
3.70E−03
2.49E−03
1.47E−03
−1
4.205
ANGPT1


204197_s_at
NM_004350
125
2.10E−02
3.03E−02
2.22E−02
−1
4.271
RUNX3


205683_x_at
NM_003294
126
2.10E−02
2.17E−02
8.61E−03
−1
4.292
TPSAB1


207134_x_at
NM_024164
127
2.10E−02
2.17E−02
8.61E−03
−1
4.292
TPSB2


216474_x_at
AF206667
128
2.10E−02
2.17E−02
8.61E−03
−1
4.292
TPSAB1,










TPSB2


214487_s_at
NM_002886
129
2.10E−02
2.60E−02
1.21E−02
−1
4.296
RAP2A, RAP2B


214488_at
NM_002886
130
2.10E−02
2.60E−02
1.21E−02
−1
4.296
RAP2B


212674_s_at
AK002076
131
3.70E−03
3.70E−03
2.98E−02
−1
4.426
DHX30


209253_at
AF037261
132
2.10E−02
2.60E−02
7.17E−03
−1
4.520
SCAM-1


210619_s_at
AF173154
133
2.10E−02
3.03E−02
9.84E−03
−1
4.648
HYAL1


213221_s_at
AB018324
134
3.70E−03
3.70E−03
2.54E−03
−1
4.758
SIK2


216939_s_at
Y08756
135
2.10E−02
7.04E−03
6.08E−03
−1
4.910
HTR4,










KIAA1985


202415_s_at
NM_012267
136
2.10E−02
7.04E−03
1.51E−02
−1
4.922
HSPBP1


205798_at
NM_002185
137
2.10E−02
3.03E−02
2.08E−02
−1
5.081
IL7R


207938_at
NM_015886
138
2.10E−02
2.60E−02
4.15E−02
−1
5.111
PI15


217060_at
U03115
139
2.10E−02
3.03E−02
4.35E−02
−1
5.115
TCRBV


207900_at
NM_002987
140
2.10E−02
1.14E−02
6.92E−03
−1
5.125
CCL17


205189_s_at
NM_000136
141
2.10E−02
1.14E−02
1.89E−02
−1
5.128
FANCC


208009_s_at
NM_014448
142
2.10E−02
2.17E−02
2.24E−02
−1
5.145
ARHGEF16


206148_at
NM_002183
143
2.10E−02
3.03E−02
2.62E−02
−1
5.170
IL3RA


204816_s_at
AI924903
144
2.10E−02
3.03E−02
8.65E−03
−1
5.215
DHX34


210306_at
U89358
145
3.70E−03
3.70E−03
7.60E−04
−1
5.331
L3MBTL


206398_s_at
NM_001770
146
2.10E−02
1.14E−02
2.36E−03
−1
5.693
CD19


212540_at
BG476661
147
2.10E−02
2.17E−02
7.82E−03
−1
5.949
CDC34


207694_at
NM_000307
148
2.10E−02
1.14E−02
5.97E−03
−1
6.007
POU3F4


202268_s_at
NM_003905
149
2.10E−02
2.60E−02
1.30E−02
−1
6.029
APPBP1


202909_at
NM_014805
150
2.10E−02
3.03E−02
1.30E−02
−1
6.919
EPM2AIP1


205798_at
NM_002185
151
2.10E−02
3.03E−02
1.54E−02
−1
6.948
IL7R


203421_at
NM_006034
152
2.10E−02
2.60E−02
1.35E−02
−1
6.959
TP53I11


207403_at
NM_003604
153
2.10E−02
2.60E−02
1.79E−03
−1
6.981
IRS4


202312_s_at
K01228
154
2.10E−02
3.03E−02
3.84E−02
−1
7.027
COL1A1


201497_x_at
NM_022844
155
2.10E−02
3.03E−02
1.40E−02
−1
7.285
MYH11


203683_s_at
NM_003377
156
2.10E−02
3.03E−02
4.63E−03
−1
7.314
VEGFB


210438_x_at
M25077
157
2.10E−02
1.14E−02
1.40E−02
−1
8.214
SSA2


205805_s_at
NM_005012
158
2.17E−05
2.17E−05
2.07E−05
−1
8.307
ROR1


207222_at
NM_003561
159
2.10E−02
3.03E−02
8.61E−03
−1
9.192
PLA2G10


203329_at
NM_002845
160
4.11E−04
2.17E−04
3.04E−05
−1
9.736
PTPRM


207036_x_at
NM_000836
161
2.10E−02
1.14E−02
1.43E−02
−1
9.843
GRIN2D


208580_x_at
NM_021968
162
3.70E−03
2.49E−03
7.90E−03
−1
10.804
HIST1H4J,










HIST1H4K


214463_x_at
NM_003541
163
3.70E−03
2.49E−03
7.90E−03
−1
10.804
HIST1H4J,










HIST1H4K


208091_s_at
NM_030796
164
2.10E−02
7.04E−03
6.78E−03
−1
10.908
DKFZP564K0822


206516_at
NM_000479
165
4.11E−04
4.11E−04
8.60E−04
−1
11.814
AMH


200834_s_at
NM_001024
166
3.70E−03
3.70E−03
1.35E−02
−1
11.908
RPS21


208105_at
NM_000164
167
2.10E−02
2.60E−02
3.55E−03
−1
12.069
GIPR


207959_s_at
NM_004662
168
2.10E−02
2.60E−02
4.54E−02
−1
12.457
DNAH9


210345_s_at
AF257737
169
2.10E−02
2.60E−02
4.54E−02
−1
12.457
DNAH9


202315_s_at
NM_004327
170
4.11E−04
2.17E−04
3.36E−06
−1
12.559
BCR


208733_at
AW301641
171
3.70E−03
1.36E−03
6.96E−04
−1
12.569
RAB2


221847_at
BF665706
172
2.10E−02
2.60E−02
5.69E−03
−1
13.019



214481_at
NM_003514
173
2.10E−02
3.03E−02
1.06E−02
−1
13.748
HIST1H2AM


214644_at
BF061074
174
3.70E−03
2.49E−03
1.08E−03
−1
14.563
HIST1H2AK


217192_s_at
AL022067
175
2.10E−02
1.14E−02
7.64E−03
−1
14.756
PRDM1


220937_s_at
NM_014403
176
2.10E−02
1.14E−02
6.81E−03
−1
15.370
SIAT7D


221551_x_at
AW044319
177
2.10E−02
1.14E−02
6.81E−03
−1
15.370
SIAT7D


215266_at
AL096732
178
2.10E−02
2.60E−02
8.04E−03
−1
17.220
DNAH3


203851_at
NM_002178
179
2.10E−02
2.17E−02
4.92E−03
−1
17.962
IGFBP6


209466_x_at
M57399
180
2.10E−02
7.04E−03
6.13E−03
−1
21.339
PTN


211737_x_at
BC005916
181
2.10E−02
7.04E−03
6.13E−03
−1
21.339
PTN


209686_at
BC001766
182
2.10E−02
3.03E−02
8.52E−03
−1
22.433
S100B


216867_s_at
X03795
183
2.10E−02
2.60E−02
1.47E−02
−1
26.553
PDGFA


210229_s_at
M11734
184
2.10E−02
2.17E−02
1.02E−02
−1
36.065
CSF2


209651_at
BC001830
185
2.10E−02
3.03E−02
6.32E−03
−1
42.216
TGFB1I1


206616_s_at
AF155382
186
4.11E−04
4.11E−04
8.65E−04
−1
59.650
ADAM22


208226_x_at
NM_004194
187
4.11E−04
4.11E−04
8.65E−04
−1
59.650
ADAM22


208227_x_at
NM_021721
188
4.11E−04
4.11E−04
8.65E−04
−1
59.650
ADAM22


208237_x_at
NM_021722
189
4.11E−04
4.11E−04
8.65E−04
−1
59.650
ADAM22


216993_s_at
U32169
190
2.10E−02
2.60E−02
1.55E−02
−1
65.430
COL11A2


209726_at
AB018195
191
3.70E−03
1.36E−03
1.03E−02
−1
74.847
CA11


206944_at
AF007141
192
2.10E−02
7.04E−03
3.15E−02
−1
382.037
HTR6


203503_s_at
NM_004565
193
2.10E−02
3.03E−02
4.90E−02
−1
NA
PEX14


214994_at
BF508948
194
3.70E−03
2.49E−03
1.12E−03
1
1.010
APOBEC3F


202270_at
NM_002053
195
3.70E−03
3.70E−03
2.70E−03
1
1.012
GBP1


201975_at
NM_002956
196
4.11E−04
2.17E−04
4.98E−04
1
1.070
RSN


206584_at
NM_015364
197
4.11E−04
2.17E−04
1.05E−02
1
1.095
LY96


212561_at
AA349595
198
2.10E−02
3.03E−02
3.47E−02
1
1.126
RAB6IP1


209413_at
BC002431
199
3.70E−03
2.49E−03
4.18E−04
1
1.156
B4GALT2


214507_s_at
NM_014285
200
3.70E−03
2.49E−03
8.67E−04
1
1.249
EXOSC2


212819_at
AF055024
201
3.70E−03
1.36E−03
1.93E−03
1
1.260
ASB1


206364_at
NM_014875
202
2.10E−02
3.03E−02
9.68E−03
1
1.328
KIF14


206421_s_at
NM_003784
203
2.10E−02
2.60E−02
3.81E−02
1
1.388
SERPINB7


209961_s_at
M60718
204
2.10E−02
2.60E−02
4.30E−02
1
1.389
HGF


204202_at
NM_017604
205
2.10E−02
7.04E−03
8.75E−03
1
1.413
IQCE


207872_s_at
NM_006863
206
3.70E−03
2.49E−03
6.77E−04
1
1.434
LILRA1


215906_at
S65921
207
2.10E−02
3.03E−02
3.41E−02
1
1.459
ACCLC


204849_at
NM_006602
208
3.70E−03
3.70E−03
2.40E−02
1
1.506
TCFL5


211832_s_at
AF201370
209
2.10E−02
7.04E−03
1.33E−02
1
1.527
MDM2


207867_at
NM_006193
210
2.10E−02
2.60E−02
2.32E−02
1
1.539
PAX4


203501_at
NM_006102
211
3.70E−03
3.70E−03
3.39E−03
1
1.549
PGCP


208454_s_at
NM_016134
212
3.70E−03
3.70E−03
3.39E−03
1
1.549
PGCP


206426_at
NM_005511
213
2.10E−02
2.60E−02
4.10E−02
1
1.558
MLANA


208193_at
NM_000590
214
2.10E−02
2.60E−02
2.01E−02
1
1.561
IL9


201538_s_at
AL048503
215
2.10E−02
7.04E−03
3.54E−02
1
1.600
DUSP3


214872_at
AL080129
216
2.10E−02
1.14E−02
1.73E−03
1
1.613
Rif1


205885_s_at
NM_000885
217
2.10E−02
1.14E−02
5.96E−03
1
1.631
ITGA4


205062_x_at
NM_002892
218
2.10E−02
1.14E−02
2.08E−02
1
1.637
ARID4A


209245_s_at
AB014606
219
2.10E−02
2.60E−02
8.48E−03
1
1.645
KIF1C


212976_at
R41498
220
2.10E−02
1.14E−02
2.83E−02
1
1.645
TA-LRRP


200797_s_at
AI275690
221
3.70E−03
3.70E−03
4.15E−02
1
1.650
MCL1


203266_s_at
NM_003010
222
2.10E−02
1.14E−02
2.07E−02
1
1.655
MAP2K4


208042_at
NM_013303
223
3.70E−03
1.36E−03
7.53E−03
1
1.655
VG5Q


207037_at
NM_003839
224
2.10E−02
2.17E−02
7.68E−03
1
1.667
TNFRSF11A


206911_at
NM_005082
225
2.10E−02
7.04E−03
2.82E−03
1
1.671
TRIM25


208359_s_at
NM_004981
226
2.10E−02
2.60E−02
2.42E−02
1
1.674
KCNJ4


211451_s_at
U24056
227
2.10E−02
2.60E−02
2.42E−02
1
1.674
KCNJ4


212882_at
AB018338
228
2.10E−02
2.60E−02
1.03E−02
1
1.676
KLHL18


205141_at
NM_001145
229
2.10E−02
7.04E−03
9.28E−03
1
1.713
ANG


216695_s_at
AF082559
230
3.70E−03
1.36E−03
3.13E−03
1
1.721
TNKS


205312_at
NM_003120
231
2.10E−02
2.60E−02
1.99E−02
1
1.722
SPI1


205990_s_at
NM_003392
232
2.10E−02
3.03E−02
3.56E−02
1
1.746
WNT5A


202260_s_at
NM_003165
233
3.70E−03
3.70E−03
2.60E−03
1
1.747
STXBP1


214624_at
AA548647
234
3.70E−03
1.36E−03
1.08E−02
1
1.750
UPK1A


211561_x_at
L35253
235
4.11E−04
2.17E−04
9.33E−05
1
1.760
MAPK14


216817_s_at
AJ302604
236
2.10E−02
1.14E−02
7.08E−03
1
1.764
OR2H1


207044_at
NM_000461
237
2.10E−02
7.04E−03
1.51E−02
1
1.766
THRB


208724_s_at
BC000905
238
3.70E−03
3.70E−03
3.32E−02
1
1.770
RAB1A


214111_at
AF070577
239
2.10E−02
7.04E−03
4.11E−02
1
1.774
AF070577


205261_at
NM_002630
240
2.10E−02
7.04E−03
1.86E−02
1
1.781
PGC


207406_at
NM_000780
241
2.10E−02
7.04E−03
1.64E−02
1
1.787
CYP7A1


206218_at
NM_002364
242
2.10E−02
7.04E−03
9.00E−04
1
1.791
MAGEB2


207010_at
NM_000812
243
2.10E−02
2.60E−02
4.13E−03
1
1.801
GABRB1


217301_x_at
X71810
244
2.10E−02
3.03E−02
6.23E−03
1
1.822
RBBP4


211108_s_at
U31601
245
3.70E−03
3.70E−03
5.90E−03
1
1.826
JAK3


211109_at
U31601
246
3.70E−03
3.70E−03
5.90E−03
1
1.826
JAK3


206902_s_at
NM_005728
247
3.70E−03
1.36E−03
1.14E−04
1
1.853
ENDOGL1


206903_at
NM_005728
248
3.70E−03
1.36E−03
1.14E−04
1
1.853
ENDOGL2


209260_at
BC000329
249
2.10E−02
3.03E−02
1.58E−02
1
1.879
SFN


214099_s_at
AK001619
250
3.70E−03
2.49E−03
1.54E−02
1
1.883
PDE4DIP


201453_x_at
NM_005614
251
2.10E−02
3.03E−02
1.52E−02
1
1.891
RHEB


213404_s_at
BF033683
252
2.10E−02
3.03E−02
1.52E−02
1
1.891
RHEB


202805_s_at
NM_004996
253
4.11E−04
4.11E−04
6.42E−04
1
1.892
ABCC1


215008_at
AA582404
254
2.10E−02
7.04E−03
2.49E−03
1
1.897
TLL2


205619_s_at
NM_004527
255
2.10E−02
3.03E−02
1.38E−02
1
1.905
MEOX1


215130_s_at
AC002550
256
2.10E−02
3.03E−02
4.39E−02
1
1.906
MGC35048


215131_at
AC002550
257
2.10E−02
3.03E−02
4.39E−02
1
1.906
MGC35048


207675_x_at
NM_003976
258
2.10E−02
7.04E−03
4.31E−03
1
1.922
ARTN


216052 x_at
AF115765
259
2.10E−02
7.04E−03
4.31E−03
1
1.922
ARTN


205962_at
NM_002577
260
2.10E−02
1.14E−02
8.39E−04
1
1.930
PAK2


207143_at
NM_001259
261
3.70E−03
1.36E−03
1.29E−02
1
1.932
CDK6


203755_at
NM_001211
262
4.11E−04
2.17E−04
5.38E−04
1
1.941
BUB1B


202166_s_at
NM_006241
263
3.70E−03
3.70E−03
1.74E−02
1
1.948
PPP1R2


206570_s_at
NM_002785
264
2.10E−02
2.17E−02
1.17E−02
1
1.953
PSG8, PSG4,










PSG9, PSG3,










PSG7


202886_s_at
M65254
265
2.10E−02
7.04E−03
5.42E−03
1
1.959
PPP2R1B


200889_s_at
NM_003144
266
3.70E−03
2.49E−03
4.88E−04
1
1.965
SSR1


206942_s_at
NM_002674
267
3.70E−03
1.36E−03
6.48E−03
1
1.969
PMCH


207862_at
NM_006760
268
2.10E−02
7.04E−03
2.52E−03
1
1.976
UPK2


200771_at
NM_002293
269
2.10E−02
1.14E−02
3.76E−03
1
1.978
LAMC1


206145_at
AF178841
270
2.10E−02
3.03E−02
9.28E−03
1
1.995
RHAG


206609_at
NM_005462
271
2.10E−02
7.04E−03
7.65E−04
1
2.006
MAGEC1


215116_s_at
AF035321
272
2.10E−02
7.04E−03
3.89E−02
1
2.021
DNM1


208229_at
NM_022975
273
2.10E−02
7.04E−03
1.97E−03
1
2.048
FGFR2


211349_at
AB001328
274
2.10E−02
2.60E−02
6.74E−04
1
2.049
SLC15A1


207654_x_at
NM_001938
275
3.70E−03
1.36E−03
3.30E−03
1
2.070
DR1


209188_x_at
AW516932
276
3.70E−03
1.36E−03
3.30E−03
1
2.070
DR1


216652_s_at
AL137673
277
3.70E−03
1.36E−03
3.30E−03
1
2.070
DR1


214565_s_at
NM_012390
278
2.10E−02
2.17E−02
2.50E−03
1
2.092
PROL3, PROL5


214566_at
NM_012390
279
2.10E−02
2.17E−02
2.50E−03
1
2.092
PROL5


215719_x_at
X83493
280
3.70E−03
1.36E−03
4.50E−04
1
2.136
TNFRSF6


216252_x_at
Z70519
281
2.10E−02
7.04E−03
2.33E−03
1
2.136
TNFRSF6


208405_s_at
NM_006016
282
3.70E−03
3.70E−03
1.33E−03
1
2.137
CD164


216857_at
L48728
283
2.10E−02
1.14E−02
1.47E−03
1
2.147
TCRB PS7


216865_at
M64108
284
3.70E−03
3.70E−03
6.10E−03
1
2.151
COL14A1


216866_s_at
M64108
285
3.70E−03
3.70E−03
6.10E−03
1
2.151
COL14A1


212942_s_at
AB033025
286
3.70E−03
1.36E−03
8.72E−04
1
2.192
KIAA1199


209737_at
AB014605
287
3.70E−03
1.36E−03
6.90E−04
1
2.199
AIP1


207325_x_at
NM_004988
288
2.10E−02
7.04E−03
2.44E−02
1
2.232
MAGEA1


215017_s_at
AW270932
289
3.70E−03
3.70E−03
3.32E−04
1
2.233
C1orf39


203806_s_at
NM_000135
290
2.10E−02
7.04E−03
2.51E−03
1
2.249
FANCA


203440_at
M34064
291
2.10E−02
2.60E−02
1.61E−02
1
2.355
CDH2


205339_at
NM_003035
292
2.10E−02
7.04E−03
8.46E−04
1
2.355
SIL


209648_x_at
AL136896
293
3.70E−03
1.36E−03
3.23E−03
1
2.357
SOCS5


205429_s_at
NM_016447
294
2.10E−02
7.04E−03
1.09E−03
1
2.361
MPP6


208340_at
NM_003723
295
3.70E−03
1.36E−03
1.07E−03
1
2.369
CASP13


211203_s_at
U07820
296
2.10E−02
2.60E−02
6.09E−03
1
2.386
CNTN1


206437_at
NM_003775
297
3.70E−03
1.36E−03
3.29E−03
1
2.397
EDG6


207663_x_at
NM_001473
298
2.10E−02
1.14E−02
1.79E−03
1
2.404
GAGE3


207739_s_at
NM_001472
299
2.10E−02
1.14E−02
1.79E−03
1
2.404
GAGE8,










GAGE4,










GAGE5,










GAGE7,










GAGE2,










GAGE1,










GAGE6,










GAGE3,










GAGE7B


203889_at
NM_003020
300
2.10E−02
1.14E−02
2.16E−03
1
2.410
SGNE1


217210_at
AL031737
301
2.10E−02
1.14E−02
2.41E−03
1
2.431



204844_at
L12468
302
3.70E−03
1.36E−03
2.31E−03
1
2.461
ENPEP


214726_x_at
AL556041
303
2.10E−02
1.14E−02
1.09E−02
1
2.466
ADD1


206702_at
NM_000459
304
2.10E−02
1.14E−02
1.23E−03
1
2.467
TEK


209835_x_at
BC004372
305
2.10E−02
3.03E−02
4.58E−02
1
2.468
CD44


210916_s_at
AF098641
306
2.10E−02
3.03E−02
4.58E−02
1
2.468
CD44


212014_x_at
AI493245
307
2.10E−02
3.03E−02
4.58E−02
1
2.468
CD44


203594_at
NM_003729
308
2.10E−02
2.60E−02
3.38E−02
1
2.474
RTCD1


206537_at
NM_001167
309
4.11E−04
4.11E−04
2.90E−04
1
2.483
BIRC4


201196_s_at
M21154
310
2.10E−02
2.60E−02
7.72E−03
1
2.525
AMD1


207705_s_at
NM_025176
311
2.10E−02
3.03E−02
3.93E−02
1
2.532
KIAA0980


210992_x_at
U90939
312
2.10E−02
3.03E−02
4.34E−02
1
2.544
FCGR2C


211395_x_at
U90940
313
2.10E−02
3.03E−02
4.34E−02
1
2.544
FCGR2C


205446_s_at
NM_001880
314
2.10E−02
1.14E−02
4.31E−02
1
2.552
ATF2


204979_s_at
NM_007341
315
2.10E−02
7.04E−03
2.37E−03
1
2.587
SH3BGR


208525_s_at
NM_012369
316
2.10E−02
1.14E−02
2.40E−03
1
2.639
OR2F1, OR2F2


208526_at
NM_012369
317
2.10E−02
1.14E−02
2.40E−03
1
2.639
OR2F1


204072_s_at
NM_023037
318
2.10E−02
1.14E−02
2.67E−02
1
2.651
13CDNA73


214151_s_at
AU144243
319
2.10E−02
2.60E−02
1.49E−02
1
2.682
PIGB


221511_x_at
AF212228
320
2.10E−02
2.60E−02
1.49E−02
1
2.682
CCPG1


222156_x_at
AK022459
321
2.10E−02
2.60E−02
1.49E−02
1
2.682
CCPG1


207360_s_at
NM_002531
322
2.10E−02
7.04E−03
2.96E−03
1
2.733
NTSR1


206577_at
NM_003381
323
2.10E−02
3.03E−02
4.09E−02
1
2.750
VIP


201756_at
NM_002946
324
2.10E−02
2.60E−02
8.61E−03
1
2.779
RPA2


220266_s_at
AF105036
325
2.10E−02
1.14E−02
1.09E−02
1
2.807
KLF4


211024_s_at
BC006221
326
3.70E−03
1.36E−03
5.86E−03
1
2.869
TITF1


206131_at
NM_001832
327
2.10E−02
1.14E−02
2.88E−03
1
2.879
CLPS


214642_x_at
AI200443
328
2.10E−02
7.04E−03
2.99E−03
1
2.940
MAGEA5


200769_s_at
BC001686
329
4.11E−04
4.11E−04
6.60E−04
1
2.942
MAT2A


213363_at
AW170549
330
2.10E−02
7.04E−03
9.28E−04
1
2.945
CA5BL


206762_at
NM_002234
331
2.10E−02
2.60E−02
1.88E−03
1
2.952
KCNA5


203952_at
NM_007348
332
2.10E−02
7.04E−03
4.68E−03
1
2.967
ATF6


201521_s_at
NM_007362
333
2.10E−02
1.14E−02
2.09E−03
1
3.026
NCBP2


204227_s_at
NM_004614
334
2.10E−02
2.17E−02
4.48E−03
1
3.055
TK2


204643_s_at
NM_006375
335
4.11E−04
4.11E−04
2.98E−05
1
3.068
COVA1


204668_at
AL031670
336
2.10E−02
2.60E−02
1.94E−02
1
3.073
RNF24


202404_s_at
NM_000089
337
2.10E−02
2.60E−02
9.81E−03
1
3.074
COL1A2


210040_at
AF208159
338
3.70E−03
1.36E−03
6.35E−03
1
3.131
SLC12A5


215634_at
AF007137
339
3.70E−03
3.70E−03
4.86E−04
1
3.167
GRIA1


206091_at
NM_002381
340
2.10E−02
3.03E−02
2.69E−03
1
3.209
MATN3


210166_at
AF051151
341
2.10E−02
1.14E−02
1.52E−02
1
3.226
TLR5


200713_s_at
NM_012325
342
2.10E−02
3.03E−02
2.12E−02
1
3.241
MAPRE1


205732_s_at
NM_006540
343
3.70E−03
3.70E−03
6.93E−04
1
3.257
NCOA2


204493_at
NM_001196
344
2.10E−02
2.17E−02
8.04E−03
1
3.263
BID


207780_at
NM_001340
345
2.17E−05
2.17E−05
1.82E−06
1
3.302
CYLC2


217056_at
X61070
346
2.10E−02
3.03E−02
1.17E−02
1
3.434
TRA@


209189_at
BC004490
347
3.70E−03
3.70E−03
6.05E−03
1
3.463
FOS


216392_s_at
AK021846
348
3.70E−03
2.49E−03
1.69E−04
1
3.480
SEC23IP


215486_at
AW072461
349
3.70E−03
2.49E−03
8.93E−03
1
3.516
PRPS1L1


213131_at
R38389
350
2.10E−02
2.17E−02
3.62E−03
1
3.537
OLFM1


207681_at
NM_001504
351
2.10E−02
2.17E−02
2.78E−02
1
3.601
CXCR3


207224_s_at
NM_016543
352
2.10E−02
7.04E−03
3.05E−03
1
3.879
SIGLEC7


207307_at
NM_000868
353
3.70E−03
2.49E−03
1.40E−04
1
4.032
HTR2C


203676_at
NM_002076
354
3.70E−03
2.49E−03
6.66E−04
1
4.040
GNS


210729_at
U36269
355
2.10E−02
3.03E−02
2.59E−02
1
4.143
NPY2R


208743_s_at
BC001359
356
2.10E−02
1.14E−02
4.88E−02
1
4.276
YWHAB


207643_s_at
NM_001065
357
2.10E−02
1.14E−02
5.70E−04
1
4.356
TNFRSF1A


205321_at
NM_001415
358
3.70E−03
1.36E−03
1.74E−03
1
4.406
EIF2S3


214348_at
NM_001057
359
3.70E−03
1.36E−03
1.09E−03
1
4.453
TACR2


206556_at
NM_014410
360
3.70E−03
2.49E−03
2.77E−04
1
4.518
CLUL1


216621_at
AL050032
361
3.70E−03
3.70E−03
1.12E−03
1
4.628
AL050032.1


203779_s_at
NM_005797
362
3.70E−03
2.49E−03
2.04E−03
1
4.695
EVA1


201894_s_at
NM_001920
363
3.70E−03
3.70E−03
5.62E−04
1
4.765
SSR1


206826_at
NM_002677
364
2.10E−02
3.03E−02
4.05E−02
1
4.767
PMP2


202319_at
NM_015571
365
4.11E−04
2.17E−04
1.38E−05
1
4.842
SENP6


210417_s_at
U81802
366
2.10E−02
1.14E−02
2.68E−02
1
4.869
PIK4CB


208607_s_at
NM_030754
367
3.70E−03
1.36E−03
8.25E−04
1
5.042
SAA1, SAA2


214456_x_at
M23699
368
3.70E−03
1.36E−03
8.25E−04
1
5.042
SAA1


205126_at
NM_006296
369
2.10E−02
1.14E−02
1.06E−02
1
5.075
VRK2


206902_s_at
NM_005728
370
3.70E−03
3.70E−03
1.30E−03
1
5.146
ENDOGL1


206903_at
NM_005728
371
3.70E−03
3.70E−03
1.30E−03
1
5.146
ENDOGL2


210224_at
AF031469
372
3.70E−03
3.70E−03
4.32E−03
1
5.155
MR1


205408_at
NM_004641
373
2.10E−02
7.04E−03
5.13E−04
1
5.249
MLLT10


215157_x_at
AI734929
374
3.70E−03
1.36E−03
2.13E−03
1
5.277
PABPC1


210996_s_at
U43430
375
4.11E−04
2.17E−04
7.55E−04
1
5.403
YWHAE


207236_at
NM_003419
376
2.10E−02
7.04E−03
3.06E−03
1
5.517
ZNF345


212850_s_at
AA584297
377
2.10E−02
2.60E−02
8.55E−03
1
5.626
LRP4


209581_at
BC001387
378
4.11E−04
2.17E−04
7.63E−04
1
5.674
HRASLS3


203066_at
NM_014863
379
2.10E−02
1.14E−02
4.28E−03
1
5.675
GALNAC4S-










6ST


200641_s_at
BC003623
380
2.10E−02
3.03E−02
4.18E−03
1
5.731
YWHAZ


205538_at
NM_003389
381
2.10E−02
2.60E−02
4.30E−02
1
5.793
CORO2A


204886_at
AL043646
382
2.10E−02
1.14E−02
1.17E−03
1
5.855
PLK4


206439_at
NM_004950
383
2.10E−02
2.17E−02
4.94E−03
1
6.348
DSPG3


212617_at
AB002293
384
2.10E−02
2.60E−02
2.60E−03
1
6.788
ZNF609


212461_at
BF793951
385
2.10E−02
3.03E−02
3.54E−02
1
6.845



214602_at
D17391
386
2.10E−02
1.14E−02
3.49E−03
1
7.218
COL4A4


206812_at
NM_000025
387
3.70E−03
2.49E−03
6.95E−03
1
7.330
ADRB3


202620_s_at
NM_000935
388
3.70E−03
3.70E−03
8.23E−03
1
7.350
PLOD2


206925_at
NM_005668
389
2.10E−02
2.60E−02
7.44E−03
1
7.681
SIAT8D


205530_at
NM_004453
390
2.10E−02
3.03E−02
3.39E−02
1
7.717
ETFDH


203834_s_at
NM_006464
391
3.70E−03
1.36E−03
7.54E−04
1
7.727
TGOLN2


202923_s_at
NM_001498
392
2.10E−02
7.04E−03
1.04E−03
1
7.744
GCLC


203517_at
NM_006554
393
2.10E−02
3.03E−02
3.91E−03
1
8.001
MTX2


209754_s_at
AF113682
394
3.70E−03
3.70E−03
1.91E−03
1
8.351
TMPO


207245_at
NM_001077
395
3.70E−03
2.49E−03
4.63E−03
1
8.575
UGT2B17


207392_x_at
NM_001076
396
3.70E−03
2.49E−03
4.63E−03
1
8.575
UGT2B15


206692_at
NM_002241
397
2.10E−02
7.04E−03
3.37E−03
1
8.819
KCNJ10


208050_s_at
NM_001224
398
4.11E−04
2.17E−04
2.66E−05
1
9.015
CASP2


210055_at
BE045816
399
2.10E−02
2.60E−02
3.07E−03
1
9.077
TSHR


205174_s_at
NM_012413
400
2.10E−02
7.04E−03
3.72E−03
1
10.221
QPCT


210375_at
X83858
401
3.70E−03
2.49E−03
8.20E−03
1
10.333
PTGER3


206099_at
NM_006255
402
2.10E−02
1.14E−02
1.38E−02
1
10.583
PRKCH


203550_s_at
NM_006589
403
2.10E−02
1.14E−02
7.88E−04
1
10.893
C1orf2


210331_at
AB048365
404
2.10E−02
3.03E−02
1.00E−02
1
10.931
NEDL1


205206_at
NM_000216
405
3.70E−03
1.36E−03
1.76E−04
1
10.990
KAL1


215993_at
AF070543
406
2.10E−02
1.14E−02
8.81E−03
1
12.226
ODZ2


213090_s_at
AI744029
407
2.10E−02
2.60E−02
1.93E−02
1
12.921
TAF4


202430_s_at
NM_021105
408
2.10E−02
2.60E−02
6.43E−03
1
13.006
PLSCR1


215996_at
AI446234
409
3.70E−03
2.49E−03
2.92E−04
1
13.631
pre-TNK


204073_s_at
NM_013279
410
2.10E−02
3.03E−02
3.88E−02
1
14.189
C11orf9


215599_at
X83300
411
2.10E−02
3.03E−02
1.34E−02
1
15.668
SMA4


207725_at
NM_004575
412
2.10E−02
1.14E−02
1.37E−02
1
16.065
POU4F2


212720_at
AI670847
413
3.70E−03
2.49E−03
5.17E−04
1
16.903
PAPOLA


209459_s_at
AF237813
414
2.10E−02
2.60E−02
3.44E−03
1
17.443
ABAT


209460_at
AF237813
415
2.10E−02
2.60E−02
3.44E−03
1
17.443
ABAT


203626_s_at
NM_005983
416
2.10E−02
1.14E−02
1.27E−03
1
17.743
SKP2


210567_s_at
BC001441
417
2.10E−02
1.14E−02
1.27E−03
1
17.743
SKP2


216984_x_at
D84143
418
3.70E−03
1.36E−03
6.07E−03
1
19.947
IGLVJ


201817_at
NM_014671
419
2.10E−02
2.17E−02
1.70E−03
1
21.762
UBE3C


213371_at
AI803302
420
2.10E−02
2.17E−02
1.20E−03
1
28.386
LDB3


216887_s_at
AJ133768
421
2.10E−02
2.17E−02
1.20E−03
1
28.386
LDB3


204003_s_at
NM_007342
422
2.10E−02
2.17E−02
7.68E−04
1
34.471
NUPL2


216430_x_at
AF043586
423
2.10E−02
7.04E−03
1.18E−03
1
37.955
IGLJ3


216908_x_at
AF001549
424
4.11E−04
4.11E−04
5.94E−04
1
39.235
LOC94431


215719_x_at
X83493
425
2.10E−02
7.04E−03
2.33E−03
1
42.507
TNFRSF6


203279_at
NM_014674
426
2.10E−02
2.60E−02
2.19E−03
1
48.486
EDEM1


201798_s_at
NM_013451
427
3.70E−03
2.49E−03
2.54E−03
1
131.626
FER1L3


208363_s_at
NM_001566
428
2.10E−02
1.14E−02
1.99E−02
1
208.057
INPP4A


208364_at
NM_001566
429
2.10E−02
1.14E−02
1.99E−02
1
208.057
INPP4A


206225_at
NM_014910
430
3.70E−03
2.49E−03
1.55E−03
1
326.316
ZNF507


211749_s_at
BC005941
431
2.10E−02
2.17E−02
1.41E−03
1
NA
VAMP3





Table 2: Genetic markers which are differentially expressed between multiple sclerosis patients having good or poor clinical outcome are provided (the Probeset ID of the Affymetrix Gene Chip), along with the corresponding GenBank accession number (GenBank Acc. No.), the gene symbol, the SEQ ID NO., the p values using the TNOM, Info and t-Test statistical tests, the direction of change in gene expression (“1” - upregulation; “−1” - downregulation) and the fold change (F/C) in MS patients having poor clinical outcome as compared to good clinical outcome (Poor/Good).


NA—not available.






Altogether, these results demonstrate the MS clinical outcome prediction ability of the identified 431 genes which are differentially expressed between RRMS patients with good or poor clinical outcome.


Example 2
Identification of RRMS Clinical Outcome Predicting Genes

Experimental and Statistical Results


Predictive clinical outcome gene expression signature—As is shown in FIG. 6, application of the SVM on data from 19/26 patients with good (9 patients) or poor (10 patients) outcome as a training set, and 9/27 additional patients from the validation group as test set, resulted in a high classification rate of 89%. This high classification was achieved by the Forward feature selection algorithm using 34 gene transcripts (29 genes) (Table 3, hereinbelow) accordingly defined as predictive. Classification rate was 70.4% using only one gene (RRN3) and reached a rate of 85.2% using 6 genes (RRN3, KLF4, HAB1, TPSB2, IGLJ3, COL11A2). Addition of one or all of the remaining predictive genes resulted in maximal classification rate of 89.0%. This suggests that a predictive ability with an accuracy of 89% could be achieved using only 7 genes.









TABLE 3







Genes capable of predicting the clinical outcome of RRMS
















Corresponding
SEQ




F/C




GenBank Acc.
ID
TNOM
Info
t-Test

(Poor/
Gene


Probeset ID
No.
NO:
PValue
PValue
PValue
Dir
Good)
Symbol


















203683_s_at
NM_003377
156
2.10E−02
3.03E−02
4.63E−03
−1
7.31
VEGFB


206148_at
NM_002183
143
2.10E−02
3.03E−02
2.62E−02
−1
5.17
IL3RA


207134_x_at
NM_024164
127
2.10E−02
2.17E−02
8.61E−03
−1
4.29
TPSB2


207532_at
NM_006891
46
3.70E−03
3.70E−03
2.66E−02
−1
2.06
CRYGD


207705_s_at
NM_025176
311
2.10E−02
3.03E−02
3.93E−02
1
2.53
KIAA0980


207900_at
NM_002987
140
2.10E−02
1.14E−02
6.92E−03
−1
5.13
CCL17


208687_x_at
AF352832
74
3.70E−03
3.70E−03
3.18E−02
−1
2.47
HSPA8


209188_x_at
AW516932
276
3.70E−03
1.36E−03
3.30E−03
1
2.07
DR1


209466_x_at
M57399
180
2.10E−02
7.04E−03
6.13E−03
−1
21.34
PTN


209686_at
BC001766
182
2.10E−02
3.03E−02
8.52E−03
−1
22.43
S100B


209726_at
AB018195
191
3.70E−03
1.36E−03
1.03E−02
−1
74.85
CA11


210328_at
AF101477
61
2.10E−02
2.60E−02
3.52E−02
−1
2.26
GNMT


210916_s_at
AF098641
306
2.10E−02
3.03E−02
4.58E−02
1
2.47
CD44


213815_x_at
AI913329
115
2.10E−02
2.60E−02
3.04E−03
−1
3.82
NY-REN24


214613_at
AW024085
97
2.10E−02
7.04E−03
1.72E−02
−1
3.02
GPR3


214726_x_at
AL556041
303
2.10E−02
1.14E−02
1.09E−02
1
2.47
ADD1


215116_s_at
AF035321
272
2.10E−02
7.04E−03
3.89E−02
1
2.02
DNM1


215766_at
AL096729
50
2.10E−02
7.04E−03
2.58E−03
−1
2.12
GSTA1


215778_x_at
AJ006206
16
2.10E−02
2.60E−02
2.29E−02
−1
1.59
HAB1


215781_s_at
D87012
63
2.10E−02
2.60E−02
2.98E−02
−1
2.27
TOP3B


215954_s_at
AI200896
117
2.10E−02
2.60E−02
3.04E−03
−1
3.82
NY-REN24


215993_at
AF070543
406
2.10E−02
1.14E−02
8.81E−03
1
12.23
ODZ2


216430_x_at
AF043586
423
2.10E−02
7.04E−03
1.18E−03
1
37.95
IGLJ3


216474_x_at
AF206667
128
2.10E−02
2.17E−02
8.61E−03
−1
4.29
TPSAB1,










TPSB2


216652_s_at
AL137673
277
3.70E−03
1.36E−03
3.30E−03
1
2.07
DR1


216699_s_at
L10038
47
2.10E−02
7.04E−03
5.23E−03
−1
2.07
KLK1


216875_x_at
X83412
17
2.10E−02
2.60E−02
2.29E−02
−1
1.59
HAB1


216908_x_at
AF001549
424
4.11E−04
4.11E−04
5.94E−04
1
39.23
RRN3


216984_x_at
D84143
418
3.70E−03
1.36E−03
6.07E−03
1
19.95
IGLVJ


216993_s_at
U32169
190
2.10E−02
2.60E−02
1.55E−02
−1
65.43
COL11A2


217060_at
U03115
139
2.10E−02
3.03E−02
4.35E−02
−1
5.11
TCRBV


217109_at
AJ242547
102
3.70E−03
3.70E−03
2.34E−02
−1
3.21
MUC4


217110_s_at
AJ242547
103
3.70E−03
3.70E−03
2.34E−02
−1
3.21
MUC4


220266_s_at
AF105036
325
2.10E−02
1.14E−02
1.09E−02
1
2.81
KLF4









Independent validation of the predictive clinical outcome gene expression signature—Applying the resulting SVM generated classifier, based on the 34 predictive genes to an additional data set of 18/27 patients from the validation group maintained the high classification rate of 88.9%, p<0.00001.


Altogether, these results demonstrate the identification of 34 genes which are capable of predicting the outcome of RRMS (e.g., poor or good clinical outcome) with a classification rate of about 90%.


In addition, these results demonstrate that gene expression profiling combined with carefully chosen learning algorithms allow the prediction of disease outcome and can be incorporated into clinical decision making in relapsing-remitting MS. Since MS has a winding course and the rate of disease progression differs between patients, the results obtained from the present study can predict patient outcome and may be incorporated in individualized tailored management of RRMS. Application of the invention may enable planning of tailored therapeutic strategies and allow delineation of patients at high-risk that may benefit from early therapy.


Example 3
Biological Regulation of the Predictive Clinical Outcome Gene Expression

Functional Annotation Results


Functional annotation of the 34 predictive genes described in Table 3, Example 2, hereinabove, demonstrated that this group of genes was significantly enriched with zinc-ion binding protein genes (S100B, KLF4, CA11) and with genes exhibiting cytokine activity (CCL17, MUC4, PTN VEGFB), p=0.02 and p=0.005, respectively (FIG. 7). The Genomica software confirmed the enrichment by zinc-ion binding gene family and by cytokine activity genes using all the 431 differentiating gene expression signature data (FIGS. 8a-c). Using these enriched gene-families, regulatory pathways were reconstructed (FIGS. 9 and 10). These pathways suggest that apoptosis regulation through zinc-ion binding and cytokine activity is responsible for Th1/Th2 cytokine activity shift and may play a role in the clinical outcome of RRMS. Genomica reconstruction of regulatory gene expression networks based on all 431 differentiating genes resulted in a regulation pathway in which the predictive zinc-ion binding gene KLF4 in association with CLPP and RRLP mediate downstream genes including S100B (FIG. 10). Other interesting functional groups in the 29 predictive genes include adhesion and cell migration like CD44 and COL11A2, and T cell receptor genes like TCRVB, all play an important role in MS pathogenesis.


Example 4
Selection of Differentiating Genes

Computational Results


Selection of differentiating genes and determination of their predictive power—To evaluate the power of each of the 431 differentiating genes identified in this study to predict the prognosis (good or poor clinical outcome) of a subject diagnosed with multiple sclerosis, the study sample was randomly divided into 80% of the subjects as a “training set” and 20% of the subjects as a “test set” and a model was build using the SVM based on RBF kernel. For each of the differentiating genes the predictability of the training set on the clinical outcome of the test set was computed and the average error following 50 permutations was calculated. Genes with the lowest average error were selected, then, for each selected gene, the remaining genes were added one after the other, by selecting the next gene such that the average error after 50 repeats of the group of genes including the new gene has the lowest average error as compared to the addition of another gene. This process was repeated 430 times for each additional genes added to the previous group of genes. The resulting average error plot is shown in FIG. 12, and the average error for each gene combination is demonstrated in Table 4, hereinbelow, wherein the first gene in row number 1 (SEQ ID NO:158; NM005012) exhibits the best predictive power (error average of “0”).









TABLE 4







Average error of gene combination with predictive


ability of Multiple Sclerosis clinical outcome













SEQ


Aver-



Row
ID

Gene Bank
age


Number
NO:
Probeset ID
ID
error
Gene Symbol















1
158
205805_s_at
NM_005012
0
ROR1


2
68
200949_x_at
NM_001023
0
RPS20


3
5
201783_s_at
NM_021975
0
RELA


4
58
200647_x_at
NM_003752
0
EIF3S8


5
329
200769_s_at
NM_005911
0
MAT2A


6
120
200936_at
NM_000973
0
RPL8


7
380
200641_s_at
U28964
0
YWHAZ


8
342
200713_s_at
NM_012325
0
MAPRE1


9
88
200790_at
NM_002539
0
ODC1


10
166
200834_s_at
NM_001024
0
RPS21


11
266
200889_s_at
AI016620
0
SSR1


12
51
201023_at
NM_005642
0
TAF7


13
310
201196_s_at
M21154
0
AMD1


14
91
201513_at
AI659180
0
TSN


15
427
201798_s_at
NM_013451
0
FER1L3


16
22
201889_at
NM_014888
0
FAM3C


17
84
201418_s_at
NM_003107
0
SOX4


18
269
200771_at
NM_002293
0
LAMC1


19
388
202620_s_at
NM_000935
0
PLOD2


20
155
201497_x_at
NM_022844
0
MYH11


21
333
201521_s_at
NM_007362
0
NCBP2


22
215
201538_s_at
NM_004090
0
DUSP3


23
195
202270_at
NM_002053
0
GBP1


24
419
201817_at
NM_014671
0
KIAA0010/







UBE3C


25
75
202314_at
NM_000786
0
CYP51A1


26
125
204197_s_at
NM_004350
0
RUNX3


27
11
203205_at
NM_014663
0
JMJD2


28
251
201453_x_at
NM_005614
0
RHEB


29
253
202805_s_at
NM_004996
0
ABCC1


30
337
202404_s_at
NM_000089
0
COL1A2


31
110
202140_s_at
NM_003992
0
CLK3


32
222
203266_s_at
NM_003010
0
MAP2K4


33
56
202799_at
NM_006012
0
CLPP


34
324
201756_at
NM_002946
0
RPA2


35
156
203683_s_at
NM_003377
0
VEGFB


36
7
203145_at
NM_006461
0
SPAG5


37
57
202127_at
AB011108
0
PRPF4B


38
233
202260_s_at
NM_003165
0
STXBP1


39
149
202268_s_at
NM_003905
0
APPBP1


40
363
201894_s_at
NM_001920
0
DCN


41
107
203422_at
NM_002691
0
POLD1


42
193
203503_s_at
NM_004565
0
PEX14


43
393
203517_at
NM_006554
0
MTX2


44
265
202886_s_at
M65254
0
PPP2R1B


45
160
203329_at
NM_002845
0
PTPRM


46
41
202732_at
NM_007066
0
PKIG


47
38
203022_at
NM_006397
0
RNASEH2A


48
90
203340_s_at
NM_003705
0
SLC25A12


49
70
203701_s_at
NM_017722
0
FLJ20244


50
85
202410_x_at
NM_000612
0
IGF2


51
403
203550_s_at
NM_006589
0
C1orf2


52
304
206702_at
NM_000459
0
TEK


53
426
203279_at
NM_014674
0
EDEM1


54
240
205261_at
NM_002630
0
PGC


55
49
203193_at
NM_004451
0
ESRRA


56
294
205429_s_at
NM_016447
0
MPP6


57
136
202415_s_at
NM_012267
0
HSPBP1


58
150
202909_at
NM_014805
0
EPM2AIP1


59
232
205990_s_at
NM_003392
0
WNT5A


60
10
203426_s_at
M65062
0
IGFBP5


61
392
202923_s_at
NM_001498
0
GCLC


62
89
203339_at
AI887457
0
SLC25A12


63
332
203952_at
NM_007348
0
ATF6


64
290
203806_s_at
NM_000135
0
FANCA


65
422
204003_s_at
NM_007342
0
NUPL2


66
291
203440_at
M34064
0
CDH2


67
114
202554_s_at
AL527430
0
GSTM3


68
309
206537_at
NM_001167
0
BIRC4


69
203
206421_s_at
NM_003784
0
SERPINB7


70
362
203779_s_at
NM_005797
0
EVA1


71
397
206692_at
NM_002241
0
KCNJ10


72
334
204227_s_at
NM_004614
0
TK2


73
302
204844_at
L12468
0
ENPEP


74
179
203851_at
NM_002178
0
IGFBP6


75
171
208733_at
AW301641
0
RAB2


76
53
204319_s_at
NM_002925
0
RGS10


77
402
206099_at
NM_006255
0
PRKCH


78
315
204979_s_at
NM_007341
0
SH3BGR


79
271
206609_at
NM_005462
0
MAGEC1


80
218
205062_x_at
NM_002892
0
RBBP1


81
154
202312_s_at
NM_000088
0
COL1A1


82
243
207010_at
NM_000812
0
GABRB1


83
211
203501_at
NM_006102
0
PGCP


84
180
209466_x_at
M57399
0
PTN


85
412
207725_at
NM_004575
0
POU4F2


86
300
203889_at
NM_003020
0
SGNE1


87
131
212674_s_at
AK002076
0
DHX30


88
71
210463_x_at
BC002492
0
FLJ20244


89
398
208050_s_at
NM_001224
0
CASP2


90
289
215017_s_at
AW270932
0
FLJ20275


91
371
206903_at
NM_005728
0
ENDOGL1


92
118
204144_s_at
NM_004204
0
PIGQ


93
220
212976_at
R41498
0
TA-LRRP


94
82
203801_at
AA013164
0
SIP


95
42
205013_s_at
NM_000675
0
ADORA2A


96
430
206225_at
NM_014910
0
KIAA1084


97
64
204163_at
NM_007046
0
EMILIN1


98
144
204816_s_at
NM_014681
0
DHX34


99
2
205034_at
NM_004702
0
CCNE2


100
205
204202_at
NM_017604
0
KIAA1023


101
405
205206_at
NM_000216
0
KAL1


102
318
204072_s_at
NM_023037
0
13CDNA73


103
146
206398_s_at
NM_001770
0
CD19


104
314
205446_s_at
NM_001880
0
ATF2


105
12
203324_s_at
NM_001233
0
CAV2


106
416
203626_s_at
NM_005983
0
SKP2


107
267
206942_s_at
NM_002674
0
PMCH


108
105
206759_at
NM_002002
0.005
FCER2


109
353
207307_at
NM_000868
0
HTR2C


110
296
211203_s_at
U07820
0
CNTN1


111
224
207037_at
NM_003839
0.005
TNFRSF11A


112
165
206516_at
NM_000479
0
AMH


113
113
208041_at
NM_002929
0
RHOK


114
345
207780_at
NM_001340
0
CYLC2


115
387
206812_at
NM_000025
0.005
ADRB3


116
61
210328_at
AF101477
0
GNMT


117
250
214099_s_at
AK001619
0.005
PDE4DIP


118
59
210949_s_at
BC000533
0.005
EIF3S8


119
235
211561_x_at
L35253
0.005
MAPK14


120
382
204886_at
AL043646
0.005
STK18


121
143
206148_at
NM_002183
0.005
IL3RA


122
361
216621_at
AL050032
0



123
372
210224_at
AF031469
0.005
MR1


124
199
209413_at
BC002431
0
B4GALT2


125
79
206879_s_at
NM_013982
0
NRG2


126
116
214892_x_at
BC004262
0
NY-REN-24


127
162
208580_x_at
NM_021968
0.005
HIST1H4J


128
322
207360_s_at
NM_002531
0.005
NTSR1


129
354
203676_at
NM_002076
0
GNS


130
391
203834_s_at
NM_006464
0.01
TGOLN2


131
377
212850_s_at
AA584297
0.005
LRP4


132
255
205619_s_at
NM_004527
0.005
MEOX1


133
270
206145_at
NM_000324
0.005
RHAG


134
373
205408_at
NM_004641
0.01
MLLT10


135
104
212852_s_at
AL538601
0.005
SSA2


136
400
205174_s_at
NM_012413
0.01
QPCT


137
67
222206_s_at
AA781143
0.005
LOC56926


138
167
208105_at
NM_000164
0.005
GIPR


139
423
216430_x_at
AF043586
0.005
IGLJ3


140
188
208227_x_at
NM_021721
0.01
ADAM22


141
182
209686_at
BC001766
0.005
S100B


142
106
206760_s_at
NM_002002
0.015
FCER2


143
54
209477_at
BC000738
0.015
EMD


144
326
211024_s_at
BC006221
0.005
TITF1


145
164
208091_s_at
NM_030796
0.01
DKFZP564K0822


146
307
212014_x_at
AI493245
0
CD44


147
383
206439_at
NM_004950
0.01
DSPG3


148
260
205962_at
NM_002577
0.005
PAK2


149
340
206091_at
NM_002381
0.01
MATN3


150
357
207643_s_at
NM_001065
0.005
TNFRSF1A


151
390
205530_at
NM_004453
0.01
ETFDH


152
161
207036_x_at
NM_000836
0.005
GRIN2D


153
6
213457_at
BF739959
0.005
MFHAS1


154
316
208525_s_at
NM_012369
0.005
OR2F1


155
272
215116_s_at
AF035321
0.01
DNM1


156
338
210040_at
AF208159
0.01
SLC12A5


157
241
207406_at
NM_000780
0
CYP7A1


158
367
208607_s_at
NM_030754
0.005
SAA2


159
379
203066_at
NM_014863
0.01
GALNAC4S-6ST


160
40
212242_at
AL565074
0.005
TUBA1


161
381
205538_at
NM_003389
0.015
CORO2A


162
231
205312_at
NM_003120
0.01
SPI1


163
39
203071_at
NM_004636
0.015
SEMA3B


164
256
215130_s_at
AC002550
0.02
MGC35048


165
286
212942_s_at
AB033025
0.02
KIAA1199


166
8
210822_at
U72513
0.015
na


167
151
205798_at
NM_002185
0.005
IL7R


168
399
210055_at
BE045816
0.01
TSHR


169
33
205272_s_at
NM_006250
0.02
PRH1


170
254
215008_at
AA582404
0.01
TLL2


171
295
208340_at
NM_003723
0.025



172
141
205189_s_at
NM_000136
0.02
FANCC


173
429
208364_at
NM_001566
0.02
INPP4A


174
65
207954_at
NM_002050
0.015
GATA2


175
229
205141_at
NM_001145
0.03
RNASE4


176
259
216052_x_at
AF115765
0.025
ARTN


177
355
210729_at
U32500
0.02
NPY2R


178
298
207663_x_at
NM_001473
0.02
GAGE4


179
173
214481_at
NM_003514
0.02
HIST1H2AM


180
371
206903_at
NM_005728
0.025
ENDOGL1


181
86
204400_at
NM_005864
0.015
EFS


182
45
208059_at
NM_005201
0.02
CCR8


183
305
209835_x_at
BC004372
0.03
CD44


184
127
207134_x_at
NM_024164
0.025
TPSB2


185
133
210619_s_at
AF173154
0.03
HYAL1


186
200
214507_s_at
NM_014285
0.025
RRP4


187
313
211395_x_at
U90940
0.015
FCGR2B


188
370
206902_s_at
NM_005728
0.025
ENDOGL1


189
112
210249_s_at
U59302
0.025
NCOA1


190
226
208359_s_at
NM_004981
0.03
KCNJ4


191
249
209260_at
BC000329
0.025
SFN


192
80
205463_s_at
NM_002607
0.025
PDGFA


193
299
207739_s_at
NM_001472
0.025
GAGE1


194
196
201975_at
NM_002956
0.025
RSN


195
27
209031_at
AL519710
0.025
IGSF4


196
308
203594_at
NM_003729
0.03
RTCD1


197
288
207325_x_at
NM_004988
0.025
MAGEA1


198
349
215486_at
AW072461
0.03
LOC221823


199
108
202766_s_at
NM_000138
0.04
FBN1


200
311
207705_s_at
NM_025176
0.035
KIAA0980


201
14
217165_x_at
M10943
0.04
MT1F


202
130
214488_at
NM_002886
0.035
RAP2B


203
285
216866_s_at
M64108
0.035
COL14A1


204
207
215906_at
S65921
0.045



205
365
202319_at
NM_015571
0.035
SUSP1


206
275
207654_x_at
NM_001938
0.025
DR1


207
284
216865_at
M64108
0.03
COL14A1


208
96
212999_x_at
AW276186
0.04
HLA-DQB1


209
172
221847_at
BF665706
0.035



210
76
214095_at
AW190316
0.03
LOC56901


211
279
214566_at
NM_012390
0.03
PROL5


212
413
212720_at
AI670847
0.03
PAPOLA


213
351
207681_at
NM_001504
0.035
CXCR3


214
202
206364_at
NM_014875
0.045
KIF14


215
37
212534_at
AU144066
0.035
ZNF24


216
74
208687_x_at
AF352832
0.03
HSPA8


217
375
210996_s_at
U43430
0.035
YWHAE


218
360
206556_at
NM_014410
0.035
CLUL1


219
170
202315_s_at
NM_004327
0.03
BCR


220
147
212540_at
BG476661
0.03
CDC34


221
151
205798_at
NM_002185
0.04
IL7R


222
306
210916_s_at
AF098641
0.035
CD44


223
366
210417_s_at
U81802
0.05
PIK4CB


224
217
205885_s_at
L12002
0.045
ITGA4


225
394
209754_s_at
AF113682
0.035
TMPO


226
192
206944_at
AF007141
0.045
HTR6


227
341
210166_at
AF051151
0.045
TLR5


228
352
207224_s_at
NM_016543
0.04
SIGLEC7


229
83
215746_at
L34409
0.03



230
157
210438_x_at
M25077
0.03
SSA2


231
122
206327_s_at
NM_004933
0.05
CDH15


232
350
213131_at
R38389
0.04
OLFM1


233
30
204066_s_at
NM_014914
0.035
CENTG2


234
25
213262_at
AI932370
0.04
SACS


235
260
205962_at
NM_002577
0.045
PAK2


236
238
208724_s_at
BC000905
0.04
RAB1A


237
428
208363_s_at
NM_001566
0.05
INPP4A


238
278
214565_s_at
NM_012390
0.04
PROL5


239
252
213404_s_at
BF033683
0.045



240
395
207245_at
NM_001077
0.055
UGT2B17


241
343
205732_s_at
NM_006540
0.05
NCOA2


242
325
220266_s_at
NM_004235
0.045
KLF4


243
31
206846_s_at
NM_006044
0.05
HDAC6


244
386
214602_at
D17391
0.05
COL4A4


245
301
217210_at
AL031737
0.045



246
317
208526_at
NM_012369
0.065
OR2F1


247
29
212912_at
AI992251
0.05
RPS6KA2


248
407
213090_s_at
AI744029
0.05
TAF4


249
72
204703_at
NM_006531
0.04
TG737


250
283
216857_at
L48728
0.045



251
227
211451_s_at
U24056
0.055
KCNJ4


252
191
209726_at
AB018195
0.05
CA11


253
126
205683_x_at
NM_003294
0.06
TPSB2


254
132
209253_at
AF037261
0.055
SCAM-1


255
187
208226_x_at
NM_004194
0.045
ADAM22


256
94
209320_at
AF033861
0.05
ADCY3


257
66
210358_x_at
BC002557
0.05
GATA2


258
109
216065_at
AL031228
0.05
C6orf11


259
46
207532_at
NM_006891
0.03
CRYGD


260
212
208454_s_at
NM_016134
0.06
PGCP


261
24
206910_x_at
NM_005666
0.055
HFL3


262
69
214003_x_at
BF184532
0.055
RPS20


263
425
215719_x_at
X83493
0.06
TNFRSF6


264
1
207160_at
NM_000882
0.05
IL12A


265
347
209189_at
BC004490
0.065
FOS


266
197
206584_at
NM_015364
0.065
LY96


267
263
202166_s_at
NM_006241
0.06
PPP1R2


268
273
208229_at
NM_022975
0.06
FGFR2


269
344
204493_at
NM_001196
0.07
BID


270
181
211737_x_at
BC005916
0.065
PTN


271
177
221551_x_at
AB035172
0.065
SIAT7D


272
356
208743_s_at
BC001359
0.065
YWHAB


273
257
215131_at
AC002550
0.065
MGC35048


274
148
207694_at
NM_000307
0.07
POU3F4


275
244
217301_x_at
X71810
0.065
RBBP4


276
13
207509_s_at
NM_002288
0.07
LAIR2


277
73
216582_at
AL021808
0.07



278
420
213371_at
AI803302
0.06
LDB3


279
36
209156_s_at
AY029208
0.065
COL6A2


280
236
216817_s_at
AJ302604
0.065



281
210
207867_at
NM_006193
0.08
PAX4


282
43
221792_at
AW118072
0.08



283
174
214644_at
BF061074
0.07
HIST1H2AK


284
121
204065_at
NM_004854
0.055
CHST10


285
138
207938_at
NM_015886
0.075
PI15


286
87
210880_s_at
AB001467
0.075
EFS


287
194
214994_at
BF508948
0.065
KIAA0907


288
389
206925_at
NM_005668
0.085
SIAT8D


289
44
208135_at
NM_006481
0.065
TCF2


290
396
207392_x_at
NM_001076
0.08
UGT2B15


291
184
210229_s_at
M11734
0.085
CSF2


292
408
202430_s_at
NM_021105
0.07
PLSCR1


293
242
206218_at
NM_002364
0.08
MAGEB2


294
152
203421_at
NM_006034
0.08
TP53I11


295
47
216699_s_at
L10038
0.065
KLK1


296
268
207862_at
NM_006760
0.085
UPK2


297
128
216474_x_at
AF206667
0.075
TPSB2


298
230
216695_s_at
AF082559
0.085
TNKS


299
225
206911_at
NM_005082
0.085
ZNF147


300
431
211749_s_at
BC005941
0.07
VAMP3


301
60
215230_x_at
AA679705
0.085
EIF3S8


302
206
207872_s_at
NM_006863
0.085
LILRB1


303
424
216908_x_at
AF001549
0.085



304
378
209581_at
BC001387
0.085
HRASLS3


305
358
205321_at
NM_001415
0.085
EIF2S3


306
262
203755_at
NM_001211
0.09
BUB1B


307
246
211109_at
U31601
0.075
JAK3


308
98
201269_s_at
AB028991
0.1
KIAA1068


309
320
221511_x_at
AB033080
0.095
CPR8


310
117
215954_s_at
AI200896
0.075
NY-REN-24


311
401
210375_at
X83858
0.105
PTGER3


312
404
210331_at
AB048365
0.09
KIAA0322


313
264
206570_s_at
NM_002785
0.08
PSG11


314
34
214534_at
NM_005322
0.1
HIST1H1B


315
303
214726_x_at
AL556041
0.085
ADD1


316
369
205126_at
NM_006296
0.09
VRK2


317
92
204159_at
NM_001262
0.095
CDKN2C


318
277
216652_s_at
AL137673
0.095
DR1


319
93
211792_s_at
U17074
0.11
CDKN2C


320
293
209648_x_at
AL136896
0.11
SOCS5


321
153
207403_at
NM_003604
0.105
IRS4


322
142
208009_s_at
NM_014448
0.115
ARHGEF16


323
16
215778_x_at
AJ006206
0.11
HAB1


324
359
214348_at
NM_001057
0.09
TACR2


325
406
215993_at
AF070543
0.1



326
425
215719_x_at
X83493
0.115
TNFRSF6


327
327
206131_at
NM_001832
0.09
CLPS


328
321
222156_x_at
AK022459
0.09
CPR8


329
204
209961_s_at
M60718
0.11
HGF


330
384
212617_at
AB002293
0.12
KIAA0295


331
175
217192_s_at
AL022067
0.12
PRDM1


332
331
206762_at
NM_002234
0.115
KCNA5


333
297
206437_at
NM_003775
0.115
EDG6


334
410
204073_s_at
NM_013279
0.125
C11orf9


335
111
209107_x_at
U19179
0.09
NCOA1


336
209
211832_s_at
AF201370
0.145
MDM2


337
101
204895_x_at
NM_004532
0.115
MUC4


338
335
204643_s_at
NM_006375
0.115
COVA1


339
213
206426_at
NM_005511
0.115
MLANA


340
234
214624_at
AA548647
0.145
UPK1A


341
178
215266_at
AL096732
0.125
DNAH3


342
124
205608_s_at
U83508
0.12
ANGPT1


343
336
204668_at
AL031670
0.135
RNF24


344
414
209459_s_at
AF237813
0.1
NPD009


345
19
209685_s_at
M13975
0.15
PRKCB1


346
319
214151_s_at
AU144243
0.14
CPR8


347
418
216984_x_at
D84143
0.135
IGLJ3


348
376
207236_at
NM_003419
0.145
ZNF345


349
411
215599_at
X83300
0.13
SMA3


350
421
216887_s_at
AJ133768
0.145
LDB3


351
348
216392_s_at
AK021846
0.135
SEC23IP


352
81
205022_s_at
NM_005197
0.13
CHES1


353
374
215157_x_at
AI734929
0.14
PABPC1


354
140
207900_at
NM_002987
0.16
CCL17


355
62
211988_at
BG289800
0.145
SMARCE1


356
20
207504_at
NM_005182
0.13
CA7


357
168
207959_s_at
NM_004662
0.155
DNAH9


358
129
214487_s_at
NM_002886
0.15
RAP2B


359
415
209460_at
AF237813
0.13
NPD009


360
312
210992_x_at
U90939
0.115
FCGR2A


361
282
208405_s_at
NM_006016
0.185
CD164


362
370
206902_s_at
NM_005728
0.175
ENDOGL1


363
28
209453_at
M81768
0.125
SLC9A1


364
214
208193_at
NM_000590
0.145
IL9


365
223
208042_at
NM_013303
0.135
HSU84971


366
364
206826_at
NM_002677
0.16
PMP2


367
119
206592_s_at
NM_003938
0.16
AP3D1


368
95
209480_at
M16276
0.175
HLA-DQB1


369
228
212882_at
AB018338
0.17
KIAA0795


370
339
215634_at
AF007137
0.185



371
97
214613_at
AW024085
0.185
GPR3


372
9
206376_at
NM_018057
0.165
NTT73


373
102
217109_at
AJ242547
0.175
MUC4


374
276
209188_x_at
BC002809
0.2
DR1


375
417
210567_s_at
BC001441
0.175
SKP2


376
346
217056_at
X61070
0.195



377
258
207675_x_at
NM_003976
0.155
ARTN


378
328
214642_x_at
AI200443
0.165
MAGEA5


379
183
216867_s_at
X03795
0.195
PDGFA


380
208
204849_at
NM_006602
0.195
TCFL5


381
135
216939_s_at
Y08756
0.205
HTR4


382
23
209530_at
U07139
0.2
CACNB3


383
15
215175_at
AB023212
0.21
PCNX


384
185
209651_at
BC001830
0.195
TGFB1I1


385
292
205339_at
NM_003035
0.225
SIL


386
287
209737_at
AB014605
0.205
AIP1


387
186
206616_s_at
AF155382
0.225
ADAM22


388
201
212819_at
AF055024
0.22
ASB1


389
35
205498_at
NM_000163
0.205
GHR


390
239
214111_at
AF070577
0.195
OPCML


391
21
213106_at
AI769688
0.21
ATP8A1


392
368
214456_x_at
M23699
0.225
SAA2


393
221
200797_s_at
AI275690
0.235
MCL1


394
115
213815_x_at
AI913329
0.24
NY-REN-24


395
3
215935_at
AL080148
0.235
DKFZP434B204


396
52
204316_at
W19676
0.235
RGS10


397
17
216875_x_at
X83412
0.215
HAB1


398
409
215996_at
AI446234
0.215



399
48
202555_s_at
NM_005965
0.2
MYLK


400
190
216993_s_at
U32169
0.255
COL11A2


401
385
212461_at
BF793951
0.235
OAZIN


402
63
215781_s_at
D87012
0.25
TOP3B


403
99
209361_s_at
BC004153
0.215
PCBP4


404
330
213363_at
AW170549
0.235
na


405
78
214948_s_at
AL050136
0.245



406
159
207222_at
NM_003561
0.27
PLA2G10


407
100
213840_s_at
R68573
0.23
MRPS12


408
145
210306_at
U89358
0.285
L3MBTL


409
123
206328_at
NM_004933
0.23
CDH15


410
245
211108_s_at
U31601
0.28
JAK3


411
134
213221_s_at
AB018324
0.27
KIAA0781


412
198
212561_at
AA349595
0.27
RAB6IP1


413
189
208237_x_at
AF155381
0.28
ADAM22


414
139
217060_at
U03115
0.235



415
176
220937_s_at
NM_014403
0.265
SIAT7D


416
169
210345_s_at
AF257737
0.29
DNAH9


417
323
206577_at
NM_003381
0.33
VIP


418
4
204919_at
NM_007244
0.29
PROL4


419
55
216283_s_at
X64116
0.285
PVR


420
77
214096_s_at
AW190316
0.27
LOC56901


421
32
216224_s_at
AK024083
0.31
HDAC6


422
274
211349_at
AB001328
0.34
SLC15A1


423
50
215766_at
AL096729
0.335
GSTA1


424
281
216252_x_at
Z70519
0.32
TNFRSF6


425
163
214463_x_at
NM_003541
0.365
HIST1H4K


426
219
209245_s_at
AB014606
0.295
KIF1C


427
52
204316_at
W19676
0.31
RGS10


428
237
207044_at
NM_000461
0.31
THRB


429
261
207143_at
NM_001259
0.39
CDK6


430
216
214872_at
AL080129
0.3
DKFZP434D193


431
103
217110_s_at
AJ242547
0.475
MUC4





Table 4: Shown are the average errors of the differentiating genes in predicting a prognosis (poor or good clinical outcome) of the MS test group based on a model computed for each gene or a group of genes in the MS training set group. The ascending order of genes reflects combinations of genes, where each row includes the gene specified in that row and in all preceding rows. For example, the average error presented in row number 4 reflects the average error in predicting clinical outcome of MS of the group of genes described in 1, 2, 3 and 4 (i.e., SEQ ID NOs: 158, 68, 5 and 58).


Probeset ID = Affymetrix ID.






As shown in Table 4 hereinabove, the predictive power of each set of genes was evaluated using the MS training and test sets of samples. The polynucleotide exhibiting the best predictive power in determining MS prognosis (i.e., poor or good prognosis/clinical outcome) was the polynucleotide set forth by SEQ ID NO:158 (GenBank Accession No. NM005012; row No. 1 in Table 4), in which the average error between the test and training groups was “0” (zero) (100% accuracy). Similarly, the combination genes set forth by SEQ ID NOs: 158 and 68 (GenBank Accession No. NM001023; row No. 2 in Table 4) displayed a predictive power with “0” average error. Another exemplary combination is shown in row number 4 in Table 4, in which the combination of the polynucleotides set forth by SEQ ID NOs:158, 68, 5 and 58 displayed a high predictive power with “0” average error. Thus, this analysis enables one skilled in the art to select a group of polynucleotides which can give the best predictive power for the clinical outcome/prognosis of MS subjects.


It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub combination.


Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention.


REFERENCES
Additional References are Cited in Text



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Claims
  • 1. A method of predicting a prognosis of a subject diagnosed with multiple sclerosis, the method comprising determining in a cell of the subject a level of expression of at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103, wherein an alteration above a predetermined threshold in said level of expression of said at least one polynucleotide sequence in said cell of the subject relative to a level of expression of said at least one polynucleotide sequence in a reference cell is indicative of the prognosis of the subject diagnosed with multiple sclerosis.
  • 2. A method of treating a subject diagnosed with multiple sclerosis, the method comprising: predicting a prognosis of a subject diagnosed with multiple sclerosis according to the method of claim 1, and;(b) selecting a treatment regimen based on said prognosis;thereby treating the subject diagnosed with multiple sclerosis.
  • 3-4. (canceled)
  • 5. A probeset comprising a plurality of oligonucleotides and no more than 700 oligonucleotides wherein each of said plurality of oligonucleotides is capable of specifically recognizing at least one polynucleotide sequence selected from the group consisting of SEQ ID NOs:158, 68, 5, 58, 329, 120, 380, 342, 88, 166, 266, 51, 310, 91, 427, 22, 84, 269, 388, 155, 333, 215, 195, 419, 75, 125, 11, 251, 253, 337, 110, 222, 56, 324, 156, 7, 57, 233, 149, 363, 107, 193, 393, 265, 160, 41, 38, 90, 70, 85, 403, 304, 426, 240, 49, 294, 136, 150, 232, 10, 392, 89, 332, 290, 422, 291, 114, 309, 203, 362, 397, 334, 302, 179, 171, 53, 402, 315, 271, 218, 154, 243, 211, 180, 412, 300, 131, 71, 398, 289, 371, 118, 220, 82, 42, 430, 64, 144, 2, 205, 405, 318, 146, 314, 12, 416, 267, 105, 353, 296, 224, 165, 113, 345, 387, 61, 250, 59, 235, 382, 143, 361, 372, 199, 79, 116, 162, 322, 354, 391, 377, 255, 270, 373, 104, 400, 67, 167, 423, 188, 182, 106, 54, 326, 164, 307, 383, 260, 340, 357, 390, 161, 6, 316, 272, 338, 241, 367, 379, 40, 381, 231, 39, 256, 286, 8, 151, 399, 33, 254, 295, 141, 429, 65, 229, 259, 355, 298, 173, 371, 86, 45, 305, 127, 133, 200, 313, 370, 112, 226, 249, 80, 299, 196, 27, 308, 288, 349, 108, 311, 14, 130, 285, 207, 365, 275, 284, 96, 172, 76, 279, 413, 351, 202, 37, 74, 375, 360, 170, 147, 151, 306, 366, 217, 394, 192, 341, 352, 83, 157, 122, 350, 30, 25, 260, 238, 428, 278, 252, 395, 343, 325, 31, 386, 301, 317, 29, 407, 72, 283, 227, 191, 126, 132, 187, 94, 66, 109, 46, 212, 24, 69, 425, 1, 347, 197, 263, 273, 344, 181, 177, 356, 257, 148, 244, 13, 73, 420, 36, 236, 210, 43, 174, 121, 138, 87, 194, 389, 44, 396, 184, 408, 242, 152, 47, 268, 128, 230, 225, 431, 60, 206, 424, 378, 358, 262, 246, 98, 320, 117, 401, 404, 264, 34, 303, 369, 92, 277, 93, 293, 153, 142, 16, 359, 406, 425, 327, 321, 204, 384, 175, 331, 297, 410, 111, 209, 101, 335, 213, 234, 178, 124, 336, 414, 19, 319, 418, 376, 411, 421, 348, 81, 374, 140, 62, 20, 168, 129, 415, 312, 282, 370, 28, 214, 223, 364, 119, 95, 228, 339, 97, 9, 102, 276, 417, 346, 258, 328, 183, 208, 135, 23, 15, 185, 292, 287, 186, 201, 35, 239, 21, 368, 221, 115, 3, 52, 17, 409, 48, 190, 385, 63, 99, 330, 78, 159, 100, 145, 123, 245, 134, 198, 189, 139, 176, 169, 323, 4, 55, 77, 32, 274, 50, 281, 163, 219, 52, 237, 261, 216, 103.
  • 6. The probeset of claim 3, wherein each of said isolated nucleic acid sequences or said plurality of oligonucleotides is bound to a solid support.
  • 7. The probeset of claim 6, wherein said plurality of oligonucleotides are bound to said solid support in an addressable location.
  • 8. The method of claim 1, wherein said reference cell is of a subject diagnosed with multiple sclerosis which displayed within a period of two years an increase of at least 0.5 point in an Expanded Disability Status Scale (EDSS).
  • 9. The method of claim 1, wherein said reference cell is of a subject diagnosed with multiple sclerosis which displayed within a period of two years no change in an Expanded Disability Status Scale (EDSS).
  • 10. The method of claim 8, wherein said alteration is upregulation of said expression level of said at least one polynucleotide sequence in said cell of the subject relative to said reference cell, whereas said at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:1-193.
  • 11. The method of claim 10, wherein said prognosis comprises no change in an Expanded Disability Status Scale (EDSS) of the subject within a period of two years.
  • 12. The method of claim 11, wherein said prognosis further comprises no relapses within said period of said two years.
  • 13. The method of claim 9, wherein said alteration is upregulation of said expression level of said at least one polynucleotide sequence in said cell of the subject relative to said reference cell, whereas said at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:194-431.
  • 14. The method of claim 13, wherein said prognosis comprises an increase of at least 0.5 point in an Expanded Disability Status Scale (EDSS) of the subject within a period of at least two years.
  • 15. The method of claim 1, wherein said detecting said level of expression is effected using an RNA detection method.
  • 16-18. (canceled)
  • 19. The method of claim 1, wherein said at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:156, 143, 127, 46, 311, 140, 74, 276, 180, 182, 191, 61, 306, 115, 97, 303, 272, 50, 16, 63, 117, 406, 423, 128, 277, 47, 17, 424, 418, 190, 139, 102, 103 and 325.
  • 20. The method of claim 1, wherein said at least one polynucleotide sequence is selected from the group consisting of SEQ ID NOs:127, 423, 16, 17, 424, 190 and 325.
  • 21. The method of claim 1, wherein said at least one polynucleotide comprises the 7 polynucleotides set forth by SEQ ID NOs:127, 423, 16, 17, 424, 190 and 325.
  • 22. (canceled)
  • 23. The method of claim 1, wherein said at least one polynucleotide sequence is set forth in SEQ ID NO:158.
  • 24. The method of claim 1, wherein said at least one polynucleotide comprises the polynucleotide sequences set forth in SEQ ID NOs:158, 68, 5, 58, 329 and 120.
  • 25. The method of claim 1 wherein said detecting said level of expression is effected using a protein detection method.
  • 26. A kit for predicting a prognosis of a subject diagnosed with multiple sclerosis, comprising the probeset of claim 5 and a reference cell.
  • 27. The kit of claim 26, further comprising packaging materials packaging said at least one reagent and instructions for use in determining the prognosis of the subject diagnosed with multiple sclerosis.
PCT Information
Filing Document Filing Date Country Kind 371c Date
PCT/IL2007/001617 12/27/2007 WO 00 12/3/2009
Provisional Applications (1)
Number Date Country
60877680 Dec 2006 US