Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma ... View Full Text


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Article Info

DATE

2014-06-10

AUTHORS

David W Mount, Charles W Putnam, Sara M Centouri, Ann M Manziello, Ritu Pandey, Linda L Garland, Jesse D Martinez

ABSTRACT

BACKGROUND: Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges. METHODS: Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison. RESULTS: A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome. CONCLUSIONS: Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer. More... »

PAGES

33-33

References to SciGraph publications

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    DIMENSIONS

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    34 schema:description BACKGROUND: Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges. METHODS: Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison. RESULTS: A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome. CONCLUSIONS: Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer.
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    42 KM approach
    43 Kaplan-Meier analysis
    44 Kaplan-Meier approach
    45 Lung Study
    46 Meier (KM) approach
    47 Meier analysis
    48 Numerous microarray-based prognostic gene expression signatures
    49 accuracy
    50 accuracy of prediction
    51 analysis
    52 applications
    53 approach
    54 appropriate cases
    55 breast cancer
    56 breast cancer data
    57 breast carcinoma
    58 breast carcinoma cases
    59 breast studies
    60 buildings
    61 cancer
    62 cancer data
    63 carcinoma
    64 carcinoma cases
    65 careful selection
    66 cases
    67 cell carcinoma
    68 cell-related genes
    69 cells
    70 challenges
    71 clinical challenge
    72 clinical data
    73 clinical outcomes
    74 clinical utility
    75 comparison
    76 concurrence
    77 conventional KM approaches
    78 conventional Kaplan Meier (KM) approach
    79 conventional Kaplan Meier analysis
    80 data
    81 data sets
    82 death
    83 disease
    84 early death
    85 early stage squamous cell carcinoma
    86 early-stage disease
    87 expression
    88 expression data sets
    89 expression signatures
    90 four-fold
    91 gene expression
    92 gene expression data sets
    93 gene expression signatures
    94 gene lists
    95 genes
    96 group
    97 hits
    98 homogenous patient groups
    99 immune-related genes
    100 limitations
    101 list
    102 little concurrence
    103 logistic regression
    104 logistic regression analysis
    105 long survivors
    106 longer survival
    107 lung
    108 lung data
    109 methodology
    110 microarray gene expression data sets
    111 microarray-based prognostic gene expression signatures
    112 model building
    113 negative breast cancer
    114 negative breast carcinoma
    115 negative breast carcinoma cases
    116 neoplasms
    117 objective
    118 one-out approach
    119 outcomes
    120 patient group
    121 prediction
    122 predictive accuracy
    123 primary neoplasms
    124 prognostic gene expression signatures
    125 prognostic genes
    126 prognostic value
    127 regression
    128 regression analysis
    129 same data set
    130 selection
    131 set
    132 signatures
    133 significant hits
    134 similar analysis
    135 similar clinical challenges
    136 similar logistic regression analysis
    137 size
    138 sliding-window analysis
    139 squamous cell carcinoma
    140 stage I
    141 stage III cases
    142 stage squamous cell carcinoma
    143 stratification
    144 stratification of cases
    145 study
    146 subset
    147 survival
    148 survivors
    149 total
    150 triple-negative breast cancer
    151 triple-negative breast carcinoma cases
    152 triple-negative breast carcinomas
    153 utility
    154 validation
    155 validation subset
    156 values
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