A systems-based modelling approach using transurethral resection of the prostate (TURP) specimens yielded incremental prognostic significance to Gleason when predicting long-term outcome in men with localized prostate cancer.

OBJECTIVE: To develop a systems-based model for predicting prostate cancer-specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long-term follow-up. PATIENTS AND METHODS: Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour. Additional sections were analysed with two multiplex quantitative immunofluorescence (IF) assays for cytokeratin-18 (epithelial cells), 4'-6-diamidino-2-phenylindole(nuclei), p63/high-molecular-weight keratin (basal cells), androgen receptor (AR) and α-methyl CoA-racemase, Ki67, phosphorylated AKT (pAKT)and CD34. Images were acquired with spectral imaging software. H&E and IF images were evaluated with image analysis algorithms; feature data were integrated with clinical variables to construct prognostic models for outcome. RESULTS: Using a training set of 256 patients with 24% events, one clinical variable (Gleason score) and two tissue-specific characteristics (H&E morphometry and tumour-specific pAKT levels) were identified (concordance index [CoI] 0.79, sensitivity 76%, specificity 86%, hazard ratio [HR] 6.6) for predicting PCSS. Validation on an independent cohort of 269 patients with 29% events yielded a CoI of 0.76, sensitivity 59%, specificity 80% and HR of 3.6. Both H&E and IF features were selected in a multivariate setting and added incremental prognostic value to the Gleason score alone (CoI 0.77 to CoI 0.79). Furthermore, global Ki67 expression and AR levels in Gleason grade 3 tumours were both univariately associated with outcome; however, neither was selected in the final model. CONCLUSION: A previously validated prostate needle-biopsy systems modelling approach that integrates clinical data with reproducible methods to assess H&E morphometry and biomarker expression, provided incremental benefit to the TURP Gleason score for predicting PCSS. Ki67 and AR, known to be associated with outcome in the prostate needle biopsy, were not associated with PCSS in multivariate models using TURP specimens.

Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery.

OBJECTIVE: To compare the performance of a systems-based risk assessment tool with standard defined risk groups and the 10-year postoperative nomogram for predicting disease progression, including biochemical relapse and clinical (systemic) failure. PATIENTS AND METHODS: Clinical variables, biometric profiles and outcome results from a training cohort comprising 373 patients in a published postoperative systems-based prognostic model were obtained. Patients were stratified according to D'Amico standard risk groups, Kattan 10-year postoperative nomogram and prognostic scores from the postoperative tissue model. The association of pathological variables and calculated risk groups with biochemical recurrence and clinical (systemic) failure was assessed using the concordance index (C-index) and hazard ratio (HR). RESULTS: Systems-based post-prostatectomy models to predict significant disease progression (post-treatment clinical failure) were more accurate than the D'Amico defined risk groups and the Kattan 10-year postoperative nomogram (systems model: C-index, 0.84; HR, 17.46; P < 0.001 vs D'Amico: C-index, 0.73; HR, 11; P = 0.001; 10-year nomogram: C-index, 0.79; HR, 5.06; P < 0.001). The systems models were also more accurate than standard risk groups for predicting prostate-specific antigen recurrence (systems model: C-index, 0.76; HR, 8.94; P < 0.001 vs D'Amico C- index, 0.70; HR, 4.67; P < 0.001) and showed incremental improvement over the 10-year postoperative nomogram (C-index, 0.75; HR, 5.83; P < 0.001). The postoperative tissue model provided additional risk discrimination over surgical margin status and extracapsular extension for predicting disease outcome, and was most significant for the clinical (systemic) failure endpoint (surgical margin: C-index, 0.58; HR, 1.57; P= 0.2; extracapsular extension: C-index, 0.62; HR, 2.06; P = 0.04). CONCLUSIONS: Risk assessment models that incorporate characteristics from the patient's own tumour specimen are more accurate than clinical-only nomograms for predicting significant disease outcome. Systems-based tools should provide useful information concerning the appropriate receipt of adjuvant therapy in the post-surgical setting.

A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib.

PURPOSE: To identify clinical and biometric features associated with overall survival of patients with advanced refractory non-small-cell lung cancer (NSCLC) treated with gefitinib. EXPERIMENTAL DESIGN: One hundred and nine diagnostic NSCLC samples were analysed for EGFR mutation status, EGFR immunohistochemistry, histologic morphometry and quantitative immunofluorescence of 15 markers. Support vector regression modelling using the concordance index was employed to predict overall survival. RESULTS: Tumours from 4 of 87 patients (5%) contained EGFR tyrosine kinase domain mutations. A multivariate model identified ECOG performance status, and tumour morphometry, along with cyclin D1, caspase-3 activated, and phosphorylated KDR to be associated with overall survival, concordance index of 0.74 (hazard ratio (HR) 5.26, p-value 0.0002). CONCLUSIONS: System-based models can be used to identify a set of baseline features that are associated with reduced overall survival in patients with NSCLC treated with gefitinib. This is a preliminary study, and further analyses are required to validate the model in a randomised, controlled treatment setting.

Comparison of models to predict clinical failure after radical prostatectomy.

BACKGROUND: Models are available to accurately predict biochemical disease recurrence (BCR) after radical prostatectomy (RP). Because not all patients experiencing BCR will progress to metastatic disease, it is appealing to determine postoperatively which patients are likely to manifest systemic disease. METHODS: The study cohort consisted of 881 patients undergoing RP between 1985 and 2003. Clinical failure (CF) was defined as metastases, a rising prostate-specific antigen (PSA) in a castrate state, or death from prostate cancer. The cohort was randomized into training and validation sets. The accuracy of 4 models to predict clinical outcome within 5 years of RP were compared: 'postoperative BCR nomogram' and 'Cox regression CF model' based on standard clinical and pathologic parameters, and 2 CF 'systems pathology' models that integrate clinical and pathologic parameters with quantitative histomorphometric and immunofluorescent biomarker features ('systems pathology Models 1 and 2'). RESULTS: When applied to the validation set, the concordance index for the postoperative BCR nomogram was 0.85, for the Cox regression CF model 0.84, for systems pathology Model 1 0.81, and for systems pathology Model 2 0.85. CONCLUSIONS: Models predicting either BCR or CF after RP exhibit similarly high levels of accuracy because standard clinical and pathologic variables appear to be the primary determinants of both outcomes. It is possible that introducing current or novel biomarkers found to be uniquely associated with disease progression may further enhance the accuracy of the systems pathology-based platform.

Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy.

PURPOSE: For patients with prostate cancer treated by radical prostatectomy, no current personalized tools predict clinical failure (CF; metastasis and/or androgen-independent disease). We developed such a tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue. PATIENTS AND METHODS: A prospectively designed algorithm was applied retrospectively to a cohort of 758 patients with clinically localized or locally advanced prostate cancer. A model predicting distant metastasis and/or androgen-independent recurrence was derived from features selected through supervised multivariate learning. Performance of the model was estimated using the concordance index (CI). RESULTS: We developed a predictive model using a training set of 373 patients with 33 CF events. The model includes androgen receptor (AR) levels, dominant prostatectomy Gleason grade, lymph node involvement, and three quantitative characteristics from hematoxylin and eosin staining of prostate tissue. The model had a CI of 0.92, sensitivity of 90%, and specificity of 91% for predicting CF within 5 years after prostatectomy. Model validation on an independent cohort of 385 patients with 29 CF events yielded a CI of 0.84, sensitivity of 84%, and specificity of 85%. High levels of AR predicted shorter time to castrate prostate-specific antigen increase after androgen deprivation therapy (ADT). CONCLUSION: The integration of clinicopathologic variables with imaging and biomarker data (systems pathology) resulted in a highly accurate tool for predicting CF within 5 years after prostatectomy. The data support a role for AR signaling in clinical progression and duration of response to ADT.

Improved prediction of prostate cancer recurrence through systems pathology.

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.