Faisal M. Khan

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.

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.

Androgen receptor expression is associated with prostate cancer-specific survival in castrate patients with metastatic disease

Study Type – Aetiology (case series)
 Level of Evidence 4

Personalized Prediction of Tumor Response and Cancer Progression on Prostate Needle Biopsy

PURPOSE To our knowledge in patients with prostate cancer there are no available tests except clinical variables to determine the likelihood of disease progression. We developed a patient specific, biology driven tool to predict outcome at diagnosis. We also investigated whether biopsy androgen receptor levels predict a durable response to therapy after secondary treatment. MATERIALS AND METHODS We evaluated paraffin embedded prostate needle biopsy tissue from 1,027 patients with cT1c-T3 prostate cancer treated with surgery and followed a median of 8 years. Machine learning was done to integrate clinical data with biopsy quantitative biometric features. Multivariate models were constructed to predict disease progression with the C index to estimate performance. RESULTS In a training set of 686 patients (total of 87 progression events) 3 clinical and 3 biopsy tissue characteristics were identified to predict clinical progression within 8 years after prostatectomy with 78% sensitivity, 69% specificity, a C index of 0.74 and a HR of 5.12. Validation in an independent cohort of 341 patients (total of 44 progression events) yielded 76% sensitivity, 64% specificity, a C index of 0.73 and a HR of 3.47. Increased androgen receptor in tumor cells in the biopsy highly significantly predicted resistance to therapy, ie androgen ablation with or without salvage radiotherapy, and clinical failure (p <0.0001). CONCLUSIONS Morphometry reliably classifies Gleason pattern 3 tumors. When combined with biomarker data, it adds to the hematoxylin and eosin analysis, and prostate specific antigen values currently used to assess outcome at diagnosis. Biopsy androgen receptor levels predict the likelihood of a response to therapy after recurrence and may guide future treatment decisions.

Prostate biopsies from black men express higher levels of aggressive disease biomarkers than prostate biopsies from white men.

A wide array of biomarkers is being investigated as predictors of prostate cancer (PCa) diagnosis and recurrence. We compared the expression of a small panel of these biomarkers as a function of race among men undergoing radical prostatectomy (RP). Prostate needle biopsy specimens from 131 patients treated with RP at the Durham Veterans Affairs Medical Center were hematoxylin and eosin stained and immunofluorescent assayed for α-methylacyl CoA racemase (AMACR), androgen receptor (AR) and Ki67. Proprietary image analysis was used to identify six biometric feature combinations that were significantly associated with progression in a previous study. Analysis of population characteristics, stratified by race, was performed using rank-sum and χ2-test. The effect of race on expression of these biomarker profiles was analyzed using multivariate linear regression. All six biomarker features were expressed at higher levels in black men than white men, with Norm AR (P=0.006) and Ki67 (P=0.02) attaining statistical significance. On multivariate analysis, all markers were expressed at higher levels in black men, with Norm AR (P=0.001), Ki67 (P=0.007) and Ki67/lum (P=0.022) reaching significance. These data support the hypothesis that PCa may be biologically more aggressive among black men.

Comparison of models to predict clinical failure after radical prostatectomy

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.

Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging

We present a new system for automated localization and quantification of the expression of protein biomarkers in immunofluorescence (IF) microscopic images. The system includes a novel method for discriminating the biomarker signal from background, where signal may be the expression of any of the many biomarkers or counterstains used in IF. The method is based on supervised learning and represents the biomarker intensity threshold as a function of image background characteristics. The utility of the proposed system is demonstrated in predicting prostate cancer recurrence in patients undergoing prostatectomy. Specifically, features representing androgen receptor (AR) expression are shown to be statistically significantly associated with poor outcome in univariate analysis. AR features are also shown to be valuable for multivariate recurrence prediction.

Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer

Purpose: The high fatality-to-case ratio of ovarian cancer is directly related to platinum resistance. Exportin-1 (XPO1) is a nuclear exporter that mediates nuclear export of multiple tumor suppressors. We investigated possible clinicopathologic correlations of XPO1 expression levels and evaluated the efficacy of XPO1 inhibition as a therapeutic strategy in platinum-sensitive and -resistant ovarian cancer. Experimental Design: XPO1 expression levels were analyzed to define clinicopathologic correlates using both TCGA/GEO datasets and tissue microarrays (TMA). The effect of XPO1 inhibition, using the small-molecule inhibitors KPT-185 and KPT-330 (selinexor) alone or in combination with a platinum agent on cell viability, apoptosis, and the transcriptome was tested in immortalized and patient-derived ovarian cancer cell lines (PDCL) and platinum-resistant mice (PDX). Seven patients with late-stage, recurrent, and heavily pretreated ovarian cancer were treated with an oral XPO1 inhibitor. Results: XPO1 RNA overexpression and protein nuclear localization were correlated with decreased survival and platinum resistance in ovarian cancer. Targeted XPO1 inhibition decreased cell viability and synergistically restored platinum sensitivity in both immortalized ovarian cancer cells and PDCL. The XPO1 inhibitor–mediated apoptosis occurred through both p53-dependent and p53-independent signaling pathways. Selinexor treatment, alone and in combination with platinum, markedly decreased tumor growth and prolonged survival in platinum-resistant PDX and mice. In selinexor-treated patients, tumor growth was halted in 3 of 5 patients, including one with a partial response, and was safely tolerated by all. Conclusions: Taken together, these results provide evidence that XPO1 inhibition represents a new therapeutic strategy for overcoming platinum resistance in women with ovarian cancer. Clin Cancer Res; 23(6); 1552–63. ©2016 AACR.

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

Study Type – Prognostic (individual cohort)

Glandular object based tumor morphometry in H&E biopsy samples for prostate cancer prognosis

Morphological and architectural characteristics of primary prostate tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide critical information for cancer diagnosis, prognosis and therapeutic response prediction. The subjective and variable Gleason grade assessed by expert pathologists in Hematoxylin and Eosin (H&E) stained specimens has been the standard for prostate cancer diagnosis and prognosis. We propose a novel morphometric, glandular object-oriented image analysis approach for the robust quantification of H&E prostate biopsy images. We demonstrate the utility of features extracted through the proposed method in predicting disease progression post treatment in a multi-institution cohort of 1027 patients. The biopsy based features were univariately predictive for clinical response post therapy; with concordance indexes (CI) ≤ 0.4 or ≥ 0.6. In multivariate analysis, a glandular object feature quantifying tumor epithelial cells not directly associated with an intact tumor gland was selected in a model incorporating preoperative clinical data, protein biomarker and morphological imaging features. The model achieved a CI of 0.73 in validation, which was significantly higher than a CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice. This work presents one of the first demonstrations of glandular object based morphological features in the H&E stained biopsy specimen to predict disease progression post primary treatment. Additionally, it is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.