Calculator for ovarian carcinoma subtype prediction

Abstract

With the emerging evidence that the five major ovarian carcinoma subtypes (high-grade serous, clear cell, endometrioid, mucinous, and low-grade serous) are distinct disease entities, management of ovarian carcinoma will become subtype specific in the future. In an effort to improve diagnostic accuracy, we set out to determine if an immunohistochemical panel of molecular markers could reproduce consensus subtype assignment. Immunohistochemical expression of 22 biomarkers were examined on tissue microarrays constructed from 322 archival ovarian carcinoma samples from the British Columbia Cancer Agency archives, for the period between 1984 and 2000, and an independent set of 242 cases of ovarian carcinoma from the Gynaecologic Tissue Bank at Vancouver General Hospital from 2001 to 2008. Nominal logistic regression was used to produce a subtype prediction model for each of these sets of cases. These models were then crossvalidated against the other cohort, and then both models were further validated in an independent cohort of 81 ovarian carcinoma samples from five different centers. Starting with data for 22 markers, full model fit, backwards, nominal logistic regression identified the same nine markers (CDKN2A, DKK1, HNF1B, MDM2, PGR, TFF3, TP53, VIM, WT1) as being most predictive of ovarian carcinoma subtype in both the archival and tumor bank cohorts. These models were able to predict subtype in the respective cohort in which they were developed with a high degree of sensitivity and specificity (j statistics of 0.88 ± 0.02 and 0.86 ± 0.04, respectively). When the models were cross-validated (ie using the model developed in one case series to predict subtype in the other series), the prediction equation's performances were reduced (j statistics of 0.70 ± 0.04 and 0.61 ± 0.04, respectively) due to differences in frequency of expression of some biomarkers in the two case series. Both models were then validated on the independent series of 81 cases, with very good to excellent ability to predict subtype (j ¼ 0.85±0.06 and 0.78±0.07, respectively). A nine-marker immunohistochemical maker panel can be used to objectively support classification into one of the five major subtypes of ovarian carcinoma.

Reproducibility of histological cell type in high-grade endometrial carcinoma

“…The model was started with fitting the full 12-marker panel and utilized a manual, iterative backwards elimination process. 23 The criterion for the exclusion of a particular marker was based on the highest P-value in the effect likelihood ratio test. From the model predictions diagnosis of endometrioid carcinoma FIGO grade 3 or serous carcinoma, a receiver operator characteristic area under the curve was calculated and a confusion matrix of model predicted versus morphological consensus type was generated.…”

“…We chose a marker panel that is in routine diagnostic use in many laboratories and appended some experimental markers from the recent literature that might aid in the differential diagnosis of endometrioid, serous, or clear-cell types. [6][7][8]11,[16][17][18][19][20][21][22][23] …”

Cancer of the ovary, fallopian tube, and peritoneum

“…Serous carcinomas are graded in a twograde system befitting their biology. High-grade serous carcinomas, including both classic-appearing and those with SET features (solid, endometrioid-like, and transitional) carry a high frequency of mutations in TP53 [20][21][22]. Low-grade serous carcinomas are often associated with borderline or atypical proliferative serous tumors, often contain mutations in BRAF and KRAS and contain wild-type TPp53.…”

“…Low-grade serous carcinomas are often associated with borderline or atypical proliferative serous tumors, often contain mutations in BRAF and KRAS and contain wild-type TPp53. Most "moderately differentiated" serous carcinomas carry mutations in TP53 and should be combined with the high-grade tumors [19,[21][22][23].…”

Specific features of current intraperitoneal therapy in patients with ovarian cancer

“…Необходимо помнить, что РЯ -крайне гетеро-генная болезнь, а метастазы, как правило, находятся на поверхности брюшины, поэтому более эффектив-ные стратегии необходимо развивать с внедрением новых форм препаратов для местной терапии, позво-ляющих снизить влияние факторов, приводящих к ле-карственной резистентности, таких как опухолевая микросреда с особенностями фенотипа раковых клеток [24]. Увеличение эффективности лекарств на этапе оптимизации в / б терапии возможно за счет нескольких факторов: повышение температуры рас-твора, объема жидкости или давления, выбор пра-вильного растворителя (высокомолекулярные лучше, чем низкомолекулярные), а также применение вазо-констрикторов (адреналин, вазопрессин) одновремен-но с цисплатином.…”