We studied the whole-genome point mutation and structural variation patterns of 133 tumors (59 high-grade serous (HGSC), 35 clear cell (CCOC), 29 endometrioid (ENOC), and 10 adult granulosa cell (GCT)) as a substrate for class discovery in ovarian cancer. Ab initio clustering of integrated point mutation and structural variation signatures identified seven subgroups both between and within histotypes. Prevalence of foldback inversions identified a prognostically significant HGSC group associated with inferior survival. This finding was recapitulated in two independent cohorts (n = 576 cases), transcending BRCA1 and BRCA2 mutation and gene expression features of HGSC. CCOC cancers grouped according to APOBEC deamination (26%) and age-related mutational signatures (40%). ENOCs were divided by cases with microsatellite instability (28%), with a distinct mismatch-repair mutation signature. Taken together, our work establishes the potency of the somatic genome, reflective of diverse DNA repair deficiencies, to stratify ovarian cancers into distinct biological strata within the major histotypes.
Purpose: We aimed to find key molecules associated with chemoresistance in ovarian cancer using gene expression profiling as a screening tool. Experimental Design: Using two newly established paclitaxel-resistant ovarian cancer cell lines from an original paclitaxel-sensitive cell line and four supersensitive and four refractory surgical ovarian cancer specimens from paclitaxel-based chemotherapy, molecules associated with chemoresistance were screened with gene expression profiling arrays containing 39,000 genes. We further analyzed 44 genes that showed significantly different expressions between paclitaxelsensitive samples and paclitaxel-resistant samples with permutation tests, which were common in cell lines and patients' tumors. Results: Eight of these genes showed reproducible results with real-time reverse transcription-PCR, of which indoleamine 2,3-dioxygenase gene expression was the most prominent and consistent. Moreover, by immunohistochemical analysis using a total of 24 serous-type ovarian cancer surgical specimens (stage III, n = 21; stage IV, n = 7), excluding samples used for GeneChip analysis, the Kaplan-Meier survival curve showed a clear relationship between indoleamine 2,3-dioxygenase staining patterns and overall survival (log-rank test, P = 0.0001). All patients classified as negative survived without relapse.The 50% survival of patients classified as sporadic, focal, and diffuse was 41, 17, and 11months, respectively. Conclusion: The indoleamine 2,3-dioxygenase screened with the GeneChip was positively associated with paclitaxel resistance and with impaired survival in patients with serous-type ovarian cancer.
In this paper, we will give a complete geometric background for the geometry of Painlevé V I and Garnier equations. By geometric invariant theory, we will construct a smooth fine moduli space Mņ (t, -, L) of stable parabolic connections on P 1 with logarithmic poles at D(t) = t 1 +· · ·+t n as well as its natural compactification. Moreover the moduli space R(P n,t ) a of Jordan equivalence classes of SL 2 (C)-representations of the fundamental group π 1 (P 1 \ D(t), * ) are defined as the categorical quotient. We define the Riemann-Hilbert correspondence RH : Mņ (t, -, L) −→ R(P n,t ) a and prove that RH is a bimeromorphic proper surjective analytic map. Painlevé and Garnier equations can be derived from the isomonodromic flows and Painlevé property of these equations are easily derived from the properties of RH. We also prove Michi-aki Inaba, Katsunori Iwasaki and Masa-Hiko Saito that the smooth parts of both moduli spaces have natural symplectic structures and RH is a symplectic resolution of singularities of R(P n,t ) a , from which one can give geometric backgrounds for other interesting phenomena, like Hamiltonian structures, Bäcklund transformations, special solutions of these equations. §1. Introduction §1.1. The purposeThe purpose of the series of papers is to give a complete geometric background for Painlevé equations of type V I or more generally for the so-called Garnier equations.As is well-known, these nonlinear differential equations have the Painlevé property which means that generic solutions of these equations have no movable singularity except for poles so that solutions have the analytic continuations on whole of the universal covering of the space of time variables.Besides the Painlevé property, there are several interesting phenomena related to these equations which have been investigated by many authors.• Each of these equations can be written in a Hamiltonian system by a natural symplectic coordinate system ([Mal], [O3], [Iw1], [Iw2], [K], [ST]).• These equations have natural parameters λ = (λ 1 , . . . , λ n ) ∈ C n . Moreover there exist birational symmetries of these equations, called Bäcklund transformations of these equations, which act on both of variables and the parameters and preserve the equations. ([O4]).• In Painlevé V I case, the group of all Bäcklund transformations is isomorphic to the affine Weyl group W (D4 ) of the type D . ([O4], [Sakai], [AL2], [NY], [IIS0]).• In Painlevé V I case, if λ ∈ C 4 lies on a reflection hyperplane of a reflection in W (D • A natural compactification of each space of initial conditions for P V I , introduced by Okamoto [O1], can be obtained by a series of explicit blowing-ups ofThe compactification is given by a smooth projective rational surface S and it has a unique anti-canonical divisors −K S = Y such that S \ Y red is the space of initial conditions for P V I . The pair (S, Y ) becomes an Okamoto-Painlevé pair of type D (1) 4 in the sense of [STT]. (See also [Sakai]). Moduli of Stable Parabolic Connections 989Though these pheno...
Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Na€ ve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble meth-ods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival.Conclusions: Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients.NOTE: There were too few early-stage EOC patients with residual tumor. A definition for the significance of bold is P value of < 0.05.
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