Clinicopathological and radiological stratification within FIGO 2018 stages improves risk-prediction in cervical cancer.

OBJECTIVE: Assess the added prognostic value of the updated International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system, and to identify clinicopathological and radiological biomarkers for improved FIGO 2018 prognostication. METHODS: Patient data were retrieved from a prospectively collected patient cohort including all consenting patients with cervical cancer diagnosed and treated at Haukeland University Hospital during 2001-2022 (n = 948). All patients were staged according to the FIGO 2009 and FIGO 2018 guidelines based on available data for individual patients. MRI-assessed maximum tumor diameter and stromal tumor invasion, as well as histopathologically assessed lymphovascular space invasion were applied to categorize patients according to the Sedlis criteria. RESULTS: FIGO 2018 stage yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) (0.86 versus 0.81 for FIGO 2009) for predicting disease-specific survival. The most common stage migration in FIGO 2018 versus FIGO 2009 was upstaging from stages IB/II to stage IIIC due to suspicious lymph nodes identified by PET/CT and/or MRI. In FIGO 2018 stage III patients, extent and size of primary tumor (p = 0.04), as well as its histological type (p = 0.003) were highly prognostic. Sedlis criteria were prognostic within FIGO 2018 IB patients (p = 0.04). CONCLUSIONS: Incorporation of cross-sectional imaging increases prognostic precision, as suggested by the FIGO 2018 guidelines. The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor and histological type. The FIGO IB risk prediction could be improved by applying MRI-assessed tumor size and stromal invasion.

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. RESULTS: The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION: Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Visceral fat percentage for prediction of outcome in uterine cervical cancer.

OBJECTIVE: The prognostic role of adiposity in uterine cervical cancer (CC) is largely unknown. Abdominal fat distribution may better reflect obesity than body mass index. This study aims to describe computed tomography (CT)-assessed abdominal fat distribution in relation to clinicopathologic characteristics, survival, and tumor gene expression in CC. METHODS: The study included 316 CC patients diagnosed during 2004-2017 who had pre-treatment abdominal CT. CT-based 3D segmentation of total-, subcutaneous- and visceral abdominal fat volumes (TAV, SAV and VAV) allowed for calculation of visceral fat percentage (VAV% = VAV/TAV). Liver density (LD) and waist circumference (at L3/L4-level) were also measured. Associations between CT-derived adiposity markers, clinicopathologic characteristics and disease-specific survival (DSS) were explored. Gene set enrichment of primary tumors were examined in relation to fat distribution in a subset of 108 CC patients. RESULTS: High TAV, VAV and VAV% and low LD were associated with higher age (≥44 yrs.; p ≤ 0.017) and high International Federation of Gynecology and Obstetrics (FIGO) (2018) stage (p ≤ 0.01). High VAV% was the only CT-marker predicting high-grade histology (p = 0.028), large tumor size (p = 0.016) and poor DSS (HR 1.07, p < 0.001). Patients with high VAV% had CC tumors that exhibited increased inflammatory signaling (false discovery rate [FDR] < 5%). CONCLUSIONS: High VAV% is associated with high-risk clinical features and predicts reduced DSS in CC patients. Furthermore, patients with high VAV% had upregulated inflammatory tumor signaling, suggesting that the metabolic environment induced by visceral adiposity contributes to tumor progression in CC.

Single-cell profiling of low-stage endometrial cancers identifies low epithelial vimentin expression as a marker of recurrent disease.

BACKGROUND: Identification of aggressive low-stage endometrial cancers is challenging. So far, studies have failed to pinpoint robust features or biomarkers associated with risk of recurrence for these patients. METHODS: Imaging mass cytometry was used to examine single-cell expression of 23 proteins in 36 primary FIGO IB endometrial cancers, of which 17 recurred. Single-cell information was extracted for each tumor and unsupervised clustering was used to identify cellular phenotypes. Distinct phenotypes and cellular neighborhoods were compared in relation to recurrence. Cellular differences were validated in a separate gene expression dataset and the TCGA EC dataset. Vimentin protein expression was evaluated by IHC in pre-operative samples from 518 patients to validate its robustness as a prognostic marker. FINDINGS: The abundance of epithelial, immune or stromal cell types did not associate with recurrence. Clustering of patients based on tumor single cell marker expression revealed distinct patient clusters associated with outcome. A cell population neighboring CD8+ T cells, defined by vimentin, ER, and PR expressing epithelial cells, was more prevalent in non-recurrent tumors. Importantly, lower epithelial vimentin expression and lower gene expression of VIM associated with worse recurrence-free survival. Loss and low expression of vimentin was validated by IHC as a robust marker for recurrence in FIGO I stage disease and predicted poor prognosis also when including all patients and in endometrioid patients only. INTERPRETATION: This study reveals distinct characteristics in low-stage tumors and points to vimentin as a clinically relevant marker that may aid in identifying a here to unidentified subgroup of high-risk patients. FUNDING: A full list of funding that contributed to this study can be found in the Acknowledgements section.

APOBEC3A/B deletion polymorphism and endometrial cancer risk.

BACKGROUND: A common 30 kb deletion affecting the APOBEC3A and APOBEC3B genes has been linked to increased APOBEC activity and APOBEC-related mutational signatures in human cancers. The role of this deletion as a cancer risk factor remains controversial. MATERIALS AND METHODS: We genotyped the APOBEC3A/B deletion in a sample of 1,470 Norwegian endometrial cancer cases and compared to 1,918 healthy controls. For assessment across Caucasian populations, we mined genotypes of the SNP rs12628403, which is in strong linkage disequilibrium with the deletion, in a GWAS dataset of 4,274 cases and 18,125 healthy controls, through the ECAC consortium. RESULTS: We found the APOBEC3A/B deletion variant to be significantly associated with reduced risk of endometrial cancer among Norwegian women (OR = 0.75; 95% CI = 0.62-0.91; p = 0.003; dominant model). Similar results were found in the subgroup of endometrioid endometrial cancer (OR = 0.64; 95% CI = 0.51-0.79; p = 3.6 × 10-5 ; dominant model). The observed risk reduction was particularly strong among individuals in the range of 50-60 years of age (OR = 0.51; 95% CI = 0.33-0.78; p = 0.002; dominant model). In the different populations included in the ECAC dataset, the ORs varied from 0.85 to 1.05. Although five out of six populations revealed ORs <1.0, the overall estimate was nonsignificant and, as such, did not formally validate the findings in the Norwegian cohort. CONCLUSION: The APOBEC3A/B deletion polymorphism is associated with a decreased risk of endometrial cancer in the Norwegian population.

Mismatch repair markers in preoperative and operative endometrial cancer samples; expression concordance and prognostic value.

BACKGROUND: The endometrial cancer mismatch repair (MMR) deficient subgroup is defined by loss of MSH6, MSH2, PMS2 or MLH1. We compare MMR status in paired preoperative and operative samples and investigate the prognostic impact of differential MMR protein expression levels. METHODS: Tumour lesions from 1058 endometrial cancer patients were immunohistochemically stained for MSH6, MSH2, PMS2 and MLH1. MMR protein expression was evaluated as loss or intact to determine MMR status, or by staining index to evaluate the prognostic potential of differential expression. Gene expression data from a local (n = 235) and the TCGA (n = 524) endometrial cancer cohorts was used for validation. RESULTS: We identified a substantial agreement in MMR status between paired curettage and hysterectomy samples. Individual high expression of all four MMR markers associated with non-endometrioid subtype, and high MSH6 or MSH2 strongly associated with several aggressive disease characteristics including high tumour grade and FIGO stage, and for MSH6, with lymph node metastasis. In multivariate Cox analysis, MSH6 remained an independent prognostic marker, also within the endometrioid low-grade subgroup (P < 0.001). CONCLUSION: We demonstrate that in addition to determine MMR status, MMR protein expression levels, particularly MSH6, may add prognostic information in endometrial cancer.

What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?

BACKGROUND: Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear. MATERIAL AND METHODS: This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002-2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAXimaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan-Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses. RESULTS: All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81-0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42-1.76, p < 0.001 for all). Only MAXimaging independently predicted survival (HR = 1.51, p = 0.03) in the model including all size variables. The optimal cutoff for maximum tumor diameter (≥ 4.0 cm) yielded sensitivity (specificity) of 83% (73%) for predicting disease-specific death after 5 years. Inter-reader agreement for MRI-based primary tumor size measurements was excellent, with ICCs of 0.83-0.85. CONCLUSION: Among all MRI-derived tumor size measurements, MAXimaging was the only independent predictor of survival. MAXimaging ≥ 4.0 cm represents the optimal cutoff for predicting long-term disease-specific survival in cervical cancer. Inter-reader agreement for MRI-based tumor size measurements was excellent.

A radiogenomics application for prognostic profiling of endometrial cancer.

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

A Gene Signature Identifying CIN3 Regression and Cervical Cancer Survival.

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Genomic alterations associated with mutational signatures, DNA damage repair and chromatin remodeling pathways in cervical carcinoma.

Despite recent advances in the prevention of cervical cancer, the disease remains a leading cause of cancer-related deaths in women worldwide. By applying the GISTIC2.0 and/or the MutSig2CV algorithms on 430 whole-exome-sequenced cervical carcinomas, we identified previously unreported significantly mutated genes (SMGs) (including MSN, GPX1, SPRED3, FAS, and KRT8), amplifications (including NFIA, GNL1, TGIF1, and WDR87) and deletions (including MIR562, PVRL1, and NTM). Subset analyses of 327 squamous cell carcinomas and 86 non-squamous cell carcinomas revealed previously unreported SMGs in BAP1 and IL28A, respectively. Distinctive copy number alterations related to tumors predominantly enriched for *CpG- and Tp*C mutations were observed. CD274, GRB2, KRAS, and EGFR were uniquely significantly amplified within the Tp*C-enriched tumors. A high frequency of aberrations within DNA damage repair and chromatin remodeling genes were detected. Facilitated by the large sample size derived from combining multiple datasets, this study reveals potential targets and prognostic markers for cervical cancer.

Genomic Characterization and Therapeutic Targeting of HPV Undetected Cervical Carcinomas.

Cervical cancer tumors with undetectable HPV (HPVU) have been underappreciated in clinical decision making. In this study, two independent CC datasets were used to characterize the largest cohort of HPVU tumors to date (HPVU = 35, HPV+ = 430). Genomic and transcriptome tumor profiles and patient survival outcomes were compared between HPV+ and HPVU tumors. In vitro analyses were done to determine efficacy of the selective CDK4/6 inhibitor palbociclib on HPVU cancer cell lines. Patients with HPVU CC tumors had worse progression-free and overall survival outcomes compared to HPV+ patients. TP53, ARID1A, PTEN, ARID5B, CTNNB1, CTCF, and CCND1 were identified as significantly mutated genes (SMGs) enriched in HPVU tumors, with converging functional roles in cell cycle progression. In vitro HPVU, but not HPV+, cancer cell lines with wild type RB1 were sensitive to palbociclib monotherapy. These results indicate that HPVU status can be translated into the clinic as a predictive biomarker of poor patient response to standard of care treatments. We suggest primary cervix tumors be routinely tested for HPV prior to treatment to identify patients who will benefit from more aggressive precision-driven therapy. Our results identify palbociclib as a lead candidate as an alternative treatment strategy for HPVU CC patients.

A 10-gene prognostic signature points to LIMCH1 and HLA-DQB1 as important players in aggressive cervical cancer disease.

BACKGROUND: Advanced cervical cancer carries a particularly poor prognosis, and few treatment options exist. Identification of effective molecular markers is vital to improve the individualisation of treatment. We investigated transcriptional data from cervical carcinomas related to patient survival and recurrence to identify potential molecular drivers for aggressive disease. METHODS: Primary tumour RNA-sequencing profiles from 20 patients with recurrence and 53 patients with cured disease were compared. Protein levels and prognostic impact for selected markers were identified by immunohistochemistry in a population-based patient cohort. RESULTS: Comparison of tumours relative to recurrence status revealed 121 differentially expressed genes. From this gene set, a 10-gene signature with high prognostic significance (p = 0.001) was identified and validated in an independent patient cohort (p = 0.004). Protein levels of two signature genes, HLA-DQB1 (n = 389) and LIMCH1 (LIM and calponin homology domain 1) (n = 410), were independent predictors of survival (hazard ratio 2.50, p = 0.007 for HLA-DQB1 and 3.19, p = 0.007 for LIMCH1) when adjusting for established prognostic markers. HLA-DQB1 protein expression associated with programmed death ligand 1 positivity (p < 0.001). In gene set enrichment analyses, HLA-DQB1high tumours associated with immune activation and response to interferon-γ (IFN-γ). CONCLUSIONS: This study revealed a 10-gene signature with high prognostic power in cervical cancer. HLA-DQB1 and LIMCH1 are potential biomarkers guiding cervical cancer treatment.

Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma.

BACKGROUND: In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy. METHODS: Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing. RESULTS: LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype. CONCLUSIONS: We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.

High degree of heterogeneity of PD-L1 and PD-1 from primary to metastatic endometrial cancer.

OBJECTIVE: PD-L1 and PD-1 are predictive markers for immunotherapy and increasingly relevant in endometrial cancer. The reported fraction of positive primary tumors has been inconsistent. We investigated the expression of PD-L1 and PD-1 in primary tumors, also stratified by MSI. As immunotherapy is foremost relevant for metastatic disease, PD-L1 and PD-1 expression was also assessed in corresponding metastatic lesions. METHODS: PD-L1 and PD-1 was investigated in a prospective, population based endometrial cancer cohort of 700 patients with corresponding metastatic lesions from 68 and 74 patients respectively. Fresh tissue was used for gene expression analysis. RESULTS: In primary tumors, PD-L1 and PD-1 are expressed in 59% and 63%, respectively, but with no impact on survival, nor when stratified for MSS and MSI. Expression patterns of PD-L1 and PD-1 are similar in MSI and MSS tumors. Available metastatic lesions show heterogeneous expression of PD-L1 and PD-1. In gene expression analysis several genes related to immunological activity, including CD274 (encoding for PD-L1), were upregulated in PD-1 positive tumors. CONCLUSION: PD-L1 and PD-1 are frequently expressed in endometrial cancer and expression patterns are similar across MSS and MSI tumors. Expression in corresponding metastatic lesions is discordant compared to primary tumors. These findings are in particular relevant for treatment decisions in advanced and recurrent disease.

Blood Metabolites Associate with Prognosis in Endometrial Cancer.

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820-0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management.

PIK3CA Amplification Associates with Aggressive Phenotype but Not Markers of AKT-MTOR Signaling in Endometrial Carcinoma.

PURPOSE: Amplification of PIK3CA, encoding the PI3K catalytic subunit alpha, is common in uterine corpus endometrial carcinoma (UCEC) and linked to an aggressive phenotype. However, it is unclear whether PIK3CA amplification acts via PI3K activation. We investigated the association between PIK3CA amplification, markers of PI3K activity, and prognosis in a large cohort of UCEC specimens. EXPERIMENTAL DESIGN: UCECs from 591 clinically annotated patients including 83 tumors with matching metastasis (n = 188) were analyzed by FISH to determine PIK3CA copy-number status. These data were integrated with mRNA and protein expression and clinicopathologic data. Results were verified in The Cancer Genome Atlas dataset. RESULTS: PIK3CA amplifications were associated with disease-specific mortality and with other markers of aggressive disease. PIK3CA amplifications were also associated with other amplifications characteristic of the serous-like somatic copy-number alteration (SCNA)-high subgroup of UCEC. Tumors with PIK3CA amplification also demonstrated an increase in phospho-p70S6K but had decreased levels of activated phospho-AKT1-3 as assessed by Reverse Phase Protein Arrays and an mRNA signature of MTOR inhibition. CONCLUSIONS: PIK3CA amplification is a strong prognostic marker and a potential marker for the aggressive SCNA-high subgroup of UCEC. Although PIK3CA amplification associates with some surrogate measures of increased PI3K activity, markers for AKT1-3 and MTOR signaling are decreased, suggesting that this signaling is not a predominant pathway to promote cancer growth of aggressive serous-like UCEC. Moreover, these associations may reflect features of the SCNA-high subgroup of UCEC rather than effects of PIK3CA amplification itself.

Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer.

We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.

Preoperative imaging markers and PDZ-binding kinase tissue expression predict low-risk disease in endometrial hyperplasias and low grade cancers.

PURPOSE: Distinguishing complex atypical hyperplasia (CAH) from grade 1 endometrioid endometrial cancer (EECG1) preoperatively may be valuable in order to prevent surgical overtreatment, particularly in patients wishing preserved fertility or in patients carrying increased risk of perioperative complications. MATERIAL AND METHODS: Preoperative histological diagnosis and radiological findings were compared to final histological diagnosis in patients diagnosed with CAH and EECG1. Imaging characteristics at preoperative magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography/computer tomography (FDG-PET/CT) were compared with tumor DNA oligonucleotide microarray data, immunohistochemistry findings and clinicopathological annotations. RESULTS: MRI assessed tumor volume was higher in EECG1 than in CAH (p=0.004) whereas tumor apparent diffusion coefficient value was lower in EECG1 (p=0.005). EECG1 exhibited increased metabolism with higher maximum and mean standard uptake values (SUV) than CAH (p≤0.002). Unsupervised clustering of EECG1 and CAH revealed differentially expressed genes within the clusters, and identified PDZ-binding kinase (PBK) as a potential marker for selecting endometrial lesions with less aggressive biological behavior. CONCLUSION: Both PBK expression and preoperative imaging yield promising biomarkers that may aid in the differentiation between EECG1 and CAH preoperatively, and these markers should be further explored in larger patient series.

PIK3CA exon9 mutations associate with reduced survival, and are highly concordant between matching primary tumors and metastases in endometrial cancer.

Mutations of the phosphoinositide-3-kinase (PI3K) catalytic subunit alpha gene (PIK3CA) are frequent in endometrial cancer. We sequenced exon9 and exon20 of PIK3CA in 280 primary endometrial cancers to assess the relationship with clinicopathologic variables, patient survival and associations with PIK3CA mRNA and phospho-AKT1 by gene expression and protein data, respectively. While PIK3CA mutations generally had no impact on survival, and were not associated with clinicopathological variables, patients with exon9 charge-changing mutations, providing a positive charge at the substituted amino acid residue, were associated with poor survival (p = 0.018). Furthermore, we characterized PIK3CA mutations in the metastatic setting, including 32 patients with matched primary tumors and metastases, and found a high level of concordance (85.7%; 6 out of 7 patients), suggesting limited heterogeneity. PIK3CA mRNA levels were increased in metastases compared to the primary tumors (p = 0.031), independent of PIK3CA mutation status, which rather associated with reduced PIK3CA mRNA expression. PIK3CA mutated tumors expressed higher p-AKT/AKT protein levels, both within primary (p < 0.001) and metastatic lesion (p = 0.010). Our results support the notion that the PI3K signaling pathway might be activated, both dependent- and independently of PIK3CA mutations, an aspect that should be considered when designing PIK3 pathway targeting strategies in endometrial cancer.