A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

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Class I Phosphoinositide 3-Kinase PIK3CA/p110α and PIK3CB/p110β Isoforms in Endometrial Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms19123931 ◽

2018 ◽

Vol 19 (12) ◽

pp. 3931 ◽

Cited By ~ 4

Author(s):

Fatemeh Mazloumi Gavgani ◽

Victoria Smith Arnesen ◽

Rhîan Jacobsen ◽

Camilla Krakstad ◽

Erling Hoivik ◽

...

Keyword(s):

Endometrial Cancer ◽

Gene Copy Number ◽

Biochemical Properties ◽

Class I ◽

Gene Copy ◽

Gene Encoding ◽

Cellular Processes ◽

Pi3k Signalling ◽

Phosphoinositide 3 Kinase ◽

Phosphatase And Tensin Homologue

The phosphoinositide 3-kinase (PI3K) signalling pathway is highly dysregulated in cancer, leading to elevated PI3K signalling and altered cellular processes that contribute to tumour development. The pathway is normally orchestrated by class I PI3K enzymes and negatively regulated by the phosphatase and tensin homologue, PTEN. Endometrial carcinomas harbour frequent alterations in components of the pathway, including changes in gene copy number and mutations, in particular in the oncogene PIK3CA, the gene encoding the PI3K catalytic subunit p110α, and the tumour suppressor PTEN. PIK3CB, encoding the other ubiquitously expressed class I isoform p110β, is less frequently altered but the few mutations identified to date are oncogenic. This isoform has received more research interest in recent years, particularly since PTEN-deficient tumours were found to be reliant on p110β activity to sustain transformation. In this review, we describe the current understanding of the common and distinct biochemical properties of the p110α and p110β isoforms, summarise their mutations and highlight how they are targeted in clinical trials in endometrial cancer.

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Abstract 2701: Multicenter Study For Prediction And Validation Of Intracranial Hemorrhage Risk In The Natural History Of Brain Arteriovenous Malformation Patients

Stroke ◽

10.1161/str.43.suppl_1.a2701 ◽

2012 ◽

Vol 43 (suppl_1) ◽

Author(s):

Helen Kim ◽

Tony Pourmohamad ◽

Charles E McCulloch ◽

Michael T Lawton ◽

Jay P Mohr ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Natural History ◽

Prediction Model ◽

Intracranial Hemorrhage ◽

Prediction Models ◽

Statistical Significance ◽

Venous Drainage ◽

Cox Proportional Hazards ◽

Natural Course

Background: BAVM is an important cause of intracranial hemorrhage (ICH) in younger persons. Accurate and reliable prediction models for determining ICH risk in the natural history course of BAVM patients are needed to help guide management. The purpose of this study was to develop a prediction model of ICH risk, and validate the performance independently using the Multicenter AVM Research Study (MARS). Methods: We used 3 BAVM cohorts from MARS: the UCSF Brain AVM Study Project (n=726), Columbia AVM Study (COL, n=640), and Scottish Intracranial Vascular Malformation Study (SIVMS, n=218). Cox proportional hazards analysis of time-to-ICH in the natural course after diagnosis was performed, censoring patients at first treatment, death, or last visit, up to 10 years. UCSF served as the model development cohort. We chose a simple model, including known risk factors that are reliably measured across cohorts (age at diagnosis, gender, initial hemorrhagic presentation, and deep venous drainage); variables were included without regard to statistical significance. Tertiles of predicted probabilities corresponding to low, medium, and high risk were obtained from UCSF and risk thresholds were validated in COL and SIVMS using Kaplan-Meier survival curves and log-rank tests (to assess whether the model discriminated between risk categories). Results: Overall, 82 ICH events occurred during the natural course: 28 in UCSF, 41 in COL, and 13 in SIVMS. Effects in the prediction model (estimated from UCSF data) were: age in decades (HR=1.1, 95% CI=0.9-1.4, P=0.41), initial hemorrhagic presentation (HR=3.6, 95% CI=1.5-8.6, P=0.01), male gender (HR=1.1, 95% CI=0.48-2.6; P=0.81), and deep venous drainage (HR=0.8, 95% CI=0.2-2.8 P=0.72). Tertiles of ICH risk are shown in the Figure , demonstrating good separation of curves into low, medium and high risk after 3 years in UCSF (left, log-rank P=0.05). The model validated well in the COL referral cohort with better discrimination of curves (middle, P<0.001). In SIMVS, a population-based study, the model separated curves in the earlier years but a consistent pattern was not observed (right, P=0.51), possibly due to the small number of ICH events. Conclusion: Our current prediction model for predicting ICH risk in the natural history course validates well in another referral population, but not as well in a population cohort. Inclusion of additional cohorts and risk factors after data harmonization may improve overall prediction and discrimination of ICH risk, and provide a generalizable model for clinical application.

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Evolutionary Trajectories toward Ceftazidime-Avibactam Resistance in Klebsiella pneumoniae Clinical Isolates

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00574-21 ◽

2021 ◽

Author(s):

Alessandra Carattoli ◽

Gabriele Arcari ◽

Giulia Bibbolino ◽

Federica Sacco ◽

Dario Tomolillo ◽

...

Keyword(s):

High Risk ◽

Klebsiella Pneumoniae ◽

Copy Number ◽

Residual Activity ◽

Gene Copy Number ◽

Resistance Mechanisms ◽

University Hospital ◽

Gene Copy ◽

Carbapenemase Gene ◽

Evolutionary Trajectories

From January 2019 to April 2020, 32 KPC-producing, ceftazidime-avibactam (CZA) resistant Klebsiella pneumoniae strains were isolated in a university hospital in Rome, Italy. These strains belonged to the ST512, ST101 and ST307 high-risk clones. Nine different CZA-resistant KPC-3 protein variants were identified, five of them never previously reported (KPC-66 to KPC-70). Among them, KPC-31, KPC-39, KPC-49, KPC-66, KP-68, KPC-69 and KPC-70 showed amino acid substitutions, insertions and deletions in the Ω loop of the protein. KPC-29 has the duplication, while the novel KPC-67 has the triplication of the KDD triplet in the 270-loop of the protein. Genomics performed on contemporary resistant and susceptible clones underlined that those novel mutations emerged in bla KPC-3 genes located on conserved plasmids: in ST512, all bla KPC-3 mutant genes were located in pKpQIL plasmids, while the three novel bla KPC-3 mutants identified in ST101 were on FIIk-FIA(HI1)-R plasmids. Selection also promoted multiplication of the carbapenemase gene copy number by transposition, recombination, and fusion of resident plasmids. When expressed in Escherichia coli recipient cells cloned in the high-copy number pTOPO vector, the Ω loop mutated variants showed CZA-resistant phenotype associated with susceptibility to carbapenems, while KPC variants with insertions in the 270-loop showed residual activity on carbapenems. The investigation of CZA-resistance mechanisms offered the unique opportunity to study vertical, horizontal, and oblique evolutionary trajectories of K. pneumoniae high-risk clones.

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Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

Journal of Clinical Oncology ◽

10.1200/jco.2016.70.3413 ◽

2017 ◽

Vol 35 (15) ◽

pp. 1660-1667 ◽

Cited By ~ 38

Author(s):

Tuomo J. Meretoja ◽

Kenneth Geving Andersen ◽

Julie Bruce ◽

Lassi Haasio ◽

Reetta Sipilä ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prediction Model ◽

Prediction Models ◽

Persistent Pain ◽

Cancer Surgery ◽

Breast Cancer Surgery ◽

Outcome Variable ◽

Axillary Lymph ◽

Operating Characteristics

Purpose Persistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort. Results Moderate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area ( P < .001), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

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The Impact Prediction Models of Neoplasia for Lung Nodules in High-Risk Patients

Revista Científica Hospital Santa Izabel ◽

10.35753/rchsi.v3i3.47 ◽

2020 ◽

Vol 3 (3) ◽

pp. 138-146

Author(s):

Camilla Matos Pedreira ◽

José Alves Barros Filho ◽

Carolina Pereira ◽

Thamine Lessa Andrade ◽

Ricardo Mingarini Terra ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Prediction Models ◽

Lung Nodule ◽

Smoking History ◽

Predictive Values ◽

Impact Prediction ◽

High Risk Patients ◽

Risk Patients ◽

The Impact

Objectives: This study aims to evaluate the impact of using three predictive models of lung nodule malignancy in a population of patients at high-risk for neoplasia according to previous analysis by physicians, as well as evaluate the clinical and radiological malignancy-predictors of the images. Material and Methods: This is a retrospective cohort study, with 135 patients, undergone surgical in the period from 01/07/2013 to 10/05/2016. The study included nodules with dimensions between 5mm and 30mm, excluding multiple nodules, alveolar consolidation, pleural effusion, and lymph node enlargement. The main variables analyzed were age, sex, smoking history, extrathoracic cancer, diameter, location, and presence of spiculation. The calculation of the area under the ROC curve assessed the accuracy of each prediction model. Results: The study analyzed 135 individuals, of which 96 (71.1%) had malignant nodules. The areas under the ROC curves for each prediction model were: Swensen 0.657; Brock 0.662; and Herder 0.633. The models Swensen, Brock, and Herder presented positive predictive values in high-risk patients, corresponding to 83.3%, 81.8%, and 82.9%, respectively. Patients with the intermediate and low-risk presented a high malignant nodule rate, ranging from 69.3-72.5% and 42.8-52.6%, respectively. Conclusion: None of the three quantitative models analyzed in this study was considered satisfactory (AUC> 0.7) and should be used with caution after specialized evaluation to avoid underestimation of the risk of neoplasia. The pretest calculations might not contemplate other factors than those predicted in the regressions, that could present a role in the clinical decision of resection.

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Copy number gain of CMTM6 increases the expression of PD-L1 in undifferentiated pleomorphic sarcoma

10.21203/rs.3.rs-201625/v1 ◽

2021 ◽

Author(s):

Shin Ishihara ◽

Takeshi Iwasaki ◽

Kenichi Kohashi ◽

Yuichi Yamada ◽

Yu Toda ◽

...

Keyword(s):

Copy Number ◽

Transmembrane Domain ◽

Gene Copy Number ◽

Copy Number Gain ◽

The Cancer Genome Atlas ◽

Gene Copy ◽

Undifferentiated Pleomorphic Sarcoma ◽

Pleomorphic Sarcoma ◽

Number Variation ◽

Cancer Genome Atlas

Abstract Background Undifferentiated pleomorphic sarcoma (UPS) is a sarcoma with a poor prognosis. A clinical trial, SARC028, revealed that treatment with anti-PD-1 drugs was effective against UPS. Studies have reported that UPS expresses PD-L1, sometime strongly (≥ 50%). However, the mechanism of PD-L1 expression in UPS has remained still unclear. CKLF-like MARVEL transmembrane domain containing 6 (CMTM6) was identified as a novel regulator of PD-L1 expression. The positive relationship between PD-L1 and CMTM6 has been reported in several studies. The aim of this study was to examine CMTM6 expression in UPS and evaluate the relationship between PD-L1 and CMTM6. Materials and methods Fifty-one primary UPS samples were subjected to CMTM6 and PD-L1 immunostaining. CMTM6 expression was assessed using proportion and intensity scores. CMTM6 gene copy number was also evaluated using a real-time PCR-based copy number assay. We also analyzed the mRNA expression and copy number variation of PD-L1 and CMTM6 in The Cancer Genome Atlas (TCGA) data. Results TCGA data indicated that the mRNAs encoded by genes located around 3p22 were coexpressed with CMTM6 mRNA in UPS. Both proportion and intensity scores of CMTM6 positively correlated with strong PD-L1 expression (≥ 50%) (both p = 0.023). CMTM6 copy number gain increased CMTM6 expression. Patients with UPS with a high CMTM6 intensity score had worse prognosis for overall survival. Conclusions CMTM6 expression was significantly correlated with PD-L1 expression. CMTM6 expression induced strong PD-L1 expression (≥ 50%). CMTM6 copy number gain promoted CMTM6 expression and increased PD-L1 expression in UPS.

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A radiogenomics application for prognostic profiling of endometrial cancer

Communications Biology ◽

10.1038/s42003-021-02894-5 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Erling A. Hoivik ◽

Erlend Hodneland ◽

Julie A. Dybvik ◽

Kari S. Wagner-Larsen ◽

Kristine E. Fasmer ◽

...

Keyword(s):

Endometrial Cancer ◽

High Risk ◽

Treatment Strategies ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Tumor Segmentation ◽

Poor Survival ◽

Cancer Genome Atlas ◽

Automated Machine Learning ◽

Magnetic Resonance Imaging Mri

AbstractPrognostication 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.

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Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

10.21203/rs.3.rs-1151996/v1 ◽

2021 ◽

Author(s):

Fariba Tohidinezhad ◽

Dario Di Perri ◽

Catharina M.L. Zegers ◽

Jeanette Dijkstra ◽

Monique Anten ◽

...

Keyword(s):

High Risk ◽

Radiation Dose ◽

Brain Tumors ◽

Prediction Model ◽

Prediction Models ◽

Verbal Learning ◽

Risk Of Bias ◽

Neurocognitive Impairments ◽

Radiation Induced ◽

Neurocognitive Decline

Abstract Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy”, “brain”, “prediction model”, and “neurocognitive impairments”. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n=7) and the trail making tests (n=4) were the most frequent outcome assessment tools. All studies used regression (n=14 linear, n=8 logistic, and n=4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: develop a standardized instrument for neurocognitive assessment in patients with brain tumors; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

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Incidence and Clinicopathological Relevance of Gene Copy Number Abnormalities Detected by a Single Quantitative PCR Assay in Mantle Cell Lymphomas.

Blood ◽

10.1182/blood.v112.11.1770.1770 ◽

2008 ◽

Vol 112 (11) ◽

pp. 1770-1770

Author(s):

Fabrice Jardin ◽

Jean-Michel Picquenot ◽

Francoise Parmentier ◽

Marie Cornic ◽

Sandrine Vaudaux ◽

...

Keyword(s):

High Risk ◽

Copy Number ◽

Cell Lymphoma ◽

Risk Group ◽

Gene Copy Number ◽

Low Risk ◽

Gene Copy ◽

Intermediate Risk ◽

Pcr Assay ◽

Mantle Cell

Abstract The genetic hallmark of mantle cell lymphoma (MCL) is the t(11;14)(q13;q32). Fluorescence in situ hybridisation (FISH) and microarray based comparative genomic hybridisation (array CGH) experiments have demonstrated that additional genetic clonal alterations occur in the majority of MCL and may have prognostic or pathological relevance. We previously developed and validated by CGH or FISH an inexpensive and sensitive genomic PCR assay (Multiplex PCR of Short Fluorescent Fragments, QMPSF) to detect gene copy number abnormalities in diffuse large B-cell lymphoma and chronic lymphocytic leukemia (Haematologica 2008,93:543-Leukemia, 2007,21:1460). In the aim to determine the incidence and clinical relevance of recurrent additional genomic number abnormalities, we specifically designed a single QMPSF assay dedicated for MCL and correlated results with pathological and clinical data. For this purpose, a series of 42 newly diagnosed MCL cases with available frozen-and paraffin-embedded lymph node tissues and clinical features were selected [median age=67y; median MCL international prognostic index (MIPI)=6.1; low risk 21%, intermediate risk 33%, high risk 45%; 3-year overall survival (OS) rate=38%]. The assay was designed according to the most frequent gene copy number abnormalities reported in MCL, allowing simultaneous analysis of 8 relevant genes (CDKN2A, RB1, ATM, CDK2, TP53, MYC, CDKN1B, and MDM2) and 2 reference genes (SEM4F and CECR1). DNA copy number gains of MYC, CDK2, CDKN1B (p27kip1) and MDM2 are observed in an equal frequency (10%). Losses of DNA copies of RB1, CDNK2A, ATM, or TP53 are observed in 38, 31, 26, and 10 % of cases respectively. Some genes are almost exclusively gained (MYC,CDK2), deleted (RB1, ATM, TP53, CDNK2A) or both (CDK2, CDKN1B). Deletions of ATM and RB1 appear strongly associated (21% of cases, p=.001). CDKN2A (p14arf and p16ink4a) homozygous deletions and CDKN1B gains are more frequently observed in blastoid variants (p=.04 and .005 respectively). According to the MIPI score, the number of gene copy abnormalities tends to increase in the high/intermediate risk group (median = 1, range 0–6), as compared to the low risk group (median=0, range 0–3, p=.07). More specifically, MYC gain is exclusively observed in the high risk group (p=.04) and CDKN2A deletions are observed in patients with the highest MIPI. The prognostic relevance of the assay was tested in 42 patients. With a median follow-up of 22 months, CDK2 (3y OS=0%) or MDM2 (3y OS=0%) gains and CDKN2A (3y OS=20%) or TP53 (3y OS=25%) losses correlate to a shorter OS (p <.0001, p=.0007, p=.003 and p=.03 respectively). CDKN2A deletions remain predictive of the outcome in patient with a high MIPI (p =.0005). Furthermore, PCR performed in 5 cases at the time of relapse showed an increase of gene copy number abnormalities compared with initial diagnosis (median = 4 vs.1; p =.02), suggesting that gene losses/gains are involved in a dynamic and selected process. To conclude, we developed a reliable and routinely applicable PCR assay which delineates distinct MCL oncogenic pathways with strong prognostic impact that could be used in combination with the recently defined MIPI.

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