Primary radiation as initial management in endometrial cancer: investigating EBRT, IMRT and HDR brachytherapy

For patients with endometrial cancer at increased risk of perioperative morbidity, primary radiation therapy is an effective alternative treatment option. However, there has been no consensus on radiation technique and little data on outcomes. Our aim was to identify factors which determine patient selection for primary radiation, investigate treatment efficacy of radiation compared to surgical management of endometrial cancer and to evaluate different radiation modalities including external beam radiation therapy alone or with a boost of either high dose rate brachytherapy or intensity-modulated radiation therapy for differences in toxicities, recurrence-free interval, cancer-specific survival and overall survival

A predictive model for serous epithelial ovarian cancer chemo-response using clinical characteristics

One of the prognostic factors most highly associated with ovarian cancer survival is response to initial chemotherapy. Current prediction models of chemo-response built with comprehensive molecular datasets, like The Cancer Genome Atlas (TCGA), could be improved by including clinical and outcomes data designed to study response to treatment. The objective of this study was to create a prediction model of ovarian cancer chemo-response using clinical-pathological features, and to compare its performance with a similar TCGA clinical model

The C. elegans Opa1 Homologue EAT-3 Is Essential for Resistance to Free Radicals

The C. elegans eat-3 gene encodes a mitochondrial dynamin family member homologous to Opa1 in humans and Mgm1 in yeast. We find that mutations in the C. elegans eat-3 locus cause mitochondria to fragment in agreement with the mutant phenotypes observed in yeast and mammalian cells. Electron microscopy shows that the matrices of fragmented mitochondria in eat-3 mutants are divided by inner membrane septae, suggestive of a specific defect in fusion of the mitochondrial inner membrane. In addition, we find that C. elegans eat-3 mutant animals are smaller, grow slower, and have smaller broodsizes than C. elegans mutants with defects in other mitochondrial fission and fusion proteins. Although mammalian Opa1 is antiapoptotic, mutations in the canonical C. elegans cell death genes ced-3 and ced-4 do not suppress the slow growth and small broodsize phenotypes of eat-3 mutants. Instead, the phenotypes of eat-3 mutants are consistent with defects in oxidative phosphorylation. Moreover, eat-3 mutants are hypersensitive to paraquat, which promotes damage by free radicals, and they are sensitive to loss of the mitochondrial superoxide dismutase sod-2. We conclude that free radicals contribute to the pathology of C. elegans eat-3 mutants

Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA

In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4–6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure

Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer

Nearly one-third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial treatment with platinum-based therapy. Genomic and clinical characterization of these patients may lead to potential alternative therapies. Here, the objective is to classify non-responders into subsets using clinical and molecular features. Using patients from The Cancer Genome Atlas (TCGA) dataset with platinum-resistant or platinum-refractory HGSC, we performed a genome-wide unsupervised cluster analysis that integrated clinical data, gene copy number variations, gene somatic mutations, and DNA promoter methylation. Pathway enrichment analysis was performed for each cluster to identify the targetable processes. Following the unsupervised cluster analysis, three distinct clusters of non-responders emerged. Cluster 1 had overrepresentation of the stage IV disease and suboptimal debulking, under-expression of miRNAs and mRNAs, hypomethylated DNA, “loss of function” TP53 mutations, and the overexpression of genes in the PDGFR pathway. Cluster 2 had low miRNA expression, generalized hypermethylation, MUC17 mutations, and significant activation of the HIF-1 signaling pathway. Cluster 3 had more optimally cytoreduced stage III patients, overexpression of miRNAs, mixed methylation patterns, and “gain of function” TP53 mutations. However, the survival for all clusters was similar. Integration of genomic and clinical data from patients that do not respond to chemotherapy has identified different subgroups or clusters. Pathway analysis further identified the potential alternative therapeutic targets for each cluster

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

Optimal spindle detection parameters for predicting cognitive performance

STUDY OBJECTIVES: Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. METHODS: Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. RESULTS: Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. CONCLUSIONS: Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition

Initial efficacy and safety results from ENGOT-ov60/GOG-3052/RAMP 201: A phase 2 study of avutometinib (VS-6766) ± defactinib in recurrent low-grade serous ovarian cancer (LGSOC).

peer reviewed5515 Background: LGSOC is a RAS/MAPK pathway driven cancer that constitutes ≤10% of ovarian cancer. There are no FDA approved treatments specifically for LGSOC. Avutometinib is a novel small molecule RAF/MEK clamp. Focal adhesion kinase (FAK) activation is a resistance mechanism to RAF/MEK inhibition, and defactinib, a small molecule inhibitor of FAK, has shown synergistic antitumor activity with avutometinib in preclinical models. The combination of avutometinib and defactinib has demonstrated a high rate of confirmed and durable responses (overall response rate [ORR] = 46%) in recurrent LGSOC (Banerjee S, ESMO 2021). Methods: A registration-directed phase 2, adaptive, multicenter, randomized study was initiated to evaluate avutometinib ± defactinib in patients with KRAS mutant (mt) and KRAS wild-type (wt) recurrent LGSOC to identify the optimal regimen based on confirmed ORR by blinded independent central review (Part A) and determine the efficacy of the optimal regimen (Part B) (NCT04625270). Pts were randomized to avutometinib 4 mg orally (PO), twice weekly, 3 weeks on, 1 week off (mono) or avutometinib 3.2 mg PO twice weekly + defactinib 200 mg PO BID 3 weeks on, 1 week off (combo). Key inclusion criteria include histologically confirmed recurrent LGSOC, known KRAS status and prior systemic therapy with platinum chemotherapy. Unlimited additional prior lines, including prior MEK inhibitor, were permitted. Here we present efficacy results from Part A (evaluable patients, N=59) and safety data from all pts enrolled (N=121). Results: In Part A, the median number of prior systemic regimens was 3 for mono, and 4 for combo. In evaluable patients, a confirmed ORR of 7% (2/30) was observed for mono (13% KRAS mt, 0% KRAS wt), and an ORR of 28% (8/29) was observed for combo (27% KRAS mt, 29% KRAS wt). Two of 4 patients previously treated with a MEK inhibitor showed a confirmed partial response (PR) on the combination arm. A high disease control rate (PR or SD ≥ 8 weeks) was observed for both mono (90%) and combo (93%). The majority of treatment related adverse events (AEs, any grade) for combo (N=57) were mild to moderate. The most common Grade ≥3 AEs for combo were blood CPK increase (15.8%), fatigue (5.3%), diarrhea (3.5%), dermatitis acneiform (1.8%), and rash (1.8%). A similar AE profile was observed for mono (N=64). Most AEs were manageable/reversible. On the combo arm, 90.6% (±20%) of planned doses were given and 9% (n=5) of pts discontinued due to AEs [asymptomatic elevated blood CPK (n=3) and fatigue (n=2)]. Conclusions: The interim data support avutometinib + defactinib as an active go-forward regimen in heavily-pretreated recurrent LGSOC, regardless of KRAS status. No new safety signals were observed, and most AEs were mild to moderate. Enrollment continues in Part B for the combination of avutometinib and defactinib. Clinical trial information: NCT04625270

Linking biodiversity, ecosystem services and human wellbeing : towards a new framework

International audienc