| 1. |
- Huvila, J., et al.
(författare)
-
Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma
- 2018
-
Ingår i: Gynecologic Oncology. - : Academic Press Inc.. - 0090-8258 .- 1095-6859. ; 149:1, s. 173-180
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information. Methods: A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability. Results: A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (29.5%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P < 0.001) of dying of EEC compared to the low-risk group. Conclusions: P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities.
|
|
| 2. |
- Alevronta, Eleftheria, et al.
(författare)
-
Dose-response relationships of intestinal organs and excessive mucus discharge after gynaecological radiotherapy
- 2021
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 16:4 April
-
Tidskriftsartikel (refereegranskat)abstract
- Background The study aims to determine possible dose-volume response relationships between the rectum, sigmoid colon and small intestine and the ‘excessive mucus discharge’ syndrome after pelvic radiotherapy for gynaecological cancer. Methods and materials From a larger cohort, 98 gynaecological cancer survivors were included in this study. These survivors, who were followed for 2 to 14 years, received external beam radiation therapy but not brachytherapy and not did not have stoma. Thirteen of the 98 developed excessive mucus discharge syndrome. Three self-assessed symptoms were weighted together to produce a score interpreted as ‘excessive mucus discharge’ syndrome based on the factor loadings from factor analysis. The dose-volume histograms (DVHs) for rectum, sigmoid colon, small intestine for each survivor were exported from the treatment planning systems. The dose-volume response relationships for excessive mucus discharge and each organ at risk were estimated by fitting the data to the Probit, RS, LKB and gEUD models. Results The small intestine was found to have steep dose-response curves, having estimated dose-response parameters: γ : 1.28, 1.23, 1.32, D : 61.6, 63.1, 60.2 for Probit, RS and LKB respectively. The sigmoid colon (AUC: 0.68) and the small intestine (AUC: 0.65) had the highest AUC values. For the small intestine, the DVHs for survivors with and without excessive mucus discharge were well separated for low to intermediate doses; this was not true for the sigmoid colon. Based on all results, we interpret the results for the small intestine to reflect a relevant link. Conclusion An association was found between the mean dose to the small intestine and the occurrence of ‘excessive mucus discharge’. When trying to reduce and even eliminate the incidence of ‘excessive mucus discharge’, it would be useful and important to separately delineate the small intestine and implement the dose-response estimations reported in the study.
|
|
| 3. |
- Li, Xiangyu, et al.
(författare)
-
Classification of clear cell renal cell carcinoma based on PKM alternative splicing
- 2020
-
Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 6:2
-
Tidskriftsartikel (refereegranskat)abstract
- Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.
|
|
| 4. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
-
A novel federated deep learning scheme for glioma and its subtype classification
- 2023
-
Ingår i: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
|
|
| 5. |
- Eriksson, Erik, 1977, et al.
(författare)
-
Participation in a Swedish cervical cancer screening program among women with psychiatric diagnoses: a population-based cohort study.
- 2019
-
Ingår i: BMC public health. - : Springer Science and Business Media LLC. - 1471-2458. ; 19:1
-
Tidskriftsartikel (refereegranskat)abstract
- In Sweden, organized screening programs have significantly reduced the incidence of cervical cancer. For cancers overall, however, women with psychiatric diagnoses have lower survival rates than other women. This study explores whether women with psychiatric diagnoses participate in cervical cancer screening programs to a lesser extent than women on average, and whether there are disparities between psychiatric diagnostic groups based on grades of severity.Between 2000 and 2010, 65,292 women within screening ages of 23-60 had at least two ICD-10 (International Statistical Classification of Diseases and Related Health Problems - Tenth Revision) codes F20*-F40* registered at visits in primary care or psychiatric care in Region Västra Götaland, Sweden. Participation in the cervical cancer screening program during 2010-2014 was compared with the general female population using logistic regression adjusted for age.Relative risk for participation (RR) for women diagnosed within psychiatric specialist care RR was 0.94 compared with the general population, adjusted for age. RR for diagnoses outside specialist care was 0.99. RR for psychoses (F20*) was 0.81.Women with less-severe psychiatric diagnoses participate in the screening program to the same extent as women overall. Women who have received psychiatric specialist care participate to a lesser extent than women overall. The lowest participation rates were found among women diagnosed with psychoses.
|
|
| 6. |
- Ge, Chenjie, 1991, et al.
(författare)
-
Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
-
Tidskriftsartikel (refereegranskat)abstract
- This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.
|
|
| 7. |
- Morgell, Ann, et al.
(författare)
-
Metabolic Characterization of Plasma and Cyst Fluid from Cystic Precursors to Pancreatic Cancer Patients Reveal Metabolic Signatures of Bacterial Infection
- 2021
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3907 .- 1535-3893. ; 20:5, s. 2725-2738
-
Tidskriftsartikel (refereegranskat)abstract
- Pancreatic cancer is the seventh leading cause of cancer-related death worldwide, with a 5 year survival rate as low as 9%. One factor complicating the management of pancreatic cancer is the lack of reliable tools for early diagnosis. While up to 50% of the adult population has been shown to develop precancerous pancreatic cysts, limited and insufficient approaches are currently available to determine whether a cyst is going to progress into pancreatic cancer. Recently, we used metabolomics approaches to identify candidate markers of disease progression in patients diagnosed with intraductal papillary mucinous neoplasms (IPMNs) undergoing pancreatic resection. Here, we enrolled an independent cohort to verify the candidate markers from our previous study with orthogonal quantitative methods in plasma and cyst fluid from serous cystic neoplasm and IPMN (either low- or high-grade dysplasia or pancreatic ductal adenocarcinoma). We thus validated these markers with absolute quantitative methods through the auxilium of stable isotope-labeled internal standards in a new independent cohort. Finally, we identified novel markers of IPMN status and disease progression - including amino acids, carboxylic acids, conjugated bile acids, free and carnitine-conjugated fatty acids, purine oxidation products, and trimethylamine-oxide. We show that the levels of these metabolites of potential bacterial origin correlated with the degree of bacterial enrichment in the cyst, as determined by 16S RNA. Overall, our findings are interesting per se, owing to the validation of previous markers and identification of novel small molecule signatures of IPMN and disease progression. In addition, our findings further fuel the provoking debate as to whether bacterial infections may represent an etiological contributor to the development and severity of the disease in pancreatic cancer, in like fashion to other cancers (e.g., Helicobacter pylori and gastric cancer).
|
|
| 8. |
- Borrelli, Pablo, et al.
(författare)
-
Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
- 2021
-
Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 41:1, s. 62-67
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy-proven PCa who had undergone(18)F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117;p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111;p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
|
|
| 9. |
- Hofving, Tobias, 1989, et al.
(författare)
-
177 Lu-octreotate therapy for neuroendocrine tumours is enhanced by Hsp90 inhibition
- 2019
-
Ingår i: Endocrine-Related Cancer. - 1479-6821 .- 1351-0088. ; 26:4, s. 437-449
-
Tidskriftsartikel (refereegranskat)abstract
- Lu-177-octreotate is an FDA-approved radionuclide therapy for patients with gastroenteropancreatic neuroendocrine tumours (NETs) expressing somatostatin receptors. The Lu-177-octreotate therapy has shown promising results in clinical trials by prolonging progression-free survival, but complete responses are still uncommon. The aim of this study was to improve the Lu-177-octreotate therapy by means of combination therapy. To identify radiosensitising inhibitors, two cell lines, GOT1 and P-STS, derived from small intestinal neuroendocrine tumours (SINETs), were screened with 1224 inhibitors alone or in combination with external radiation. The screening revealed that inhibitors of Hsp90 can potentiate the tumour cell-killing effect of radiation in a synergistic fashion (GOT1; false discovery rate < 3.2 x 10(-11)). The potential for Hsp90 inhibitor ganetespib to enhance the anti-tumour effect of Lu-177-octreotate in an in vivo setting was studied in the somatostatin receptor-expressing GOT1 xenograft model. The combination led to a larger decrease in tumour volume relative to monotherapies and the tumour-reducing effect was shown to be synergistic. Using patient-derived tumour cells from eight metastatic SINETs, we could show that ganetespib enhanced the effect of Lu-177-octreotate therapy for all investigated patient tumours. Levels of Hsp90 protein expression were evaluated in 767 SINETs from 379 patients. We found that Hsp90 expression was upregulated in tumour cells relative to tumour stroma in the vast majority of SINETs. We conclude that Hsp90 inhibitors enhance the tumour-killing effect of Lu-177-octreotate therapy synergistically in SINET tumour models and suggest that this potentially promising combination should be further evaluated.
|
|
| 10. |
- Lee, SangWook, et al.
(författare)
-
Network analyses identify liver-specific targets for treating liver diseases
- 2017
-
Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8
-
Tidskriftsartikel (refereegranskat)abstract
- We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co-expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased de novo lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver-specific genes co-expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver-specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver-specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin-like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.
|
|
