| 1. |
- Huvila, J., et al.
(författare)
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Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma
- 2018
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Ingår i: Gynecologic Oncology. - : Academic Press Inc.. - 0090-8258 .- 1095-6859. ; 149:1, s. 173-180
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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.
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| 2. |
- Brennan, Donal J., et al.
(författare)
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Tumour-specific HMG-CoAR is an independent predictor of recurrence free survival in epithelial ovarian cancer
- 2010
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407 .- 1471-2407. ; 10, s. 125-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Our group previously reported that tumour-specific expression of the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutharyl-coenzyme A reductase (HMG-CoAR) is associated with more favourable tumour parameters and a good prognosis in breast cancer. In the present study, the prognostic value of HMG-CoAR expression was examined in tumours from a cohort of patients with primary epithelial ovarian cancer. Methods: HMG-CoAR expression was assessed using immunohistochemistry (IHC) on tissue microarrays (TMA) consisting of 76 ovarian cancer cases, analysed using automated algorithms to develop a quantitative scoring model. Kaplan Meier analysis and Cox proportional hazards modelling were used to estimate the risk of recurrence free survival (RFS). Results: Seventy-two tumours were suitable for analysis. Cytoplasmic HMG-CoAR expression was present in 65% (n = 46) of tumours. No relationship was seen between HMG-CoAR and age, histological subtype, grade, disease stage, estrogen receptor or Ki-67 status. Patients with tumours expressing HMG-CoAR had a significantly prolonged RFS (p = 0.012). Multivariate Cox regression analysis revealed that HMG-CoAR expression was an independent predictor of improved RFS (RR = 0.49, 95% CI (0.25-0.93); p = 0.03) when adjusted for established prognostic factors such as residual disease, tumour stage and grade. Conclusion: HMG-CoAR expression is an independent predictor of prolonged RFS in primary ovarian cancer. As HMG-CoAR inhibitors, also known as statins, have demonstrated anti-neoplastic effects in vitro, further studies are required to evaluate HMG-CoAR expression as a surrogate marker of response to statin treatment, especially in conjunction with current chemotherapeutic regimens.
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| 3. |
- Danielsson, H., et al.
(författare)
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Blood protein profiles related to preterm birth and retinopathy of prematurity
- 2022
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Ingår i: Pediatric Research. - : Springer Science and Business Media LLC. - 0031-3998 .- 1530-0447. ; 91:4, s. 937-946
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Tidskriftsartikel (refereegranskat)abstract
- Background Nearly one in ten children is born preterm. The degree of immaturity is a determinant of the infant's health. Extremely preterm infants have higher morbidity and mortality than term infants. One disease affecting extremely preterm infants is retinopathy of prematurity (ROP), a multifactorial neurovascular disease that can lead to retinal detachment and blindness. The advances in omics technology have opened up possibilities to study protein expressions thoroughly with clinical accuracy, here used to increase the understanding of protein expression in relation to immaturity and ROP. Methods Longitudinal serum protein profiles the first months after birth in 14 extremely preterm infants were integrated with perinatal and ROP data. In total, 448 unique protein targets were analyzed using Proximity Extension Assays. Results We found 20 serum proteins associated with gestational age and/or ROP functioning within mainly angiogenesis, hematopoiesis, bone regulation, immune function, and lipid metabolism. Infants with severe ROP had persistent lower levels of several identified proteins during the first postnatal months. Conclusions The study contributes to the understanding of the relationship between longitudinal serum protein levels and immaturity and abnormal retinal neurovascular development. This is essential for understanding pathophysiological mechanisms and to optimize diagnosis, treatment and prevention for ROP. Impact Longitudinal protein profiles of 14 extremely preterm infants were analyzed using a novel multiplex protein analysis platform combined with perinatal data. Proteins associated with gestational age at birth and the neurovascular disease ROP were identified. Among infants with ROP, longitudinal levels of the identified proteins remained largely unchanged during the first postnatal months. The main functions of the proteins identified were angiogenesis, hematopoiesis, immune function, bone regulation, lipid metabolism, and central nervous system development. The study contributes to the understanding of longitudinal serum protein patterns related to gestational age and their association with abnormal retinal neuro-vascular development.
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| 4. |
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| 5. |
- Lundin, J., et al.
(författare)
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Using 360-degree videos for virtual reality exposure in CBT for panic disorder with agoraphobia : A feasibility study
- 2022
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Ingår i: Behavioural and Cognitive Psychotherapy. - : Cambridge University Press (CUP). - 1352-4658 .- 1469-1833. ; 50:2, s. 158-170
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Tidskriftsartikel (refereegranskat)abstract
- Background: Cognitive behavioural therapy (CBT) is an effective treatment for panic disorder with agoraphobia (PDA). However, implementation of some of the procedures involved, particularly in vivo exposure, can be time consuming and taxing for routine health care services. CBT with exposure taking place in virtual reality (VR-CBT) is a more time-efficient option and has shown promising results in the treatment of PDA. However, VR-CBT requires expensive equipment and appropriate virtual environments, which historically has been costly and cumbersome to produce. Thus, access to VR-CBT has been sparse in regular care environments. Aims: The aim of this study was to investigate whether VR-CBT using filmed virtual environments produced with a low-cost 360-degree film camera can be a feasible and acceptable treatment for PDA when implemented in a primary care context. Method: This was an open feasibility trial with a within-group design, with assessments conducted at pre-test, post-test, and 6-month follow-up. Participants (n = 12) received a 10-12 week treatment programme of VR-CBT and PDA-related symptoms were assessed by the primary outcome measure The Mobility Inventory for Agoraphobia (MIA) and the Panic-Disorder Severity Scale-Self Rated (PDSS-SR). Results: The results showed that treatment satisfaction was high and participants were significantly improved on PDA-related measures at post-treatment and at 6-month follow-up with large effect sizes (Cohen's d range = 1.46-2.82). All 12 participants completed the treatment. Conclusions: These findings suggest that VR-CBT with 360-degree video virtual environments delivered to primary care patients with PDA is feasible, acceptable, and potentially efficacious.
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| 6. |
- Sinzinger, Fabian, et al.
(författare)
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Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- ObjectiveSurvival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features. MethodsIn the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model. ResultsThe proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 +/- 0.03 vs. 0.62 +/- 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively. DiscussionThe experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.
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| 7. |
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| 8. |
- Edqvist, Per-Henrik D., et al.
(författare)
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Loss of ASRGL1 expression is an independent biomarker for disease-specific survival in endometrioid endometrial carcinoma
- 2015
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Ingår i: Gynecologic Oncology. - : Elsevier BV. - 0090-8258 .- 1095-6859. ; 137:3, s. 529-537
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Tidskriftsartikel (refereegranskat)abstract
- Objective For endometrial carcinoma, prognostic stratification methods do not satisfactorily identify patients with adverse outcome. Currently, histology, tumor grade and stage are used to tailoring surgical treatment and to determine the need for adjuvant treatment. Low-risk patients are not considered to require adjuvant therapy or staging lymphadenectomy. For patients with intermediate or high risk, some guidelines recommend tailoring adjuvant treatment according to additional negative prognostic factors. Our objective was to evaluate the biomarker potential of the ASRGL1 protein in endometrial carcinoma. Methods Using The Human Protein Atlas (www.proteinatlas.org), the l-asparaginase (ASRGL1) protein was identified as an endometrial carcinoma biomarker candidate. ASRGL1 expression was immunohistochemically evaluated with an extensively validated antibody on two independent endometrial carcinoma cohorts (n = 229 and n = 286) arranged as tissue microarrays. Staining results were correlated with clinical features. Results Reduced expression of ASRGL1, defined as < 75% positively stained tumor cells, was significantly associated with poor prognosis and reduced disease-specific survival in endometrioid endometrial adenocarcinoma (EEA). In multivariate analysis the hazard ratios for disease-specific survival were 3.55 (95% CI = 1.10-11.43; p = 0.003) and 3.23 (95% CI = 1.53-6.81; p = 0.002) in the two cohorts, respectively. Of the 48 cases with Grade 3 Stage I tumor all disease-related deaths were associated with low ASRGL1 expression. Conclusions Loss of ASRGL1 in EEA is a powerful biomarker for poor prognosis and retained ASRGL1 has a positive impact on survival. ASRGL1 immunohistochemistry has potential to become an additional tool for prognostication in cases where tailoring adjuvant treatment according to additional prognostic factors besides grade and stage is recommended.
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| 9. |
- Gyllensten, Ulf B., et al.
(författare)
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Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer
- 2022
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:7
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4-7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort. Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.
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