| 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. |
- Benfeitas, Rui, et al.
(författare)
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Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis
- 2019
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Ingår i: Ebiomedicine. - : Elsevier BV. - 2352-3964. ; 40, s. 471-487
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Tidskriftsartikel (refereegranskat)abstract
- Background: Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing. Methods: Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data. Findings: We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs - dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups. Interpretation: Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC.
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| 3. |
- Darmanis, Spyros, et al.
(författare)
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Identification of Candidate Serum Proteins for Classifying Well-Differentiated Small Intestinal Neuroendocrine Tumors
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:11, s. e81712-
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPatients with well-differentiated small intestine neuroendocrine tumors (WD-SI-NET) are most often diagnosed at a metastatic stage of disease, which reduces possibilities for a curative treatment. Thus new approaches for earlier detection and improved monitoring of the disease are required.Materials and methodsSuspension bead arrays targeting 124 unique proteins with antibodies from the Human Protein Atlas were used to profile biotinylated serum samples. Discoveries from a cohort of 77 individuals were followed up in a cohort of 132 individuals both including healthy controls as well as patients with untreated primary WD-SI-NETs, lymph node metastases and liver metastases.Results A set of 20 antibodies suggested promising proteins for further verification based on technically verified statistical significance. Proceeding, we assessed the classification performance in an independent cohort of patient serum, achieving, classification accuracy of up to 85% with different subsets of antibodies in respective pairwise group comparisons. The protein profiles of nine targets, namely IGFBP2, IGF1, SHKBP1, ETS1, IL1α, STX2, MAML3, EGR3 and XIAP were verified as significant contributors to tumor classification.ConclusionsWe propose new potential protein biomarker candidates for classifying WD-SI-NET at different stage of disease. Further evaluation of these proteins in larger sample sets and with alternative approaches is needed in order to further improve our understanding of their functional relation to WD-SI-NET and their eventual use in diagnostics.
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| 4. |
- Spjuth, Ola, 1977-, et al.
(författare)
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E-Science technologies in a workflow for personalized medicine using cancer screening as a case study
- 2017
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press. - 1067-5027 .- 1527-974X. ; 24:5, s. 950-957
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Tidskriftsartikel (refereegranskat)abstract
- Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.
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| 5. |
- Karnevi, Emelie, et al.
(författare)
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Translational study reveals a two-faced role of RBM3 in pancreatic cancer and suggests its potential value as a biomarker for improved patient stratification
- 2018
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Ingår i: Oncotarget. - : Impact Journals LLC. - 1949-2553. ; 9:5, s. 6188-6200
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Tidskriftsartikel (refereegranskat)abstract
- Periampullary adenocarcinoma, including pancreatic cancer, is a heterogeneous group of tumors with dismal prognosis, partially due to lack of reliable targetable and predictive biomarkers. RNA-binding motif protein 3 (RBM3) has previously been shown to be an independent prognostic and predictive biomarker in several types of cancer. Herein, we examined the prognostic value of RBM3 in periampullary adenocarcinoma, as well as the effects following RBM3 suppression in pancreatic cancer cells in vitro. RBM3 mRNA levels were examined in 176 pancreatic cancer patients from The Cancer Genome Atlas. Immunohistochemical expression of RBM3 was analyzed in tissue microarrays with primary tumors and paired lymph node metastases from 175 consecutive patients with resected periampullary adenocarcinoma. Pancreatic cancer cells were transfected with anti-RBM3 siRNA in vitro and the influence on cell viability following chemotherapy, transwell migration and invasion was assessed. The results demonstrated that high mRNA-levels of RBM3 were significantly associated with a reduced overall survival (p = 0.026). RBM3 protein expression was significantly higher in lymph node metastases than in primary tumors (p = 0.005). High RBM3 protein expression was an independent predictive factor for the effect of adjuvant chemotherapy and an independent negative prognostic factor in untreated patients (p for interaction = 0.003). After siRNA suppression of RBM3 in vitro, pancreatic cancer cells displayed reduced migration and invasion compared to control, as well as a significantly increased resistance to chemotherapy. In conclusion, the strong indication of a positive response predictive effect of RBM3 expression in pancreatic cancer may be highly relevant in the clinical setting and merits further validation.
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| 6. |
- 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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| 7. |
- Chelebian, Eduard, et al.
(författare)
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Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:19
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions. Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
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| 8. |
- Pozzoli, Susanna, et al.
(författare)
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Domain expertise–agnostic feature selection for the analysis of breast cancer data*
- 2020
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 108
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Tidskriftsartikel (refereegranskat)abstract
- Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise–agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.
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| 9. |
- Lee, SangWook, et al.
(författare)
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Network analyses identify liver-specific targets for treating liver diseases
- 2017
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8
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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.
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| 10. |
- Björn, Niclas, 1990-, et al.
(författare)
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Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
- 2020
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Ingår i: npj Systems Biology and Applications. - : Nature Publishing Group. - 2056-7189. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.
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