| 1. |
- Trac, Quang Thinh, et al.
(författare)
-
Prediction model for drug response of acute myeloid leukemia patients
- 2023
-
Ingår i: npj Precision Oncology. - : Springer Nature. - 2397-768X. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/.
|
|
| 2. |
- Bylesjö, Max, et al.
(författare)
-
K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space
- 2008
-
Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9, s. 1-7
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.Results: We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.Conclusion: The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
|
|
| 3. |
- Bylesjö, Max, et al.
(författare)
-
OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
- 2006
-
Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 341-351
-
Tidskriftsartikel (refereegranskat)abstract
- The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.
|
|
| 4. |
- Drong, Alexander W, et al.
(författare)
-
The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue.
- 2013
-
Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:2
-
Tidskriftsartikel (refereegranskat)abstract
- Genetic variants that associate with DNA methylation at CpG sites (methylation quantitative trait loci, meQTLs) offer a potential biological mechanism of action for disease associated SNPs. We investigated whether meQTLs exist in abdominal subcutaneous adipose tissue (SAT) and if CpG methylation associates with metabolic syndrome (MetSyn) phenotypes. We profiled 27,718 genomic regions in abdominal SAT samples of 38 unrelated individuals using differential methylation hybridization (DMH) together with genotypes at 5,227,243 SNPs and expression of 17,209 mRNA transcripts. Validation and replication of significant meQTLs was pursued in an independent cohort of 181 female twins. We find that, at 5% false discovery rate, methylation levels of 149 DMH regions associate with at least one SNP in a ±500 kilobase cis-region in our primary study. We sought to validate 19 of these in the replication study and find that five of these significantly associate with the corresponding meQTL SNPs from the primary study. We find that none of the 149 meQTL top SNPs is a significant expression quantitative trait locus in our expression data, but we observed association between expression levels of two mRNA transcripts and cis-methylation status. Our results indicate that DNA CpG methylation in abdominal SAT is partly under genetic control. This study provides a starting point for future investigations of DNA methylation in adipose tissue.
|
|
| 5. |
- Georgoudaki, Anna-Maria, et al.
(författare)
-
Reprogramming Tumor-Associated Macrophages by Antibody Targeting Inhibits Cancer Progression and Metastasis
- 2016
-
Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 15:9, s. 2000-2011
-
Tidskriftsartikel (refereegranskat)abstract
- Tumors are composed of multiple cell types besides the tumor cells themselves, including innate immune cells such as macrophages. Tumor-associated macrophages (TAMs) are a heterogeneous population of myeloid cells present in the tumor microenvironment (TME). Here, they contribute to immunosuppression, enabling the establishment and persistence of solid tumors as well as metastatic dissemination. We have found that the pattern recognition scavenger receptor MARCO defines a subtype of suppressive TAMs and is linked to clinical outcome. An anti-MARCO monoclonal antibody was developed, which induces anti-tumor activity in breast and colon carcinoma, as well as in melanoma models through reprogramming-TAM-populations to a pro-inflammatory phenotype and increasing tumor immunogenicity. This anti-tumor activity is dependent on the inhibitory Fc-receptor, Fc gamma RIIB, and also enhances the efficacy of checkpoint therapy. These results demonstrate that immunotherapies using antibodies designed to modify myeloid cells of the TME represent a promising mode of cancer treatment.
|
|
| 6. |
- Holm, Johanna, et al.
(författare)
-
Assessment of breast cancer risk factors reveals subtype heterogeneity
- 2017
-
Ingår i: Cancer Research. - Stockholm : Karolinska Institutet, Dept of Medical Epidemiology and Biostatistics. - 0008-5472. ; 77:13, s. 3708-3717
-
Tidskriftsartikel (refereegranskat)abstract
- Subtype heterogeneity for breast cancer risk factors has been suspected, potentially reflecting etiological differences and implicating risk prediction. Reports are conflicting regarding presence of heterogeneity for many exposures. To examine subtype heterogeneity across known breast cancer risk factors, we conducted a case-control analysis of 2,632 breast cancers and 15,945 controls in Sweden. Molecular subtype was predicted from pathology-record derived immunohistochemistry markers by a classifier trained on PAM50 subtyping. Multinomial logistic regression estimated separate odds ratios for each subtype by the exposures parity, age at first birth, breastfeeding, menarche, HRT use, somatotype at age 18, benign breast disease, mammographic density, polygenic risk score, family history of breast cancer and BRCA mutations. We found clear subtype heterogeneity for genetic factors and breastfeeding. The polygenic risk score was associated with risk of all subtypes except for the basal-like (p heterogeneity < 0.0001). Parous women who never breastfed were at higher risk of basal-like subtype (OR 4.17; 95% CI 1.89 to 9.21) compared to both nulliparous (reference) and breastfeeding women. Breastfeeding was not associated with risk of HER2-overexpressing type, but protective for all other subtypes. The observed heterogeneity in risk of distinct breast cancer subtypes for germline variants supports heterogeneity in etiology and has implications for their use in risk prediction. The increased risk of basal-like subtype among women who never breastfed merits more research into potential causal mechanisms and confounders.
|
|
| 7. |
- Kartasalo, Kimmo, et al.
(författare)
-
Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps
- 2021
-
Ingår i: European Urology Focus. - : Elsevier. - 2405-4569. ; 7:4, s. 687-691
-
Forskningsöversikt (refereegranskat)abstract
- Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under-and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. Patient summary: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.
|
|
| 8. |
- Kato, Bernet S., et al.
(författare)
-
Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model
- 2011
-
Ingår i: Proteome Science. - : Springer Science and Business Media LLC. - 1477-5956. ; 9:1, s. 73-
-
Tidskriftsartikel (refereegranskat)abstract
- The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. RESULTS: Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). CONCLUSION: The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discover with exploratory, high-throughput and multiplexed methods.
|
|
| 9. |
- Mareschal, Sylvain, et al.
(författare)
-
Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML
- 2021
-
Ingår i: Blood Advances. - : American Society of Hematology. - 2473-9529 .- 2473-9537. ; 5:4, s. 1003-1016
-
Tidskriftsartikel (refereegranskat)abstract
- Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing-based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)-defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA-related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss 200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing-based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.
|
|
| 10. |
- Mer, Arvind Singh, et al.
(författare)
-
Biological and therapeutic implications of a unique subtype of NPM1 mutated AML
- 2021
-
Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification. Here we report two distinct subtypes within NPM1 mutated AML patients, which we label as primitive and committed based on the respective presence or absence of a stem cell signature. Using gene expression (RNA-seq), epigenomic (ATAC-seq) and immunophenotyping (CyToF) analysis, we associate each subtype with specific molecular characteristics, disease differentiation state and patient survival. Using ex vivo drug sensitivity profiling, we show a differential drug response of the subtypes to specific kinase inhibitors, irrespective of the FLT3-ITD status. Differential drug responses of the primitive and committed subtype are validated in an independent AML cohort. Our results highlight heterogeneity among NPM1 mutated AML patient samples based on stemness and suggest that the addition of kinase inhibitors to the treatment of cases with the primitive signature, lacking FLT3-ITD, could have therapeutic benefit. Molecular heterogeneity of acute myeloid leukaemia (AML) across patients is a major challenge for prognosis and therapy. Here, the authors show that NPM1 mutated AML is a heterogeneous class, consisting of two subtypes which exhibit distinct molecular characteristics, differentiation state, patient survival and drug response.
|
|
