| 1. |
- Jonsson, Pär, et al.
(författare)
-
Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples
- 2015
-
Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 11:6, s. 1667-1678
-
Tidskriftsartikel (refereegranskat)abstract
- Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Licciardello, Marco P., et al.
(författare)
-
A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor
- 2017
-
Ingår i: Nature Chemical Biology. - : Nature Publishing Group. - 1552-4450 .- 1552-4469. ; 13:7, s. 771-778
-
Tidskriftsartikel (refereegranskat)abstract
- Approved drugs are invaluable tools to study biochemical pathways, and further characterization of these compounds may lead to repurposing of single drugs or combinations. Here we describe a collection of 308 small molecules representing the diversity of structures and molecular targets of all FDA-approved chemical entities. The CeMM Library of Unique Drugs (CLOUD) covers prodrugs and active forms at pharmacologically relevant concentrations and is ideally suited for combinatorial studies. We screened pairwise combinations of CLOUD drugs for impairment of cancer cell viability and discovered a synergistic interaction between flutamide and phenprocoumon (PPC). The combination of these drugs modulates the stability of the androgen receptor (AR) and resensitizes AR-mutant prostate cancer cells to flutamide. Mechanistically, we show that the AR is a substrate for gamma-carboxylation, a post-translational modification inhibited by PPC. Collectively, our data suggest that PPC could be repurposed to tackle resistance to antiandrogens in prostate cancer patients.
|
|
| 5. |
- Schumacher, Fredrick R., et al.
(författare)
-
Genome-wide association study identifies new prostate cancer susceptibility loci
- 2011
-
Ingår i: Human Molecular Genetics. - London : IRL Press. - 0964-6906 .- 1460-2083. ; 20:19, s. 3867-3875
-
Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have identified at least 30 distinct loci associated with small differences in risk. We conducted a GWAS in 2782 advanced PrCa cases (Gleason grade >= 8 or tumor stage C/D) and 4458 controls with 571 243 single nucleotide polymorphisms (SNPs). Based on in silico replication of 4679 SNPs (Stage 1, P < 0.02) in two published GWAS with 7358 PrCa cases and 6732 controls, we identified a new susceptibility locus associated with overall PrCa risk at 2q37.3 (rs2292884, P = 4.3 x 10(-8)). We also confirmed a locus suggested by an earlier GWAS at 12q13 (rs902774, P = 8.6 x 10(-9)). The estimated per-allele odds ratios for these loci (1.14 for rs2292884 and 1.17 for rs902774) did not differ between advanced and non-advanced PrCa (case-only test for heterogeneity P = 0.72 and P = 0.61, respectively). Further studies will be needed to assess whether these or other loci are differentially associated with PrCa subtypes.
|
|
| 6. |
- Westerberg, Marcus, et al.
(författare)
-
Different assumptions about missingness in registration of metastatic status have important implications for following trends in cancer presentation
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: We assessed approaches for handling missing M stage when estimating incidence trends of metastatic prostate cancer at diagnosis (M1) in Sweden. Study design and setting: We used different implementations of deterministic- and multiple imputation to handle missing M stage and estimate the age-standardized incidence of M1. Each method was assessed by studying adjusted survival of men with known and imputed M stage.Results: Missing data in M stage was high (66%) and varied over calendar time and risk groups. The estimated incidence of M1 differed greatly depending on the method of imputation, however all indicated a downward trend. A combination of deterministic imputation and multiple imputation produced adjusted survival curves for men with imputed M stage that best resembled the survival curves for men with known M stage. Conclusions: Deterministic imputation of missing M stage to M0 among men with low risk of metastatic disease in combination with multiple imputation appeared to the best method to estimate incidence of metastatic prostate cancer. Simply substituting missing M stage with M0 most likely underestimates the true incidence of M1. Inclusion of key clinical variables is important to generate plausible imputations. It is important to include data on use of imaging to handle missing M stage appropriately and assess potential bias of a chosen imputation method.
|
|
| 7. |
- Westerberg, Marcus
(författare)
-
Modelling short and long term consequences of changes in diagnostic activity and treatment
- 2020
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the late 90’s the diagnostic activity for prostate cancer has increased in Sweden, primarily due to increased use of PSA testing, and this has led to a large increase in diagnoses. Simultaneously, there have been changes in treatment strategies, and more effective treatments have been introduced. This thesis aims to increase the understanding of short and long term consequences of these changes by use of high quality data on virtually all men diagnosed with prostate cancer in Sweden.In paper I, the survival of men with metastatic prostate cancer at diagnosis was investigatedby use of survival models, including Kaplan-Meier analyses and Cox proportional hazards regression.The median survival from diagnosis increased with 6 months when comparing mendiagnosed 1998-2001 with men diagnosed 2010-2015, and the risk of death decreased with 13%, while median levels of prostate specific antigen at diagnosis dropped with up to 50%.In paper II, the interplay between diagnostic activity, incidence and risk of death by prostate cancer was modelled using a discrete time model. Data on diagnostic activity, e.g. in termsof testing frequencies, was not available and therefore a proxy for the diagnostic activity wasused. The hazards were estimated within the framework of generalized additive models. Two simulations were performed, assuming low and high diagnostic activity respectively, to compare incidence and mortality from 2017-2060. Higher diagnostic activity, compared to lower, led to more men being diagnosed, primarily with lower risk prostate cancer, but in the long run it led to fewer men diagnosed with metastatic disease and fewer prostate cancer deaths.
|
|
| 8. |
- Westerberg, Marcus, 1990-
(författare)
-
Prostate cancer incidence, treatment and mortality : Empirical longitudinal register-based studies and methods for handling missing data
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The diagnostic activity for prostate cancer has increased substantially in Sweden, primarily due to increased use of prostate-specific antigen (PSA) testing in asymptomatic men, and this has led to a large increase in diagnoses. There have also been changes in the diagnostic workup, guidelines, treatment strategies, and more effective treatments have been introduced in different phases of the disease. This thesis aims to increase the understanding of consequences of changes in diagnostic activity and treatment, with a focus on empirical studies, methodological development, and handling of missing data.In paper I, the survival of men with metastatic prostate cancer was investigated across calendar time periods by use of Kaplan-Meier analyses and Cox regression. The median survival from diagnosis increased with six months comparing men diagnosed 1998-2001 with men diagnosed 2010-2015, while median PSA decreased.In paper II, a discrete time multivariate longitudinal model was combined with a proxy for the unobserved level of diagnostic activity to produce prognoses of incidence and mortality. Simulations indicated that a higher diagnostic activity was associated with fewer men diagnosed with metastatic disease and fewer prostate cancer deaths.In paper III, we looked for clinical variables predictive of the survival of men with castration-resistant prostate cancer (CRPC). A new data base was created including longitudinal data on prescriptions of hormonal treatment, PSA, and cause of death. We found that PSA doubling time and PSA at time of CRCP were highly predictive and could be used for treatment decision.In paper IV, we estimated annual incidence of metastatic prostate cancer using different methods for handling missing data in metastatic status (M stage). Missing data in M stage was high and varied over calendar time and risk groups, yet each method indicated a downward trend in incidence. Although men with unknown metastatic status cannot be assumed to have nonmetastatic disease in general, this may be reasonable among those with tumour characteristics that indicate a low risk of metastases.In paper V, the estimation of multivariate longitudinal models was considered in a context where some events are observed on a coarser level (e.g. grouped) at some time points, causing gaps in the data. The likelihood function, score and observed information were derived under an independent coarsening mechanism. A simulation study was conducted comparing properties of several estimators including direct maximum likelihood and Monte Carlo Expectation Maximisation.
|
|
| 9. |
- Westerberg, Marcus, et al.
(författare)
-
Simulation model of disease incidence driven by diagnostic activity
- 2021
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 40:5, s. 1172-1188
-
Tidskriftsartikel (refereegranskat)abstract
- It is imperative to understand the effects of early detection and treatment of chronic diseases, such as prostate cancer, regarding incidence, overtreatment and mortality. Previous simulation models have emulated clinical trials, and relied on extensive assumptions on the natural history of the disease. In addition, model parameters were typically calibrated to a variety of data sources. We propose a model designed to emulate real-life scenarios of chronic disease using a proxy for the diagnostic activity without explicitly modeling the natural history of the disease and properties of clinical tests. Our model was applied to Swedish nation-wide population-based prostate cancer data, and demonstrated good performance in terms of reconstructing observed incidence and mortality. The model was used to predict the number of prostate cancer diagnoses with a high or limited diagnostic activity between 2017 and 2060. In the long term, high diagnostic activity resulted in a substantial increase in the number of men diagnosed with lower risk disease, fewer men with metastatic disease, and decreased prostate cancer mortality. The model can be used for prediction of outcome, to guide decision-making, and to evaluate diagnostic activity in real-life settings with respect to overdiagnosis and prostate cancer mortality.
|
|
