| 1. |
- Wang, Anqi, et al.
(författare)
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Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants
- 2023
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Ingår i: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 55:12, s. 2065-2074
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Tidskriftsartikel (refereegranskat)abstract
- The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.
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| 2. |
- Conti, David, V, et al.
(författare)
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Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction
- 2021
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Ingår i: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 53:1, s. 65-75
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction. A meta-analysis of genome-wide association studies across different populations highlights new risk loci and provides a genetic risk score that can stratify prostate cancer risk across ancestries.
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| 3. |
- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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| 4. |
- Bonde, Tiago M., et al.
(författare)
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Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
- 2022
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Ingår i: Scandinavian journal of urology. - : Taylor & Francis Group. - 2168-1805 .- 2168-1813. ; 56:3, s. 169-175
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC).Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death.Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5).Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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| 5. |
- Byström, Sanna, et al.
(författare)
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Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density
- 2018
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Ingår i: Breast Cancer Research. - : BIOMED CENTRAL LTD. - 1465-5411 .- 1465-542X. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- Background: Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Methods: Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Results: Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Conclusions: Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.
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| 6. |
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| 7. |
- Li, Jingmei, et al.
(författare)
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Prevalence of BRCA1 and BRCA2 pathogenic variants in a large, unselected breast cancer cohort
- 2019
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 144:5, s. 1195-1204
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Tidskriftsartikel (refereegranskat)abstract
- Breast cancer patients with BRCA1/2-driven tumors may benefit from targeted therapy. It is not clear whether current BRCA screening guidelines are effective at identifying these patients. The purpose of our study was to evaluate the prevalence of inherited BRCA1/2 pathogenic variants in a large, clinically representative breast cancer cohort and to estimate the proportion of BRCA1/2 carriers not detected by selectively screening individuals with the highest probability of being carriers according to current clinical guidelines. The study included 5,122 unselected Swedish breast cancer patients diagnosed from 2001 to 2008. Target sequence enrichment (48.48 Fluidigm Access Arrays) and sequencing were performed (Illumina Hi-Seq 2,500 instrument, v4 chemistry). Differences in patient and tumor characteristics of BRCA1/2 carriers who were already identified as part of clinical BRCA1/2 testing routines and additional BRCA1/2 carriers found by sequencing the entire study population were compared using logistic regression models. Ninety-two of 5,099 patients with valid variant calls were identified as BRCA1/2 carriers by screening all study participants (1.8%). Only 416 study participants (8.2%) were screened as part of clinical practice, but this identified 35 out of 92 carriers (38.0%). Clinically identified carriers were younger, less likely postmenopausal and more likely to be associated with familiar ovarian cancer compared to the additional carriers identified by screening all patients. More BRCA2 (34/42, 81.0%) than BRCA1 carriers (23/50, 46%) were missed by clinical screening. In conclusion, BRCA1/2 mutation prevalence in unselected breast cancer patients was 1.8%. Six in ten BRCA carriers were not detected by selective clinical screening of individuals.
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| 8. |
- Olsson, Henrik, et al.
(författare)
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Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
- 2022
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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| 9. |
- Pihl, Elsa, et al.
(författare)
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The proximal hamstring avulsion clinical trial (PHACT)-a randomised controlled non-inferiority trial of operative versus non-operative treatment of proximal hamstrings avulsions : study protocol
- 2019
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 9:9
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Tidskriftsartikel (refereegranskat)abstract
- Introduction The treatment of proximal hamstring avulsions is controversial. While several trials have investigated the outcome for patients treated surgically, there is today no prospective trial comparing operative treatment with non-operative treatment. This protocol describes the design for the proximal hamstring avulsion clinical trial (PHACT)-the first randomised controlled trial of operative versus non-operative treatment for proximal hamstring avulsions. Methods and analysis PHACT is a multicentre randomised controlled trial conducted across Sweden, Norway and Finland. Eligible patients (60 participants/treatment arm) with a proximal hamstring avulsion of at least two of three tendons will be randomised to either operative or non-operative treatment. Participants allocated to surgery will undergo reinsertion of the tendons with suture anchors. The rehabilitation programme will be the same for both treatment groups. When patient or surgeon equipoise for treatment alternatives cannot be reached and randomisation therefore is not possible, patients will be invited to participate in a parallel observational non-randomised cohort. The primary outcome will be the patient-reported outcome measure Perth hamstring assessment tool at 24 months. Secondary outcomes include the Lower Extremity Functional Score, physical performance and muscle strength tests, patient satisfaction and MR imaging. Data analysis will be blinded and intention-to-treat analysis will be preformed. Ethics and dissemination Ethical approval has been granted by the Ethical Committee of Uppsala University (DNR: 2017-170) and by the Norwegian ethical board (REC: 2017/1911). The study will be conducted in agreement with the Helsinki declaration. The findings will be disseminated in peer-reviewed publications.
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| 10. |
- Szulkin, Robert, et al.
(författare)
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Prediction of individual genetic risk to prostate cancer using a polygenic score.
- 2015
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Ingår i: The Prostate. - : Wiley. - 0270-4137 .- 1097-0045. ; 75:13, s. 1467-74
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.METHODS: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls.RESULTS: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk.CONCLUSIONS: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.
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