| 1. |
- Bratulic, Sinisa, 1981, et al.
(författare)
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Noninvasive detection of any-stage cancer using free glycosaminoglycans.
- 2022
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 1091-6490 .- 0027-8424. ; 119:50
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Tidskriftsartikel (refereegranskat)abstract
- Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.
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| 2. |
- Enroth, Stefan, 1976-, et al.
(författare)
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A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer
- 2018
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Ingår i: Clinical Proteomics. - : Springer Science and Business Media LLC. - 1542-6416 .- 1559-0275. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundOver 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.MethodsIn the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n=106), endometrial cancer (n=74), benign ovarian tumors (n=150) and healthy population controls (n=399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n=280), endometrial cancer (n=228), women with benign ovarian tumors (n=76) and healthy controls (n=57).ResultsIn the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III-IV with a sensitivity of 0.88 and specificity of 0.92 (AUC=0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p=9.56e-22). Also, population controls could be distinguished from ovarian cancer stage III-IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC=0.89).ConclusionThe PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.FundingThe Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.
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| 3. |
- Enroth, Stefan, 1976-, et al.
(författare)
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Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
- 2022
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundOvarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.MethodsProtein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.ResultsThe assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.ConclusionThe robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.
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| 4. |
- Enroth, Stefan, 1976-, et al.
(författare)
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High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer
- 2019
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I-IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.
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| 5. |
- Glimelius, Bengt, et al.
(författare)
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U-CAN : a prospective longitudinal collection of biomaterials and clinical information from adult cancer patients in Sweden.
- 2018
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Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 57:2, s. 187-194
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Tidskriftsartikel (refereegranskat)abstract
- Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population.Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data.Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort.Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.
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| 6. |
- 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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| 7. |
- Gyllensten, Ulf B., et al.
(författare)
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Preoperative Fasting and General Anaesthesia Alter the Plasma Proteome
- 2020
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Blood plasma collected at time of surgery is an excellent source of patient material for investigations into disease aetiology and for the discovery of novel biomarkers. Previous studies on limited sets of proteins and patients have indicated that pre-operative fasting and anaesthesia can affect protein levels, but this has not been investigated on a larger scale. These effects could produce erroneous results in case-control studies if samples are not carefully matched. Methods: The proximity extension assay (PEA) was used to characterize 983 unique proteins in a total of 327 patients diagnosed with ovarian cancer and 50 age-matched healthy women. The samples were collected either at time of initial diagnosis or before surgery under general anaesthesia. Results: 421 of the investigated proteins (42.8%) showed statistically significant differences in plasma abundance levels comparing samples collected at time of diagnosis or just before surgery under anaesthesia. Conclusions: The abundance levels of the plasma proteome in samples collected before incision, i.e., after short-time fasting and under general anaesthesia differs greatly from levels in samples from awake patients. This emphasizes the need for careful matching of the pre-analytical conditions of samples collected from controls to cases at time of surgery in the discovery as well as clinical use of protein biomarkers.
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| 8. |
- Hedlund Lindberg, Julia, et al.
(författare)
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Toward ovarian cancer screening with protein biomarkers using dried, self-sampled cervico-vaginal fluid
- 2024
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Ingår i: iScience. - 2589-0042. ; 27:2
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Tidskriftsartikel (refereegranskat)abstract
- Early detection is key for increased survival in ovarian cancer, but no general screening program exists today due to lack of biomarkers and overall cost versus benefit over traditional clinical methods. Here, we used dried cervico-vaginal fluid (CVF) as sampling matrix coupled with mass spectrometry for detection of protein biomarkers. We find that self-collected CVF on paper cards yields robust results and is suitable for high-throughput proteomics. Artificial intelligence–based methods were used to identify an 11-protein panel that separates cases from controls. In validation data, the panel achieved a sensitivity of 0.97 (95% CI 0.91–1.00) at a specificity of 0.67 (0.40–0.87). Analyses of samples collected prior to development of symptoms indicate that the panel is informative also of future risk of disease. Dried CVF is used in cervical cancer screening, and our results opens the possibility for a screening program also for ovarian cancer, based on self-collected CVF samples.
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| 9. |
- Alfonzo, Emilia, et al.
(författare)
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No survival difference between robotic and open radical hysterectomy for women with early-stage cervical cancer: results from a nationwide population-based cohort study
- 2019
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Ingår i: European Journal of Cancer. - : Elsevier BV. - 0959-8049 .- 1879-0852. ; 116, s. 169-177
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The aim of the study was to compare overall survival (OS) and disease-free survival (DFS) after open and robotic radical hysterectomy for early-stage cervical cancer. Patients and methods: This was a nationwide population-based cohort study on all women with cervical cancer stage IA1-IB of squamous, adenocarcinoma or adenosquamous histological subtypes, from January 2011 to December 2017, for whom radical hysterectomy was performed. The Swedish Quality Register of Gynaecologic Cancer was used for identification. To ensure quality and conformity of data and to disclose patients not yet registered, hospital registries were reviewed and validated. Cox and propensity score regression analysis and univariable and multivariable regression analysis were performed in regard to OS and DFS. Results: There were 864 women (236 open and 628 robotic) included in the study. The 5-year OS was 92% and 94% and DFS was 84% and 88% for the open and robotic cohorts, respectively. The recurrence pattern was similar in both groups. Using propensity score analysis and matched cohorts of 232 women in each surgical group, no significant differences were seen in survival: 5-year OS of 92% in both groups (hazard ratio [HR], 1.00; 95% confidence interval [CI], 0.50–2.01) and DFS of 85% vs 84% in the open and robotic cohort, respectively (HR, 1.08; 95% CI, 0.66–1.78). In univariable and multivariable analysis with OS as the end-point, no significant factors were found, and in regard to DFS, tumour size (p < 0.001) and grade 3 (p = 0.02) were found as independent significant risk factors. Conclusion: In a complete nationwide population-based cohort, where radical hysterectomy for early-stage cervical cancer is highly centralised, neither long-term survival nor pattern of recurrence differed significantly between open and robotic surgery. © 2019 The Authors
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| 10. |
- Alvez, Maria Bueno, et al.
(författare)
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Next generation pan-cancer blood proteome profiling using proximity extension assay
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
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