| 1. |
- Nowak, Michael, et al.
(författare)
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Prognostic significance of phospho-histone H3 in prostate carcinoma
- 2014
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Ingår i: World journal of urology. - : Springer-Verlag New York. - 0724-4983 .- 1433-8726. ; 32:3, s. 703-707
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer is the second most common cancer in men and the sixth most common cause of death from cancer in men worldwide. Currently, a sufficient pathological distinction between patients requiring further treatment and those for which active surveillance remains an option is still lacking, which leads to the problem of overtreatment. Cell proliferation is routinely assessed by detecting Ki-67 antigen. While Ki-67 is expressed throughout the interphase of proliferating cells, phosphorylation of the chromatin constituent histone H3 occurs only during the late G2 phase and mitosis thus providing a more strict assessment of the mitotic activity. We undertook this study to test whether expression of the recently introduced proliferation marker phospho-histone H3 (pHH3) in prostate carcinoma tissue sections exhibits prognostic significance in comparison with Ki-67. Protein expression of pHH3 and Ki-67 was assessed on TMA consisting of paraffin-embedded tissue from men that had undergone radical prostatectomy. The analysis included triplicate tissue cores of a total of 339 tumor foci. Immunohistochemical staining of pHH3 and Ki-67 was performed and analyzed using Definiens imaging software. Prostate cancer tissue exhibited a significantly higher frequency of pHH3-positive cells compared to benign prostate tissue. pHH3 expression was significantly correlated with Ki-67 expression. Furthermore, statistical analysis revealed positive correlation between pHH3 expression and PSA levels at diagnosis and in addition negatively correlated with overall survival. In contrast to Ki-67 staining, pHH3 expression did not correlate with Gleason grade. Our data point to a conceivable role of pHH3 as prognostic biomarker in prostate carcinoma.
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| 2. |
- Roswall, Pernilla, et al.
(författare)
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Microenvironmental control of breast cancer subtype elicited through paracrine platelet-derived growth factor-CC signaling
- 2018
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Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1546-170X .- 1078-8956. ; 24, s. 463-473
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Tidskriftsartikel (refereegranskat)abstract
- Breast tumors of the basal-like, hormone receptor-negative subtype remain an unmet clinical challenge, as there is high rate of recurrence and poor survival in patients following treatment. Coevolution of the malignant mammary epithelium and its underlying stroma instigates cancer-associated fibroblasts (CAFs) to support most, if not all, hallmarks of cancer progression. Here we delineate a previously unappreciated role for CAFs as determinants of the molecular subtype of breast cancer. We identified paracrine crosstalk between cancer cells expressing platelet-derived growth factor (PDGF)-CC and CAFs expressing the cognate receptors in human basal-like mammary carcinomas. Genetic or pharmacological intervention of PDGF-CC activity in mouse models of cancer resulted in conversion of basal-like breast cancers into a hormone receptor-positive state that enhanced sensitivity to endocrine therapy in previously resistant tumors. We conclude that specification of breast cancer to the basal-like subtype is under microenvironmental control and is therapeutically actionable.
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| 3. |
- Shaikhibrahim, Zaki, et al.
(författare)
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MED15, encoding a subunit of the mediator complex, is overexpressed at high frequency in castration-resistant prostate cancer
- 2014
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Ingår i: International Journal of Cancer. - Hoboken : Wiley-Blackwell. - 0020-7136 .- 1097-0215. ; 135:1, s. 19-26
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Tidskriftsartikel (refereegranskat)abstract
- The mediator complex is an evolutionary conserved key regulator of transcription of protein-coding genes and an integrative hub for diverse signaling pathways. In this study, we investigated whether the mediator subunit MED15 is implicated in castration-resistant prostate cancer (CRPC). MED15 expression and copy number/rearrangement status were assessed by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), respectively on 718 prostate cancer (PCa) specimens and sequenced by Sanger on a subset. Furthermore, SMAD3 phosphorylation, androgen receptor (AR) and proliferation markers were evaluated by IHC. In PCa cells, siRNA/shRNA knockdown of MED15 was followed by proliferation assays with/without dihydrotestosterone (DHT), and treatments with recombinant TGF-beta 3. Our results show that MED15 is overexpressed in 76% of distant metastatic CRPC (CRPCMET) and 70% of local-recurrent CRPC (CRPCLOC), in contrast to low frequencies in androgen-sensitive PCa, and no expression in benign prostatic tissue. Furthermore, MED15 overexpression correlates with worse clinical outcome thus defining a highly lethal phenotype. Moreover, TGF-beta signaling activation associates with MED15 overexpression in PCa tissues, and leads to increased expression of MED15 in PCa cells. MED15 knockdown effects phosphorylation and shuttling of p-SMAD3 to the nucleus as well as TGF-beta-enhanced proliferation. In PCa tissues, MED15 overexpression associates with AR overexpression/amplification and correlates with high proliferative activity. MED15 knockdown decreases both androgen-dependent and -independent proliferation in PCa cells. Taken together, these findings implicate MED15 in CRPC, and as MED15 is evolutionary conserved, it is likely to emerge as a lethal phenotype in other therapeutic-resistant diseases, and not restricted to our disease model.
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| 4. |
- 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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| 5. |
- Ström, Peter, et al.
(författare)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally 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 (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).
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