| 1. |
- Backman, Samuel, et al.
(författare)
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The Evolutionary History of Metastatic Pancreatic Neuroendocrine Tumours Reveals a Therapy Driven Route to High-Grade Transformation.
- 2024
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Ingår i: medRxiv : the preprint server for health sciences.
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Tidskriftsartikel (refereegranskat)abstract
- Tumour evolution with acquisition of more aggressive disease characteristics is a hallmark of disseminated cancer. Metastatic pancreatic neuroendocrine tumours (PanNETs) in particular, show frequent progression from a low/intermediate to a high-grade disease. To understand the molecular mechanisms underlying this phenomenon, we performed multi-omics analysis of 32 longitudinal samples from six metastatic PanNET patients. Following MEN1 inactivation, PanNETs exhibit genetic heterogeneity on both spatial and temporal dimensions with parallel and convergent tumuor evolution involving the ATRX/DAXX and mTOR pathways. Following alkylating chemotherapy treatment, some PanNETs develop mismatch repair deficiency and acquire a hypermutator phenotype. This DNA hypermutation phenotype was only found in cases that also showed transformation into a high-grade PanNET. Overall, our findings contribute to broaden the understanding of metastatic PanNET, and suggests that therapy driven disease evolution is an important hallmark of this disease.
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| 2. |
- Crona, Joakim, et al.
(författare)
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Genotype-phenotype correlations in pheochromocytoma and paraganglioma : a systematic review and individual patient meta-analysis
- 2019
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Ingår i: Endocrine-Related Cancer. - : BIOSCIENTIFICA LTD. - 1351-0088 .- 1479-6821. ; 26:5, s. 539-550
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Forskningsöversikt (refereegranskat)abstract
- Pheochromocytoma and paraganglioma (PPGL) can be divided into at least four molecular subgroups. Whether such categorizations are independent factors for prognosis or metastatic disease is unknown. We performed a systematic review and individual patient meta-analysis aiming to estimate if driver mutation status can predict metastatic disease and survival. Driver mutations were used to categorize patients according to three different molecular systems: two subgroups (SDHB mutated or wild type), three subgroups (pseudohypoxia, kinase signaling or Wnt/unknown) and four subgroups (tricarboxylic acid cycle, VHL/EPAS1, kinase signaling or Wnt/unknown). Twenty-one studies and 703 patients were analyzed. Multivariate models for association with metastasis showed correlation with SDHB mutation (OR 5.68 (95% CI 1.79-18.06)) as well as norepinephrine (OR 3.01 (95% CI 1.02-8.79)) and dopa mine (OR 6.39 (95% CI 1.62-25.24)) but not to PPGL location. Other molecular systems were not associated with metastasis. In multivariate models for association with survival, age (HR 1.04 (95% CI 1.02-1.06)) and metastases (HR 6.13 (95% CI 2.86-13.13)) but neither paraganglioma nor SDHB mutation remained significant. Other molecular subgroups did not correlate with survival. We conclude that molecular categorization accordingly to SDHB provided independent information on the risk of metastasis. Driver mutations status did not correlate independently with survival. These data may ultimately be used to guide current and future risk stratification of PPGL.
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| 3. |
- Ghosal, Suman, et al.
(författare)
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Immune signature of pheochromocytoma and paraganglioma in context of neuroendocrine neoplasms associated with prognosis
- 2023
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Ingår i: Endocrine. - : Springer Nature. - 1355-008X .- 1559-0100. ; 79:1, s. 171-179
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo understand prognostic immune cell infiltration signatures in neuroendocrine neoplasms (NENs), particularly pheochromocytoma and paraganglioma (PCPG), we analyzed tumor transcriptomic data from The Cancer Genome Atlas (TCGA) and other published tumor transcriptomic data of NENs.MethodsWe used CIBERSORT to infer immune cell infiltrations from bulk tumor transcriptomic data from PCPGs, in comparison to gastroenteropancreatic neuroendocrine tumors (GEPNETs) and small cell lung carcinomas (SCLCs). PCPG immune signature was validated with NanoString immune panel in an independent cohort. Unsupervised clustering of the immune infiltration scores from CIBERSORT was used to find immune clusters. A prognostic immune score model for PCPGs and the other NENs were calculated as a linear combination of the estimated infiltration of activated CD8+/CD4+ T cells, activated NK cells, and M0 and M2 macrophages.ResultsIn PCPGs, we found five dominant immune clusters, associated with M2 macrophages, monocytes, activated NK cells, M0 macrophages and regulatory T cells, and CD8+/CD4+ T cells respectively. Non-metastatic tumors were associated with activated NK cells and metastatic tumors were associated with M0 macrophages and regulatory T cells. In GEPNETs and SCLCs, M0 macrophages and regulatory T cells were associated with unfavorable outcomes and features, such as metastasis and high-grade tumors. The prognostic immune score model for PCPGs and the NENs could predict non-aggressive and non-metastatic diseases. In PCPGs, the immune score was also an independent predictor of metastasis-free survival in a multivariate Cox regression analysis.ConclusionThe transcriptomic immune signature in PCPG correlates with clinical features like metastasis and prognosis.
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| 4. |
- Ghosal, Suman, et al.
(författare)
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Long intergenic noncoding RNA profiles of pheochromocytoma and paraganglioma : A novel prognostic biomarker
- 2020
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 146:8, s. 2326-2335
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Tidskriftsartikel (refereegranskat)abstract
- Many long intergenic noncoding RNAs (lincRNAs) serve as cancer biomarkers for diagnosis or prognostication. To understand the role of lincRNAs in the rare neuroendocrine tumors pheochromocytoma and paraganglioma (PCPG), we performed first time in-depth characterization of lincRNA expression profiles and correlated findings to clinical outcomes of the disease. RNA-Seq data from patients with PCPGs and 17 other tumor types from The Cancer Genome Atlas and other published sources were obtained. Differential expression analysis and a machine-learning model were used to identify transcripts specific to PCPGs, as well as established PCPG molecular subtypes. Similarly, lincRNAs specific to aggressive PCPGs were identified, and univariate and multivariate analysis was performed for metastasis-free survival. The results were validated in independent samples using RT-PCR. From a pan-cancer context, PCPGs had a specific and unique lincRNA profile. Among PCPGs, five different molecular subtypes were identified corresponding to the established molecular classification. Upregulation of 13 lincRNAs was found to be associated with aggressive/metastatic PCPGs. RT-PCR validation confirmed the overexpression of four lincRNAs in metastatic compared to non-metastatic PCPGs. Kaplan-Meier analysis identified five lincRNAs as prognostic markers for metastasis-free survival of patients in three subtypes of PCPGs. Stratification of PCPG patients with a risk-score formulated using multivariate analysis of lincRNA expression profiles, presence of key driver mutations, tumor location, and hormone secretion profiles showed significant differences in metastasis-free survival. PCPGs thus exhibit a specific lincRNA expression profile that also corresponds to the established molecular subgroups and can be potential marker for the aggressive/metastatic PCPGs.
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