Syöpälääkkeiden aiheuttaman pahoinvoinnin yksilöllinen hoito

Syöpälääkehoidon aiheuttama pahoinvointi ja oksentelu heikentävät yhä syöpäpotilaiden elämänlaatua. Pahoinvointi voidaan jakaa akuuttiin, viivästyneeseen, syöpälääkehoitoa ennakoivaan, hoitoon huonosti reagoivaan ja läpilyöntipahoinvointiin. Kaikilla näillä muodoilla arvellaan olevan erilainen syntymekanismi, ja siksi niiden hoidot ovat erilaiset. Akuutti pahoinvointi ilmaantuu 24 tunnin kuluessa syöpälääkehoidosta. Viivästynyt pahoinvointi alkaa 24 tunnin kuluttua syöpälääkehoidon jälkeen ja voi kestää jopa useita päiviä hoidon päätyttyä. Pahoinvointiriskiin vaikuttavat syöpälääkehoidon emetogeenisuus eli ominaisuus aiheuttaa pahoinvointia sekä potilaaseen liittyvät riskitekijät. Estohoidossa käytettäviä lääkkeitä ovat 5-HT3- ja NK1-reseptorien salpaajat sekä deksametasoni, olantsapiini ja metoklopramidi. Syöpähoitojen aiheuttaman pahoinvoinnin hoidon tavoite eli pahoinvoinnin ja oksentelun täydellinen ehkäisy voidaan saavuttaa räätälöimällä estolääkitys yksilöllisesti.publishedVersionPeer reviewe

Rintasyövän neoadjuvanttihoidon toteutuminen ja tulokset

publishedVersionPeer reviewe

Dosentit : yliopiston merkittävä voimavara

“Dosentit - yliopiston merkittävä voimavara” -teos kerää yksiin kansiin Tampereen Dosenttiyhdistyksen järjestämän Dosenttien päivän 20.4.2023 mielenkiintoiset ja ajankohtaiset luennot. Kaikki kyseiset dosentit ovat saaneet dosentin arvon Tampereen yliopistosta. Tekstit ovat kansantajuisia ja suurelle yleisölle suunnattuja

Detecting Activation of Ribosomal Protein S6 Kinase by Complementary DNA and Tissue Microarray Analysis

Background: Studies by comparative genomic hybridization (CGH) have shown that chromosomal region 17q23 is amplified in up to 20% of primary breast cancers. We used microarray analyses to measure the expression levels of genes in this region and to explore their prognostic importance. Methods: A microarray that contained 4209 complementary DNA (cDNA) clones was used to identify genes that are overexpressed in the MCF-7 breast cancer cell line as compared with normal mammary tissue. Fluorescence in situ hybridization was used to analyze the copy number of one overexpressed gene, ribosomal protein S6 kinase (S6K), and to localize it to the 17q23 region. Northern and western blot analyses were used to measure S6K gene and protein expression, and an enzymatic assay was used to measure S6K activity. Tumor tissue microarray analysis was used to study amplification of S6K and the HER-2 oncogene, another 17q-linked gene, and the relationship between amplification and prognosis was analyzed. The Kaplan-Meier method was used for data analysis, and the log-rank test was used for statistical analysis. All P values are two-sided. Results: S6K was amplified and highly overexpressed in MCF-7 cells relative to normal mammary epithelium, and protein expression and enzyme activity were increased. S6K was amplified in 59 (8.8%) of 668 primary breast tumors, and a statistically significant association between amplification and poor prognosis (P = .0021) was observed. Amplification of both S6K and HER-2 implied particularly poor survival (P = .0001). Conclusions: The combination of CGH information with cDNA and tissue microarray analyses can be used to identify amplified and overexpressed genes and to evaluate the clinical implications of such genes and genomic rearrangements. S6K is likely to be one of the genes at 17q23 that is amplified during oncogenesis and may adversely affect the prognosis of patients with this amplificatio

Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data

Background The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training.Methods Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital.Results Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint.Conclusions Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

17q22-q24 Geenimonistuma rintasyövässä.

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