Proteomic identification of prognostic tumour biomarkers, using chemotherapy-induced cancer-associated fibroblasts

Cancer cells grow in highly complex stromal microenvironments, which through metabolic remodelling, catabolism, autophagy and inflammation nurture them and are able to facilitate metastasis and resistance to therapy. However, these changes in the metabolic profile of stromal cancer-associated fibroblasts and their impact on cancer initiation, progression and metastasis are not well-known. This is the first study to provide a comprehensive proteomic portrait of the azathioprine and taxol-induced catabolic state on human stromal fibroblasts, which comprises changes in the expression of metabolic enzymes, myofibroblastic differentiation markers, antioxidants, proteins involved in autophagy, senescence, vesicle trafficking and protein degradation, and inducers of inflammation. Interestingly, many of these features are major contributors to the aging process. A catabolic stroma signature, generated with proteins found differentially up-regulated in taxol-treated fibroblasts, strikingly correlates with recurrence, metastasis and poor patient survival in several solid malignancies. We therefore suggest the inhibition of the catabolic state in healthy cells as a novel approach to improve current chemotherapy efficacies and possibly avoid future carcinogenic processes

Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer

The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers

A Controloc gyorsteszt és a polimeráz láncreakció értékelése a Helicobacter pylori fertőzés eradikáció előtti diagnózisában

A szerzõk a Controloc ureáz gyorsteszt és a polimeráz láncreakció eredményességét értékelik ki 168 beteg adatait feldolgozva. A meghatározásokat antrum és corpus szövetmintából végezték. Arany standardként a szövettani vizsgálatot (módosított Giemsa-festés) alkalmazták. Mindhárom teszt esetében kiszámították a Helicobacter pylori fertõzés gyakoriságát, illetve a Controloc gyorsteszt és a PCR szenzitivitását, specificitását, pozitív és negatív prediktív értékét. A szövettan az antrumban 62,1, a corpusban 53,2%-ban, a Controloc gyorsteszt 62,1 és 56,2%-ban, a polimeráz láncreakció 65,0, illetve 64,4%-ban mutatta ki a kórokozót. A Controloc gyorsteszt szenzitivitása 89,2%-os az antrumban és 96%-os a corpusban, specificitása 87,9 és 93,7%- os, a polimeráz láncreakció szenzitivitása 93,3 és 94,5%-os, specificitása 81,0 és 87,3%-os. A szerzõk a Helicobacter-fertõzés eradikáció elõtti diagnosztikájában elsõsorban a szövettani vizsgálatot ajánlják, mivel pontos és kettõs, patológiai és bakteriológiai információt ad. A Controloc teszt pontossága és gyorsasága miatt rutin diagnosztikai módszerként kiválóan alkalmazható. A polimeráz láncreakció szintén pontos, de idõ-, munka- és költségigényes, az eradikáció elõtti diagnózisban akkor indokolt, ha egyéb módszerek nem eredményesek

Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Benzyl Isothiocyanate potentiates p53 signaling and antitumor effects against breast cancer through activation of p53-LKB1 and p73-LKB1 axes.

Functional reactivation of p53 pathway, although arduous, can potentially provide a broad-based strategy for cancer therapy owing to frequent p53 inactivation in human cancer. Using a phosphoprotein-screening array, we found that Benzyl Isothiocynate, (BITC) increases p53 phosphorylation in breast cancer cells and reveal an important role of ERK and PRAS40/MDM2 in BITC-mediated p53 activation. We show that BITC rescues and activates p53-signaling network and inhibits growth of p53-mutant cells. Mechanistically, BITC induces p73 expression in p53-mutant cells, disrupts the interaction of p73 and mutant-p53, thereby releasing p73 from sequestration and allowing it to be transcriptionally active. Furthermore, BITC-induced p53 and p73 axes converge on tumor-suppressor LKB1 which is transcriptionally upregulated by p53 and p73 in p53-wild-type and p53-mutant cells respectively; and in a feed-forward mechanism, LKB1 tethers with p53 and p73 to get recruited to p53-responsive promoters. Analyses of BITC-treated xenografts using LKB1-null cells corroborate in vitro mechanistic findings and establish LKB1 as the key node whereby BITC potentiates as well as rescues p53-pathway in p53-wild-type as well as p53-mutant cells. These data provide first in vitro and in vivo evidence of the integral role of previously unrecognized crosstalk between BITC, p53/LKB1 and p73/LKB1 axes in breast tumor growth-inhibition

A kalcium reguláló hormonok és a csontanyagcsere klinikai és kísérletes vizsgálata fiziológiás és patológiás állapotokban = Clinical and experimental investigations of calcium regulating hormones and bone metabolisms in physiologic and pathologic conditions

A veseelégtelenség progressziója és az ezzel járó kardiovasculáris, gastroenterologiai és hormonális betegségek a betegek életminőségének jelentős korlátjai. Projektünk célja volt a veseelégtenségben szenvedő betegek közül megtalálni azokat, akik fokozottan veszélyeztetettek a szövődmények kialakulására. Az volt a célunk, hogy jellemző mintákat találjunk, melyek alkalmasak a betegek szűrésére és monitorozására. Kutatásaink a proteinszintézis, a metabolizmus és az immunválasz genetikai hátterének kimutatására koncentrált, melyek jobban jelzik a betegség klinikai lefolyását, a szövődmények rizikóját vagy az alkalmazott hatóanyagok hatásosságát és mellékhatását. Kutatásaink legfontosabb eredményei új gén variánsok azonosítására volt, amit in silico technikákkal, a fehérjék háromdimenziós struktúrájának computerizált modellezésével, és az in vitro expressziós rendszerek funkcionális konzekvenciájának jellemzésével igazoltunk. Kifejlesztettünk olyan prognosztikai technikát, amely genetikai információk kimutatásával jellemzi ezt a betegcsoportot. Ezekkel a vizsgálatokkal különösen a több éves hosszú távú kezelésben részesülő betegeknél a betegség kezdetén kell elvégezni. Az eredményeinket nemzetközi folyóiratban publikáltuk. | The progression of kidney failure bone and the associated cardiovascular, gastroenterological and hormonal disorders have a major burden on patients' quality of life. Our project targeted patients with end stage renal disease in order to identify those individuals who are at increased risk for these complications. Our aims were to find characteristic patterns that are suitable for screening and monitoring purposes. Our research focused on variants of genes implicated in protein biosynthesis, metabolism and immune response and that may predict more precisely the clinical course, risk of complications, or the efficacy and/or side-effects of agents used for therapy. The main achievements of research include identification of novel gene variants using in silico techniques, computerized modelling of their effects on three-dimensional protein structure, and characterization of their functional consequences using in vitro expression systems. We developed prognostic techniques, that is featuring to this group of patients by using the genetic information of the examinations. By these methods the risk-factors that must be more focused on during the more-year-long treatment, can be identified at the beginning of the disease. The results were published in international scientific papers

MEK1 is associated with carboplatin resistance and is a prognostic biomarker in epithelial ovarian cancer

BACKGROUND: Primary systemic treatment for ovarian cancer is surgery, followed by platinum based chemotherapy. Platinum resistant cancers progress/recur in approximately 25% of cases within six months. We aimed to identify clinically useful biomarkers of platinum resistance. METHODS: A database of ovarian cancer transcriptomic datasets including treatment and response information was set up by mining the GEO and TCGA repositories. Receiver operator characteristics (ROC) analysis was performed in R for each gene and these were then ranked using their achieved area under the curve (AUC) values. The most significant candidates were selected and in vitro functionally evaluated in four epithelial ovarian cancer cell lines (SKOV-3-, CAOV-3, ES-2 and OVCAR-3), using gene silencing combined with drug treatment in viability and apoptosis assays. We collected 94 tumor samples and the strongest candidate was validated by IHC and qRT-PCR in these. RESULTS: All together 1,452 eligible patients were identified. Based on the ROC analysis the eight most significant genes were JRK, CNOT8, RTF1, CCT3, NFAT2CIP, MEK1, FUBP1 and CSDE1. Silencing of MEK1, CSDE1, CNOT8 and RTF1, and pharmacological inhibition of MEK1 caused significant sensitization in the cell lines. Of the eight genes, JRK (p = 3.2E-05), MEK1 (p = 0.0078), FUBP1 (p = 0.014) and CNOT8 (p = 0.00022) also correlated to progression free survival. The correlation between the best biomarker candidate MEK1 and survival was validated in two independent cohorts by qRT-PCR (n = 34, HR = 5.8, p = 0.003) and IHC (n = 59, HR = 4.3, p = 0.033). CONCLUSION: We identified MEK1 as a promising prognostic biomarker candidate correlated to response to platinum based chemotherapy in ovarian cancer