AKT regulates NPM dependent ARF localization and p53mut stability in tumors

Nucleophosmin (NPM) is known to regulate ARF subcellular localization and MDM2 activity in response to oncogenic stress, though the precise mechanism has remained elusive. Here we describe how NPM and ARF associate in the nucleoplasm to form a MDM2 inhibitory complex. We find that oligomerization of NPM drives nucleolar accumulation of ARF. Moreover, the formation of NPM and ARF oligomers antagonizes MDM2 association with the inhibitory complex, leading to activation of MDM2 E3-ligase activity and targeting of p53. We find that AKT phosphorylation of NPM-Ser48 prevents oligomerization that results in nucleoplasmic localization of ARF, constitutive MDM2 inhibition and stabilization of p53. We also show that ARF promotes p53 mutant stability in tumors and suppresses p73 mediated p21 expression and senescence. We demonstrate that AKT and PI3K inhibitors may be effective in treatment of therapeutically resistant tumors with elevated AKT and carrying gain of function mutations in p53. Our results show that the clinical candidate AKT inhibitor MK-2206 promotes ARF nucleolar localization, reduced p53(mut) stability and increased sensitivity to ionizing radiation in a xenograft model of pancreatic cancer. Analysis of human tumors indicates that phospho-S48-NPM may be a useful biomarker for monitoring AKT activity and in vivo efficacy of AKT inhibitor treatment. Critically, we propose that combination therapy involving PI3K-AKT inhibitors would benefit from a patient stratification rationale based on ARF and p53(mut) status

Time-Resolved Transcriptome Analysis of Bacillus subtilis Responding to Valine, Glutamate, and Glutamine

Microorganisms can restructure their transcriptional output to adapt to environmental conditions by sensing endogenous metabolite pools. In this paper, an Agilent customized microarray representing 4,106 genes was used to study temporal transcript profiles of Bacillus subtilis in response to valine, glutamate and glutamine pulses over 24 h. A total of 673, 835, and 1135 amino-acid-regulated genes were identified having significantly changed expression at one or more time points in response to valine, glutamate, and glutamine, respectively, including genes involved in cell wall, cellular import, metabolism of amino-acids and nucleotides, transcriptional regulation, flagellar motility, chemotaxis, phage proteins, sporulation, and many genes of unknown function. Different amino acid treatments were compared in terms of both the global temporal profiles and the 5-minute quick regulations, and between-experiment differential genes were identified. The highlighted genes were analyzed based on diverse sources of gene functions using a variety of computational tools, including T-profiler analysis, and hierarchical clustering. The results revealed the common and distinct modes of action of these three amino acids, and should help to elucidate the specific signaling mechanism of each amino acid as an effector

A Novel Composite Biomarker Panel For Detection Of Early Stage Non-small Cell Lung Cancer

Purpose: To investigate a novel composite methodology of using targeted serum microRNAs (micro ribonucleic acid; miRNA) and urine metabolites for the accurate detection of early stage non-small cell lung cancer (NSCLC). Methods: Consecutively consenting NSCLC patients and matched control subjects were recruited to provide samples of serum for miRNA and/or urine for metabolite analyses. Serum miRNA levels were measured using quantitative real-time reverse-transcription with exogenous control, and the comparative delta cycle threshold (&#9651CT) method was used to calculate relative miRNA expression of two targeted miRNAs (miR-21 and miR-223). The concentrations of six targeted urinary metabolites in patients and healthy controls were measured using proton nuclear magnetic resonance (1H NMR) spectroscopy. A composite methodology of using the 35 accruals with both serum and urine biomarkers was then established with binary logistic regression, receiver operating characteristic (ROC) models with or without artificial intelligence (AI). Results: The ROC analysis of miRNA expression yielded a sensitivity of 96.4% and a specificity of 88.2% for the detection of early stage NSCLC, with area under the curve (AUC) = 0.91 (CI 95%: 0.80-1.0). Relative urinary concentrations of 4-methoxyphenylacetic acid (4MPLA) were significantly different between NSCLC and healthy control (p=0.008). The ROC analysis of 4MPLA yielded a sensitivity of 82.1% and a specificity of 88.2%, with AUC = 0.85. The composite process combining miRNA and metabolite expression demonstrated a sensitivity and specificity of nearly 100% and AUC=1. Conclusions: A highly specific, sensitive and non-invasive detection method for NSCLC was developed. Pending validation, this can potentially improve the early detection and, hence, the treatment and survival outcomes of patients

ARCII:A phase II trial of the HIV protease inhibitor Nelfinavir in combination with chemoradiation for locally advanced inoperable pancreatic cancer

AbstractBackground and purposeNelfinavir can enhance intrinsic radiosensitivity, reduce hypoxia and improve vascularity. We conducted a phase II trial combining nelfinavir with chemoradiotherapy (CRT) for locally advanced inoperable pancreatic cancer (LAPC).Materials and methodsRadiotherapy (50.4Gy/28 fractions; boost to 59.4Gy/33 fractions) was administered with weekly gemcitabine and cisplatin. Nelfinavir started 3–10days before and was continued during CRT. The primary end-point was 1-year overall survival (OS). Secondary end-points included histological downstaging, radiological response, 1-year progression free survival (PFS), overall survival (OS) and treatment toxicity. An imaging sub-study (n=6) evaluated hypoxia (18F-Fluoromisonidazole-PET) and perfusion (perfusion CT) during induction nelfinavir.ResultsThe study closed after recruiting 23 patients, due to non-availability of Nelfinavir in Europe. The 1-year OS was 73.4% (90% CI: 54.5–85.5%) and median OS was 17.4months (90% CI: 12.8–18.8). The 1-year PFS was 21.8% (90% CI: 8.9–38.3%) and median PFS was 5.5months (90% CI: 4.1–8.3). All patients experienced Grade 3/4 toxicity, but many were asymptomatic laboratory abnormalities. Four of 6 patients on the imaging sub-study demonstrated reduced hypoxia and increased perfusion post-nelfinavir.ConclusionsCRT combined with nelfinavir showed acceptable toxicity and promising survival in pancreatic cancer

Menin determines K-RAS proliferative outputs in endocrine cells

Endocrine cell proliferation fluctuates dramatically in response to signals that communicate hormone demand. The genetic alterations that override these controls in endocrine tumors often are not associated with oncogenes common to other tumor types, suggesting that unique pathways govern endocrine proliferation. Within the pancreas, for example, activating mutations of the prototypical oncogene KRAS drive proliferation in all pancreatic ductal adenocarcimomas but are never found in pancreatic endocrine tumors. Therefore, we asked how cellular context impacts K-RAS signaling. We found that K-RAS paradoxically suppressed, rather than promoted, growth in pancreatic endocrine cells. Inhibition of proliferation by K-RAS depended on antiproliferative RAS effector RASSF1A and blockade of the RAS-activated proproliferative RAF/MAPK pathway by tumor suppressor menin. Consistent with this model, a glucagon-like peptide 1 (GLP1) agonist, which stimulates ERK1/2 phosphorylation, did not affect endocrine cell proliferation by itself, but synergistically enhanced proliferation when combined with a menin inhibitor. In contrast, inhibition of MAPK signaling created a synthetic lethal interaction in the setting of menin loss. These insights suggest potential strategies both for regenerating pancreatic β cells for people with diabetes and for targeting menin-sensitive endocrine tumors

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

BACKGROUND: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.</p

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score.

Funder: University of California (#C21CR2060), the Prostate Cancer FoundationFunder: Research Council of Norway (#223273), KG Jebsen Stiftelsen, and South East Norway Health AuthorityBACKGROUND: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations