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// Maurizio Muscaritoli 1 , Simone Lucia 1 , Alessio Farcomeni 2 , Vito Lorusso 3 , Valeria Saracino 3 , Carlo Barone 4 , Francesca Plastino 4 , Stefania Gori 5 , Roberto Magarotto 5 , Giacomo Carteni 6 , Bruno Chiurazzi 6 , Ida Pavese 7 , Luca Marchetti 7 , Vittorina Zagonel 8 , Eleonora Bergo 8 , Giuseppe Tonini 9 , Marco Imperatori 9 , Carmelo Iacono 10 , Luigi Maiorana 10 , Carmine Pinto 11 , Daniela Rubino 11 , Luigi Cavanna 12 , Roberto Di Cicilia 12 , Teresa Gamucci 13 , Silvia Quadrini 13 , Salvatore Palazzo 14 , Stefano Minardi 14 , Marco Merlano 15 , Giuseppe Colucci 16 and Paolo Marchetti 17, 18 , on behalf of the PreMiO Study Group 19 1 Department of Clinical Medicine, Sapienza University of Rome, Rome, Italy 2 Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy 3 Department of Medical Oncology, National Cancer Research Centre Giovanni Paolo II, Bari, Italy 4 Department of Medical Oncology, Catholic University of Sacred Heart, Largo A. Gemelli, Rome, Italy 5 Medical Oncology Unit, Ospedale Sacro Cuore Don Calabria, Verona, Italy 6 Oncology Unit, Antonio Cardarelli Hospital, Naples, Italy 7 Oncology Unit, San Pietro Fatebenefratelli Hospital, Rome, Italy 8 Department of Clinical and Experimental Oncology, Medical Oncology 1, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy 9 Department of Oncology, University Campus Bio-Medico of Rome, Rome, Italy 10 Department of Medical Oncology, Azienda Ospedaliera Civile - Maria Paterno Arezzo, Ragusa, Italy 11 Medical Oncology, Clinical Cancer Centre, IRCCS-Arcispedale S. Maria Nuova, Reggio Emilia, Italy 12 Department of Oncology-Hematology, Guglielmo da Saliceto Hospital, Piacenza, Italy 13 Medical Oncology Unit, S.S. Trinita Hospital, Sora, Italy 14 Division of Medical Oncology, Mariano Santo Hospital, Azienda Ospedaliera, Cosenza, Italy 15 Medical Oncology, Oncology Department, S. Croce & Carle Teaching Hospital, Cuneo, Italy 16 Medical Oncology Department, National Cancer Research Centre Giovanni Paolo II, Bari, Italy 17 Department of Clinical and Molecular Medicine, Faculty of Medicine and Psychology Sapienza, St. Andrea Hospital, Rome, Italy 18 IDI-IRCCS, Rome, Italy 19 The PreMiO Study group also included investigators who contributed to patients’ enrollment Correspondence to: Maurizio Muscaritoli, email: maurizio.muscaritoli@uniroma1.it Keywords: malnutrition, cancer, cachexia, sarcopenia, oncology Received: April 27, 2017 Accepted: June 20, 2017 Published: August 10, 2017 ABSTRACT Background: In cancer patients, malnutrition is associated with treatment toxicity, complications, reduced physical functioning, and decreased survival. The Prevalence of Malnutrition in Oncology (PreMiO) study identified malnutrition or its risk among cancer patients making their first medical oncology visit. Innovatively, oncologists, not nutritionists, evaluated the nutritional status of the patients in this study. Methods: PreMiO was a prospective, observational study conducted at 22 medical oncology centers across Italy. For inclusion, adult patients (>18 years) had a solid tumor diagnosis, were treatment-naive, and had a life expectancy >3 months. Malnutrition was identified by the Mini Nutritional Assessment (MNA), appetite status with a visual analog scale (VAS), and appetite loss with a modified version of Anorexia-Cachexia Subscale (AC/S-12) of the Functional Assessment of Anorexia-Cachexia Therapy (FAACT). Findings: Of patients enrolled ( N= 1,952), 51% had nutritional impairment; 9% were overtly malnourished, and 43% were at risk for malnutrition. Severity of malnutrition was positively correlated with the stage of cancer. Over 40% of patients were experiencing anorexia, as reported in the VAS and FAACT questionnaire. During the prior six months, 64% of patients lost weight (1–10 kg). Interpretation: Malnutrition, anorexia, and weight loss are common in cancer patients, even at their first visit to a medical oncology center.

Background The aim of the study was to identify p53 gene mutations by FAMA (fluorescence-assisted mismatch analysis) in colorectal cancers. Patients and methods Analytical scanning of the p53 gene (exons 5–9) was performed in colon cancer samples from 44 consecutive patients by FAMA. FAMA is a semiautomatic scanning approach based on the chemical cleavage of the mismatch in fluorescently labeled heteroduplex DNA, obtained from the combination of a normal and a mutated allele. FAMA has already shown optimal levels of diagnostic accuracy and sensitivity in detecting gene mutations (nucleotide substitutions, insertions/deletions) both at the germline and somatic level. The peculiar feature of FAMA is its ability to detect and localize mutations, by a redundant pattern of signals due to fluorescent DNA fragments generated by chemical cleavage. Moreover, previous data have demonstrated that normal contaminating DNA from stromal cells in the sample does not affect the sensitivity of the procedure, leading to the identification of the mutation even when the ratio mutant/normal allele is 10%. Results Eighteen mutations (12 missense, one nonsense, two deletions, three nucleotide substitutions at the level of the splice-junctions) and two polymorphisms were detected by FAMA in 17 patients (39%) and then confirmed by automated sequence analysis. Six of 18 mutations (33%) were not previously reported for colon cancer samples and two of 18 lesions (11%) were identified as novel p53 mutations. Conclusions Analytical scanning of the p53 gene by FAMA in DNA from colon cancer samples provides a sensitive, accurate and specific diagnostic procedure for routine clinical application.

Clear evidence exists for heritability of humanlongevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118nonagenarian Caucasian sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11 (LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1228 unrelated nonagenarian and 1907 geographically matched controls. Using a fixed-effect meta-analysis approach, rs4420638 at the TOMM40/ APOE/APOC1 gene locus showed significant association with longevity (P-value = 9.6 × 10 -8). By combined modeling of linkage and association, we showed that association of longevity with APOEe4 and APOEe2 alleles explain the linkage at 19q13.11-q13.32 with P-value = 0.02 and P-value = 1.0 × 10 -5, respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.

The occurrence of mutations in the p53 tumor suppressor gene is a specific and recurring genetic event in solid tumors. P53 plays a pivotal role in multiple cellular processes such as cell growth control, DNA repair and programmed cell death. Genotoxic damage, also induced by chemotherapy or radiotherapy, induces p53 overexpression in order to control the rate of proliferating damaged cells, thus triggering the mismatch repair or apoptotic pathways. P53 inactivation determines a condition of genetic instability, justifying the subsequent susceptibility to acquire mutations of different other genes. P53 mutations are associated with worse prognosis and with chemo/radioresistance, due to the inability to trigger p53-dependent programmed cell death. Molecular diagnostic strategies show 32% p53 mutations in breast cancer. The analysis of the p53 gene performed by FAMA (Fluorescence Assisted Mismatch Analysis) in high-risk breast cancer patients withor = 10 involved axillary nodes may help identify a subset of very high risk BC patients (vHR-BC) with poorer prognosis and a subset with better prognosis, potentially responsive to medical treatments. The accurate evaluation of the p53 status can predict prognosis and sensitivity to chemotherapy, thus representing the first step toward better definition of therapeutic strategies according to the molecular characterization of the individual patient.