Is HLA type a possible cancer risk modifier in Lynch syndrome?

Lynch syndrome (LS) is the most common inherited cancer syndrome. It is inherited via a monoallelic germline variant in one of the DNA mismatch repair (MMR) genes. LS carriers have a broad 30% to 80% risk of developing various malignancies, and more precise, individual risk estimations would be of high clinical value, allowing tailored cancer prevention and surveillance. Due to MMR deficiency, LS cancers are characterized by the accumulation of frameshift mutations leading to highly immunogenic frameshift peptides (FSPs). Thus, immune surveillance is proposed to inhibit the outgrowth of MMR-deficient cell clones. Recent studies have shown that immunoediting during the evolution of MMR-deficient cancers leads to a counter-selection of highly immunogenic antigens. The immunogenicity of FSPs is dependent on the antigen presentation. One crucial factor determining antigen presentation is the HLA genotype. Hence, a LS carrier's HLA genotype plays an important role in the presentation of FSP antigens to the immune system, and may influence the likelihood of progression from precancerous lesions to cancer. To address the challenge of clarifying this possibility including diverse populations with different HLA types, we have established the INDICATE initiative (Individual cancer risk by HLA type, ), an international network aiming at a systematic evaluation of the HLA genotype as a possible cancer risk modifier in LS. Here we summarize the current knowledge on the role of HLA type in cancer risk and outline future research directions to delineate possible association in the scenario of LS with genetically defined risk population and highly immunogenic tumors.Peer reviewe

Pravastatin for early-onset pre-eclampsia:a randomised, blinded, placebo-controlled trial

Objective: Women with pre-eclampsia have elevated circulating levels of soluble fms-like tyrosine kinase-1 (sFlt-1). Statins can reduce sFlt-1 from cultured cells and improve pregnancy outcome in animals with a pre-eclampsia-like syndrome. We investigated the effect of pravastatin on plasma sFlt-1 levels during pre-eclampsia. Design: Blinded (clinician and participant), proof of principle, placebo-controlled trial. Setting: Fifteen UK maternity units. Population: We used a minimisation algorithm to assign 62 women with early-onset pre-eclampsia (24 +0–31 +6 weeks of gestation) to receive pravastatin 40 mg daily (n = 30) or matched placebo (n = 32), from randomisation to childbirth. Primary outcome: Difference in mean plasma sFlt-1 levels over the first 3 days following randomisation. Results: The difference in the mean maternal plasma sFlt-1 levels over the first 3 days after randomisation between the pravastatin (n = 27) and placebo (n = 29) groups was 292 pg/ml (95% CI −1175 to 592; P = 0.5), and over days 1–14 was 48 pg/ml (95% CI −1009 to 913; P = 0.9). Women who received pravastatin had a similar length of pregnancy following randomisation compared with those who received placebo (hazard ratio 0.84; 95% CI 0.50–1.40; P = 0.6). The median time from randomisation to childbirth was 9 days [interquartile range (IQR) 5–14 days] for the pravastatin group and 7 days (IQR 4–11 days) for the placebo group. There were three perinatal deaths in the placebo-treated group and no deaths or serious adverse events attributable to pravastatin. Conclusions: We found no evidence that pravastatin lowered maternal plasma sFlt-1 levels once early-onset pre-eclampsia had developed. Pravastatin appears to have no adverse perinatal effects. Tweetable abstract: Pravastatin does not improve maternal plasma sFlt-1 or placental growth factor levels following a diagnosis of early preterm pre-eclampsia #clinicaltrial finds

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

Rationale: Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its high burden, representation of African ancestry individuals in asthma genome-wide association studies (GWAS) has been inadequate to date, and true associations in these underrepresented minority groups may have been missed. Here, we report the largest asthma GWAS to date from the Consortium on Asthma among African Ancestry Populations (CAAPA). Methods: CAAPA participants (7009 asthmatics, 7645 controls) were genotyped using the African Diaspora Power Chip (ADPC), an array designed to complement existing genome-wide array data, as well as Illumina’s Multi-Ethnic Genotyping array. Genotypes were imputed using the CAAPA whole genome-sequence reference panel. Logistic mixed effects models were used to test for association between allelic dosage and asthma, separately for each study. Results were meta-analyzed using a meta-regression approach that accounts for heterogeneity in allelic effects among ethnic groups. Results: We identified two novel loci that may be specific to asthma risk in African ancestry populations (lead SNP rs13277810, intronic to LOC101927815, p=3E-8; lead SNP rs114647118, intronic to TATDN1, p=3E-7). We found strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations (p\u3c0.05/810 candidate SNPs investigated), including the chr12q13 region, a novel locus identified by the Trans-National Asthma Genetic Consortium (TAGC) that has previously not been replicated. Conclusions: We report two associations that may bespecific to asthma risk in African ancestry populations. Our results also suggest some asthma risk loci discovered in non-African populations are relevant in African ancestry populations

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

OLIVEIRA, Ricardo Riccio. Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil. Michelle Daya1, Nicholas Rafaels1, Tonya M. Brunetti1, Sameer Chavan1, Albert M. Levin2, Aniket Shetty1, Christopher R. Gignoux1, Meher Preethi Boorgula1, Genevieve Wojcik 3, Monica Campbell1, Candelaria Vergara 4, Dara G. Torgerson5, Victor E. Ortega6, Ayo Doumatey7, Henry Richard Johnston8, Nathalie Acevedo9, Maria Ilma Araujo10, Pedro C. Avila 11, Gillian Belbin12, Eugene Bleecker13, Carlos Bustamante3, Luis Caraballo9, Alvaro Cruz14, Georgia M. Dunston15, Celeste Eng5, Mezbah U. Faruque16, Trevor S. Ferguson 17, Camila Figueiredo18, Jean G. Ford19, Weiniu Gan20, Pierre-Antoine Gourraud21, Nadia N. Hansel4, Ryan D. Hernandez22, Edwin Francisco Herrera-Paz 23,24, Silvia Jiménez9, Eimear E. Kenny12, Jennifer Knight-Madden17, Rajesh Kumar25, Leslie A. Lange1, Ethan M. Lange1, Antoine Lizee21, Pissamai Maul26, Trevor Maul26, Alvaro Mayorga27, Deborah Meyers13, Dan L. Nicolae28, Timothy D. O’Connor29, Ricardo Riccio Oliveira30, Christopher O. Olopade31, Olufunmilayo Olopade28, Zhaohui S. Qin 32, Charles Rotimi 7, Nicolas Vince 21, Harold Watson33, Rainford J. Wilks17, James G. Wilson34, Steven Salzberg 35, Carole Ober36, Esteban G. Burchard22, L. Keoki Williams37, Terri H. Beaty 38, Margaret A. Taub39, Ingo Ruczinski39, CAAPA, Rasika A. Mathias4 & Kathleen C. Barnes1, Ayola Akim Adegnika40, Ganiyu Arinola41, Ulysse Ateba-Ngoa40, Gerardo Ayestas23, Hilda Bjarnadóttir42, Adolfo Correa 43, Said Omar Leiva Erazo23, Marilyn G. Foreman44, Cassandra Foster4, Li Gao4, Jingjing Gao45, Leslie Grammer11, Mark Hansen46, Tina Hartert47, Yijuan Hu32, Iain Königsberg1, Kwang-Youn A. Kim 48, Pamela Landaverde-Torres23, Javier Marrugo49, Beatriz Martinez49, Rosella Martinez23, Luis F. Mayorga23, Delmy-Aracely Mejia-Mejia50, Catherine Meza49, Solomon Musani43, Shaila Musharoff3, Oluwafemi Oluwole28, Maria Pino-Yanes 5, Hector Ramos23, Allan Saenz23, Maureen Samms-Vaughan51, Robert Schleimer11, Alan F. Scott52, Suyash S. Shringarpure3, Wei Song29, Zachary A. Szpiech 22, Raul Torres 53, Gloria Varela23, Olga Marina Vasquez54, Francisco M. De La Vega3, Lorraine B. Ware47 & Maria Yazdanbakhsh 5. 1Department of Medicine, University of Colorado Denver, Aurora, CO 80045, USA. 2Department of Public Health Sciences, Henry Ford Health System, Detroit, MI 48202, USA. 3Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. 4Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA. 5Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA. 6Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem 27157, USA. 7Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD 20892, USA. 8Department of Human Genetics, Emory University, Atlanta, GA 30322, USA. 9Institute for Immunological Research, Universidad de Cartagena, Cartagena 130000, Colombia 10Immunology Service, Universidade Federal da Bahia, Salvador 401110170, Brazil. 11Department of Medicine, Northwestern University, Chicago, IL 60611, USA. 12Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 13Department of Medicine, University of Arizona College of Medicine, Tucson, AZ 85724, USA. 14Universidade Federal da Bahia, Salvador 401110170, Brazil. 15Department of Microbiology, Howard University College of Medicine, Washington, DC 20059, USA. 16National Human Genome Center, Howard University College of Medicine, Washington, DC 20059, USA. 17Caribbean Institute for Health Research, The University of the West Indies, Kingston 00007, Jamaica. 18Departamento de Biorregulacao, Universidade Federal da Bahia, Salvador 401110170, Brazil. 19Department of Medicine, Einstein Medical Center, Philadelphia, PA 19141, USA. 20National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA. 21Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064ATIP-Avenir, Equipe 5, Nantes, France. 22Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, USA. 23Facultad de Medicina, Universidad Católica de Honduras, San Pedro Sula 21102, Honduras. 24Universidad Tecnológica Centroamericana (UNITEC), Facultad de Ciencias Médicas, Tegucigalpa, Honduras. 25Department of Pediatrics, Northwestern University, Chicago, IL 60611, USA. 26Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael BB11115, Barbados. 27Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras. 28Department of Medicine, University of Chicago, Chicago, IL 60637, USA. 29Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. 30Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador 40296-710, Brazil. 31Department of Medicine and Center for Global Health, University of Chicago, Chicago, IL 60637, USA. 32Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA. 33Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael BB11000, Barbados. 34Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA. 35Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. 36Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 37Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI 48202, USA. 38Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. 39Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. These authors contributed equally: Rasika A. Mathias, Kathleen C. Barnes.40Centre de Recherches Médicales de Lambaréné, BP:242, Lambaréné 13901, Gabon. 41Department of Chemical Pathology, University of Ibadan, Ibadan 900001, Nigeria. 42Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland. 43Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA. 44Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA. 45Data and Statistical Sciences, AbbVie, North Chicago, IL 60064, USA. 46Illumina, Inc., San Diego, CA 92122, USA. 47Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA. 48Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA. 49Instituto de Investigaciones Immunologicas, Universidad de Cartagena, Cartagena 130000, Colombia. 50Facultad de Ciencias de la Salud, Universidad Tecnológica Centroamericana (UNITEC), San Pedro Sula 21102, Honduras. 51Department of Child Health, The University of the West Indies, Kingston 00007, Jamaica. 52Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. 53Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA. 54Centro Medico de la Familia, San Pedro Sula 21102, Honduras. 55Department of Parasitology, Leiden University Medical Center, Leiden 02333, NetherlandsSubmitted by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:18:22Z No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:36:07Z (GMT) No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Made available in DSpace on 2019-03-25T16:36:07Z (GMT). No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5) Previous issue date: 2019Múltipla - ver em NotasAsthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations