Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database

Enteric methane (CH4) from ruminants is the major driver of global warming and climate change. Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (~ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.Fil: Congio, Guilhermo F.S.. Universidade de Sao Paulo; BrasilFil: Bannink, André. University of Agriculture Wageningen; Países BajosFil: Mayorga, Olga L.. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Rodrigues, João P.P.. Universidade Federal Rural do Rio de Janeiro; BrasilFil: Bougouin, Adeline. University of California at Davis; Estados UnidosFil: Kebreab, Ermias. University of California at Davis; Estados UnidosFil: Carvalho, Paulo C.F.. Universidade Federal do Rio Grande do Sul; BrasilFil: Berchielli, Telma T.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Mercadante, Maria E.Z.. São Paulo Agribusiness Technology Agency; BrasilFil: Valadares-Filho, Sebastião C.. Universidade Federal de Viçosa; BrasilFil: Borges, Ana L.C.C.. Universidade Federal de Minas Gerais; BrasilFil: Berndt, Alexandre. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Rodrigues, Paulo H.M.. Universidade de Sao Paulo; BrasilFil: Ku Vera, Juan C.. Universidad Autonoma de Yucatan (uady);Fil: Molina Botero, Isabel C.. Universidad Nacional Agraria La Molina; PerúFil: Arango, Jacobo. Centro Internacional de Agricultura Tropical; ColombiaFil: Reis, Ricardo A.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Posada Ochoa, Sandra L.. Universidad de Antioquia; ColombiaFil: Tomich, Thierry R.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Castelán Ortega, Octavio A.. Universidad Autónoma del Estado de México; MéxicoFil: Marcondes, Marcos I.. Washington State University; Estados UnidosFil: Gómez, Carlos. Universidad Nacional Agraria La Molina; PerúFil: Ribeiro Filho, Henrique M.N.. Universidade Do Estado de Santa Catarina; BrasilFil: Gere, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Ariza-Nieto, Claudia. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Giraldo, Luis A.. Universidad Nacional de Colombia; ColombiaFil: Gonda, Horacio Leandro. Sveriges Lantbruksuniversitet (slu);Fil: Cerón Cucchi, María Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Hernández, Olegario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Ricci, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Hristov, Alexander N.. State University of Pennsylvania; Estados Unido