Moderated peer assessment of individual contribution to group work

UCL Engineering trains students to use engineering knowledge within extended group practical activities to prepare them for their careers after graduation. However, despite the substantial educational benefits of getting students to work in teams, providing individual assessment can be challenging. Students frequently express dissatisfaction if all members of a team are given the same mark regardless of the individual effort. Here, we aim to promote student engagement and improve student experience during group work by giving each student an individual mark. The individual mark results from multiplying the overall “group mark” by a personal contribution factor. This personal contribution is assessed directly by peers, who are aware of each team member’s contribution, encouraging self-reflection, and moderated by tutors when necessary. This practice has been well received by students in other universities. We are working with a student committee to identify and evaluate various methods and e-learning systems that would aid us to run this practice efficiently even for large numbers of students. This includes rules to flag cases requiring moderation. This project, partially funded by ELDG 2015, fits with our aim of increasing students’ satisfaction and engagement with assessment. We have combined it with our ‘360 degrees peer assessment method’, which we presented at last year’s conference, to provide a reliable and individual peer assessment of group work. We provide a novel approach to group assessment which encourages self-reflection and is intended to improve the learning experience and student satisfaction during group work, in line with UCL 2034

Functional genomic landscape of acute myeloid leukaemia

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. [Display omitted] •Acute myeloid leukemia patient cohort with clinical, molecular, drug response data•Validation and discovery of diverse biological features of drug response•Broad mapping of tumor cell differentiation state affecting response to drugs•Modeling reveals a strong and targetable determinant of clinical outcome Bottomly et al. present a functional genomic resource composed of molecular, clinical, and drug response data on acute myeloid leukemia patient specimens. Through integration of all of these data, they identify genetic and cell differentiation state features that predict drug response, and they utilize modeling to identify targetable determinants of clinical outcome