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John Le Quesne

Researcher at University of Cambridge

Publications -  83
Citations -  6663

John Le Quesne is an academic researcher from University of Cambridge. The author has contributed to research in topics: Medicine & Lung cancer. The author has an hindex of 25, co-authored 56 publications receiving 4615 citations. Previous affiliations of John Le Quesne include University Hospitals of Leicester NHS Trust & Leicester Royal Infirmary.

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Tracking the Evolution of Non–Small-Cell Lung Cancer

Mariam Jamal-Hanjani, +82 more
TL;DR: Intratumor heterogeneity mediated through chromosome instability was associated with an increased risk of recurrence or death, a finding that supports the potential value of chromosome instability as a prognostic predictor.
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Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

Christopher Abbosh, +119 more
- 25 May 2017 - 
TL;DR: It is shown that phylogenetic ct DNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies.
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Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Nicholas McGranahan, +219 more
- 30 Nov 2017 - 
TL;DR: It is found that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity.
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Fc-Optimized Anti-CD25 Depletes Tumor-Infiltrating Regulatory T Cells and Synergizes with PD-1 Blockade to Eradicate Established Tumors.

Frederick Arce Vargas, +250 more
- 18 Apr 2017 - 
TL;DR: Use of an anti‐CD25 antibody with enhanced binding to activating Fc&ggr;Rs led to effective depletion of tumor‐infiltrating Treg cells, increased effector to Treg cell ratios, and improved control of established tumors.
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Tracking Genomic Cancer Evolution for Precision Medicine: The Lung TRACERx Study

TL;DR: TRACERx, a prospective study of patients with primary non-small cell lung cancer, aims to map the genomic landscape of lung cancer by tracking clonal heterogeneity and tumour evolution from diagnosis to relapse.