Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab

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Abstract

<p id="P1">The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T-cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T-cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab mechanism of action. </p><p id="P2">Mutation burden decreases with successful checkpoint blockade therapy in patients with melanoma, suggesting that selection against protective mutant neoepitopes may be a critical mechanism of action of Nivolumab </p><p id="P3"> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/b365cdf3-d01d-4f70-9579-191a3cf7862d/PubMedCentral/image/nihms907788u1.jpg"/> </div> </p>

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Morten L. Nielsen, Claus Lundegaard, Peder Worning … (2003)

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

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Elaine R. Mardis, Daniel C. Koboldt, Ken Chen … (2012)

The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample. In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls. Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X. delarson@wustl.edu; lding@wustl.edu Supplementary data are available at Bioinformatics online.

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Combination therapy with anti-CTLA-4 and anti-PD-1 leads to distinct immunologic changes in vivo.

Rituparna Das, Rakesh Verma, Mario Sznol … (2015)

Combination therapy concurrently targeting PD-1 and CTLA-4 immune checkpoints leads to remarkable antitumor effects. Although both PD-1 and CTLA-4 dampen the T cell activation, the in vivo effects of these drugs in humans remain to be clearly defined. To better understand biologic effects of therapy, we analyzed blood/tumor tissue from 45 patients undergoing single or combination immune checkpoint blockade. We show that blockade of CTLA-4, PD-1, or combination of the two leads to distinct genomic and functional signatures in vivo in purified human T cells and monocytes. Therapy-induced changes are more prominent in T cells than in monocytes and involve largely nonoverlapping changes in coding genes, including alternatively spliced transcripts and noncoding RNAs. Pathway analysis revealed that CTLA-4 blockade induces a proliferative signature predominantly in a subset of transitional memory T cells, whereas PD-1 blockade instead leads to changes in genes implicated in cytolysis and NK cell function. Combination blockade leads to nonoverlapping changes in gene expression, including proliferation-associated and chemokine genes. These therapies also have differential effects on plasma levels of CXCL10, soluble IL-2R, and IL-1α. Importantly, PD-1 receptor occupancy following anti-PD-1 therapy may be incomplete in the tumor T cells even in the setting of complete receptor occupancy in circulating T cells. These data demonstrate that, despite shared property of checkpoint blockade, Abs against PD-1, CTLA-4 alone, or in combination have distinct immunologic effects in vivo. Improved understanding of pharmacodynamic effects of these agents in patients will support rational development of immune-based combinations against cancer.