Tumor monocyte content predicts immunochemotherapy outcomes in esophageal adenocarcinoma.

For inoperable esophageal adenocarcinoma (EAC), identifying patients likely to benefit from recently approved immunochemotherapy (ICI+CTX) treatments remains a key challenge. We address this using a uniquely designed window-of-opportunity trial (LUD2015-005), in which 35 inoperable EAC patients received first-line immune checkpoint inhibitors for four weeks (ICI-4W), followed by ICI+CTX. Comprehensive biomarker profiling, including generation of a 65,000-cell single-cell RNA-sequencing atlas of esophageal cancer, as well as multi-timepoint transcriptomic profiling of EAC during ICI-4W, reveals a novel T cell inflammation signature (INCITE) whose upregulation correlates with ICI-induced tumor shrinkage. Deconvolution of pre-treatment gastro-esophageal cancer transcriptomes using our single-cell atlas identifies high tumor monocyte content (TMC) as an unexpected ICI+CTX-specific predictor of greater overall survival (OS) in LUD2015-005 patients and of ICI response in prevalent gastric cancer subtypes from independent cohorts. Tumor mutational burden is an additional independent and additive predictor of LUD2015-005 OS. TMC can improve patient selection for emerging ICI+CTX therapies in gastro-esophageal cancer.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm.

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

T cell receptor sequence clustering and antigen specificity.

There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.

SMAC mimetics promote NIK-dependent inhibition of CD4+ TH17 cell differentiation.

Second mitochondria-derived activator of caspase (SMAC) mimetics (SMs) are selective antagonists of the inhibitor of apoptosis proteins (IAPs), which activate noncanonical NF-κB signaling and promote tumor cell death. Through gene expression analysis, we found that treatment of CD4+ T cells with SMs during T helper 17 (TH17) cell differentiation disrupted the balance between two antagonistic transcription factor modules. Moreover, proteomics analysis revealed that SMs altered the abundance of proteins associated with cell cycle, mitochondrial activity, and the balance between canonical and noncanonical NF-κB signaling. Whereas SMs inhibited interleukin-17 (IL-17) production and ameliorated TH17 cell-driven inflammation, they stimulated IL-22 secretion. Mechanistically, SM-mediated activation of NF-κB-inducing kinase (NIK) and the transcription factors RelB and p52 directly suppressed Il17a expression and IL-17A protein production, as well as the expression of a number of other immune genes. Induction of IL-22 production correlated with the NIK-dependent reduction in cMAF protein abundance and the enhanced activity of the aryl hydrocarbon receptor. Last, SMs also increased IL-9 and IL-13 production and, under competing conditions, favored the differentiation of naïve CD4+ T cells into TH2 cells rather than TH17 cells. These results demonstrate that SMs shape the gene expression and protein profiles of TH17 cells and inhibit TH17 cell-driven autoimmunity.

Improved Targeting of Cancers with Nanotherapeutics.

Targeted cancer nanotherapeutics offers numerous opportunities for the selective uptake of toxic chemotherapies within tumors and cancer cells. The unique properties of nanoparticles, such as their small size, large surface-to-volume ratios, and the ability to achieve multivalency of targeting ligands on their surface, provide superior advantages for nanoparticle-based drug delivery to a variety of cancers. This review highlights various key concepts in the design of targeted nanotherapeutics for cancer therapy, and discusses physicochemical parameters affecting nanoparticle targeting, along with recent developments for cancer-targeted nanomedicines.

Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples.

The diversity of an organism's B- and T-cell repertoires is both clinically important and a key measure of immunological complexity. However, diversity is hard to estimate by current methods, because of inherent uncertainty in the number of B- and T-cell clones that will be missing from a blood or tissue sample by chance (the missing-species problem), inevitable sampling bias, and experimental noise. To solve this problem, we developed Recon, a modified maximum-likelihood method that outputs the overall diversity of a repertoire from measurements on a sample. Recon outputs accurate, robust estimates by any of a vast set of complementary diversity measures, including species richness and entropy, at fractional repertoire coverage. It also outputs error bars and power tables, allowing robust comparisons of diversity between individuals and over time. We apply Recon to in silico and experimental immune-repertoire sequencing data sets as proof of principle for measuring diversity in large, complex systems.

Antibody repertoire deep sequencing reveals antigen-independent selection in maturing B cells.

Antibody repertoires are known to be shaped by selection for antigen binding. Unexpectedly, we now show that selection also acts on a non-antigen-binding antibody region: the heavy-chain variable (VH)-encoded "elbow" between variable and constant domains. By sequencing 2.8 million recombined heavy-chain genes from immature and mature B-cell subsets in mice, we demonstrate a striking gradient in VH gene use as pre-B cells mature into follicular and then into marginal zone B cells. Cells whose antibodies use VH genes that encode a more flexible elbow are more likely to mature. This effect is distinct from, and exceeds in magnitude, previously described maturation-associated changes in heavy-chain complementarity determining region 3, a key antigen-binding region, which arise from junctional diversity rather than differential VH gene use. Thus, deep sequencing reveals a previously unidentified mode of B-cell selection.

Absolute quantification of protein copy number using a single-molecule-sensitive microarray.

We report the use of a microfluidic microarray incorporating single molecule detection for the absolute quantification of protein copy number in solution. In this paper we demonstrate protocols which enable calibration free detection for two protein detection assays. An EGFP protein assay has a limit of detection of <30 EGFP proteins in a microfluidic analysis chamber (limited by non-specific background binding), with a measured limit of linearity of approximately 6 × 10(6) molecules of analyte in the analysis chamber and a dynamic range of >5 orders of magnitude in protein concentration. An antibody sandwich assay was used to detect unlabelled human tumour suppressor protein p53 with a limit of detection of approximately 21 p53 proteins and a dynamic range of >3 orders of magnitude. We show that these protocols can be used to calibrate data retrospectively to determine the absolute protein copy number at the single cell level in two human cancer cell lines.

A first step towards practical single cell proteomics: a microfluidic antibody capture chip with TIRF detection.

We have developed a generic platform to undertake the analysis of protein copy number from single cells. The approach described here is 'all-optical' whereby single cells are manipulated into separate analysis chambers using an optical trap; single cells are lysed by a shock wave caused by laser-induced microcavitation, and the protein released from a single cell is measured by total internal reflection microscopy as it is bound to micro-printed antibody spots within the device. The platform was tested using GFP transfected cells and the relative precision of the measurement method was determined to be 88%. Single cell measurements were also made on a breast cancer cell line to measure the relative levels of unlabelled human tumour suppressor protein p53 using a chip incorporating an antibody sandwich assay format. These results suggest that this is a viable method for measuring relative protein levels in single cells.