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.

Immune checkpoint inhibitors (ICI) were recently approved as a first-line treatment for inoperable esophageal adenocarcinomas (EAC) in combination with chemotherapy (CTX). Unfortunately, even though EAC has one of the highest tumor mutation burdens among all cancer types, response to immunochemotherapy (ICI+CTX) is highly variable, and the underlying molecular basis is incompletely understood. While genomic features such as mutations and copy number alterations in EAC are highly variable across samples, DNA methylation array data from numerous studies suggested that EAC can be clustered into a few consistent subtypes. We thus hypothesize that epigenetic heterogeneity of EAC may contribute to or associate with patients’ heterogeneous response to ICI+CTX, possibly through modulation of key genes or neoantigens. To test this hypothesis, we took advantage of a unique ICI+CTX LUD2015-005 trial in which inoperable EAC patients received first-line ICI for four weeks (ICI-4W), followed by ICI+CTX. Instead of methylation array, we also used a new DNA methylation sequencing technology, TET-Assisted Pyridine-Borane Sequencing (TAPS), on 64 tumor and 15 adjacent normal tissue samples collected from 23 EAC patients before and throughout treatment. Unlike prior studies that used methylation arrays which only cover ~2.5% of all CpG sites in the genome, TAPS detects genome-wide, base-resolution DNA methylation information. Furthermore, many previous studies did not account for variability in tumor content between samples, which could impact downstream DNA methylation analyses. In view of this, we proposed an analytical framework that includes tumor content and local copy number as key parameters, which estimates the tumor and stromal methylation and tests for differentially methylated regions (DMR). We also performed unsupervised clustering based on large scale genome-wide methylation pattern and revealed 2 major tumor clusters. Using the whole genome data, we identified a large set of shared tumor-specific DMRs, revealing that hypomethylation across wide regions of the genome and hypermethylation of certain gene bodies are common features in EAC. We also identified a set of shared outcome-associated DMRs in pre-treatment samples, which predicts better progression-free survival at 12 months. We further performed subgroup analysis of the 2 tumor clusters. Interestingly, we found higher numbers of cluster-specific prognostic DMRs with stronger effect sizes. This suggests that tumor subtypes may respond differently to treatment, and should be considered separately in statistical analyses. Altogether, these results indicate that a detailed understanding of tumor epigenetic heterogeneity will improve patient stratification in immunochemotherapy. Citation Format: Phil F. Xie, Jaeho Chang, Paulina Siejka-Zielińska, Masato Inoue, Magdalena Drożdż, Joseph A. Chadwick, Thomas M. Carroll, Richard P. Owen, Michael J. White, Joseph Kaplinsky, Robert Amess, Mark Middleton, Skirmantas Kriaucionis, Chunxiao Song, Benjamin Schuster-Böckler, Xin Lu. Association between epigenetic heterogeneity of esophageal adenocarcinoma and response to first-line immunochemotherapy in LUD2015-005 Trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB131.

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.

For patients with inoperable esophageal adenocarcinoma (EAC), prognosis on conventional chemotherapy (CTX) remains poor. In 2021, the FDA approved two αPD-1 immune checkpoint inhibitors (ICI) for addition to fluoropyrimidine/platinum-containing CTX in this first-line setting. As ICI+CTX enters the clinic, understanding ICI responses and predicting which patients will benefit from ICI addition are key challenges. To address these challenges, we assessed clinical and molecular profiles from the experimental LUD2015-005 trial (NCT02735239, EudraCT 2015-005298-19). Treatment consisted of an initial four-week ICI-only window with durvalumab (αPD-L1) with or without a single dose of tremelimumab (αCTLA-4), followed by 6 cycles of ICI+CTX (CapOx). 38 inoperable patients received treatment (35 EAC; 3 ESCC); median overall survival (OS) and progression-free survival (PFS) were 13.4 and 9.3 months, respectively. All patients reported at least one treatment emergent adverse event (TEAE), with 29 (76.3%) reporting grade 3 or higher TEAEs. EAC patients with available samples (n = 33) were taken forward for biomarker analysis, using tumor and adjacent normal biopsies collected at pre-treatment (PreTx), after four weeks of ICI-only (ICI-4W), and at the end of ICI+CTX (PostTx). Transcriptomic comparison of paired PreTx and ICI-4W EAC biopsies (n = 28) revealed ICI-induced upregulation of a novel T-cell inflammation signature (termed INCITE). Stronger INCITE upregulation correlated with greater tumor shrinkage during the ICI-only window, and tumors with minimal INCITE upregulation showed markers of ICI resistance, including Innate PD-1 Resistance (IPRES). Despite correlation with ICI-only responses, INCITE changes were not associated with overall ICI+CTX outcomes. To find predictive biomarkers of ICI+CTX outcomes, we conducted comprehensive genomic and transcriptomic profiling of PreTx EAC biopsies (n = 33). First, we generated a novel 65,000 cell scRNA-seq dataset and designed a deconvolution workflow to resolve tumor cell composition. Unexpectedly, monocyte composition was strongly linked with greater overall survival (OS) (HR: 0.40 [0.23-0.69]; p = 0.001; FDR = 0.047). Coding tumor mutational burden (TMB) was also associated with improved OS (HR: 0.50 [0.28-0.89]; p = 0.019). Multivariate modelling suggested monocyte composition and TMB were independent and complementary predictors of outcomes. Neither factor was associated with outcomes in a TCGA cohort of EAC patients not treated with ICI, suggesting these biomarkers may be specific to ICI or ICI+CTX. Our findings suggest monocyte composition and TMB may identify EAC patients likely to benefit from ICI+CTX. INCITE upregulation may also serve as a useful monitor of ICI efficacy. These timely findings further our understanding of ICI response and resistance and may help inform patient selection for ICI+CTX. Citation Format: Thomas M. Carroll, Joseph A. Chadwick, Richard P. Owen, Michael J. White, Joseph Kaplinsky, Iliana Peneva, Anna Frangou, Jaeho Chang, Phil F. Xie, Andrew Roth, Bob Amess, Hantao Lou, Katy J. McCann, Georgina Berridge, Roman Fischer, Chansavath Phetsouphanh, Ayo O. Omiyale, Brittany-Amber Jacobs, David Ahern, Simon R. Lord, Stewart Norris-Bulpitt, Sam T. Dobbie, Lucinda Griffiths, Kristen Aufiero Ramirez, Toni Ricciardi, Mary J. Macri, Aileen Ryan, Ralph R. Venhaus, Benoit J. Van den Eynde, Ioannis Karydis, Benedikt M. Kessler, Benjamin Schuster-Böckler, Mark R. Middleton, Xin Lu. Comprehensive molecular profiling to predict first-line immunochemotherapy outcomes in inoperable esophageal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1247.

We propose TCRDivER, a global approach to T-cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. As immunotherapies improve, the long standing biological interest in connecting outcome with T cell receptor (TCR) repertoire status has become more urgent. Here we show that new insights can be extracted from high throughput repertoire sequencing data. Most current efforts focus on identification of immunisation-specific sequence motifs or on monitoring changes in frequency of individual clones. Applying TCRDivER to murine spleen samples shows it characterises an additional dimension of repertoire variation, beyond conventional diversity estimates, allowing distinction between immunised and non-immunised samples. We further apply TCRDivER to repertoires from human blood. In both cases we show characteristic relationships between repertoire features. These reveal biologically interpretable relationships between sequence similarity and clonal expansions. We thereby demonstrate a new tool for investigation in clinical and research applications.

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.

A fundamental challenge in immunology is diagnostic classification based on repertoire sequence. We used the principle of maximum entropy (MaxEnt) to build compact representations of antibody (IgH) and T-cell receptor (TCRβ) CDR3 repertoires based on the statistical biophysical patterns latent in the frequency and ordering of repertoires’ constituent amino acids. This approach results in substantial advantages in quality, dimensionality, and training speed compared to MaxEnt models based solely on the standard 20-letter amino-acid alphabet. Descriptor-based models learn patterns that pure amino-acid-based models cannot. We demonstrate the utility of descriptor models by successfully classifying influenza vaccination status (AUC=0.97, p=4×10 -3 ), requiring only 31 samples from 14 individuals. Descriptor-based MaxEnt modeling is a powerful new method for dissecting, encoding, and classifying complex repertoires.

By promoting noncanonical NF-κB signaling, SMAC mimetics can reprogram CD4 + T helper cell differentiation and curb autoimmunity.

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.

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 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 (V-H)-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 V-H gene use as pre-B cells mature into follicular and then into marginal zone B cells. Cells whose antibodies use V-H 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 V-H gene use. Thus, deep sequencing reveals a previously unidentified mode of B-cell selection.

Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a highly effective treatment modality for many hematologic malignancies, a major treatment-associated toxicity is the induction of a prolonged state of T cell immunodeficiency in the transplant recipient, which in turn contributes to critical clinical outcomes such as infectious complications, and the risk of relapse. Targeted deep sequencing of the T cell receptor beta-chain (TCRβ) has emerged as a promising technology for enabling the qualitative and quantitative monitoring of T cell recovery following transplant with unprecedented resolution. Major challenges remain, however, in the establishment of informative analysis tools for characterization of global TCRβ repertoire dynamics. In the current work, we developed and applied a novel analysis approach as a mean to gain detailed biological insight into T cell reconstitution following allo-HSCT. To this end, we isolated naïve and memory CD4+ and CD8+ T cells from peripheral blood mononuclear cells of 14 patients with advanced chronic lymphocytic leukemia who underwent allo-HSCT following reduced-intensity doses of fludarabine and busulfan. From these T cell subpopulations, genomic DNA was extracted at post-transplant day 30 (d30) and later time points informative for thymic-independent (4 month post-transplant; d120) and thymic-dependent (1 year post-transplant; d365) T cell immune recovery. Subsequently, a template library for sequencing on an Illumina GA2 system was generated through PCR amplification of the TCRβ CDR3 region using an established panel of 45 Vβ- and 13 Jβ-specific primers. We obtained a median of 394,872 (range 0-26,426,784) productive reads across our 168 samples. As a comparison group, we further studied repertoire data from naïve and memory CD4+ and CD8+ T cells collected from 9 healthy adult volunteers. To characterize how transplant perturbs the TCR repertoire, we first compared VDJ usage between the transplanted patients and the healthy controls. For each of the post-transplant and control samples, we tallied the number of clones from all sequenced compartments (CD4+ and CD8+, naïve and memory) that used each of the several thousand possible VDJ combinations. We performed pairwise comparisons of the resulting VDJ distributions for all 253 sample pairs at days 30, 120 and 365 by calculating the R2 and, separately, X2 statistics. Permutation analysis demonstrated that control samples were more similar to each other than either post-transplant day 30, 120 or 365 samples (P=2.5-5.0x10-5, 2.5-5.0x10-5 and ≤2.5x10-5 by X2; 2.5-5.0x10-5, 5.5-5.7x10-4 and 1.0-1.2x10-4 by R2, respectively). Of note, whereas control samples demonstrated a similar VDJ usage, such similarity was not observed among post-transplant samples at day 30, 120 or 365 (P=0.65, 0.53, and 0.60 by X2; P=0.014, 0.38, and 0.43 by R2, respectively). These results demonstrate that VDJ usage in transplant recipients remains more heterogeneous than in healthy controls throughout the entire first year of reconstitution. To understand whether this heterogeneity reflects equilibrium or dynamic changes of the TCR repertoire, we visualized the time course of reconstitution using principal component analysis of VDJ usage. We observed marked dynamism, in which most transplant recipients both experienced a greater degree of change than was represented by the controls, and explored regions of VDJ usage very different from that of controls. Preliminarily, we observed that several transplant recipients became more similar to controls over time, while others did not. Our results demonstrate that post-transplant T cell reconstitution follows both personal and highly dynamic trajectories across a range of clinical courses, and suggest that TCR sequencing in larger sample sizes is a promising avenue for future study. Ongoing analyses focus on investigating the correlates of this dynamism among the 14 transplant recipients through subgroup analysis based on their clinical course and sequence-level analysis. The results obtained through these novel computational and systems methods will be integrated with other experimental measures of immune reconstitution including immunophenotyping and TCR excision circle (TREC) analysis. Disclosures: No relevant conflicts of interest to declare.

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.

We have previously demonstrated that multidimensional optical spectroscopy (EVV 2DIR) can conveniently and easily quantify tryptophan (W), tyrosine (Y), phenylalanine (F) and the total methyl (CH3) content of a protein , but more independent variables need to be quantified in order to uniquely identify a higher proportion of the proteome. Bioinformatics analysis shows that only a small number of amino acids need be quantified to uniquely identify a substantial proportion of the human proteome. For example, if the approximate mass (± 10%) of a protein is also known as well as the W, F, Y and CH3 content, then the number of proteins uniquely identified increases from 2% to 15% of the entries in the human protein database (Ensemble release 44) containing 33100 proteins. In this paper, we demonstrate a comprehensive protein identification and characterisation strategy based on the combination and principles of nonlinear electrokinetics together with EVV-2DIR spectroscopy. A complex mixture is spatially resolved via Capillary Zone Electrophoresis (CZE) and the electrophoretic mobilities of the resulting fractions are analysed in a manner that facilitates the assignment of values to a substantial range of physicochemical properties such as the molecular weight and surface charge densities to each fraction with an accuracy larger than 80%4. The two analysis methods are joined up with a specially-devised CZE-EVV interface platform which enables the preservation of the spatial resolution of the fractions and facilitates upstream optical interrogation of each individual fraction5.