Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma

A comprehensive evaluation of diversity measures for TCR repertoire profiling

BACKGROUND

T cells play a crucial role in adaptive immunity, as they monitor internal and external immunogenic signals through their specific receptors (TCRs). Using high-throughput sequencing, one can assess TCR repertoire in various clinical settings and describe it quantitatively by calculating a diversity index. Multiple diversity indices that capture the richness of TCRs and the evenness of their distribution have been proposed in the literature; however, there is no consensus on gold-standard measures and interpretation of each index is complex. Our goal was to examine the performance characteristics of 12 commonly used diversity indices in simulated and real-world data.

RESULTS

Simulated data were generated to evaluate how data richness and evenness affect index values using three nonparametric models. Fourteen real-world TCR datasets were obtained to examine differences in indices by analysis protocols and test their robustness to subsampling. Pielou, Basharin, d50, and Gini primarily describe evenness and highly correlate with one another. They are best suited for measuring the representation of TCR clones. Richness is best captured by S index, next Chao1 and ACE which also consider information on evenness. Shannon, Inv.Simspon, D3, D4, and Gini.Simpson measure richness and increasingly more information on evenness. More skewed TCR distributions provided more stable results in subsampling. Gini-Simpson, Pielou, and Basharin were the most robust in both simulated and experimental data.

CONCLUSIONS

Our results could guide investigators to select the best diversity index for their particular experimental question and draw attention to factors that can influence the accuracy and reproducibility of results.

Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist

Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

TCR CDR3-antigen chemical complementarity associated with poor ovarian cancer outcomes: A vestigial immune response to early cancer antigens?

Ovarian cancer continues to present significant challenges for early detection and treatment, indicating a need for novel approaches to improve disease outcomes. In this report, we applied a previously described algorithm for detecting chemical complementarity between candidate cancer antigens and complementarity determining region-3 (CDR3) amino acid sequences from tumor resident T-cell receptors. Current literature indicates an association between high CDR3-cancer antigen complementarity and improved survival outcomes. For example, high CDR3-BRAF electrostatic complementarity is associated with a better melanoma outcome. However, such CDR3-cancer antigen chemical complementarity in ovarian cancer was largely associated with worse outcomes. Specifically, high CDR3-MAGEB4 and CDR3-TDRD1 electrostatic complementarity was associated with lower ovarian cancer disease free survival (DFS). Additionally, high CDR3-MAGEB4 and CDR3-TDRD1 electrostatic complementarity was associated with decreased MAGEB4/TDRD1 gene expression and gene copy numbers, consistent with a selection against ovarian cancer cells expressing these antigens. However, when TDRD1 was split into fragments, high CDR3-TDRD1 hydrophobicity complementarity, for a specific TDRD1 fragment, was associated with increased DFS and higher immune marker expression levels. This dichotomy highlights the myriad of opportunities to establish risk stratifications and to identify potential, actionable cancer antigens using immunogenomic parameters.

Epithelial Ovarian Cancer: Providing Evidence of Predisposition Genes

Epithelial ovarian cancer (EOC) is one of the cancers most influenced by hereditary factors. A fourth to a fifth of unselected EOC patients carry pathogenic variants (PVs) in a number of genes, the majority of which encode for proteins involved in DNA mismatch repair (MMR) pathways. PVs in BRCA1 and BRCA2 genes are responsible for a substantial fraction of hereditary EOC. In addition, PV genes involved in the MMR pathway account for 10–15% of hereditary EOC. The identification of women with homologous recombination (HR)-deficient EOCs has significant clinical implications, concerning chemotherapy regimen planning and development as well as the use of targeted therapies such as poly(ADP-ribose) polymerase (PARP) inhibitors. With several genes involved, the complexity of genetic testing increases. In this context, next-generation sequencing (NGS) allows testing for multiple genes simultaneously with a rapid turnaround time. In this review, we discuss the EOC risk assessment in the era of NGS.

TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences

T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5' RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.

Dr Effects of Adiponectin, Plasma D-Dimer, Inflammation and Tumor Markers on Clinical Characteristics and Prognosis of Patients with Ovarian Cancer

 [Abstract] Objective: To investigate the effects of adiponectin (ADPN), plasma D-dimer (D-D), inflammation and tumor markers on clinical characteristics and prognosis of patients with ovarian cancer. Methods: A total of 80 patients with ovarian cancer treated in our hospital from April 2017 to November 2019 were enrolled as study subjects and evenly divided into observation group (patients with ovarian cancer) and control group (patients with benign ovarian tumor) based on the results of postoperative pathological biopsy. The levels of ADPN, plasma D-D, inflammatory factors and serum tumor markers [carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4) and risk of ovarian malignancy algorithm (ROMA)] were compared between the two groups. The diagnostic value of serum tumor markers CA125, HE4 and ROMA in ovarian cancer was explored. The correlations of the changes of ROMA with the changes in the levels of ADPN, plasma D-D, high-sensitivity C-reactive protein (hs-CRP), CA125 and HE4 were analyzed. Additionally, the related risk factors affecting the development of ovarian cancer were subjected to univariate and multivariate logistic regression analyses. Results: In comparison with control group, observation group exhibited a lowered ADPN level (p<0.05), notably raised levels of plasma D-D, inflammatory factors hs-CRP and interleukin-6 (IL-6) and serum tumor markers CA125 and HE4 and an evidently increased ROMA (p<0.05). Besides, the detection of serum ROMA showed the highest specificity and sensitivity and low false positive rate and false negative rate. The changes of ROMA were positively correlated with the changes in the levels of plasma D-D, hs-CRP, CA125 and HE4 (p<0.05), and negatively associated with the changes in ADPN level (p<0.05). The results of univariate analysis showed that abnormal ADPN, D-D, hs-CRP, IL-6, CA125 and HE4 levels were related risk factors affecting the development of ovarian cancer. It was found through multivariate logistic regression analysis that decreased ADPN level and increased D-D, hs-CRP, IL-6, CA125 and HE4 levels were independent risk factors affecting the development of ovarian cancer. Conclusion: In the case of ovarian cancer, the ADPN level declines, while the levels of plasma D-D, inflammatory factors, and serum tumor markers CA125, HE4 and ROMA rise obviously. Besides, the ROMA level displays a positive relation to the content of CA125, HE4, plasma D-D and inflammatory factors and a negative association with ADPN level.

A comparison of immune receptor recombination databases sourced from tumour exome or RNAseq files: Verifications of immunological distinctions between primary and metastatic melanoma

It became apparent several years ago that RNAseq and exome files prepared from tissue could be mined for adaptive immune receptor (IR) recombinations, which has given extra value to datasets originally intended for gene expression or mutation studies. For example, recovery of IR recombination reads from tumour specimen genomics files can correlate with survival rates. In particular, many benchmarking processes have been applied to the two sets of the IR recombination reads obtained from the cancer genome atlas files, but these two sets have never been directly compared. Here we show that both sets largely agree regarding several parameters. For example, recovery of TRB recombination reads from both WXS and RNAseq files representing metastatic melanoma was associated with a better outcome (p < .0004 in both cases); and T-cell receptor recombination read recovery, for both genomics file types, associated very strongly with T-cell gene expression markers. However, the use of CDR3 chemical features for survival distinctions was not consistent. This topic, and the surprising result that both datasets indicated that primary melanoma with recovery of IR recombination reads, in stark contrast to metastatic melanoma, represents a worse outcome, are discussed.

Reconstituting T cell receptor selection in-silico

Each T cell receptor (TCR) gene is created without regard for which substances (antigens) the receptor can recognize. T cell selection culls developing T cells when their TCRs (i) fail to recognize major histocompatibility complexes (MHCs) that act as antigen presenting platforms or (ii) recognize with high affinity self-antigens derived from healthy cells and tissue. While T cell selection has been thoroughly studied, little is known about which TCRs are retained or removed by this process. Therefore, we develop an approach using TCR gene sequencing and machine learning to identify patterns in TCR protein sequences influencing the outcome of T cell receptor selection. We verify the trained models classify TCRs from developing T cells as being before selection and TCRs from mature T cells as being after selection. Our approach may provide future avenues for studying the relationship between T cell selection and conditions like autoimmune diseases.

T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates

Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor β chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.

Immunogenomics of colorectal adenocarcinoma: Survival distinctions represented by immune receptor, CDR3 chemical features and high expression of BTN gene family members

Immunogenomics studies of colon cancer have lagged behind other cancer types, such as melanoma and lung cancer, potentially limiting immunotherapy approaches to colon cancer, also less common than in the cases of melanoma and lung cancer. Here we applied an extensively benchmarked algorithm for retrieving immune receptor recombination sequencing reads from colon cancer exomes available via the cancer genome atlas. Assessment of the complementarity determining region-3 chemical features represented by the reads revealed associations of distinct chemical features with better or worse survival rates, for both T-cell and B-cell receptor, recombination reads. A follow up assessment of immune gene expression correlations with the recovery of the recombination reads revealed a consistent association of high level expression of BTN gene family members and better survival rates. Overall, these approaches provide several striking consistencies connecting immunogenomics features with colon cancer survival rates, potentially providing a basis for guiding immuno-therapy applications.