Landscape of B cell immunity and related immune evasion in human cancers

Abstract

Tumor-infiltrating B cells are an important component in the microenvironment with unclear anti-tumor impacts. We enhanced our previous computational algorithm TRUST to extract the B cell immunoglobulin (Ig) hypervariable regions from bulk tumor RNA-seq data. TRUST assembled over 30 million complementarity-determining region 3 (CDR3s) of the B cell heavy chain (IgH) from The Cancer Genome Atlas (TCGA). Widespread B cell clonal expansions and Ig subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations in MICA and MICB genes related to antibody-dependent cell mediated cytotoxicity (ADCC) were identified in tumors with elevated B cell activity. IgG3-1 subclass switch interacts with the B cell receptor affinity maturation and defects in the ADCC pathway. Comprehensive pan-cancer analyses of tumor-infiltrating B cell receptor repertoires identified novel tumor immune evasion mechanisms through genetic alterations. The IgH sequences identified here are potentially useful resources for future development of immunotherapies.

DeepBCR: Deep learning framework for cancer-type classification and binding affinity estimation using B cell receptor repertoires

“…We previously developed a computational method TRUST to assemble over 30 million B cell receptors (BCRs) from TCGA bulk tumor RNA-seq data 5 . We now describe a deep learning framework, DeepBCR, to model complex antigen-antibody interactions starting from BCR sequences.…”

“…One cause of such contradictory outcomes is due to the diversity in the B cell isotypes which interact with receptors preferentially expressed on different immune cells 2 . For example, IgA antibodies reduce the anti-tumor effects in liver cancer 3 and melanoma 4 , although the role of IgA antibodies are more complex in other cancer types 5 . On the other hand, the binding affinity of tumor infiltrating B cells is believed to be high regardless of downstream regulations, because of the wide-spread somatic hypermutations observed in tens of millions of tumor-infiltrating B-cell receptors (BCRs) 5 .…”

“…Although billions of BCRs have been sequenced in published studies 6 , they came from donors of limited clinical annotations and most of them are naive B cells. The over 30 million BCRs derived from our previous study from TCGA 5 have rich clinical annotation and so are more likely to gain insights on the general antibody-antigen binding patterns. To understand the power of associating antibodies with 1 antigens, we start with a simpler problem---whether we can predict cancer type from the BCR repertoire.…”

An ultrasensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data

“…characterization of CDR3 repertoires remains a great challenge. To date, several tools, such as MiXCR (Bolotin et al 2017), RTCR (Gerritsen et al 2016), IMSEQ (Kuchenbecker et al 2015), LymAnalyzer (Yu et al 2016), TraCeR (Stubbington et al 2016), and TRUST (Hu et al 2019), have been developed for to characterizing CDR3 repertoires in TCR-Seq and/or RNA-Seq data. However, these methods have some limitations and require improvements in terms of 1) discarding reads without complete CDR3s or with low frequency, which may cause massive loss of CDR3 sequences and bias toward TCR repertoires, 2) parameter sensitivity and reliance on hands-on settings for model optimization, 3) poor performance in datasets with short read length and low TCR content, and 4) excessive consumption of time and computational resource, which roadblocks the usage of these methods To overcome these limitations, we developed a sensitive and accurate method, named CATT (CharActerizing TCR reperToires, http://bioinfo.life.hust.edu.cn/CATT), for characterizing CDR3 repertoires in both bulk and single-cell TCR(RNA)-Seq datasets.…”

“…few methods have been developed for CDR3 characterization, several limitations roadblock their wide applications. For example, TRUST(Hu et al 2019) was specifically designed to detect CDR3 sequences in bulk RNA-Seq data; LymAnalyzer(Yu et al 2016), RTCR(Gerritsen et al 2016), and IMSEQ(Kuchenbecker et al 2015) …”