Immune receptor CDR3 chemical features that preserve sequence information are highly efficient in reflecting survival distinctions: A pan‑cancer analysis
- Authors:
- Published online on: June 9, 2022 https://doi.org/10.3892/br.2022.1551
- Article Number: 68
Abstract
Introduction
Over the last several years a huge volume of adaptive immune receptor (IR) recombinations has been obtained and analyzed, due to the increased use of genomic approaches and computational opportunities. In some cases, these recombinations have been obtained through an immune repertoire approach whereby PCR-based techniques lead to the amplification of a large number of clonotypes (1,2), although in several cases, such an approach indicates only a few relatively dominant clonotypes. Other sources of adaptive IR recombination reads have been genomics files representing tissues that included T-cells and B-cells, such as exome (WXS) and RNAseq files (3-6). The obvious limitation of mining such files is the lower level of IR read recoveries in comparison to a PCR-based approach specific for IRs, i.e., lack of depth. However, the advantage of the study of IR recombination reads from genomics files has been the opportunity for breadth, including the opportunity to integrate the IR recombination read data with other data platforms, especially gene expression and clinical data (7-10) over very large patient sets.
Analysis of the IR functional implications, based on the recovery of IR recombination reads from genomics files, has involved two strategies. First, a summary (single value) physicochemical assessment of the complementarity determining region-3 (CDR3) amino acid (AA) sequences has been correlated with survival distinctions. This approach is based on the simple idea that the CDR3 is the most important region of the IR for antigen recognition (11), and that, particularly in a big data setting, a dominant physicochemical feature could represent a minimum for successful antigen binding and immune mediated reductions in tumor cell numbers (12). From the outset, algorithms were developed to exploit the physicochemical features, and their correlations with data represented by other platforms, such as survival and immune biomarker expression, to identify possible antigen partners (9,12-16). For example, single value, physicochemical assessments of tumor resident breast cancer B-cell receptor CDR3s revealed not only a strong survival distinction when those assessments included an assessment of the chemical interaction potential of same-patient TP53 mutants but also revealed clear associations of immune markers with the higher surviving, chemical complementarity group. More recently, the chemical sequence motif approach, which preserves the presumed AA sequence functional role in the physicochemical assignments and which has already been employed in immune repertoire approaches (17), has been applied to IR CDR3s, and to CDR3-antigen matching algorithms, where the IR CDR3s have been identified in large genomics file sets (10,18). For example, an algorithm for establishing basic chemical complementarity over a large dataset of CDR3s and mutants, that employed a chemical sequence motif approach, identified correlations between TRG CDR3, AA sequence-PIK3CA mutant chemical complementarity and survival rates and immune marker expression (10). This latter approach involved a series of alignments of mutant peptides throughout the breast cancer dataset with all of the same-patient, tumor resident TRG CDR3s, to establish in each case the highest possible chemical complementarity score indicated by the algorithm developed and employed in that report. Patients representing the highest chemical complementarity scores also represented the highest survival. In another case, the chemical sequence motif, survival distinctions were established with blood resident, TRG CDR3 AA chemical sequence motifs in the absence of any potential assessments of chemically complementary antigens (18).
Given the high likelihood of continued characterizations of immune receptor CDR3 chemical features for either antigen identification or basic correlative work, it became of interest to make a comparison of the potential of a relatively simple, single value physicochemical parameter to represent the CDR3 AA sequence, which is efficient in a big data setting, compared with the value of the chemical sequence motif approach, which is more processing intensive for all subsequent correlative or chemical interaction approaches and which may be overly restrictive for correlative studies and biomarker discovery. Thus, we undertook an accounting of each approach for indicating survival distinctions among The Cancer Genome Atlas (TCGA) cancer datasets, to evaluate one specific area of use for these CDR3 AA sequences, with results strongly indicating that the chemical sequence motif assessments were more productive than a single parameter physicochemical CDR3 AA assessment.
Materials and methods
IR recombination read recoveries
The approach to the recovery of the IR recombination reads has been extensively described (5,19,20). The software for using WXS files as input is freely available at https://github.com/bchobrut-USF/blanck_group, including a readme file; and as a container version at https://hub.docker.com/r/bchobrut/vdj, including a readme file. Briefly, the approach involved a pre-screen of the WXS files with 10-mers matching the V- and J-gene segments for all seven of the adaptive human IRs, followed by a high stringency validation of the V- and J-gene IDs (Table SI). All data used in this project were obtained under the NIH dbGaP project approval no. 6300.
The single value, physicochemical determinations and AA chemical sequence motif representations
The above GitHub and docker links include IR recombination read processing software for establishing the single value, physicochemical characters for the CDR3 AA sequences, e.g., net charge per residue or aromaticity (Table SII). These software modules follow the precedents of the Pappu lab (21). The CDR3 AA chemical sequence motifs were established for each CDR3 according to reference (18) and Table SIII; and the python code used to make the symbol replacements to establish the chemical sequence motifs is freely available at https://github.com/bchobrut-USF/brca_motif.
Survival distinctions
Survival distinctions based on the single value, physicochemical features of the CDR3 AA sequences were assessed as described (12), with an example output in Table SIV; and survival distinctions based on the AA chemical sequence motif approach were assessed as previously indicated (18), with example outputs in Table SV, Table SVI, Table SVII, Table SVIII, Table SIX, Table SX, Table SXI and Table SXII, representing Table I in the results section below.
 | Table IDetection of overall survival distinctions via either the AA chemical sequence motif approach or the single value, physicochemical features approach, for the IR CDR3 AAs recovered from tumor WXS files representing eight cancer datasets. |
Overview of the comparison of the survival distinctions indicated by the single value, physicochemical feature compared with the AA chemical motif sequence approach
Given the above survival distinction outputs, exemplified by Table SIV, for the single value, physicochemical approach, and Table SV, Table SVI, Table SVII, Table SVIII, Table SIX, Table SX, Table SXI and Table SXII for the CDR3 AA chemical sequence motif approach, we totaled all survival distinctions representing a Kaplan-Meier (KM) logrank P-value of less than 0.05, for any of the above values or motifs, for either TRA or TRB CDR3s. The exact same set of TRA, TRB CDR3s (exact same input) was used to obtain the survival distinctions for the single value, physicochemical and the chemical sequence motif approaches, respectively, for each of the datasets.
Results
The adaptive IR recombination reads from every blood and tumor WXS file in the TCGA database and in several publications were obtained (5,12,20,22). For numerous cancer datasets, it became apparent that it was possible to observe survival distinctions based on single value, physicochemical features of the CDR3 AA sequences (Table SIV) (23), with related approaches taken by others (17). For the purpose of this report, the physicochemical features approach, exemplified by reference (23), will be referred to as the single value, physicochemical feature(s), and in all cases, the approach of reference (23) represents a single number for characterizing a single physicochemical parameter of the CDR3 AA sequence, for example, the net charge per residue (Table SII). More recently, we have observed survival distinctions with an AA chemical sequence motif approach, whereby the actual CDR3 AA sequence is reduced to a sequence representing physicochemical features of similar AAs (Table SIII) (18), for example NNP, for ‘negative, negative, positive’ to represent the R-groups, at physiological pH, of the E,E,K AA sequence. To appreciate the different potential of the two approaches, we tabulated the opportunity to note survival distinctions, based on a KM analysis with a resulting P-value of less than 0.05. In the case of the single value, physicochemical feature(s) approach, 22 distinct parameters were assessed for their potential to indicate a survival distinction, whereas, in the case of the AA chemical sequence motif approach, the minimum length in the assessment was four AAs.
Keeping in mind the aspects of the two approaches described in the preceding paragraph, we assessed overall survival (OS) distinctions using all TRA and TRB CDR3 AA sequences, respectively, available from all TCGA cancer datasets, with a basic result being that more such distinctions could be had using the CDR3 AA chemical sequence motif approach. For example, eight cancer types representing TCGA tumor WXS files demonstrated 20 OS distinctions for the AA chemical sequence motif approach, representing the TRA CDR3s, vs. 5 OS distinctions for the single value physicochemical features approach (Table I). For the TRB CDR3s, the eight cancer types assessed represented 14 and 11 OS distinctions for the AA chemical sequence motif and single value, physicochemical features approaches, respectively (Table I). In the case of the OS distinctions represented by the CDR3 AA chemical sequence motif approach, the OS distinction was between TCGA cases representing the AA chemical sequence motif vs. those without the chemical motif, but with a recovery of the corresponding IR recombination read, e.g., TRA. In the case of the OS distinctions representing the single value, physicochemical features, the distinctions also always represented only cases with a recovery of at least one read representing the indicated IR recombination. In the case of the single value, physicochemical features approach, this was out of necessity, as this approach compares the top and bottom 50th percentiles for the given physicochemical feature.
More recently, the AA chemical sequence motif and the single value, physicochemical features approaches, respectively, have been applied to IR CDR3 AA sequences obtained from the blood WXS files of cancer patients and have indicated survival distinctions (8,16,18), not surprising considering the relatively limited clonality of blood-borne IRs at the typical older ages of cancer patients; and considering more precise work in single patient settings indicating the detection of blood-borne, anti-cancer antigen IRs (24). Thus, we considered a comparison of the opportunity to detect survival distinctions for the AA sequence chemical motif vs. the single value, physicochemical features approach using IR recombination reads obtained from the cancer patient, blood WXS files. The same eight cancer types were compared, as were compared using only tumor specimen WXS files, and the comparison for the blood WXS files revealed 155 AA chemical sequence motif-based OS distinctions vs. 5 such distinctions for the single value, physicochemical features approach, for the TRA CDR3s (Table II). Example KM analyses representing the AA chemical sequence motif and the single parameter, physicochemical motif approaches applied to detect the OS distinctions, respectively, using the IR recombination reads recovered from the blood WXS files, are shown in Fig. 1. For the TRB CDR3 AA sequences, the comparison revealed 33 vs. 13 OS distinctions, for the AA chemical motif and single value, physicochemical features approaches, respectively (Table II).
| Table IIDetection of overall survival distinctions via either the AA chemical sequence motif approach or the single value, physicochemical features approach, for the IR CDR3 AAs recovered from blood WXS files representing eight cancer datasets. |
Given the robustness of the AA chemical sequence motif approach in assessing whether an OS distinction could be had, we assessed the opportunity to distinguish OS rates using the AA chemical sequence motif vs. all remaining cases, rather than simply all remaining cases with an IR recombination read recovery, as was done for the data in Tables I and II. In sum, the analyses indicated a robust opportunity to identify OS distinctions, for TRA and TRB CDR3s, and for CDR3s recovered from both tumor and blood WXS files, when defining cases based on the recovery of a specific CDR3 AA chemical sequence motif (Tables III and IV).
| Table IIIDetection of OS distinctions via the AA chemical sequence motif approach via a comparison of specific AA chemical sequence motifs vs. all remaining samples, regardless of the recovery, or not, of an IR recombination read. The results represent IR recombination reads obtained from tumor specimen WXS files. |
| Table IVDetection of OS distinctions via the AA chemical sequence motif approach via a comparison of specific AA chemical sequence motifs vs. all remaining samples, regardless of the recovery, or not, of an IR recombination read. The below results represent IR recombination reads obtained from blood WXS files. |
Discussion
Over the last several years there has been an extensive effort to group IR CDR3s based on sequence homologies and based on chemical features, to develop CDR3 fingerprints of various conditions and to bioinformatically attempt to link the CDR3s to antigens. We have observed two basic approaches in the literature, the single value, physicochemical assessment approach and the chemical sequence approach, as detailed above. As with the above datasets, these approaches have been applied in the cancer setting, by our group (8-10,12-16,18,23,25), by Ostmeyer et al (17), and by others (26,27). In addition, CDR3 AA sequence homology assessments have been applied in the SARS-CoV-2 and other viral settings (28,29), in cases of allergies (30), in Alzheimer's disease and in other neurological disorders (31,32). A key issue in this development is the focus on the CDR3 AA sequences in the big data setting. Also, this focus is based on the CDR3 being highly important in IR antigen binding and has allowed for the efficient establishment of distinctions related to diseases. This is in contrast to a goal of appreciating the role of the entire IR in antigen binding, whether by in silico approaches, such as molecular modeling, or with in vitro approaches. The focus on the CDR3, and the patient distinctions provided by the different CDR3-based parameters, represents a goal of efficiency, particularly in meeting the longer-term goal of identifying patients in particular risk groups.
It is not possible to make a controlled comparison of the two CDR3-based approaches considered in this report, but it is possible to assemble the record of results over a very large dataset, particularly when these assessments are based on IR recoveries from genomics files. This goal is important, again, in the interest of maximizing efficiency, for answering the simple question, is there a role for IR-antigen binding specificity in a given condition or disease? And, the single value physicochemical assessment represents another step, likely only applicable in the big data setting, away from unnecessarily intensive approaches to processing. In most cases, a very large amount of data regarding the single value physicochemical features of CDR3s, integrated with clinical information, can be processed with common skills on a common laptop. However, the comparison in this report does indicate that a negative result with a single value physicochemical representing the CDR3s could not rule out the possibility that an AA sequenced based, homology motif approach would uncover patient-based risks.
As noted above, these IR recoveries have been obtained from tens of thousands of genomics files, which are available in conjunction with other data platforms, such as survival data. Thus, the recovery of these IR CDR3s, followed by their single value physicochemical or chemical sequence motif assessments, can be linked to distinct survival rates in many cases. Again, keeping in mind that a final assessment of which approach is more successful is not possible, what can be said is that the two approaches that have been applied in the past do differ in the quantity of their output for the same mega-dataset, namely eight cancer datasets from TCGA. Thus, there are two limitations to understanding the value of the CDR3-limited, chemical sequence motif approach over the CDR3-limited, single value physicochemical feature assessment. First, it is possible that with a PCR-based, immune repertoire approach, the vastly increased numbers of CDR3s would be sufficient to provide as many, or almost as many patient-distinction opportunities using a single value physicochemical approach as is apparently provided by the chemical sequence motif approach. In particular, even in the above work, there was no attempt to render the chemical sequence motifs back to single value physicochemical features, which may have led to a convergence of the many chemical sequence motif results. Having noted that, it is less likely that chemical sequence motifs that are ‘outliers’, that cannot be summarized by a single value physicochemical feature, would be missed when using the chemical sequence motif approach from the start, in categorizing patient risks. The second limitation in this report is the fact that all distinctions based on the single value physicochemical or chemical sequence motif approach were cancer survival distinctions. Thus, it remains to be learned whether the apparent, increased value of grouping the chemical sequence motif for interrogating clinical data, compared with providing only a single value physicochemical assessment, would be maintained in a viral infection setting, for example, a setting that could be much more immunologically simpler than cancer, particularly with regard to patient clinical features. In other words, would a particular single value physicochemical feature shifted in a certain direction always reflect the infection of a specific virus, especially when other clinical parameters related to a suspected viral infection are known?
However, if a comparison of the approaches in this report remains consistent over additional datasets, with additional investigators, a reasonable interpretation would be that, by preserving sequence information, i.e., the order of the AAs in the CDR3, it is more likely that groups of CDR3s can be identified as being associated with given clinical features. With the single value, physicochemical approach, it is likely that many CDR3s, which happen to group with parameter-distinctive CDR3s because of a tally of a chemical feature across multiple AAs, without regard to AA positioning in the sequence, will be specifically irrelevant to that parameter distinction. Depending on the goal, that may not make the single value, physicochemical assessment pointless, but it is possible that a lack of a result with a single value, physicochemical feature would then lead to a second effort with the chemical sequence motif approach to have a desired, clinically relevant CDR3 fingerprint.
Supplementary Material
All TRA recoveries from all TCGA-BRCA primary tumor exome files.
All single parameter physicochemical values assigned to all TRA CDR3 AA sequences recovered from TCGA-BRCA exome files. These values were used to generate the TCGA-BRCA, TRA results of Table S4.
Amino acid symbols representing distinct chemical features for evaluation of CDR3 motifs.
Survival distinctions represented by single parameter, TRA CDR3 physicochemical assessments for TCGA-BRCA and TCGA-STAD
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (SKCM).
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (BRCA).
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (STAD). These data represent 100% of the analysis output with no p-values less than 0.05.
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (LUAD).
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (LUSC).
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (UCEC).
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (HNSC). These data represent 100% of the analysis output with no p-values less than 0.05.
CDR3 amino acid chemical sequence motifs representing survival distinctions for the eight cancer datasets in article Table 1 (OV). These data represent 100% of the analysis output with no p-values less than 0.05.
Acknowledgements
Authors wish to thank Ms. Corinne Walters (University of South Florida) for administrative support related to NIH dataset access.
Funding
Funding: AC, BIC were RISE fellowship recipients. BIC was an American Hematology Association fellowship recipient.
Availability of data and materials
Parts of the article represents datasets available from previously published supporting online material files. Any additional raw data is available via email to the corresponding author.
Authors' contributions
BEM and GB conceived the study, performed the analyses, and wrote and edited the manuscript. BIC, AC, ECG and MJD performed the analyses. All authors have read and approved the final manuscript. BIC and GB confirm authenticity of all the raw data.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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