Research ArticleClinical Studies
Open Access

TCR CDR3-CMV Antigen Chemical Complementaries Are Associated With a Worse Outcome for Renal Cell Carcinoma

LILA ALKASSAB, MICHAEL J. DIAZ, ELIZABETH A. FLETCHER, TAHA I. HUDA, SRIJIT PAUL, SHIVANSHU KUMAR, ANDREA CHOBRUTSKIY, BORIS I. CHOBRUTSKIY, JOANNA J. SONG and GEORGE BLANCK
Anticancer Research April 2024, 44 (4) 1505-1511; DOI: https://doi.org/10.21873/anticanres.16947

Abstract

Background/Aim: Due to still unresolved questions regarding viruses as either a primary cause or a comorbidity in cancer, we examined a potential immune response to cytomegalovirus (CMV) in the renal cell carcinoma (RCC) setting using genomics and bioinformatics approaches. Materials and Methods: Specifically, we assessed chemical complementarity scores (CSs) for solid tissue normal resident, T-cell receptor (TCR) complementarity determining region 3 (CDR3s) and CMV antigens and determined whether higher or lower CS groups were associated with a higher or lower survival probability. Results: This was indeed the case, with all such analyses consistently indicating a lower overall and progression-free survival for the cases representing the higher TCR CDR3-CMV antigen chemical CSs. This basic result was obtained for two separate RCC datasets and multiple CMV antigens. Conclusion: The results raise the question, to what extent a systemic CMV infection may represent an important co-morbidity for RCC.

Key Words:

Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney and represents the sixth most common cancer in males. The strongest risk factor for RCC is smoking (1, 2). Genetic factors may also play a role, such as the presence of the mutated form of the VHL gene (2). RCC generally has a good prognosis with a five-year survival rate of 75%. However, the successful management of RCC depends on early diagnosis and prompt treatment (2).

There are many cancers that are known to be linked to a particular virus (3). In addition, certain populations are at higher risk of certain types of cancer because they are more likely to be exposed to a particular virus. For example, most patients of Burkitt lymphoma are found in Sub Saharan Africa, and in most cases, the tumor development is associated with Epstein Barr Virus infection (3). In addition to cancer cases where the virus likely plays a direct role in the initiation and development of the cancer, there are also many indications of viral infections representing co-morbidities (3). For example, CMV has been associated with worse outcomes for neuroblastoma (4, 5), and in this case, it is likely, although not certain, that CMV infection is a comorbidity. Thus, to determine whether CMV could be of relevance in RCC, we assessed RCC related T-cell receptor (TCR) CDR3s. In particular, we evaluated the TCR CDR3s obtained from surgical marginal, normal tissue, and blood for their chemical complementarity to previously described CMV antigens. Results indicated that patients that had apparently been exposed to CMV had a worse outcome.

Materials and Methods

Extraction of T-cell receptor-alpha (TRA) and T-cell receptor-beta (TRB) recombination reads from RCC genomics files. Exome (WXS) files representing The Cancer Genome Atlas (TCGA)-KIRC (phs000178) dataset and RNAseq files representing the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-RCC (phs001287) datasets were downloaded from the genomic data commons (GDC) via database of genotypes and phenotypes (dbGaP) approved protocols, 6300 and 31752, respectively. The TCR recombination reads were extracted from the WXS files as described (6-9), using original software freely available at: https://github.com/kcios/2021. The TCR recombination read extraction process has been extensively benchmarked, particularly in ref. (10). The TCR recombination read outputs used in this report are available at Supplementary Tables S1 and S2.

Use of the Adaptive Match web tool. The Adaptive Match web tool (adaptivematch.com) performs an alignment of input CDR3 AA sequences and a candidate antigen. The web tool software then assesses a chemical complementarity score (CS) based on either electrostatic interactions, hydrophobic interactions or both (referred to as the Combo CS). The web tool calculations are based on ref. (11), and the web tool has been particularly benchmarked in refs. (12, 13). Briefly, the alignment of the CDR3 AA sequences and the candidate antigen sequences leads to a relatively high CS when there is proximity, in the alignment, of attractive charges or hydrophobic AAs, respectively, and to a low CS if there is proximity of like electrostatic charges or polar AA aligned with non-polar AAs. The alignment is then shifted by one AA and the CS is recalculated. For a given candidate antigen, the best CS is kept and output by adaptivematch.com. Also, for each case, with a variable number of TCR CDR3s per case, the maximal CS is used to place that case in an upper or lower 50th percentile group, representing the maximal CSs. Example input files, for CDR3s, antigens, and survival data are represented by Tables S3-S5, respectively. The adaptivematch.com website provides details related to input file formats. Example output files are represented by Tables S6, S7. Table S6 represents a summary output. Table S7 represents a detailed output, including case IDs, CSs, and the CDR3-candidate antigen alignments that led to the CSs. To be clear, the complete, detailed CS calculation process is provided in ref. (11). In summary, the Adaptive Match web tool is designed to match CDR3s and candidate antigens in a big data setting, where thousands of cases and tens of thousands of CDR3s can be processed in small time frames.

Survival analyses. From the output data from adaptivematch.com represented by Table S7, we created a pivot table consisting of two columns represented by “Rows” and “Values”; Rows included case IDs of selected RCC patients and their corresponding maximal Combo CSs. The Combo CSs were then sorted, high to low, and their respective cases were divided into upper and lower 50th percentile groups. For the TCGA-KIRC dataset, adaptivematch.com results were verified at cbioportal.org (14, 15), using the “PanCancer” version of the TCGA-KIRC; for the CPTAC-RCC dataset, the adaptivematch.com output was verified at the “Analyses” section of the GDC. Results were re-verified with the R software package and the R software package was used to generate the figures for this report, as previously described (16). Finally, the single time point, two-proportion test, for the CPTAC-RCC samples, was conducted using the following web tool: https://www.medcalc.org/calc/comparison_of_proportions.php.

Results

To determine whether the TCR CDR3s isolated from the RCC marginal surgical, normal tissues (herein referred to as solid tissue normal samples) had chemical complementarity to CMV antigens, we employed a Combo chemical complementarity scoring algorithm (11) and the adaptivematch.com webtool (12, 13). The Combo CS algorithm takes into consideration both electrostatic and hydrophobic interactions when assessing the chemical CS for the CDR3 and a candidate antigen. We first assessed the TCGA-KIRC dataset and considered previously identified CMV antigens (4, 17), beginning with pp65. Thus, we obtained the Combo CSs for solid tissue normal, TCR CDR3s and pp65, using both TRA and TRB CDR3 AA sequences, herein referred to as TCR CDR3s, recovered from the TCGA-KIRC dataset, and determined whether there was a survival probability distinction for the cases (patients) representing the upper and lower 50th percentile Combo CS groups, with the Combo CSs representing the maximal Combo CS for each case. For what was termed for this report, Fragment 1 of the pp65 antigen (Table I; Table S3), we observed worse overall survival (OS) and progression-free survival (PFS) probabilities for the upper 50th percentile group of cases based on the Combo CSs (Figure 1A and B). Note that, the pp65 fragments assessed, as well as other fragments in Table I, discussed below, represented an arbitrary subdivision of the CMV antigens into several fragments, as specified, in the case of pp65, in Table S3.

View this table:
Table I.

Amino acid (AA) sequences of CMV protein fragments studied in this report (See also Table S3).

Figure 1.
Figure 1.

Kaplan–Meier (KM) analysis of the upper and lower 50th percentile complementarity score (CS) groups, based on Combo CSs calculated for the CMV pp65 antigen. (A) Overall survival (OS) analysis of TCGA-KIRC cases representing the upper (black line, arrowhead, n=33) and lower (grey line, n=34) 50th percentile groups for Combo CSs based on solid tissue normal resident TCR CDR3s and pp65 Fragment 1 (Table I, Table S3) (Logrank p-value=0.004; Cox univariate regression p-value=0.0017; Table S6). (B) Progression-free survival (PFS) analysis of TCGA-KIRC cases representing the upper (black line, arrowhead, n=33) and lower (grey line, n=34) 50th percentile groups for Combo CSs based on solid tissue normal resident TCR CDR3s and pp65 Fragment 1 (Logrank p-value=0.026; Cox univariate regression p-value=0.0088). (C) OS analysis of TCGA-KIRC cases representing the upper (black line, arrowhead, n=12) and lower (grey line, n=11) 50th percentile groups for Combo CSs based on blood-sourced TCR CDR3s and pp65 Fragment 1 (Logrank p-value=0.077; Cox univariate regression p-value=0.1901). See also Table S9.

Next, we considered the possibility that a similar survival probability distinction could be observed using blood-sourced TCR CDR3s to calculate the Combo CSs representing pp65 Fragment 1 (Table I). Results indicated that was indeed the case for OS probabilities (Figure 1C).

To determine whether the above results with the CMV pp65 Fragment 1 peptide could be reproduced with another CMV antigen, we assessed the Combo CSs for the TCR CDR3s, as recovered from the solid tissue normal samples, and the CMV UL40 antigen. For the Kaplan–Meier analysis, the TCGA-KIRC cases were separated based on whether the maximal Combo CS, for each case, was in the upper or lower 50th percentile of the maximal Combo CS values. Results indicated that the upper 50th percentile of CSs for the TCR CDR3s and the UL40 antigen Fragment 5 represented a worse OS and PFS probability, in comparison to the cases representing the lower 50th percentile of Combo CSs (Table I; Figure 2).

Figure 2.
Figure 2.

Kaplan–Meier analysis of the upper and lower 50th percentile complementarity score (CS) groups, based on Combo CSs calculated for the CMV UL40 antigen. (A) Overall survival (OS) analysis of TCGA-KIRC cases representing the upper (black line, arrowhead, n=33) and lower (grey line, n=34) 50th percentile groups for Combo CSs based on solid tissue normal resident TCR CDR3s and UL40 Fragment 5 (Table I) (Logrank p-value=0.0002; Cox univariate regression p-value=0.0039). (B) Progression-free survival (PFS) of the upper (black line, arrowhead, n=33) and lower (grey line, n=34) 50th percentile groups for Combo CSs based on solid tissue normal resident TCR CDR3s and UL40 Fragment 5 (Logrank p-value=0.02; Cox univariate regression p-value=0.0221).

We next evaluated the CPTAC RCC dataset for OS probabilities for cases representing TCR CDR3s, obtained from solid tissue normal RNAseq files (Table S2), that were an exact match to known anti-CMV CDR3 AA sequences (Table S8) versus cases that had no such matches to the anti-CMV CDR3s represented by the VDJdb dataset (18). Results indicated that, at the 4.5 year post-diagnosis time point, a two-proportion comparison represented a significant difference, whereby cases with an exact match to an anti-CMV CDR3 had a worse OS than cases lacking any exact matches to the dataset of anti-CMV CDR3s (p-value=0.0246).

We next evaluated the CPTAC, TCR CDR3s from the solid tissue normal samples for chemical complementarity with CMV antigens, as in the TCGA-KIRC analysis above. Results indicated that the upper 50th percentile of cases as represented by the Combo CSs for solid tissue based CDR3s and the CMV IE1 antigen, termed Fragment 3 (Table I), represented a worse OS probability than the lower 50th percentile (Figure 3A). An analogous result was observed for PFS (Figure 3B).

Figure 3.
Figure 3.

Clinical Proteomic Tumor Analysis Consortium Kaplan–Meier analysis of the upper and lower 50th percentile complementarity score (CS) groups, based on Combo CSs calculated for the CMV IE1 antigen. (A) Overall survival (OS) of the upper (black line, arrowhead, n=51) and lower (grey line, n=55) 50th percentile groups for Combo CSs based on solid tissue normal resident TCR CDR3s and IE1 Fragment 3 (Table I; Materials and Methods) (Logrank p-value=0.011; Cox univariate regression p-value=0.0740). (B) Progression-free survival (PFS) of the upper (black line, arrowhead, n=45) and lower (grey line, n=48) 50th percentile groups based on solid tissue normal resident TCR CDR3s and IE1 Fragment 3 (Table I; Materials and Methods) (Logrank p-value=0.089; Cox univariate regression p-value=0.1893).

To further assess the above associations of TCR-CMV antigen CSs with OS with greater specificity, we determined the OS distinctions for Combo CSs based on only TRB CDR3s and previously identified CMV IE1 TCR epitopes. Results indicated that for three such CMV epitopes, the higher Combo CSs were associated with a worse outcome, consistent with the above results (Table II), in particular for IE1 Fragment 3 (Table I). Note that this assessment was repeated with random peptides with the same average AA length as the CMV epitopes from IEDB. In the case of the random peptides, the overall statistical assessment, including the Cox univariate p-value and co-efficient indicated that the association of the higher Combo CSs based on the CMV TCR epitopes with OS represented a higher statistical significance than that observed with what were effectively control, random peptides. In particular, the Cox p-values were higher, and the Cox co-efficient was lower, and represented both an association and inverse correlation with OS in the case of the random peptides (Table II).

View this table:
Table II.

Summary of a comparison of overall survival distinctions represented by combo complementarity score (CS) for CPTAC-renal cell carcinoma solid tissue normal T-cell receptor beta (TRB) CDR3s and three previously identified CMV IE1 T-cell receptor (TCR) epitopes and random peptide controls.

A multivariate analysis was performed to identify potential confounding variables in the association of the OS and PFS with Combo CSs based on solid tissue normal resident TCR CDR3s and CMV-UL40 fragment 5 (Table III and Table IV) and -pp65 fragment 1 (Table V and Table VI) epitopes. We used the following factors to conduct the multivariate analysis: Combo CS, diagnosis age, sex, and race (Asian, black or African American and white). The analyses retained the significant p-value for the Combo CS characterization in all four groups (Table III, Table IV, Table V, and Table VI). For the OS groups, diagnosis age, and sex also yielded significant p-values (Table III and Table V).

View this table:
Table III.

Multivariate Cox proportional hazard model of overall survival for TCGA-KIRC cases including case combo complementarity score (CS) representing solid tissue normal TCR CDR3s and CMV pp65 Fragment 1.

View this table:
Table IV.

Multivariate Cox proportional hazard model of PFS for TCGA-KIRC cases including case Combo complementarity score (CS) representing solid tissue normal TCR CDR3s and CMV pp65 Fragment 1.

View this table:
Table V.

Multivariate Cox proportional hazard model of overall survival for TCGA-KIRC cases including case combo complementarity scores representing solid tissue normal TCR CDR3s and CMV UL40 Fragment 5.

View this table:
Table VI.

Multivariate Cox proportional hazard model of progression-free survival for TCGA-KIRC cases including case combo complementarity scores representing solid tissue normal TCR CDR3s and CMV UL40 Fragment 5.

Discussion

The above indicated assessments indicated that, for two different RCC datasets, TCR CDR3s with a relatively high chemical complementarity to certain previously identified CMV antigens were recovered from RCC patients with worse outcomes. These results are consistent with the possibility that RCC patients who have been exposed to CMV infection are more likely to have a worse outcome. The approach and the data above suffer from several limitations. First, mining the TCR recombination reads from WXS and RNAseq files is likely to provide a less comprehensive picture of the TCRs than a PCR-based, immune repertoire approach to obtaining the TCR recombination reads. However, it is true that, particularly over large patient datasets, the recovery of recombination reads from genomics files obtained from tumor tissue has been extensively benchmarked, for example, via correlations with immune signature gene expression levels, patient outcomes, especially in melanoma; and via cogent antigen chemical complementarity assessments, such as with melanoma TCRs and MAGE antigens (12, 19-22). In the case of anti-viral CDR3s in particular, in a previous report, there was very high level correspondence between patient outcomes with exact matches to antiviral CDR3s and TCR CDR3-viral antigen chemical complementarities (4) (In this report, for RCC, very little data was available representing exact matches to previous identified anti-CMV CDR3s).

A second limitation of the study is the comparatively low standard for establishing a connection between the TCR CDR3s and the CMV antigens. While TCR CDR3-CMV antigen chemical complementarity may represent an efficient and useful prescreen in the big data setting, a more sophisticated approach, such as molecular docking, as well as in vitro and in vivo studies would be required for a firm conclusion regarding the binding of any one, specific TCR variable region to a CMV antigen.

Keeping in mind the consistency and successes and the limitations of the approach used in this report, an important question arises: are RCC patients having a worse outcome because of a systemic CMV infection, effectively a comorbidity? As noted, all recombination reads in this report were sourced from surgical, marginal tissue. As normal tissue, this may reflect a system-wide presence of the virus. Or, the TCR recombination reads may reflect T-cells responding to a CMV-driven tumor in neighboring tissue. Overall, establishing a role for CMV infection in cancer etiology has been challenging. However, certain cancers, such as glioblastoma, very likely represent a cancer where at least a subset of the cases are caused by CMV (23-25). There can also be considerations of indirect impacts on cancer development, as distinct from an independent infection comorbidity. For example, high levels of CMV may lead to more overall inflammation that supports cancer growth.

In summary, the above approach and data suggest a highly user-friendly biomarker approach to identifying RCC patients at greater risk of a worse outcome and provide an indication that a more extensive assessment of CMV infections in RCC patients would be helpful for prognoses. Finally, with additional knowledge in this setting, it is possible that anti-CMV therapies could improve RCC outcomes.

Acknowledgements

The Authors thank USF research computing; Ms. Corinne Walters for extensive admin support for database access; and the taxpayers of the State of Florida.

Footnotes

  • Authors’ Contributions

    LA: Conceptualization; Formal analysis; Methodology; Visualization; Writing - review & editing. MJD: Methodology; Visualization. EAF: Methodology; Visualization. TIH: Resources; Methodology; Software. SP: Methodology; Software. SK: Methodology; Software. AC: Formal analysis; Methodology; Software; Visualization. BIC: Formal analysis; Methodology; Software; Visualization. JJS: Resources; Methodology; Software. GB: Methodology; Project administration; Resources; Supervision; Writing - review & editing.

  • Supplementary Data

    All supplementary data are available at: https://usf.box.com/s/gc0hlu9ku0l9web7p7w1dqy0b1x0axsu; or are publicly available; or, in the case of controlled access material, such as the raw WXS files, are available with proper application to dbGaP, as indicated in Methods.

  • Conflicts of Interest

    The Authors have no conflicts of interest to declare in relation to this study.

  • Received December 22, 2023.
  • Revision received January 13, 2024.
  • Accepted January 15, 2024.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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