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Supplementary Table 7: Model weights for all 17 breast cancer models

HLA information for crystal structures

Supplementary Table 5: Model weights of all 15 colorectal cancer models.

multiple 4-mers per CDR3

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor–encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays.Significance:This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299

supplementary tables 1 - 4 and 6 and figure legends

model weights for colorectal cancer model

The improvement of next-generation sequencing technologies has promoted the field of immunogenetics and produced numerous immunogenomics data. Modern data-driven research has the power to promote novel biomedical discoveries through secondary analysis of such data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific and articulate writing, reporting and interpreting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability (p=0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCR-Seq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community.

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see “The AIRR Community Guide to Repertoire Analysis” chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1–9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71–77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274–1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.

Cardiac complications, particularly myocarditis and pericarditis, have been associated with SARS-CoV-2 (the virus that causes COVID-19) infection (1-3) and mRNA COVID-19 vaccination (2-5). Multisystem inflammatory syndrome (MIS) is a rare but serious complication of SARS-CoV-2 infection with frequent cardiac involvement (6). Using electronic health record (EHR) data from 40 U.S. health care systems during January 1, 2021-January 31, 2022, investigators calculated incidences of cardiac outcomes (myocarditis; myocarditis or pericarditis; and myocarditis, pericarditis, or MIS) among persons aged ≥5 years who had SARS-CoV-2 infection, stratified by sex (male or female) and age group (5-11, 12-17, 18-29, and ≥30 years). Incidences of myocarditis and myocarditis or pericarditis were calculated after first, second, unspecified, or any (first, second, or unspecified) dose of mRNA COVID-19 (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) vaccines, stratified by sex and age group. Risk ratios (RR) were calculated to compare risk for cardiac outcomes after SARS-CoV-2 infection to that after mRNA COVID-19 vaccination. The incidence of cardiac outcomes after mRNA COVID-19 vaccination was highest for males aged 12-17 years after the second vaccine dose; however, within this demographic group, the risk for cardiac outcomes was 1.8-5.6 times as high after SARS-CoV-2 infection than after the second vaccine dose. The risk for cardiac outcomes was likewise significantly higher after SARS-CoV-2 infection than after first, second, or unspecified dose of mRNA COVID-19 vaccination for all other groups by sex and age (RR 2.2-115.2). These findings support continued use of mRNA COVID-19 vaccines among all eligible persons aged ≥5 years.

Lymphocytes play a critical role in antitumor immune responses. They are directly targeted by some therapies, and the composition and spatial organization of intratumor T-cell populations is prognostic in some cancer types. A better understanding of lymphocyte population dynamics over the course of disease and in response to therapy is urgently needed to guide therapy decisions and to develop new therapy targets. Deep sequencing of the repertoire of antigen receptor–encoding genes expressed in a lymphocyte population has become a widely used approach for profiling the population's immune status. Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richness and diversity of lymphocyte populations; to track clone members over time, between tissues, and across lymphocyte subsets; to detect clonal expansion; and to detect the recruitment of new clones into a tissue. Repertoire sequencing is thus a critical complement to other methods of lymphocyte and immune profiling in cancer. This review describes the current state of knowledge based on repertoire sequencing studies conducted on human cancer patients, with a focus on studies of the T-cell receptor beta chain locus. The review then outlines important questions left unanswered and suggests future directions for the field.

New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear.To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2.This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing.International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated).A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01];20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%.In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML. The proliferation of molecular biology and bioinformatics tools necessary to generate huge quantities of immune receptor data has not been matched by frameworks that allow easy data analysis. The authors present immuneML, an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Purpose: This phase II clinical trial evaluated whether the addition of stereotactic ablative radiotherapy (SAbR), which may promote tumor antigen presentation, improves the overall response rate (ORR) to high-dose IL2 (HD IL2) in metastatic renal cell carcinoma (mRCC). Patients and Methods: Patients with pathologic evidence of clear cell renal cell carcinoma (RCC) and radiographic evidence of metastasis were enrolled in this single-arm trial and were treated with SAbR, followed by HD IL2. ORR was assessed based on nonirradiated metastases. Secondary endpoints included overall survival (OS), progression-free survival (PFS), toxicity, and treatment-related tumor-specific immune response. Correlative studies involved whole-exome and transcriptome sequencing, T-cell receptor sequencing, cytokine analysis, and mass cytometry on patient samples. Results: Thirty ethnically diverse mRCC patients were enrolled. A median of two metastases were treated with SAbR. Among 25 patients evaluable by RECIST v1.1, ORR was 16% with 8% complete responses. Median OS was 37 months. Treatment-related adverse events (AE) included 22 grade ≥3 events that were not dissimilar from HD IL2 alone. There were no grade 5 AEs. A correlation was observed between SAbR to lung metastases and improved PFS (P = 0.0165). Clinical benefit correlated with frameshift mutational load, mast cell tumor infiltration, decreased circulating tumor-associated T-cell clones, and T-cell clonal expansion. Higher regulatory/CD8+ T-cell ratios at baseline in the tumor and periphery correlated with no clinical benefit. Conclusions: Adding SAbR did not improve the response rate to HD IL2 in patients with mRCC in this study. Tissue analyses suggest a possible correlation between frameshift mutation load as well as tumor immune infiltrates and clinical outcomes.

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. Here, we show that we can accurately predict from the TCR if any T cell is pre- or post-selection (and by extension if that T cell would be culled). First, we analyze millions of TCR genes sequenced from mouse spleen using high-throughput receptor sequencing. To get examples of TCRs pre-selection, we develop a way to translate non-productive TCR genes that otherwise cannot express protein, obtaining candidate TCR protein sequences never subjected to T cell selection. To get examples of TCRs post-selection, we translate productive TCR genes sequenced from a peripheral location such as spleen, obtaining TCR protein sequences representing T cells that survived T cell selection. Next, we train a machine learning model to find sequence level patterns that discriminate between these two categories of TCR protein sequences, achieving a balanced classification accuracy of 73% (AUC of 0.8) on de-novo TCR β-chain protein sequences. This classification accuracy may be near the achievable limit using only the β-chain representing half of the TCR heterodimer. To verify our approach, we show our machine learning model accurately predicts that (i) productive TCRs from thymus (where T selection occurs) are predominantly pre-selection and (ii) productive TCRs from peripheral tissues like skin are post-selection.

Purpose: Renal cysts are a frequent incidental finding on cross-sectional radiographic imaging. While most cysts are indolent, individuals with such cysts are frequently monitored for interval growth and potential malignant transformation, which is ultimately rare. In this study, we aimed to assess patients’ values and preferences (believes and attitudes) about renal cysts. Methods: We deployed a cross-sectional survey to a random sample of patients with a diagnosis of renal cysts who were identified by billing code and self-identification. We collected data about demographics, insurance status, family history and overall health, and characteristics of patients with renal cysts. We performed a binary regression analysis (adjusted for age, gender, family history of cancer and kidney disease, and treatment plan for renal cysts) to determine anxiety predictors in patients with renal cysts. Results: We included 301 respondents in whom billing code and self-identification corresponded; of these, 138 had renal cysts and 163 did not. In an adjusted regression analysis, there was a suggestion that a clear management plan (OR = 0.49, 95% CI [0.22–1.11]) (p value 0.08) may be associated with less anxiety and a family history of renal disease may be associated with more anxiety (OR = 1.94 [0.76–4.94]) (p value 0.17). Family history of cancer also did not significantly predict anxiety (OR = 0.54 [0.24–1.19]) (p value 0.13). All these results were not statistically significant and had wide confidence intervals of the effect estimates make the results imprecise. Conclusion: Findings of this pilot study suggest a clear management plan for the renal cyst(s) management may be associated with a lower level of anxiety, thereby by emphasizing the importance of good communication, patient engagement and evidence-based guidance. More definitive, adequately powered studies are needed to evaluate this finding further. In addition, further studies exploring differences in imaging practices, patient symptomatology and patient engagement by different provider types would be insightful. Ultimately, tools to improve shared decision-making are needed to provide more patient-centered care.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

The adaptive immune system in vertebrates has evolved to recognize non-self-antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared six metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.