Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition immunotherapy.

Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors. We studied 29 patients in a basket trial comprising 12 different tumor types, treated with 10 different checkpoint inhibition regimens. Our analysis revealed that even across this diverse cohort, patients achieving clinical benefit had significantly higher neoepitope load, higher expression of T cell signatures, and higher PD-L2 expression, which also correlated with improved progression-free and overall survival. Importantly, the combination of biomarkers serves as a better predictor than each of the biomarkers alone. Basket trials are frequently used in modern immunotherapy trial design, and here we identify a set of biomarkers of potential relevance across multiple cancer types, allowing for the selection of patients that most likely will benefit from immune checkpoint inhibition.

Human endogenous retroviruses form a reservoir of T cell targets in hematological cancers.

Human endogenous retroviruses (HERV) form a substantial part of the human genome, but mostly remain transcriptionally silent under strict epigenetic regulation, yet can potentially be reactivated by malignant transformation or epigenetic therapies. Here, we evaluate the potential for T cell recognition of HERV elements in myeloid malignancies by mapping transcribed HERV genes and generating a library of 1169 potential antigenic HERV-derived peptides predicted for presentation by 4 HLA class I molecules. Using DNA barcode-labeled MHC-I multimers, we find CD8+ T cell populations recognizing 29 HERV-derived peptides representing 18 different HERV loci, of which HERVH-5, HERVW-1, and HERVE-3 have more profound responses; such HERV-specific T cells are present in 17 of the 34 patients, but less frequently in healthy donors. Transcriptomic analyses reveal enhanced transcription of the HERVs in patients; meanwhile DNA-demethylating therapy causes a small and heterogeneous enhancement in HERV transcription without altering T cell recognition. Our study thus uncovers T cell recognition of HERVs in myeloid malignancies, thereby implicating HERVs as potential targets for immunotherapeutic therapies.

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide-MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the high-throughput technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103-397, n = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4-18, n = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0-43%, n = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

Corrigendum: An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.

[This corrects the article on p. 1566 in vol. 8, PMID: 29187854.].

An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.

Personalization of cancer immunotherapies such as therapeutic vaccines and adoptive T-cell therapy may benefit from efficient identification and targeting of patient-specific neoepitopes. However, current neoepitope prediction methods based on sequencing and predictions of epitope processing and presentation result in a low rate of validation, suggesting that the determinants of peptide immunogenicity are not well understood. We gathered published data on human neopeptides originating from single amino acid substitutions for which T cell reactivity had been experimentally tested, including both immunogenic and non-immunogenic neopeptides. Out of 1,948 neopeptide-HLA (human leukocyte antigen) combinations from 13 publications, 53 were reported to elicit a T cell response. From these data, we found an enrichment for responses among peptides of length 9. Even though the peptides had been pre-selected based on presumed likelihood of being immunogenic, we found using NetMHCpan-4.0 that immunogenic neopeptides were predicted to bind significantly more strongly to HLA compared to non-immunogenic peptides. Investigation of the HLA binding strength of the immunogenic peptides revealed that the vast majority (96%) shared very strong predicted binding to HLA and that the binding strength was comparable to that observed for pathogen-derived epitopes. Finally, we found that neopeptide dissimilarity to self is a predictor of immunogenicity in situations where neo- and normal peptides share comparable predicted binding strength. In conclusion, these results suggest new strategies for prioritization of mutated peptides, but new data will be needed to confirm their value.

MuPeXI: prediction of neo-epitopes from tumor sequencing data.

Personalization of immunotherapies such as cancer vaccines and adoptive T cell therapy depends on identification of patient-specific neo-epitopes that can be specifically targeted. MuPeXI, the mutant peptide extractor and informer, is a program to identify tumor-specific peptides and assess their potential to be neo-epitopes. The program input is a file with somatic mutation calls, a list of HLA types, and optionally a gene expression profile. The output is a table with all tumor-specific peptides derived from nucleotide substitutions, insertions, and deletions, along with comprehensive annotation, including HLA binding and similarity to normal peptides. The peptides are sorted according to a priority score which is intended to roughly predict immunogenicity. We applied MuPeXI to three tumors for which predicted MHC-binding peptides had been screened for T cell reactivity, and found that MuPeXI was able to prioritize immunogenic peptides with an area under the curve of 0.63. Compared to other available tools, MuPeXI provides more information and is easier to use. MuPeXI is available as stand-alone software and as a web server at http://www.cbs.dtu.dk/services/MuPeXI .

Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes.

Identification of the peptides recognized by individual T cells is important for understanding and treating immune-related diseases. Current cytometry-based approaches are limited to the simultaneous screening of 10-100 distinct T-cell specificities in one sample. Here we use peptide-major histocompatibility complex (MHC) multimers labeled with individual DNA barcodes to screen >1,000 peptide specificities in a single sample, and detect low-frequency CD8 T cells specific for virus- or cancer-restricted antigens. When analyzing T-cell recognition of shared melanoma antigens before and after adoptive cell therapy in melanoma patients, we observe a greater number of melanoma-specific T-cell populations compared with cytometry-based approaches. Furthermore, we detect neoepitope-specific T cells in tumor-infiltrating lymphocytes and peripheral blood from patients with non-small cell lung cancer. Barcode-labeled pMHC multimers enable the combination of functional T-cell analysis with large-scale epitope recognition profiling for the characterization of T-cell recognition in various diseases, including in small clinical samples.

A robust prognostic gene expression signature for early stage lung adenocarcinoma.

BACKGROUND: Stage I lung adenocarcinoma is usually not treated with adjuvant chemotherapy; however, around half of these patients do not survive 5 years. Therefore, a reliable prognostic biomarker for early stage patients would be critical to identify those most likely to benefit from early additional treatments. Several studies have searched for gene expression prognostic biomarkers for lung adenocarcinoma, but these have not yielded a widely accepted prognosticator. RESULTS: We analyzed gene expression from seven published lung adenocarcinoma cohorts for which we included only stage I and II patients who were not given adjuvant therapy. Seven genes consistently obtained statistical significance in Cox regression for overall survival. The combined signature has a weighted mean hazard ratio of 3.2 in all cohorts and 3.0 (C.I. 1.3-7.4, p < 0.01) in an independent validation cohort and is strongly correlated with previously published signatures of chromosomal instability and cell cycle progression. CONCLUSIONS: The new prognostic signature, if validated prospectively, may enable better stratification and treatment of early stage lung cancer patients.

TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen.

BACKGROUND: A substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available. METHODS: We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing. RESULTS: The cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95 % accuracy (32 %/75 % of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data. CONCLUSIONS: Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

Relationship of postoperative thrombocytosis and survival of patients with colorectal cancer.

INTRODUCTION: Thrombocytosis accompanying solid tumors and predicting the prognosis of malignant tumors has been the subject of intensive research lately. Reports so far have evaluated the role of preoperative platelet count. In our present study we looked at the effect of tumor removal on platelet count and the predictive power of postoperative thrombocytosis on the survival of patients with colorectal cancer (CRC). METHODS: We retrospectively evaluated the clinical and histopathological data of 336 patients operated due to CRC between 2001 and 2011. Thrombocytosis was defined as a platelet count exceeding 400 × 10(3)/µL. Preoperative platelet count was compared with the value measured 1 month postoperatively. RESULTS: The platelet count significantly decreased after the removal of the primary tumor (paired Wilcoxon test p < 0.001). In univariate analysis preoperative thrombocytosis was a significant marker of overall survival (OS) with HR 2.2, p < 0.001 while the postoperative thrombocytosis was nearly significant with HR = 1.59, p = 0.087. In multivariate setting, when corrected for location, stage, tumor size and controlling for gender and age (> 65 years vs. ≤ 65 years), both pre- and postoperative thrombocytosis were significant independent prognostic markers with HR 1.80, p = 0.20 and HR = 1.98, p = 0.018, respectively. DISCUSSION AND CONCLUSION: Although the pathomechanism of thrombocytosis related to solid tumors is not known the decrease of platelet count after the removal of the primary tumor raises the possibility that the tumor may play an active role in the development of thrombocytosis. Furthermore, the observation of postoperative thrombocytosis with significant worse outcome underlines the predictive power of elevated platelet count.