Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma.

BACKGROUND: Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. RESULTS: We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. CONCLUSIONS: CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.

A liposomal RNA vaccine inducing neoantigen-specific CD4+ T cells augments the antitumor activity of local radiotherapy in mice.

Antigen-encoding, lipoplex-formulated RNA (RNA-LPX) enables systemic delivery to lymphoid compartments and selective expression in resident antigen-presenting cells. We report here that the rejection of CT26 tumors, mediated by local radiotherapy (LRT), is further augmented in a CD8+ T cell-dependent manner by an RNA-LPX vaccine that encodes CD4+ T cell-recognized neoantigens (CD4 neoantigen vaccine). Whereas CD8+ T cells induced by LRT alone were primarily directed against the immunodominant gp70 antigen, mice treated with LRT plus the CD4 neoantigen vaccine rejected gp70-negative tumors and were protected from rechallenge with these tumors, indicating a potent poly-antigenic CD8+ T cell response and T cell memory. In the spleens of CD4 neoantigen-vaccinated mice, we found a high number of activated, poly-functional, Th1-like CD4+ T cells against ME1, the immunodominant CD4 neoantigen within the poly-neoantigen vaccine. LRT itself strongly increased CD8+ T cell numbers and clonal expansion. However, tumor infiltrates of mice treated with CD4 neoantigen vaccine/LRT, as compared to LRT alone, displayed a higher fraction of activated gp70-specific CD8+ T cells, lower PD-1/LAG-3 expression and contained ME1-specific IFNγ+ CD4+ T cells capable of providing cognate help. CD4 neoantigen vaccine/LRT treatment followed by anti-CTLA-4 antibody therapy further enhanced the efficacy with complete remission of gp70-negative CT26 tumors and survival of all mice. Our data highlight the power of combining synergistic modes of action and warrants further exploration of the presented treatment schema.

SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.

BACKGROUND: Various approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated. RESULTS: We present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a multiple ungapped alignment approach to call indels. For germline variant calling, we model allele counts per site to follow a multinomial conditional distribution. For somatic variant calling, we rely on paired tumor-normal pairs from identical individuals and introduce a hybrid subtraction and joint sample analysis approach by modeling tumor-normal allele counts per site to follow a joint multinomial conditional distribution. A comprehensive performance evaluation has been conducted using a diversity of variant calling benchmarks. For germline variant calling, SNVSniffer demonstrates highly competitive accuracy with superior speed in comparison with the state-of-the-art FaSD, GATK and SAMtools. For somatic variant calling, our algorithm achieves comparable or even better accuracy, at fast speed, than the leading VarScan2, SomaticSniper, JointSNVMix2 and MuTect. CONCLUSIONS: SNVSniffers demonstrates the feasibility to develop integrated solutions to fast and efficient identification of germline and somatic variants. Nonetheless, accurate discovery of genetic variations is critical yet challenging, and still requires substantially more research efforts being devoted. SNVSniffer and synthetic samples are publicly available at http://snvsniffer.sourceforge.net .

Identification of a tumor-reactive T-cell repertoire in the immune infiltrate of patients with resectable pancreatic ductal adenocarcinoma.

PURPOSE: The devastating prognosis of patients with resectable pancreatic ductal adenocarcinoma (PDA) presents an urgent need for the development of therapeutic strategies targeting disseminated tumor cells. Until now, T-cell therapy has been scarcely pursued in PDA, due to the prevailing view that it represents a poorly immunogenic tumor. EXPERIMENTAL DESIGN: We systematically analyzed T-cell infiltrates in tumor biopsies from 127 patients with resectable PDA by means of immunohistochemistry, flow cytometry, T-cell receptor (TCR) deep-sequencing and functional analysis of in vitro expanded T-cell cultures. Parallel studies were performed on tumor-infiltrating lymphocytes (TIL) from 44 patients with metastatic melanoma. RESULTS: Prominent T-cell infiltrates, as well as tertiary lymphoid structures harboring proliferating T-cells, were detected in the vast majority of biopsies from PDA patients. The notion that the tumor is a site of local T-cell expansion was strengthened by TCR deep-sequencing, revealing that the T-cell repertoire in the tumor is dominated by highly frequent CDR3 sequences that can be up to 10,000-fold enriched in tumor as compared to peripheral blood. In fact, TCR repertoire composition in PDA resembled that in melanoma. Moreover, in vitro expansion of TILs was equally efficient for PDA and melanoma, resulting in T-cell cultures displaying HLA class I-restricted reactivity against autologous tumor cells. CONCLUSIONS: The tumor-infiltrating T-cell response in PDA shows striking similarity to that in melanoma, where adoptive T-cell therapy has significant therapeutic impact. Our findings indicate that T-cell-based therapies may be used to counter disease recurrence in patients with resectable PDA.

TCLP: an online cancer cell line catalogue integrating HLA type, predicted neo-epitopes, virus and gene expression.

Human cancer cell lines are an important resource for research and drug development. However, the available annotations of cell lines are sparse, incomplete, and distributed in multiple repositories. Re-analyzing publicly available raw RNA-Seq data, we determined the human leukocyte antigen (HLA) type and abundance, identified expressed viruses and calculated gene expression of 1,082 cancer cell lines. Using the determined HLA types, public databases of cell line mutations, and existing HLA binding prediction algorithms, we predicted antigenic mutations in each cell line. We integrated the results into a comprehensive knowledgebase. Using the Django web framework, we provide an interactive user interface with advanced search capabilities to find and explore cell lines and an application programming interface to extract cell line information. The portal is available at http://celllines.tron-mainz.de.

Mutated tumor alleles are expressed according to their DNA frequency.

The transcription of tumor mutations from DNA into RNA has implications for biology, epigenetics and clinical practice. It is not clear if mutations are in general transcribed and, if so, at what proportion to the wild-type allele. Here, we examined the correlation between DNA mutation allele frequency and RNA mutation allele frequency. We sequenced the exome and transcriptome of tumor cell lines with large copy number variations, identified heterozygous single nucleotide mutations and absolute DNA copy number, and determined the corresponding DNA and RNA mutation allele fraction. We found that 99% of the DNA mutations in expressed genes are expressed as RNA. Moreover, we found a high correlation between the DNA and RNA mutation allele frequency. Exceptions are mutations that cause premature termination codons and therefore activate nonsense-mediated decay. Beyond this, we did not find evidence of any wide-scale mechanism, such as allele-specific epigenetic silencing, preferentially promoting mutated or wild-type alleles. In conclusion, our data strongly suggest that genes are equally transcribed from all alleles, mutated and wild-type, and thus transcribed in proportion to their DNA allele frequency.