Enhanced Immunogenicity of Mitochondrial-Localized Proteins in Cancer Cells.

Epitopes derived from mutated cancer proteins elicit strong antitumor T-cell responses that correlate with clinical efficacy in a proportion of patients. However, it remains unclear whether the subcellular localization of mutated proteins influences the efficiency of T-cell priming. To address this question, we compared the immunogenicity of NY-ESO-1 and OVA localized either in the cytosol or in mitochondria. We showed that tumors expressing mitochondrial-localized NY-ESO-1 and OVA proteins elicit significantdly higher frequencies of antigen-specific CD8+ T cells in vivo. We also demonstrated that this stronger immune response is dependent on the mitochondrial location of the antigenic proteins, which contributes to their higher steady-state amount, compared with cytosolic localized proteins. Consistent with these findings, we showed that injection of mitochondria purified from B16 melanoma cells can protect mice from a challenge with B16 cells, but not with irrelevant tumors. Finally, we extended these findings to cancer patients by demonstrating the presence of T-cell responses specific for mutated mitochondrial-localized proteins. These findings highlight the utility of prioritizing epitopes derived from mitochondrial-localized mutated proteins as targets for cancer vaccination strategies.S

Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers

Infiltration of human cancers by T cells is generally interpreted as a sign of immune recognition, and there is a growing effort to reactivate dysfunctional T cells at such tumor sites1. However, these efforts only have value if the intratumoral T cell receptor (TCR) repertoire of such cells is intrinsically tumor reactive, and this has not been established in an unbiased manner for most human cancers. To address this issue, we analyzed the intrinsic tumor reactivity of the intratumoral TCR repertoire of CD8+ T cells in ovarian and colorectal cancer—two tumor types for which T cell infiltrates form a positive prognostic marker2,3. Data obtained demonstrate that a capacity to recognize autologous tumor is limited to approximately 10% of intratumoral CD8+ T cells. Furthermore, in two of four patient samples tested, no tumor-reactive TCRs were identified, despite infiltration of their tumors by T cells. These data indicate that the intrinsic capacity of intratumoral T cells to recognize adjacent tumor tissue can be rare and variable, and suggest that clinical efforts to reactivate intratumoral T cells will benefit from approaches that simultaneously increase the quality of the intratumoral TCR repertoire

Generation of Tumor-Reactive T Cells by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids

Cancer immunotherapies have shown substantial clinical activity for a subset of patients with epithelial cancers. Still, technological platforms to study cancer T-cell interactions for individual patients and understand determinants of responsiveness are presently lacking. Here, we establish and validate a platform to induce and analyze tumor-specific T cell responses to epithelial cancers in a personalized manner. We demonstrate that co-cultures of autologous tumor organoids and peripheral blood lymphocytes can be used to enrich tumor-reactive T cells from peripheral blood of patients with mismatch repair-deficient colorectal cancer and non-small-cell lung cancer. Furthermore, we demonstrate that these T cells can be used to assess the efficiency of killing of matched tumor organoids. This platform provides an unbiased strategy for the isolation of tumor-reactive T cells and provides a means by which to assess the sensitivity of tumor cells to T cell-mediated attack at the level of the individual patient

Generation of Tumor-Reactive T Cells by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids

Cancer immunotherapies have shown substantial clinical activity for a subset of patients with epithelial cancers. Still, technological platforms to study cancer T-cell interactions for individual patients and understand determinants of responsiveness are presently lacking. Here, we establish and validate a platform to induce and analyze tumor-specific T cell responses to epithelial cancers in a personalized manner. We demonstrate that co-cultures of autologous tumor organoids and peripheral blood lymphocytes can be used to enrich tumor-reactive T cells from peripheral blood of patients with mismatch repair-deficient colorectal cancer and non-small-cell lung cancer. Furthermore, we demonstrate that these T cells can be used to assess the efficiency of killing of matched tumor organoids. This platform provides an unbiased strategy for the isolation of tumor-reactive T cells and provides a means by which to assess the sensitivity of tumor cells to T cell-mediated attack at the level of the individual patient. A modified patient-derived tumor organoids system allows the expansion of tumor-specific T cells from blood for personalized analysis of their anti-cancer properties

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community