Tumour evolution and microenvironment interactions in 2D and 3D space.

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin.

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.

Plasma Ceramide C24:0/C16:0 Ratio is Associated with Improved Survival in Patients with Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a high fatality rate, with surgery as the only curative treatment. Identification of new biomarkers related to survival may help guide discovery of new pathophysiologic pathways and potential therapeutic targets. As long-chain ceramides have been linked to tumor proliferation, we sought to determine if ceramide levels were prognostic in PDAC. METHODS: Patients from two phase I studies of PDAC were followed for all-cause mortality. Ceramide levels (C24:0, C22:0, and C16:0) were quantified before treatment and at study intervals. Multivariable Cox regression models assessed the association of ceramide levels and mortality after adjusting for other univariable predictors, including time-dependent tumor resection. The ability of repeated ceramide measures to discriminate patients at risk for mortality was also assessed using multivariable modeling and the c-statistic. RESULTS: Higher plasma C16:0 concentration was associated with higher all-cause mortality in univariable and multivariable analysis (adjusted hazard ratio [aHR] 1.41, 95% confidence interval [CI] 1.09-1.82; p < 0.01). In contrast, a higher plasma C24:0/C16:0 ratio was associated with lower all-cause mortality in multivariable analysis (aHR 0.69, 95% CI 0.49-0.97; p = 0.032). Discrimination of mortality was significantly improved with the addition of either plasma C16:0 or C24:0/C16:0 levels, with optimal discrimination occurring using repeated measures of the C24:0/C16:0 ratio (c-statistic 0.73 vs. c-statistic 0.66; p < 0.001). CONCLUSIONS: Higher plasma C16:0 and lower C24:0/C16:0 ratios are independently associated with mortality in PDAC and show an ability to improve discrimination of mortality in this deadly disease. Further studies are needed to confirm this association and evaluate this novel pathway for potential therapeutic targets.

Induction of cancer neoantigens facilitates development of clinically relevant models for the study of pancreatic cancer immunobiology.

Neoantigen burden and CD8 T cell infiltrate are associated with clinical outcome in pancreatic ductal adenocarcinoma (PDAC). A shortcoming of many genetic models of PDAC is the lack of neoantigen burden and limited T cell infiltrate. The goal of the present study was to develop clinically relevant models of PDAC by inducing cancer neoantigens in KP2, a cell line derived from the KPC model of PDAC. KP2 was treated with oxaliplatin and olaparib (OXPARPi), and a resistant cell line was subsequently cloned to generate multiple genetically distinct cell lines (KP2-OXPARPi clones). Clones A and E are sensitive to immune checkpoint inhibition (ICI), exhibit relatively high T cell infiltration, and have significant upregulation of genes involved in antigen presentation, T cell differentiation, and chemokine signaling pathways. Clone B is resistant to ICI and is similar to the parental KP2 cell line in terms of relatively low T cell infiltration and no upregulation of genes involved in the pathways noted above. Tumor/normal exome sequencing and in silico neoantigen prediction confirms successful generation of cancer neoantigens in the KP2-OXPARPi clones and the relative lack of cancer neoantigens in the parental KP2 cell line. Neoantigen vaccine experiments demonstrate that a subset of candidate neoantigens are immunogenic and neoantigen synthetic long peptide vaccines can restrain Clone E tumor growth. Compared to existing models, the KP2-OXPARPi clones better capture the diverse immunobiology of human PDAC and may serve as models for future investigations in cancer immunotherapies and strategies targeting cancer neoantigens in PDAC.

T Cell- and Monocyte-Specific RNA-Sequencing Analysis in Septic and Nonseptic Critically Ill Patients and in Patients with Cancer.

Sepsis is characterized as life-threatening organ dysfunction caused by a dysregulated host immune response to infection. The purpose of this investigation was to determine the differential effect of sepsis on innate versus adaptive immunity, in humans, by examining RNA expression in specific immune cell subsets, including monocytes/macrophages and CD4 and CD8 T cells. A second aim was to determine immunosuppressive mechanisms operative in sepsis that might be amenable to immunotherapy. Finally, we examined RNA expression in peripheral cells from critically ill nonseptic patients and from cancer patients to compare the unique immune response in these disorders with that occurring in sepsis. Monocytes, CD4 T cells, and CD8 T cells from septic patients, critically ill nonseptic patients, patients with metastatic colon cancer, and healthy controls were analyzed by RNA sequencing. Sepsis induced a marked phenotypic shift toward downregulation of multiple immune response pathways in monocytes suggesting that impaired innate immunity may be fundamental to the immunosuppression that characterizes the disorder. In the sepsis cohort, there was a much more pronounced effect on gene transcription in CD4 T cells than in CD8 T cells. Potential mediators of sepsis-induced immunosuppression included Arg-1, SOCS-1, and SOCS-3, which were highly upregulated in multiple cell types. Multiple negative costimulatory molecules, including TIGIT, Lag-3, PD-1, and CTLA-4, were also highly upregulated in sepsis. Although cancer had much more profound effects on gene transcription in CD8 T cells, common immunosuppressive mechanisms were present in all disorders, suggesting that immunoadjuvant therapies that are effective in one disease may also be efficacious in the others.

Targeting tumour-associated macrophages with CCR2 inhibition in combination with FOLFIRINOX in patients with borderline resectable and locally advanced pancreatic cancer: a single-centre, open-label, dose-finding, non-randomised, phase 1b trial.

BACKGROUND: In pancreatic ductal adenocarcinoma, the CCL2-CCR2 chemokine axis is used to recruit tumour-associated macrophages for construction of an immunosuppressive tumour microenvironment. This pathway has prognostic implications in pancreatic cancer, and blockade of CCR2 restores anti-tumour immunity in preclinical models. We aimed to establish the safety, tolerability, and recommended phase 2 oral dose of the CCR2 inhibitor PF-04136309 in combination with FOLFIRINOX chemotherapy (oxaliplatin and irinotecan plus leucovorin and fluorouracil). METHODS: We did this open-label, dose-finding, non-randomised, phase 1b study at one centre in the USA. We enrolled treatment-naive patients aged 18 years or older with borderline resectable or locally advanced biopsy-proven pancreatic ductal adenocarcinoma, an Eastern Cooperative Oncology Group performance status of 1 or less, measurable disease as defined by Response Evaluation Criteria in Solid Tumors version 1.1, and normal end-organ function. Patients were allocated to receive either FOLFIRINOX alone (oxaliplatin 85 mg/m(2), irinotecan 180 mg/m(2), leucovorin 400 mg/m(2), and bolus fluorouracil 400 mg/m(2), followed by 2400 mg/m(2) 46-h continuous infusion), administered every 2 weeks for a total of six treatment cycles, or in combination with oral PF-04136309, administered at a starting dose of 500 mg twice daily in a standard 3 + 3 dose de-escalation design. Both FOLFIRINOX and PF-04136309 were simultaneously initiated with a total treatment duration of 12 weeks. The primary endpoints were the safety, tolerability, and recommended phase 2 dose of PF-04136309 plus FOLFIRINOX, with an expansion phase planned at the recommended dose. We analysed the primary outcome by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01413022. RESULTS: Between April 19, 2012, and Nov 12, 2014, we treated 47 patients with FOLFIRINOX alone (n=8) or with FOLFIRINOX plus PF-04136309 (n=39). One patient had a dose-limiting toxic effect in the dose de-escalation group receiving FOLFIRINOX plus PF-04136309 at 500 mg twice daily (n=6); this dose was established as the recommended phase 2 dose. We pooled patients in the expansion-phase group (n=33) with those in the dose de-escalation group that received PF-04136309 at the recommended phase 2 dose for assessment of treatment-related toxicity. Six (75%) of the eight patients receiving FOLFIRINOX alone were assessed for treatment toxicity, after exclusion of two (25%) patients due to insurance coverage issues. The median duration of follow-up for treatment toxicity was 72·0 days (IQR 49·5-89·0) in the FOLFIRINOX alone group and 77·0 days (70·0-90·5) in the FOLFIRINOX plus PF-04136309 group. No treatment-related deaths occurred. Two (5%) patients in the FOLFIRINOX plus PF-04136309 group stopped treatment earlier than planned due to treatment-related toxic effects. Grade 3 or higher adverse events reported in at least 10% of the patients receiving PF-04136309 included neutropenia (n=27), febrile neutropenia (n=7), lymphopenia (n=4), diarrhoea (n=6), and hypokalaemia (n=7). Grade 3 or higher adverse events reported in at least 10% of patients receiving FOLFIRINOX alone were neutropenia (n=6), febrile neutropenia (n=1), anaemia (n=2), lymphopenia (n=1), diarrhoea (n=2), hypoalbuminaemia (n=1), and hypokalaemia (n=3). Therapy was terminated because of treatment-related toxicity in one (17%) of the six patients receiving FOLFIRINOX alone. 16 (49%) of 33 patients receiving FOLFIRINOX plus PF-04136309 who had undergone repeat imaging achieved an objective tumour response, with local tumour control achieved in 32 (97%) patients. In the FOLFIRINOX alone group, none of the five patients with repeat imaging achieved an objective response, although four (80%) of those patients achieved stable disease. INTERPRETATION: CCR2-targeted therapy with PF-04136309 in combination with FOLFIRINOX is safe and tolerable. FUNDING: Washington University-Pfizer Biomedical Collaborative.

Tumor-induced STAT3 activation in monocytic myeloid-derived suppressor cells enhances stemness and mesenchymal properties in human pancreatic cancer.

Pancreatic cancer (PC) mobilizes myeloid cells from the bone marrow to the tumor where they promote tumor growth and proliferation. Cancer stem cells (CSCs) are a population of tumor cells that are responsible for tumor initiation. Aldehyde dehydrogenase-1 activity in PC identifies CSCs, and its activity has been correlated with poor overall prognosis in human PC. Myeloid cells have been shown to impact tumor stemness, but the impact of immunosuppressive tumor-infiltrating granulocytic and monocytic myeloid-derived suppressor cells (Mo-MDSC) on ALDH1(Bright) CSCs and epithelial to mesenchymal transition is not well understood. In this study, we demonstrate that Mo-MDSC (CD11b(+)/Gr1(+)/Ly6G(-)/Ly6C(hi)) significantly increase the frequency of ALDH1(Bright) CSCs in a mouse model of PC. Additionally, there was significant upregulation of genes associated with epithelial to mesenchymal transition. We also found that human PC converts CD14(+) peripheral blood monocytes into Mo-MDSC (CD14(+)/HLA-DR(low/-)) in vitro, and this transformation is dependent on the activation of the STAT3 pathway. In turn, these Mo-MDSC increase the frequency of ALDH1(Bright) CSCs and promote mesenchymal features of tumor cells. Finally, blockade of STAT3 activation reversed the increase in ALDH1(Bright) CSCs. These data suggest that the PC tumor microenvironment transforms monocytes to Mo-MDSC by STAT3 activation, and these cells increase the frequency of ALDH1(Bright) CSCs. Therefore, targeting STAT3 activation may be an effective therapeutic strategy in targeting CSCs in PC.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer 's disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow.

The novel drug candidate S2/IAPinh improves survival in models of pancreatic and ovarian cancer.

Cancer selective apoptosis remains a therapeutic challenge and off-target toxicity has limited enthusiasm for this target clinically. Sigma-2 ligands (S2) have been shown to enhance the cancer selectivity of small molecule drug candidates by improving internalization. Here, we report the synthesis of a novel drug conjugate, which was created by linking a clinically underperforming SMAC mimetic (second mitochondria-derived activator of caspases; LCL161), an inhibitor (antagonist) of inhibitor of apoptosis proteins (IAPinh) with the sigma-2 ligand SW43, resulting in the new chemical entity S2/IAPinh. Drug potency was assessed via cell viability assays across several pancreatic and ovarian cancer cell lines in comparison with the individual components (S2 and IAPinh) as well as their equimolar mixtures (S2 + IAPinh) both in vitro and in preclinical models of pancreatic and ovarian cancer. Mechanistic studies of S2/IAPinh-mediated cell death were investigated in vitro and in vivo using syngeneic and xenograft mouse models of murine pancreatic and human ovarian cancer, respectively. S2/IAPinh demonstrated markedly improved pharmacological activity in cancer cell lines and primary organoid cultures when compared to the controls. In vivo testing demonstrated a marked reduction in tumor growth rates and increased survival rates when compared to the respective control groups. The predicted mechanism of action of S2/IAPinh was confirmed through assessment of apoptosis pathways and demonstrated strong target degradation (cellular inhibitor of apoptosis proteins-1 [cIAP-1]) and activation of caspases 3 and 8. Taken together, S2/IAPinh demonstrated efficacy in models of pancreatic and ovarian cancer, two challenging malignancies in need of novel treatment concepts. Our data support an in-depth investigation into utilizing S2/IAPinh for the treatment of cancer.

Overexpression of human DNA polymerase theta is a biomarker of aggressive and DNA repair-deficient papillary thyroid cancers.

BACKGROUND: DNA polymerase theta (POLQ) is an enzyme that repairs double-strand DNA breaks. POLQ is overexpressed in several cancer types, and increased expression is associated with a poor prognosis. Ablating POLQ function in vitro increases drug sensitivity to agents that cause double-strand DNA breaks, including chemotherapies and ionizing radiation. POLQ's role in thyroid cancer remains poorly understood. METHODS: Expression of POLQ and other genes of interest were analyzed in 513 papillary thyroid cancers (505 primary tumors and 8 metastatic lesions) and 59 normal thyroid samples available in the Cancer Genome Atlas. The Cancer Genome Atlas RNA and DNA sequencing data were queried with the Xena platform. The Recombination Proficiency Score was calculated to assess DNA repair efficiency. Other signaling events associated with thyroid tumorigenesis and clinical outcomes were analyzed. Univariate and multivariate analyses were performed. Treatment with the POLQ inhibitors ART558 and Novobiocin tested the effect of POLQ inhibition on in vitro thyroid cancer growth. RESULTS: POLQ expression was increased in papillary thyroid cancers compared to normal thyroid tissue (P < .05). POLQ expression levels were inversely correlated with Recombination Proficiency Score levels (P < .05). POLQ expression was highest in tall cell papillary thyroid cancers and in metastases. Higher POLQ expression was also associated with dedifferentiation, BRAF signaling, and shorter progression-free intervals (P < .05). Treatment with POLQ inhibitors decreased in vitro thyroid cancer growth (P < .05). CONCLUSION: These findings suggest that increased POLQ expression could serve as a valuable clinical marker and a potential therapeutic target in the treatment of thyroid cancer.

Single-cell transcriptomics reveals long noncoding RNAs associated with tumor biology and the microenvironment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous and lethal. Long noncoding RNAs (lncRNAs) are an important class of genes regulating tumorigenesis and progression. Prior bulk transcriptomic studies in PDAC have revealed the dysregulation of lncRNAs but lack single-cell resolution to distinguish lncRNAs in tumor-intrinsic biology and the tumor microenvironment (TME). We analyzed single-cell transcriptome data from 73 multiregion samples in 21 PDAC patients to evaluate lncRNAs associated with intratumoral heterogeneity and the TME in PDAC. We found 111 cell-specific lncRNAs that reflected tumor, immune and stromal cell contributions, associated with outcomes, and validated across orthogonal datasets. Single-cell analysis of tumor cells revealed lncRNAs associated with TP53 mutations and FOLFIRINOX treatment that were obscured in bulk tumor analysis. Lastly, tumor subcluster analysis revealed widespread intratumor heterogeneity and intratumoral lncRNAs associated with cancer hallmarks and tumor processes such as angiogenesis, epithelial-mesenchymal transition, metabolism and immune signaling. Intratumoral subclusters and lncRNAs were validated across six datasets and showed clinically relevant associations with patient outcomes. Our study provides the first comprehensive assessment of the lncRNA landscape in PDAC using single-cell transcriptomic data and can serve as a resource, PDACLncDB (accessible at https://www.maherlab.com/pdaclncdb-overview), to guide future functional studies.

Engineering a Novel Modular Adenoviral mRNA Delivery Platform Based on Tag/Catcher Bioconjugation.

mRNA vaccines have attracted widespread research attention with clear advantages in terms of molecular flexibility, rapid development, and potential for personalization. However, current mRNA vaccine platforms have not been optimized for induction of CD4/CD8 T cell responses. In addition, the mucosal administration of mRNA based on lipid nanoparticle technology faces challenges in clinical translation. In contrast, adenovirus-based vaccines induce strong T cell responses and have been approved for intranasal delivery. To leverage the inherent strengths of both the mRNA and adenovirus platforms, we developed a novel modular adenoviral mRNA delivery platform based on Tag/Catcher bioconjugation. Specifically, we engineered adenoviral vectors integrating Tag/Catcher proteins at specific locales on the Ad capsid proteins, allowing us to anchor mRNA to the surface of engineered Ad viruses. In proof-of-concept studies, the Ad-mRNA platform successfully mediated mRNA delivery and could be optimized via the highly flexible modular design of both the Ad-mRNA and protein bioconjugation systems.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival.

Numerous cell states are known to comprise the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME). However, the developmental stemness and co-occurrence of these cell states remain poorly defined. Here, we performed single-cell RNA sequencing (scRNA-seq) on a cohort of treatment-naive PDAC time-of-diagnosis endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) samples (n = 25). We then combined these samples with surgical resection (n = 6) and publicly available samples to increase statistical power (n = 80). Following annotation into 25 distinct cell states, cells were scored for developmental stemness, and a customized version of the Ecotyper tool was used to identify communities of co-occurring cell states in bulk RNA-seq samples (n = 268). We discovered a tumor microenvironmental community comprised of aggressive basal-like malignant cells, tumor-promoting SPP1+ macrophages, and myofibroblastic cancer-associated fibroblasts associated with especially poor prognosis. We also found a developmental stemness continuum with implications for survival that is present in both malignant cells and cancer-associated fibroblasts (CAFs). We further demonstrated that high-dimensional analyses predictive of survival are feasible using standard-of-care, time-of-diagnosis EUS-FNB specimens. In summary, we identified tumor microenvironmental and developmental stemness characteristics from a high-dimensional gene expression analysis of PDAC using human tissue specimens, including time-of-diagnosis EUS-FNB samples. These reveal new connections between tumor microenvironmental composition, CAF and malignant cell stemness, and patient survival that could lead to better upfront risk stratification and more personalized upfront clinical decision-making.

Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment.

BACKGROUND: Interactions between immune and tumor cells are critical to determining cancer progression and response. In addition, preclinical prediction of immune-related drug efficacy is limited by interspecies differences between human and mouse, as well as inter-person germline and somatic variation. To address these gaps, we developed an autologous system that models the tumor microenvironment (TME) from individual patients with solid tumors. METHOD: With patient-derived bone marrow hematopoietic stem and progenitor cells (HSPCs), we engrafted a patient's hematopoietic system in MISTRG6 mice, followed by transfer of patient-derived xenograft (PDX) tissue, providing a fully genetically matched model to recapitulate the individual's TME. We used this system to prospectively study tumor-immune interactions in patients with solid tumor. RESULTS: Autologous PDX mice generated innate and adaptive immune populations; these cells populated the TME; and tumors from autologously engrafted mice grew larger than tumors from non-engrafted littermate controls. Single-cell transcriptomics revealed a prominent vascular endothelial growth factor A (VEGFA) signature in TME myeloid cells, and inhibition of human VEGF-A abrogated enhanced growth. CONCLUSIONS: Humanization of the interleukin 6 locus in MISTRG6 mice enhances HSPC engraftment, making it feasible to model tumor-immune interactions in an autologous manner from a bedside bone marrow aspirate. The TME from these autologous tumors display hallmarks of the human TME including innate and adaptive immune activation and provide a platform for preclinical drug testing.

Combination TIGIT/PD-1 blockade enhances the efficacy of neoantigen vaccines in a model of pancreatic cancer.

Background: Cancer neoantigens are important targets of cancer immunotherapy and neoantigen vaccines are currently in development in pancreatic ductal adenocarcinoma (PDAC) and other cancer types. Immune regulatory mechanisms in pancreatic cancer may limit the efficacy of neoantigen vaccines. Targeting immune checkpoint signaling pathways in PDAC may improve the efficacy of neoantigen vaccines. Methods: We used KPC4580P, an established model of PDAC, to test whether neoantigen vaccines can generate therapeutic efficacy against PDAC. We focused on two immunogenic neoantigens associated with genetic alterations in the CAR12 and CDK12 genes. We tested a neoantigen vaccine comprised of two 20-mer synthetic long peptides and poly IC, a Toll-like receptor (TLR) agonist. We investigated the ability of neoantigen vaccine alone, or in combination with PD-1 and TIGIT signaling blockade to impact tumor growth. We also assessed the impact of TIGIT signaling on T cell responses in human PDAC. Results: Neoantigen vaccines induce neoantigen-specific T cell responses in tumor-bearing mice and slow KPC4580P tumor growth. However, KPC4580P tumors express high levels of PD-L1 and the TIGIT ligand, CD155. A subset of neoantigen-specific T cells in KPC4580P tumors are dysfunctional, and express high levels of TIGIT. PD-1 and TIGIT signaling blockade in vivo reverses T cell dysfunction and enhances neoantigen vaccine-induced T cell responses and tumor regression. In human translational studies, TIGIT signaling blockade in vitro enhances neoantigen-specific T cell function following vaccination. Conclusions: Taken together, preclinical and human translational studies support testing neoantigen vaccines in combination with therapies targeting the PD-1 and TIGIT signaling pathways in patients with PDAC.

Neoantigen vaccine platforms in clinical development: understanding the future of personalized immunotherapy.

INTRODUCTION: Derived from genetic alterations, cancer neoantigens are proteins with novel amino acid sequences that can be recognized by the immune system. Recent evidence demonstrates that cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity, while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction ?algorithms,? and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. These advances have generated considerable enthusiasm for ?the ?development of neoantigen vaccines. Several neoantigen vaccine platforms are currently being evaluated in early phase clinical trials including the synthetic long peptide (SLP), RNA, dendritic cell (DC), and DNA vaccine platforms. AREAS COVERED: In this review, we describe, evaluate the mechanism(s) of action, compare the advantages and disadvantages, and summarize early clinical experience with each vaccine platform. We provide perspectives on the future directions of the neoantigen vaccine field. All data are derived from PubMed and ClinicalTrials search updated in October 2020. EXPERT OPINION: Although the initial clinical experience is promising, significant challenges to the success of neoantigen vaccines include limitations in neoantigen identification and the need to successfully target the immunosuppressive tumor microenvironment.

Challenges targeting cancer neoantigens in 2021: a systematic literature review.

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.