pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Background: Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. Results: We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information. Conclusions: pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

Use of patient-reported outcomes (PRO) data to complement exposure-response analysis in early clinical cancer drug development.

BACKGROUND: This proof-of-concept retrospective case study investigated whether patient-reported outcomes (PRO) instruments, designed to capture symptomatic adverse event data, could identity a known exposure-response (ER) relationship for safety characterized in an original FDA analysis of an approved anti-cancer agent. PRO instruments have been designed to uniquely quantify the tolerability aspects of exposure-associated symptomatic adverse events. We explored whether standard ER analyses of clinician-reported safety data for symptomatic adverse events could be complemented by ER analysis using PRO data that capture and quantify the tolerability aspects of these same symptomatic adverse events. METHODS: Exposure-associated adverse event data for diarrhea were analyzed in parallel in 120 patients enrolled in a clinical trial using physician reported Common Terminology Criteria for Adverse Events (CTCAE) and patient-reported symptomatic adverse event data captured by the National Cancer Institute's (NCI) PRO Common Terminology Criteria for Adverse Events (PRO-CTCAE) instrument. Comparative ER analyses of diarrhea were conducted using the same dataset. Results from the CTCAE and PRO-CTCAE ER analyses were assessed for consistency with the ER relationship for diarrhea established in the original NDA using a 750-patient dataset. The analysis was limited to the 120-patient subset with parallel CTCAE and PRO-CTCAE assessments. RESULTS: Within the same 120-patient dataset, ER analysis using dense, longitudinal PRO-CTCAE-derived data was sensitive to identify the known ER relationship for diarrhea, whereas the standard CTCAE based ER analysis was not. CONCLUSIONS: ER analysis using PRO assessed symptomatic adverse event data may be a sensitive tool to complement traditional ER analysis. Improved identification of relationships for safety, by including quantification of the tolerability aspect of symptomatic adverse events using PRO instruments, may be useful to improve the sensitivity of exposure response analysis to support early clinical trial dosage optimization strategies, where decision making occurs within limited small patient datasets.

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.

Neoantigen-targeted CD8+ T cell responses with PD-1 blockade therapy.

Neoantigens are peptides derived from non-synonymous mutations presented by human leukocyte antigens (HLAs), which are recognized by antitumour T cells1-14. The large HLA allele diversity and limiting clinical samples have restricted the study of the landscape of neoantigen-targeted T cell responses in patients over their treatment course. Here we applied recently developed technologies15-17 to capture neoantigen-specific T cells from blood and tumours from patients with metastatic melanoma with or without response to anti-programmed death receptor 1 (PD-1) immunotherapy. We generated personalized libraries of neoantigen-HLA capture reagents to single-cell isolate the T cells and clone their T cell receptors (neoTCRs). Multiple T cells with different neoTCR sequences (T cell clonotypes) recognized a limited number of mutations in samples from seven patients with long-lasting clinical responses. These neoTCR clonotypes were recurrently detected over time in the blood and tumour. Samples from four patients with no response to anti-PD-1 also demonstrated neoantigen-specific T cell responses in the blood and tumour to a restricted number of mutations with lower TCR polyclonality and were not recurrently detected in sequential samples. Reconstitution of the neoTCRs in donor T cells using non-viral CRISPR-Cas9 gene editing demonstrated specific recognition and cytotoxicity to patient-matched melanoma cell lines. Thus, effective anti-PD-1 immunotherapy is associated with the presence of polyclonal CD8+ T cells in the tumour and blood specific for a limited number of immunodominant mutations, which are recurrently recognized over time.

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer.

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

FDA Approval Summary: Capmatinib and Tepotinib for the Treatment of Metastatic NSCLC Harboring MET Exon 14 Skipping Mutations or Alterations.

The FDA approved capmatinib and tepotinib on May 6, 2020, and February 3, 2021, respectively. Capmatinib is indicated for patients with metastatic non-small cell lung cancer (mNSCLC) whose tumors have a mutation leading to mesenchymal-epithelial transition (MET) exon 14 skipping as detected by an FDA-approved test. Tepotinib is indicated for mNSCLC harboring MET exon 14 skipping alterations. The approvals were based on trials GEOMETRY mono-1 (capmatinib) and VISION (tepotinib). In GEOMETRY mono-1, overall response rate (ORR) per Blinded Independent Review Committee (BIRC) was 68% [95% confidence interval (CI), 48-84] with median duration of response (DoR) 12.6 months (95% CI, 5.5-25.3) in 28 treatment-naïve patients and 41% (95% CI: 29, 53) with median DoR 9.7 months (95% CI, 5.5-13) in 69 previously treated patients with NSCLC with mutations leading to MET exon 14 skipping. In VISION, ORR per BIRC was 43% (95% CI: 32, 56) with median DoR 10.8 months (95% CI, 6.9-not estimable) in 69 treatment-naïve patients and 43% (95% CI, 33-55) with median DoR 11.1 months (95% CI, 9.5-18.5) in 83 previously-treated patients with NSCLC harboring MET exon 14 alterations. These are the first two therapies to be FDA approved specifically for patients with metastatic NSCLC with MET exon 14 skipping.

FDA Approval Summary: Osimertinib for Adjuvant Treatment of Surgically Resected Non-Small Cell Lung Cancer, a Collaborative Project Orbis Review.

On December 18, 2020, the FDA approved osimertinib as adjuvant therapy in patients with non-small cell lung cancer (NSCLC) whose tumors have EGFR exon 19 deletions or exon 21 (L858R) mutations, as detected by an FDA-approved test. The approval was based on the ADAURA study, in which 682 patients with NSCLC were randomized to receive osimertinib (n = 339) or placebo (n = 343). Disease-free survival (DFS) in the overall population (stage IB-IIIA) was improved for patients who received osimertinib, with an HR of 0.20; 95% confidence interval (CI), 0.15-0.27; P < 0.0001. Median DFS was not reached for the osimertinib arm compared with 27.5 months (95% CI, 22.0-35.0) for patients receiving placebo. Overall survival data were not mature at the time of the approval. This application was reviewed under FDA's Project Orbis, in collaboration with Australia Therapeutic Goods Administration, Brazil ANVISA, Health Canada, Singapore Health Sciences Authority, Switzerland Swissmedic, and the United Kingdom Medicines and Healthcare products Regulatory Agency. This is the first targeted therapy adjuvant approval for NSCLC and has practice-changing implications.

FDA Approval Summary: Tucatinib for the Treatment of Patients with Advanced or Metastatic HER2-positive Breast Cancer.

On April 17, 2020, the FDA approved tucatinib in combination with trastuzumab and capecitabine for the treatment of patients with advanced unresectable or metastatic HER2-positive breast cancer, including patients with brain metastases, who have received one or more prior anti-HER2-based regimens in the metastatic setting. This was the first new molecular entity evaluated under Project Orbis, an FDA Oncology Center of Excellence initiative, which supports concurrent review of oncology drugs by multiple global health authorities. Approval was based on the HER2CLIMB trial, which randomized patients to receive tucatinib or placebo with trastuzumab and capecitabine. Tucatinib demonstrated efficacy compared with placebo in progression-free survival [PFS; HR: 0.54; 95% confidence interval (CI): 0.42-0.71; P < 0.00001] and overall survival (OS; HR: 0.66; 95% CI, 0.50-0.87; P = 0.00480). Patients with either treated and stable or active brain metastases made up 48% of the study population. PFS in patients with brain metastases confirmed benefit (HR: 0.48; 95% CI, 0.34-0.69; P < 0.00001). The benefit in patients with brain metastases allowed for inclusion of this specific population in the indication. Important safety signals included diarrhea and hepatotoxicity which are listed under Warnings and Precautions. This article summarizes the FDA thought process and data supporting the favorable benefit-risk profile and approval of tucatinib.

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

Best practices for bioinformatic characterization of neoantigens for clinical utility.

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Effects of antioxidant gene therapy on the development of diabetic retinopathy and the metabolic memory phenomenon.

PURPOSE: The purpose of this study was to determine the therapeutic effect and mechanism of AAV-MnSOD by intravitreal injection on diabetic retinopathy (DRP) and the metabolic memory phenomenon. METHODS: The effect of hyperglycemia and metabolic memory on the thickness of basement membrane, ratio of pericyte area and cross-sectional area of capillary vessels in the nerve fiber layer and outer plexiform layer; retinal capillary cell apoptosis; number of acellular capillaries and activities of retinal MnSOD and catalase were examined and compared with intravitreal injection of AAV-MnSOD by transmission electron microscopy, TUNEL assay, ELISA, and immunohistochemistry. RESULTS: Hyperglycemia increased the thickness of capillary basement membranes in the nerve fiber layer and outer plexiform layer, decreased the ratio of pericyte area and cross-sectional area of capillary vessels, increased numbers of acellular capillaries and apoptosis of retinal capillary cells, and decreased activities of retinal MnSOD and catalase. Termination of hyperglycemia cannot reverse pathological changes listed above. Intra-vitreal injection of AAV-MnSOD dramatically elevated the level and activities of retinal MnSOD and catalase, and effectively prevented the progression of DRP and the metabolic memory phenomenon. CONCLUSIONS: Increasing reactive oxygen species concentration and continuous decreasing of antioxidant enzyme activity play important roles in DRP and the metabolic memory phenomenon. AAV-MnSOD gene therapy provides a promising strategy to inhibit this blinding disease.

Targeting VE-PTP activates TIE2 and stabilizes the ocular vasculature.

Retinal and choroidal neovascularization (NV) and vascular leakage contribute to visual impairment in several common ocular diseases. The angiopoietin/TIE2 (ANG/TIE2) pathway maintains vascular integrity, and negative regulators of this pathway are potential therapeutic targets for these diseases. Here, we demonstrated that vascular endothelial-protein tyrosine phosphatase (VE-PTP), which negatively regulates TIE2 activation, is upregulated in hypoxic vascular endothelial cells, particularly in retinal NV. Intraocular injection of an anti-VE-PTP antibody previously shown to activate TIE2 suppressed ocular NV. Furthermore, a small-molecule inhibitor of VE-PTP catalytic activity (AKB-9778) activated TIE2, enhanced ANG1-induced TIE2 activation, and stimulated phosphorylation of signaling molecules in the TIE2 pathway, including AKT, eNOS, and ERK. In mouse models of neovascular age-related macular degeneration, AKB-9778 induced phosphorylation of TIE2 and strongly suppressed NV. Ischemia-induced retinal NV, which is relevant to diabetic retinopathy, was accentuated by the induction of ANG2 but inhibited by AKB-9778, even in the presence of high levels of ANG2. AKB-9778 also blocked VEGF-induced leakage from dermal and retinal vessels and prevented exudative retinal detachments in double-transgenic mice with high expression of VEGF in photoreceptors. These data support targeting VE-PTP to stabilize retinal and choroidal blood vessels and suggest that this strategy has potential for patients with a wide variety of retinal and choroidal vascular diseases.

CCN2/CTGF regulates neovessel formation via targeting structurally conserved cystine knot motifs in multiple angiogenic regulators.

Blood vessels are formed during development and tissue repair through a plethora of modifiers that coordinate efficient vessel assembly in various cellular settings. Here we used the yeast 2-hybrid approach and demonstrated a broad affinity of connective tissue growth factor (CCN2/CTGF) to C-terminal cystine knot motifs present in key angiogenic regulators Slit3, von Willebrand factor, platelet-derived growth factor-B, and VEGF-A. Biochemical characterization and histological analysis showed close association of CCN2/CTGF with these regulators in murine angiogenesis models: normal retinal development, oxygen-induced retinopathy (OIR), and Lewis lung carcinomas. CCN2/CTGF and Slit3 proteins worked in concert to promote in vitro angiogenesis and downstream Cdc42 activation. A fragment corresponding to the first three modules of CCN2/CTGF retained this broad binding ability and gained a dominant-negative function. Intravitreal injection of this mutant caused a significant reduction in vascular obliteration and retinal neovascularization vs. saline injection in the OIR model. Knocking down CCN2/CTGF expression by short-hairpin RNA or ectopic expression of this mutant greatly decreased tumorigenesis and angiogenesis. These results provided mechanistic insight into the angiogenic action of CCN2/CTGF and demonstrated the therapeutic potential of dominant-negative CCN2/CTGF mutants for antiangiogenesis.

Role of connective tissue growth factor in the retinal vasculature during development and ischemia.

PURPOSE: To investigate the function of connective tissue growth factor (CTGF), a matricellular protein of the CCN (Cyr61/CTGF/Nov) family, in retinal vasculature during development and ischemia. METHODS: CTGF expression was determined using RT-PCR, immunohistochemistry, and transgenic mice carrying CTGF promoter-driven-GFP. CTGF antibody was intraocularly injected into neonates at postnatal day (P)2, and its effect on retinal angiogenesis was analyzed at P4. Transgenic animals expressing GFP regulated by the glial fibrillary acidic protein promoter were used for astrocyte visualization. Retinal vascular occlusion was introduced by rose Bengal and laser photocoagulation on chimeric mice that were reconstituted with GFP+ bone marrow cells. Vascular repair in response to VEGF-A and CTGF was analyzed. RESULTS: A temporal increase in CTGF at both mRNA and protein levels was observed in the ganglion cell layer and inner nuclear layer during development. Endothelial cells and pericytes were identified as the main cellular sources of CTGF during retinal angiogenesis. CTGF stimulated the migration of astrocytes, retinal endothelial cells, and pericytes in vitro. Inhibition of CTGF by specific antibody affected vascular filopodial extension, growth of the superficial vascular plexus, and astrocyte remodeling. In adult mice, CTGF was prominently expressed in the perivascular cells of arteries. CTGF activated bone marrow-derived perivascular cells and promoted fibrovascular membrane formation in the laser-induced adult retinopathy model. CONCLUSIONS: CTGF is expressed in vascular beds and acts on multiple cell types. It is important for vessel growth during early retinal development and promotes the fibrovascular reaction in murine retinal ischemia after laser injury.