TRBC1-targeting antibody-drug conjugates for the treatment of T cell cancers.

Antibody and chimeric antigen receptor (CAR) T cell-mediated targeted therapies have improved survival in patients with solid and haematologic malignancies1-9. Adults with T cell leukaemias and lymphomas, collectively called T cell cancers, have short survival10,11 and lack such targeted therapies. Thus, T cell cancers particularly warrant the development of CAR T cells and antibodies to improve patient outcomes. Preclinical studies showed that targeting T cell receptor ß-chain constant region 1 (TRBC1) can kill cancerous T cells while preserving sufficient healthy T cells to maintain immunity12, making TRBC1 an attractive target to treat T cell cancers. However, the first-in-human clinical trial of anti-TRBC1 CAR T cells reported a low response rate and unexplained loss of anti-TRBC1 CAR T cells13,14. Here we demonstrate that CAR T cells are lost due to killing by the patient's normal T cells, reducing their efficacy. To circumvent this issue, we developed an antibody-drug conjugate that could kill TRBC1+ cancer cells in vitro and cure human T cell cancers in mouse models. The anti-TRBC1 antibody-drug conjugate may provide an optimal format for TRBC1 targeting and produce superior responses in patients with T cell cancers.

Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.

PURPOSE: Delay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass. METHODS: Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas. RESULTS: Forty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semi-quantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels. CONCLUSION: Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.

Identification of nonsense-mediated decay inhibitors that alter the tumor immune landscape.

Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen implicated disruption of kinase SMG1's phosphorylation of UPF1 as a potential disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro . Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases. One Sentence Summary: Disruption of the nonsense-mediated decay pathway with a newly developed SMG1 inhibitor with in-vivo activity increases the expression of T-cell targetable cancer neoantigens resulting from truncating mutations.

Preclinical studies show that Co-STARs combine the advantages of chimeric antigen and T cell receptors for the treatment of tumors with low antigen densities.

Two types of engineered T cells have been successfully used to treat patients with cancer, one with an antigen recognition domain derived from antibodies [chimeric antigen receptors (CARs)] and the other derived from T cell receptors (TCRs). CARs use high-affinity antigen-binding domains and costimulatory domains to induce T cell activation but can only react against target cells with relatively high amounts of antigen. TCRs have a much lower affinity for their antigens but can react against target cells displaying only a few antigen molecules. Here, we describe a new type of receptor, called a Co-STAR (for costimulatory synthetic TCR and antigen receptor), that combines aspects of both CARs and TCRs. In Co-STARs, the antigen-recognizing components of TCRs are replaced by high-affinity antibody fragments, and costimulation is provided by two modules that drive NF-κB signaling (MyD88 and CD40). Using a TCR-mimic antibody fragment that targets a recurrent p53 neoantigen presented in a common human leukocyte antigen (HLA) allele, we demonstrate that T cells equipped with Co-STARs can kill cancer cells bearing low densities of antigen better than T cells engineered with conventional CARs and patient-derived TCRs in vitro. In mouse models, we show that Co-STARs mediate more robust T cell expansion and more durable tumor regressions than TCRs similarly modified with MyD88 and CD40 costimulation. Our data suggest that Co-STARs may have utility for other peptide-HLA antigens in cancer and other targets where antigen density may limit the efficacy of engineered T cells.

Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.