Rational design of chimeric antigen receptor T (CAR-T) cells based on the recognition of antigenic epitopes capable of evoking the most potent CAR activation is an important objective in optimizing immune therapy. In solid tumors, the B7-H3 transmembrane protein is an emerging target that harbours two distinct epitope motifs, IgC and IgV, in its ectodomain. Here, we generate dromedary camel nanobodies targeting B7-H3 and demonstrate that CAR-T cells, based on the nanobodies recognizing the IgC but not IgV domain, had potent antitumour activity against large tumors in female mice. These CAR-T cells are characterized by highly activated T cell signaling and significant tumor infiltration. Single-cell transcriptome RNA sequencing coupled with functional T-cell proteomics analysis uncovers the top-upregulated genes that might be critical for the persistence of polyfunctional CAR-T cells in mice. Our results highlight the importance of the specific target antigen epitope in governing optimal CAR-T activity and provide a nanobody-based B7-H3 CAR-T product for use in solid tumor therapy.

Hairy cell leukemia variant (HCLv) responds poorly to purine analogue monotherapy. Rituximab concurrent with cladribine (CDAR) improves response rates, but long-term outcomes are unknown. We report final results of a phase 2 study of CDAR for patients with HCLv. Twenty patients with 0 to 1 prior courses of cladribine and/or rituximab, including 8 who were previously untreated, received cladribine 0.15 mg/kg on days 1 to 5 with 8 weekly rituximab doses of 375 mg/m2 beginning day 1. Patients received a second rituximab course ≥6 months after cladribine, if and when minimal residual disease (MRD) was detected in blood. The complete remission (CR) rate from CDAR was 95% (95% confidence interval, 75-100). Sixteen (80%) of 20 patients (95% confidence interval, 56-94) became MRD negative according to bone marrow at 6 months. The median duration of MRD-negative CR was 70.1 months, and 7 of 16 are still MRD negative up to 120 months. With a median follow-up of 69.7 months, 11 patients received delayed rituximab, and the 5-year progression-free survival (PFS) and overall survival (OS) were 63.3% and 73.9%, respectively. Five patients with TP53 mutations had shorter PFS (median, 36.4 months vs unreached; P = .0024) and OS (median, 52.4 months vs unreached; P = .032). MRD-negative CR at 6 months was significantly associated with longer PFS (unreached vs 17.4 months; P < .0001) and OS (unreached vs 38.2 months; P < .0001). Lack of MRD in blood at 6 months was also predictive of longer PFS and OS (P < .0001). After progression following CDAR, median OS was 29.7 months. CDAR is effective in HCLv, with better outcomes in patients who achieve MRD-negative CR. This trial is registered at www.clinicaltrials.gov as #NCT00923013.

Mesothelin (MSLN) is a cell surface glycoprotein normally expressed only on serosal surfaces, and not found in the parenchyma of vital organs. Many solid tumors also express MSLN, including mesothelioma and pancreatic adenocarcinoma. Due to this favorable expression profile, MSLN represents a viable target for directed anti-neoplastic therapies, such as recombinant immunotoxins (iToxs). Pre-clinical testing of MSLN-targeted iTox’s has yielded a strong body of evidence for activity against a number of solid tumors. This has led to multiple clinical trials, testing the safety and efficacy of the clinical leads SS1P and LMB-100. While promising clinical results have been observed, neutralizing anti-drug antibody (ADA) formation presents a major challenge to overcome in the therapeutic development process. Additionally, on-target, off-tumor toxicity from serositis and non-specific capillary leak syndrome (CLS) also limits the dose, and therefore, impact anti-tumor activity. This review summarizes existing pre-clinical and clinical data on MSLN-targeted iTox’s. In addition, we address the potential future directions of research to enhance the activity of these anti-tumor agents.

Background: Current classification of protein folds are based, ultimately, on visual inspection of similarities. Previous attempts to use computerized structure comparison methods show only partial agreement with curated databases, but have failed to provide detailed statistical and structural analysis of the causes of these divergences. Results: We construct a map of similarities/dissimilarities among manually defined protein folds, using a score cutoff value determined by means of the Receiver Operating Characteristics curve. It identifies folds which appear to overlap or to be "confused" with each other by two distinct similarity measures. It also identifies folds which appear inhomogeneous in that they contain apparently dissimilar domains, as measured by both similarity measures. At a low (1%) false positive rate, 25 to 38% of domain pairs in the same SCOP folds do not appear similar. Our results suggest either that some of these folds are defined using criteria other than purely structural consideration or that the similarity measures used do not recognize some relevant aspects of structural similarity in certain cases. Specifically, variations of the "common core" of some folds are severe enough to defeat attempts to automatically detect structural similarity and/or to lead to false detection of similarity between domains in distinct folds. Structures in some folds vary greatly in size because they contain varying numbers of a repeating unit, while similarity scores are quite sensitive to size differences. Structures in different folds may contain similar substructures, which produce false positives. Finally, the common core within a structure may be too small relative to the entire structure, to be recognized as the basis of similarity to another. Conclusion: A detailed analysis of the entire available protein fold space by two automated similarity methods reveals the extent and the nature of the divergence between the automatically determined similarity/dissimilarity and the manual fold type classifications. Some of the observed divergences can probably be addressed with better structure comparison methods and better automatic, intelligent classification procedures. Others may be intrinsic to the problem, suggesting a continuous rather than discrete protein fold space.