Chimeric antigen receptor T cells (CAR-T cell) targeting CD19 are effective against several subtypes of CD19-expressing hematologic malignancies. Centralized manufacturing has allowed rapid expansion of this cellular therapy, but it may be associated with treatment delays due to the required logistics. We hypothesized that point of care manufacturing of CART cells on the automated CliniMACS Prodigy R device allows reproducible and fast delivery of cells for the treatment of patients with non-Hodgkin lymphoma. Here we describe cell manufacturing results and characterize the phenotype and effector function of CART cells used in a phase I/II study. We utilized a lentiviral vector delivering a second-generation CD19 CAR construct with 4-1BB costimulatory domain and TNFRSF19 transmembrane domain. Our data highlight the successful generation of CART cells at numbers sufficient for all patients treated, a shortened duration of production from 12 to 8 days followed by fresh infusion into patients, and the detection of CART cells in patient circulation up to 1-year post-infusion.

The RV144 vaccine trial showed reduced risk of HIV-1 acquisition by 31.2%, although mechanisms that led to protection remain poorly understood. Here we identify transcriptional correlates for reduced HIV-1 acquisition after vaccination. We assess the transcriptomic profile of blood collected from 223 participants and 40 placebo recipients. Pathway-level analysis of HIV-1 negative vaccinees reveals that type I interferons that activate the IRF7 antiviral program and type II interferon-stimulated genes implicated in antigen-presentation are both associated with a reduced risk of HIV-1 acquisition. In contrast, genes upstream and downstream of NF-κB, mTORC1 and host genes required for viral infection are associated with an increased risk of HIV-1 acquisition among vaccinees and placebo recipients, defining a vaccine independent association with HIV-1 acquisition. Our transcriptomic analysis of RV144 trial samples identifies IRF7 as a mediator of protection and the activation of mTORC1 as a correlate of the risk of HIV-1 acquisition.

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Background: Salvage regimens for chemorefractory aggressive lymphoma achieve response rates of approximately 30%. Anti-CD19 CAR-T cells have demonstrated anti-lymphoma activity, but patients (pts) with rapidly progressive disease and urgent need for therapy have worse prognosis and many are not able to receive CAR-T cells in time. Decreasing the time from apheresis to infusion can make CAR-T cells available to pts with rapid progression of their disease. We present the results of a phase I clinical trial using on-site CAR-T manufacture for treatment of relapsed / refractory (r/r) B cell non Hodgkin lymphoma (NHL). Methods: Adult pts with r/r CD19+ B cell lymphomas who failed ≥ 2 lines of therapy were enrolled. Autologous T cells were transduced with a lentiviral vector (Lentigen Technology, Inc, LTG1563) encoding an anti-CD19 binding motif, CD8 linker and tumor necrosis receptor superfamily 19 (TNFRS19) transmembrane region, and 4-lBB/CD3z intracellular signaling domains. GMP-compliant manufacture was done using CliniMACS Prodigy, in a 12-day culture. Dose escalation was conducted according to a 3+3 design. Lymphodepletion was done with cyclophosphamide (60mg/kg x 1) and fludarabine (25mg/m2/d x 3). Cytokine release syndrome (CRS) and CAR-T related encephalopathy syndrome (CRES) were graded using the Lee and CARTOX criteria, respectively. Results: As of July 30, 2019 , 12 pts were enrolled and treated. Baseline characteristics are listed in table 1. 10/12 pts were refractory to the prior line of therapy, 5 had bulky disease and 9 had symptomatic disease at the time of lymphocyte collection. CAR-T cell product manufacture was successful in all pts. Median transduction rate was 48% [range 29-62] with and median culture expansion of 43-fold [range 30-79]. High dimensional flow cytometry showed the infused CD4 and CD8 CAR-T cells express a central memory and transition to memory - like profile, with enrichment for CD27 and high CCR7 expression. In addition, a subset of CD4 and CD8 CAR-T cells expressed effector transcription factors T-BET and GATA3 while CD4 CAR-T clusters express low levels of immune checkpoint blockers PD-1 and BTLA. All enrolled pts received their infusion of anti-CD19 CAR-T cells. CAR-T cell doses were 0.5 x 106/kg (n = 4) and 1 x 106/kg (n = 8). Median apheresis to infusion time was 13 days [range 13-20], 10 products were infused fresh. CAR-T persistence, based on vector sequence, peaked in peripheral blood MNCs between days 14-21. All responding subjects have had CAR-T persistence on follow up PCR measurements (range 1 - 12 months). CAR-T cell dose did not have an impact in the time to peak in vivo CAR-T cell expansion or in the rate of CAR-T cell persistence. Five pts experienced CRS. Grade 1 - 2 CRS was observed in 4 pts; whereas 1 pt died as a consequence of severe CRS in the context of bulky disease. Pharmacologic interventions for CRS included tocilizumab (n = 5), siltuximab (n = 2) and corticosteroids (n = 2). Two subjects presented grade 4 CRES with resolution after corticosteroids, no other grade ≥3 non-hematologic toxicity was observed. The most common all grade non - hematologic toxicity was fatigue, observed in 6 subjects. Hematologic toxicity was common, with grade ≥ 3 neutropenia observed in all subjects, with 4 subjects presenting grade 3 neutropenia without fever beyond day 30. Among 11 pts evaluable for response, 8 pts have achieved complete response (CR) and one had partial response (PR). Two pts did not respond. For the intention to treat population (n=12), the CR rate was 67% and overall response rate (ORR) was 75%. Overall response rates were equal between both dose levels (75%), but CR rates were higher in pts treated with 1 x 106 CAR-T cells (75% vs. 50%). Two pts have died, causes of death include progressive disease (n=1) and CRS (n=1). After a median follow up 3 months (range 1 - 12) all responding pts are alive; 1 subject relapsed 6 months after treatment with CD19+ disease and entered CR after anti-CD19 antibody drug immunoconjugate treatment. Conclusions: In this phase 1 study, second generation anti-CD19 CAR-T cells with TNFRS19 transmembrane domain have potent clinical activity. The short manufacture times achieved by local CAR-T cell manufacture with the CliniMACS Prodigy enables treatment of a very high risk NHL population that would otherwise not be able to receive CAR-T products due to rapidly progressive disease. Disclosures Caimi: ADC Therapeutics: Research Funding; Celgene: Speakers Bureau; Genentech: Research Funding. Schneider:Lentigen Technology, A Miltenyi Biotec Company: Employment. Bakalarz:Genentech: Speakers Bureau. Kruger:Lentigen Technology Inc., A Miltenyi Biotec Company: Employment. Worden:Lentigen Technology, A Miltenyi Biotec Company: Employment. Kadan:Lentigen Technology Inc., A Miltenyi Biotec Company: Employment. Malek:Adaptive: Consultancy; Janssen: Speakers Bureau; Amgen: Speakers Bureau; Celgene: Consultancy; Takeda: Consultancy; Sanofi: Consultancy; Medpacto: Research Funding. Metheny:Takeda: Speakers Bureau; Incyte: Speakers Bureau. Dropulic:Lentigen Technology, A Miltenyi Biotec Company: Employment. OffLabel Disclosure: Clinical Trial of on - site manufactured CAR-T cells. This manufacturing process is under research.

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MotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations.ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks.ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations.AvailabilityThe code and data are available at compbio.case.edu/NetKSA.

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, N et KSA, outperforms two state-of-the-art algorithms, including KinomeXplorer and LinkPhinder, in overall KSA prediction. By stratifying the ranking of kinases, N et KSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases.