CAR T cell therapy in multiple myeloma, where are we now and where are we heading for?

The introduction of chimeric antigen receptor (CAR) T cells revolutionized treatment of relapsed and refractory multiple myeloma (RRMM) in recent years. Currently, two CAR T cell products-idecabtagene vicleucel and ciltacabtagene autoleucel-are approved in the United States and the European Union to treat patients with three prior lines of therapy, including a proteasome inhibitor, an immunomodulatory drug, and an anti-CD38 antibody. Moreover, seminal phase III trials of both agents in earlier lines of therapy have been published recently. Despite unprecedented rates of deep and lasting remissions in RRMM, there are still areas of uncertainty regarding the optimal use and distribution of CAR T cells in multiple myeloma. In the current review, we discuss the available data on approved CAR T cell products as well as unmet clinical needs and ongoing developments to optimize usage of this promising treatment modality in multiple myeloma.

Cellular dynamics following CAR T cell therapy are associated with response and toxicity in relapsed/refractory myeloma.

B-cell maturation antigen (BCMA)-targeting chimeric antigen receptor (CAR) T cells revolutionized the treatment of relapsed/refractory multiple myeloma (RRMM). However, data on cellular (CAR) T cell dynamics and the association with response, resistance or the occurrence of cytokine release syndrome (CRS) are limited. Therefore, we performed a comprehensive flow cytometry analysis of 27 RRMM patients treated with Idecabtagene vicleucel (Ide-cel) to assess the expansion capacity, persistence and effects on bystander cells of BCMA-targeting CAR T cells. Additionally, we addressed side effects, like cytokine release syndrome (CRS) and cytopenia. Our results show that in vivo expansion of CD8+ CAR T cells is correlated to response, however persistence is not essential for durable remission in RRMM patients. In addition, our data provide evidence, that an increased fraction of CD8+ T cells at day of leukapheresis in combination with successful lymphodepletion positively influence the outcome. We show that patients at risk for higher-grade CRS can be identified already prior to lymphodepletion. Our extensive characterization contributes to a better understanding of the dynamics and effects of BCMA-targeting CAR T cells, in order to predict the response of individual patients as well as side effects, which can be counteracted at an early stage or even prevented.

Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma.

Markers that predict response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected mononuclear cells isolated from peripheral blood and bone marrow before and after the application of approved B cell maturation antigen-directed CAR T cells to single-cell multiomic analyses to identify markers associated with resistance and early relapse. Differences between responders and nonresponders were identified at the time of leukapheresis. Nonresponders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressed CD8+ T cell and natural killer cell function. Analysis of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded clones compared to low/intermediate expanded clones. We identified potential immunotherapy targets on CAR T cells, like PD1, to improve their functionality and durability. Our work provides evidence that an immunosuppressive microenvironment causes resistance to CAR T cell therapies in multiple myeloma.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Activation of distinct inflammatory pathways in subgroups of LR-MDS.

Aberrant innate immune signaling has been identified as a potential key driver of the complex pathophysiology of myelodysplastic neoplasms (MDS). This study of a large, clinically and genetically well-characterized cohort of treatment-naïve MDS patients confirms intrinsic activation of inflammatory pathways in general mediated by caspase-1, interleukin (IL)-1ß and IL-18 in low-risk (LR)-MDS bone marrow and reveals a previously unrecognized heterogeneity of inflammation between genetically defined LR-MDS subgroups. Principal component analysis resolved two LR-MDS phenotypes with low (cluster 1) and high (cluster 2) levels of IL1B gene expression, respectively. Cluster 1 contained 14/17 SF3B1-mutated cases, while cluster 2 contained 8/8 del(5q) cases. Targeted gene expression analysis of sorted cell populations showed that the majority of the inflammasome-related genes, including IL1B, were primarily expressed in the monocyte compartment, consistent with a dominant role in determining the inflammatory bone marrow environment. However, the highest levels of IL18 expression were found in hematopoietic stem and progenitor cells (HSPCs). The colony forming activity of healthy donor HSPCs exposed to monocytes from LR-MDS was increased by the IL-1ß-neutralizing antibody canakinumab. This work reveals distinct inflammatory profiles in LR-MDS that are of likely relevance to the personalization of emerging anti-inflammatory therapies.

Decision Support for Oropharyngeal Cancer Patients Based on Data-Driven Similarity Metrics for Medical Case Comparison.

Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.

The Cosine Similarity Technique: A new method for smart EXCIMER laser control.

PURPOSE: To introduce additional steps towards smart laser control in eye surgery, with the use of the cosine similarity technique to analyze the spectra of organic polymers obtained using non-contact photoacoustic spectroscopy (NCPAS). METHODS: The experiments were performed with two organic polymers: polyethylene and polyamide. A 193 nm excimer laser was used for photoablation at a repetition rate of 200Hz. The resulting acoustic signal of the ablation process was recorded by a capacitor microphone and then preamplified and digitized. For each specimen, four measurements with 1000 single pulses were taken. The cosine similarity technique was then used to compare the spectra of the polymers. The performance of the discrimination technique was evaluated by receiver operating characteristic analysis. RESULTS: It was possible to correctly recognize a material with a probability of approximately 98% using the cosine similarity technique at a laser repetition rate and recording rate of 200 Hz, which represents the acoustic signal of one laser pulse. CONCLUSIONS: The determination of materials with the cosine similarity method (CSM) is a fast, precise and promising approach towards smart laser control. Additional steps could include the design of a database containing generic spectra, using higher repetition rates, and the combination of NCPAS results with the position of the laser beam.