Integrative multiomics analysis of myeloma identifies 12 disease subtypes defined by specific patterns of genetic alterations.

T-cell redirection therapy using chimeric antigen receptor (CAR) T cells and bispecific antibodies (BiAbs) has shown promising efficacy in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM), leading to the approval of 2 CAR T-cell products and numerous BiAb trials. Data on the outcomes after relapse following BiAbs are urgently required to develop strategies for sequencing salvage therapies. We identified 58 patients progressing after a BiAb trial at Mount Sinai Hospital. Progression-free survival (PFS) to the first salvage (PFS1), second salvage therapy (PFS2), and overall survival (OS) were estimated using the Kaplan-Meier method. The median age of the patients was 67 years, and 78% had high-risk cytogenetics. They had a median of 6 prior therapy lines, 89% were triple-class refractory, and 44% were penta-drug refractory. After the BiAb trial, patients were followed for a median of 30.5 months and received a median of 2 additional salvage therapies (range, 1-9). The most common first salvage was T-cell redirection in 19 patients (10 BiAb and 9 CAR T cells). Ten patients underwent T-cell redirection as a second salvage treatment. T-cell redirection therapy as first or second salvage was feasible and associated with a median PFS1 of 28.9 months, PFS2 of 30.9 months, and an OS of 62% at 2 years. The sequential use of different T-cell redirection therapies is possible and may lead to deep and durable responses following the relapse after BiAb therapy in RRMM.

The domestic cat is an important human companion animal that can also serve as a relevant model for ~250 genetic diseases, many metabolic and degenerative conditions, and forms of cancer that are analogous to human disorders. MicroRNAs (miRNAs) play a crucial role in many biological processes and their dysregulation has a significant impact on important cellular pathways and is linked to a variety of diseases. While many species already have a well-defined and characterized miRNAome, miRNAs have not been carefully studied in cats. As a result, there are no feline miRNAs present in the reference miRNA databases, diminishing the usefulness of medical research on spontaneous disease in cats for applicability to both feline and human disease. This study was undertaken to define and characterize the cat miRNAome in normal feline tissues. High-throughput sequencing was performed on 12 different normal cat tissues. 271 candidate feline miRNA precursors, encoding a total of 475 mature sequences, were identified, including several novel cat-specific miRNAs. Several analyses were performed to characterize the discovered miRNAs, including tissue distribution of the precursors and mature sequences, genomic distribution of miRNA genes and identification of clusters, and isomiR characterization. Many of the miRNAs were regulated in a tissue/organ-specific manner.

Purpose: Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. Methods: We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. Results: We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. Conclusion: Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

The advent of precision medicine has revolutionized cancer treatment by integrating individual genetic, lifestyle, and environmental factors to tailor patient care (Huang et al., 2020; Ginsburg and Phillips, 2018). However, the complexity and heterogeneity of diseases like Multiple Myeloma (MM) pose significant challenges in leveraging the vast amounts of genomic data and biomedical literature available for personalized treatment planning (Rajkumar, 2014; Röllig et al., 2015). To address this, we present an innovative Retrieval-Augmented Generation (RAG) based chatbot framework that harnesses the power of Natural Language Processing (NLP) and state-of-the-art language models to curate and analyze MM-specific literature and provide personalized treatment recommendations based on patient-specific genomic data (Lewis et al., 2020). Our framework integrates the BioMed-RoBERTa-base model for embedding generation (Gururangan et al., 2020) and the Mistral-7B language model for question answering (Anthropic, 2023), enabling effective understanding and response to complex clinical queries. The retrieval component is enhanced by Amazon OpenSearch Service, ensuring fast and accurate access to relevant information. A comprehensive data analysis pipeline, including exploratory data analysis, semantic search, clustering, and topic modeling, provides valuable insights into the MM research landscape, informing the chatbot’s knowledge base and uncovering potential research directions (Blei et al., 2003; Mikolov et al., 2013). Deployed using Amazon Kendra, our RAG chatbot offers a user-friendly and scalable platform for accessing MM information, incorporating features such as user authentication, customizable web interface, and continuous improvement based on user feedback. The framework aims to democratize access to precision medicine by providing clinicians with a sophisticated tool for interpreting complex genomic data in the context of MM, streamlining clinical workflows, and facilitating the development of personalized treatment plans (Patel et al., 2015). This paper presents the conceptualization, development, and potential impact of our RAG-based chatbot framework on the landscape of MM treatment and precision medicine. We argue that the synergistic integration of AI, NLP, and domain-specific knowledge marks a new era of healthcare, characterized by highly personalized, data-driven, and effective treatment modalities (Thong et al., 2021). Our framework not only advances the field of precision medicine in MM but also serves as a blueprint for the development of similar systems in other complex diseases, ultimately improving patient outcomes and quality of life.