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Deciphering the Immune Evolution Landscape of Multiple Myeloma Long-Term Survivors Using Single Cell Genomics

Mahmoud Aly Mohamed, Abdelrahman

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Abstract

Multiple myeloma (MM) is a malignant bone marrow (BM) disease characterized by somatic hypermutation and DNA damage in plasma cells; leading to the overproduction of dysfunctional malignant myeloma cells. Accumulation of myeloma cells has direct and indirect effects on the BM and other organs. Despite the development of new therapeutic options; MM remains incurable and only a small fraction of patients experiences long-term survival (LTS). The past has shown that ultimately all patients still relapse; leading to the hypothesis that a state of active immune-surveillance is required to control the residual disease.

To understand the long-term survival phenomenon and its link to the immune-phenotypes in MM disease; we collected paired bone marrow samples from 24 patients who survived for about 7 to 17 years after Autologous Stem Cell Transplant (ASCT), with a high plasma cell infiltration in the BM (median 49.5%) at diagnosis time. Response assessment according to the International Myeloma Working Group (IMWG) revealed that 15 patients were in complete remission (CR), whereas 9 patients were in non-complete remission (non-CR) that had tumor cells which remained stable over recent years.

We performed single-cell RNA-seq sequencing on more than 290,000 bone marrow cells from 11 patients before treatment (BT) and in LTS, as well as three healthy controls using 10x Genomics technology. I developed a computational approach using the state-of-the-art single cell methods, statistical inference and machine learning models to decipher the bone marrow immune cell types and states across all clinical groups. I performed in-depth analyses of the bone marrow immune microenvironment across all captured cell types, and provided the global landscape of cellular states across all clinical groups.

In this work, I defined new cellular states, marker genes, and gene signatures associated with the patients’ clinical and survival states. Additionally, I defined a new myeloid population termed Myeloma-associated Neutrophils (MAN) cells and a T cell exhaustion population termed Aberrant Memory Cytotoxic (AMC) CD8+ T cells in newly diagnosed Multiple Myeloma patients.

Moreover, I propose new therapeutic targets CXCR3 and NR4A2 in AMC CD8+ T cells, which could be further investigated to reverse the T cell exhaustion state in newly diagnosed MM patients. Furthermore, I defined new prognostic markers in the CD8+ T cell compartment which could be predictive for the global disease state.

Finally, I propose that MM long-term survivors go through a complex and evolving immune landscape and acquire cellular states in a stepwise manner. Furthermore, I propose the Continuum Immune Landscape (CIL) Model which explains the immune landscape of MM patients before and after long-term survival. Additionally, I introduced the Disease-State Trajectories (DST) hypothesis regarding the disease-associated dysregulated cellular states in MM context, which could be generalized into other tumor entities and diseases.

Item Type: Dissertation
Supervisor: Brors, Prof. Dr. Benedikt
Place of Publication: Heidelberg
Date of thesis defense: 28 January 2021
Date Deposited: 03 Mar 2021 13:50
Date: 2021
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Service facilities > German Cancer Research Center (DKFZ)
Subjects: 004 Data processing Computer science
310 General statistics
600 Technology (Applied sciences)
610 Medical sciences Medicine
Uncontrolled Keywords: Applied computer science, Systems biology, Learning system
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