Mudappathi, Rekha

ABSTRACT Germline variation shapes cancer risk, yet its influence on the evolutionary dynamics of established tumors remains poorly understood. In multiple myeloma, subclonal diversification drives disease progression and treatment failure, but the heritable factors that modulate this process are unknown. Here, we show that germline variation is associated with tumor evolutionary features, implicating inherited regulation in subclonal expansion. Integrating germline variation with tumor evolutionary parameters identifies variants associated with evolutionary features, with signals enriched in regulatory regions, consistent with a transcriptional basis. We further identify TBKBP1 as a key locus linking germline variation to tumor evolution and clinical outcome. Germline variation at this locus is associated with TBKBP1 expression and subclonal expansion, and TBKBP1 expression correlates with adverse prognosis, consistent across independent cohorts. Functional analyses demonstrate that TBKBP1 promotes proliferation and activates MYC, mTORC1 and non-canonical NF-κB signaling pathway. Together, these findings establish germline regulatory variation as a determinant of tumor evolutionary dynamics and identify TBKBP1 as a mediator linking inherited variation to subclonal expansion and disease progression in multiple myeloma.

Abstract Motivation Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. Results We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. Availability and implementation R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Abstract Intracellular protein transport (ICT) is a tightly regulated process that orchestrates protein localization and expression, ensuring proper cellular function. Dysregulated ICT can lead to aberrant expression of surface proteins involved in cell-cell communication, adhesion, and immune responses, contributing to disease progression and therapeutic resistance. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables the simultaneous measurement of mRNA and surface protein levels in the same cell, providing a powerful opportunity to investigate the molecular mechanisms underlying surface protein regulation. In this study, we introduce a novel computational frame for Modeling Protein Expression and Transport (MPET) that evaluates the contribution of ICT activity to differential surface protein expression using CITE-seq data. MPET comprises three modules for identification of ICT– surface protein regulatory circuits across biological scales and their contributions to phenotypic variation. We applied MPET to analyze single-cell data from COVID-19 patients with varying disease severity. Our analysis revealed context-dependent recruitment of ICT genes and pervasive rewiring of ICT pathways throughout the course of disease progression. Notably, we found that even when the transcriptional levels of key immune response proteins remained stable, their expression on cell surface were significantly altered due to dysregulated ICT. MPET provides a valuable new tool for dissecting complex regulatory networks and offers mechanistic insight into post-transcriptional regulation of cell surface proteins in diseases.

ABSTRACT Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (es t imating e volution a ry events t hrough s i ngle-ti m epoint s e quencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

Abstract Waldenstrom macroglobulinemia (WM) is a lymphoplasmacytic lymphoma associated with an increase in serum immunoglobulin M (IgM) monoclonal protein. The underlying causes of the disease are still not fully understood, but cells in the bone marrow (BM) microenvironment (TME) are anticipated to play a significant role in disease progression. We previously reported that myeloid-derived suppressor cells (MDSCs), a phenotypically heterogeneous group of cells known for their ability to suppress immune responses, are expanded in the bone marrow of patients with WM and that the expansion of MDSCs is particularly notable for increased numbers of polymorphonuclear MDSCs (PMN-MDSCs). In the present study, we evaluated the ability of WM cells to attract MDSCs to the tumor microenvironment (TME). We also evaluated whether PMN-MDSCs suppress the cytotoxic function of T-cells and thereby promote the survival of WM cells. Our study confirms the expansion of MDSCs, notably PMN-MDSCs in BM of WM patients as compared to normals. We also found that PMN-MDSCs expressing CD66b was a predominant population in WM patients. To determine the phenotype and transcriptome of both PMN-MDSCs and monocytic MDSCs (M-MDSCS) in WM, we compared BM aspirates from WM patients to normal bone marrow (NBM) and performed cellular indexing of transcriptomes and epitopes by sequencing (CITEseq) analysis (WM=3, NBM=2) followed by qPCR validation. We found that MDSCs in WM express significantly higher levels of HIF1-α overall (p<0.02) compared to NBM. Furthermore, PMN-MDSCs are more abundant in WM bone marrow than M-MDSCs and express significantly higher levels of CXCR4 (p<0.001), myeloperoxidase (MPO; p<0.0003), and azuricidin (AZU1; p<0.0004), suggesting an activated phenotype. To determine whether WM cells attract MDSCs to the BM, we performed a transwell assay and showed that WM cells (BCWM.1 cell line) attracted MDSCs, particularly PMN-MDSCs, to a greater extent than M-MDSCs (n=3; p<0.001). We next evaluated whether MDSCs prevent cytotoxic T-cells from lysing WM cells. In-vitro co-culture studies with BCWM.1 cells and T-cytotoxic cells (B:CD8-T) showed that T-cytotoxic cells were able to lyse the WM cell line by 46.4% , (median range 46.4 - 47.8%, p=0.001). However, when BCWM.1 cells and T-cytotoxic cells were co-cultured in the presence of MDSCs (i.e B:CD8-T:M), the specific lysis of BCWM.1 cells was reduced by 12.6% respectively (range 2.2- 12.6%, p<0.001). Furthermore, we observed that B:CD8-T:M co-culture significantly reduced CD8-T cell activation as compared to activated T cells alone (17.9%) (median range 17.8- 18%, p<0.001). In contrast, no affect was observed with B: CD8-T coculture by 91% (range 90.6- 91.4% p<.001) confirming that MDSCs suppress CD8-T cell activation. Our study therefore provides evidence that MDSCs in WM have an activated phenotype and that PMN-MDSCs in WM are attracted by malignant cells in the bone marrow. Furthermore, we find that the increased presence of PMN-MDSCs in the WM bone marrow environment suppresses T-cell lysis of malignant cells and may thereby contribute to disease progression. These findings provide a strong rationale for targeting MDSCs as a potential therapeutic strategy in WM.