Colin Harry Lamont

• Lamont, Colin Harry (2026). Optimal control and the dynamics of ancestral lineages. Genetics. ISSN 0016-6731. 233(4). doi: 10.1093/genetics/iyag122. Fulltekst i vitenarkiv
• Fjørtoft, Marit Otterlei; Garred, Øystein; Lingjærde, Ole Christian; Johannesen, Karin Teien; Ottestad, Lars & Bergheim, Inger Riise [Vis alle 29 forfattere av denne artikkelen] (2025). Single-cell analysis reveals tumour size as a key driver of immune cell profile alterations in primary breast tumours and corresponding lymph nodes. BJC Reports. 3(1). doi: 10.1038/s44276-025-00152-3. Fulltekst i vitenarkiv Vis sammendrag

  At diagnosis, 30–40% of women with breast cancer have metastases in sentinel (SN) or axillary lymph nodes (ALN). Nodal status is a strong prognostic factor and guides treatment decisions. Immune checkpoint inhibition has shown some efficacy, which can increase in the neoadjuvant setting. A better understanding of how tumour cells in primary tumours and metastatic lymph nodes shape the local immune microenvironment may provide clues for more individualized therapeutic interventions. Methods We conducted deep immunophenotypic analysis of 29 primary breast tumours and 36 lymph nodes from 38 patients with primary operable breast cancer. Results The immune profile of the primary tumour was not predictive of the lymph node immune profile or metastatic status. Primary tumours showed prominent CD8 T cell exhaustion and activated regulatory T cells, and the frequencies of these subsets were associated with tumour size. The immune cell profile in lymph nodes were different from the profile in primary tumours, except for the ALN+ nodes, which displayed a T-cell profile more similar to primary tumours. The frequencies of the T cell subsets in lymph nodeswere associated with metastatic size. Tumour cells from smaller metastases exhibited a distinct phenotype compared to those from larger tumour deposits, and the size of the tumour cell deposit impacted the local immune cell composition. Conclusion The tumour size of primary tumours and metastatic size in lymph nodes are the main drivers of changes in immune cell composition

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• Lamont, Colin Harry (2026). Can We Reconstruct Evolution's Hidden History?—A Conversation with Colin LaMont - Genetics in Your World | Acast. Fulltekst i vitenarkiv Vis sammendrag

  Listen to Can We Reconstruct Evolution's Hidden History?—A Conversation with Colin LaMont from Genetics in Your World. In this episode of Genetics in Your World, GSA Early Career Scientist Multimedia Subcommittee co-chair Anindya Ganguly and member Racheal Asaolu interview Colin LaMont of University of Oslo. They discuss LaMont's work which presents a new mathematical method for calculating how strongly selection favors a genetic variant directly from an evolutionary tree, without knowledge of exact population history, finding that ancestral lineages act like strategists anticipating future selection rather than just responding to it in the moment. Read his paper titled, "Optimal control and the dynamics of ancestral lineages," published in the August 2026 issue of GENETICS: https://doi.org/10.1093/genetics/iyag122 Music: Loopster Kevin MacLeod (incompetech.com). Licensed under Creative Commons: By Attribution 3.0 License, http://creativecommons.org/licenses/by/3.0/

• Lamont, Colin Harry; Nilsson, Daniel; Huse, Kanutte; Spasevska, Ivana; Olsson, Lina M. & Porwit, Anna [Vis alle 10 forfattere av denne artikkelen] (2026). Tumor cell expression of Sox11 and cyclin D1 differently impact the spatial tumor immune cell landscape in Mantle cell lymphoma- a translational Nordic MCL2/3 study. HemaSphere. 10(S1). Fulltekst i vitenarkiv Vis sammendrag

  Background Mantle cell lymphoma (MCL) is a heterogeneous B cell lymphoma with frequent relapses. Intensified first line chemoimmunotherapy with cytarabine followed by autologous stem cell transplantation (ASCT) improves outcomes in younger patients, yet high risk disease (high Ki67/MIPI B and TP53 aberrations) remains difficult to control. The tumor microenvironment (TME) contributes to this heterogeneity as immune depleted, macrophage rich MCL have been linked to adverse prognosis and resistance. However, the spatial organization of TME and associated transcriptional programs in clinically annotated cohorts are incompletely defined. Aims To integrate spatial proteomics with compartment resolved transcriptomics to map TME architecture in MCL and relate it to tumor biology and outcome. Methods We included TMA sections containing 135 diagnostic tissue cores from 98 patients treated on Nordic Lymphoma Group trials MCL2/3 with long term follow-up clinical data. Imaging mass cytometry (IMC, Hyperion) used a 31 marker panel covering tumor, T cell, myeloid, endothelial and checkpoint proteins. GeoMx™ DSP profiled 1,811 transcripts (Cancer Transcriptomic Atlas) in 190 ROIs (~2 ROIs/patient), segmented into CD20+ tumor, CD8+ cytotoxic T cells, and CD3+/CD8− helper T cells. Results Fourteen distinct cell types/subsets were identifed in IMC data and unsupervised analyses of these cell type frequencies identified three recurrent TME clusters: tumor rich, M2 like macrophage rich, and T cell rich. Patients with M2 rich TME had inferior progression free survival and had tumor cells with lower SOX11 and higher Ki67 and cyclin D1 protein expression. Transcriptomics analysis of tumor cell segments revealed that SOX11 low tumors (enriched in the M2 rich cluster) had upregulated proliferation and cell cycle programs (e.g., Cyclin D1/Ki67 signatures), in contrast to SOX11+ tumor cells characterized by B cell differentiation/activation program and immune regulatory genes. Exhaustion associated markers (LAG3, TIGIT) were detected in T cells within M2 rich regions, yet accompanied by higher activation marker expression relative to the other clusters. Canonical correlation analysis highlighted the inverse relationship between cyclin D1 and SOX11, and SOX11 as the strongest tumor cell feature linked to neighborhood composition: SOX11 expression correlated positively with dendritic cells and PD 1+ CD8 T cells and negatively with PD 1− CD4 and CD8 T cells and macrophage abundance. Conclusion MCL exhibits distinct spatial TIME architectures with prognostic relevance. A macrophage dominant, SOX11 low state couples increased tumor proliferation with altered T cell phenotypes and inferior outcome. SOX11 emerges as a central axis, connecting tumor intrinsic programs to immune neighborhood structure, supporting its use for risk stratification and for guiding microenvironment targeted strategies (e.g., macrophage reprogramming and checkpoint/exhaustion pathway combinations) in high risk MCL.

• Lamont, Colin Harry (2025). Spatial Data In Julia! doi: https:/github.com/OBIWOW/OBiWoW-2025/tree/main/12-Friday/spatial-data-in-julia. Fulltekst i vitenarkiv Vis sammendrag

  Spatial biology is rapidly evolving, no one is really sure what the best approach or standard analysis will look like. With a fun and fast programming language like Julia, you can test out new solutions and approaches quickly in a reactive and reproducible notebook environment. Images are just big matrices, cell data just a big data frame. Why not wrangle them with a nicely designed language that makes vectorizing functions as easy as ".", and arrays are as easy as [1,2,3]?

• Lamont, Colin Harry; Nilsson, Daniel; Huse, Kanutte; Spasevska, Ivana; Olsson, Lina M. & Kolstad, Arne [Vis alle 10 forfattere av denne artikkelen] (2025). Tumor immune cell landscape of Mantle cell lymphoma. Fulltekst i vitenarkiv Vis sammendrag

  Mantle cell lymphoma (MCL) is a rare form of B-cell non-Hodgkin lymphoma (B-NHL). The malignancy commonly has an aggressive clinical course but is biologically and clinically heterogeneous. MCL overexpress cyclin D1 due to the t(11;14) translocation and most cases have aberrant expression of the neural transcription factor Sox11. Whereas the genetic landscape of MCL is well characterized, an in-dept characterization of the tumor immune microenvironment (TIME) is lacking. Here, we employed spatial proteomics to map the TIME architecture and explored how gene expression in tumor cells and T cells changed dependent on the neighborhood type. The cohort was based on TMA sections containing 134 diagnostic tumor tissues from 98 MCL patients included in the Nordic clinical phase II studies MCL2/MCL3. TMA neighbor sections were analyzed by imaging mass cytometry (IMC, Hyperion) and GeoMx (Nanostring DSP) was used to collect region of interest (ROI) spatially resolved genes expression profiles (immunopanel) from CD20+ cells (tumor), CD3+CD8+ T cells and CD3+CD8- T cells. By IMC, we identified 14 different cell types and 3 main TIME subtypes: T cell rich (47%), tumor rich (41%) and M2 rich (12%). The M2 rich subtype was enriched for cases with high protein expression of cyclin D1, low or negative expression of Sox 11 and higher gene expression of genes involved in DNA replication (MCM5, PLK1), MHC class I antigen presentation (TAP1) and interferon signaling (STAT1) in tumor cells. Sox11 expression was positively correlated with the cell ratios of DC/macrophages, PD-1+/PD1- CD8 T cells and PD-1+/PD-1- CD4 T cells, suggesting more antigen presentation to T cells in Sox11high tumors. Neighborhood analysis organizing MCL TIME into tumor, margin, follicle and T cell zones revealed that more PD-1+ CD4 T cells than PD-1+ CD8 T-cells co-localized to tumor zones. Survival analysis by applying univariate or multivariate proportional hazard analysis confirmed that patients with high M2/M1 macrophages had poor outcome whereas cases with remnant follicular zones had better outcome. Unsupervised integrative analysis using multi-omics factor analysis (MOFA), which included clinical, IMC and transcriptome data identified major dimensions of disease heterogeneity in MCL. Collectively, our data identified TIME subtypes and spatial features that refine disease heterogeneity and aggressiveness of MCL.

• Sharma, Ankush; Kongsrud, Karoline; Spasevska, Ivana; Lamont, Colin Harry; Steen, Chloe Beate & Lorenz, Susanne [Vis alle 10 forfattere av denne artikkelen] (2025). Deciphering Tumor Microenvironment Complexity in Lymphoma Using Spatial Transcriptomics. Fulltekst i vitenarkiv Vis sammendrag

  The tumor microenvironment (TME) in lymphomas, such as Diffuse Large B-cell Lymphoma (DLBCL) and Follicular Lymphoma (FL), is highly heterogeneous and plays a critical role in modulating therapeutic outcomes, including responses to CD19 CAR T-cell therapy. To unravel the spatial complexity of the TME, we employed an integrative approach combining spatial transcriptomics (Visium, 10x Genomics) and spatial proteomics (imaging mass cytometry, IMC) on FFPE tissue sections from FL and DLBCL patients. Using tools such as CytoSPACE and CARD, we mapped single-cell RNA-seq data to spatial coordinates and validated cellular compositions with IMC data. We identified spatially distinct immune and stromal cell populations, including GC B cells, Tregs, macrophages, dendritic cells, and endothelial cells, across follicular and T-cell rich regions. CytoSPACE-based mapping revealed consistent enrichment of key markers (e.g., CD79B, FOXP3, GRZA) with corresponding protein expression profiles. We also developed R based ShinySpatial framework to visualize spatial data enhancing the accessibility and interpretability of complex spatial datasets. This pilot study lays the groundwork for identifying spatial biomarkers that predict therapeutic responses and resistance in lymphoma, with ongoing integration of data from patients enrolled in the JULIET, BELINDA, and ELARA trials.

• Lamont, Colin Harry; Huse, Kanutte; Spasevska, Ivana; Nilsson, Daniel; Kolstad, Arne & Jerkeman, Mats [Vis alle 8 forfattere av denne artikkelen] (2024). Mapping the tumor microenvironment in mantle cell lymphoma by imaging mass cytometry. Fulltekst i vitenarkiv Vis sammendrag

  Background: Mantle cell lymphoma (MCL) is a rare aggressive form of B cell non-Hodgkin lymphoma, accounting for less than 7% of all cases of newly diagnosed lymphoma. MCL is characterized by specific hallmarks, including the t(11;14)(q13;q32) translocation that results in constitutive expression of Cyclin D1, and overexpression of the SOX11 transcription factor. Although the molecular profile and the genetic landscape of MCL and their impact on the disease aggressiveness have been well characterized, the tumor microenvironment (TME) and its role are still not fully understood. Here, we aimed to explore TME in MCL, determine how tumor cell features shape the TME and link TME features to clinical high-risk factors. Methods: Diagnostic biopsies from 102 MCL patients included in the two 2 prospective trials by the Nordic Lymphoma Group, MCL2 and MCL3, were imaged by Hyperion imaging mass cytometry (IMC) using a 31-marker antibody panel. Images were segmented into single cells, batch-corrected and annotated using a novel binary clustering strategy. Using CytoMAP, the cells were divided into 30 µm-radius neighborhoods and clustered to define five types of tissue regions. Results: We identified 13 different clusters of cells present in the MCL TME, with CD4 T cells being the most prevalent immune cell population, with a median of 10% (range 2.2-28%), followed by CD8 T cells with a median of 9% (range 1.5-22%) and CD163- macrophages 8% (range 2.1-23%). T follicular helper cells are notably absent and the follicular dendritic cell (FDC) networks are heterogenous. Neighborhood analysis reveals five types of microenvironments across all patients: tumor exclusive (22%), immune-infiltrating tumor (31%) immune-dominant tumor (19%), T cell zone like (6%), and FDC/follicle (14%). Hierarchical clustering of patients reveals that differences in the microenvironment composition are associated with architectural motifs and follicle organization. In our dataset, 89 patients were SOX11+, and high expression of SOX11 was associated with higher CD19 (p = 0.03, two sided Mann-Whitney test) and lower Cyclin D1 (p = 2x10⁻⁴) and Ki67 (p = 1.7 x10⁻³) expression and linked to a longer time to progression (p = 0.04). Conclusion: Our study contributes to an improved understanding of the MCL tumor microenvironment. Future analysis will determine how the observed tumor microenvironment shapes clinical outcomes.

• Lamont, Colin Harry (2024). Flexible and Integrated Pipeline for Spatial Biology Data Analysis. Fulltekst i vitenarkiv

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