Tumor Microenvironment Analysis Techniques

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor—a representative cold tumor—we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample—a hot tumor—we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures. Interesting spatial proteomics study by Matthias Mann and larger team. https://lnkd.in/edAHnaWz 

Very excited to share our new study in #CancerCell on post-treatment scarring, and recurrence in brain tumors, led by the amazing Spencer Watson and Anoek Zomer in my lab, with many wonderful collaborators! https://lnkd.in/enMQzQUa  We discovered a fibrotic scar response, following a range of different therapies - that promotes cancer cell survival - and ultimately tumor #relapse This study led on from our previous investigation of macrophage-targeted therapies in #GBM in preclinical models, when a wonderful former postdoc, Daniela Quail, found that all mice responded to the treatment, but over time, tumors recurred in ~50% of individuals - and this was ALWAYS next to a scar.. (Quail et al, Science, 2016:) So, we naturally next wanted to ask – is this scarring a cause of tumor recurrence, or simply a consequence? Two amazing postdocs, Spencer Watson and Anoek Zomer, joined my lab and set out to address this question.. First, they investigated whether the #fibrotic #scar formation was unique to macrophage-targeted therapies? Turns out, it is not – we also find post-treatment scars in the context of radiation, surgical excision, and in multiple mouse models of different glioma subtypes.. Critically, we also found scars associated with recurrent human GBM following standard of care therapy.. Through a deep analysis of the tumor #microenvironment - using orthogonal multi-omics strategies, incl. mass-spec proteomics, spatial transcriptomics, sc-RNAseq & sequential IF, integrated w/ computational analyses - we identified the key pathways driving the scar formation.. The power of this integrated strategy, in which we also captured the dynamic evolution of the response & emergence of recurrence, revealed #inflammation and #TGFb signaling among the highest enriched pathways in scars – and so we targeted these together in preclinical models. We found the combination of TGFb inhibition w/ the anti-inflammatory agent dexamethasone blocked scar formation in the context of macrophage-targeted CSF1R inhibition, and dramatically prolonged survival in mice - indicating that scars form a niche for tumor cells to survive I have many people to thank who made this possible - Spencer and Anoek first & foremost, and all our collaborators and co-authors. We are extremely grateful to all #patients who were part of this study, and our clinical colleagues at CHUV Lausanne - Giulia Cossu, Andreas Hottinger, Monika Hegi and Roy Daniel; Marike Broekman & Erik Abels, Leiden; Jason Huse, MDACC. Huge thanks to Nadine Fournier, Joao Lourenco & Sina Nassiri for invaluable computational analyses that helped us gain key biological insights; Pauline Aubel, Simona Avanthay, and Davide Croci for critical experimental support; Aga Chryplewicz & Doug Hanahan for helping us investigate other glioma models; Krisztian Homiccko for helping us with the Xenium experiments; and Manfredo Quadroni and his amazing mass-spec facility at UNIL!

🔬 Why We Should Stop Throwing Away the "Clumps" in Single-Cell Analysis To study tumors at single-cell resolution, scientists routinely dissociate tissue into individual cells using enzymes. Any clumps that survive get filtered out as "doublets"—assumed to be artifacts—before flow cytometry or sequencing. A new Nature paper from Daniel Peeper's lab shows this filtering discards the most valuable cells. CD8+ T cells physically clustered with tumor cells or APCs are 9-fold more likely to be tumor-reactive than singlets. These aren't random aggregates—they're cells caught mid-synapse, actively engaging their targets. The clustered T cells are enriched for CD39+PD-1+, the molecular signature of tumor reactivity. They maintain a TCF7+ stem-like phenotype—exactly the durable responders you want for adoptive cell therapy. And remarkably, they survive brief enzymatic digestion. I think this reframes how we should approach TIL isolation for therapy. The authors show that simply relaxing doublet gates and expanding clusters separately yields a population with dramatically higher tumor-killing capacity. It also makes a strong case for spatial methods. Technologies like Xenium, MERSCOPE, and CosMx now measure 20,000+ genes at single-cell resolution, plus protein panels—all while preserving tissue architecture and cell-cell contacts. No dissociation, no lost clusters, no guessing which cells were neighbors. I think we're approaching an inflection point where dissociation-based workflows become the exception rather than the rule. 📄 https://lnkd.in/e9BAWynu Sofia Ibañez Molero, Johanna Veldman, Daniel Peeper — The Netherlands Cancer Institute

Microsoft Research Pathology-AI Breakthrough https://lnkd.in/gaiERFUV GigaTIME uses AI to translate routine Hematoxylin and Eosin (H&E) pathology slides directly into virtual multiplex immunofluorescence (mIF) images, effectively mapping the spatial proteomic landscape of the Tumor Immune Microenvironment (TIME). • How it Works: The framework was trained on an immense dataset of 40 million cells with paired H&E and mIF data across 21 proteins, allowing it to bridge cellular morphology and protein states. • Scale and Impact: We applied GigaTIME to a massive virtual population dataset—14,256 patients from 51 hospitals, generating 299,376 virtual mIF slides spanning 24 cancer types. • Clinical Discovery: This population-scale analysis, previously impossible due to mIF data scarcity, uncovered 1,234 statistically significant associations linking proteins, biomarkers, staging, and survival. • Future of Pathology: GigaTIME enables large-scale clinical discovery and patient stratification directly from routine, cost-effective H&E slides, revealing new spatial and combinatorial protein activation patterns. This work marks a significant step by Microsoft toward democratizing high-resolution TIME analysis and accelerating the development of precision cancer therapies. #Microsoft #AIinMedicine #Pathology #CancerResearch #SpatialProteomics #PrecisionMedicine #GigaTIME Figure Courtesy: Cell

NotebookLM:"...[described is a] multi-omics study employing spatial technologies to develop biomarkers for predicting immunotherapy outcomes in non-small cell lung cancer (NSCLC) patients. Researchers utilized spatial proteomics and transcriptomics to deeply profile the tumor immune microenvironment (TIME), identifying distinct cell-type and gene signatures associated with either resistance or response to PD-1-based immunotherapy. The study defined a resistance signature within the tumor compartment, characterized by proliferating tumor cells, granulocytes, and vessels, and a response signature in the stroma involving M1/M2 macrophages and CD4 T cells, demonstrating the critical spatial context of these prognostic markers. These findings led to the development of robust, independent gene signatures that can predict progression-free survival, supporting a framework for precision immunotherapy in NSCLC by identifying high-risk and high-benefit patients." From the source: "Our study presents a spatial multi-omic framework for developing biomarkers associated with PD-1-based immunotherapies in solid tumors. In this study, rather than directly generating protein-based signatures, we transformed protein expression into phenotypic cell states and then derived cell-type signatures linked to immunotherapy outcomes. Genes were subsequently extracted from these cell types to construct transcriptomic signatures associated with immunotherapy outcomes. The rationale behind this approach lies in the difficulty of directly combining proteomic and transcriptomic data, which is challenging due to the nonlinear dependency between protein and RNA expression. Incorporating spatial context further strengthens this approach by enabling the identification of not only relevant cell types and genes but also their localization within the TIME. Spatial coordinates embedded in outcome models provide a layer of evidence, capturing interactions shaped by tissue architecture." https://lnkd.in/e3X3U3Em

Cracking the Code of Deadly Brain Cancer: Serial Biopsies Reveal Hidden Immune Battle in Glioblastoma Traditional MRI scans often mislead doctors about whether glioblastoma (GBM) treatments actually work. To uncover the real biological effects, researchers performed serial biopsies during treatment with the oncolytic virus CAN-3110, directly injected into brain tumors. Advanced multiomic analyses—combining single-cell RNA-seq, proteomics, and spatial imaging—revealed that repeated CAN-3110 injections reshaped the tumor microenvironment, triggered immune activation, and expanded tumor-specific T cell clones. Surprisingly, even when MRI scans suggested tumor progression, molecular data showed active antitumor immune responses, proving that standard imaging can hide real therapeutic success. This groundbreaking approach may redefine how we evaluate brain cancer treatments. https://lnkd.in/gcmJ834B https://lnkd.in/gy2nZ_x8

A new spatial atlas of colorectal cancer reveals how stromal and immune niches actively reprogram tumors toward a stem-like, treatment-resistant state. This study leverages high-resolution CosMx spatial transcriptomics, profiling 846,000+ cells across late-stage CRC and normal colon, to dissect the cellular ecosystems that shape tumor evolution. Key findings: ◾ Nine spatial niches were mapped, including granulocyte-rich microenvironments and lymphoid aggregates (LAs). These niches were highly conserved within normal colon but profoundly restructured in tumors. ◾ Granulocyte infiltration (especially neutrophils) was strongly linked to stromal reprogramming. Granulocyte-driven TNF/IL1 signaling suppressed pro-differentiation CXCL14⁺ fibroblasts and expanded MMP⁺ remodeling fibroblasts, shifting epithelial cells toward a foetal/stem-like state. ◾ Epithelial tumor cells exhibited a consistent FXYD5⁺/PIGR⁻ signature, enabling clean separation of tumor-intrinsic programs from microenvironment-induced ones. This revealed a core proliferative signature layered atop TME-driven hypoxia, EMT, and inflammatory cues. ◾ Lymphoid aggregates contained a CCR7/SELL⁺ IL7R⁺ CD4 T cell population associated with B cell activation and metabolic remodeling, suggesting these niches function as adaptive immune “reaction centers” within or adjacent to tumors. ◾ Granulocyte chemoattractants (CXCL8, CXCL5) were upregulated in both tumor epithelial cells and stromal compartments, indicating a self-reinforcing inflammatory loop. This atlas offers potential for next-generation CRC interventions, especially those targeting tumor plasticity, immune remodeling, and stromal-epithelial crosstalk. The data reinforce an emerging paradigm: dedifferentiation is not purely mutation-driven but is spatially induced by microenvironmental inflammation and fibroblast states. For therapeutics, this highlights opportunities to: ▪️ Block neutrophil recruitment or activation to prevent stem-state reversion. ▪️ Restore pro-differentiation fibroblast programs (e.g., CXCL14⁺/BMP-mediated). ▪️ Target tumor-specific markers like FXYD5 for diagnostics or antibody-based therapies. ▪️ Leverage LA-associated immune subsets as biomarkers of responsiveness to immunotherapy. For translational research, the dataset provides a high-resolution map of spatial trajectories linking necrosis → inflammation → stromal rewiring → epithelial progenitor states, an actionable framework for intercepting aggressive tumor phenotypes. Spatial biology is rapidly shifting our understanding of tumor ecosystems. As technologies mature, integrating spatial transcriptomics into preclinical models, patient stratification, and therapy response prediction will be essential. CRC (long known for its heterogeneity) stands to benefit immensely from this paradigm. Full preprint: https://lnkd.in/eberHyze #ColorectalCancer #SpatialTranscriptomics #TumorMicroenvironment #OncologyResearch #DrugDevelopment

Tumor Immune Microenvironment Studies in 3D Cell Co-culture Models The tumor immune microenvironment (TIME) plays a critical role in cancer progression, immune evasion, and therapeutic response. Traditional 2D cell cultures are often unable to replicate the complex interactions within the TIME, prompting the adoption of 3D cell co-culture models as a more physiologically relevant approach. These models integrate cancer cells, immune cells, and other stromal components in three-dimensional structures to more effectively mimic the in vivo tumor microenvironment. 1. Main applications: (1) Immune cell recruitment and function 3D co-culture systems allow the study of immune cell behavior, including recruitment, activation, and cytotoxicity. For example, interactions between tumor cells and T cells can be observed to evaluate the efficacy of immune checkpoint inhibitors. (2) Cytokine and chemokine dynamics 3D environments enable the analysis of cytokine and chemokine secretion, revealing how tumors manipulate immune responses to promote evasion or suppression. (3) Immune evasion mechanisms Tumor-associated macrophages (TAMs) and regulatory T cells (Tregs) can be incorporated into 3D models to study their role in suppressing anti-tumor immunity. (4) Immunotherapy testing By incorporating immune cells into co-cultures, these models are very useful for testing immunotherapies such as immune checkpoint inhibitors, CAR-T cells, or tumor vaccines. 2. Advantages of 3D co-culture models: (1) Enhanced complexity: Replicating cell-cell and cell-matrix interactions within TIME. (2) Dynamic interactions: Tracking real-time immune cell infiltration and tumor killing. (3) High predictive value: Better correlation with in vivo results compared to 2D models. 3D co-culture models provide a powerful platform to advance our understanding of the tumor immune microenvironment and optimize immunotherapies, bridging the gap between preclinical research and clinical application. Reference [1] Peiyuan Mu et al., Journal of Experimental and Clinical Cancer Research 2023 (https://lnkd.in/e3JKWabm) #TumorMicroenvironment #3DCellModels #Immunotherapy #CancerResearch #ImmuneEvasion #DrugDiscovery #TIME #BiomedicalResearch #Newsletter #CancerImmunotherapy #InnovationInResearch

Recently, a study published in Nature Immunology caught my eye. In it, the authors undertook an extensive study that charts generic variations influencing the tumour microenvironment (TME). The TME plays a crucial role in tumour progression and response to treatment. Understanding the genetic underpinnings of the TME could help pave the way for novel therapeutic approaches and enhanced treatment targeting. One of the study's most interesting aspects is its use of machine learning methods and advanced bioinformatic approaches to analyze and integrate large-scale datasets. The advanced computational methods used enabled identification of genetic variations that may have otherwise been overlooked, highlighting the power of computational biology in advancing our understanding of cancer. Leveraging these techniques, the researchers created a detailed atlas of genetic factors impacting the TME, which they refer to as immunity quantitative trait loci (immunQTLs), and showed that many of these genetic factors were likely co-localized with previously known expression quantitative trait loci. This observation suggests that the immunQTLs may contribute to the cellular heterogeneity observed within the TME by influencing the expression of genes modulating immune infiltration. Going beyond their initial discovery-driven computational work to further validate their findings, they mapped immunQTLs across >1,600 genes and 23 cancers that are associated with cancer pathogenesis and immune regulation. Diving even deeper, they went on to experimentally validate that one of the identified genes, CCL2, which is implicated in promoting colorectal carcinoma (CRC) progression by allowing tumour cells to evade immunity, may be a promising therapeutic target. This finding demonstrates the potential of the depth of the data set and how it might be used to identify and validate targets. This publication presents a significant amount of work that I have only scratched the surface of here. It offers new insights into the complexity of genetic factors influencing the TME, providing a comprehensive genetic map of the TME and its implications for cancer therapy. The authors have made their data available through a publicly accessible database to help propel further work by the research community. To me, an exciting aspect of this work is that it may help open the door to future combination therapeutic approaches that target both the tumour cells and their microenvironment. https://lnkd.in/ezRckvFh

Why location matters in cancer 🧬 In oncology, we often focus on what mutations exist. But an equally important question is where biological processes occur inside the tumour. Tumours are not uniform masses. They are spatially organised ecosystems made of cancer cells, immune cells, fibroblasts, and extracellular matrix interacting within structured niches. Traditional bulk sequencing averages signals from mixed cell populations. This approach can miss how location inside the tumour shapes biology. Spatial omics is helping change that 🔬 By preserving tissue architecture, these technologies allow us to measure molecular signals directly within their spatial context. This reveals patterns we could not see before: 🧬 Immune-rich tumor margins 🧬 Hypoxic cores driving aggressive behavior 🧬 Stromal niches supporting tumor growth These spatial patterns are increasingly being explored to: 🏥 Predict therapy response 🏥 Identify resistance niches 🏥 Discover spatial biomarkers This idea is not entirely new to pathology. During my PhD, I worked with breast cancer tissue samples using immunohistochemistry (IHC) to study predictive protein biomarker location. What mattered was not only whether the protein was present, but where it was located, either in the cytoplasm or the nucleus. 🔬 Nuclear localization 🔬 Cytoplasmic localization 🔬 Distribution across tumour regions The spatial context of protein expression often carries important biological meaning. Today, spatial omics technologies are expanding this concept across the entire transcriptome and proteome. Instead of mapping one biomarker at a time, we can now map thousands of molecular signals directly within tumour tissue architecture. In many ways, spatial omics is scaling up what pathologists have long recognised: Location matters. 👇 Which spatial feature of tumours do you think holds the most clinical promise for precision oncology? #oncology #spatialomics #cancerbiology #translationalresearch #tumormicroenvironment #precisiononcology