Multiplex immunofluorescence is a crucial technique in identifying biomarkers (PMID: 31502166). Detecting markers at different expression levels carries biological meaning (PMID: 34112666), but it is challenging and requires improved detection (PMID: 25242720). We introduce seqLA™, an automated method for amplifying individual markers in multiplex panels for efficient staining cycles.

Using the sequential immunofluorescence (seqIF™, PMID: 37813886) assays on the COMET™ platform, the seqLA™ method augments the number of detection antibodies per primary antibody cyclically, leading to intensified signals. The amplification complex can be eluted, enabling successive staining cycles while upholding tissue integrity. Using formalin-fixed paraffin-embedded tissue sections from human colorectal carcinoma and breast cancer, a 26-plex panel comprising 20 standard seqIF™ markers for immune and stroma compartments, with 6 markers (LAG-3, ICOS, TCF-1, FOXP3, PD-1, and PD-L1) detected with the seqLA™ method. Staining was compared with single-plex chromogenic immunohistochemistry (IHC) for each amplified marker (PMID: 32414858). Lunaphore HORIZON™ software was used for the quantification of the dynamic ranges and image analysis.

We successfully incorporated 6 amplified markers into a 20-plex seqIF™ panel. Our amplification method yielded an adjustable signal intensity increase between low- and high-expressing cell populations, signifying a broader dynamic range compared to unamplified seqIF™ markers. Notably, amplification facilitated the detection of the full expression spectrum of PD-1 and PD-L1, delving into various low-expressing subtypes of regulatory immune cells like Treg and macrophages within the TME. The staining patterns of amplified markers exhibited a strong correlation with chromogenic IHC staining. Combining amplified markers within a larger panel enabled an enhanced analysis of the TME and complex immune cell classification within tertiary lymphoid structures. 

This novel amplification strategy will enable the identification of markers expressed at low levels, thereby capturing the complete expression spectrum of crucial immune checkpoint markers.

Abstract
Introduction:
Breast cancer is the most frequent cancer among women worldwide and in Morocco. This study analyzes the socio-demographic, clinical, and molecular characteristics of breast cancer in patients treated at the Mohamed VI Cancer Treatment Center in Casablanca (CM-VI).
Materials and Methods :
This retrospective descriptive study included 833 patients diagnosed with histologically confirmed breast cancer in 2019 at CM-VI. Data were extracted from the center’s digitized medical records.
Results:
The cohort was predominantly female (98.7%), with most patients covered by social health insurance (88.8%) and married (57.8%). Overweight and obesity affected 42.7% of patients. Family history of cancer was noted in 28.6%, while only 2.9% had a personal history of cancer. The right breast was affected in 49.4% of cases. Invasiveductal carcinoma was the predominant histological type (82.6%), with Grade II tumors accounting for 56.2%. Hormonal receptor expression was common in estrogen receptors (75.7%) and progesterone receptors (70.8%), while HER2 was positive in 23% of cases. Stage II disease represented nearly half of cases (48.1%). Regarding molecular subtypes, luminal B was most frequent (50.1%), followed by luminal A (27.3%), triple negative (16.2%), and HER2-enriched (6.4%). Molecular subtype distribution showed significant associations with patient age (p=0.02) and disease stage (p=0.001), but not with sex, family, or personal cancer history, histology, or body mass index.
Conclusion:
These findings align with the international literature and provide the first integrated overview of breast cancer characteristics at the national level in Morocco. Further studies are required to validate and generalize these results to the broader Moroccan population, considering regional specificities.
Impact:
This study highlights the need to explore genetic profiles to support personalized treatment strategies based on age, stage, and molecular subtype; stresses targeting intratumor heterogeneity; and enhancing the screening of high-risk groups.

Background
For decades, scientists have searched for ways not just to slow the rate of cancer growth but to eliminate it source. A key clue lies in mutations within the promoter region of the human telomerase reverse transcriptase (hTERT) gene — especially the C228T mutation. These changes trigger abnormal telomerase activation, allowing cancer cells to multiply constantly and resist cell death. While today’s treatments aim to slow tumor growth, few target this underlying cause.

Objective
We propose a forward-looking combined strategy that directly addresses the hTERT promoter mutation — aiming not just to treat but to heal with long-lasting results — while integrating spatial technologies to reveal how these mutations are distributed within the tumor microenvironment.

Methods
This multi-step approach is designed with both care and precision.

1. First, reduce abnormal tumor cell division using temporary regulatory agents.

2. Then, apply gene-editing tools to repair the hTERT promoter mutation — restoring proper cellular control.

3. At the same time, enhance immune surveillance through cytokine support and checkpoint inhibitors.

4. Incorporate spatial transcriptomics and multiplex imaging to map hTERT promoter mutation patterns across tumor niches, thereby tailoring interventions to microenvironmental heterogeneity.

Supportive therapies are included to protect the digestive, neurological, and cardiovascular systems during treatment, ensuring overall patient well-being.

Conclusion
This strategy offers more than suppression — it seeks restoration. By correcting the mutation at its source, while also mapping and targeting its spatial distribution within the tumor ecosystem, it represents a scientifically grounded and personalized path toward durable remission of aggressive tumors.

The transformation of normal intestinal epithelium into colorectal cancer (CRC) involves coordinated changes across molecular, cellular, and architectural scales; yet, how these layers integrate remains poorly resolved. Here, we survey colorectal tumorigenesis by combining whole-transcriptome spatial molecular imaging (WTx CosMx SMI) with single-nucleus RNA-sequencing (snPATHO-seq) and digital histopathology on colon samples containing reference mucosa, adenomas and carcinomas, as well as a metastatic lymph node. Leveraging (discrete) histological annotations and (continuous) data-driven trajectories, we quantify the dynamics of cellular density, heterogeneity, function and signaling along the reference-adenoma–carcinoma axis, which is concordant in its spatial and molecular definition. This combination of analytical approaches across different data views let us chart tissue transformation across dimensions (physical/transcriptional) and scales (cell/tissue).

We resolve ∼3.5 million cells into 43 epithelial, immune, and stromal subpopulations that exhibit a bi-furcating tumor evolution: On the one hand, LGR5+ stem-like epithelial cells are enriched in highly homogeneous proliferative tumor cores. On the other hand, MMP7+ fetal-like states are restricted to immunosuppressive invasive fronts, rich in cancer-associated fibroblasts (CAFs) and tumor-associated macrophages. These subpopulations form concentric spatial layers that organize transformed regions, and their compound aligns with histological malignancy. We further define ‘transition crypts’ – single colonic crypts with divided histological and transcriptional makeup – that arise from rare crypt fusion or abrupt transformation events. Finally, we trace MMP7+ fetal-like tumor cell states and concomitant myofibroblast-like FAP+ CAFs to lymphovascular invasion sites and matched lymph node metastases, thereby recapitulating invasive programs at single-cell resolution and across sites.

In all, we present single cell- and WTx-resolved spatial data that are among the first of their kind. These open up spatial-centric, out-of-the-box analytical avenues to resolve the molecular, cellular and architectural dynamics that attend tissue transformation during CRC onset, progression and dissemination.

Background: Clear Cell Renal Cell Carcinoma (ccRCC), the most prevalent and aggressive histopathological subtype of kidney cancer, originates from the cells lining the proximal tubules of the nephron. ccRCC tumors show significant intra-tumoral heterogeneity, which impairs drug penetration and therapeutic efficacy. Radiopharmaceutical (RP) therapies have emerged as a promising approach for treating those resistant cancers as they bind specifically to molecular biomarkers expressed in malignant cells.

Methods: In this regard, to move beyond the one-size-fits-all paradigm in nuclear oncology, we developed an oncological digital twin for predicting the intra-tumor uptake of RPT, specifically [89Zr] Zr-girentuximab in RCC. The proposed Deep Learning model captures various mechanisms underlying tumor response from histological sections and the compartmental model of the RP agent. Global temporal dynamics of drug penetration were inferred from the immuno-kinetic compartmental model of [89Zr] Zr-girentuximab. Spatial drug distribution was resolved via tissue characterization, including segmentation of blood vessels and neoplastic regions. Additionally, the model incorporates analyses of proxies for tumoral heterogeneity: immunohistochemistry-derived parameters (e.g., biomarker expression). Spatial correlation techniques were used to identify parameters unraveling the drug uptake patterns in space.

Results: Our study primarily identified key parameters linking tumoral heterogeneity to uneven drug distribution. Thereafter, the multimodal tumor digital twin revealed high-fidelity predictive capabilities in RP drug retention validated against ex-vivo microPET imaging.

Conclusion: The framework adopts a comprehensive approach to account for various aspects of RP therapy absorption in tumors including macroscopic heterogeneity measurements. This patient-specific Digital Twin paves the way for the predictive comparison of treatment efficacy enabling therapeutic optimization and improved clinical outcomes.

Colorectal cancer (CRC) is a highly prevalent malignancy marked by significant inter-patient heterogeneity in immune and metabolic traits, which critically influences therapeutic response and clinical outcomes. This complexity reflects the dynamic tumor ecosystem, where diverse cellular and molecular interactions drive disease progression and resistance. Despite therapeutic advances, metastatic CRC (mCRC), particularly microsatellite stable (MSS) tumors, remains largely refractory to immunotherapy and prone to relapse. A deeper understanding of tumor-specific metabolic reprogramming is essential to identify actionable vulnerabilities and guide personalized therapies.

In this study, we present a multi-omics framework that integrates transcriptomic, metabolomic, and functional data into Genome-Scale Metabolic Models (GSMMs) to computationally model the tumor ecosystem and redefine CRC stratification based on metabolic phenotypes. This approach enabled the identification and preclinical validation of specific metabolic vulnerabilities, advancing precision oncology by translating multi-omics data into actionable targets. Although not spatially resolved, the framework is compatible with spatially annotated datasets, offering future opportunities to dissect intratumoral metabolic heterogeneity.

In parallel, to further refine stratification, we developed an immune–metabolic gene signature capturing key immune and metabolic traits in CRC. This signature supported a predictive model that classified tumors into two main clusters: one predominantly glycolytic and another characterized by enhanced metabolic flexibility and oxidative phosphorylation (OXPHOS). These profiles support more precise patient selection and inform tailored therapeutic strategies targeting immune–metabolic vulnerabilities.

Altogether, our results demonstrate that multi-omics data modeling enables refined stratification of CRC tumors, uncovering metabolic vulnerabilities that can be exploited for precision therapy design. This approach provides a robust framework for the development of personalized treatment strategies based on tumor-specific metabolic rewiring.

Acknowledgments
Authors acknowledge support from MICIU/AEI/10.13039/501100011033–European Commission FEDER funds (PID2023-150539OB-I00); CIBER-EHD (EHD20PI03, CB17/04/00023); AGAUR (2021-SGR-00350); ICREA Foundation (ICREA Academia award to M.C.); and the Spanish Structures of María de Maeztu program (CEX2021-001202-M).

Cancer stem cells (CSCs) represent a critical subpopulation within tumors, endowed with self-renewal and differentiation capacities, and are implicated in tumor initiation, progression, metastasis, therapeutic resistance, and recurrence. This study sought to establish and validate a microfluidic device (MD) for the enrichment, functional assessment, and therapeutic evaluation of CSC populations derived from experimental models and primary tumor samples. Murine (LM38LP) and human (BPR6) breast cancer cell lines were cultured within MDs to promote sphere formation. CSC enrichment was confirmed through the expression analysis of pluripotency-associated genes (Oct4, Sox2, Nanog, and CD44) by means of quantitative PCR (qPCR) and immunofluorescence. Sphere number, size, and gene expression profiles were quantitatively assessed before (control) and after chemotherapeutic exposure. To validate the MD platform against a conventional scale, parallel experiments were performed in 12-well plates. To extend translational relevance, three primary canine tumor samples (solid thyroid carcinoma, simple tubular carcinoma, and reactive lymph node) were mechanically disaggregated and processed within MDs for CSC characterization. The MD platform enabled consistent CSC population enrichment, showing significant sphere growth modulation parameters and stemness marker expression following treatment. A notable reduction in both size and growth rate was observed in spheres treated with Doxorubicin or Paclitaxel after 8 days of culture, compared to controls. These findings are particularly significant, as this technique can be used to assess cell heterogeneity and the potential of cells to form tumors. The MD also supported immunofluorescence staining and allowed for real-time monitoring of individual cell growth. Sphere formation efficiency and CSC marker expression were demonstrated in primary veterinary tumor cultures, highlighting the device’s cross-species applicability. Microfluidic-based sphere assays represent a robust, reproducible, and scalable platform for the functional interrogation of CSC dynamics and therapeutic responses. This methodology holds great promise for advancing CSC-targeted therapies and supporting personalized oncology in both human and veterinary settings.

Introduction:
Oncofetal gene reactivation is a hallmark of cancer plasticity, but its spatial dynamics and integration with tumor regulatory circuits remain unclear in colorectal cancer (CRC). We investigated how a fetal-like state, defined by an oncofetal signature (OnFS), contributes to intratumoral heterogeneity and how it is shaped by silencing AP-1 transcription factors (FOS and JUND), using single-cell and spatial transcriptomics to map its organization within the tumor microenvironment (TME).

Methods:
Single-cell RNA sequencing and spatial transcriptomics were applied to CRC patient-derived organoids (PDOs) and matched tumor biopsies, including shFOS and shJUND lines. Analyses included UMAP, Louvain clustering, SingleR-based cell annotation, differential expression, pathway enrichment (ssGSEA), and trajectory inference (PAGA and Monocle3). Spatial co-localization assessed OnFS-high enrichment in TME niches (e.g., invasive fronts, hypoxic cores, and immune-excluded regions). OnFS activation was quantified with AddModuleScore and its association with EMT, stemness, and AP-1 disruption evaluated.

Expected Outcomes:
We anticipate a gradient of OnFS activation across PDOs and tumor sections, enriched at invasion zones and stromal interaction sites. OnFS-high cells are expected to act as hubs in phenotypic trajectories and localize near immunosuppressive and hypoxic niches. Co-expression analysis should reveal modules linked to EMT, hypoxia, and therapy resistance, with strong overlap between OnFS-high and AP-1–silenced profiles, suggesting convergent spatial mechanisms.

Discussion:
Fetal-like reprogramming in CRC emerges as a spatially organized and dynamic driver of tumor heterogeneity, functionally tied to AP-1 regulation. Spatial mapping shows OnFS-driven plasticity concentrates in biologically active niches, providing potential biomarkers and therapeutic targets to disrupt tumor adaptation and resistance.

Glioblastoma (GBM) remains the most aggressive and lethal form of brain tumour, highlighting the urgent need for new therapeutic strategies. Gold nanorod (GNR)-mediated photothermal therapy (PTT) is a promising approach that uses near-infrared (NIR) irradiation to induce hyperthermia and selectively ablate tumour cells containing GNRs. However, improving the targeting specificity and reducing the treatment intensity are necessary for the clinical translation of this therapy to minimise off-target damage. Furthermore, achieving a uniform spatial distribution of GNRs within the tumour is essential for enhancing efficacy.

This study evaluates three strategies for functionalising the surface of GNRs: PEGylation; conjugation with Protein G; and a novel construct combining Protein G with anti-CD133 antibodies for targeted delivery to glioblastoma stem-like cells. The nanoparticles were characterised using dynamic light scattering (DLS) and zeta potential analysis to confirm successful conjugation and stability.

Spatial biology analyses were conducted using optical microscopy to visualise the anchoring of GNRs on target cells, thereby confirming the specific localisation of CD133-functionalised nanoparticles. In CT2A glioblastoma cells, CD133-GNRs exhibited the highest level of cytotoxicity (86%) under baseline conditions (4.5 W laser, 3 µg/mL), enabling a reduction in both the laser power (3 W) and the GNR dose (2 µg/mL) while maintaining effectiveness (85%).

To enhance the distribution of GNRs in tumours, macrophages were investigated as delivery vehicles due to their innate tumour-tropic behaviour. GNR-loaded macrophages were co-cultured with glioblastoma cells and their spatial migration was monitored. Upon irradiation, this strategy enhanced tumour ablation, demonstrating improved intratumoral dispersion and cell death.

These results support the use of CD133-targeted GNRs as a safer and more effective PTT modality. Furthermore, spatial analyses and intratumoral mapping confirmed the importance of localisation strategies for nanoparticles, providing valuable insights into the development of advanced nanomedicine platforms for GBM.

High-grade serous ovarian cancer (HGSC) is typically diagnosed at an advanced stage, with frequent chemoresistant relapse despite strong response to platinum/taxane-based chemotherapy. Recent studies have shown that cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME) produce modifying factors, like cytokines, that significantly modulate malignant phenotypes in HGSC. We identified an unrecognized mechanism wherein metabolic reprogramming via an upregulated glutamine anabolic pathway occurs in reactive CAFs within HGSC tissue. This dysfunctional pathway endows CAFs with atypical metabolic flexibility, using noncanonical carbon and nitrogen sources, producing glutamine in the nutrient-poor tumor microenvironment. We demonstrated that normally fed C57BL/6 mice injected with syngeneic fallopian epithelial cell-derived cancer cells exhibited markedly higher tumor burden than those on a glutamine-free (GF) diet. Tumor-bearing mice treated with a glutaminase inhibitor showed reduced tumor burden relative to vehicle-treated controls, indicating that glutamine depletion hinders tumor growth and inhibits disease progression. Imaging mass cytometry (IMC) and mass spectrometry imaging (MSI) applied to serial sections from murine tissue microarrays investigated whether the tumor immune microenvironment mediates the effect of glutamine deprivation on reducing tumor burden. Our results demonstrated that GF diet mice exhibited alterations in the cellular composition of the TME with significantly reduced tumor burden. Compared to control mice, GF diet mice had enhanced B-cell-related immune response, decreased stemness and levels of epithelial–mesenchymal transition (EMT) cancer cells, and lower density of activated CAFs. Neighborhoods surrounding EPCAM+ PANCK+ tumor cells in GF-fed mice were more densely populated with various cell types, particularly neutrophils, and enriched in metabolic signatures such as kynurenic acid and indole-3-carbinol. Sequential immunofluorescence (seqIF) studies validated IMC and MSI findings, characterizing the functional properties of cell phenotypes while showing different neighborhoods between GF and control groups. Our findings suggest that glutamine is central in metabolic reprogramming of the ovarian tumor immune microenvironment, effecting spatially resolved cellular and metabolic profiles of the ovarian TME.