Tracking Dynamics of Spontaneous Tumors in Mice Using Photon-Counting Computed Tomography

Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments

Immune cells within the tumor microenvironment impact cancer progression, resistance, response to treatments. Despite remarkable outcomes for some cancer patients, immunotherapies remain unsatisfactory for others. Here, we designed an experimental setting using the Alb-R26 Met "inside-out" mouse model, faithfully recapitulating molecular features of liver cancer patients, to explore the effects of distinct anticancer targeted therapies on the tumor immune landscape. Using two treatments in clinical trials for different cancer types, Decitabine and MEK+BCL-XL blockage, we show their capability to trigger tumor regression in Alb-R26 Met mice and to superimpose distinct profiles of immune cell types and immune-checkpoints, impacting immunotherapy response. A machine learning approach processing tumor imaging and immune profile data identified a putative signature predicting tumor treatment response in mice and patients. Outcomes exemplify how the tumor immune microenvironment is differentially reshaped by distinct anticancer agents and highlight the importance of measuring its modulation during treatment to optimize oncotherapy and immunotherapy combinations.

Mammary Cancer Models: An Overview from the Past to the Future

Breast cancer research heavily relies on diverse model systems to comprehend disease progression, develop novel diagnostics, and evaluate new therapeutic strategies. This review offers a comprehensive overview of mammary cancer models, covering both ex vivo and in vivo approaches. We delve into established techniques, such as cell culture and explore cutting-edge advancements, like tumor-on-a-chip and bioprinting. The in vivo section encompasses spontaneous, induced, and transplanted models, genetically engineered models, chick chorioallantoic membrane assays, and the burgeoning field of in silico models. Additionally, this article briefly highlights the key discoveries made using these models, significantly enhancing our understanding of breast cancer. In essence, this article serves as a comprehensive compass, charting the trajectory of mammary cancer modeling from its early beginnings to the promising vistas of tomorrow.

Nanoparticle Contrast Agents for Photon-Counting Computed Tomography: Recent Developments and Future Opportunities

The clinical availability of photon-counting computed tomography (PCCT) has ushered in a new era of CT imaging. Spectral imaging coupled with superior contrast resolution, and ultrahigh spatial resolution (200 μm) offered by PCCT has the potential to revolutionize value-driven imaging. The potential of multicolor PCCT has generated excitement, and renewed interest, in novel contrast agent development for comprehensive disease interrogation, prediction and monitoring of treatment outcomes. Nanoparticles provide a versatile and powerful platform for the development of next generation contrast agents for spectral PCCT. In this article, we review recent developments and use of nanoparticle contrast agents for PCCT. We also discuss future research and translational opportunities for nanoparticle-based CT contrast agents enabled by the advent of PCCT and describe key considerations for their clinical translation.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Objective. Photon-counting micro-computed tomography (micro-CT) is a major advance in small animal preclinical imaging. Small molecule- and nanoparticle-based contrast agents have been widely used to enable the differentiation of liver tumors from surrounding tissues using photon-counting micro-CT. However, there is a notable gap in the application of these market-available agents to the imaging of breast and ovarian tumors using photon-counting micro-CT. Herein, we have used photon-counting micro-CT to determine the effectiveness of these contrast agents in differentiating ovarian and breast tumor xenografts in live, intact mice.Approach. Nude mice carrying different types of breast and ovarian tumor xenografts (AU565, MDA-MB-231 and SKOV-3 human cancer cells) were injected with ISOVUE-370 (a small molecule-based agent) or Exitron Nano 12000 (a nanoparticle-based agent) and subjected to photon-counting micro-CT. To improve tumor visualization using photon-counting micro-CT, we developed a novel color visualization method, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand.Main results. Ourin vivoexperiments confirm the effectiveness of this color visualization approach, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggest that Exitron Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 delivers a more comprehensive tumor enhancement but with significant variance between scans due to its short blood half-time. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based contrast agent (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemical analysis.Significance. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor progression and response to treatments.

Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model

Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Photon-counting micro computed tomography (micro-CT) offers new potential in preclinical imaging, particularly in distinguishing materials. It becomes especially helpful when combined with contrast agents, enabling the differentiation of tumors from surrounding tissues. There are mainly two types of contrast agents in the market for micro-CT: small molecule-based and nanoparticle-based. However, despite their widespread use in liver tumor studies, there is a notable gap in research on the application of these commercially available agents for photon-counting micro-CT in breast and ovarian tumors. Herein, we explored the effectiveness of these agents in differentiating tumor xenografts from various origins (AU565, MDA-MB-231, and SKOV-3) in nude mice, using photon-counting micro-CT. Specifically, ISOVUE-370 (a small molecule-based agent) and Exitrone Nano 12000 (a nanoparticle-based agent) were investigated in this context. To improve tumor visualization, we proposed a novel color visualization method for photon-counting micro-CT, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Our in vivo experiments confirm its effectiveness, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggested that Exitrone Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 gives more comprehensive tumor enhancement but with a significant variance between scans due to its short blood half-time. This variability leads to high sensitivity to timing and individual differences among mice. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemistry. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor development and response to treatments.

Fluorescence molecular tomography for quantum yield and lifetime

Fluorescence molecular tomography (FMT) is a promising modality for noninvasive imaging of internal fluorescence agents in biological tissues, especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting microcomputed tomography (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving the image reconstruction stability. Our numerical experiments demonstrate the accuracy and stability of the proposed reconstruction method in the presence of data noise, achieving a reconstruction error of 0.02 ns for the fluorescence lifetime and an average relative error of 18% for quantum yield reconstruction.

ADAMTSL5 is an epigenetically activated gene underlying tumorigenesis and drug resistance in hepatocellular carcinoma

BACKGROUND & AIMS

The tumour microenvironment shapes tumour growth through cellular communications that include both direct interactions and secreted factors. The aim of this study was to characterize the impact of the secreted glycoprotein ADAMTSL5, whose role in cancer has not been previously investigated, on hepatocellular carcinoma (HCC).

METHODS

ADAMTSL5 methylation status was evaluated through bisulfite sequencing, and publicly available data analysis. ADAMTSL5 RNA and protein expression were assessed in mouse models and HCC patient samples and compared to data from published datasets. Functional studies, including association of ADAMTSL5 depletion with responsiveness to clinically relevant drugs, were performed in cellular and in vivo models. Molecular alterations associated with ADAMTSL5 targeting were determined using proteomics, biochemistry, and reverse-transcription quantitative PCR.

RESULTS

Methylome analysis revealed hypermethylated gene body CpG islands at the ADAMTSL5 locus in both mouse and human HCC, correlating with higher ADAMTSL5 expression. ADAMTSL5 targeting interfered with tumorigenic properties of HCC cells in vitro and in vivo, whereas ADAMTSL5 overexpression conferred tumorigenicity to pre-tumoural hepatocytes sensitized to transformation by a modest level of MET receptor expression. Mechanistically, ADAMTSL5 abrogation led to a reduction of several oncogenic inputs relevant to HCC, including reduced expression and/or phosphorylation levels of receptor tyrosine kinases MET, EGFR, PDGFRβ, IGF1Rβ, or FGFR4. This phenotype was associated with significantly increased sensitivity of HCC cells to clinically relevant drugs, namely sorafenib, lenvatinib, and regorafenib. Moreover, ADAMTSL5 depletion drastically increased expression of AXL, accompanied by a sensitization to bemcentinib.

CONCLUSIONS

Our results point to a role for ADAMTSL5 in maintaining the function of key oncogenic signalling pathways, suggesting that it may act as a master regulator of tumorigenicity and drug resistance in HCC.

LAY SUMMARY

The environment of cancer cells has profound effects on establishment, progression, and response of a tumour to treatment. Herein, we show that ADAMTSL5, a protein secreted by liver cancer cells and overlooked in cancer so far, is increased in this tumour type, is necessary for tumour formation and supports drug resistance. Adamtsl5 removal conferred sensitivity of liver cancer cells to drugs used in current treatment. This suggests ADAMTSL5 as a potential marker in liver cancer as well as a possible drug target.

The frontier of live tissue imaging across space and time

What you see is what you get-imaging techniques have long been essential for visualization and understanding of tissue development, homeostasis, and regeneration, which are driven by stem cell self-renewal and differentiation. Advances in molecular and tissue modeling techniques in the last decade are providing new imaging modalities to explore tissue heterogeneity and plasticity. Here we describe current state-of-the-art imaging modalities for tissue research at multiple scales, with a focus on explaining key tradeoffs such as spatial resolution, penetration depth, capture time/frequency, and moieties. We explore emerging tissue modeling and molecular tools that improve resolution, specificity, and throughput.

Modeling Heterogeneity of Triple-Negative Breast Cancer Uncovers a Novel Combinatorial Treatment Overcoming Primary Drug Resistance

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype characterized by a remarkable molecular heterogeneity. Currently, there are no effective druggable targets and advanced preclinical models of the human disease. Here, a unique mouse model (MMTV-R26Met mice) of mammary tumors driven by a subtle increase in the expression of the wild-type MET receptor is generated. MMTV-R26Met mice develop spontaneous, exclusive TNBC tumors, recapitulating primary resistance to treatment of patients. Proteomic profiling of MMTV-R26Met tumors and machine learning approach show that the model faithfully recapitulates intertumoral heterogeneity of human TNBC. Further signaling network analysis highlights potential druggable targets, of which cotargeting of WEE1 and BCL-XL synergistically kills TNBC cells and efficiently induces tumor regression. Mechanistically, BCL-XL inhibition exacerbates the dependency of TNBC cells on WEE1 function, leading to Histone H3 and phosphoS33RPA32 upregulation, RRM2 downregulation, cell cycle perturbation, mitotic catastrophe, and apoptosis. This study introduces a unique, powerful mouse model for studying TNBC formation and evolution, its heterogeneity, and for identifying efficient therapeutic targets.

C3G Is Upregulated in Hepatocarcinoma, Contributing to Tumor Growth and Progression and to HGF/MET Pathway Activation

The complexity of hepatocellular carcinoma (HCC) challenges the identification of disease-relevant signals. C3G, a guanine nucleotide exchange factor for Rap and other Ras proteins, plays a dual role in cancer acting as either a tumor suppressor or promoter depending on tumor type and stage. The potential relevance of C3G upregulation in HCC patients suggested by database analysis remains unknown. We have explored C3G function in HCC and the underlying mechanisms using public patient data and in vitro and in vivo human and mouse HCC models. We found that C3G is highly expressed in progenitor cells and neonatal hepatocytes, whilst being down-regulated in adult hepatocytes and re-expressed in human HCC patients, mouse HCC models and HCC cell lines. Moreover, high C3G mRNA levels correlate with tumor progression and a lower patient survival rate. C3G expression appears to be tightly modulated within the HCC program, influencing distinct cell biological properties. Hence, high C3G expression levels are necessary for cell tumorigenic properties, as illustrated by reduced colony formation in anchorage-dependent and -independent growth assays induced by permanent C3G silencing using shRNAs. Additionally, we demonstrate that C3G down-regulation interferes with primary HCC tumor formation in xenograft assays, increasing apoptosis and decreasing proliferation. In vitro assays also revealed that C3G down-regulation enhances the pro-migratory, invasive and metastatic properties of HCC cells through an epithelial-mesenchymal switch that favors the acquisition of a more mesenchymal phenotype. Consistently, a low C3G expression in HCC cells correlates with lung metastasis formation in mice. However, the subsequent restoration of C3G levels is associated with metastatic growth. Mechanistically, C3G down-regulation severely impairs HGF/MET signaling activation in HCC cells. Collectively, our results indicate that C3G is a key player in HCC. C3G promotes tumor growth and progression, and the modulation of its levels is essential to ensure distinct biological features of HCC cells throughout the oncogenic program. Furthermore, C3G requirement for HGF/MET signaling full activation provides mechanistic data on how it works, pointing out the relevance of assessing whether high C3G levels could identify HCC responders to MET inhibitors.