Molecular differences of angiogenic versus vessel co-opting colorectal cancer liver metastases at single-cell resolution

Vessel co-option: a unique vascular-immune niche in liver cancer

Tumor vasculature is pivotal in regulating tumor perfusion, immune cell infiltration, metastasis, and invasion. The vascular status of the tumor is intricately linked to its immune landscape and response to immunotherapy. Vessel co-option means that tumor tissue adeptly exploits pre-existing blood vessels in the para-carcinoma region to foster its growth rather than inducing angiogenesis. It emerges as a significant mechanism contributing to anti-angiogenic therapy resistance. Different from angiogenic tumors, vessel co-option presents a distinctive vascular-immune niche characterized by varying states and distribution of immune cells, including T-cells, tumor-associated macrophages, neutrophils, and hepatic stellate cells. This unique composition contributes to an immunosuppressive tumor microenvironment that is crucial in modulating the response to cancer immunotherapy. In this review, we systematically reviewed the evidence and molecular mechanisms of vessel co-option in liver cancer, while also exploring its implications for anti-angiogenic drug resistance and the immune microenvironment, to provide new ideas and clues for screening patients with liver cancer who are effective in immunotherapy.

Role of imbalanced gut microbiota in promoting CRC metastasis: from theory to clinical application

Metastasis poses a major challenge in colorectal cancer (CRC) treatment and remains a primary cause of mortality among patients with CRC. Recent investigations have elucidated the involvement of disrupted gut microbiota homeostasis in various facets of CRC metastasis, exerting a pivotal influence in shaping the metastatic microenvironment, triggering epithelial-mesenchymal transition (EMT), and so on. Moreover, therapeutic interventions targeting the gut microbiota demonstrate promise in enhancing the efficacy of conventional treatments for metastatic CRC (mCRC), presenting novel avenues for mCRC clinical management. Grounded in the "seed and soil" hypothesis, this review consolidates insights into the mechanisms by which imbalanced gut microbiota promotes mCRC and highlights recent strides in leveraging gut microbiota modulation for the clinical prevention and treatment of mCRC. Emphasis is placed on the considerable potential of manipulating gut microbiota within clinical settings for managing mCRC.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases

OBJECTIVE

To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM).

METHODS

186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis.

RESULTS

186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038).

CONCLUSION

ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.

Disturbed endothelial cell signaling in tumor progression and therapy resistance

Growth of new blood vessels is considered requisite to cancer progression. Recent findings revealed that in addition to inducing angiogenesis, tumor-derived factors alter endothelial cell gene transcription within the tumor mass but also systemically throughout the body. This subsequently contributes to immunosuppression, altered metabolism, therapy resistance and metastasis. Clinical studies demonstrated that targeting the endothelium can increase the success rate of immunotherapy. Single-cell technologies revealed remarkable organ-specific endothelial heterogeneity that becomes altered by the presence of a tumor. In metastases, endothelial transcription differs remarkably between newly formed and co-opted vessels which may provide a basis for developing new therapies to target endothelial cells and overcome therapy resistance more effectively. This review addresses how cancers impact the endothelium to facilitate tumor progression.

The Inhibition of Vessel Co-Option as an Emerging Strategy for Cancer Therapy

Vessel co-option (VCO) is a non-angiogenic mechanism of vascularization that has been associated to anti-angiogenic therapy. In VCO, cancer cells hijack the pre-existing blood vessels and use them to obtain oxygen and nutrients and invade adjacent tissue. Multiple primary tumors and metastases undergo VCO in highly vascularized tissues such as the lungs, liver or brain. VCO has been associated with a worse prognosis. The cellular and molecular mechanisms that undergo VCO are poorly understood. Recent studies have demonstrated that co-opted vessels show a quiescent phenotype in contrast to angiogenic tumor blood vessels. On the other hand, it is believed that during VCO, cancer cells are adhered to basement membrane from pre-existing blood vessels by using integrins, show enhanced motility and a mesenchymal phenotype. Other components of the tumor microenvironment (TME) such as extracellular matrix, immune cells or extracellular vesicles play important roles in vessel co-option maintenance. There are no strategies to inhibit VCO, and thus, to eliminate resistance to anti-angiogenic therapy. This review summarizes all the molecular mechanisms involved in vessel co-option analyzing the possible therapeutic strategies to inhibit this process.

Interleukin-1 receptor antagonist: An alternative therapy for cancer treatment

The interleukin-1 receptor antagonist (IL-1Ra) is an anti-inflammatory cytokine and a naturally occurring antagonist of the IL-1 receptor. It effectively counteracts the IL-1 signaling pathway mediated by IL-1α/β. Over the past few decades, accumulating evidence has suggested that IL-1 signaling plays an essential role in tumor formation, growth, and metastasis. Significantly, anakinra, the first United States Food and Drug Administration (FDA)-approved IL-1Ra drug, has demonstrated promising antitumor effects in animal studies. Numerous clinical trials have subsequently incorporated anakinra into their cancer treatment protocols. In this review, we comprehensively discuss the research progress on the role of IL-1 in tumors and summarize the significant contribution of IL-1Ra (anakinra) to tumor immunity. Additionally, we analyze the potential value of IL-1Ra as a biomarker from a clinical perspective. This review is aimed to highlight the important link between inflammation and cancer and provide potential drug targets for future cancer therapy.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases

OBJECTIVE

To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM).

METHODS

Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images.

RESULTS

All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01).

CONCLUSION

Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

BACKGROUND

Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM.

METHODS

A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened.

RESULTS

We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT).

CONCLUSIONS

Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Dual-Targeting Biomimetic Nanomaterials for Photo-/Chemo-/Antiangiogenic Synergistic Therapy

Avoiding the low specificity of phototheranostic reagents at the tumor site is a major challenge in cancer phototherapy. Meanwhile, angiogenesis in the tumor is not only the premise of tumor occurrence but also the basis of tumor growth, invasion, and metastasis, making it an ideal strategy for tumor therapy. Herein, biomimetic cancer cell membrane-coated nanodrugs (mBPP NPs) have been prepared by integrating (i) homotypic cancer cell membranes for evading immune cell phagocytosis to increase drug accumulation, (ii) protocatechuic acid for tumor vascular targeting along with chemotherapy effect, and (iii) near-infrared phototherapeutic agent diketopyrrolopyrrole derivative for photodynamic/photothermal synergetic therapy. The mBPP NPs exhibit high biocompatibility, superb phototoxicity, excellent antiangiogenic ability, and double-trigging cancer cell apoptosis in vitro. More significantly, mBPP NPs could specifically bind to tumor cells and vasculature after intravenous injection, inducing fluorescence and photothermal imaging-guided tumor ablation without recurrence and side effects in vivo. The biomimetic mBPP NPs could cause drug accumulation at the tumor site, inhibit tumor neovascularization, and improve phototherapy efficiency, providing a novel avenue for cancer treatment.