Prediction of therapeutic response of advanced hepatocellular carcinoma to combined targeted immunotherapy by MRI

Tissue-matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma

Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three-dimensional (3D) printing was employed for tissue sampling, to match the multi-parametric MRI data with tumor tissues, followed by flow cytometry analysis and next-generation RNA-sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL-related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T-cells, CD4+ T-cells, CD8+ T-cells, PD1 + CD8+ T-cells, B-cells, macrophages, and regulatory T-cells (correlation coefficients ranged from -0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non-inflamed subclasses, with the proportion of T-cells, CD8+ T-cells, and PD1 + CD8+ T-cells significantly higher in the inflamed group compared to the non-inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z-score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721-0.910) and a cut-off value of -0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z-score was related to the gene expression of T-cell activation, chemokine production, and cell adhesion. The tissue-matched analysis of multi-parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses.

Magnetic resonance imaging-based approaches for detecting the efficacy of combining therapy following VEGFR-2 and PD-1 blockade in a colon cancer model

BACKGROUND

Angiogenesis inhibitors have been identified to improve the efficacy of immunotherapy in recent studies. However, the delayed therapeutic effect of immunotherapy poses challenges in treatment planning. Therefore, this study aims to explore the potential of non-invasive imaging techniques, specifically intravoxel-incoherent-motion diffusion-weighted imaging (IVIM-DWI) and blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI), in detecting the anti-tumor response to the combination therapy involving immune checkpoint blockade therapy and anti-angiogenesis therapy in a tumor-bearing animal model.

METHODS

The C57BL/6 mice were implanted with murine MC-38 cells to establish colon cancer xenograft model, and randomly divided into the control group, anti-PD-1 therapy group, and combination therapy group (VEGFR-2 inhibitor combined with anti-PD-1 antibody treatment). All mice were imaged before and, on the 3rd, 6th, 9th, and 12th day after administration, and pathological examinations were conducted at the same time points.

RESULTS

The combination therapy group effectively suppressed tumor growth, exhibiting a significantly higher tumor inhibition rate of 69.96% compared to the anti-PD-1 group (56.71%). The f value and D* value of IVIM-DWI exhibit advantages in reflecting tumor angiogenesis. The D* value showed the highest correlation with CD31 (r = 0.702, P = 0.001), and the f value demonstrated the closest correlation with vessel maturity (r = 0.693, P = 0.001). While the BOLD-MRI parameter, R2* value, shows the highest correlation with Hif-1α(r = 0.778, P < 0.001), indicating the capability of BOLD-MRI to evaluate tumor hypoxia. In addition, the D value of IVIM-DWI is closely related to tumor cell proliferation, apoptosis, and infiltration of lymphocytes. The D value was highly correlated with Ki-67 (r = - 0.792, P < 0.001), TUNEL (r = 0.910, P < 0.001) and CD8a (r = 0.918, P < 0.001).

CONCLUSIONS

The combination of VEGFR-2 inhibitors with PD-1 immunotherapy shows a synergistic anti-tumor effect on the mouse colon cancer model. IVIM-DWI and BOLD-MRI are expected to be used as non-invasive approaches to provide imaging-based evidence for tumor response detection and efficacy evaluation.

Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective

The latest developments in cancer immunotherapy, namely the introduction of immune checkpoint inhibitors, have led to a fundamental change in advanced cancer treatments. Imaging is crucial to identify tumor response accurately and delineate prognosis in immunotherapy-treated patients. Simultaneously, advances in image acquisition techniques, notably functional and molecular imaging, have facilitated more accurate pretreatment evaluation, assessment of response to therapy, and monitoring for tumor recurrence. Traditional approaches to assessing tumor progression, such as RECIST, rely on changes in tumor size, while new strategies for evaluating tumor response to therapy, such as the mRECIST and the EASL, rely on tumor enhancement. Moreover, the assessment of tumor volume, enhancement, cellularity, and perfusion are some novel techniques that have been investigated. Validation of these novel approaches should rely on comparing their results with those of standard evaluation methods (EASL, mRECIST) while considering the ultimate outcome, which is patient survival. More recently, immunotherapy has been used in the management of primary liver tumors. However, little is known about its efficacy. This article reviews imaging modalities and techniques for assessing tumor response and survival in immunotherapy-treated patients with primary hepatic malignancies.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.