Transarterial Radioembolization in Patients With Unresectable Colorectal Cancer Liver Metastases

Chemotherapy Rechallenge or Reintroduction Compared to Regorafenib or Trifluridine/Tipiracil for Pretreated Metastatic Colorectal Cancer Patients: A Propensity Score Analysis of Treatment Beyond Second Line (Proserpyna Study)

BACKGROUND

The optimal treatment for metastatic colorectal cancer (mCRC) beyond second line is still questioned. Besides the standard of care agents (regorafenib, REG, or trifluridine/tipiracil, FTD/TPI), chemotherapy rechallenge or reintroduction (CTr/r) are commonly considered in clinical practice, despite weak supporting evidence. The prognostic performance of CTr/r, REG and FTD/TPI in this setting are herein evaluated.

PATIENTS AND METHODS

PROSERpYNa is a multicenter, observational, retrospective study, in which patients with refractory mCRC, progressing after at least 2 lines of CT, treated with CTr/r, REG or FTD/TPI, are considered eligible and were enrolled in 2 independent data sets (exploratory and validation). Primary endpoint was overall survival (OS); secondary endpoints were investigator-assessed progression-free survival (PFS), objective response rate (RR) and safety. A propensity score adjustment was accomplished for survival analyses.

RESULTS

Data referring to patients treated between Jan-10 and Jan-19 from 3 Italian institutions were gathered (341 and 181 treatments for exploratory and validation data sets respectively). In the exploratory cohort, median OS (18.5 vs. 6.5 months), PFS (6.1 vs. 3.5 months) and RR (28.6% vs. 1.4%) were significantly longer for CTr/r compared to REG/FTD/TPI. Survival benefits were retained at the propensity score analysis, adjusted for independent prognostic factors identified at multivariate analysis. Moreover, these results were confirmed within the validation cohort analyses.

CONCLUSIONS

Although the retrospective fashion, CTr/r proved to be a valuable option in this setting in a real-world context, providing superior outcomes compared to standard of care agents at the price of a moderate toxicity.

Optimal treatment strategies for borderline resectable liver metastases from colorectal cancer

BACKGROUND

Traditionally, colorectal liver metastases (CRLMs) are divided into "initially resectable" and "initially unresectable." The terminology "borderline resectable" continues to be elusive without any common consensus or definition. This narrative review aims to decode the conundrum of "borderline resectable CRLM (BR-CRLM)" and to discuss optimal treatment strategies.

METHODS

A comprehensive review was performed using Medline/PubMed and Web of Science databases with a search period ending on January 1, 2024. Using PubMed, the terms "CRLM," "BR-CRLM," and "management of BR-CRLM" were searched.

RESULTS

The 2016 European Society for Medical Oncology guidelines defined the term "resectability" in CRLM using the "technical (surgical) criteria" and the "oncologically criteria." These 2 criteria form the basis of defining BR-CRLM. Thus, BR-CRLM can be either technically easy but with unfavorable oncologically criteria or technically difficult with favorable oncologically criteria. Although defining BR-CRLM by incorporating both these criteria seems to be the most logical way forward, there is currently a lot of heterogeneity in the literature. It is generally agreed upon that some form of chemotherapy needs to be administered in BR-CRLM before embarking on surgery. Conversion chemotherapy is used in patients with BR-CRLM in which there is a possibility of resection after effective downsizing. Along with improved effective chemotherapy, great strides have been made in pushing the limits of surgery to achieve resectability in this subset of patients.

CONCLUSION

Advanced surgical techniques and locoregional liver-directed therapies coupled with perioperative chemotherapy with or without targeted therapy have made long-term survival benefit, a reality in patients with BR-CRLM. Thus, the time has come to recognize "BR-CRLM" as a distinct entity.

Colorectal cancer subtyping and prognostic model construction based on interleukin-related genes

Members of the interleukin (IL) family are closely linked to cancer development and progression. However, research on the prognosis of colorectal cancer (CRC) related to IL is still lacking. This study investigated new CRC prognostic markers and offered new insights for CRC prognosis and treatment. CRC-related data and IL gene data were collected from public databases. Sample clustering was done with the NMF package to divide samples into different subtypes. Differential, enrichment, survival, and immune analyses were conducted on subtypes. A prognostic model was constructed using regression analysis. Drug sensitivity analysis was performed using GDSC database. Western blot analysis was performed to assess the effect of IL-7 on the JAK/STAT signaling pathway. Flow cytometry was used to examine the impact of IL-7 on CD8+ T cell apoptosis. Two CRC subtypes based on IL-associated genes were obtained. Cluster 1 had a higher survival rate than cluster 2, and they showed differences in some immune levels. The two clusters were mainly enriched in the JAK-STAT signaling pathway, T helper 17 cell differentiation, and the IL-17 signaling pathway. An 11-gene signature was built, and risk score was an independent prognosticator for CRC. The low-risk group showed a higher sensitivity to nine common targeted anticancer drugs. Western blot and flow cytometry results demonstrated that IL-7 could phosphorylate STAT5 and promote survival of CD8+ T cells. In conclusion, this study divided CRC samples into two IL-associated subtypes and obtained an 11-gene signature. In addition, targeted drugs that may improve the prognosis of patients with CRC were identified. These findings are of paramount importance for patient prognosis and CRC treatment.NEW & NOTEWORTHY We identified two clusters with significant survival differences in colorectal cancer (CRC) based on interleukin-related genes, constructed an 11-gene risk score model that can independently predict the prognosis of CRC, and explored some targeted drugs that may improve the prognosis of patients with CRC. The results of this study have important implications for the prognosis and treatment of CRC.

Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objective

For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.

Methods

In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).

Results

For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).

Conclusions

This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

Ytrrium-90 transarterial radioembolization in patients with gastrointestinal malignancies

Transarterial radioembolization (TARE) with yttrium-90 (Y90) is a promising alternative strategy to treat liver tumors and liver metastasis from colorectal cancer (CRC), as it selectively delivers radioactive isotopes to the tumor via the hepatic artery, sparring surrounding liver tissue. The landscape of TARE indications is constantly evolving. This strategy is considered for patients with hepatocellular carcinoma (HCC) with liver-confined disease and preserved liver function in whom neither TACE nor systemic therapy is possible. In patients with liver metastases from CRC, TARE is advised when other chemotherapeutic options have failed. Recent phase III trials have not succeeded to prove benefit in overall survival; however, it has helped to better understand the patients that may benefit from TARE based on subgroup analysis. New strategies and treatment combinations are being investigated in ongoing clinical trials. The aim of this review is to summarize the clinical applications of TARE in patients with gastrointestinal malignancies.