Dataset Information

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.

ABSTRACT:

Background

Lenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.

Methods

We retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups.

Results

Seven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001).

Conclusions

The MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.

SUBMITTER: Li X

PROVIDER: S-EPMC10546830 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.

Li Xiaomi X Ding Xiaoyan X Liu Mei M Wang Jingyan J Li Wei W Chen Jinglong J

The oncologist 20231001 10

<h4>Background</h4>Lenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.<h4>Methods</h4>We retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median o ...[more]

PMID: 37105140

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Project description:BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

Project description:BACKGROUND:CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood. METHODS:We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets. FINDINGS:A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms "liver cancer with EPCAM up", "tumor invasiveness up", "methyltransferase complex", and "translational initiation" were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC. INTERPRETATION:Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis. FUND: This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).

Project description:On August 16, 2018, the U.S. Food and Drug Administration approved lenvatinib (Lenvima, Eisai Inc.) for first-line treatment of patients with unresectable hepatocellular carcinoma (HCC). Approval was based on an international, multicenter, randomized, open-label, noninferiority trial (REFLECT; NCT01761266) conducted in 954 patients with previously untreated metastatic or unresectable HCC. Patients were randomized (1:1) to receive lenvatinib (12 mg orally once daily for patients with a baseline body weight ≥60 kg and 8 mg orally once daily for patients with a baseline body weight <60 kg) or sorafenib (400 mg orally twice daily) until radiological disease progression or unacceptable toxicity. REFLECT demonstrated that lenvatinib was noninferior but not statistically superior to sorafenib for overall survival (OS; hazard ratio, [HR] 0.92; 95% confidence intervals [CI], 0.79-1.06), with median OS of 13.6 and 12.3 months in the lenvatinib and sorafenib arms, respectively. REFLECT also demonstrated statistically significant improvements in investigator-assessed progression-free survival (PFS; HR, 0.66; 95% CI, 0.57-0.77]; p < 0.001), corresponding to median PFS of 7.4 and 3.7 months and overall response rate of 24.1% vs 9.2% per modified RECIST for HCC (mRECIST) in the lenvatinib and sorafenib arms, respectively. Consistent results were observed by an independent review facility per RECISTv1.1 and per mRECIST. The most common adverse reactions observed in the lenvatinib-treated patients (≥20%) in decreasing frequency were hypertension, fatigue, diarrhea, decreased appetite, arthralgia/myalgia, decreased weight, abdominal pain, palmar-plantar erythrodysesthesia syndrome, proteinuria, dysphonia, hemorrhagic events, hypothyroidism, and nausea. IMPLICATIONS FOR PRACTICE: This article describes the U.S. Food and Drug Administration's review of data from a single trial, REFLECT, that supported the approval of lenvatinib, as a single agent, for the first-line treatment of unresectable hepatocellular carcinoma (HCC). REFLECT was an open-label, noninferiority trial that randomized 954 patients with HCC who were ineligible for liver-directed therapy with no prior systemic therapy for HCC to lenvatinib or sorafenib. REFLECT demonstrated that lenvatinib-treated patients had similar survival, more responses, and longer time to progression than those receiving sorafenib. Serious side effects were more common among lenvatinib-treated patients. Lenvatinib is an effective treatment for patients with previously untreated HCC.

Dataset Information

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.

Background

Methods

Results

Conclusions

Publications

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.

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