Exploring ALK fusion in colorectal cancer: a case series and comprehensive analysis.

Anaplastic lymphoma kinase (ALK) fusion-positive colorectal cancer (CRC) is a rare and chemotherapy-refractory subtype that lacks established and effective treatment strategies. Additionally, the efficacy and safety of ALK inhibitors (ALKi) in CRC remain undetermined. Herein, we examined a series of ALK-positive CRC patients who underwent various lines of ALKi treatment. Notably, we detected an ALK 1196M resistance mutation in a CRC patient who received multiple lines of chemotherapy and ALKi treatment. Importantly, we found that Brigatinib and Lorlatinib demonstrated some efficacy in managing this patient, although the observed effectiveness was not as pronounced as in non-small cell lung cancer cases. Furthermore, based on our preliminary analyses, we surmise that ALK-positive CRC patients are likely to exhibit inner resistance to Cetuximab. Taken together, our findings have important implications for the treatment of ALK-positive CRC patients.

Methylated ctDNA predicts early recurrence risk in patients undergoing resection of initially unresectable colorectal cancer liver metastases.

Background: Patients with initially unresectable colorectal cancer liver metastases (IU-CRLM) might benefit from using an effective systemic treatment followed by resection of liver metastases but the curative success rate is quite low. Indeed, nearly one-third of patients exhibit early recurrence within the first 6 months after surgery, and these individuals often have poor overall survival. Objectives: This study aims to clarify the application value of serial circulating tumor DNA (ctDNA) analysis in predicting the clinical outcome of IU-CRLM patients following liver metastasectomy. Design: A retrospective study was conducted on a cohort of patients with IU-CRLM between February 2018 and April 2021. Methods: Plasma samples at different time points during CRLM treatment [baseline (BL), preoperation (PRE), postoperation (POST), end-of-treatment (EOT), and progressive disease (PD)] were retrospectively collected from patients with initially unresectable CRLM enrolled at the Sun Yat-sen University Cancer Center. Dynamic changes of SEPTIN 9 (SEPT9) and Neuropeptide Y (NPY) methylated circulating tumor DNA (MetctDNA) levels in serial plasma samples were detected using droplet-digital PCR (ddPCR). Results: SEPT9 and NPY genes were hypermethylated in colon cancer cell lines and tissues while no difference was observed between primary and metastatic tumors. Patients with MetctDNA positive at POST or EOT had significantly lower recurrence-free survival (RFS) compared to patients with MetctDNA negative at these time points [POST: Hazard ratio (HR) 9.44, 95% confidence interval (CI) 5.15-17.30, p < 0.001; EOT: HR 11.48, 95% CI 3.27-40.31, p < 0.001]. Multivariate analysis demonstrated that POST (OR 33.96, 95% CI 4.03-286.10, p = 0.001) and EOT (OR 18.36, 95% CI 1.14-295.71, p = 0.04) MetctDNA was an independent risk factor for early recurrence. Time-dependent receiver operating characteristic curve (T-ROC) analysis revealed that area under the curve (AUC) value was greatest at the relapse time point of 6 months post-intervention, with POST-AUC and EOT-AUC values of 0.74 (95% CI 0.66-0.81) and 0.73 (95% CI 0.53-0.94), respectively. Serial MetctDNA analysis showed that RFS was significantly lower in patients with no MetctDNA clearance compared with those with MetctDNA clearance (HR 26.05, 95% CI 4.92-137.81, p < 0.001). Conclusion: Our study confirmed that serial ctDNA analysis of NPY and SEPT9 gene methylation could effectively predict early recurrence in IU-CRLM patients, especially at POST and EOT.

Clinical and molecular characteristics of RNF43 mutations as promising prognostic biomarkers in colorectal cancer.

Background: Transmembrane E3 ubiquitin ligase (RNF43) mutations are present in approximately 6-18% of colorectal cancers (CRC) and could enhance Wnt/ß-catenin signaling, which is emerging as a promising therapeutic target. This study aims to investigate the clinical and molecular characteristics and potential heterogeneity of RNF43-mutant CRC. Methods: A total of 78 patients with RNF43-mutant CRC were enrolled from July 2013 to November 2022. Demographic data, clinical characteristics, treatment regimens used, and survival outcomes were collected and analyzed. Results: Our study uncovered that patients with RNF43 mutations in the N-terminal domain (NTD; n = 50) exhibited shorter overall survival (OS; median months, 50.80 versus not reached; p = 0.043) compared to those in the C-terminal domain (CTD; n = 17). Most RNF43 mutations in NTD had positive primary lymph node status, low tumor mutation burden (TMB-L), and correlated with proficient mismatch repair (pMMR)/microsatellite stable (MSS) status. By contrast, RNF43 mutations in CTD were significantly enriched in deficient MMR (dMMR)/microsatellite instability (MSI-H) tumors with high TMB (TMB-H). N-terminal RNF43-mutated tumors harbored a hotspot variant (RNF43 R117fs), which independently predicted a significantly worse outcome in pMMR/MSS CRC with a median OS of 18.9 months. Patients with RNF43 mutations and the BRAF V600E alterations demonstrated sensitivity to BRAF/EGFR inhibitors. Moreover, we observed that pMMR/MSS patients with RNF43 R117fs mutation had a higher incidence of stage IV, ⩾2 metastatic sites, low TMB, and none of them received PD-1/PD-L1 inhibitor therapy. Conclusion: Our findings provide the first evidence that RNF43 mutations in NTD and the R117fs variant correlate with a poorer prognosis in CRC patients, providing strategies for Wnt-targeted therapy to improve clinical efficacy.

An Immune Risk Score Predicts Survival of Patients with Acute Myeloid Leukemia Receiving Chemotherapy.

PURPOSE: Prediction models for acute myeloid leukemia (AML) are useful, but have considerable inaccuracy and imprecision. No current model includes covariates related to immune cells in the AML microenvironment. Here, an immune risk score was explored to predict the survival of patients with AML. EXPERIMENTAL DESIGN: We evaluated the predictive accuracy of several in silico algorithms for immune composition in AML based on a reference of multi-parameter flow cytometry. CIBERSORTx was chosen to enumerate immune cells from public datasets and develop an immune risk score for survival in a training cohort using least absolute shrinkage and selection operator Cox regression model. RESULTS: Six flow cytometry-validated immune cell features were informative. The model had high predictive accuracy in the training and four external validation cohorts. Subjects in the training cohort with low scores had prolonged survival compared with subjects with high scores, with 5-year survival rates of 46% versus 19% (P < 0.001). Parallel survival rates in validation cohorts-1, -2, -3, and -4 were 46% versus 6% (P < 0.001), 44% versus 18% (P = 0.041), 44% versus 24% (P = 0.004), and 62% versus 32% (P < 0.001). Gene set enrichment analysis indicated significant enrichment of immune relation pathways in the low-score cohort. In multivariable analyses, high-risk score independently predicted shorter survival with HRs of 1.45 (P = 0.005), 2.12 (P = 0.004), 2.02 (P = 0.034), 1.66 (P = 0.019), and 1.59 (P = 0.001) in the training and validation cohorts, respectively. CONCLUSIONS: Our immune risk score complements current AML prediction models.

Systematic Construction and Validation of a Metabolic Risk Model for Prognostic Prediction in Acute Myelogenous Leukemia.

Background: Acute myelogenous leukemia (AML) is a heterogeneous disease with recurrent gene mutations and variations in disease-associated gene expression, which may be useful for prognostic prediction. Methods: RNA matrix and clinical data of AML were downloaded from GEO, TCGA, and TARGET databases. Prognostic metabolic genes were identified by LASSO analysis to establish a metabolic model. Prognostic accuracy of the model was quantified by time-dependent receiver operating characteristic curves and the area under the curve (AUC). Survival analysis was performed by log-rank tests. Enriched pathways in different metabolic risk statuses were evaluated by gene set enrichment analyses (GSEA). Results: We identified nine genes to construct a prognostic model of shorter survival in the high-risk vs. low-risk group. The prognostic model showed good predictive efficacy, with AUCs for 5-year overall survival of 0.78 (0.73-0.83), 0.76 (0.62-0.89), and 0.66 (0.57-0.75) in the training, adult external, and pediatric external cohorts, respectively. Multivariable analysis demonstrated that the metabolic signature had independent prognostic value with hazard ratios of 2.75 (2.06-3.66), 1.89 (1.09-3.29), and 1.96 (1.00-3.84) in the training, adult external, and pediatric external cohorts, respectively. Combining metabolic signatures and classic prognostic factors improved 5-year overall survival prediction compared to the prediction by classic prognostic factors (p < 0.05). GSEA revealed that most pathways were metabolism-related, indicating potential mechanisms. Conclusion: We identified dysregulated metabolic features in AML and constructed a prognostic model to predict the survival of patients with AML.

Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

This study aimed to construct immune-related predictors to identify responders to anti-PD1 therapy of melanoma through CIBERSORT algorithm. Using the least absolute shrinkage and selection operator (LASSO) logistic regression, we constructed an immunoscore consisting of 8 immune subsets to predict the anti-PD1 response. This score achieved an overall accuracy of AUC = 0.77, 0.80 and 0.73 in the training cohort, validation cohort and on-anti-PD1 cohort, respectively. Patients with high immunoscores had significantly higher objective response rates (ORRs) than did those with low immunoscores (ORR: 53.8% vs 17.7%, P < 0.001 for entire pre-anti-PD1 cohort; 42.1% vs 15.1%, P = 0.022 for on-anti-PD1 cohort; 66.7% vs 16.7%, P = 0.038 for neoadjuvant anti-PD1 cohort). Prolonged survival trends were observed in high-immunoscore group (1-year PFS: 42.4% vs 14.3%, P = 0.059; 3-year OS: 41.5% vs 31.6%, P = 0.057). Furthermore, we found that high-immunoscore group exhibited higher fractions of tumor-infiltrating lymphocytes and an increased IFN-γ response. Analysis of the results of the GSEA indicated a significant enrichment of antitumor immunity pathways in the high-immunoscore group. Therefore, this study indicated that we constructed a robust immunoscore model to predict the anti-PD1 response of metastatic melanoma and the neoadjuvant anti-PD1 response of resectable melanoma.