Superior outcomes and high-risk features with carfilzomib, lenalidomide, and dexamethasone combination therapy for patients with relapsed and refractory multiple myeloma: Results of the multicenter KMMWP2201 study.

Carfilzomib, lenalidomide, and dexamethasone (KRd) combination therapy improves the survival of patients with relapsed and/or refractory multiple myeloma (RRMM). Nonetheless, evidence on the use of KRd in Asian populations remains scarce. Accordingly, this study aimed at investigating this regimen's efficacy in a large group of patients. This retrospective study included patients with RRMM who were treated with KRd at 21 centers between February 2018 and October 2020. Overall, 364 patients were included (median age: 63 years). The overall response rate was 90% in responseevaluable patients, including 69% who achieved a very good partial response or deeper responses. With a median follow-up duration of 34.8 months, the median progression-free survival (PFS) was 23.4 months and overall survival (OS) was 59.5 months. Among adverse factors affecting PFS, highrisk cytogenetics, extramedullary disease, and doubling of monoclonal protein within 2 to 3 months prior to start of KRd treatment significantly decreased PFS and overall survival (OS) in multivariate analyses. Patients who underwent post-KRd stem cell transplantation (i.e.delayed transplant) showed prolonged PFS and OS. Grade 3 or higher adverse events (AEs) were observed in 56% of the patients, and non-fatal or fatal AE's that resulted in discontinuation of KRd were reported in 7% and 2% of patients, respectively. Cardiovascular toxicity was comparable to that reported in the ASPIRE study. In summary, KRd was effective in a large real-world cohort of patients with RRMM with long-term follow-up. These findings may further inform treatment choices in the treatment of patients with RRMM.

Enhanced Expression of Glycolytic Enzymes and Succinate Dehydrogenase Complex Flavoprotein Subunit A by Mesothelin Promotes Glycolysis and Mitochondrial Respiration in Myeloblasts of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by rapid growth and uncontrolled proliferation of undifferentiated myeloid cells. Metabolic reprogramming is commonly observed in the bone marrow of AML patients, as leukemia cells require increased ATP supply to support disease progression. In this study, we examined the potential role of mesothelin as a metabolic modulator in myeloid cells in AML. Mesothelin is a well-known marker of solid tumors that promotes cancer cell proliferation and survival. We initially analyzed alterations in mesothelin expression in the myeloblast subpopulations, defined as SSC-Alow/CD45dim, obtained from the bone marrow of AML patients using flow cytometry. Our results showed overexpression of mesothelin in 34.8% of AML patients. Subsequently, metabolic changes in leukemia cells were evaluated by comparing the oxygen consumption rates (OCR) of bone marrow samples derived from adult AML patients. Notably, a higher OCR was observed in the mesothelin-positive compared to the mesothelin-low and non-expressing groups. Treatment with recombinant human mesothelin protein enhanced OCR and increased the mRNA expression of glycolytic enzymes and mitochondrial complex II in KG1α AML cells. Notably, siRNA targeting mesothelin in KG1α cells led to the reduction of glycolysis-related gene expression but had no effect on the mitochondrial complex gene. The collective results demonstrate that mesothelin induces metabolic changes in leukemia cells, facilitating the acquisition of a rapid supply of ATP for proliferation in AML. Therefore, the targeting of mesothelin presents a potentially promising approach to mitigating the progression of AML through the inhibition of glycolysis and mitochondrial respiration in myeloid cells.

Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.

This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT). A total of 1280 patients with NSCLC who underwent contrast-enhanced CT scans and EGFR mutation testing before treatment were selected for the final study. Regions of interest were segmented from the CT images to extract radiomics features and obtain tumor images. These tumor images were input into a convolutional neural network model to extract 512 image features, which were combined with radiographic features and clinical data to predict the EGFR mutation. The generalization performance of the model was evaluated using external institutional data. The internal and external datasets contained 324 and 130 EGFR mutants, respectively. Sex, height, weight, smoking history, and clinical stage were significantly different between the EGFR-mutant patient groups. The EGFR mutations were predicted by combining the radiomics and clinical features, and an external validation dataset yielded an area under the curve (AUC) value of 0.7038. The model utilized 1280 tumor images, radiomics features, and clinical characteristics as input data and exhibited an AUC of approximately 0.81 and 0.78 during the primary cohort and external validation, respectively. These results indicate the feasibility of integrating radiomics analysis with deep learning for predicting EGFR mutations. CT-image-based genetic testing is a simple EGFR mutation prediction method, which can improve the prognosis of NSCLC patients and help establish personalized treatment strategies.

Development of a multi-modal learning-based lymph node metastasis prediction model for lung cancer.

PURPOSE: This study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information. METHODS: We utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification. To further enhance classification performance, a soft-voting ensemble technique was applied to integrate the outcomes of multiple multi-modal models. RESULTS: A comparison of the classification performance revealed that the multi-modal model, which integrated CT images and clinical information, outperformed the single-modal models. Among the four multi-modal models, the Xception model demonstrated the highest classification performance, with an area under the curve (AUC) of 0.756 for the internal test dataset and 0.736 for the external validation dataset. The ensemble model (SEResNet50_DenseNet121_Xception) exhibited even better performance, with an AUC of 0.762 for the internal test dataset and 0.751 for the external validation dataset, surpassing the multi-modal model's performance. CONCLUSIONS: Integrating CT images and clinical information improved the performance of the lymph node metastasis prediction models in patients with NSCLC. The proposed 3D multi-modal lymph node prediction model can serve as an auxiliary tool for evaluating lymph node metastasis in patients with non-pretreated NSCLC, aiding in patient screening and treatment planning.

Geriatric risk model for older patients with diffuse large B-cell lymphoma (GERIAD): a prospective multicenter cohort study.

BACKGROUND/AIMS: Optimal risk stratification based on simplified geriatric assessment to predict treatment-related toxicity and survival needs to be clarified in older patients with diffuse large B-cell lymphoma (DLBCL). METHODS: This multicenter prospective cohort study enrolled newly diagnosed patients with DLBCL (≥ 65 yr) between September 2015 and April 2018. A simplified geriatric assessment was performed at baseline using Activities of Daily Living (ADL), Instrumental ADL (IADL), and Charlson's Comorbidity Index (CCI). The primary endpoint was event-free survival (EFS). RESULTS: The study included 249 patients, the median age was 74 years (range, 65-88), and 125 (50.2%) were female. In multivariable Cox analysis, ADL, IADL, CCI, and age were independent factors for EFS; an integrated geriatric score was derived and the patients stratified into three geriatric categories: fit (n = 162, 65.1%), intermediate-fit (n = 25, 10.0%), and frail (n = 62, 24.9%). The established geriatric model was significantly associated with EFS (fit vs. intermediate-fit, HR 2.61, p < 0.001; fit vs. frail, HR 4.61, p < 0.001) and outperformed each covariate alone or in combination. In 87 intermediate-fit or frail patients, the relative doxorubicin dose intensity (RDDI) ≥ 62.4% was significantly associated with worse EFS (HR, 2.15, 95% CI 1.30-3.53, p = 0.002). It was related with a higher incidence of grade ≥ 3 symptomatic non-hematologic toxicities (63.2% vs. 27.8%, p < 0.001) and earlier treatment discontinuation (34.5% vs. 8.0%, p < 0.001) in patients with RDDI ≥ 62.4% than in those with RDDI < 62.4%. CONCLUSION: This model integrating simplified geriatric assessment can risk-stratify older patients with DLBCL and identify those who are highly vulnerable to standard dose-intensity chemoimmunotherapy.