UNASSIGNED: Liposarcoma is a malignant tumor that originates from adipose tissue and can occur in any part of the body. There is currently no clear conclusion on whether there are significant differences in prognosis between liposarcoma at different anatomical locations, especially retroperitoneal liposarcoma (RLPS) and non retroperitoneal liposarcoma (NRLPS). The aim of this study is to reveal whether there are differences in prognosis between these two locations of liposarcoma, and further explore the fundamental reasons behind these differences.
UNASSIGNED: We conducted an in-depth investigation into the factors affecting the prognosis of patients with liposarcoma by analyzing the data from the Surveillance, Epidemiology, and End Results Program (SEER) database. Then, we used propensity score matching (PSM) to balance these prognostic factors for comparative analysis of survival between RLPS and NRLPS. In addition, by analyzing transcriptome and whole exome data from TCGA and the Japan Genotypic Phenotype Archive (JGA), we identified genes with significant expression differences and explored changes in the immune microenvironment.
UNASSIGNED: Through analysis of RLPS and NRLPS patients in the SEER database, we observed significant prognostic differences between the two groups, with RLPS exhibiting worse prognosis (p < 0.001). Even after adjusting for confounding factors through PSM, these survival rate differences remained significant, with RLPS still showing worse prognosis (p = 0.017). Furthermore, our analysis of transcriptomic data led to the identification of 467 differentially expressed genes. Additionally, we noted significant differences in the immune microenvironment and whole exome sequencing data between the two groups.
UNASSIGNED: There are significant differences between patients with RLPS and NRLPS. Therefore, from clinical research to treatment strategies, RLPS and NRLPS should be considered as two distinct types of tumors, necessitating differentiated approaches for their study and treatment.

BACKGROUND: Mucinous liposarcoma myxoid liposarcoma is a malignant mucoid soft tissue tumor derived from undifferentiated stromal cells in perivascular, subbody cavity and intermuscular space, and composed of cells at different stages of differentiation from preadipocytes to mature cells. In rare cases, it may change from lipoma malignancy. The main manifestations is painless mass, relatively slow growth, the course can last decades, the prevalence of liposarcoma in the population is 14% to 18%, mainly in adults, male prevalence is higher than women, but not significant. The main good hair part is the thigh, have mucinous sex, high differentiation type, dedifferentiation type, polymorphic type. Clinical diagnosis is difficult, and there are no obvious symptoms in the early stage, so the diagnosis should be combined with B ultrasound, MRI, CT, and other auxiliary examinations. The gold standard is pathological examination. In December 2023, our department admitted a patient with a mucinous abdominal mass. The report is as follows.
METHODS: Does liposarcoma metastasize? Is any chemotherapy required after surgery? Will it ever relapse in the future? What is the survival period after surgery?
METHODS: Mucinous liposarcoma.
METHODS: Surgical resection of the sarcoma.
RESULTS: The nodule sample was 33 * 28 * 13 cm, with complete capsule, gray and yellow sections, fine texture, soft, gray, red, grayish, and yellow mucoid nodules in some areas, and the maximum diameter of the nodules was 21cm. Immunohistochemistry was: CD34 (+), CDK 4 (+), CK (-), Desmin (weak +), Ki67 (index 5%), MDM 2 (-), p16 (weak +), S-100P (+), Vimentin (+), BCL-2 (+). He was also sent to the Department of Pathology of Peking Union Medical College Hospital for consultation with Professor Lu Zhaohui, whose consultation opinion was in line with myxoliposarcoma.
CONCLUSIONS: Retroperitoneal liposarcoma is a common retroperitoneal tumor, but it is relatively rare in clinical practice; the overall morbidity is low, mainly manifested as abdominal pain and abdominal distension, abdominal distension, and a long course of disease; it is not sensitive to radiotherapy and chemotherapy, and should be closely follow up by CT examination to understand the recurrence and metastasis.

Liposarcoma (LPS) is a rare soft tissue sarcoma that develops from the differentiation of fat cells, typically occurring in the lower extremities and retroperitoneal space. Depending on its histological morphology and molecular changes, LPS can be divided into various subtypes, each exhibiting distinct biological behaviors. During treatment, especially for LPS arising in the retroperitoneum, the extent and quality of the initial surgery are critically important. Treatment strategies must be tailored to the specific type of LPS. Over the past few decades, the treatment of LPS has undergone numerous advancements, with new therapeutic approaches such as targeted drugs and immunotherapies continually emerging. This paper reviews the biological characteristics, molecular alterations, as well as surgical and pharmacological treatments of various LPS subtypes, with the aim of enhancing clinicians\' understanding and emphasizing the importance of individualized precision therapy. With a deeper understanding of the biological characteristics and molecular alterations of LPS, future treatment trends are likely to focus more on developing personalized treatment plans to better address the various types of LPS.

BACKGROUND: As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is essential to evaluate the prognosis of LPS patients.
METHODS: By comprehensive analysis of data derived from GEO datasets, differentially expressed genes (DEGs) were obtained. Univariate and Lasso Cox regressions were subsequently employed to reveal distant recurrence-free survival (DRFS)-associated DEGs and develop a prognostic gene signature, which was assessed by Kaplan-Meier survival and ROC curve. GSEA and immune infiltration analyses were conducted to illuminate molecular mechanisms and immune correlations of this model in LPS progression. Furthermore, a correlation analysis was involved to decipher the therapeutic significance of this model for LPS.
RESULTS: A six-gene signature was developed to predict DRFS of LPS patients and showed higher precision performance in more aggressive LPS subtypes. Then, a nomogram was further established for clinical application based on this risk model. Via GSEA, the high-risk group was significantly enriched in cell cycle-related pathways. In the LPS microenvironment, neutrophils, memory B cells and resting mast cells exhibited significant differences in cell abundance between high-risk and low-risk patients. Moreover, this model was significantly correlated with therapeutic targets.
CONCLUSIONS: A prognostic six-gene signature was developed and significantly associated with cell cycle pathways and therapeutic target genes, which could provide new insights into risk assessment of LPS progression and therapeutic strategies for LPS patients to improve their prognosis.

BACKGROUND: Retroperitoneal liposarcoma (RLPS) constitutes the majority of retroperitoneal sarcomas. While surgical resection remains the sole curative approach, determining the optimal surgical strategy for RLPS remains elusive. This study addresses the ongoing debate surrounding the optimal surgical strategy for RLPS.
METHODS: We recruited 77 patients with RLPS who underwent aggressive surgical policies. Patients were categorized into three surgical subtypes: suprapancreatic RLPS, pancreatic RLPS, and subpancreatic RLPS. Our standardized surgical strategy involved resecting macroscopically uninvolved adjacent organs according to surgical subtypes. We collected clinical, pathological and prognostic data for analyses.
RESULTS: The median follow-up was 45.5 months. Overall survival (OS) and recurrence-free survival (RFS) were significantly correlated with multifocal RLPS, pathological subtype, recurrent RLPS and histological grade (P for OS = 0.011, 0.004, 0.010, and < 0.001, P for RFS = 0.004, 0.001, < 0.001, and < 0.001, respectively). The 5-Year Estimate OS of well-differentiated liposarcoma (WDLPS), G1 RLPS, de novo RLPS and unifocal RLPS were 100%, 89.4%, 75.3% and 69.1%, respectively. The distant metastasis rate was 1.4%. The morbidity rates (≥ grade III) for suprapancreatic, pancreatic, and subpancreatic RLPS were 26.7%, 15.6%, and 13.3%, respectively. The perioperative mortality rate is 2.6%.
CONCLUSIONS: Standardized aggressive surgical policies demonstrated prognostic benefits for RLPS, particularly for G1 RLPS, WDLPS, unifocal RLPS, and de novo RLPS. This approach effectively balanced considerations of adequate exposure, surgical safety, and thorough removal of all fat tissue. G1 RLPS, WDLPS, unifocal RLPS, and de novo RLPS could be potential indications for aggressive surgical policies.

OBJECTIVE: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroperitoneal well-differentiated liposarcomas (WDLPS) and lipomas.
METHODS: This retrospective multi-center study included 167 patients (training/external test cohort, 104/63) with MDM2-positive WDLPS or MDM2-negative lipomas. Clinical data and CECT features were independently measured and analyzed by two radiologists. A clinico-radiological model, radiomics signature (RS), deep learning and radiomics signature (DLRS), and a DLRN incorporating radiomics and deep learning features were developed to differentiate between WDLPS and lipoma. The model utility was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA).
RESULTS: The DLRN showed good performance for distinguishing retroperitoneal lipomas and WDLPS in the training (AUC, 0.981; accuracy, 0.933) and external validation group (AUC, 0.861; accuracy, 0.810). The DeLong test revealed the DLRN was noticeably better than clinico-radiological and RS models (training: 0.981 vs. 0.890 vs. 0.751; validation: 0.861 vs. 0.724 vs. 0.700; both P < 0.05); however, no discernible difference in performance was seen between the DLRN and DLRS (training: 0.981 vs. 0.969; validation: 0.861 vs. 0.837; both P > 0.05). The calibration curve analysis and DCA demonstrated that the nomogram exhibited good calibration and offered substantial clinical advantages.
CONCLUSIONS: The DLRN exhibited strong predictive capability in predicting WDLPS and retroperitoneal lipomas preoperatively, making it a promising imaging biomarker that can facilitate personalized management and precision medicine.

BACKGROUND: The mouse double minute 2 homolog (MDM2) oncogene exerts oncogenic activities in many cancers and represents a potential therapeutic target. This trial evaluated the safety, pharmacokinetics, pharmacodynamics, and preliminary efficacy of alrizomadlin (APG-115), a novel MDM2/p53 inhibitor, in patients with advanced solid tumors.
METHODS: Patients with histologically confirmed advanced solid tumors who had progressed to standard treatment or lacked effective therapies were recruited. Alrizomadlin was administered once daily every other day for 21 days of a 28-day cycle until disease progression or intolerable toxicity.
RESULTS: A total of 21 patients were enrolled and treated with alrizomadlin; 57.1% were male and the median age was 47 (25-60) years. The maximum tolerated dose of alrizomadlin was 150 mg and the recommended phase II dose was 100 mg. One patient in the 200-mg cohort experienced dose-limiting toxicity of thrombocytopenia and febrile neutropenia. The most common grade 3/4 treatment-related adverse events were thrombocytopenia (33.3%), lymphocytopenia (33.3%), neutropenia (23.8%), and anemia (23.8%). Alrizomadlin demonstrated approximately linear pharmacokinetics (dose range 100-200 mg) and was associated with increased plasma macrophage inhibitory cytokine-1, indicative of p53 pathway activation. Of the 20 assessable patients, 2 [10%, 95% confidence interval (CI) 1.2% to 31.7%] patients achieved partial response and 10 (50%, 95% CI 27.2% to 72.8%) showed stable disease. The median progression-free survival was 6.1 (95% CI 1.7-10.4) months, which was significantly longer in patients with wild-type versus mutant TP53 (7.9 versus 2.2 months, respectively; P < 0.001). Among patients with MDM2 amplification and wild-type TP53, the overall response rate was 25% (2/8) and the disease control rate was 100% (8/8).
CONCLUSIONS: Alrizomadlin had an acceptable safety profile and demonstrated promising antitumor activity in MDM2-amplified and TP53 wild-type tumors. This study supports further exploration of alrizomadlin with recommended doses of 100 mg q.o.d. in 21 days on and 7 days off regimen.

OBJECTIVE: Radical resection of retroperitoneal liposarcoma (RLPS) may necessitate vascular resection and reconstruction. The study was conducted to assess surgical outcomes of surgery for RLPS with major vascular involvement.
METHODS: Patients with RLPS who underwent surgical resection at the Sarcoma Center of Peking University Cancer Hospital between April 2011 and December 2022 were identified from a prospectively maintained database. Patients were classified into two groups: vascular resection and non-vascular resection groups. A propensity score matching analysis was performed to eliminate baseline differences between the groups. Surgical details and postoperative outcomes were analyzed. Furthermore, prognostic factors for local recurrence-free survival (LRFS) and overall survival (OS) were assessed.
RESULTS: Overall, 199 patients were identified and the median follow-up period was 48 (interquartile range [IQR] 45-69) months. Vascular resection was performed in 42 (21%) patients, 25 of whom had vascular infiltration. A total of 39 patients had vascular replacement and 3 patients underwent partial resection (side-wall resection). Vascular resection was burdened by higher rates of major morbidity (38% vs. 14%, p < 0.001) and 30-day mortality (7.1% vs. 1.3%, p = 0.005). After propensity-matched analysis, patients who underwent vascular resection had 5-year LRFS and OS rates comparable to those without vascular involvement. Major vascular resection was not an independent risk factor for LRFS or OS.
CONCLUSIONS: Although accompanied by increased risks of major morbidity and mortality, the major vascular resection enabled radical resection in patients with advanced RLPS, affording comparable 5-year LRFS and OS rates compared to those who did not.

OBJECTIVE: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.
METHODS: We collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component-wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C-index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox-Snell residual plot. We also used time-dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model-agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model.
RESULTS: The final ML models are consisted of six factors: patient\'s age, gender, marital status, surgical history, as well as tumor\'s histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS.
CONCLUSIONS: The ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

The feasibility of surgery after immunotherapy for mediastinal liposarcoma remains uncertain. Besides, the case of immunotherapy for liposarcoma is still lacking. We report a case of recurrence after resection of a left mediastinal liposarcoma. After recurrence, one course of pembrolizumab plus anlotinib hydrochloride showed no tumor shrinkage, and genetic testing showed CDK4 amplification and PD-L1 TPS <1%; therefore, the plan was changed to one course of pembrolizumab plus palbociclib, but the tumor still did not shrink. Thus, second tumor resection was performed. In addition, the postoperative pathology was still well-differentiated liposarcoma. The significance of immunotherapy in liposarcoma still needs to be further explored. In the absence of surgical contraindications, secondary surgery might be feasible.