Leiomyosarcoma Patients Research Articles

Detection of Alternative Lengthening of Telomeres via Chromogenic in situ Hybridization (ALT-CISH) for the Prognostication of PanNETs and Other Neoplasms

Molecular studies have shown ALT to be an important prognostic biomarker of shorter relapse-free survival (RFS) for patients with pancreatic neuroendocrine tumors (PanNETs) and other neoplasms. However, the preferred method of detecting ALT in tissue is by fluorescence in situ hybridization (FISH), which has several clinical limitations. These issues necessitate the creation of a chromogenic ALT assay that can be easily implemented into routine practice. A CISH assay was developed using genetically modified osteosarcoma cell lines, 20 normal pancreata, 20 ALT-positive PanNETs, and 20 ALT-negative PanNETs. Thereafter, it was validated on a multi-institutional cohort of 360 surgically resected PanNETs and correlated with multiple clinicopathologic features, RFS, and FISH results. Separately, 109 leiomyosarcomas (LMS) were evaluated by both CISH and FISH, and, similarly, the prognostic significance of ALT status was assessed. Upon optimization, ALT-CISH was identified in 112 of 360 (31%) primary PanNETs and was 100% concordant with FISH testing. ALT correlated with several adverse prognostic findings and distant metastasis (all p<0.004). The 5-year RFS for patients with ALT-positive PanNETs was 35% as compared to 94% for ALT-negative PanNETs. By multivariate analysis, ALT was an independent prognostic factor for shorter RFS. Similarly, ALT was associated with shorter RFS in LMS patients and, analogous to PanNETs, a negative, independent prognostic factor. ALT-CISH was developed and validated in not only PanNETs, but also sarcomas, specifically LMS. CISH testing has multiple advantages over FISH that facilitate its widespread clinical use in the detection of ALT and prognostication of patients with diverse neoplasms.

Individualized evaluation of risk and prognosis in uterine leiomyosarcoma patients with synchronous distant metastases: a real-world retrospective study.

Uterine leiomyosarcoma (uLMS) accounts for roughly 70% of all uterine sarcomas, with recurrence and mortality rates notably higher than those of other uterine tumors. The prognosis of uLMS patients who have distant metastases remains poor. The objective of this study was to determine independent risk variables related to distant metastases in patients with uLMS and prognostic factors for those with distant metastases. Subsequently, two practical nomograms were developed and validated to assess the probability of distant metastases and predict survival outcomes for these with distant metastases, respectively. A real-world retrospective study was carried out using data from patients diagnosed with primary uLMS in the Surveillance, Epidemiology, and End Results (SEER) database spanning the years 2010 to 2015. Univariate andmultivariate logistic regression analyses were utilized to identify clinicopathological characteristics related to the risk of distant metastases, while univariate and multivariate Cox regressions were employed to determine prognostic factors. Then, a risk nomogram incorporating independent risk variables and a prognostic nomogram integrating independent prognostic factors were established in the training cohort and validated for accuracy in the validation cohort, respectively. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration curves were utilized to measure the accuracy of nomograms, while decision curve analysis (DCA) curves were employed to assess their clinical benefit capacity. Based on the median total point derived from the prognostic nomogram, patients were stratified into high- and low-risk groups. The differentiation ability of the prognostic nomogram was evaluated using Kaplan-Meier survival analysis with the log-rank test. The study encompassed 1,362 patients diagnosed with uLMS, among whom 337 cases (24.7%) manifested synchronous distant metastases at the initial diagnosis. Univariate and multivariate logistic regression analyses identified race, histological grade, T stage, N stage, tumor size, surgery, and chemotherapy as independent risk factors for distant metastases in uLMS patients. The outcomes of both univariate and multivariate Cox analyses indicated that surgery and chemotherapy emerged as independent protective factors for prognosis in uLMS patients with distant metastases, whereas higher histological grade and T stage were identified as independent risk factors. The risk nomogram incorporating independent risk variables and the prognostic nomogram integrating independent prognostic factors could respectively predict the risk of metastases and the prognosis very effectively in both training and validation cohorts. In summary, we developed the novel well-validated risk nomogram to precisely assess the probability of metastases in uLMS patients and prognostic nomogram to predict the prognosis of those with distant metastases, providing decision-making guidance for tailoring individualized clinical management of these patients.

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Immune infiltrate analysis to reveal distinct immune classes for leiomyosarcomas.

11546 Background: Leiomyosarcomas (LMS) are aggressive malignancies of smooth-muscle origin. Few targeted molecular options exist and immunotherapy (IO) agents against T cells have been disappointing. In this study, we examine the immune infiltrate of LMS tumors by immunohistochemistry (IHC) and gene expression. Our objective was to identify actionable targets for immunotherapy in patients with LMS. Methods: Tumor samples from a series of LMS patients were prospectively collected and banked for DNA and RNA sequencing as part of the MD Anderson Patient Mosaic. Associated clinical data for the patients were collected and maintained. IHC was performed on available separate tissue microarrays. Differential gene expression (DGE) analysis was done using the DESeq2 package for R and the Benjamini-Hochberg procedure used to control for false discovery rate. Immune cell fractions were estimated using the MCPcounter package. All gene expression analyses were repeated on The Cancer Genome Atlas (TCGA) dataset as an independent dataset. Results: A total of 104 TCGA samples and 72 samples from Mosaic from 53 patients with uterine or soft tissue LMS were included in the analysis. Tumors in the TCGA group were treatment naive. Of the samples in the Mosaic group, 61 (85%) had been previously treated with chemotherapy, 8 (11%) had been radiated, and 5 (7.5%) had been previously treated with immunotherapy. Thirty-one (43%) tumors were metastatic. Fifty-one (70%) were soft tissue LMS and 21 (30%) were uterine LMS. Four distinct immune classes were seen in both the TGCA and Mosaic: a predominantly B-cell group, a monocyte/macrophage group, a mixed infiltrate group and a group with a neutrophil signature. DGE performed between the subgroups revealed a unique gene expression pattern for tumors in the B cell and monocyte/macrophage subgroups. Interrogation of specific immune-associated genes revealed low levels of expression in the neutrophil group, suggesting this may be the most immunologically “cold” subset. Some of these results were reproduced by IHC using a TMA: the presence of a cluster of samples with B-cells and higher expression of immune checkpoints, a macrophage driven cluster, and an immunologically cold cluster. Conclusions: Analyses of LMS samples across two large datasets demonstrate the existence of 4 distinct immune classes of tumors that persist despite variation in clinical and treatment conditions: B cell, monocytic/macrophage, mixed-infiltrate and neutrophil rich. The B cell and monocytic groups have increased expression of immune markers which may be amenable to targeted therapies.

Surgically treated pelvic liposarcoma and leiomyosarcoma: The effect of tumor size on cancer-specific survival

IntroductionIn soft tissue pelvic liposarcoma and leiomyosarcoma, it is unknown whether a specific tumor size cut-off may help to better predict prognosis, defined as cancer-specific survival (CSS). We tested whether different tumor size cut-offs, could improve CSS prediction. Materials and methodsSurgically treated non-metastatic soft tissue pelvic sarcoma patients were identified (Surveillance, Epidemiology, and End Results 2004–2019). Kaplan-Meier plots, univariable and multivariable Cox-regression models and receiver operating characteristic-derived area under the curve (AUC) estimates were used. ResultsOverall, 672 (65 %) liposarcoma (median tumor size 11 cm, interquartile range [IQR] 7–16) and 367 (35 %) leiomyosarcoma (median tumor size 8 cm, IQR 5–12) patients were identified. The p-value derived ideal tumor size cut-off was 17.1 cm, in liposarcoma and 7.0 cm, in leiomyosarcoma. In liposarcoma, according to p-value derived cut-off, five-year CSS rates were 92 vs 83 % (≤17.1 vs > 17.1 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 83.8 to 86.8 % (Δ = 3 %). Similarly, among previously established cut-offs (5 vs 10 vs 15 cm), also 15 cm represented an independent predictor of CSS and improved prognostic ability from 83.8 to 87.0 % (Δ = 3.2 %). In leiomyosarcoma, according to p-value derived cut-off, five-year CSS rates were 86 vs 55 % (≤7.0 vs > 7.0 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 68.6 to 76.5 % (Δ = 7.9 %). ConclusionsIn liposarcoma, the p-value derived tumor size cut-off was 17.1 cm vs 7.0 cm, in leiomyosarcoma. In both histologic subtypes, these cut-offs exhibited the optimal statistical characteristics (univariable, multivariable and AUC analyses). In liposarcoma, the 15 cm cut-off represented a valuable alternative.

Abstract 5135: Tertiary lymphoid structures and T-cell aggregates signal a robust anti-tumor immune response in a subset of leiomyosarcoma patients

Abstract Cancer immunotherapy has ushered in a new era for treatment of many difficult to treat cancers such as Leiomyosarcoma (LMS), one of the most common subtypes of sarcomas. There are few biomarkers which can be used to identify patients who might benefit from immunotherapy. There is an urgent need to discover novel biomarkers which might be prognostic of a strong anti-tumor response. We thus sought to comprehensively profile the microenvironment of LMS tumors to identify features which might be suggestive of an active immune response and molecular features prognostic of response to immunotherapy. We began our analysis by classifying the LMS tumors from The Cancer Genome Atlas (TCGA) into 4 subtypes based on previously identified molecular features and comparing the enrichment of various immune gene signatures. Two of the subtypes had a high expression of various immune gene signatures including B Cells, M2 macrophages and Cytotoxic T cells. Visual inspection of the Hematoxylin and Eosin-stained tissue slides available with TCGA pointed to a presence of lymphoid aggregates or Tertiary lymphoid structures (TLS) in 10-15% of the tumors - predominantly belonging to one of the two subtypes. This subtype displayed features of smooth muscle differentiation and was also associated with improved survival. To validate our findings, we performed immunohistochemistry (IHC) and bulk RNA sequencing of LMS specimens (n=66) obtained from surgical resections from the Johns Hopkins Hospital. We identified mature CD21+ TLS in 15% of these cases and non-germinal center T cell aggregates present in more than 30% of the cases and these features were associated with improved survival. We digitally merged the different IHC sections to study if the markers were spatially correlated. Interestingly, spatial analysis showed that these CD8 T cells aggregates co-localized with neighborhoods of high density of PD-L1+ cells, hinting at the possibility of immune mediated upregulation of PD-L1 as means of immune suppression. The deconvolution of the transcriptome of these samples showed a marked presence of cytotoxic CD8 T cells and plasma B cells in this subset of patients, an enrichment of pathways related to T cell trafficking, activation and proliferation. Single cell RNA sequencing of tumor infiltrating immune cells showed an oligoclonal expansion of B and T cells. Taken together, this study shows that a subset of LMS patients have a strong anti-tumor immune response which is characterized by the presence of Tertiary Lymphoid Structures and patches of high density of PD-L1 expressing cells. These features might serve as biomarkers in screening LMS patients which might benefit from immunotherapy. Citation Format: Aditya Suru, Alexandre Maalouf, Lingling Chen, Ada Tam, William Villasi, Kaushal Sharma, Lindy Zhang, Christian Meyer, Jonathan Greer, Fabian Johnston, Carol Morris, Sophia Strike, Adam Levin, Daniel Rhee, Brian Ladle, Kornel Schuebel, Yan Zhang, Rulin Wang, Srinivasan Yegnasubramanian, John Gross, Robert Anders, Andrew Pardoll, Nicolas Llosa. Tertiary lymphoid structures and T-cell aggregates signal a robust anti-tumor immune response in a subset of leiomyosarcoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5135.

Abstract 1991: Leiomyosarcoma poly-aneuploid cancer cells form in response to chemotherapy, contribute to chemoresistance, and lead to higher rates of metastatic recurrence

Abstract Poly-aneuploid cancer cells (PACCs) or as also known as “giant cells”, are a treatment resistant stem cell-like cancer phenotype. PACCs have been identified in numerous cancer types, and their presence appears to play a significant role in chemoresistance and poor patient outcomes. PACCs employ a multitude of polyploidization programs to enter this transient state in response to stress. After removal of stress, PACCs give rise to non-PACC progeny through potential mechanisms of neosis or depolyploidization that maintain chemotherapeutic resistance. Leiomyosarcoma (LMS) is a difficult to treat sarcoma that grows in smooth muscle that often develops chemoresistance. Here we report the first description of PACCs in LMS cell culture, LMS xenografts, and LMS patient tumor microarrays. LMS cells treated (doxorubicin, docetaxel, and gemcitabine) in vitro and in vivo lead to the development of mononucleated and multinucleated polypoid cells as the predominant phenotype via polyploidization. Polyploidization mechanisms identified included endocycling, endomitosis, and cell-cell fusion. In vitro, LMS PACCs had a significant increase in size and DNA content as well as an increase in resistance to chemotherapy as compared to parents. LMS PACCs also repopulated the culture with non-PACC LMS progeny cells. The PACC progeny cells maintained a significantly decreased (p&amp;lt;0.0001) response to chemotherapy compared to parent cells. PACCs were also identified in clinical samples from patients with LMS. Twenty-nine percent of samples (N=289) contained abnormally large tumor cells and fifty-eight percent contained abnormal nuclei. A positive correlation was observed in patient tumor samples between PACCs in metastatic tumors and recurrence (p=0.0277) and survival (p=0.0009) than those patient tumors without PACCs. The high rate of functional loss of TP53 in LMS (&amp;gt;90%) may contribute to the ability of PACCs to form abnormal, multiple nuclei. We therefore transfected LMS PACCs to express TP53 and observed an increased apoptotic response. Additionally, combining functional TP53 expression with low doses of doxorubicin increases the apoptotic response of LMS cells, potentially by targeting PACCs. LMS PACCs may contribute to the lack of response in patients with LMS. Identifying therapeutic approaches that prevent or target LMS PACCs, including TP53 replacement, may improve response to chemotherapy and improve outcomes for patients. Citation Format: Ryan Kennington, Gareth Mitchell, Emily Payne, Niraja Bhachech, Jonathan A. Fletcher, Ting Liu, Matt van de Rijn, Sarah Amend, Kenneth J. Pienta, Joshua D. Schiffman, Lisa M. Abegglen. Leiomyosarcoma poly-aneuploid cancer cells form in response to chemotherapy, contribute to chemoresistance, and lead to higher rates of metastatic recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1991.

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma

PurposeMachine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremity LMS. MethodsThe Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologic extremity LMS (n = 634). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of extremity LMS patients (n = 46). ResultsAll ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.75–0.76 at the 5-year time point. The Random Forest (RF) model was the best performing model and used for external validation. This model also performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.90 and 0.87, respectively. The RF model was well calibrated on external validation. This model has been made publicly available at https://rachar.shinyapps.io/lms_app/ ConclusionsML models had excellent performance for survival prediction of extremity LMS. Future studies incorporating a larger institutional cohort may be needed to further validate the ML model for LMS prognostication.

A novel nomogram and recursive partitioning analysis for predicting cancer-specific survival of patients with subcutaneous leiomyosarcoma

Accurately predicting prognosis subcutaneous leiomyosarcoma (LMS) is crucial for guiding treatment decisions in patients. The objective of this study was to develop prediction models for cancer-specific survival (CSS) in patients with subcutaneous LMS. The collected cases of diagnosed subcutaneous LMS were randomly divided into a training cohort and a validation cohort at a 6:4 ratio based on tumor location and histological code. The X-tile program was utilized to determine the optimal cutoff points for age index. Univariate and Cox multivariate regression analyses were conducted to identify independent risk factors for subcutaneous LMS patients. Nomograms were constructed to predict CSS, and their performance was assessed using C-index and calibration plots. Additionally, a decision tree model was established using recursive partitioning analysis to determine the total score for CSS prediction in subcutaneous LMS patients based on the nomogram model. A total of 1793 patients with subcutaneous LMS were found. X-tile software divides all patients into ≤ 61 years old, 61–82 years old, and ≥ 82 years old. The most important anatomical sites were the limbs (including the upper and lower limbs, 48.0%). Only 6.2% of patients received chemotherapy, while 44% had a history of radiotherapy and 92.9% underwent surgery. The independent risk factors for patients with subcutaneous LMS were age, summary stage, grade, and surgery. CSS was significantly decreased in patients with distant metastases, which showed the highest independent risk predictor (HR 4.325, 95% CI 3.010–6.214, p < 0.001). The nomogram prediction model of LMS was constructed based on four risk factors. The C-index for this model was 0.802 [95% CI 0.781–0.823] and 0.798 [95% CI 0.768–0.829]. The training cohort's 3-, 5-, and 10-year AUCs for CSS in patients with subcutaneous LMS were 0.833, 0.830, and 0.859, and the validation cohort's AUC for predicting CSS rate were 0.849, 0.830, and 0.803, respectively. Recursive segmentation analysis divided patients into 4 risk subgroups according to the total score in the nomogram, including low-risk group < 145, intermediate-low-risk group ≥ 145 < 176, intermediate-high-risk group ≥ 176 < 196, and high-risk group ≥ 196; The probability of the four risk subgroups is 9.1%, 34%, 49%, and 79% respectively. In this retrospective study, a novel nomogram or corresponding risk classification system for patients with subcutaneous LMS were developed, which may assist clinicians in identifying high-risk patients and in guiding the clinical decision.

Trabectedin and low-dose radiation therapy in patients with advanced leiomyosarcoma.

Dimensional response is an unmet need in second lines of advanced soft tissue sarcomas (STS). Indeed, the three approved drugs, pazopanib, trabectedin, and eribulin, achieved an overall response rate (ORR) of less than 10%. This fact potentially hinders the options for fast symptomatic relief or surgical rescue. The combination of trabectedin plus low-dose radiation therapy (T-XRT) demonstrated a response rate of 60% in phase I/II trial, while real-life data achieved 32.5% ORR, probably due to a more relaxed timing between treatments. These results were obtained in progressing and advanced STS. In this study, the merged databases (trial plus real life) have been analyzed, with a special focus on leiomyosarcoma patients. As responses were seen in a wide range of sarcoma histologies (11), this study planned to analyze whether leiomyosarcoma, the largest subtype with 26 cases (30.6%) in this series, exhibited a better clinical outcome with this therapeutic strategy. In addition, four advanced and progressing leiomyosarcoma patients, all with extraordinarily long progression-free survival of over 18 months, were collected. A total of 847 cycles of trabectedin were administered to 85 patients, with the median number of cycles per patient being 7 (1-45+). A trend toward a longer progression-free survival (PFS) was observed in leiomyosarcoma patients with median PFS (mPFS) of 9.9 months [95% confidence interval (CI): 1.1-18.7] versus 5.6 months (95% CI: 3.2-7.9) for the remaining histologies, p = 0.25. When leiomyosarcoma and liposarcoma were grouped, this difference reached statistical significance, probably due to the special sensitivity of myxoid liposarcoma. The mPFS for L-sarcomas was 12.7 months (95% CI: 7-18.5) versus 4.3 months (95% CI: 3.3-5.3) for the remaining histologies, p = 0.001. Cases with long-lasting disease control are detected among leiomyosarcoma patients. Even when extraordinarily long-lasting responses do exist among leiomyosarcoma patients treated with T-XR, we were unable to demonstrate a significant difference favoring leiomyosarcoma patients in clinical outcomes.

FOXO3a deregulation in uterine smooth muscle tumors

ObjectiveThe present study aimed to investigate FOXO3a deregulation in Uterine Smooth Muscle Tumors (USMT) and its potential association with cancer development and prognosis. MethodsThe authors analyzed gene and protein expression profiles of FOXO3a in 56 uterine Leiomyosarcomas (LMS), 119 leiomyomas (comprising conventional and unusual leiomyomas), and 20 Myometrium (MM) samples. The authors used techniques such as Immunohistochemistry (IHC), FISH/CISH, and qRT-PCR for the present analyses. Additionally, the authors conducted an in-silico analysis to understand the interaction network involving FOXO3a and its correlated genes. ResultsThis investigation revealed distinct expression patterns of the FOXO3a gene and protein, including both normal and phosphorylated forms. Expression levels were notably elevated in LMS, and Unusual Leiomyomas (ULM) compared to conventional Leiomyomas (LM) and Myometrium (MM) samples. This upregulation was significantly associated with metastasis and Overall Survival (OS) in LMS patients. Intriguingly, FOXO3a deregulation did not seem to be influenced by EGF/HER-2 signaling, as there were minimal levels of EGF and VEGF expression detected, and HER-2 and EGFR were negative in the analyzed samples. In the examination of miRNAs, the authors observed upregulation of miR-96-5p and miR-155-5p, which are known negative regulators of FOXO3a, in LMS samples. Conversely, the tumor suppressor miR-let7c-5p was downregulated. ConclusionsIn summary, the outcomes of the present study suggest that the imbalance in FOXO3a within Uterine Smooth Muscle Tumors might arise from both protein phosphorylation and miRNA activity. FOXO3a could emerge as a promising therapeutic target for individuals with Unusual Leiomyomas and Leiomyosarcomas (ULM and LMS), offering novel directions for treatment strategies.

A web-based nomogram to predict overall survival for postresection leiomyosarcoma patients with lung metastasis.

To investigate the overall survival of post-resection leiomyosarcoma (LMS) patients with lung metastasis, data of post-resection LMS patients with lung metastasis between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The clinical characteristics and survival data for post-resection LMS patients with lung metastasis at Tianjin Medical University Cancer Hospital & Institute (TJMUCH) between October 2010 and July 2018 were collected. Patients derived from the SEER database and TJMUCH were divided into training and validation cohorts, respectively. Univariate and multivariate Cox regression analyses were performed and a nomogram was established. The area under the curve (AUC) and the calibration curve were used to evaluate the nomogram. A web-based nomogram was developed based on the established nomogram. Eventually, 226 patients from the SEER database who were diagnosed with LMS and underwent primary lesion resection combined with lung metastasis were enrolled in the training cohort, and 17 patients from TJMUCH were enrolled in the validation cohort. Sex, race, grade, tumor size, chemotherapy, and bone metastasis were correlated with overall survival in patients with LMS. The C-index were 0.65 and 0.75 in the SEER and Chinese set, respectively. Furthermore, the applicable AUC values of the ROC curve in the SEER cohort to predict the 1-, 3-, 5- years survival rate were 0.646, 0.682, and 0.689, respectively. The corresponding AUC values in the Chinese cohort were 0.970, 0.913, and 0.881, respectively. The calibration curve showed that the nomogram performed well in predicting the overall survival in post-resection LMS patients with lung metastasis. A web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) was established. The web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) is an accurate and personalized tool for predicting the overall survival of post-resection LMS with lung metastasis.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Cancer is a heterogenous, dynamic target. Individual patients, lesions, and cell populations may present with different characteristics and varying sensitivities to anticancer therapies. The purpose of this study was to investigate the utility of image-derived (radiomic) biomarkers for prediction of lesion-specific treatment response. Using a database of serial imaging from a Phase III clinical trial (NCT01440088), all visible lung metastases were contoured on chest computed tomography performed at baseline and after 2 cycles of chemotherapy. Metastatic leiomyosarcoma patients received either doxorubicin monotherapy (DM) or doxorubicin plus evofosfamide (DE). Percent change in volume with respect to baseline was evaluated as a metric of treatment response. Lesions that experienced a positive volume change greater than 50% were labeled as 'progressors'; all other lesions were labeled as 'non-progressors'. Radiomic features were extracted at baseline from the whole lesion and three peritumoral regions using PyRadiomics (version 3.0.1). Features were compared using the Mann-Whitney U-test and further used to classify between response categories. Logistic classification models were evaluated with 10-fold cross-validation using Area Under the Curve for the Receiver Operating Characteristic (AUROC) and Precision-Recall (AUPRC) curves as measures of model performance. Significance was evaluated by permutation tests. All code used to derive features is open source and available for download. In total, 204 lung metastases from 80 patients were evaluated. The fraction of progressing lesions was 19% and 27% for the DM and DE regimes, respectively. Differences in individual lesion response were observed in 20% of patients with multiple lung metastases. Significant differences in entropy were observed between response categories (false discovery rate < 5% after multiple testing correction): interior regions for lesions receiving DM, and peripheral regions for lesions receiving DE. The best performing models for the DM and DE regimens had AUROC of 0.87 and 0.76 and AUPRC 0.60 and 0.50, respectively (p < 0.05). Predicting lesion-specific responses using radiomic features represents a paradigm shift. Lesion-specific radiomic models indicate a 2 to 3-fold increase in predictive capacity in comparison to a no-skill classifier. These models, although preliminary, achieved a strong predictive value and could be used to predict lesion-specific treatment response.

Comprehensive Immune Profiling Unveils a Subset of Leiomyosarcoma with "Hot" Tumor Immune Microenvironment.

Purpose: To investigate the immune biomarker in Leiomyosarcoma (LMS), which is rare and recognized as an immune cold cancer showing a poor response rate (<10%) to immune checkpoint inhibitors (ICIs). However, durable response and clinical benefit to ICIs has been observed in a few cases of LMS, including, but not only, LMS with tertiary lymphoid structure (TLS) structures. Patients and methods: We used comprehensive transcriptomic profiling and a deconvolution method extracted from RNA-sequencing gene expression data in two independent LMS cohorts, the International Cancer Genome Consortium (ICGC, N = 146) and The Cancer Genome Atlas (TCGA, N = 75), to explore tumor immune microenvironment (TIME) in LMS. Results: Unsupervised clustering analysis using the previously validated two methods, 90-gene signature and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), identified immune hot (I-H) and immune high (I-Hi) LMS, respectively, in the ICGC cohort. Similarly, immune active groups (T-H, T-Hi) were identified in the TCGA cohort using these two methods. These immune active ("hot") clusters were significantly associated, but not completely overlapping, with several validated immune signatures such as sarcoma immune class (SIC) classification and TLS score, T cell inflamed signature (TIS) score, immune infiltration score (IIS), and macrophage score (M1/M2), with more patients identified by our clustering as potentially immune hot. Conclusions: Comprehensive immune profiling revealed a subset of LMS with a distinct active ("hot") TIME, consistently associated with several validated immune signatures in other cancers. This suggests that the methodologies that we used in this study warrant further validation and development, which can potentially help refine our current immune biomarkers to select the right LMS patients for ICIs in clinical trials.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.

Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection. Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots. 118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves. The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

Heterogeneity in the gene regulatory landscape of leiomyosarcoma.

Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

A phase II study of cabozantinib and temozolomide in patients with unresectable or metastatic leiomyosarcoma and other soft tissue sarcomas.

11505 Background: Potentiation of chemotherapy, without increased toxicity, to improve outcome is an area of interest. Preclinical and clinical evidence shows simultaneous utilization of antiangiogenic agents and chemotherapy have a synergistic effect. We hypothesized that the dual targeting of VEGF and c-MET pathways with cabozantinib (CAB) would result in clinical benefit for patients with soft tissue sarcoma (STS) when combined with temozolomide (TMZ), an alkylating agent. Utilization of CAB/TMZ is anticipated to produce a synergistic antitumor effect worthwhile to explore in STS. Methods: A multicenter phase II study of CAB/TMZ in patients with unresectable/metastatic leiomyosarcoma (LMS) and other STS was conducted by the Midwest Sarcoma Trials Partnership. Age ≥ 18, adequate performance status, organ function, measurable disease (RECIST 1.1), and 0-5 prior chemotherapy regimens were required. CAB 40 mg PO daily plus TMZ 150-200 mg/m2 PO 1-5 days were given in 28-day cycles until disease progression or unacceptable toxicity. Patients enrolled in 2 cohorts: 1. Uterine and non-uterine LMS 2. Other STS. The first 14 subjects to be enrolled in the study in either cohort served as a safety lead in. The primary endpoint was progression-free rate (PFR) at 12 weeks for cohort 1. Secondary endpoints include overall response rate (ORR), clinical benefit rate, median progression-free survival (PFS), overall survival, and safety and tolerability. The exploratory endpoint was to estimate the correlation of PFR and OS to expression levels of VEGF, amongst others. A Simon 2-stage design was used. Results: A total of 42 patients were enrolled in Cohort 1, and 30 in Cohort 2, across 5 sites. For Cohort 1, median age was 59 years (range 35-86), 85.7% were non-Hispanic, 73.8% were female, 61.9% had ECOG of 0. The PFR at 12 weeks was 45.9% (for 37 evaluable patient) with a median PFS of 6.4 months (95% CI 4.6-6.7). Five PR were observed for an ORR of 13.5%. For Cohort 2, median age was 63 (20-86), 83.3% were non-Hispanic, 36.7% were female, 53.3% had ECOG of 0. The PFR at 12 weeks was 50% (for 28 evaluable patients) with a median PFS of 3.2 months (95% CI 1.4-4.6). One PR was observed. Reported grade 3-4 adverse events attributed to one or both intervention drugs affecting ≥10% of patients (n = 70) included hypertension (10%), decreased neutrophils (18.7%), decreased platelets (31%). Conclusions: The combination therapy of CAB/TMZ in patients with unresectable or metastatic LMS exceeded its primary endpoint with a PFR at 12 weeks &gt; 39%. The PFS of 6.4 months in Cohort 1 exceeds rates achieved with second line tyrosine kinase inhibitors (TKIs) and alkylators in LMS patients. Treatment was feasible and did not reveal any previously unreported toxicities. The combination with CAB with TMZ demonstrated meaningful clinical benefit and is worth further exploration in LMS. Clinical trial information: NCT04200443 .

Loss of the DNA repair gene RNase H2 and a unique subset of DDR-deficient leiomyosarcomas.

11549 Background: Targeting the DNA damage response (DDR)/Homologous Recombination (HR) pathway is an emerging therapeutic approach for leiomyosarcoma (LMS). Loss of RNase H2 decreases DNA repair via the Non-Homologous End-Joining (NHEJ) pathway, leading to increased double stranded breaks, replication stress, and increased cell death. Therefore, we developed an assay to screen for RNase H2 loss in STS patient samples to determine its prevalence and prognostic significance, particularly in LMS patients. Methods: RNASEH2B homozygous deletion (HomDel) calls from TCGA samples were based on the Allele-Specific Copy number Analysis of Tumors (ASCAT2) algorithm. Immunohistochemistry (IHC) of RNase H2 was performed on tissue microarrays (TMAs) of uterine (U-LMS) and soft tissue (ST-LMS) leiomyosarcoma samples from MD Anderson Cancer Center (MDACC) using a selective antibody developed by Repare Therapeutics. RNase H2 loss was defined as &lt; = 10% cells without nuclear IHC staining and scored by experienced pathologists in 2 separate cohorts. Genomic analysis was performed using SNiPDx™, a targeted-NGS assay designed to detect biallelic loss of function in select DDR related genes and whole genome sequencing (WGS). MDACC TMA samples were clinically annotated by retrospective review. Results: Using TCGA data, RNASEH2B HomDels were seen in 6% (5/80) of all LMS cases, with a higher proportion in U-LMS (15%; 4/27) as compared to ST-LMS (2%; 1/53). In a pan-tumor TMA, RNase H2 loss by IHC was found in 3.8% (32/843) of samples overall, and in 10% (9/88) of LMS samples. This proportion of RNase H2 loss was higher in a larger MDACC LMS cohort, where negative staining of RNase H2 was found in 30% (33/110) of U-LMS and 38% (39/102) of ST-LMS cases. 30 MDACC LMS cases (15 U-LMS and 15 ST-LMS) were analyzed for RNASEH2B HomDels by SNiPDx. In U-LMS, RNASEH2B HomDels were detected in 64% (7/11) of RNase H2 IHC loss cases vs. 0% (0/3) in RNase H2 IHC intact cases. No RNASEH2B HomDels were detected in ST-LMS cases (10 IHC loss, 5 intact). The median overall survival (mOS) of MDACC U-LMS patients (n = 109) was 4.3 years. No significant mOS difference was seen in RNase H2 IHC intact cases versus loss (mOS 4.4 v 3.3 years, p = 0.54). In a separate cohort, 45 U-LMS samples were screened by IHC; 22% (10/45) had RNase H2 loss. RNASEH2B HomDels were detected in 70% (7/10) of RNase H2 IHC loss cases using SNiPDx and confirmed using WGS. In contrast, RNASEH2B HomDels were detected in 3% (1/33) of RNase H2 IHC intact cases. In the combined U-LMS cohort with IHC and SNiPDx results (n = 71), the diagnostic accuracy, sensitivity and specificity of RNase H2 IHC for detecting RNASEH2B HomDels was 76%, 93% and 71% respectively. Conclusions: RNase H2 loss via RNASEH 2B HomDels is prevalent in U-LMS and is unique from other DDR pathway alterations. RNase H2 IHC is an effective screening tool and is being developed for future clinical trials targeting DDR in LMS.

Abstract 1686: Leiomyosarcoma poly-aneuploid cancer cells form in response to chemotherapy and contribute to chemoresistance

Abstract Poly-aneuploid cancer cells (PACCs) are large, treatment resistant cancer cells identified in a number of different cancer types, sometimes called “giant cells.” PACCs have stem cell-like phenotypes and appear to play a role in treatment resistance that leads to poor patient outcomes. The PACC phenotype is a transient state that cancer cells can adopt to protect themselves from stress. PACCs can give rise to non-PACC progeny that maintain resistance to chemotherapy. While PACCs are documented in many cancer types, they have not yet been documented in sarcoma. However, we identified PACCs in cell culture from leiomyosarcoma (LMS) patient tumors. The goal of this study was to characterize PACCs in LMS in vitro, in vivo, and in patient samples. When LMS cells grown in vitro or in mice were treated with chemotherapy (doxorubicin, gemcitabine, or docetaxel), large cells with atypical nuclei emerged as the predominant cellular phenotype. In vitro, these LMS PACCs were more resistant to chemotherapy and repopulated the culture with non-PACC LMS cells, potentially through a process called neosis. PACCs were also identified in clinical samples from patients with LMS. Current efforts are underway to test for a correlation between the amount of PACCs in patient samples and initial response to therapy, as well as long-term patient outcomes. LMS tumors often do not respond to chemotherapy so defining the role of PACCs in LMS may have important clinical implications. LMS PACCs, which are non-dividing cells, may contribute to the lack of response. Identifying therapeutic approaches that target and kill LMS PACCs may improve response to chemotherapy and improve outcomes for patients. LMS tumors have a very high rate of TP53 loss of function reported to be &amp;gt;90%; TP53 loss may contribute to the ability of these giant cells to form with such abnormal nuclei. LMS PACCs transfected to express functional TP53 undergo apoptosis. Additionally, combining functional TP53 expression with low doses of doxorubicin increases the apoptotic response of LMS cells, potentially by targeting PACCs. Ongoing investigations may support the development of effective TP53 based therapeutics to target PACCs and to improve outcomes for LMS patients. Citation Format: Lisa M. Abegglen, Niraja Bhachech, Gareth Mitchell, Ryan Kennington, Brad Nelson, Miranda Sharp, Matthew Buccilli, Anthony Iovino, Aarushi Rohaj, Jonathan A. Fletcher, Ting Liu, Matt van de Rijn, Sarah R. Amend, Kenneth J. Pienta, Joshua D. Schiffman. Leiomyosarcoma poly-aneuploid cancer cells form in response to chemotherapy and contribute to chemoresistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1686.

Systemic Inflammatory Indices in Second-Line Soft Tissue Sarcoma Patients: Focus on Lymphocyte/Monocyte Ratio and Trabectedin.

A second-line standard of treatment has not yet been identified in patients with soft tissue sarcomas (STS), so identifying predictive markers could be a valuable tool. Recent studies have shown that the intratumoral and inflammatory systems significantly influence tumor aggressiveness. We aimed to investigate prognostic values of pre-therapy neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory index (SII), progression-free survival (PFS), and overall survival (OS) of STS patients receiving second-line treatment. In this single-center retrospective analysis, ninety-nine patients with STS were enrolled. All patients received second-line treatment after progressing to anthracycline. PFS and OS curves were calculated using the Kaplan-Meier method of RNA sequencing, and CIBERSORT analysis was performed on six surgical specimens of liposarcoma patients. A high NLR, PLR, and SII were significantly associated with worse PFS (p = 0.019; p = 0.004; p = 0.006). Low LMR was significantly associated with worse OS (p = 0.006). Patients treated with Trabectedin showed a better PFS when the LMR was low, while patients treated with other regimens showed a worse PFS when the LMR was low (p = 0.0154). The intratumoral immune infiltrates analysis seems to show a correlation between intratumoral macrophages and LMR. PS ECOG. The metastatic onset and tumor burden showed prognostic significance for PFS (p = 0.004; p = 0.041; p = 0.0086). According to the histologies, PFS was: 5.7 mo in liposarcoma patients vs. 3.8 mo in leiomyosarcoma patients vs. 3.1 months in patients with other histologies (p = 0.053). Our results confirm the prognostic role of systemic inflammatory markers in patients with STS. Moreover, we demonstrated that LMR is a specific predictor of Trabectedin efficacy and could be useful in daily clinical practice. We also highlighted a possible correlation between LMR levels and the percentage of intratumoral macrophages.