Osteochondroma-like parosteal osteosarcoma: A case highlighting diagnostic challenge and surgical advances.

Parosteal osteosarcomas are uncommon malignant bone tumors that arise from the bone surface. Their heterogenous components can present challenges in diagnosis. We present a case of a rare variant of this tumor known as an osteochondroma-like parosteal osteosarcoma, which was initially misdiagnosed as a cartilaginous tumor on core needle biopsy. Surgical resection of the tumor ultimately allowed for definitive diagnosis. Our case demonstrates the limitations of needle biopsy in diagnosing variants of parosteal osteosarcoma and the vital role of multidisciplinary discussions in guiding diagnosis and treatment. Furthermore, our case utilizes 3-dimensional printing technology in the surgical treatment, and illustrates the recent advances in patient-specific surgical techniques.

Genetic Screen in a Pre-Clinical Model of Sarcoma Development Defines Drivers and Therapeutic Vulnerabilities.

PURPOSE: High-grade complex karyotype sarcomas are a heterogeneous group of tumors with a uniformly poor prognosis. Within complex karyotype sarcomas, there are innumerable genetic changes but identifying those that are clinically relevant has been challenging. EXPERIMENTAL DESIGN: To address this, we utilized a pooled genetic screening approach, informed by TCGA data, to identify key drivers and modifiers of sarcoma development that were validated in vivo. RESULTS: YAP1 and wildtype KRAS were validated as drivers and transformed human mesenchymal stem cells into two distinct sarcoma subtypes, undifferentiated pleomorphic sarcoma (UPS) and myxofibrosarcoma (MFS), respectively. A subset of tumors driven by CDK4 and PIK3CA reflected leiomyosarcoma (LMS) and osteosarcoma (OS) demonstrating the plasticity of this approach and the potential to investigate sarcoma subtype heterogeneity. All generated tumors histologically reflected human sarcomas and had increased aneuploidy as compared to simple karyotype sarcomas. Comparing differential gene expression of TCGA samples to model data identified increased oxidative phosphorylation signaling in YAP1 tumors. Treatment of a panel of soft tissue sarcomas with a combination of YAP1 and oxidative phosphorylation inhibitors led to significantly decreased viability. CONCLUSIONS: Transcriptional co-analysis of TCGA patient samples to YAP1 and KRAS model tumors support that these sarcoma subtypes lie along a spectrum of disease and adds guidance for further transcriptome-based refinement of sarcoma subtyping. This approach can be used to begin to understand pathways and mechanisms driving human sarcoma development, the relationship between sarcoma subtypes and to identify and validate new therapeutic vulnerabilities for this aggressive and heterogeneous disease.

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Low-Dose Sorafenib Promotes Cancer Stem Cell Expansion and Accelerated Tumor Progression in Soft Tissue Sarcomas.

The cancer stem cell (CSC) hypothesis postulates that heterogeneous human cancers harbor a population of stem-like cells which are resistant to cytotoxic therapies, thus providing a reservoir of relapse following conventional therapies like chemotherapy and radiation (RT). CSCs have been observed in multiple human cancers, and their presence has been correlated with worse clinical outcomes. Here, we sought to evaluate the impact of drug dosing of the multi-tyrosine kinase inhibitor, sorafenib, on CSC and non-CSCs in soft tissue sarcoma (STS) models, hypothesizing differential effects of sorafenib based on dose and target cell population. In vitro, human cancer cell lines and primary STS from surgical specimens were exposed to escalating doses of sorafenib to determine cell viability and expression of CSC marker aldehyde dehydrogenase (ALDH). In vivo, ALDHbright CSCs were isolated, exposed to sorafenib, and xenograft growth and survival analyses were performed. We observed that sarcoma CSCs appear to paradoxically respond to the tyrosine kinase inhibitor sorafenib at low doses with increased proliferation and stem-like function of CSCs, whereas anti-viability effects dominated at higher doses. Importantly, STS patients receiving neoadjuvant sorafenib and RT on a clinical trial (NCT00864032) showed increased CSCs post therapy, and higher ALDH scores post therapy were associated with worse metastasis-free survival. These data suggest that low-dose sorafenib may promote the CSC phenotype in STS with clinically significant effects, including increased tumor growth and higher rates of metastasis formation in sarcoma patients.

Effect of ALDH1A1 and CD44 on Survival and Disease Recurrence in Patients With Osteosarcoma.

PURPOSE: Emerging evidence suggests that osteosarcoma stem cells (OSCs) may be responsible for tumor initiation propagation, recurrence, and resistance to therapy. We set out to evaluate the relationship between the abundance of ALDH1A1 and CD44-positive cells in biopsy and resection samples on disease recurrence and overall survival. METHODS: A retrospective review of 20 patients, including biopsy and resection samples, was performed at a comprehensive cancer center. Additionally, we queried the publicly available TARGET dataset of osteosarcoma patients. RESULTS: Neither the percentages of ALDH1A1-positive cells nor CD44-positive cells were significantly associated with overall mortality or disease recurrence in either biopsy or resection samples. Unlike our institutional data, overall survival was significantly correlated to higher ALDH1A1 expression in the TARGET dataset both in univariate and age-adjusted analyses. CONCLUSIONS: ADLH1 and CD44, potential markers of OSCs, were not found to be reliable clinical immunohistochemical prognostic markers for osteosarcoma patient survival, specifically disease-free survival. Osteosarcoma patients with high ALDH1A1 RNA expression showed improved overall survival in examining a national genomic database of osteosarcoma patients but again no association with disease-free survival. The potential of CD44 and ALDH1A1 as cellular-specific prognostic markers of survival, and as possible molecular targets, may be limited in osteosarcoma.

Characterization of natural killer and cytotoxic T-cell immune infiltrates in pancreatic ductal adenocarcinoma.

BACKGROUND AND OBJECTIVES: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor response to systemic therapies, including immunotherapy. Given the immunotherapeutic potential of natural killer (NK) cells, we evaluated intratumoral NK cell infiltrates along with cytotoxic T cells in PDAC to determine their association with patient outcomes. METHODS: We analyzed tumors from 93 PDAC patients treated from 2012 to 2020. Predictor variables included tumor-infiltrating lymphocytes (TILs), T-cell markers (CD3, CD8, CD45RO), NK marker (NKp46), and NK inhibitory marker (major histocompatibility complex class I [MHC-I]) by immunohistochemistry. Primary outcome variables were recurrence-free survival (RFS) and overall survival (OS). RESULTS: Mean TILs, CD3, and NKp46 scores were 1.3 ± 0.63, 20.6 ± 17.5, and 3.1 ± 3.9, respectively. Higher expression of CD3 and CD8 was associated with higher OS, whereas NK cell infiltration was not associated with either RFS or OS. There was a tight positive correlation between MHC-I expression and all T-cell markers, but not with NKp46. CONCLUSIONS: Overall NK cell infiltrates were low in PDAC and did not predict clinical outcomes, whereas T-cell infiltrates did. Further characterization of the immune infiltrate in PDAC, including inhibitory signals and suppressive cell types, may yield better biomarkers of prognosis and immune targeting in this refractory disease.

Intratumoral NKp46+ natural killer cells are spatially distanced from T and MHC-I+ cells with prognostic implications in soft tissue sarcoma.

Introduction: Soft tissue sarcomas (STS) are rare, heterogenous malignancies with an unmet need for novel immunotherapies. Tumor infiltrating lymphocytes (TILs) have been linked with favorable outcomes in STS patients, though the contribution of natural killer (NK) cells and spatial relationships of TILs with MHC-I expressing cells lacks detailed characterization. Experimental design: Using archived and prospectively collected specimens, we evaluated intratumoral NK cells by immunohistochemistry (IHC), flow cytometry, and immunofluorescence (IF). We assessed spatial localization of NK and T cells by multiplex IF, analyzing the effects of MHC-I expression status on NK and T cell clustering. Results: Both intratumoral NKp46 and CD56dim expression were associated with significantly improved overall survival (P=0.05), while higher infiltrates of CD56bright NK cells predicted a worse prognosis (P=0.05). The presence of intratumoral NK cells was inversely proportional to CD3+ T cells. Spatial analyses showed NK cells preferentially clustering close to other NK cells with sparse CD3+ T and CD8+ T cells in range (P<0.0001). Additionally, CD3+ T and CD8+ T cells showed significantly greater co-localization with MHC-I+ cells, compared to NK cells (P<0.0001). After neoadjuvant radiotherapy, there was greater CD8 clustering, while after neoadjuvant chemotherapy, there was overall lower TIL clustering. Conclusion: Intratumoral NK cells are prognostic in STS and localize closer to MHC-I- cells than T cells. Although both NK and T cells are associated with improved survival in STS, their differential distribution in the TME based on MHC-I expression status may serve as a biomarker for improved immunotherapy treatment selection.

Langerhans cell histiocytosis with aneurysmal bone cyst-like changes: a case-based literature review.

BACKGROUND: Langerhans cell histiocytosis (LCH) is a neoplastic transformation of myeloid precursors that commonly presents as an osteolytic lesion of the long or flat bones in children. Aneurysmal bone cysts (ABC) are benign neoplasms that frequently affect the metaphysis of long bones and the spine, often revealing a rapidly expansile lesion with fluid-fluid levels. LCH with secondary ABC-like changes is a rare condition that has only been reported five times, with two presentations in the cranium. The aim of this paper is to review the etiology, clinical and radiographic presentations, and treatment of this condition, as well as to present a novel case on the topic. CASE DESCRIPTION: We describe a 5-year-old boy with a rapidly growing head mass and eye pain resulting in a diagnosis of LCH with secondary ABC-like changes. Radiography demonstrated an expansile, lytic lesion of the left parietal bone with fluid-fluid levels. A confirmatory diagnosis was made through histopathology, demonstrating an inflammatory, histiocytic infiltrate staining positive for CD1a, CD68, CD207 (Langerin), and S-100. The lesion was surgically excised, and the patient recovered without any complications. CONCLUSION: We present a novel case of LCH with secondary ABC-like changes managed with surgical excision. While a radiographic workup with multiple imaging modalities is helpful for diagnosis, a thorough immunohistochemical analysis is essential as imaging characteristics are variable and nonspecific. Furthermore, surgical excision should be considered first-line treatment for lesions involving the skull in surgically accessible areas as it is curative, alleviates symptoms, and allows for histopathological diagnosis.

Synchronous Undetected HPV+ Cancer in a HPV- Tongue Cancer Patient.

We report a case of a 63-year-old male who presented with synchronous pT1N1 p16-positive squamous cell carcinoma (SCC) of the left tonsil and pT4N0 p16-negative SCC of the left tongue.

Coccidioidomycosis in Joint Replacement: A Review of the Literature With Case Presentations.

Coccidioidomycosis is a fungal infection endemic to certain regions of the Americas. In some cases, the organism may infect the musculoskeletal system, resulting in a prosthetic joint infection (PJI). Due to its difficulty in diagnosis, treatment of coccidioidomycosis in PJI is often delayed. Furthermore, with limited number of case reports, a standard of care in treatment has yet to be established. We present 2 cases of coccidioidomycosis PJI, the extensive evaluation that led to the diagnosis, and the treatment provided. This report highlights the natural progression of coccidioidomycosis in a prosthetic joint, the diagnostic features including histology, advanced imaging, and final treatment administered.

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

Human soft tissue sarcomas harbor an intratumoral viral microbiome which is linked with natural killer cell infiltrate and prognosis.

BACKGROUND: Groundbreaking studies have linked the gut microbiome with immune homeostasis and antitumor immune responses. Mounting evidence has also demonstrated an intratumoral microbiome, including in soft tissue sarcomas (STS), although detailed characterization of the STS intratumoral microbiome is limited. We sought to characterize the intratumoral microbiome in patients with STS undergoing preoperative radiotherapy and surgery, hypothesizing the presence of a distinct intratumoral microbiome with potentially clinically significant microbial signatures. METHODS: We prospectively obtained tumor and stool samples from adult patients with non-metastatic STS using a strict sterile collection protocol to minimize contamination. Metagenomic classification was used to estimate abundance using genus and species taxonomic levels across all classified organisms, and data were analyzed with respect to clinicopathologic factors. RESULTS: Fifteen patients were enrolled. Most tumors were located at an extremity (67%) and were histologic grade 3 (87%). 40% were well-differentiated/dedifferentiated liposarcoma histology. With a median follow-up of 24 months, 4 (27%) patients developed metastases, and 3 (20%) died. Despite overwhelming human DNA (>99%) intratumorally, we detected a small but consistent proportion of bacterial DNA (0.02-0.03%) in all tumors, including Proteobacteria, Bacteroidetes, and Firmicutes, as well as viral species. In the tumor microenvironment, we observed a strong positive correlation between viral relative abundance and natural killer (NK) infiltration, and higher NK infiltration was associated with superior metastasis-free and overall survival by immunohistochemical, flow cytometry, and multiplex immunofluorescence analyses. CONCLUSIONS: We prospectively demonstrate the presence of a distinct and measurable intratumoral microbiome in patients with STS at multiple time points. Our data suggest that the STS tumor microbiome has prognostic significance with viral relative abundance associated with NK infiltration and oncologic outcome. Additional studies are warranted to further assess the clinical impact of these findings.

Case report: Treatment of metastatic dedifferentiated chondrosarcoma with pembrolizumab yields sustained complete response.

Dedifferentiated chondrosarcomas (DDCS) are aggressive tumors with poor outcomes. Treatment of localized DDCS is primarily surgical, though most patients present with unresectable or metastatic disease. Systemic treatment options for advanced DDCS are limited, and the benefits of chemotherapy in this patient population remain controversial. Among other systemic therapy options, there is emerging clinical evidence to support the use of immunotherapy in patients with advanced DDCS. However, studies regarding the efficacy of immunotherapy in advanced DDCS are limited. Here, we present the case of a patient with metastatic, programmed death-ligand 1 (PD-L1)-positive DDCS treated with pembrolizumab who showed a sustained complete response for 24 months after initiation of therapy. To our knowledge, this case represents one of few documented cases of metastatic chondrosarcoma with sustained response to immunotherapy. The impressive response seen with PD-L1 inhibition in our patient indicates that immunotherapy is a successful treatment option in a subset of DDCS patients, and further investigation is needed to identify potential responders to immunotherapy.

Transcriptome Analysis of Tumor-Infiltrating Lymphocytes Identifies NK Cell Gene Signatures Associated With Lymphocyte Infiltration and Survival in Soft Tissue Sarcomas.

Purpose: Clinical successes using current T-cell based immunotherapies have been limited in soft tissue sarcomas (STS), while pre-clinical studies have shown evidence of natural killer (NK) cell activity. Since tumor immune infiltration, especially tumor-infiltrating lymphocytes, is associated with improved survival in most solid tumors, we sought to evaluate the gene expression profile of tumor and blood NK and T cells, as well as tumor cells, with the goal of identifying potential novel immune targets in STS. Experimental Design: Using fluorescence-activated cell sorting, we isolated blood and tumor-infiltrating CD3-CD56+ NK and CD3+ T cells and CD45- viable tumor cells from STS patients undergoing surgery. We then evaluated differential gene expression (DGE) of these purified populations with RNA sequencing analysis. To evaluate survival differences and validate primary DGE results, we also queried The Cancer Genome Atlas (TCGA) database to compare outcomes stratified by bulk gene expression. Results: Sorted intra-tumoral CD3+ T cells showed significant upregulation of established activating (CD137) and inhibitory genes (TIM-3) compared to circulating T cells. In contrast, intra-tumoral NK cells did not exhibit upregulation of canonical cytotoxic genes (IFNG, GZMB), but rather significant DGE in mitogen signaling (DUSP4) and metabolic function (SMPD3, SLC7A5). Tumors with higher NK and T cell infiltration exhibited significantly increased expression of the pro-inflammatory receptor TLR4 in sorted CD45- tumor cells. TCGA analysis revealed that tumors with high TLR4 expression (P = 0.03) and low expression of STMN1 involved in microtubule polymerization (P < 0.001) were associated with significantly improved survival. Conclusions: Unlike T cells, which demonstrate significant DGE consistent with upregulation of both activating and inhibiting receptors in tumor-infiltrating subsets, NK cells appear to have more stable gene expression between blood and tumor subsets, with alterations restricted primarily to metabolic pathways. Increased immune cell infiltration and improved survival were positively correlated with TLR4 expression and inversely correlated with STMN1 expression within tumors, suggesting possible novel therapeutic targets for immunotherapy in STS.

Unusual chondroblastoma of the hand with large extraosseous soft tissue component.

Chondroblastoma is a rare, benign primary cartilaginous bone tumor that typically arises in the epiphyses of the long bones. Radiologically, a well-defined lytic lesion with thin sclerotic margins is commonly found. The tumor is characterized histologically as an admixture of chondroblasts and multinucleated giant cells with chondroid matrix and pericellular calcifications. We present a case of a chondroblastoma of the hand with an unusual large extraosseous soft tissue component. The mass demonstrated diffuse calcifications and radiolucent lesions in the dorsal aspect of the hamate and metacarpals. Differential diagnoses included synovial chondromatosis, soft tissue chondroma, and tenosynovial giant cell tumor. The patient underwent open biopsy of the mass with plans for excision. Final histopathologic diagnosis was of chondroblastoma of the hamate with a large soft tissue component. A marginal excision of the lesion with curettage and cementation was performed.

Machine learning for rhabdomyosarcoma histopathology.

Correctly diagnosing a rare childhood cancer such as sarcoma can be critical to assigning the correct treatment regimen. With a finite number of pathologists worldwide specializing in pediatric/young adult sarcoma histopathology, access to expert differential diagnosis early in case assessment is limited for many global regions. The lack of highly-trained sarcoma pathologists is especially pronounced in low to middle-income countries, where pathology expertise may be limited despite a similar rate of sarcoma incidence. To address this issue in part, we developed a deep learning convolutional neural network (CNN)-based differential diagnosis system to act as a pre-pathologist screening tool that quantifies diagnosis likelihood amongst trained soft-tissue sarcoma subtypes based on whole histopathology tissue slides. The CNN model is trained on a cohort of 424 centrally-reviewed histopathology tissue slides of alveolar rhabdomyosarcoma, embryonal rhabdomyosarcoma and clear-cell sarcoma tumors, all initially diagnosed at the originating institution and subsequently validated by central review. This CNN model was able to accurately classify the withheld testing cohort with resulting receiver operating characteristic (ROC) area under curve (AUC) values above 0.889 for all tested sarcoma subtypes. We subsequently used the CNN model to classify an externally-sourced cohort of human alveolar and embryonal rhabdomyosarcoma samples and a cohort of 318 histopathology tissue sections from genetically engineered mouse models of rhabdomyosarcoma. Finally, we investigated the overall robustness of the trained CNN model with respect to histopathological variations such as anaplasia, and classification outcomes on histopathology slides from untrained disease models. Overall positive results from our validation studies coupled with the limited worldwide availability of sarcoma pathology expertise suggests the potential of machine learning to assist local pathologists in quickly narrowing the differential diagnosis of sarcoma subtype in children, adolescents, and young adults.