Tumors Of Origin Research Articles

B-314 Utility of he4 in differential diagnosis of pleural effusions

Abstract Background The usefulness of tumor markers in the differential diagnosis of cancer in patients with pleural effusions (PE) remains controversial. Few studies have reported the measurement of human epididymal protein 4 (HE4) in pleural fluid. HE4 is a tumor marker associated with different types of cancer. An elevated serum concentration has been observed in ovarian and lung cancers. Other benign pathologies can increase HE4 levels, which can be detect with different laboratory tests identified with adenosine deaminase (ADA), C-reactive protein (CRP), % polynuclear cell count (%PMN) and glomerular filtration rate (GFR) can be used in order to detect possible false positives. The objective of this study was to assess the diagnostic usefulness of HE4 in malignant pleural effusions by comparing HE4 concentrations between benign and malignant samples, while considering the causes of HE4 false positives. Methods HE4, ADA, differential leukocyte count, CRP and GFR concentrations were determined in 238 pleural effusion samples. Diagnostic efficacy analysis using receiver operating curves (ROC) identified the ability of HE4 to detect malignancy, searching for cut-off points with 100% specificity. Results HE4 concentrations showed significant differences (P<0.01) between malignant (median 1065pmol/L) and benign (median 699pmol/L) pleural effusions. Significant differences (P<0.01) were found in HE4 concentrations for gynecological and lung cancer origin (median 7397 pmol/L and 5150 pmol/L respectively) and other malignant pleural effusions (median 1065 pmol/L). Patients with benign diseases with renal failure showed a higher HE4 concentration (median 964pmol/L) than in patients without these pathologies (median 696pmol/L) identified with GFR<30 mL/min. No differences were demonstrated in HE4 concentration levels using ADA, differential leukocyte count or CRP, so only glomerular filtration rate was considered cause of false positives. A sensitivity of 23% was obtained for HE4, with a cut-off point of 3050pmol/L and a specificity of 100% for the whole group. In the potential false positive group, the sensitivity of HE4 was 28% with a cutoff point of 3050 pmol/L with a specificity of 100%. In the group without potential false positives, we obtained a sensitivity of 30% with a specificity of 100% for a cutoff point of 1992pmol/L. When we used a specific cut-off point for each group, we obtained a sensitivity of 38% with a specificity of 100%. Conclusions HE4 levels are higher in malignant pleural effusions compared to other pathologies. HE4 levels allow the identification of malignant pleural effusions with moderate sensitivity and maximum specificity. HE4 levels can differentiate between tumors of gynecological or lung origin and others. Glomerular filtration rate (GFR<30 mL/min) was considered the main cause of false positives. If we identify possible false positives, we can use specific cut-offs and improve sensitivity up to 38% with maximum specificity compared to using a single cut-off for all effusions.

A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids

Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.

A unique presentation of pericardial epithelioid angiosarcoma with multifaceted complications

Introduction and importance: Angiosarcomas are rare tumors of endothelial origin and may arise in any organ. Epithelioid angiosarcomas are a subtype of angiosarcoma that are rapidly progressive and typically fatal. Case presentation: The authors report a case of a 25-year-old previously healthy female who presented initially for dyspnea and palpitations, on further evaluation she was found to have large bilateral pleural effusions and cardiac tamponade. Clinical discussion: Pericardiocentesis and thoracentesis were performed alongside biopsies that revealed atypical cellular proliferation. Fluorodeoxyglucose-positron emission tomography (FDG-PET) showed avid uptake in the anterior mediastinum, perivascular, paratracheal, subcarinal and pleural lymph nodes with large FDG uptake in the bilateral pleural effusion. Mediastinoscopy was done and biopsies showed an overtly malignant, epithelioid neoplasm with foci of vaso-formation; Keeping with high-grade epithelioid angiosarcoma of the pericardium. She received six cycles of weekly paclitaxel, but imaging for abdominal pain incidentally showed evidence of metastasis to the liver and spine so she was switched to Adriamycin-Ifosfamide for which she received one cycle so far. Her hospital course was complicated by high-output pleural effusions, chylothorax, left atrial thrombus formation and an intensive care unit stay for septic shock. Conclusion: Pericardial epithelioid angiosarcoma has been reported rarely in the literature. The authors aim to report a case of extensive metastatic pericardial epithelioid angiosarcoma in a young patient; which we believe can be an addition to the literature of a malignancy associated with poor prognosis and no definitive proven treatment regimen.

Differentiating primary from metastatic ovarian tumors of gastrointestinal origin by CT

PurposeTo determine differentiating CT imaging features of primary ovarian cancers from ovarian metastases of gastrointestinal origin. MethodsRetrospective study of 50 patients with new ovarian lesions on CT, half were primary ovarian cancers and half gastrointestinal metastases. Two blinded independent readers described tumor characteristics on CT (size, laterality, margin, etc.) and ancillary features (ascites, peritoneal seeding, lymphadenopathy, etc.). Patient age, sex, cancer history, and tumor marker levels for CA-125 and CEA were collected. Wilcoxon test and Pearson's chi-squared test were used for statistical analysis. Results50 patients with mean age of 62.1 years were included. Ovarian metastases were more likely to be cystic/mainly cystic (p=0.013), have smooth margins (p=0.011), and have no/mild enhancement (p<0.001). Primary ovarian lesions were associated with moderate to large volume of ascites (p=0.047) and more commonly seen with lymphadenopathy (p=0.008). Laterality was not significantly different between the two groups. CA-125 level was more commonly elevated in primary ovarian lesions (87% vs 50%, p=0.018), and with much higher values (1076.5 vs 155.1, p=0.013). CEA level was more commonly elevated in metastatic ovarian lesions (83.3% vs 15.4%, p<0.001), and with higher values (72.4 vs 2.1, p<0.001). ConclusionOvarian metastases were more frequently smooth-margined and cystic with little enhancement. Primary ovarian lesions were more commonly associated with lymphadenopathy and larger volume of ascites. Tumor markers CEA and CA-125 were more frequently elevated in metastatic and primary lesions, respectively. Cancer history was the only variable that increased the odds of metastasis and therefore it is important to always correlate with history of cancer.

Predicting somatic mutation origins in cell-free DNA by semi-supervised GAN models

MotivationDistinguishing between pathogenic cancer-associated mutations and other somatic variants present in cell-free DNA (cfDNA) is one of the challenges in the field of liquid biopsy. This distinction is critical, since the misclassification of mutations stemming from clonal hematopoiesis (CH) as tumor-derived and vice versa could result in inaccurate diagnoses and inappropriate therapeutic interventions for patients. ResultsWe addressed this by developing a specialized machine learning technique to differentiate tumor- or CH-related mutations in cfDNA. We established a comprehensive in-house reference catalog, comprising approximately 25,000 single nucleotide variants (SNVs), each linked to either tumor or CH origin. This reference serves as a foundation for training a deep learning model, which is structured on the semi-supervised generative adversarial network (SSGAN) architecture. By analyzing genomic coordinates and nucleotide composition of cfDNA variants, our model attains 95 % area under the curve (AUC) in classifying uncharacterized variants as CH or tumor-derived. In conclusion, our research emphasizes the potential of genomic feature prediction, using cfDNA data, to stand as a robust alternative to conventional multi-analyte sequencing methods. This approach not only enhances the accuracy of distinguishing CH from tumor mutations in liquid biopsy data, but also highlights the potential of advanced data analysis techniques and machine learning in genomics and personalized medicine. Availability: https://github.com/FPalizban/SSGAN.

Multifaceted modeling of small intestinal neuroendocrine tumors

Small intestinal neuroendocrine tumors, siNETs, are a group of rare cancers that arise from neuroendocrine cells in the lining of the jejunum and ileum, which are either classified as tumors, siNETs, or carcinomas, siNECs. Current treatment strategies for low grade tumors include surgical resection, peptide radionucleotide receptor therapy, and somatostatin analogues, while high grade and recurrent tumors may receive cytotoxic chemotherapy. These limited treatment options are linked to the lack of representative models that can both reflect the biology of the tumor and are amenable to mid-to-high throughput experimentation. Cell line generation is challenging considering the indolent nature of primary lesions, although some attempts have been successful using a variety of methods and include the primary P-STS line and those derived from metastatic lesions including GOT1, CNDT2.5 and HC45. Patient-derived modeling, including organoids and xenografting, have allowed for multicellular and three-dimensional representations of the original tumor. These specific models allow for multicellular populations derived from the tumor, providing better tumor representation for use in drug screening and in vitro assays. Currently, there are limited, although increasing, published models of siNETs implanted as xenografts in mice and zebrafish. As these cellular and animal models provide insights on siNET biology, theragnostic modeling has provided key information on the clinical progression and treatment of this disease. Significant strides toward more representative models have been made throughout the last decade. In this review, details of these attempts as well as future directions and strategies for more robust models will be addressed.

A patient-derived HCC spheroid system to model the tumor microenvironment and treatment response

Background & AimsHepatocellular carcinoma (HCC) is the third leading and fastest rising cause of cancer-related death worldwide. The discovery and preclinical development of compounds targeting HCC is hampered by the absence of authentic tractable systems recapitulating the heterogeneity of patient HCC tumors and the tumor microenvironment (TME). MethodsWe established a novel and simple patient-derived multicellular tumorspheroid model based on clinical HCC tumor tissues, processed using enzymatic and mechanical dissociation. After quality controls, 22 HCC tissues and 17 HCC serum were selected for tumorspheroid generation and perturbation studies. Cells were grown in 3D in optimized medium in presence of patient serum. Characterization of the tumorspheroid cell populations was performed by flow cytometry, immunohistochemistry and functional assays. As a proof of concept, we treated patient-derived spheroids with FDA approved anti-HCC compounds. ResultsThe model was successfully established independently from cancer etiology and grade from 22 HCC tissues. We show that the use of HCC patient serum was essential for tumorspheroid generation, the TME function and to maintain cell viability. The tumorspheroids comprised the main cell compartments including epithelial cancer cells as well as all major cell populations of the TME (cancer-associated fibroblasts, macrophages and T cells, endothelial cells). Tumorspheroids reflect HCC heterogeneity, including variability in cell type proportions and TME, and mimic the original tumor features. Moreover, we observed differential responses to FDA HCC approved drugs between donors as observed in patients. ConclusionThis patient HCC serum-tumorspheroid model provides novel opportunities for drug discovery and development as well as mechanism of action studies including compounds targeting the TME. This model will likely contribute to improve the dismal outcome of patients with HCC. Impact and implicationsHepatocellular carcinoma (HCC) is a leading and fast rising cause of cancer-related death worldwide. Despite approval of novel therapies, the outcome of advanced HCC remains unsatisfactory. By developing a novel patient-derived tumorspheroid model recapitulating tumor heterogeneity and microenvironment, we provide new opportunities for HCC drug development and analysis of mechanism of action in authentic patient tissues. The application of the patient-derived tumorspheroids combined with other HCC models will likely contribute to drug development and to improve the dismal outcome of patients with HCC.

Which Prognostic Model Best Predicts Poor Prognosis in Patients with Spinal Metastases? A Comparative Analysis of 8 Scoring Systems

Existing scoring system's comparative effectiveness in identifying patients with poor prognosis (i.e., <6 months survival) has not been thoroughly explored. We compared the predictive performance of eight prognostic scoring systems (Tomita, modified Tokuhashi, modified Bauer, Rades, Oncological Spinal Prognostic Index, Lei, New England Spinal Metastasis Score, and the skeletal oncology research group (SORG) nomogram) with the area under curve (AUC) from receiver operating characteristic (ROC) curves and evaluated the predictive accuracy for 6-month survival across different primary tumor origins, and 1-month survival. Logistic regression was used to identify factors associated with 6-month survival. 641 patients with spinal metastasis treated between 1994 and 2022 were included. The SORG nomogram showed best performance with low discriminative power in predicting 6-month survival (AUC [95% confidence interval (CI)]: 0.664 [0.584-0.744]). Logistic regression analysis identified significant factors influencing 6-month survival, including primary cancer type in Lei's classification, preoperative Frankel grades C and D, or grades A and B compared with grade E, preoperative WBC, preoperative albumin, and preoperative chemotherapy. For 1-month survival predictions, both the SORG nomogram (AUC [95% CI]: 0.750 [0.648-0.851]) and modified Tokuhashi score (AUC [95% CI]: 0.667 [0.552-0.781]) showed significance, albeit with moderate to low discriminative power. This study shows that most scoring systems have low discriminative power, with only the SORG nomogram having moderate power for predicting poor prognosis. Recent and future advances in treatment, laboratory markers, and our understanding of tumor biology should be incorporated into prognostic models to improve their accuracy.

Review of deep learning-based pathological image classification: From task-specific models to foundation models

Pathological diagnosis is considered the gold standard in cancer diagnosis, playing a crucial role in guiding treatment decisions and prognosis assessment for patients. However, achieving accurate diagnosis of pathology images poses several challenges, including the scarcity of pathologists and the inherent subjective variability in their interpretations. The advancements in whole-slide imaging technology and deep learning methods provide new opportunities for digital pathology, especially in low-resource settings, by enabling effective pathological image classification. In this article, we begin by introducing the datasets, which include both unimodal and multimodal types, as essential resources for advancing pathological image classification. We then provide a comprehensive overview of deep learning-based pathological image classification models, covering task-specific models such as supervised, unsupervised, weakly supervised, and semi-supervised learning methods, as well as unimodal and multimodal foundation models. Next, we review tumor-related indicators that can be predicted from pathological images, focusing on two main categories: indicators that can be recognized by pathologists, such as tumor classification, grading, and region recognition; and those that cannot be recognized by pathologists, including molecular subtype prediction, tumor origin prediction, biomarker prediction, and survival prediction. Finally, we summarize the key challenges in digital pathology and propose potential future directions.

A CASE OF METASTATIC INTRACARDIAC RHABDOMYOSARCOMA

Intracardiac metastasis of rhabdomyosarcoma (RMS) is a rare occurrence, with only a few cases reported in the literature. RMS is a malignant tumor of skeletal muscle origin, primarily affects children and young adults and rarely adults. It is characterized by an aggressive spread, often metastasizing to the lungs, bone marrow, and lymph nodes. However, cardiac involvement is uncommon and typically presents late in the disease course, complicating management and worsening prognosis. This article explores a rare case of intracardiac metastasis of RMS, discussing the clinical presentation, diagnostic challenges, treatment modalities, and prognosis. We also review relevant literature to provide a comprehensive understanding of this rare phenomenon.

Merkel Cell Carcinoma: A Case Report and Literature Review

Merkel cell carcinoma (MCC) is a rare but very aggressive cutaneous malignant tumor of neuroendocrine origin with an increasing incidence rate. Its pathogenesis is linked to Merkel cell polyomavirus, and advanced age, white skin exposed to UV and immunodeficiency are among these risk factors. Its treatment is based on surgery in localized disease and immunotherapy in locally advanced or metastatic disease. We report here the case of a 79-year-old patient, followed at the Oncology-Radiotherapy Department of the Mohammed VI University Hospital in Marrakech, for treatment of a MCC of the lower lip. He benefited from primary surgery followed by adjuvant radiotherapy with an encouraging response. Currently he is under surveillance. Knowledge of this rare malignant tumor will allow health professionals to improve its management by establishing the diagnosis early and indicating adequate treatment, because therapeutic advances in recent years have significantly improved survival.

Microfluidic Applications in Prostate Cancer Research.

Prostate cancer is a disease in which cells in the prostate, a gland in the male reproductive system below the bladder, grow out of control and, among men, it is the second-most frequently diagnosed cancer (other than skin cancer). In recent years, prostate cancer death rate has stabilized and, currently, it is the second-most frequent cause of cancer death in men (after lung cancer). Most deaths occur due to metastasis, as cancer cells from the original tumor establish secondary tumors in distant organs. For a long time, classical cell cultures and animal models have been utilized in basic and applied scientific research, including clinical applications for many diseases, such as prostate cancer, since no better alternatives were available. Although helpful in dissecting cellular mechanisms, these models are poor predictors of physiological behavior mainly because of the lack of appropriate microenvironments. Microfluidics has emerged in the last two decades as a technology that could lead to a paradigm shift in life sciences and, in particular, controlling cancer. Microfluidic systems, such as organ-on-chips, have been assembled to mimic the critical functions of human organs. These microphysiological systems enable the long-term maintenance of cellular co-cultures in vitro to reconstitute in vivo tissue-level microenvironments, bridging the gap between traditional cell cultures and animal models. Several reviews on microfluidics for prostate cancer studies have been published focusing on technology advancement and disease progression. As metastatic castration-resistant prostate cancer remains a clinically challenging late-stage cancer, with no curative treatments, we expanded this review to cover recent microfluidic applications related to prostate cancer research. The review includes discussions of the roles of microfluidics in modeling the human prostate, prostate cancer initiation and development, as well as prostate cancer detection and therapy, highlighting potentially major contributions of microfluidics in the continuous march toward eradicating prostate cancer.

The roles of patient-derived xenograft models and artificial intelligence toward precision medicine.

Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

PIPAC Pharmacologic and Clinical Data.

Pressurized intraperitoneal aerosol chemotherapy (PIPAC) emerged as an innovative intraperitoneal chemotherapy delivery system to overcome the issue of limited efficacy of systemic therapies to induce response in peritoneal malignancies. Promising results for patients with mesothelioma peritonei and peritoneal metastasis from gastric, ovarian, colorectal, pancreatic, and hepatobiliary tumors origin are changing the landscape for patients otherwise just facing palliative treatment. Ongoing trials will shed more light on the actual benefits of PIPAC.

PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.

Despite advanced diagnostics, 3%-5% of cases remain classified as cancer of unknown primary (CUP). DNA methylation, an important epigenetic feature, is essential for determining the origin of metastatic tumors. We presented PathMethy, a novel Transformer model integrated with functional categories and crosstalk of pathways, to accurately trace the origin of tumors in CUP samples based on DNA methylation. PathMethy outperformed seven competing methods in F1-score across nine cancer datasets and predicted accurately the molecular subtypes within nine primary tumor types. It not only excelled at tracing the origins of both primary and metastatic tumors but also demonstrated a high degree of agreement with previously diagnosed sites in cases of CUP. PathMethy provided biological insights by highlighting key pathways, functional categories, and their interactions. Using functional categories of pathways, we gained a global understanding of biological processes. For broader access, a user-friendly web server for researchers and clinicians is available at https://cup.pathmethy.com.

Unicystic Ameloblastoma Masquerading as Vascular Lesion

Ameloblastoma is a benign epithelial tumor of odontogenic origin which shows variations in its presentation that intrigue the authors. Authors report a case of unicystic ameloblastoma which presented, clinically and radiographically mimicking features of a vascular lesion. Patient reported with the chief complaint of pain and swelling in the left lower-third of the face. The investigations performed gave a diagnosis dilemma and hence deciding on appropriate investigations played a pivotal role to enable precise diagnosis of the lesion and facilitated surgeons to decide on the treatment plan.

Timeline of surgery in localized angiosarcoma of the breast: Improving outcome following multidisciplinary treatment optimization

Primary (PAS) and radiation-associated angiosarcomas (RAAS) of the breast are rare tumors of vascular origin with poor survival. In this retrospective cohort study, we aimed to assess the impact of multidisciplinary treatment optimization on the prognosis of patients who underwent surgery at a national referral center. Cases of operable angiosarcoma of the breast evaluated by a multidisciplinary team including surgeons, medical oncologists and radiation oncologists expert in the field and treated from January 2012 to January 2023 were retrieved from a prospectively maintained database. The outcomes of three treatment groups, defined by the timing of surgery in relation to adjuvant and neoadjuvant therapies, were compared. Fifty-nine patients with operable angiosarcomas of the breast (49 RAAS and 10 PAS) were retrospectively identified. The five-year overall survival was 85.2% (95% CI 73.9-98.2) and event-free survival was significantly better in patients with grade 1 than those with grade 2 or 3 tumors. Patients receiving neoadjuvant chemotherapy had significantly better outcomes than those treated with primary surgery. Pathological complete response was significantly higher in patients receiving neoadjuvant radiotherapy after neoadjuvant chemotherapy, and a trend towards better distant-disease-free survival was found for patients with complete response at time of surgery. Optimization of angiosarcoma treatment based on specialized, multidisciplinary assessment regarding the type and timing of surgery and the use of neoadjuvant chemoradiotherapy can improve outcomes. The findings of this study support the use of neoadjuvant chemotherapy as well as adjuvant and neoadjuvant radiotherapy in clinical practice.

GDF-15 predicts epithelioid hemangioendothelioma aggressiveness and is down-regulated by sirolimus through ATF4/ATF5 suppression.

Epithelioid hemangioendothelioma (EHE) poses a therapeutic challenge due to limited efficacy of conventional chemotherapy in advanced cases, necessitating exploration of new treatment avenues and identification of novel aggressiveness biomarkers. This study aimed at i) utilizing an EHE patient-derived xenograft (PDX) model and its associated cell line to assess the efficacy of sirolimus and ii) analyzing two distinct patient cohorts to pinpoint circulating biomarkers of EHE aggressiveness. A PDX model and corresponding cell line were established from an advanced EHE patient, demonstrating consistency with the original tumor in terms of histomorphology, WWTR1::CAMTA1 fusion presence, and genomic and transcriptomic profiles. Two independent patient series were employed to investigate the association between Growth/Differentiation Factor 15 (GDF-15) serum levels and EHE aggressiveness. ELISA analyses on EHE cell culture medium and blood from EHE-carrying mice revealed the release of GDF-15 by EHE cells. Sirolimus exhibited markedly higher anti-tumor activity compared to doxorubicin, concurrently reducing GDF-15 expression/release both in vivo and in vitro. This reduction was attributed to the drug-induced inhibition of phosphorylation/activation of 4E-BP1 and subsequent downregulation of the GDF-15 transcription factors ATF4 and ATF5. Blood sample analyses from two independent patient series showed a significant correlation between GDF-15 and EHE aggressiveness. This study identifies GDF-15 as a novel biomarker of EHE aggressiveness and underscores the superior efficacy of sirolimus compared to doxorubicin in our experimental models. The observed inhibition of GDF-15 release by sirolimus suggests its potential as a biomarker for monitoring the drug's activity in patients.

Modified trident technique for surgical approach to facial lipoma.

Lipomas are the most common tumors of mesenchymal origin throughout the body. Although they have low incidence in the oral cavity, they surgical approach can be challenging. 10-year-old male with a giant lipoma in the buccal and masticator space, an intraoral surgical approach was chosen using by modifying trident technique of Ramírez-Oropeza. The main advantages and limitations of this intraoral approach are examined. An intraoral approach was selected because of less possibility of injuring the facial nerve, better esthetic results and less invasive, obtaining excellent results.

Abstract B042: Dissection of the T cell infiltrate in mouse pancreatic tumors reveals a diverse tumor-reactive T cell repertoire targeting neoantigens and tumor-associated antigens

Abstract Although pancreatic cancer is generally refractory to immune checkpoint blockade (ICB), recent studies of tumor-infiltrating T cells in human tumor samples demonstrated the presence of in vivo expanded, tumor-reactive T cell receptor (TCR) clonotypes. Here, we explored the T cell repertoire in ICB-refractory murine pancreatic cancer models by combining single-cell transcriptomics with functional TCR characterization. Our initial studies in an orthotopic, transplantable model revealed a remarkable diversity of tumor-reactive TCR clonotypes that could be distinguished from bystander T cells based on gene expression signatures. We identified TCRs exclusively reactive against the tumor of origin, TCRs that also react against an independently derived pancreatic tumor line, and TCRs broadly reactive to tumor cells of diverse tissue origin (e.g. MC38), the latter category being predominant. Subsequent studies were performed in an autochthonous mouse model, in which single focal tumors were induced in the pancreas by means of in vivo electroporation of gene constructs encoding mutant Kras and inducing CRISPR/Cas9-mediated inactivation of tumor suppressor genes. We analyzed the T cell repertoire in tumors with different levels of mutational burden by also inactivating DNA damage repair genes. Interestingly, even in mismatch repair-deficient tumors that featured a high mutational load, the anti-tumor T cell response was primarily directed against recurrent tumor-associated antigens (TAAs) rather than ‘private’ mutanome-encoded neoantigens. Several TAA-reactive TCRs also mediated reactivity against non-transformed cells. Immunopeptidome analysis thus far revealed the identity of 5 cognate antigens targeted by the tumor-reactive TCR repertoire: a mutanome-encoded neoantigen, an epitope encoded by an ectopically expressed endogenous retroviral provirus, and three epitopes derived from TAAs: Lon Peptidase 2 (LONP2), ATPase Na+/K+ Transporting Subunit Beta 3 (ATP1B3) and SEC24B, a component of secretory vesicles. Notably, the overexpression of LONP2 and ATP1B3 was found associated with progression of human cancers, whereas silencing was shown to suppress tumor cell growth. Overexpression of SEC24B in cancers has not been reported, but this protein plays an essential role in the secretory pathway of acinar cells. In summary, in-depth analysis of the natural T cell response in murine pancreatic cancer models revealed a diverse tumor-reactive T cell repertoire that is predominantly directed against TAAs, some of which are also expressed in normal somatic cells. These findings underline the importance of uncovering the antigen-specificity of the natural tumor-reactive TCR repertoire, both in mouse models and human tumor samples, to assess its therapeutic potential and safety with regard to personalized immunotherapy. Citation Format: Stefan Zens, Hannes Kehm, Daniel Baumann, Aline Konrad, Zibo Meng, Arnoud H. de Ru, Julian Mochayedi, Francesca Lucato, Angelika B. Riemer, Isabel Poschke, Michael Volkmar, Peter A. van Veelen, Rienk Offringa. Dissection of the T cell infiltrate in mouse pancreatic tumors reveals a diverse tumor-reactive T cell repertoire targeting neoantigens and tumor-associated antigens [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B042.