Tumor Heterogeneity Research Articles

Characterizing the Transformation and Diagnosis of Atypical Lipomatous Tumor to Dedifferentiated Liposarcoma: Single Institutional Outcomes.

Atypical lipomatous tumor (ALT) in the extremities is a locally aggressive adipocytic tumor with the potential risk of transformation into dedifferentiated liposarcoma (DDLS). Studies seldom differentiate whether DDLS was diagnosed on initial biopsy, final resected specimen, or subsequent recurrence. Our study seeks to characterize how and when patients received their ALT or DDLS diagnoses to better understand the relationship between the two neoplasms. We performed a retrospective review of patients diagnosed with ALT or DDLS of the extremities. Clinical characteristics, including the method of diagnosis of an ALT or DDLS, time between diagnoses, and tumor recurrence was recorded. Univariate/multivariate analysis was performed to identify risk factors. Forty-five patients were diagnosed with ALT after core needle biopsy (CNB) and 41 of them received marginal en bloc excision. Three (7.3%) of these patients had a heterogeneous tumor on final resection, pathology revealed both ALT and DDLS. Four patients (8.2%) were diagnosed with DDLS from CNB and received negative margin en bloc excision. One of these tumors was identified as heterogeneous ALT/DDLS after resection. Fifty-three patients received marginal en bloc resection without CNB after a benign lipomatous mass was suspected on CT/MRI. Among these, one (1.9%) had a tumor with a heterogeneous composition of both ALT and DDLS on pathology. There were 11 (11.7%) ALT recurrences and 1 (1.0%) DDLS recurrence after ALT resection. Obtaining a proper diagnosis whether ALT or DDLS is critical. Our cohort found that amongst those concerning lipomatous lesions biopsied, 7.84% will show biopsy proven DDLS. Additionally, 6.67% of the biopsies will be false negatives and show DDLS on final pathology. Furthermore, our local recurrence for ALT was 11.7% recurring as ALT and 1.0% recurring as DDLS.

High-Throughput Hybridization Assay as First-Line Diagnostic Test for Sarcomas: Clinical Assessment in a Tertiary Referral Center.

Sarcomas are rare and highly heterogeneous mesenchymal tumors with deceptive morphologic features that pose a challenge for precise diagnostics. Chromosomal rearrangements generating pathognomonic gene fusions are useful diagnostic markers, traditionally tested using single-plex standard of care assays with limited diagnostic yield. NanoString nCounter technology has emerged as a robust solution with multiplexing capabilities. To optimize NanoString effective coverage of specific entities and conduct a validation study to support its clinical implementation. We reconfigured a NanoString's codeset by including a set of probes for detecting gene fusion variants of solitary fibrous tumors, low-grade fibromyxoid sarcomas/sclerosing epithelioid fibrosarcomas, and undifferentiated small round cell sarcomas, totaling 188 probes. A technical validation study was conducted with 96 retrospective samples. Additionally, 76 prospective samples were evaluated to assess the assay's clinical performance. Both technical and clinical validation studies showed that NanoString's codeset reached >88% sensitivity and 100% specificity, compared with standard of care methods, and superior diagnostic yield as a first-line test. Our design enabled the detection of almost all fusion variants of NGFI-A binding protein 2 (NAB2) with signal transducer and activator of transcription 6 (STAT6) in solitary fibrous tumors, as well as cAMP responsive element binding protein 3 like 1/2 (CREB3L1/2) rearrangements in all low-grade fibromyxoid sarcoma/sclerosing epithelioid fibrosarcoma cases. Identification of specific gene fusions of undifferentiated small round cell sarcoma was also improved, but additional strategies are necessary to attain full coverage. The NanoString platform demonstrated good sensitivity, specificity, and superior diagnostic yield. It is a cost-effective assay with rapid turnaround time, low sample consumption, streamlined analysis, and easy customization. Therefore, it is a promising alternative first-line diagnostic tool for routine sarcoma testing.

Exploring the role of ADAMTSL2 across multiple cancer types: A pan-cancer analysis and validated in colorectal cancer

BackgroundRecent studies have established a correlation between ADAMTSL2 (ADAMTS-like 2) and the development of various cancers. This study aims to conduct a comprehensive pan-cancer analysis in 37 cancer types and investigate its potential role in colon and rectal adenocarcinoma (COADREAD).MethodPan-cancer and mutation data were sourced from The Cancer Genome Atlas (TCGA) database and analyzed using Sangerbox analysis platform. We explored the expression patterns and prognostic implications of ADAMTSL2, and investigated its relationships with tumor heterogeneity, stemness, immune checkpoint genes, immune cell infiltration, RNA modifications, and mutational profiles across different cancers. Additionally, with Ethics Committee approval, we conducted immunohistochemical (IHC) analysis on 120 COADEAD samples to evaluate ADAMTSL2 expression and its association with clinicopathological parameters.ResultsADAMTSL2 expression was positively correlated with the hazard ratio of OS, DSS, DFI and PFI for ESCA and COADREAD. A negative correlation was observed between ADAMTSL2 expression and NEO levels in COAD. Gene alterations in ADAMTSL2 were observed, with a mutation frequency of 5.0% in COAD. There is a significant correlation between ADAMTSL2 expression and immune cell infiltration in a variety of cancers. The expression level of ADAMTSL2 protein was associated with T stage, N stage, M stage (p < 0.05). Kaplan‒Meier survival curves demonstrated that the high ADAMTSL2 group had a shorter OS time (p = 0.047) and progression free survival time (p = 0.026) than the low ADAMTSL2 group.ConclusionIn summary, we conducted a comprehensive pan-cancer analysis of ADAMTSL2 and we demonstrated that ADAMTSL2 may serve as a novel prognostic biomarker and immunotherapy target in COADREAD.

Identification of a senescence-related transcriptional signature to uncover molecular subtypes and key genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer caused by abnormal cell growth due to faulty signal transduction. Cells secrete tumor suppressor factors in response to potential carcinogenic signals, inducing cellular senescence (CS) as a countermeasure. However, accurately measuring CS levels in different types of tumors is challenging due to tumor heterogeneity and the lack of universal and specific CS markers. Machine learning has revealed unique molecular traits in HCC patients, leading to clinical advantages. More research is needed to understand senescence-related molecular features in these patients. In this study, the gene expression profile features of patients with HCC were analyzed by integrating single-cell RNA sequencing and bulk RNA-seq datasets from HCC samples. The analysis identified the senescence-related pathways exhibiting HCC specificity. Subsequently, genes from these pathways were used to identify senescence-related molecular subtypes in HCC, showing significant variations in biological and clinical attributes. An HCC-specific CS risk model developed in this study revealed substantial associations between the patients' CS scores and prognosis grouping, clinical staging, immune infiltration levels, immunotherapy response, and drug sensitivity levels. Within the constructed model, G6PD was identified as a key gene, potentially serving as a senescence-related target in liver cancer. Molecular biology experiments demonstrated that overexpression of G6PD effectively promotes the proliferative, invasive, and migration capacities of HepG2 and SK-HEP-1 cells. In conclusion, this analysis offers a valuable framework for understanding senescence in HCC and introduces a new biomarker. These findings improve our understanding of senescence in HCC and have potential for future research.

Spatial transcriptomics: a new frontier in accurate localization of breast cancer diagnosis and treatment.

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients' survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of high-resolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes.

Automatic detection and segmentation of lesions in 18 F-FDG PET/CT imaging of patients with Hodgkin lymphoma using 3D dense U-Net.

The accuracy of automatic tumor segmentation in PET/computed tomography (PET/CT) images is crucial for the effective treatment and monitoring of Hodgkin lymphoma. This study aims to address the challenges faced by certain segmentation algorithms in accurately differentiating lymphoma from normal organ uptakes due to PET image resolution and tumor heterogeneity. Variants of the encoder-decoder architectures are state-of-the-art models for image segmentation. Among these kinds of architectures, U-Net is one of the most famous and predominant for medical image segmentation. In this study, we propose a fully automatic approach for Hodgkin lymphoma segmentation that combines U-Net and DenseNet architectures to reduce network loss for very small lesions, which is trained using the Tversky loss function. The hypothesis is that the fusion of these two deep learning models can improve the accuracy and robustness of Hodgkin lymphoma segmentation. A dataset with 141 samples was used to train our proposed network. Also, to test and evaluate the proposed network, we allocated two separate datasets of 20 samples. We achieved 0.759 as the mean Dice similarity coefficient with a median value of 0.767, and interquartile range (0.647-0.837). A good agreement was observed between the ground truth of test images against the predicted volume with precision and recall scores of 0.798 and 0.763, respectively. This study demonstrates that the integration of U-Net and DenseNet architectures, along with the Tversky loss function, can significantly enhance the accuracy of Hodgkin lymphoma segmentation in PET/CT images compared to similar studies.

Single nucleus DNA sequencing of flow sorted archived frozen and formalin fixed paraffin embedded solid tumors.

Archived samples, including frozen and formalin fixed paraffin embedded (FFPE) tissues, are a vast resource of clinically annotated materials for the application of high-definition genomics to improve patient management and provide a molecular basis for the delivery of personalized cancer therapeutics. Notably, FFPE tissues are stable, provide repeat sampling of tissues of interest, and can be stored indefinitely at ambient temperature. The development of single cell DNA sequencing (scDNA-seq) technologies provides an unparalleled opportunity for the study of tumor heterogeneity and the identification of often rare subclonal cell populations that drive tumor evolution and progression to advanced therapy resistant disease. However, major limitations to the use of archived tissues for scDNA-seq include the low yields of intact cells in the presence of high levels of subcellular debris in biopsies, and the highly variable quantity and quality of the DNA extracted from samples of interest. The latter is of high significance for the use of FFPE tissues due to the presence of DNA-protein crosslinks. In addition, many samples, notably tumors arising in solid tissues, contain admixtures of reactive stroma, inflammatory cells, and necrosis in immediate contact with tumor cells. To expand their use for translational studies, we optimized flow sorting and sequencing of single nuclei from archived fresh frozen (FF) and FFPE tumor tissues. Our methods, which include isolation of intact nuclei suitable for library preparations, quality control (QC) metrics for each step, and a single cell sequencing bioinformatic processing and analysis pipeline, were validated with flow sorted nuclei from matching FF and FFPE ovarian cancer surgical samples and a sequencing panel of 553 amplicons targeting single nucleotide and copy number variants in genes of interest. Our flow sorting based protocol provides intact nuclei suitable for snDNA-seq from archival FF and FFPE tissues. Furthermore, we have developed QC steps that optimize the preparation and selection of samples for deep single cell clonal profiling. Our data processing pipeline captures rare subclones in tumors with highly variable genomes based on variants in genes of interest.

An engineered model of metastatic colonization of human bone marrow reveals breast cancer cell remodeling of the hematopoietic niche

Incomplete understanding of metastatic disease mechanisms continues to hinder effective treatment of cancer. Despite remarkable advancements toward the identification of druggable targets, treatment options for patients in remission following primary tumor resection remain limited. Bioengineered human tissue models of metastatic sites capable of recreating the physiologically relevant milieu of metastatic colonization may strengthen our grasp of cancer progression and contribute to the development of effective therapeutic strategies. We report the use of an engineered tissue model of human bone marrow (eBM) to identify microenvironmental cues regulating cancer cell proliferation and to investigate how triple-negative breast cancer (TNBC) cell lines influence hematopoiesis. Notably, individual stromal components of the bone marrow niche (osteoblasts, endothelial cells, and mesenchymal stem/stromal cells) were each critical for regulating tumor cell quiescence and proliferation in the three-dimensional eBM niche. We found that hematopoietic stem and progenitor cells (HSPCs) impacted TNBC cell growth and responded to cancer cell presence with a shift of HSPCs (CD34+CD38-) to downstream myeloid lineages (CD11b+CD14+). To account for tumor heterogeneity and show proof-of-concept ability for patient-specific studies, we demonstrate that patient-derived tumor organoids survive and proliferate in the eBM, resulting in distinct shifts in myelopoiesis that are similar to those observed for aggressively metastatic cell lines. We envision that this human tissue model will facilitate studies of niche-specific metastatic progression and individualized responses to treatment.

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p &lt; 0.001) and peritumoral contrast enhancement (p &lt; 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p &lt; 0.001) as well as in the radiomics analysis (p &lt; 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.

KRAS mutations detection methodology: from RFLP to CRISPR/Cas based methods.

In personalized cancer medicine, the identification of KRAS mutations is essential for making treatment decisions and improving patient outcomes. This work presents a comprehensive review of the current approaches for detection of KRAS mutations in different cancers. We highlight the value of fast and reliable KRAS mutations discovery and the effectiveness of molecular testing for selecting individuals who might benefit from targeted therapy. We provide an overview of various methods and tools available for detecting KRAS mutations, such as digital droplet PCR, next-generation sequencing (NGS), and polymerase chain reaction (PCR). We also address the difficulties and limitations in the identification of KRAS mutations, namely tumor heterogeneity and the emergence of resistance mechanisms. This article aims to guide clinicians in KRAS mutation identification.

6436 Adrenal Venous Congestion Clinically Mimicking Carcinoma In A Large Degenerated Adrenocortical Adenoma

Abstract Disclosure: K. Ishiwata: None. S. Suzuki: None. S. Watanabe: None. Y. Yamazaki: None. H. Sasano: None. K. Yokote: None. Introduction: A large heterogenous adrenal tumor is often malignant. Only eight reported cases of adrenal adenoma harboring hemorrhage or venous thrombosis have been reported but their details not. We encountered a case of large degenerated adrenocortical adenoma harboring adrenal venous congestion mimicking adrenocortical carcinoma. Case: Adrenal tumor was incidentally detected in a 71-year-old woman with a history of hypertension, dyslipidemia, and osteoporosis in her regular health check-up. Abdominal CT and MRI demonstrated a heterogenous mass measuring 7.6 cm with peripheral enhancement in the left adrenal gland. [1]3[1]I-adosterol scintigraphy revealed accumulation only in the left tumor margins with no accumulation in [1]8F-FDG-PET scan. Results of endocrinologic evaluation were consistent with subclinical Cushing syndrome. The large size and heterogeneous nature of the mass clinically suggested adrenocortical malignancy, and left laparoscopic adrenalectomy was subsequently performed. The resected left adrenal lesion was encapsulated and measured 6.5 x 6.3 x 5.8 cm appearing almost black and partially yellow on the cut surface. Microscopically, the nodule was composed of SF-1 positive, clear cortical cells with abundant hyaline stroma containing numerous dilated veins with CD31/34-positive endothelial cells, and no foci of necrosis or hemorrhage were detected. The tumor cells above were positive for steroidogenic enzymes including CTP17, HSD3β and CYP11B1 but negative for CYP11B2. Ki-67 labeling index was &amp;lt;2% in the tumor. The criteria of Weiss (0 out of 9) revealed that the tumor was cortisol-producing adrenocortical adenoma. Discussion: In this case, clusters of SF1-positive cortisol producing adrenocortical adenoma cells were present in the hyalinized stroma containing many dilated blood vessels. These findings indicated that those histological features were caused by ischemic degeneration due to intratumoral circulatory disturbance, which clinically simulated adrenocortical carcinoma. Conclusion: We have experienced a very rare case of cortisol-producing adrenocortical adenoma with adrenal venous congestion, resulting in marked enlargement of the lesion. Presentation: 6/3/2024

Utilizing Clinical Transformation Criteria for Prognostic Stratification in Follicular Lymphoma Prior to Initial Immunochemotherapy.

Although the prognosis of follicular lymphoma (FL) has improved, some patients experience early disease progression, including progression of disease within 24 months (POD24). Histological transformation is a critical event in FL. However, the heterogeneity of FL tumors makes it challenging to diagnose transformation accurately. We retrospectively applied the clinical transformation criteria used for FL transformation assessments at relapse or disease progression to conduct transformation assessments before the initial immunochemotherapy. Sixty-six FL patients who first received immunochemotherapy between January 2009 and February 2023 at our institution were selected. Twenty-three were clinical-transformation-positive (CLT+). The progression-free survival (PFS) rate of the CLT+ patients was significantly lower than that of the clinical-transformation-negative (CLT-) patients. In the POD24 assessment subgroup, the CLT+ patients had a higher incidence of POD24 than the CLT- patients. There was no significant difference in PFS between the patients treated with CHOP-like regimens and those treated with bendamustine regimens. In the CHOP-like group, the CLT+ patients exhibited significantly lower PFS than the CLT- patients. In the bendamustine group, the clinical transformation did not affect PFS. Clinical transformation criteria may be useful for the prognostic stratification of FL prior to immunochemotherapy. Additionally, they may serve as predictors of POD24.

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone’s regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-β signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Clinical applications of STING agonists in cancer immunotherapy: current progress and future prospects.

The STING (Stimulator of Interferon Genes) pathway is pivotal in activating innate immunity, making it a promising target for cancer immunotherapy. STING agonists have shown potential in enhancing immune responses, particularly in tumors resistant to traditional therapies. This scholarly review examines the diverse categories of STING agonists, encompassing CDN analogues, non-CDN chemotypes, CDN-infused exosomes, engineered bacterial vectors, and hybrid structures of small molecules-nucleic acids. We highlight their mechanisms, clinical trial progress, and therapeutic outcomes. While these agents offer significant promise, challenges such as toxicity, tumor heterogeneity, and delivery methods remain obstacles to their broader clinical use. Ongoing research and innovation are essential to overcoming these hurdles. STING agonists could play a transformative role in cancer treatment, particularly for patients with hard-to-treat malignancies, by harnessing the body's immune system to target and eliminate cancer cells.

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

Combinatory Immunotherapy for Glioblastoma Treatment

AbstractDespite advancements in the treatment of glioblastoma, it faces challenges due to tumor heterogeneity, the blood‐brain barrier, recurrence, immune evasion, and conventional strategies, leading to low survival and a poor prognosis. Therefore, it is crucial to develop novel, useful treatment strategies for improving brain distribution to overcome blood‐brain barriers (BBB) and help the treatment of glioblastoma. Conventional immunotherapy like checkpoint inhibitors, CAR‐T cell therapy, monoclonal antibodies, cancer vaccines, and adoptive cell transfer often suffers from low therapeutic outcomes because the singular therapy generally has shortcomings, such as the cold immune microenvironment of brain tumors. In contrast, the emerging combinatory immunotherapy integrated with chemo‐immunotherapy, photothermal‐immunotherapy, and radio‐immunotherapy has shown great promise to modulate tumoral immune microenvironment and boost treatment outcomes. This review discusses the immune microenvironment of GBM, its impact on immunological effects, current immunotherapy methods, and advanced studies. It also introduces combinational GBM immunotherapy with traditional cancer therapies like chemo‐immunotherapy, surgery‐immunotherapy, photothermal‐immunotherapy, and radiotherapy‐immunotherapy. In the future, it is anticipated that this article will provide beneficial information and a path for the strategy of innovative, efficient combination immunotherapy in the treatment of glioblastoma.

RNA lipid nanoparticles as efficient in vivo CRISPR-Cas9 gene editing tool for therapeutic target validation in glioblastoma cancer stem cells

In vitro and ex-vivo target identification strategies often fail to predict in vivo efficacy, particularly for glioblastoma (GBM), a highly heterogenous tumor rich in resistant cancer stem cells (GSCs). An in vivo screening tool can improve prediction of therapeutic efficacy by considering the complex tumor microenvironment and the dynamic plasticity of GSCs driving therapy resistance and recurrence. This study proposes lipid nanoparticles (LNPs) as an efficient in vivo CRISPR-Cas9 gene editing tool for target validation in mesenchymal GSCs. LNPs co-delivering mRNA (mCas9) and single-guide RNA (sgRNA) were successfully formulated and optimized facilitating both in vitro and in vivo gene editing. In vitro, LNPs achieved up to 67 % reduction in green fluorescent protein (GFP) expression, used as a model target, outperforming a commercial transfection reagent. Intratumoral administration of LNPs in GSCs resulted in ∼80 % GFP gene knock-out and a 2-fold reduction in GFP signal by day 14. This study showcases the applicability of CRISPR-Cas9 LNPs as a potential in vivo screening tool in GSCs, currently lacking effective treatment. By replacing GFP with a pool of potential targets, the proposed platform presents an exciting prospect for therapeutic target validation in orthotopic GSCs, bridging the gap between preclinical and clinical research.

Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location.

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

Activating Invasion and Metastasis in Small Cell Lung Cancer: Role of the Tumour Immune Microenvironmentand Mechanisms of Vasculogenesis, Epithelial-Mesenchymal Transition, Cell Migration, and Organ Tropism.

Small cell lung cancer (SCLC) harbours the most aggressive phenotype of all lung cancers to correlate with its bleak prognosis. The aggression of SCLC is partially attributable to its strong metastatic tendencies. The biological processes facilitating the metastasis in SCLC are still poorly understood and garnering a deeper understanding of these processes may enable the exploration of additional targets against this cancer hallmark in the treatment of SCLC. This narrative review will discuss the proposed molecular mechanisms by which the cancer hallmark of activating invasion and metastasis is featured in SCLC through important steps of the metastatic pathway, and address the various molecular targets that may be considered for therapeutic intervention. The tumour immune microenvironment plays an important role in facilitating immunotherapy resistance, whilst the poor infiltration of natural killer cells in particular fosters a pro-metastatic environment in SCLC. SCLC vasculogenesis is achieved through VEGF expression and vascular mimicry, and epithelial-mesenchymal transition is facilitated by the expression of the transcriptional repressors of E-cadherin, the suppression of the Notch signalling pathway and tumour heterogeneity. Nuclear factor I/B, selectin and B1 integrin hold important roles in SCLC migration, whilst various molecular markers are expressed by SCLC to assist organ-specific homing during metastasis. The review will also discuss a recent article observing miR-1 mRNA upregulation as a potential therapeutic option in targeting the metastatic activity of SCLC. Treatment of SCLC remains a clinical challenge due to its recalcitrant and aggressive nature. Amongst the many hallmarks used by SCLC to enable its aggressive behaviour, that of its ability to invade surrounding tissue and metastasise is particularly notable and understanding the molecular mechanisms in SCLC metastasis can identify therapeutic targets to attenuate SCLC aggression and improve mortality.

Resectable Non-Small Cell Lung Cancer Heterogeneity and Recurrence Assessed by Tissue Next-Generation Sequencing Genotyping and Circulating Tumor Cell EZH2 Characterization

IntroductionNon-small cell lung cancer (NSCLC) is the most common type of lung neoplasm. Despite surgical resection, it has a high relapse rate, accounting for 30–55% of all cases. Next-generation sequencing (NGS) based on a customized gene panel and the analysis of circulating tumor cells (CTCs) can help identify heterogeneity, stratify high-risk patients, and guide treatment decisions. In this descriptive study involving a small prospective cohort, we focus on the phenotypic characterization of CTCs, particularly concerning EZH2 expression (a member of the Polycomb Repression Complex 2), as well as on the mutation profiles of the tissue using a customized gene panel and their association with poor outcomes in NSCLC. MethodsIsolation and characterization of EZH2 on CTCs were evaluated before surgical resection (CTC1) and one month after surgery (CTC2) in resectable NSCLC patients. Targeted NGS was performed using a customized 50-gene panel on tissue samples from a subset of patients. Results76 patients with resectable NSCLC were recruited. The top mutated genes in the cohort included TP53, FLT1, MUC5AC, EGFR, and NLRP3. Pair of genes that had mutually exclusive mutations was TP53-RIN3, and pairs of genes with co-occurring mutations were CD163-TLR4, FGF10-FOXP2, ADAMTSL3-FLT1, ADAMTSL3-MUC5AC and MUC5AC-NLRP3. CTCs decreased significantly between the two time points CTC1 and CTC2 (p<0.0001), and CTCs+ patients with high EZH2 expression had an 87% increased risk of death (p=0.018). ConclusionsIntegrating molecular profiling of tumors and CTC characterization can provide valuable insights into tumor heterogeneity and improve patient stratification for resectable NSCLC.