Tumor Heterogeneity Research Articles

CELL-INTRINSIC METABOLIC PHENOTYPES IDENTIfiED IN GLIOBLASTOMA PATIENTS USING MASS SPECTROMETRY IMAGING OF 13C-LABELED GLUCOSE METABOLISM

Abstract AIMS Glioblastoma (GB) is an inherently heterogenous and invasive primary brain tumour. Genetic and transcriptomic studies have attempted to classify GB into subtypes that can identify therapeutic vulnerabilities and predict survival. An outstanding question and technical challenge is to visualise the metabolism of a tumour within its native microenvironment and understand to what extent its metabolism is driven by the microenvironment. METHOD Patients with GB tumours were infused with [U-13C]glucose and intra-operative multi-regional tumour samples were rapidly snap frozen for metabolic tracing analysis using mass spectrometry imaging (MSI). Tissue microenvironment was assessed using multiplexing of antibodies on immunocytochemistry (IMC) of parallel sections. Isotope tracing in human derived neurospheres and patient derived xenografts were used to confirm the presence of metabolic phenotypes detected in the human samples and to test susceptibility of each phenotype to a panel of drugs targeting cellular metabolism. RESULTS Three metabolic signatures were identified in patient GB tumours: glycolytic, oxidative and a mixed glycolytic/oxidative phenotype. The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation. The phenotypes were retained when patient derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. The phenotypes also demonstrate differential drug sensitivities. CONCLUSION This is the first report to demonstrate high resolution imaging of metabolic fluxes in a human tumour in vivo. In conjunction with studies on patient-derived neurospheres and orthotopically implanted xenografts we have demonstrated the presence of different metabolic phenotypes within GB that are tumour cell intrinsic and largely independent of the tumour microenvironment. These can be imaged clinically and used to stratify patients for personalised therapy.

Programmable Circular Multivalent Nanobody‐Targeting Chimeras (mNbTACs) for Multireceptor‐Mediated Protein Degradation and Targeted Drug Delivery

Multispecific therapeutics hold significant promise in drug delivery, protein degradation, and cell recruitment to address clinical issues of tumor heterogeneity, resistance, and immune evasion. However, their modular engineering remains challenging. We developed a targeted degradation platform, termed multivalent nanobody‐targeting chimeras (mNbTACs), by encoding diverse nanobody codons on a circular template using DNA printing technology. The homo‐ or hetero‐ mNbTACs specifically recognized membrane targets in a multivalent manner and simultaneously recruited scavenger receptors to favor clathrin‐/caveolae‐dependent endocytosis and lysosomal degradation of multiple proteins with high efficiency and selectivity. We demonstrated that a bispecific doxorubicin‐loaded mNbTAC, named Doxo‐mvNbsPPH, passively accumulated at tumor sites, specifically interacted with PD‐L1 and HER2 targets, and was rapidly transported into lysosome, inducing potent immunogenic cell death and alleviating immune checkpoint evasion. The synergistic boosting of innate and adaptive immunity promoted the infiltration and proliferation of CD8+ T cells in tumor microenvironment (an 11‐fold increase) with high toxicity and low exhaustion, eventually enhancing antitumor efficacy. Our mNbTAC platform provides multispecific therapeutics with variable valences and programmed species, whereas it induces targeted protein degradation through multireceptor‐mediated endocytosis and lysosomal degradation without the need for lysosome‐targeting receptors, representing a general and modular tool to harness extracellular proteome for disease treatment.

Programmable Circular Multivalent Nanobody-Targeting Chimeras (mNbTACs) for Multireceptor-Mediated Protein Degradation and Targeted Drug Delivery.

Multispecific therapeutics hold significant promise in drug delivery, protein degradation, and cell recruitment to address clinical issues of tumor heterogeneity, resistance, and immune evasion. However, their modular engineering remains challenging. We developed a targeted degradation platform, termed multivalent nanobody-targeting chimeras (mNbTACs), by encoding diverse nanobody codons on a circular template using DNA printing technology. The homo- or hetero- mNbTACs specifically recognized membrane targets in a multivalent manner and simultaneously recruited scavenger receptors to favor clathrin-/caveolae-dependent endocytosis and lysosomal degradation of multiple proteins with high efficiency and selectivity. We demonstrated that a bispecific doxorubicin-loaded mNbTAC, namedDoxo-mvNbsPPH, passively accumulated at tumor sites, specifically interacted with PD-L1and HER2targets, and was rapidly transported into lysosome, inducing potent immunogenic cell death and alleviating immune checkpoint evasion. The synergistic boosting of innate and adaptive immunity promoted the infiltration and proliferation of CD8+T cells in tumor microenvironment (an 11-fold increase) with high toxicity and low exhaustion, eventually enhancing antitumor efficacy. Our mNbTAC platform provides multispecific therapeutics with variable valences and programmed species, whereas it induces targeted protein degradation through multireceptor-mediated endocytosis and lysosomal degradation without the need for lysosome-targeting receptors, representing a general and modular tool to harness extracellular proteome for disease treatment.

Flower-Shaped Plasma Cells in Multiple Myeloma with Morphological Heterogeneity.

Flower-shaped nuclei in plasma cells are rare in multiple myeloma. We report on an 88-year-old male who presented with a mass lesion in the clavicular region. A biopsy of the mass revealed an increase in mature plasma cells with round nuclei. In contrast, a bone marrow examination showed increased plasma cells with flower-shaped nuclei. The patient tested negative for human T-lymphotropic virus type-1 and was diagnosed with multiple myeloma. While multiple myeloma is known for intra-tumor heterogeneity, reports of morphological heterogeneity based on the site of tumor sampling are limited. In this case, the presence of plasma cells with flower-shaped nuclei enabled the identification of site-dependent morphological tumor heterogeneity.

KRAS, a New Target for Precision Medicine in Colorectal Cancer?

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, with significant public health concerns. This review examines the landscape of KRAS inhibition in colorectal cancer (CRC), focusing on recent advances in therapeutic strategies targeting this oncogene. Historically deemed undruggable due to its complex structure and essential role in tumorigenesis, KRAS mutations are prevalent in CRC and are associated with poor prognosis. However, breakthroughs in drug development have led to the emergence of KRAS inhibitors as promising treatment options. This review discusses various classes of KRAS inhibitors, including covalent and non-covalent inhibitors, as well as combination therapies aimed at enhancing efficacy and overcoming resistance mechanisms. It highlights recent clinical trials evaluating the efficacy of KRAS inhibitors either as monotherapy or in combination with other agents, such as anti-EGFR antibodies. Despite challenges such as resistance mechanisms and tumor heterogeneity, the development of KRAS inhibitors represents a significant advance in CRC treatment and holds promise for improving patient outcomes in the future.

Nutritional aspects in neuroendocrine neoplasms. bridging the gap between dietary interventions and cancer care strategies: a scoping review.

Neuroendocrine neoplasms (NENs) represent heterogeneous tumors arising from neuroendocrine cells in different organs. Despite growing interest in the nutritional aspects of NEN management, research in this area is limited. Aim of this review is to summarize the current state of knowledge, highlight research gaps, and underscore the significance of nutrition in the comprehensive care of NEN patients. We conducted an extensive bibliographic search focusing on studies (including retrospective and prospective studies, systematic reviews, case series, and guidelines) exploring the relationship between nutritional assessments, dietary interventions, micronutrient deficiencies, and their impact on NEN outcomes. Significant gaps exist in current research, particularly in understanding the specific nutritional needs of NEN patients and how tailored nutritional interventions can improve clinical outcomes. Evidence suggests that a high-fat Western diet may promote the growth of NEN, while a Mediterranean diet may help lower insulin levels and strengthen the immune system, potentially preventing tumor development. The ketogenic diet and intermittent fasting may also have positive impacts. Addressing common micronutrient deficiencies, such as vitamin D and niacin, is crucial to mitigate disease progression. There's a crucial need for future studies to include a comprehensive nutritional assessment incorporating patient-reported outcomes, to fully capture the impact of nutritional strategies. Nutritional management, an important but under-researched facet of NEN treatment, significantly improves patients' quality of life and survival. Integrating nutrition into personalized cancer care is essential, highlighting the role of nutritional strategies in optimizing patient outcomes.

Natural Phycocyanin/Paclitaxel Micelle Delivery of Therapeutic P53 to Activate Apoptosis for HER2 or ER Positive Breast Cancer Therapy.

The P53 gene is commonly mutated in breast cancer, protein based the gene as anticancer drugs could provide efficient and stable advantages by restoring the function of the wild-type P53 protein. In this study, we describe the creation and utilization of a micelle composed by natural phycocyanin and paclitaxel and grafting anti-HER2 (PPH), which effectively packages and transports recombinant P53 protein with anti-ER (PE), resulting in a new entity designated as PE@PPH, to address localization obstacles and modify cellular tropism to the cell membrane or nucleus. The results indicate that PE@PPH has strong antitumor properties, even at low doses of PTX both in vitro and in vivo. These findings suggest that PE@PPH could be an enhancing micelle for delivering therapeutic proteins and promoting protein functional recovery, particularly in addressing the challenges posed by tumor heterogeneity in breast cancer.

Fine Needle Aspiration Cytology of Pancreatoblastoma in Adult/Adolescent Patients, One With Histology Mimicking NUT Carcinoma.

Pancreatoblastoma is a rare malignant neoplasm. Cytologic diagnosis is challenging due to the tumor's heterogeneity and requirement of the presence of squamoid nests. Commonly affects children, but the tumor rarely is seen in adult patients. We are reporting three cases from two patients. First patient was a 38-year-old male with a mass in the pancreatic body and numerous hepatic lesions. Fine-needle aspiration (FNA) of the pancreas showed a biphasic malignancy, predominantly composed of a primitive component with intermingled squamoid nests. Subsequent Liver FNA from the same patient showed a similar biphasic malignancy. NUT carcinoma was the top differential and was ruled out by molecular testing. Second patient was a 24-year-old female with a history of pancreatoblastoma related to Gardner's syndrome initially diagnosed in 2015 at age 17, status post distal pancreatectomy and chemotherapy. Celiac lymph node FNA in 2021 showed few cohesive clusters of atypical epithelioid cells, which were highlighted by beta-catenin. Lastly, the literature was reviewed; differential diagnosis and ancillary testing were discussed.

T-Cell Dynamics Predicts Prognosis of Patients with Hepatocellular Carcinoma Receiving Atezolizumab Plus Bevacizumab.

Atezolizumab and bevacizumab show promise for treating hepatocellular carcinoma (HCC), but identifying responsive patients remains challenging, due to tumor heterogeneity. This study explores immune dynamics following this combination therapy. Between 2020 and 2023, 29 patients with advanced HCC who received atezolizumab plus bevacizumab at Severance Hospital, Seoul, were enrolled in this study. Peripheral blood mononuclear cells were analyzed using flow cytometry and statistical methods to assess immune alterations and identify biomarkers. Baseline characteristics showed a diverse HCC cohort with a mean age of 64 years and 82.8% male predominance. Absence of extrahepatic metastasis was associated with better overall survival. Immune responses revealed distinct CD4+ T-cell phenotypes between the 'partial response (PR) + stable disease (SD)' and 'progressive disease (PD)' groups, with an overall increase in CD8+ T-cell phenotypes. Patients with higher frequencies of CD8+PD-1+Ki-67+ T cells experienced significantly improved overall survival, while those with lower frequencies of CD4+Foxp3+PD-1+LAG3+ T cells also had notable survival benefits. These findings enhance the overall understanding of immune responses to this combination therapy, facilitating improved patient stratification and personalized therapeutic approaches for HCC.

Habitat-based CT radiomics enhances the ability to predict spread through air spaces in stage T1 invasive lung adenocarcinoma.

Spread through air spaces (STAS) represents a novel invasive pattern in lung adenocarcinoma (LUAD) and is a risk factor for poor prognosis in stage T1 LUAD. This study aims to develop and validate a CT habitat imaging analysis model for predicting STAS in stage T1 invasive LUAD. We retrospectively analyzed 217 patients with preoperative stage T1 invasive LUAD (115 STAS-positive and 102 STAS-negative cases, including 151 in the train set and 66 in the test set). Semi-automatic segmentation was performed on the regions of interest (ROIs) in all CT images, with an automatic 3mm expansion around the tumor, considering the intratumoral and peritumoral 3mm area. This area was divided into three sub-regions via K-means clustering, and 1197 radiomic features were extracted from each sub-region and the overall combined region. After dimension reduction through the Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator(LASSO), the best features for each sub-region and overall were selected. Models were then built using the selected radiomic features through the Adaptive Boosting (AdaBoost) and Multilayer Perceptron (MLP) classifiers. Four different models were established based on different sub-regions and the overall features. The performance of these models was evaluated through receiver operating characteristic curves (AUC) under the DeLong test, calibration curves via the Hosmer-Lemeshow test, and decision curve analysis to assess the performance of these features. In this study, we evaluated the predictive performance of AdaBoost and MLP classifiers on rad feature models across various subregions and the overall dataset. In the test set, the AdaBoost classifier achieved a maximum AUC of 0.871 in Habitat 3, whereas the MLP classifier demonstrated slightly superior performance with an AUC of 0.879. Both classifiers exhibited high efficiency in habitat 3, with the MLP algorithm showing enhanced model performance. CT habitat imaging analysis for the preoperative prediction of STAS in stage T1 invasive LUAD shows satisfactory diagnostic performance, with the habitat3 model exhibiting the highest efficacy, reflecting tumor heterogeneity.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy.

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

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.