Understanding Of Tumor Heterogeneity Research Articles

DNA damage repair profiling of esophageal squamous cell carcinoma uncovers clinically relevant molecular subtypes with distinct prognoses and therapeutic vulnerabilities

DNA damage repair (DDR) is a critical process that maintains genomic integrity and plays essential roles at both the cellular and organismic levels. Here, we aimed to characterize the DDR profiling of esophageal squamous cell carcinoma (ESCC), investigate the prognostic value of DDR-related features, and explore their potential for guiding personalized treatment strategies. We analyzed bulk and single-cell transcriptomics data from 377 ESCC cases from our institution and other publicly available cohorts to identify major DDR subtypes. The heterogeneity in cellular and functional properties, tumor microenvironment (TME) characteristics, and prognostic significance of these DDR subtypes were investigated using immunogenomic analysis and invitro experiments. Additionally, we experimentally validated a combinatorial immunotherapy strategy using syngeneic mouse models of ESCC. DDR alteration profiling enabled us to identify two distinct DDR subtypes, DDRactive and DDRsilent, which exhibited independent prognostic values in locoregional ESCC but not in metastatic ESCC. The DDRsilent subtype was characterized by an inflamed but immune-suppressed microenvironment with relatively high immune cell infiltration, abnormal immune checkpoint expression, T-cell exhaustion, and enrichment of cancer-related pathways. Moreover, DDR subtyping indicates that BRCA1 and HFM1 are robust and independent prognostic factors in locoregional ESCC. Finally, we proposed and verified that the concomitant triggering of GITR or blockade of BTLA with PD-1 blockade or cisplatin chemotherapy represents effective combination strategies for high-risk locoregional ESCC tumors. Our discovery of DDR-based molecular subtypes will enhance our understanding of tumor heterogeneity and have significant clinical implications for the therapeutic and management strategies of locoregional ESCC. This study was supported by the National Key R&D Program of China (2021YFC2501000, 2022YFC3401003), National Natural Science Foundation of China (82172882), the Beijing Natural Science Foundation (7212085), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091), and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT310027).

From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies.

Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.

Grade Progression and Intrapatient Tumor Heterogeneity as Potential Contributors to Resistance in Gastroenteropancreatic Neuroendocrine Tumors.

Gastroenteropancreatic neuroendocrine neoplasms (NENs) are a heterogenous group of tumors that are incurable when metastatic, regardless of grade. The aim of this article is to understand tumor heterogeneity and grade progression as possible contributors to drug resistance in gastroentropancreatic neuroendocrine tumors (GEP-NETs). Heterogeneity has been observed in the genetic, pathological, and imaging features of these tumors at baseline. Diagnostic challenges related to tumor sampling and the potential for changes in grade over time further confound our ability to optimize therapy for patients. A better understanding of NEN biology and tumor heterogeneity at baseline and over time could lead to the development of new therapeutic avenues.

Circulating Tumor DNA as a Minimal Residual Disease Assessment and Recurrence Risk in Patients Undergoing Curative-Intent Resection with or without Adjuvant Chemotherapy in Colorectal Cancer: A Systematic Review and Meta-Analysis.

Emerging data have suggested that circulating tumor DNA (ctDNA) can be a reliable biomarker for minimal residual disease (MRD) in CRC patients. Recent studies have shown that the ability to detect MRD using ctDNA assay after curative-intent surgery will change how to assess the recurrence risk and patient selection for adjuvant chemotherapy. We performed a meta-analysis of post-operative ctDNA in stage I-IV (oligometastatic) CRC patients after curative-intent resection. We included 23 studies representing 3568 patients with evaluable ctDNA in CRC patient post-curative-intent surgery. Data were extracted from each study to perform a meta-analysis using RevMan 5.4. software. Subsequent subgroup analysis was performed for stages I-III and oligometastatic stage IV CRC patients. Results showed that the pooled hazard ratio (HR) for recurrence-free survival (RFS) in post-surgical ctDNA-positive versus -negative patients in all stages was 7.27 (95% CI 5.49-9.62), p < 0.00001. Subgroup analysis revealed pooled HRs of 8.14 (95% CI 5.60-11.82) and 4.83 (95% CI 3.64-6.39) for stages I-III and IV CRC, respectively. The pooled HR for RFS in post-adjuvant chemotherapy ctDNA-positive versus -negative patients in all stages was 10.59 (95% CI 5.59-20.06), p < 0.00001. Circulating tumor DNA (ctDNA) analysis has revolutionized non-invasive cancer diagnostics and monitoring, with two primary forms of analysis emerging: tumor-informed techniques and tumor-agnostic or tumor-naive techniques. Tumor-informed methods involve the initial identification of somatic mutations in tumor tissue, followed by the targeted sequencing of plasma DNA using a personalized assay. In contrast, the tumor-agnostic approach performs ctDNA analysis without prior knowledge of the patient's tumor tissue molecular profile. This review highlights the distinctive features and implications of each approach. Tumor-informed techniques enable the precise monitoring of known tumor-specific mutations, leveraging the sensitivity and specificity of ctDNA detection. Conversely, the tumor-agnostic approach allows for a broader genetic and epigenetic analysis, potentially revealing novel alterations and enhancing our understanding of tumor heterogeneity. Both approaches have significant implications for personalized medicine and improved patient outcomes in the field of oncology. The subgroup analysis based on the ctDNA method showed pooled HRs of 8.66 (95% CI 6.38-11.75) and 3.76 (95% CI 2.58-5.48) for tumor-informed and tumor-agnostic, respectively. Our analysis emphasizes that post-operative ctDNA is a strong prognostic marker of RFS. Based on our results, ctDNA can be a significant and independent predictor of RFS. This real-time assessment of treatment benefits using ctDNA can be used as a surrogate endpoint for the development of novel drugs in the adjuvant setting.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

Mutational profiling of true single circulating tumor cells (sCTC) in metastatic lung cancer to determine genomic heterogeneity and drug-resistance signatures.

e21024 Background: Omics of single Circulating tumor cells (CTCs) is highly envisioned though challenging. . Genotyping of TRUE sCTC capture and omics data can determine the molecular heterogeneity of a single cell without the stringent need for deep sequencing compared to tissue or blood (1/100th fold compared to ctDNA). Here, we show the utility of sCTC genome profiling to determine inter and intra-patient mutational heterogeneity and compare the outcome with matched ensembled ctDNA mutational profiles. Methods: OncoRADAR an antibody-affinity-based platform was used to capture live sCTCs without cell fixing. 50 sCTCs (90 %) and clusters (10 %) were isolated from 20 metastatic lung cancer pts for mutational genotyping. Amplified whole genomes were subjected to targeted hybrid-capture comprehensive genome profiling. Libraries prepared using a custom-designed comprehensive gene panel OncoIndx, targeting 600 cancer-relevant exons including MSI and HRR pathway genes, were sequenced in paired-end mode on the Illumina platform. Variant calling was performed using an in-house developed bioinformatics pipeline. Pathway enrichment analysis was performed manually using data enriched from different gene pathway databases. Results: 81 mutations were detected in 56 genes across all sCTCs with pathogenic, likely pathogenic, and unknown significance. The mutational landscape included SNVs (38.6%), splice variants (10%), frameshift mutations (0.8%), and truncations (50.4%). From a subset of patients (10 %), multiple sCTCs showed PTEN variant cooccurring with ESR1 and MET. This suggest a clonal selection and emergence of subclones during progression. Pathways related to drug resistance, migration, deregulated cell cycle, and signaling were significantly enriched in multiple sCTCs when compared to plasma genotyping. Low average TMB (1.89), LOH (3.9) and HRD (14.67%), and high MSI (28.68%) scores were observed. In contrast, matching ctDNA had higher average LOH (18.67), HRD (45.08%), and lower MSI (0.99) scores. Multiple sCTCs from a small fraction of patients had comparable values for genome stability indicators, suggesting maintained genome stability. Genotyping of sCTC among patients showed a high variability compared to comprehensive analysis at the ctDNA level. Conclusions: Targeted gene profiling of true sCTCs revealed multiple variants that remained undetected in ctDNA, enabling a better understanding of tumor heterogeneity. Analysis of multiple sCTCs from the same patient underscores the importance of clonal evolution and plasticity during metastasis. Longitudinal monitoring of sCTCs may reveal personalized markers for disease prevalence, metastasis and relapse. sCTC genotyping may help design personalized therapy with better precision.

89Zr]-Atezolizumab-PET Imaging Reveals Longitudinal Alterations in PDL1 during Therapy in TNBC Preclinical Models.

Triple-negative breast cancers (TNBCs) currently have limited treatment options; however, PD-L1 is an indicator of susceptibility to immunotherapy. Currently, assessment of PD-L1 is limited to biopsy samples. These limitations may be overcome with molecular imaging. In this work, we describe chemistry development and optimization, in vitro, in vivo, and dosimetry of [89Zr]-Atezolizumab for PD-L1 imaging. Atezolizumab was conjugated to DFO and radiolabeled with 89Zr. Tumor uptake and heterogeneity in TNBC xenograft and patient-derived xenograft (PDX) mouse models were quantified following [89Zr]-Atezolizumab-PET imaging. PD-L1 expression in TNBC PDX models undergoing therapy and immunohistochemistry (IHC) was used to validate imaging. SUV from PET imaging was quantified and used to identify heterogeneity. PET/CT imaging using [89Zr]-Atezolizumab identified a significant increase in tumor:muscle SUVmean 1 and 4 days after niraparib therapy and revealed an increased trend in PD-L1 expression following other cytotoxic therapies. A preliminary dosimetry study indicated the organs that will receive a higher dose are the spleen, adrenals, kidneys, and liver. [89Zr]-Atezolizumab PET/CT imaging reveals potential for the noninvasive detection of PD-L1-positive TNBC tumors and allows for quantitative and longitudinal assessment. This has potential significance for understanding tumor heterogeneity and monitoring early expression changes in PD-L1 induced by therapy.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications.

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Clinical implications and perspectives of portal venous circulating tumor cells in pancreatic cancer

Despite recent improvements in the diagnosis and treatment of pancreatic cancer (PC), clinical outcomes remain dismal. Moreover, there are no effective prognostic or predictive biomarkers or options beyond carbohydrate antigen 19-9 for personalized and precise treatment. Circulating tumor cells (CTCs), as a member of the liquid biopsy family, could be a promising biomarker; however, the rarity of CTCs in peripheral venous blood limits their clinical use. Because the first venous drainage of PC is portal circulation, the portal vein can be a more suitable location for the detection of CTCs. Endoscopic ultrasound-guided portal venous sampling of CTCs is both feasible and safe. Several studies have suggested that the detection rate and number of CTCs may be higher in the portal blood than in the peripheral blood. CTC counts in the portal blood are highly associated with hepatic metastasis, recurrence after surgery, and survival. The phenotypic and genotypic properties measured in the captured portal CTCs can help us to understand tumor heterogeneity and predict the prognosis of PC. Small sample sizes and heterogeneous CTC detection methods limit the studies to date. Therefore, a large number of prospective studies are needed to corroborate portal CTCs as a valid biomarker in PC.

Abstract 1340: Type XVII collagen biology is central in the fibro-immune axis of tumor fibrosis: serological assessment of type XVII collagen

Abstract Background: Collagens are the main components of the desmoplastic reaction of the tumors stroma, resulting in tumors fibrosis. Immuno-oncology (IO) is a central part of treating and understanding solid tumor patients. Alterations in the extracellular matrix (ECM) are known to contribute to tumor progression and metastasis. Collagen XVII (BP180) is a type II transmembrane protein that plays a unique role in the ECM with both fibrotic and auto-antigenicity properties, both central aspects in tumor biology. Aberrant expression and ectodomain shedding of collagen XVII have been associated with tumor invasiveness and metastasis in multiple tumor types such as colorectal, breast, pancreatic or ovarian carcinoma. In this study, we developed an ELISA targeting the collagen XVII ectodomain and investigate the non-invasive biomarker potential across different tumor types. Methods: An ELISA (PRO-C17) was developed based on a monoclonal antibody raised against the NC16a ectodomain of collagen XVII. The ELISA was optimized and validated for accuracy and precision in human serum. PRO-C17 biomarker levels were measured in 214 patients with different kinds of cancer including bladder (n = 19), breast (n = 19), colorectal (n = 20), head and neck (n = 20), kidney (n = 20), lung (n = 17), melanoma (n = 20), ovarian (n = 20), pancreatic (n = 20), prostate (n = 19), and stomach cancer (n = 20). PRO-C17 levels in cancer patients were compared to healthy individuals (n = 23) by Kruskal Wallis H test and Dunn’s multiple comparisons test. The diagnostic power of the biomarker was assessed by the area under the curve (AUC) of the receiver-operating characteristics (ROC). Results: PRO-C17 was technically robust and specific for the collagen XVII ectodomain, as showed by western blot of supernatant from A431 cells expressing and shedding collagen XVII. PRO-C17 was significantly increased in serum from patients with bladder (p = 0.04, AUC = 0.752), breast (p = 0.01, AUC = 0.777), colorectal (p &amp;lt; 0.0001, AUC = 0.904), head and neck (p = 0.002, AUC = 0.828), kidney (p = 0.01, AUC = 0.789), and ovarian cancer (p = 0.004, AUC = 0.76) compared to healthy individuals. No difference was observed in patients with lung (p = 0.23, AUC = 0.739), melanoma (p = 0.06, AUC = 0.737), pancreatic (p &amp;gt; 0.9, AUC = 0.722), prostate (p = 0.29, AUC = 0.727), and stomach cancer (p &amp;gt; 0.9, AUC = 0.690). Conclusion: Type XVII collagen was important across solid tumor types. When quantified in serum, type XVII collagen levels were significantly elevated in serum from patients with specific types of cancer only, which may indicate the disruption of the adhesion of epithelial cells to the basement membrane during tumor invasion. These findings suggest PRO-C17 as a promising non-invasive biomarker that can aid in understanding tumor heterogeneity as well as elaborate on the role of collagen XVII in the balance of fibrosis and auto-autoimmunity of tumor progression. Citation Format: Marina Crespo-Bravo, Rasmus S. Pedersen, Jeppe Thorlacius-Ussing, Nicholas Willumsen, Morten A. Karsdal. Type XVII collagen biology is central in the fibro-immune axis of tumor fibrosis: serological assessment of type XVII collagen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1340.

Immune characteristics and genetic markers of esophageal cancer by single-cell analysis: implications for immunotherapy.

Esophageal cancer (EC) is one of the most common cancers worldwide. The prognoses for patients with the same stage of EC can vary substantially. The progress of single-cell analysis technology has furthered the understanding of tumor heterogeneity. This paper aimed to apply single-cell analysis to explore the characteristics of the tumor environment of EC and provide a basis for personalized treatment. The latest gene expression data and clinical follow-up information of single-cell sequencing results of EC samples were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) Application Programming Interface (API). A differential gene function analysis of the immune infiltration signature agents in the tumor microenvironment (TME) was performed using bioinformatics analytical methods, and potential molecular targets were sought. We identified specific cell subsets in the EC and paracancerous samples, including panel cells, natural killer (NK) cells, exhausted cluster of differentiation (CD)8+ T cells, CD8+ memory T (Tcm) cells, and effector memory T (Tem) cells, including B cell enrichment in the cancer samples. Differences were detected between B cells and monocytes in stage II and III tumors, which may be related to RNA transcription and degradation. The CXCL8 protein was identified as a valid potential prognostic marker. Cell groups with homogenous cell surface markers exhibit intercellular variations that exert a considerable effect on cell function. Our study will contribute to the understanding of the TME and cellular heterogeneity in EC patients and serve as a valuable resource for in-depth exploration of the pathogenesis of EC and the identification of potential therapeutic targets in the future.

RHAMM marks proliferative subpopulation of human colorectal cancer stem cells.

The cancer stem cell (CSC) theory features typically rare self-renewing subpopulations that reconstitute the heterogeneous tumor. Identification of molecules that characterize the features of CSCs is a key imperative for further understanding tumor heterogeneity and for the development of novel therapeutic strategies. However, the use of conventional markers of CSCs is still insufficient for the isolation of bona fide CSCs. We investigated organoids that are miniature forms of tumor tissues by reconstructing cellular diversity to identify specific markers to characterize CSCs in heterogeneous tumors. Here, we report that the receptor for hyaluronan-mediated motility (RHAMM) expresses in a subpopulation of CD44+ conventional human colorectal CSC fraction. Single-cell transcriptomics of organoids highlighted RHAMM-positive proliferative cells that revealed distinct characteristics among the various cell types. Prospectively isolated RHAMM+CD44+ cells from the human colorectal cancer tissues showed highly proliferative characteristics with a self-renewal ability in comparison with the other cancer cells. Furthermore, inhibition of RHAMM strongly suppressed organoid formation in vitro and inhibited tumor growth in vivo. Our findings suggest that RHAMM is a potential therapeutic target because it is a specific marker of the proliferative subpopulation within the conventional CSC fraction.

Characterization of heterogeneous metabolism in hepatocellular carcinoma identifies new therapeutic target and treatment strategy.

Metabolic reprogramming is a well-known hallmark of cancer. Systematical identification of clinically relevant metabolic subtypes of Hepatocellular carcinoma (HCC) is critical to understand tumor heterogeneity and develop efficient treatment strategies. We performed an integrative analysis of genomic, transcriptomic, and clinical data from an HCC patient cohort in The Cancer Genome Atlas (TCGA). Four metabolic subtypes were defined: mHCC1, mHHC2, mHCC3, and mHCC4. These subtypes had distinct differences in mutations profiles, activities of metabolic pathways, prognostic metabolism genes, and immune features. The mHCC1 was associated with poorest outcome and was characterized by extensive metabolic alterations, abundant immune infiltration, and increased expression of immunosuppressive checkpoints. The mHHC2 displayed lowest metabolic alteration level and was associated with most significant improvement in overall survival in response to high CD8+ T cell infiltration. The mHHC3 was a "cold-tumor" with low immune infiltration and few metabolic alterations. The mHCC4 presented a medium degree of metabolic alteration and high CTNNB1 mutation rate. Based on our HCC classification and in vitro study, we identified palmitoyl-protein thioesterase 1 (PPT1) was a specific prognostic gene and therapeutic target for mHCC1. Our study highlighted mechanistic differences among metabolic subtypes and identified potential therapeutic targets for subtype-specific treatment strategies targeting unique metabolic vulnerabilities. The immune heterogeneities across metabolic subtypes may help further clarify the association between metabolism and immune environment and guide the development of novel strategies through targeting both unique metabolic vulnerabilities and immunosuppressive triggers.

Data-Driven Radiogenomic Approach for Deciphering Molecular Mechanisms Underlying Imaging Phenotypes in Lung Adenocarcinoma: A Pilot Study.

The heterogeneity of lung tumor nodules is reflected in their phenotypic characteristics in radiological images. The radiogenomics field employs quantitative image features combined with transcriptome expression levels to understand tumor heterogeneity molecularly. Due to the different data acquisition techniques for imaging traits and genomic data, establishing meaningful connections poses a challenge. We analyzed 86 image features describing tumor characteristics (such as shape and texture) with the underlying transcriptome and post-transcriptome profiles of 22 lung cancer patients (median age 67.5 years, from 42 to 80 years) to unravel the molecular mechanisms behind tumor phenotypes. As a result, we were able to construct a radiogenomic association map (RAM) linking tumor morphology, shape, texture, and size with gene and miRNA signatures, as well as biological correlates of GO terms and pathways. These indicated possible dependencies between gene and miRNA expression and the evaluated image phenotypes. In particular, the gene ontology processes "regulation of signaling" and "cellular response to organic substance" were shown to be reflected in CT image phenotypes, exhibiting a distinct radiomic signature. Moreover, the gene regulatory networks involving the TFs TAL1, EZH2, and TGFBR2 could reflect how the texture of lung tumors is potentially formed. The combined visualization of transcriptomic and image features suggests that radiogenomic approaches could identify potential image biomarkers for underlying genetic variation, allowing a broader view of the heterogeneity of the tumors. Finally, the proposed methodology could also be adapted to other cancer types to expand our knowledge of the mechanistic interpretability of tumor phenotypes.

Interactive gene identification for cancer subtyping based on multi-omics clustering.

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Rank ectopic expression in the presence of Neu and PyMT oncogenes alters mammary epithelial cell populations and their tumorigenic potential

Determination of the mammary epithelial cell that serves as the cell of origin for breast cancer is key to understand tumor heterogeneity and clinical management. In this study, we aimed to decipher whether Rank expression in the presence of PyMT and Neu oncogenes might affect the cell of origin of mammary gland tumors. We observed that Rank expression in PyMT+/− and Neu+/− mammary glands alters the basal and luminal mammary cell populations already in preneoplasic tissue, which may interfere with the tumor cell of origin restricting their tumorigenesis ability upon transplantation assays. In spite of this, Rank expression eventually promotes tumor aggressiveness once tumorigenesis is established.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Advent of Single Tumor Cell Analysis in Oral Squamous Cell Carcinoma – An Insight

Oral squamous cell carcinoma OSCC has a complex portfolio comprised of genetic and epigenetic instability. Gene expression studies are necessary for molecular typing of oral cancer. The conventional studies on gene expression obtained from whole cell populations average the differences between individual cells. The evolving tools and techniques using single tumor cell analysis STCA allows identification and analysis of individual cell type within a system and enables better characterization of cancer and for prognostication. The present paper intends to highlight the available evidence on STCA in OSCC. STCA has been carried out in carcinomas of the breast prostate pancreas colon and lung. However much needs to be explored in the region of head and neck especially OSCC. STCA enables better understanding of tumor heterogeneity and its microenvironment. Further it helps in identification and detection of cancer stem cells and circulating tumor cells. STCA has been remarkable in understanding clonal evolution of cancer stem cells tumor specific neo-antigens and evaluation of drug resistance mechanisms. Few limitations of STCA could be artefactual errors procedural difficulties and such others. STCA when combined with other technologies could facilitate the integration of multidimensional datasets having a significant impact in the analysis of risk stratification diagnosis and treatment of patients with OSCC worldwide.

The Emerging Role of Tumor Microenvironmental Stimuli in Regulating Metabolic Rewiring of Liver Cancer Stem Cells

Primary liver cancer (PLC) is one of the most devastating cancers worldwide. Extensive phenotypical and functional heterogeneity is a cardinal hallmark of cancer, including PLC, and is related to the cancer stem cell (CSC) concept. CSCs are responsible for tumor growth, progression, relapse and resistance to conventional therapies. Metabolic reprogramming represents an emerging hallmark of cancer. Cancer cells, including CSCs, are very plastic and possess the dynamic ability to constantly shift between different metabolic states depending on various intrinsic and extrinsic stimuli, therefore amplifying the complexity of understanding tumor heterogeneity. Besides the well-known Warburg effect, several other metabolic pathways including lipids and iron metabolism are altered in PLC. An increasing number of studies supports the role of the surrounding tumor microenvironment (TME) in the metabolic control of liver CSCs. In this review, we discuss the complex metabolic rewiring affecting liver cancer cells and, in particular, liver CSCs. Moreover, we highlight the role of TME cellular and noncellular components in regulating liver CSC metabolic plasticity. Deciphering the specific mechanisms regulating liver CSC-TME metabolic interplay could be very helpful with respect to the development of more effective and innovative combinatorial therapies for PLC treatment.

Protein‐Coated Biodegradable Gas‐Stabilizing Nanoparticles for Cancer Therapy and Diagnosis Using Focused Ultrasound

AbstractSurface‐engineered hydrophobic nanoparticles that can stabilize small gas pockets on their surfaces (i.e., gas‐stabilizing nanoparticles, GSNs) have been recently shown to be excellent contrast and cavitation agents for ultrasound theranostics. However, previously developed GSNs are not biodegradable, which limits their clinical translation potential. Here the development of biodegradable GSNs is shown by coating hydrophobically modified mesoporous silica nanoparticles with different protein solutions. It is found that these novel GSNs retain strong cavitation activity while rapidly degrading in simulated body fluid (SBF) or in vivo in days or a few weeks, respectively. Interestingly, GSNs coated with other stabilizing layers, Pluronic F127 polymer or phospholipids, demonstrated significantly slower degradation rates with only partial degradation even after a month of incubation in SBF. Next, it is shown that these biodegradable GSNs can be used to ablate tumor xenografts at lower ultrasound intensities, thus avoiding the side effects of high‐intensity ultrasound. Finally, it is shown that only tumors treated with GSNs and ultrasound can specifically enrich for circulating tumor DNA, which will improve liquid biopsies for understanding tumor heterogeneity and treatment response. Overall, this study details a simple yet effective method for preparing biodegradable GSNs with broad potential for applications in cancer diagnosis and therapy.