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Rybinski, Brad, and Kyuson Yun. "Addressing intra-tumoral heterogeneity and therapy resistance." Oncotarget 7, no. 44 (September 6, 2016): 72322–42. http://dx.doi.org/10.18632/oncotarget.11875.
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Korpershoek, Esther, Claudia K. Stobbe, Francien H. van Nederveen, Ronald R. de Krijger, and Winand N. M. Dinjens. "Intra-tumoral molecular heterogeneity in benign and malignant pheochromocytomas and extra-adrenal sympathetic paragangliomas." Endocrine-Related Cancer 17, no. 3 (September 2010): 653–62. http://dx.doi.org/10.1677/erc-10-0072.
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Pheochromocytomas (PCCs) and extra-adrenal sympathetic paragangliomas (sPGLs) are catecholamine-producing tumors occurring in the context of hereditary tumor syndromes, with known germline mutations, and as sporadic tumors. The pathogenesis of sporadic PCC and sPGL is poorly understood, and little is known about intra-tumoral heterogeneity with respect to molecular aberrations. Since knowledge on intra-tumoral heterogeneity is important for understanding the pathogenesis of these tumors, we investigated 12 benign and 8 malignant PCCs and sPGLs for loss of heterozygosity (LOH) on DNA extracted from different regions of each tumor and from metastases. LOH markers were selected on chromosomal regions frequently deleted in PCC, including 1p, 3q, 3p, and 11p. Benign tumors were found to have less intra-tumoral heterogeneity (overall 8%) than malignant tumors (overall 23%), with the highest frequencies for chromosome 1p36 in the benign tumors (17%) and 1p13 and 3q24 in malignant tumors (both 38%). In addition, differences in LOH patterns were detected between paired primary malignant tumors, and their metastases and different LOH patterns were observed in bilateral PCC of a multiple endocrine neoplasia type 2 patient. We demonstrate that malignant PCC and sPGL have more intra-tumoral molecular heterogeneity than benign tumors, which suggests that benign and malignant PCC and sPGL have a different pathogenesis.
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Landau, Dan A. "Epigenetic Heterogeneity in Non-Hodgkin Lymphoma." Blood 132, Supplement 1 (November 29, 2018): SCI—35—SCI—35. http://dx.doi.org/10.1182/blood-2018-99-109500.
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Abstract In chronic lymphocytic leukemia (CLL) genetically distinct subpopulations are commonly observed and predict future evolutionary and clinical trajectories. Similar to the case of intra-tumoral genetic diversity, intra-tumoral epigenetic diversity occurs at the level of DNA methylation, leading to massive stochastic diversification in methylation patterns. Importantly, stochastic "epimutations" impact transcription, clonal evolution and clinical outcome. To robustly differentiate "epidrivers" from the majority of random passenger DNA methylation changes, a novel statistical inference framework has been developed that accounts for the varying epimutation rate across the genome. This framework can also include histone modifications revealing a decrease in the coherence between different epigenetic marks in CLL and consistent with intra-leukemic epigenetic diversity. Finally, multi-omic single-cell sequencing techniques further allow for tracking of epimutations and inferring high-resolution lineage trees. Disclosures Landau: Pharmacylcics: Honoraria.
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Wang, Lujia, Hairong Wang, Fulvio D’Angelo, Lee Curtin, Christopher P. Sereduk, Gustavo De Leon, Kyle W. Singleton, et al. "Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm." PLOS ONE 19, no. 4 (April 3, 2024): e0299267. http://dx.doi.org/10.1371/journal.pone.0299267.
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Background and objective Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. Methods We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. Results WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. Conclusions This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
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Wu, Qiong, Anders E. Berglund, Robert J. Macaulay, and Arnold B. Etame. "The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma." Cells 13, no. 11 (May 30, 2024): 942. http://dx.doi.org/10.3390/cells13110942.
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Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.
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Matsumoto, Yuji, Omkar Singh, Jose Garcia, Sunwoo Kwak, Zied Abdullaev, Nelson Freeburg, Dana Silverbush, Kenneth Aldape, Christos Davatzikos, and MacLean Nasrallah. "EPCO-15. INTRA-TUMORAL HETEROGENEITY OF DNA METHYLATION PROFILING AND CELLULAR COMPOSITION IN GLIOBLASTOMA." Neuro-Oncology 26, Supplement_8 (November 1, 2024): viii4. http://dx.doi.org/10.1093/neuonc/noae165.0014.
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Abstract BACKGROUND DNA methylation profiling has become an important diagnostic and exploratory tool in neuro-oncology. Intra-tumoral molecular heterogeneity is a crucial factor in the treatment resistance of IDH-wildtype glioblastoma (GBM). However, the intra-tumoral heterogeneity of methylation profiles in GBM remains poorly elucidated. This study aimed to investigate the intra-tumoral heterogeneity of DNA methylation subclasses and deconvolve methylation profiles to infer the cellular composition of the tumor. METHODS We conducted genome-wide DNA methylation analysis and applied the DKFZ/Heidelberg CNS tumor classifier to FFPE tissue samples from a multi-sampling cohort with multiple samples per patient and a single-sampling cohort with one sample per patient. Only samples classified as GBM were included. We conducted two different methylation-based deconvolution analyses. Deconvolution 1 inferred the fractions of tumor subtypes (RTK_I, RTK_II, MES_TYP, and MES_ATYP) and cell types in the microenvironment. Deconvolution 2 estimated the abundance of malignant cell states (stem-like and differentiated cell components) and cell types in the microenvironment. RESULTS The multi-sampling cohort included 32 patients with 113 samples, and the single-sampling cohort included 87 patients with 87 samples. In the multi-sampling cohort, 8 patients (25%) exhibited intra-patient heterogeneity in methylation subclasses across their samples. Deconvolution 1 indicated that each tumor subclass component was heterogeneously distributed within samples, and the abundance of the myeloid cell component correlated with the MES_TYP component across samples. Deconvolution 2 similarly revealed heterogeneity in the immune component abundance across samples within patients. Integrating the results of Deconvolution 1 and Deconvolution 2 using the entire cohort revealed significant correlations between RTK_I and stem-like, RTK_II and differentiated, and MES_TYP and immune components. These findings were also replicated in TCGA dataset. CONCLUSIONS Our findings underscore the importance of considering intra-tumoral heterogeneity in methylation profiles and the biological characteristics of each methylation subclass in developing novel GBM therapies.
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Schupp, Patrick, Samuel Shelton, Daniel Brody, Rebecca Eliscu, Brett Johnson, Tali Mazor, Kevin Kelley, et al. "EPCO-39. CLARIFYING THE MOLECULAR CONSEQUENCES OF ONCOGENIC MUTATIONS THROUGH MULTISCALE AND MULTIOMIC ANALYSIS OF INDIVIDUAL TUMORS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v132—v133. http://dx.doi.org/10.1093/neuonc/noad179.0501.
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Abstract Understanding the molecular consequences of oncogenic mutations is an important goal that may reveal essential features of malignant cells and new therapeutic targets for diverse cancers. However, this task is complicated by substantial inter- and intra-tumoral heterogeneity, which obscure the molecular consequences of oncogenic mutations in malignant cells. Although single-cell methods hold promise for this task, it remains non-trivial to isolate DNA and RNA from the same cell at scale. Here we describe a statistically motivated strategy that controls for inter-tumoral heterogeneity and exploits intra-tumoral heterogeneity to reveal the most consistent molecular phenotypes of malignant cells. By combining deep, multiscale sampling of IDH-mutant astrocytomas with integrative, multiomic analysis, we reconstruct and validate the phylogenies, spatial distributions, and molecular profiles of distinct malignant clones. Importantly, by genotyping nuclei for driver mutations, we show that existing strategies for inferring malignancy from gene expression profiles of single cells may be inaccurate. We identify genes that are consistently upregulated in the truncal clone of IDH-mutant astrocytomas, which are significantly enriched with genes involved in RNA splicing, mRNA transport and the nuclear pore, and WNT / MYC signaling. Furthermore, we find that expression phenotypes of malignancy persist despite loss of the mutant IDH1 protein following chr2q deletion in a subset of malignant cells. More broadly, our work provides a generalizable strategy for precisely deconstructing intra-tumoral heterogeneity and determining the most robust molecular consequences of oncogenic mutations in any kind of solid tumor.
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Moon, Chang-In, William Tompkins, Yuxi Wang, Abigail Godec, Xiaochun Zhang, Patrik Pipkorn, Christopher A. Miller, Carina Dehner, Sonika Dahiya, and Angela C. Hirbe. "Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST." Genes 11, no. 5 (May 1, 2020): 499. http://dx.doi.org/10.3390/genes11050499.
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Sarcomas are highly aggressive cancers that have a high propensity for metastasis, fail to respond to conventional therapies, and carry a poor 5-year survival rate. This is particularly true for patients with neurofibromatosis type 1 (NF1), in which 8%–13% of affected individuals will develop a malignant peripheral nerve sheath tumor (MPNST). Despite continued research, no effective therapies have emerged from recent clinical trials based on preclinical work. One explanation for these failures could be the lack of attention to intra-tumoral heterogeneity. Prior studies have relied on a single sample from these tumors, which may not be representative of all subclones present within the tumor. In the current study, samples were taken from three distinct areas within a single tumor from a patient with an NF1-MPNST. Whole exome sequencing, RNA sequencing, and copy number analysis were performed on each sample. A blood sample was obtained as a germline DNA control. Distinct mutational signatures were identified in different areas of the tumor as well as significant differences in gene expression among the spatially distinct areas, leading to an understanding of the clonal evolution within this patient. These data suggest that multi-regional sampling may be important for driver gene identification and biomarker development in the future.
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Servidei, Tiziana, Donatella Lucchetti, Pierluigi Navarra, Alessandro Sgambato, Riccardo Riccardi, and Antonio Ruggiero. "Cell-of-Origin and Genetic, Epigenetic, and Microenvironmental Factors Contribute to the Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma." Cancers 13, no. 23 (December 3, 2021): 6100. http://dx.doi.org/10.3390/cancers13236100.
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Intra-tumoral heterogeneity (ITH) is a complex multifaceted phenomenon that posits major challenges for the clinical management of cancer patients. Genetic, epigenetic, and microenvironmental factors are concurrent drivers of diversity among the distinct populations of cancer cells. ITH may also be installed by cancer stem cells (CSCs), that foster unidirectional hierarchy of cellular phenotypes or, alternatively, shift dynamically between distinct cellular states. Ependymoma (EPN), a molecularly heterogeneous group of tumors, shows a specific spatiotemporal distribution that suggests a link between ependymomagenesis and alterations of the biological processes involved in embryonic brain development. In children, EPN most often arises intra-cranially and is associated with an adverse outcome. Emerging evidence shows that EPN displays large intra-patient heterogeneity. In this review, after touching on EPN inter-tumoral heterogeneity, we focus on the sources of ITH in pediatric intra-cranial EPN in the framework of the CSC paradigm. We also examine how single-cell technology has shed new light on the complexity and developmental origins of EPN and the potential impact that this understanding may have on the therapeutic strategies against this deadly pediatric malignancy.
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Ramakrishnan, Valya, Gatikrushna Singh, Beibei Xu, Johnny Ackers, and Clark Chen. "DNAR-04. MICRORNA DEGRADATION ENHANCES GLIOBLASTOMA INTRA-TUMORAL HETEROGENEITY TO AUGMENT ACQUIRED TEMOZOLOMIDE RESISTANCE." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v98—v99. http://dx.doi.org/10.1093/neuonc/noad179.0370.
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Abstract INTRODUCTION O6 methyl-guanine methyltransferase (MGMT) restores alkylated DNA to its undamaged form and dictates clonal evolution in response to chemotherapy. As such, MGMT is a major determinant of glioblastoma resistance to the standard-of-care chemotherapy temozolomide. miR-181d is a MGMT suppressing microRNA, whose expression inversely correlate with survival expectation after temozolomide treatment. Here, we characterize the expression of miR-181d and MGMT in response to temozolomide treatment. Method: Laboratory characterization, single cell analysis, and murine modeling RESULTS Treatment of glioblastoma cells with temozolomide (TMZ) induced a feed-forward cascade resulting in an ataxia telangiectasia and Rad3 (ATR) and polyribonucleotide nucleotidyl transferase 1 (PNPT1) dependent degradation of miR-181d. Single-cell analyses revealed such miR-181d degradation: 1) increased mean level of MGMT expression in a cell population and 2) enhanced cell-to-cell variability in MGMT expression to increase intra-tumoral heterogeneity in DNA repair capacity. This increased intra-tumoral heterogeneity enhanced glioblastoma resistance to temozolomide through a mechanism distinct from lowering the mean MGMT expression of the tumor population. Suppression of this heterogeneity by exogenous miR-181d over-expression enhanced the tumoricidal activity of temozolomide against glioblastomas. CONCLUSION We identified an interface between DNA damage response and miRNA degradation that mediate intra-tumoral heterogeneity and subsequent temozolomide resistance. This resistance mechanism is distinct from modulation of the mean MGMT expression of the population.
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Zhou, Yi-Hong, Kambiz Afrasiabi, and Mark E. Linskey. "Extracellular control of chromosomal instability and maintenance of intra-tumoral heterogeneity." Journal of Cancer Metastasis and Treatment 4, no. 8 (August 2, 2018): 41. http://dx.doi.org/10.20517/2394-4722.2018.16.
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Moon, Hye-ran, Altug Ozcelikkale, Yi Yang, Bennett D. Elzey, Stephen F. Konieczny, and Bumsoo Han. "An engineered pancreatic cancer model with intra-tumoral heterogeneity of driver mutations." Lab on a Chip 20, no. 20 (2020): 3720–32. http://dx.doi.org/10.1039/d0lc00707b.
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We present a microfluidic tumor model with intra-tumoral heterogeneity of key driver mutations of pancreatic cancers including Kras, p16 and p53. We demonstrate its potential use of drug screening, and identify a new drug resistance mechanism.
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Richards, Cathy E., Katherine M. Sheehan, Elaine W. Kay, Charlotta Hedner, David Borg, Joanna Fay, Anthony O’Grady, Arnold D. K. Hill, Karin Jirström, and Ann M. Hopkins. "Development of a Novel Weighted Ranking Method for Immunohistochemical Quantification of a Heterogeneously Expressed Protein in Gastro-Esophageal Cancers." Cancers 13, no. 6 (March 13, 2021): 1286. http://dx.doi.org/10.3390/cancers13061286.
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High expression of Junctional Adhesion Molecule-A (JAM-A) has been linked with poor prognosis in several cancers, including breast cancers overexpressing the human epidermal growth factor receptor-2 (HER2). Furthermore, JAM-A expression has been linked with regulating that of HER2, and associated with the development of resistance to HER2-targeted therapies in breast cancer patients. The purpose of this study was to establish a potential relationship between JAM-A and HER2 in HER2-overexpressing gastro-esophageal (GE) cancers. Interrogation of gene expression datasets revealed that high JAM-A mRNA expression was associated with poorer survival in HER2-positive gastric cancer patients. However, high intra-tumoral heterogeneity of JAM-A protein expression was noted upon immunohistochemical scoring of a GE cancer tissue microarray (TMA), precluding a simple confirmation of any relationship between JAM-A and HER2 at protein level. However, in a test-set of 25 full-face GE cancer tissue sections, a novel weighted ranking system proved effective in capturing JAM-A intra-tumoral heterogeneity and confirming statistically significant correlations between JAM-A/HER2 expression. Given the growing importance of immunohistochemistry in stratifying cancer patients for the receipt of new targeted therapies, this may sound a cautionary note against over-relying on cancer TMAs in biomarker discovery studies of heterogeneously expressed proteins. It also highlights a timely need to develop validated mechanisms of capturing intra-tumoral heterogeneity to aid in future biomarker/therapeutic target development for the benefit of cancer patients.
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Lau, Hannah Si Hui, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Yirong Sim, Jelmar Quist, Aye Aye Thike, Puay Hoon Tan, Shazib Pervaiz, Anita Grigoriadis, and Kanaga Sabapathy. "Abstract P64: Adipose-enriched Peri-tumoral Stroma Prognosticates Poorer Survival in Breast Cancers." Cancer Research 84, no. 8_Supplement (April 15, 2024): P64. http://dx.doi.org/10.1158/1538-7445.fcs2023-p64.
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Abstract Background: Breast cancers display a large degree of diversity between and within tumors. Intra-tumoral heterogeneity is contributed by variations in tumor cells and non-malignant stromal and immune cells of the tumor microenvironment (TME). So far, studies on the TME have focused on intra-tumoral stroma, whereas little attention has been given to the peri-tumoral microenvironment, extending beyond the tumor margins into the surrounding histologically normal-appearing tissues. Here, we hypothesized that intrinsic intra-tumoral heterogeneity impacts the more distal peri-tumoral stroma, which may in turn affect tumor growth and development. Methods: A systematic, multi-regional transcriptomic profiling analysis of the tumor and peri-tumoral regions from 8 ER+/PR+/HER2− invasive breast carcinomas was performed using the human Clariom™ S Assay. For each patient, 5 spatially distinct tumor samples were obtained, while peri-tumoral samples were collected from 4 different sides and at varying distances up to 7 cm from tumor margins. Pathway enrichment analyses and cellular deconvolution were performed to determine their molecular characteristics and cell type compositions. Gene expression signatures identified from peri-tumoral samples were applied to tumor-adjacent normal RNA-seq samples from 50 TCGA ER+/PR+/HER2− breast cancer patients to determine their prognostic value. Results: The 8 tumors displayed varying levels of spatial diversity in their biological properties, including 3 tumors with basal-classified regions. Four peri-tumoral clusters with distinct pathway activities and cell compositions were identified, but were independent of distance and location to the tumor. Two clusters reflect the major breast peri-tumoral states – a pro-inflammatory, adipose-enriched phenotype which was significantly associated with poorer overall survival (HR=4.28, p-value=0.02), and an adaptive immune cell- and myofibroblast-enriched phenotype. Conclusion: This suggests that the peri-tumoral stroma may be an important determinant in cancer progression. Moreover, independent of spatially-defined patterns, the peri-tumoral phenotype appears to be driven by inter-individual variations in the proportion of fibroglandular and fat tissue of the breast. Citation Format: Hannah Si Hui Lau, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Yirong Sim, Jelmar Quist, Aye Aye Thike, Puay Hoon Tan, Shazib Pervaiz, Anita Grigoriadis, Kanaga Sabapathy. Adipose-enriched Peri-tumoral Stroma Prognosticates Poorer Survival in Breast Cancers [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P64.
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Mittal, Karuna, Hongxiao Li, Dennis Wylie, Jaspreet Kaur, Rishab Kolachina, Bikram Sahoo, Guanhao Wei, et al. "Molecular profiling and quantitative image analysis reveal spatial intratumor heterogeneity in TNBC." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e12536-e12536. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e12536.
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e12536 Background: Triple negative breast cancer (TNBC) is a molecularly complex and heterogeneous subtype with distinct biological features and clinical behavior. Extensive intra-tumor heterogeneity is suspected to be a major cause of therapeutic failure. Spatially-distinct parts of the tumor harbor diverse and divergent clonal populations of cancer cells. Thus it is likely increasing the likelihood that some of those clones resist treatment, expand in numbers and eventually, repopulate the tumor leading to recurrence and spread. Therefore, a deeper understanding of this complexity is fundamental to gaining insights into this clinically important issue. In this study, we used RNA sequencing and machine learning approaches to examine the molecular and phenotypic/cellular profiles of various tumor samples from the same patient tumor to reveal spatial intra-tumor heterogeneity in TNBCs. Methods: We used 34 samples (2-4 samples from each patient tumor) from a total of 11 unique TNBC patients. RNA-sequencing was performed to quantify differential gene expression and machine learning (ML) approaches (deep learning regression modeling) were used to quantify the percentage of tumor and tumor-infiltrating lymphocytes (TILs) in the H&E stained tissue sections. The extent of concordance/discordance between the multiple tumor samples that originated from the same patient was analyzed by analyzing the intra- and inter-patient variance of normalized tumor cell, TIL % and gene expression. We also performed pathway analysis to identify signaling pathways dysregulated within (intra-tumoral heterogeneity) and between tumors (inter-tumoral heterogeneity) and performed molecular subtype analysis. Results: We observed that gene expression variance as higher within-patient (intra-tumor) compared to between-patient (inter-tumor). Tumor samples from 70% of patients showed different molecular subtypes representing extensive intra-tumor heterogeneity. Our ML-based image analysis showed that intra-patient tumor cell and TILs density/percentage variance was greater than inter-patient variance. In addition, patients with high within-patient gene expression variability had a high tumor and TIL variance. Among the within-patient expression variability, the genes associated with the PLK1 and Notch signaling were enriched. Conclusions: Our results suggest that TNBCs exhibit higher intra-tumor gene expression and cellular variance compared to inter-tumor gene expression and cellular variance suggesting higher intra-tumor heterogeneity in TNBCs.
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Moffet, Joel, Oluwaseun Fatunla, James Whittle, Jones Jordan, Samuel Roberts-Thomson, Anna Pavenko, David Scoville, et al. "TMIC-36. SPATIAL ARCHITECTURE OF HIGH-GRADE GLIOMA REVEALS TUMOR HETEROGENEITY WITHIN DISTINCT DOMAINS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v286. http://dx.doi.org/10.1093/neuonc/noad179.1102.
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Abstract High-grade gliomas are aggressive primary brain cancers with poor response to standard regimens, driven by immense heterogeneity. In isocitrate dehydrogenase (IDH) wild-type high-grade glioma (glioblastoma, GBM), increased intra-tumoral heterogeneity is associated with more aggressive disease. Recently, spatial technologies have emerged to dissect this complex heterogeneity within the tumor ecosystem by preserving cellular organization in situ. Here, we construct a high-resolution molecular landscape of GBM and IDH-mutant high-grade glioma patient samples to investigate the cellular subtypes and spatial communities that compose high-grade glioma using digital spatial profiling and spatial molecular imaging. This uncovered striking diversity of the tumor and immune microenvironment, that is embodied by the heterogeneity of the inferred copy-number alterations in the tumor. Reconstructing the tumor architecture revealed two distinct niches, one composed of tumor cell states that most closely resemble normal glial cells, associated with microglia; and the other niche populated by monocytes and mesenchymal tumor cells. We further reveal that communication between tumor and immune cells is underpinned by tumor-specific ligands, such as TGFb signaling in astrocyte-like tumor cells. This primary study reveals high levels of intra-tumoral heterogeneity in high-grade gliomas, associated with a diverse immune landscape within spatially localized regions.
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Kersch, Cymon, Cheryl Claunch, Prakash Ambady, Elmar Bucher, Daniel Schwartz, Ramon Barajas, Jeffrey Iliff, Laura Heiser, Leslie Muldoon, and Edward Neuwelt. "PATH-63. TRANSCRIPTIONAL SIGNATURES IN HISTOLOGIC STRUCTURES WITHIN GLIOBLASTOMA TUMORS MAY PREDICT PERSONALIZED TREATMENT SENSITIVITY AND SURVIVAL." Neuro-Oncology 21, Supplement_6 (November 2019): vi157. http://dx.doi.org/10.1093/neuonc/noz175.658.
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Abstract OBJECTIVE Personalized treatment strategies in Glioblastoma multiforme (GBM) has been hampered by intra-tumoral heterogeneity. The goals of this study were to (1) determine the impact of intra-tumoral heterogeneity on established predictive and prognostic transcriptional signatures in human GBM, and (2) develop methods to mitigate the impact of tissue heterogeneity on transcriptomic-based patient stratification. METHODS We analyzed transcriptional profiles of GBM histological structures from the open-source Ivy Glioblastoma Atlas Project. To generate these data, infiltrative tumor, leading edge, cellular tumor [CT], perinecrotic zones, pseudopalisading cells, hyperplastic blood vessels and microvascular proliferation were microdissected from 34 newly diagnosed GBM and underwent RNA sequencing. Data from The Cancer Genome Atlas were used for validation. Principle component analysis, network analysis and gene set enrichment analysis were used to probe gene expression patterns. RESULTS Distinct biological networks were enriched in each tumor histological structure. Classification of patients into GBM molecular subtypes varied based on the structure assessed, with many patients classified as every subtype depending on the structure analyzed. Using only CT to classify subtypes, we identified biologically unique patterns suggesting that proneural and mesenchymal tumors may be more sensitive to chemoradiotherapy and immunotherapy, respectively. Survival outcome predicted by an established multigene panel was confounded by histologic structure. Utilizing CT transcriptomics we developed a novel survival prediction gene signature that identified the highest-risk GBM patients in both CT and bulk tissue gene expression profiles. CONCLUSIONS Histologic structures contribute to intra-tumoral heterogeneity in GBM. Using mixed-structure biopsy samples could incorrectly subtype tumors and produce invalid patient stratification. Limiting transcriptomic analysis to the CT allowed us to develop a new survival prediction gene signature that appears accurate even in mixed tissue samples. The biological patterns uncovered in the subtypes and risk-stratified groups have important implications for guiding the development of precision medicine in GBM.
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Gutiérrez, María Laura, Luis Muñoz-Bellvís, and Alberto Orfao. "Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact." Cancers 13, no. 17 (September 3, 2021): 4451. http://dx.doi.org/10.3390/cancers13174451.
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Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities—i.e., classical vs. squamous subtypes of PDAC—leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.
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Chapman, Owen, Sunita Sridhar, Robert J. Wechsler-Reya, Lukas Chavez, and Jill P. Mesirov. "MEDB-66. Investigating intra-tumoral heterogeneity of extrachromosomal DNA in SHH medulloblastoma." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i121—i122. http://dx.doi.org/10.1093/neuonc/noac079.440.
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Abstract Extrachromosomal circular DNA (ecDNA) is an important driver of aggressive cancers, including medulloblastoma (MB), the most common malignant pediatric brain tumor. Our study’s aim is to better understand how ecDNA containing cells can potentiate malignant growth. EcDNA’s role in the development of treatment resistance and association with poor outcomes is hypothesized to arise from its contribution to intra-tumoral heterogeneity and its potential to promote oncogene dependency switching. To analyze the intra-tumoral distribution of ecDNA, we have now simultaneously analyzed the accessible chromatin and gene expression in single cells of a SHH medulloblastoma (MB) patient using multiome single-cell ATAC-seq and gene expression (10X Genomics). Whole genome sequencing (WGS) of this tumor previously revealed a heterozygous somatic TP53 mutation and two distinct ecDNAs: a 3.2Mbp amplicon comprising 3 regions of chr1 and another 4.5Mbp amplicon comprising 23 segments originating from chr7 and chr17. We then used multimodal analysis to describe the tumor cell types, gene expression, variant signatures and estimate ecDNA copy number in the medulloblastoma tumor sample. We identified 12 distinct clusters in the human tumor, 5 of which were determined to be normal non-tumor [OSC1] cells, as identified by specific cell type markers, and 7 of which were determined to be tumor cells. Enrichment of ecDNA was restricted to only one of these tumor clusters. In addition, we also performed the same multiome single-cell analyses in an orthotopic xenograft mouse model derived from this SHH MB patient tumor. In the PDX, 17 clusters were identified, all of which were determined to be tumor cells and enriched for ecDNA. Our preliminary results indicate that tumor cells with ecDNA in the human tumor (particularly the ecDNA enriched cluster) almost exclusively account for [OSC2] the cells in the corresponding PDX, emphasizing the aggressiveness of ecDNA containing cells.
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Jeantet, Marion, David Tougeron, Gaelle Tachon, Ulrich Cortes, Céline Archambaut, Gaelle Fromont, and Lucie Karayan-Tapon. "High Intra- and Inter-Tumoral Heterogeneity of RAS Mutations in Colorectal Cancer." International Journal of Molecular Sciences 17, no. 12 (December 1, 2016): 2015. http://dx.doi.org/10.3390/ijms17122015.
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Wilson, MS, CML West, GD Wilson, SA Roberts, RD James, and PF Schofield. "Intra-tumoral heterogeneity of tumour potential doubling times (Tpot) in colorectal cancer." British Journal of Cancer 68, no. 3 (September 1993): 501–6. http://dx.doi.org/10.1038/bjc.1993.376.
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Carmona Echeverria, L. M., E. Johnston, Y. Shanmugabavan, A. Rowan, G. Goh, R. Scott, M. Hung, et al. "Prostate cancer intra-tumoral heterogeneity: Correlation between clinical parameters, mpMRI and biomarkers." European Urology Supplements 16, no. 3 (March 2017): e358-e359. http://dx.doi.org/10.1016/s1569-9056(17)30273-7.
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Tougeron, David, Marion Jeantet, Ulrich Cortes, Celine Archambaut, Gaelle Fromont, Jean-Marc Tourani, and Lucie Karayan-Tapon. "High intra- and inter-tumoral heterogeneity of RAS mutations in colorectal cancer." Journal of Clinical Oncology 34, no. 15_suppl (May 20, 2016): 3577. http://dx.doi.org/10.1200/jco.2016.34.15_suppl.3577.
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Raviram, Ramya, Anugraha Raman, Sebastian Preissl, Shaoping Wu, Tomoyuki Koga, Chenxu Zhu, Jens Luebeck, et al. "DDDR-24. INTEGRATED ANALYSIS OF SINGLE CELL CHROMATIN ACCESSIBILITY AND RNA EXPRESSION IDENTIFIED COMMON VULNERABILITY DESPITE GLIOBLASTOMA HETEROGENEITY." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii104. http://dx.doi.org/10.1093/neuonc/noac209.389.
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Abstract INTRODUCTION In 2021, the World Health Organization (WHO) reclassified glioblastoma, the most common form of adult brain cancer, into isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade IV IDH mutant (G4 IDHm) astrocytomas. For both tumor types, intra-tumoral heterogeneity is a key contributor to therapeutic failure. METHODS we applied integrated genome-wide chromatin accessibility (snATACseq) and transcription (snRNAseq) profiles to clinical specimens derived IDHwt glioblastomas and G4 IDHm) astrocytomas, with goal of therapeutic target discovery. RESULTS The integrated analysis achieved resolution of intra-tumoral heterogeneity not previously possible, providing a molecular landscape of extensive regional and cellular variability. snATACseq delineated focal amplification down to an ~40 KB resolution. The snRNA analysis elucidated distinct cell types and cell states (neural progenitor/oligodendrocyte cell-like or astrocyte/mesenchymal cell-like) that were superimposable onto the snATACseq landscape. Paired-seq (parallel snATACseq and snRNAseq using the same clinical sample) provided high resolution delineation of extrachromosomal circular DNA (ecDNA), harboring oncogenes including CCND1 and EGFR. Importantly, the copy number of ecDNA genes correlated closely with the level of RNA expression. Integrated analysis across all specimens profiled suggests that IDHm grade 4 astrocytoma and IDHwt glioblastoma cells shared a common chromatin structure defined by open regions enriched for Nuclear Factor 1 transcription factors (NFIA and NFIB). Silencing of NF1A or NF1B suppressed in vitro and in vivo growth of patient-derived IDHwt glioblastomas and G4 IDHm astrocytoma models that mimic distinct glioblastoma cell states. CONCLUSION Our findings suggest despite distinct genotypes and cell states, glablastoma/G4 astrocytoma cells share dependency on core transcriptional programs, yielding an attractive platform for addressing therapeutic challenges associated with intra-tumoral heterogeneity.
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Raviram, Ramya, Anugraha Raman, Sebastian Preissl, Jianfang Ning, Shaoping Wu, Kinsey Deynze, Clark Chen, and Bing Ren. "EPCO-19. INTEGRATED ANALYSIS OF INTRA-TUMORAL HETEROGENEITY IN GLIOBLASTOMAS THROUGH SINGLE NUCLEI ATAC-SEQ AND RNA-SEQ." Neuro-Oncology 22, Supplement_2 (November 2020): ii73. http://dx.doi.org/10.1093/neuonc/noaa215.298.
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Abstract Glioblastoma, the most common form of primary brain cancer in adult, consists of an ecosystem of cancer cells characterized by cell-to-cell variations in genotypes and phenotypes. This intra-tumoral heterogeneity forms the substrate for cancer evolution, which in turn fuels therapeutic failure and resistance. To better define this heterogeneity, we profiled clinical glioblastoma specimens using single nuclei assays for Transposase-Accessible Chromatin with sequencing (snATAC-seq), RNA-Seq (snRNA-Seq), Paired snATAC/snRNA-seq, and whole genome-seq (WGS). We found that snATAC-Seq detected focal amplifications (~40kb-2MB) of genomic regions, revealing magnitudes of intra-tumoral heterogeneity previously unappreciated. snATAC-seq and WGS provided high resolution chromatin-state maps of extrachromosomal DNA (ecDNA), while Paired-seq delineated gene expression patterns associated with these chromatin-states. sn-RNAseq confirmed distinct cell states previously defined by others, including oligodendrocyte progenitor cell (OPC), neural progenitor cell (NPC), astrocyte, and mesenchymal -like glioblastomas. Analysis of snATAC-seq profiles in this context revealed shared dependency of these cell states on the Nuclear Factor 1 complex (NFIA and NFIB). Our results demonstrate the utility of cross-platform integration of single cell genomic technologies and suggest that, despite the overwhelming genotypic and phenotypic heterogeneity, shared vulnerability of predominant glioblastoma cell states can be identified.
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Rassamegevanon, Treewut, Steffen Löck, Michael Baumann, Mechthild Krause, and Cläre von Neubeck. "Heterogeneity of γH2AX Foci Increases in Ex Vivo Biopsies Relative to In Vivo Tumors." International Journal of Molecular Sciences 19, no. 9 (September 4, 2018): 2616. http://dx.doi.org/10.3390/ijms19092616.
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The biomarker for DNA double stand breaks, gammaH2AX (γH2AX), holds a high potential as an intrinsic radiosensitivity predictor of tumors in clinical practice. Here, two published γH2AX foci datasets from in and ex vivo exposed human head and neck squamous cell carcinoma (hHNSCC) xenografts were statistically re-evaluated for the effect of the assay setting (in or ex vivo) on cellular geometry and the degree of heterogeneity in γH2AX foci. Significant differences between the nucleus areas of in- and ex vivo exposed samples were found. However, the number of foci increased linearly with nucleus area in irradiated samples of both settings. Moreover, irradiated tumor cells showed changes of nucleus area distributions towards larger areas compared to unexposed samples, implying cell cycle alteration after radiation exposure. The number of residual γH2AX foci showed a higher degree of intra-tumoral heterogeneity in the ex vivo exposed samples relative to the in vivo exposed samples. In the in vivo setting, the highest intra-tumoral heterogeneity was observed in initial γH2AX foci numbers (foci detected 30 min following irradiation). These results suggest that the tumor microenvironment and the culture condition considerably influence cellular adaptation and DNA damage repair.
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Deuss, Eric, Cornelius Kürten, Lara Fehr, Laura Kahl, Stefanie Zimmer, Julian Künzel, Roland H. Stauber, Stephan Lang, Timon Hussain, and Sven Brandau. "Standardized Digital Image Analysis of PD-L1 Expression in Head and Neck Squamous Cell Carcinoma Reveals Intra- and Inter-Sample Heterogeneity with Therapeutic Implications." Cancers 16, no. 11 (May 31, 2024): 2103. http://dx.doi.org/10.3390/cancers16112103.
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For practical reasons, in many studies PD-L1 expression is measured by combined positive score (CPS) from a single tumor sample. This does not reflect the heterogeneity of PD-L1 expression in head and neck squamous cell carcinoma (HNSCC). We investigated the extent and relevance of PD-L1 expression heterogeneity in HNSCC analyzing primary tumors and recurrences (LRs), as well as metastases. Tumor tissue from 200 HNSCC patients was immunohistochemically stained for PD-L1 and analyzed using image-analysis software QuPath v3.4 with multiple specimens per patient. CPS was ≥20 in 25.6% of primary tumors. Intra-tumoral heterogeneity led to a therapeutically relevant underestimation of PD-L1 expression in 28.7% of patients, when only one specimen per patient was analyzed. Inter-tumoral differences in PD-L1 expression between primary tumors and lymph node metastasis (LNM) or LR occurred in 44.4% and 61.5% (CPS) and in 40.6% and 50% of cases (TPS). Overall survival was increased in patients with CPS ≥ 1 vs. CPS < 1 in primary tumors and LNM (hazard ratio: 0.46 and 0.35; p < 0.005); CPS in LR was not prognostic. Our analysis shows clinically relevant intra- and inter-sample heterogeneity of PD-L1 expression in HNSCC. To account for heterogeneity and improve patient selection for immunotherapy, multiple sample analyses should be performed, particularly in patients with CPS/TPS < 1.
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Cook, Daniel J., John Whitman, Nicole Liadis, and John Cole. "Abstract P1-05-07: Spatially-resolved single-cell tumor heterogeneity captured by TumorScope biophysical modeling software using MR Imaging." Cancer Research 82, no. 4_Supplement (February 15, 2022): P1–05–07—P1–05–07. http://dx.doi.org/10.1158/1538-7445.sabcs21-p1-05-07.
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Abstract Background: Dysregulated cellular metabolism is a hallmark of breast cancer, and targeting it has promising implications for improving care and patient outcomes. Specifically, heterogeneity in tumor metabolism is thought to play a role in determining chemotherapy response, the development of resistance, and promoting metastastasis. Despite this, metabolic tumor heterogeneity for individual breast cancer patients has not been characterized completely. Methods: In this study, we used state-of-the-art techniques to characterize metabolic heterogeneity within individual patient tumors by integrating single cell RNA-seq data with genome-scale metabolic modeling. Using SimBioSys’ TumorScope - a commercially available biophysical modeling platform, we compared intra-tumoral metabolic heterogeneity from experimental single cell RNA-seq data to simulated intra tumoral heterogeneity. Results: Using single cell RNA-seq data, we found that intra-tumoral gradients in nutrient availability are widely present within patient tumors (for a single luminal A patient, glucose import flux ranged from 0.19 - 1.25 g/gDW/day, while glutathione import ranged from 0.004 - 0.054 g/gDW/day). We also found that these gradients lead to cellular growth rate gradients within individual tumors (for our representative patient, median SGR = 0.62 %/day +/- 0.33 %/day stdev). Using TumorScope, we found this same gradient behavior within patient tumors. Selecting a similarly growing luminal A patient from our TumorScope simulations resulted in gradients in glucose import (range = 0.17 - 1.26 g/gDW/day), glutathione import (range = 0.024 - 0.058 g/gDW/day), and tumor SGR (median = 0.40 %/day, stdev = 0.42 %/day), which closely match metabolism from single cells (comparing maximum-scaled SGR distributions between single cells and TumorScope yielded a p-value = 0.10). We next examined which nutrients govern heterogeneity in tumor SGR. We found that glucose availability with the tumor microenvironment is more limiting to cell growth than oxygen availability, and this result was consistent between metabolic profiles from both single cell RNA-seq data and TumorScope simulations. TumorScope’s spatially resolved simulations offered the additional insight that gradients in nutrient availability are caused by heterogeneity in the distribution of macro- and micro-vasculature and the composition of the tumor microenvironment. We then used data reduction techniques to compare populations of single cells with differing metabolic phenotypes to identify molecular behavior at the single cells in higher molecular resolution. We found that single cells collected from the clinic co-cluster with single cells from TumorScope simulations, suggesting that a significant amount of intra-tumoral metabolic heterogeneity observed in patients is captured by TumorScope simulations. Conclusion: Accessing tumor heterogeneity has traditionally required specialized equipment, analytic expertise, and invasive procedures, largely limiting its study to large, academic hospitals. Currently, metabolic heterogeneity is only understood in 2D and for few markers (using pathology slides) or in relatively few cells with little or no spatial resolution (for single cell RNA-seq). TumorScope provides a novel approach to simulate metabolic heterogeneity at the single cell scale in 3D across a whole tumor. TumorScope democratizes the study of tumor heterogeneity by making it accessible to clinicians and researchers from MRI data alone. TumorScope has the capability to capture tumor metabolic heterogeneity at a higher scale than previously achievable which will allow for a dramatic increase in our understanding of tumor biology and ultimately improve clinical decision making. Citation Format: Daniel J Cook, John Whitman, Nicole Liadis, John Cole. Spatially-resolved single-cell tumor heterogeneity captured by TumorScope biophysical modeling software using MR Imaging [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-05-07.
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Nakashima, Takuma, Yusuke Funakoshi, Hirohisa Yajima, Ryo Yamamoto, Yuriko Sugihara, Shohei Nambu, Yoshiki Arakawa, et al. "EPCO-32. DISSECTING THE INTRA- AND INTER-TUMORAL HETEROGENEITY UNDERLYING GLIOBLASTOMA PATHOGENESIS UTILIZING MULTI-OMICS ANALYSIS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v131. http://dx.doi.org/10.1093/neuonc/noad179.0495.
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Abstract Glioblastoma (GBM) is the most common malignant brain tumor with a dismal prognosis. Considering its extensive molecular diversity underlies therapeutic resistance to multimodal treatment, it is imperative to reveal intra- and inter-tumoral heterogeneity, leading to further understanding of GBM pathogenesis. We analyzed 289 whole-genome sequencing data (WGS) including 159 unpublished deep WGS (≥ ×120 coverage) along with RNA-seq, DNA methylation array, whole-genome bisulfite sequencing, and assay for transposase-accessible chromatin with sequencing (ATAC-seq) to uncover the multi-omics molecular heterogeneity of GBM. Along with inter-tumoral heterogeneity of genetic driver alterations, deep WGS enables us to delineate a fine view of clonal architecture where mutational signatures differ across clonal and subclonal mutations, supporting that different mutational processes contribute to GBM pathogenesis depending on the developmental stage. As well, tumor cell differentiation status detected by transcriptional deconvolution analysis is associated with multi-layer profiles including genome, transcriptome, epigenome, and chromatin status. ATAC-seq demonstrated distinct features of genome-wide chromatin accessibility associated with gene expression subtypes of GBM. Motif enrichment analysis detects the differentially accessible regions where the proneural subtypes are enriched with the SOX10 motif, known as a transcription factor for mesenchymal transition whereas the mesenchymal and classical subtypes are enriched with the CREB/ATF motif, a regulator of TGF beta associated with a poor prognosis. Our findings support a model in which GBM evolves through generating genetic and epigenetic intra-tumoral heterogeneity suggesting that specific cell states may have distinct vulnerability to mutational processes and epigenetic modifications. Our analysis reveals the molecular mechanisms underlying the progression of GBM enhancing our understanding of the pathogenesis.
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Borga, Chiara, Carlo Alberto Dal Pozzo, Elisabetta Trevellin, Francesca Bergamo, Sabina Murgioni, Anna Caterina Milanetto, Claudio Pasquali, et al. "mTOR pathway and somatostatin receptors expression intratumor-heterogeneity in ileal NETs." Endocrine-Related Cancer 28, no. 7 (July 1, 2021): 449–56. http://dx.doi.org/10.1530/erc-21-0052.
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The knowledge of the molecular landscape of ileal neuroendocrine tumors (NETs) is affected by the lack of systematic studies investigating intra-tumoral heterogeneity. In this study, intra-tumoral heterogeneity was investigated in 27 primary ileal G1-NETs and their matched nodal and liver metastases in order to assess the tumor grading, the expression status of two somatostatin receptor isoforms (i.e. SSTR2A and SSTR5) and mTOR signaling dysregulation (ph-mTOR, ph-p70S6K, ph-4EBP1, PTEN and miR-21). Among the 27 G1 primary tumors, 4 shifted to G2 in the matched liver metastasis. Although mTOR activation was pretty consistent between primary and secondary malignancies, mTOR effectors (ph-p70S6K and ph-4EBP1) were overexpressed in matched liver metastases, whereas PTEN expression profile changed in only two cases. MiR-21 was significantly up-regulated in the metastatic setting. Although SSTRs expression was present in most of the primary tumors and matched metastasis, we found SSTR5 expression to be significantly increased in liver metastases. Notably, SSTRs expression was heterogeneous within the same lesions in most of the lesions. Overall, despite primary and metastatic ileal NETs show a similar molecular landscape, tumor grading and mTOR signaling pathway may diverge in the metastatic setting, thus affecting prognosis and treatment.
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Bayley, Nicholas, Jenna Minami, Christopher Tse, Henan Zhu, Jennifer Salinas, Cassidy Andrasz, Weihong Yan, et al. "TMET-17. LIPID METABOLIC HETEROGENEITY OF GLIOMA CELLULAR STATES REVEALS ENVIRONMENTAL DEPENDENCIES." Neuro-Oncology 26, Supplement_8 (November 1, 2024): viii291. http://dx.doi.org/10.1093/neuonc/noae165.1155.
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Abstract Malignant growth and survival of cancer is supported through reprogrammed lipid metabolism. In gliomas, genetic alterations can drive lipid metabolic dysregulation revealing therapeutic opportunities to exploit lipid metabolic vulnerabilities within genetically defined subsets of glioma tumors. However, it remains unclear whether inter- and intra-tumoral transcriptomic heterogeneity in gliomas relates to distinct lipid programs and potential dependencies. Here we set out to characterize pan-glioma lipid metabolic heterogeneity through genomic, transcriptomic, and lipidomic profiling of a large and diverse cohort of patient tumor samples. We define key axes of lipid metabolic variability across gliomas and identify relationships between lipid metabolic gene expression programs and glioma lipidomic profiles. Intriguingly, intersection of lipid gene expression signatures with single cell RNA sequencing revealed patterns of lipid metabolic heterogeneity that distinguish neurodevelopmental cellular states of gliomas, mirroring patterns of lipid metabolic diversity across cell types within the normal brain. Consequently, tumors with opposing cellular state enrichment have distinct lipid metabolism and environmental dependencies. Tumors enriched for Radial Glia-like states have high capacity for de novo fatty acid synthesis while tumors enriched for oligodendrocyte progenitor-like states are dependent on exogenous sources of lipids for survival and growth. These findings connect inter- and intra-tumoral heterogeneity of cellular states to variability in lipid metabolic reprogramming to reveal future avenues for precision medicine.
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Koay, Eugene J., Flavio E. Baio, Alexander Ondari, Mark J. Truty, Vittorio Cristini, Ryan M. Thomas, Rong Chen, et al. "Intra-tumoral heterogeneity of gemcitabine delivery and mass transport in human pancreatic cancer." Physical Biology 11, no. 6 (November 26, 2014): 065002. http://dx.doi.org/10.1088/1478-3975/11/6/065002.
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Southern, S. A., and C. S. Herrington. "Assessment of intra-tumoral karyotypic heterogeneity by interphase cytogenetics in paraffin wax sections." Molecular Pathology 49, no. 5 (October 1, 1996): M283—M289. http://dx.doi.org/10.1136/mp.49.5.m283.
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Torrecilla, Sara, Daniela Sia, Andrew N. Harrington, Zhongyang Zhang, Laia Cabellos, Helena Cornella, Agrin Moeini, et al. "Trunk mutational events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma." Journal of Hepatology 67, no. 6 (December 2017): 1222–31. http://dx.doi.org/10.1016/j.jhep.2017.08.013.
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Aedo Lopez, Veronica L., Reem Saleh, Benjamin Blyth, Xin Du, Dane Vassiliadis, Katie Fennell, Davide Moi, et al. "Abstract B001: Intra- and inter-tumoral heterogeneity of melanoma across different metastatic sites." Cancer Research 84, no. 22_Supplement (November 17, 2024): B001. http://dx.doi.org/10.1158/1538-7445.tumbody-b001.
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Abstract Melanoma, a highly metastatic skin cancer, exhibits variations in prognosis and response to therapy based on the site of metastasis. Despite the success of immunotherapy and targeted therapies in melanoma, over half of metastatic melanoma patients will experience disease progression due to therapy resistance. The heterogeneity and plasticity of melanoma cells contribute to metastatic dissemination and therapy resistance. Our aim is to determine whether distinct clones and/or their transcriptional cell states can predict the formation of tumors in various organs and assess how these clones change over time. To identify melanoma clones across different metastatic sites, NOD scid gamma (NSG) mice and C57BL/6 mice were injected subcutaneously, intravenously or intracranially with the same pool of cells of barcoded luciferase expressing YUMMER1.7 murine melanoma cells. Transduction conditions ensured that one DNA barcode integrated into cell genomes at one barcode per cell, serving as a lineage tag. Bioluminescence imaging was performed once a week to monitor tumor growth of mice injected intravenously and intracranially. Subcutaneous tumors were measured by calliper. Mice were euthanized at different time points; day 8, 15, 22, 29 post-implantation and at ethical endpoints. Tumors were harvested and DNA sequencing was performed to identify barcodes expressed by the tumor cells. All mice developed tumors, with 100% penetrance in NSG mice. However, in C57BL/6 mice, 10% of mice intravenously injected and 27% of subcutaneously implanted mice showed complete lesion regression, suggesting that the immune system may be responsible for tumor regression. Analysis of barcodes allowed us to assess the heterogeneity of melanoma tumors at different metastatic sites and their evolution over time. Lineage tracing and clonal heterogeneity will be evaluated using the state of art technology, SPLINTR (Single-cell Profiling and LINeage Tracing), enabling us to match a cells evolution with changes in transcriptional states. Barcode analysis performed before implanting the cells and, at different timepoints in subcutaneous and lung tumors showed that dominant subclones at the baseline were also dominant in subcutaneous and lung tumors in immunocompromised mice. In contrast, dominant subclones in immunocompetent mice was those present in lower proportion at baseline. Additionally, greater variability of subclones was observed especially in lung and brain tumors, across immunocompetent mice, likely as a mechanism of resistance, enabling these tumors to overcome immunoediting. Understanding the variability of clonality between different metastatic sites and over time will improve our comprehension of the role of different subclones in organ- specific metastasis and their transcriptional cell states. Citation Format: Veronica L. Aedo Lopez, Reem Saleh, Benjamin Blyth, Xin Du, Dane Vassiliadis, Katie Fennell, Davide Moi, Roberta Mazzieri, Riccardo Dolcetti, Karen E. Sheppard, Grant A. McArthur. Intra- and inter-tumoral heterogeneity of melanoma across different metastatic sites [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B001.
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Reuben, Alexandre, Zachary A. Cooper, Rodabe N. Amaria, Marie A. Forget, Cara Haymaker, Chantale Bernatchez, Pei-ling Chen, et al. "Intra-patient intra-tumoral immune heterogeneity is evident at progression on targeted therapy and immunotherapy for melanoma." Journal for ImmunoTherapy of Cancer 2, Suppl 3 (2014): P200. http://dx.doi.org/10.1186/2051-1426-2-s3-p200.
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Zhu, Yao, Junlong Wu, Jian Pan, Beihe Wang, Yu Wei, and Ding-Wei Ye. "Tumor heterogeneity and treatment response assessment using next generation imaging and liquid biopsy in patients with metastatic castration resistant prostate cancer receiving abiraterone (ANGELA): A single-center prospective observational trial." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 5057. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.5057.
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5057 Background: Metastatic castration resistant prostate cancer (mCRPC) is a disease of intra-patient tumor heterogeneity which poses obstacles in treatment and response evaluation. With the development of next generation imaging (NGI) and liquid biopsy, precise assessment of heterogeneity has entered a new era. In this study, we used longitudinal NGI (68Ga-PSMA and 18F-FDG PET/CT) and circulating tumor DNA (ctDNA) testing to assess tumor heterogeneity and treatment response in mCRPC patients receiving abiraterone. Methods: ANGELA is a single-center prospective observational trial (NCT05188911). We recruited mCRPC patients who received abiraterone as initial hormonal treatment. Eligible patients should have a life expectancy >12 months. CtDNA testing and NGI were performed at baseline and after 12 weeks of abiraterone (Week 13). Conventional imaging and PSA testing were performed at Week 1 and every 12 weeks. Primary end-point of this study is intra-patient lesion heterogeneity evaluated by 3 dimensions: (1) Imaging heterogeneity: defined as at least 1 lesion with FDG+/PSMA- phenotype at baseline and/or Week 13; (2) Genomic heterogeneity: quantified by ctDNA fraction increase and mutational changes per gene between baseline and Week 13; (3) Response heterogeneity: defined as at least 1 new tumoral lesion with either PSMA or FDG abnormal uptake at Week 13. Secondary end-points are correlations of heterogeneity with conventional-evaluated progression-free survival (PFS) and 2-year overall survival (OS). Results: Between May 20, 2020 and Feb 02, 2021, we included 33 eligible patients in our study. FDG+/PSMA- tumoral lesion was observed in 7/33 (21.2%) and 8/33 (24.2%) of patients at baseline and Week 13, respectively. Notably, 5 of 8 patients with imaging heterogeneity at Week 13 had no FDG+/PSMA- lesion at baseline. For genomic heterogeneity, we detected ctDNA fraction increase in 4/33 patients (12.1%). In addition, we found new gene mutations detected in Week 13 ctDNA testing in 5/33 patients (15.2%), 2 of 5 had ctDNA fraction increase. For response heterogeneity, we observed 15/33 patients (45.5%) patients developed at least 1 new tumoral lesion (FDG+ or PSMA+) at Week 13. We correlated heterogeneity factors with prognosis, and found that detection of new mutated genes (P=0.005 and P<0.001), new PSMA+ lesion (P<0.001 and P<0.001), and new FDG+ lesion (P=0.008 and P<0.0001) at Week 13 were significant predictors of shorter PFS and decreased 2-year OS. Detailed biomarker study for response analysis is still in process. Conclusions: Intra-patient tumor heterogeneity was common among mCRPC patients receiving abiraterone. Combining liquid biopsy with NGI can monitor changes in tumor heterogeneity and indicate early combination therapy. Clinical trial information: NCT05188911 .
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Dent, Anglin J., Kevin Faust, Brian Lam, Alberto J. Leon, Queenie Tsang, and Phedias Diamandis. "Abstract 1685: Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1685. http://dx.doi.org/10.1158/1538-7445.am2022-1685.
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Abstract BACKGROUND: Emerging evidence strongly implicates intra-tumoral heterogeneous biology in treatment resistance and disease progression across many cancer types. Thus, there is a need for workflows capable of systematically resolving and targeting distinct tumor subpopulations1. Using glioblastoma (GBM) as a prototype, I have aimed to leverage the computational power of Artificial Intelligence (AI) and deep learning to develop an autonomous workflow for the objective definition of biologically distinct tumor subpopulations2. OBJECTIVES: I hypothesize that AI may be leveraged as a tool to resolve spatial heterogeneity, by identifying tumoral subpopulations with unique molecular profiles and therapeutic targets. To highlight the need for routine analysis of tumor heterogeneity, I will address if: METHODS: I apply our developed image clustering workflows to quantify AI-defined subregions within a clinical cohort of 10 GBM patient tumors2,3. Laser capture microdissection and mass spectrometry-based proteomics are leveraged to address if AI-defined subregions show intra-tumoral molecular variation. Further, existing pharmacogenomic databases are utilized to carry out drug sensitivity and transcriptional clustering to define AI-defined region-specific therapeutic sensitivities and resistances across my clinical GBM cohort4. RESULTS: Preliminary data shows that region to region heterogeneity can be found in IDH wild-type GBM using our unbiased omics approach, in addition to predicting different pharmacogenomic sensitivities. CONCLUSIONS: This project aims to develop the first AI-driven tool to guide the routine and systematic molecular analysis of spatial morphogenomic heterogeneity. Further, this tool may have the potential to provide novel approaches for personalized care by selecting drug combinations that target a larger fraction of a tumor’s true biology. 1. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (80-. ). 344, 1396-1401 (2014). 2. Faust, K. et al. Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network. JCO Clin. Cancer Informatics 811-821 (2020) doi:10.1200/cci.20.00035. 3. Roohi, A., Faust, K., Djuric, U. & Diamandis, P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surgical Pathology Clinics vol. 13 349-358 (2020). 4. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603-607 (2012). Citation Format: Anglin J. Dent, Kevin Faust, Brian Lam, Alberto J. Leon, Queenie Tsang, Phedias Diamandis. Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1685.
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Matsumoto, Yuji, Jose Garcia, Anahita Fathi Kazerooni, Zied Abdullaev, Omkar Singh, Christos Davatzikos, Kenneth Aldape, and MacLean P. Nasrallah. "NIMG-83. ELUCIDATING INTRA-TUMORAL HETEROGENEITY AND DISTINCT RADIOMIC FEATURES IN GLIOBLASTOMA METHYLATION SUBCLASSES." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v205. http://dx.doi.org/10.1093/neuonc/noad179.0778.
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Abstract Glioblastoma, IDH-wildtype (GBM), is characterized by the inter- and intra-tumoral molecular heterogeneity. Genome-wide DNA methylation profiling has recently emerged as a promising tool offering complementary value to the classification of central nervous system tumors. In adult GBM patients, the most common methylation subclasses comprise RTK I, RTK II, and MES. We hypothesize that quantification of intricate and spatially complex radiomic features extracted from multi-parametric MRI (mpMRI) is informative to non-invasively determining characteristics of GBM methylation subclasses and their heterogeneity. In this study, mpMRI scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of newly diagnosed 23 GBM patients were retrospectively collected. We derived 6339 radiomic features, including histograms, morphologic and textural descriptors. For each case, 3-4 multiple samplings from different parts of the tumor were conducted. DNA was extracted from FFPE for each of sample and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC array. Classification of methylation subclasses was performed using the MNP brain tumor classifier v12b6 of the DKFZ. Analysis of variance (ANOVA) with Bonferroni correction was employed to extract subclass-specific radiomic features. Of the 23 patients, 7 indicated intra-tumoral heterogeneity in methylation subclasses. We identified 68 radiomic features (p< 0.05) and 12 radiomic features (p< 0.01) that showed statistically significant differences among the methylation subclasses as determined by ANOVA with Bonferroni correction. A heatmap illustrating the relationships between subclasses and extracted radiomic features displayed distinct trends among subclasses and between heterogeneous and homogeneous cases within the same subclass. Our findings suggest the presence of heterogeneity within methylation subclasses and distinctive imaging patterns for each subclass. These findings potentially pave the way for a more personalized approach to glioblastoma management, highlighting the importance of further validation through the analysis of additional cases.
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Karagiannis, Dimitris, and Theodoros Rampias. "HDAC Inhibitors: Dissecting Mechanisms of Action to Counter Tumor Heterogeneity." Cancers 13, no. 14 (July 16, 2021): 3575. http://dx.doi.org/10.3390/cancers13143575.
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Intra-tumoral heterogeneity presents a major obstacle to cancer therapeutics, including conventional chemotherapy, immunotherapy, and targeted therapies. Stochastic events such as mutations, chromosomal aberrations, and epigenetic dysregulation, as well as micro-environmental selection pressures related to nutrient and oxygen availability, immune infiltration, and immunoediting processes can drive immense phenotypic variability in tumor cells. Here, we discuss how histone deacetylase inhibitors, a prominent class of epigenetic drugs, can be leveraged to counter tumor heterogeneity. We examine their effects on cellular processes that contribute to heterogeneity and provide insights on their mechanisms of action that could assist in the development of future therapeutic approaches.
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Dontenwill, M., M. Mercier, G. Gillmann, D. Reita, I. Lelong-Rebel, F. Noulet, A. Idbaih, et al. "P11.59 Integrin a5 heterogeneous expression in glioblastoma is related to glioma stem cell subpopulations." Neuro-Oncology 21, Supplement_3 (August 2019): iii57. http://dx.doi.org/10.1093/neuonc/noz126.205.
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Abstract BACKGROUND Glioblastoma (GBM) is the most aggressive primary brain tumor. Treatment failure and recurrence are explained in part by tumoral heterogeneity. Our previous results showed that the integrin α5β1 is implicated in GBM aggressiveness and represents a relevant therapeutic target. Recently, we observed intra- and inter-tumor heterogeneity of integrin α5β1 expression. Heterogeneity may be linked to different glioma stem cell populations. MATERIAL AND METHODS Ten glioma stem cell lines were grown as neurospheres in stem cell medium and their differentiation was induced by serum and/or ATRA. Two cell lines (NCH421k and NCH644) were selected and were modified by depletion (CrisprCas9) or transfection of the α5 integrin gene. Polyclonal lines and individual clones were analyzed phenotypically in vitro, before and after differentiation, and in vivo in orthotopic xenografts of 2x104 cells in nude mice. TCGA datasets were used to validate the heterogeneous expression of α5 integrin in GBM. RESULTS TCGA data validate that α5 integrin mRNA was only over-expressed in the mesenchymal subclass of GBM. Our results show that α5 integrin protein is not expressed in stem cell culture conditions. However, α5 integrin expression is induced after differentiation in only half of the cell lines supporting the notion of tumoral heterogeneity of glioma stem cells. Interestingly, single cell-derived clone evaluation showed that intra-tumoral stem cell heterogeneity also exists at the level of α5 protein expression. When glioma stem cells are programmed or transduced to express α5 integrin, differentiated cells became more aggressive. Notably, they acquired a fibronectin-dependent motility and a proliferative phenotype. Interestingly, integrin α5 remained expressed in secondary stem cells obtained after dedifferentiation. The in vivo assays suggested that glioma stem cells, programmed to express the integrin, were prone to form larger tumors. CONCLUSION Our data support the hypothesis that some glioma stem cells are programmed to express the α5 integrin subunit in their differentiated progeny to form a more aggressive tumor. They add new evidences that both cell populations may be considered for new therapeutic strategies against GBM.
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Golebiewska, Anna, Anne Dirkse, Thomas Buder, Yahaya A. Yabo, Arnaud Muller, Petr V. Nazarov, Rolf Bjerkvig, et al. "STEM-09. INTRINSIC TUMOR PLASTICITY IN GLIOBLASTOMA ALLOWS FOR RECREATION OF STEM LIKE-STATES AND EFFICIENT TUMOR CELL ADAPTATION TO NEW MICROENVIRONMENTS." Neuro-Oncology 21, Supplement_6 (November 2019): vi235. http://dx.doi.org/10.1093/neuonc/noz175.983.
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Abstract BACKGROUND Cellular heterogeneity is a hallmark of numerous cancer types, including Glioblastoma (GBM). Cancer stem cells (CSC) have been accounted for the generation of phenotypic heterogeneity and tumor progression in GBM. Recent data, however, suggest that CSCs may not represent a stable entity and intrinsic plasticity plays a key role in tumor adaptation to changing microenvironments. The question arises whether CSCs are a defined subpopulation of GBM or whether they represent a cellular state that any cancer cell can adopt. METHODS We interrogated intra-tumoral phenotypic heterogeneity at the single cell transcriptomic and proteomic level in GBM biopsies, patient-derived stem-like cultures and orthotopic xenografts (PDOXs). Tumor cell subpopulations, classified based on their expression of four proposed stem cell markers (CD133, CD15, A2B5 and CD44), were FACS isolated and functionally characterized under various microenvironmental conditions. Mathematical Markov modelling was applied to calculate state transitions. RESULTS GBM patient biopsies, PDOXs and stem-like cell cultures displayed remarkable stem cell-associated intra-tumoral heterogeneity. However independent of marker expression, all analysed tumor subpopulations carried stem-cell properties and recreated phenotypic heterogeneity. Mathematical modeling revealed a different propensity in reforming heterogeneity over time, which was independent of the proliferation index but linked to in vivo tumorigenic potential. Although GBM subpopulations varied in their potential to adapt to new environments, all were able to reach a steady state microenvironment-specific equilibrium. CONCLUSIONS Our results suggest that phenotypic heterogeneity in GBM results from intrinsic plasticity allowing tumor cells to adapt to changing microenvironmental conditions. Cellular states are non-hierarchical, reversible and occur via stochastic state transitions, striving towards a microenvironment-instructed equilibrium. Our data provides evidence that CSCs do not represent a defined clonal entity, but rather a cellular state determined by environmental conditions, which has implications for the design of treatment strategies targeting CSC-like states.
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Tran, Maxine, Arash Latifoltojar, Joana B. Neves, Marianthi-Vasiliki Papoutsaki, Fiona Gong, Arnaud Comment, Ana S. H. Costa, et al. "First-in-human in vivo non-invasive assessment of intra-tumoral metabolic heterogeneity in renal cell carcinoma." BJR|case reports 5, no. 3 (September 2019): 20190003. http://dx.doi.org/10.1259/bjrcr.20190003.
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Intratumoral genetic heterogeneity and the role of metabolic reprogramming in renal cell carcinoma have been extensively documented. However, the distribution of these metabolic changes within the tissue has not been explored. We report on the first-in-human in vivo non-invasive metabolic interrogation of renal cell carcinoma using hyperpolarized carbon-13 (13C) MRI and describe the validation of in vivo lactate metabolic heterogeneity against multi regional ex vivo mass spectrometry. hyperpolarized carbon-13 (13C)-MRI provides an in vivo assessment of metabolism and provides a novel opportunity to safely and non-invasively assess cancer heterogeneity.
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Lim, Y., S. Kang, H. Kim, J. Mun, M. Roh, N. Gulati, H. Yang, J. Moon, C. Won, and C. Park. "631 Determining intra-tumoral heterogeneity and immune escape mechanisms in melanoma using spatial transcriptomics." Journal of Investigative Dermatology 142, no. 8 (August 2022): S109. http://dx.doi.org/10.1016/j.jid.2022.05.642.
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Sarode, Gargi, Sachin C. Sarode, Jagdish Tupkari, and Shankargouda Patil. "Is oral squamous cell carcinoma unique in terms of intra- and inter-tumoral heterogeneity?" Translational Research in Oral Oncology 2 (January 2017): 2057178X1770357. http://dx.doi.org/10.1177/2057178x17703578.
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Jacob, Fadi, Ryan D. Salinas, Daniel Y. Zhang, Phuong T. T. Nguyen, Jordan G. Schnoll, Samuel Zheng Hao Wong, Radhika Thokala, et al. "A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity." Cell 180, no. 1 (January 2020): 188–204. http://dx.doi.org/10.1016/j.cell.2019.11.036.
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Chien, Franklin, Marina Michaud, Mojtaba Bakhtiari, James L. Ross, Chanel Schroff, Matija Snuderl, Tobey MacDonald, and Manoj Bhasin. "MDB-106. MEDULLOBLASTOMA TUMOR MICROENVIRONMENTS SHOW INTRA- AND INTER-TUMORAL HETEROGENEITY ON SPATIAL TRANSCRIPTOMICS." Neuro-Oncology 26, Supplement_4 (June 18, 2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.554.
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Abstract BACKGROUND Medulloblastoma defines a heterogeneous set of neuroepithelial neoplasms of the posterior fossa. Four groups of medulloblastoma are recognized: wingless (WNT), sonic hedgehog (SHH), group 3, and group 4. The tumor microenvironment (TME) influences tumor progression and response to therapy and has emerged as a growing target for the study of novel therapeutic approaches. METHODS We show spatial gene expression architecture through 10X Visium Spatial Sequencing on 16 formalin-fixed paraffin-embedded (FFPE) samples representing all molecular groups of medulloblastoma. Spatial transcriptomics data is correlated with clinical parameters, including M-stage at diagnosis, tumor histological classification, and outcomes data including survival and relapse status. RESULTS Spatial sequencing demonstrates both intra- and inter-tumoral TME heterogeneity across molecular subgroups. Unsupervised clustering and uniform manifold approximate projection illustrate clusters of distinct spatial gene expression representing cell states from different components of the TME, including tumor-associated astrocytes (TAA), macrophages (TAM), and vascular endothelium. Medulloblastoma cells constitute the majority of cells and express marker genes representing different stages of neuronal differentiation and cell cycle. Pathway analysis reveals the enrichment of pathways associated with cell motility (RHO GTPase, MET, MAPK signaling), heat shock response, growth factor signaling, and adaptive immune response (TLR, MHC-II signaling). In addition, molecular pathways known in medulloblastoma, including NF-kB and TP53 regulatory pathways, were also enriched. Neighborhood-enrichment analysis reveals the co-localization of TAMs and TAAs in close spatial relationship with mitotic progenitor-like medulloblastoma cells. Additionally, these two cell types constitute a greater proportion of the TME in patients at relapse compared to initial diagnosis. CONCLUSION Spatial transcriptomics enables new insight into the heterogeneity of the medulloblastoma TME. Notably, our analysis elucidates the spatial association of TAMs and TAAs with proliferating tumor cells and an increased abundance of these cell types following relapse. These initial results support the role of TAMs and TAAs in medulloblastoma progression.
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Barelli, Carlotta, Flaminia Kaluthantrige Don, Raffaele M. Iannuzzi, Stefania Faletti, Ilaria Bertani, Isabella Osei, Simona Sorrentino, et al. "Morphoregulatory ADD3 underlies glioblastoma growth and formation of tumor–tumor connections." Life Science Alliance 8, no. 2 (November 26, 2024): e202402823. http://dx.doi.org/10.26508/lsa.202402823.
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Glioblastoma is a major unmet clinical need characterized by striking inter- and intra-tumoral heterogeneity and a population of glioblastoma stem cells (GSCs), conferring aggressiveness and therapy resistance. GSCs communicate through a network of tumor–tumor connections (TTCs), including nanotubes and microtubes, promoting tumor progression. However, very little is known about the mechanisms underlying TTC formation and overall GSC morphology. As GSCs closely resemble neural progenitor cells during neurodevelopment, we hypothesized that GSCs’ morphological features affect tumor progression. We identified GSC morphology as a new layer of tumoral heterogeneity with important consequences on GSC proliferation. Strikingly, we showed that the neurodevelopmental morphoregulator ADD3 is sufficient and necessary for maintaining proper GSC morphology, TTC abundance, cell cycle progression, and chemoresistance, as well as required for cell survival. Remarkably, both the effects on cell morphology and proliferation depend on the stability of actin cytoskeleton. Hence, cell morphology and its regulators play a key role in tumor progression by mediating cell–cell communication. We thus propose that GSC morphological heterogeneity holds the potential to identify new therapeutic targets and diagnostic markers.
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Roberts, Cardenas, and Tedja. "The Role of Intra-Tumoral Heterogeneity and Its Clinical Relevance in Epithelial Ovarian Cancer Recurrence and Metastasis." Cancers 11, no. 8 (July 30, 2019): 1083. http://dx.doi.org/10.3390/cancers11081083.
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Epithelial ovarian cancer is the deadliest gynecologic cancer, due in large part to recurrent tumors. Recurrences tend to have metastasized, mainly in the peritoneal cavity and developed resistance to the first line chemotherapy. Key to the progression and ultimate lethality of ovarian cancer is the existence of extensive intra-tumoral heterogeneity (ITH). In this review, we describe the genetic and epigenetic changes that have been reported to give rise to different cell populations in ovarian cancer. We also describe at length the contributions made to heterogeneity by both linear and parallel models of clonal evolution and the existence of cancer stem cells. We dissect the key biological signals from the tumor microenvironment, both directly from other cell types in the vicinity and soluble or circulating factors. Finally, we discuss the impact of tumor heterogeneity on the choice of therapeutic approaches in the clinic. Variability in ovarian tumors remains a major barrier to effective therapy, but by leveraging future research into tumor heterogeneity, we may be able to overcome this barrier and provide more effective, personalized therapy to patients.
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Gudenas, Brian, Bernhard Englinger, Anthony P. Y. Liu, Sheikh Tanveer Ahmad, David Meredith, Elke Pfaff, Leena Paul, et al. "ATRT-08. THE SINGLE-CELL LANDSCAPE OF PINEOBLASTOMA IDENTIFIES DEVELOPMENTAL ORIGINS AND EXPOSES TUMORIGENIC DEPENDENCIES." Neuro-Oncology 25, Supplement_1 (June 1, 2023): i2. http://dx.doi.org/10.1093/neuonc/noad073.008.
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Abstract Pineoblastoma (PB), a rare and aggressive brain tumor affecting children, presents with a highly variable age distribution and treatment outcome. Our recent bulk tumor analyses of DNA methylation and mutational landscapes uncovered four discrete PB molecular subgroups (PB-miRNA1, PB-miRNA2, PB-MYC/FOXR2, and PB-RB), providing a major advance in our understanding of biological and clinical heterogeneity. However, developmental origins of PB subgroup heterogeneity and mechanisms governing how specific genetic alterations promote malignancy remain unknown. We conducted single-nucleus RNA sequencing of 38 primary tumors, including cases from all subgroups, to uncover intra-tumoral heterogeneity, developmental origins and expose selective dependencies. Transcriptional programs driving intra-tumoral heterogeneity showed subgroup-specific patterns such as mRNA splicing associated with PB-miRNA1/2 and phototransduction in PB-RB and PB-MYC/FOXR2. Next, we created a single-cell transcriptional atlas of the murine pineal gland across 11 developmental stages and found significant associations between PB subgroups and specific differentiation states of the pinealocyte lineage, suggesting subgroup-specific developmental origins. Characterization of pineal development informed generation of biologically faithful disease models, including a novel genetically engineered mouse model of the PB-RB subgroup. PB-Rb1 mouse tumors were histologically and molecularly validated for their fidelity to human tumor counterparts, exhibiting up-regulation of key pinealocyte lineage markers that are diagnostic in patients. Using cell lines derived from our PB-Rb1 mouse model we identified several transcription factor dependencies we believe are high-jacked during normal pinealocyte development. Our findings inform the developmental origins of pineoblastoma as well as provides candidates for potential therapeutic targets.