Analysis Of Tumor Heterogeneity Research Articles

Forming Single-Cell-Derived Colon Cancer Organoid Arrays on a Microfluidic Chip for High Throughput Tumor Heterogeneity Analysis.

Single-cell-derived tumor organoids (STOs) possess a distinct genetic background, making them valuable tools for demonstrating tumor heterogeneity. In order to fulfill the high throughput demands of STO assays, we have developed a microfluidic chip containing 30 000 microwells, which is dedicated to a single cell culture approach for selective expansion and differential induction of cancer stem cells. The microwells are coated with a hydrophilic copolymer to eliminate cell adhesion, and the cell culture is supported by poly(ethylene glycol) (PEG) to establish a nonadhesive culture environment. By utilizing an input cell density of 7 × 103·mL-1, it is possible to construct a 4000 single cell culture system through stochastic cell occupation. We demonstrate that the addition of 15% PEG10000 in the cell culture medium effectively prevents cell loss while facilitating tumor stem cell expansion. As were demonstrated by HCT116, HT29, and SW480 colon cancer cells, the microfluidic approach achieved a STO formation rate of ∼20%, resulting in over 800 STOs generated from a single culture. Comprehensive analysis through histomorphology, immunohistochemistry, drug response evaluation, assessment of cell invasion, and biomarker detection reveals the heterogeneity among individual STOs. Specifically, the smaller STOs exhibited higher invasion and drug resistance capabilities compared with the larger ones. The developed microfluidic approach effectively facilitates STO formation and offers promising prospects for investigating tumor heterogeneity, as well as conducting personalized therapy-focused drug screening.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Melanoma Clonal Heterogeneity Leads to Secondary Resistance after Adoptive Cell Therapy with Tumor-Infiltrating Lymphocytes.

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TIL) is effective in patients with melanoma, although long-term responses seem restricted in patients who have complete remissions. Many patients develop secondary resistance to TIL-ACT but the involved mechanisms are unclear. In this study, we describe a case of secondary resistance to TIL-ACT possibly due to intratumoral heterogeneity and selection of a resistant tumor cell clone by the transferred T cells. To the best our knowledge, this is the first case of clonal selection of a pre-existing nondominant tumor cell clone; this report demonstrates the mechanism involved in secondary resistance to TIL-ACT that can potentially change current clinical practice because it advocates for T-cell collection from multiple tumor sites and analysis of tumor heterogeneity before treatment with TIL-ACT.

Abstract 4305: Analysis of tumor heterogeneity in syngeneic models; CT26.WT colon carcinoma and 4T1-Luc2-1A4 breast carcinoma in female BALB/cAnNHsd mice

Abstract Cancer is a complex disease that involves both malignant cells and the surrounding microenvironment. The tumor microenvironment (TME) is comprised of various components, including cancer-associated fibroblasts, immune cells, blood vessels, and extracellular matrix elements, all of which interact with cancer cells. It is increasingly recognized that the components of the TME are a critical determinant of therapeutic response and resistance, and as such targeting the TME has emerged as a promising strategy in cancer therapy. Syngeneic models are necessary to study the TME, and more specifically the tumor infiltrating immune cell populations, as these models allow us to monitor various aspects of the immune response. Another important aspect to consider is tumor heterogeneity, which can cause issues during analysis due to immune cell populations not being evenly distributed throughout the tumor. The decision of whether to analyze whole tumors or halved tumors when monitoring changes in immune cell populations is an important consideration. When considering multiple endpoints from a single tumor sample, halved tumors are oftentimes considered as we remain mindful of the 3Rs (replacement, reduction and refinement) in animal research. As such, it is important to understand the effects of splitting samples and how it can affect the data that is generated. To determine the effects of analyzing whole and halved tumor samples on immune cell subsets, studies were designed to subcutaneously implant either CT26.WT or 4T1-Luc2-1A4 cells into female BALB/cAnNHsd mice and harvest tumor samples after the completion of treatment. Mice were treated with isotype control (Clone MPC-11) or anti-mCTLA-4 (Clone 9D9). At the time of sample collection, tumors were kept whole intact or halved and analyzed via flow cytometry to quantify lymphoid and myeloid immune cell subsets within tumor samples. The data revealed differences in immune cell subset populations for both isotype vs. anti-CTLA-4 treated tumors and whole vs. halved tumors. Changes in the lymphoid cell compartment were observed for whole vs. halved tumors of ± ~25-100% and ± ~0-40% for isotype treated and of ± ~0-30% and ± ~25-2600% for anti-CTLA-4 treated CT26.WT or 4T1-Luc2-1A4 tumors, respectively. Changes in the myeloid cell compartment of ± ~5-45% and ± ~10-65% for isotype treated and of ± ~15-100% and ± ~0-70% for anti-CTLA-4 treated CT26.WT or 4T1-Luc2-1A4 tumors, respectively, were also observed. Additionally, differences were observed between the two tumor halves, not just between whole and halved tumors. Taken together, the data support that splitting tumor samples can negatively impact the analysis of the immune cell populations. Tumors can be split if required to perform multiple analyses, however, this should be done with caution, as it can result in trend differences and increases variability within the dataset. Citation Format: Justin Snider, Derrik Germain, Lauren Kucharczyk, Anita Zaitouna, Sheri Barnes, David Draper, Scott Wise. Analysis of tumor heterogeneity in syngeneic models; CT26.WT colon carcinoma and 4T1-Luc2-1A4 breast carcinoma in female BALB/cAnNHsd mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4305.

Identification of ALYREF in pan cancer as a novel cancer prognostic biomarker and potential regulatory mechanism in gastric cancer

ALYREF is considered as a specific mRNA m5C-binding protein which recognizes m5C sites in RNA and facilitates the export of RNA from the nucleus to the cytoplasm. Expressed in various tissues and highly involved in the transcriptional regulation, ALYREF has the potential to become a novel diagnostic marker and therapeutic target for cancer patients. However, few studies focused on its function during carcinogenesis and progress. In order to explore the role of ALYREF on tumorigenesis, TCGA and GTEx databases were used to investigate the relationship of ALYREF to pan-cancer. We found that ALYREF was highly expressed in majority of cancer types and that elevated expression level was positively associated with poor prognosis in many cancers. GO and KEGG analysis showed that ALYREF to be essential in regulating the cell cycle and gene mismatch repair in tumor progression. The correlation analysis of tumor heterogeneity indicated that ALYREF could be specially correlated to the tumor stemness in stomach adenocarcinoma (STAD). Furthermore, we investigate the potential function of ALYREF on gastric carcinogenesis. Prognostic analysis of different molecular subtypes of gastric cancer (GC) unfolded that high ALYREF expression leads to poor prognosis in certain subtypes of GC. Finally, enrichment analysis revealed that ALYREF-related genes possess the function of regulating cell cycle and apoptosis that cause further influences in GC tumor progression. For further verification, we knocked down the expression of ALYREF by siRNA in GC cell line AGS. Knockdown of ALYREF distinctly contributed to inhibition of GC cell proliferation. Moreover, it is observed that knocked-down of ALYREF induced AGS cells arrested in G1 phase and increased cell apoptosis. Our findings highlighted the essential function of ALYREF in tumorigenesis and revealed the specific contribution of ALYREF to gastric carcinogenesis through pan-cancer analysis and biological experiments.

Computer tomography-based shape of tumor contour and texture of tumor heterogeneity are independent prognostic indicators for clinical T1b-T2 renal cell carcinoma.

To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes. Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death. A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively.

Analysis of Tumor Heterogeneity Through AXL Activation in Primary Resistance to EGFR Tyrosine Kinase Inhibitors

IntroductionEGFR tyrosine kinase inhibitors are standard therapeutic agents for patients with advanced NSCLC harboring EGFR mutations. Nevertheless, some patients exhibit primary resistance to EGFR tyrosine kinase inhibitors in the first-line treatment setting. AXL, a member of the TYRO3, AXL, and MERTK family of receptor tyrosine kinases, is involved in primary resistance to EGFR tyrosine kinase inhibitors in EGFR-mutated NSCLC. MethodsWe investigated spatial tumor heterogeneity using autopsy specimens and a patient-derived cell line from a patient with EGFR-mutated NSCLC having primary resistance to erlotinib plus ramucirumab. ResultsQuantitative polymerase chain reaction analysis revealed that AXL mRNA expression differed at each metastatic site. In addition, AXL expression levels were likely to be negatively correlated with the effectiveness of erlotinib plus ramucirumab therapy. Analysis of a patient-derived cell line established from the left pleural effusion before initiation of treatment revealed that the combination of EGFR tyrosine kinase inhibitors and an AXL inhibitor remarkably inhibited cell viability and increased cell apoptosis in comparison with EGFR tyrosine kinase inhibitor monotherapy or combination therapy of these inhibitors with ramucirumab. ConclusionsOur observations suggest that AXL expression may play a critical role in the progression of spatial tumor heterogeneity and primary resistance to EGFR tyrosine kinase inhibitors in patients with EGFR-mutated NSCLC.

Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI

Predicting glioma grade plays a pivotal role in treatment and prognosis. However, several current methods for grading depend on the characteristics of the whole tumor. Predicting grade by analyzing tumor subregions has not been thoroughly investigated, which aims to improve the prediction performance. To predict glioma grade via analysis of tumor heterogeneity with features extracted from tumor subregions, it is mainly divided into four magnetic resonance imaging (MRI) sequences, including T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), pre-gadolinium T1-weighted (T1), and post-gadolinium T1-weighted methods. This study included the data of 97 patients with glioblastomas and 42 patients with low-grade gliomas before surgery. Three subregions, including enhanced tumor (ET), non-enhanced tumor, and peritumoral edema, were obtained based on segmentation labels generated by the GLISTRBoost algorithm. One hundred radiomic features were extracted from each subregion. Feature selection was performed using the cross-validated recursive feature elimination with a support vector machine (SVM) algorithm. SVM classifiers with grid search were established to predict glioma grade based on unparametric and multiparametric MRI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the classifiers, and the performance of the subregions was compared with the results of the whole tumor. In uniparametric analysis, the features from the ET subregion yielded a higher AUC value of 0.8697, 0.8474, and 0.8474 than those of the whole tumor of FLAIR, T1, and T2. In multiparametric analysis, the ET subregion achieved the best performance (AUC = 0.8755), which was higher than the uniparametric results. Radiomic features from the tumor subregion can potentially be used as clinical markers to improve the predictive accuracy of glioma grades.

Single-cell transcriptomic analysis of tumor heterogeneity and intercellular networks in human urothelial carcinoma.

Heterogeneity of tumor cells and the tumor microenvironment (TME) is significantly associated with clinical outcomes and treatment responses in patients with urothelial carcinoma (UC). Comprehensive profiling of the cellular diversity and interactions between malignant cells and TME may clarify the mechanisms underlying UC progression and guide the development of novel therapies. This study aimed to extend our understanding of intra-tumoral heterogeneity and the immunosuppressive TME in UC and provide basic support for the development of novel UC therapies. Seven patients with UC were included who underwent curative surgery at our hospital between July 2020 and October 2020. We performed single-cell RNA sequencing (scRNA-seq) analysis in seven tumors with six matched adjacent normal tissues and integrated the results with two public scRNA-seq datasets. The functional properties and intercellular interactions between single cells were characterized, and the results were validated using multiplex immunofluorescence staining, flow cytometry, and bulk transcriptomic datasets. All statistical analyses were performed using the R package with two-sided tests. Wilcoxon-rank test, log-rank test, one-way analysis of variance test, and Pearson correlation analysis were used properly. Unsupervised t-distributed stochastic neighbor embedding clustering analysis identified ten main cellular subclusters in urothelial tissues. Of them, seven urothelial subtypes were noted, and malignant urothelial cells were characterized with enhanced cellular proliferation and reduced immunogenicity. CD8 + T cell subclusters exhibited enhanced cellular cytotoxicity activities along with increased exhaustion signature in UC tissues, and the recruitment of CD4 + T regulatory cells was also increased in tumor tissues. Regarding myeloid cells, coordinated reprogramming of infiltrated neutrophils, M2-type polarized macrophages, and LAMP3 + dendritic cells contribute to immunosuppressive TME in UC tissues. Tumor tissues demonstrated enhanced angiogenesis mediated by KDR + endothelial cells and RGS5 + /ACTA2 + pericytes. Through deconvolution analysis, we identified multiple cellular subtypes may influence the programmed death-ligand 1 (PD-L1) immunotherapy response in patients with UC. Our scRNA-seq analysis clarified intra-tumoral heterogeneity and delineated the pro-tumoral and immunosuppressive microenvironment in UC tissues, which may provide novel therapeutic targets.

A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis.

The rapid development of single-cell technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them. Distinguishing different cell types and subtypes can identify the components of different immune cells and different tumor clones in the tumor microenvironment, which is the basic work of tumor heterogeneity analysis and can help researchers understand the mechanism of tumor immune escape. Existing algorithms treat both cell types and subtypes as populations of cells with specific gene expression patterns, which is not conducive to accurate cell typing. For that, we proposed a cell similarity metric that unifies cell type recognition and subtype identification (UCRSI), with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner. Our method calculates these two kinds of differences separately, and then combines them using a consensus adjacency matrix, and finally cell typing is completed using spectral clustering algorithm. The results show that UCRSI can reconstruct expert annotation of single-cell RNA sequencing datasets more robustly than existing methods. And, UCRSI is useful for analyzing tumor heterogeneity and improving visualization of large-scale cell clustering.

Renal tumor heterogeneity analysis in Von Hippel-Lindau disease using single-cell RNA sequencing

Renal tumor heterogeneity analysis in Von Hippel-Lindau disease using single-cell RNA sequencing

Technological and computational advances driving high-throughput oncology.

Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell-cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales.

Deconvolution of tumor composition using partially available DNA methylation data

BackgroundDeciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions.ResultsIn this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance.ConclusionsOur method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth).

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

Prostate-specific Membrane Antigen Positron Emission Tomography (PSMA-PET) and Gleason grading system based Artificial Intelligence (AI) Model in Diagnosis and Staging of Prostate Cancer

Introduction: Prostate cancer is the second-most commonly occurring cancer and the fifth major cause of death among men worldwide. Early diagnosis and treatment planning are very crucial in reducing the mortality rate due to prostate cancer. Gleason grading is the most used prostate cancer prognosis tool by doctors for a long time. It is used to determine the aggressiveness of the cancer for planning treatment options. The process requires very trained pathologists to look at multiple biopsy samples under the microscope and assign a grade to the cancer based on its severity. Methods: In this work, we are adding Gleason grading capabilities to prostate-specific membrane antigen positron emission tomography/ computed tomography (PSMA PET-CT) scans for tumor habitats and classify them as aggressive or indolent type. Tagging habitats with Gleason grade to categorize them as aggressive or indolent type helps in biopsy planning to extract the right tissue samples and tagging helps to target aggressive tumors during radiation therapy. We have developed a machine learning-based model to automatically classify tumor habitat regions of interest from PSMA PET and CT imaging data into Gleason grade groups of indolent and aggressive. Results: The 68Ga-PSMA PET-CT scans are very effective in detecting the presence of different habitats within the tumor with distinct volumes, each with a specific combination of flow, cell density, necrosis, and edema. Habitat distribution through tumor heterogeneity analysis in patients with prostate cancers can be enabled to discriminate between cancers that progress quickly and those that are more indolent. Conclusion: We have developed an AI model to classify habitat tumors present in the gross tumor volume into indolent and aggressive types based on the ground truth generated using Gleason grade groups on pathology samples by Healthcare Global Cancer Hospital, Bangalore, India. Habitat analysis helps radiotherapists to target active tumor cells within gross tumor volume and helps in selecting the right tissue for performing biopsy. The currently developed model is performing with an overall accuracy of 90% on test data.

Abstract 6089: Novel workflow enables enhanced tissue dissociation and gentle clean-up for single cell applications

Abstract With genomic instability being a hallmark of cancer, heterogeneity within solid tumors is prevalent. Intra-tumoral heterogeneity contributes strongly to clinical outcomes. In fact, heterogeneity is often cited as a driver of therapeutic failure, chemo-resistance, and recurrence of disease. Current bulk profiling analysis of tumors is limited in its ability to provide the level of granularity necessary for uncovering aspects of heterogeneity that contribute to poor outcomes. Thus, it is imperative to gain a deeper understanding of tumors at the single-cell level. To properly analyze tumor heterogeneity at the single-cell level it is critical that quality methods for tissue dissociation and cell purification be utilized. While the most frequently used workflows rely on a combination of enzymatic and mechanical dissociation of the tissue followed by RBC lysis to remove contaminating red blood cells, these approaches tend to be harsh on cells leading to activation of cell stress pathways or even the loss of target cells due to their death during processing. The analysis of tumor heterogeneity has long been hampered by issues with low yield and sample preparation-induced alterations in cell physiology. To alleviate these issues, we present a workflow that couples Cellsonics’ SimpleFlow™ with Akadeum’s Buoyancy Activated Cell Sorting (BACS™) Microbubbles for gentle and effective tumor dissociation and clean-up that does not compromise precious samples. Cellsonics’ SimpleFlow™ system features the proprietary Bulk Lateral Ultrasonic™ (BLU™) energy to generate single cells from solid tissue with unparalleled speed, quality, and sample integrity. SimpleFlow™ is engineered to enable any lab to obtain the full spectrum of heterogeneous cell populations while maintaining the native gene expression patterns across all cell types. Single cell samples can be generated from solid tissues in 4-10 minutes under cold temperature without the use of enzymes. Akadeum’s BACS™ Microbubble technology features low-density microbubbles that are functionalized to capture an array of analytes including particular cell types. Akadeum’s BACS™ Microbubbles enable the purification of cell types of interest quickly, easily, and without sacrificing cell health and physiology. In this study, The SimpleFlow™ system was used to dissociate fresh tissues from mice to obtain a heterogeneous suspension of single cells. Following the dissociation, up to 99% of RBCs were removed from samples using Akadeum’s Mouse RBC Depletion. In subsequent studies, cancer cells were spiked into tissue samples prior to processing to assess their recovery and physiology. The novel workflow presented here combing SimpleFlow™ and microbubbles demonstrates the recovery of the full spectrum of cells and did not harm tumor cell recovery or health. Citation Format: Chananat Klomsiri, Edward Grimley, Jon Roussey, Carolina Livi, Casey Wegner, Brandon H. McNaughton, Steve Levers, Vibhu Vivek, Dan Snyder, Doug Summers, Ma Phiengsai. Novel workflow enables enhanced tissue dissociation and gentle clean-up for single cell applications [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 6089.

Abstract 1685: Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma

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. &amp; 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.

ATHENA: analysis of tumor heterogeneity from spatial omics measurements.

SummaryTumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA uses graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems.Availability and implementationATHENA is available as a Python package under an open-source license at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/. The data presented in this article are publicly available on Figshare at https://figshare.com/articles/dataset/zurich_pkl/19617642/2.Supplementary information Supplementary data are available at Bioinformatics online.

Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer

BackgroundCancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.MethodsOrgan-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.ResultsComparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.ConclusionsWe performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.

BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud.

Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline. We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools. http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics. Supplementary data are available at Bioinformatics online.