Understanding Of Tumor Heterogeneity Research Articles

Microfluidic Tandem Mechanical Sorting System for Enhanced Cancer Stem Cell Isolation and Ingredient Screening.

Robust isolation of cancer stem cells (CSCs) in a high-throughput, label-free manner is critical for understanding tumor heterogeneity and developing therapeutic strategies targeting CSCs. Cell-mechanics-based microfluidic sorting systems provide efficient and specific platforms for investigation of stem cell-like characteristics on the basis of cell deformability and cell-substrate adhesion properties. In the present study, a microfluidic tandem mechanical sorting system is developed to enrich CSCs with high flexibility and low adhesive capacity. In the integrated microfluidic system, cancer cells are driven by hydrodynamic forces to flow continuously through two featured devices, which are functionalized with sequentially variable microbarriers and surface-coated fluid mixing microchannels, respectively. Collected deformable and low-adhesive cancer cells exhibit enhanced stem cell-like properties with higher stemness and metastasis capacity both in vitro and in vivo, compared with each single device separation. Using these devices, bioactive natural compound screening targeting CSCs is performed and a potent therapeutic compound isoliquiritigenin from licorice is identified to inhibit the lung cancer stem cell phenotype. Taken together, this microfluidic tandem mechanical sorting system can facilitate drug screening targeting CSCs and the analysis of signals regulating CSC function in drug resistance.

Assessing heterogeneity in spatial data using the HTA index with applications to spatial transcriptomics and imaging.

MotivationTumour heterogeneity is being increasingly recognized as an important characteristic of cancer and as a determinant of prognosis and treatment outcome. Emerging spatial transcriptomics data hold the potential to further our understanding of tumour heterogeneity and its implications. However, existing statistical tools are not sufficiently powerful to capture heterogeneity in the complex setting of spatial molecular biology.ResultsWe provide a statistical solution, the HeTerogeneity Average index (HTA), specifically designed to handle the multivariate nature of spatial transcriptomics. We prove that HTA has an approximately normal distribution, therefore lending itself to efficient statistical assessment and inference. We first demonstrate that HTA accurately reflects the level of heterogeneity in simulated data. We then use HTA to analyze heterogeneity in two cancer spatial transcriptomics datasets: spatial RNA sequencing by 10x Genomics and spatial transcriptomics inferred from H&E. Finally, we demonstrate that HTA also applies to 3D spatial data using brain MRI. In spatial RNA sequencing, we use a known combination of molecular traits to assert that HTA aligns with the expected outcome for this combination. We also show that HTA captures immune-cell infiltration at multiple resolutions. In digital pathology, we show how HTA can be used in survival analysis and demonstrate that high levels of heterogeneity may be linked to poor survival. In brain MRI, we show that HTA differentiates between normal ageing, Alzheimer’s disease and two tumours. HTA also extends beyond molecular biology and medical imaging, and can be applied to many domains, including GIS.Availability and implementationPython package and source code are available at: https://github.com/alonalj/hta.Supplementary information Supplementary data are available at Bioinformatics online.

Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications.

Glioblastomas (GBM) are the most common and aggressive tumors of the central nervous system. Rapid tumor growth and diffuse infiltration into healthy brain tissue, along with high intratumoral heterogeneity, challenge therapeutic efficacy and prognosis. A better understanding of spatiotemporal tumor heterogeneity at the histological, cellular, molecular, and dynamic levels would accelerate the development of novel treatments for this devastating brain cancer. Histologically, GBM is characterized by nuclear atypia, cellular pleomorphism, necrosis, microvascular proliferation, and pseudopalisades. At the cellular level, the glioma microenvironment comprises a heterogeneous landscape of cell populations, including tumor cells, non-transformed/reactive glial and neural cells, immune cells, mesenchymal cells, and stem cells, which support tumor growth and invasion through complex network crosstalk. Genomic and transcriptomic analyses of gliomas have revealed significant inter and intratumoral heterogeneity and insights into their molecular pathogenesis. Moreover, recent evidence suggests that diverse dynamics of collective motion patterns exist in glioma tumors, which correlate with histological features. We hypothesize that glioma heterogeneity is not stochastic, but rather arises from organized and dynamic attributes, which favor glioma malignancy and influences treatment regimens. This review highlights the importance of an integrative approach of glioma histopathological features, single-cell and spatially resolved transcriptomic and cellular dynamics to understand tumor heterogeneity and maximize therapeutic effects.

Abstract 2227: Pan-cancer shedding patterns of tumor circulating cell free DNA

Abstract Introduction: Tumor circulating cell-free DNA (ctDNA) comprises DNA molecules cast from tumors that reach and survive in a patient's bloodstream. Given its non-invasive nature, the liquid biopsy of ctDNA has remarkable potential for diagnosis, prognosis, disease progression tracking, and treatment monitoring. Nonetheless, little is known about which tumor features yield higher representation of ctDNA in blood or even which regions in the tumor genome are more inclined to shed and thus be observed as ctDNA. Typically, studies of ctDNA have focused on a limited and well-established set of genes and recurrent variants. However, these limited gene panels may not capture the breadth of genetic alterations that reflect tumor biology. Their limited footprint hinders a comprehensive understanding of tumor heterogeneity, mechanisms of resistance, and DNA shedding patterns. To address these limitations, we developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy™, that enables sensitive detection and tracking of mutations in over twenty thousand genes from plasma samples. Results: Here we profile over 50 tumor, normal, and plasma matched samples using NeXT Liquid Biopsy to investigate pan-cancer patterns of DNA shedding. We observe varying levels of ctDNA shedding in plasma, suggesting that tracking tumors with an exome-scale set of variants, as opposed to with a targeted panel, can benefit greatly from the higher sensitivity and granularity of our enhanced exome sequencing. Next, we sought to investigate the patterns of somatic variants representation in plasma. For this, we integrate shedding ratios with transcriptome and epigenome data from healthy paired tissues and examine variant shedding biases associated with transcription rate and histone modifications in associated nucleosomes. Conclusion: We sequenced and analyzed one of the largest tumor, normal, and plasma cohorts to date with comprehensive coverage of somatic variants across all human genes. Our results suggest that tracking exome-scale somatic variants adds invaluable information to understand a tumor's biology. Moreover, our comprehensive coverage of the tumor genome can be used to unveil biases of tumor genome shedding patterns. We are able to investigate the shedding ratio across distinct genomic features deriving, to our knowledge, the first exome scale pan-cancer shedding resource of the human genome. Citation Format: Fabio C. P. Navarro, Simo Zhang, Mengyao Tan, Charles Abbott, Josette Northcott, John Lyle, Gabor Bartha, Jason Harris, John West, Richard Chen, Sean Michael Boyle. Pan-cancer shedding patterns of tumor circulating cell free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2227.

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

BackgroundDrawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution.MethodsHere, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones.ResultsWe apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones.ConclusionsThe integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).

"Zooming in" on Glioblastoma: Understanding Tumor Heterogeneity and its Clinical Implications in the Era of Single-Cell Ribonucleic Acid Sequencing.

Glioblastoma (GBM) is the most common primary brain malignancy in adults and one of the most aggressive of all human cancers. It is highly recurrent and treatment-resistant, in large part due to its infiltrative nature and inter- and intratumoral heterogeneity. This heterogeneity entails varying genomic landscapes and cell types within and between tumors and the tumor microenvironment (TME). In GBM, heterogeneity is a driver of treatment resistance, recurrence, and poor prognosis, representing a substantial impediment to personalized medicine. Over the last decade, sequencing technologies have facilitated deeper understanding of GBM heterogeneity by "zooming in" progressively further on tumor genomics and transcriptomics. Initial efforts employed bulk ribonucleic acid (RNA) sequencing, which examines composite gene expression of whole tumor specimens. While groundbreaking at the time, this bulk RNAseq masks the crucial contributions of distinct tumor subpopulations to overall gene expression. This work progressed to the use of bulk RNA sequencing in anatomically and spatially distinct tumor subsections, which demonstrated previously underappreciated genomic complexity of GBM. A revolutionary next step forward has been the advent of single-cell RNA sequencing (scRNAseq), which examines gene expression at the single-cell level. scRNAseq has enabled us to understand GBM heterogeneity in unprecedented detail. We review seminal studies in our progression of understanding GBM heterogeneity, with a focus on scRNAseq and the insights that it has provided into understanding the GBM tumor mass, peritumoral space, and TME. We highlight preclinical and clinical implications of this work and consider its potential to impact neuro-oncology and to improve patient outcomes via personalized medicine.

Tumor Heterogeneity: A Great Barrier in the Age of Cancer Immunotherapy.

Simple SummaryDespite great advances in cancer therapy, tumor heterogeneity continues to be a great barrier for the successful treatment of cancer. It has long been established that tumor heterogeneity is prevelant in most cancer patients and is a major driver of acquired resistance to all forms of cancer therapy. In the case of immunotherapy, the response of the immune system against specific tumor antigens can drive selective pressure towards antigen-negative cells, which is a common cause of relapse in the clinic. In this review we summarize the mechanisms in which tumor heterogeity can arise. Furthermore, we discuss recent advances in the field of oncology that can be used to better identify, study, and overcome tumor heterogeneity.Throughout the history of oncology research, tumor heterogeneity has been a major hurdle for the successful treatment of cancer. As a result of aberrant changes in the tumor microenvironment such as high mutational burden, hypoxic conditions and abnormal vasculature, several malignant subpopulations often exist within a single tumor mass. Therapeutic intervention can also increase selective pressure towards subpopulations with acquired resistance. This phenomenon is often the cause of relapse in previously responsive patients, drastically changing the expected outcome of therapy. In the case of cancer immunotherapy, tumor heterogeneity is a substantial barrier as acquired resistance often takes the form of antigen escape and immunosuppression. In an effort to combat intrinsic resistance mechanisms, therapies are often combined as a multi-pronged approach to target multiple pathways simultaneously. These multi-therapy regimens have long been a mainstay of clinical oncology with chemotherapy cocktails but are more recently being investigated in the emerging landscape of immunotherapy. Furthermore, as high throughput technology becomes more affordable and accessible, researchers continue to deepen their understanding of the factors that influence tumor heterogeneity and shape the TME over the course of treatment regimens. In this review, we will investigate the factors that give rise to tumor heterogeneity and the impact it has on the field of immunotherapy. We will discuss how tumor heterogeneity causes resistance to various treatments and review the strategies currently being employed to overcome this challenging clinical hurdle. Finally, we will outline areas of research that should be prioritized to gain a better understanding of tumor heterogeneity and develop appropriate solutions.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Deciphering Tumor Heterogeneity in Hepatocellular Carcinoma (HCC)-Multi-Omic and Singulomic Approaches.

Tumor heterogeneity, a key hallmark of hepatocellular carcinomas (HCCs), poses a significant challenge to developing effective therapies or predicting clinical outcomes in HCC. Recent advances in next-generation sequencing-based multi-omic and single cell analysis technologies have enabled us to develop high-resolution atlases of tumors and pull back the curtain on tumor heterogeneity. By combining multiregion targeting sampling strategies with deep sequencing of the genome, transcriptome, epigenome, and proteome, several studies have revealed novel mechanistic insights into tumor initiation and progression in HCC. Advances in multiparametric immune cell profiling have facilitated a deeper dive into the biological complexity of HCC, which is crucial in this era of immunotherapy. Moreover, studies using liquid biopsy have demonstrated their potential to circumvent the need for tissue sampling to investigate heterogeneity. In this review, we discuss how multi-omic and single-cell sequencing technologies have advanced our understanding of tumor heterogeneity in HCC.

Evaluation of Intra-Tumoral Vascularization in Hepatocellular Carcinomas.

Intratumoral neovascularization has intricate effects on tumor growth, metastasis, and treatment. Over the last 30 years, Microvessel density (MVD) has been the standard method for laboratory and clinical evaluation of angiogenesis. Hepatocellular carcinoma (HCC) is a typical hypervascularized tumor, and the predictive value of MVD for prognosis is still controversial. According to previous viewpoints, this has been attributed to the determination of hotspot, counting methods, vascular endothelial markers, and different definitions of high and low vascular density; however, the heterogeneity of tumor angiogenesis patterns should be factored. The breakthroughs in artificial intelligence and algorithm can improve the objectivity and repeatability of MVD measurement, thus saving a lot of manpower. Presently, anti-angiogenesis therapy is the only effective systematic treatment for liver cancer, and the use of imaging technology-assisted MVD measurement is expected to be a reliable index for evaluating the curative effect. MVD in multinodular hepatocellular carcinoma represents a subject area with huge understudied potential, and exploring it might advance our understanding of tumor heterogeneity.

Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.

Abstract 2507: Understanding endometrial cancer heterogeneity at single-cell resolution

Abstract Endometrial cancer is the fourth most common gynecologic malignancy in the US. Tumor- associated genetic signatures are known to be associated with poorer prognosis of patients with endometrial cancer. In general, frequent gene alterations are enrichments of p53 and KRAS, PTEN, PIK3CA and PI3K/AKT mutations with variable CNV. Considerable evidence for intratumor heterogeneity indicate that bulk genomic sequencing approaches may be insufficient since endometrial cancer is characterized by extensive tumor heterogeneity. Here, to get new insight about endometrial cancer heterogeneity, we applied single cell sequencing since it has the potential to improve our understanding of this genetic feature by providing clonotype and variant information in high resolution. We examined cancer heterogeneity using single cell DNA sequencing Copy Number Variation (sc-DNAseq-CNV) analysis and clonotype detection in three tumors from patients with endometrial cancer. The technology used for processing single cell was 10x ChromiumTM Technology that provides detection of 100 Kb CNV events, calling clonotypes down to 10 of 1000 cell inputs. By processing these samples resulted in the sequencing of around 800 cells. Genome-wide ploidy analysis of the tumor cells showed variable median ploidies per sample. The regional copy number was estimated by processing our data in 20 Kb cases of reads. Multiple sub-clones were identified per sample number one where four clusters of sub-clones characterized cancer heterogeneity. Copy number changes on entire chromosome arms and mutations at variant detection level were detected since the resolution provided by single cell technology applied. Among the variant detection we identified previously reported mutations in POLE, POLE2, TP53, FGFR2, ARID1A, CTNNBI, PIK3CA, PIK3RI, KRAS and SWI/SNF in specific sub clones. Our study addresses the predominant challenge to determine tumor heterogeneity by performing better approaches to detect genomic differences and identifying molecular signatures among women with endometrial cancer. Better understanding of tumor heterogeneity may benefit patients diagnosed with endometrial cancer, or patients with specific molecular characteristics linked to endometrial cancer. Citation Format: Enrique I. Velazquez Villarreal, John D. Carpten, David W. Craig. Understanding endometrial cancer heterogeneity at single-cell resolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2507.

Molecular and Cellular Complexity of Glioma. Focus on Tumour Microenvironment and the Use of Molecular and Imaging Biomarkers to Overcome Treatment Resistance.

This review highlights the importance and the complexity of tumour biology and microenvironment in the progression and therapy resistance of glioma. Specific gene mutations, the possible functions of several non-coding microRNAs and the intra-tumour and inter-tumour heterogeneity of cell types contribute to limit the efficacy of the actual therapeutic options. In this scenario, identification of molecular biomarkers of response and the use of multimodal in vivo imaging and in particular the Positron Emission Tomography (PET) based molecular approach, can help identifying glioma features and the modifications occurring during therapy at a regional level. Indeed, a better understanding of tumor heterogeneity and the development of diagnostic procedures can favor the identification of a cluster of patients for personalized medicine in order to improve the survival and their quality of life.

Abstract IA05: Circulating tumor DNA as a tool for prognostication, translational discovery, and early cancer detection

Abstract New sequencing technologies have facilitated the detection and profiling of circulating tumor DNA (ctDNA) in the blood of patients with cancer. Recent efforts have demonstrated that ctDNA can be detected in the blood of pediatric patients with solid malignancies, using assays specifically designed for the unique somatic profiles observed in these cancers. Once identified in a blood sample, ctDNA levels appear to correlate with outcome and can be tracked over time to measure response to therapy and detect recurrence. Deep profiling of ctDNA can identify subclonal somatic variants that are not detectable in single-tumor biopsy samples, enabling a broader understanding of tumor heterogeneity and the ability to discover novel patterns of tumor evolution and treatment resistance. Validation of the prognostic and translational value of these assays requires leveraging large prospective studies, and this work is ongoing. One obvious application of this new technology is the potential to detect the presence of cancer in pediatric patients with increased cancer risks, including cancer predisposition syndromes. Collaborative multi-institutional efforts are under way to collect samples and develop new ctDNA assays for early cancer detection. Citation Format: Brian Crompton. Circulating tumor DNA as a tool for prognostication, translational discovery, and early cancer detection [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA05.

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy.

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

Abstract C063: Associations of leptin and leptin receptor protein and gene expression with breast cancer clinicopathologic features

Abstract The adipokine, leptin (LEP) and its receptor (leptin receptor, LEPR), are hypothesized to play a role in breast cancer (BrCa) outcomes disparities. The objective of this study was to address the gaps in knowledge regarding the epidemiologic associations of LEP and LEPR protein and gene expression with breast cancer clinicopathologic features (namely, estrogen receptor [ER] status, and tumor grade, stage, size, and subtype). In the Women's Circle of Health Study, we used immunohistochemistry to assess protein expression in breast tumor tissue microarrays among a sample of 711 early stage BrCa cases. LEP and LEPR protein expression was scored semiquantitatively by a board-certified pathologist and we used NanoString digital, multiplexed assays to quantitatively assess gene expression of these biomarkers. Multivariable-adjusted logistic regression models were used to examine the associations of interest. Compared to ER+, LEP protein expression was lower (OR 0.54, 95% CI 0.30, 1.00), and LEPR protein (OR 3.95, 95% CI 2.02, 7.71) and gene expression (OR 1.003, 95% CI 1.001, 1.005) were higher among ER- BrCa cases. Compared to well/moderately differentiated tumors, LEPR protein (OR 2.46, 95% CI 1.40, 4.31) and gene expression (OR 1.002, 95% CI 1.000, 1.003) were higher among poorly differentiated tumors. Compared to luminal A, LEP protein expression was higher among non-luminal HER2-expressing (OR 3.34, 95% CI 1.20, 9.32) and TNBC subtypes (OR 2.02, 95% CI 1.00, 4.08), LEPR protein expression was lower among non-luminal HER2-expressing (OR 0.39, 95% CI 0.16, 0.94) and TNBC (OR 0.28, 95% CI 0.13, 0.57), and LEPR gene expression was lower in TNBC (OR 0.997, 95% CI 0.995, 0.999). LEP gene expression and LEPR protein and gene expression were inversely associated with tumor size (P-values <0.05). No major associations were observed between LEP and LEPR expression profiles and tumor stage. These findings suggest that LEP and LEPR protein and gene expression in the breast tumor microenvironment may serve as biomarkers contributing to interindividual differences in BrCa prognostic indicators and are important for understanding tumor heterogeneity and outcomes disparities. Funding: This study was supported by grants from the National Cancer Institute (K01CA193527, P01CA151135, R01CA100598, R01CA185623). Tumor samples were received, processed and tracked under the auspices of the Roswell Park Comprehensive Cancer Center DataBank and BioRepository Shared Resource, supported by P30CA16056 with support from the Breast Cancer Research Foundation to CBA. This work was also supported by the Biomedical Informatics and the Biospecimen Repository and Histopathology Service shared resources of Rutgers Cancer Institute of New Jersey, supported by P30CA072720. Citation Format: Adana A.M. Llanos, Baichen Xu, Bo Qin, Wenjin Chen, Marina A. Chekmareva, Lei Cong, Chi-Chen Hong, Song Yao, Christine B. Ambrosone, Elisa V. Bandera, Kitaw Demissie. Associations of leptin and leptin receptor protein and gene expression with breast cancer clinicopathologic features [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C063.

Heterogeneity at the invasion front of triple negative breast cancer cells

Identifying better predictive and prognostic biomarkers for the diagnosis and treatment of triple negative breast cancer (TNBC) is complicated by tumor heterogeneity ranging from responses to therapy, mutational burden, and clonal evolution. To overcome the gap in our understanding of tumor heterogeneity, we hypothesized that isolating and studying the gene expression profile of invasive tumor cell subpopulations would be a crucial step towards achieving this goal. In this report, we utilized a fluidic device previously reported to be capable of supporting long-term three-dimensional growth and invasion dynamics of cancer cells. Live invading and matched non-invading SUM149 inflammatory breast cancer cells were enriched using this device and these two functionally distinct subpopulations were tested for differences in gene expression using a gene expression microarray. 305 target genes were identified to have altered expression in the invading cells compared to the non-invading tumoroid cells. Gene ontology analysis of the gene panel identified multiple biological roles ranging from extracellular matrix reorganization to modulation of the immune response and Rho signaling. Interestingly, the genes associated with the invasion front differ between different samples, consistent with inter- and intra-tumor heterogeneity. This work suggests the impact of heterogeneity in biomarker discovery should be considered as cancer therapy increasingly heads towards a personalized approach.

Characterization of Heterogeneous Metabolism in Hepatocellular Carcinoma Identifies New Therapeutic Target and Treatment Strategy

Background: Metabolic reprogramming is a well-known hallmark of cancer. Systematical identification of clinically relevant metabolic subtypes of Hepatocellular carcinoma (HCC) is critical to understand tumor heterogeneity and develop efficient treatment strategies. Methods: We performed an integrative analysis of genomic, transcriptomic, and clinical data from an HCC patient cohort in The Cancer Genome Atlas (TCGA). In vitro experiments were carried out for preliminary validation. Findings: Four metabolic subtypes were defined: mHCC1, mHHC2, mHCC3, and mHCC4. These subtypes had distinct differences in mutations profiles, activities of metabolic pathways, prognostic metabolism genes, and immune features. The mHCC1 was associated with poorest outcome and was characterized by extensive metabolic alterations, abundant immune infiltration, and increased expression of immunosuppressive checkpoints. The mHHC2 displayed lowest metabolic alteration level and was associated with most significant improvement in overall survival in response to high CD8+ T cell infiltration. The mHHC3 was a “cold-tumor” with low immune infiltration and few metabolic alterations. The mHCC4 presented a medium degree of metabolic alteration and high CTNNB1 mutation rate. Based on our HCC classification and in vitro study, we identified palmitoyl-protein thioesterase 1 (PPT1) was a specific prognostic gene and therapeutic target for mHCC1. Interpretation: Our study highlighted mechanistic differences among metabolic subtypes and identified potential therapeutic targets for subtype-specific treatment strategies targeting unique metabolic vulnerabilities. The immune heterogeneities across metabolic subtypes may help further clarify the association between metabolism and immune environment, and guide the development of novel strategies through targeting both unique metabolic vulnerabilities and immunosuppressive triggers. Funding Statement: This work was financially supported by the Outstanding Youth Scholars Project of Guangdong Provincial People's Hospital (No. KJ012019096). Declaration of Interests: All financial interests are unrelated to this study. All authors declare no competing interests.

Essential gene expression pattern of head and neck squamous cell carcinoma revealed by tumor-specific expression rule based on single-cell RNA sequencing

Head and neck squamous cell carcinoma (HNSCC) has been widely reported and considered as one of the most threatening diseases to human health. Derived from complicated tissue subtypes, HNSCC has diverse symptoms and pathogenesis. They make the identification of the core carcinogenic factors of such diseases at the multi-cell level difficult. With the development of single-cell sequencing technologies, the effects of non-malignant cells on traditional bulk sequencing data can be eliminated directly. On the basis of fresh single-cell RNA-seq data, we set up a computational filtering strategy for tumor cell identification in an expression rule manner. This strategy can reveal the accurate expression distinction between tumor cells and adjacent tumor microenvironment, which are all supported by literature reports. Validated by several independent datasets, these rule genes can further group HNSCC patients with significant difference on survival risks. Thus, the establishment of our computational approach may not only provide an efficient tool to identify malignant cells in the tumor ecosystem but also deepen our understanding of tumor heterogeneity and tumorigenesis.

Uncovering Differently Expressed Markers and Heterogeneity on Human Pancreatic Cancer

Discovery of biomarkers is critical to understand tumor heterogeneity and microenvironment. To determine differently expressed makers on cancer tissue for comprehensive profiling, the multiplexed tissue imaging mass cytometer (IMC) which uniquely combines time-of-flight mass spectrometry with metal-labeling technology to enable breakthrough discovery on single cell level was employed to investigate the expression of seven markers related to the epithelial-to-mesenchymal transition [α-smooth muscle actin (α-SMA), vimentin, collagen I, cytokeratin 7, pan-keratin], tumor proliferation (Ki-67), and human leucocyte antigen (HLA-DR) on human pancreatic cancer tissue. The difference was analyzed using bioinformatic tools. We observed the high expression of α-SMA, vimentin, collagen I, and Ki-67 on grade I but not on grade III. HLA-DR was highly expressed on grade I/III but not on grade II. Overall, the expression of markers has elucidated the heterogeneity intratumors. Additionally, to identify biomarkers on pancreatic cancer cells by blind systematic evolution of ligands by exponential enrichment (SELEX), aptamer pull-down assay and liquid chromatography–tandem mass spectrometry were used. Mortalin was identified as a potential a prognostic marker of pancreatic cancer. Our studies demonstrate that the IMC and blind SELEX might be implemented to discover biomarkers which can be used to better understand tumor biology and biomedical research applications.