Heterogeneity Of Human Cancers Research Articles

Early, precise, and safe clinical evaluation of the pharmacodynamic effects of novel agents in the intact human tumor microenvironment.

Introduction: Drug development is systemically inefficient. Research and development costs for novel therapeutics average hundreds of millions to billions of dollars, with the overall likelihood of approval estimated to be as low as 6.7% for oncology drugs. Over half of these failures are due to a lack of drug efficacy. This pervasive and repeated low rate of success exemplifies how preclinical models fail to adequately replicate the complexity and heterogeneity of human cancer. Therefore, new methods of evaluation, early in the development trajectory, are essential both to rule-in and rule-out novel agents with more rigor and speed, but also to spare clinical trial patients from the potentially toxic sequelae (high risk) of testing investigational agents that have a low likelihood of producing a response (low benefit). Methods: The clinical in vivo oncology (CIVO®) platform was designed to change this drug development paradigm. CIVO precisely delivers microdose quantities of up to 8 drugs or combinations directly into patient tumors 4-96h prior to planned surgical resection. Resected tissue is then analyzed for responses at each site of intratumoral drug exposure. Results: To date, CIVO has been used safely in 6 clinical trials, including 68 subjects, with 5 investigational and 17 approved agents. Resected tissues were analyzed initially using immunohistochemistry and in situ hybridization assays (115 biomarkers). As technology advanced, the platform was paired with spatial biology analysis platforms, to successfully track anti-neoplastic and immune-modulating activity of the injected agents in the intact tumor microenvironment. Discussion: Herein we provide a report of the use of CIVO technology in patients, a depiction of the robust analysis methods enabled by this platform, and a description of the operational and regulatory mechanisms used to deploy this approach in synergistic partnership with pharmaceutical partners. We further detail how use of the CIVO platform is a clinically safe and scientifically precise alternative or complement to preclinical efficacy modeling, with outputs that inform, streamline, and de-risk drug development.

γδ T cell dichotomy with opposing cytotoxic and wound healing functions in human solid tumors.

γδ T cells are important tissue-resident, innate T cells that are critical for tissue homeostasis. γδ cells are associated with positive prognosis in most tumors; however, little is known about their heterogeneity in human cancers. Here, we phenotyped innate and adaptive cells in human colorectal (CRC) and endometrial cancer. We found striking differences in γδ subsets and function in tumors compared to normal tissue, and in the γδ subsets present in tumor types. In CRC, an amphiregulin (AREG)-producing subset emerges, while endometrial cancer is infiltrated by cytotoxic cells. In humanized CRC models, tumors induced this AREG phenotype in Vδ1 cells after adoptive transfer. To exploit the beneficial roles of γδ cells for cell therapy, we developed an expansion method that enhanced cytotoxic function and boosted metabolic flexibility, while eliminating AREG production, achieving greater tumor infiltration and tumor clearance. This method has broad applications in cellular therapy as an 'off-the-shelf' treatment option.

370 Single-Cell Analysis of Human Glioma and Immune Cells Identifies S100A4 as an Immunotherapy Target

INTRODUCTION: Tumor-associated myeloid cells are critical regulators of tumor progression, metastasis, and immune evasion and are promising therapeutic targets. However, elucidating their functional and molecular heterogeneity in human cancers has been challenging given a lack of lineage-specific markers and their highly plastic nature, which precludes faithful modeling and analysis in vitro. Therefore a major rate-limiting step in developing more effective immunotherapies for glioblastoma (GBM) is our inadequate understanding of the cellular complexity and the molecular heterogeneity of immune infiltrates in gliomas. METHODS: Via RNAseq we integrated analysis of 201,986 human glioma, immune, and other stromal cells at the single cell level generating multi-regional and -dimensional analyses of human gliomas and in doing so map GBM cellular heterotypia and spatial, molecular, and functional heterogeneity of glioma and associated stromal cells, including immune cells. RESULTS: We discovered extensive spatial and molecular heterogeneity in immune infiltrates. We identify molecular signatures for nine distinct myeloid cell subtypes, of which five are independent prognostic indicators of glioma patient survival. Furthermore, we identify S100A4 as a regulator of immune suppressive T and myeloid cells in GBM and demonstrate that deleting S100A4 in non-cancer cells is sufficient to reprogram the immune landscape and significantly improve survival. CONCLUSIONS: This study provides insights into spatial, molecular, and functional heterogeneity of glioma and glioma-associated immune cells and demonstrates the utility of this dataset for discovering therapeutic targets for this poorly immunogenic cancer.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment.

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

Abstract 1809: Engineered destabilized ARE on the 3UTR of MYC degrades oncogenic c-MYC transcript and protein and induces apoptosis in multiple cancer types

Abstract The c-MYC oncogene is overexpressed in more than 18 human cancers, including triple negative breast cancer (TNBC), prostate cancer, and osteosarcoma, yet there is no clinically approved drug that targets this major oncogenic driver. We discovered that the 3’UTR of c-MYC in many cancers is enriched with stabilizing poly U tract sequences that can be destabilized with our novel engineered stabilizing AU Rich Elements (ARE), leading to degradation of c-MYC. We hypothesize that c-MYC destabilizing constructs will degrade c-MYC transcript and proteins across a range of cancers that overexpress this oncogene. We used heterogeneous panels of TNBC, prostate cancer, and osteosarcoma cell lines that are reflective of the heterogeneity of human cancers. We introduced our c-MYC destabilizing construct and assessed cell viability. We then performed immunofluorescence and western blot for assessment of c-MYC protein expression. Vector constructs were used as controls. Introduction of destabilizing elements achieved the degradation of both c-MYC transcript and proteins within four days, as well as a subsequent loss in cell viability. We observed that treated cells disintegrated from their nucleus, leading to increased active caspase 3/7 and cell death. Taken together, we have developed a novel technology that reliably targets and degrades the c-MYC oncogene across a range of highly aggressive and difficult to treat cancers Citation Format: Chidiebere Awah, Fu Dong, Yana Glemaud, Fayola Levine, Daniel Weiser, Olorunseun Ogunwobi. Engineered destabilized ARE on the 3UTR of MYC degrades oncogenic c-MYC transcript and protein and induces apoptosis in multiple cancer types [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 1809.

Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations.

Functional precision medicine is a strategy whereby live tumor cells from affected individuals are directly perturbed with drugs to provide immediately translatable, personalized information to guide therapy. The heterogeneity of human cancer has led to the realization that personalized approaches are needed to improve treatment outcomes. Precision oncology has traditionally used static features of the tumor to dictate which therapies should be used. Static features can include expression of key targets or genomic analysis of mutations to identify therapeutically targetable "drivers." Although a surprisingly small proportion of individuals derive clinical benefit from the static approach, functional precision medicine can provide additional information regarding tumor vulnerabilities. We discuss emerging technologies for functional precision medicine as well as limitations and challenges in using these assays in the clinical trials that will be necessary to determine whether functional precision medicine can improve outcomes and eventually become a standard tool in clinical oncology.

IDM-SPS: Identifying driver module with somatic mutation, PPI network and subcellular localization

Mutation profiles together with prior knowledge such as interactions between genes/proteins provide abundant critical information for the identification of driver modules, which is very important for analyzing mutational heterogeneity in human cancers. Due to the negative effects of inevitable false positive interactions in the PPI network, subcellular localization data are exerted to filter out them firstly, and somatic mutation profiles are used to weight the retained interactions. Five novel recombination operators are introduced basing on the vertex degrees and the edge weights in the PPI network, and a parthenogenetic algorithm is devised for solving the presented identification model which takes into account network connectivity, mutual exclusivity, coverage, and hops between genes within a module. Extensive experimental results indicate that compared with two state-of-the-art computational methods Hotnet2 and MEXCOwalk, the proposed method exhibits competitive performance in most cases in terms of recovering known cancer genes, providing modules that have satisfied coverage and mutual exclusivity, and are enriched for mutations in specific cancer types. Many identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in the literature. In addition, the proposed method does identify many cancer related genes missed by methods Hotnet2 and MEXCOwalk, including some recognized genes covering many types of cancers but having low mutation frequency.

Intratumor Epigenetic Heterogeneity-A Panel Gene Methylation Study in Thyroid Cancer.

BackgroundThyroid cancer (TC) is the most common endocrine malignancy, and the incidence is increasing very fast. Surgical resection and radioactive iodine ablation are major therapeutic methods, however, around 10% of differentiated thyroid cancer and all anaplastic thyroid carcinoma (ATC) are failed. Comprehensive understanding the molecular mechanisms may provide new therapeutic strategies for thyroid cancer. Even though genetic heterogeneity is rigorously studied in various cancers, epigenetic heterogeneity in human cancer remains unclear.MethodsA total of 405 surgical resected thyroid cancer samples were employed (three spatially isolated specimens were obtained from different regions of the same tumor). Twenty-four genes were selected for methylation screening, and frequently methylated genes in thyroid cancer were used for further validation. Methylation specific PCR (MSP) approach was employed to detect the gene promoter region methylation.ResultsFive genes (AP2, CDH1, DACT2, HIN1, and RASSF1A) are found frequently methylated (>30%) in thyroid cancer. The five genes panel is used for further epigenetic heterogeneity analysis. AP2 methylation is associated with gender (P < 0.05), DACT2 methylation is associated with age, gender and tumor size (all P < 0.05), HIN1 methylation is associated to tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01). RASSF1A methylation is associated with lymph node metastasis (P < 0.01). For heterogeneity analysis, AP2 methylation heterogeneity is associated with tumor size (P < 0.01), CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension (all P < 0.01). The multivariable analysis suggested that the risk of lymph node metastasis is 2.5 times in CDH1 heterogeneous methylation group (OR = 2.512, 95% CI 1.135, 5.557, P = 0.023). The risk of extra-thyroidal extension is almost 3 times in HIN1 heterogeneous methylation group (OR = 2.607, 95% CI 1.138, 5.971, P = 0.023).ConclusionFive of twenty-four genes were found frequently methylated in human thyroid cancer. Based on 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression.

Comprehensive molecular and clinical characterization of SLC1A5 in human cancers

Although SLC1A5 has been reported to be closely associated with some cancer types, a comprehensive and systematic assessment of SLC1A5 across human cancers is lacking. Thus, Pan-cancer analysis of SLC1A5 was performed across 30 types of human cancers in this study. We examined mRNA expression, protein expression, copy number variation (CNV), DNA methylation, clinical relevance, cell functions, drug response and total immune infiltrates of SLC1A5 in more than 9000 patients across 30 human cancer types from The Cancer Genome Atlas (TCGA) dataset. Additionally, nine independent Gene Expression Omnibus datasets, more than 800 cancer cell lines from the Cancer Cell Line Encyclopedia dataset and the Project Achilles dataset were used to validate our findings in the TCGA dataset. Landscapes of SLC1A5 were established across multiple cancers. We showed that SLC1A5 is upregulated in multiple cancers, particularly in digestive and respiratory system cancers. SLC1A5 upregulation may be driven by CNV gain and DNA hypomethylation in human cancers. Furthermore, SLC1A5 overexpression is associated with tumor progression and poor survival in multiple cancers. Moreover, we systematically explored the potential effects of SLC1A5 expression on cell functions and drug response in human cancers. SLC1A5 knockdown showed significant proliferation-inhibiting effects in most human cancer types, especially in the digestive system and KRAS-mutant cancers. SLC1A5 expression is associated with proliferation activities of KRAS-mutant cancer cell lines and drug response of many anti-cancer drugs. Finally, we demonstrated that SLC1A5-realted tumor immune microenvironment characteristics showed strong heterogeneity in human cancers. Taken together, our findings highlight the important roles of SLC1A5 in tumorigenesis, progression, prognosis and therapy.

A large-scale snapshot of intratumor heterogeneity in human cancer

Using sophisticated statistical analyses on population-scale cancer whole-genome sequences, a new study published in Cell characterizes the genomic architecture of intratumor heterogeneity (ITH). It results in an unprecedented snapshot of subclones in about 30 cancer types, generating a wealth of insight into the underlying mutational events, processes, and their selection.

Abstract PO-083: Utilizing spatial transcriptomics to elucidate tertiary lymphoid structure heterogeneity in human cancer

Abstract Blockade of the inhibitory PD1 pathway on CD8+ and CD4+ tumor infiltrating lymphocytes (TILs) has revolutionized standard of care for cancer patients. However, these approaches only benefit approximately 20% of cancer patients, thus a more comprehensive understanding of the immune response is paramount for the development of new therapeutic approaches and enhancement of the anti-tumor immune response. The presence of tertiary lymphoid structures (TLS) correlates with enhanced anti-tumor immunity and improved prognosis in several solid tumors. These ectopic lymphoid aggregates can exhibit features like secondary lymphoid organs (SLOs), including high endothelial venules, a T cell zone with mature dendritic cells (DCs), and a germinal center (GC) with follicular DCs and B cells, that facilitate the induction of immune responses in situ. Furthermore, the presence of TLS correlate with superior responses to immunotherapy and mature, GC-containing TLS correlate with increased T cell function in human tumors. However, current immunotherapies do not target TLS despite their predominance in the tumor microenvironment (TME) and key role in the adaptive immune response. Developing therapies that adequately induce TLS is limited by a lack of objective, bioinformatic analyses to elucidate the cellular and locational heterogeneity of TLS. TLS differences in composition are thought to define maturation levels i.e. early TLS have B and T cell aggregates but lack follicular DC and GC appear as the TLS mature. However, cellular composition is just one aspect of TLS heterogeneity. To successfully increase mature TLS in cancer patients for maximal humoral immunity, we must first understand the complete transcriptomic and proteomic profile of TLS in cancer patients. We have utilized the innovative Nanostring GeoMx platform to spatially interrogate the transcriptomics and proteomics associated with TLS. By pairing this technology with high level multispectral immunofluorescence via the Vectra Polaris system, we can link TLS heterogeneity to TLS position within the tumor. Further, we can analyze the complete activation profile of B and T cells to understand how TLS heterogeneity affects lymphocyte function. Our data have indicated that TLS can be linked to increased CD8+ T cell infiltrate and function. In fact, when mature TLS are not present, CD8+ T cell and Treg interactions increase, ultimately creating a more immunosuppressive TME. Lastly, utilizing a novel, robust bioinformatic pipeline, we have identified new genes that will aid in an objective definition of TLS. Specifically, CD27 is increased within TLS that are proximal to the tumor compared to those in the normal adjacent tissue. These studies will revolutionize the way in which TLS are defined within the TME, ultimately offering comprehensive gene and protein analysis of TLS that includes spatial geography of the structures and an unbiased, objective definition of TLS for future clinical utility and immunotherapeutic targeting. Citation Format: Dongyan Liu, Xiang Li, Rajesh Acharya, Ernest M. Meyer, Shelley Reynolds, Ayana Ruffin, Robert L. Ferris, Dario A.A. Vignali, Riyue Bao, Tullia C. Bruno. Utilizing spatial transcriptomics to elucidate tertiary lymphoid structure heterogeneity in human cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-083.

Patient-Derived In Vitro Models for Drug Discovery in Colorectal Carcinoma.

Lack of relevant preclinical models that reliably recapitulate the complexity and heterogeneity of human cancer has slowed down the development and approval of new anti-cancer therapies. Even though two-dimensional in vitro culture models remain widely used, they allow only partial cell-to-cell and cell-to-matrix interactions and therefore do not represent the complex nature of the tumor microenvironment. Therefore, better models reflecting intra-tumor heterogeneity need to be incorporated in the drug screening process to more reliably predict the efficacy of drug candidates. Classic methods of modelling colorectal carcinoma (CRC), while useful for many applications, carry numerous limitations. In this review, we address the recent advances in in vitro CRC model systems, ranging from conventional CRC patient-derived models, such as conditional reprogramming-based cell cultures, to more experimental and state-of-the-art models, such as cancer-on-chip platforms or liquid biopsy.

Abstract SY21-01: Analyzing tumor metabolism in patients

Abstract Metabolism is dynamic and responds to a wide range of cell-intrinsic and cell-extrinsic factors. Many human diseases involve perturbations of metabolism at the cellular level, and in many cases normalizing the metabolic state has proven to be therapeutically valuable. Cancer is one such disease, in which factors both intrinsic and extrinsic to the malignant cell impact tumor biology and disease progression. In cancer, cell-intrinsic influences on metabolism include somatically-acquired mutations in oncogenes and tumor suppressor genes, many of which regulate metabolic activity. Cell-extrinsic factors include nutrient access, which may become limiting due to inadequate vasculature and intense fuel utilization, and metabolic interactions with stromal and immune cells. A major challenge in cancer metabolism research is to understand how these various factors culminate in the metabolic phenotype of an intact tumor, and ultimately to identify which altered pathways represent potential therapeutic targets. Among the many metabolic pathways reprogrammed in cancer, nucleotide metabolism and responses to DNA damage are highly integrated with aberrant signaling and gene regulatory networks in cancer. We have taken two parallel approaches to understand metabolic complexity and identify potential therapeutic liabilities in human cancer. The first uses a combination of multi-parametric imaging and intraoperative stable isotope infusions to assess metabolic fluxes in patients with solid tumors, and to compare fluxes between tumors and adjacent tissue. Genomic, histological and metabolic analysis of tumor samples allows us to correlate various intrinsic and extrinsic factors to specific aspects of the metabolic phenotype. The second approach uses standardized culture conditions to assess cell-intrinsic heterogeneity of metabolic preferences and dependencies in large panels of human cancer cell lines. This approach has uncovered liabilities associated with specific molecular subtypes of non-small cell and small cell lung cancerm ncluding several related to nucleotide and nucleic acid metabolism. I will discuss the application of these approaches to metabolic heterogeneity in human cancer, with an emphasis on features relevant to intact tumors and therapeutic liabilities and the role of metabolic imaging in studying cancer metabolism. Citation Format: Ralph J. Deberardinis. Analyzing tumor metabolism in patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY21-01.

Abstract IA23: Metabolic complexity in cancer cells and tumors

Abstract Metabolism is dynamic and responds to a wide range of cell-intrinsic and cell-extrinsic factors. In cancer, cell-intrinsic influences on metabolism include somatically acquired mutations in oncogenes and tumor suppressor genes, many of which regulate metabolic activity. Cell-extrinsic factors include nutrient access, which may become limiting due to inadequate vasculature and intense fuel utilization, and metabolic interactions with stromal and immune cells. A major challenge in cancer metabolism research is to understand how these various factors culminate in the metabolic phenotype of an intact tumor, and ultimately to identify which altered pathways represent potential therapeutic targets. We have taken two parallel approaches to understand metabolic complexity in human cancer. The first uses a combination of multiparametric imaging and intraoperative stable isotope infusions to assess metabolic fluxes in patients with solid tumors, and to compare fluxes between tumors and adjacent benign tissue. Genomic, histologic, and metabolic analysis of tumor samples allows us to correlate various intrinsic and extrinsic factors to specific aspects of the metabolic phenotype. The second approach uses standardized culture conditions to assess cell-intrinsic heterogeneity of metabolic preferences and dependencies in large panels of human cancer cell lines. This approach has uncovered liabilities associated with specific molecular subtypes of non-small cell and small cell lung cancer. I will discuss the application of these two approaches to metabolic heterogeneity in human cancer, with an emphasis on features relevant to intact tumors and therapeutic liabilities. Citation Format: Ralph J. DeBerardinis. Metabolic complexity in cancer cells and tumors [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr IA23.

Abstract B54: Dissecting immune cell heterogeneity in human cancer by single-cell RNA-sequencing

Abstract Checkpoint blockade therapies that aim to reactivate anti-tumor immune responses have revolutionized cancer treatment, resulting in durable responses in a significant proportion of patients with advanced disease. Nevertheless, many patients fail to reach long-term clinical benefit, and therefore, a better insight into the mechanisms underlying response to immunotherapy is required to enhance its success rates. Recent findings suggest that immune cell infiltrates in cancer can be highly heterogeneous, potentially contributing to differential therapy outcomes. However, immune cell states have thus far been studied mainly in patients with end-stage melanoma and diverse treatment histories, whereas patients with early-stage metastatic disease are generally treatment naïve, allowing assessment of naturally evolved T cell functionality and immune cell composition. To characterize the heterogeneity in the tumor immune environment within and between patients, we profiled the tumor microenvironment of six stage III melanoma patients by single-cell RNA sequencing, generating an unbiased map of the expression signatures of immune cells as well as tumor and stromal cells. In parallel, we also derived the T cell receptor sequences for single T cells, and used them to determine intra- and inter-tumoral T cell clonality and infer the functionality of clonally expanded T cell populations in the tumor. A series of different cell types and cellular states that have been described previously in end-stage melanoma was also observed in this early-stage metastatic disease, including cytotoxic, exhausted, and regulatory T cells, as well as various myeloid subsets. Notably, despite identical disease stage and treatment background, the composition of the immune cell populations differed considerably between patients. In addition, while analysis of T cell states showed high variability between and within patients, clonally expanded T cells identified within patients predominantly adapted similar cell profiles. Currently we are assessing whether the occurrence of distinct T cell states can be coupled to other aspects of the immune infiltrate. In addition to describing T cell heterogeneity, our data suggest the existence of novel immune cell subsets within the tumor microenvironment. Our findings demonstrate that, using our single-cell RNA-sequencing approach, we can determine the immunological make-up in melanoma during early stages of metastatic disease, in an unbiased manner. Collectively, our approach should be helpful in determining the mechanisms underlying the development and effectiveness of tumor-specific immune responses in metastatic melanoma. Citation Format: Ido Yofe, Hanjie Li, Anne van der Leun, Lubling Yaniv, Assaf Weiner, Alexander van Akkooi, Amos Tanay, Ton Schumacher, Ido Amit. Dissecting immune cell heterogeneity in human cancer by single-cell RNA-sequencing [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr B54.

RNA sequencing to highlight the heterogeneity in circulating exosomes from patients with esophageal squamous cell carcinoma.

e15546 Background: Dissecting tumor heterogeneity is crucial for understanding tumor prognosis, response to therapy, and metastasis. But current tissue biopsy-based strategies for characterizing molecular heterogeneity are invasive and may be confounded by intra-tumor heterogeneity. Here we explore whether exosomes that contain bioactive molecules from the cell of origin can provide new noninvasive means to delineate the heterogeneity of human cancers. Methods: We used RNA-sequencing (RNA-seq) to perform unbiased profiling of mRNAs and long noncoding RNAs (lncRNAs) in plasma exosomes isolated from patients with esophageal squamous cell carcinoma (ESCC, n = 6), patients with esophagitis (n = 6), and healthy controls (n = 6). Results: The number of expressed genes detected in our data set is 63355, including 29615 lncRNAs. We found that exosomes from ESCC have dramatically distinct transcriptome and lncRNA landscapes from that of esophagitis and healthy controls, with 2278 genes and 584 lncRNAs showing differential expression between ESCC and controls; and 854 genes and 126 lncRNAs displaying differential expression between ESCC and esophagitis. We also observed variable expression of diverse transcriptional patterns related to immune response, signal transduction, cell mobility, and transmembrane protein binding, as well as differentially expressed 953 lncRNAs between Stage I and Stage II ESCC samples. Finally, we discovered that both gene and lncRNA expression profiles are variably across exosomal samples from different ESCC patients. Conclusions: Our data reveals that exosomes from ESCC contain distinct transcriptional and lncRNA profiles that separate ESCC from benign esophagitis and healthy controls. Our analysis also identifies unappreciated molecular heterogeneity in exosomes of ESCC, which may pave the way for using exosomal RNA-seq to decode molecular heterogeneity in cancers.

The genetic basis and clinical application of heterogeneity of breast cancer

Breast cancer is comprised of a group of heterogeneous diseases that differ significantly in their clinic-pathologic characteristics and molecular profiles. For many years, this disease has been classified according to the integral clinicopathological factors, to provide a dictates for institution of therapeutic approach. Over the past decades, several waves of technology have advanced the characterization of mutations in cancer genomes, especially the emerge and application of high-through next-generation sequencing technique. It has enabled systematic characterization of the full repertoire of molecular diversity of cancer cells, and yielded substantial insights into the genomic mutation and evolution, the intrinsic genetic nature of biology and heterogeneity in human cancer. This article aims to describe recent advances and application of advanced technologies towards the identification of heterogeneity of breast cancer.

Abstract 2395: Direct assessment of sequence heterogeneity in human cancers by Duplex Sequencing

Abstract The extent of heterogeneity in DNA sequence within human cancers has been difficult to fully assess, due to the absence of methods with sufficient sensitivity to detect minority variants. For example, next-generation sequencing could in principle detect sub-clonal mutations at any genomic position; however due to amplification errors and inaccuracies in the sequencing platform itself, such approaches are limited to detection of mutations present in &amp;gt;1% of cells. We have overcome this limitation by developing an approach, Duplex Sequencing, which improves the accuracy of next-generation sequencing by &amp;gt;100,000 fold. Duplex Sequencing is based upon separately tagging and sequencing the two strands of single molecules of duplex DNA. True mutations are present at the same position in both DNA strands and are complementary, whereas artifacts arising from amplification or sequencing errors are seen in only one strand. The calculated error rate of our approach is less than one artifactual mutation per billion nucleotides sequenced (ref. 1). We have also developed an enrichment approach based on sequential rounds of hybridization to biotinylated probes which enables efficient sequencing of targeted regions of the genome (ref. 2). Duplex Sequencing thereby enables exceptionally sensitive detection of sequence heterogeneity within any set of genes. We have now applied Duplex Sequencing to study of sequence heterogeneity in both normal and malignant samples. In acute myeloid leukemia (AML), we find multiple sub-clonal mutations which fall below the detection limit of conventional approaches, some of which encode mutations that can drive chemotherapy resistance and cancer progression. Our results indicate that prior studies have under-estimated the burden of sub-clonal mutations in AML by more than 1,000-fold. We have additionally studied chronic myeloid leukemia (CML); treatment of CML with inhibitors directed against the Abl kinase represents the prototypical targeted cancer therapy. With Duplex Sequencing, we find that Abl mutations are extremely uncommon at the time of CML diagnosis. In contrast, refractory patients who fail therapy frequently possess multiple sub-clonal mutations within the Abl kinase, most of which are below the resolution of conventional approaches but are readily detected with our method. In ongoing work, we have found multiple sub-clonal mutations within human prostate cancer and colon cancer, suggestive of widespread intratumor heterogeneity that may limit the efficacy of single-agent therapy in these diseases.

Unbiased Selection of Peptide-Peptoid Hybrids Specific for Lung Cancer Compared to Normal Lung Epithelial Cells.

To develop widely applicable diagnostic and potentially therapeutic approaches overcoming protein heterogeneity in human cancer, we have developed a technology to unbiasedly select high specificity compound(s) that bind any biomolecule (e.g., proteins, lipids, carbohydrates) presented on the cancer cell surface but not on normal cells. We utilized a peptidomimetic based on-bead two-color (OBTC) combinatorial cell screen that can detect differences between two cell surfaces at high accuracy by looking for beads (where each bead in the library had one peptide-peptoid hybrid on the surface) that only bound cancer but not normal cells. We screened a library of 393 216 compounds targeting HCC4017 lung adenocarcinoma cells (labeled in red) in the presence of HBEC30KT normal bronchial epithelial cells (labeled in green) derived from the same tissue of the same patient. This screen identified a peptide-peptoid hybrid called PPS1 which displayed high specific binding for HCC4017 cancer cells over HBEC30KT cells. Specificity was validated through on-bead, ELISA-like and magnetic bead pulldown studies, while a scrambled version of PPS1 did not show any binding. Of interest, the simple dimeric version (PPS1D1) displayed cytotoxic activity on HCC4017 cells, but not on normal HBEC30KT cells. PPS1D1 also strongly accumulated in HCC4017 lung cancer xenografts in mice over control constructs. We conclude that such combinatorial screens using tumor and normal cells from the same patient have significant potential to develop new reagents for cancer biology, diagnosis, and potentially therapy.

Production of Uniform 3D Microtumors in Hydrogel Microwell Arrays for Measurement of Viability, Morphology, and Signaling Pathway Activation.

Despite significant investments in cancer research and drug discovery/development, the rate of new cancer drug approval is ≤5% and most cases of metastatic cancer remain incurable. Ninety-five percent of new cancer drugs fail in clinical development because of a lack of therapeutic efficacy and/or unacceptable toxicity. One of the major factors responsible for the low success rate of anticancer drug development is the failure of preclinical models to adequately recapitulate the complexity and heterogeneity of human cancer. For throughput and capacity reasons, high-throughput screening growth inhibition assays almost exclusively use two-dimensional (2D) monolayers of tumor cell lines cultured on tissue culture-treated plastic/glass surfaces in serum-containing medium. However, these 2D tumor cell line cultures fail to recapitulate the three-dimensional (3D) context of cells in solid tumors even though the tumor microenvironment has been shown to have a profound effect on anticancer drug responses. Tumor spheroids remain the best characterized and most widely used 3D models; however, spheroid sizes tend to be nonuniform, making them unsuitable for high-throughput drug testing. To circumvent this challenge, we have developed defined size microwell arrays using nonadhesive hydrogels that are applicable to a wide variety of cancer cell lines to fabricate size-controlled 3D microtumors. We demonstrate that the hydrogel microwell array platform can be applied successfully to generate hundreds of uniform microtumors within 3-6 days from many cervical and breast, as well as head and neck squamous cell carcinoma (HNSCC) cells. Moreover, controlling size of the microwells in the hydrogel array allows precise control over the size of the microtumors. Finally, we demonstrate the application of this platform technology to probe activation as well as inhibition of epidermal growth factor receptor (EGFR) signaling in 3D HNSCC microtumors in response to EGF and cetuximab treatments, respectively. We believe that the ability to generate large numbers of HNSCC microtumors of uniform size and 3D morphology using hydrogel arrays will provide more physiological in vitro 3D tumor models to investigate how tumor size influences signaling pathway activation and cancer drug efficacy.