The novel drug candidate S2/IAPinh improves survival in models of pancreatic and ovarian cancer.

Cancer selective apoptosis remains a therapeutic challenge and off-target toxicity has limited enthusiasm for this target clinically. Sigma-2 ligands (S2) have been shown to enhance the cancer selectivity of small molecule drug candidates by improving internalization. Here, we report the synthesis of a novel drug conjugate, which was created by linking a clinically underperforming SMAC mimetic (second mitochondria-derived activator of caspases; LCL161), an inhibitor (antagonist) of inhibitor of apoptosis proteins (IAPinh) with the sigma-2 ligand SW43, resulting in the new chemical entity S2/IAPinh. Drug potency was assessed via cell viability assays across several pancreatic and ovarian cancer cell lines in comparison with the individual components (S2 and IAPinh) as well as their equimolar mixtures (S2 + IAPinh) both in vitro and in preclinical models of pancreatic and ovarian cancer. Mechanistic studies of S2/IAPinh-mediated cell death were investigated in vitro and in vivo using syngeneic and xenograft mouse models of murine pancreatic and human ovarian cancer, respectively. S2/IAPinh demonstrated markedly improved pharmacological activity in cancer cell lines and primary organoid cultures when compared to the controls. In vivo testing demonstrated a marked reduction in tumor growth rates and increased survival rates when compared to the respective control groups. The predicted mechanism of action of S2/IAPinh was confirmed through assessment of apoptosis pathways and demonstrated strong target degradation (cellular inhibitor of apoptosis proteins-1 [cIAP-1]) and activation of caspases 3 and 8. Taken together, S2/IAPinh demonstrated efficacy in models of pancreatic and ovarian cancer, two challenging malignancies in need of novel treatment concepts. Our data support an in-depth investigation into utilizing S2/IAPinh for the treatment of cancer.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Engineering a Novel Modular Adenoviral mRNA Delivery Platform Based on Tag/Catcher Bioconjugation.

mRNA vaccines have attracted widespread research attention with clear advantages in terms of molecular flexibility, rapid development, and potential for personalization. However, current mRNA vaccine platforms have not been optimized for induction of CD4/CD8 T cell responses. In addition, the mucosal administration of mRNA based on lipid nanoparticle technology faces challenges in clinical translation. In contrast, adenovirus-based vaccines induce strong T cell responses and have been approved for intranasal delivery. To leverage the inherent strengths of both the mRNA and adenovirus platforms, we developed a novel modular adenoviral mRNA delivery platform based on Tag/Catcher bioconjugation. Specifically, we engineered adenoviral vectors integrating Tag/Catcher proteins at specific locales on the Ad capsid proteins, allowing us to anchor mRNA to the surface of engineered Ad viruses. In proof-of-concept studies, the Ad-mRNA platform successfully mediated mRNA delivery and could be optimized via the highly flexible modular design of both the Ad-mRNA and protein bioconjugation systems.

Single-cell transcriptomics reveals long noncoding RNAs associated with tumor biology and the microenvironment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous and lethal. Long noncoding RNAs (lncRNAs) are an important class of genes regulating tumorigenesis and progression. Prior bulk transcriptomic studies in PDAC have revealed the dysregulation of lncRNAs but lack single-cell resolution to distinguish lncRNAs in tumor-intrinsic biology and the tumor microenvironment (TME). We analyzed single-cell transcriptome data from 73 multiregion samples in 21 PDAC patients to evaluate lncRNAs associated with intratumoral heterogeneity and the TME in PDAC. We found 111 cell-specific lncRNAs that reflected tumor, immune and stromal cell contributions, associated with outcomes, and validated across orthogonal datasets. Single-cell analysis of tumor cells revealed lncRNAs associated with TP53 mutations and FOLFIRINOX treatment that were obscured in bulk tumor analysis. Lastly, tumor subcluster analysis revealed widespread intratumor heterogeneity and intratumoral lncRNAs associated with cancer hallmarks and tumor processes such as angiogenesis, epithelial-mesenchymal transition, metabolism and immune signaling. Intratumoral subclusters and lncRNAs were validated across six datasets and showed clinically relevant associations with patient outcomes. Our study provides the first comprehensive assessment of the lncRNA landscape in PDAC using single-cell transcriptomic data and can serve as a resource, PDACLncDB (accessible at https://www.maherlab.com/pdaclncdb-overview), to guide future functional studies.

High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival.

Numerous cell states are known to comprise the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME). However, the developmental stemness and co-occurrence of these cell states remain poorly defined. Here, we performed single-cell RNA sequencing (scRNA-seq) on a cohort of treatment-naive PDAC time-of-diagnosis endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) samples (n = 25). We then combined these samples with surgical resection (n = 6) and publicly available samples to increase statistical power (n = 80). Following annotation into 25 distinct cell states, cells were scored for developmental stemness, and a customized version of the Ecotyper tool was used to identify communities of co-occurring cell states in bulk RNA-seq samples (n = 268). We discovered a tumor microenvironmental community comprised of aggressive basal-like malignant cells, tumor-promoting SPP1+ macrophages, and myofibroblastic cancer-associated fibroblasts associated with especially poor prognosis. We also found a developmental stemness continuum with implications for survival that is present in both malignant cells and cancer-associated fibroblasts (CAFs). We further demonstrated that high-dimensional analyses predictive of survival are feasible using standard-of-care, time-of-diagnosis EUS-FNB specimens. In summary, we identified tumor microenvironmental and developmental stemness characteristics from a high-dimensional gene expression analysis of PDAC using human tissue specimens, including time-of-diagnosis EUS-FNB samples. These reveal new connections between tumor microenvironmental composition, CAF and malignant cell stemness, and patient survival that could lead to better upfront risk stratification and more personalized upfront clinical decision-making.

Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment.

BACKGROUND: Interactions between immune and tumor cells are critical to determining cancer progression and response. In addition, preclinical prediction of immune-related drug efficacy is limited by interspecies differences between human and mouse, as well as inter-person germline and somatic variation. To address these gaps, we developed an autologous system that models the tumor microenvironment (TME) from individual patients with solid tumors. METHOD: With patient-derived bone marrow hematopoietic stem and progenitor cells (HSPCs), we engrafted a patient's hematopoietic system in MISTRG6 mice, followed by transfer of patient-derived xenograft (PDX) tissue, providing a fully genetically matched model to recapitulate the individual's TME. We used this system to prospectively study tumor-immune interactions in patients with solid tumor. RESULTS: Autologous PDX mice generated innate and adaptive immune populations; these cells populated the TME; and tumors from autologously engrafted mice grew larger than tumors from non-engrafted littermate controls. Single-cell transcriptomics revealed a prominent vascular endothelial growth factor A (VEGFA) signature in TME myeloid cells, and inhibition of human VEGF-A abrogated enhanced growth. CONCLUSIONS: Humanization of the interleukin 6 locus in MISTRG6 mice enhances HSPC engraftment, making it feasible to model tumor-immune interactions in an autologous manner from a bedside bone marrow aspirate. The TME from these autologous tumors display hallmarks of the human TME including innate and adaptive immune activation and provide a platform for preclinical drug testing.

Induction of cancer neoantigens facilitates development of clinically relevant models for the study of pancreatic cancer immunobiology.

Neoantigen burden and CD8 T cell infiltrate are associated with clinical outcome in pancreatic ductal adenocarcinoma (PDAC). A shortcoming of many genetic models of PDAC is the lack of neoantigen burden and limited T cell infiltrate. The goal of the present study was to develop clinically relevant models of PDAC by inducing cancer neoantigens in KP2, a cell line derived from the KPC model of PDAC. KP2 was treated with oxaliplatin and olaparib (OXPARPi), and a resistant cell line was subsequently cloned to generate multiple genetically distinct cell lines (KP2-OXPARPi clones). Clones A and E are sensitive to immune checkpoint inhibition (ICI), exhibit relatively high T cell infiltration, and have significant upregulation of genes involved in antigen presentation, T cell differentiation, and chemokine signaling pathways. Clone B is resistant to ICI and is similar to the parental KP2 cell line in terms of relatively low T cell infiltration and no upregulation of genes involved in the pathways noted above. Tumor/normal exome sequencing and in silico neoantigen prediction confirms successful generation of cancer neoantigens in the KP2-OXPARPi clones and the relative lack of cancer neoantigens in the parental KP2 cell line. Neoantigen vaccine experiments demonstrate that a subset of candidate neoantigens are immunogenic and neoantigen synthetic long peptide vaccines can restrain Clone E tumor growth. Compared to existing models, the KP2-OXPARPi clones better capture the diverse immunobiology of human PDAC and may serve as models for future investigations in cancer immunotherapies and strategies targeting cancer neoantigens in PDAC.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin.

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.

Combination TIGIT/PD-1 blockade enhances the efficacy of neoantigen vaccines in a model of pancreatic cancer.

Background: Cancer neoantigens are important targets of cancer immunotherapy and neoantigen vaccines are currently in development in pancreatic ductal adenocarcinoma (PDAC) and other cancer types. Immune regulatory mechanisms in pancreatic cancer may limit the efficacy of neoantigen vaccines. Targeting immune checkpoint signaling pathways in PDAC may improve the efficacy of neoantigen vaccines. Methods: We used KPC4580P, an established model of PDAC, to test whether neoantigen vaccines can generate therapeutic efficacy against PDAC. We focused on two immunogenic neoantigens associated with genetic alterations in the CAR12 and CDK12 genes. We tested a neoantigen vaccine comprised of two 20-mer synthetic long peptides and poly IC, a Toll-like receptor (TLR) agonist. We investigated the ability of neoantigen vaccine alone, or in combination with PD-1 and TIGIT signaling blockade to impact tumor growth. We also assessed the impact of TIGIT signaling on T cell responses in human PDAC. Results: Neoantigen vaccines induce neoantigen-specific T cell responses in tumor-bearing mice and slow KPC4580P tumor growth. However, KPC4580P tumors express high levels of PD-L1 and the TIGIT ligand, CD155. A subset of neoantigen-specific T cells in KPC4580P tumors are dysfunctional, and express high levels of TIGIT. PD-1 and TIGIT signaling blockade in vivo reverses T cell dysfunction and enhances neoantigen vaccine-induced T cell responses and tumor regression. In human translational studies, TIGIT signaling blockade in vitro enhances neoantigen-specific T cell function following vaccination. Conclusions: Taken together, preclinical and human translational studies support testing neoantigen vaccines in combination with therapies targeting the PD-1 and TIGIT signaling pathways in patients with PDAC.

Neoadjuvant FOLFIRINOX Therapy Is Associated with Increased Effector T Cells and Reduced Suppressor Cells in Patients with Pancreatic Cancer.

PURPOSE: FOLFIRINOX has demonstrated promising results for patients with pancreatic ductal adenocarcinoma (PDAC). Chemotherapy-induced immunogenic cell death can prime antitumor immune responses. We therefore performed high-dimensional profiling of immune cell subsets in peripheral blood to evaluate the impact of FOLFIRINOX on the immune system. EXPERIMENTAL DESIGN: Peripheral blood mononuclear cells (PBMC) were obtained from treatment-naïve (n = 20) and FOLFIRINOX-treated patients (n = 19) with primary PDAC tumors at the time of resection. PBMCs were characterized by 36 markers using mass cytometry by time of flight (CyTOF). RESULTS: Compared with treatment-naïve patients, FOLFIRINOX-treated patients showed distinct immune profiles, including significantly decreased inflammatory monocytes and regulatory T cells (Treg), increased Th1 cells, and decreased Th2 cells. Notably, both monocytes and Treg expressed high levels of immune suppression-associated CD39, and the total CD39+ cell population was significantly lower in FOLFIRINOX-treated patients compared with untreated patients. Cellular alterations observed in responders to FOLFIRINOX included a significantly decreased frequency of Treg, an increased frequency of total CD8 T cells, and an increased frequency of CD27-Tbet+ effector/effector memory subsets of CD4 and CD8 T cells. CONCLUSIONS: Our study reveals that neoadjuvant chemotherapy with FOLFIRINOX enhances effector T cells and downregulates suppressor cells. These data indicate that FOLFIRINOX neoadjuvant therapy may improve immune therapy and clinical outcome in patients with PDAC.

Challenges targeting cancer neoantigens in 2021: a systematic literature review.

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.

Tumor-on-chip modeling of organ-specific cancer and metastasis.

Every year, cancer claims millions of lives around the globe. Unfortunately, model systems that accurately mimic human oncology - a requirement for the development of more effective therapies for these patients - remain elusive. Tumor development is an organ-specific process that involves modification of existing tissue features, recruitment of other cell types, and eventual metastasis to distant organs. Recently, tissue engineered microfluidic devices have emerged as a powerful in vitro tool to model human physiology and pathology with organ-specificity. These organ-on-chip platforms consist of cells cultured in 3D hydrogels and offer precise control over geometry, biological components, and physiochemical properties. Here, we review progress towards organ-specific microfluidic models of the primary and metastatic tumor microenvironments. Despite the field's infancy, these tumor-on-chip models have enabled discoveries about cancer immunobiology and response to therapy. Future work should focus on the development of autologous or multi-organ systems and inclusion of the immune system.

Optimized polyepitope neoantigen DNA vaccines elicit neoantigen-specific immune responses in preclinical models and in clinical translation.

BACKGROUND: Preclinical studies and early clinical trials have shown that targeting cancer neoantigens is a promising approach towards the development of personalized cancer immunotherapies. DNA vaccines can be rapidly and efficiently manufactured and can integrate multiple neoantigens simultaneously. We therefore sought to optimize the design of polyepitope DNA vaccines and test optimized polyepitope neoantigen DNA vaccines in preclinical models and in clinical translation. METHODS: We developed and optimized a DNA vaccine platform to target multiple neoantigens. The polyepitope DNA vaccine platform was first optimized using model antigens in vitro and in vivo. We then identified neoantigens in preclinical breast cancer models through genome sequencing and in silico neoantigen prediction pipelines. Optimized polyepitope neoantigen DNA vaccines specific for the murine breast tumor E0771 and 4T1 were designed and their immunogenicity was tested in vivo. We also tested an optimized polyepitope neoantigen DNA vaccine in a patient with metastatic pancreatic neuroendocrine tumor. RESULTS: Our data support an optimized polyepitope neoantigen DNA vaccine design encoding long (≥20-mer) epitopes with a mutant form of ubiquitin (Ubmut) fused to the N-terminus for antigen processing and presentation. Optimized polyepitope neoantigen DNA vaccines were immunogenic and generated robust neoantigen-specific immune responses in mice. The magnitude of immune responses generated by optimized polyepitope neoantigen DNA vaccines was similar to that of synthetic long peptide vaccines specific for the same neoantigens. When combined with immune checkpoint blockade therapy, optimized polyepitope neoantigen DNA vaccines were capable of inducing antitumor immunity in preclinical models. Immune monitoring data suggest that optimized polyepitope neoantigen DNA vaccines are capable of inducing neoantigen-specific T cell responses in a patient with metastatic pancreatic neuroendocrine tumor. CONCLUSIONS: We have developed and optimized a novel polyepitope neoantigen DNA vaccine platform that can target multiple neoantigens and induce antitumor immune responses in preclinical models and neoantigen-specific responses in clinical translation.

Neoantigen vaccine platforms in clinical development: understanding the future of personalized immunotherapy.

INTRODUCTION: Derived from genetic alterations, cancer neoantigens are proteins with novel amino acid sequences that can be recognized by the immune system. Recent evidence demonstrates that cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity, while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction ?algorithms,? and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. These advances have generated considerable enthusiasm for ?the ?development of neoantigen vaccines. Several neoantigen vaccine platforms are currently being evaluated in early phase clinical trials including the synthetic long peptide (SLP), RNA, dendritic cell (DC), and DNA vaccine platforms. AREAS COVERED: In this review, we describe, evaluate the mechanism(s) of action, compare the advantages and disadvantages, and summarize early clinical experience with each vaccine platform. We provide perspectives on the future directions of the neoantigen vaccine field. All data are derived from PubMed and ClinicalTrials search updated in October 2020. EXPERT OPINION: Although the initial clinical experience is promising, significant challenges to the success of neoantigen vaccines include limitations in neoantigen identification and the need to successfully target the immunosuppressive tumor microenvironment.

In silico epitope prediction analyses highlight the potential for distracting antigen immunodominance with allogeneic cancer vaccines.

Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types. Results show limited therapeutic benefit in clinical trials and currently there are no FDA approved allogeneic cancer vaccines. We used recently developed bioinformatics tools including the pVAC-seq suite of software tools to analyze DNA/RNA sequencing data from the TCGA to examine the repertoire of antigens presented by a typical allogeneic cancer vaccine, and to simulate allogeneic cancer vaccine clinical trials. Specifically, for each simulated clinical trial we modeled the repertoire of antigens presented by allogeneic cancer vaccines consisting of three hypothetical cancer cell lines to 30 patients with the same cancer type. Simulations were repeated ten times for each cancer type. Each tumor sample in the vaccine and the vaccine recipient was subjected to HLA typing, differential expression analyses for tumor associated antigens (TAAs), germline variant calling, and neoantigen prediction. These analyses provided a robust, quantitative comparison between potentially beneficial TAAs and neoantigens versus distracting antigens present in the allogeneic cancer vaccines. We observe that distracting antigens greatly outnumber shared TAAs and neoantigens, providing one potential explanation for the lack of observed responses to allogeneic cancer vaccines. This analysis provides additional rationale for the redirection of efforts towards a personalized cancer vaccine approach.

Tumor-on-a-chip platform to interrogate the role of macrophages in tumor progression.

Tumor-infiltrating leukocytes, in particular macrophages, play an important role in tumor behavior and clinical outcome. The spectrum of macrophage subtypes ranges from antitumor 'M1'-type to protumor 'M2'-type macrophages. Tumor-associated macrophages (TAMs) typically display phenotypic features of both M1 and M2, and the population distribution is thought to be dynamic and evolves as the tumor progresses. However, our understanding of how TAMs impact the tumor microenvironment remains limited by the lack of appropriate 3D in vitro models that can capture cell-cell dynamics at high spatial and temporal resolution. Using our recently developed microphysiological 'tumor-on-a-chip' (TOC) device, we present here our findings on the impact of defined macrophage subsets on tumor behavior. The TOC device design contains three adjacent and connected chambers in which both the upper and lower chambers are loaded with tumor cells, whereas the central chamber contains a dynamic, perfused, living microvascular network. Introduction of human pancreatic or colorectal cancer cells together with M1-polarized macrophages significantly inhibited tumor growth and tumor-induced angiogenesis. Protein analysis and antibody-based neutralization studies confirmed that these effects were mediated through production of C-X-C motif chemokines (CXCL9), CXCL10 and CXCL11. By contrast, M2-macrophages mediated increased tumor cell migration into the vascularized chamber and did not inhibit tumor growth or angiogenesis. In fact, single-cell RNA sequencing showed that M2 macrophages further segregated endothelial cells into two distinct subsets, corresponding to static cells in vessels versus active cells involved in angiogenesis. The impact of M2 macrophages was mediated mostly by production of matrix metalloproteinase 7 and angiopoietin 2. In summary, our data demonstrate the utility of the TOC device to mechanistically probe biological questions in a 3D in vitro microenvironment.

The clonal evolution of metastatic colorectal cancer.

Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine.