Patent Grant
11462296
This disclosure relates generally to methods for identifying and analyzing nucleic acids and, more particularly, methods for identifying and analyzing cell-free nucleic-acid biomarkers.
The composition and abundance of an organism's nucleic acids provide biomarkers indicative of various aspects of the organism's genome and transcriptional expression, including the organism's predisposition toward particular biological states, as well as the presence and progression of such biological states. Much of a living, multicellular organism's total nucleic acid complement is located intracellularly: DNA is chiefly located within the nuclei of the cells, whereas RNA of numerous types is abundant within the various organelles and cytoplasm of cells. Thus, nucleic acids may be derived from cells and used as biomarkers to determine a biological state of organism, such as the presence of a disease or the biological behavior of the disease. In applications such as xenografting, tissue from a donor animal may be grafted into a host animal, and then the biological behavior of the donor tissue may be evaluated in the host animal by analyzing nucleic-acid biomarkers derived from cells of the donor tissue.
This disclosure describes example techniques and systems for determining the composition and abundance of nucleic acids in the blood of a host animal that has received a tissue xenograft and detecting biomarkers of a biological state via identification of the nucleic acids. Such techniques may include creating a combined reference genome that incorporates gene sequences from both the genome of the host animal and the genome of the xenograft donor animal. Cell-free nucleic acid sequences isolated from a blood sample of the host animal may then be aligned with the combined reference genome. In order to distinguish between sequences originating from the donor animal and those originating from the host animal, those sequences that align with a single region of the combined reference genome may be retained for further analysis. The retained sequences may then be analyzed to determine their identity, species of origin, abundance, and association with predetermined gene clusters that represent known biochemical pathways. In some examples, the techniques described herein may enable accurate identification and analysis of biomarkers associated with a biological state of xenograft donor tissue or xenograft host tissue based on a blood sample of the host animal, in part by eliminating from consideration confounding sequences originating from one of the host animal or the donor animal.
In one example, a method comprises obtaining a plurality of exosomes from a sample of bodily fluid derived from a first organism of a first species, wherein the first organism of the first species comprises tissue derived from both the first organism of the first species and tissue derived from a second organism of a second species, and wherein the plurality of exosomes comprises a plurality of molecules of ribonucleic acid (RNA); determining, for substantially each molecule of the plurality of molecules of RNA, a corresponding RNA sequence; determining, for each corresponding RNA sequence, whether the RNA sequence is substantially aligned with exactly one corresponding gene sequence of a combined reference genome; determining one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome, wherein the one or more characteristics include at least one of: a gene name of the corresponding gene sequence; a species associated with the corresponding gene sequence, wherein the species is one of the first species and the second species; determining an approximate number of times that each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of blood; and determining, based on one or more of the one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome or the approximate number of times each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of bodily fluid, whether the tissue derived from the second organism of the second species contains a biomarker indicative of at least one of a disease status, a response of the first organism of the first species to the tissue derived from the second organism of the second species, or a response of tissue derived from the second organism to transplantation within the organism of the first species.
In another example, a method comprises determining a corresponding RNA sequence for substantially each molecule of a plurality of molecules of ribonucleic acid (RNA), wherein a plurality of exosomes from a sample of bodily fluid derived from a first organism of a first species comprises the plurality of molecules of RNA, and wherein the first organism of the first species comprises tissue derived from both the first organism of the first species and tissue derived from a second organism of a second species; determining, for each corresponding RNA sequence, whether the RNA sequence is substantially aligned with exactly one corresponding gene sequence of a combined reference genome; determining one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome; and determining an approximate number of times that each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of bodily fluid; and determining, based on one or more of the one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome or the approximate number of times each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of bodily fluid, whether the tissue derived from the second organism of the second species contains a biomarker indicative of at least one of a disease status, a response of the first organism of the first species to the tissue derived from the second organism of the second species, or a response of tissue derived from the second organism to transplantation within the first organism of the first species.
In another example, a system comprises a reservoir configured to receive a sample of bodily fluid; and processing circuitry configured to: determine a corresponding RNA sequence for substantially each molecule of a plurality of molecules of ribonucleic acid (RNA), wherein a plurality of exosomes from the sample of bodily fluid is derived from a first organism of a first species and comprises the plurality of molecules of RNA, and wherein the first organism of the first species comprises tissue derived from both the first organism of the first species and tissue derived from a second organism of a second species; determine, for each corresponding RNA sequence, whether the RNA sequence is substantially aligned with exactly one corresponding gene sequence of a combined reference genome; determine one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome; determine an approximate number of times that each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of bodily fluid; and determine, based on one or more of the one or more characteristics of each RNA sequence substantially aligned with exactly one corresponding gene sequence of the combined reference genome or the approximate number of times each RNA sequence substantially aligned with exactly one corresponding gene occurs in the sample of bodily fluid, whether the tissue derived from the second organism of the second species contains a biomarker indicative of at least one of a disease status, a response of the first organism of the first species to the tissue derived from the second organism of the second species, or a response of tissue derived from the second organism to transplantation within the first organism of the first species.
The details of one or more example are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.
Favorable clinical outcomes of many medical conditions depend, to varying degrees, on factors such as the accurate prediction of a patient's risk of a condition or disease, reliable testing for early detection of the condition or disease, accurate prediction of disease progression, or the selection and administration of appropriate therapies. In some cases, the composition and abundance of nucleic acids present in the patient's tissue may be used as one or more biomarkers corresponding to the patient's risk of the condition or disease, or to the presence, progression, or potential response of the condition or disease to a particular therapy. Identification of such biomarkers present in the patient's tissue thus may facilitate one or more interventions associated with a favorable clinical outcome of the patient.
At present, diagnostics and interventions based on nucleic-acid biomarkers largely rely on the identification of single nucleic-acid biomarkers, which may be identified based on cells derived from tissue samples (e.g., tumor biopsies) obtained from multiple patients having a particular disease. Tissue samples from another patient then may be analyzed to determine whether the biomarker is present in the sample. While some such biomarkers pass predetermined statistical thresholds for biomarker identification, the use of single biomarkers in diagnosis and treatment-selection is subject to several drawbacks. For example, in the case of a particular type of cancer, there may be inherent genetic heterogeneity within tumors in individual patients and among different patients with the same type of cancer. Due to such heterogeneity, analysis of a tissue sample from a patient may lead to a false-negative diagnosis that the patient does not have the particular type of cancer because the single biomarker was not detected in the tissue sample.
In addition, methods for identifying and detecting biomarkers based on nucleic acids from cells derived from tissue samples are subject to their own limitations. For example, such methods limit the scope of inquiry to the tissue samples themselves. Although nucleic-acid biomarkers identified in tissue samples (e.g., tumor biopsies) may indicate the presence of a disease or condition, such biomarkers do not reflect aspects of the disease or condition that occur outside of the sampled tissue. For example, while much of a cell's nucleic acids are located within the cell, some nucleic acids, such as RNA, can be transported out of the cell inside vesicles called exosomes. In particular, cell-free RNA may be found in the bloodstream of animals inside exosomes.
The RNA contained within exosomes may be indicative of its parent cell's transcription profile, and may provide biomarkers indicative of a patient's biological state. For example, the RNA contained within exosomes found within a patient's bloodstream may be indicative of one or more of the patient's risk of developing a condition or disease, the presence of the condition or disease, the progress of the condition or disease, or the potential future progress of the condition or disease. Indeed, some species of RNA contained within exosomes may be indicative of a metastatic cancer phenotype (i.e., that a cancerous condition is likely to metastasize), and may indicate that a process of metastasis of a primary tumor has begun. Such species of RNA may be microRNAs, which may be packaged into exosomes, secreted from a tumor cell into the bloodstream of an organism, and disseminated to distant tissue sites. Once such microRNAs have infiltrated a distant tissue site, they may perform a signaling or conditioning function that causes non-cancerous cells at the distant tissue site to undergo changes that make the tissue site more favorable to colonization by tumor cells.
In some examples, monitoring of the composition and abundance of such RNA species present within the bloodstream of a patient may reveal a progressive increase in the abundance of such RNA species, which in turn may serve as a biomarker of progress of a cancerous condition toward metastasis. Such findings may inform a clinician's decision to select a particular type of therapy over another type of therapy, as different types of therapy may be predictable more appropriate at different stages of the condition or disease.
Thus, a complement of multiple cell-free nucleic acid biomarkers derived from blood may provide a better indicator of the presence, progress and future progression of a condition or disease than nucleic acid biomarkers derived from cells taken from a tissue sample. The collection of blood samples for the detection and analysis of cell-free nucleic acid biomarkers may be less invasive, costly, and time consuming than tissue biopsies and/or imaging procedures that may be involved in the detection of nucleic acid biomarkers from tissue samples. Thus, diagnostic and treatment-selection methods based on the detection of cell-free nucleic acid biomarkers derived from exosomes present in a patient's bloodstream may provide numerous clinical benefits over interventions based on the detection of a single nucleic acid biomarker derived from cells.
Described herein are example techniques and system for cell-free nucleic acid biomarker identification that may be used to account for the genetic heterogeneity of many conditions and diseases, as well as example methods for detecting such biomarkers within the blood of a patient. Such methods may be used for virtually any disease or condition for which there are cell lines that grow as xenografts, or patient derived xenografts that reflect the expected variance in a disease or condition. One method involves the use of gene cluster expression summary scores. These scores account for coordinated transcriptional regulation of multiple genes, overcoming deficiencies of single biomarkers. Another method includes the use of reconstructed hybrid genomes (e.g., mouse host and tumor donor species) and bioinformatics approaches to identify mRNAs expressed exclusively in the tumor cells (donor) and mRNAs expressed exclusively in supporting stromal cells (host). Such methods may distinguish mRNAs present in donor-derived exosomes from mRNAs present in host-derived exosomes, enabling a novel means to identify candidate biomarkers. Because the approach is not restricted to identification of donor-derived exosomes, it can also measure biomarkers of host response as well as biomarkers that can define response to therapy. The example methods described herein thus may provide efficient and cost-effective ways to discover biomarkers that can inform the risk of diseases or conditions, their diagnosis and prognosis, may be used to predict response to therapy, and may be rapidly validated in patient samples.
In some methods described herein, identification of cell-free nucleic acid biomarkers may be conducted via xenograft procedures. In such procedures, a tissue sample may be obtained from a donor animal of a first species. Whole tissue or cell lines cultured from the tissue sample may then be grafted into a host animal of another species. In some examples, the tissue sample or cell lines derived from the donor animal may be derived from tissue harboring a disease or condition, such as tissue from a cancerous tumor. In such examples, healthy tissue or cell lines derived from another donor animal may be introduced into another host animal as a control. In other examples, the tissue derived from the donor animal may be healthy organ tissue, and it may be desirable to identify cell-free nucleic acid biomarkers associated with a response of the host tissue and/or the donor organ tissue to a graft procedure. In either example, blood samples subsequently may be obtained from the host animal, from which exosomes containing RNA may be extracted. The RNA then may be sequenced, quantified, and aligned with a hybrid genome prepared by combining the genome of the donor-animal species and the host-animal species. In order to distinguish RNA sequences derived from the donor species from RNA sequences derived from the host species, sequences derived from the respective species may be analyzed separately. In examples in which cell-free nucleic acid biomarkers of a disease or condition are to be determined, donor sequences resulting from control donor animals also may be compared to donor sequences resulting test donor animals. RNA sequences that are differentially expressed in the tissue derived from the control donor animals and the tissue derived from the test donor animals may be identified as biomarker sequences for use in disease diagnosis or analysis of disease behavior. Similarly, RNA sequences that are differentially expressed in instances of organ-tissue acceptance or rejection by the host animals may be identified as biomarker sequences for use in analysis of organ-transplant feasibility.
In other examples, a biological status associated with a particular biomarker may be associated with acceptance or rejection of the tissue of the donor animal by the body of a host animal. Example techniques described herein also may include identifying a predisposition toward a particular biological state of the donor animal based on the presence of a biomarker, such as a predisposition toward acceptance or rejection of donor tissue. In other examples, such techniques may include identifying targeted therapies based on the presence of a biomarker that indicates that the body of the host animal is accepting or rejecting the tissue of the donor animal, such as in the case of organ tissue transplanted into the host animal to assess the long-term feasibility of organ transplant from the donor animal into a different host animal of yet another species.
For the sake of illustration, the example techniques described herein are described within the context of an example in which the host animal is a mouse harboring cells associated with a disease of humans or companion animals, such as cells derived from a canine osteosarcoma tumor. The xenografts described in the example presented herein represent two molecular phenotypes of osteosarcoma (OS-1 and OS-2) with distinct biological behavior that are highly conserved between dogs and humans. However, it should be understood that the example techniques may be used in the identification and detection of biomarkers associated with other diseases or conditions of humans or other animal species.
Osteosarcoma (OS) is a heterogeneous disease with a disproportionate human impact, as it mainly affects children and adolescents, and is the most common malignant pediatric tumor of bone. Standard therapy for OS comprises neoadjuvant chemotherapy, surgery and adjuvant chemotherapy. The 5-year survival rates of OS patients with localized and operable OS is 60-70%, but the outcome of patients with non-resectable or metastatic OS is poor, as more than half of patients with OS succumb to metastatic disease.
OS is also the most common primary malignant tumor of bone in dogs, and it is particularly prevalent in large and giant breeds. OS is an incurable, highly prevalent cancer of large and giant breed dogs that has been identified as a high priority for health research by over 25 AKC Parent breed clubs. In contrast to humans, OS occurs most commonly in older dogs. The number of diagnoses per year has been estimated at >8,000, and possibly as high as 80,000 in the US, with the lifetime risk for OS in some breeds being as high as 1 in 5 to 1 in 7. Similarly to humans, the outcome of canine patients with metastatic OS is poor. Tumors at the primary site may be managed surgically, but most dogs with OS die from metastasis to lungs or to other bones or organs.
These collective statistics illustrate that progress in managing OS has been hindered by its heterogeneity in both humans and in dogs. For example, neither the histological appearance nor the propensity of the tumor cells to elaborate bone, cartilage, or collagen matrices are predictive of behavior, and while recurrent molecular events have been described, these are yet to be adopted as prognostic or predictive biomarkers for this disease. Thus, clarification of the etiology of the disease, development of better strategies to manage disease progression, and methods to guide personalized treatments are among the unmet health needs for both human OS patients and canine patients. These needs may be met by models (e.g., models in species other than humans or canines) that accurately recapitulate the natural heterogeneity of OS in both humans and in dogs. Such models may provide a better understanding of the events that underlie OS tumor heterogeneity and contribute to disease progression may enable the development of effective strategies to manage OS and to improve outcomes. In some cases, a single model may be applicable to both humans and dogs, because spontaneous OS may be a homologous cellular and molecular disease of humans and dogs. For example, prognostically significant gene- and microRNA-expression signatures have been discovered that are evolutionarily conserved in human and canine OS. Such expression signatures may predict both the biological behavior OS and patient survival. While not necessarily linked to metastatic potential, the molecular components of such gene and microRNA expression profiles may reflect tumor growth, invasive potential, time to metastasis, or patient response to therapy.
Techniques for modeling OS to obtain a better understanding of OS disease-events in are within the scope of this disclosure and are described in further detail below. Such techniques also may enable a better understanding of events that occur in other diseases, such as other diseases that may affect dogs, other non-human animals, or humans. In addition, such techniques may enable a better understanding of events that occur in other medical situations, such as in tissue-transplant or other situations.
In the example described below with respect to
In some examples, cells used for xenografts are called OS-1 (OSCA-32) and OS-2 (OSCA-40). Such cells may be derived from canine tumors with distinct biological behavior and recapitulate this behavior in xenografts. In this example, the cross-species hybrid genome approach may be used to identify separate canine and mouse sequences from tumor xenografts that inform the progression of disease (in the mouse). Thus, it is possible to use tumor samples grown in mice to determine the contribution of dog sequences (derived from the implanted, growing tumor cells) and mouse sequences (derived from infiltrating stroma) to define features of progression for tumors arising from implantation of the different cell lines. In the following description, references are made to illustrative examples. It is understood that other examples may be utilized without departing from the scope of the disclosure.
Exosomes may first be isolated from the serum (102). In some examples, this may be accomplished by using ExoQuick kits from System Biosciences, Inc. (SBI), although other suitable techniques may be used.
Next, total RNA may be isolated from the exosomes (104). For example, this may be accomplished by using the Complete SeraMir Exosome RNA Amplification kit from SBI and precipitated with the Dr. GenTLE (Gene Trapping by Liquid Extraction) System from SBI, although other suitable techniques may be used.
Sequencing libraries may be generated (108) from the RNA by using Nextera XT DNA Library Preparation Kit (Clontech) at the University of Minnesota Genomics Center (UMGC), although other suitable techniques and facilities may be used. In some examples, sequencing-library preparation may include RNA purification, reverse-transcriptase PCR production of cDNA from the RNA molecules, PCR amplification of the resulting cDNA molecules, and transcription of the cDNA molecules into RNA. Sequencing may be done at UMGC on a 50 base-pair paired-end (PE) run on a HiSeq 2500 nucleic acid sequencing instrument using Rapid chemistry. In some examples, it may be desirable to use 8 samples per lane and generate >120 M reads, which may be fairly well balanced across projects. Preferably, average quality scores may be above Q30 for all PE reads.
The sequences obtained at block 106 are then compared to a cross-species hybrid genome is performed, followed by bioinformatic analyses (110). A summary of example bioinformatics methods for creation and mapping to cross-species hybrid genome and the workflow of data analysis steps with illustrations is described below.
Next, multi-mapped genes are excluded from the analysis (208). Excluding multi-mapped reads from the abundance estimation step may be useful to help avoid incorrectly identifying the presence of graft-derived nucleic acids. Aligning RNA-Seq reads only to the reference genome of the graft species may result in the spurious identification of graft-derived genes in cases where the genes have identical sequences in both species. It may be desirable to compare gene expressions levels from a xenograft sample with a negative control sample way provide further power to reduce false-positives. The identity and abundance of genes originating from the donor animal, which in this example may be a dog, is then determined (210). As described in further detail below, the determined identity and abundance of genes originating from the donor animal may be used to determine the presence of disease and disease progression, and may inform treatment decisions.
The bioinformatics analysis illustrated in
In some examples, a disease status may be a predisposition to a disease, the presence of a disease, or the progress or potential progression of a disease of the dog donor-animal. In some examples, the disease status may enable a clinician or experimenter to select an appropriate treatment for the dog donor-organism or the mouse host-organism. In other examples, instead of a disease status, the bioinformatics analysis may indicate a response of the mouse host-organism to non-diseased cells or tissue derived from the dog donor-organism, or a response of the non-diseased cells or tissue derived from the dog donor-organism to transplantation within the mouse host-organism. In such examples, the characteristics of the RNA sequences that align with the combined reference genome or the number of times that such RNA sequences occur in the sample of bodily fluid then may be used to determine whether the dog-donor organism is a good candidate to receive a tissue transplant (e.g., an organ transplant).
In some examples, the bioinformatics analysis illustrated in
As illustrated in
Memory 226 may include any volatile or non-volatile media, such as a random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, and the like. As mentioned above, memory 226 may store information including instructions for execution by processing circuitry 228 such as, but not limited to, instructions for performing the techniques described herein. Communication module 244 may provide one or more channels for receiving and/or transmitting information. Communication module 244 may be configured to perform wired and/or wireless communication with other devices, such as radio frequency communications. In other examples, communication module 244 may not be implemented, and instead, memory 226 may be removable (e.g., a removable flash memory).
Power source 240 delivers operating power to various components of computing device 218. Power source 240 may generate operational power from an alternating current source (e.g., residential or commercial electrical power outlet) or direct current source such as a rechargeable or non-rechargeable battery and a power generation circuit to produce the operating power. In other examples, non-rechargeable storage devices may be used for a limited period of time.
In one or more examples, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media forming a tangible, non-transitory medium. Instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general purpose microprocessors, or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to one or more of any of the foregoing structure or any other structure suitable for implementation of the techniques described herein.
In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and/or discrete electrical circuitry, residing in an IMD and/or external programmer.
Further aspects of the disclosure will now be discussed, including further details of the techniques described herein. The example laboratory techniques described herein for accomplishing routine laboratory tasks, such as the collection of blood and the isolation of serum from blood, as well as others, are not intended to be limiting and may be performed by any suitable laboratory techniques. In addition to the techniques described above, supplementary techniques, as described below, may be employed. The example techniques described herein may be used to identify cell-free transcripts or other nucleic acids in blood that are specifically associated with the particular tumor and the host response, thereby creating sets of biomarkers with distinct diagnostic utility. In some examples, the human or animal disease may be re-created in a mouse (xenograft, xenotransplants), and to recognize that certain components of the response will be absent when immunodeficient mice are used (for example, the host T-cell component in athymic nude mice).
Tumor cells may regularly enter the circulation. Most of these cells die because they fail to adapt to conditions of growth outside the tumor niche. However, rare cells survive at distant sites and re-establish a niche that is suitable for tumor growth. It is known that tumors with different metastatic potential (including OS) display distinct patterns of gene expression, which may be derived from tumor cell-autonomous properties, but may be influenced by the local tumor microenvironment (TME) stratification of OS into prognostically significant groups. Canine and human OS may be stratified into more and less aggressive groups based on their molecular signatures. The differences in behavior among these subtypes of OS are intrinsic—that is, they are not entirely due to therapeutic management. Moreover, while OS generally has high potential to metastasize, somewhere between 30-40% of children and 10%-25% of dogs treated for this disease, respectively, will survive more than 10 years and more than 2 years. Thus, some tumors metastasize more slowly than others, and this may true for tumor xenografts in immunodeficient mice.
The example described herein illustrates that OS-2 xenografts may have a greater propensity to disseminate to the lung than OS-1 xenografts. Highly expressed genes present in stromal cells of OS-2 xenograft tumors are associated with inflammation and immune response. In addition, pulmonary fibroblasts treated with OS-2 exosomes show decreased expression of chemokines that are strongly chemotactic for lymphocytes and that establish an environment favoring polarization to the Th17 phenotype. OS-2 derived exosomes contain genes involved in immune regulation and inflammation. Both OS-1 and OS-2 exosomes may enable the re-programming of the tumor micro-environment and establishing a favorable niche for tumor dissemination and metastases.
Tumor exosomes package bioactive molecules such mRNAs and microRNAs. Thus, nucleic acids enriched in OS tumors with distinct biological behaviors may be contained in exosomes. Exosomes in canine OS cell lines have been characterized in situ, by flow cytometry, and by biochemical analysis. The data suggest there may be preferential accumulation of mRNA species in cells derived from tumors with different behavior. Thus, at least some the in vivo behavior of OS may be associated with an altered composition of exosomes that enter the circulation and influence distant environments in preparation for tumor dissemination.
For this study, cell lines derived from two spontaneous canine OS with distinctly different biological behavior (OS-1 and OS-2) were used for heterotypic in vivo modeling that recapitulates the heterogeneous biology and behavior of this disease. Both cell lines demonstrated stability of the transcriptome when grown as orthotopic xenografts in athymic nude mice. Consistent with the behavior of the original tumors, OS-2 xenografts grew more rapidly at the primary site and had greater propensity to disseminate to lung and establish microscopic metastasis. Moreover, OS-2 promoted formation of a different tumor-associated stromal environment than OS-1 xenografts. In addition to comprising a larger fraction of the tumors, a robust pro-inflammatory population dominated the stromal cell infiltrates in OS-2 xenografts, while a mesenchymal population with a gene signature reflecting myogenic signaling dominated those in the OS-1 xenografts. The studies described herein show that canine OS cell lines maintain intrinsic features of the tumors from which they were derived and recapitulate the heterogeneous biology and behavior of bone cancer in mouse models. This system provides a resource to understand interactions between tumor cells and the stromal environment that may drive progression and metastatic propensity of OS.
Understanding the heterogeneous biology and behavior of OS may be useful to fully elucidate the pathogenesis of osteosarcoma and other diseases. As described herein, orthotopic canine OS xenografts preserve the biological, molecular, and heterotypic heterogeneity observed in the tumors from which they were derived. Moreover, transcriptome analysis of xenograft tumors revealed a strong OS cell specific stromal response, which may provide evidence that intrinsic genetic tumor characteristics and cross-talk between tumor and stromal cells might underlie heterogeneity of biological behavior in OS patients. These data may provide insight into tumor-host interactions and identify targets that may play a role in treatment strategies for OS patients.
Results
In contrast, OS-2 tumors had a more aggressive appearance with spindle-shaped, anaplastic cells that had round to elongate nuclei, moderate anisocytosis and anisokaryosis, and frequent mitotic activity (
The different metastatic propensity of OS-1 and OS-2 was confirmed histologically (
As shown in
When dog and mouse genes were analyzed together, expression of mouse-specific genes was not detected in the canine cell lines (
The heatmap in
To better understand the differences between OS-1 and OS-2, the upregulated and downregulated murine genes in OS-2 were considered as separate lists and used IPA to identify enriched biological functions and transcription factors that regulate these genes. The 482 differentially expressed murine genes included 240 that were upregulated, and 242 that were downregulated in OS-2 tumor xenografts relative to OS-1 tumor xenografts. The most upregulated murine gene in the OS-2 xenografts was Mcpt 1 (+11.25 fold), whereas the most downregulated murine gene was Nkx2-1 (−10.97 fold) (see table S2).
Based on biological function and processes, the most upregulated murine genes in OS-2 tumors were proteases, metallopeptidases, cytokines, and chemokines involved in cell movement, leukocyte migration, inflammation, and angiogenesis (
In the example described herein, mouse xenografts were used to study the heterogeneity and biological behavior of OS in vivo. Specifically, this approach creates opportunities to examine tumor-intrinsic properties, as well as organotypic, tumor-stromal interactions that influence tumor progression. Cells were injected at the orthotopic site to simulate the biology of the spontaneous disease. The anatomical site of implantation may be considered carefully, as the biological behavior of tumors is dependent on both the intrinsic properties of tumor cells and host factors that differ between tissues and organs. The microenvironment in subcutaneous xenografts consists of desmoplastic mouse stromal cells that do not resemble the organization seen in autochthonous tumors. These properties also apply to OS: orthotopic canine OS xenografts in nude mice produced osteoid matrix and metastasized spontaneously, while subcutaneous xenografts did not.
The data show that heterogeneity of biological behavior (including metastatic propensity) can be recapitulated to a limited extent in tumors from cell lines, but more readily by utilizing multiple cell lines that cover the spectrum of tumor behavior. Further, the data show that the major genetic drivers that distinguish the two canine OS cell lines in vitro were retained in the orthotopic xenografts. In addition to stability of the transcriptome, the cell lines show stable morphology from the primary canine tumors to the primary orthotopic tumors, and to the metastatic tumors. Confirmation of genetic and morphologic stability over many passages was validated the utility of the present model to understand OS tumor heterogeneity.
As predicted from the original behavior of the spontaneous tumors in the dogs and from their gene and microRNA expression signatures, the logarithmic expansion phase of OS-2 primary xenografts was faster than that of OS-1 primary xenografts. However, both cell lines seemed to reach the tumor endpoints at approximately the same time. Two factors might account for this. First, the tumors are growing within a cavity surrounded by bone, and despite the fact that OS-2 xenografts showed greater epiphyseal destruction and invasion, the bone constrains the maximum size achievable by the primary tumors within the experimental time frame. Second, mice with OS-2 xenografts did not show greater morbidity than mice with OS-1 xenografts, determined by the absence of lameness, ambulatory deficits, and other behaviors associated with chronic pain. This could be due to adaptive behavior of prey species to hide pain; however, previous work has shown that painful intramedullary bone tumors produce behavioral changes in mice. It should be noted that these cell lines accurately represent the biological behavior of the tumors from which they were originally derived, and more broadly the classification of more aggressive and less aggressive tumors. Furthermore, such properties have been verified independently by other groups using one of these cell lines, and they generally extend to human and murine osteosarcoma.
Beyond growth at the primary site, biological behavior can be quantified by metastatic propensity and successful spread to distant sites. Again, the predictions from the original spontaneous tumors were confirmed experimentally in the models described herein. OS-2 cells were a representative example from a group of highly aggressive tumors (worse prognosis) that showed high expression of cell cycle and DNA damage repair associated genes, with concomitant reduced expression of a complement of genes that defined “microenvironment interactions. This reduced expression of molecules that mediate local cell communication could explain, at least in part, the observation that cells injected intratibially achieved rapid systemic distribution, spreading to the lungs within 6 hours; i.e., there was nothing to hold the cells in place, and they had no preference to remain in the local bone environment.
The results suggest that even though both OS-1 and OS-2 cell lines can establish a metastatic niche, they do so with different kinetics, creating a suitable model to study intrinsic differences in metastatic propensity, as well as host-related factors that contribute to the metastatic niche in OS.
Based on these observations, two distinct mechanisms for the different metastatic potential of OS-1 and OS-2 xenografts are herein proposed. OS-2 cells might have greater metastatic potential due to their interaction with the local microenvironment in the bone, which leads to reduced retention, and potentially to an increased capability to condition the distant site. The alternative possibility is that OS-2 cells seed the lungs shortly after inoculation, and even though many of these cells might leave the lungs or die, accounting for the loss of luciferase signal by 24 hours, some cells remain and eventually form the pulmonary lesions (i.e., equivalent to seeding or colonization by intravenous inoculation). Preliminary experiments suggest that OS-1 and OS-2 cells have low efficiency of pulmonary colonization upon intravenous injection which would indicate the first possibility occurs.
Highly expressed mouse genes present in the OS-2 xenografts were associated with B cell signaling, inflammation, and immune response, whereas mouse genes in the OS-1 cells xenografts were associated with patterning, and especially with muscle formation. Increased expression of myogenic regulators in mouse stromal cells in OS-1 xenografts raises interesting questions regarding possible effects of OS-1 tumor cells on marrow derived mesenchymal stromal cells.
Intriguingly, the most downregulated murine gene in the OS-2 xenografts was the transcription factor Nkx2-1, which is known to regulate lung epithelial cell morphogenesis and differentiation. Down-regulation of NKX2-1 has been shown to precede dissemination of lung adenocarcinoma cells. NKX2-1 amplification has been reported in one human OS patient but there are no reports of down regulation or loss of NKX2-1 in OS patients.
Thus, xenograft models that recapitulated the heterogeneous biological behavior of OS have been developed and have been described herein. These models may be useful to understand the mechanisms that drive progression and metastasis of OS, as they are expandable into additional cell lines to represent a wider spectrum of disease.
Materials and Methods
Cells and culture conditions: Two canine OS cell lines representing previously described “highly aggressive” and “less aggressive” molecular phenotypes (OS-1 and OS-2), were used in this study. OS-1 and OS-2 are derivatives of the OSCA-32 and OSCA-40 and OSCA-40 cell lines. Specifically, OS-1 represents a subline that successfully established tumors after orthotopic implantation, as the parental OSCA-32 did not establish heterotopic or orthotopic tumors in every occasion. OS-2 represents the parental OSCA-40, which reliably formed tumors after orthotopic implantation in every experiment done.
Cell lines were validated using STR Short Tandem Repeats (STR) profiles by DNA Diagnostics Center (DDC Medical) (Fairfield, Ohio). OS-1 and OS-2 cells were modified to stably express green fluorescent protein (GFP) and firefly luciferase as described (Scott et al., 2015) and used for orthotopic injections in mice. After transfection and selection, it was confirmed that the GFP/luciferase construct was stably integrated in each cell line by fluorescence in situ hybridization, and it was corroborated that the two cell lines had approximately equivalent luciferase activity on a per cell basis using conventional luciferase assays. All cell lines were grown in DMEM (Gibco, Grand Island, N.Y.) containing 5% glucose and L-glutamine, supplemented with 10% fetal bovine serum (Atlas Biologicals, Fort Collins, Colo.), 10 mM4-(2-hydroxyethyl)-1-piperazine ethanesulphonic acid buffer (HEPES) and 0.1% Primocin (Invivogen, San Diego, Calif.) and cultured at 37° C. in a humidified atmosphere of 5% CO2. Canine OS cell lines are available for distribution through Kerafast, Inc. (Boston, Mass.). Each cell line was passaged more than 15 times before the experiments when they were inoculated into mice.
Mice: Six-week old, female, athymic nude mice (strain NCrnu/nu) were obtained from Charles River Laboratories (Wilmington, Mass.). The University of Minnesota Institutional Animal Care and Use Committee approved protocols for mouse experiments of this study (Protocol No.: 1307-30806A).
Tumor xenografts: Eight animals per group provide >95% power to identify a 15% change in the median time to tumor when the σ for both populations is <2.0 and the acceptable α error is 5% (p<0.05). Experimental replicates increased statistical robustness, accounting for the expected heterogeneity.
Four replicate experiments were done to assess orthotopic growth and metastatic dissemination of OS-1 and OS-2 cells. For the first pilot experiment, groups of three mice were used to validate the approach. All of the mice receiving OS-1 xenografts showed successful implantation, but only two of the three mice receiving OS-2 xenografts showed successful implantation. For the second experiment, groups of 16 mice were used to establish significance. In this experiment, all of the mice receiving OS-2 xenografts showed successful implantation, but eight mice injected with OS-1 xenografts had significant adverse effects during anesthesia and were not recovered (i.e., they were humanely euthanatized). For the third experiment, nine mice were inoculated with OS-2 cells to verify the unexpected effects of rapid dissemination to the lung. No mice received OS-1 for this experiment. Finally, for the fourth experiment, five mice were inoculated with each cell line (OS-1 and OS-2) to achieve a biological replicate of experiment two, maintaining the sample size at a number to maximize a positive outcome. Appropriate censoring was used to include all animals in the analyses, only excluding any which succumbed acutely or subacutely during the intratibial injection procedure. Thus, 16 mice inoculated with OS-1 were included in the analyses of tumor growth, and 32 mice inoculated with OS-2 were included in the analyses of tumor growth.
It was previously determined that four samples per group approximate the point of minimal returns using large genomic datasets for gene expression profiling, and these estimates hold true from microarrays to RNAseq where the fidelity of replication within samples is high, despite orders of magnitude more data (see analysis of RNA sequencing below).
Animals were assigned to separate cages (4 animals each) in random order for each experiment. All of the animals in each cage received the same treatment. OS-1 and OS-2 cells expressing GFP and firefly luciferase were injected intratibially. Mice were anesthetized with xylazine (10 mg/kg, I.P.) and ketamine (100 mg/kg, I.P.), and 1×105 cells suspended in 10 μl of sterile PBS were injected into the left tibia using a tuberculin syringe with 29-gauge needle. Buprenorphine (0.075 mg/kg, I.P. q.8 hours; Buprenex®, Reckitt Benckiser Healthcare, Richmond, Va.) was used for pain control over the first 24 hours after injection of tumor cells, and prophylactic ibuprofen administrated in the water was used over the next 3.
Tumor growth was monitored by measuring width-and-length of the proximal tibia and the stifle joint weekly using calipers, as well as by in vivo imaging as described (Kim et al., 2014). Bioluminescence imaging (Xenogen IVIS spectrum, Caliper Life Sciences, Hopkinton, Mass.) was done after injection of D-luciferin (Gold biotechnology, St. Louis, Mo.) following isoflurane inhalant anesthesia and analyzed with Living Image Software (Caliper Life Sciences). Bone tissue volume was calculated from both tibiae using the equation V=L×W2×0.52 (Banerjee et al., 2013) and tumor volume was estimated by subtracting the normal bone tissue volume of the contralateral unaffected (right) tibia from the volume of the affected (left) tibia.
Mice were observed for up to 8 weeks or until tumor endpoint criteria were reached (ill thrift, tumor reaching 1 cm in the largest diameter, visible lameness, pain, or severe weight loss), at which time they were humanely euthanized with pentobarbital sodium and sodium phenytoin solution (Beuthanasia-D Special®, Schering-Plough Animal Health, Union, N.J.). Primary bone tumors and lung tissues were dissected and a portion of each was stored at −80° C. for RNA extraction. The remaining tissues were fixed in 10% neutral-buffered formalin, and processed for routine histological examination.
Luciferase activity and tumor sizes were compared using multiple t-test and Holm-Sidak method with Prism 6 software (GraphPad). p<0.05 was used as the level of significance.
RNA extraction, library preparation, and RNA sequencing: Total RNA was extracted from primary intratibial tumors and from cell lines using miRNeasy Mini Kit (QIAGEN, Valencia, Calif.). RNA integrity was examined using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, Calif.) and RIN values of all samples were >8.0. Sequencing libraries were prepared with TruSeq Library Preparation Kit (Illumina, San Diego, Calif.). RNA sequencing (100-bp paired-end) with HiSeq 2500 (Illumina) was done at the University of Minnesota Genomics Center (UMGC). A minimum of ten million read-pairs was generated for each sample.
Analysis of RNA sequencing data: Initial quality control analysis of RNA sequencing (FASTQ) data for each sample was performed using the FastQC software (version 0.11.2)(Andrews). FASTQ data were trimmed with Trimmomatic (Bolger, 2014). HISAT2 (Kim et al., 2015) was used to map paired-end reads from eight xenograft tumors (four tumors of OS-1 and four tumors of OS-2) and four parental cell line samples (two each for OS-1 and OS-2 cell lines). For accurate alignment of sequencing reads to canine and murine genes within xenograft tumors a HISAT2 index for mapping was built from a multi-sequence fasta file containing both the canine (canFam3) and murine (mm10) genomes. Insertion size metrics were calculated for each sample using Picard software (version 1.126) (http://picard.sourceforge.net.). Samtools (version 1.0_BCFTools_HTSLib) was used to sort and index the bam files (Li et al., 2009). Transcript abundance estimates were generated using the Rsubread featureCounts program for differential gene expression analysis (Liao et al., 2014).
Gene counts for each xenograft sample were imported into RStudio (v. 3.2.3) for differential gene expression (DGE) analysis with EdgeR. Lowly expressed genes were removed by filtering. A gene was considered expressed if had log 2-transformed read counts per million (CPM) >1 in at least two of the eight xenograft tumors. Biological variation within xenograft sample groups was estimated by common dispersion and biological coefficient of variation (BCV) calculations. Pair-wise empirical analysis of differential gene expression was performed on sample groups (OS-1 and OS-2) using ‘Exact Test’ for two-group comparisons with trimmed mean of M-values (TMM) normalization (Robinson and Oshlack, 2010). Tagwise dispersion (individual dispersion for each gene) was used to adjust for abundance differences across biological replicates (n=4) within each xenograft group (OS-1 and OS-2). Gene counts as CPM, were imported into Partek Genomic Suite for clustering analysis and visualization. The Pearson similarity metric and average linkage clustering method were used for hierarchical clustering of mean-centered CPM values. Enriched pathway and functional classification analyses of DGEs were performed using IPA. The reference set for all IPA analyses was the Ingenuity Knowledge Base (genes only) and human Entrez gene names were used as the output format. To understand the high level functions and utilities that each gene identified as differentially expressed between OS-1 and OS-2 was associated with, Metacore software (Thompson Reuters) was used to identify statistically over-represented cellular processes in the dataset.
The following table, referred to above as Table S2, provides a list of differentially-expressed murine genes in OS-2 relative to OS-1 xenografts. Fold change (FC), p values, and FDR-adjusted p values were calculated by pair-wise Exact Test comparisons in EdgeR. Genes were annotated with the Ingenuity Knowledge Base of IPA.
Various aspects of the disclosure have been described. These and other aspects are within the scope of the following claims.
This application claims the benefit of U.S. Provisional Patent Application No. 62/407,987, filed Oct. 13, 2016, the entire content of which being incorporated herein by reference.
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| Number | Date | Country | |
|---|---|---|---|
| 20180105866 A1 | Apr 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62407987 | Oct 2016 | US |
