# Patent

## Title
Methods and compositions for identification of tumor models

## Abstract

The disclosure provides methods and compositions, e.g., kits, for identifying or authenticating a sample, e.g., a tumor model, based on the genotype of the sample at a group of SNP loci.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of PCT/CN2020/079067 filed Mar. 12, 2020, which claims priority to application PCT/CN2019/077750, filed Mar. 12, 2019, the disclosure of which is incorporated herein by reference.


SEQUENCE LISTING
The sequence listing that is contained in the file named “078272-8002US01_SL_ST25”, which is 187 KB (as measured in Microsoft Windows) and was created on May 25, 2021, is filed herewith by electronic submission and is incorporated by reference herein.
FIELD OF THE INVENTION
The present invention generally relates to molecular biology, cancer biology and animal models.
BACKGROUND
Cell lines, organoids, xenograft and homograft models are useful model systems in oncology and other biomedical researches. Model authentication and characterization helps their proper utilization and alleviates a series of problems such as misidentification and misuse, cross-contamination, erroneous cancer classification, genomic change due to longtime culture and genetic drift, all were well noted especially in cell lines due to their popular use. For example, various studies reported about 10-40% misidentification/contamination rates for cell line banks.
There are a variety of methods for authenticating cell lines including cell morphology examining, isoenzymology, cytogenetic analysis (karyotyping and FISH), human lymphocyte antigen (HLA) typing, short-tandem repeat (STR) profiling, single-nucleotide polymorphism (SNP) typing, DNA and RNA sequencing (Freedman, L. P. et al. Biotechniques 59, 189-90, 192 (2015); Nims, R. W. & Reid, Y. In Vitro Cell Dev Biol Anim 53, 880-887 (2017)). Among these technologies, STR profiling has been most widely used and there is a standard (ASN-0002) to guide its application on authenticating human cell lines (Almeida, J. L., Cole, K. D. & Plant, A. L. PLoS Biol 14, e1002476 (2016)). A panel of 19 STR markers for mouse cell lines were also developed (Zaaijer, S. et al. Elife 6(2017)). The sensitivity of STR assays for detecting contaminant is about 5-10% (Yu, M. et al. Nature 520, 307-11 (2015)). In recent years, SNP typing is becoming increasingly used for cell line and biosample authentication owning to its improved accuracy, sensitivity and reduced cost. SNPs can be profiled by PCR and next-generation sequencing (NGS) including transcriptomic sequencing or RNA-seq, whole exome sequencing (WES) and whole genome sequencing (WGS). Current SNP assays have detection sensitivities at about 3-5%. There are also databases with STR, SNP and other information for cell lines to facilitate their authentication and characterization.
Besides cell lines, organoids and mouse tumor models are widely used in oncology research and drug development. Organoids are in vitro three-dimensional culture deriving from stem cells, primary and engineered tumor samples, and xenografted human tumors that maintain many organismal structures and functions. Mouse tumor models are in vivo systems including patient-derived xenograft (PDX), cell line derived xenograft (CDX), syngeneic or mouse cell line-derived models, mouse homograft models, etc. Some of these models, like PDX, can more faithfully capture histopathology and genomics to primary tumors than cell lines. Like cell lines, these tumor models have similar quality control issues, but there are additional problems. In xenograft models, tumors contain human tumor cells and mouse stromal cells, the latter gradually replace human counterparts during the passaging of models, which, when compounded with genomic heterogeneity, implantation site difference (subcutaneous and orthotopic), growth variation and dissection randomness, makes the human-mouse genetic compositions of tumors from even same PDX differ considerably, to the extent that some samples are nearly pure human or mouse content. Such tumor-host mixing and interference occurs to all implanted tumors models, causing fluctuation of allele frequencies for STR markers and SNPs, therefore adversely impacting traditional STR and SNP based authentication methods. Large-scale sample authentication is also a logistic burden and error-prone, especially for biobanks where many kinds of in vitro and in vivo models are simultaneously maintained and used. Therefore, there is a need to develop new SNP based assay to identify and authenticate tumor models.
SUMMARY OF INVENTION
In one aspect, the present disclosure provides a method for identifying or authenticating a sample. In one embodiment, the method comprises: obtaining a nucleic acid from a sample; detecting a genotype for the sample at a plurality of human single nucleotide polymorphism (SNP) loci or at a plurality of mouse SNP loci; comparing the genotype for the sample to a reference genotype; and determining the identification of the sample. In certain embodiments, the human SNP is selected from the group as shown in Table 1. In certain embodiments, the mouse SNP is selected from the group as shown in Table 2
In certain embodiments, the sample is a cell, a tissue, an organoid, or a combination thereof. In certain embodiments, the sample is a cell line or a tumor tissue. In certain embodiments, the sample is derived from a xenograft or homograft tumor model. In certain embodiments, the sample is derived from patient-derived xenograft (PDX), cell line derived xenograft (CDX), syngeneic or mouse cell line-derived models, mouse homograft models.
In certain embodiments, the sample comprises a contaminant, the method further comprises determining the percentage of the contaminant in the sample. In certain embodiments, the method further comprises determining the identity of the contaminant.
In certain embodiments, the detecting step uses next-generation sequencing (NGS) or a sequencing-based SNP array. In certain embodiments, the nucleic acid is barcoded.
In certain embodiments, the method further comprises identifying the gender of a subject from which the sample is obtained. In certain embodiments, the method further comprises identifying the ethnicity of a subject from which the sample is obtained. In certain embodiments, the method further comprises detecting the presence of virus or mycoplasma in the sample. In certain embodiments, the method further comprises determining strain of an immunodeficient mouse from which the sample is obtained.
In another aspect, the present disclosure provides a method for determining the alleles in a sample. In some embodiments, the method comprises: obtaining a nucleic acid from the sample; selecting a set of single nucleotide polymorphism (SNP) of the sample that can be amplified together in a multiplex amplification reaction, wherein the set of SNP loci are selected from the group as shown in Table 1 or Table 2; providing a set of oligonucleotide primer pairs, wherein each oligonucleotide primer pair in the set flanks a single locus in the set of SNP loci, and wherein each oligonucleotide primer pair is capable of amplifying a single locus from the set of SNP loci in a multiplex amplification reaction; co-amplifying the set of SNP loci in a multiplex amplification reaction, wherein the product of the multiplex amplification reaction comprises a mixture of amplified alleles from each of the co-amplified loci in the set of SNP loci; and evaluating the products of the co-amplification reaction to determine the alleles present at each of the loci analyzed in the set of SNP loci within the sample.
In another aspect, the present disclosure provides a method of authenticating a sample comprising a human component and a mouse component. In certain embodiments, the method comprises obtaining a nucleic acid from the sample; detecting a genotype of the sample at 100 or more mouse genomic loci, each of the mouse genomic loci having a corresponding homologous human genomic locus, wherein each of mouse genomic loci and the corresponding homologous human genomic locus have identical flanking nucleotide sequences; and determining the ratio of the mouse component in the sample based on the genotype. In certain embodiments, the mouse genomic loci are selected from Table 6.
In another aspect, the present disclosure provides a kit for identifying a sample. In certain embodiments, the kit comprises primers for detecting in a sample at a group of human SNP loci or at a group of mouse SNP loci. In certain embodiments, the kit further comprises an agent for amplifying DNA fragments containing the human or mouse SNPs using the primers.
In another aspect, the present disclosure provides a microarray for identifying a human or mouse sample. In certain embodiments, the microarray comprises probes for detecting a genotype of a sample at a group of human or mouse SNP loci.
In yet another aspect, the present disclosure provides a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to: retrieve a genotype of a sample at a group of human or mouse SNP loci; compare the genotype of the sample to a reference genotype; and determine the identification of the sample.
In yet another aspect, the present disclosure provides a method for authenticating a sample comprising a major component and a minor component. In certain embodiments, the method comprises detecting a genotype of the sample at 100 or more SNP loci; determining an SNP heterogeneity ratio for each of the SNP loci according to the formula shown in Table 11; determining a sample heterogeneity ratio based on the SNP heterogeneity ratios for the SNP loci using a Gaussian mixture distribution that models the genotype; and determining the major component of the sample by: comparing the genotype of the sample to a group of reference genotypes, each detected in a reference sample, identifying a reference sample that has a reference genotype with the highest identity to the genotype of the sample, determining that the major component is the reference sample if: (i) the reference genotype is more than 90% identical to the genotype of the sample and the sample heterogeneity ratio is less than 10%, or (ii) the reference genotype is more than 80% identical to the genotype of the sample and the sample heterogeneity ratio is more than 10%.
In certain embodiments, the method further comprises determining the minor component of the sample. In certain embodiments, the method further comprises determining the percentage of the major component and minor component in the sample.



BRIEF DESCRIPTION OF THE DRAWINGS
The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.
FIG. 1A-1C shows the cell line authentication and sample genetic heterogeneity. FIG. 1A: Genotype similarities for unrelated/mismatch, identical and closely related cell line pairs. FIG. 1B: Heterogeneity ratios in 118 uncontaminated cell lines, 220 PDX and 31 PDXO models. FIG. 1C: Heterogeneity ratio is positively correlated with mouse ratio in PDX models.
FIG. 2A-2D show that the heterogeneity ratio can be used to detect and quantify contamination. FIG. 2A-2D: A serial mixes of cell lines MV-4-11(MV411) and LNCaP clone FGC (LNCAPCLONEFGC) with cell ratios 5%, 2.5%, 1.25% and 0.625% for the latter, FIG. 2E: pure LNCaP clone FGC cell line, FIG. 2F: pure MV-4-11 cell line. Each tick above the horizontal axis represents an informative SNP site with corresponding SNP heterogeneity ratio. Probability density was estimated by assuming a 2/3-component Gaussian mixture. Sample serial number is labeled in the top-right box with the major component cell line in parenthesis. Sample heterogeneity ratio is shown underneath.
FIG. 3A-3F show the contamination detection, contaminant inference and contamination ratio estimation. FIG. 3A: Sample 19R58129 is MV411 mixed with minor contaminating cell line LNCaP clone FGC (LNCAPCLONEFGC). LNCAPCLONEFGC was correctly identified as the contaminant (p-value=5.01E-17) with a contamination ratio of 1.41%. LNCaP-C4-2 (C42) and LNCAPCLONEFGC were both derived from LNCaP and share high genetic identity. In the quantile-quantile plot, each dot is a reference cell line, theoretical and sample quantiles were calculated from a beta distribution fitted to genotype similarities between MV411 and 1055 reference cell lines. The 99% confidence band is shaded. FIG. 3B: Accuracy of inferring the contaminating second cell line in a cell line under different heterogeneity ratios. A total of 94 cell line samples with known contaminating second cell line were tested, samples were binned by heterogeneity ratio. FIG. 3C: Cell line “G-292 clone A141B1” had a sample heterogeneity ratio of 7.62% with a distinct right peak in the probability density of SNP heterogeneity ratios, indicating it was contaminated. FIG. 3D: OCI-AML-2 was inferred as the contaminant (p-value=1.58E-07) in cell line “G-292 clone A141B1” with a contamination ratio of 6.21%. FIG. 3E: Near perfect correlation between estimated and known contamination ratios in simulated cell line mixtures. FIG. 3F: High correlation between heterogeneity ratios and contamination ratios for cell line samples with known contamination.
FIG. 4A-4D show the estimation of mouse ratio in human-mouse mixtures. FIG. 4A: Accurate estimation of mouse ratio by the deep NGS sequencing in a serial of human-mouse DNA mixtures with mouse ratios 90%, 80%, 70%, 50%, 30%, 20%, 10%, 7%, 5% and 0%. FIG. 4B-4C: Mouse ratios estimated in 220 PDX and 31 PDX-derived organoid models by three approaches, assayed on the same sample for each model. FIG. 4D: A quadratic relationship between mouse ratios estimated by the deep NGS sequencing and WES in 220 PDX models.



DETAILED DESCRIPTION OF THE INVENTION
Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, and as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described.
All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided could be different from the actual publication dates that may need to be independently confirmed.
As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.
Definitions
The following definitions are provided to assist the reader. Unless otherwise defined, all terms of art, notations and other scientific or medical terms or terminology used herein are intended to have the meanings commonly understood by those of skill in the chemical and medical arts. In some cases, terms with commonly understood meanings are defined herein for clarity and/or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a substantial difference over the definition of the term as generally understood in the art.
As used herein, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise.
The term “allele” refers to one of two or more existing genetic variants of a specific polymorphic locus.
The term “amount” or “level” refers to the quantity of a polynucleotide of interest or a polypeptide of interest present in a sample. Such quantity may be expressed in the absolute terms, i.e., the total quantity of the polynucleotide or polypeptide in the sample, or in the relative terms, i.e., the concentration of the polynucleotide or polypeptide in the sample.
The terms “amplicon,” “amplification product” and “amplified sequence” are used interchangeably herein and refer to the product of a amplification technique for increasing polynucleotide sequences, either linearly or exponentially. An amplicon can be double-stranded or single-stranded and can include the separated component strands obtained by denaturing a double-stranded amplification product. In certain embodiments, the amplicon of one amplification cycle can serve as a template in a subsequent amplification cycle. Exemplary amplification techniques include, but are not limited to, PCR or any other method employing a primer extension step. Other nonlimiting examples of amplification include, but are not limited to, ligase detection reaction (LDR) and ligase chain reaction (LCR). Amplification methods can comprise thermal-cycling or can be performed isothermally. In various embodiments, the term “amplification product” and “amplified sequence” includes products from any number of cycles of amplification reactions.
As used herein, “amplify” refers to the process of enzymatically increasing the amount of a specific nucleotide sequence. This amplification is not limited to but is generally accomplished by PCR, which involves multiple cycles of a process comprising the steps of denaturation, annealing and extension. As used herein, “denaturation” refers to the separation of two complementary nucleotide strands from an annealed state. Denaturation can be induced by a number of factors, such as, for example, ionic strength of the buffer, temperature, or chemicals that disrupt base pairing interactions. As used herein, “annealing” refers to the specific interaction between strands of nucleotides wherein the strands bind to one another substantially based on complementarity between the strands as determined by Watson-Crick base pairing. It is not necessary that complementarity be 100% for annealing to occur. As used herein, “extension” refers to the amplification cycle after the primer oligonucleotide and target nucleic acid have annealed to one another, wherein the polymerase enzyme catalyzes primer extension, thereby enabling amplification, using the target nucleic acid as a replication template.
As used herein, the term “cancer” or “tumor” refers to any diseases involving an abnormal cell growth and include all stages and all forms of the disease that affects any tissue, organ or cell in the body. The term includes all known cancers and neoplastic conditions, whether characterized as malignant, benign, soft tissue, or solid, and cancers of all stages and grades including pre- and post-metastatic cancers. In general, cancers can be categorized according to the tissue or organ from which the cancer is located or originated and morphology of cancerous tissues and cells. As used herein, cancer types include, without limitation, acute lymphoblastic leukemia (ALL), acute myeloid leukemia, adrenocortical carcinoma, anal cancer, astrocytoma, childhood cerebellar or cerebral, basal-cell carcinoma, bile duct cancer, bladder cancer, bone tumor, brain cancer, cerebellar astrocytoma, cerebral astrocytoma/malignant glioma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumors, visual pathway and hypothalamic glioma, breast cancer, Burkitt's lymphoma, cervical cancer, chronic lymphocytic leukemia, chronic myelogenous leukemia, colon cancer, emphysema, endometrial cancer, ependymoma, esophageal cancer, Ewing's sarcoma, retinoblastoma, gastric (stomach) cancer, glioma, head and neck cancer, heart cancer, Hodgkin lymphoma, islet cell carcinoma (endocrine pancreas), Kaposi sarcoma, kidney cancer (renal cell cancer), laryngeal cancer, leukaemia, liver cancer, lung cancer, neuroblastoma, non-Hodgkin lymphoma, ovarian cancer, pancreatic cancer, pharyngeal cancer, prostate cancer, rectal cancer, renal cell carcinoma (kidney cancer), retinoblastoma, Ewing family of tumors, skin cancer, stomach cancer, testicular cancer, throat cancer, thyroid cancer, vaginal cancer.
A “cell”, as used herein, can be prokaryotic or eukaryotic. A prokaryotic cell includes, for example, bacteria. A eukaryotic cell includes, for example, a fungus, a plant cell, and an animal cell. The types of an animal cell (e.g., a mammalian cell or a human cell) includes, for example, a cell from circulatory/immune system or organ (e.g., a B cell, a T cell (cytotoxic T cell, natural killer T cell, regulatory T cell, T helper cell), a natural killer cell, a granulocyte (e.g., basophil granulocyte, an eosinophil granulocyte, a neutrophil granulocyte and a hypersegmented neutrophil), a monocyte or macrophage, a red blood cell (e.g., reticulocyte), a mast cell, a thrombocyte or megakaryocyte, and a dendritic cell); a cell from an endocrine system or organ (e.g., a thyroid cell (e.g., thyroid epithelial cell, parafollicular cell), a parathyroid cell (e.g., parathyroid chief cell, oxyphil cell), an adrenal cell (e.g., chromaffin cell), and a pineal cell (e.g., pinealocyte)); a cell from a nervous system or organ (e.g., a glioblast (e.g., astrocyte and oligodendrocyte), a microglia, a magnocellular neurosecretory cell, a stellate cell, a boettcher cell, and a pituitary cell (e.g., gonadotrope, corticotrope, thyrotrope, somatotrope, and lactotroph)); a cell from a respiratory system or organ (e.g., a pneumocyte (a type I pneumocyte and a type II pneumocyte), a clara cell, a goblet cell, an alveolar macrophage); a cell from circular system or organ (e.g., myocardiocyte and pericyte); a cell from digestive system or organ (e.g., a gastric chief cell, a parietal cell, a goblet cell, a paneth cell, a G cell, a D cell, an ECL cell, an I cell, a K cell, an S cell, an enteroendocrine cell, an enterochromaffin cell, an APUD cell, a liver cell (e.g., a hepatocyte and Kupffer cell)); a cell from integumentary system or organ (e.g., a bone cell (e.g., an osteoblast, an osteocyte, and an osteoclast), a teeth cell (e.g., a cementoblast, and an ameloblast), a cartilage cell (e.g., a chondroblast and a chondrocyte), a skin/hair cell (e.g., a trichocyte, a keratinocyte, and a melanocyte (Nevus cell)), a muscle cell (e.g., myocyte), an adipocyte, a fibroblast, and a tendon cell), a cell from urinary system or organ (e.g., a podocyte, a juxtaglomerular cell, an intraglomerular mesangial cell, an extraglomerular mesangial cell, a kidney proximal tubule brush border cell, and a macula densa cell), and a cell from reproductive system or organ (e.g., a spermatozoon, a Sertoli cell, a leydig cell, an ovum, an oocyte). A cell can be normal, healthy cell; or a diseased or unhealthy cell (e.g., a cancer cell). A cell further includes a mammalian zygote or a stem cell which include an embryonic stem cell, a fetal stem cell, an induced pluripotent stem cell, and an adult stem cell. A stem cell is a cell that is capable of undergoing cycles of cell division while maintaining an undifferentiated state and differentiating into specialized cell types. A stem cell can be an omnipotent stem cell, a pluripotent stem cell, a multipotent stem cell, an oligopotent stem cell and a unipotent stem cell, any of which may be induced from a somatic cell. A stem cell may also include a cancer stem cell. A mammalian cell can be a rodent cell, e.g., a mouse, rat, hamster cell. A mammalian cell can be a lagomorpha cell, e.g., a rabbit cell. A mammalian cell can also be a primate cell, e.g., a human cell. In certain examples, the cells are those used for mass bioproduction, e.g., CHO cells.
The term “complementarity” refers to the ability of a nucleic acid to form hydrogen bond(s) with another nucleic acid sequence by either traditional Watson-Crick or other non-traditional types. A percent complementarity indicates the percentage of residues in a nucleic acid molecule which can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 9, 10 out of 10 being 50%, 60%>, 70%>, 80%>, 90%, and 100% complementary). “Perfectly complementary” means that all the contiguous residues of a nucleic acid sequence will hydrogen bond with the same number of contiguous residues in a second nucleic acid sequence. “Substantially complementary” as used herein refers to a degree of complementarity that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%. 97%, 98%, 99%, or 100% over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more nucleotides, or refers to two nucleic acids that hybridize under stringent conditions.
It is noted that in this disclosure, terms such as “comprises”, “comprised”, “comprising”, “contains”, “containing” and the like have the meaning attributed in United States Patent law; they are inclusive or open-ended and do not exclude additional, un-recited elements or method steps. Terms such as “consisting essentially of” and “consists essentially of” have the meaning attributed in United States Patent law; they allow for the inclusion of additional ingredients or steps that do not materially affect the basic and novel characteristics of the claimed invention. The terms “consists of” and “consisting of” have the meaning ascribed to them in United States Patent law; namely that these terms are close ended.
As used herein, the term “contaminant” means a component present in a sample that is different from the major component in the sample or cause impurity or other undesirable effect of the sample, such as spoiling, corruption, infection.
The terms “determining,” “assessing,” “assaying,” “measuring” and “detecting” can be used interchangeably and refer to both quantitative and semi-quantitative determinations. Where either a quantitative and semi-quantitative determination is intended, the phrase “determining a level” of a polynucleotide or polypeptide of interest or “detecting” a polynucleotide or polypeptide of interest can be used.
The term “genome” refers to the total genetic information carried by an individual organism or cell, represented by the complete DNA sequences of its chromosomes.
The term “hybridizing” refers to the binding, duplexing, or hybridizing of a nucleic acid molecule preferentially to a particular nucleotide sequence under stringent conditions. The term “stringent conditions” refers to conditions under which a probe will hybridize preferentially to its target subsequence, and to a lesser extent to, or not at all to, other sequences in a mixed population (e.g., a cell lysate or DNA preparation from a tissue biopsy). A “stringent hybridization” and “stringent hybridization wash conditions” in the context of nucleic acid hybridization (e.g., as in array, microarray, Southern or northern hybridizations) are sequence dependent, and are different under different environmental parameters. An extensive guide to the hybridization of nucleic acids is found in, e.g., Tijssen Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes part 1, Ch. 2, “Overview of principles of hybridization and the strategy of nucleic acid probe assays,” (1993) Elsevier, N.Y. Generally, highly stringent hybridization and wash conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. The Tm is the temperature (under defined ionic strength and pH) at which 50% of the target sequence hybridizes to a perfectly matched probe. Very stringent conditions are selected to be equal to the Tm for a particular probe. An example of stringent hybridization conditions for hybridization of complementary nucleic acids which have more than 100 complementary residues on an array or on a filter in a Southern or northern blot is 42° C. using standard hybridization solutions (see, e.g., Sambrook and Russell Molecular Cloning: A Laboratory Manual (3rd ed.) Vol. 1-3 (2001) Cold Spring Harbor Laboratory, Cold Spring Harbor Press, NY). An example of highly stringent wash conditions is 0.15 M NaCl at 72° C. for about 15 minutes. An example of stringent wash conditions is a 0.2×SSC wash at 65° C. for 15 minutes. Often, a high stringency wash is preceded by a low stringency wash to remove background probe signal. An example medium stringency wash for a duplex of, e.g., more than 100 nucleotides, is 1×SSC at 45° C. for 15 minutes. An example of a low stringency wash for a duplex of, e.g., more than 100 nucleotides, is 4×SSC to 6×SSC at 40° C. for 15 minutes.
The term “locus” refers to any segment of DNA sequence in a genome defined by chromosomal coordinates in a reference genome known to the art, irrespective of biological function. A DNA locus can contain multiple genes or no genes; it can be a single base pair or millions of base pairs.
The term “nucleic acid” and “polynucleotide” are used interchangeably and refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides, or analogs thereof. Polynucleotides may have any three-dimensional structure, and may perform any function, known or unknown. Non-limiting examples of polynucleotides include a gene, a gene fragment, exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, ribozymes, cDNA, shRNA, single-stranded short or long RNAs, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, control regions, isolated RNA of any sequence, nucleic acid probes, and primers. The nucleic acid molecule may be linear or circular.
The term “oligonucleotide” refers to a nucleic acid sequence of at least about five nucleotides to about 500 nucleotides (e.g. 5, 6, 7, 8, 9, 10, 12, 15, 18, 20, 21, 22, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450 or 500 nucleotides). In some embodiments, for example, an oligonucleotide can be from about 15 nucleotides to about 30 nucleotides, or about 20 nucleotides to about 25 nucleotides, which can be used, for example, as a primer in a polymerase chain reaction (PCR) amplification assay and/or as a probe in a hybridization assay or in a microarray. Oligonucleotides of this invention can be natural or synthetic, e.g., DNA, RNA, PNA, LNA, modified backbones, etc., as are well known in the art.
The term “polymorphic locus” refers to a genomic locus at which two or more alleles have been identified.
The term “primer” refers to an oligonucleotide and analogs thereof that are capable of selectively hybridizing to a target nucleic acid or “template”, a target region flanking sequence or to a corresponding primer-binding site of an amplification product; and allows the synthesis of a sequence complementary to the corresponding polynucleotide template, flanking sequence or amplification product from the primer's 3′ end. Typically, a primer can be between about 10 to 100 nucleotides in length and can provide a point of initiation for template-directed synthesis of a polynucleotide complementary to the template, which can take place in the presence of appropriate enzyme(s), cofactors, substrates such as nucleotides (dNTPs) and the like. As used herein, the terms “amplification primer” and “oligonucleotide primer” are used interchangeably and refer to an oligonucleotide, capable of annealing to an RNA or DNA region adjacent a target sequence, and serving as an initiation primer for DNA synthesis under suitable conditions well known in the art. Typically, a PCR reaction employs an “amplification primer pair” also referred to as an “oligonucleotide primer pair” including an “upstream” or “forward” primer and a “downstream” or “reverse” primer, which delimit a region of the RNA or DNA to be amplified. A first primer and a second primer may be either a forward or reverse primer and are used interchangeably herein and are not to be limiting.
The term “reference genotype” as used herein refers to a predetermined genotype of one or more genomic loci that is present in a reference sample, e.g., a sample with known identity. The reference genotype is suitable for the use of a method of the present invention, to serve as a basis for comparing the genotype of specific genomic loci that is present in a test sample. A reference genotype may vary depending on the nature of the sample as well as other factors such as the gender, age, ethnicity of the subjects based on whom such a reference sample is established.
The term “sample” or “biological sample” used herein refers to any cell, tissue, organoid or any other sample that contains one or more nucleic acid molecule(s) of interest. In certain embodiments, the sample is a cell (e.g., normal cell, cancer cell, cell line), a tissue (e.g., a normal tissue, a cancer tissue, a xenograft or allograft tissue), an organoid, etc.
The term “single nucleotide polymorphism” or “SNP” refers to a single nucleotide position in a genomic sequence where two or more alternative alleles are present at appreciable frequency within a population, e.g., >1%. SNPs can occur within a coding sequence of a gene, within noncoding regions of a gene and/or in an intergenic (e.g., intron) region of a gene. SNPs that are not in protein coding regions can still have effects on gene splicing, transcription factor binding and/or the sequence of non-coding RNA. The SNP nomenclature provided herein refers to the official Reference SNP (rs) identification number as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI), which is available in the GenBank® database.
As used herein, the term “subject” refers to a human or any non-human animal (e.g., mouse, rat, rabbit, dog, cat, cattle, swine, sheep, horse or primate). A human includes pre and post-natal forms. In many embodiments, a subject is a human being. A subject can be a patient, which refers to a human presenting to a medical provider for diagnosis or treatment of a disease. The term “subject” is used herein interchangeably with “individual” or “patient.” A subject can be afflicted with or is susceptible to a disease or disorder but may or may not display symptoms of the disease or disorder.
The term “substrate” when used in the context of an array refers to material capable of supporting associated assay components (e.g., assay regions, cells, test compounds, etc.). Examples of substrates include, but are not limited to glass, Si-based materials, functionalized polystyrene, functionalized polyethylene-glycol, functionalized organic polymers, nitrocellulose or nylon membranes, paper, cotton, and materials suitable for synthesis. Substrates need not be flat and include any type of shape including spherical shapes (e.g., beads). Materials attached to a substrate may be attached to any portion of the substrate (e.g., may be attached to an interior portion of a porous substrate material). Preferred embodiments of the present technology have nucleic acid probes attached to a substrate. A nucleic acid probe is “attached” to a substrate when it is associated with the substrate through a non-random chemical or physical interaction. In some preferred embodiments, the attachment is through a covalent bond, e.g., as provided by a linker.
The term “tumor models”, as used herein, refer to cells, tissues or animals used to study the development and progression of cancer, and to test treatments before they are given to human.
The term “tumor sample” includes a biological sample or a sample from a biological source that contains one or more tumor cells. Biological samples include samples from body fluids, e.g., blood, plasma, serum, or urine, or samples derived, e.g., by biopsy, from cells, tissues or organs, preferably tumor tissue suspected to include or essentially consist of cancer cells.
SNPs for Identification of Tumor Samples
Misidentification and contamination of biobank samples (e.g., cell lines) have plagued biomedical research. Short-tandem repeat (STR) and single-nucleotide polymorphism (SNP) assays are widely used to authenticate biosamples and can detect contamination at a sensitivity of 5-10% and 3-5%, respectively. The present disclosure in one aspect provides a method with ≤1% sensitivity for detecting contamination. It can further identify the contaminant and estimate the contamination ratio for mixed cell line samples. It is by far the most sensitive and accurate method reported for cell line authentication. In certain embodiments, the method can also detect interspecies contamination in human-mouse mixed samples such as xenograft tumors, and accurately estimate the mouse ratio. In certain embodiments, mycoplasma and mollicutes are among the searching targets as well. In certain embodiments, this multi-functional method simultaneously infers population structure and gender of human samples. In certain embodiments, owning to DNA barcoding technology, the method disclosed herein can profile 100-200 samples in a single run at per-sample cost comparable to conventional STR assays, making it truly high-throughput and low-cost tool for maintaining high-quality biobanks.
The methods and compositions described herein are based, in part, on the discovery of a group of SNP loci that can be used to identify and authenticate a sample obtained from a tumor model. In certain embodiment, the tumor model is a human tumor model, including primary human tumor, patient-derived xenografts (PDX), human tumor cell line, human cell-line derived xenograft and human organoids. In certain embodiments, SNPs are selected from human SNPs based on the RNAseq or Whole-Exome Sequencing (WES) data of a number of human tumor models. The selected human SNPs are located in exonic regions of highly expressed genes that are located in mostly non-linkage-disequilibrium (non-LD) blocks across 22 autosomes. Each human tumor model therefore has a unique genotype (i.e., SNP fingerprint) at the selected human SNP loci.
In certain embodiments, the human SNP loci selected have homology in mouse genome. When a sample is amplified using primers targeting such human SNP loci, nucleotide sequences of corresponding mouse loci may be generated if the sample is mixed with mouse cell or tissue. Such human SNPs may be used to estimate the percentage of mouse content in the mixture of human and mouse cells/tissues, e.g., based on the number of mouse and human reads of these SNPs.
In certain embodiments, the human SNPs used herein are selected from the group as shown in Table 1.
In certain embodiments, the SNPs include a group of mouse SNPs to identify and authenticate mouse tumor models such as mouse tumor cell line. In some embodiments, the mouse SNPs used herein are selected from the group as shown in Table 2.
In certain embodiments, the SNPs further include human SNPs in sex chromosomes (chromosome X and chromosome Y) to determine the gender of a subject from which the sample is obtained. In certain embodiments, the sex chromosome SNPs are selected from the group as shown in Table 3.
In certain embodiments, the SNPs further include mouse SNPs that can be used to determine the strain of an immunodeficient mouse from which the sample is obtained. In some embodiments, the SNPs are shown in Table 4.
Methods
In one aspect, the present disclosure provides a method for identifying and authenticating a sample.
In certain embodiments, the method disclosed herein is to match a sample to a reference (e.g. standard cancer cell lines). Conventional STR and SNP assays largely used genotype-based Tanabe-Masters algorithm and its variations. STR assays generate analog signals for a dozen of markers. SNP assays genotype often many more SNPs. Therefore, higher similarity thresholds are often used by SNP assay to call two samples match. However, the matching power of conventional assays can be severely compromised for contaminated samples even with  ̃100 SNPs. In certain embodiments, the method disclosed herein performed high-depth (3000λ) sequencing of 237 SNP sites for human samples, and showed 100% accuracy in identifying a sample or the major component of contaminated samples.
In certain embodiments, the method disclosed herein is to detect contamination in biological samples. The sensitivity for detecting contamination in cell lines is about 5-10% for STR assays and 3-5% for SNP assays. However, performance can be rather unstable, to the extent that even a >20% contamination was not detected in a mixture of two unrelated cell lines by a 96-SNP assay (Liang-Chu, M. M. et al. PLoS One 10, e0116218 (2015)). In certain embodiments, the method disclosed herein consistently reaches 2% sensitivity when only using the heterogeneity ratio, by both its value and distinct bi/tri-modal distribution. The sensitivity reaches 1% if the contaminant is in a library of reference samples with SNP fingerprint. Such sensitivity is virtually the theoretic detection limit, because uncontaminated cell lines, due to multiclonality and sequencing errors, exhibit a comparable level of genetic heterogeneity to cell line samples with  ̃1% contamination.
In certain embodiments, the method disclosed herein is to identify contaminants. Cross-contamination of cell lines is common in biobanks. The composition of a contaminated culture changes over time due to different growth rates of cell lines. Cell lines differ in genomics such as gene mutations and may respond differently to drug treatment, causing erroneous results in drug screening. The inventors of the present disclosure constructed a SNP fingerprint library for over 1000 cancer cell lines, with that a contaminating cell line can be unambiguously identified. Further the contamination ratio can be accurately estimated. Besides checking cell line quality, this capacity can have other utilizations such as monitoring the dynamic composition of two cell lines under biological or chemical interference.
Besides intraspecies contamination, in certain embodiments, the method disclosed herein is able to accurately detect and quantify interspecies contamination between human and mouse. In certain embodiments, the method disclosed herein uses not SNPs but 108 homologous DNA segments that are diverged between the two species but have identical flanking nucleotide sequences, so common primers can be designed for unbiased amplification of human and mouse DNA segments. This approach showed perfect performance in a serial of mouse-human DNA mixture benchmark samples. The homology-based principle can be used for detecting other interspecies contaminations.
In certain embodiments, the power of the method disclosed herein comes from several novel features. The first is deep NGS sequencing, which obtains both the genotype and nucleotide frequency of SNPs, while conventional STR and SNP assays only profile SNP genotypes. Secondly, beside SNP profiling, the method disclosed herein performs targeted sequencing for detecting mycoplasma contamination and estimating mouse-human mix ratios. Thirdly, a suit of statistical models and algorithms have been developed to exploit the deep NGS sequencing data, making the authentication process automatic, robust and objective. Finally, DNA barcode technology is used to enable parallel sequencing of 100-200 samples simultaneously that drastically reduces cost.
The high-throughput low-cost methods disclosed herein that can be routinely used by biobanks to maintain authentic and high-quality samples. The method can be broadly adapted for samples from other species and even microbiome, and can be implemented on any NGS sequencing platforms.
In one embodiment, the method comprises: obtaining a nucleic acid from a sample; detecting a genotype for the sample at a plurality of human or mouse single SNP loci disclosed herein; comparing the genotype for the sample to a reference genotype detected in a reference sample; and determining the identification of the sample. In certain embodiments, the genotype at 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more SNP loci is detected.
The nucleic acid obtained from a sample can be RNA or DNA. In certain embodiments, the nucleic acid obtained from a sample is genomic DNA isolated from the sample. In certain embodiments, the nucleic acid obtained from a sample is genomic DNA is total RNA or mRNA isolated from the sample. In certain embodiments, the nucleic acid obtained from a sample is amplified, e.g. by PCR reaction or PCR following reverse transcription.
The genotype for the sample at SNP loci can be detected based on any suitable methods known in the art, for example, but not limited to, sequencing based methods and hybridization-based methods.
In certain embodiments, the detecting step involves an amplification step. In such case, the detecting agent comprises at least a pair of primers which can hybridize to the genomic region containing the SNP locus and amplify a polynucleotide sequence encompassing the SNP locus in the presence of a polymerase. The pair of primers used to amplify the genomic region containing the SNP has sufficient identity with or complementarity to at least a portion of the genomic region such that the primer or the probe can specifically hybridize to the genomic region or to its complementary strand. “Specifically hybridize” as used herein means the primer or probe can hybridize to the intended sequence under stringent conditions. “Stringent condition” as used herein refers to hybridizing at 42° C. in a solution consisting of 5×SSPE, 5×Denhardt's solution, 0.5% SDS, and 100 ug/mL denatured salmon sperm DNA, and then washing at 42° C. with a solution comprising 0.5×SSC and 0.1% SDS.
The method of designing the pair of primers for a specific SNP locus is generally known in the art. For example, Primer3 software, available online from the Massachusetts Institute of Technology, may be used to design PCR primers to flank the STR regions by inputting the sequences for the SNP locus.
In certain embodiments, the amplification step involves amplifying alleles at multiple loci in one reaction. In certain embodiments, the amplification step comprises selecting a set of single nucleotide polymorphism (SNP) of the sample that can be amplified together in a multiplex amplification reaction, wherein the set of SNP loci are selected from the group as shown in Table 1 or Table 2; providing a set of oligonucleotide primer pairs, wherein each oligonucleotide primer pair in the set flanks a single locus in the set of SNP loci, and wherein each oligonucleotide primer pair is capable of amplifying a single locus from the set of SNP loci in a multiplex amplification reaction; co-amplifying the set of SNP loci in a multiplex amplification reaction, wherein the product of the multiplex amplification reaction comprises a mixture of amplified alleles from each of the co-amplified loci in the set of SNP loci; and evaluating the products of the co-amplification reaction to determine the alleles present at each of the loci analyzed in the set of SNP loci within the sample. An example of a set of SNP loci with the oligonucleotide primer pairs that can be amplified together in a multiplex amplification reaction is shown in Table 12.
After amplification by a suitable nucleic acid amplification method such as PCR, the sequence or the SNP in the amplification product is detected. In certain embodiments, the amplification product has a length of 50 bp-500 bp. In certain embodiments, the sequence of the SNP in the amplification product is detected using sequencing-based methods, e.g., next-generation sequencing (NGS) methods. In certain embodiments, NGS methods are used to determine the sequences in a large number of SNP loci. In certain embodiments, NGS methods can be used to simultaneously determine the sequences of SNP loci from a number of samples by barcoding the nucleic acid obtained from each sample.
When the nucleic acid obtained from a sample is RNA, the amplification step may optionally comprise a reverse transcription step to produce cDNA of the RNA in the sample. The cDNA is then amplified using the primers to allow detection of presence of the SNP.
In some embodiments, microarrays, e.g., are employed to detect the SNPs in the nucleic acid. Microarray consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. The presence of a SNP can be detected by measuring the intensity of the labeled RNA or DNA that bind to specific probes on the array.
Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261. Although a planar array surface is often employed the array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may also be nucleic acids on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation of an all-inclusive device.
The probes and primers necessary for practicing the present invention can be synthesized and labeled using well known techniques. Oligonucleotides used as probes and primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12:6159-6168.
In certain embodiments, the method further comprises identifying the gender of a subject from which the sample is obtained, e.g., by detecting sex chromosome SNPs selected from the group as shown in Table 3. In certain embodiments, the method further comprises identifying the ethnicity of a subject from which the sample is obtained. In certain embodiments, the method further comprises determining strain of an immunodeficient mouse from which the sample is obtained, e.g., by detecting vendor SNPs as shown in Table 4.
In certain embodiments, the method disclosed herein further includes detecting common viral infection and mycoplasma contamination in tumor models, including hepatisis A/B/C virus (HAV/HBV/HCV), human immunodeficiency virus (HIV), Epstein-Barr virus (EBV), and human papillomavirus (HPV). In certain embodiments, the markers used to detect viral infection and mycoplasma contamination are shown in Table 5.
In certain embodiments, the method disclosed herein can be used to authenticating a sample comprising a major component and a minor component. In certain embodiments, the method comprises estimating heterogeneity ratios; determining major component of the sample; determining minor component of the sample; and estimating mixture ratio of the major and minor components.
In certain embodiments, the heterogeneity ratios can be estimated as follows. There are six informative genotype combinations that can be used to estimate heterogeneity ratios from the deep NGS sequencing data (Table 11). They exhibit four distinct nucleotide frequency patterns. Combinations 1 and 2 generate the same pattern, and we use an average formula to calculate the percentage of the minor component S2, or the heterogeneity ratio. The formula produces an exact estimate of the ratio when the two combinations occur with equal frequency, a scenario that should be closely approximated when the number of SNPs is large. Similar averaging approach is used for Combinations 4 and 5. When the heterogeneity ratio is low, sequencing error may interfere the inference of heterogeneity ratio. To alleviate this, a 2-step statistical procedure can be used. Assuming sequencing error is e=0.001 and the sequencing depth is n (n≥500, any SNP with n<500 is discarded) at a given SNP site, the probability of observing k erroneous nucleotides follows a binomial distribution with parameters n and e.



 
  
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To determine the major component in the sample, the genotype at a SNP site is determined using only nucleotides with allele frequencies larger than a threshold, 10% for reference samples and 25% for test samples which may be contaminated. The genotype similarity between a reference sample and a test sample is the percentage of SNPs with identical genotypes, excluding SNPs with sequencing depth less than 500 in the test sample. The major component of the test sample is the reference sample with the highest genotype similarity, which must be greater than 90% (or 80%) if the heterogeneity ratio of the test sample is <10% (or >10%). Otherwise, no major component is called.
After the estimation of heterogeneity ratio and determination of major component, the minor component of a test sample can be determined. For a mixture of the major component and one of the other reference samples (e.g., all cell lines with genomic data), a chimeric genotype can be obtained, with possibly 1 to 4 nucleotides, at every SNP site. Frequencies of nucleotides are calculated using the heterogeneity ratio. Similarly, the chimeric genotype of the test sample is obtained. The two chimeric genotypes are considered identical if they harbor same nucleotides and frequencies of each nucleotide are within three folds. The genotype similarity between the test sample and each reference sample combined with the major component is then calculated. The set of all pairwise genotype similarities are then fitted by a beta distribution with parameters (α,β)



 
  
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The mix ratio for two reference samples can be estimated as follows. Assume that two component S1 and S2 are mixed with ratio θ for S1 and (1−θ) for S2 where 0≤θ≤1. From deep NGS sequencing data, nucleotide frequencies of all n SNPs in both component can be accurately estimated. For a SNP, its four nucleotide frequencies are denoted, which sum to 1, as {A1, T1, G1, C1} for component 51 and {A2, T2, G2, C2} for component S2. In principle, one of the frequencies is close to 1 if the SNP is homozygous, and two frequencies are both close to 0.5 if the SNP is heterozygous. Actual data may have some deviations due to sequencing errors and randomness, as well as multiclonality of cell lines.
From sequencing data of the mix sample, the actual occurrences of the four nucleotides are denoted as x={nA, nT, nG, nC}. The likelihood of such observation is



 
  
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Kits and Microarrays
In another aspect, the present disclosure provides kits for use in the methods described above. The kits may comprise any or all of the reagents to perform the methods described herein. In certain embodiments, the kit comprises primers for detecting in a sample at a group of human SNP loci or at a group of mouse SNP loci. In certain embodiments, the kit further comprises primers for detecting sex chromosome SNPs to identify the gender of a subject from which the sample is obtained. In certain embodiments, the kit further comprises primers for detecting ethnicity SNPs to identify the ethnicity of a subject from which the sample is obtained. In certain embodiments, the kit further comprises primers for detecting vendor SNPs to determine the strain of an immunodeficient mouse from which the sample is obtained. In certain embodiments, the kit further comprises primers for detecting virus infection or mycoplasma contamination in the sample.
In certain embodiments, the kit further comprises an agent for amplifying DNA fragments containing the human or mouse SNPs using the primers. In addition, the kits may include instructional materials containing directions (i.e., protocols) for the practice of the methods provided herein. While the instructional materials typically comprise written or printed materials they are not limited to such. Any medium capable of storing such instructions and communicating them to an end user is contemplated by this invention. Such media include, but are not limited to electronic storage media (e.g., magnetic discs, tapes, cartridges, chips), optical media (e.g., CD ROM), and the like. Such media may include addresses to internet sites that provide such instructional materials.
In another aspect, the present disclosure provides oligonucleotide probes attached to a solid support, such as an array slide or chip, e.g., as described in Eds., Bowtell and Sambrook DNA Microarrays: A Molecular Cloning Manual (2003) Cold Spring Harbor Laboratory Press. Construction of such devices are well known in the art, for example as described in US patents and patent Publications U.S. Pat. No. 5,837,832; PCT application WO95/11995; U.S. Pat. Nos. 5,807,522; 7,157,229, 7,083,975, 6,444,175, 6,375,903, 6,315,958, 6,295,153, and 5,143,854, 2007/0037274, 2007/0140906, 2004/0126757, 2004/0110212, 2004/0110211, 2003/0143550, 2003/0003032, and 2002/0041420. Nucleic acid arrays are also reviewed in the following references: Biotechnol Annu Rev (2002) 8:85-101; Sosnowski et al. Psychiatr Genet (2002)12(4): 181-92; Heller, Annu Rev Biomed Eng (2002) 4: 129-53; Kolchinsky et al., Hum. Mutat (2002) 19(4):343-60; and McGail et al., Adv Biochem Eng Biotechnol (2002) 77:21-42.
A microarray can be composed of a large number of unique, single-stranded polynucleotides, usually either synthetic antisense polynucleotides or fragments of cDNAs, fixed to a solid support. Typical polynucleotides are preferably about 6-60 nucleotides in length, more preferably about 15-30 nucleotides in length, and most preferably about 18-25 nucleotides in length. For certain types of arrays or other detection kits/systems, it may be preferable to use oligonucleotides that are only about 7-20 nucleotides in length. In other types of arrays, such as arrays used in conjunction with chemiluminescent detection technology, preferred probe lengths can be, for example, about 15-80 nucleotides in length, preferably about 50-70 nucleotides in length, more preferably about 55-65 nucleotides in length, and most preferably about 60 nucleotides in length.
Computer-Implemented Methods, Systems and Devices
Any of the methods described herein may be totally or partially performed with a computer system including one or more processors, which can be configured to perform the steps. Thus, embodiments are directed to computer systems configured to perform the steps of any of the methods described herein, potentially with different components performing a respective step or a respective group of steps. Although presented as numbered steps, steps of methods herein can be performed at a same time or in a different order. Additionally, portions of these steps may be used with portions of other steps from other methods. Also, all or portions of a step may be optional. Any of the steps of any of the methods can be performed with modules, circuits, or other means for performing these steps.
Any of the computer systems mentioned herein may utilize any suitable number of subsystems. In some embodiments, a computer system includes a single computer apparatus, where the subsystems can be the components of the computer apparatus. In other embodiments, a computer system can include multiple computer apparatuses, each being a subsystem, with internal components. The subsystems can be interconnected via a system bus. Additional subsystems include, for examples, a printer, keyboard, storage device(s), monitor, which is coupled to display adapter, and others. Peripherals and input/output (I/O) devices, which couple to I/O controller, can be connected to the computer system by any number of means known in the art, such as serial port. For example, serial port or external interface (e.g. Ethernet, Wi-Fi, etc.) can be used to connect computer system to a wide area network such as the Internet, a mouse input device, or a scanner. The interconnection via system bus allows the central processor to communicate with each subsystem and to control the execution of instructions from system memory or the storage device(s) (e.g., a fixed disk, such as a hard drive or optical disk), as well as the exchange of information between subsystems. The system memory and/or the storage device(s) may embody a computer readable medium. Any of the data mentioned herein can be output from one component to another component and can be output to the user.
A computer system can include a plurality of the same components or subsystems, e.g., connected together by external interface or by an internal interface. In some embodiments, computer systems, subsystem, or apparatuses can communicate over a network. In such instances, one computer can be considered a client and another computer a server, where each can be part of a same computer system. A client and a server can each include multiple systems, subsystems, or components.
It should be understood that any of the embodiments of the present disclosure can be implemented in the form of control logic using hardware (e.g., an application specific integrated circuit or field programmable gate array) and/or using computer software with a generally programmable processor in a modular or integrated manner. As used herein, a processor includes a multi-core processor on a same integrated chip, or multiple processing units on a single circuit board or networked. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and/or methods to implement embodiments of the present disclosure using hardware and a combination of hardware and software.
Any of the software components or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Java, C++ or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer readable medium for storage and/or transmission, suitable media include random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like. The computer readable medium may be any combination of such storage or transmission devices.
Such programs may also be encoded and transmitted using carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet. As such, a computer readable medium according to an embodiment of the present invention may be created using a data signal encoded with such programs. Computer readable media encoded with the program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer readable medium may reside on or within a single computer product (e.g. a hard drive, a CD, or an entire computer system), and may be present on or within different computer products within a system or network. A computer system may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.
The following examples are provided to better illustrate the claimed invention and are not to be interpreted as limiting the scope of the invention. All specific compositions, materials, and methods described below, in whole or in part, fall within the scope of the present invention. These specific compositions, materials, and methods are not intended to limit the invention, but merely to illustrate specific embodiments falling within the scope of the invention. One skilled in the art may develop equivalent compositions, materials, and methods without the exercise of inventive capacity and without departing from the scope of the invention. It will be understood that many variations can be made in the procedures herein described while still remaining within the bounds of the present invention. It is the intention of the inventors that such variations are included within the scope of the invention.
Example 1
Materials and Methods
Nucleic Acid Extraction
Genomic DNA from cells, PDXs and PDXOs was purified using DNeasy Blood & Tissue Kit (QIAGEN, Cat. 69506, CA) according to the manufacturer's instructions. DNA integrity was determined by 2100 Bioanalyser (Agilent) and quantified using the NanoDrop (Thermo Scientific). One aliquot of high-quality DNA sample (OD260/280=1.8 ̃2.0, OD260/230≥2.0, >1 μg) was used for the deep NGS sequencing and WES sequencing. Total RNA from cells, PDXs and PDXOs was purified using RNeasy Mini Kit (QIAGEN, Cat. 74106, CA) according to the manufacturer's instructions. Integrity of the total RNA was determined by 2100 Bioanalyser (Agilent) and quantified using the NanoDrop (Thermo Scientific). One aliquot of high-quality RNA sample (OD260/280=1.8 ̃2.2, OD260/230≥2.0, RIN≥8.0, >1 μg) was used for the deep NGS sequencing and RNAseq sequencing.
Cell Line Mixture Preparation
A cell line mixture was prepared by mixing cells from two cell lines with given ratios. Based on cell growth rate, cells were seeded in 15 ml medium in T75 that allowed cell confluence to reach 60%-80%, followed by overnight incubation at CO2 Water Jacketed Incubator (SANYO). Cells were harvested during the logarithmic growth period, and counted with hemocytometer (Chongguang) for the calculation of concentration. Cells from two cell lines were then mixed according to predefined ratios to create a cell line mixture that was subsequently centrifuged at 3,000 rpm for 5 minutes. Supernatant was aspirated and cell pellets were stored at −20° C. for DNA extraction.
Human-Mouse DNA Mixture Preparation
A serial of mouse-human DNA mixture benchmark samples were prepared by mixing mouse spleen DNA and human genomic DNA (Thermo Scientific, Cat. 4312660). Mouse spleen DNA was purified using DNeasy Blood & Tissue Kit (QIAGEN, Cat. 69506, CA) according to the manufacturer's instructions and quantified using the NanoDrop (Thermo Scientific). Mouse spleen DNA and human genomic DNA were diluted to 200 ng/μL, then mixed by predefined ratios. The DNA mixture was used for the deep NGS sequencing later.
Barcode Deep NGS Sequencing
Multiplex PCR was used to prepare target sequencing libraries for Illumina sequencers with a paired-end read length of 150 bp (pE150). The NGS deep sequencing covered 630 amplicons, sizes of which ranged from 160 bp to 260 bp. Genomic DNA was amplified by using IGT-EM808 polymerase mixture (iGene TechBioscience Co., Ltd, 95° C. for 3 min 30 secs, 18 cycles of incubation at 98° C. for 20 secs and 60° C. for 8 min, hold at 72° C. for 5 min) and then purified by AMPure XP beads (Beckman, Cat. A63881).
Barcoding was executed by a second round of amplification. Briefly, purified target amplicons were taken as templates and added with upstream IGT-I5 index (10 μM), downstream IGT-17 index (10 μM) and polymerase mixture for PCR reaction. The mixture was then placed in a thermal cycler for amplification with the following settings: 95° C. for 3 min 30 secs, 9 cycles of incubation at 98° C. for 20 secs, 58° C. for 1 min and 72° C. for 30 secs, hold-on at 72° C. for 5 min. The barcoded library was then purified by using AMPure XP beads (Beckmen, Cat. A63881).
After library construction, Qubit 3.0 fluorometer dsDNA HS Assay (Thermo Fisher Scientific) was used to quantify concentrations of the resulting sequencing libraries. Agilent BioAnalyzer 2100 (Agilent) was used to analyze size distribution ranging from 280 bp to 420 bp. Paired-end sequencing was performed using an Illumina system following Illumina-provided protocols for 2×150 bp paired-end sequencing.
RNAseq and WES Sequencing
In RNAseq sequencing, the mRNA-focused sequencing libraries were constructed from total RNA. Poly-A mRNA was purified from total RNA using oligo-dT-attached magnetic beads and then fragmented by fragmentation buffer. Using the short fragments as templates, first stranded cDNA was synthesized using reverse transcriptase and random primers, followed by second stranded cDNA synthesis. Then the synthesized cDNA was subjected to end-repair, phosphorylation and ‘A’ base addition according to library construction protocol. Then sequencing adapters were added to both ends of the cDNA fragments. After PCR amplification for cDNA fragments, the targeted 250-350 bp fragments were cleaned up. After library construction, Qubit 3.0 fluorometer dsDNA HS Assay (Thermo Fisher Scientific) was used to quantify concentrations of the resulting sequencing libraries, while the size distribution was analyzed using Agilent BioAnalyzer 2100 (Agilent). After library validation, Illumina CBOT cluster generation system with HiSeq PE Cluster Kits (Illumina) was used to generate clusters. Paired-end sequencing was performed using an Illumina system following Illumina-provided protocols for 2×150 paired-end sequencing.
WES was performed by Wuxi Nextcode Co. Ltd. (Shanghai, China). Briefly, genomic DNA was extracted and fragmented to an average size of 180-280 bp. DNA libraries were generated by Illumina's manufacturer paired-end protocols. Exons were captured by Agilent SureSelect Human All Exon V6, and subsequently sequenced by the Illumina NovaSeq platform (Illumina Inc., San Diego, Calif., USA) to generate 150 bp paired-end reads.
SNP Selection and Profiling
The inventors selected a panel SNPs for human sample authentication by several criteria: 1) SNPs are in exons, 2) SNPs are located on all 22 autosomes and are sufficient away from each other since chromosome abnormality, including deletions and duplications of large chromosome segments, are common in tumors, 3) SNPs are in highly expressed genes, 4) the minor allele frequency (MAF) of a SNP is close to 0.5 in 3 reference populations of the International HapMap Project, namely Han Chinese (CHB), Nigeria Yoruba (YRI) and Utah residents with Northern and Western European ancestry from the CEPH collection (CEU).
Benchmark Samples and Data
Two cell line benchmark sample sets were prepared. The first set has 78 samples for 3 pairs of cell lines including PANC-1 and RT4, MV-4-11 and “LNCaP clone FGC”, CAL27 and Raji. Each pair has 26 samples including the pure two cell lines and 3 replicates for 8 mix ratios by cell count (Supp. Table S2). The second set has 22 cell lines each contaminated by a known second cell line by a mostly small but unspecified ratio (Supp. Table S3).
Estimating Heterogeneity Ratios
There are six informative genotype combinations that can be used to estimate heterogeneity ratios from the deep NGS sequencing data (Table 11). They exhibit four distinct nucleotide frequency patterns. Combinations 1 and 2 generate the same pattern, and we use an average formula to calculate the percentage of the minor component S2, or the heterogeneity ratio. The formula produces an exact estimate of the ratio when the two combinations occur with equal frequency, a scenario that should be closely approximated when the number of SNPs is large. Similar averaging approach is used for Combinations 4 and 5. When the heterogeneity ratio is low, sequencing error may interfere the inference of heterogeneity ratio. To alleviate this, we use a 2-step statistical procedure. Assuming sequencing error is e=0.001 and the sequencing depth is n (n≥500, any SNP with n<500 is discarded) at a given SNP site, the probability of observing k erroneous nucleotides follows a binomial distribution with parameters n and e.



 
  
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For each n, we calculate the cumulative density function and obtain a threshold h so that the probability of observing more than h erroneous nucleotides out of the n nucleotides is smaller than 0.01. In the sequencing data, any low-frequency nucleotide with number of reads smaller than a corresponding threshold h is discarded. We then use an Expectation-Maximization algorithm (package mclust in R, version 3.5.3 (Team, R. C. R: A language and environment for statistical computing. 3.5.3 edn (R Foundation for Statistical Computing, Vienna, Austria, 2018))) to estimate parameters of a Gaussian mixture (with 1 to 3 components) that models the distribution of nucleotide frequencies smaller than a maximal heterogeneity (0.2 used for all samples in this study). If there is only a single Gaussian component or the Gaussian component with smallest mean accounts for more than 60% of all data points, median of all data points is taken as the sample heterogeneity ratio, otherwise, median of data points in the other Gaussian component(s) is taken as the sample heterogeneity ratio.
Determining Major Component of a Sample
The genotype at a SNP site is determined using only nucleotides with allele frequencies larger than a threshold, 10% for reference samples and 25% for test samples which may be contaminated. The genotype similarity between a reference sample and a test sample is the percentage of SNPs with identical genotypes, excluding SNPs with sequencing depth less than 500 in the test sample. The major component of the test sample is the reference sample with the highest genotype similarity, which must be greater than 90% (or 80%) if the heterogeneity ratio of the test sample is <10% (or >10%). Otherwise, no major component is called.
Determining Minor Component of a Sample
After the estimation of heterogeneity ratio and determination of major component, we determine the minor component of a test sample. For a mixture of the major component and one of the other reference samples (e.g., all cell lines with genomic data), we obtain a chimeric genotype, with possibly 1 to 4 nucleotides, at every SNP site. Frequencies of nucleotides are calculated using the heterogeneity ratio. Similarly, we get the chimeric genotype of the test sample. The two chimeric genotypes are considered identical if they harbor same nucleotides and frequencies of each nucleotide are within three folds. We then calculate the genotype similarity between the test sample and each reference sample combined with the major component. The set of all pairwise genotype similarities are then fitted by a beta distribution with parameters (α,β)



 
  
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In the equation, Γ(α) is the gamma function, x is genotype similarity. Its parameters were estimated by package fitdistrplus in R (version 3.5.3). From the fitted beta distribution we then calculated the probability of observing any genotype similarity larger than a specific value. A quantile-quantile graph with 99% confidence band was plotted for all observed genotype similarities for visualization. A reference sample was considered the minor component if (1) it has the highest genotype similarities, (2) its genotype similarity is above the 99% confidence upper bound in the quantile-quantile graph, and (3) its p-value<1.0E-6 in the fitted beta distribution.
Estimating Mixture Ratio of Two Cell Lines
Cell lines are used to explain the estimation of mix ratio for two reference samples. Assume that two cell lines S1 and S2 are mixed with ratio B for S1 and (1-θ) for S2 where From deep NGS sequencing data, nucleotide frequencies of all n SNPs in both cell lines can be accurately estimated. For a SNP, its four nucleotide frequencies are denoted, which sum to 1, as {A1, T1, G1, C1} for cell line S1 and {A2, T2, G2, C2} for cell line S2. In principle, one of the frequencies is close to 1 if the SNP is homozygous, and two frequencies are both close to 0.5 if the SNP is heterozygous. Actual data may have some deviations due to sequencing errors and randomness, as well as multiclonality of cell lines.
From sequencing data of the mix sample, the actual occurrences of the four nucleotides are denoted as x={nA, nT, nG, nC}. The likelihood of such observation is

(θ|x)=Pθ(x)=const×ΠME{A,T,G,C}(θM1+(1−θ)M2)nM 

The likelihood Pθ (xi) can be calculated for any SNP i∈(1, 2, . . . , n) with observed data xi, the likelihood of observing data X={x1, x2, . . . , xn} for all SNPs is



 
  
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θ that maximizes the likelihood can then be solved by stepwise increment of θ. The above procedure can be used for mixture of any two human samples as well.
Simulation of Cell Line Mixture for Contaminant Detection
Simulation was performed for 3 cell line pairs including PANC-1 and RT4, MV-4-11 and “LNCaP clone FGC”, CAL27 and Raji. All six cell lines were profiled by deep NGS sequencing to obtain their SNP fingerprints. Two cell lines in a pair were mixed in silico where ratio of the first cell line is r, and r takes the following values: 0.15%, 0.30%, 0.625%, 1.25%, 2.5%, 5%, 10%, 15%, and 20%. For each SNP site, r×n nucleotides were obtained from the first cell line where n was a random integer from 500 to 5000, r×n were further distributed into 4 nucleotides (A, T, G, C) according to their frequencies in the first cell line. Similarly, (1−r)×n nucleotides were obtained from the second cell line. The ratio was then reversed so a symmetric sampling was done with ratio r for the second cell line.
Estimating Mouse Ratio from RNAseq and WES Datasets
Sequencing reads were mapped to human (hg19) and mouse (mm10) genomes using mapping tools STAR (Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013)) for RNAseq data and BWA (Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-60 (2009)) for WES data with default parameters. If a read was only mapped to human genome, or had fewer mismatches to human genome than to mouse genome, it was classified as human read. Mouse reads were similarly assigned. If a read was mapped to both genomes with close number of mismatches, off by at most 2, the read was unclassifiable and discarded. The mouse ratio was the proportion of mouse reads out of all kept reads.
Example 2
This example illustrates the human sample authentication and contamination detection.
SNP Profiling and Fingerprint
A panel of SNPs were selected for authenticating human samples including cell lines, xenografts and organoids (Table 1). SNPs were profiled by deep NGS sequencing with an average depth of 3000. Each sample has a unique SNP fingerprint consisted of both nucleotide identities and frequencies for all the SNPs. It shall be emphasized that a cell line can have fluctuating SNP fingerprints between passages and among biobanks due to genetic drift and heterogeneity, so a current SNP fingerprint can be profiled for better curation. The SNP fingerprints can be generated, with reduced precision, by relatively low-depth NGS data. In this example, the inventors generated SNP fingerprints for 1050 cell lines from RNAseq data profiled by the inventors and CCLE, which serve as references.
The inventors illustrated the authentication, characterization, intraspecies and interspecies contamination detection using SNP profiling data from deep NGS sequencing for 217 cell line samples, 220 PDX and 31 PDX-derived organoid (PDXO) samples. For the cell line samples, the inventors tested the mixtures of two cell lines with known mix ratios from serial dilutions and 6 corresponding pure cell lines (Table 7), the mixtures of two cell lines with unknown mix ratios (Table 8), and 117 unmixed cell lines (Table 9).
Authentication of Human Samples
Identity of a sample, or the major component of a contaminated sample, was determined by its genotype similarity to a library of reference samples. In 217 tested cell line samples, genotype similarities between same cell lines were always >90% with an average of 98.6%, and the lowest was 91.7% for an A-875 cell culture with 16.7% contamination of JEG-3 (FIG. 1A, Table 8). In contrast, genotype similarities between unrelated cell lines were almost always below 50%. Still there were cell lines that are closely related or in the same synonymous group by various reasons including mislabeling, contamination, deriving from same patient, one cell line being parental to another, etc. For example, HCT-15 and HCT-8 likely were derived from the same patient; QGY-7701 is contaminated and a HeLa derivative. Genotype similarities for 16 such cell line pairs in the dataset range from 84% to 96% (Table 10). These cell line pairs can be distinguished except for almost identical ones such as HLE and HLF. Genotype similarities between same models on average are 98.0% (87.2 ̃100%) for 220 PDX and 31 PDX-derived organoid (PDXO) samples, and nearly all are below 50% between different models.
Estimation of Genetic Heterogeneity
If a sample is uncontaminated and is purely monoclonal diploid, then a SNP site is either homozygous or heterozygous, and the observed nucleotide frequency is close to 1 or 0.5 in deep NGS sequencing data, difference only coming from errors and randomness in sequencing. In reality, cell lines may have minor clones, are aneuploid or are contaminated (contaminants), so not only did the inventors observe frequencies far away from 0.5 and 1, but also 3 or 4 nucleotides at a SNP site. Such information can be used to estimate genetic heterogeneity of a sample.
The dominant clone is the major component of a sample, minor clones and contaminants are the minor component. There are six informative genotype combinations of the major and minor components that can be used to estimate SNP heterogeneity ratios, based on the four observed nucleotide frequency patterns (Table 11). A SNP site is informative if it emits one of the four patterns. Subsequently, sample heterogeneity ratio is estimated from individual SNP heterogeneity ratios by a statistical modeling approach (see Example 1). Using the test samples, the inventors found that uncontaminated cell lines on average have 107 informative SNP sites, while contaminated cell lines have a slightly more 112. On average, PDX and PDXO models have 156 and 111 informative SNP sites, respectively, which reflects higher genetic heterogeneity and/or mouse contamination in PDX models.
Detection and Quantification of Contamination
The inventors detected sample contamination by combining three analyses. First, contaminated samples can have high heterogeneity ratios, while uncontaminated ones do not. In the test samples, 115 of 118 (97.5%) presumably uncontaminated cell lines have heterogeneity ratios <2% and all <3% (FIG. 1B). In contrast, the inventors observed high heterogeneity ratios for contaminated cell lines, for example, an A-875 cell culture mixed with JEG-3 cell had heterogeneity ratio 15.5% (Table 8). As shown supra, heterogeneity ratio is proportional to contamination ratio (percentage of contaminants), and therefore is a good indicator for contamination. Human tumors dissected from PDX models contain mouse stroma, and indeed the inventors observed higher heterogeneity ratios in PDX tumors (FIG. 1B), caused by mouse contamination (FIG. 1C). PDXOs, as in vitro culture of PDXs, have significantly smaller heterogeneity ratios due to much smaller and often only trace amount of mouse cells (FIG. 1B).
Contamination was also indicated by a distinct right peak in the probability density of SNP heterogeneity ratios for a sample (FIG. 2A-2F). The peak shifted right as contamination and heterogeneity ratio increase, and sometimes splits into two peaks. The bi/tri-modal distribution vanished or only marginally showed up for uncontaminated cell lines or cell lines with very low contamination ratios (<1%) and heterogeneity ratios (<2%).
Finally, contaminants can be directly detected by statistical modeling that gives intuitive visualization and rigorous probabilistic measurement (see Example 1, FIG. 3A). In 94 cell line samples each mixed with another cell line, the inventors can always correctly infer the minor contaminant cell line in a cell line when the heterogeneity ratio is ≥2% (FIG. 3B). Accuracy goes down to about 80% and 50% when the heterogeneity ratio is 1-2% and <1%. For the 8 missed samples, seven samples were characterized as clean and only one was marked by a wrong contaminating cell line. Of course, such inference is only feasible when the contaminating cell line is also one with known SNP fingerprint. The inventors detected several contaminated cell lines in our biobank, one example is cell line “G-292 clone A141B1” which had a high heterogeneity ratio of 7.62% (FIG. 3C), and it was contaminated by 6.21% OCI-AML-2 (FIG. 3D).
After identifying the contaminating cell line, the inventors can estimate the contamination ratio (i.e. percentage of the second cell line) using a maximum-likelihood approach (see Example 1). Simulation studies showed that the estimated contamination ratios are extremely close to known ratios (FIG. 3E). The inventors observed a tight linear correlation between heterogeneity ratios and contamination ratios (FIG. 3F). Therefore, as discussed before, heterogeneity ratio is a good estimator of contamination, and is particularly useful when contaminants are not standard cell lines. Still in contaminated samples, contaminants contributed only part, though sometimes majority, of the genetic heterogeneity, consequently contamination ratios were generally smaller than corresponding heterogeneity ratios (see Table 8), the few violations were caused by data processing methods.
In summary, heterogeneity ratio, by its value and distribution, is a reliable contamination measure for human samples. Cell line samples with heterogeneity ratio 2% are highly likely contaminated, and when the contaminant is another cell line also with SNP fingerprint information, its identity can be inferred and the contamination ratio can be estimated with an unprecedented sensitivity at measured by cell or DNA mix ratios (Table 7 and 8).
Example 3
This example illustrates the mouse tumor model authentication.
A panel of mouse SNPs (see Table 2) were selected for authenticating 32 syngeneic mouse tumor models commonly used in preclinical immunomodulatory drug development, including 4T1, A20, B16-BL6, B16-F0, B16-F1, B16-F10, C1498, Colon26, CT26WT, E.G7-Ova, EL4, EMT6, H22, Hepa1-6, J558, J774A1, JC, KLN205, L1210, L5178-R, LLC, MBT2, MC38, MPC-11, Neuro-2a, P388D1, P815, Pan02, Renca, RM1, S91, and WEHI164. Most models have 6 unique SNPs. Colon26 and CT26WT are mouse colon adenocarcinoma models originated from BALB/c mouse strain, each has 12 SNPs with 6 common ones for a total of 18 unique ones. B16-BL6, B16-F0, B16-F1, and B16-F10 are mouse melanoma cell lines in C57BL/6 mouse strain and were all derived from B16 thus share high genetic similarity. Specifically, B16 is the parental line of B16-F0, which in turn is the parental line of B16-F1. B16-F10 is the 10th serial passage of B16-F0 and is the parental line of B16-BL646. The inventors used 7 common SNPs to first assign a test cell line into this group, then to B16-BL6, B16-F0 and B16-F10 each with 6 unique SNPs, and when none of the 18 SNPs is observed, the test cell line is assigned B16-F1. Authentication on these model models achieved 100% accuracy.
Example 4
This example illustrates the human-mouse interspecies contamination detection.
The inventors compared human hg19 and mouse mm10 genomes, and identified a group of 100-300 bp segments (see Table 3) such that each segment significantly diverged—by insertion, deletion and point mutation—between human and mouse (31-97% sequence similarities), yet has identical flanking sequences so that a common pair of primers can be designed. After NGS sequencing, the inventors separated human and mouse reads, calculated mouse ratios for all segments, and took median of these ratios as the mouse ratio in a human-mouse mixed sample. This method demonstrated extremely high accuracy in a set of benchmark samples in which mouse and human DNA was mixed by serial dilutions (FIG. 4A). The inventors also developed methods of estimating mouse content from RNAseq and WES data (see Example 1). The inventors compared three methods in estimating mouse ratios in 220 PDX and 31 PDXO models (FIG. 4B-C). DNA (for WES and the deep NGS sequencing) and RNA (for RNAseq) were extracted and sequenced from same sample of a model to remove sample variance. PDXO models generally had low mouse content. In PDX models, mouse ratios accurately estimated from deep NGS sequencing data were the highest, followed by RNAseq then WES. This is mainly because the exon-capture kit used in WES was designed to enrich human exons and had low hybridization affinity to homologous mouse exons. RNAseq used polyA-enrichment protocol with no species preference but gene expression has great temporospatial variability in human tumor and mouse stroma of PDX. Indeed, the inventors observed a very strong quadratic relationship for mouse ratios between the deep NGS sequencing data and WES data (R=0.96, FIG. 4D), but a much weaker linear correlation between the deep sequencing data and RNAseq data (R=0.62).
Example 5
This example illustrates the detection of mycoplasma in the samples.
The inventors used one pair of universal primers for the detection of all mycoplasma species, and 11 pairs for detecting 11 mollicutes including A. laidlawii, M. arginine, M. fermentans, M. genitalium, M. hominis, M. hyorhinis, M. orale, M. pneumonia, M. salivarium, and U. urealyticum with proven effectiveness (Molla Kazemiha, V. et al. Cytotechnology 61, 117-24 (2009)). The inventors identified one mycoplasma contaminated cell line in the biobank by the deep NGS sequencing method and subsequently validated it by a mycoplasma detection kit.
Example 6
This example illustrates the population structure analysis and gender determination.
Of the panel of SNPs used for human sample authentication, 143 were characterized by the International HapMap Project (International HapMap, C. The International HapMap Project. Nature 426, 789-96 (2003)). The inventors used fastSTRUCTURE (Raj, A., Stephens, M. & Pritchard, J. K. Genetics 197, 573-89 (2014)) to perform population structure analysis of three reference populations: Han Chinese (CHB), Nigeria Yoruba (YRI) and Utah residents with Northern and Western European ancestry from the CEPH collection (CEU). All 406 individuals were unambiguously assigned with high probabilities. The inventors then profiled 423 PDX models derived from East Asian patients and 634 PDX models derived from Western patients in the U.S. All the East Asian PDX models have dominant CHB composition with only one exception. Majority of the Western PDX models have predominantly CEU composition, the rest have major CHB or YRI compositions or mixture of two or three of the reference populations. The inventors also used 3 SNPs at Y chromosome for gender inference (Table 3), which was always accurate except for tumor samples with lost Y chromosome.
While the disclosure has been particularly shown and described with reference to specific embodiments (some of which are preferred embodiments), it should be understood by those having skill in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure as disclosed herein.
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    50. Hideyuki Tanabe, Y. T., Daisuke Minegishi, Miharu Kurematsu, Tohru Masui, Hiroshi Mizusawa. Cell line individualization by STR multiplex system in the cell bank found cross-contamination between ECV304 and EJ-1/T24. Tiss. Cult. Res. Commun. 18, 329-338 (1999).
    51. Team, R. C. R: A language and environment for statistical computing. 3.5.3 edn (R Foundation for Statistical Computing, Vienna, Austria, 2018).
    52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).
    53. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-60 (2009).









TABLE 1







Human SNP











Locus
Rs#
SEQ ID NO.















chr1_10473196
rs2229687
1



chr1_1222267
rs11260579
2



chr1_1249187
rs12142199
3



chr1_151733335
rs8480
4



chr1_169345868
rs1028180
5



chr1_169519112
rs6020
6



chr1_201969082
rs1130790
7



chr1_201973565
rs3820439
8



chr1_202304868
rs14451
9



chr1_20977000
rs1043424
10



chr1_20982631
rs4704
11



chr1_220154768
rs1061160
12



chr1_226352498
rs2306120
13



chr1_230829139
rs1051038
14



chr1_234529570
rs2175593
15



chr1_234614390
rs117698521
16



chr1_46095272
rs1135812
17



chr1_46660295
rs2292487
18



chr1_52264064
rs1770791
19



chr1_54683856
rs15921
20



chr2_10712278
rs3732114
21



chr2_109513601
rs3827760
22



chr2_109543883
rs922452
23



chr2_109552936
rs3215127
24



chr2_109580638
rs260691
25



chr2_207006676
rs1801318
26



chr2_220046975
rs3731900
27



chr2_232326417
rs1131171
28



chr2_238672703
rs3739038
29



chr2_242572846
rs3208142
30



chr2_242618050
rs1131195
31



chr2_27260469
rs1124649
32



chr2_27550967
rs1049817
33



chr2_33226533
rs4952330
34



chr2_3504687
rs9950
35



chr2_68388823
rs1137930
36



chr2_69659126
rs4453725
37



chr2_71365676
rs357756
38



chr2_85769711
rs1078004
39



chr2_99995804
rs1376443
40



chr3_108188993
rs9868484
41



chr3_121500699
rs17849995
42



chr3_14174427
rs4685076
43



chr3_183560195
rs13091
44



chr3_183861243
rs843358
45



chr3_186509517
rs187868
46



chr3_193374964
rs9851685
47



chr3_33907945
rs3183987
48



chr3_50334231
rs2269432
49



chr3_50355730
rs35455589
50



chr3_50378176
rs4688725
51



chr3_52727257
rs2289247
52



chr3_58154327
rs8640
53



chr4_10099340
rs13441
54



chr4_1330759
rs1128427
55



chr4_164435265
rs2304802
56



chr4_1737502
rs11248073
57



chr4_183815688
rs4742
58



chr4_186097045
rs6855305
59



chr4_25419283
rs9174
60



chr4_39458051
rs2125313
61



chr4_57889677
rs1056364
62



chr4_83795806
rs10025654
63



chr5_137902339
rs1042665
64



chr5_140048209
rs2251860
65



chr5_141014494
rs2530223
66



chr5_176940384
rs335438
67



chr5_179290845
rs30386
68



chr5_311478
rs2244029
69



chr5_31409252
rs2241337
70



chr5_33951693
rs16891982
71



chr5_33964210
rs183671
72



chr5_61694379
rs247264
73



chr5_72798845
rs14010
74



chr5_74069863
rs6874609
75



chr6_105726036
rs1051484
76



chr6_150114745
rs4816
77



chr6_26545632
rs4871
78



chr6_2745352
rs6927195
79



chr6_42992825
rs3749903
80



chr6_49403282
rs8589
81



chr6_70407465
rs12648
82



chr6_89793894
rs1130809
83



chr6_90039670
rs10502
84



chr6_97339078
rs6684
85



chr7_116528240
rs4808
86



chr7_127721507
rs322825
87



chr7_128607384
rs8043
88



chr7_140159721
rs10243155
89



chr7_150916228
rs6949587
90



chr7_158536267
rs2305473
91



chr7_2577781
rs1043291
92



chr7_6063283
rs4560
93



chr7_6066461
rs2639
94



chr7_75959188
rs2072435
95



chr7_897492
rs10950789
96



chr7_99047978
rs883403
97



chr7_99747130
rs12878
98



chr811710888
rs12338
99



chr8_125498547
rs3812471
100



chr8_144662353
rs1062391
101



chr8_144697041
rs1049832
102



chr8_95877787
rs713113
103



chr9_100823084
rs13049
104



chr9_124914613
rs4679
105



chr9_127177161
rs4574
106



chr9_131397479
rs4837291
107



chr9_131767668
rs2287363
108



chr9_135974100
rs886017
109



chr9_139298593
rs1051957
110



chr9_26978259
rs11555693
111



chr9_33026572
rs20583
112



chr9_86278817
rs7866234
113



chr9_96238578
rs10821135
114



chr10_102746503
rs2863095
115



chr10_1046712
rs2306409
116



chr10_120920588
rs10749291
117



chr10_12209752
rs6686
118



chr10_16796919
rs1049632
119



chr10_27434483
rs2274634
120



chr10_72179746
rs1043098
121



chr10_99219885
rs2152092
122



chr11_118967758
rs643788
123



chr11_120107411
rs882856
124



chr11_122928622
rs4802
125



chr11_125479363
rs2241502
126



chr11_3028140
rs729662
127



chr11_4159457
rs9937
128



chr11_47188411
rs3740691
129



chr11_65632262
rs558114
130



chr11_6630833
rs1043390
131



chr11_67200819
rs4930427
132



chr11_82973004
rs8789
133



chr12_104341103
rs7645
134



chr12_109994870
rs9593
135



chr12_112037000
rs695871
136



chr12_118473054
rs9788041
137



chr12_12967127
rs1051374
138



chr12_2968169
rs3742076
139



chr12_2997397
rs2907608
140



chr12_49230035
rs1057908
141



chr12_50529736
rs3741562
142



chr12_6638116
rs740850
143



chr12_6647109
rs1803621
144



chr12_67706466
rs1060350
145



chr13_111298392
rs436462
146



chr13_115004914
rs2296971
147



chr13_25000617
rs7571
148



chr13_28239970
rs14105
149



chr14_102514227
rs13749
150



chr14_105222037
rs1132975
151



chr14_106208082
rs11621259
152



chr14_106208086
rs1045853
153



chr14_106236128
rs12890621
154



chr14_21967916
rs1139130
155



chr14_24615435
rs4575
156



chr14_24736027
rs14193
157



chr14_49294391
rs34609389
158



chr14_51716188
rs7161242
159



chr14_64908845
rs2236225
160



chr14_75359670
rs2230237
161



chr15_40328665
rs8208
162



chr15_44038899
rs2411284
163



chr15_48384907
rs2250072
164



chr15_48426484
rs1426654
165



chr15_48485926
rs2413887
166



chr15_52901433
rs12915981
167



chr15_63937209
rs2229749
168



chr15_63988357
rs2255243
169



chr15_75189930
rs1130741
170



chr15_75650836
rs1128933
171



chr15_75932129
rs13737
172



chr15_77344793
rs11737
173



chr15_89858602
rs1138465
174



chr15_91525197
rs2301826
175



chr16_11773662
rs3190321
176



chr16_15129970
rs7200543
177



chr16_2049640
rs2286469
178



chr16_2285357
rs26840
179



chr16_27238110
rs1127228
180



chr16_70515355
rs11054
181



chr16_70602221
rs12909
182



chr16_718514
rs7204542
183



chr16_75590092
rs3743601
184



chr16_75646576
rs3743599
185



chr16_81010073
rs1127390
186



chr17_19247075
rs4924987
187



chr17_38179492
rs2302777
188



chr17_40722029
rs665268
189



chr17_5294976
rs14231
190



chr17_61908556
rs13030
191



chr17_7217463
rs2292064
192



chr17_73016621
rs1044228
193



chr17_74056413
rs2665998
194



chr17_80008392
rs9916764
195



chr17_80039481
rs1140616
196



chr18_12351342
rs11080572
197



chr18_33750046
rs8299
198



chr18_77805856
rs3744872
199



chr19_10226256
rs7710
200



chr19_1110829
rs2302109
201



chr19_13885484
rs10104
202



chr19_17628587
rs6743
203



chr19_19023853
rs3177137
204



chr19_1997363
rs1610045
205



chr19_2762585
rs2302491
206



chr19_39196745
rs3745859
207



chr19_39322087
rs9419
208



chr19_39926521
rs17626
209



chr19_4362691
rs243261
210



chr19_4454000
rs11909
211



chr19_45490570
rs3786505
212



chr19_49513273
rs1062708
213



chr19_51301456
rs4802741
214



chr19_580665
rs4682
215



chr19_8468337
rs2230876
216



chr20_25260931
rs2227890
217



chr20_2638579
rs6753
218



chr20_2996497
rs1178016
219



chr20_31427635
rs2070090
220



chr20_3193978
rs8362
221



chr20_391170
rs7059
222



chr20_43530234
rs4931
223



chr20_568696
rs6053171
224



chr21_38568308
rs6579
225



chr21_46271452
rs235314
226



chr22_32795641
rs5749426
227



chr22_32887150
rs9726
228



chr22_38273749
rs9466
229



chr22_39134715
rs1062687
230



chr22_42276742
rs2228314
231



chr22_42912106
rs1812240
232



chr22_42970032
rs137055
233



chr22_43195147
rs1128013
234



chr22_43610207
rs138993
235



chr22_50885775
rs1053744
236



chrX_75004529
rs1343879
237

















TABLE 2







Mouse SNP










Locus
Gene
SEQ ID NO.
Cell line





chr1_91387260
Ilkap
238
4T1


chr5_136026554
Dtx2
239
4T1


chr11_100695233
Dhx58
240
4T1


chr11_69740416
Polr2a
241
4T1


chr12_110649884
Dync1h1
242
4T1


chr19_47898131
Itprip
243
4T1


chr2_29843149
Urm1
244
A20


chr2_122052069
Eif3j1
245
A20


chr10_117355993
Cpsf6
246
A20


chr10_127064881
Cdk4
247
A20


chr11_101288969
Becn1
248
A20


chr11_69589186
Trp53
249
A20 & MC38


chr11_69589202
Trp53
250
A20 & MC38


chr5_90215024
Cox18
251
B16BL6


chr13_37985317
Ssr1
252
B16BL6


chr14_117186703
Gpc6
253
B16BL6


chr14_55745832
Nop9
254
B16BL6


chr15_80929461
Tnrc6b
255
B16BL6


chr18_60692027
Ndst1
256
B16BL6


chr3_100144344
Wdr3
257
B16BL6; B16F0; B16F1; B16F10


chr4_109883738
Faf1
258
B16BL6; B16F0; B16F1; B16F10


chr5_108087329
Mtf2
259
B16BL6; B16F0; B16F1; B16F10


chr15_97790681
Slc48a1
260
B16BL6; B16F0; B16F1; B16F10


chr16_57166188
Nit2
261
B16BL6; B16F0; B16F1; B16F10


chr19_32799998
Pten
262
B16BL6; B16F0; B16F1; B16F10


chr19_5716287
Ehbp111
263
B16BL6; B16F0; B16F1; B16F10


chr2_10091850
Kin
264
B16F0


chr2_5053146
Optn
265
B16F0


chr3_122275655
Dnttip2
266
B16F0


chr4_129639489
Txlna
267
B16F0


chr7_131362539
2310057M21Rik
268
B16F0


chr9_109100255
Plxnb1
269
B16F0


chr5_115985533
Cit
270
B16F10


chr8_119599093
Taf1c
271
B16F10


chr9_86564707
Pgm3
272
B16F10


chr13_21445463
Zscan26
273
B16F10


chr18_12197207
Npc1
274
B16F10


chrX_169313612
Hccs
275
B16F10


chr3_69030400
Smc4
276
C1498


chr4_116074925
Uqcrh
277
C1498


chr11_101411430
Aarsd1
278
C1498


chr11_69588367
Trp53
279
C1498; JC


chr11_69588367
Trp53
280
C1498; JC


chr17_84712802
Lrpprc
281
C1498


chr18_24638470
Elp2
282
C1498


chr8_83571885
Tecr
283
Colon26


chr12_84609021
Abcd4
284
Colon26


chr13_4135258
Akr1c18
285
Colon26


chr13_93880550
Arsb
286
Colon26


chr18_80197758
Rbfa
287
Colon26


chrX_41825559
Thoc2
288
Colon26


chr3_95659269
Mcl1
289
CT26WT


chr6_124749315
Atn1
290
CT26WT


chr14_56693508
Mphosph8
291
CT26WT


chr17_35881376
Dhx16
292
CT26WT


chr18_60550220
Dctn4
293
CT26WT


chrX_164071728
Siah1b
294
CT26WT


chr13_74667993
Erap1
295
CT26WT Colon26


chr14_101695962
Uch13
296
CT26WT Colon26


chr14_34343682
Glud1
297
CT26WT Colon26


chr15_12924098
Drosha
298
CT26WT Colon26


chr15_99404465
Tmbim6
299
CT26WT Colon26


chrX_157454879
Sms
300
CT26WT Colon26


chr2_105013312
Eif3m
301
EG7Ova


chr3_32728469
Mrpl47
302
EG7Ova


chr7_19514493
Trappc6a
303
EG7Ova


chr7_48803057
Zdhhc13
304
EG7Ova


chr15_103243005
Hnrnpa1
305
EG7Ova


chrX_73788891
Ssr4
306
EG7Ova


chr3_97168031
Acp6
307
EL4


chr3_14557226
Lrrcc1
308
EL4


chr4_98934494
Usp1
309
EL4


chr8_122890806
Ankrd11
310
EL4


chr14_14114149
Psmd6
311
EL4


chr17_56424099
Ptprs
312
EL4


chr5_33643749
Slbp
313
EMT6


chr9_3430504
Cwfl912
314
EMT6


chr10_50849489
Ascc3
315
EMT6


chr11_70014640
Acadvl
316
EMT6


chr19_8770295
Nxfl
317
EMT6


chrX_13158898
Usp9x
318
EMT6


chr5_149623543
Hsph1
319
H22


chr6_131370348
Ybx3
320
H22


chr7_135698360
Mki67
321
H22


chr9_109842576
Nme6
322
H22


chr9_21757780
Spc24
323
H22


chr12_24711691
Rrm2
324
H22


chr3_145578132
Znhit6
325
Hepa16


chr9_72617951
Rfx7
326
Hepa16


chr13_3573709
BC016423
327
Hepa16


chr15_31594403
Cct5
328
Hepa16


chr17_23676012
Tnfrsf12a
329
Hepa16


chr19_15956304
Cep78
330
Hepa16


chr4_55378242
Rad23b
331
J558


chr7_126371216
Spns1
332
J558


chr9_111230959
Mlh1
333
J558


chr11_50210648
Sqstm1
334
J558


chr11_69588647
Trp53
335
J558 & Renca


chr11_69588703
Trp53
336
J558 & Renca


chrX_101293807
Med12
337
J558


chr5_3559131
Fam133b
338
J774A1


chr8_72255808
Ap1m1
339
J774A1


chr10_26872682
Arhgap18
340
J774A1


chr13_114826176
Mocs2
341
J774A1


chr17_35835434
Tubb5
342
J774A1


chr19_37387069
Kif11
343
J774A1


chr8_72739404
Sin3b
344
JC


chr9_119918477
Wdr48
345
JC


chr12_17277245
Pdia6
346
JC


chr18_12189845
3110002H16Rik
347
JC


chr19_8831307
Hnmpul2
348
JC


chr2_25372768
Sapcd2
349
KLN205


chr3_145596142
Znhit6
350
KLN205


chr5_145132963
Pdap1
351
KLN205


chr8_122571980
Cdt1
352
KLN205


chr17_84706019
Lrpprc
353
KLN205


chrX_140472073
Prps1
354
KLN205


chr5_38234081
Lyar
355
L1210


chr5_69566389
Guf1
356
L1210


chr6_135023351
Ddx47
357
L1210


chr10_40850958
Cdc40
358
L1210


chr12_54768043
Snx6
359
L1210


chrX_94078824
Zfx
360
L1210


chr1_55080340
Hspd1
361
L5178R


chr3_96579869
Polr3gl
362
L5178R


chr9_57256682
1700017B05Rik
363
L5178R


chr10_116498369
Cnot2
364
L5178R


chr11_109436637
Amz2
365
L5178R


chrX_105874791
Atrx
366
L5178R


chr4_127047898
Zmym1
367
LLC


chr4_129008072
Ak2
368
LLC


chr4_155855159
Dvl1
369
LLC


chr5_145244535
Zfp655
370
LLC


chr6_145246772
Kras
371
LLC


chr14_86866528
Diap3
372
LLC


chr1_161074777
Cenpl
373
MBT2


chr2_112406248
Katnbl1
374
MBT2


chr2_69194469
Spc25
375
MBT2


chr7_19006050
Irf2bp1
376
MBT2


chr13_104144156
Trappe13
377
MBT2


chr16_48999045
C330027C09Rik
378
MBT2


chr7_41625342
2610021A01Rik
379
MC38


chr8_70180548
Tmem161a
380
MC38


chr9_22013055
Prkesh
381
MC38


chr13_74646321
Erap1
382
MC38


chr15_34485603
Hrsp12
383
MC38


chr5_145144973
Bud31
384
MPC11


chr6_145232109
Kras
385
MPC11


chr13_3575438
BC016423
386
MPC11


chr15_61989534
Myc
387
MPC11


chr17_35016227
Vars
388
MPC11


chr19_46076132
Nolc1
389
MPC11


chr2_170515838
Pfdn4
390
Neuro2a


chr7_123428178
Lcmt1
391
Neuro2a


chr11_96911133
Cdk5rap3
392
Neuro2a


chr13_97191232
Hexb
393
Neuro2a


chr15_31598022
Cct5
394
Neuro2a


chr16_20680966
Eif4g1
395
Neuro2a


chr8_122482698
Piezo1
396
P388D1


chr8_70296436
Ddx49
397
P388D1


chr9_24424805
Dpy1911
398
P388D1


chr11_98694175
Psmd3
399
P388D1


chr12_73982520
Snapc1
400
P388D1


chr13_69811634
Med10
401
P388D1


chr1_43983175
Tpp2
402
P815


chr7_105636932
Arfip2
403
P815


chr9_36759241
Stt3a
404
P815


chr11_94634572
Lrrc59
405
P815


chr12_108812956
Yy1
406
P815


chr13_104811305
Cwc27
407
P815


chr1_63152796
Ndufs1
408
Pan02


chr7_127972166
Fus
409
Pan02


chr7_45156316
Pih1d1
410
Pan02


chr16_16983639
Mapk1
411
Pan02


chr17_75538733
Fam98a
412
Pan02


chr19_6920138
Esrra
413
Pan02


chr4_147941100
2510039O18Rik
414
Renca


chr12_69579944
Mettl21d
415
Renca


chr13_19376528
Stard3n1
416
Renca


chr17_71517626
Ndc80
417
Renca


chrX_93420657
Pola1
418
Renca


chr1_24711551
Lmbrd1
419
RM1


chr4_140702160
Rcc2
420
RM1


chr11_120720063
Lrrc45
421
RM1


chr11_69589607
Trp53
422
RM1


chr15_12890119
Drosha
423
RM1


chr17_46648312
Mrpl2
424
RM1


chr3_142810708
Pkn2
425
S91-P1-150414


chr3_94864330
Pogz
426
S91-P1-150414


chr4_131865081
Mecr
427
S91-P1-150414


chr9_15308558
Taf1d
428
S91-P1-150414


chr17_33925530
Tapbp
429
S91-P1-150414


chr17_35668634
Gtf2h4
430
S91-P1-150414


chr5_29441373
Nom1
431
WEHI164


chr13_106947227
Dimt1
432
WEHI164


chr16_56029611
Pcnp
433
WEHI164


chr17_45419631
Cdc51
434
WEHI164


chr18_35572424
Matr3
435
WEHI164


chr19_23676211
Gm6563
436
WEHI164
















TABLE 3







Human Y


Chromosome SNP










Locus
Rs#







chrY_14832620
rs7067496



chrY_15467824
rs2032654



chrY_15591537
rs2032653




















TABLE 4







chromosome
position









chr11
78371203



chr16
15839180





















TABLE 5







virus
genome
Sequence









EBV
NC_009334
86-249



EBV
NC_009334
549-765



EBV
NC_009334
1037-1189



EBV
NC_009334
2571-2732



HBV
NC_003977
304-489



HBV
NC_003977
1393-1618



HPV16
NC_001526
7152-7271



HPV16
NC_001526
7402-7901



HPV16
NC_001526
86-406



HPV18
NC_001357
30-1774



HIV
NC_001802
20-177



HIV
NC_001802
8443-8949



mycoplasma
CP029295.1
505718-506180

















TABLE 6







Mouse Genome Sequence Homologous to Human










Locus
SEQ ID NO.







chr1:105664142-105664584
437



chr1:131599847-131600265
438



chr1:133620651-133621057
439



chr1:38175197-38175663
440



chr1:42255546-42255968
441



chr1:43553833-43554279
442



chr1:55148075-55148405
443



chr1;55914041-55914488
444



chr1:55987400-55987857
445



chr2:114047027-114047475
446



chr2:114049076-114049523
447



chr2:114734704-114735308
448



chr2:114938795-114939209
449



chr2:116075697-116076099
450



chr2:119326991-119327420
451



chr2:119411547-119411942
452



chr2:140659041-140659312
453



chr3:144089441-144089832
454



chr3:34504194-34504633
455



chr3:36986772-36987094
456



chr3:37025884-37026323
457



chr3:6002716-6003025
458



chr4:100853347-100853768
459



chr4:102760274-102760632
460



chr4:41519808-41520239
461



chr4:43443358-43443775
462



chr4:43445287-43445715
463



chr4:76039612-76039883
464



chr5:106322349-106322777
465



chr5:122861456-122861856
466



chr5:122988599-122989028
467



chr6:108664368-108664777
468



chr7:102097926-102098361
469



chr7:102428566-102429007
470



chr7:102698517-102698966
471



chr7:105384072-105384436
472



chr7:105635729-105636145
473



chr7:105740673-105740944
474



chr7:107665615-107666028
475



chr7:108754667-108755104
476



chr8:103447835-103448254
477



chr8:115428668-115429087
478



chr8:123892100-123892548
479



chr9:119495272-119495684
480



chr9:120929616-120930041
481



chr9:124124306-124124697
482



chr9:24974244-24974660
483



chr9:82866266-82866706
484



chr9:84973186-84973624
485



chr10:29698549-29699004
486



chr10:75061569-75061989
487



chr11:101189377-101189820
488



chr11:101277344-101277788
489



chr11:101867409-101867715
490



chr11:102509968-102510377
491



chr11:114183205-114183653
492



chr11:115849863-115850303
493



chr12:101040300-101040687
494



chr12:107638060-107638501
495



chr12:66469994-66470265
496



chr13:31911885-31912294
497



chr13:38196485-38196783
498



chr13:39523526-39523952
499



chr13:43200327-43200776
500



chr13:43200696-43201123
501



chr13:44317005-44317340
502



chr13:44375738-44376192
503



chr14:100461399-100461810
504



chr14:100950247-100950697
505



chr14:100978309-100978722
506



chr14:103095102-103095512
507



chr14:105815614-105816038
508



chr14:111681168-111681625
509



chr14:114547663-114548108
510



chr14:123186854-123187271
511



chr14:52463385-52463656
512



chr15:102430819-102431236
513



chr15:102811273-102811660
514



chr15:102966510-102966781
515



chr15:103298047-103298653
516



chr15:103524801-103525249
517



chr15:34141703-34142120
518



chr16:29666774-29667232
519



chr16:29875939-29876394
520



chr16:6057738-6058045
521



chr16:6776352-6776786
522



chr16:76321297-76321735
523



chr16:78941222-78941652
524



chr16:80265994-80266442
525



chr16:80434685-80435068
526



chr16:87128020-87128361
527



chr16:87319732-87320189
528



chr16:91114059-91114450
529



chr17:15370365-15370824
530



chr17:26742420-26742783
531



chr17:26935067-26935511
532



chr17:27876446-27876890
533



chr17:30292965-30293355
534



chr18:19963182-19963628
535



chr18:25632724-25633184
536



chr18:34606137-34606581
537



chr18:34641045-34641374
538



chr18:34759289-34759725
539



chr18:34863942-34864380
540



chr19:41962522-41962964
541



chr19:46061929-46062390
542



chr19:46251931-46252398
543



chr19:46306521-46306978
544

















TABLE 7







Authentication and contaminant detection of cell line pairs with serial dilutions*



















Minor




Con-






component



Minor
taminant





Minor
ratio

Heterogeneity
Major
component
ratio




Major
Component
(percent-
#Informative
ratio
component
(contaminant)
(percent-
P-


Cell line mixture
Component
(Contaminant)
age)**
SNPs
(percentage)
inferred***
inferred****
age)***
value*****



















PANC1:RT4
PANC1
—
—
118
1.4
PANC1 (99.46%)
—
—
—



RT4
—
—
122
1.65
RT4 (98.54%)
—
—
—



RT4
PANC1
5
86
3.3
RT4 (97.78%)
PANC1 (96.73%)
2.88
2.98E−16



RT4
PANC1
2.5
80
1.85
RT4 (97.93%)
PANC1 (94.79%)
1.08
6.65E−12



RT4
PANC1
1.25
81
1.1
RT4 (97.76%)
PANC1 (88.14%)
0.41
3.97E−09



RT4
PANC1
0.625
80
1.06
RT4 (97.76%)
—
—




PANC1
RT4
5
131
8.09
PANC1 (99.50%)
RT4 (98.33%)
7.21
5.01E−17



PANC1
RT4
2.5
128
4.06
PANC1 (99.35%)
RT4 (95.50%)
2.81
5.01E−17



PANC1
RT4
1.25
132
2.75
PANC1 (99.49%)
RT4 (90.73%)
1.48
5.01E−17



PANC1
RT4
0.625
139
2.62
PANC1 (99.47%)
RT4 (81.61%)
1.08
1.67E−08


LNCAPCLONEFGC:
LNCAPCLONEFGC
—
—
97
1.005
LNCAPCLONEFGC (99.03%)
—
—
—


MV411
MV411
—
—
93
0.965
MV411 (99.03%)
—
—
—



MV411
LNCAPCLONEFGC
5
99
9
MV411 (99.45%)
LNCAPCLONEFGC (96.83%)
9.08
5.01E−17



MV411
LNCAPCLONEFGC
2.5
111
4.51
MV411 (99.50%)
LNCAPCLONEFGC (98.34%)
4.14
5.01E−17



MV411
LNCAPCLONEFGC
1.25
117
2.18
MV411 (99.18%)
LNCAPCLONEFGC (90.32%)
2.01
1.67E−09



MV411
LNCAPCLONEFGC
0.625
112
1.58
MV411 (99.00%)
LNCAPCLONEFGC (89.44%)
0.0148
1.67E−09



LNCAPCLONEFGC
MV411
5
102
2.35
LNCAPCLONEFGC (98.99%)
MV411 (94.57%)
2.14
5.01E−17



LNCAPCLONEFGC
MV411
2.5
101
1.67
LNCAPCLONEFGC (99.04%)
MV411 (91.58%)
0.88
2.37E−11



LNCAPCLONEFGC
MV411
1.25
98
1.49
LNCAPCLONEFGC (99.03%)
MV4U (87.77%)
0.71
2.76E−11



LNCAPCLONEFGC
MV411
0.625
105
1.36
LNCAPCLONEFGC (99.03%)
—
—
—


CAL28:RAJI
CAL27
—
—
39
1.39
CAL27 (97.39%)
—
—
—



RAJI
—
—
114
1.18
RAJI (98.56%)
—
—
—



RAJI
CAL27
5
116
5.36
RAJI (98.66%)
CAL27 (99.12%)
5.54
5.01E−17



RAJI
CAL27
2.5
127
4.17
RAJI (98.32%)
CAL27 (94.70%)
4.01
8.37E−11



RAJI
CAL27
1.25
121
1.84
RAJI (98.51%)
CAL27 (90.83%)
2.21
8.37E−06



RAJI
CAL27
0.625
116
2.49
RAJI (98.50%)
CAL27 (90.43%)
2.28
1.67E−07



CAL27
RAJI
5
121
7.11
CAL27 (99.17%)
RAJI (99.51%)
5.41
5.01E−17



CAL27
RAJI
2.5
113
3.79
CAL27 (98.94%)
RAJI (92.06%)
2.41
4.30E−13



CAL27
RAJI
1.25
112
2.14
CAL27 (98.61%)
RAJI (83.42%)
1.21
4.20E−07



CAL27
RAJI
0.625
112
1.4
CAL27 (98.49%)
RAJI (83.75%)
0.61
1.59E−07





*average values for each cell line mixture with 3 technical replicates except the unmixed ones


**percentage of the minor cell line based on cell counts


***genotype similarity shown in parenthesis


****chimeric genotype similarity shown in parenthesis


*****probability that the inferred minor component is incorrect













TABLE 8







Authentication and contaminaut detection of cell line mixtures


















Con-







Minor
taminant





Heterogeneity
Major
component
ratio




#Informative
ratio
component
(contaminant)
(percent-
P-


Cell line mixture
SNPs
(percentage)
inferred***
inferred****
age)***
value*****
















ME180:143B
119
6.54
ME180 (98.06%)
143B (97.09%)
7.01
5.01E−17


143B:ME180
135
3.24
143B (98.55%)
ME180 (94.17%)
3.21
5.01E−17


JEG3:A875
104
1.63
JEG3 (98.49%)
A875 (87.94%)
1.21
5.01E−13


A875:JEG3
93
15.50
A875 (91.71%)
JEG3 (99.00%)
16.71
5.01E−17


HT3:C33A
115
3.54
HT3 (100%)
C33A (97.06%)
3.41
5.01E−17


C33A:HT3
90
4.34
C33A (99.01%)
HT3 (100%)
4.61
0


DOTC24510:CASKI
136
5.47
DOTC24510 (98.99%)
CASKI (93.97%)
5.21
5.01E−17


CASKI:DOTC24510
129
4.26
CASKI (98.98%)
DOTC24510 (91.84%)
4.11
5.01E−17


HLE:HCC94
163
2.62
HLE (99.0%), 
HCC94 (91.46%)
2.91
5.01E−17





HLF (96.08%)





HCC94:HLE
133
10.65
HCC94 (97.6%)
HLE (96.63%), 
10.11
5.01E−17






HLF (96.63%)




NCIH1993:LS174T
141
3.97
NCIH1993 (98.05%)
LS174T (95.12%),
4.21
5.01E−17






LS180 (95.12%),








HM7 (94.63%)




LS174T:NCIH1993
114
4.88
LS174T (99.02%), 
NCIH1993 (97.06%)
4.71
5.01E−17





LS180 (99.03%),








HM7 (98.54%)





OSC19:SF763
152
7.03
OSC19 (98.08%)
SF763 (96.15%)
5.71
5.01E−17


SF763:OSC19
133
3.35
SF763 (99.02%)
OSC19 (90.15%)
2.91
2.51E−16


SW626:SJCRH30
155
11.67
SW626 (95.63%)
SJCRH30 (98.54%)
13.21
5.01E−17


SJCRH30:SW626
88
1.79
SJCRH30 (98.55%)
SW626 (94.2%)
2.01
1.58E−16


A875:ME180
115
2.68
A875 (98.56%)
ME180 (95.67%)
2.31
5.01E−17


DOTC24510:CASKI
144
1.75
DOTC24510 (98.5%)
CASKI (86%)
1.71
1.00E−15


OSC19:SF763
130
2.68
OSC19 (98.56%)
SF763 (93.27%)
2.11
5.01E−17


NOZ:SW626
127
0.82
NOZ (97.56%)
SW626 (82.93%)
0.71
3.98E−11


SNU739:MM1R
121
2.29
SNU739 (99.01%)
MM1R (94.03%),
1.71
5.01E−17






MM1S (94.03%)




U251:SR
127
1.09
U251 (98.54%)
SR (89.76%)
2.11
5.01E−17





*in the format of major cell line: minor/contaminating cell line


**genotype similarity shown in parenthesis


***chimeric genotype similarity shown in parenthesis


****probability that the inferred minor component is wrong













TABLE 9







Authentication of cell lines











# of
Heterogeneity




informative
ratio



Cell line
SNPs
(percentage)
Cell line inferred*













G292CloneA14B1
176
7.62
G292CLONEA141B1 (98.53%)


HPAF-II
153
6.63
HPAFII(99.01%)


PL45
135
4.49
PL45(98.42%), PANC1005(98.43%)


HCC827
193
3.46
HCC827(99.51%)


Hela
138
2.99
HELA(99.52%), HELA229(99.06%)


OCI-AML-2
143
2.77
OCIAML2(98.1%)


K-562
158
2.56
K562(98.52%)


OVCAR-5
154
2.14
OVCAR5(97.52%)


A-427
115
2.05
A427(97.45%)


8505C
158
1.96
8505C(98.05%)


NOZ
143
1.87
NOZ(97.94%)


Hep3B
106
1.77
HEP3B217(95.73%)


SF268
113
1.63
SF268(93.85%)


NCI-H1993
101
1.57
NCIH1993(96.25%)


NCI-H1793
139
1.56
NCIH1793(98.55%)


NCI-H1688
120
1.55
NCIH1688(99.48%)


MX-1
141
1.51
MX 1(100%)


NCI-N87
120
1.45
NCIN87(98.03%)


RBE
110
1.43
RBE(98.94%)


SiHa-579
121
1.43
SIHA(98.84%)


KPL-4
118
1.42
KPL4(96.43%)


EVSA-T
132
1.41
EVSAT(97.86%)


Ishikawa
90
1.29
ISHIKAWA(100%),





ISHIKAWAHERAKLIO02ER(100%)


IM95m
100
1.28
IM95M(98.35%), IM95(98.34%)


MES-SA
99
1.26
MESSA(99.5%)


MHH-CALL-2
189
1.26
MHHCALL2(100%)


OZ
107
1.26
OZ(98.95%)


SH-SY5Y
86
1.25
SHSY5Y(99.47%), SKNSH(99.47%)


PC-9
116
1.24
PC9(98.48%), PC14(97.99%)


Calu-3
117
1.23
CALU3(98.02%)


ME-180
89
1.21
ME180(98.89%)


SUM159PT
89
1.20
SUM159PT(98.94%)


NCI-H322
107
1.18
NCIH322(99.46%)


LS174T
85
1.18
LS174T(99.44%), HM7(98.89%),





LS180(99.45%)


NCI-H292
95
1.18
NCIH292(100%)


NCI-H1568
153
1.16
NCIH1568(97.57%)


GTL-16
113
1.15
GTL16(98.46%), MKN45(97.97%)


KG-1a
104
1.15
KG1A(97.34%), KG1(97.93%)


HM-7
104
1.15
HM7(98.38%), LS180(98.92%),





LS174T(98.92%)


HCCLM3
119
1.14
HCCLM3(98.92%)


MHCC97-H
75
1.08
MHCC97H(99.34%)


NCI-H1373
137
1.08
NCIH1373(98.16%)


Capan-2
139
1.06
CAPAN2(99.52%)


ML-2
111
1.06
ML2(99.49%)


SW1463
144
1.06
SW1463(93.06%)


NCI-H1395
110
1.06
NCIH1395(98.93%)


A-673
112
1.05
A673(98.54%)


DU-145
149
1.04
DU145(99.52%)


NCI-H1781
154
1.04
NCIH1781(99.51%)


SK-NEP-1
79
1.04
SKNEP1(99.49%)


A-431
75
1.04
A431(98.5%)


HepG2C3A
84
1.02
HEPG2C3A(99.32%)


U251
102
1.01
U251MG(99.45%)


SNU-354
88
1.01
SNU354(94.42%)


Hs445
93
1.00
HS445(99.49%)


JEG-3
94
1.00
JEG3(99.39%)


Z-138
94
1.00
Z138(98.54%)


IHH-4
87
1.00
IHH4(100%)


SF763
84
1.00
SF763(98.44%)


SNU-739-P1
95
1.00
SNU739(98.95%)


HeLa299
78
0.99
HELA(99.43%), HELA229(99.43%)


L-82
106
0.99
L82(97.96%)


MMIR
105
0.98
MM1R(100%), MM1S(99.48%)


HT-3
118
0.97
HT3(99.48%)


SCH
92
0.97
SCH(99.41%)


U118MG
128
0.97
U118MG(96.17%)


OSC-19
118
0.96
OSC19(98.41%)


JVM-13
89
0.94
JVM13(98.97%)


WiDr
113
0.94
HT29(97.14%)


SK-N-SH
78
0.93
SKNSH(99.5%), SHSY5Y(99.50%)


KYSE-410
147
0.92
KYSE410(97.55%)


HBL-1
100
0.92
HBL1(100%)


NAMALWACSN
104
0.91
NAMALWACSN(98.98%),





NAMALWA(98.46%)


SNU-368
23
0.91
SNU368(95.89%)


Jurkat
101
0.90
JURKAT(98.34%),





JURKATCLONEE61(98.37%)


SK-N-AS
124
0.89
SKNAS(99.04%)


SNU-2535
85
0.88
SNU2535(99.47%)


HCCC-9810
94
0.86
HCCC9810(98.31%)


COLO320DM
96
0.85
COLO320DM(98.47%)


MS751
103
0.85
MS751(97.88%)


SW48
34
0.85
SW48(98.2%)


CCRF-CEM
72
0.84
COC1DDP(99.02%),





CCRFCEM(99.02%), COC1(99.02%)


YCC-2
121
0.84
YCC2(97.98%)


TJ905
100
0.84
TJ905(97.53%)


CoC1DDP
92
0.83
COC1DDP(99.46%),





CCRFCEM(99.46%), COC1(99.46%)


SK-UT-1
94
0.83
SKUT1(99.03%)


D283
77
0.81
D283MED(98.98%)


DoTc24510
105
0.80
DOTC24510(98.97%)


MCCAR
84
0.80
MCCAR(98.52%)


OCI-LY7
105
0.80
OCILY7(98.04%)


A253
141
0.79
A253(98.06%)


JAR
79
0.79
JAR(98.33%)


SU-DHL-6
134
0.79
SUDHL6(99%)


SR
66
0.79
SR(99.46%)


AN3CA
107
0.78
AN3CA(96.65%)


Hutu80
80
0.78
HUTU80(100%), AZ521(100%)


OCUM-2M
106
0.78
OCUM2M(98.47%)


SW684
74
0.77
SW684(98.98%)


SJCRH30
71
0.76
SJCRH30(98.97%)


SW982
71
0.75
SW982(99.48%)


Y-79
84
0.75
Y79(97.38%)


MKN45
103
0.73
MKN45(98.99%), GTL16(98.47%)


143B
91
0.72
143B(98.96%)


Molt-4
87
0.72
MOLT4(98.98%), MOLT3(99.03%)


RT4
105
0.72
RT4(99.11%)


HCT-8
93
0.71
HCT8(98.5%)


NCI-H460
127
0.71
NCIH460(99.53%)


SW480
141
0.71
SW480(100%)


T.Tn
129
0.71
T.T(95.22%)


YCC-10
99
0.71
YCC10(98.97%)


C-33A
80
0.71
C33A(98.51%)


ASH-3
99
0.67
ASH3(97.06%)


SW626
112
0.66
SW626(97.98%)


SW756
96
0.66
SW756(99.02%)


A-875
80
0.63
A875(98.06%)


CoC1
72
0.61
COC1DDP(98.98%),





CCRFCEM(98.98%), COC1(98.98%)


JurkatcloneE6-1
92
0.57
JURKATCLONEE61(98.12%),





JURKAT(98.08%)





* genotype similarity shown in parenthesis













TABLE 10







Genotype similarities of related cell lines











Cell line tested
Related cell line
Genotype similarity















Hela
SMMC7721
0.8431



Hela
BEL7402
0.8529



Hela
QGY7701
0.8657



Hela
QGY7703
0.8683



SW480
SW620
0.8852



NCIH1993
NCIH2073
0.8932



OCUM-2M
OCUM2D
0.9



143B
HOS
0.9179



SR
SR786
0.9235



SJCRH30
RH30
0.932



143B
KHOSNP
0.9563



Hep3B
HEP3B217
0.9573



HCT-8
HCT15
0.9608



HepG2C3A
HEPG2
0.9622



HCCLM3
MHCC97H
0.9626

















TABLE 11







Six informative genotype combinations to estimate heterogeneity/contamination ratio*













Combination
1
2
3
4
5
6





S1 genotype
AA
AA
AA
AT
AT
AT


S2 genotype
TT
AT
TG
GG
AG
GC


S2 ratio 
T/(A + T)**
2T/(A + T)**
(T + G)/(A + T + G)
G/(A + T + G)***
G/(T + G)***
(G + C)/


(SNP 





(A + T + G + C)


heterogeneity








ratio)








Nucleotide 
large A 
large A 
large A 
large A and 
large A and 
large A and T 


frequency
small T
small T
small T and G
T small G
T small G
small G and C


pattern





*S1 is the major component and S2 is the minor/contaminating component in the mixed sample. Each combination uses specific nucleotides to represent a class of combinations, for example, the first combination denotes that both are homozygous genotypes with different nucleotides In the formulas for calculating S2 ratio, a nucleotide denotes its count (total number of reads) in NGS sequencing data.


**Combinations 1 and 2 cannot be distinguished from observed NGS data so 1.5T/(A + T) is used for both


**Combinations 4 and 5 cannot be distinguished from observed NGS data so 1.5G/(A + T + G) is used for both













TABLE 12







Sequencing Primers













SEQ

SEQ




ID

ID


Locus
Forward Primer
NO.
Reverse Primer
NO.





chr1_10473196
ctgcatgtaggcctttgaggat
545
gggcccatgtgaaaagcataac
546


chr1_1222267
gccggcaactctgactcc
547
tcaccagtctgaaccccact
548


chr1_1249187
ccgacgggtgtggatgtg
549
cgagaaggccaaccactactac
550


chr2_10712278
atcaatgaaacgaccgtcctctt
551
acggttaccagaaaagaggtatagaatt
552


chr2_109513601
tgagtagctcagggatgctgta
553
cccacggagctgccattt
554


chr2_109543883
cccactaattctgcagatggct
555
gcctggctacggttcagac
556


chr3_14174427
cgggctctgagttgattcctc
557
aaacccatgtcccacattttcaac
558


chr3_50334231
gcaatggaggtcccttggg
559
atggagggcctggaggtc
560


chr3_50355730
tcaccactccagcccaagta
561
gccagtaccttcctgcatctc
562


chr4_1330759
actacaccagctgggaaacaatt
563
tgggaggacaagagtggca
564


chr4_1737502
cccgtgtgtgttaggggatg
565
cggcgcacatacctgct
566


chr4_183815688
taagatcaaacacatcagcaatgagc
567
gcaaccaaagtttttctttctttccc
568


chr5_141014494
agccttgcatattggtgggg
569
ctctctactgacttaaggattgtggg
570


chr5_176940384
ctccaatcagcttcagggagac
571
gcgacagaccctgctcttc
572


chr5_179290845
ccaccacctggctctcct
573
aggacctgtaccacgccat
574


chr6_26545632
agccacagaggagatcagct
575
agtacagagctctcaaaaatgtacatttc
576


chr6_2745352
gagagcacagacaaccccg
577
ctcgtgttgtatttcccccagat
578


chr10_102746503
ccattgatgggttccatttgcc
579
gaccacgttccgcggg
580


chr10_1046712
tggaggataggaacaccatcga
581
acacacaccttgttgatgaagaga
582


chr10_99219885
gcatggaagccctggacc
583
ccctgatgtacctcaaaggctc
584


chr11_118967758
cccaggatgccaatgatcaca
585
agtccatttctccttgcagatcc
586


chr11_120107411
gggacagggagtatcaggct
587
ctctctcagacttgctcactgatc
588


chr11_3028140
cagccccgggagctct
589
ccatgtctgagggaactgctc
590


chr12_109994870
agggatcctccaagctccc
591
gegttgatctctcatttcaaaccat
592


chr12_112037000
atggtgaggggcccataca
593
gactgttttggtagcaacggc
594


chr12_6638116
cgaccccgagcctcaga
595
caatacttcatgatggtgtggaaagg
596


chr13_111298392
gctccatgagttcctccacag
597
cagcaccaagagggccg
598


chr13_115004914
gatgagcggcacttctgttttc
599
ttctgccacgtaatgagggc
600


chr14_105222037
agactcaatggccatgcagg
601
atctgcccacgtgcagc
602


chr14_106208082
gatgtcgctgggatagaagcc
603
aaccatctccaaagccaaaggt
604


chr14_24736027
gggtcctgcacatctccttg
605
ccatcacctccttcctccct
606


chr15_40328665
gtgaaagcaggaaatgtatgccc
607
ggcttattcaaacctccttagagcta
608


chr15_44038899
gacaacttcgagagtcgcatct
609
cacaggaatgaagggcccc
610


chr15_75189930
gtctgagctgcactgccttat
611
gacagcaggcacggaatatca
612


chr16_11773662
gtgcccccgctgtaagac
613
tctctccagaaaggacctaagtgt
614


chr16_15129970
cctgcgaggttcagatgctt
615
gctctccggtccttctacct
616


chr16_2049640
gccgagcgctgggaag
617
ccccgcccgctacct
618


chr17_19247075
atttgtgggctagctcctatgtg
619
acctgtgttcttctgtgttccc
620


chr17_38179492
tcaaagatgtggatggagcgg
621
gcagacagccacgcagat
622


chr17_40722029
gccattcctgggagtacacag
623
cacgctgacagctcctgg
624


chr18_12351342
ctacactcatgagcactggacc
625
ttgttatctttcaggttttaatacaacaacaaat
626


chr18_33750046
gatgcttgaaatgctctcaagtcc
627
gggccaatgttgtgctcaatac
628


chr18_77805856
gagccgaccacaagctcc
629
caggtcatcttcaacctcctcg
630


chr19_10226256
ggacaccccggcaatgg
631
tcccgcatctacctggctaa
632


chr19_1110829
cgaaggtgtctgagaagtactgg
633
cgtggagaagggtgagtgc
634


chr19_13885484
ctgaaaagaatcggggccca
635
ctgatccccgggctcca
636


chr20_25260931
gctttcagagggctccagatc
637
gcgctccaaggcctcag
638


chr20_3193978
tctgtttccctgataagtgccg
639
tggcaaaggaaggcagtgtt
640


chr20_391170
attcccagattcctcatggtgc
641
cgaacccctgaattctagctgaata
642


chr21_38568308
ctgatcggaagcagcctgtt
643
atcaggaaacctcagttcgataaagtat
644


chr21_46271452
ctgcaagacgagaggactgtc
645
gtttaaagaagaaaacccgtatgctagat
646


chr22_32795641
ttgcccctccaaagtgagttac
647
actagcaccttttatacttatccagagac
648


chr22_38273749
ctccaccccatccccagat
649
aaagttcttcatagacttgtgggtca
650


chr22_39134715
cgaagtccttgggggcac
651
gcacactgagggctggtc
652

## Claims

1. A method for determining the authentication of a human or mouse sample, the method comprising:
obtaining a nucleic acid from the human or mouse sample;
providing a set of oligonucleotide primer pairs, wherein each oligonucleotide primer pair in the set flanks a single locus in a set of single nucleotide polymorphism (SNP) loci of (i) SEQ ID NOs: 1-237 or (ii) SEQ ID NOs: 238-436, and wherein each oligonucleotide primer pair is capable of amplifying a single locus from the set of SNP loci in a multiplex amplification reaction;
co-amplifying the set of SNP loci in a multiplex amplification reaction, wherein the product of the multiplex amplification reaction comprises a mixture of amplified alleles from each of the co-amplified loci in the set of SNP loci;
evaluating the products of the co-amplification reaction to determine the alleles present at each of the set of SNP loci within the human or mouse sample;
comparing the alleles present at each of the set of SNP loci within the sample with alleles present at each of the set of SNP loci in a human or mouse reference sample; and
determining whether (i) the human or mouse sample is the same as the human or mouse reference sample, or (ii) the human or mouse sample contains a contaminant other than the human or mouse reference sample.

2. The method of claim 1, wherein the human or mouse sample is a cell, a tissue, an organoid, or a combination thereof.

3. The method of claim 2, wherein the human or mouse sample is a cell line or a tumor tissue.

4. The method of claim 1, wherein the human or mouse sample contains a contaminant, the method further comprising determining the percentage of the contaminant in the human or mouse sample.

5. The method of claim 1, wherein the human or mouse sample contains a contaminant, the method further comprising determining the identity of the contaminant.

6. The method of claim 1, wherein the products of the co-amplification reaction are evaluated by next-generation sequencing (NGS).

7. The method of claim 1, wherein the nucleic acid is barcoded.

8. The method of claim 1, further comprising identifying the gender of a subject from which the sample is obtained by analyzing at least one sex chromosome SNP locus.

9. The method of claim 1, wherein the human or mouse sample is a mouse sample from a mouse tumor model selected from the group consisting of 4T1, A20, B16-BL6, B16-F0, B16-F1, B16-F10, C1498, Colon26, CT26WT, E.G7-Ova, EL4, EMT6, H22, Hepa1-6, J558, J774A1, JC, KLN205, L1210, L5178-R, LLC, MBT2, MC38, MPC-11, Neuro-2a, P388D1, P815, Pan02, Renca, RM1, S91, and WEHI164.