Methods for detecting and treating fungal infections

Abstract

The present disclosure provides a method for determining whether a subject has or is at risk of developing a fungal infection, such as candidemia, and a method for treating the subject based on the determination. The determination may include rapid detection of one or more pathogen classes, such as fungi, viruses, and bacteria, at one time. Systems useful therefor are also provided.

Description

[Technical field]

[0001] [CROSS REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 146,212, filed February 5, 2021, the disclosure of which is incorporated herein by reference in its entirety.

[0002] [Federal Government Grant Description] This invention was made with Government support under Federal Grant Number R21AI132978-01 awarded by the National Institute of Allergy and Infectious Diseases (NIH / NIAID). The Government has certain rights in this invention. [Background technology]

[0003] Candidemia is the most common nosocomial bloodstream infection in the United States and causes significant morbidity and mortality in hospitalized patients. Improved rapid diagnostic methods capable of distinguishing candidemia from other causes of febrile illness in hospitalized patients are of paramount importance. Diagnostic methods based on pathogen class-specific biomarkers, such as those focusing on host gene expression patterns in circulating leukocytes, may provide a promising alternative.

[0004] U.S. Patent Application Publication No. 2016 / 0194715 to Zaas et al. discusses methods for identifying fungal infections such as candidiasis by proteomic assays of peripheral blood samples. Summary of the Invention

[0005] This Summary is provided to introduce a selection of concepts that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

[0006] According to some embodiments, provided herein is a method for classifying a subject, the method comprising: (a) obtaining a biological sample from the subject; (b) measuring a signature indicative of one or more of a fungal infection, and optionally a bacterial infection, a viral infection, health, and / or a non-infectious illness in the biological sample on a platform, where the signature comprises gene expression levels of a predefined set of genes; (c) inputting the gene expression levels into a fungal classifier, and optionally one or more additional classifiers selected from a bacterial infection classifier, a viral classifier, and a control classifier (health and / or a non-infectious illness), where the classifier comprises predefined weighting values ​​(i.e., coefficients) for each of the genes of the predefined set of genes for the platform; and (d) classifying the subject as having a fungal infection, and / or a bacterial infection, a viral infection, or a control based on the gene expression levels and the classifiers.

[0007] In some embodiments, the method comprises normalizing the gene expression levels to generate normalized gene expression values, wherein the inputting step comprises inputting the normalized gene expression values ​​into a classifier, and wherein the classifying step comprises calculating a probability for a fungal infection, and optionally a bacterial infection, a viral infection, or a control, based on said normalized gene expression values ​​and the classifier.

[0008] In some embodiments, the method further comprises generating a report that assigns the subject a score indicative of the probability of fungal infection, and optionally, bacterial infection, viral infection, health and / or non-infectious disease.

[0009] In some embodiments, the method further comprises (e) administering an appropriate treatment to the subject based on the classifying step.

[0010] In some embodiments, the defined gene set is a set of 1, 5, 10, 15, or 20 to 30, 40, 50, 60, or 70 genes. In some embodiments, the defined gene set is a set of 1, 5, 10, 15, or 20 to 30, 40, 50, 60, or 70 genes listed in Tables 1-5. In some embodiments, the defined gene set is a set of 1, 5, or 10 to 15, 20, 25, 30, or 33 genes listed in Tables 6-10 (e.g., selected from the genes listed in bold in Tables 6-10).

[0011] In some embodiments, the subject has symptoms of an infection (e.g., fever). In some embodiments, the subject has symptoms of sepsis.

[0012] In some embodiments, the biological sample is selected from the group consisting of peripheral blood, sputum, cerebrospinal fluid, urine, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, and combinations thereof. In some embodiments, the biological sample comprises a peripheral blood sample. In some embodiments, the biological sample comprises bronchoalveolar lavage.

[0013] In some embodiments, the measuring step includes or is preceded by one or more of the steps of purifying cells from the sample, disrupting the cells of the sample, and isolating RNA from the sample.

[0014] In some embodiments, the measuring step comprises PCR amplification, isothermal amplification, sequencing, and / or nucleic acid probe hybridization. In some embodiments, the platform comprises an array platform, a thermal cycler platform (e.g., a multiplexed and / or real-time PCR platform), a hybridization and multiplexed signal-coded (e.g., fluorescent) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, an isothermal amplification platform, or a combination thereof.

[0015] In some embodiments, the fungal infection involves a yeast, such as Candida, Trichosporon, or Cryptococcus.

[0016] In some embodiments, the fungal classifier is / was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to be suffering from a fungal infection; (ii) obtaining biological samples from a plurality of non-hospitalized healthy controls and / or a plurality of subjects known to be suffering from a non-communicable disease; (iii) measuring on a platform gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

[0017] In some embodiments, the fungal classifier is / was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to suffer from a fungal infection; (ii) obtaining biological samples from a plurality of subjects known to suffer from a bacterial infection; (iii) measuring on a platform gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

[0018] In some embodiments, the fungal classifier is / was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to suffer from a fungal infection; (ii) obtaining biological samples from a plurality of subjects known to suffer from a viral infection; (iii) measuring on a platform gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

[0019] In some embodiments, the generating step comprises iteratively (i) assigning a weight for each normalized gene expression value, inputting the weights and expression values ​​for each gene into a classifier (e.g., a linear regression classifier) ​​equation to determine a score for each outcome in a plurality of subjects, then (ii) determining the accuracy of classification for each outcome across the plurality of subjects, then (iii) adjusting the weights until the accuracy of classification is optimized to provide said fungal, bacterial, viral, and / or control classifiers for the platform, where genes with non-zero weights are included in each classifier, and optionally uploading the components of each classifier (genes, weights, and / or etiology thresholds) to one or more databases.

[0020] According to some embodiments, a method for detecting a fungal infection in a subject comprises providing a biological sample from the subject and measuring on a platform differential expression of a defined set of genes, wherein the defined set of genes is from 5, 10, 15, 20, 25, or 30 to 50, 60, 70, 80, 90, or all 94 of the genes listed in Tables 1-5; for example, 3, 5, 8, 10, 12, 15, 18, 20, 30, 40, 50, 60, 70, 80, 90, or all 94 of the genes listed in Table 1. and optionally, all 3, 5, 8, 10, 12, 15, or 18 of the genes listed in Table 2; all 3, 5, 8, 10, 12, 15, 18, or 19 of the genes listed in Table 3; all 3, 5, 8, 10, 12, 15, 18, or 19 of the genes listed in Table 4; and / or all 3, 4, 5, 6, 7, 8, 9, or 10 of the genes listed in Table 5, or any of the above defined genes. The child set may include all 5, 10, 15, 20, 25, 30, or 33 of the genes (e.g., measurable using oligonucleotide probes homologous to said genes) listed in Tables 6-10; e.g., all 1, 2, 3, 4, or 5 of the genes listed in Table 6; and optionally all 1, 2, 3, 4, 5, 6, 7, 8, or 9 of the genes listed in Table 7; all 1, 2, 3, 4, 5, 6, 7, or 8 of the genes listed in Table 8; all 1, 2, 3, 4, 5, 6, 7, or 8 of the genes listed in Table 9. and / or one, two, three, or all four of the genes listed in Table 10, or said defined set of genes comprises ITGA2B, MKI67, and AZU1; and optionally HDAC4, DCAF15, SDHC, SAP30L, DNASE1, and DCAF15; PIGT, HERC6, and LY6E; SLC35E1, WIPI2, RELL1, MAP1LC3B, CASZ1, and GABBR1;and / or RPS24 and CTSB, wherein differential expression of a defined set of genes indicates the presence or absence of a fungal infection in a subject.

[0021] In some embodiments, the measuring step includes or is preceded by one or more of the steps of purifying cells from the sample, disrupting cells of the sample, and isolating RNA from the sample.

[0022] In some embodiments, the measuring step comprises semi-quantitative PCR, isothermal amplification, and / or nucleic acid probe hybridization. In some embodiments, the platform comprises an array platform, a thermal cycler platform (e.g., a multiplexed and / or real-time PCR platform), an isothermal amplification platform, a hybridization and multiplexed signal-coded (e.g., fluorescent) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof.

[0023] In some embodiments, the subject is suffering from a symptom of infection (e.g., fever). In some embodiments, the subject is suffering from a symptom of sepsis.

[0024] In some embodiments, the method further comprises treating said subject for a fungal infection if the presence of a fungal infection is detected.

[0025] According to some aspects, there is further provided a method of treating a fungal infection in a subject, comprising administering an appropriate treatment regimen to the subject when the subject is determined to have a fungal infection by the methods as taught herein. Also provided is the use of an appropriate treatment regimen to treat a fungal infection in a subject when the subject is determined to have a fungal infection by the methods as taught herein.

[0026] In some embodiments, the appropriate treatment regimen includes administering an antifungal antibiotic. In some embodiments, the appropriate treatment regimen includes administering a therapeutic agent selected from the group consisting of echinocandins (e.g., caspofungin, micafungin, anidulafungin), azole antifungals (e.g., fluconazole, voriconazole, isavuconazole, posaconazole), polyenes (e.g., amphotericin B), pyridine analogs (e.g., 5-fluorocytosine (5-FC, or flucytosine)), APX001 (fosmanogepix), APX879, benzothioureas, clofazimine, hydrazicins (e.g., BHBM and B0), ibomycin, monoclonal antibody 18B7, aminopyrazole resorcylate (e.g., Compound 112), sertraline, tamoxifen, VT-1598, and the like, as well as combinations thereof.

[0027] In some embodiments, the methods further comprise the step of monitoring the subject for efficacy of an appropriate treatment regimen by use of a method for detecting a fungal infection as taught herein.

[0028] According to some embodiments, a system for detecting a fungal infection in a subject includes at least one processor; a sample input circuit configured to accept a biological sample from the subject; a sample analysis circuit coupled to the at least one processor and configured to determine gene expression levels in the biological sample of a set of genes predefined as indicative of a fungal infection; an input / output circuit coupled to the at least one processor; a memory circuit coupled to the at least one processor and configured to store data, parameters, and / or the set of genes; and a memory coupled to the processor and configured to, when executed by the at least one processor, operate the at least one processor. The system further includes a memory including computer readable program code embodied in the memory to cause the system to perform operations including controlling / performing measurement by a sample analysis circuit of gene expression levels of a predefined set of genes in the biological sample; normalizing the gene expression levels to generate normalized gene expression values; retrieving from the memory circuitry predefined weighting values ​​(i.e., coefficients) for each of the genes of the predefined set of genes; calculating a likelihood of fungal infection based on the weighted values ​​of the normalized gene expression values; and controlling output by an input / output circuit of a determination of the presence or absence of a fungal infection.

[0029] In some embodiments, the defined gene set includes 5, 10, 15, 20, 25, or 30, up to all 50, 60, 70, 80, 90, or 94, of the genes listed in Tables 1-5; for example, 3, 5, 8, 10, 12, 15, 18, 20, 25, or all 29, of the genes listed in Table 1; and, optionally, 3, 5, 8, 10, 12, 15, of the genes listed in Table 2. or all 18 of the genes listed in Table 3; all 3, 5, 8, 10, 12, 15, 18, or 19 of the genes listed in Table 4; and / or all 3, 4, 5, 6, 7, 8, 9, or 10 of the genes listed in Table 5; or the defined gene set includes 5, 10, 15, 20, 22, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, For example, 1, 2, 3, 4, or all 5 of the genes listed in Table 6; and, optionally, 1, 2, 3, 4, 5, 6, 7, 8, or all 9 of the genes listed in Table 7; 1, 2, 3, 4, 5, 6, 7, or all 8 of the genes listed in Table 8; 1, 2, 3, 4, 5, 6, or all 7 of the genes listed in Table 9; and / or 1, 2, 3, 4, 5, 6, or all 7 of the genes listed in Table 10; The defined set of genes may include one, two, three, or all four of the genes listed in 10, or the defined set of genes may include ITGA2B, MKI67, and AZU1; and optionally, HDAC4, DCAF15, SDHC, SAP30L, DNASE1, and DCAF15; PIGT, HERC6, and LY6E; SLC35E1, WIPI2, RELL1, MAP1LC3B, CASZ1, and GABBR1; and / or RPS24 and CTSB.

[0030] In some embodiments, the system comprises computer readable code for converting the quantitative or semi-quantitative detection of gene expression into a cumulative score or probability of fungal infection.

[0031] In some embodiments, the system includes an array platform, a thermal cycler platform (e.g., a multiplexed and / or real-time PCR platform), a hybridization and multiplexed signal-coding (e.g., a fluorescent) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, an isothermal amplification platform, or a combination thereof.

[0032] The accompanying figures and examples are offered by way of illustration and not by way of limitation. The foregoing aspects and other features of the present disclosure are explained in the following description and taken in conjunction with the accompanying figure examples ("FIG.") which relate to one or more embodiments. [Brief description of the drawings]

[0033] [Figure 1] FIG. 1 is a schematic diagram showing the experimental design for decomposition of discovery and validation cohorts by infection phenotype, according to one embodiment of the present disclosure. [Figure 2A] Differentially expressed genes (adjusted P<0.05) in response to different infection phenotypes. All genes, infection phenotypes, are compared to all others. [Figure 2B] Figure 1 shows differentially expressed genes (adjusted P<0.05) in response to different infection phenotypes. All genes, Candida genus, are compared to each other phenotype. [Diagram 3] Graphs showing multinomial gene expression classifiers according to embodiments of the present disclosure. Panel A: ROC of the multinomial classifier performance for each infection phenotype in the discovery cohort. Panel B: Boxplots showing the predicted probability of the classifier for each infection phenotype in the discovery cohort. Panel C: ROC of the multinomial classifier performance for each infection phenotype in the validation cohort. Panel D: Boxplots showing the predicted probability of the classifier for each infection phenotype in the validation cohort. [Figure 4]Graphs showing validation cohorts according to embodiments of the present disclosure. ROC (Panel A) and box plots (Panel B) of multinomial classifier performance for each infection phenotype in the Tsalik et al. cohort. ROC (Panel C) and box plots (Panel D) of multinomial classifier performance for each infection phenotype in the Ramilo et al. cohort. ROC (Panel E) and box plots (Panel F) of multinomial classifier performance for each infection phenotype in the in vitro cohort. The infection class as established by the classifier was determined by the phenotype with the highest predicted probability per subject. [Diagram 5]FIG. 11 is a block diagram of a classification system and / or computer program product that may be used in a platform according to the present invention. The classification system and / or computer program product 1100 may include a processor subsystem 1140, which includes one or more central processing units (CPUs) on which one or more operating systems and / or one or more applications run. Although one processor 1140 is shown, it will be understood that there may be multiple processors 1140, which may be either electrically interconnected or separate. The processor 1140 is configured to execute computer program code from a memory device, such as the memory 1150, for performing at least a portion of the operations and methods described herein. The memory circuitry 1170 may store a database that provides access to data / parameters / classifiers used by the classification system 1110, such as signatures, weights, thresholds, etc. The input / output circuitry 1160 may include a display and / or a user input device, such as a keyboard, a touch screen, and / or a pointing device. Devices attached to the input / output circuitry 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input / output circuitry 1160 may include networking or communication controllers, input devices (keyboard, mouse, touch screen, etc.), and output devices (printers or displays). An optional update circuitry 1180 may be included as an interface for providing updates to the classification system 1100, for example, updates to the code executed by the processor 1140 stored in the memory 1150 and / or the storage circuitry 1170. Updates provided via the update circuitry 1180 may also include updates to portions of the storage circuitry 1170 related to databases and / or other data storage formats that maintain information about the classification system 1100, such as signatures, weights, thresholds, etc.The sample input circuit 1110 provides an interface for the classification system 1100 to accept a biological sample to be analyzed. The sample processing circuit 1120 can further process the biological sample within the classification system 1100 to prepare the biological sample for automated analysis. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] The disclosures of all patent references cited herein are hereby incorporated by reference to the extent they are consistent with the disclosure set forth herein.

[0035] To promote an understanding of the principles of the disclosure, reference will now be made to preferred embodiments and specific language will be used to describe the same, although it will be understood that no limitation of the scope of the disclosure is thereby intended, and that variations and further modifications of the disclosure as exemplified herein are contemplated as would normally occur to one skilled in the art.

[0036] As used herein, the articles "a," "an," and "the" are used to refer to one or to more than one (i.e., at least one) of the grammatical object of the article. By way of example, "an element" means at least one element and can include more than one element.

[0037] "About" is used to provide flexibility to the numerical range endpoints by providing that the indicated value may be slightly above or below the endpoints (e.g., by 2%, 5%, 10%, or 15%) without affecting the desired result.

[0038] Use of the terms "including," "comprising," or "having," and their variations herein are intended to encompass the subsequently listed elements and equivalents thereof, as well as additional elements. As used herein, "and / or" refers to and includes any and all possible combinations of one or more of its associated listed items, as well as the lack of combinations when interpreted in the alternative ("or").

[0039] As used herein, the transitional phrase "consisting essentially of" (and grammatical variations) may be construed to include the recited materials or steps, and those that do not materially affect the basic and novel characteristics of the claimed invention. Thus, as used herein, the term "consisting essentially of" should not be construed as the equivalent of "comprising."

[0040] Moreover, the present disclosure also contemplates that in some embodiments, any feature or combination of features set forth herein may be excluded or omitted. To illustrate, if the specification states that a complex comprises components A, B, and C, it is specifically contemplated that any or combination of A, B, or C, singly or in any combination, may be omitted and discarded.

[0041] The recitation of ranges of values ​​herein, unless otherwise indicated herein, is merely intended to serve as a shorthand method of individually referring to each separate value falling within the range, and each separate value is incorporated herein as if it were individually recited herein. For example, if a concentration range is stated as 1% to 50%, it is intended that values ​​such as 2% to 40%, 10% to 30%, or 1% to 3%, are expressly recited herein. These are merely examples of what is specifically intended, and all possible combinations of numerical values ​​between the lowest recited value and the highest recited value, including those values, should be considered to be expressly recited in this disclosure.

[0042] As used herein, the term "signature" refers to a set of biological analytes and measurable amounts of said analytes, the specific combination of which indicates the presence or absence of a specified biological state. These signatures are found in multiple subjects with known conditions (e.g., confirmed bacterial infection, viral infection, fungal infection, or control (healthy and / or non-infectious disease)) and can distinguish (individually or jointly) one or more categories or outcomes of interest. These measurable analytes, also known as biological markers, can be (but are not limited to) gene expression levels, protein or peptide levels, or metabolite levels. See also U.S. Patent Application Publication No. 2015 / 0227681 to Courchesne et al.; U.S. Patent Application Publication No. 2016 / 0153993 to Eden et al.

[0043] In some embodiments as disclosed herein, a "signature" is a particular combination of genes that, when the expression levels of the genes are incorporated into a classifier as taught herein, identifies a condition, such as a fungal infection. See, for example, the examples provided below. However, the signature may be processed / interpreted in other ways, such as those mentioned in U.S. Patent Application Publication No. 2015 / 0227681 to Courchesne et al. and U.S. Patent Application Publication No. 2016 / 0153993 to Eden et al. As a non-limiting example, U.S. Patent No. 10,533,224 to Khatri et al. discusses the use of comparison of biomarker levels to a reference value range of uninfected control subjects, such as a time-matched reference value range, and the geometric mean of biomarker expression compared to the control reference value for the biomarker, to identify a condition or biological state.

[0044] As used herein, the terms "classifier" and "predictor" are used interchangeably and refer to a mathematical function that uses the values ​​of a signature (e.g., gene expression levels for a defined set of genes) and the determined coefficients (or weights) for each signature component to generate a score for a given observation or individual patient for assignment to a category. A classifier can be linear and / or probabilistic. A classifier is linear when the score is a function of the sum of the signature values ​​weighted by a set of coefficients. Furthermore, a classifier is probabilistic when the function of the signature values ​​generates a probability, where a value between 0 and 1.0 (or between 0% and 100%) quantifies the likelihood that a subject or observation will belong to a particular category or produce a particular outcome, respectively. Probit regression and logistic regression are examples of probabilistic linear classifiers that use probit and logistic link functions, respectively, to generate a probability.

[0045] Classifiers as taught herein can be obtained by a procedure known as "training," which uses a set of data containing observations with known category membership (e.g., fungi, viruses, bacteria, controls, etc.). Specifically, training seeks to find an optimal signature as well as optimal coefficients (i.e., weights) for each component (e.g., gene expression level components) of a given signature, with the optimal result being determined by the highest achievable classification accuracy.

[0046] As used herein, "classifying" or "classification" refers to a method of assigning a subject suffering from or at risk of a biological condition, such as an infection (e.g., a fungal infection), to one or more categories or outcomes (e.g., the patient is infected with a pathogen or is not infected). The outcome or category is determined by the value of the score provided by the classifier, which may be compared to a cutoff or threshold, confidence level, or limit. In other scenarios, the probability of belonging to a particular category may be given (e.g., when the classifier reports a probability).

[0047] As used herein, the term "indicating" when used in conjunction with gene expression levels means that the gene expression levels are up-regulated or down-regulated, altered, or changed compared to the expression levels in another biological state or control. The term "indicating" when used in conjunction with protein levels means that the gene expression levels are higher or lower, increased or decreased, altered, or changed compared to the standard protein levels in another biological state.

[0048] In some embodiments, the classifier / classification is "agnostic" in that it indicates a general biological condition, such as a fungal infection, bacterial infection, viral infection, or SIRS, but does not provide an indication of a specific organism (genus, and optionally, species) as the cause of the condition (e.g., the particular fungus or bacterium causing the infection).

[0049] As used herein, the term "biomarker" or "biological marker" is used interchangeably and refers to a naturally occurring biological molecule present in a subject at various concentrations that is useful for predicting the risk or incidence of a disease or condition, such as fungal infection. For example, a biomarker can be a protein or gene expression that is present in a higher or lower amount in a subject at risk of or suffering from a fungal infection, such as candidemia. A biomarker can include, but is not limited to, a nucleic acid, a ribonucleic acid, or a polypeptide that is used as an indicator or marker for a biological state in a subject. In some embodiments, a biomarker comprises RNA. In other embodiments, a biomarker comprises DNA. In yet other embodiments, a biomarker comprises a protein. A biomarker can also include any naturally occurring or non-existent polymorphism (e.g., single nucleotide polymorphism (SNP)) or gene variant present in a subject that is useful for predicting the risk or incidence of a fungal infection, such as candidemia.

[0050] As used herein, "treating", "treatment", "therapy" and / or "therapeutic regimen" refer to a clinical intervention made in response to a disease, disorder, physiological condition, or biological condition (e.g., fungal infection) manifested by or to which the patient may be susceptible. The goal of treatment includes alleviating or preventing / reducing symptoms, slowing or halting the progression or worsening of the disease, disorder, or condition, and / or ameliorating the disease, disorder, or condition, such as an infection. As used herein, the terms "prevent", "preventing", "prevention", "prophylactic treatment", and the like refer to reducing the probability of developing a disease, disorder, or condition (e.g., fungal infection such as candidemia) in a subject who does not have the disease, disorder, or condition but is at risk of or susceptible to developing it. The term "effective amount" or "therapeutically effective amount" refers to an amount sufficient to produce beneficial or desired biological and / or clinical results.

[0051] As used herein, the term "administering" an agent, such as a therapeutic entity, to an animal or cell is intended to refer to dosing, delivering, or applying the agent (e.g., drug, treatment, etc.) to an intended target. With respect to a therapeutic agent, the term "administering" is intended to refer to contacting or dosing, delivering, or applying the therapeutic agent to a subject by any suitable route for delivery of the therapeutic agent to a desired location in an animal, including delivery by either parenteral or oral routes, intramuscular, injection, subcutaneous / intradermal injection, intravenous injection, intrathecal administration, buccal administration, transdermal delivery, topical administration, and administration by intranasal or respiratory routes.

[0052] The term "appropriate treatment regimen" or "appropriate treatment" refers to the standard treatment required to treat a particular disease or disorder. Such regimen often requires the act of administering to a subject a therapeutic agent capable of producing a curative effect in a disease state. For example, a therapeutic agent for treating a subject with a fungal infection (e.g., candidemia, cryptococcus infection, etc.) may include, for example, an antifungal antibiotic. Specific therapeutic agents for treating a subject having a fungal infection may include, but are not limited to, drugs such as echinocandins (e.g., caspofungin, micafungin, anidulafungin), azole antifungals (e.g., fluconazole, voriconazole, isavuconazole, posaconazole), polyenes (e.g., amphotericin B), pyridine analogs (e.g., 5-fluorocytosine (5-FC, or flucytosine)), APX001 (fosmanogepix), APX879, benzothioureas, clofazimine, hydrazicines (e.g., BHBM and B0), ibomycin, monoclonal antibody 18B7, aminopyrazole resorcylates (e.g., Compound 112), sertraline, tamoxifen, VT-1598, and the like, as well as combinations thereof. See, for example, Iyer et al., “Treatment strategies for crypococcal infection: challenges, advances and future outlook,” Nature Reviews Microbiology 19, 454-466 (2021).

[0053] Treatment of bacterial infections may include antibiotics, including but not limited to penicillins, cephalosporins, fluoroquinolones, tetracyclines, macrolides, and aminoglycosides.Therapeutic agents for treating subjects with viral infections include but are not limited to oseltamivir, RNAi antivirals, inhaled ribavirin, monoclonal antibody respigam, zanamivir, and neuraminidase blockers.The present disclosure contemplates the use of the methods taught herein to determine treatment with antifungal, antiviral, or antibiotic agents that are not yet commercially available.

[0054] Such regimens may also include administering to the subject a therapeutic agent capable of causing a reduction in symptoms associated with a disease or biological condition. Examples of such therapeutic agents include, but are not limited to, NSAIDS, acetaminophen, antihistamines, beta-agonists, antitussives, or other agents that reduce symptoms associated with a disease or infectious process.

[0055] As used herein, the term "biological sample" includes, but is not limited to, a sample containing tissue, cells, and / or biological fluid isolated from a subject. Examples of biological samples include, but are not limited to, tissue, cells, biopsy, blood (e.g., peripheral blood), lymph, serum, plasma, cerebrospinal fluid, urine, saliva, mucus, tears, sputum, nasopharyngeal swabs, nasopharyngeal washes, bronchoalveolar lavage fluid, endotracheal aspirates, and the like. In some embodiments, the biological sample comprises peripheral blood. In some embodiments, the biological sample comprises bronchoalveolar lavage fluid. Biological samples can be obtained directly from a subject (e.g., by blood or tissue sampling) or from a third party (e.g., received from an intermediary such as a health care provider or laboratory technician).

[0056] The term "genetic material" refers to the material corresponding to that used to store genetic information in the nucleus or mitochondria of the cells of an organism. Examples of genetic material include, but are not limited to, double-stranded and single-stranded DNA, cDNA, RNA, and mRNA.

[0057] As used herein, the terms "subject" and "patient" are used interchangeably herein and refer to both human and non-human animals. The term "non-human animal" of the present disclosure includes all vertebrates, e.g., mammals and non-mammals, e.g., non-human primates, sheep, dogs, cats, horses, cows, chickens, amphibians, reptiles, etc. The methods and compositions disclosed herein can be used either in vitro on a sample (e.g., on an isolated cell or tissue) or in vivo on a subject (i.e., a living organism, such as a patient). In some embodiments, the subject includes a human suffering from or at risk of suffering from a fungal infection, such as candidemia. In some embodiments, the subject has symptoms of infection (e.g., fever). In some embodiments, the subject has symptoms of sepsis.

[0058] As used herein, "sepsis" refers to organ damage caused by a dysregulated host response to infection. See Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 801 (2016). Organ damage can be determined, for example, by an increase in the Sequential Organ Failure Assessment (also known as Sepsis-Related Organ Failure Assessment or SOFA) score of 2 or more points above baseline.

[0059] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0060] One aspect of the present disclosure provides a method for generating pathogen class-specific classifiers for a platform capable of identifying and distinguishing fungal, viral, and / or bacterial infections across various hosts with a high degree of accuracy, comprising, consisting of, or consisting essentially of: (i) obtaining biological samples from a plurality of subjects known to be suffering from a fungal infection; (ii) obtaining biological samples from a plurality of non-hospitalized healthy controls; (iii) measuring gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii) on the platform; (iv) optionally normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating one or more classifiers capable of identifying and distinguishing fungal infections across various hosts with a high degree of accuracy.

[0061] In some embodiments, the method further provides for obtaining biological samples from a plurality of subjects suffering from viral and / or bacterial infections and / or non-infectious illnesses (SIRS) for use in the generating step.

[0062] In some embodiments, the measuring step includes or is preceded by one or more of the steps of purifying cells from the sample, disrupting the cells of the sample, and isolating RNA from the sample.

[0063] In some embodiments, the measuring step comprises PCR, reverse transcription (of mRNA to cDNA), isothermal amplification, and / or nucleic acid probe hybridization.

[0064] As used herein, "fungal infection" refers to an infection (e.g., blood infection, pulmonary infection, etc.) of a host subject by a pathogenic fungus (e.g., yeast, mold, dematiaceous fungus). Fungi may include, but are not limited to, fungi such as Candida (causing candidemia and candidiasis), Cryptococcus (e.g., Cryptococcus neoformans), Aspergillus, Histoplasma (e.g., Histoplasma capsulatum), Pneumocystis, Coccidioides (e.g., Coccidioides immitis), Paracoccidioides (e.g., Paracoccidioides brasiliensis), Sporothrix (e.g., Sporothrix schenckii), and the like.

[0065] In some embodiments, the fungus is a yeast, such as Candida, Trichosporon, or Cryptococcus. Representative species of Candida include, but are not limited to, Candida albicans, Candida glabrata, Candida tropicalis, Candida dubliniensis, Candida krusei, Candida lusitanae, Candida parapsilosis, and Candida zeylanoides. Representative species of Trichosporon include, but are not limited to, Trichosporon fungemia. Representative species of the genus Cryptococcus include, but are not limited to, Cryptococcus neoformans and Cryptococcus gattii.

[0066] As used herein, the term "platform" or "technology" refers to an apparatus (e.g., instruments and associated parts, computers, computer-readable media including one or more databases as taught herein, reagents, etc.) that can be used to measure a signature, e.g., gene expression levels, in accordance with the present disclosure. Examples of platforms include, but are not limited to, array platforms, thermal cycler platforms (e.g., multiplexed and / or real-time PCR platforms), nucleic acid sequencing platforms, isothermal amplification platforms (e.g., loop-mediated isothermal amplification (LAMP, RT-LAMP)), hybridization and / or multiplexed signal-encoded (e.g., fluorescent) detector platforms, etc., nucleic acid mass spectrometry platforms, magnetic resonance platforms, Northern blotting, and combinations thereof (e.g., a combination of PCR and isothermal amplification - see, e.g., Varlamov et al., "Combinations of PCR and Isothermal Amplification Techniques Are Suitable for Fast and Sensitive Detection of SARS-CoV-2 Viral RNA," Front. Bioeng. Biotechnol., 2020).

[0067] In some embodiments, the platform is configured to measure gene expression levels semi-quantitatively (i.e., rather than measuring in individual or absolute expression, expression levels are measured as estimates and / or relative to each other or to specified markers (e.g., the expression of another "standard" or "reference" gene).

[0068] In some embodiments, semi-quantitative measurements include "real-time PCR" by performing PCR cycles until a signal indicative of a specified mRNA is detected, and the number of PCR cycles required until detection is used to provide an estimated or relative expression level of the genes in the signature.

[0069] Real-time PCR platforms include, for example, TaqMan® Low Density Array (TLDA), where samples undergo multiplexed reverse transcription followed by real-time PCR on an array card with a group of wells in which real-time PCR is performed. See Kodani et al. 2011, J. Clin. Microbial. 49(6):2175-2182. Real-time PCR platforms also include, for example, Biocartis Idylla™ sample-to-result technology, where cells are lysed, DNA / RNA is extracted, real-time PCR is performed, and the results are detected.

[0070] Magnetic resonance platforms include, for example, T2 Biosystems® T2 Magnetic Resonance (T2MR®) technology, in which molecular targets can be identified in biological samples without the need for purification.

[0071] The terms "array", "microarray" and "microarray" are interchangeable and refer to the arrangement of a group of nucleotide sequences presented on a substrate. Any type of array can be utilized in the methods provided herein. For example, the array can be on a solid substrate such as a glass slide (solid-phase array) or on a semi-solid substrate such as a nitrocellulose membrane. The array can also be presented on beads, i.e., bead arrays. These beads are typically microscopic and can be made, for example, from polystyrene. The array can also be presented on nanoparticles, which can be made, for example, from gold, especially, but also from silver, palladium, or platinum. See, for example, the Nanosphere Verigene® System, which uses gold nanoparticle probe technology. Magnetic nanoparticles can also be used. Other examples include nuclear magnetic resonance microcoils. The nucleotide sequences can be DNA, RNA, or any permutation thereof (e.g., nucleotide analogs such as locked nucleic acids (LNA)). In some embodiments, the nucleotide sequences span exon / intron boundaries to detect gene expression of spliced ​​or mature RNA species rather than genomic DNA. The nucleotide sequences can also be partial sequences from genes, primers, entire gene sequences, non-coding sequences, coding sequences, published sequences, known sequences, or novel sequences. The arrays can additionally contain other compounds, such as antibodies, peptides, proteins, tissues, cells, chemicals, carbohydrates, etc., that specifically bind proteins or metabolites.

[0072] The host-derived biomarker approach as taught herein offers the potential to fill an important diagnostic niche, including rapid (even point-of-care) detection of one or more pathogen classes at once. In some embodiments, detection may be performed by the platform in less than 48 hours, less than 36 hours, or less than 24 hours. In some embodiments, detection may be performed by the platform in less than 22 hours, less than 20 hours, or less than 16 hours. In some embodiments, detection may be performed by the platform in less than 12 hours, less than 10 hours, or less than 8 hours. In some embodiments, detection may be performed by the platform in less than 6 hours, less than 4 hours, or less than 2 hours. In some embodiments, detection may be performed by the platform in less than 60 minutes, less than 45 minutes, or less than 30 minutes. Specific examples of such platforms include, but are not limited to, PCR-based platforms.

[0073] In some embodiments, generating a classifier involves iteratively (i) assigning a weight for each normalized gene expression value, inputting the weights and expression values ​​for each gene into a classifier (e.g., a linear regression classifier) ​​equation to determine a score for each outcome in a plurality of subjects, then (ii) determining the accuracy of the classification for each outcome across the plurality of subjects, then (iii) adjusting the weights until the accuracy of the classification is optimized to provide said bacterial, viral, fungal, and / or control classifiers for the platform, and optionally uploading the components (genes, weights, and / or etiology thresholds) of each classifier to one or more databases, where genes with non-zero weights are included in each classifier.

[0074] In another embodiment, the classifier includes a linear regression classifier and the generating step includes converting scores of the classifier to probabilities.

[0075] In another embodiment, the method further comprises validating the classifier against a known dataset comprising at least two relevant clinical traits.

[0076] Another aspect of the disclosure provides fungal, viral, bacterial, and / or control classifiers made according to the methods of the disclosure, where the classifiers include expression levels of 5, 10, 15, 20, 25, or 30, up to 50, 60, 70, 80, 90, or all 94 of the genes listed in Tables 1-5 (e.g., measurable using oligonucleotide probes homologous to said genes). (Note that one gene - TMEM199 appears in both the fungal and viral classifiers of Tables 1 and 3, respectively, albeit with a negative coefficient (weight) in the fungal classifier and a positive coefficient (weight) in the viral classifier.) Genome Reference: Homo sapiens GRCh38, Release 96, downloaded from ftp.ensembl.org / pub / release-96 / fasta / homo_sapiens / dna / on June 15, 2019. Transcript Reference: Homo sapiens GRCh38, Release 96, downloaded from here ftp.ensembl.org / pub / release-96 / gtf / homo_sapiens / . For example, a classifier may include expression levels of 1, 5, 10, 15, or 20, up to 30, 40, 50, 60, or 70 genes among those listed in Tables 1-5.

[0077] [Table 1-1] [Table 1-2]

[0078] [Table 2]

[0079] [Table 3]

[0080] [Table 4]

[0081] [Table 5]

[0082] For example, a fungal classifier may include 3, 5, 8, 10, 12, 15, 18, 20, 25, or all 29 of the genes listed in Table 1; a bacterial classifier may include 3, 5, 8, 10, 12, 15, or all 18 of the genes listed in Table 2; a viral classifier may include 3, 5, 8, 10, 12, 15, 18, or all 19 of the genes listed in Table 3; a SIRS classifier may include 3, 5, 8, 10, 12, 15, 18, or all 19 of the genes listed in Table 4; and / or a health classifier may include 3, 4, 5, 6, 7, 8, 9, or all 10 of the genes listed in Table 5.

[0083] One or more of these classifiers may be included in carrying out the methods taught by the present disclosure, for example, but not limited to, a fungal classifier only; a fungal classifier and a bacterial classifier; a fungal classifier and a viral classifier; a fungal classifier, a bacterial classifier, and a viral classifier; a fungal classifier and a non-infectious illness (SIRS) classifier; a fungal classifier and a health classifier; a fungal classifier, a SIRS classifier, and a health classifier; a fungal classifier, a bacterial classifier, a viral classifier, and a SIRS classifier; a fungal classifier, a bacterial classifier, a viral classifier, and a health classifier; and a fungal classifier, a bacterial classifier, a viral classifier, a SIRS classifier, and a health classifier. As an example, the method may include the use of a fungal classifier and a bacterial classifier to determine the presence or absence of a fungal infection and a bacterial infection. As another example, the method may include the use of a fungal classifier and a SIRS classifier to determine the presence or absence of a fungal infection and a non-infectious illness in a subject. As another example, the methods may include the use of fungal classifiers, bacterial classifiers, and SIRS classifiers to determine the presence or absence of fungal infection, bacterial infection, and non-infectious disease in a subject.

[0084] Another embodiment of the disclosure provides fungal, viral, bacterial, and / or control classifiers generated according to the methods of the disclosure that include expression levels of 5, 10, 15, 20, 25, 30, or all 33 of the genes (e.g., measurable using oligonucleotide probes homologous to said genes) listed in Tables 6-10. Genes that overlap with the example classifiers of Tables 1-5 are highlighted in bold.

[0085] [Table 6]

[0086] [Table 7]

[0087] [Table 8]

[0088] [Table 9]

[0089] [Table 10]

[0090] Another aspect of the disclosure provides fungal, viral, bacterial, and / or control classifiers created according to the methods of the disclosure, comprising the expression levels of the genes in bold listed in Tables 6-10. That is, the fungal classifier includes ITGA2B, MKI67, and AZU1 (each having a positive coefficient); the bacterial classifier includes HDAC4, DCAF15, SDHC, SAP30L, and DNASE1 (each having a positive coefficient), and DCAF15 (having a negative coefficient); the viral classifier includes PIGT, HERC6, and LY6E (each having a positive coefficient); the SIRS classifier includes SLC35E1, WIPI2, RELL1, and MAP1LC3B2 (each having a positive coefficient), and CASZ1 and GABBR1 (each having a negative coefficient); and the health classifier includes RPS24 (having a positive coefficient) and CTSB (having a negative coefficient).

[0091] As mentioned above, one or more of these classifiers may be included in carrying out the methods taught by the present disclosure, for example, but not limited to, a fungal classifier only; a fungal classifier and a bacterial classifier; a fungal classifier and a viral classifier; a fungal classifier, a bacterial classifier, and a viral classifier; a fungal classifier and a non-infectious illness (SIRS) classifier; a fungal classifier and a health classifier; a fungal classifier, a SIRS classifier, and a health classifier; a fungal classifier, a bacterial classifier, a viral classifier, and a SIRS classifier; a fungal classifier, a bacterial classifier, a viral classifier, and a health classifier; and a fungal classifier, a bacterial classifier, a viral classifier, a SIRS classifier, and a health classifier. As an example, the method may include the use of a fungal classifier and a bacterial classifier to determine the presence or absence of a fungal infection and a bacterial infection. As another example, the method may include the use of a fungal classifier and a SIRS classifier to determine the presence or absence of a fungal infection and a non-infectious illness in a subject. As another example, the methods may include the use of fungal classifiers, bacterial classifiers, and SIRS classifiers to determine the presence or absence of fungal infection, bacterial infection, and non-infectious disease in a subject.

[0092] In some embodiments, use of these signatures can identify multiple different etiologies (fungal infections such as candidemia, bacterial infections, viral infections, non-infectious illnesses ("SIRS"), and / or health) at once with a high degree of accuracy. For example, in some embodiments, the etiologies have an area under the receiver operating characteristic curve (auROC or ROC), which is the probability that a subject will have the correctly assigned etiology of at least 0.90, e.g., at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99; or at least 0.80, e.g., at least 0.81, at least 0.82, at least 0.83, at least 0.84, at least 0.85, at least 0.86, at least 0.87, at least 0.88, or at least 0.89. As is known in the art, an auROC of 0.80 means that the correct assignment will be 80% of the time, and an auROC above 0.80 is considered to be excellent performance of the classifier.

[0093] An aspect of the disclosure is a method for classifying a subject, comprising: (a) obtaining a biological sample from the subject; (b) measuring a signature indicative of one or more of a fungal infection, and optionally a bacterial infection, a viral infection, health, and / or a non-infectious illness in the biological sample on a platform, where the signature comprises gene expression levels of a predefined set of genes; (c) inputting the gene expression levels into a fungal classifier, and optionally one or more additional classifiers selected from a bacterial infection classifier, a viral classifier, and a control classifier (health and / or non-infectious illness), where the classifiers comprise predefined weighting values ​​(i.e., coefficients) for each of the genes of the predefined set of genes for the platform; and (d) classifying the subject as having a fungal infection, and / or a bacterial infection, a viral infection, or a control based on the gene expression levels and the classifiers. In some embodiments, the method comprises normalizing the gene expression levels to generate a normalized gene expression value, wherein the inputting comprises inputting the normalized gene expression value into a classifier, and wherein the classifying comprises calculating a probability for a fungal infection, and optionally, a bacterial infection, a viral infection, or a control, based on the normalized gene expression value and the classifier. In some embodiments, the method further comprises generating a report assigning to the subject a score indicative of a probability of a fungal infection, and optionally, a bacterial infection, a viral infection, health, and / or a non-infectious illness. In some embodiments, the method further comprises (e) administering an appropriate treatment to the subject based on the classifying step.

[0094] Another aspect of the disclosure is a method for diagnosing and / or treating a fungal infection, such as candidemia, in a subject suffering from or at risk for a fungal infection, comprising: (a) obtaining a biological sample from the subject; (b) measuring gene expression levels (i.e., signatures) of a predefined set of genes in the biological sample on a platform; (c) optionally normalizing the gene expression levels to generate normalized gene expression values; (d) inputting the gene expression values ​​into one or more classifiers selected from a bacterial infection classifier, a viral classifier, a fungal classifier, and / or a control classifier, wherein said classifiers are selected from the group consisting of: The method further comprises, comprising, or consisting essentially of: (a) a classifier comprising predefined weighting values ​​(i.e., coefficients) for each of the genes of a predefined gene set for the platform, and optionally, the classifier is retrieved from one or more databases; (e) calculating a probability for one or more of bacteria, viruses, and fungi, and / or controls based on the normalized gene expression values ​​and the classifier, thereby determining whether a fungal infection such as candidemia is present in the subject, or the likelihood of the subject developing such a fungal infection; and (f) optionally, administering an appropriate treatment.

[0095] In some embodiments, the method further comprises generating a report that assigns the subject a score indicative of the probability of a fungal infection, such as candidemia.

[0096] In some embodiments, the defined gene set includes expression levels of 5, 10, 15, 20, 25, or 30, to 50, 60, 70, 80, 90, or all 94 of the genes listed in Tables 1-5. For example, a classifier may include expression levels of 1, 5, 10, 15, or 20, to 30, 40, 50, 60, or 70 genes listed in Tables 1-5.

[0097] By way of example, a defined set may include all 3, 5, 8, 10, 12, 15, 18, 20, 25, or 29 of the genes listed in Table 1; and optionally all 3, 5, 8, 10, 12, 15, or 18 of the genes listed in Table 2; all 3, 5, 8, 10, 12, 15, 18, or 19 of the genes listed in Table 3; all 3, 5, 8, 10, 12, 15, 18, or 19 of the genes listed in Table 4; and / or all 3, 4, 5, 6, 7, 8, 9, or 10 of the genes listed in Table 5, in any combination.

[0098] As another example, the predefined gene list may include expression levels of 5, 10, 15, 20, 25, 30, or all 33 of the genes listed in Tables 6-10. As a further example, the predefined gene list may include expression levels of the genes in bold listed in Tables 6-10.

[0099] In some embodiments, the biological sample is selected from the group consisting of peripheral blood, sputum, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, cerebrospinal fluid, urine, and combinations thereof. In certain embodiments, the biological sample comprises a peripheral blood sample. In certain embodiments, the biological sample comprises bronchoalveolar lavage.

[0100] <Classification System> 5, the classification system and / or computer program product 1100 may be employed within or by a platform according to various embodiments described herein. The classification system and / or computer program product 1100 may be embodied as one or more enterprise, application, personal, pervasive, and / or embedded computer systems operable to receive, transmit, process, and / or store data using any suitable combination of software, firmware, and / or hardware, and may include various types of tangible, non-transitory computer readable media, standalone and / or interconnected by any conventional, public and / or private, real and / or virtual, wired and / or wireless networks, including all or a portion of the global communications network known as the Internet.

[0101] 5, the classification system 1100 may include a processor subsystem 1140 including one or more central processing units (CPUs) on which one or more operating systems and / or one or more applications run. While one processor 1140 is shown, it will be understood that there may be multiple processors 1140, which may be electrically interconnected or separate. The processor 1140 is configured to execute program code from a memory device, such as memory 1150, to perform at least a portion of the operations and methods described herein, and may be any conventional or special purpose processor, including, but not limited to, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), and a multi-core processor.

[0102] The memory subsystem 1150 may include a hierarchy of memory devices such as random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, and / or any other solid-state memory device. A storage circuit 1170 may also be provided, which may include, for example, a portable computer diskette, a hard disk, a portable compact disk read-only memory (CDROM), an optical storage device, a magnetic storage device, and / or any other type of disk or tape based storage subsystem. The storage circuit 1170 may provide non-volatile storage of data / parameters / classifiers for the classification system 1100. The storage circuit 1170 may include disk drives and / or network storage components. The storage circuit 1170 may be used to store code to be executed and / or data to be accessed by the processor 1140. In some embodiments, the storage circuit 1170 may store a database that provides access to the data / parameters / classifiers used in the classification system 1110, such as signatures, weights, thresholds, etc. Any combination of one or more computer-readable media may be utilized by the storage circuit 1170. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.As used herein, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0103] The input / output circuitry 1160 may include a display and / or user input devices, such as a keyboard, touch screen, and / or pointing device. Devices attached to the input / output circuitry 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input / output circuitry 1160 may include network or communication controllers, input devices (keyboard, mouse, touch screen, etc.), and output devices (printers or displays). The input / output circuitry 1160 may also provide an interface to devices, such as displays and / or printers, to which results of the operations of the classification system 1100 may be communicated for provision to a user of the classification system 1100.

[0104] An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100. Updates may include updates to code executed by the processor 1140 that is stored in the memory 1150 and / or the storage circuit 1170. Updates provided by the update circuit 1180 may also include updates to portions of the storage circuit 1170 associated with databases and / or other data storage formats that maintain information about the classification system 1100, e.g., signatures, weights, thresholds, etc.

[0105] The sample input circuit 1110 of the classification system 1100 may provide an interface for a platform as described above to accept a biological sample to be analyzed. The sample input circuit 1110 may include mechanical as well as electrical elements that accept a biological sample provided by a user into the classification system 1100 and transport the biological sample within the classification system 1100 and / or platform to be processed. The sample input circuit 1110 may include a barcode reader that identifies a barcoded container for sample and / or test order identification. The sample processing circuit 1120 may further process the biological sample within the classification system 1100 and / or platform to prepare the biological sample for automated analysis. The sample analysis circuit 1130 may automatically analyze the processed biological sample. The sample analysis circuit 1130 may be used, for example, to measure gene expression levels of a predefined set of genes for a biological sample provided to the classification system 1100. The sample analysis circuit 1130 may also normalize the gene expression levels to generate normalized gene expression values. The sample analysis circuit 1130 may retrieve a fungal infection classifier, and optionally one or more of a viral infection classifier, a bacterial infection classifier, a non-infectious illness classifier, and a healthy subject classifier, from the memory circuit 1170. The sample analysis circuit 1130 may input the normalized gene expression values ​​into the classifiers. The sample analysis circuit 1130 may calculate an etiology probability or likelihood for a fungal infection, and optionally one or more of a viral infection, a bacterial infection, a non-infectious illness, and a healthy subject, based on the output of the classifiers and controls by the input / output circuit 1160.

[0106] The sample input circuit 1110, the sample processing circuit 1120, the sample analysis circuit 1130, the input / output circuit 1160, the storage circuit 1170, and / or the update circuit 1180 may be executed, at least in part, under the control of one or more processors 1140 of the classification system 1100. As used herein, executing "under the control" of the processor 1140 means that the operations performed by the sample input circuit 1110, the sample processing circuit 1120, the sample analysis circuit 1130, the input / output circuit 1160, the storage circuit 1170, and / or the update circuit 1180 may be executed and / or directed, at least in part, by the processor 1140, but does not exclude that at least a portion of the operations of those components are separately, electrically or mechanically automated. The processor 1140 may control the operation of the classification system 1100 as described herein through the execution of computer program code.

[0107] Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, the "C" programming language, traditional procedural programming languages ​​such as Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the classification system 1100, partly on the classification system 1100, as a standalone software package, partly on the classification system 1100 and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the classification system 1100 through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., through the Internet using an Internet Service Provider), or in a cloud computing environment, or provided as a service, such as Software as a Service (SaaS).

[0108] In some embodiments, the system includes computer readable code capable of converting quantitative or semi-quantitative detection of gene expression into a cumulative score or probability of one or more of the following etiologies: fungal infection, and also optionally viral infection, bacterial infection, non-infectious disease, and healthy subjects.

[0109] In some embodiments, the system is a sample prep to result system whose components are integrated such that a user can simply insert the biological sample to be tested and then receive a result output from the system some time later (preferably within a short period of time, e.g., up to 3 minutes or 45 minutes, or up to 1 hour, 2 hours, or 3 hours, or 8 hours, 12 hours, 24 hours, or 48 hours).

[0110] Another aspect of the present disclosure provides all that is described and exemplified herein.

[0111] The following examples are offered by way of illustration and not by way of limitation. EXAMPLES

[0112] Example 1. The host transcriptional response to candidemia is governed by neutrophil activation and heme biosynthesis, supporting a novel diagnostic approach. A. Method Subject Enrollment: All study patients were enrolled at Duke University Medical Center (DUMC) after informed consent. The study was approved by the Institutional Review Board (IRB) at DUMC (Pro00083484) and conducted in accordance with the Declaration of Helsinki. Forty-eight hospitalized patients with candidemia were enrolled by the Infectious Disease Data and Specimen Archive Program at Duke University (Durham, NC) at the time of the first positive blood culture for Candida species. Whole blood was collected from these subjects into PAXGene tubes for RNA sequencing, and serum was collected from each subject for additional analysis. Each subject with candidemia had at least one and at most 14 samples collected over the course of the study. Subjects with viral, bacterial, or non-infectious diseases. RNA sequencing data from previously enrolled subjects (DUMC, Durham VA Health Care System, UNC Health Care, and Henry Ford Hospital) who presented to the emergency department were also run along with the candidemia samples. Peripheral blood samples were also collected from a population of non-hospitalized healthy controls. Clinical adjudication served as the reference standard and was performed after enrollment but before gene expression measurements. The adjudication method used here has been described previously. Non-infectious subjects were labeled as having a systemic inflammatory response syndrome (SIRS) phenotype - defined by no evidence of infection and at least two SIRS criteria (temperature < 36° Celsius (° C) or > 38° C, tachycardia > 90 beats / min, tachypnea > 20 breaths / min or PaCO2 < 32 mmHg, white blood cell count < 4,000 cells / mm3 or > 12,000 cells / mm3, or > 10% neutrophil band type).

[0113] RNA extraction, library preparation and sequencing: Total RNA was extracted from human blood stored and archived in PAXgene blood RNA tubes using the Qiagen PAXgene Blood miRNA kit according to the manufacturer's protocol. RNA quantity and quality were assessed using a Nanodrop 2000 spectrophotometer (Thermo Scientific) and an Agilent 2100 Bioanalyzer, respectively. RNA sequencing libraries were generated using the NuGEN Universal mRNA-seq kit with AnyDeplete Globin (NuGEN Technologies, Redwood City, CA) and sequenced on an Illumina NovaSeq 6000 instrument using an S2 flow cell and 50bp paired-end reads (performed by the Duke Sequencing and Genomic Technologies Core).

[0114] RNA Sequencing Data Processing: For both discovery and validation datasets, RNA sequences were mapped to the human genome (hg) and gene expression was quantified using STAR with the parameters quantMode: "GeneCounts"; outSAMtype: "None"; outSAMmode: "None"; readFilesCommand: "zcat" and the ENSEMBL gene reference Homo sapiens GRCh38 DNA, release 96, downloaded from ftp: / / ftp.ensembl.org / pub / release-96 / fasta / homo_sapiens / dna / (for gene quantification). All other parameters were left at their default values ​​for STAR version 2.7.1a. Samples with low numbers of mapped reads (<12 million reads) or low average pairwise correlations (<0.70) were excluded from the analysis. Genes with 0 counts or counts / million <2 in ≥50% of samples in the discovery cohort were excluded. The validation cohort was reduced to the gene sets that passed quality control in the discovery cohort, and the remaining gene counts were normalized within each cohort using TMM.

[0115] <Statistical analysis> Differential expression: For both discovery and validation datasets, mean expression for each outcome group - candidemia, bacterial, viral, SIRS, and healthy - was estimated using the R Bioconductor package limma, adjusting for age, sex, and ethnicity using empirical Bayesian linear modeling with voom weights. Generalized linear hypothesis tests (i.e., contrasts) were used to test for differential expression between specific infection type groups (i.e., candidemia vs. healthy). A false discovery rate of less than 5% was used to determine statistical significance for each comparison. Differential expression results from the discovery and validation cohorts were combined with cohort random effects using inverse variance-weighted meta-analysis of log2 fold changes as implemented by the R package meta.

[0116] Diagnostic classifier development and validation: Regularized multinomial logistic regression (lasso) implemented by the R package glmnet was used to identify multigene signatures of infection type. Three different unbiased feature selections were used before building the model: 1) the top 1000 most variable genes, 2) the top 2000 most variable genes, and 3) all ~11,100 genes that passed quality control. Multinomial model performance was estimated using nested leave one sample out cross validation (LOOCV). Specifically, for each sample, one sample was held out and the remaining samples were used to estimate the model. Within (N-1) samples, the sparsity parameter was optimized using 10-fold cross validation. The optimal sparsity parameter was then used to estimate the model in N-1 samples. The resulting model was used to estimate the predicted class probabilities in the held-out samples. After completing LOOCV, the predicted class probabilities from the held-out samples were used to evaluate the following training performance metrics: auROC per class, confusion matrix, overall sensitivity, and overall specificity. The full model was estimated using the full data with the sparsity parameter optimized by 10-fold cross-validation of the discovery dataset. This full model was used to predict infection class probabilities in other sequenced samples from other datasets. Model testing performance metrics included the area under the receiver operating characteristic curve (auROC) per class and confusion matrix.

[0117] Additional validation: Independent external validation was performed on two human microarray gene expression datasets. For the Ramilo dataset, Affymetrix CEL files and sample characteristics were downloaded from GEO (GSE6269-GPL96). CEL files were imported and processed using the R Bioconductor packages readAffy. Expression values ​​were normalized using gcrma. Probes detected in fewer than four samples and Affymetrix control probes were excluded. For the Tsalik dataset, Affymetrix microarray gene expression was previously processed and normalized as described previously. For both the Ramilo and Tsalik datasets, microarray probes were mapped to ensemble gene identifiers to reduce them to a subset of probes that were located in the classifier gene list. The resulting expression values ​​were log2 transformed and analyzed to re-estimate model weights using the same normalized multinomial modeling, cross-validation procedures, and performance metrics used in the discovery analysis.

[0118] Additional validation was performed on an in vitro PBMC microarray dataset consisting of viral (influenza), bacterial (Escherichia coli and Streptococcus pneumoniae), and fungal (Candida albicans, Cryptococcus neoformans, and Cryptococcus gattii) infections of healthy human PBMCs. Similar to the Ramilo and Tsalik dataset, CEL files were imported and processed using the R Bioconductor package readAffy, and gcrma was used to normalize and eliminate low-expressing and control probes, defined as detected in fewer than four samples. Microarray probe identifiers were mapped to ensemble genes; data were reduced to the subset of probes that were located in the classifier gene list; and log2 transformed. The same regularized multinomial modeling, cross-validation, and performance metrics used in the discovery analysis were applied here to estimate classifier models on different gene expression platforms.

[0119] Biological pathway analysis: The gene list was analyzed using Database for Annotation, Visualization and Integrated Discovery (DAVID, www.david.abcc.ncifcrf.gov) to identify significantly enriched pathways. We also applied weighted gene co-expression network analysis (WGCNA) to the discovery dataset (i.e., 11,131 genes in 136 samples). Using these parameters, power parameter=6; UPGMA clustering; method=“hybrid”, deepSplit=2, and dynamic tree cutting with minclustersize=30, we identified 41 clusters (or “modules”). The aggregate expression of all genes assigned to a module can be aggregated using PCA, where the first principal component (named eigengene) is used as an aggregate measure of module gene expression. Since each module eigengene can be considered as the aggregate expression of all of the genes in that module, we can use the eigengene value to test for association with infection type. Each module-specific gene was tested for association with candidemia infection using linear regression. Modules with parameter estimates with Benjamini-Hochburg adjusted p-values ​​<5% were considered statistically significant. Additionally, each module was assessed for enrichment of KEGG and GO pathways using the functions goana and kegga available in the R bioconductor package limma. Ensemble gene identifiers were mapped to entrez gene identifiers and enrichment was assessed for the gene set within the module compared to all genes that passed quality control and were located in the entrez genes. The enrichment p-values ​​were adjusted for multiple testing within each module using the Benjamini-Hochberg adjustment.

[0120] Beta-D-glucan testing: Serum samples from all subjects with candidemia, 5 healthy subjects, and 20 subjects with viral infection were BDG tested (Viracor Eurofins) (range <31 to >500). Values ​​>500 were treated as 501, and values ​​<31 were treated as previously described. auROCs were calculated for BDG test values ​​and the candidemia component of the gene expression signature separately for the discovery and validation cohorts restricted to the subset of subjects with both BDG test and gene expression. auROCs for BDG and gene expression were compared using the DeLong test. BDG and gene expression data were also compared by Spearman correlation. The Mann-Whitney test was used for comparison of means.

[0121] B. Results i. Study population Forty-eight hospitalized adult subjects were enrolled at Duke University Medical Center from 2011 to 2014, along with sequential sampling of a subset of patients, at the time of the first positive blood culture for Candida species (minimum 2 days after initial blood culture collection). In addition, we enrolled patients with similar clinical backgrounds but with documented acute respiratory viral infections, acute bacterial (pneumonia or bacteremia) infections, or clinically determined non-infectious illnesses, as well as uninfected healthy subjects (n=151, FIG. 1). The study included subjects from a variety of clinical backgrounds, including solid organ transplants, stem cell transplants, hematological malignancies, and ICU patients with central venous catheters. A total of seven different Candida species were identified, with the most common being C. albicans and C. glabrata.

[0122] [Table 11-1] [Table 11-2]

[0123] ii. Discovery and validation cohorts Subjects and controls were randomly divided into discovery and validation cohorts for the initial analysis. The discovery and validation cohorts included 138 and 61 subjects, respectively (Figure 1). In the discovery cohort, 23 subjects were determined to have a bloodstream infection with Candida species and no other type of infection. As controls, 35 subjects with confirmed bacterial infection and 48 with confirmed viral infection (both monomicrobial). In addition, 17 patients with acute non-infectious illness were included and labeled systemic inflammatory response syndrome (SIRS) because patients may also present clinically with acute non-infectious illness. In the validation cohort, there were 25 subjects with candidemia, plus 10 subjects with confirmed bacterial infection and 11 subjects with confirmed viral infection (both monomicrobial). Fifteen healthy subjects were also included in each cohort as controls - the mean age of the healthy controls was 20.9 years in the discovery dataset and 33.5 years in the validation dataset. Sixty-five percent of the candidemic subjects in the discovery cohort and 80% in the validation cohort were on antifungal treatment at the time of first sampling.

[0124] iii. The transcriptional response to candidemia is robust and reveals an antifungal defense mechanism. Candidemia elicited a strong transcriptomic response in the human host, with 1,641 genes being differentially upregulated compared to healthy controls. These upregulated genes corresponded to known components of the host immune response to fungal infection, including innate immune response, defense response to fungi, leukocyte migration, and response to yeast. Other stress-related pathways included response to cytokines, inflammatory response, cellular response to oxidative stress, and host control of heme synthesis and iron metabolism. There were 2,316 downregulated genes that clustered into immune processes such as adaptive immune response, control of immune response, B cell proliferation, humoral immune response, immunoglobulin production, and T cell costimulation. To further elucidate how the transcriptomic response limits active biological pathways in the host, weighted gene co-expression network analysis (WGCNA) was performed to identify clusters of correlated genes associated with candidemia compared to healthy controls. Clusters that were significantly upregulated in candidemia included pathways of immune activation and inflammation, including the innate immune response and neutrophil activation, migration, and degranulation.

[0125] iv. The transcriptional response to candidemia is unique compared to other infectious triggers. In addition to healthy controls, univariate comparisons were also performed between the transcriptomic response to candidemia and acute bacterial and viral infections, as well as non-infectious SIRS. While there were several conserved components of the host response observed across infection phenotypes, there were also 342 (12%) genes that were uniquely differentially expressed during candidemia compared to all others. When examining differential expression of genes for Candida compared to other bed-bound phenotypes, the greatest differences were seen between candidemia and bacterial infections (2,407 unique genes), followed by candidemia and viral infections and candidemia and SIRS (740 and 149 genes, respectively) (Figures 2A-2B). This highlights that the transcriptional response to candidemia has unique characteristics compared to other classes of infections. Interestingly, when the transcriptomic response to candidemia was compared with that of other pathogen classes, the top genes upregulated in candidemia again clustered into pathways biased towards neutrophil activation and heme biosynthesis, further highlighting the strength of these responses during fungal infection.

[0126] v. A multinomial gene expression classifier distinguishes candidemia from viral or bacterial infections. Regularized multinomial logistic regression analysis was then used to determine the most consistently co-regulated gene sets (signatures) across samples from each group of infected subjects. For Candida infection, previous studies in mouse models have demonstrated that gene expression signatures discriminate between early and late invasive candidiasis, and that signal intensity decreases with time. Therefore, for diagnostic classifier development, we utilized only the first RNA sample obtained for each Candida subject after the first positive blood culture (median 5 days, range 2-23 days). All other acute infection phenotypes only had one RNA sample per subject per episode, taken at the time of the first presentation of the respective infection.

[0127] Model performance was assessed with auROC and confusion matrices for all infection classes. All performance measures were cross-validated. A 94-gene classifier was identified that could accurately distinguish between candidemia, bacterial, viral, SIRS, and healthy phenotypes. (Figure 3) auROC was 0.98 (95% CI 0.96-1) for candidemia, 0.99 (95% CI 0.98-1) for both bacterial and viral infections, 0.99 (95% CI 0.97-1) for SIRS, and 0.99 (95% CI 0.96-1) for healthy subjects (Figure 4, Supplementary Table 7). Signatures derived from the discovery cohort were then used to predict infection classes in the validation dataset. auROC and confusion matrices per class were computed computationally. Performance in the validation cohort was equally good: auROC was 0.97 (95% CI 0.90 to 1) for candidemia and 1 for bacterial infections (95% CI 1 to 1), viral infections (95% CI 1 to 1), and healthy subjects (95% CI 0.99 to 1).

[0128] vi. A candidemia blood-based gene expression signature is maximally expressed at the height of the disease and decreases in intensity over time. Once a Candida-specific diagnostic signature was identified, it was sought to examine signal intensity over time, as discriminating between early and late disease and defining response to treatment may impact patient clinical care, treatment options, and prognosis. A total of 28 subjects with candidemia were sampled on more than one date after culture positivity, ranging from 2 to 14 samples per subject. Samples were collected 2 to 80 days after the first culture. When comparing quantitative levels of expression of genes in the signature for these subjects, we found that the overall trend in signal intensity decreased from the first to the last time point in subjects with isolated candidemia. However, there was a notable variability in quantitative signal intensity and temporal resolution between subjects. There was an expected inverse correlation seen between quantitative gene expression and days since positive blood culture (ρ=-0.441, p=.0009). In several subjects for whom adequate samples were available, the predicted probabilities derived from the candidemia signature decreased over time with treatment, and eventually the subjects were predicted by the model to be healthy once candidemia had resolved.

[0129] Given the uniqueness of this dataset and the lack of public gene expression data on candidemia subjects, for validation we next applied the classifier to two independent gene expression datasets (Ramilo et al. and Tsalik et al.) from human subjects with acute bacterial and viral illnesses (Figure 4). When applied to the Ramilo et al. dataset, the novel classifier performed well, with an auROC of 0.97 (0.95% CI 0.94-1). When applied to the Tsalik et al. dataset, the auROC was 0.87 (95% CI 0.80-0.93) for bacterial infections, 0.88 (95% CI 0.82-0.92) for viral infections, and 0.89 (95% CI 0.84-0.94) for non-infectious illnesses.

[0130] Candidemia results were then compared to gene expression data from an in vitro stimulation assay, in which peripheral blood mononuclear cells (PBMCs) were isolated from healthy individuals and then exposed to pathogens from multiple classes. In this model, cells were then harvested 24 hours after exposure to analyze transcriptomic responses during experimental viral (influenza), bacterial (Streptococcus pneumoniae or Escherichia coli), and fungal (Candida albicans or Cryptococcus neoformans or Cryptococcus gattii) infections. The Human Candidemia Classifier was then applied to these data, where it correctly identified the associated pathogen exposure - auROC was 0.94 (95% CI 0.88-0.99) for fungal-infected cells, 0.96 (95% CI 0.89-1) for bacterial-infected cells, 0.90 (95% CI 0.69-1) for viral-infected cells, and 0.94 (95% CI 0.86-0.99) for healthy control cells (Figure 4).

[0131] vii. Comparison with BDG We next sought to compare the diagnostic accuracy of serum BDG levels with the novel transcriptomic biomarker signature. The mean level of BDG at the time of the first positive blood culture for candidemia was 246 pg / mL ± 192 (range <31 to >500), which was not significantly higher than the mean for the last BDG of 235 pg / mL ± 189 (range <31 to >500, p=0.85). Sequential BDG measurements showed that only 43% (13 / 30) of subjects had decreasing values ​​of BDG in response to treatment, and the rate of decrease was highly variable. The overall BDG auROC was 0.90 (95% CI 0.80 to .97). Although this result was not statistically significant, when decomposed into discovery and validation cohorts, the candidemia component of the gene expression classifier had higher performance characteristics than BDG. The discovery auROC for gene expression was 1 (95%CI 1 to 1) compared to 0.98 (95%CI 0.94 to 1) for BDG (p=0.39), and the validation auROC was 0.94 (95%CI 0.81 to 1) for gene expression compared to 0.83 (95%CI 0.63 to 0.97) for BDG (p=0.35).BDG levels were found to be moderately inversely correlated with days since positive blood culture (ρ=-0.29, p=0.05) and mildly correlated with quantitative gene expression (ρ=0.258, p=0.084).

[0132] C. Consideration Multiple pathogen-based diagnostic modalities for candidemia are currently available, but are often hampered by delayed time to results and / or suboptimal sensitivity and specificity. Host-derived biomarker approaches offer the potential to fill important diagnostic niches, including rapid (even point-of-care) detection of multiple pathogen classes at once, and improved specificity through identification of pathological host responses. In this study, we defined for the first time the host response to candidemia as seen from a transcriptomic perspective in circulating leukocytes. This enabled the development of host signatures that can distinguish acute fungal infections from viral, bacterial, and SIRS phenotypes that can cause similar acute illness in at-risk hosts.

[0133] The host response to Candida infection has both shared and unique features compared to other pathogen classes, which are manifested at the transcriptional level in peripheral blood. Over 1,600 differentially expressed genes (DEGs) were found in the presence of candidemia compared to healthy controls. Many of these DEGs reflected known components of the immune response to fungal infection or severe illness, hence cellular responses to such cytokine signaling, inflammatory responses, and oxidative stress. While some, such as neutrophil activation and migration, are known to play a role in antifungal defense, the strength of these responses was surprising even when compared to similarly ill subjects with acute bacterial infections, highlighting the critical importance of these pathways in clearing Candida species. Other enriched pathways identify potentially novel host response mechanisms to Candida infection, such as changes in the control of heme synthesis. Iron is known to be important for fungal pathogens such as Candida in vitro, but the results suggest that the human host may manipulate this system as part of its response to fungal infection.

[0134] By multinomial logistic regression analysis, we identified a unifying signature that can model the host response to multiple different etiologies at once with a high degree of accuracy (auROC 0.98 for candidemia). The candidemia component of this classifier outperformed the standard of care diagnostic BDG test. Importantly, the candidemia signature showed robust performance, even though more than 70% of the cohort was on active empirical antifungal treatment at the time of first testing (a common clinical approach that undermines many traditional pathogen detection strategies such as blood cultures). Furthermore, the classifier performs well across a variety of typical clinical contexts, including neutropenia and multiple types of immunosuppression, as well as across seven different Candida species. Another advantage of the multinomial approach presented herein is that a single test can simultaneously inform the diagnosis of multiple conditions (i.e., fungal, bacterial, viral, SIRS, health).

[0135] One limitation of this study is that although the in silico and in vitro validation data supported generalizability, this was a single-center study, and validation in other candidemia populations would be necessary if additional cohorts / datasets were available. Although the cohorts were diverse, the relatively small candidemia sample size limited subgroup analysis, and further studies on larger groups of neutropenic and other types of immunocompromised patients would be necessary. Furthermore, the study design limits our ability to identify test performance at earlier time points during the Candida infection period, when treatment may be most effective, as subjects were not enrolled until their blood cultures had turned positive. Although this study defines the performance of a transcriptomic signature for the diagnosis of candidemia, it is not known how such a signature would perform in or be affected by the presence of other fungal diseases, such as invasive mold infections. Finally, this study did not directly evaluate the performance of the signature in cases of invasive candidiasis (esophageal, abdominal) that did not involve candidemia, so signal strength and efficacy in these infections will need to be formally explored.

[0136] D. Conclusion The host response to candidemia in hospitalized adults is highly conserved and distinct from transcriptomic responses to acute viral and bacterial infections. Clinically available platforms capable of operationalizing PCR-based signatures of the size presented herein already exist, providing a proximal route to clinical application of these findings. Harnessing these pathogen class-specific responses will enable a better understanding of the immunopathogenesis of fungal infections in the human host and holds promise for the development of host gene expression-based assays that simultaneously differentiate multiple types of clinical disease in acute-phase patients.

[0137] Example 2. Performance of fungal classifiers in Cryptococcus infections As described in Example 1 above, we compared candidemia results with gene expression data from an in vitro stimulation assay, where peripheral blood mononuclear cells (PBMCs) were isolated from healthy individuals and then exposed to pathogens from multiple classes. In this model, cells were then harvested 24 hours after exposure to analyze transcriptomic responses during experimental viral (influenza), bacterial (Streptococcus pneumoniae or Escherichia coli), and fungal (Candida albicans or Cryptococcus neoformans or Cryptococcus gattii) infections. The human candidemia classifier was then applied to these data, which accurately identified the associated pathogen exposure - auROC was 0.94 (95% CI 0.88 to 0.99) for fungal-infected cells, 0.96 (95% CI 0.89 to 1) for bacterial-infected cells, 0.90 (95% CI 0.69 to 1) for viral-infected cells, and 0.94 (95% CI 0.86 to 0.99) for healthy control cells (Figure 4).

[0138] To further clarify the difference in signature performance between Candida and Cryptococcus, we examined the prediction probability and confusion matrix at the agonist level. We observed no statistically significant difference between Candida and Cryptococcus (ANOVA F-test p-value=0.2866).

[0139] Thus, the fungal classifier trained on Candida-infected samples was able to identify other fungal infections, such as those from the Cryptococcus genus, supporting its use to identify fungal infections more generally.

[0140] [Example 3. Example of adding a classifier] A size-reduced gene expression signature was created using lasso logistic regression with a nested cross-validation procedure similar to that used to create the full model as described in Example 1 above, with one modification: the lasso model was specified such that the maximum number of features or genes in the model was 40. The resulting classifiers are presented in Table 12.

[0141] [Table 12-1] [Table 12-2]

[0142] As mentioned above, the size-reduced gene signature was newly created using the same procedure as reported in Example 1, but with a restriction on the number of genes included. This may result in some variation of genes between the signatures. Therefore, it is not just a subset of the initial signature, even though some genes appear in both.

[0143] Those skilled in the art will readily appreciate that the present disclosure is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The disclosure described herein represents preferred embodiments, which are illustrative and are not intended as limitations on the scope of the disclosure. Those skilled in the art will recognize modifications thereof and other uses which are encompassed within the spirit of the disclosure as defined by the scope of the claims.

[0144] No admission is made that any reference, including any non-patent or patent document cited in this specification constitutes prior art. In particular, it will be understood that reference to any document in this specification does not constitute an admission that any of these documents form part of the common general knowledge in the art in the United States or any other country, unless otherwise expressly stated. Any discussion of references states what their authors assert, and the applicants reserve the right to question the accuracy and pertinence of any of the documents cited herein. All references cited herein are incorporated by reference in their entirety, unless expressly indicated otherwise. In the event of any substantial discrepancy between any definitions and / or descriptions found in the cited references, the present disclosure shall control.

[0145] The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. The present invention is defined by the following claims, together with equivalents of the claims that may be included therein.

Claims

1. 1. A method for classifying an object, comprising: (a) providing a biological sample obtained from a subject; (b) measuring on a platform in said biological sample a signature indicative of a fungal infection, wherein said signature comprises gene expression levels of a predefined set of genes; (c) inputting the gene expression levels into a fungal classifier that includes predefined weighting values ​​for each of the genes in the predefined gene set for the platform, wherein the fungal classifier was generated using data from subjects with confirmed fungal infection, data from healthy subjects, and data from subjects with confirmed non-communicable disease; and (d) classifying the subject as having the fungal infection based on the gene expression levels and the classifier. A method comprising:

2. the method comprising normalizing gene expression levels to generate the normalized gene expression values, the inputting step comprising inputting the normalized gene expression values ​​into the fungal classifier; The method of claim 1 , wherein the classifying step comprises calculating a probability of the fungal infection based on the normalized gene expression values ​​and the classifier.

3. The method of claim 2, further comprising the step of generating a report assigning a score to the subject indicating the probability of said fungal infection.

4. A method according to any one of claims 1 to 3, wherein step (b) of measuring on the platform comprises measuring signatures indicative of the fungal and viral infections, and step (c) of inputting the gene expression levels comprises inputting the gene expression levels into the fungal classifier and the viral classifier.

5. A method according to any one of claims 1 to 4, wherein step (b) of measuring on the platform comprises measuring signatures indicative of the fungal and bacterial infections, and step (c) of inputting the gene expression levels comprises inputting the gene expression levels into the fungal classifier and bacterial classifier.

6. 6. The method of any one of claims 1 to 5, wherein the defined set of genes is a set of 1, 5, 10, 15 or 20, to 30, 40, 50, 60 or 70 genes listed in Tables 1 to 5.

7. 7. The method of any one of claims 1 to 6, wherein the defined set of genes is a set of 1, 5 or 10, up to 15, 20, 25, 30 or 33 genes listed in Tables 6 to 10.

8. The method of any one of claims 1 to 7, wherein the subject has symptoms of an infection.

9. The method of any one of claims 1 to 8, wherein the subject has symptoms of sepsis.

10. 10. The method of any one of claims 1 to 9, wherein the biological sample is selected from the group consisting of blood, sputum, cerebrospinal fluid, urine, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage fluid, endotracheal aspirate, and combinations thereof.

11. The method of any one of claims 1 to 9, wherein the biological sample comprises a peripheral blood sample.

12. The method of any one of claims 1 to 9, wherein the biological sample comprises bronchoalveolar lavage fluid.

13. 13. The method according to any one of claims 1 to 12, wherein the measuring step comprises or is preceded by one or more of the steps of purifying cells from the biological sample, disrupting cells of the biological sample, and isolating RNA from the biological sample.

14. The method of any one of claims 1 to 13, wherein the measuring step comprises using PCR amplification, isothermal amplification, sequencing and / or nucleic acid probe hybridization.

15. The method of claim 1, wherein (b) comprises measuring on a platform a signature indicative of one or more biological conditions selected from the group consisting of the fungal infection, and a bacterial infection, a viral infection, health and a non-infectious disease.

16. The method of any one of claims 1 to 15, wherein (c) comprises inputting the gene expression levels into the fungal classifier and one or more additional classifiers selected from the group consisting of a bacterial classifier, a viral classifier, a health classifier and a non-infectious disease classifier.

17. A method according to any one of claims 1 to 16, comprising classifying the subject as having the fungal infection, bacterial infection, viral infection, healthy or non-infectious disease in a single test.

18. A method according to any one of claims 1 to 17, comprising classifying the subject as having said fungal infection, bacterial infection, viral infection, healthy or non-infectious disease in a single test with an area under the receiver operating characteristic curve (auROC) of at least 0.80 for each biological condition.

19. 19. The method of any one of claims 1 to 18, wherein the fungal infection comprises a yeast such as Candida, Trichosporon or Cryptococcus.

20. The method of any one of claims 1 to 19, wherein the defined set of genes comprises one or more genes selected from the group consisting of HDAC4, DCAF15, SDHC, SAP30L, DNASE1, DCAF15, PIGT, HERC6, LY6E, SLC35E1, WIPI2, RELL1, MAP1LC3B, CASZ1, GABBR1, RPS24, CTSB, ITGA2B, MKI67 and AZU1.

21. The method of any one of claims 1 to 20, wherein the defined set of genes includes one or more genes selected from the group consisting of ITGA2B, MKI67 and AZU1.

22. 22. The method of any one of claims 1 to 21, wherein the fungal classifier was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to be suffering from a fungal infection; (ii) obtaining biological samples from a plurality of non-hospitalized healthy controls or a plurality of subjects known to be suffering from a non-communicable disease; (iii) measuring on a platform the gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

23. 23. The method of any one of claims 1 to 22, wherein the fungal classifier was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to suffer from a fungal infection; (ii) obtaining biological samples from a plurality of subjects known to suffer from a bacterial infection; (iii) measuring on a platform the gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

24. 24. The method of any one of claims 1 to 23, wherein the fungal classifier was generated by a process comprising the steps of: (i) obtaining biological samples from a plurality of subjects known to suffer from a fungal infection; (ii) obtaining biological samples from a plurality of subjects known to suffer from a viral infection; (iii) measuring on a platform the gene expression levels of a plurality of genes in each of the samples from steps (i) and (ii); (iv) normalizing the gene expression levels obtained in step (iii) to generate normalized gene expression values; and (f) generating a fungal classifier.

25. 25. The method of any one of claims 22 to 24, wherein the generating step comprises: (i) assigning a weight for each normalized gene expression value, inputting the weights and normalized gene expression values ​​for each gene into a classifier equation to determine a score for each outcome of the plurality of subjects, then (ii) determining an accuracy of classification for each outcome across the plurality of subjects, then (iii) adjusting the weights until accuracy of classification is optimized to provide the fungal, bacterial, viral and / or control classifier for the platform, wherein genes with non-zero weights are included in the respective classifiers.

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