Biodosimetry panel and method

Abstract

The present invention relates to methods and kits for assessing the absorbed dose of ionizing radiation and the severity of tissue damage from radiation in a patient. It provides the ability to detect erroneously overexposed individuals, select individuals, optimize the schedule of countermeasure doses used for treatment, and monitor the effectiveness of tools for a particular individual.SOLUTION: A multiplex assay kit for use in assessing absorbed doses of ionizing radiation or radiation-induced tissue injury in a patient sample, said kit configured to measure the level of a plurality of biomarkers in said sample, wherein said plurality of biomarkers comprises: (a) DNA damage biomarkers; (b) inflammatory response biomarkers; (c) tissue damage biomarkers; (d) tissue damage repair biomarkers; (e) hematology surrogate markers; and (f) combinations thereof.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 61 / 540,584, filed September 29, 2011, the entire contents of which are incorporated herein by reference.

[0002] FIELD OF THE INVENTION This application relates to assay methods, modules and kits for performing diagnostic assays useful in detecting radiation exposure and the severity of tissue injury to radiation.

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH This invention was made with Federal support under HHSO100201000009C awarded by the U.S. Department of Health and Human Services. The U.S. Government has certain rights in this invention. [Background technology]

[0004] In the aftermath of an accident in which a significant number of civilians are exposed to radiation or radioactive materials, health authorities must be able to rapidly identify individuals exposed to significant, life-threatening radiation doses. The fatal effects of ionizing radiation (IR) are widespread and include systemic and organ-specific damage. The acute effects of large doses of ionizing radiation (>2 Gy) include depletion of certain peripheral blood cell types, immunosuppression, mucosal damage, and potential damage to other sites, such as bone and bone marrow niche cells, the digestive system, lungs, kidneys, and the central nervous system. Furthermore, exposure to low or moderate doses of ionizing radiation (1–3 Gy) can result in increased mortality if accompanied by physical injury, opportunistic infections, and / or bleeding. Long-term effects include widespread organ and tissue dysfunction or fibrosis, cataracts, and ultimately an increased risk of cancer. In many cases, the effects of radiation exposure can be mitigated by early triage and treatment.

[0005] While radioactive materials can be detected using instruments, assessing radiation dose or injury already received by humans is more difficult. Current and foreseeable medical countermeasures for radiation injury are often expensive, labor-intensive, time-consuming to administer (and monitor), have limited availability, and are sometimes associated with serious toxicity; therefore, they should be administered only to subjects likely to benefit from their use. Fast, accurate radiation dose and tissue injury assessment can greatly facilitate the identification of exposed subjects who can benefit from early medical intervention.

[0006] No rapid diagnostics based on samples collected at a single time point can easily identify the level of IR exposure. Complete blood counts, especially lymphocyte counts, are useful, but optimally, at least two samples separated by several hours to several days are required to estimate dose. The diagnostic "gold standard" in the field of radiation biodosimetry is the dicentric chromosome assay, but it is labor-intensive and slow, making its use in high-casualty situations questionable. Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, there is a need for sensitive and specific biodosimetry dose assessment tools that can be used to identify patients requiring urgent medical attention, improve risk assessment for sequelae or late effects of radiation exposure, improve the efficiency of patient tracking for repeated observation or therapeutic administration, and play a role in surveillance and long-term follow-up. Such tools also fill a critical need for monitoring radiation received during medical care, such as from medical imaging devices, as part of medical treatments (e.g., cancer treatments), or in preparation for stem cell transplantation. The tools provide the ability to detect accidentally overexposed individuals, select individuals, optimize countermeasure dose schedules used for treatment, and monitor the effectiveness of the tools for specific individuals. [Means for solving the problem]

[0008] The present invention provides biodosimetry assay panels and methods for measuring multiple radiation-sensitive protein biomarkers to assess radiation dose and tissue injury. The methods of the present invention can be used to triage and guide the treatment of individuals exposed to ionizing radiation after a major radiological or nuclear event. These tools can also be used to guide the treatment of individuals exposed to ionizing radiation as a result of medical therapy or accidental exposure.

[0009] Thus, the present invention provides a multiplex assay kit for use in assessing the absorbed dose of ionizing radiation or radiation-induced tissue damage in a patient sample, the kit being configured to measure the levels of a plurality of biomarkers in the sample, the plurality of biomarkers including (a) DNA damage biomarkers, (b) inflammatory response biomarkers, (c) tissue damage biomarkers, (d) tissue damage repair biomarkers, (e) hematology-surrogate markers, and (f) combinations thereof. Also contemplated is a device capable of housing such a kit or components of the kit for measuring the levels of the plurality of biomarkers, the device being operatively associated with a computer system storing a computer program that, when executed by the computer system, causes the computer program to perform a method comprising correlating the levels of the plurality of biomarkers present in the sample with the radiation dose absorbed by the patient.

[0010] Further provided is a multiplex assay kit for use in assessing absorbed dose of ionizing radiation or radiation-induced tissue damage in a patient sample, the kit being configured to measure levels of a plurality of biomarkers in the sample, the plurality of biomarkers including: (i) one or more biomarkers including Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, and combinations thereof; and (ii) additional biomarkers including: (a) a DNA damage biomarker; (b) an inflammatory response biomarker; (c) a tissue damage biomarker; (d) a tissue damage repair biomarker; (e) a hematology surrogate marker; and (f) a combination thereof. In a preferred embodiment, the plurality of biomarkers includes Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, and IL-12.

[0011] The present invention also provides a method for assessing an absorbed dose of ionizing radiation in a patient sample, the method comprising: (a) measuring levels of a plurality of biomarkers in the sample; and (b) applying, by a processor, an algorithm for assessing the absorbed dose in the patient based on the levels of the plurality of biomarkers in the sample, wherein the plurality of biomarkers comprises: (i) DNA damage biomarkers; (ii) inflammatory response biomarkers; (iii) tissue damage biomarkers; (iv) tissue damage repair biomarkers; (v) hematology surrogate markers; and (vi) combinations thereof.

[0012] The present invention further contemplates a method for assessing an absorbed dose of ionizing radiation in a patient sample, the method comprising: (a) measuring levels of a plurality of biomarkers in the sample; and (b) applying, by a processor, an algorithm for assessing the absorbed dose in the patient based on the levels of the plurality of biomarkers in the sample, wherein the plurality of biomarkers comprises (i) one or more biomarkers including Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, and combinations thereof; and (ii) additional biomarkers including (a) a DNA damage biomarker; (b) an inflammatory response biomarker; (c) a tissue damage biomarker; (d) a tissue damage repair biomarker; (e) a hematology surrogate marker; and (f) a combination thereof. In a preferred embodiment, the plurality of biomarkers comprises Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, and IL-12.

[0013] The present invention provides a number of multiplex biodosimetry assay kits for use in assessing the absorbed dose of ionizing radiation in a patient sample, the kits being configured to measure the levels of a plurality of biomarkers in the sample, wherein the plurality of biomarkers comprises: (a) Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, and / or TPO; and / or (b) Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, and / or IL-12.

[0014] The present invention also provides various biodosimetry assay kits for use in assessing absorbed doses of ionizing radiation in patient samples, wherein the kit(s) are configured to measure levels of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, and compare said level(s) with those of normal controls.

[0015] Another embodiment of the present invention is a method for assessing absorbed dose of ionizing radiation in a patient sample, the method comprising: (a) measuring the level of a plurality of biomarkers in said sample; (b) applying, by a processor, an algorithm to estimate the absorbed dose in the patient based on the levels of the plurality of biomarkers in the sample. the plurality of biomarkers comprises (a) Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO; or (b) Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, IL-12.

[0016] Additionally, the present invention includes a method for assessing absorbed dose of ionizing radiation in a patient sample, the method comprising: (a) measuring the levels of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, and TPO in the sample; and (b) applying, by a processor, an algorithm to estimate the absorbed dose in the patient based on the levels of the plurality of biomarkers in the sample. Includes:

[0017] Another embodiment of the present invention is a method for determining an injury severity value, comprising: (a) measuring the levels of a plurality of biomarkers in a patient sample, wherein one or more of the biomarkers are altered compared to a normal control in the event of injury in the patient; (b) fitting, by a processor, the measured levels to a response surface model as a function of injury severity index and / or time; (c) computing a cost function for combining the plurality of biomarkers; and (d) identifying injury severity values ​​that minimize the cost function over a known time interval; Includes:

[0018] The present invention further includes a method for determining radiation dose, comprising: (a) measuring the levels of a plurality of biomarkers in a patient sample, wherein one or more of the plurality of biomarkers is altered compared to a normal control in the event of radiation exposure; (b) fitting, by a processor, the measured levels to a reaction surface model as a function of radiation dose or time; (c) computing a cost function for combining the plurality of biomarkers; and (d) selecting a radiation dose that minimizes the cost function over a known time interval; Includes:

[0019] Furthermore, the present invention provides a method for determining levels of a plurality of biomarkers in a patient sample, comprising: providing a computer system operatively connected to an assay system configured to measure levels of a plurality of biomarkers in a patient sample; (a) fitting the measured levels to a response surface model as a function of injury severity index or time; (b) computing a cost function for combining the plurality of biomarkers; and (c) identifying injury severity values ​​that minimize the cost function over a known time interval; A computer readable medium having stored thereon a computer program for causing the method to calculate an injury severity value by a method comprising:

[0020] In a further embodiment, the present invention provides a method for determining levels of a plurality of biomarkers in a patient sample, comprising the steps of: (a) fitting the measured levels to a reaction surface model as a function of radiation dose or time; (b) computing a cost function for combining the plurality of biomarkers; and (c) selecting a radiation dose that minimizes the cost function over a known time interval; The present invention also includes a computer-readable recording medium storing a computer program for causing a method for calculating a radiation dose by a method including the steps of:

[0021] Additional embodiments include multiplex hematology surrogate biomarker assay kits configured to measure the levels of a plurality of biomarkers in a sample, the plurality of biomarkers comprising lymphocyte cell surface markers, neutrophil cell surface markers, and combinations thereof.

[0022] Yet a final embodiment of the present invention is a method for assaying peripheral blood leukocyte status in a sample, comprising: (a) measuring the levels of a plurality of hematological surrogate biomarkers in a sample, wherein the plurality of biomarkers comprises lymphocyte cell surface markers, neutrophil cell surface markers, and combinations thereof; (b) comparing the level of the biomarker in the sample with the level of the biomarker in a normal control sample; and (c) determining the peripheral blood leukocyte status based on the comparison step (b). Includes: [Brief explanation of the drawings]

[0023] [Figure 1](a)-(b) show the effect of radiation on plasma Flt-3L levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 8 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 2] (a)-(b) show the effect of radiation on plasma SAA levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 9 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 3](a)-(b) show the effect of radiation on plasma G-CSF levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 10 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 4] (a)-(b) show the effect of radiation on plasma GM-CSF levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 11 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 5] (a)-(b) show the results of the mouse radiation dose study, specifically plasma IL-6 response showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control (unpaired t-test, highlighted p-values ​​<0.01) for the response to each irradiation condition (Table 12). [Figure 6](a)-(b) show the effect of radiation on plasma TPO levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 13 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 7] (a)-(b) show the effect of radiation on plasma EPO levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 14 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 8] (a)-(b) show the effect of radiation on plasma IL-5 levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 15 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 9] (a)-(b) show the results of a mouse radiation dose study, specifically plasma IL-10 responses showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control (unpaired t-test, highlighted p-values ​​<0.01) for the response to each irradiation condition (Table 16). [Figure 10] (a)-(b) show the results of a mouse radiation dose study, specifically plasma KC / GRO responses showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control for response to each irradiation condition (unpaired t-test, highlighted p-value < 0.01). [Figure 11] (a)-(b) show the results of a mouse radiation dose study, specifically plasma TNF-α responses showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control (unpaired t-test, highlighted p-values ​​<0.01) for the response to each irradiation condition (Table 17). [Figure 12] (a)-(b) show the results of a mouse radiation dose study, specifically showing the response of γ-H2AX in blood cell pellets showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control (unpaired t-test, highlighted p-values ​​<0.01) for the response to each irradiation condition (Table 19). [Figure 13] (a)-(b) show the results of a mouse radiation dose study, specifically p53 responses in blood cell pellets showing concentration versus time (panel (a)), concentration versus dose (panel (b)), and p-values ​​for irradiated versus control (unpaired t-test, highlighted p-values ​​<0.01) for the response to each irradiation condition (Table 20). [Figure 14](a)-(b) show the effect of radiation on plasma CD-27 levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 21 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 15] (a)-(b) show the effect of radiation on plasma IL-12 levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 22 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 16] (a)-(b) show the effect of radiation on plasma CD45 levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 23 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 17] (a)-(b) show the effect of radiation on plasma CD26 levels in mice. Panel (a) shows biomarker levels as a function of time after radiation (60Co gamma rays), with each line representing a different dose. Table 24 shows the significance (p-value, unpaired t-test, p-value < 0.01 highlighted) for changes in biomarker levels for each irradiation condition compared to the 0 Gy control. Panel (b) shows the results of a combined injury study comparing biomarker levels in mice receiving radiation alone (filled circles) versus mice receiving radiation combined with a 15% surface area stab wound (open circles). Each point on both plots represents the average level from eight replicate animals. [Figure 18a] Scatter plots showing predicted dose as a function of actual dose for the entire mouse radiation dose study sample set using a multiparameter dose assessment algorithm. Different colors correspond to different sampling times. The data are slightly dithered along the x-axis to make the points more clearly visible. Points on the solid line exactly match the actual dose. Points within the dashed line are within 1.5 Gy of the actual dose (below 6 Gy) or within 25% of the actual dose (above 6 Gy). Panel (a) contains results using an optimal five-biomarker panel (Flt-3L, G-CSF, GM-CSF, EPO, IL12 / 23). [Figure 18b] Scatter plot showing predicted dose as a function of actual dose for the entire mouse radiation dose study sample set using a multiparameter dose assessment algorithm. Different colors correspond to different sampling times. The data has been slightly dithered along the x-axis to make the points more clearly visible. Points on the solid line correspond exactly to the actual dose. Points within the dashed line are within 1.5 Gy of the actual dose (below 6 Gy) or within 25% of the actual dose (above 6 Gy). Panel (b) contains results using a smaller LPS-unresponsive panel (Flt-3L, EPO). [Figure 19a]Figure 1 shows ROC curves for distinguishing the 6 Gy dose from the non-irradiated control (blue) and the 6 Gy dose from the 3 Gy dose. The ROC curves were generated by varying the predicted dose values ​​used to classify the samples. The histogram inset shows the distribution of predicted doses for the 6 Gy and non-irradiated sample sets, illustrating the separation of the two distributions. Panel (a) contains results using the optimal five-biomarker panel (Flt-3L, G-CSF, GM-CSF, EPO, IL12 / 23). [Figure 19b] Figure 1 shows ROC curves for distinguishing the 6 Gy dose from the non-irradiated control (blue) and the 6 Gy dose from the 3 Gy dose. The ROC curves were generated by varying the predicted dose values ​​used to classify the samples. The histogram inset shows the distribution of predicted doses for the 6 Gy and non-irradiated sample sets, illustrating the separation of the two distributions. Panel (b) contains results using a smaller panel of LPS-unresponsive antibodies (Flt-3L, EPO). [Figure 20] (a)-(b) Dose classification accuracy for mouse biomarker discovery data using a multiparameter algorithm. Two performance metrics are plotted as a function of the number of biomarkers used. Each point represents the performance of a different combination of biomarkers; all possible combinations of the 12 most radiosensitive biomarkers are shown. In panel (a), accuracy is presented as the percentage of samples correctly classified by dose (within 1.5 Gy for doses ≤ 6 Gy and within 25% for doses > 6 Gy). In panel (b), prediction error is presented as the RMS error in dose prediction across all samples. Two high-performing biomarker combinations were selected for each panel size: a panel that did not include injury-sensitive markers (yellow data points) and a panel that included injury-sensitive markers (red data points). Performance metrics for these selected panels are listed in Table 8. [Figure 21]This figure shows the performance of the multiparameter algorithm and the optimal six-biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for assessing radiation dose in a mouse model. All samples tested in this study were blinded to the individuals conducting the study during testing and dose prediction analysis. The plot shows the predicted dose as a function of the actual dose for samples collected between days 1 and 7 after irradiation. Different colors correspond to the time of sample collection. The data are slightly dithered along the x-axis to make the points more clearly visible. Points whose predicted dose exactly matches the actual dose fall on the solid line. Points on the dashed line meet our dose prediction accuracy criteria and are within 1.5 Gy of the actual dose (below 6 Gy) or within 25% of the actual dose (above 6 Gy). The inset shows the percentage of predicted doses that fall within our accuracy criteria, as well as the root mean square error in predicted doses across the entire dataset. [Figure 22] This figure shows the performance of the multiparameter algorithm and the optimal six-biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for classifying mouse samples by radiation dose. All samples tested in this study were blinded to the individuals conducting the study during testing and dose prediction analysis. The focus of the analysis shown in this plot is the ability of the algorithm to accurately classify samples above or below a critical 2 Gy dose threshold in humans, which is approximately equivalent to 5 Gy in the mouse model. The plot shows receiver operating characteristic curves for distinguishing doses of 6 Gy or more from unirradiated controls (blue) and for distinguishing doses of 6 Gy or more from doses of 3 Gy or less. The ROC curves were generated by varying the predicted dose values ​​used to classify the samples. The histogram inset shows the distribution of predicted doses for samples receiving 0 Gy, 3 Gy, or 6 Gy doses, demonstrating the separation of these distributions. Classification performance at the optimal predicted dose threshold is given in the table below the plot. [Figure 23]This figure shows the performance of a multiparameter algorithm and the effect of a 15% wound injury on an optimal six-plex biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for assessing radiation dose in a mouse model. The sample set included samples from mice with or without a skin wound (a puncture wound covering 15% of the surface area) that received 0 Gy or 6 Gy of radiation (60Co gamma rays). Samples were collected at different time points up to 7 days after exposure. There were an equal number of replicates for each dose / injury / time condition. The plot shows the predicted dose as a function of the actual dose for samples collected between 1 and 7 days after irradiation. Different colors correspond to the time of sample collection. "Injured" data points are shown as triangles, and "non-injured" data points are shown as circles. The data are slightly dithered along the x-axis to allow for clear visualization of the points. Points where the predicted dose exactly matches the actual dose fall on the solid line. Points within the dashed line meet our dose prediction accuracy criteria and are within 1.5 Gy of the actual dose (below 6 Gy) or within 25% of the actual dose (above 6 Gy). The inset shows the percentage of predicted doses that fall within our accuracy criteria, as well as the root mean square error in predicted doses across the entire dataset. [Figure 24-1] Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-2]Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-3] Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-4]Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-5] Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-6]Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-7] Figure 25 shows the effect of radiation on plasma biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivary amylase (AMY), CRP, TIMP-1, and TNF-RII, respectively) in rhesus macaques. The plots show levels as a function of time after irradiation. The left panel reflects results using samples exposed to 60Co γ-rays, and the right panel reflects results using samples exposed to 3MV LINAC photons. Each point in the plot represents 5-6 animals for the 60Co γ-ray samples, 3-4 animals for the 3MV LINAC samples up to the 9-day time point, and 2 animals for the time points beyond 9 days. The graphs are linear except for Figures 25(b) and (d), which are logarithmic. [Figure 24-8] Analysis of CD20, CD177, neutrophils, and lymphocytes in irradiated NHP samples illustrates the potential of these biomarkers as surrogate radioreactive biomarkers in place of blood cell counts (). Each graph shows the effect of radiation over time on a biomarker or biomarker ratio in NHPs (n = 6 for each dose and time cohort). [Figure 24-9]Analysis of CD20, CD177, neutrophils, and lymphocytes in irradiated NHP samples illustrates the potential of these biomarkers as surrogate radioreactive biomarkers in place of blood cell counts (). Each graph shows the effect of radiation over time on a biomarker or biomarker ratio in NHPs (n = 6 for each dose and time cohort). [Figure 25] (a)-(b) show the performance of a multiparameter algorithm for classifying NHP samples from sample set (A) by radiation dose. The data set was used to train and test the algorithm using a random subsampling approach to avoid training bias. The focus of the analysis shown in this plot is the algorithm's ability to accurately classify samples above or below the critical 2 Gy dose threshold in humans, which is roughly equivalent to 3 Gy in NHP models. Panel A shows the receiver operating characteristic curves for distinguishing doses of 3.5 Gy or greater from unirradiated controls (blue) and for distinguishing doses of 3.5 Gy or greater from doses of 1 Gy or less. The ROC curves were generated by varying the predicted dose values ​​used to classify the samples. The histogram inset shows the distribution of predicted doses for samples receiving 0 Gy, 1 Gy, or 3.5 Gy doses, demonstrating the separation of these distributions. Classification performance at the optimal predicted dose threshold is given in the table below. Panel B shows the predicted doses for NHP samples plotted as a function of actual dose. [Figure 26] Figure 1 shows a comparison of biomarker levels in plasma from a normal human population and plasma from individuals with prevalent chronic diseases. Data for each group—normal, asthma, hypertension (HBP), and rheumatoid arthritis (RA)—are presented in box and whisker format, providing the median (center of the box), lower and upper quartiles (top and bottom of the box), and 1.5 interquartile range (whiskers). Outliers are indicated as black dots directly above or below the box and whiskers. Concentrations are given in pg / mL, with the exception of SAA and CRP, which are given in ng / mL. [Figure 27] (a)-(b) show the results of an attempt to model increased baseline variability in normal humans compared to non-irradiated mice. Random noise was added to biomarker levels from non-irradiated mice in the biomarker discovery data so that the observed standard deviation of the data (in the log domain) matched the observed standard deviation for similar markers measured in normal human level studies versus the observed standard deviation for similar markers in normal human level studies. Panel A is a histogram comparing the distribution of baseline levels of one of the biomarkers (Flt-3L) before and after the addition of noise. A table of the observed standard deviations in mice and humans versus the standard deviations after noise addition is shown below the plot. Panel B is a scatter plot showing predicted doses for 0 Gy samples from the blinded study before and after the addition of noise. For comparison, predicted doses for 6 Gy samples are also shown (without added noise) as the optimal threshold was selected using the original data to classify samples as 0 Gy or ≥ 6 Gy (see Figure 27). The addition of noise did not result in any additional misclassifications and the classification specificity remained at 100%. [Figure 28]This figure shows biomarker levels in plasma from melanoma patients who underwent lymphocyte-depleting chemotherapy in preparation for cell transplantation therapy. The study had two treatment arms: one set of patients also underwent TBI 3 days after receiving chemotherapy, while the other received either TBI or chemotherapy. The plot compares biomarker levels in samples from non-TBI patients (Mela-Cntrl-0Gy), samples collected from TBI patients before radiation therapy (Mela-TBI-0Gy), and samples collected from TBI patients 5–6 hours after receiving a single 2-Gy fraction (Mela-TBI-2Gy). Biomarker levels from 40 normal blood donors (see Figure 27) are also provided for comparison. Concentrations are given in pg / mL, with the exception of SAA and CRP, which are given in ng / mL. AMY1A (salivary amylase) and p53 levels in patients receiving the 2 Gy fraction showed significant elevations compared with pre-exposure levels and levels in the non-TBI control treatment group of the study (p<0.05). [Figure 29] (a)-(b) show biomarker levels in plasma from lung cancer patients (top) and GI cancer patients (bottom) who underwent regional radiation therapy (2 Gy fractions, 5 weekly fractions, 6 weeks). Plots show biomarker levels before radiation therapy and after cumulative doses of 30 Gy and 60 Gy (lung) or 54 Gy (GI). Biomarker levels from 40 normal blood donors (see Figure 27) are also provided for comparison. Concentrations are given in pg / mL, with the exception of SAA and CRP, which are given in ng / mL. [Figure 30] (a)-(e) show the results of a confounding effect mouse study for plasma levels of Flt-3L, SAA, G-CSF, GM-CSF, and IL-6. The y-axis is scaled to make the radiation response visible. In some cases, the LPS response is off-scale. The maximum response for various conditions can be displayed on a log scale in the figures. Results from the 0 Gy and 6 Gy runs from the radiation dose study are plotted side-by-side for comparison. [Figure 31](a)-(e) show the results of the confounding effect mouse study for plasma levels of TPO, EPO, IL-12 / 23, IL-5, and IL-10. The y-axis is scaled to make the radiation response visible. In some cases, the LPS response is off-scale. The maximum response for various conditions can be displayed on a log scale in the figure. Results from the 0 Gy and 6 Gy runs from the radiation dose study are plotted side-by-side for comparison. [Figure 32] (a)-(d) show the results of the confounding effect mouse study for plasma levels of KC / GRO and TNFα, and blood cell pellet levels of p53 and γH2AX. The y-axis is scaled to make the radiation response visible. In some cases, the LPS response is off-scale. The maximum response for the various conditions can be displayed on a log scale in the figures. Results from the 0 Gy and 6 Gy runs from the radiation dose study are plotted side by side for comparison. [Figure 33] A summary of the confounding effect studies is provided. For each assay, the bar graphs show the mean concentration in control mice and the mean concentration at the condition producing the maximal response to G-CSF and LPS across all conditions tested in both the radiation dose study and the confounding effect study. The signal for each assay is normalized to the maximal radiation response, which is set at 100%. [Figure 34] (a)-(d) show the results of testing stored plasma samples from irradiated NHPs for Flt-3L, EPO, CRP, and SAA. SAA was measured using a commercially available ELISA kit. Each point represents the average value of three different animals. [Figure 35] (a)-(d) show the results of testing pooled plasma samples from irradiated NHPs for IL-6, BPI, TPO, and p53. Each point represents the average value of three different animals. [Figure 36](a)-(b) show the results of testing stored plasma samples from irradiated NHPs for the lymphocyte cell surface marker CD20 and the neutrophil cell surface marker CD177. Biomarker concentrations for the two tested doses (1.0 and 3.5 Gy) are shown as filled circles, with the y-axis scale given to the left of the plot. For comparison, lymphocyte and neutrophil cell counts measured in the same animals at the same time are shown as open circles, with the y-axis scale given to the right of the plot. Each point represents the average value from three different animals. [Figure 37] We present results from a panel of six plasma markers (Flt-3L, EPO, p53, CD20, CD177, and SAA) that provide good discrimination between animals receiving >3.5 Gy (equivalent to approximately 2 Gy in humans) and <3.5 Gy, and further provide high accuracy for semi-quantitative dose prediction. [Figure 38a] FIG. 1 illustrates the use of the statistical methods described herein to generate injury severity values ​​or radiation doses from patient samples by analyzing one or more biomarkers in the patient sample and correlating the level(s) of those biomarkers with injury severity values ​​or radiation doses, respectively. [Figure 38b] FIG. 1 illustrates the use of the statistical methods described herein to generate injury severity values ​​or radiation doses from patient samples by analyzing one or more biomarkers in the patient sample and correlating the level(s) of those biomarkers with injury severity values ​​or radiation doses, respectively. [Figure 38c] FIG. 1 illustrates one non-limiting example of a system used to analyze samples using the statistical methods described herein, where the system includes a processor and an algorithm module. DETAILED DESCRIPTION OF THE INVENTION

[0024] Unless otherwise defined herein, scientific and technical terms used herein shall have the meanings commonly understood by those skilled in the art. Furthermore, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. The articles "a" and "an" are used herein to refer to one or more than one (i.e., at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0025] One embodiment of the present invention is a multiplex biodosimetry assay kit and method that can be used to assess the absorbed dose of ionizing radiation in patient samples. The method and kit of the present invention can be used to assess the likelihood or risk of developing acute radiation syndrome (ARS) and / or to assess the clinical severity of ARS in patients. Depending on the type of radiation exposure, the method and kit of the present invention can be used to assess radiation dose in any appropriate unit of measurement. For example, the absorbed dose of ionizing radiation after external exposure can be measured in various appropriate units of measurement, including, but not limited to, gray (or rad) and sievert (or rem); the radiation dose after internal contamination is measured as committed effective dose equivalent (CEDE); and the dose associated with external and internal exposure is measured as total effective dose equivalent (TEDE).

[0026] The assay panel(s) used in certain embodiments of the present invention comprise multiple radiation biomarkers used to assess radiation exposure. Radiation biomarkers may be any substance that acts as an indicator of exposure of an organism to radiation, including, but not limited to, proteins, nucleic acids, carbohydrates, and metabolites. In one embodiment, all of the biomarkers included in the panel are proteins.

[0027] A suitable assay panel includes at least one radiation biomarker from at least one, two, three, four, or five of the following biomarker types: DNA damage biomarkers, inflammatory response biomarkers, tissue damage biomarkers, tissue damage repair biomarkers, and hematology surrogate biomarkers. As used herein, a DNA damage biomarker is a radiation biomarker associated with the host response to radiation-induced DNA damage. An inflammatory response biomarker is a radiation biomarker that is upregulated or downregulated during a systemic or local inflammatory response resulting from radiation exposure. A tissue damage biomarker is a radiation biomarker released from tissue as a result of radiation-induced local tissue damage, while a tissue damage repair biomarker is a protein that is upregulated or downregulated during repair, regeneration, or the fibroblastic phase after tissue injury. A tissue damage repair biomarker may also include proteins associated with soft tissue repair processes, including, but not limited to, fibroblast formation, collagen synthesis, tissue remodeling, and reorganization. Finally, hematological surrogate biomarkers are cell surface markers of blood cells that can be used as a substitute for conventional blood cell counts to assess the effects of radiation on specific blood cell populations. Useful hematological surrogate markers include markers found on general cell types (e.g., white blood cells), or on more specific cell types within those types, such as lymphocytes, neutrophils, and platelets, or even more specifically, T cells or B cells.

[0028] There may be some overlap between the categories of biomarkers described above. For example, some inflammatory response biomarkers may also be associated with tissue damage repair. In one embodiment, a radiation biomarker panel includes at least one inflammatory response biomarker and at least one tissue damage repair biomarker. In an alternative embodiment, the panel includes biomarkers that are both inflammatory response biomarkers and tissue damage repair biomarkers.

[0029] A non-limiting list of biomarkers that can be used in the present invention is provided in Table 1 below.

[0030] [Table 1]

[0031] In a preferred embodiment, the assay panel used in the present invention is configured to measure the level of multiple biomarkers in a sample, wherein the multiple biomarkers include one or more of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, and combinations thereof, and / or additional biomarkers including DNA damage biomarkers, inflammatory response biomarkers, tissue damage biomarkers, tissue damage repair biomarkers, hematology surrogate biomarkers, and combinations thereof. In a specific embodiment, the panel includes Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, IL-12, and combinations thereof. In one embodiment, the IL-12 assay is specific for the p40 subunit of IL-12 in the p70 heterodimer of IL-12 and may cross-react with IL-23 (including the p40 subunit). In another embodiment, the IL-12 assay is specific for the p70 heterodimer of intact IL-12.

[0032] The selected biomarkers for assessing exposure to radiation are preferably not significantly affected by chronic diseases with high prevalence in the human population, such as diabetes, asthma, hypertension, heart disease, arthritis, and / or other chronic inflammatory or autoimmune diseases. The selected biomarkers for assessing exposure to radiation are also preferably not affected by other types of trauma (e.g., wounds, burns, and / or psychological stress) that an individual may experience during a radiation event. In one embodiment, the biomarker response associated with whole-body radiation exposure (e.g., 2, 6, 10, or 12 Gy) is less than the biomarker response associated with wounds, burns, and / or psychological trauma. Such a comparison can be determined through the use of a combined injury animal model. The inventors note that biomarkers with significant confounding effects from confounding diseases or trauma are further selected and have value in the dose assessment algorithm. In one embodiment, biomarkers that may be affected by such confounding effects are included, and information regarding the presence or absence of such confounding conditions is included in the algorithm for dose assessment. For example, if a potentially confounding condition is identified in a patient (e.g., a confounding disease or trauma), approaches that can be taken to minimize the impact of the confounding condition on the accuracy of the dose assessment algorithm include: i) excluding the patient from analysis with the algorithm; ii) applying the algorithm but using an edited biomarker panel that excludes or applies lower weights to biomarkers that may be affected by the confounding condition; or iii) applying a different algorithm that uses a biomarker panel selected to be robust to the confounding condition.

[0033] The kits of the invention may further include devices, reagents, and / or consumables for measuring hematological parameters such as peripheral blood cell counts or for measuring "acute phase response" (APR) biomarkers. Such assay components may be variations of commercially available products for assessing blood cell counts and APR biomarkers, such as the Quikread CRP fingerprick device (Orion Diagnostica, Finland), which measures levels of C-reactive protein.

[0034] In a preferred embodiment, the present invention includes assays for lymphocyte and neutrophil cell surface markers that are useful as surrogates for lymphocyte and neutrophil counts. The present invention provides methods and kits for performing multiplexed hematology surrogate biomarker assays, including kits configured to measure levels of multiple biomarkers in a sample, including lymphocyte and / or neutrophil cell surface markers. In one embodiment, the lymphocyte surface markers include CD5, CD20, CD26, CD27, CD40, or a combination thereof. Additionally, the neutrophil cell surface markers include CD16b, CD177, or a combination thereof. The hematology surrogate marker assay methods of the present invention can be performed on samples including whole blood, blood cell pellets, serum, and / or plasma. In one embodiment, measurements are performed on samples prepared by reconstituting dried blood spots. In another embodiment, such measurements are performed using serum and / or plasma samples. In a preferred embodiment, measurements are performed using plasma samples. Surprisingly, the inventors have discovered that free (i.e., cell-unbound) forms of neutrophil and lymphocyte surface markers can be measured in plasma, and that the levels of these markers in plasma after radiation exposure provide useful diagnostic information for assessing the effects of radiation on neutrophils and lymphocytes.

[0035] Those skilled in the art of biological assays are aware of many suitable approaches and instruments for measuring the biomarkers and biomarker panels of the present invention. In one embodiment, the kit is configured to measure biomarker levels using an immunoassay. In a preferred embodiment, the kit includes a multi-well assay plate containing multiple assay wells configured to measure multiple biomarkers in one or more samples. Preferably, the wells are configured to allow the use of individual wells for multiplexed measurements of multiple different biomarkers. In one such assay plate, the wells of the assay plate contain multiple assay domains, at least two of which contain reagents for measuring different biomarkers. In an alternative preferred embodiment, the kit includes an assay cartridge for measuring biomarkers in a sample. Preferably, the cartridge includes a flow cell having an inlet, an outlet, and a detection chamber, the inlet, the detection chamber, and the outlet defining a flow path through the flow cell, and the detection chamber configured to measure the levels of multiple biomarkers in a sample. Kits used in the methods of the invention may further include one or more additional assay reagents for use in the assay, which may be provided in one or more vials, containers, or compartments of the kit. Additionally, kits for assessing radiation exposure may also include (a) a barcoded patient identification tag; (b) a dried blood spot collection card containing a barcode, which may be used, for example, to facilitate sample identification; (c) a sample transport bag containing a desiccant; (d) a capillary with a plunger; and / or (e) a lancet.

[0036] Samples that can be analyzed in the kits and methods of the present invention include, but are not limited to, any biological fluid, cell, tissue, organ, or combination or portion thereof that contains or potentially contains a biomarker for a disease, disorder, or abnormal condition of interest. For example, a sample may be a histological section of a specimen obtained by biopsy, or cells placed in or adapted to tissue culture. Furthermore, a sample may be a subcellular fraction or extract, or a crude or substantially pure nucleic acid molecule or protein preparation. In one embodiment, the sample analyzed in the assays of the present invention is blood or a blood fraction, such as a blood pellet, serum, or plasma. Other suitable samples include biopsy tissue, intestinal mucosa, urine, parotid gland, hematological tissue, intestine, liver, pancreas, or nervous system. Samples may be taken from any patient, including, but not limited to, animals, mammals, primates, non-human primates, humans, etc. In one embodiment, levels are measured using an immunoassay. The radiation biomarker panels disclosed herein may be used at the onset and throughout the course of acute radiation syndrome to assess and monitor patient health. In a preferred embodiment, the sample is collected from the patient within about 1 to 7 days after radiation exposure.

[0037] As used herein, a "biomarker" is a substance associated with a particular biological state, which may be a disease or abnormal condition. Changes in biomarker levels can be correlated with the risk or progression of a disease or disorder, or the susceptibility of a disease or disorder to a given treatment. Biomarkers can be useful in diagnosing the risk of a disease or the presence of a disease in an individual, or can be useful in tailoring an individual's treatment (drug therapy or dosing regimen options) for a disease. In evaluating potential drug therapies, biomarkers can be used as surrogates for natural endpoints such as survival or irreversible morbidity. When a treatment alters a biomarker that is directly linked to improved health, the biomarker serves as a "surrogate endpoint" for assessing clinical benefit.

[0038] As used herein, the term "level" refers to the amount, concentration, or activity of a biomarker. It can also refer to the rate of change in the amount, concentration, or activity of a biomarker. The level can be expressed by the amount or synthesis rate of messenger RNA (mRNA) encoded by a gene, the amount or synthesis rate of a polypeptide corresponding to a specific amino acid sequence encoded by a gene, or the amount or synthesis rate of a biochemical form of the biomarker accumulated in a cell, including the amount of a biomarker after a specific synthetic modification, such as a polypeptide, nucleic acid, or small molecule. This term can refer to the absolute amount of a biomarker in a sample or the relative amount of a biomarker, including amounts or concentrations determined under steady-state or non-steady-state conditions. The level can also refer to an assay signal that correlates with the amount, concentration, activity, or rate of change of a biomarker. The level of a biomarker can be determined relative to a control marker in a sample.

[0039] Specific biomarkers useful for distinguishing between normal and diseased / exposed patients can be identified, for example, by visual classification of data plotted on one- or multidimensional graphs, or by using statistical methods such as characterizing statistically weighted differences between control individuals and diseased patients, and / or by using receiver operating characteristic (ROC) curve analysis. A variety of suitable methods for identifying useful biomarkers and setting detection thresholds / algorithms are known in the art and will be apparent to those skilled in the art.

[0040] For example, but not limited to, biomarkers of diagnostic value may first be:

number

[0041] According to one embodiment of the present invention, biomarkers that result in a statistically weighted difference between control individuals and diseased / exposed patients of greater than, for example, 1, 1.5, 2, 2.5 or 3 could identify markers of diagnostic value.

[0042] Another method of statistical analysis to identify biomarkers is the use of z-scores, as described, for example, in Skates et al. (2007) Cancer Epidemiol. Biomarkers Prev. 16(2):334-341.

[0043] Another statistical analysis method that can be useful in the inventive method of the present invention for determining the effectiveness of a specific candidate analyte, such as a specific biomarker, to act as a diagnostic marker is ROC curve analysis. ROC curve is a graphical approach to examine the effect of cutoff criteria, such as the cutoff value for a diagnostic indicator, such as the level of an assay signal or analyte in a sample, on the diagnostic ability to correctly identify positive or negative samples or subjects. One axis of the ROC curve is the true positive rate (TPR, i.e., the probability that a true positive sample / subject is correctly identified as positive), or the false negative rate (FNR=1-TPR, the probability that a true positive sample / subject is incorrectly identified as negative). The other axis is the true negative rate, i.e., TNR, the probability that a true negative sample is correctly identified as negative, or the false positive rate (FPR=1-TNR, the probability that a true negative sample is incorrectly identified as positive). ROC curve is created by varying the diagnostic cutoff value used to identify samples / subjects as positive or negative, and plotting the calculated values ​​of TRP or FNR and TNR or FPR for each cutoff value using the assay results for a group of samples / subjects.The area under the ROC curve (referred to herein as AUC) is an indicator of the diagnostic ability to distinguish between positive and negative samples / subjects.In one embodiment, biomarkers provide AUC≧0.7.In another embodiment, biomarkers provide AUC≧0.8.In another embodiment, biomarkers provide AUC≧0.9.

[0044] The diagnostic indicator analyzed by ROC curve analysis may be the level of an analyte, e.g., a biomarker, or an assay signal. Alternatively, the diagnostic indicator may be a function of multiple measurements, e.g., the levels / assay signals of multiple analytes, e.g., multiple biomarkers, or a function of combining the levels or assay signals of one or more analytes with a patient scoring value determined based on the patient's visual, radiological, and / or histological evaluation. Multiparameter analysis can provide a more accurate diagnosis compared to analysis of a single marker.

[0045] Candidates for multi-analyte panels can be selected by using criteria such as, for example, individual analyte ROC areas, median differences between groups normalized by geometric interquartile ranges (LQRs), etc. The goal is to partition the analyte space to improve separation between groups (e.g., normal and disease populations) or minimize misclassification rates.

[0046] One approach is to define the panel response as a weighted combination of the responses of individual analytes, and then calculate an objective function, such as the product of ROC area, sensitivity, and specificity. See, for example, WO2004 / 058055 and US2006 / 0205012, the disclosures of which are incorporated herein by reference in their entirety. The weighting coefficients define the sorting objective; for linear combinations, the objective is a two-dimensional line, a three-dimensional plane, or a hyperplane in higher dimensions. The optimal coefficients can be determined using algorithms such as gradient descent, downhill simplex, and simulated annealing to maximize the objective function and find the extremum of the function in multiple dimensions; more details can be found in "Numerical Recipes in C, The Art of Scientific Computing," W. Press et al., Cambridge University Press, 1992.

[0047] Another approach is to use discriminant analysis, in which a multivariate probability distribution (normal, multinomial, etc.) is used to describe each group. Some distributions result in a partitioning hyperplane in analyte space. One advantage of this approach is the ability to simultaneously classify measurements into multiple groups (e.g., normal, disease 1, disease 2) rather than just two at a time. For further details, see "Principles of Multivariate Analysis, A User's Perspective," WJ Krzanowski, Oxford University Press, 2000, and "Multivariate Observations," GAF Seber, John Wiley, 2004.

[0048] Once the partitioning hyperplane is determined, the structural stability of different assay panels can be compared by evaluating the distance metric to the separating hyperplane for each group. It is worth noting that the algorithms described above are designed to find the best classification between groups; therefore, these algorithms can also be used to distinguish between different disease or disease populations or subgroups or populations. Finally, categorical data (e.g., age, sex, race, ethnicity) can also be coded into different levels and used as optimization variables in this method.

[0049] In one embodiment, the present invention provides a radiation dose-calculation algorithm comprising: (a) measuring the levels of multiple radiation biomarkers in a patient sample; (b) fitting the measured levels to a response surface model for the response of the biomarkers as a function of radiation dose or sample time; (c) computing a cost function for combining the multiple biomarkers; (d) selecting the calculated radiation dose and calculated sample time that minimizes the cost function; and, optionally, (e) comparing the calculated radiation dose to a threshold value to classify individuals according to the dose received (e.g., to distinguish irradiated from unexposed individuals or to identify patients who will benefit from a treatment option).

[0050] As used herein, sample time refers to the time between the radiation exposure event and the time the sample was collected. In applications where the sample time is known or expected to be known, for example, when the exposure occurred during a predetermined time frame, the actual sample time can be provided to the algorithm. In these cases, only the calculated radiation dose needs to be selected in step (d) above.

[0051] In a preferred embodiment, a two-parameter response function, i.e., M i (dose, time) is determined, in this case M i is the expected level of marker i as a function of dose and sample time. Alternatively, M i may also represent the expected level of a derived value i derived from the levels of one or more biomarkers, such as the reciprocal of the marker level, the log of the marker level, the ratio or product of two marker levels, etc. Preferably, the response function is established based on existing data from human, animal, or in vitro studies.

[0052] Based on these established response functions, one can determine the radiation exposure dose (and possibly sample time, if the dose is known but the sample time is not) that gives the best overall fit to the response functions of each of the different markers (or derived values). One general form of cost function (F) that can be minimized to find the best fit is given by the following equation: E i is the measured level (or derived value) of the biomarker i(m i ) and the expected level (or derived value) of the biomarker predicted by the response surface for a given dose-time condition (M i is a function that gives a value associated with the discrepancy between (dose, time). i is a weighting function for each marker i, which may be dose-dependent and / or time-dependent. The weighting function may be used to vary the importance given to a particular marker in a particular dose and time range. In one embodiment, the weight is determined at that dose and time point based on the statistical significance of the measurement. Many different weighting functions can be used, including, for example, the inverse of the coefficient of variation (CV) of the biomarker level at that dose and time. In some cases, the weighting function may be omitted.

[0053]

number

[0054] E i An example of a possible way to calculate m i and M i and then calculating the absolute value of the difference, or calculating the square of the difference. i and M iThe values ​​of are normalized to avoid overemphasizing more biomarkers, for example, by dividing the biomarkers by the minimum, maximum, median, or mean value for normal samples, or for all expected samples. One specific example of a preferred cost function with normalization coefficients is given below, which corresponds to a "least squares fit," where each term is normalized to the product of the measurement and fit value and scaled by a weighting function.

[0055]

number

[0056] The cost function is M i and m i In the preferred cost function described below, the log value is used to minimize overemphasis on biomarkers with the largest fold changes, and also to minimize bias for biomarkers with higher abundance.

[0057]

number

[0058] Additionally, the measured and fitted values ​​for biomarker levels may be linearly or orthogonally added to the limit of detection (LOD) or lower limit of quantitation to minimize the impact on the cost function of changes in levels close to the limit of detection, as in the following function:

[0059]

number

[0060] The above-described algorithm for assessing radiation dose can also be more generally applied to disease conditions that can be assessed in a clinical setting using a severity index, i.e., a classification scale used by clinicians to characterize the stage of a disease or disorder. A variety of conditions can be assessed using a severity index, including traumatic brain injury, stroke, embolism, liver disease, kidney disease, heart disease, inflammatory bowel disease, Alzheimer's disease, dementia, thyroid disease, rheumatoid arthritis, multiple sclerosis, psoriasis, systemic lupus erythematosus, Hashimoto's thyroiditis, pernicious anemia, Addison's disease, type 1 diabetes, dermatomyositis, Sjögren's syndrome, myasthenia gravis, reactive arthritis, Graves' disease, celiac disease, and cancer.

[0061] In this regard, the algorithm described above can be used as an injury severity value calculation algorithm, which includes the steps of: (a) measuring the levels of a plurality of biomarkers in a patient sample, where one or more of the plurality of biomarkers are altered in the event of injury in the patient compared to a normal control; (b) fitting the measured levels to an injury severity index or response surface model as a function of time; (c) computing a cost function for combining the plurality of biomarkers; (d) identifying an injury severity value that minimizes the cost function over a known time interval; and, optionally, (e) comparing the injury severity value to a threshold, where an injury severity value exceeding the threshold indicates the relative severity of the injury. This is shown in Figure 38(a), in which a user (3801) measures a plurality of biomarkers in a patient sample (3802) using an assay system (3803) (including a processor (3804) and an algorithm module (3805) shown in Figure 38(c)). The assay system's processor and algorithm module fits the measured biomarker levels (3806) to a response surface model (3807), calculates a cost function (3808), and identifies a severity index (3809). Similarly, the use of statistical methods to analyze radiation dose in a sample is illustrated in Figure 38(b), where a radiation dose (3810) is generated using statistical methods.

[0062] In this embodiment, the cost function is:

number

number

[0063] Other statistical methods can be used to perform multivariate analysis of biomarker levels. For example, neural network approaches can be used (see, for example, Musavi et al., Neural Networks (1992) (5): 595-603; Wang et al., Artif. Intell. Med. (2010) 48 (2-3): 119-127; Lancashire, L. et al., Computational Intelligence in Bioinformatics and Computational Biology (2005): pp. 1-6, 14-15). Neural networks are a broad class of flexible models used to simulate nonlinear systems. They often consist of a large number of "neurons," i.e., single linear or nonlinear computer elements, often interconnected in complex ways, and often organized into layers. Other modeling approaches include, but are not limited to, linear models, support vector machines, and discriminant analysis (Lancashire et al., "Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma," Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, November 14-15, 2005, pp. 1-6; Wang et al., "Method of Regulatory Networks that Can Explore Protein Regulations for Disease Classification," Artif Intell Med., 48(2-3) (2010), pp. 119-127).

[0064] Thus, the methods of the present invention can be used to assess the absorbed dose of ionizing radiation in a patient sample by measuring the levels of multiple biomarkers in the sample and applying an algorithm to assess the absorbed dose in the sample based on the levels of the multiple biomarkers in the sample, where the multiple biomarkers include DNA damage biomarkers, inflammatory response biomarkers, tissue damage biomarkers, tissue damage repair biomarkers, or hematology surrogate biomarkers. In a preferred embodiment, the algorithm quantifies the absorbed dose of ionizing radiation in the range of about 1 to 10 Gy, preferably between about 1 to 6 Gy, more preferably between about 2 to 6 Gy, or between about 6 to 10 Gy.

[0065] All or one or more portions of the algorithms and statistical methods disclosed herein may be performed by or executed on a processor, general-purpose or special-purpose or other such machine, integrated circuit, or any combination thereof. Furthermore, software instructions for performing the algorithm(s) and statistical method(s) disclosed herein may be stored, in whole or in part, on a computer-readable medium, i.e., a storage device, for use by a computer, a processor, general-purpose or special-purpose or other such machine, or any combination thereof. A non-limiting list of suitable storage devices includes, but is not limited to, a computer hard drive, a compact disc, a transitory propagated signal, a network, or a portable medium readable by an appropriate drive or via an appropriate connection.

[0066] In addition to biomarker measurements, biodosimetry assessments can benefit from additional inputs, such as information on clinical symptoms. For example, the Biodosimetry Assessment Tool (BAT) is a software application that equips healthcare providers with diagnostic information relevant to the management of human radiation disasters (e.g., clinical signs and symptoms, body dosimetry). Preliminarily designed for immediate use after a radiation accident, the software application facilitates the retrieval, integration, and storage of data obtained from exposed individuals. Template-collected data is compared with established radiation dose responses from the literature to provide multiparameter dose assessments. The program preserves clinical information useful for disaster management (e.g., extent of radioactive contamination, wounds, infections) and presents relevant diagnostic information in a concise format, making it suitable for managing both military and civilian radiation accidents.

[0067] Biomarker levels can be measured using any of a number of techniques available to those skilled in the art, including, for example, direct physical measurements (e.g., mass spectrometry) or binding assays (e.g., immunoassays, agglutination assays, and immunochromatographic assays). The biomarkers identified herein can be measured by any suitable immunoassay method, including, but not limited to, ELISA, microsphere-based immunoassays, lateral flow test strips, antibody-based dot blots, or Western blots. This method can also include measuring signals resulting from chemical reactions, such as changes in light absorption, changes in fluorescence, the generation of chemiluminescence or electrochemiluminescence, changes in reflectance, refractive index, or light scattering, the accumulation or release of a detectable label from a surface, oxidation or reduction or redox species, changes in current or potential, or magnetic fields. Suitable detection techniques can detect binding events by measuring the engagement of a labeled binding reagent through measurement of the label, via photoluminescence (e.g., via measurement of fluorescence, time-resolved fluorescence, evanescent wave fluorescence, upconverting fluorophores, multiphoton fluorescence, etc.), chemiluminescence, electrochemiluminescence, light scattering, light absorption, radioactivity, magnetic fields, enzymatic activity (e.g., by measuring enzyme activity via an enzymatic reaction that causes a change in optical absorption or fluorescence, or that causes chemiluminescence). Alternatively, detection techniques that do not require the use of labels can be used, for example, techniques based on measurement of mass (e.g., surface acoustic wave measurements), refractive index (e.g., surface plasmon resonance measurements), or intrinsic luminescence of the analyte.

[0068] Binding assays for measuring biomarker levels can be used in solid-phase or homogeneous formats.Suitable assay methods include sandwich assays or competitive binding assays.Examples of sandwich immunoassays are described in U.S. Patent No. 4,168,146 and U.S. Patent No. 4,366,241, both of which are incorporated herein by reference in their entirety.Examples of competitive immunoassays include those disclosed in U.S. Patent No. 4,235,601, U.S. Patent No. 4,442,204 and U.S. Patent No. 5,208,535, each of which is incorporated herein by reference in its entirety.

[0069] Multiple biomarkers can be measured using multiplex assay formats, such as multiplexing through the use of binding reagent arrays, multiplexing using spectral discrimination of labels, or multiplexing flow cytometry analysis of binding assays performed on particles, e.g., using the Luminex® system. Suitable multiplexing methods include array-based binding assays that use patterned arrays of immobilized antibodies directed against the biomarkers of interest. Various approaches for performing multiplexed assays have been reported (see, e.g., US20040022677; US20050052646; US20030207290; US20030113713; US20050142033; and US20040189311, each of which is incorporated herein by reference. One approach to multiplexed binding assays involves the use of patterned arrays of binding reagents, see, for example, U.S. Pat. Nos. 5,807,522 and 6,110,426, for more details; Delehanty JB., Printing functional protein microarrays using piezoelectric capillaries, Methods Mol. Bio. (2004) 278:135-44; Lue RY et al., Site-specific immobilization of biotinylated proteins for protein microarray analysis, Methods Mol. Biol. (2004) 278:85-100; Lovett, Toxicogenomics: Toxicologists Brace for the Genomics Revolution, Science (2000) 289:536-537; Berns A, Cancer: Gene expression in diagnosis, Nature (2000), 403, 491-92; Walt, Molecular Biology: Bead-based Fiber-Optic Arrays, Science (2000) 287:451-52).Another approach involves the use of binding reagents coated on beads that can be individually identified and interrogated. See, for example, WO9926067, which describes the use of magnetic particles of different sizes to assay multiple analytes; particles belonging to different distinct size ranges are used to assay different analytes. The particles are designed to be individually identified and interrogated by flow cytometry. Vignali reported a multiplexed binding assay using microparticles of 64 different bead sets, each containing two dyes in a uniform and well-defined ratio (Vignali, D.A. A, "Multiplexed Particle-Based Flow Cytometric Assays," J. Immunol Meth. (2000) 243:243-55). A similar approach involving a set of 15 different beads of different sizes and fluorescence has been disclosed as useful for simultaneous typing of multiple pneumococcal serotypes (Park, M.K. et al., "A Latex Bead-Based Flow Cytometric Immunoassay Capable of Simultaneous Typing of Multiple Pneumococcal Serotypes (Multibead Assay)," Clin. Diag. Lab Immunol (2000) 7:4869). Bishop, J.E. et al. have reported a multiplex sandwich assay for the simultaneous quantification of six human cytokines (Bishop, L.E. et al., "Simultaneous Quantification of Six Human Cytokines in a Single Sample Using Microparticle-based Flow Cytometric Technology," Clin. Chem (1999) 45:1693-1694).

[0070] A diagnostic test can be performed in a single assay chamber, such as a single well of an assay plate or an assay chamber of a cartridge. Assay modules (e.g., assay plates or cartridges or multi-well assay plates), methods, and devices for performing assay measurements suitable for the present invention are described, for example, in US20040022677; US20050052646; US20050142033; US20040189311, each of which is incorporated herein by reference in its entirety. Assay plates and plate readers are currently commercially available (MULT1-SPOT® and MULTI-ARRAY® plates and SECTOR® instruments, a division of MESO SCALE DISCOVERY®, Meso Scale Diagnostics, LLC, Gaithersburg, MD).

[0071] See the specific examples illustrating the above constructs and methods. It should be understood that the examples are given to illustrate, rather than limit, the scope of various embodiments of the invention. [Example]

[0072] method Assays. Assays were developed as a number of different single- or multiplexed panels in MSD MULTI-ARRAY 96-well plates and analyzed using ECL detection on an MSD plate reader (e.g., the SECTOR or PR2 line of commercially available plate readers from Meso Scale Discovery, a division of Meso Scale Diagnostics, LLC, Gaithersburg, MD). Biomarkers analyzed in assay panels include those listed in Table 1.

[0073] Before performing assay measurements using an assay panel, samples were first diluted with the appropriate sample diluent to the dilution specified for that panel. The diluted sample (typically approximately 10-25 μL) was then combined with an additional volume of sample diluent (typically one-third the diluted sample volume) into the wells of a MULTI-ARRAY assay containing an array of capture antibodies for the targets in the panel. The plate was incubated with shaking for approximately 2 hours, the samples were removed, and the wells were washed three times with phosphate-buffered saline. A 50 μL volume of a mixture of labeled (MSD SULFO-TAG™-labeled, an ECL-label also available from MESO SCALE DISCOVERY) detection antibodies against the targets in the wells was added, and the plate was incubated with shaking for approximately 1 hour. The wells were washed three times with phosphate-buffered saline, and approximately 125 μL of MSD T Read buffer (commercially available from MESO SCALE DISCOVERY) was added. Plates were read using an MSD ECL plate reader (commercially available from MESO SCALE DISCOVERY), which records the assay signal for each array element in relative ECL units.

[0074] The same procedure was used to analyze intracellular markers in blood cell pellets, except that the initial sample was prepared using histone extraction buffer (50 mM TRIS pH 7.5, 500 mM NaCl, 0.5% Na-deoxycholate, 1% Triton X100, 2 mM EDTA, 1% PhIC, 1% PIC, and 1 mM PMSF; 10 6 The leukocytes were prepared by extracting a blood cell pellet in 200 μL of water (200 μL per 100 leukocytes).

[0075] The experimental plate layout included negative QC controls, positive QC controls, and an eight-point calibration curve, all performed in duplicate. The calibration curve was fitted with a four-parameter logistic (4-PL) fit using 1 / y weighting and used to calculate sample concentrations.

[0076] Radiation dose study in mice. Female mice (strain B6D2F1 / J) 60 Mice were subjected to total body irradiation (TBI) at a range of doses (doses in one study included 0, 1.5, 3, 6, 10, and 14 Gy) using a Co γ-ray source at a dose rate of approximately 0.6 Gy / min. Mice from these dose levels were sampled at 6 h, 1, 2, 3, 5, and 7 days post-irradiation (see Table 2). Whole blood was collected at the sampling times and processed into platelet-poor EDTA plasma fraction and peripheral blood leukocyte pellet (PBL). Separate aliquots of whole blood were withdrawn and blood counts were performed. In a given study, typically 6–8 mice were tested per dose / time condition. In one study (the "blinded study"), samples covering the conditions listed in Table 3 were submitted for analysis in a blinded manner to allow for unbiased characterization of the performance of the dose estimation algorithm.

[0077] Combined injury study in mice. This study examined the effect of a dorsal stab wound covering 15% of the total body surface area on radiation biomarker levels (the wound model is described in Ledney et al., 2010). Female mice (B6D2F1 / J strain) exposed to 0 (sham irradiation), 3, 6, or 10 Gy were then subjected to a stab wound covering 15% of the total body surface area within 1 hour of irradiation. Plasma samples were collected 6 hours, 1, 2, 3, 5, or 7 days after irradiation. Irradiation conditions and sample collection were as described above for the biomarker discovery study. As non-injured controls, an equal number of mice were subjected to the same dose / time conditions and treatments as the injured mice but did not receive a stab wound. Samples were also collected from true negative control mice, which were not subjected to the sham wound and irradiation treatments. At least eight replicate mice were subjected to each dose / time / wound condition. Prior to conducting the full combined injury study, a smaller pilot combined injury study was conducted, which was limited to the 0 and 6 Gy dose conditions and only six replicate mice per dose / time / injury condition, respectively (see Tables 3 and 4 for a summary of the test conditions for the pilot and full combined injury studies, respectively).

[0078] Radiation Dose Study with NHP Samples. Remnant non-human primate (NHP) samples (rhesus macaques - macaques) from a previous radiation study were evaluated as follows: Remnant sample set A was irradiated and 60 Remnant sample set B included EDTA-plasma samples collected at various times after TBI irradiation with 0, 1.0, 3.5, 6.5, or 8.5 Gy from a Co gamma source, and remnant sample set B included EDTA-plasma samples collected before irradiation and at various times after TBI irradiation with 7.5, 10.0, or 11.5 Gy (6MV LINAC photons, 0.80 Gy / min). Table 5 summarizes the samples tested in the feasibility study. Sample set A was provided and included blood cell counts measured at the time of sample collection.

[0079] Human Samples. To measure expected normal variations in radiological biomarkers, remnant platelet-poor EDTA plasma was collected from individual blood samples donated at a blood donation center (via Bioreclamation, LLC, Liverpool, NY). Samples were collected from 40 normal individuals and up to 10 self-identified individuals with each of four highly prevalent chronic diseases (hypertension, diabetes, asthma, or rheumatoid arthritis). Samples are summarized in Table 6 and were selected to be diverse in gender, age, and race.

[0080] To evaluate potential novel human models of radiation exposure, remnant human samples were collected from radiation oncology patients. EDTA plasma samples were collected from patients undergoing standard radiation therapy for lung cancer (15 patients) and GI cancer (8 patients). These treatments involve localized but relatively large-area irradiation of affected organs. A typical treatment schedule involves patients receiving 1.8 or 2.0 Gy per day, 5 days per week for 6 weeks, for a total dose of 54–60 Gy. Samples were collected before irradiation, 3, and 6 weeks after irradiation. EDTA plasma was also collected from melanoma patients (13 patients) undergoing TBI in preparation for cell transplant therapy (samples were collected 6 hours before and after receiving a 2 Gy dose). All treatments also included chemotherapy. More details on the samples and protocols can be found in Table 7.

[0081] [Table 2]

[0082] [Table 3]

[0083] [Table 4]

[0084] [Table 5]

[0085] [Table 6]

[0086] [Table 7]

[0087] Data Analysis. When assessing the dose and time response of individual biomarkers, the significance of differences in observed responses to different test conditions was determined by calculating p-values ​​using a two-tailed unpaired t-test. The measured dose and time responses of individual biomarkers, as determined in mouse radiation dose studies, were used to develop a multiparameter algorithm for dose prediction. The basic approach is to model the dose and time response for each biomarker. To predict dose, biomarker levels are measured in patients (or animal models) and the dose that provides the best compromise for fitting each biomarker to the response surface model is calculated. In the studies described herein, it is assumed that exposure times are known, and therefore, only the dose that provides the best compromise fit needs to be calculated.

[0088] result Mouse Radiation Dose and Combined Injury Study - Individual Biomarker Response. Biomarker testing results for the mouse radiation dose study are shown in Figures 1-17. Each figure includes plots of biomarker levels versus dose for each collection time and plots of biomarker levels versus time for each dose. Each data point for a marker in panels A-D represents the average across 7-8 mice for the control condition and across 10-12 mice for the sham and irradiated conditions, with the exception of EPO, IL-5, IL10, KC / GRO, and TNF-α measurements. Data points for these assays represent the average across 3-4 mice for the control and across 6-8 mice for the sham and irradiated conditions. The figures also provide a table of p-values ​​indicating the significance of the change in biomarker level for each condition compared to the non-irradiated control.

[0089] Figures are provided for selected biomarkers showing significant changes (p<0.05) across the range of dose / time conditions. DNA damage and inflammatory markers were early radiation markers, peaking at 6 hours or 1 day and declining significantly by 2 days, although there was some evidence that IL-6 and SAA were elevated at later time points due to higher doses, similar to the progression of acute radiation syndrome. Exceptions were IL-5 and IL-12, which showed a strong response at later time points. IL-12 was the only marker to show a strong dose-dependent decrease in concentration. Biomarkers of tissue damage repair tended to increase at or beyond 2 days post-radiation, but G-CSF had strong early and late responses, and Flt-3L, all but one, showed a significant response at the earliest (6 hours) time point.

[0090] Figures 1-17 also include plots of biomarker levels versus time for each dose and injury condition tested in the full composite injury study. Focusing on the 0 Gy (non-irradiated) condition, comparison of biomarker levels for uninjured mice (filled circles) and injured mice (open circles) showed that there were numerous biomarkers (Flt-3L, GM-CSF, TPO, EPO, IL-5, and CD27) that were not altered by radiation exposure or injury. In contrast, SAA, G-CSF, and CD-26 were significantly elevated by wounding (p<0.05), and the effect of wounding was greater in magnitude than the effect of radiation exposure. IL-12 and CD-45 showed a moderate wound effect that was statistically significant at some time points, but less significant in magnitude than the effect of radiation exposure. The results obtained using the combined injury model are largely consistent with those using the LPS injection model, as described herein, except that GM-CSF and IL-12 levels, which are strongly elevated in response to LPS, are unaffected (GM-CSF) or only weakly affected (IL-12) by wounding.

[0091] If a biomarker is insensitive to wounding in non-irradiated animals, wounding also generally did not affect biomarker levels after radiation exposure. The only exception was TPO; wounding appeared to accelerate the kinetics of the appearance of elevated TPO levels in irradiated animals. The effect was most pronounced for animals receiving a 6 Gy dose. On day 5 after irradiation, wounded animals had mean TPO levels nearly five times higher than non-wounded animals. By day 7, levels in wounded and non-wounded animals were roughly comparable. We predict that the use of biomarkers with this type of effect will not affect the ability to identify irradiated patients, but could potentially result in dose overestimation. However, adjusting the dose assessment algorithm based on wound or other trauma information would further improve the accuracy of the algorithm.

[0092] Mouse Radiation Dose Study—Algorithm Development and Testing. A multiparameter algorithm (as described in the Methods section) using five biomarkers (Flt-3L, G-CSF, GM-CSF, EPO, IL12 / 23) was applied to the full sample set from the initial mouse radiation dose and time study. For each sample, along with this set of biomarker measurements, a predicted dose was calculated using a repeated random subsampling approach (see Methods). Figure 18(a) shows a plot of predicted dose versus actual prescribed dose for the full set of samples. Dashed lines indicate a range of ±1.5 Gy from 0–6 Gy and ±25% beyond 6 Gy. Accounting for the 2.5-fold higher sensitivity of humans to radiation dose compared to the mouse model (LD50 / 30 of the B6D2F1 / J female mouse model is approximately 9.5 Gy—see Lednye et al., 2010—vs. approximately 3–4 Gy in humans), the corresponding human equivalent dose range was ±0.6 from 0–2.4 Gy and ±25% above 2.4 Gy.

[0093] The ability of the algorithm to correctly classify doses into the appropriate range can be calculated from Figure 18(a). For all dosages across the time window of 1 to 7 days after exposure, the percentage of samples falling within the defined boundaries in Figure 18(a) is 90±3%, where standard deviation is the variation in the calculated percentage across different combinations of test and training sets. Note that when the time range is extended to include the 6-hour time point, the accuracy decreases slightly to 88±3%.

[0094] The algorithm was also characterized for its overall ability to distinguish between >6 Gy and non-irradiated control mice (0 Gy) and between doses above and below 3 Gy. Given the mouse model's lower relative survival sensitivity to radiation dose compared to humans (the LD50 / 30 is nearly 2.5-fold higher for the mouse model), this classification should roughly correspond to its ability to classify doses above or below approximately 2-3 Gy in humans. Figure 19(a) shows ROC curves generated by varying the predicted dose threshold used to classify samples. The ROC curves demonstrated excellent classification ability, with an area under the curve (AOC) of 0.999 for distinguishing 0 Gy from ≥6 Gy and 0.956 for distinguishing ≤3 Gy from ≥6 Gy.

[0095] Supplemental Mouse Radiation Dose Study—Algorithm Development and Testing. Algorithm development proceeded using the results of the second mouse radiation dose study. To select the optimal biomarker panel for use with our multiparameter dose-estimation algorithm (described above), the performance of each possible combination of the 12 most radiation-sensitive biomarkers was tested against the dataset from the biomarker discovery study. A repeated random subsampling approach was used so that training and test samples were typically independent. Algorithm performance was calculated using two different metrics: (i) a prediction error metric given as the root mean square error (RMSE) in predicted dose across the complete sample set; (ii) an accuracy metric, in this case, we defined precision as the percentage of samples with predicted doses between 0 and 6 Gy that were within ±1.5 Gy and above 6 Gy, ±25%. Accounting for the 2.5-fold higher sensitivity of humans to radiation dose compared to the mouse model (LD50 / 30 of the B6D2F1 / J female mouse model is approximately 9.5 Gy—see Lednye et al., 2010—vs. approximately 3–4 Gy in humans), the corresponding human equivalent dose range was ±0.6 Gy from 0–2.4 Gy and ±25% above 2.4 Gy.

[0096] Figure 20 shows the RMSE and accuracy metrics for different possible biomarker combinations (each point in the graph represents a different combination of 1 to 12 biomarkers). Focusing on the best-performing combination for each possible panel size (top point for each panel size), the figure shows that there is no advantage to panel sizes larger than six markers. Table 8 shows performance metrics for the two top-performing panels for each panel: a top-performing panel containing one of the three highly injury-sensitive biomarkers (SAA, G-CSF, and CD26), and a top-performing panel that does not contain any of these three markers. Because there is no evidence that injury-sensitive markers are required for optimal performance, a six-marker panel that does not contain injury-sensitive biomarkers (CD27, Flt-3L, GM-CSF, CD45, IL12, and TPO) was selected as the preferred panel for further performance characterization.

[0097] [Table 8]

[0098] Performance of the algorithm for predicting dose for blinded samples. Using the complete biomarker discovery data as a training set for the multiparameter algorithm, the optimal six-biomarker panel was used to calculate the estimated dose for each sample from the blinded study. In this analysis, sampling time information was available and used for dose estimation, but the actual dose information was hidden from the analyst until dose estimation was complete. Figure 21 shows the correlation between predicted and actual dose, including the dotted line representing the accuracy criteria described above. Using this approach, 94.7% of predicted doses were within the accuracy criteria for all doses across the time window from 1 to 7 days after exposure. The RMS error in dose estimation was 1.14 Gy (necessary to be approximately equivalent to ±0.46 Gy in humans). Figure 21 also shows that the majority of points outside the accuracy criteria were for samples collected 1 day after radiation, indicating there may be an opportunity to improve performance by identifying additional early biomarkers and weighting them more heavily.

[0099] The algorithm was also characterized for its overall ability to distinguish doses ≥6 Gy from unirradiated control mice (0 Gy) and to distinguish doses ≥6 Gy from doses ≤3 Gy. Given the lower sensitivity of mouse models to radiation than humans (the LD50 / 30 is nearly 2.5-fold higher for mouse models), this classification should roughly correspond to the ability to classify doses around the critical 2 Gy threshold in humans. Figure 22 shows ROC curves generated by varying the predicted dose threshold used to classify samples. The ROC curves demonstrate excellent classification ability, with an area under the curve (AOC) of 1.000 distinguishing 0 Gy from ≥6 Gy and 0.998 distinguishing ≤3 Gy from ≥6 Gy. Using the optimal classification determined by ROC analysis, we achieved perfect separation between 0 Gy and ≥6 Gy samples (100% sensitivity, 100% specificity) and near-perfect separation between ≤3 Gy and ≥6 Gy samples (99.2% sensitivity and 100% specificity).

[0100] Effect of Combined Injury on Algorithm Performance. To provide a preliminary view of the algorithm's robustness to the potentially confounding effects of injury, the algorithm (using the selected optimal six-biomarker panel) was used to predict dose in samples from a pilot combined injury study (0 and 6 Gy, with and without a 15% superficial wound). Figure 23 provides a plot of predicted dose versus actual dose. The percentage of samples that fell within this criterion for dose prediction accuracy was 93.3%, closely matching the value observed for studies without an injury component (the % accuracy for the blinded study was 94.7%). The RMSE error for dose (0.85 Gy) was lower than that observed for the blinded study (1.14 Gy), likely because the blinded study included a higher dose. It was also possible to set a classification threshold in the predicted dose that perfectly distinguished between non-irradiated and irradiated animals (100% sensitivity, 100% specificity), despite the inclusion of injured animals.

[0101] Preliminary testing of radiation biomarkers in NHP samples. Stored plasma samples from irradiated NHPs (rhesus macaques) were tested using the NHP biomarker assay panel. However, for nearly every dose / time condition, at least five of six replicate samples were tested for all assays.

[0102] Figure 24(a-z) shows the radiation dose response for 13 biomarkers selected for radiosensitivity. Figure 24(a-z) shows that five of the six biomarkers selected for the mouse dose assessment algorithm were radioresponsive in NHPs (Flt-3L, CD27, TPO, and IL-12) and / or had mechanistic analogs (i.e., the neutrophil surface marker CD177 as an analog for CD45 in mice, and the lymphocyte surface marker CD20 as an analog for CD27 in mice). One marker from the mouse panel, GM-CSF, was not detected in control and irradiated NHPs; the assay may simply not be sensitive enough to native rhesus GM-CSF. Figure 24(a-z) also shows that three markers that demonstrated radiosensitivity in the mouse model (SAA, EPO, and G-CSF) but were not selected for use in the dose assessment algorithm also responded to radiation in the NHP model. Figure 24(a-z) shows the response of two markers (CRP and salivary amylase) used to assess radiation exposure in humans, but not in mice, confirming that these biomarkers respond to radiation in the NHP model. Finally, Figure 24(a-z) shows the dose response for two novel early responders to radiation (TIMP-1 and TNF-RII) identified by screening a cancer biomarker panel. TIMP-1 (tissue inhibitor of metalloproteinase 1) regulates various physiological processes through the inhibition of metalloproteinases and also possesses erythropoietic activity. Soluble TNF-RII (soluble TNF receptor II) is released into plasma by proteolytic cleavage of cell-bound TNF receptors and is associated with a number of inflammatory conditions.

[0103] A first-pass test using a multiparameter algorithm for dose assessment in NHP models was performed using a biomarker panel selected to roughly correspond to the preferred mouse panel. The panel included Flt-3L, TPO, IL-12, CD20, CD27, CD177, and salivary amylase. CD20 and CD177 are mechanistically similar to CD27 and CD45 in the mouse panel. GM-CSF was not included because the NHP GM-CSF assay does not appear to be sensitive enough to detect native GM-CSF in plasma from normal or irradiated mice. The one marker without an analog in the mouse panel was salivary amylase, an established marker in NHPs but not affected by radiation in mice. The dataset collected from NHP sample set A was analyzed using a slight modification to the algorithm used for the mouse study, in which the contributions of different markers were weighted based on their dose-response over a specific time range. The algorithm was trained and tested on the dataset using a random subsampling method to avoid training bias. Figure 24 (a–z) shows the algorithm's performance for distinguishing between animals receiving doses of 3.5 Gy or more and non-irradiated control animals (0 Gy), and between animals receiving doses of 3.5 Gy or more and those receiving doses of 1 Gy or less. Given the lower sensitivity of the NHP model to radiation than humans (the LD50 / 30 is approximately 1.5-fold higher for the mouse model), this classification should roughly correspond to the ability to classify doses around the critical 2 Gy threshold in humans (3 Gy in NHPs). Figure 25 shows ROC curves generated by varying the predicted dose threshold used to classify samples. The ROC curves demonstrate excellent classification ability, with an area under the curve (AOC) of 1.000 distinguishing between 0 Gy and ≥3.5 Gy, and an AOC of 0.995 distinguishing between ≤1 Gy and ≥3.5 Gy. The optimal classification determined by ROC analysis was used to completely separate 0 Gy and ≥3.5 Gy samples (100% sensitivity, 100% specificity) and almost completely separate ≤1 Gy and ≥3.5 Gy samples (96.9% sensitivity and 98.5% specificity).Figure 25 also shows the correlation between predicted and actual doses, and shows that this correlation is very good near the critical 3 Gy point.

[0104] Human normal and disease samples. The human biomarker panel was tested using remnant plasma samples from blood donors, including 42 normal healthy individuals (Table 6), as well as a set of samples from donors who self-reported suffering from one of four prevalent chronic diseases: hypertension (10 samples), rheumatoid arthritis (6 samples), asthma (10 samples), and diabetes (9 samples). The results are plotted in bar and whisker format in Figure 26. There was no evidence that the disease population was significantly different (p<0.05) from the normal population for any of the biomarkers.

[0105] When judging the ability of mouse model data to support the use of dose-assessment algorithms in humans, one consideration is whether the increased normal range expected for biomarkers in diverse human populations (relative to inbred mouse strains) increases the likelihood of false positives. We decided to investigate this issue by adding random noise to biomarker levels measured from non-irradiated mice in biomarker discovery studies, so that the variation in "normal" mouse levels would match the variation observed in normal human populations. We applied this noise to four of the biomarkers in our preferred six-biomarker panel (Flt-3L, CD27, GM-CSF, and IL-12). CD45 was not measured in the human sample set; therefore, there was no reference for comparison. TPO actually showed lower variation in the human sample set than in the mouse sample set, so we left the mouse levels unchanged.

[0106] The data were then analyzed to determine how the added noise affected the specificity by which non-irradiated control mice could be distinguished from mice exposed to 6 Gy. Classification of 0 Gy or ≥ 6 Gy was performed using the optimal threshold selected in the absence of injected noise. As shown in Figure 27, increasing baseline biomarker variability did not result in any additional misclassification, and the specificity measured for classification remained at 100%.

[0107] Human Samples from Patients Undergoing Radiation Oncology. A sample set from cancer patients undergoing radiation (Table 7) was evaluated as a potential model for assessing biodosimetry algorithms. One set of samples was from melanoma patients who received lymphodepleting chemotherapy prior to cell transplant therapy. The study included one treatment group that also received total-body irradiation (for 3 days after chemotherapy) and one that did not. Samples were only available pre-irradiation, 5–6 hours after the first 2 Gy fraction; therefore, the sample set was relevant for early-onset biomarkers. The results, shown in Figure 28, indicate that biomarker levels in patients in the pre-irradiation draw and non-TBI treatment groups may differ substantially from normal ranges due to the chemotherapy regimen. Nevertheless, two biomarkers appeared significantly elevated in the post-irradiation group compared to the pre-irradiation and non-TBI treatment groups: salivary amylase (p=0.0012), a well-known early-onset radiation marker, and p53 (p=0.013), a marker identified as an early (less than 1 day) marker.

[0108] Samples were also examined from patients who received localized radiation therapy (2 Gy per day, 5 times per week for 6 weeks) to the lung for GI cancer, in combination with neoadjuvant or concurrent chemotherapy. Biomarker levels were measured in samples collected before radiation and after cumulative doses of 30 and 54–60 Gy (Figure 29). Overall, no significant changes in biomarker levels were observed as a result of these localized radiation therapies. There was evidence of small increases in mean levels of hematopoietic markers (Flt-3L and TPO) and small decreases in levels of soluble lymphocyte surface markers (CD5 and CD20), but the changes were modest (approximately 2-fold), and there was significant overlap between distributions.

[0109] Mouse Confounding Effect Study - Individual Biomarker Response. Results of a preliminary mouse confounding effect study are shown in Figures 30-32 for biomarkers indicating radiation sensitivity. The figures show biomarker levels versus recovery time in the absence of a confounder or after injection of LPS or G-CSF. Plots are provided for both unirradiated mice and mice exposed to 6 Gy 2 hours after treatment with the confounder. Because the preliminary confounding effect study used a different radiation source (X-rays) than the radiation dose-response study (γ-rays), the 0 and 6 Gy conditions from the radiation dose study were overlaid to assess the consistency of results between studies. Figure 33 provides bar graphs showing the relative magnitude of the maximum observed LPS and G-CSF responses compared to the maximum observed radiation response (including samples from the radiation response study), allowing for a rapid assessment of which markers may be subject to confounding effects.

[0110] Figure 33 shows that several assays (SAA, G-CSF, GM-CSF, IL-6, IL-12, IL-12 / 23, and KC / GRO) showed LPS responses on the same scale as or greater than the maximum radiation response. These assays were primarily cytokine or inflammatory response markers. Flt-3L, EPO, TPO, p53, and γH2AX, in contrast, were insensitive or showed only slight increases in response to LPS. Interestingly, TNF-α showed little or no radiation response but strongly responded to LPS, potentially making it useful for identifying samples with significant inflammation. Most markers showed no response to G-CSF treatment. Even when responses were observed, for example, for EPO, the responses were relatively small relative to the scale of the radiation response.

[0111] Mouse Confounding Effect Study—Algorithm with Reduced LPS-Insensitive Biomarker Set. One approach to address the confounding effect of non-radiation-related inflammatory responses is to remove inflammatory biomarkers from the dose assessment algorithm for patients with overt trauma or infection. Algorithm performance was characterized after removing LPS-sensitive biomarkers from the preferred five-panel biomarker set to generate a two-biomarker LPS-insensitive panel (Flt-3L and G-CSF). Figures 18b and 19b provide plots of predicted dose versus actual dose and ROC curves for classifying samples with doses ≥ 6 Gy, generated similarly to the curves presented in Figures 18a and 19a for the full five-marker panel. The reduced panel was still useful for dose prediction, but with somewhat reduced performance compared to the full panel. Accuracy of classifying dose within 1.5 Gy for doses up to 6 Gy or within 25% for doses greater than 6 Gy was 74 ± 4%, compared to 91 ± 3% for the full panel. The AUC for the ROC curve to distinguish doses ≥ 6 Gy from non-irradiated controls was 0.989 compared to 0.999 for the full panel. The AUC to distinguish doses ≥ 6 Gy from doses ≤ 6 Gy was 0.882 compared to 0.956 for the full panel.

[0112] Preliminary Study of NHP Samples. Figures 34-35 show results based on a preliminary study of a subset of rhesus macaque plasma samples collected 0-9 days after TBI using 1 or 3.5 Gy (3 monkeys per dose). The results largely confirm those observed using the mouse model; differences are highlighted below. The NHP model generally showed higher radiosensitivity, with a stronger response at approximately 3 Gy. Many markers showed a good response at 1 Gy. EPO and SAA responded over a wider time range (1-9 days for EPO, 1-3 days for SAA) than observed in mice. CRP, while non-radioresponsive in mice, showed a strong early response in NHPs. BPI and p53 were both observed as very early (6 h) markers. GM-CSF did not respond to radiation in the NHP model (data not shown).

[0113] Figure 36 shows interesting results obtained when plasma samples were tested with the CD20 (lymphocyte) and CD177 (neutrophil) surrogate markers. Preliminary studies using a small set of stored blood pellet samples collected 0, 1, or 2 days after irradiation showed that CD20 and CD177 levels were significantly elevated by cell count (data not shown). Even more interestingly, Figure 36 shows that for plasma samples from the 1 and 3.5 Gy cohorts, measurable levels of free (non-cell-bound) CD-20 and CD-177 were present in the plasma, and changes in plasma levels of these markers over dose and time provide useful diagnostic information.

[0114] Additional NHP Sample Testing and Dose Assessment Algorithm Development for the NHP Model. Data from the NHP model was used to evaluate an approach to assess radiation dose by fitting multiplexed biomarker data to a time-dose response surface for each biomarker (the same approach described above for the mouse data). Results showed that a panel of six plasma markers (Flt-3L, EPO, p53, CD20, CD177, and SAA) could provide good discrimination between animals receiving >3.5 Gy (equivalent to approximately 2 Gy in humans) and those receiving <3.5 Gy, and further provided high accuracy for semi-quantitative dose prediction. Results for these six biomarkers are shown below in Table 26 and Figure 37. Table 26 also shows the discriminatory effectiveness of additional biomarkers measured in this study.

[0115] [Table 9]

[0116] The impact of replacing the individual values ​​of CD20 and CD177 in the model with the CD177 / CD20 ratio was evaluated to determine whether this change improved the accuracy of the algorithm (similar to the use of the neutrophil / lymphocyte ratio for dose assessment based on hematological results). As shown in Table 27(a)-(b), the use of the ratio provided accuracy roughly equivalent to the use of the individual values.

[0117] [Table 10]

[0118] Alternative algorithms for dose estimation were also evaluated. A simple linear model (Dose = A1C1 + A2C2 + A3C3 + , where Ci is the concentration of marker i and Ai is an empirically determined coefficient) was equivalent to a response surface-based model for distinguishing between animals exposed to 0 and 3.5 Gy (both models were able to correctly classify all samples from animals in these two groups). The linear model has the advantage of not requiring knowledge of the time between exposure and sample collection, but it does not quantify the dose and surface model (the root-mean-square error for dose estimation of the optimal panel was approximately 1 Gy versus approximately 0.3 Gy for the response surface model).

[0119] Various publications and test methods are cited herein, the disclosures of which are incorporated herein by reference in their entirety. In the event that the present specification and materials incorporated by reference and / or referred to herein contain conflicting disclosures and / or inconsistent use of technical terms and / or the incorporated / referenced materials use or define terms differently than those used or defined herein, the present specification shall control.

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Claims

1. A method for assaying the state of peripheral blood leukocytes in a sample, (a) A step of measuring the levels of multiple hematological surrogate biomarkers in a sample, wherein the multiple biomarkers include lymphocyte cell surface markers, neutrophil cell surface markers, or a combination thereof, The above step, wherein the neutrophil cell surface marker includes CD16b, CD177, or a combination thereof; (b) a step of comparing the level of the hematological surrogate biomarker in the sample with the level of the hematological surrogate biomarker in a normal control sample; and (c) A step of determining the peripheral blood leukocyte state based on the comparison step (b) above. The above method, including.

2. The method according to claim 1, wherein the lymphocyte cell surface marker includes CD5, CD20, CD26, CD27, CD40, or a combination thereof.

3. The method according to claim 1 or 2, wherein the sample is a blood cell pellet, serum, or plasma.

4. The method according to claim 3, wherein the sample is plasma.

5. The method according to claim 1 or 2, wherein the sample includes a dried blood spot, and the dried blood spot is reconstructed before step (a).

6. The method according to any one of claims 1 to 5, further comprising evaluating the effects of radiation on neutrophils and lymphocytes based on the peripheral blood leukocyte status determined in step (c).

7. A multiplex assay kit for use in evaluating the peripheral blood leukocyte status of a sample, the kit being configured to measure the levels of multiple biomarkers using the method described in any one of claims 1 to 6.

8. A hematological surrogate biomarker multiplex assay kit configured to measure the levels of multiple biomarkers in a sample, wherein the multiple biomarkers include lymphocyte surface markers, neutrophil surface markers, or a combination thereof.

9. The kit according to claim 8, wherein the lymphocyte cell surface markers include CD5, CD20, CD26, CD27, CD40, or a combination thereof.

10. The kit according to claim 8 or 9, wherein the neutrophil cell surface marker comprises CD-16b, CD177, or a combination thereof.

11. The kit according to any one of claims 7 to 10, wherein the sample is a blood cell pellet, serum, or plasma.

12. The kit according to claim 11, wherein the sample is plasma.

13. The kit according to any one of claims 7 to 10, wherein the sample comprises a dried blood spot.

14. The kit according to any one of claims 7 to 13, further comprising instructions for relating the levels of the plurality of biomarkers present in the sample to the dose of radiation absorbed by the patient.

15. The kit according to any one of claims 7 to 14, further comprising a computer-readable recording medium that, when executed by a computer system, causes the computer system to perform a method including relating the levels of the plurality of biomarkers present in a sample to the dose of radiation absorbed by a patient.

16. A device capable of housing a kit according to any one of claims 7 to 15 or components of the kit for measuring the levels of the plurality of biomarkers, wherein the device is operably associated with a computer system, the computer system storing a computer program, and when executed by the computer system, the computer program performs a method comprising relating the levels of the plurality of biomarkers present in a sample to the dose of radiation absorbed by a patient.

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