EP3628068A1 - Systèmes et procédés pour déterminer les attributs d'échantillons biologiques - Google Patents

Systèmes et procédés pour déterminer les attributs d'échantillons biologiques

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Publication number
EP3628068A1
EP3628068A1 EP17908722.6A EP17908722A EP3628068A1 EP 3628068 A1 EP3628068 A1 EP 3628068A1 EP 17908722 A EP17908722 A EP 17908722A EP 3628068 A1 EP3628068 A1 EP 3628068A1
Authority
EP
European Patent Office
Prior art keywords
quantitation
exposure
post
sample
attributes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP17908722.6A
Other languages
German (de)
English (en)
Other versions
EP3628068A4 (fr
Inventor
Patrick Lilley
Beau WALKER
Michael John COLBUS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Liquid Biosciences Inc
Original Assignee
Liquid Biosciences Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Liquid Biosciences Inc filed Critical Liquid Biosciences Inc
Publication of EP3628068A1 publication Critical patent/EP3628068A1/fr
Publication of EP3628068A4 publication Critical patent/EP3628068A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR

Definitions

  • the field of the invention is biological sample testing. Background
  • Biospecimens in Biobanking identify that pre-measurement errors account for most errors in clinical laboratory results. They propose that laboratory personnel increase the rigor of protocols and documentation surrounding pre-measurement collection and handling of biological samples as a solution for limiting diminished sample quality.
  • U.S. Patent No. 5,846,492 to Jacobs et al. describes a spectrophotometric method for determining sample quality measurement in the dispensing tip of an analyzer.
  • the tip can be scanned in a light-tight enclosure that will analyze the absorbance spectra of the liquid.
  • this reference fails to appreciate advances in technology that facilitate new ways of determining sample quality.
  • the present invention provides apparatuses, systems, and methods related to a determining an unknown pre-quantitation attribute of a target biological sample (e.g., a blood sample, a protein serum sample, a tissue sample, a CSF sample, a urine sample, and a stool sample).
  • a target biological sample e.g., a blood sample, a protein serum sample, a tissue sample, a CSF sample, a urine sample, and a stool sample.
  • a method of determining an unknown pre-quantitation attribute of a target biological sample (e.g., an indication of quality of the target biological sample) is contemplated.
  • Embodiments of the method include several steps. In one step, it requires receiving a set of biological data pairs, where each biological data pair corresponds to an altered biological sample (e.g., a deliberately degraded or incidentally degraded biological sample having a known degradation) and comprises a known pre-quantitation attribute and a set of post- quantitation attributes. In another step, it requires using the set of biological data pairs to computationally develop a model describing a relationship between post- quantitation attributes and the known pre-quantitation attribute. And in another step, it requires applying a set of target biological sample post-quantitation attributes to the model to determine the pre-quantitation attribute of the target biological sample (which is otherwise unknown).
  • the known pre-quantitation attribute comprises a type of deliberate degradation that the altered biological sample has been subjected to.
  • Deliberate degradation can come in the form of elapsed time, exposure to heat, exposure to cold, exposure to vibration, exposure to acceleration, exposure to ultraviolet light, exposure to exogenous substances, and exposure to other environmental forces or factors, or any combination thereof.
  • a post-quantitation attribute can include an output of results from a mass
  • post-quantitation attributes can include: protein quantitation, protein abundance, protein concentration, protein activity, protein presence, peptide quantitation, peptide presence, peptide abundance, RNA activity, wavelength emission measurement, and a mass to charge ratio value.
  • the model that is computationally developed can include a plurality of models (e.g., a system of models, competing models, or an ensemble of models that work together).
  • the step of using the set of biological data pairs to computationally develop a model additionally includes identifying and disregarding unnecessary post-quantitation attributes (e.g., not all information from a mass spectrometer will be useful in determining the unknown pre-quantitative attribute, so the unnecessary post-quantitative attributes are disregarded).
  • unnecessary post-quantitation attributes e.g., not all information from a mass spectrometer will be useful in determining the unknown pre-quantitative attribute, so the unnecessary post-quantitative attributes are disregarded.
  • a method of determining a quality of a target biological sample is contemplated. This method includes several steps.
  • each biological data pair corresponding to an altered biological sample and comprising a known quality and a set of post-quantitation attributes.
  • it requires using the set of biological data pairs to computationally develop a model describing a relationship between (1 ) a subset of post-quantitation attributes and (2) the known quality.
  • it requires applying a set of target biological sample post- quantitation attributes to the model to determine the quality of the target biological sample.
  • the model receives an input comprising the target biological sample post-quantitation attributes and produces an output comprising the quality of the target biological sample.
  • a target biological sample e.g. , a blood sample, a protein serum sample, a tissue sample, a CSF sample, a urine sample, and a stool sample
  • the altered biological sample is degraded such that the degradation corresponds to the known quality, and the known quality can be expressed as a continuum ranging from low quality to high quality.
  • a post-quantitation attribute can be, for example, a protein quantitation, a protein abundance, a protein concentration, a protein activity, a protein presence, a peptide quantitation, a peptide presence, a peptide abundance, an RNA activity, a wavelength emission measurement, and a mass to charge ratio value.
  • the model can be a single model, a system of models, competing models, or an ensemble of models that work together.
  • a system for use with an instrument develops a model to determine an unknown pre-quantitation attribute of a target biological sample.
  • the system includes a computational modeling device communicatively coupled with the instrument.
  • the instrument is configured to analyze altered biological samples to produce sets of post-quantitation attributes corresponding to the altered biological samples. Each altered biological sample that has been analyzed by the instrument has a
  • each biological data pair includes a known pre-quantitation attribute and a set of post-quantitation attributes.
  • the computational modeling device performs several functions. It receives sets of biological data pairs as input, and then it computationally develops a model describing a relationship between (1 ) post-quantitation attributes and (2) the known pre-quantitation attribute.
  • the model can be applied to a set of target biological sample post-quantitation attributes to determine the unknown pre-quantitation attribute of the target biological sample.
  • the system can be, for example, a mass
  • the known pre-quantitation attribute is a type of deliberate degradation that the altered biological sample has been exposed to.
  • the altered biological sample can be exposed to: heat, cold, and ultraviolet light.
  • the model that is computationally developed can include a plurality of models (e.g. , a system of models, competing models, or an ensemble of models that work together).
  • a plurality of models e.g. , a system of models, competing models, or an ensemble of models that work together.
  • Figure 1 shows a set of biological samples before and after analysis by an instrument.
  • Figure 2 shows a model-building set of biological samples before and after analysis by an instrument.
  • Figure 3 shows a biological data pair corresponding to a biological sample from a model-building set of biological samples.
  • Figure 4 shows biological data pairs used in model development to create a model.
  • Figure 5 shows a set of post-quantitation target biological samples.
  • Figure 6 shows a biological data pair of a post-quantitation target biological sample having an unknown pre-quantitation attribute.
  • Figure 7 shows a model being applied to solve for unknown pre- quantitation attributes that correspond to target biological samples.
  • Figure 8 shows a system where a computational modeling device is both physically and informationally coupled with the quantitation instrument.
  • Figure 9 shows a system where a computational modeling device is informationally, but not physically coupled with the quantitation instrument.
  • Figure 10 is a flow chart of a method where the pre-quantitation is measure of quality.
  • inventive subject matter provides example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus, if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining elements.
  • Coupled to is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements).
  • Coupled to and “coupled with” are used synonymously.
  • the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term "about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed considering the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable.
  • any language directed to a computer should be read to include any suitable combination of computing devices, including servers, interfaces, systems, databases, agents, peers, Engines, controllers, or other types of computing devices operating individually or collectively.
  • the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.).
  • the software instructions preferably configure the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus.
  • the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods.
  • Data exchanges preferably are conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network.
  • the following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided in this application is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
  • the systems and methods described in this application are directed to innovations related to development of models that describe relationships between known post-quantitation attributes of biological samples (e.g., attributes discovered after analyzing a biological sample using an instrument such as a mass
  • spectrometer and unknown pre-quantitation attributes of biological samples (e.g., an unknown metric, quality, or other attribute that is a feature of a biological sample prior to analysis by an instrument).
  • the models described according to the inventive subject matter are developed using biological data pairs. Each biological data pair includes a known pre-quantitation attribute and a known set of post-quantitation attributes that both correspond to a biological sample.
  • Methods of the inventive subject matter include several steps that are discussed in more detail below.
  • set notation expressed as ⁇ ... , ... , ... ⁇ is used in this application to express a set of items (e.g., biological samples, attributes, etc.). For example, if a set is expressed as ⁇ a, ...
  • the set comprises / quantity of a.
  • Sets are sometimes also described in text instead of using set notation, and so it should be understood that even if set notation is not used, that does not preclude the possibility that a particular item could be also be expressed as a set.
  • Prime notation is used to differentiate between biological samples that have been analyzed by an instrument and biological samples that have not.
  • a member of a set of biological samples is denoted as, for example, a', that means it is a post-quantitation sample having a corresponding set of post-quantitation attributes.
  • prime notation and non-prime notation can be used interchangeably as it still refers to the same set of biological samples.
  • a set of biological samples is expressed as the set of samples ⁇ b-i , ... ,bj ⁇ . All the biological samples ⁇ b-i , ... ,bj ⁇ are eventually run through an instrument 102 as shown in Figure 1. After the biological samples ⁇ b-i, ... ,bj ⁇ have been run through the instrument 102, they are notated as the set ⁇ b'i, ... ,b'j ⁇ to indicate they have been analyzed by the instrument.
  • pre- quantitation refers to a status before a sample has been analyzed by an instrument
  • post-quantitation refers to a status after a sample has been analyzed by an instrument.
  • a pre-quantitation attribute is an attribute of a biological sample that is quantifiable prior to analysis by an instrument (e.g. , quality of a biological sample)
  • a post-quantitation attribute is a result of analysis by an instrument (e.g., the results of an analysis).
  • Post-quantitation attributes often come in sets, depending on the instrument used and the analysis performed.
  • the set of biological samples ⁇ bi , ... , b, ⁇ can be, for example, one, or any combination of, a blood sample, a protein serum sample, a tissue sample, a cerebrospinal fluid (CSF) sample, a urine sample, and a stool sample.
  • a blood sample a protein serum sample
  • tissue sample a tissue sample
  • CSF cerebrospinal fluid
  • urine sample a urine sample
  • stool sample a stool sample.
  • all the samples in the set of biological samples are of the same type.
  • the set of biological samples can include a variety of different types of biological samples.
  • a set of biological samples that includes biological samples of different types all the biological samples in the set are still preferably related in some manner.
  • the samples can, for example, have some other attribute or attributes in common.
  • a set of biological samples that includes some combination of blood samples, protein serum samples, tissue samples, CSF samples, urine samples, and stool samples can still be used as the set of biological samples ⁇ b-i, ... ,bj ⁇ as seen in Figure 1 if the samples are related in some way outside of sample type.
  • "Related" biological samples could have an overlap in terms of coming from the same patient, the same hospital, the same region, the same underlying ailment, etc.
  • the biological samples have other attributes in common, such as being produced by similar systems with a body (e.g., urine samples and stool samples are both excrement).
  • a set of biological samples includes both blood samples and tissue samples
  • the biological samples in that set could be related by having the same or similar proteins, antibodies (e.g., Immunoglobulin G), cell densities, electrolytes, DNA, blood cells, etc.
  • antibodies e.g., Immunoglobulin G
  • cell densities e.g., electrolytes, DNA, blood cells, etc.
  • This same set of overlapping attributes can be applicable to many different types of biological samples, not just blood and tissue samples.
  • Instrument 102 can be a variety of instruments including: a mass spectrometer, a colorimeter, a spectrophotometer, a chromatograph, a gel electrophoresis system, a blood chemistry analyzer, a spectrofluorometer, an immunoassay system, proteomic assay systems, and an immunoturbidimetric system.
  • Other contemplated instruments include: genomic instruments - instruments to measure gene expression of DNA, miRNA, mRNA, IncRNA, such as the
  • Nanostring® nCounter® genomic instruments - gene sequencers, such as lllumina® next-generation sequencers (NGS); and proteomic instruments - protein assays, such as SomaLogic® SOMAscan Assay and SDS Page instruments.
  • genomic instruments - gene sequencers such as lllumina® next-generation sequencers (NGS)
  • proteomic instruments - protein assays such as SomaLogic® SOMAscan Assay and SDS Page instruments.
  • model-building set of biological samples is denoted as the set ⁇ bm-i, ... ,brri j ⁇ , as seen in Figure 2.
  • the model-building set of biological samples ⁇ bm-i, ... ,brri j ⁇ belongs to the set of biological samples ⁇ b-i , ... ,bj ⁇ shown in Figure 1.
  • model building set of biological samples ⁇ bm-i, ... ,brri j ⁇ is a subset of the set of biological samples ⁇ b- ⁇ , ... ,bj ⁇ , then j ⁇ i. But in other words
  • the model building set of biological samples ⁇ bm-i, ... ,brri j ⁇ is not a subset of the set of biological samples ⁇ bi , ... ,bj ⁇ . In those embodiments, it is still the case that j ⁇ i. This is because methods of the inventive subject matter are most useful for taking a relatively small set of model-building biological samples to create a model that enables determination of an otherwise unknown pre-quantitation attribute of biological samples belonging to the set ⁇ b-i , ... ,bj ⁇ .
  • the set of biological samples ⁇ b- ⁇ , ... ,bj ⁇ can be of one or more types (as discussed above), while the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ can be of one or more types sharing no overlap in type with the set of biological samples ⁇ b-i , ... ,bj ⁇ . It is important for different types of biological samples used in methods of the inventive subject matter to be related. For example, a model developed using a model-building set of biological samples of one type could take into account post-quantitation attributes that also pertain to other types of biological samples. Thus, the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ does not need to be the same type of biological sample as the set of biological samples ⁇ bi , ... ,bi ⁇ .
  • Each biological sample in the model-building set of biological samples ⁇ bmi , ... ,brri j ⁇ has a known pre-quantitation attribute (e.g. , an alteration or a degradation).
  • each biological sample in the model-building set of biological samples is altered in a measurable way prior to analysis (or quantitation) by the instrument of Figure 1.
  • Each altered biological sample i.e. , each biological sample in the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇
  • Alteration of a biological sample e.g.
  • an alteration of a biological sample is expressed as a pre-quantitation attribute.
  • a pre-quantitation attribute can be, for example: identity of a person or entity that harvested the sample; a location where the sample was harvested; steps taken to process the sample prior to quantitation; or any other attribute that pertains to the sample but is not directly related to a patient outcome (e.g. , diagnosis of disease, mistake in protocol, or mistake in sample acquisition such as mistake in blood draw, tissue sampling, etc.).
  • the alteration can also be: exposure to heat; exposure to cold; exposure to ultraviolet light; exposure to chemical means; or exposure to a denaturing reagent via, for example, an acid, a base, an inorganic salt, an organic solvent (e.g.
  • a pre-quantitation attribute can additionally be expressed as a set of several pre-quantitation attributes (e.g., any combination of pre-quantitation attributes discussed in this application).
  • a pre-quantitation attribute can be a known absence of an alteration.
  • a known alteration or lack thereof is a known pre-quantitation attribute corresponding to a model-building biological sample.
  • the pre-quantitation attribute simply indicates an absence of alteration.
  • model-building set of biological samples ⁇ bm-i, ... ,brri j ⁇ exists as a subset of the biological samples ⁇ b-i, ... ,bj ⁇ , it is also contemplated that the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ does not necessarily need to belong to the broader set of biological samples ⁇ b-i , ... ,bj ⁇ . In embodiments where the model- building set of biological samples ⁇ bm-i , ... ,brri j ⁇ is not a subset of the biological samples ⁇ b-i, ...
  • model-building set of biological samples could nevertheless be useful for determining unknown pre-analytical attributes biological samples from the set of biological samples ⁇ b-i , ... ,bj ⁇ , so long as the model-building set of biological samples ⁇ bmi , ... ,brri j ⁇ are related to the set of biological samples ⁇ b-i, ... ,bj ⁇ .
  • the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ are related to the set of biological samples ⁇ bi , ... , b, ⁇ , whether a subset of the set of biological samples ⁇ bi , ...
  • a model developed using the model- building set of biological samples ⁇ bm-i , ... ,brri j ⁇ can still be applied to the post- quantitation attributes of the set of biological samples by virtue of that relatedness.
  • the biological samples ⁇ bi, ... ,bj ⁇ and the model- building set of biological samples ⁇ bmi , ... ,brri j ⁇ are run through the same type of instrument (e.g. , both sets are analyzed by a mass spectrometer - it does not need to be the exact same mass spectrometer as long as the same types of post- quantitation attributes are generated). It is contemplated that biological samples used in methods of the inventive subject matter can be run through an instrument either locally or remotely as long as the instrument generates post-quantitation attributes that are useful in model development (described in more detail below).
  • Biological samples could be run through the instrument in groups, in sequence, all at once, etc. This process can occur over the course of hours, days, weeks, or months - it is contemplated that there is no time limit on when the samples must be run through an instrument other than constraints affecting the biological samples themselves (e.g., shelf life).
  • the amount of time that has elapsed between collecting each member of the model-building set of biological samples and running those members through an instrument can be a pre- quantitation attribute that is used in model development.
  • model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ is also run through an instrument (e.g. , the same instrument as the biological samples ⁇ b-i , ... ,bj ⁇ ) to create a set of post-quantitation model-building biological samples ⁇ bm'-i , ... ,bm' j ⁇ , as seen in Figure 2
  • Post-quantitation biological samples ⁇ b'i , ... ,b'j ⁇ each have a corresponding set of post- quantitation attributes.
  • post-quantitation model-building biological samples ⁇ bm'-i , ... ,bm' j ⁇ also each have corresponding sets of post-quantitation attributes.
  • post-quantitation attributes can include, for example, a protein quantitation, a protein abundance, a protein concentration, a protein activity, a protein presence, a peptide quantitation, a peptide presence, a peptide abundance, a RNA activity, a wavelength emission measurement, and a mass to charge ratio value. Additionally, each post-quantitation attribute can itself be a set of data.
  • post-quantitation attributes can include results directly measured by an instrument (e.g. , a mass spectrometer), it is also contemplated that post-quantitation attributes can additionally include information that is inferred from results directly measured by the instrument. For example, information not directly measured can be inferred from the raw data output by mass spectrometer (e.g. , via the raw, unmatched mass to charge ratio values). In another example, after a protein is synthesized (e.g. , translated from RNA) it is often modified by either the addition of small molecules or the removal of peptides.
  • a mass spectrometer can help identify these modifications, albeit indirectly, and the presence, absence, or level of modification can be a post-quantitation attribute.
  • a biological data pair 300 is produced, as shown in Figure 3.
  • Each post-quantitation biological sample bm' j therefore has a corresponding pre- quantitation attribute 302 and a set of post-quantitation attributes 304, both of which make up a biological data pair 300.
  • Biological data pairs are unique for each post- quantitation biological sample bm' j having a known pre-quantitation attribute (e.g. , biological samples belonging to the model-building set of biological samples).
  • biological data pairs 402, 404, 406, & 408 corresponding to each post-quantitation biological sample bm' j in the model-building set of biological samples ⁇ bm'-i , ... ,bm' j ⁇ are used to develop a model 410, as shown in Figure 4.
  • the model 410 expresses a target pre-quantitation attribute as a function of a set of post-quantitation attributes, where the target pre-quantitation attribute that can be solved for by using the model is the same as the known pre- quantitation attributes from the biological data pairs 402, 404, 406, & 408.
  • model 410 can include a plurality of models (e.g. , a system of models, competing models, or an ensemble of models that work together).
  • the model 410 is developed computationally. During model development, a computer receives biological data pairs corresponding to the model-building set of biological samples as input, as shown in Figure 4 and described above. Once the model is developed, it can be used to determine the value of unknown pre- quantitation attributes corresponding to target biological samples belonging to a set of target biological samples ⁇ bt'i , ... ,bt' k ⁇ (denoted as a "prime" set since these samples would need to be post-quantitation samples having post-quantitation attributes associated with them), as shown in Figure 5. For example, if each biological sample ⁇ bm'-i , ...
  • the model would enable solving for exposure to heat in post-quantitation target biological samples ⁇ bt'i , ... ,bt' k ⁇ where that pre-quantitation attribute is unknown for each sample bt' k .
  • the target set of biological samples could have as few as one sample in the set, while the upper bound is theoretically unlimited.
  • the set of target biological samples must all have been run through the instrument that the set of model-building biological samples were run through (e.g., the same instrument or same type of instrument).
  • each target biological sample bt' k After running the set of target biological samples through an instrument, each target biological sample bt' k , as shown in Figure 6, then has a corresponding biological data pair 600, where the biological data pair 600 has a known set of post-quantitation attributes 602 (also denoted as t_post k ) and an unknown, target pre-quantitation attribute 604 (also denoted as t_pre k ).
  • post-quantitation attributes 602 also denoted as t_post k
  • target pre-quantitation attribute 604 also denoted as t_pre k
  • the model-building set of biological samples ⁇ bm-i , ... ,brri j ⁇ is a subset of the set of biological samples ⁇ bi , ... , b, ⁇
  • the target set of biological samples could be the members of the set of post-quantitation biological samples ⁇ b'i , ... , b', ⁇ minus the post-quantitation model-building set of biological samples ⁇ bm'i, ... ,bm' j ⁇ .
  • the model would be easily applicable to the remaining members of the set of post-quantitation biological samples
  • a target set of biological samples ⁇ bt'i, ... ,bt' k ⁇ can be completely different types of biological samples than the set of biological samples ⁇ b'-i , ... ,b'i ⁇ , even if the target set of biological samples ⁇ bt'- ⁇ , ... ,bt' k ⁇ is a subset of the post-quantitation biological samples ⁇ b'i , ... ,b'j ⁇ ). It is also not a requirement that the target set of biological samples ⁇ bt'i, ... ,bt' k ⁇ be members of the set of biological samples.
  • a target biological sample is a different type of biological sample than the type (or types) of biological samples comprising the model-building set of biological samples
  • the set of post- quantitation attributes corresponding to the target biological sample can still fit into a model that has been developed using the model-building set of biological samples.
  • models generated according to the inventive subject matter can be used successfully to determine target pre-quantitation attributes when the input set of post-quantitation attributes are sufficient for implementation of the model, regardless of target biological sample type.
  • the set of post-quantitation attributes that the model interprets as signaling an alteration or degradation of a tissue sample may be the same as, or similar to, the markers of alteration or degradation (i.e., the set of post-quantitation attributes) in blood samples because many different types of biological samples, including blood samples and tissue samples, degrade in similar ways.
  • the pre-quantitation attributes and sets of post- quantitation attributes can be grouped into biological data pairs corresponding to the target biological sample, expressed as ⁇ t_post k ,t_pre k ⁇ .
  • an electronic device can be informationally coupled with an instrument to facilitate information and data exchange. It is additionally contemplated that the electronic device can instead be implemented as software on an existing computing device (e.g., a computing device that already exists with an instrument, on a server, on a network of device or servers, etc.).
  • an existing computing device e.g., a computing device that already exists with an instrument, on a server, on a network of device or servers, etc.
  • an electronic device 800 is both physically and informationally coupled with an instrument 802.
  • the device 900 can be informationally coupled with the instrument 902, but not physically coupled with the instrument 902.
  • the electronic device is implemented to handle tasks such as model development and storage and manipulation of data (e.g., pre-quantitation attributes, post-quantitation attributes, biological data pairs, etc.) as necessary to facilitate implementation of methods of the inventive subject matter.
  • a virtual environment e.g., as software
  • they can be implemented on, for example, a server or set of servers that are configured to exchange data with the instrument (e.g., cloud servers).
  • Information exchange between the device and the instrument can occur via a network connection, but it can also occur by manual data exchange (e.g., transferring data using a portable data storage device such as a flash drive or portable hard drive).
  • an electronic device receives a set of biological data pairs, where each biological data pair corresponds to an altered biological sample belonging to a model-building set of biological samples.
  • Each biological data pair comprises a known quality (e.g., a quality determination based on information about the alteration of the biological sample and expressed as a pre-quantitation attribute) and a set of post-quantitation attributes (e.g., the results of analysis using an instrument).
  • the device uses the set of biological data pairs to computationally develop a model describing a relationship between (1 ) a subset of post-quantitation attributes (where the subset can also be the entire set) and (2) the known quality (e.g.
  • the next step 1004 is to apply a set of target biological sample post-quantitation attributes to the model to determine (e.g., solve for) the quality (e.g., the unknown pre-quantitation attribute) of each target biological sample.
  • An implementation of the inventive subject matter could be used in determining quality of blood serum samples belonging to a set of 1000 blood serum samples. To do this, first a set of 30 blood serum samples from 5 different healthy patients is chosen. Each sample belonging to the set of 30 is subjected to a different degradation regime, which would be recorded as degradation data (i.e., a known pre-quantitation attribute).
  • one third of the samples for each patient could be exposed to a temperature greater than 40C, which is generally considered too high; one third could alternatively be exposed to a denaturing reagent such as alcohol; and the final one third could be reserved as controls, with no degradation.
  • the model-building set of biological samples with known degradation levels i.e. , known pre-quantitation attributes
  • a mass spectrometry instrument to produce output values (e.g. , either the raw mass to charge values or imputed values, all of which are expressed as a set of post- quantitation attributes) which are expressed as sets of post-quantitation attributes that correspond to each blood serum sample in the model-building set.
  • the resulting post-quantitation attributes, along with the degradation data (i.e. , the pre-quantitation attribute), would be used to generate a model, where the model would identify markers/variables from the output values (i.e. , post-quantitation attributes) that correspond to/predict degradation (i.e. , the pre-quantitation attribute).
  • a second set of biological samples i.e. , a set of target biological samples
  • the model is then applied to the output values (i.e. , the post-quantitation attributes) for this second set, and the quality level (i.e. , the pre-quantitation attribute) of each of the second set of samples can be determined.
  • Methods of the inventive subject matter can also be useful when, for example, a medical researcher has a set of blood samples to analyze and is aware that at least one of the samples was exposed to too much heat prior to analysis by a mass spectrometer, but the researcher only knows of one or two specific samples that were exposed to this condition.
  • the researcher could first run a biological sample from the set that they know was exposed to heat to generate a model that relates post-quantitation attributes to heat degradation, and then subsequently run all the rest of the samples with unknown qualities (e.g. , unknown heat degradations) through that model to determine which of those samples has suffered heat-related degradation.

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Abstract

L'invention concerne des systèmes et des procédés pour déterminer des attributs de pré-quantification d'échantillons biologiques à l'aide des attributs de post-quantification desdits échantillons. En modifiant un ensemble d'échantillons biologiques de manière mesurable avant de le soumettre à un instrument (p. ex., spectromètre de masse), il est possible de développer un modèle qui permet de déterminer les attributs de pré-quantification inconnus dans d'autres échantillons biologiques en fonction desdits attributs de post-quantification.
EP17908722.6A 2017-05-02 2017-05-02 Systèmes et procédés pour déterminer les attributs d'échantillons biologiques Withdrawn EP3628068A4 (fr)

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US4075475A (en) * 1976-05-03 1978-02-21 Chemetron Corporation Programmed thermal degradation-mass spectrometry analysis method facilitating identification of a biological specimen
US20050064422A1 (en) * 2001-11-29 2005-03-24 Barnidge David R Polypeptide quantitation
CA2630796A1 (fr) * 2005-11-29 2007-06-07 Denator Aktiebolag Procede de determination de la qualite d'un echantillon biologique
US8133701B2 (en) * 2006-12-05 2012-03-13 Sequenom, Inc. Detection and quantification of biomolecules using mass spectrometry
US20130103321A1 (en) * 2011-10-24 2013-04-25 Somalogic, Inc. Selection of Preferred Sample Handling and Processing Protocol for Identification of Disease Biomarkers and Sample Quality Assessment
WO2013170011A2 (fr) * 2012-05-09 2013-11-14 William Beaumont Hospital Procédé pour déterminer la qualité d'un spécimen biologique
EP2956127A4 (fr) * 2013-02-14 2016-10-05 Metanomics Health Gmbh Moyens et procédés pour évaluer la qualité d'un échantillon biologique
WO2015145387A1 (fr) * 2014-03-26 2015-10-01 Metanomics Health Gmbh Moyens et méthodes de détermination de la qualité d'échantillons sanguins en fonction d'un panel de métabolites
US10890592B2 (en) * 2015-11-04 2021-01-12 Metabolon, Inc. Automated sample quality assessment

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