COLLECTION PROBE AND METHODS FOR THE USE THEREOF
20220310378 · 2022-09-29
Assignee
Inventors
- Livia Schiavinato Eberlin (Austin, TX)
- Thomas Milner (Austin, TX)
- Jialing Zhang (Austin, TX)
- John Lin (Austin, TX)
- John Rector (Austin, TX)
- Nitesh Katta (Austin, TX)
- Aydin Zahedivash (Austin, TX)
Cpc classification
B01L3/502
PERFORMING OPERATIONS; TRANSPORTING
G01N2560/00
PHYSICS
H01J49/04
ELECTRICITY
H01J49/0031
ELECTRICITY
International classification
H01J49/04
ELECTRICITY
A61B10/00
HUMAN NECESSITIES
B01L3/00
PERFORMING OPERATIONS; TRANSPORTING
Abstract
Method and devices are provided for assessing tissue samples from a plurality of tissue sites in a subject using molecular analysis. In certain aspects, devices of the embodiments allow for the collection of liquid tissue samples and delivery of the samples for mass spectrometry analysis.
Claims
1-43. (canceled)
44. A method for assessing tissue samples from a subject, the method comprising: (a) applying a fixed or discrete volume of a solvent to a tissue site in the subject; (b) collecting the applied solvent to obtain a liquid sample; (c) subjecting the liquid sample to mass spectrometry analysis, thereby obtaining a mass spectrometry profile comprising a plurality of mass-to-charge (m/z) ratios; and (d) characterizing the liquid sample based on the mass spectrometry profile; wherein characterizing the liquid sample comprises determining whether the tissue site comprises: normal lung, breast, ovarian, or thyroid cells; benign lung, breast, ovarian, or thyroid cells; or cancerous lung, breast, ovarian, or thyroid cells.
45. (canceled)
46. The method of claim 44, wherein the fixed or discrete volume of a solvent is applied as a droplet.
47. The method of claim 44, wherein the fixed or discrete volume of a solvent is applied at using a pressure of less than 100 psig.
48. (canceled)
49. The method of claim 44, wherein the fixed or discrete volume of a solvent is applied at using a mechanical pump to move the solvent through a solvent conduit.
50. The method of claim 44, wherein collecting the applied solvent comprises applying a negative pressure to pull the sample into a collection conduit and/or applying a gas pressure to push the sample into a collection conduit.
51. (canceled)
52. The method of claim 50, wherein the solvent is applied through a solvent conduit that is separate from the collection conduit.
53. The method of claim 52, wherein the gas pressure is applied through a gas conduit that is separate from the solvent conduit and the collection conduit.
54. (canceled)
55. The method of claim 44, wherein the method produces no detectable physical damage to the tissue.
56-60. (canceled)
61. The method of claim 44, wherein the solvent comprises water, ethanol, or a combination thereof.
62. (canceled)
63. (canceled)
64. The method of claim 44, wherein the discrete volume of solvent is between about 0.1 and 100 μL.
65. (canceled)
66. The method of claim 44, wherein collecting the applied solvent is between 0.1 and 30 seconds after the applying step.
67. (canceled)
68. (canceled)
69. The method of claim 44, further comprising collecting a plurality liquid samples from a plurality of tissue sites.
70. The method of claim 69, wherein the liquid samples are collected with a probe and wherein the probe is washed between collection of the different samples; wherein the probe is disposable and is changed between collection of the different samples; or wherein the probe comprises a collection tip and further comprising ejecting the collection tip from the probe after the liquid samples are collected.
71-76. (canceled)
77. The method of claim 44, further defined as an intraoperative method.
78-80. (canceled)
81. The method of claim 77, further comprising resecting tissue sites that are identified to include cancerous lung, breast, ovarian, or thyroid cells.
82. The method of claim 44, wherein the liquid sample is obtained from the tissue site in vivo.
83. The method of claim 44, wherein the liquid sample is obtained from the tissue site ex vivo.
84-110. (canceled)
111. The method of claim 44, wherein when the profile comprises at least 5 mass-to-charge (mn/z) ratios selected from the group consisting of 175.02, 187.01, 201.04, 215.03, 306.08, 313.16, 330.98, 332.90, 357.10, 409.23, 615.17, 722.51, 744.55, 747.52, 748.52, 771.52, 773.53, 861.55, 863.57, 885.55, and 886.55, then the tissue site is identified as comprising cancerous lung cells.
112-121. (canceled)
122. The method of claim 44, wherein when the profile comprises at least 3 mass-to-charge (m/z) ratios selected from the group consisting of 124.01, 175.02, 175.03, 283.27, 313.16, and 341.27, then the tissue site is identified as comprising cancerous ovarian cells.
123-127. (canceled)
128. The method of claim 44, wherein when the profile comprises at least 5 mass-to-charge (m/z) ratios selected from the group consisting of 175.02, 191.02, 191.05, 283.27, 341.27, 353.16, 432.20, 433.21, 615.17, 822.47, and 822.48, then the tissue site is identified as comprising cancerous thyroid cells.
129-134. (canceled)
135. The method of claim 44, wherein when the profile comprises at least 5 mass-to-charge (m/z) ratios selected from the group consisting of 187.04, 268.80, 279.92, 283.27, 341.27, 345.16, 381.21, 687.51, 742.54, and 766.54, then the sample is identified as comprising cancerous breast cells.
136. (canceled)
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.
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DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
I. THE PRESENT EMBODIMENTS
[0048] In certain aspects, the instant application provides methods and devices for molecular assessment of samples, such as tissue samples. In particular, aspects the methods can be used to assess multiple tissue sites during an operation (or biopsy) of the tissue. This feature allows for accurate identification of diseased tissues (e.g., tissue sites retaining cancer cells) in “real-time” allowing surgeons to more accurately address only the diseased tissue relative to surrounding normal tissues. In particular aspects, the methods disclosed here can involve delivery of a fixed or discrete volume of solvent to a tissue site, followed by collection of a liquid sample from the site and analysis of the liquid sample by mass spectrometry. Importantly, rather than being applied in a high pressure spray, solvent is applied as discreet droplets and at low pressure. These methods allow for accurate collection of samples from a distinct tissue site while avoiding damage to the tissue being assessed. The resulting mass spectrometry profile from collected samples allows for differentiation of diseased versus normal tissue sites. The method can be repeated at multiple sites of interest to very accurately map molecular changes (e.g., in a tissue). Importantly, the profiles of samples could be differentiated even with-out the use an ionization source. Thus, while methods of the embodiments could be used in conjunction with an ionization source, the use of such a source is not required. These methodologies can allow assessment of plurality of tissue sites over a short range of time, thereby allowing for very accurate assessment of the boundaries of diseased versus normal tissues.
[0049] In some aspects, the methods detailed herein can be used to collect and analyze samples from a wide range of sources. For example, the methods can be used to assess forensic, agriculture, drug of abuse, pharmaceutical, and/or oil/petroleum samples.
[0050] In some aspects, the materials (PDMS and PTFE) and solvent (e.g., water only solvents) used in the devices of the embodiments are biologically compatible, such that they can be used in surgery in for real-time analysis. Furthermore, because the devices can be very compact, it can be hand-held or integrated to a robotic surgical system, such as the Da Vinci surgical system (e.g., in an automated system). Thus, many regions of the human body cavity can be quickly sampled during surgery, and analyzed (e.g., by using a database of molecular signatures and machine learning algorithms). Therefore, the diagnostic results may be provided in real time for each sampled region. Exemplary devices for use in these methods are detailed below.
[0051] Referring initially to
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[0053] It is understood that in certain embodiments, each of conduits 111, 112 and 113 (which can be of any desired length) may comprise separate components. For example, the portion of each of the conduits within probe 110 may be formed as integral channels during the manufacturing of probe 110. In addition, the portions of each of the conduits between probe 110 and chamber 120, pressurized gas supply 130 and mass spectrometer 140 may be tubing or other components suitable for providing fluid flow.
[0054] In this embodiment, apparatus 100 may comprise a pump 125 configured to transfer the solvent from chamber 120 to the first conduit 111 and reservoir 115. In the embodiment shown, apparatus 100 can also comprise a first valve 121 configured to control a sample flow from reservoir 115 through third conduit 113 to mass spectrometer 140. Apparatus 100 can also comprise a second valve 122 configured to control a flow of pressurized gas through second conduit 112 to reservoir 115.
[0055] A control system 160 can be configured to control operating parameters of apparatus 100. For example, control system 160 can be configured to control a flow of solvent from chamber 120 through first conduit 111 to reservoir 115 by controlling the operation of pump 125. In addition, control system 160 can be configured to control the sample flow from reservoir 115 to mass spectrometer 140 by controlling the opening and closing of first valve 121. Control system 160 can further be configured to control the pressurized gas flow from pressurized gas container 130 to reservoir 115 by controlling the opening and closing of second valve 122.
[0056] During operation of apparatus 100, a user can position probe 110 so that reservoir 115 is placed on sample site 150. Control system 160 can operate pump 125 for specific periods of time to transfer a desired volume of the solvent from chamber 120 to reservoir 115 via first conduit 111. In exemplary embodiments, the solvent in chamber 120 can assist in the efficient extraction of molecules from a tissue sample site 150 for analysis.
[0057] In addition, control system 160 can allow a particular period of time between the operation of pump 125 and the opening of first valve 121. This can allow a vacuum from mass spectrometer 140 (or a separate, auxiliary vacuum system) to draw sample materials (e.g. molecules from tissue sample site 150) from reservoir 115 to mass spectrometer 140 via third conduit 113.
[0058] When first valve 121 is opened, control system 160 can also open second valve 122 to allow an inert gas (e.g. N.sub.2 or CO.sub.2) to be transferred from pressurized gas supply 130 to reservoir 115 via second conduit 112. The inert gas can assist in sample tissue drying prior to analysis, as well as prevent a solvent gap in first conduit 111 (e.g. as a result of a vacuum pulled by mass spectrometer 140 when reservoir 115 contacts sample site 150). The inert gas can also assist in solvent transport from sample site 150 to mass spectrometer 140 through third conduit 113.
[0059] Control system 160 may comprise software and hardware suitable for operating the various components of apparatus 100. Particular embodiments of the various components shown in the schematic of
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[0066] As shown in the embodiment of
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II. ASSAY METHODOLOGIES
[0070] In some aspects, the present disclosure provides methods of determining the presence of diseased tissue (e.g., tumor tissue) or detecting a molecular signature of a biological specimen by identifying specific patterns of a mass spectrometry profile. Biological specimens for analysis can be from animals, plants or any material (living or non-living) that has been in contact with biological molecules or organisms. A biological specimen can be samples in vivo (e.g. during surgery) or ex vivo.
[0071] A profile obtained by the methods of the embodiments can correspond to, for example, proteins, metabolites, or lipids from analyzed biological specimens or tissue sites. These patterns may be determined by measuring the presence of specific ions using mass spectrometry. Some non-limiting examples of ionizations methods that can be coupled to this device include chemical ionization, laser ionization, atmospheric-pressure chemical ionization, electron ionization, fast atom bombardment, electrospray ionization, thermal ionization. Additional ionization methods include inductively coupled plasma sources, photoionization, glow discharge, field desorption, thermospray, desorption/ionization on silicon, direct analysis in real time, secondary ion mass spectroscopy, spark ionization, and thermal ionization.
[0072] In particular, the present methods may be applied or coupled to an ambient ionization source or method for obtaining the mass spectral data such as extraction ambient ionization source. Extraction ambient ionization sources are methods with, in this case, liquid extraction processes dynamically followed by ionization. Some non-limiting examples of extraction ambient ionization sources include air flow-assisted desorption electrospray ionization (AFADESI), direct analysis in real time (DART), desorption electrospray ionization (DESI), desorption ionization by charge exchange (DICE), electrode-assisted desorption electrospray ionization (EADESI), electrospray laser desorption ionization (ELDI), electrostatic spray ionization (ESTASI), Jet desorption electrospray ionization (JeDI), laser assisted desorption electrospray ionization (LADESI), laser desorption electrospray ionization (LDESI), matrix-assisted laser desorption electrospray ionization (MALDESI), nanospray desorption electrospray ionization (nano-DESI), or transmission mode desorption electrospray ionization (TM-DESI).
[0073] As with many mass spectrometry methods, ionization efficiency can be optimized by modifying the collection or solvent conditions such as the solvent components, the pH, the gas flow rates, the applied voltage, and other aspects which affect ionization of the sample solution. In particular, the present methods contemplate the use of a solvent or solution which is compatible with human issue. Some non-limiting examples of solvent which may be used as the ionization solvent include water, ethanol, methanol, acetonitrile, dimethylformamide, an acid, or a mixture thereof. In some embodiments, the method contemplates a mixture of acetonitrile and dimethylformamide. The amounts of acetonitrile and dimethylformamide may be varied to enhance the extraction of the analytes from the sample as well as increase the ionization and volatility of the sample. In some embodiments, the composition contains from about 5:1 (v/v) dimethylformamide:acetonitrile to about 1:5 (v/v) dimethylformamide:acetonitrile such as 1:1 (v/v) dimethylformamide:acetonitrile. However, in preferred embodiment the solvent for use according to the embodiments is a pharmaceutically acceptable solvent, such as sterile water or a buffered aqueous solution.
III. EXAMPLES
[0074] The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.
Example 1—Smart MasSpec Pen Design
[0075] The MasSpec Pen (
[0076] The system developed consists of three main parts: 1) a syringe pump that is programmed to deliver a discrete solvent volume using a controlled flow rate; 2) tubing systems integrated to two-way pinch valves for controlled solvent transport; 3) a probe tip which is used for direct sampling of biological tissues. Several iterations of the system were explored and optimized with the ultimate goal of minimizing tissue damage, maximizing tissue-analyte extraction, and maximizing solvent transmission to the mass spectrometer.
[0077] The optimized system contains three primary components: 1) a syringe pump that is programmed to deliver a defined water volume (4-10 μL) to the sampling probe; 2) small diameter (ID 800 μm) polytetrafluoroethylene (PTFE) tubing conduits which are integrated to a fast (8 ms) two-way pinch valves for controlled solvent transport from pump to tissue, and from the tissue to the mass spectrometer; 3) a handheld pen-sized probe for direct sampling of biological tissues.
[0078] The main component of the handheld pen-sized probe is a 3D-printed polydimethylsiloxane (PDMS) tip (
[0079] The three conduit tubes used are made of polytetrafluoroethylene (PTFE), which is also biologically compatible. Tube 111 is used to deliver solvent from syringe pump to the probe tip. Tube 112 is used, in some cases, to deliver an inert gas (N.sub.2 or CO.sub.2) to the probe tip. The gas serves three main purposes: 1) tissue drying prior to analysis; 2) prevent solvent gap in tube 111 due to the mass spectrometer's vacuum when the reservoir is closed by contacting the tissue specimen; 2) assist solvent transport from tissue to the mass spectrometer through tube 113. However, in some circumstances there is no need for use of a gas. Tube 113 is directly connected to the inlet of the mass spectrometer so that the positive pressure of the mass spectrometer vacuum system is used to drive the droplet from the reservoir to the mass spectrometer inlet for ionization.
[0080] The time events involved in the device operation are automated and precisely controlled by software that communicates with an Arduino system and two two-way pinch valves. All pinch valves are closed until the process is initiated when: 1. under 300 μL/min, a pulse is sent to the pump to infuse the solvent for two seconds and stop, generating a 10 μL droplet filling in the MasSpec Pen reservoir; 2. Tubes 112 and 113 are closed, allowing the solvent in the reservoir to interact with the tissue for three seconds to extract the molecules; 3. The pinch valves controlling tubes 112 and 113 are opened simultaneously, allowing the droplet to transfer to the mass spectrometer for ionization and molecular analysis. 4. A pulse is sent to the pump to infuse the solvent for another 12 seconds and stop, to completely drive all the extracted molecules into the mass spectrometer. 5. Leave tube 112 and 113 open for another 20 seconds to allow all the solvent in tube 113 to go into the mass spectrometry. The total analyzing time is 37 seconds.
[0081] The tip design using three conduit tubes and high speed actuated pinch valves allowed precise control of droplet motion and showed excellent performance and robustness. The entire process from sampling to mass spectral acquisition is completed in 10 s or less and is fully automated using an Arduino microcontroller, so that each acquisition and analysis is individually triggered through a one-step click using a foot pedal. System automation ensures that each solvent droplet is delivered separately to the inlet, yielding several mass spectra that are averaged for a final molecular profile of the sample. Further, controlled droplet delivery allowed the mass spectrometer to operate without any evident performance degradation. After each use, the MasSpec Pen can be cleaned if residues are observed through a rapid and automated cleaning flush, or by replacing the disposable tip.
Example 2—Molecular Profiles and Analysis
[0082] The system described herein operates by directly connecting the collection conduit to the mass spectrometer inlet for transporting the analyte-containing solvents to the mass spectrometer for molecular analysis. This set up greatly simplifies operational details and precludes the use of ionization sources. After the probe interacts with the tissue, the solvent is then transported to the mass spectrometer and directly infused without the need of an additional ionization source. Since the system is fully automated so that each 10 μL solvent droplet is delivered separately to the inlet, the mass spectrometer operates without any impact on its performance. Rich molecular information is obtained in this manner, similar to what is observed from other solvent-extraction ambient ionization techniques such as desorption electrospray ionization. The ionization mechanism may be similar to inlet ionization. For inlet ionization methods, the ionization occurs in the inlet pressure drop region between atmosphere and vacuum. Several solvent systems can be used in the device. In this example, to assure full biological compatibility of the device, water was used as the only solvent, although mixtures of ethanol and water in different ratios were also explored and yielded similar results. To demonstrate these samples were analyzed after extraction with a solvent composed of 5:1 and 20:1 (H.sub.2O:EtOH) and found out EtOH will help extract more PE lipids, like PE (40:6) (m/z 790.539) and PE (38:4) (m/z 766.540) (see results in
[0083] The effectiveness of the MasSpec Pen in obtaining molecular information was tested by analyzing thin tissue sections and pieces of tissue samples. First, 16 μm thick tissue sections were analyzed on standard histologic glass slides following the automated operational steps described above for the MasSpec Pen, using pure water as the solvent. Several probe tips with different reservoir diameters of the MasSpec Pen were tested, yielding mass spectra presenting lipids species characteristic of mouse brain tissue grey matter, white matter, or mixed composition for larger sampling sizes.
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[0085] The negative ion mode mass spectra obtained from the grey matter region of the mouse brain tissue section presented rich molecular information including a variety of ions corresponding to deprotonated or chloride adducts of lipid species commonly detected from biological tissues using solvent-based ambient ionization MS techniques. Peaks at high relative abundancies were identified as fatty acids (FA) from m/z 120-350, sphingolipids such as sulfatides from m/z 700-1100 and chloride adducts of ceramides (Cer) from m/z 500-700, and glycerophospholipids (GL) such as glycerophosphoinositols (PI), glycerophosphoethanolamines (PE), glycerophosphoserines (PS) and doubly charged cardiolipins (CL) from m/z 700-1100. In the higher mass range from m/z 1100-1800, GL dimers and singly charged CL were observed. A variety of peaks tentatively identified as small metabolites including glutamine at m/z 145.061, glutamate at m/z 146.045, N-acetylaspartic acid at m/z 174.041 and chloride adduct of hexose at m/z 215.033 were detected in lower mass range from m/z 120-250, based on high mass accuracy measurements and tandem mass spectrometry data (Table 1). Importantly, the negative ion mode mass spectra obtained from the grey matter from different tissue sections of the same mouse brain were reproducible (RSD=9.3%, n=9), comparable to what reported using the same method for DESI-MSI (RSD=8.0%, n=5) In the positive ion mode, the mass spectra obtained presented high relative abundances of commonly observed molecular species identified as diacylglycerols (DG), PE, and glycerophosphocholine (PC) (
[0086] The MasSpec Pen spectrum was compared with the DESI spectrum which was acquired under similar MS parameters but using the commonly applied acetonitrile and dimethylformamide solvent system due to its high efficiency for extracting lipids from biological tissue. Interestingly, in the negative ion mode, the spectrum from MasSpec Pen using water as the extraction solvent shared large amount of molecular species from m/z 500 to m/z 1800 with the spectrum from DESI using ACN and DMF, with slightly higher ratios of PE lipids, such as PE (40:6) (m/z 790.539) and PE (38:4) (m/z 766.539).
[0087] Further analysis compared the molecular species detected in the negative ion mode with those observed in a DESI mass spectrum acquired from a serial tissue section of the same mouse brain using water as the solvent and analogous experimental conditions. The mass spectra obtained using the MasSpec Pen and DESI were similar with a calculated cosine similarity of 0.9, sharing a large number of molecular species at comparable relative abundances and signal-to-noise (S/N) ratios (
[0088] To evaluate the system performance, consecutive analysis was conducted on the same tissue section and different tissue sections and demonstrated that the system is highly reproducible within samples and across different samples.
[0089] Molecular Analysis of Human Cancer and Normal tissues sections. Ambient ionization mass spectrometry has been extensively investigated for molecular diagnosis of human cancerous tissues. To test the capability of MasSpec Pen system described herein for differentiating the normal and tumorous samples, 62 human tissue samples of five different tissue types including breast, kidney, lymph node, thyroid and ovary, were analyzed. The mass spectra obtained in the negative ion mode using water as solvent system for each tissue type showed molecular ions commonly observed by DESI-MS, with high relative abundances of metabolites and lipids. Principal component analysis (PCA) was employed to statistically evaluate the performance of MasSpec Pen in interspecific and intraspecific analyses of human specimen. It should be noted that the first three components, which all encompassed more than 85% of the total variance, are used in the present work. As can be seen in
[0090] The capability of the MasSpec Pen was tested to analyze 20 thin tissue sections of normal and tumor human breast (n=5 normal breast, n=5 breast ductal carcinoma) and thyroid (n=5 normal thyroid, n=4 papillary thyroid carcinoma, and n=1 follicular thyroid adenoma) tissues. The mass spectra obtained in the negative ion mode for each tissue type presented a rich variety of molecular ions commonly observed from human tissues by DESI-MSI, with high relative abundances of metabolites, fatty acids, and complex lipids. For example, the mass spectra obtained for papillary thyroid carcinoma tissue sections presented lipid species previously identified as diagnostic markers by DESI-MSI (Zhang et al., Cancer Research, 76, 2016), including a variety of doubly-charged CL, and other glycerophospholipids such as PI (38:4) (m/z 885.550), PI (36:4) (m/z 857.518), PE (38:4) (m/z 766.539), and PE (36:2) (m/z 742.539) (Table 2). A distinct mass spectral profile was obtained for normal thyroid tissue section, presenting high relative abundances of m/z 126.904, identified as iodine, m/z 145.050, identified as glutamine, m/z 175.024, identified as ascorbic acid, m/z 822.472, tentatively assigned to C.sub.36H.sub.78O.sub.9N.sub.3I, and m/z 885.551, identified as PI (38:4) (
[0091] Molecular Analysis of Fresh Tissue Samples. The MasSpec Pen device was designed to operate on fresh tissue samples independently of morphology. To test the device for fresh tissue analysis, fresh mouse brain tissue was used in the beginning. No significant differences were observed in the spectra obtained from mouse brain tissue sections or fresh brain tissues.
[0092] It should be noted that all the frozen specimens that were obtained from tissue banks, had been well preserved under −80° C. in freezer and were thawed at room temperature before use. The data collected from fresh human specimens were also processed by PCA. PCA of the spectra recorded shows a clear distinction between the normal and tumorous samples (
TABLE-US-00001 TABLE 1 Data obtained for the identification of selected negative ion mode molecular ions from mouse brain tissue. Main Fragment Proposed Proposed Measured Theoretical Mass error ions upon Identification formula m/z m/z (ppm) MS/MS.sup.a Thymosin β-4 C.sub.212H.sub.350N.sub.56O.sub.78S.sub.1 991.091 (−5) 991.090 (−5) <1 (−5) NA 1239.113 (−4) 1239.114 (−4) <1 (−4) 1652.484 (−3) 1652.488 (−3) 2.4 (−3) ST t42:1 C.sub.48H.sub.92NO.sub.12S 906.634 906.635 −1.1 NA PI 38:4 C.sub.47H.sub.82O.sub.13P 885.550 885.550 <1 152.995, 241.011, 283.264, 303.233, 419.257, 581.309 PS 40:6 C.sub.46H.sub.77NO.sub.10P 834.529 834.529 <1 152.994, 283.264, 327.233, 419.256, 437.267, 747.497 PE 40:6 C.sub.45H.sub.77NO.sub.8P 790.539 790.539 <1 283.243, 283.264, 327.232, 480.309 38:4 C.sub.43H.sub.77NO.sub.8P 766.539 766.539 0 259.243, 283.263, 303.232, 480.309 P-38:6 .sup. C.sub.43H.sub.73NO.sub.7P 746.513 746.513 <1 283.243, 327.232, 436.282 O-36:3 C.sub.41H.sub.77NO.sub.7P 726.545 726.544 1.4 140.010, 152.994, 281.248, 444.288, 462.299 P-36:4 .sup. C.sub.41H.sub.73NO.sub.7P 722.513 722.513 <1 152.994, 259.243, 303.233, 418.273, 436.283 Cer 36:1 C.sub.36H.sub.71NO.sub.3Cl 600.513 600.513 <1 NA FA 22:6 C.sub.22H.sub.31O.sub.2 327.233 327.233 <1 229.195, 283.243, 309.174 20:4 C.sub.20H.sub.31O.sub.2 303.233 303.233 <1 205.195, 259.243, 284.991 18:0 C.sub.18H.sub.35O.sub.2 283.264 283.264 <1 265.130 16:0 C.sub.16H.sub.31O.sub.2 255.233 255.233 <1 237.043 N- C.sub.6H.sub.8NO.sub.5 174.040 174.041 −5.7 58.028, Acetylaspartic 88.039, acid 130.049 hexose C.sub.6H.sub.12O.sub.6Cl 215.033 215.034 −4.7 NA glutamate C.sub.5H.sub.8NO.sub.4 146.045 146.046 −6.8 102.054, 128.034 Glutamine C.sub.5H.sub.9N.sub.2O.sub.3 145.061 145.062 −6.9 127.050, 128.034 .sup.aNA (not available) means that only high mass accuracy was used for tentative ion identification.
TABLE-US-00002 TABLE 2 Data obtained for the identification of selected negative ion mode molecular ions from human thyroid tissue. Main Fragment Proposed Proposed Measured Theoretical Mass error ions upon Identification formula m/z m/z (ppm) MS/MS.sup.a PI 40:5 C.sub.49H.sub.84O.sub.13P 911.566 911.566 <1 NA 38:4 C.sub.47H.sub.82O.sub.13P 885.550 885.550 <1 152.994, 223.006, 241.011, 283.264, 303.233, 419.256, 581.310 36:4 C.sub.45H.sub.78O.sub.13P 857.518 857.518 <1 152.994, 241.011, 279.233, 415.226, 577.278 34:1 C.sub.45H.sub.80O.sub.13P 835.534 835.534 <1 152.994, 241.011, 255.232, 391.226, 553.277 PE 38:4 C.sub.43H.sub.77NO.sub.8P 766.539 766.539 <1 140.010, 152.995, 259.243, 283.264, 303.233, 480.309 36:2 C.sub.41H.sub.77NO.sub.8P 742.539 742.540 −1.3 140.010, 152.994, 281.248 P-36:4 .sup. C.sub.41H.sub.73NO.sub.7P 722.513 722.513 <1 140.010, 196.037, 259.243, 303.233, 418.270, 436.283 CL 74:7 C.sub.83H.sub.146O.sub.17P.sub.2 738.502 738.502 <1 NA 72:8 C.sub.81H.sub.140O.sub.17P.sub.2 723.479 723.479 <1 NA Cer 34:1 C.sub.34H.sub.67NO.sub.3Cl 572.481 572.482 −1.7 NA FA 20:4 C.sub.20H.sub.31O.sub.2 303.233 303.233 <1 205.195, 259.243, 284.992 18:0 C.sub.18H.sub.35O.sub.2 283.265 283.264 3.5 265.130 18:1 C.sub.18H.sub.33O.sub.2 281.250 281.249 3.6 NA 18:2 C.sub.18H.sub.31O.sub.2 279.234 279.233 3.6 261.222 16:0 C.sub.18H.sub.31O.sub.2 255.233 255.233 <1 NA Ascorbic C.sub.6H.sub.7O.sub.6 175.024 175.025 −5.7 87.007, acid 115.002 Glutamine C.sub.5H.sub.9N.sub.2O.sub.3 145.050 145.062 −8.3 NA I- 126.904 126.905 −7.9 NA .sup.aNA (not available) means that only high mass accuracy was used for tentative ion identification.
TABLE-US-00003 TABLE 3 Data obtained for the identification of selected negative ion mode molecular ions from human ovarian tissue. Main Fragment Proposed Proposed Measured Theoretical Mass error ions upon Identification formula m/z m/z (ppm) MS/MS.sup.a PI 40:4 C.sub.49H.sub.86O.sub.13P 913.581 913.581 <1 223.000, 241.011, 283.264, 331.264, 419.257, 581.309 38:4 C.sub.47H.sub.82O.sub.13P 885.549 885.550 −1.1 152.994, 223.000, 241.011, 283.264, 303.233, 419.256, 439.225, 581.309 36:1 C.sub.45H.sub.84O.sub.13P 863.565 863.566 −1.2 152.995, 241.011, 281.248, 283.264, 419.256 34:1 C.sub.43H.sub.80O.sub.13P 835.534 835.534 <1 152.994, 223.000, 241.011, 255.233, 281.248, 391.225, 553.278 PS 38:3 C.sub.44H.sub.79NO.sub.10P 812.544 812.545 −1.2 152.994, 283.264, 305.248, 419.256, 437.266, 725.514 36:1 C.sub.42H.sub.79NO.sub.10P 788.545 788.545 <1 281.248, 283.264, 417.242, 419.256, 437.268, 701.512 PE 38:4 C.sub.43H.sub.80O.sub.13P 766.539 766.539 <1 259.243, 283.264, 303.233, 480.309 O-38:5 C.sub.43H.sub.77NO.sub.7P 750.544 750.544 <1 259.243, 303.233, 446.303, 464.313 P-35:4 .sup. C.sub.41H.sub.73NO.sub.7P 722.512 722.513 −1.4 259.243, 303.233, 418.273, 436.283 FA 16:0 C.sub.16H.sub.31O.sub.2 255.232 255.233 −3.9 NA Ascorbic acid C.sub.6H.sub.7O.sub.6 175.024 175.024 <1 87.007, 115.002 .sup.aNA (not available) means that only high mass accuracy was used for tentative ion identification.
TABLE-US-00004 TABLE 4 Data obtained for the identification of selected negative ion mode molecular ions from human lung tissue. Main Mass Fragment Proposed Proposed Measured Theoretical error ions upon Identification formula m/z m/z (ppm) MS/MS.sup.a PI 40:4 C.sub.49H.sub.86O.sub.13P 913.580 913.581 −1.1 152.994, 223.000, 241.010, 283.264, 331.264, 419.256, 581.311 38:4 C.sub.47H.sub.82O.sub.13P 885.550 885.550 <1 152.994, 223.000, 241.011, 283.264, 303.233, 419.256, 581.311 36:1 C.sub.45H.sub.84O.sub.13P 863.565 863.566 −1.2 152.994, 241.011, 281.248, 283.264, 419.256, 581.311 36:2 C.sub.45H.sub.82O.sub.13P 861.548 861.549 −1.2 152.994, 223.000, 241.011, 281.256, 417.241 PG 36:2 C.sub.42H.sub.78O.sub.10P 773.542 773.534 10 152.994, 281.256, 417.241, 491.278, 509.288 34:1 C.sub.40H.sub.76O.sub.10P 747.514 747.517 −4.0 152.994, 255.233, 281.256, 391.226, 417.241, 491.277 PE 38:4 C.sub.43H.sub.77NO.sub.8P 766.535 766.539 −5.2 140.010, 283.256, 303.233, 480.309, 36:1 C.sub.41H.sub.79NO.sub.8P 744.552 744.555 −4.0 140.011, 281.256, 283.264, 480.307 P-38:4 .sup. C.sub.43H.sub.77NO.sub.7P 750.534 750.544 −13 259.243, 303.233, 464.314 P-36:4 .sup. C.sub.41H.sub.73NO.sub.7P 722.511 722.513 −2.8 259.243, 303.233, 418.273, 436.283 O-34:2 C.sub.39H.sub.75NO.sub.7P 700.527 700.529 −2.9 NA Cer 34:1 C.sub.34H.sub.67NO.sub.3Cl 572.479 572.482 −5.2 NA FA 18:1 C.sub.18H.sub.33O.sub.2 281.249 281.249 <1 NA Ascorbic Acid C.sub.6H.sub.7O.sub.6 175.023 175.024 −5.7 115.002 .sup.aNA (not available) means that only high mass accuracy was used for tentative ion identification.
TABLE-US-00005 TABLE 5 Data obtained for the identification of selected negative ion mode molecular ions from human breast tissue. Main Proposed Mass Fragment Identifi- Proposed Measured Theoretical error ions upon cation formula m/z m/z (ppm) MS/MS.sup.a PI 38:4 C.sub.47H.sub.82O.sub.13P 885.550 885.550 <1 152.994, 223.000, 241.011, 283.264, 303.233, 419.257, 581.310, 599.319 36:1 C.sub.45H.sub.84O.sub.13P 863.565 863.566 −1.2 152.994, 223.000, 241.011, 281.248, 283.264, 419.256, 581.309 PG 36:2 C.sub.42H.sub.78O.sub.10P 773.542 773.534 10 152.994, 281.248, 417.240, 491.276 FA 20:4 C.sub.20H.sub.31O.sub.2 303.233 303.233 <1 205.195, 259.243, 284.991 18:1 C.sub.18H.sub.33O.sub.2 281.249 281.249 <1 NA .sup.aNA (not available) means that only high mass accuracy was used for tentative ion identification.
TABLE-US-00006 TABLE 6 Patient demographics of the 253 human tissue samples used in this study. Number of Number of patients by patients by race Median age, Age range, gender (White, Black, Patient Diagnosis Years Years (male, female) Asian, Unknown) Breast Normal 47 24-76 (0, 29) (21, 7, 1, 0) Cancer 58 41-75 (2, 14) (10, 2, 4, 0) Lung Normal 57 12-82 (33, 14) (35, 12, 0, 0) Cancer 66 22-84 (25, 23) (35, 7, 0, 6) Ovary Normal 50 31-80 (0, 29) (22, 7, 0, 0) Cancer 62 30-83 (0, 28) (25, 2, 0, 1) Thyroid Normal 40 18-80 (10, 17) (18, 7, 0, 2) Tumor 49 16-81 (12, 17) (21, 4, 0, 4)
Materials and Methods
[0093] Mass Spectrometer. Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Scientific, San Jose, Calif.) was used. Full-scan was carried out at the range of m/z 120-1800, and the other mass spectrometric parameters were listed as follows: resolving power 140 000, micro scan 2, maximum injection time 300 ms, capillary temperature 350° C. and S-lens RF level 100.
[0094] Biological Tissues. Wild-type mouse brains were purchased from Bioreclamation IVT. 62 frozen human tissue specimens including breast, thyroid, lymph node, ovarian, and kidney were obtained from Cooperative Human Tissue Network and Baylor College Tissue Bank. Samples were stored in a −80° C. freezer. Tissue slides were sectioned at 16 μm using a CryoStar™ NX50 cryostat. Frozen tissue specimen were thawed under room temperature before use.
[0095] Statistical Analysis. IBM SPSS Statistics 22.0 (IBM Corporation, Armonk, N.Y., USA) was used to perform principal component analysis (PCA) to reveal patterns in the data. The analysis was performed directly using the raw data. The 10 peaks of the top relative intensities in the m/z range of 700-900 were used for PCA. Typically, the first three components, which all encompassed more than 85% of the total variance, are used in the present results.
Example 3—System Automation for Handheld and Laparoscopic Use
[0096] Because all the materials (PDMS and PTFE) and solvent (only water) used in the MasSpec Pen design are biologically compatible, the system has a high potential to be used in surgery in handheld way for real-time analysis. More than that, due to the small dimension of the device, it can even be integrated to a robotic surgical system, such as the Da Vinci surgical system through an accessory port or one of its robotic arms. Several regions of the human body cavity can be quickly sampled during surgery, and analyzed by using a database of molecular signatures and machine learning algorithms. Therefore, the diagnosing results may be provided in real time for each sampled region. This system can be broadly used in a wide variety of oncological and other surgical interventions (such as endometriosis) for which real-time characterization and diagnosis of tissues are needed.
Example 4—Predictive Analysis of Tissue Samples
[0097] The MasSpec Pen design was used to analyze tissue samples from patients with breast cancer, lung cancer, ovarian cancer, or thyroid cancer along with normal tissue samples. Before these samples were analyzed, the samples were processed by rounding the mass to charge ratio (m/z) to the nearest 0.01 and normalizing the total ion chromatogram (TIC). All background m/z peaks and those peaks which appeared in less than 10% of the patient samples were also removed. The full mass range was used in the analysis. The trained classifier was a lasso logistic regression model. For tissue samples in which the presence of cancer was being analyzed, the overall performance results for all classifiers is shown in Table 7. The overall results has an accuracy of 96.3%, sensitivity of 96.4%, and specificity of 96.2%.
TABLE-US-00007 TABLE 7 Tissue Sample Prediction Relative to True Determination of All Normal vs All Cancer* Predicted Normal Cancer True Normal 127 5 Cancer 4 106 *not including Benign Thyroid
[0098] For the tissue samples in which the presence of lung cancer was being analyzed, Table 8 shows the mass to charge values (m/z) used in the differentiation of the tissue samples along with the associated coefficient for that particular value.
TABLE-US-00008 TABLE 8 Lung Cancer Mass to Charge Values (m/z) and Coefficients for Normal Lung vs Lung Cancer m/z Coefficient 175.02 32.51042 187.01 492.94937 201.04 324.19856 215.03 −134.54101 313.16 −711.31964 330.98 31.73486 332.90 −49.54229 357.10 −903.32504 409.23 218.36836 615.17 −418.02900 722.51 42.39442 744.55 780.14488 747.52 −248.52283 748.52 −494.98929 771.52 6.80739 773.53 −292.30917 863.57 −722.21921 885.55 703.46083 886.55 8.82125
[0099] Table 9 shows the analysis rate and the classification of each sample with the true (histological) determination in rows and the predicted value in the columns. Of the cancer tissue samples, the samples were identified with an accuracy of 96.8%, a sensitivity of 97.9%, specificity of 95.7% and AUC of 0.97.
TABLE-US-00009 TABLE 9 Tissue Sample Prediction Relative to True Determination for Lung Cancer Predicted Prop. Normal Cancer correct True Normal 45 2 0.957 Cancer 1 47 0.979
[0100] Similar analysis was performed for normal lung vs adenocarcinoma samples as shown in Table 10 and Table 11. The samples were identified with 92.2% accuracy, 88.2% sensitivity, 93.6% specificity, and AUC of 0.98.
TABLE-US-00010 TABLE 10 Lung Cancer Mass to Charge Values (m/z) and Coefficients for Normal Lung vs Adenocarcinoma m/z Coefficient 175.02 78.79492 201.04 113.95819 747.52 −134.59620 773.53 −17.30482 885.55 205.16262
TABLE-US-00011 TABLE 11 Tissue Sample Prediction Relative to True Determination for Lung Squamous Cell Carcinoma Predicted Prop. Normal Cancer Correct True Normal 44 3 0.936 Cancer 2 15 0.882
[0101] Similar analysis was performed for normal lung vs squamous samples as shown in Table 12 and Table 13. The samples were identified with 93.8% accuracy, 88.2% sensitivity, 95.7% specificity, and AUC of 0.93.
TABLE-US-00012 TABLE 12 Lung Cancer Mass to Charge Values (m/z) and Coefficients for Normal Lung vs Squamous Cell Lung Cancer m/z Coefficient 201.04 203.209288 306.08 2.171805 747.52 −83.325218 773.53 −101.591552 861.55 −22.995934 885.55 248.475559
TABLE-US-00013 TABLE 13 Tissue Sample Prediction Relative to True Determination for Lung Cancer Predicted Prop. Normal Cancer Correct True Normal 45 2 0.957 Cancer 2 15 0.882
[0102] Similar, to the analysis carried out for lung cancer described above, a similar analysis was carried out with ovarian, thyroid, and breast cancer and showing the respective m/z peaks and coefficients for each set of samples. Ovarian cancer samples were detected with 94.7% accuracy, 100% sensitivity, 89.7% specificity, and AUC of 0.98. The thyroid cancer samples were detected with 94.7% accuracy, 90.9% sensitivity, 96.3% specificity, and AUC of 0.93. Finally, breast cancer samples were detected with 95.6% accuracy, 87.5% sensitivity, 100% specificity, and AUC of 1.00.
TABLE-US-00014 TABLE 14 Ovarian Cancer Mass to Charge Values (m/z) and Coefficients m/z Coefficient 124.01 −0.39418349 175.02 −0.44099907 175.03 −0.65091248 283.27 −0.19534503 313.16 0.13896620 341.27 −0.01845538
TABLE-US-00015 TABLE 15 Tissue Sample Prediction Relative to True Determination for Ovarian Cancer Predicted Prop. Normal Cancer Correct Normal 26 3 0.897 Cancer 0 28 1.000
TABLE-US-00016 TABLE 16 Thyroid Cancer Mass to Charge Values (m/z) and Coefficients for Normal Thyroid vs Benign Tumor m/z Coefficient 175.02 0.050122579 191.02 −0.009462112 191.05 −0.354060964 283.27 −0.471995496 341.27 −0.151684619 615.17 −0.208451792 822.47 −1.009896669 822.48 −1.045185471
TABLE-US-00017 TABLE 17 Tissue Sample Prediction Relative to True Determination for Normal Thyroid vs Benign Tumor Predicted Prop. Normal Cancer Correct True Normal 26 1 0.963 Cancer 1 10 0.909
TABLE-US-00018 TABLE 18 Thyroid Cancer Mass to Charge Values (m/z) and Coefficients for Normal Thyroid vs Malignant Tumor m/z Coefficient 175.02 −0.13520642 283.27 −0.41455282 341.27 −0.16730814 353.16 −0.06014487 432.20 −0.31647335 433.21 −0.07291166 615.17 −0.61749889 822.47 −0.53746679 822.48 −1.04230818
TABLE-US-00019 TABLE 19 Tissue Sample Prediction Relative to True Determination for Normal Thyroid vs Benign Tumor Predicted Prop. Normal Cancer Correct True Normal 26 1 0.963 Cancer 1 10 0.909
TABLE-US-00020 TABLE 20 Thyroid Cancer Mass to Charge Values (m/z) and Coefficients for Normal Breast vs Breast Cancer m/z Coefficient 187.04 476.70006 268.80 −190.32304 279.92 79.49933 283.27 −31.45926 341.27 −11.77054 345.16 −154.78978 381.21 −68.13689 687.51 −39.13906 742.54 1771.27018 766.54 1663.80192
TABLE-US-00021 TABLE 21 Tissue Sample Prediction Relative to True Determination for Normal Breast vs Breast Cancer Predicted Prop. Normal Cancer Correct True Normal 29 0 1.000 Cancer 2 14 0.875
Example 5—Spatial Resolution of the MasSpec Pen System
[0103] The spatial resolution of the MasSpec Pen system was tested and determined that higher spatial resolution could be determined using a specific spot. Testing was carried out using white vs. grey matter in a mouse brain. Shown in
Example 6—Non-Destructive Molecular Analysis of Tissue Samples
[0104] The MasSpec Pen was designed to operate directly on tissue specimens independently of tissue stiffness and morphology. The performance of the MasSpec Pen was tested to analyze soft tissue samples (0.1-5 g) from different organs including mouse brain and human breast, thyroid, lung and ovary tissues. Tissue analyses were performed in ambient conditions through a simple one-step experiment, following the same automated operational steps described previously. The MasSpec Pen tip was gently contacted to the surface of the tissue sample for a period of 3 s while extraction took place. The mass spectra obtained for a region of grey matter from the mouse brain was reproducible (RSD=4.6%, n=10) and highly similar to the mouse brain tissue section mass spectra (cosine similarity of 0.93) (
[0105] Visual and microscopic inspection of all the tissue samples after MasSpec Pen analysis revealed no detectable damage to the tissue sample morphology in the region probed.
Example 7—Molecular Diagnosis and Statistical Prediction of Cancer in Human Tissues
[0106] It was next evaluated if the molecular information obtained from human tissue samples using the MasSpec Pen was diagnostic and predictive of disease state. A total of 253 human tissue specimens using the MasSpec Pen, including 95 lung samples (47 normal and 48 cancer samples including 17 adenocarcinoma, 17 squamous cell carcinoma, and 14 cancer samples of other histologic subtypes), 57 ovary samples (29 normal and 28 HGSC), 57 thyroid samples (27 normal, 11 follicular thyroid adenoma and 18 papillary thyroid carcinoma), and 45 breast samples (29 normal and 16 ductal carcinoma) (
[0107] To evaluate if the MasSpec Pen molecular signatures are predictive of cancer and normal tissues, the Lasso method was applied to build classification models using the histologically validated mass spectral database. The performance of the model was evaluated through a leave-one-patient-out cross-validation approach, and measured by sensitivity and specificity for cancer, as well as accuracy and AUC (Table 22). For breast cancer (n=45), 87.5% sensitivity, 100% specificity (AUC=1.0), an overall accuracy of 95.6% was achieved, which is comparable to the results reported using DESI-MSI (98.2% accuracy, n=126) (Guenther et al., Cancer Research, 75, 2015)), the iKnife (95.5% accuracy, n=10) (Balog et al., Science Translational Medicine, 5, 2013), and MALDI imaging of lipids and proteins (94.1% accuracy, n=68) (31). For HGSC (n=57), 100% sensitivity, 89.7% specificity, and 94.7% accuracy was achieved (AUC=0.98), which is also similar to classification results obtained by DESI-MSI (97.1% accuracy, n=31) (Sans et al., Cancer Research, 2017). For lung cancer (n=956), 98.097.9% sensitivity, 95.7% specificity, and 96.89% accuracy was achieved (AUC=0.97). When predicting based on lung cancer histologic subtypes, 93.8% and 92.2% accuracy was achieved for squamous cell carcinoma and adenocarcinoma, respectively. Thyroid tumor samples investigated included benign follicular thyroid adenoma (FTA) and malignant papillary thyroid carcinoma (PTC) samples. A classifier for each was built yielding 94.7% accuracy for FTA and 97.8% accuracy for PTC. Overall, 96.4% sensitivity, 96.2% specificity and 96.3% accuracy was achieved for all the four types of cancer investigated. These results demonstrate that the molecular information obtained from human tissue samples by the MasSpec Pen is highly predictive of cancer. Further, the results indicate that the statistical classifiers built on the molecular data acquired using the MasSpec Pen are robust and may be used in an automated approach for rapid clinical diagnosis of tissue samples.
TABLE-US-00022 TABLE 22 Description of the samples and results obtained using the MasSpec Pen. Pathological diagnosis, number of patient samples, and the Lasso prediction sensitivity, specificity, accuracy, and area under the curve obtained using a leave-one-out cross validation approach are shown. Pathologic Evaluation Number of Lasso Prediction Organ Diagnosis Histologic Type Patients Sensitivity Specificity Accuracy AUC Breast Normal 29 87.5% 100.0% 95.6% 1.00 Cancer Ductal Carcinoma 16 Lung.sup.a Normal 47 98.0% 95.7% 96.9% 0.97 Cancer Adenocarcinoma 17 88.2% 93.6% 92.2% 0.98 Squamous Cell 17 88.2% 95.7% 93.8% 0.93 Other 14 — — — — Ovary Normal 29 100.0% 89.7% 94.7% 0.98 Cancer High Grade Serous 28 Thyroid.sup.b Normal 27 — — — — Tumor Papillary Carcinoma 18 94.4% 100.0% 97.8% 0.99 Follicular Adenoma 11 90.9% 96.3% 94.7% 0.93 .sup.aLasso prediction results for lung cancer are shown for normal versus all cancer tissues (first row), followed by normal versus lung adernocarcinoma (middle row) and normal versus squamous cell carcinoma (last row). .sup.bLasso prediction results for thyroid cancer are shown for normal versus malignant papillary carcinoma, and normal versus benign follicular adenoma.
Example 8—Intra-Sample Analysis of Histologic Distinct and Cancer Margin Tissue Regions
[0108] The ability of the MasSpec Pen to identify histologically distinct regions was evaluated in a single human tissue sample that contained regions of HGSC adjacent to normal ovarian stroma tissue. Five consecutive spots in the tissue sample were analyzed using a MasSpec Pen with a 1.5 mm diameter, as demarcated in the optical image shown in
Example 9—In Vivo Analysis of a Murine Model of Human Breast Cancer During surgery
[0109] The MasSpec Pen was designed with biocompatible materials to ensure full compatibility as an in vivo molecular diagnostic tool. The MasSpec Pen was tested for in vivo tissue analysis using a murine model of human breast cancer. BT474 HER2+breast cancer cells were implanted subcutaneously in nude athymic mice (n=3). The tumors were grown to an average of 250 mm.sup.3 over a period of 4 weeks. All surgical and MasSpec Pen analysis procedures were performed under anesthesia. A surgical blade was used to open a flap of skin surrounding the tumor, and then the skin flap was sharply dissected from the surface of the tumor. The exposed tumor was then analyzed using the MasSpec Pen following the same automated experimental steps described previously.
Example 10—Materials and Methods
[0110] Study design: The objective of this study was to evaluate the potential of a new mass spectrometry-based probe to non-destructively analyze and diagnose cancer in human tissue samples. In this study, the molecular profiles of human tissue samples obtained from 282 patients including normal and cancer breast, lung, thyroid, and ovary tissues were investigated. All patient samples were obtained from the Cooperative Human Tissue Network (CHTN), Asterand Biosciences (Detroit, Mich.), the MD Anderson Tissue Bank, and the Baylor College of Medicine Tissue Bank, under approved Institutional Review Board (IRB) protocol. The mass spectra obtained using the MasSpec Pen in tissue samples were normalized, background subtracted and analyzed using a statistical technique to build classification models. Expert, board-certified pathologists (J. L, W. Y, and N. C) evaluated H&E stained tissue sections obtained from the tissue samples analyzed. The pathologists were blind to any information about the acquisition from mass spectrometry analysis. Samples were excluded from statistical analysis if they were determined by the pathologist to have substantial heterogeneity in cell composition, which included 28 samples. The in vivo animal model experiments were conducted under approved Institutional Animal Care and Use Committee (IACUC) protocol.
[0111] Design and Engineering of the MasSpec Pen: A 3D printer (Model uPrint SE plus) was used to print the key component—PDMS (Dow Corning, Midland, Mich., USA) probe tip. The pen tips were fabricated by casting an elastomer from a negative mold and then dissolving the mold away. The negative molds were designed using SolidWorks computer aided design (CAD) software and then fused deposition modeled with the 3D printer using ABS plastic (Stratasys, Eden Prairie, Minn., USA) and soluble support material. The parts were then washed to remove support material, using a support cleaning apparatus (SCA-1200HT, SCA) and solvent (EcoWorks) at 70° C. for 24 hrs or until support material was fully dissolved. For the casting, a mixture of PDMS elastomer base and curing agent (Sylgard 184, Dow Corning) were prepared in a weight ratio of 10:1, respectively. The mixture was poured into the 3-D printed molds, cured in an oven (10GCE-LT, Quincy Lab) at 74° C. for 1 h, and then placed in a closed container with acetone (Fisher Scientific, Waltham, Mass., USA) to dissolve. The final washing step had the tips sonicated in acetone to remove any remaining ABS. PTFE tubing (ID 1/32 inch, OD 1/16 inch, Cole-Parmer, Vernon Hills, Ill., USA) was directly inserted into the probe tip for experiments.
[0112] Data acquisition: All experiments were performed on a Q Exactive hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, San Jose, Calif.). Full-scan was carried out at the range of m/z 120-1800, using resolving power 140,000, capillary temperature of 350° C. and S-lens RF level of 100. Wild-type mouse brain were purchased from BioreclamationIVT (Westbury, N.Y.). A total of 282 human tissue specimens including breast, thyroid, ovary, and lung were obtained frozen and stored in a −80° C. freezer until analysis, when they were thawed in room temperature. The tissues were placed in a surface and analyzed by the MasSpec Pen using the experimental steps described. After experiments, the tissue regions analyzed were annotated, frozen, and 16 μm tissue sections prepared using a CryoStar™ NX50 cryostat. Additional tissue sections at different regions of the tissue piece were obtained for MS analysis. Tissue sections were kept frozen until analysis, when they were in room temperature and analyzed by the MasSpec Pen. Tissue sections were then H&E stained and evaluated by histopathology. The pathologic diagnosis was used as the reference for our molecular database.
[0113] In Vivo Experiments: In vivo experiments were performed during surgical resection of tumors using murine animal models while the mice were under anesthesia (2% isoflurane, 98% O.sub.2). BT474 HER2+ cells were grown in improved minimal essential medium (IMEM, Invitrogen, Carlsbad, Calif.) supplemented with 10% FBS, 1% L-glutamine, and 1% insulin, to 80-90% confluency in 5% O.sub.2 and 37° C. Cells were counted via hemocytometer and trypan blue dye exclusion. Nude athymic female mice (N=3) were subcutaneously implanted with a 0.72 mg, 60-day release, 17β-estradiol pellet (Innovative Research of America, Sarasota, Fla.) in the nape of the neck. Approximately 24 hours later, BT474 breast cancer cells (10.sup.7) in serum-free IMEM media with 20% growth factor-reduced Matrigel were injected subcutaneously into the right flank of the mouse (total injection of 100 μL). Tumors were monitored weekly for growth until they reached 0.7-1.0 cm in diameter (average of 250 mm.sup.3). At that time point, all surgical procedures were performed while the mice were under anesthesia (2% isoflurane, 98% O.sub.2). A surgical blade was used to open a flap of skin, leaving an estimated 1-2 cm of space around the tumors, and then the skin flap was dissected from the surface of the tumor. The skin was flapped to expose the tumor and adjacent normal tissues, which were analyzed in several regions using the MasSpec Pen. Pieces of the tumor were then resected using a scalpel and analyzed ex vivo. Tumor tissue regions analyzed by the MasSpec pen were annotated, flash frozen, sectioned, and subjected to H&E staining for diagnosis.
[0114] Statistical Analysis: Averages of three mass spectra obtained during each 10 seconds MasSpec Pen analysis were used to build molecular databases. The Xcalibur raw data was converted to Microsoft Excel spreadsheet format. The full mass range of the spectra were partitioned into bins by rounding m/z values to the nearest hundredth. All mass spectra were first normalized according to total ion count (TIC) or to the absolute intensity of m/z 885.55, to account for slight fluctuations in signal intensities that may occur between experiments. Then, background peaks as well as peaks not appearing in at least 10% of the samples analyzed were excluded to reduce random noise.
[0115] For each tissue section (breast or thyroid), four representative mass spectra for each tissue section analyzed were imported to metaboanalyst (http://www.metaboanalyst.ca/) for principal component analysis (PCA) using the website built-in function. Score plots and loading plots were generated through the website for each tissue type. For each soft tissue sample type (breast, thyroid, lung, and ovary), the data was imported to R programming language. PCA was performed by centering the pre-processed data to mean zero and computing principal components using the prcomp function in R. The first three principal components were visualized with the rgl and pca3d packages for R. For tissue classification, the Lasso method was applied using the glmnet package in the CRAN R language library. Models generated using the Lasso are simpler to interpret than other regularization methods, as it yields “sparse” models, that is, models that involve only a subset of the features. A mathematical weight for each statistically informative feature is calculated by the Lasso depending on the importance that the mass spectral feature has in characterizing a certain class (cancer versus normal, or a cancer subtype versus normal). Classification was performed using a leave-one-out cross-validation approach to assess the predictive accuracy within the training set. Performance of trained classifiers was measured by sensitivity, specificity, accuracy, and AUC.
[0116] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.