Spatially co-registered genomic and imaging (SCORGI) data elements for fingerprinting microdomains

Abstract

The present disclosure relates to spatially co-registered genomic and imaging (SCORGI) data elements for fingerprinting of microdomains. A method of generating a plurality of spatially co-registered data elements, each spatially co-registered data element associated with and generated from a pair of co-registered tissue portions obtained from adjacent locations taken from a core of a tissue sample, and includes an image data portion and a genomic data portion. For each pair of co-registered tissue portions, the method comprises: (i) obtaining a plurality of multiplexed-to-hyper images from an imaging data portion of the co-registered tissue portions and storing it as part of a data element, (ii) generating image data from the plurality of multiplexed-to-hyper images and storing it as part of a data element, and (iii) generating genomic data from the genomic data portion of the associated co-registered tissue portion and storing it as part of the data element.

Description

[0001] This application is a divisional application of a Chinese patent application with an application date of August 21, 2019, an application number of 201980061036.1, and an invention title of "Spatially Co-Registered Genomic and Imaging (SCORGI) Data Elements for Fingerprinting Microdomains".

[0002] Cross-reference to related applications

[0003] This application claims the priority of U.S. Provisional Patent Application No. 62 / 721,018, titled "Spatially CO-Registered Genomic and Imaging (SCORGI) Data Elements for Fingerprinting Microdomains", filed on August 22, 2018, the disclosure of which is incorporated herein by reference.

[0004] Government contracts

[0005] This invention was made with government support under Grant No. CA204826 awarded by the National Institutes of Health (NIH). The government has certain rights in this invention. Technical field

[0006] The present invention relates to digital pathology, and in particular to a method for creating spatially co-registered genomic and imaging (SCORGI) data elements for fingerprinting microdomains in tumors and / or related tissues, including a new tumor sampling strategy for characterizing spatial heterogeneity in solid tumors, and to a digital pathology system employing such a method. Background art

[0007] Digital pathology refers to the acquisition, storage, and display of histologically stained tissue samples, and initially gained attention in niche applications such as second opinion telepathology, immunohistochemical interpretation, and intraoperative telepathology. Generally, a large amount of patient data is generated from biopsy samples, consisting of 3 - 50 slides, and visually evaluated by pathologists under a microscope, but evaluated using digital technology by viewing on a high-definition monitor. Due to the manual labor involved, current workflow practices are time-consuming, error-prone, and subjective.

[0008] Cancer is a heterogeneous disease. In tissue images stained with hematoxylin and eosin (H&E), heterogeneity is characterized by the presence of various histological structures, such as carcinoma in situ, invasive carcinoma, adipose tissue, blood vessels, and normal ducts. Moreover, for many malignancies, molecular and cellular heterogeneity is a prominent feature among tumors of different patients, between different sites of a tumor in a single patient, and within an individual tumor. Intra-tumor heterogeneity involves phenotypically distinct clonal subpopulations of cancer cells and other cell types, including the tumor microenvironment (TME). These cancer cell clonal subpopulations and other cell types include local and bone marrow-derived stromal stem and progenitor cells, subsets of immune-inflammatory cells that promote or kill tumors, cancer-associated fibroblasts, endothelial cells, and pericytes. The TME can be viewed as an evolving ecosystem in which cancer cells engage in heterotypic interactions with these other cell types and use available resources to proliferate and survive. Consistent with this view, the spatial relationship between cell types within the TME (i.e., spatial heterogeneity) appears to be one of the major drivers of disease progression and treatment resistance. Therefore, it is imperative to define the spatial heterogeneity within the TME to correctly diagnose the subtypes of specific diseases and identify the optimal treatment regimens for individual patients.

[0009] To date, intra-tumor heterogeneity has been explored using three main approaches. The first approach is to take core samples from specific tumor regions to measure overall averages. Heterogeneity in the samples is measured by analyzing multiple cores within the tumor using a variety of techniques, including whole exome sequencing, epigenetics, proteomics, and metabolomics. The second approach involves "single cell analysis" using the above methods, RNA Seq, imaging after isolating cells from tissue, or flow cytometry. The third approach uses the spatial resolution of light microscopy imaging and combines it with molecule-specific tags to measure biomarkers in situ within cells. Although each of these methods provides a certain degree of validity, they all have various drawbacks and limitations.

[0010] In addition, one of the greatest challenges in assessing the clinical significance of tumor heterogeneity is the lack of tools for spatial analysis of samples at the single cell level and the limited tools for integrating genomic and proteomic measurements in appropriately clinically and molecularly annotated sample sets.

[0011] Therefore, there is room for improvement in the field of intra-tumor heterogeneity characterization. Summary of the Invention

[0012] In one embodiment, a method of creating multiple tissue portions for imaging and genomic analysis is provided. The method includes obtaining a plurality of cores from a tissue sample, and for each core, obtaining a plurality of imaging data portions and a plurality of genomic data portions from the core in an alternating manner along the length of the core such that each imaging data portion is associated with a corresponding one of the genomic data portions to form a plurality of series pairs of adjacent tissue portions.

[0013] In another embodiment, a plurality of tissue microarray (TMA) slides are provided that include tissue portions for imaging and genomic analysis. The plurality of TMA slides includes: a plurality of first TMA slides having a plurality of imaging data portions fixed thereto for each of a plurality of cores in a tissue sample; and a plurality of second TMA slides having a plurality of genomic data portions fixed thereto for each of a plurality of cores in a tissue sample. For each core, the plurality of imaging data portions and the plurality of genomic data portions are from alternating portions along the length of the core such that each imaging data portion is associated with a corresponding one of the genomic data portions for the core to form a plurality of consecutive pairs of adjacent tissue portions.

[0014] In yet another embodiment, a method of generating a plurality of spatially co-registered data elements from a tissue sample is provided. The method includes obtaining a plurality of cores from a tissue sample, and for each core, obtaining a plurality of imaging data portions and a plurality of genomic data portions from the core in an alternating manner along the length of the core such that each imaging data portion is associated with a corresponding one of the genomic data portions to form a plurality of series pairs of adjacent, co-registered tissue portions. The method further includes, for each imaging data portion: (i) obtaining a plurality of multiplexed to hyperplexed images by repeatedly labeling with a plurality of fluorescent tags, (ii) analyzing the multiplexed to hyperplexed images to obtain image data therefrom, and (iii) identifying a plurality of regions of interest in the genomic data portion associated with the imaging data portion based on the obtained image data for the imaging data portion. The method further includes, for each genomic data portion, performing genomic analysis on each region of interest in the genomic data portion to generate genomic data for the genomic data portion; and for each co-registered tissue portion, correlating the following with each other as co-registered imaging and genomic data elements: (i) the multiplexed to hyperplexed image of the imaging data portion of the co-registered tissue portion, (ii) the image data of the imaging data portion of the co-registered tissue portion, and (iii) the genomic data of the genomic data portion of the co-registered tissue portion.

[0015] In yet another embodiment, a non-transitory computer-readable medium storing a plurality of spatially co-registered data elements is provided. Each spatially co-registered data element is associated with and generated from a pair of co-registered tissue parts obtained from adjacent positions of a core taken from a tissue sample, and includes an image data portion and a genomic data portion. Each spatially co-registered data element includes: (i) a plurality of multiplexed-to-superpath images obtained from the imaging data portion of the associated pair of co-registered tissue parts; (ii) image data generated from the plurality of multiplexed-to-superpath images obtained from the imaging data portion of the associated pair of co-registered tissue parts; and (iii) genomic data generated from the genomic data portion of the associated pair of co-registered tissue parts.

[0016] In another embodiment, a method for generating a plurality of spatially co-registered data elements is provided. Each spatially co-registered data element is associated with and generated from a pair of co-registered tissue parts obtained from adjacent positions of a core taken from a tissue sample, and includes an image data portion and a genomic data portion. The method includes, for each pair of co-registered tissue parts: (i) obtaining a plurality of multiplexed-to-superpath images from the imaging data portion of the co-registered tissue parts and storing them as part of the data element; (ii) generating image data from the plurality of multiplexed-to-superpath images and storing it as part of the data element; and (iii) generating genomic data from the genomic data portion of the associated co-registered tissue parts and storing it as part of the data element.

[0017] In yet another embodiment, a system for generating and storing spatially co-registered data elements is provided. The spatially co-registered data elements are associated with and generated from a pair of co-registered tissue parts obtained from adjacent positions of a core taken from a tissue sample, and the pair of co-registered tissue parts includes an image data portion and a genomic data portion. The system includes processing means, which includes: (i) an image data generation component configured to generate image data from a plurality of multiplexed-to-superpath images obtained from the imaging data portion of the associated pair of co-registered tissue parts; (ii) a region of interest identification component configured to identify a plurality of regions of interest in the genomic data portion of the associated pair of co-registered tissue parts based on the generated image data, wherein the genomic data is generated from the genomic data portion of the associated co-registered tissue parts based on the identified plurality of regions of interest; and (iii) a data element generation component configured to generate and store a spatially co-registered data element including the plurality of multiplexed-to-superpath images, the generated image data, and the generated genomic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of a sample preparation method illustrating an exemplary embodiment according to one aspect of the disclosed concept;

[0019] Figure 2 is an illustration of Figure 1 the sample preparation method shown in;

[0020] Figure 3 is a flowchart of a method for generating SCORGI data elements for co-registration and co-localization of an imaging data portion and a genomic data portion according to an exemplary embodiment of another aspect of the disclosed concept;

[0021] Figure 4 is an illustration of a method for generating Figure 3 the SCORGI data elements;

[0022] Figure 5 is a schematic diagram of an exemplary digital pathology system according to a specific, non-limiting exemplary embodiment of the disclosed concept:

[0023] Figure 6 is an illustration of the generation of a tumor map according to a non-limiting aspect of the disclosed concept. DETAILED DESCRIPTION

[0024] As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" include plural forms.

[0025] As used herein, the statement that two or more components or elements are "coupled" should mean that the components are linked, either directly or indirectly (i.e., through one or more intermediate components or elements), and operate together as long as the link occurs.

[0026] As used herein, "directly coupled" means that two elements are in direct contact with each other.

[0027] As used herein, the term "a number" should mean one or an integer greater than one (i.e., a plurality).

[0028] As used herein, the terms "component" and "system" are intended to refer to computer-related entities, either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be components. One or more components can reside within a process and / or thread of execution, and a component can be located on one computer and / or distributed between two or more computers. Although certain ways of displaying information to a user are shown and described herein with respect to certain diagrams or graphs as screenshots, those skilled in the relevant art will recognize that various other alternative ways can be employed.

[0029] As used herein, the term "multiplex imaging" shall refer to an imaging technique that employs up to 7 biomarkers, and a "multiplex image" shall refer to an image created using multiplex imaging.

[0030] As used herein, the term "hyperplex imaging" shall refer to an imaging technique that employs more than 7 biomarkers, and a "hyperplex image" shall refer to an image created using hyperplex imaging.

[0031] As used herein, the term "multiplex-to-hyperplex imaging" shall include multiplex imaging and / or hyperplex imaging, and a "multiplex-to-hyperplex image" shall include multiplex images and / or hyperplex images.

[0032] Directional phrases used herein, such as but not limited to, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of elements shown in the drawings and are not limited to the stated direction unless expressly set forth therein.

[0033] For purposes of illustration, the disclosed concepts will now be described in connection with numerous specific details in order to provide a thorough understanding of the invention. However, it will be apparent that the disclosed concepts may be practiced without these specific details, without departing from the spirit and scope of the invention.

[0034] On the one hand, the disclosed concept provides a novel tumor sampling strategy for characterizing spatial heterogeneity in solid tumors, which the inventors refer to as "SCORGI". As described in more detail herein, SCORGI is spatially co-registered genomic and imaging data elements generated from spatially co-registered adjacent tissue samples obtained from, for example but not limited to, matched primary tumors, regional lymph nodes, and distant metastases. SCORGI integrates region-specific genomic information (such as but not limited to whole exome sequencing obtained via next-generation sequencing methods, in situ transcriptomics, and epigenomics, etc.) and imaging information obtained from a large number of individual sample cores distributed within such tissue samples (such as but not limited to, obtained via antibody biomarkers with a large number of fluorescent tags). The unprecedented SCORGI-based tumor sampling strategy of the disclosed concept enables digital fingerprinting of microdomains that represent spatial, functional, and genomic intratumoral heterogeneity (ITH) among malignant cells, non-malignant cells (such as but not limited to immune cells, cancer-associated fibroblasts (CAFs), and endothelial cells), and their local interactions within the tumor microenvironment (TME), which is considered a key determinant of metastatic disease progression. Additionally, digital fingerprinting of SCORGI-based microdomains according to the disclosed concept will enable the identification and characterization of pathogenic signaling networks within the TME, potentially leading to improved treatment strategies.

[0035] Figure 1 is a flowchart, and Figure 2 is a schematic diagram that illustrates a sample preparation method according to an exemplary embodiment of one aspect of the disclosed concept. As described herein, Figure 1 and Figure 2 the sample preparation methods shown in provide a plurality of tissue samples, which can then be used to generate SCORGI data elements as described herein.

[0036] Referring to Figure 1 and Figure 2 , the method starts at step 5, where a solid tumor sample 2 as shown in Figure 2 is obtained. In a non-limiting exemplary embodiment, the tumor sample 2 is approximately 1.5 cm 2 . Also in step 5, the solid tumor sample 2 is cut into six separate tumor sections 4, labeled 4a - 4f in Figure 2 . In the exemplary embodiment, the tumor sections 4a - 4f are processed into formalin-fixed paraffin-embedded (FFPE) sections. Next, in step 10, seven separate core samples or "cores" are obtained from the distribution areas in each of the tumor sections 4a - 4f, in Figure 2Each is labeled 6. In a non-limiting exemplary embodiment, each core 6 is a macroporous 5 mm core. Thus, after step 10, a total of forty-two cores 6 will be obtained from the tumor sections 4a-4f.

[0037] Next, in step 15, forty-two cores 6 are used to create three macroporous tissue microarray (TMA) blocks 8, Figure 2 one of which is shown in. As Figure 2 shown, each TMA block 8 will include seven cores 6 from the corresponding two tissue tumor sections 4a-4f (e.g., the first TMA block 8 may include cores 6 obtained from tumor sections 4a and 4b, the second TMA block 8 may include cores 6 obtained from tumor sections 4c and 4d, and the third TMA block 8 may include cores 6 obtained from tumor sections 4e and 4f).

[0038] Then, the method proceeds to step 20, where in this non-limiting exemplary embodiment, each TMA block 8 is used to create three pairs of TMA slides (for a total of 9 pairs of TMA slides) that contain multiple pairs of co-registered and co-localized portions from the cores 6. More specifically, as Figure 2 shown, in a non-limiting exemplary embodiment, three imaging data portions 12 and three genomic data portions 14 are obtained from each core 6 in each TMA block 8. In a non-limiting exemplary embodiment, each imaging data portion 12 is a 5 μm thick, 5 mm diameter tissue portion, and each genomic data portion 14 is a 10-20 μm thick, 5 mm diameter tissue portion (the genomic data portion 14 is thicker than the imaging data portion 12 in the exemplary embodiment because generally more tissue is required for the genomic analysis to be performed on the genomic data portion 14 than for the imaging to be performed on the imaging data portion 12, as described herein). As Figure 2 shown, three imaging data portions 12 and three genomic data portions 14 are obtained from each core 6 in an alternating manner such that each imaging data portion 12 will be directly adjacent to a genomic data portion 14, so that they form adjacent tissue portions (i.e., SCORGI tissue portions) that can be used to create multiple SCORGI data elements as described herein. Thus, in the current non-limiting exemplary embodiment, a total of forty-two imaging data portions 12 and a total of forty-two genomic data portions 14 are obtained from the fourteen cores 6 provided on each TMA block 8. Thus, for the three TMA blocks 8 of the exemplary embodiment, a total of one hundred and twenty-six imaging data portions 12 and a total of one hundred and twenty-six genomic data portions 14 are obtained.

[0039] Additionally, as Figure 2As shown, for each TMA cassette 8, the acquired imaging data portions 12 are sequentially positioned on three imaging data TMA slides 16a, 16b, 16c, and the acquired genomic data portions 14 are sequentially positioned on three genomic data TMA slides 18a, 18b, 18c. Thus, for each TMA cassette 8, the above-described three pairs of TMA slides are formed, where the first pair includes imaging data TMA slide 16a and genomic data TMA slide 18a, the second pair includes imaging data TMA slide 16b and TMA slide 18b, and the third pair includes imaging data TMA slide 16c and genomic data TMA slide 18c. Additionally, as will be appreciated, for each TMA cassette 8, each pair of TMA slides will include fourteen pairs of co-registered imaging data portions 12 and genomic data portions 14, where the imaging data portions 12 are disposed on TMA slide 16 and the corresponding genomic data portions 14 are disposed at corresponding positions on TMA slide 18. Thus, upon completion of step 20, a total of nine pairs of TMA slides (nine TMA slides 16 and nine TMA slides 18) will be formed from the three TA cassettes 8 of this embodiment, resulting in a total of one hundred and twenty-six pairs of co-registered and co-localized imaging data portions 12 and genomic data portions 14 (i.e., one hundred and twenty-six SCORGI tissue portions). As described above, those co-registered and co-located pairs of imaging data portions 12 and genomic data portions 14 can be used to create multiple data elements referred to herein as SCORGI.

[0040] It should be noted that the exemplary quantities of the various items described above, e.g., the number of tumor sections 4 (e.g., six), the number of cores 6 from each tumor section 4 (e.g., seven), and the number of imaging data portions 12 and genomic data portions 14 from each core 6 (e.g., three), diameters, and / or thicknesses are merely exemplary, and other quantities of such items can be expected within the scope of the disclosed concepts. For example but not limited to, the size of the cores for multiplex to super-resolution imaging as described herein can be greater than 5 mm. Specifically, tumors typically span centimeters (e.g., 1 - 5 cm), and within the scope of the disclosed concepts, the entire tumor can be sectioned to create portions with a diameter of 1 - 5 cm. In such an implementation, whole slide images (WSIs) of these portions can be made, and multiplex to super-resolution imaging of the WSIs can be performed.

[0041] Figure 3 is a flowchart, and Figure 4is a schematic diagram that illustrates a method for generating SCORGI data elements from co-registered and co-localized pairs of image data portion 12 and genomic data portion 14 (referred to herein as SCORGI tissue portions) according to another aspect of the disclosed concepts. In particular, as described in more detail below, SCORGI is generated by performing multiplex-to-superpath imaging and region-specific genomics on each pair of co-registered and co-localized pairs of imaging data portion 12 and genomic data portion 14, respectively.

[0042] Referring Figure 3 to Figure 4 and Figure 4 , the method begins at step 25, where for each imaging data portion 12 in TMA slides 16a, 16b, and 16c, a plurality of high-resolution multiplex-to-superpath images 26 are generated from the imaging data portion 12 using multiplex-to-superpath imaging process 28, which in this exemplary embodiment includes repeatedly labeling each imaging data portion 12 with a plurality of fluorescent tags to image a plurality of biomarkers. In the illustrated exemplary embodiment, as

[0043] shown in Figure 4 , each imaging data portion 12 is divided into sixty image fields 31, fifty different fluorescent tags are applied to the imaging data portion 12, and an image 26 (e.g., at 20x) is captured for each fluorescent tag for each image field 28. Thus, in this exemplary embodiment, a total of 3,000 multiplex-to-superpath images 26 will be captured for each imaging data portion 12. It will be appreciated that the number and selection of biomarkers used in step 25 can vary and can depend on the mechanism of tumor progression of interest.

[0044] In an exemplary embodiment, a standard multiplexing machine, such as the PerkinElmer Vectra Polaris automated quantitative pathology system (http: / / www.perkinelmer.com / product / vectra-polaris-top-level-assembly-ship-cls143455), can be used to generate the multiplexed image 26 and the image data 32 just described (in this case, the standard multiplexing machine typically includes software for generating the image data 32). Alternatively, the standard multiplexing machine just described can be used to generate the multiplexed image 26, and the image data 32 can be generated therefrom using separate image analysis software, such as but not limited to, the public domain THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment) software described in Spagnolo et al., Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images, Cancer Res. November 1, 2017; 77(21):e71-e74. The THRIVE software is an open-source tool to help cancer researchers conduct interactive hypothesis testing. THRIVE provides an integrated workflow for multiplex-to-hyperplex analysis of whole-slide immunofluorescence images and tissue microarrays, including standard cell and subcellular segmentation and biomarker quantification. THRIVE quantifies spatial intratumoral heterogeneity, as well as the interactions between different cell phenotypes and non-cellular components. THRIVE is able to decipher various molecular and cellular signaling networks that support the co-evolution of malignant cells and their specific TME (e.g., cancer-associated fibroblasts, immune cells, extracellular matrix) to confer a malignant phenotype, resulting in, for example, dormancy, drug resistance, immune escape, and metastatic potential.

[0045] As yet another alternative, the hyperplexed image 26 and the image data 32 can be generated from the TMA slides 16a, 16b, and 16c as just described by sending the TMA slides 16a, 16b, and 16c to a hyperplexed immunofluorescence Clinical Laboratory Improvement Amendments (CLIA) laboratory, such as the NeoGenomics MxIF: Multimolecular Multiplexing Methodology Laboratory (https: / / neogenomics.com / pharma-services / lab-services / inultiomyx).

[0046] After step 30, the method proceeds to step 35, where for each imaging data portion 12 in TMA slides 16a, 16b, and 16c, the acquired image data 32 is used to identify a plurality of regions of interest (“ROIs,” also referred to as “microdomains”) in genomic data portion 14 that is co-registered with the imaging data portion 12 (and is included in one of TMA slides 18a, 18b, and 18c). The acquired image data 32 can be used to identify ROIs according to any one of a number of known or later-developed methods, such as, but not limited to, the methods described in detail in the following documents and implemented in the THRIVE software: U.S. Provisional Application Serial No. 62 / 675,832, filed May 24, 2018, entitled “Predicting the Recurrence Risk of Cancer Patients From Primary Tumors with Multiplexed Immunofluorescence Biomarkers and Their Spatial Correlation Statistics,” which is incorporated herein by reference; PCT Application No. PCT / US19 / 033662, filed May 23, 2019, entitled “Predicting the Recurrence Risk of Cancer Patients From Primary Tumors with Multiplexed Immunofluorescence Biomarkers and Their Spatial Correlation Statistics,” which is incorporated herein by reference; and Spagnolo et al., Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images, Cancer Res. November 1, 2017; 77(21):e71-e74. In this method, spatially resolved correlations between biomarkers are used as covariates in a multivariate survival model of outcome data (e.g., recurrence) to construct a spatial tissue map of cancer recurrence in a superpathological tissue sample. These maps depict microdomains associated with recurrence and metastatic progression. In addition, it is expected that performing region-specific genomics on phenotypically distinct microdomains of SCORGI data elements as described herein will reveal enriched subclonal populations of malignant cells, which are themselves also enriched in metastases.The integration of genomic information and imaging information as described herein in SCORGI enables modeling of phenotypic and genotypic progression and the development of predictive biomarkers mechanistically related to metastatic progression.

[0047] Referring again to Figure 4 , an exemplary genomic data portion 14 and two exemplary ROIs 34 ("Region 1" and "Region 2") are shown. At step 40, for each genomic data portion 14 in TMA slides 18a, 18b, and 18c, a genomic analysis identified by reference numeral 36 is performed on each identified ROI 34 in the genomic data portion 14. As a result of and based on the genomic analysis 36, genomic data 38 for each identified ROI 34 will be generated for each genomic data portion 14. By way of example and not limitation, the genomic analysis performed at step 40 can be whole exome sequencing obtained via next-generation sequencing methods, in situ transcriptomics, and epigenomics, etc., and the generated genomic data 38 can include, but is not limited to, data indicative of mutations and / or data indicative of in situ transcriptomics (FISSEQ, DSP).

[0048] The just-described step 40 can be performed by any suitable method and / or apparatus. For example, a microfluidic dissection method and a unit compatible with a standard optical microscope can be used to extract DNA from the ROI 34 to rapidly extract nucleic acids from small (1-5 mm) ROIs of standard FFPE tissue sections developed by Neuroindx (http: / / www.neuroindx.com / ). In this method, the location of the ROI is transferred to the microfluidic dissection unit, and the microfluidic gasket is positioned tightly above the corresponding ROI. Then, the tissue digestion buffer is automatically pumped onto the tissue and circulated within the ROI gasket. Once digested, the liquefied tissue sample (i.e., the extracted ROI) is pumped back into the reservoir and then transferred to a test tube. Thereafter, DNA can be extracted from the tissue digest and quantified using standard Qiagen and Invitrogen reagents and protocols. For genomic analysis, next-generation sSequencing, such as whole exome sequencing (WES), can be performed by an external sequencing service provider (such as the RPCI Genomic Shared Resource at Roswell Park Cancer Institute, Buffalo, NY). For example, WES can be performed using SureSelectHuman All Exon V6 plus COSMIC r2 (design ID S07604715, ~64 Mbp) as the target from Agilent. The raw reads received from such sequencing can be further analyzed using a pipeline assembled from publicly available software packages and / or custom software scripts.

[0049] In an exemplary embodiment, after step 40, the multiplexed-to-superpath images 26, image data 32, and genomic data 38 for each co-registered imaging data portion 12 and genomic data portion 14 (i.e., each SCORGI tissue portion) are stored in association with each other (in a suitable computer-readable storage medium, such as the storage media described herein) as SCORGI data elements. In the non-limiting exemplary embodiment described herein, one hundred and twenty-six SCORGI data elements will be created and stored.

[0050] As noted elsewhere herein, in one particular embodiment, the disclosed concepts can be implemented as part of a digital pathology system in a manner in which the image data 32 is generated from the multiplexed-to-superpath images 26, and the ROIs in the genomic data portion 14 are identified using the acquired image data 32 using software tools such as, but not limited to, the THRIVE software described herein, which is local to or otherwise accessible by a local computing device forming part of the digital pathology system. Additionally, as also noted elsewhere herein, in such an embodiment, certain genomic data 38 can also be generated from raw sequencing data using software tools local to or otherwise accessible by the same computing device. Figure 5 is a schematic diagram of such an exemplary system 42 according to one particular non-limiting exemplary embodiment. As Figure 5 shown, the system 42 includes a computing device, which can be, for example but not limited to, a PC, laptop computer, tablet computer, or any other suitable device configured to perform the functions described herein. The system 42 can be located, for example but not limited to, in the same laboratory where the sampling shown in Figure 1 and Figure 2 is performed and described herein. The system 42 includes an input device 44 (such as a keyboard and / or touchpad), a display 46 (such as an LCD), and a processing device 48. A user is able to provide input to the processing device 48 using the input device 44, and the processing device 48 provides an output signal to the display 46 to enable the display 46 to display information to the user (such as information related to SCORGI), as described in detail herein.

[0051] The processing device 48 of system 42 includes a processor and a memory. The processor can be, for example but not limited to, a microprocessor (μP) interfaced with the memory, a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device. The memory can be any one or more of various types of internal and / or external storage media, such as but not limited to RAM, ROM, (one or more) EPROM, (one or more) EEPROM, FLASH, etc., which provide storage registers, i.e., non-volatile computer-readable media, for data storage in a manner such as that of the internal storage area of a computer, and can be volatile memory or non-volatile memory. A plurality of routines executable by the processor are already stored in the memory, including routines for implementing various aspects of the disclosed concepts as described herein. In particular, in Figure 5 the illustrated embodiment shown, the processing device 48 is configured to receive and store the multiplexed-to-superpath image 26 as described elsewhere herein. The processing device 48 further includes an image data generation component 50 that is configured and arranged to generate the image data 32 (i.e., quantize) as described elsewhere herein from the received and stored multiplexed-to-superpath image 26. The generated image data 32 is then stored by the processing device 48. As described above, the image data generation component 50 can include the THRIVE software described elsewhere herein. Additionally, the processing device further includes an ROI identification component 52 that is configured and arranged to identify the ROI in the genomic data portion 14 using the acquired image data 32 as described elsewhere herein. The identified ROI can then be used to generate the genomic data 38 as described herein, which is then stored by the processing device 48. Also as described above, the ROI identification component 52 can similarly include the THRIVE software described elsewhere herein.

[0052] Additionally, as Figure 5 shown, the processing device 48 of the present embodiment further includes a SCORGI generation component 54. The SCORGI generation component 54 is configured and arranged to generate and store the SCORGI data elements as described herein based on the stored multiplexed-to-superpath image 26, image data 32, and genomic data 38.

[0053] Additionally, also as Figure 5 shown, according to another aspect of the present embodiment, the processing device 48 further includes a tumor atlas component 56. According to a non-limiting aspect of the disclosed concepts, the tumor atlas component 56 is configured and arranged to generate and display on the display 46 as Figure 6The tumor atlas 57 shown in [reference]. The tumor atlas 57 is a three-dimensional (3D) map that can display spatial ITH through multiple SCORGI icons 58. Specifically, each SCORGI icon 58 represents and corresponds to a SCORGI data element generated by the SCORGI generation component 54 from one or more specific tissue samples as described herein. As Figure 6 shown, each SCORGI icon 58 is positioned at a specific location in the x, y, z coordinate system of the tumor atlas 57. This specific location corresponds to and is based on the location of a specific tissue portion within the original tissue sample that was used to generate the SCORGI data element corresponding to the SCORGI icon 58. Additionally, the tumor atlas component 56 is constructed and configured to generate the tumor atlas 57 such that certain images and / or information can be selectively displayed on the display 46 when the user selects a specific one of the SCORGI icons 58 using the input device 44. For example but not limited to, as Figure 6 shown, the information displayed can include one or more of the following: (i) an image of the imaging data portion 12, (ii) an image of the genomic data portion 14, and (iii) the selected multiplexed-to-superpath image 26, (iv) the selected portion of the image data 32, and (v) the selected portion of the region-specific genomic data 38.

[0054] The tumor atlas 57 as just described is merely one application of the SCORGI-based sampling of the disclosed concept. Other applications can include using scRNAseq as a region-specific genomics process in SCORGI generation to establish phylogenetic relationships between microdomains and generate pseudo-time tumor evolution trajectories within a given biological sample and in longitudinally collected samples from the same patient (e.g., regional lymph nodes and distant metastases).

[0055] In the claims, any reference numerals placed between parentheses shall not be construed as limiting the claim. The word "comprising" or "including" does not exclude the presence of elements or steps other than those listed in the claim. In a device claim listing several components, several of these components may be implemented by one and the same piece of hardware. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. In any device claim listing several components, several of these components may be implemented by one and the same piece of hardware. The fact that certain elements are recited in mutually different dependent claims does not mean that these elements cannot be used in combination.

[0056] Although the present invention has been described in detail for purposes of illustration based on presently considered to be the most practical and preferred embodiments, it is to be understood that such details are for that purpose only and that the invention is not limited to the disclosed embodiments, but on the contrary, the invention is intended to cover modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it is to be understood that the invention contemplates that, where possible, one or more features of any embodiment may be combined with one or more features of any other embodiment.

Claims

1. A method for generating multiple spatially co - registered data elements from a tissue sample, comprising: Obtain a core from a tissue sample; Obtain a plurality of imaging data portions and a plurality of genomic data portions from the core in an alternating manner along the length of the core such that each imaging data portion is associated with a corresponding one of the genomic data portions to form a plurality of series pairs of adjacent, co-registered tissue portions, wherein for each series pair of co-registered tissue portions, the imaging data portion and the genomic data portion are obtained from immediately adjacent portions of the core along the length of the core; For each imaging data portion: (i) obtain a plurality of biomarker-labeled images from the imaging data portion by repeatedly labeling with a plurality of biomarker tags, (ii) analyze the biomarker-labeled images to obtain image data therefrom, and (iii) identify a plurality of regions of interest in the genomic data portion associated with the imaging data portion based on the obtained image data for the imaging data portion; For each genomic data portion, perform genomic analysis on each region of interest in the genomic data portion to generate genomic data for the genomic data portion; And For each series pair of co-registered tissue portions, associate the following with each other as co-registered imaging and genomic data elements: (i) the biomarker-labeled images of the imaging data portion of the series pair of co-registered tissue portions, (ii) the image data of the imaging data portion of the series pair of co-registered tissue portions; And (iii) the genomic data of the genomic data portion of the series pair of co-registered tissue portions.

2. The method according to claim 1, wherein the image of each biomarker - labeled is a multiplexed - to - hyperspectral image, and wherein each biomarker label is a fluorescent label.

3. The method according to claim 1, further comprising storing the co - registered data imaging and genomic elements of each co - registered tissue portion in a non - transitory computer - readable medium.

4. The method according to claim 1, further comprising: Generate and display on a display of a computer system a map including a plurality of co-registered data element icons, each co-registered data element icon being associated with and based on a corresponding one of the co-registered imaging and genomic data elements; 5. The method according to claim 4, further comprising: In response to selection of a particular one of the co-registered data element icons, cause one or more of the plurality of biomarker-labeled images, at least a portion of the image data, and at least a portion of the genomic data of the co-registered imaging and genomic data elements associated with the particular one of the co-registered data element icons to be displayed on the display; 6. The method according to claim 1, wherein for each imaging data portion, the imaging data portion is divided into a plurality of fields, and obtaining the plurality of biomarker - labeled images by repeatedly labeling with the plurality of biomarker labels includes obtaining a plurality of images for each field.

7. A non - transitory computer - readable medium storing a plurality of spatially co - registered data elements generated by the method according to claim 1.

8. The non - transitory computer - readable medium according to claim 7, wherein in each spatially co - registered data element, the genomic data is generated from a plurality of regions of interest in the genomic data portion of an associated pair of co - registered tissue portions, the plurality of regions of interest being identified based on image data generated from a plurality of multiplexed - to - hyperspectral images obtained from the imaging data portion of the associated pair of co - registered tissue portions.

9. A method for generating multiple spatially co - registered data elements from a tissue sample, the method comprising: Obtain a core from a tissue sample; Obtain a plurality of imaging data portions and a plurality of genomic data portions from the core in an alternating manner along the length of the core such that each imaging data portion is associated with a corresponding one of the genomic data portions to form a plurality of series pairs of adjacent, co-registered tissue portions, wherein for each series pair of co-registered tissue portions, the imaging data portion and the genomic data portion are obtained from immediately adjacent portions of the core along the length of the core; For each series pair of co-registered tissue portions: (i) obtain a plurality of biomarker-labeled images from the imaging data portion of the series pair of co-registered tissue portions by repeatedly labeling with a plurality of biomarker tags and store them as part of a data element, (ii) generate image data obtained from the plurality of biomarker-labeled images and store it as part of a data element; and (iii) generating genomic data obtained from genomic data portions of the series pairs of co-registered tissue portions and storing it as part of a data element.

10. The method according to claim 9, wherein the image of each biomarker - labeled is a multiplexed - to - hyperspectral image, and wherein each biomarker label is a fluorescent label.

11. The method according to claim 9 further comprises storing data elements of each co-registered tissue part in a non-transitory computer-readable medium.

12. The method according to claim 9 further comprises: Generating and displaying on a display of a computer system a map including a plurality of data element icons, each data element icon being associated with and based on a respective one of the data elements in the data elements.

13. The method according to claim 12 further comprises: In response to selection of a particular one of the data element icons, causing one or more of a plurality of biomarker-labeled images, at least a portion of the image data, and at least a portion of the genomic data of the data element associated with the particular one of the data element icons to be displayed on the display.

14. The method according to claim 9, wherein for each imaging data part, the imaging data part is divided into a plurality of fields, and obtaining the plurality of biomarker-labeled images by repeatedly labeling with the plurality of biomarker tags comprises obtaining a plurality of images of each field.

15. The method according to claim 9, wherein for each pair of co-registered tissue parts, genomic data is generated from a plurality of regions of interest in the genomic data part of the associated pair of co-registered tissue parts, the plurality of regions of interest being identified based on image data generated from the plurality of biomarker-labeled images, the plurality of biomarker-labeled images being obtained from the imaging data part of the associated pair of co-registered tissue parts.

16. The method according to claim 9, wherein each imaging data part and each genomic data part have a diameter of 1 cm to 5 cm.

17. A non-transitory computer-readable medium storing a plurality of data elements generated according to the method of claim 9.

18. A system for generating and storing a plurality of spatially co-registered data elements from a plurality of imaging data parts and a plurality of genomic data parts, the plurality of imaging data parts and the plurality of genomic data parts being obtained from a core of a tissue sample in an alternating manner along the length of the core such that each imaging data part is associated with a corresponding one of the genomic data parts to form a plurality of series pairs of adjacent, co-registered tissue parts, wherein for each series pair of co-registered tissue parts, the imaging data part and the genomic data part are obtained from immediately adjacent parts of the core along the length of the core, the system comprising processing means, the processing means comprising: An image data generation component configured and arranged to generate image data by repeatedly labeling, for each series pair of co-registered tissue portions, from a plurality of biomarker-labeled images, the plurality of biomarker-labeled images being obtained from the imaging data portions of the series pairs of co-registered tissue portions; A region of interest identification component configured and arranged to identify, for each series pair of co-registered tissue portions, a plurality of regions of interest in the genomic data portions of the series pairs of co-registered tissue portions based on the generated image data of the series pairs of co-registered tissue portions, wherein for each series pair of co-registered tissue portions, the genomic data is generated from the genomic data portions of the series pairs of co-registered tissue portions based on the identified plurality of regions of interest; and A data element generation component configured and arranged to generate and store spatially co-registered data elements of the plurality of spatially co-registered data elements for each series pair of co-registered tissue portions, the stored spatially co-registered data elements including the plurality of biomarker-labeled images of the series pairs of co-registered tissue portions, the generated image data, and the generated genomic data.

19. The system according to claim 18, wherein the processing means further comprises a map generation component configured and arranged to generate a map comprising a plurality of co-registered data element icons, wherein each co-registered data element icon is associated with and based on one of the spatially co-registered data elements generated by the data element generation component.

20. The system according to claim 19, wherein the processing device is configured and arranged to: in response to a selection of one of the co-registered data element icons, cause one or more of a biomarker-labeled image of the spatially co-registered data elements, at least a portion of the generated image data of the spatially co-registered data elements, and at least a portion of the generated genomic data of the spatially co-registered data elements to be displayed.

Images