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

Abstract

To provide a method of generating a plurality of spatially co-registered data elements.SOLUTION: The method includes: for each pair of spatially co-registered tissue sections, obtaining and storing, as part of a data element, a plurality of multiplexed images to hyperplexed images from an imaging data section of the co-registered tissue section; generating and storing, as part of the data element, image data from the plurality of multiplexed images to hyperplexed images; and generating and storing, as part of the data element, genomic data from a genomic data section of the associated co-registered tissue section. Each data element is associated with and generated from the pair of co-registered tissue sections obtained from adjacent positions of a core taken from a tissue sample and includes an image data section and the genomic data section.SELECTED DRAWING: None

Description

[Technical field]

[0001] <CROSS REFERENCE TO RELATED APPLICATIONS> This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 62 / 721,018, filed August 22, 2018, and entitled “Spatially CO-Registered Genomic and Imaging (SCORGI) Data Elements for Fingerprinting Microdomains,” the disclosure of which is incorporated herein by reference.

[0002] <Government contracts> 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.

[0003] <Field of the Invention> 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 from tumors and / or associated tissues, including a novel tumor sampling strategy for characterizing spatial heterogeneity in solid tumors, and a digital pathology system employing the same. [Background technology]

[0004] <Description of Related Art> Digital pathology refers to the acquisition, storage, and display of histologically stained tissue samples, and has initially gained traction in niche applications such as second opinion telepathology, immunostain interpretation, and intraoperative telepathology. Typically, biopsy samples generate large volumes of patient data consisting of 3-50 slides that pathologists evaluate visually under a microscope, but with digital technology can be evaluated by viewing on a high-definition monitor. Due to the manual steps involved, current workflow practices are time-consuming, error-prone, and subjective.

[0005] Cancer is a heterogeneous disease. In hematoxylin and eosin (H&E) stained tissue images, heterogeneity is characterized by the presence of various histological structures such as carcinoma in situ, invasive carcinoma, adipose tissue, blood vessels, normal ducts, etc. Furthermore, for many malignant tumors, molecular and cellular heterogeneity is a prominent feature found between tumors of different patients, between different sites of neoplasia in one patient, and within a single tumor. Intratumoral heterogeneity involves phenotypically distinct cancer cell clonal subpopulations and other cell types that comprise the tumor microenvironment (TME). These cancer cell clonal subpopulations and other cell types include stromal stem and progenitor cells derived from local and bone marrow, subclasses of tumor-promoting or tumor-killing immune-inflammatory cells, cancer-associated fibroblasts, endothelial cells, and peripheral cells. The TME can be considered as an evolving ecosystem in which cancer cells engage in heterogeneous interactions with these other cell types to exploit available resources to proliferate and survive. In this light, the spatial relationships between cell types within the TME (i.e., spatial heterogeneity) are believed to be one of the main drivers of disease progression and treatment resistance. Therefore, defining spatial heterogeneity within the TME is essential to properly diagnose specific disease subtypes and identify optimal treatments for individual patients.

[0006] To date, intratumor heterogeneity has been examined using three main approaches. The first approach involves taking core samples from specific regions of the tumor and measuring the population average. Sample heterogeneity is measured by analyzing multiple cores within the tumor using techniques such as whole exome sequencing, epigenetics, proteomics, and metabolomics. The second approach involves isolating cells from the tissue and then performing "single cell analysis" using the methods mentioned above, RNA sequencing, imaging, or flow cytometry. The third approach uses the spatial resolution of light microscopy imaging to measure biomarkers in cells in situ, preserving spatial context and combining with molecular-specific labels. All of these approaches have various drawbacks and limitations, although each offers some degree of validity.

[0007] Furthermore, one of the biggest 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 limited tools for integrating genomic and proteomic measurements in clinically and molecularly relevant annotated sample sets.

[0008] Therefore, there is room for improvement in the characterization of intratumor heterogeneity. Summary of the Invention

[0009] In one embodiment, a method for producing a number of tissue sections for imaging and genomic analysis is provided, the method including obtaining a number of cores from a tissue sample, and for each core, obtaining a number of imaging data sections and a number of genomic data sections from the core, alternating along a length of the core, and associating each of the imaging data sections with a respective one of the genomic data sections to form a number of serial pairs of adjacent tissue sections.

[0010] In another embodiment, a plurality of tissue microarray (TMA) slides are provided that include tissue sections for imaging and genomic analysis, the plurality of TMA slides including a number of first TMA slides having a plurality of imaging data sections fixed thereto for each of a number of cores from a tissue sample, and a number of second TMA slides having a plurality of genomic data sections fixed thereto for each of a number of cores from the tissue sample, each core having a plurality of imaging data sections and a plurality of genomic data sections arranged in alternating fashion along the length of the core, each imaging data section being associated with a respective one of the genomic data sections of the core to form a plurality of serial sets of adjacent tissue sections.

[0011] In yet another embodiment, a method for generating a plurality of spatially co-registered data elements from a tissue sample is provided, the method comprising: obtaining a number of cores from the tissue sample; and for each core, obtaining a plurality of imaging data sections and a plurality of genomic data sections from the core alternating along a length of the core, and associating each of the imaging data sections with a respective one of the genomic data sections to form a plurality of serial sets of adjacent co-registered tissue sections. The method further comprises, for each of the imaging data sections, (i) obtaining a plurality of multiplex or hyperplex images by repeated labeling with a plurality of fluorescent tags, (ii) analyzing the multiplex or hyperplex images and obtaining image data from the analyzed images, and (iii) identifying a number of regions of interest in the genomic data sections associated with the imaging data sections based on the image data obtained for the imaging data sections. The method also includes, for each genomic data section, performing genomic analysis on each of the regions of interest of the genomic data section to generate genomic data for the genomic data section, and, for each co-registered tissue section, associating as co-registered imaging data and genomic data elements: (i) a multiplex or hyperplex image of the imaging data section of the co-registered tissue section, (ii) image data of the imaging data section of the co-registered tissue section, and (iii) genomic data of the genomic data section of the co-registered tissue section.

[0012] In yet another embodiment, a non-transitory computer readable medium is provided that stores a plurality of spatially co-registered data elements, each of which includes an image data section and a genomic data section associated with and generated from a pair of co-registered tissue sections obtained from adjacent locations of a core taken from a tissue sample, each of which includes (i) a plurality of multiplex or hyperplex images obtained from the imaging data sections of the associated set of co-registered tissue sections, (ii) image data generated from the plurality of multiplex or hyperplex images obtained from the imaging data sections of the associated set of co-registered tissue sections, and (iii) genomic data generated from the genomic data sections of the associated set of co-registered tissue sections.

[0013] In another embodiment, a method is provided for generating a plurality of spatially co-registered data elements, each of which is associated with and generated from a set of co-registered tissue sections obtained from adjacent locations of a core taken from a tissue sample, and includes an image data section and a genomic data section, the method including, for each set of co-registered tissue sections, (i) obtaining and storing, as part of the data element, a plurality of multiplex or hyperplex images from the imaging data sections of the co-registered tissue sections, (ii) generating and storing, as part of the data element, image data from the plurality of multiplex or hyperplex images, and (iii) generating and storing, as part of the data element, genomic data from the genomic data section of the associated co-registered tissue section.

[0014] In yet another embodiment, a system is provided for generating and storing spatially co-registered data elements associated with and generated from a set of co-registered tissue sections obtained from adjacent locations of a core taken from a tissue sample, the set of co-registered tissue sections including an image data section and a genomic data section. The system includes a processing device that includes: (i) an image data generation component structured and configured to generate image data from a plurality of multiplex or hyperplex images obtained from a related set of imaging data sections of the co-registered tissue sections; (ii) a region of interest identification component structured and configured to identify a number of regions of interest in a related set of genomic data sections of the co-registered tissue sections based on the generated image data, where genomic data is generated from the genomic data sections of the related co-registered tissue sections based on the identified number of regions of interest; and (iii) a data element generation component structured and configured to generate and store spatially co-registered data elements, where the spatially co-registered data elements include a plurality of multiplex or hyperplex images, generated image data, and generated genomic data. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a flow chart illustrating a sample preparation method according to an exemplary embodiment of one aspect of the disclosed concepts.

[0016] [Diagram 2] FIG. 2 is a schematic diagram illustrating the sample preparation method shown in FIG.

[0017] [Diagram 3]FIG. 3 is a flowchart illustrating an exemplary embodiment of a further aspect of the disclosed concept, a method for generating SCORGI data elements from multiple sets of co-registered and co-located imaging data section and genomic data section pairs.

[0018] [Figure 4] FIG. 4 is a schematic diagram illustrating a method for generating the SCORGI data elements of FIG.

[0019] [Diagram 5] FIG. 5 is a schematic diagram of an exemplary digital pathology system in one non-limiting exemplary embodiment of the disclosed concepts.

[0020] [Figure 6] FIG. 6 is a schematic diagram illustrating tumor atlas generation according to one non-limiting aspect of the disclosed concepts. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Detailed Description of Exemplary Embodiments As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0022] As used herein, the statement that two or more parts or components are "coupled" means that, to the extent that they are associated, the parts are connected or in operation together, either directly or indirectly, i.e., through one or more intermediate parts or components.

[0023] As used herein, "directly coupled" means that two elements are in direct contact with each other.

[0024] As used herein, the term "number" means one or an integer greater than one (ie, a plurality).

[0025] The terms "component" and "system" as used herein are intended to refer to any computer-related entity, whether hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a thread of execution, a program, and / or a computer. For purposes of illustration, both an application running on a server and the server may be a component. One or more components may reside within a process and / or thread of execution, and a component may be localized on one computer and / or distributed among two or more computers. Although certain ways of displaying information to a user have been shown and described with respect to certain figures or graphs as screenshots, one of ordinary skill in the art will recognize that various other alternatives may be used.

[0026] As used herein, the term "multiplexed imaging" refers to an imaging technique that employs up to seven biomarkers, and the term "multiplexed image" refers to an image produced using multiplexed imaging.

[0027] As used herein, the term "hyperplexed imaging" refers to an imaging technique that employs more than seven biomarkers, and the term "hyperplexed image" refers to an image produced using hyperplexed imaging.

[0028] As used herein, the term "multiplex imaging or hyperplexed imaging" includes multiplex imaging and / or hyperplex imaging, and the term "multi-hyperplexed images" includes multiplex images and / or hyperplex images.

[0029] As used herein, directional terms such as up, down, left, right, above, below, front, back, and derivatives thereof, refer to the orientation of the elements illustrated in the drawings and do not limit the scope of the claims, unless expressly stated.

[0030] The disclosed concepts are described with reference to numerous specific details, for purposes of explanation so as to provide a thorough understanding of the improvements of the subject matter, however, it will be apparent that the disclosed concepts can be practiced without these specific details without departing from the spirit and scope of the invention.

[0031] In one aspect, the disclosed concept provides a novel tumor sampling strategy for characterizing spatial heterogeneity in solid tumors, which is based on what we call "SCORGIs". SCORGIs are spatially co-registered genomic and imaging data elements that are generated from spatially co-registered adjacent tissue samples, such as, but not limited to, matched primary tumors, regional lymph nodes, and distant metastases. SCORGIs integrates region-specific genomic and imaging information. Region-specific genomic information includes, but is not limited to, whole exome sequencing, in situ transcriptomics, epigenomics, etc., obtained using next-generation sequencing methods. Imaging information is information obtained from multiple individual sample cores distributed within such tissue samples, such as, but not limited to, a wide range of fluorescently labeled antibody biomarkers. The unprecedented SCORGI-based tumor sampling strategy of the present disclosure allows for digital fingerprinting of microdomains, which represent spatial, functional, and genomic intratumor heterogeneity (ITH) in malignant and non-malignant cells (e.g., but not limited to, immune cells, cancer-associated fibroblasts (CAFs), and endothelial cells) and their localized interactions within the tumor microenvironment (TME), which are believed to be key determinants of metastatic disease progression. Furthermore, SCORGI-based digital fingerprinting according to the concepts of the present disclosure allows for the identification and characterization of pathogenic signaling networks within the TME, leading to improved therapeutic strategies.

[0032] Figure 1 is a flow chart and Figure 2 is a schematic diagram illustrating a sample preparation method in an exemplary embodiment of one aspect of the disclosed concepts. As described herein, the sample preparation method illustrated in Figures 1 and 2 provides a plurality of tissue samples that can be used to generate the SCORGI data elements described herein.

[0033] 1 and 2, the method begins at step 5, where a solid tumor sample 2, shown in FIG. 2, is obtained. In a non-limiting exemplary embodiment, the tumor sample 2 is 1.5 cm 2 In step 5, the solid tumor sample 2 is sliced ​​into six individual tumor slices 4, which are labeled 4a-4f in FIG. 2. In an exemplary embodiment, the tumor slices 4a-4f are processed into formalin-fixed paraffin-embedded (FFPE) slices. Next, in step 10, seven individual core samples or "cores" (each core is labeled 6 in FIG. 2) are obtained from distributed regions in each of the tumor slices 4a-4f. In a non-limiting exemplary embodiment, each core 6 is a large core with a bore of 5 mm. Thus, after step 10, a total of 42 cores 6 are obtained from the tumor slices 4a-4f.

[0034] Next, in step 15, the 42 cores 6 are used to create three large bore tissue microarray (TMA) cassettes 8, one of which is shown in FIG. 2. As shown in FIG. 2, each TMA cassette 8 contains seven cores 6 from two of each of the tissue tumor slices 4a-4f (e.g., a first TMA cassette 8 contains cores 6 from tumor slices 4a and 4b, a second TMA cassette 8 contains cores 6 from tumor slices 4c and 4d, and a third TMA cassette 8 contains cores 6 from tumor slices 4e and 4f).

[0035] The method then proceeds to step 20, where, in a non-limiting exemplary embodiment of the present invention, each TMA cassette 8 is used to generate three sets of TMA slides containing multiple sets of co-registered and co-positioned sections from the cores 6 (for a total of nine sets of TMA slides). More specifically, in the non-limiting exemplary embodiment shown in FIG. 2, three imaging data sections 12 and three genomic data sections 14 are obtained from each core 6 in each TMA cassette 8. In a non-limiting exemplary embodiment, each imaging data section 12 is a tissue section having a thickness of 5 μm and a diameter of 5 mm, and each genomic data section 14 is a tissue section having a thickness of 10-20 μm and a diameter of 5 mm (in an exemplary embodiment, the thickness of the genomic data sections 14 is greater than the thickness of the imaging data sections 12 because, as described herein, the genomic analysis performed in the genomic data sections 14 typically requires more tissue than the imaging performed in the imaging data sections 12). As shown in FIG. 2, three imaging data sections 12 and three genomic data sections 14 are alternately obtained from each core 6 such that each imaging data section 12 is directly adjacent to a genomic data section 14, forming sequential / serial pairs of adjacent tissue sections (i.e., SCORGI tissue sections), which can be used to create multiple SCORGI data elements as described herein. Thus, in a non-limiting exemplary embodiment of the present invention, a total of 42 imaging data sections 12 and a total of 42 genomic data sections 14 are obtained from the 14 cores 6 provided to each TMA cassette 8. Thus, for the three TMA cassettes 8 of the exemplary embodiment, a total of 126 imaging data sections 12 and a total of 126 genomic data sections 14 are obtained.

[0036] 2, each TMA cassette 8 has the obtained imaging data sections 12 sequentially arranged on three imaging data TMA slides 16a, 16b, 16c, and the obtained genomic data sections 14 sequentially arranged on three genomic data TMA slides 18a, 18b, 18c. As a result, each TMA cassette 8 has three sets of TMA slides as described above, a first set including the imaging data TMA slide 16a and the genomic data TMA slide 18a, a second set including the imaging data TMA slide 16b and the genomic data TMA slide 18b, and a third set including the imaging data TMA slide 16c and the genomic data TMA slide 18c. It will be further understood that for each TMA cassette 8, each set of TMA slides includes 14 sets of co-registered imaging data sections 12 and genomic data sections 14, with the imaging data sections 12 arranged on the TMA slide 16 and the corresponding genomic data sections 14 arranged in corresponding positions on the TMA slide 18. Thus, upon completion of step 20, a total of nine TMA slides (nine TMA slides 16 and nine TMA slides 18) are formed from the three TMA cassettes 8 of this embodiment, resulting in a total of 126 co-registered and co-located sets of imaging data sections 12 and genomic data sections 14 (i.e., 126 SCORGI tissue sections). As noted above, these co-registered and co-located sets of imaging data sections 12 and genomic data sections 14 can be used to create a plurality of data elements, referred to herein as SCORGIs.

[0037] It should be noted that the illustrated numbers of various items above, such as the number of tumor slices 4 (e.g., 6), the number of cores 6 obtained from each tumor slice 4 (e.g., 7), and the number of diameters and / or thicknesses of imaging data sections 12 and genomic data sections 14 obtained from each core 6 (e.g., 3), are meant for illustrative purposes only, and other numbers of such items may be used within the scope of the disclosed concepts. For example, the size of the cores used in the multiplex imaging or hyperplex imaging described herein may be, but is not limited to, greater than 5 mm. Specifically, tumors are typically several centimeters (e.g., 1-5 cm) in diameter, and the entire tumor can be cut to create sections 1-5 cm in diameter within the scope of the disclosed concepts. In such embodiments, whole slide images (WSIs) of the sections can be created, and multiplex imaging or hyperplex imaging can be performed on the WSIs.

[0038] 3 is a flow chart and FIG. 4 is a schematic diagram illustrating an exemplary embodiment of one aspect of the disclosed further concepts, a method for generating SCORGI data elements from co-registered and co-located sets of imaging data sections 12 and genomic data sections 14 (referred to herein as SCORGI tissue sections). Specifically, SCORGIs are generated by performing multiplex or hyperplex imaging and region-specific genomics for each of multiple co-registered and co-located sets of imaging data sections 12 and genomic data sections 14, as described in more detail below.

[0039] 3 and 4, the method begins at step 25, where for each imaging data section 12 of the TMA slides 16a, 16b, 16c, a high resolution multiplex or hyperplex image 26 is generated from the imaging data section 12 using a multiplex or hyperplex imaging process 28, which in an exemplary embodiment includes repeatedly labeling each imaging data section 12 with multiple fluorescent tags to image multiple biomarkers. In the exemplary embodiment shown in FIG. 4, each imaging data section 12 is divided into 60 image fields 31, 50 different fluorescent tags are applied to the imaging data section 12, and an image 26 is acquired (e.g., 20 times) for each fluorescent tag in each image field 28. As a result, in this exemplary embodiment, a total of 3000 multiplex or hyperplex images 26 are acquired for each imaging data section 12. It will be appreciated that the number and selection of biomarkers used in step 25 can vary and may depend on the tumor progression mechanism of interest.

[0040] Next, in step 30, for each imaging data section 12 of the TMA slides 16a, 16b, 16c, the generated multiplex or hyperplex image 26 is analyzed to obtain image data 32 as shown in FIG. 4. Specifically, for each field 31, the fluorescent multiplex or hyperplex image 26 includes an intensity distribution for each biomarker. Thus, in an exemplary embodiment, the multiplex or hyperplex image 26 is processed to extract image data 32 including quantification of information extracted from the multiplex or hyperplex image 26. Such image data 32 may include quantification such as, but not limited to, intensity and localization of subcellular biomarkers in each cell of each multiplex or hyperplex image 26, morphometric measurements such as size and shape in a cell or subcellular region, and / or intensity distribution in a cell or subcellular region that may be characterized by statistical or textural means.

[0041] In an exemplary embodiment, a standard multiplexing machine, such as PerkinElmer's 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 (where the standard multiplexing machine typically includes software for generating the image data 32). Alternatively, the multiplexed image 26 can be generated using the standard multiplexing machine, and the image data 32 can be generated from the image data 26 using separate image analysis software. The image analysis software may include, but is not limited to, the public domain THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment) software, which is described in the literature [Spagnolo, et al., Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images, Cancer Res. 2017 Nov 1;77(21):e71-e74]. THRIVE software is an open source tool to assist cancer researchers in interactive hypothesis testing. THRIVE provides an integrated workflow for multiplexed or hyperplexed analysis of whole slide immunofluorescence images and tissue microarrays, including standard cellular and subcellular segmentation and biomarker quantification. THRIVE quantifies intracellular spatial heterogeneity and interactions between different cellular phenotypes and non-cellular components.THRIVE can decipher diverse molecular and cell signaling networks, supporting the resiprocal coevolution of malignant cells and their specific TME (e.g., cancer-associated fibroblasts, immune cells, extracellular matrix, etc.) to confer malignant phenotypes that result in dormancy, drug resistance, immune evasion, and metastatic potential.

[0042] As a further alternative embodiment, the generation of hyperplex images 26 and image data 32 from the TMA slides 16a, 16b, 16c described above can be performed by sending the TMA slides 16a, 16b, 16c to a CLIA (Clinical Laboratory Improvement Amendments) hyperplexing immunofluorescence lab, such as, for example, NeoGenomics MxIF (Multi-molecular multiplexing methodology lab (https: / / neogenomics.com / pharma-services / lab-services / multiomyx)).

[0043] After step 30, the method proceeds to step 35. In step 35, for each of the imaging data sections 12 of the TMA slides 16a, 16b, 16c, the acquired image data 32 is used to identify several regions of interest ("ROIs", also referred to as "microdomains") in the genomic data section 14 (contained in one TMA slide 18a, 18b, 18c) that are co-registered with the imaging data section 12. The ROIs can be identified using the acquired image data 32 by any of several known or later developed methods and implemented in the THRIVE software. Examples of this method include, but are not limited to, the method described in U.S. Provisional Application No. 62 / 675,832 (title: Predicting the Recurrurrence Risk of Cancer Patients From Primary Tumors with Multiplexed Immunofluorescence Biomarkers and Their Spatial Correlation Statistics) filed on May 24, 2018, the method described in PCT / US19 / 033662 (title: Predicting the Recurrurrence Risk of Cancer Patients From Primary Tumors with Multiplexed Immunofluorescence Biomarkers and Their Spatial Correlation Statistics) filed on May 23, 2019, and the method described in the literature (Spagnolo, et al. Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images, Cancer Res. 2017 Nov 1;77(21):e71-e74), and these methods are incorporated herein by reference.In the method, spatially resolved correlations between biomarkers as covariates in multivariate survival models of outcome data (e.g., recurrence) are used to construct maps of spatial organization of cancer recurrence in hyperplexed tissue samples. These maps delineate microdomains associated with recurrence and metastatic progression. Furthermore, performing region-specific genomics on phenotypically distinct microdomains of the SCORGI data elements described herein reveals enriched malignant cell subclonal populations that are also likely enriched within the metastases themselves. The integration of genomic and imaging information in the SCORGIs described herein allows for modeling of the phenotypic and genotypic progression and evolution of predictive biomarkers mechanistically linked to metastatic progression.

[0044] Referring again to FIG. 4, an exemplary genomic data section 14 and two exemplary ROIs 34 ("Region 1" and "Region 2") are shown. In step 40, for each of the genomic data sections 14 in the TMA slides 18a, 18b, 18c, a genomic analysis, indicated by reference numeral 36, is performed for each ROI 34 identified in the genomic data section 14. Based on the results of the genomic analysis 36, genomic data 38 is generated for each identified ROI 34 for each genomic data section 14. For example, and without limitation, the genomic analysis performed in step 40 may be whole genome sequencing obtained via next generation sequencing methods, in situ transcriptomics, epigenomics, among others, and the generated genomic data 38 may also include, but is not limited to, data indicative of mutations and / or data indicative of in situ transcriptomics (FISSEQ, DSP).

[0045] Step 40 above can be performed by any suitable method and / or device. For example, extraction of DNA from ROI 34 can be performed using a microfluidic dissection method and unit, compatible with standard optical microscopes, developed by NeuroIndx (http: / / www.neuroindx.com / ) for rapid extraction of nucleic acids from small (1-5 mm) ROIs in standard FFPE tissue sections. In this method, the location of the ROI is moved to the microfluidic dissection unit and a microfluidic gasket is firmly placed over the corresponding ROI. Tissue digestion buffer is then automatically dispensed onto the tissue and circulated within the ROI gasket. Once digested, the liquefied tissue sample (i.e., extracted ROI) is pumped back into the reservoir and then into a tube. DNA can then be extracted from the tissue digest and quantified using standard Qiagen and Invitrogen reagents and their protocols. For genomic analysis, next generation sequencing such as whole exome sequencing (WES) can be performed by external sequencing service providers, such as RPCI Genomic Shared Resource, Roswell Park Cancer Institute, Buffalo, NY. For example, WES can be performed using Agilent's SureSelect Human All Exon V6 plus COSMIC r2 (Design ID S07604715, ~64 Mbp) target. The raw reads received from such sequencing can be further analyzed using pipelines assembled from publicly available software packages and / or custom software scripts.

[0046] In an exemplary embodiment, after step 40, for each co-registered imaging data section 12 and genomic data section 14 (i.e., each SCORGI tissue section), the multiplex or hyperplex image 26, image data 32, and genomic data 38 are stored (in a suitable computer readable medium as described herein) in association with each other as a SCORGI data element. In the non-limiting exemplary embodiment described herein, 126 SCORGI data elements are created and stored.

[0047] As described elsewhere herein, in certain embodiments, the disclosed concepts may be implemented as part of a digital pathology system, where image data 32 is generated from a multiplex or hyperplex image 26, and ROIs in the genomic data section 14 are identified using the obtained image data 32 using a software tool, such as, but not limited to, the THRIVE software described herein. The software tool may be directly available (local) to the user or may otherwise be accessible by a local computing device forming part of the digital pathology system. Furthermore, as described elsewhere herein, in such embodiments, the specific genomic data 38 may also be generated from the raw sequencing data using local software or a software tool accessible by the same computing device. FIG. 5 is a schematic diagram of such an exemplary system 42 in one non-limiting exemplary embodiment. As shown in FIG. 5, the system 42 includes a computing device, which may be, but is not limited to, a PC, a laptop computer, a tablet computer, or any other suitable device configured to perform the functions described herein. System 42 may be located in the same laboratory where sampling is performed, for example, and without limitation, as shown in Figures 1 and 2 and described herein. System 42 includes an input device 44 (such as a keyboard and / or touchpad), a display 46 (such as an LCD), and a processor 48. A user can use input device 44 to input to processor 48, which can display information for the user (e.g., SCORGI-related information) on display 46, as described in detail herein.

[0048] The processing unit 48 of the system 42 includes a processor and a memory. The processor may be, for example, but not limited to, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or other suitable processing device coupled to a memory. The memory may be any one or more of various types of internal and / or external storage media, for example, but not limited to, RAM, ROM, EPROM(s), EEEPROM(s), FLASH. Others provide storage registers, i.e., non-transitory computer readable media, for data storage, for example in the form of a computer's internal storage area, and may include volatile or non-volatile memory. The memory stores several routines executable by the processor, including routines for implementing various aspects of the disclosed concepts described herein. In particular, in the illustrated embodiment shown in FIG. 5, the processing unit 48 is structured and configured to receive and store the multiplexed or hyperplexed image 26, as described elsewhere herein. The processing device 48 further includes an image data generation component 50 that is structured and configured to generate (i.e., quantify) image data 32, as described elsewhere herein, from the received and stored multiplex or hyperplex image 26. The generated image data 32 is then stored by the processing device 48. As described above, the image data generation component 50 may include the THRIVE software, as described elsewhere herein. The processing device also includes an ROI identification component 52 that is structured and configured to identify ROIs in the genomic data section 14 using the obtained image data 32, as described elsewhere herein. The identified ROIs may then be used to generate genomic data 38, as described herein, which is stored by the processing device 48. As described above, the ROI identification component 52 may also include the THRIVE software, as described elsewhere herein.

[0049] 5, the processing device 48 of the present embodiment further includes a SCORGI generation component 54. The SCORGI generation component 54 is structured and configured to generate and store the SCORGI data elements described herein based on the stored multiplex / hyperplex images 26, image data 32, and genomic data 38.

[0050] Also, as shown in FIG. 5, in a further aspect of the present embodiment, the processing device 48 further includes a tumor atlas component 56. In one non-limiting aspect of the disclosed concept, the tumor atlas component 56 is structured and configured to generate and display a tumor atlas 57 on the display 46, as shown in FIG. 6. The tumor atlas 57 is a three-dimensional (3D) map that can depict the spatial ITH via a plurality of SCORGI icons 58. Specifically, each SCORGI icon 58 represents and corresponds to a SCORGI data element generated by the SCORGI generation component 54 from a particular tissue sample described herein. As shown in FIG. 6, each SCORGI icon 58 is positioned as a specific location within the x, y, z coordinate system of the tumor atlas 57. The specific location corresponds to and is based on the location within the original tissue sample of the particular tissue section that the SCORGI icon 58 was used to generate the corresponding SCORGI data element. Additionally, the tumor atlas component 56 is structured and configured to generate the tumor atlas 57 such that particular images and / or information can be selectively displayed on the display 46 when a user selects a particular one of the SCORGI icons 58 using the input device 44. For example, as shown in FIG. 6, the displayed information can include, but is not limited to, one or more of: (i) an image of the imaging data section 12, (ii) an image of the genomic data section 14, (iii) a selected multiplex or hyperplex image 26, (iv) a selected portion of the image data 32, and (v) a selected portion of the region specific genomic data 38.

[0051] This tumor atlas57 is just one application of the disclosed concepts of SCORGI-based sampling. Other applications include using scRNAseq as a region-specific genomics step in the SCORGI generation to establish phylogenetic relationships between microdomains and generate pseudo-time tumor evolution trajectories within a given biological specimen and among samples collected longitudinally from the same patient (e.g., regional lymph nodes and distant metastases).

[0052] In the claims, any reference signs placed between parentheses shall not be construed as limiting the scope of the claim. The word "comprising" or "including" does not exclude the presence of other elements or steps than those listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. For any device invention enumerating several means in a claim, several of these means may be embodied by one and the same item of hardware. The fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.

[0053] Although the present invention has been described in detail for purposes of illustration based on what are considered to be the most practical and preferred embodiments, it should be understood that such details are for the purpose of illustration only, and that the present invention is not limited to the disclosed embodiments, but is intended to include modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it should be understood that the present invention contemplates that one or more features of any embodiment can be combined with one or more features of any other embodiment, to the extent possible.

Claims

1. 1. A method for producing multiple tissue sections for imaging and genomic analysis, comprising: Obtaining several cores from the tissue sample; for each core, obtaining from the core a plurality of imaging data sections and a plurality of genomic data sections in alternating fashion along a length of the core, and associating each of the imaging data sections with a respective one of the genomic data sections to form a plurality of serial sets of adjacent tissue sections; A method comprising:

2. 2. The method of claim 1, wherein for each serial set of tissue sections, an imaging data section and a genomic data section are obtained from immediately adjacent portions of the core along the length of the core.

3. 2. The method of claim 1, wherein each imaging data section has a first thickness and each genomic data section has a second thickness that is greater than the first thickness.

4. The method of claim 3, wherein the first thickness is 5 μm and the second thickness is 10-20 μm.

5. The method of claim 1 , wherein each imaging data section and each genomic data section has a diameter of 5 mm.

6. The method of claim 1 , wherein the tissue sample is a sample of a tumor, a regional lymph node, or a distant metastasis.

7. 2. The method of claim 1, comprising fixing each of the imaging data sections from each core to a number of first tissue microarray (TMA) slides and fixing each of the genomic data sections from each core to a number of second tissue microarray (TMA) slides.

8. The method of claim 1 , wherein the number of cores is a plurality of cores.

9. 2. The method of claim 1, wherein the tissue sample is divided into a plurality of tissue slices, and the plurality of cores comprises, for each tissue slice, a plurality of tissue slice cores obtained from discrete regions of the tissue slice.

10. 1. A plurality of tissue microarray (TMA) slides containing tissue sections for imaging and genomic analysis, comprising: a number of first TMA slides having a plurality of imaging data sections fixed thereon for each of a number of cores from the tissue sample; a number of second TMA slides having a plurality of genomic data sections fixed thereon for each of the number of cores from the tissue sample; A plurality of tissue microarray (TMA) slides, wherein for each core, the plurality of imaging data sections and the plurality of genomic data sections come from alternating portions along the length of the core, each of the imaging data sections being associated with a respective one of the genomic data sections of the core to form a plurality of serial sets of adjacent tissue sections.

11. 11. The plurality of TMA slides of claim 10, wherein for each serial set of tissue sections, the imaging data section and the genomic data section are obtained from immediately adjacent portions of the core along the length of the core.

12. 11. The plurality of TMA slides of claim 10, wherein each imaging data section has a first thickness and each genomic data section has a second thickness greater than the first thickness.

13. 13. The plurality of TMA slides of claim 12, wherein the first thickness is 5 μm and the second thickness is 10-20 μm.

14. 11. The plurality of TMA slides of claim 10, wherein each imaging data section and each genomic data section has a diameter of 5 mm.

15. 11. The plurality of TMA slides of claim 10, wherein the tissue sample is a sample of a tumor, a regional lymph node, or a distant metastasis.

16. 1. A method for generating a plurality of spatially co-registered data elements from a tissue sample, comprising: Obtaining several cores from the tissue sample; for each core, obtaining from the core a plurality of imaging data sections and a plurality of genomic data sections in alternating fashion along a length of the core, each of the imaging data sections being associated with a respective one of the genomic data sections to form a plurality of adjacent co-registered serial sets of tissue sections; For each of the imaging data sections, (i) obtaining a plurality of multiplex or hyperplex images by repeated labeling with a plurality of fluorescent tags, (ii) analyzing the multiplex or hyperplex images and obtaining image data from the analyzed images, and (iii) identifying a number of regions of interest in the genomic data section associated with the imaging data section based on the image data obtained for the imaging data section. for each genomic data section, performing genomic analysis on each of the regions of interest of the genomic data section to generate genomic data for the genomic data section; The method includes associating, for each co-registered tissue section, as co-registered imaging and genomic data elements: (i) the multiplex image or hyperplex image of the imaging data section of the co-registered tissue section, (ii) the image data of the imaging data section of the co-registered tissue section, and (iii) genomic data of the genomic data section of the co-registered tissue section.

17. 20. The method of claim 16, further comprising storing the co-registered data imaging and genomic elements for each co-registered tissue section in a non-transitory computer readable medium.

18. 17. The method of claim 16, further comprising generating and displaying on a display of the computer system a map comprising a plurality of co-registered data element icons, each of the co-registered data element icons being associated based on a respective one of the co-registered imaging data and genomic data elements.

19. 20. The method of claim 18, further comprising, in response to a selection of a particular one of the co-registered data element icons, displaying on the display one or more of a number of multiplex or hyperplex 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 in association with the particular one of the co-registered data element icons.

20. 17. The method of claim 16, wherein for each imaging data section, the imaging data section is divided into a plurality of fields, and obtaining a plurality of multiplexed or hyperplexed images by repeating labeling with a plurality of fluorescent tags includes obtaining a plurality of images for each of the fields.

21. 20. A non-transitory computer readable medium storing a plurality of spatially co-registered data elements generated by the method of claim 16.

22. 1. A non-transitory computer readable medium storing a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with a set of co-registered tissue sections obtained from adjacent locations of cores taken from a tissue sample and generated from the tissue sections, the non-transitory computer readable medium including an image data section and a genomic data section, the image data section comprising: (i) a plurality of multiplex or hyperplex images obtained from the imaging data sections of the associated set of co-registered tissue sections; (ii) image data generated from the plurality of multiplex or hyperplex images obtained from the imaging data sections of the associated set of co-registered tissue sections; and (iii) genomic data generated from the genomic data sections of the associated set of co-registered tissue sections.

23. 23. The non-transitory computer readable medium of claim 22, wherein in each spatially co-registered data element, genomic data is generated from several regions of interest in the genomic data sections of the associated set of co-registered tissue sections, the regions of interest being identified based on image data generated from a plurality of multiplexed or hyperplexed images obtained from imaging data sections of the associated set of co-registered tissue sections.

24. 1. A method for generating a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with and generated from a set of co-registered tissue sections obtained from adjacent locations of a core taken from a tissue sample, the tissue sections including an image data section and a genomic data section, the method comprising: The method includes, for each pair of co-registered tissue sections, (i) obtaining and storing as part of a data element a plurality of multiplex or hyperplex images from the imaging data sections of the co-registered tissue sections, (ii) generating and storing as part of the data element image data from the plurality of multiplex or hyperplex images, and (iii) generating and storing as part of the data element genomic data from the genomic data sections of associated co-registered tissue sections.

25. 25. The method of claim 24, wherein for each set of co-registered tissue sections, genomic data is generated from several regions of interest in the genomic data sections of the associated set of co-registered tissue sections, the regions of interest being identified based on image data generated from a plurality of multiplexed or hyperplexed images obtained from imaging data sections of the associated set of co-registered tissue sections.

26. 10. The method of claim 1, wherein each imaging data section and each genomic data section has a diameter between 1 cm and 5 cm.

27. 1. A system for generating and storing spatially co-registered data elements, the spatially co-registered data elements being associated with and generated from a set of co-registered tissue sections obtained from adjacent locations of a core taken from a tissue sample, the set of co-registered tissue sections including an image data section and a genomic data section, the system comprising a processor, the processor comprising: an image data generation component structured and configured to generate image data from a plurality of multiplexed or hyperplexed images obtained from the imaging data sections of the associated set of co-registered tissue sections; a region of interest identification component structured and configured to identify a number of regions of interest in genomic data sections of the associated set of co-registered tissue sections based on the generated image data, wherein genomic data is generated from the genomic data sections of the associated co-registered tissue sections based on the identified number of regions of interest; a data element generation component structured and configured to generate and store the spatially co-registered data elements, the spatially co-registered data elements including the plurality of multiplex or hyperplex images, the generated image data, and the generated genomic data.

28. 28. The system of claim 27, wherein the processing device further comprises a map generation component structured and configured to generate a map comprising a plurality of co-registered data element icons, one of the co-registered data element icons being associated based on the spatially co-registered data elements generated by the data element generation component.

29. 30. The system of claim 28, wherein the processing device is structured and configured to, in response to selection of one of the co-registered data element icons, display one or more of a multiplex or hyperplex 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.

Images