Introduction
Main
LCM: spacecraft for analyzing the tumor region of interest
Novel cell sorting technologies: space probe for deeper analysis
Spatial transcriptomics: cataloging the cancer universe
Spatial omics: towards onco-verse
Spatial genomics: tracking the cancer evolution
Spatial epigenomics: interpreting the blueprint of tumor
Spatial proteomics: delineating the cancer landscape
Spatial multi-omics: into the cancer multiverse
The era of spatial (pathology) atlas will lead to next-generation diagnostics and therapeutics
Target | Technology | Findings Types | Biological Findings | Cancer Type | Reference | Stats ( gene/cells (gc) or gene/mm^2 (gm) ) |
---|---|---|---|---|---|---|
RNA | Visium | Biomaker | CNVs (Copy Number Variations), such as MYC and PTEN, occur early stage of cancer | Prostate cancer | Erickson et al. (2022) [138] | 3500 genes / 7850µm^2 (100µm diameter spot) |
Visium | Biomarker | Upregulated cilia gene expression on tumor-normal cell interaction sites | Melanoma | Hunter et al. (2021) [147] | 500-3000 genes / 1600µm^2 (55µm diameter spot) | |
Visium | Biomarker | GATA3 mutation upregulates epithelial-to-mesenchymal transition and angiogenesis | Ductal Carcinoma In Situ (DCIS) of Breast cancer | Nagasawa et al. (2021) [41] | 2928 genes / 1600µm^2 (55µm diameter spot) | |
Visium | Biomarker | Macrophage population enhances inflammatory gene expression, including T-cell recruiting chemokine | Prostate Cancer | Tuong et al. (2021) [180] | ||
Visium | Biomarker & Prognosis | CDH12-enriched tumors indicate poor clinical outcome, but superior response to ICT | Bladder cancer | Gouin III et al. (2021) [181] | >1250 UMIs / 1600µm^2 (55µm diameter spot) | |
Visium | Heterogeneity | Heterogeneous response to 5ARI treatment | Prostate cancer | Joseph et al. (2021) [182] | ||
Visium | Heterogeneity | Cell type deconvolution indicates T cell interaction | HER2-positive breast cancer | Andersson et al. (2021) [133] | 0-200 cells / 7850µm^2 (100µm diameter spot) | |
Visium | Heterogeneity | Heterogeneity with discoveries of novel cell states and unknown multicellular communities | Carcinoma | Luca et al. (2021) [183] | ||
Visium | Heterogeneity | Spatial distribution of hypoxia-related heterogeneity | Pancreatic Ductal Adenocarcinoma (PDAC) | Sun et al. (2021) [141] | 2178-2541 genes / 7850µm^2 (100µm diameter spot) | |
Visium | Heterogeneity | Heterogeneous cell-type composition in each location | Pancreatic cancer | Ma et al. (2022) [184] | ||
Visium | TME | Tumor-specific keratinocyte (TSK) cells serve as a hub for intercellular communication | Cutaneous Squamous Cell Carcinoma (cSSC) | Ji et al. (2020) [144] | ~1200 genes / 9500µm^2 (110µm diameter spot) | |
Visium | TME | Generated Single-cell Tumor Immune Atlas | 13 types of cancer | Nieto et al. (2021) [146] | ||
Visium | TME | Tumor growth when arginase-1 expression by myeloid cells | Neuroblastoma | Van de Velde et al. (2021) [185] | ||
Visium | TME | Atlas of human breast cancer; Immune related composition within tumor | Breast cancer | Wu et al. (2021) [139] | / 1600µm^2 (55µm diameter spot) | |
Visium | TME | Metastatic microenvironment contains immunosuppressive cells which have better metabolic activity. | Colorectal cancer | Wu et al. (2022) [27] | 1-10 cells per spot | |
Visium | TME | Interleukin-10-releasing myeloid cells causes immunosuppressive TME by driving T cell exhaustion | Glioblastoma | Ravi et al. (2022) [145] | 4-22 cells per spot | |
Visium | TME | High FAP and SPP1 leads to therapeutic failure | Colorectal cancer | Qi et al. (2022) [48] | 2051-4937 genes per spot | |
Visium | TME | Tgfbr2 knockout converted TME leading to fibroblast activation | Lung cancer | Dhainaut et al. (2022) [186] | ||
Visium | TME & Heterogeneity | Complex heterogeneous gene expression of lymphoid area close to tumor | Melanoma (Stage III Cutaneous Malignant) | Thrane etal. (2018) [140] | ||
Visium | TME & Heterogeneity | Detection of tumor subclones of each ductal region and T cell adjacent to the tumor | Ductal Carcinoma In Situ (DCIS) of Breast cancer | Wei et al. (2022) [187] | 19-1562 genes / 1600µm^2 (55µm diameter spot) | |
Visium | TME & Heterogeneity & Biomarker | Cell-to-cell interaction exists spatially, creating restricted enriched clusters | Pancreatic Ductal Adenocarcinoma (PDAC) | Moncada et al. (2020) [142] | 1000 genes / 7850 µm^2 (100µm diameter spot) | |
MERFISH | TME | Cancer cells and immune cells interaction leads to mesenchymal-like states | Glioblastoma | Hara et al. (2021) [124] | 135 genes / 14181 cells | |
MERFISH | TME | Heterogeneous niches having different response to immune checkpoint blockade | Hepatocellular carcinoma | Magen et al. (2022) [188] | ||
ISS | Biomarker | Observation of gene mutations and profiling gene expression | Breast cancer | Ke et al. (2013) [58] | 256 genes / 1-35 cells | |
ISS | Biomarker | Uncovering sources of pro-angiogenic signaling, role of mesenchymal-like cancer cells | Glioblastoma | Ruiz-Moreno et al. (2022) [189] | 1.1 million cells | |
ISS | Heterogeneity | Detection of intratumoral heterogeneity with its specific gene expression patterns | Breast cancer | Svedlund et al. (2019) [128] | 91 genes | |
Fisseq | Biomarker | ExSeq enabled better detection of gene expression | Breast cancer | Alon et al. (2021) [190] | 297 genes / 2395 cells | |
RNAscope | Biomarker | Validation of Accurate gene expression detection | Gastric cancer | Tamma et al. (2018) [191] | ||
RNAscope | Heterogeneity | Heterogeneous spatial distribution of HER2 and ER gene expression | Breast cancer | Annaratone et al. (2017) [119] | 38191 cells | |
RNAscope | Heterogeneity | TERT gene expression spatially heterogeneous | 10 human cancer cell lines | Rowland et al. (2019) [120] | 3 copies of genes / 55-204 cells | |
Protein | Nanostring | Biomarker | MEK inhibitor and JAK/STAT3 pathway inhibitor can be a potential solution for tumorigenesis | Medulloblastoma | Zagozewski et al. (2022) [192] | 56 proteins / 12 ROIs |
Nanostring | Biomarker & Heterogeneity | Immune checkpoint protein supporting heterogeneity | Metastatic prostate cancer | Brady et al. (2021) [193] | 100-900 genes, 8-35 proteins / 1200 cells per ROI [168 ROI (500µm size ) ] | |
Nanostring | Prognosis & biomarker | Observation of gene expression in tumor due to adjuvant chemotherapy can further be used for prognosis | Triple Negative Breast Cancer (TNBC) | Kulasinghe et al. (2022) [194] | 68 targets / | |
Nanostring | TME | Discovered fetal-like reprogramming of TME causing Immunosuppressive onco-fetal ecosystem | Hepatocellular Carcinoma | Sharma et al. (2020) [195] | 96 genes / 212000 cells [12 ROI (500µm size ) per slide] | |
Nanostring | TME | Multicellular interaction networks that underlie immunologic and tumorigenic processes | Colorectal cancer | Pelka et al. (2021) [196] | 204 genes / 371223 cells [45 circular regions of interest measuring 500 μm in diameter] | |
Nanostring | TME | Anti-tumor immunity failure due to increased immune suppression within TDLN (Tumor Draining Lymph Node) | Melanoma | Van Krimpen et al. (2022) [197] | 730 genes, 58 protein markers / 5 ROIs per patient | |
Nanostring | TME | Bacterial burden was significantly high in lung tumor, corresponding to oncogenic pathways | Lung cancer | Wong-Rolle et al. (2022) [198] | ||
Nanostring | TME & Biomarker | Mechanism of Myofibroblast avoiding the adaptive immune resopnse | Pancreatic Ductal Adenocarcinoma (PDAC) | Han et al. (2022) [199] | 78 genes, 21 proteins / 24 ROI [24 ROI (300µm size ) ] | |
Nanostring | TME & Biomarker | Gene expression difference between Primary and Lymph node metastasis from oropharyngeal SCC (OPSCC) | Head and Neck Squamous cell Carcinoma | Sadeghirad et al. (2022) [200] | ||
Nanostring | TME & Heterogeneity | Nerves adjacent to tumor exhibits high stress and growth response | Oral cancer | Schmitd et al. (2022) [201] | 8162 genes / All ROI (unknown diameter) | |
Nanostring | TME & Prognosis | Proteomic changes were detected, and can be used for prognosis for neo-adjuvent HER2-targeted therapy | HER2-positive Breast Cancer | McNamara et al. (2021) [202] | 40 biomarkers / 122 samples with 2 ROIs each | |
mIHC | TME & Biomarker | Different response to CSF1R blockade from two distinct TAM(Tumor-associated Macrophage) | Colon Cancer | Zhang et al. (2020) [203] | ||
mIHC | TME | Heterogenous TME (Tumor Microenvironment) has different response to PC (preoperative chemotherapy) | Colorectal Cancer | Che et al. (2021) [204] | ||
mIHC | TME | TAM (Tumor-associated Macrophage) derived from different types of myeloid cells causes heterogeneity | Glioblastoma | Pombo Antunes et al. (2021) [205] | ||
mIHC | TME& Prognosis | PDAC Tumor immune microenvironment reflected a low immunogenic ecosystem and correlates with patient survival. | Pancreatic Ductal Adenocarcinoma (PDAC) | Mi, Haoyang, et al. (2022) [206] | 27 markers | |
mIHC | TME& Prognosis | Leukocyte heterogeneity in PDAC TiME affects patient survival | PDAC | Liudahl, Shannon M., et al. (2021) [207] | 27 markers | |
mIHC | Prognosis | Neoadjuvant chemotherapy response prediction using H&E and mIHC Tissue Microarray data in muscle-invasive bladder cancer (MIBC) | Bladder Cancer | Mi, Haoyang, et al. (2021) [208] | ||
mIHC | Prognosis | Patient survival prediction model using mIHC slides (CD8, CD20, k56) in ovarian cancer | Ovarian Cancer | Nakhli, Ramin, et al.(2023) [209] | 3 markers | |
CODEX | TME | Identification of distinct cellular neighborhoods and their impact on both TME and survival rate | Colorectal Cancer | Schürch et al. (2020) [210] | 56 markers | |
CODEX | TME & Biomarker | Low expression of intrafollicular CD4 expression indicates early failure | Follicular lymphoma | Mondello et al. (2021) [172] | 23 markers | |
CODEX | TME & Biomarker | Discovered spatial biomarker, SpatialScore, which causes pembrolizumab response | Cutaneous T cell lymphomas (CTCL) | Phillips et al. (2021) [211] | 56 markers / 117170 cells | |
CODEX | Biomarker & Prognosis | CDH12-enriched tumors indicate poor clinical outcome, but superior response to ICT | Bladder cancer | Gouin et al. (2021) [181] | 35 markers |
Target | Technology | Finding Type | Biological Findings | Cancer | Reference | Stats ( gene/cells (gc) or gene/mm^2 (gm) ) |
---|---|---|---|---|---|---|
RNA | LCM | Biomarker | AVR7 is a tumor suppressive gene in gastric resistant prostate cancer | Prostate cancer | Cato et al.(2019) [212] | |
LCM | Biomarker | COL1A1 is a promising therapeutic marker in glioma | NSCLC | Baldelli et al(2022) [213] | 6-60 cells | |
LCM | Biomarker | Invasive lobular breast cancer's stroma and CAF pathway discovery and two genes were influenced survival rates. | Invasive lobular breast cancer | Gómez-Cuadrado et al.(2022) [214] | ||
LCM | Biomarker | Detection of EGFR and KRAS mutation with few cells, approximately 50 tumor cells | Lung adenocarcinoma | Chowdhuri et al. (2012) [215] | As few as 50 cells | |
LCM | Biomarker | Efficiency of detecting EGFR and KRAS gene mutations increased significantly with LCM | Lung cancer | Malapelle et al. (2011) [216] | ||
LCM | Heterogeneity | Recurrent nasopharyngeal carcinoma has a differential gene expression from non-recurrent tumor. | Nasopharyngeal cancer | Tay et al.(2022) [217] | ||
SLACS | Biomarker | A-to-I editing events in a specific gene has a correlation with the therapeutic response | Triple Negative Breast Cancer | 3-5 c | ||
DNA | LCM | Biomarker | LOH of 10q23.3 marker for metastatic progression | Node-positive prostate cancer | Rubin et al. (2000) [218] | |
LCM | Biomarker | Allelic loss at chromosome p16 and p53 consistent during cancer progression | Metastatic bladder cancer | Cheng et al. (2001) [219] | ||
LCM | Biomarker | LOH (Loss of Heterozygosity) detection in tumor | Inflammatory Breast Cancer | Bertheau et al. (2001) [220] | 500 cells / 5000 cells | |
LCM | Biomarker | Allelic loss of activated X chromosome related to carcinogenesis and progression | Bladder cancer | Cheng et al. (2004) [221] | 400-600 cells | |
LCM | Biomarker | Observed AMACR (Alpha-methylacyl-coenzyme A racemase) regulation | Colon Adenoma-carcinoma | Zhang et al. (2009) [222] | ||
LCM | Biomarker | Detection of somatic mutations | Various solid tissues and lobular carcinoma | Ellis et al. (2020) [74] | 100-1000 cells | |
LCM | CTC genotyping | CTC genotyping in glioma | glioma | Zhu et al.(2022) [223] | ||
LCM | Heterogeneity | Heterogeneity based on the observations of LOH (Loss of Heterozygosity) | Breast cancer | Wild et al. (2000) [224] | ||
LCM | Heterogeneity | Detection of nonrandom X chromosome inactivation in different regions of same tumor sample | Bladder Carcinoma | Cheng et al. (2002) [225] | 400-600 cells | |
LCM | Heterogeneity | Detection of geneitc divergence during clonal evolution | Cell renal cell carcinoma | Jones et al. (2005) [226] | 400-1000 cells | |
LCM | Heterogeneity | Identification of frequent genetic divergence during metastases | Cutaneous melanoma | Katona et al. (2007) [227] | 400-1000 cells | |
LCM | Heterogeneity | Structural variant analysis with LCM+ grouping tumor types | Post-pubertal testicular germ-cell tumours | Bryce et al.(2019) [73] | ||
LCM | Heterogeneity | Somatic mutation pattern analysis of cancers | Various types | Olafsson et al.(2021) [228] | few hundreds cells | |
SLACS | Heterogeneity | Genomic landscape of the cells in 3D tumor mass | Breast cancer | Kim et al. (2018) [92] | 3-5 cells | |
SLACS | Prognosis | Mapping of clonal changes within hematopoietic lineages to performing prognosis liquid cancer | Myeloma and leukemia | Jeong et al. (2023) [96] | ||
Protein | LCM | Biomarker | Using RRPA and LCM found cellular signaling protein in breast cancer | Breast cancer | Cowherd et al.(2004) [229] | |
LCM | Biomarker | Observation of 12 novel TVM (tumor vascular markers) | Ovarian cancer | Buckanovich et al. (2007) [230] | -2000 cells | |
LCM | Biomarker | Finding a connection with protein and subcellular structure names invadosome about cancer | Cancer specimen | Ezzoukhry et al.(2018) [83] | 312 proteins enriched / 100, 350, 3000, 10000, 40000 cells | |
LCM | Biomarker | NMNT is a marker which affects CAF(Cancer-associated Fibroblast). | glioblastoma | Lam et al.(2022) [87] | 40,000,000 µm^2 for proteomics | |
Epigenome | LCM | Biomarker | Detected 766 up or down-regulated genes with subtype comparisons | Lung adenocarcinoma | Selamat et al. (2012) [231] | 766 genes |
LCM | Heterogeneity | Using RRBS with LCM and found DNA methylation pattern of adrenoncoroticla | adrenocortical carcinoma | Schillebeeckx et al.(2013) [232] | ||
LCM | CTC methylation profiling | Epigenetic features of CTC with LCM | Lung cancer | Zhao et al.(2021) [86] | 10, 50 cells | |
DNA + RNA | LCM | Biomarker | Finding a biomarker for TNBC patients with phase2 neoadjuvant therapy | Breast cancer | Jovanovic et al. (2017) [233] | |
LCM | Heteogeneity | CNV and gene expression profiling in ROI of oral squamous cell carcinoma | Oral squamous cell carcinoma | Chen et al. (2022) [234] | 230 cells | |
LCM | Heterogeneity | Landcape of genomic and transcriptomic of Lung Adenomatous Premalignancy | Lung cancer | Krysan et al. (2019) [235] | ||
LCM | Tumor subtyping & heterogeneity | regrouping cancer subtypes with proteome analysis which leads to overcome therapy resistance and targeting heterogeneity | TNBC | Zhu et al. (2021) [88] | 50-200 cells | |
Protein + RNA | LCM | Biomarker | Identification cancer promoting stromal component in proteomic and transciptomic aspects in canine mammary tumors | canine mammary tumors | Poschel et al (2021) [236] |