Dissecting Spatial Heterogeneity in Gynecological Cancers with M20 Spatial
Gynecological cancers, such as endometrial and ovarian cancers, pose significant threats to women's health. Globally, endometrial and ovarian cancers account for over 420,000 and 320,000 new cases annually, respectively, with high mortality rates[1]. In the United States, an estimated 69,120 uterine corpus and 20,890 ovarian cancer cases will be diagnosed in 2025, leading to approximately 26,590 deaths[2]. These statistics underscore a critical need to improve medicine’s understanding and treatment effectiveness of these malignancies.
Importance of Intratumoral Spatial Heterogeneity
In both endometrial and ovarian cancers, intratumoral spatial heterogeneity significantly influences therapeutic outcomes. Variations in tumor subtypes, molecular profiles, and gene expression patterns within the tumor microenvironment affect treatment responses and are closely associated with cancer progression, drug resistance, and recurrence.
Spatial transcriptomics has emerged as a pivotal technology for studying tumor spatial heterogeneity. The M20 Spatial technology, officially released in October 2023, represents a groundbreaking advancement as the first spatial whole-transcriptome technology based on random primers with versatile sample compatibility (https://www.m20genomics.com/3818.html). This technology enables comprehensive capture of both mRNA and non-coding RNAs across various sample types, including FFPE (formalin-fixed paraffin-embedded) tissues. In November 2024, the technology underwent significant upgrades to enhance sensitivity, resolution, and analytical depth, demonstrating exceptional performance in both murine and human clinical samples.
Application of M20 Spatial in Endometrial and Ovarian Cancers
To assess the performance of M20 Spatial in studying intratumoral spatial heterogeneity in gynecological malignancies, a research group tested M20 Spatial on FFPE samples from endometrial and ovarian cancers. The results highlight its high sensitivity, unbiased transcriptome coverage, and ability to capture comprehensive spatial RNA information in gynecological cancers.
1. High Sensitivity for Gene Detection
M20 Spatial identified 2,772 spatially resolved spots in the endometrial cancer FFPE sample, with a total of 52,927 genes detected. Each spot (50 µm diameter) had a median UMI count of 33,587 and a median gene count of 8,026 (Figure 1).
Figure 1. Left: Eosin staining image of the human endometrial cancer FFPE sample. Middle: Spatial UMI count map. Right: Spatial gene count map.
M20 Spatial identified 3,039 spatial spots for the ovarian cancer FFPE sample, where 55,306 genes were detected. Each spot had a median UMI count of 17,698 and a median gene count of 6,352 (Figure 2).
Figure 2. Left: Eosin staining image of the human ovarian cancer FFPE sample. Middle: Spatial UMI count map. Right: Spatial gene count map.
As evidenced by the detected UMI and gene counts, M20 Spatial demonstrates highly sensitive RNA capture in FFPE samples of endometrial and ovarian cancers. Its performance is comparable to that of higher-end conventional spatial transcriptomic technologies for fresh or frozen samples. Furthermore, due to its whole-transcriptome capture approach, M20 Spatial achieved a total gene count over 50,000 in both sample types, far surpassing spatial transcriptomics technologies that rely on poly(A)-capture or targeted probes.
2. Unbiased Full-Length Transcriptome Coverage
Leveraging the power of random primers, M20 Spatial has achieved unbiased, full-length coverage of gene body sequences without requiring third-generation sequencing technologies. This capability remains highly evident in the endometrial and ovarian cancer FFPE samples tested. Data from these samples showed uniform coverage from the 5’ to 3’ ends of transcripts (Figure. 3-4).
Figure 3. Read coverage along the gene body in the human endometrial cancer FFPE sample.
Figure 4. Read coverage along the gene body in the human ovarian cancer FFPE sample.
3. Comprehensive and Reliable Spatial Non-Coding RNA Information
Since its release in October 2023, the M20 Spatial technology has revolutionized the field of spatial transcriptomics by overcoming the limitation of detecting only coding regions of the transcriptome. This breakthrough enables the comprehensive detection of the entire transcriptome, including both coding and non-coding RNAs. In the current study on endometrial and ovarian cancer FFPE samples, M20 Spatial successfully captured a wide range of RNA molecules. Among these, mRNAs were the most abundant, while long non-coding RNAs (lncRNAs) and other non-coding RNA species were also detected in significant numbers (Figures 5-6).
Figure 5. Detected gene number of different types of RNA captured by M20 Spatial in the human endometrial cancer FFPE sample.
Figure 6. Detected gene number of different types of RNA captured by M20 Spatial in the human ovarian cancer FFPE sample.
Notably lncRNAs, which were the most abundant non-coding RNAs identified, have garnered significant attention for their regulatory roles in tumors. M20 Spatial was capable of capturing lncRNAs across various spatial locations in both endometrial and ovarian cancer samples (Figures 7-8).
Figure 7. The spatial distribution of lncRNAs in the human endometrial cancer FFPE sample.
Figure 8. The spatial distribution of lncRNAs in the human ovarian cancer FFPE sample.
The simultaneous detection of both non-coding and coding RNAs provides a more comprehensive view of gene expression regulation and spatial heterogeneity in gynecological malignancies, facilitating a deeper understanding of the complex biology underlying these cancers.
4. Spatial Heterogeneity of Intratumoral Subpopulations
Through dimensionality reduction and clustering analysis of M20 Spatial data, researchers identified multiple subpopulations based on their transcriptional profiles within both endometrial and ovarian cancer samples. These subpopulations exhibited unique spatial distributions within the tumor samples (Figure 9-10).
In the endometrial cancer sample, specific subpopulations expressed markers such as SCGB2A1[3] and SOX17[4] (Figure 9), which are known to be associated with endometrial cancer and its prognosis.
Figure 9. Unsupervised cell clustering (upper left), spatial projection (upper right) and marker expression in each cluster (lower panel) in the human endometrial cancer FFPE sample.
Similarly, in ovarian cancer, subpopulation markers like TIMP1[5], POSTN[6], CT45A5[7], and CT45A10[7] were identified, highlighted for their relevance to ovarian cancer outcomes (Figure 10).
Figure 10. Unsupervised cell clustering (upper left), spatial projection (upper right) and marker expression in each cluster (lower panel) in the human ovarian cancer FFPE sample.
By identifying these spatially heterogeneous subpopulations M20 Spatial offers crucial insights into the complex biological functions of these cancers.
5. Spatial Heterogeneity of Tumor-Associated Gene Expression
Utilizing the information provided by M20 Spatial, researchers further explored the spatial heterogeneity of gene expression. They first employed ESTIMATE analysis[8] to evaluate tumor purity, immune score, and stromal score in both endometrial and ovarian cancer samples. The results showed that both cancer types exhibited spatially heterogeneous distributions across these three evaluations, indicating that the spatial distribution patterns of malignant cells, immune cells, and stromal cells varied within the tumor microenvironment (Figure 11-12).
Figure 11. ESTIMATE analysis of endometrial cancer FFPE samples
Figure 12. ESTIMATE analysis of ovarian cancer FFPE samples
In addition to these findings, Figures 13-14 illustrate the expression patterns of specific genes in endometrial and ovarian cancer FFPE samples. In endometrial cancer, the presence of mismatch repair (MMR) gene defects is a crucial pathological criterion for disease pathology[9]. In this study, the expression patterns of the four MMR genes were found to differ, with MSH2 and MSH6 being relatively higher than MLH1 and PMS2 (Figure 13). Moreover, genes like CTNNB1[10] and PIK3CA[11], associated with endometrial cancer prognosis, displayed expression and spatial heterogeneity within the tumor (Figure 13).
Figure 13. Expression level of cancer-related genes in endometrial cancer FFPE samples
In the ovarian cancer sample, spatial heterogeneity in the expression of several ovarian cancer-related genes was observed. The expression of ovarian serous carcinoma-associated genes was particularly obvious, with notable expression of genes such as ESR[12] and WT1[13], whose spatial expression patterns showed heterogeneity trends consistent with epithelial cell marker EPCAM. In contrast, the expression of ovarian clear cell carcinoma-related gene HNF1B[13-14], as well as ovarian mucinous carcinoma-associated genes KRT20 (CK20), CDX2, and KRT7 (CK7)[15-16] showed minimal expression in this sample (Figure 14).
Figure 14. Expression level of cancer-related genes in ovarian cancer FFPE samples
Notably, the spatial heterogeneity trends in tumor-related gene expression in both samples aligned closely with the tumor purity pattern in Figures 11-12, thereby validating the reliability of the M20 Spatial experiment and analysis results.
Summary
The application of M20 Spatial technology to FFPE samples from gynecological malignancies highlights its robust performance in capturing comprehensive spatial transcriptomic data. By detecting both coding and non-coding RNAs, M20 Spatial offers rich insights into tumor heterogeneity and molecular characteristics of diseases, positioning it as a powerful tool for advancing cancer research. We are excited to equip clinical researchers with this powerful, reliable, and ever improving tool to accelerate groundbreaking discoveries.
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