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Background: Predicting genomic alterations from routine hematoxylin and eosin (H&E) whole-slide images (WSIs) may help triage molecular testing. Methods: We retrospectively enrolled 437 patients at Osaka Metropolitan University Hospital across 26 cancers, matched with clinical gene-panel data. We curated 1023 binary endpoints across SNV, CNV, and SV categories. We extracted slide embeddings from five pathology foundation models (Prism, GigaPath, Feather, Chief, and Titan) using a unified feature extraction pipeline and benchmarked them using a lightweight downstream Multi-Layer Perceptron (MLP) classifier. Using the best-performing patch feature system, we trained a multi-instance learning model to assess incremental benefit. Results: Titan achieved the highest and most stable transfer performance, with a median endpoint-wise Area Under the Receiver Operating Characteristic curve (AUROC) of 0.77 in the slide benchmarking; at the patch-level, prediction of APC_SNV reached an AUROC of 0.916, and prediction of KRAS_SNV reached an AUROC of 0.811 on the held-out test set. Conclusions: In a heterogeneous clinical gene-panel setting, pathology foundation models can provide strong baseline genomic-prediction signals without additional fine-tuning. We propose a practical, deployment-oriented two-stage workflow: rapid slide-embedding screening to prioritize robust representations and candidate endpoints, followed by patch-level training for high-value tasks where additional performance gains and interpretable regions are clinically worthwhile.

Genetic abnormalities of the fibroblast growth factor receptor 2 (FGFR2) gene, including amplification, fusions, and mutations, have been reported in various solid tumors. While molecular targeted therapies against FGFR2 fusion have been proved to be useful in cholangiocarcinoma, the therapeutic significance of FGFR2 inhibitors remains unclear in other various solid cancers. Genomic and clinical information from solid tumor cancer gene panel testing cases is consolidated in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database in Japan. This study aimed to utilize the C-CAT database to clarify the clinical–pathological significance of FGFR2 abnormalities. A total of 101,231 patients with solid cancer have been registered in the C-CAT database between June 2019 and June 2025. Of the 101,231 cases, 1312 cases with FGFR2 gene abnormalities were analyzed. FGFR2 alterations included amplification in 515 cases, fusion in 280 cases, and mutations in 568 cases. They were detected most frequently in the biliary tract (271 cases), esophagus/stomach (231 cases), and breast (211 cases). Amplification was frequent in the esophagus/stomach (205 cases) and breast (105 cases). Mutations were frequent in the uterus (111 cases), breast (89 cases), and biliary tract (86 cases). Among 515 FGFR2 alteration cases, FGFR2 inhibitors were administered in 85 cases. Of the 85 cases, disease control was achieved in 49 cases, 44 cases of which were biliary tract cancer. FGFR2 might be a promising therapeutic target not only for cholangiocarcinoma with fusion but also for esophagus/stomach cancer and breast cancer with FGFR2 alterations.

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Cancer-associated fibroblasts (CAFs) are a key constituent of the tumor microenvironment. CAFs may affect the development of tumor cells. The critical role of CAFs in the tumor microenvironment is linked to their epigenetic modifications, as a stable yet reversible regulation of cellular phenotypes. Current evidence indicates that their formation and function are closely linked to epigenetic mechanisms. Existing research indicates that the epigenetic alteration abnormalities are triggered by metabolic cues and stabilize the acquired phenotype of CAFs. This process is associated with transcriptional changes and patient outcomes in various tumors, providing a biological rationale and translational potential for reprogramming CAFs. Understanding of epigenetic modifications in CAFs remain insufficient, while DNA methylation in CAFs can alter CAF states through multiple pathways and thereby influence tumor progression. It is necessary to investigate the unique, identifiable epigenetic signatures of CAF. As an epigenetic reader couple histone acetylation to high-output oncogenic transcription; meanwhile, noncoding RNAs modulate CAF formation and therapeutic responses via bidirectional crosstalk between tumor cells and stroma. The interactions between different epigenetic modifications and their underlying regulatory logic may play a crucial role in developing new therapeutic strategies. This review focuses on the roles of DNA methylation, histone acetylation, and enhancer reprogramming in CAFs.

The tumor heterogeneity that is frequently observed in cancer tissues comprises not only cancer cells but also stromal cells in the tumor microenvironment. One of the major components of tumor stroma, i.e., cancer-associated fibroblasts (CAFs), play crucial roles in tumor progression and the tumor response to chemotherapy. The known subtypes of CAFs are antigen-presenting CAFs (apCAFs), myofibroblastic CAFs (myCAFs), and inflammatory CAFs (iCAFs). It has been speculated that (i) the heterogeneity of CAF subtypes might contribute to tumor progression; (ii) cell-to-cell interactions among CAF subtypes in tumors might be associated with the development of various types of carcinomas, and (iii) juxtracrine and/or paracrine signaling from CAFs may play important roles in this development. A clarification of the mechanisms that underlie the tumoral heterogeneity of CAFs could contribute to cancer treatment as precision medicine. This review explains the significance of CAF heterogeneity in tumor microenvironments, especially concerning the CAF subtypes.

The immune checkpoint inhibitor pembrolizumab is effective for the treatment of recurrent cancer with a tumor mutation burden-high (TMB-high) status. Globally, the cutoff value of TMB-high has been set as ≥10 mut/Mb, but the optimal cutoff value of TMB for treating breast cancer (BC) with pembrolizumab has not been identified. We re-evaluated the optimal cutoff value of TMB-high status in BC by using the clinical dataset from Japan’s Center for Cancer Genomics and Advanced Therapeutics (C-CAT) profiling database. We extracted 6403 BC cases that had been enrolled from the C-CAT database of 101,231 cases of various types of cancers. Of all 6403 BC cases, 683 (10.7%) showed TMB ≥ 10 mut/Mb as TMB-high. Of the 683 TMB-high cases, 175 were administered pembrolizumab. The receiver operating characteristic curve indicated that for treating BC with pembrolizumab, a TMB ≥ 18.5 mut/Mb was an adequate cutoff regarding sensitivity and specificity. The BC patients’ overall response rate was 21.4%. The disease control rate was 42.9%. The probability of time-to-treatment failure was significantly better in the BC cases with TMB ≥ 18.5 mut/Mb versus those with TMB < 18.5 mut/Mb (p = 0.007). These findings suggested that the optimal cutoff value of the TMB for treating breast cancer with pembrolizumab might be ≥18.5 mut/Mb.

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC’s heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.

Background/Objectives: The neurotrophic tropomyosin receptor kinase (NTRK) genes NTRK1, NTRK2, and NTRK3 encode tyrosine kinase receptors, and their fusion genes are known as the oncogenic driver genes for cancer. This study aimed to compare the diagnostic ability of NTRK fusion among five types of multi-cancer genome profiling tests (multi-CGP tests) and determine a useful multi-CGP test for NTRK fusion, recorded in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database in Japan. This study aimed to compare the diagnostic results for NTRK fusions among the five different CGP tests. Methods: A total of 88,688 tumor cases were enrolled in the C-CAT profiling database from 2019 to 2024. The detection frequency of NTRK fusion genes was compared to the results for five multi-CGP tests: NCC Oncopanel, FoundationOne CDx (F1), FoundationOne Liquid (F1L), GenMineTOP (GMT), and Guardant360. Results: NTRK fusion genes were detected in 175 (0.20%) of the 88,688 total cases. GMT, which is equipped with RNA sequencing function, frequently detected NTRK fusion genes (20 of 2926 cases; 0.68%) in comparison with the other four multi-CGP tests that do not have RNA sequencing analysis. GMT showed significantly (p < 0.05) higher diagnostic ability for NTRK fusions compared with the other four multi-CGP tests. Especially, NTRK2 fusion was significantly (p < 0.001) more highly determined by GMT than it was by the other four multi-CGP tests. The detection rates for FGFR1 and FGFR3 were significantly higher in GMT than in other multi-CGP tests. In contrast, the detection rates of the ALK and RET fusion genes were significantly higher in F1L. Conclusions: GMT, which is equipped with RNA sequencing analysis, might show a useful diagnostic ability for NTRK fusions, especially for NTRK2 fusion genes.

Background: Lymph node (LN) metastasis is one of the most frequent metastatic patterns in patients with gastric cancer (GC); however, few genes predictive of LN status in GC have been identified. Aims: We aimed to identify candidate genes associated with LN metastasis by analyzing the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database and performing immunohistochemical analysis of GC cases at our hospital. Patients and Methods: A total of 2028 GCs from the C-CAT database were enrolled to identify genetic alterations. A total of 360 GC patients who underwent gastrectomy at our hospital were enrolled to examine the clinical significance of CCNE1 expression via an immunohistochemical study. Results: A total of 977 cases out of 2028 GC patients showed LN metastasis. Genetic alterations of ERBB2, CCNE1, MYC, ZNF217, and GNAS were frequent in the LN metastasis group. CCNE1-positive expression was found in 108 (30.0%) of the 360 GC samples. LN metastasis was significantly (p = 0.01) more frequent in CCNE1-positive patients. In addition, the CCNE1-positive group had a significantly (p < 0.001) poorer prognosis than the CCNE1-negative group, which was especially evident for GC patients at stage I. CCNE1 positivity was significantly (p < 0.001) correlated with postoperative recurrence. Conclusions: CCNE1 gene amplification is associated with LN metastasis of GC.

Background Cancer‐associated fibroblasts (CAFs) have been reported to be tumor‐specific cells. We have recently reported that thrombospondin‐4 (THBS4) expression is exclusive to CAFs. This study aimed to clarify whether the identification of circulating CAFs (cir‐CAFs) by THBS4 is detectable in the blood of gastric cancer (GC) patients, and whether cir‐CAFs are useful for the screening test of GC. Materials and Methods CAFs and normal fibroblasts (NFs) were respectively established from 17 GC specimens. A total of 24 healthy volunteers and 77 GC patients were enrolled. Flow cytometric analysis was performed using anti‐THBS4 antibody. The sensitivity and specificity of THBS4‐positive cir‐CAFs for detection of GC were calculated. Results THBS4+ cells showed ovoid‐like cells and expressed THBS4. THBS4+ expression was significantly (p = 0.014) higher on CAFs than NFs. GC patients had a significantly (p = 0.0323) higher average number of THBS4‐positive cir‐CAFs than healthy volunteers: 212 cells versus 6.4 cells. The ROC curve indicated that 27 THBS4‐positive cells per 10,000 blood cells was an adequate cutoff for GC diagnosis. The sensitivity and specificity of cir‐CAFs were 76.6% and 100%, respectively. In contrast, the sensitivities of CEA and CA19‐9 were only 22.1% and 9.1%, respectively. The sensitivity of cir‐CAFs was high even for Stage I GC, at 73.5%, while the sensitivity of CEA and CA19‐9 was low at 14.7% and 0%, respectively. Conclusion THBS4‐positive cir‐CAFs are detectable in the blood of GC patients. The cir‐CAFs might be a useful tumor marker in a GC screening test, especially for early‐stage GC.