Objective: We investigated microsatellite instability (MSI) status and programed cell death ligand 1 (PD-L1) expression as predictors of prognosis and responsiveness to chemotherapy for stage II/III gastric cancer. Background: The clinical implications of MSI status and PD-L1 expression in gastric cancer have not been well-elucidated. Methods: Tumor specimens and clinical information were collected from patients enrolled in the CLASSIC trial—a randomized controlled study of capecitabine plus oxaliplatin-based adjuvant chemotherapy. Five quasi-monomorphic mononucleotide markers were used to assess tumor MSI status. PD-L1 expressions of tumor and stromal immune cells were evaluated using immunohistochemistry. Results: Of 592 patients, 40 (6.8%) had MSI-high (MSI-H) tumors. Among 582 patients available for immunohistochemistry evaluation, PD-L1 was positive in tumor cells (tPD-L1) of 16 patients (2.7%) and stromal immune cells (sPD-L1) of 165 patients (28.4%). Multivariable analysis of disease-free survival (DFS) showed that MSI-H and sPD-L1-positivity were independent prognostic factors [hazard ratio 0.301 (0.123–0.736), 0.714 (0.514–0.991); P = 0.008, 0.044), as were receiving chemotherapy, age, tumor grade, and TNM stage. Although adjuvant chemotherapy improved DFS in the microsatellite-stable (MSS) group (5-year DFS: 66.8% vs 54.1%; P = 0.002); no benefit was observed in the MSI-H group (5-year DFS: 83.9% vs 85.7%; P = 0.931). In the MSS group, sPD-L1-negative patients, but not sPD-L1-positive patients, had significant survival benefit from adjuvant chemotherapy compared with surgery only (5-year DFS: 66.1% vs 50.7%; P = 0.001). Conclusion: MSI status and PD-L1 expression are clinically actionable biomarkers for stratifying patients and predicting benefit from adjuvant chemotherapy after D2 gastrectomy for stage II/III gastric cancer.

Background Tumour-infiltrating lymphocytes (TILs) have a strong prognostic significance, particularly in triple-negative breast cancer (TNBC). One important source of TILs in breast cancer is tertiary lymphoid structures (TLSs). Objective To carry out a histological analysis of surgically resected TNBC to identify the location of TLSs, the relationship between TLSs and TILs and their prognostic significance in TNBC. Methods We retrospectively analysed 769 patients with TNBC. Results TILs were defined as the percentage of stroma of invasive carcinoma infiltrated by lymphocytes. TLSs were mainly present within adjacent terminal duct lobular units and around in situ components. TNBC with higher levels of TILs showed a higher nuclear grade, lower lymphovascular invasion, less accompanying in situ component, a homogeneous growth pattern, necrosis in invasive areas, low levels of tumour stroma, high levels of peritumoral lymphocytic infiltration and moderate to abundant TLSs in adjacent tissue. TILs, the degree of peritumoral lymphocytic infiltration and adjacent TLSs were prognostic factors for disease-free and overall survival. Although the TIL level did not have a prognostic value in stage I, it added significant prognostic information for stages II and III. Conversely, patients with high levels of TILs did not show prognostic differences according to the pTNM stage. Patients with high levels of TILs (>60%) and moderate to abundant TLSs had significantly better disease-free survival than those with high levels of TILs but none or few TLSs. Conclusions TLSs are frequently present in TNBC and are closely associated with TILs. TILs provide additional prognostic information in patients with TNBC with a higher pTNM stage.

Markers of the epithelial-to-mesenchymal transition (EMT) in gastric tumor tissues are associated with poor patient outcomes. We performed a screen to identify pharmacologic compounds that kill gastric cancer cells with EMT-associated gene expression patterns and investigate their mechanisms. We identified 29 gastric cancer cell lines with a gene expression signature previously associated with an EMT subtype, based on data from RNA sequence analyses, and confirmed the mesenchymal phenotypes of 7 lines (Hs746T, SNU1750, MKN1, SK4, SNU484, SNU668, and YCC11), based on invasive activity and protein markers. We screened 1,345 compounds for their ability to kill cells with the EMT signature compared with cell lines without this pattern. We tested the effects of identified compounds in BALB/c nude mice bearing GA077 tumors; mice were given intraperitoneal injections of the compound or vehicle (control) twice daily for 24 days and tumor growth was monitored. Proteins associated with the toxicity of the compounds were overexpressed in MKN1 and SNU484 cells or knocked down in MKN45 and SNU719 using small interfering RNAs. We performed immunohistochemical analyses of 942 gastric cancer tissues and investigated associations between EMT markers and protein expression patterns. The nicotinamide phosphoribosyltransferase inhibitor FK866 killed 6 of 7 gastric cancer cell lines with EMT-associated gene expression signatures but not gastric cancer cells without this signature. The 6 EMT-subtype gastric cell lines expressed significantly low levels of nicotinic acid phosphoribosyltransferase (NAPRT), which makes the cells hypersensitive to nicotinamide phosphoribosyltransferase inhibition. Gastric cell lines that expressed higher levels of NAPRT, regardless of EMT markers, were sensitized to FK866 after knockdown of NAPRT, whereas overexpression of NAPRT in deficient EMT cell lines protected them from FK866-mediated toxicity. Administration of FK866 to nude mice with tumors grown from GA077 cells (human gastric cancer tumors of the EMT subtype) led to tumor regression in 2 weeks; FK866 did not affect tumors grown from MKN45 cells without the EMT expression signature. Loss of NAPRT might promote the EMT, because it stabilizes β-catenin. We correlated the EMT gene expression signature with lower levels of NAPRT in 942 gastric tumors from patients; we also found lower levels of NAPRT mRNA in colorectal, pancreatic, and lung adenocarcinoma tissues with the EMT gene expression signature. FK866 selectively kills gastric cancer cells with an EMT gene expression signature by inhibiting nicotinamide phosphoribosyltransferase in cells with NAPRT deficiency. Loss of NAPRT expression, frequently through promoter hypermethylation, is observed in many gastric tumors of the EMT subtype. FK866 might be used to treat patients with tumors of this subtype.

The Gleason grading system, currently the most powerful prognostic predictor of prostate cancer, is based solely on the tumor’s histological architecture and has high inter-observer variability. We propose an automated Gleason scoring system based on deep neural networks for diagnosis of prostate core needle biopsy samples. To verify its efficacy, the system was trained using 1133 cases of prostate core needle biopsy samples and validated on 700 cases. Further, system-based diagnosis results were compared with reference standards derived from three certified pathologists. In addition, the system’s ability to quantify cancer in terms of tumor length was also evaluated via comparison with pathologist-based measurements. The results showed a substantial diagnostic concordance between the system-grade group classification and the reference standard (0.907 quadratic-weighted Cohen’s kappa coefficient). The system tumor length measurements were also notably closer to the reference standard (correlation coefficient, R = 0.97) than the original hospital diagnoses (R = 0.90). We expect this system to assist pathologists to reduce the probability of over- or under-diagnosis by providing pathologist-level second opinions on the Gleason score when diagnosing prostate biopsy, and to support research on prostate cancer treatment and prognosis by providing reproducible diagnosis based on the consistent standards.

The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3–82.7%), the Cohen’s kappa score (κ) was 0.650 (95% CI: 0.570–0.730), and the quadratic-weighted kappa score (κ_quad) was 0.897 (95% CI: 0.815–0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system’s accuracy reached 67.4% (95% CI: 63.2–71.6%), κ 0.553 (95% CI: 0.495–0.610), and the κ_quad 0.880 (95% CI: 0.822–0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations.