Impact of tumor localization and molecular subtypes on the prognostic and predictive significance of p53 expression in gastric cancer

We investigated the prognostic and predictive impact of p53 expression for gastric cancer (GC) patients treated without or with preoperative chemotherapy (CTx) and its relationship with specific molecular GC subtypes. Specimens from 694 GC patients (562 surgical resection specimens without or after CTx, 132 biopsies before CTx) were analyzed by p53 immunohistochemistry. High (H) and low (L) microsatellite instability (MSI) and Epstein–Barr virus positivity were determined previously. Our results show that aberrant p53 expression was a negative prognostic factor in uni- and multivariable analysis in the resection specimens cohort (each p < 0.01). Subgroup analysis showed the strongest prognostic effect for patients with distally located tumors or no CTx treatment. In the biopsy cohort before CTx, p53 did not predict response or survival. p53 expression was significantly different among the molecular subtypes in surgical resection and bioptic specimens with strong association of altered p53 with MSI-L. Patients with MSI-H and aberrant p53 showed the worst survival in the biopsy cohort. In conclusion, the prognostic impact of p53 in GC differs according to tumor localization and CTx. Altered p53 is characteristic for MSI-L, and the p53 status in biopsies before CTx delineates MSI-H subtypes with inverse prognostic impact

Elevated microsatellite instability at selected tetranucleotide (EMAST) repeats in gastric cancer: a distinct microsatellite instability type with potential clinical impact?

We investigated the clinical impact of elevated microsatellite instability at selected tetranucleotide (EMAST) repeats in the context of neoadjuvant chemotherapy (CTx) in gastric/gastro-oesophageal adenocarcinomas. We analysed 583 resected tumours (272 without and 311 after CTx) and 142 tumour biopsies before CTx. If at least two or three of the five tetranucleotide repeat markers tested showed instability, the tumours were defined as EMAST (2+) or EMAST (3+), respectively. Expression of mismatch repair proteins including MSH3 was analysed using immunohistochemistry. Microsatellite instability (MSI) and Epstein-Barr virus (EBV) positivity were determined using standard assays. EMAST (2+) and (3+) were detected in 17.8 and 11.5% of the tumours, respectively. The frequency of EMAST (2+) or (3+) in MSI-high (MSI-H) tumours was 96.2 or 92.5%, respectively, demonstrating a high overlap with this molecular subtype, and the association of EMAST and MSI status was significant (each overall p < 0.001). EMAST (2+ or 3+) alone in MSI-H and EBV-negative tumours demonstrated only a statistically significant association of EMAST (2+) positivity and negative lymph node status (42.3% in EMAST (2+) and 28.8% in EMAST negative, p = 0.045). EMAST alone by neither definition was significantly associated with overall survival (OS) of the patients. The median OS for EMAST (2+) patients was 40.0 months (95% confidence interval [CI] 16.4-63.6) compared with 38.7 months (95% CI 26.3-51.1) for the EMAST-negative group (p = 0.880). The median OS for EMAST (3+) patients was 46.7 months (95% CI 18.2-75.2) and 38.7 months (95% CI 26.2-51.2) for the negative group (p = 0.879). No statistically significant association with response to neoadjuvant CTx was observed (p = 0.992 and p = 0.433 for EMAST (2+) and (3+), respectively). In conclusion, our results demonstrate a nearly complete intersection between MSI-H and EMAST and they indicate that EMAST alone is not a distinct instability type associated with noticeable clinico-pathological characteristics of gastric carcinoma patients

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

BACKGROUND Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability

Diverse ‘just-right’ levels of chromosomal instability and their clinical implications in neoadjuvant treated gastric cancer

BACKGROUND: The Cancer Genome Atlas (TCGA) consortium described EBV positivity(+), high microsatellite instability (MSI-H), genomic stability (GS) and chromosomal instability (CIN) as molecular subtypes in gastric carcinomas (GC). We investigated the predictive and prognostic value of these subtypes with emphasis on CIN in the context of neoadjuvant chemotherapy (CTx) in GC. METHODS: TCGA subgroups were determined for 612 resected adenocarcinomas of the stomach and gastro-oesophageal junction (291 without, 321 with CTx) and 143 biopsies before CTx. EBV and MSI-H were analysed by standard assays. CIN was detected by multiplex PCRs analysing 22 microsatellite markers. Besides the TCGA classification, CIN was divided into four CIN-subgroups: low, moderate, substantial, high. Mutation profiling was performed for 52 tumours by next-generation sequencing. RESULTS: EBV(+) (HR, 0.48; 95% CI, 0.23–1.02), MSI-H (HR, 0.56; 95% CI, 0.35–0.89) and GS (HR, 0.72; 95% CI, 0.45–1.13) were associated with increased survival compared to CIN in the resected tumours. Considering the extended CIN-classification, CIN-substantial was a negative prognostic factor in uni- and multivariable analysis in resected tumours with CTx (each p < 0.05). In biopsies before CTx, CIN-high predicted tumour regression (p = 0.026), but was not prognostically relevant. CONCLUSION: A refined CIN classification reveals tumours with different biological characteristics and potential clinical implications

Significant Tumor Regression after Neoadjuvant Chemotherapy in Gastric Cancer, but Poor Survival of the Patient? Role of MHC Class I Alterations

We aimed to determine the clinical and prognostic relevance of allelic imbalance (AI) of the major histocompatibility complex (MHC) class I genes, encompassing the human leukocyte antigen (HLA) class I and beta-2 microglobulin (B2M) genes, in the context of neoadjuvant platinum/fluoropyrimidine chemotherapy (CTx). Biopsies before CTx were studied in 158 patients with adenocarcinoma of the stomach or gastroesophageal junction. The response was histopathologically evaluated. AI was detected by multiplex PCRs analysis of four or five microsatellite markers in HLA and B2M regions, respectively. AI with no marker was significantly associated with response or survival. However, subgroup analysis revealed differences. AI at marker D6S265, close to the HLA-A gene, was associated with an obvious increased risk in responding (HR, 3.62; 95% CI, 0.96–13.68, p = 0.058) but not in non-responding patients (HR, 0.92; 95% CI, 0.51–1.65, p = 0.773). Markers D6S273 and D6S2872 showed similar results. The interaction between AI at D6S265 and response to CTx was significant in a multivariable analysis (p = 0.010). No associations were observed for B2M markers. Our results underline the importance of intact neoantigen presentation specifically for responding patients and may help explain an unexpectedly poor survival of a patient despite significant tumor regression after neoadjuvant platinum/fluoropyrimidine CTx

Significant Tumor Regression after Neoadjuvant Chemotherapy in Gastric Cancer, but Poor Survival of the Patient? Role of MHC Class I Alterations

We aimed to determine the clinical and prognostic relevance of allelic imbalance (AI) of the major histocompatibility complex (MHC) class I genes, encompassing the human leukocyte antigen (HLA) class I and beta-2 microglobulin (B2M) genes, in the context of neoadjuvant platinum/fluoropyrimidine chemotherapy (CTx). Biopsies before CTx were studied in 158 patients with adenocarcinoma of the stomach or gastroesophageal junction. The response was histopathologically evaluated. AI was detected by multiplex PCRs analysis of four or five microsatellite markers in HLA and B2M regions, respectively. AI with no marker was significantly associated with response or survival. However, subgroup analysis revealed differences. AI at marker D6S265, close to the HLA-A gene, was associated with an obvious increased risk in responding (HR, 3.62; 95% CI, 0.96&ndash;13.68, p = 0.058) but not in non-responding patients (HR, 0.92; 95% CI, 0.51&ndash;1.65, p = 0.773). Markers D6S273 and D6S2872 showed similar results. The interaction between AI at D6S265 and response to CTx was significant in a multivariable analysis (p = 0.010). No associations were observed for B2M markers. Our results underline the importance of intact neoantigen presentation specifically for responding patients and may help explain an unexpectedly poor survival of a patient despite significant tumor regression after neoadjuvant platinum/fluoropyrimidine CTx

Post-neoadjuvant assessment of tumour budding according to ITBCC subgroups delivers stage- and regression-grade independent prognostic information in intestinal-type gastric adenocarcinoma

Tumour budding (TB) has been associated with adverse clinicopathological factors and poor survival in a plethora of therapy-naive carcinoma entities including gastric adenocarcinoma (GC). As conventional histopathological grading is usually omitted in the post-neoadjuvant setting of GC, our study aimed to investigate the prognostic impact of TB in GCs resected after neoadjuvant therapy. We evaluated TB according to the criteria from the International Tumour Budding Consensus Conference (ITBCC) in 167 post-neoadjuvant resections of intestinaltype GC and correlated the results with overall survival (OS) and clinicopathological parameters. GCs were categorised into Bd1 (0-4 buds, low TB), Bd2 (5-9 buds, intermediate TB), and Bd3 (>= 10 buds, high TB). Carcinomas with intermediate and high TB were significantly enriched in higher ypTNM stages and strongly associated with reduced 5-year OS in univariable analyses (p < 0.001). In multivariable analyses including sex, age, resection status, UICC stage, and tumour regression grading, TB remained a stage-independent predictor of survival (p < 0.001, hazard ratio Bd2: 2.60, Bd3: 4.74). The assessment of TB according to the ITBCC criteria provides valuable prognostic information in the post-neoadjuvant setting of intestinal-type GC and may be a considerable substitute for the conventional grading system in GCs after neoadjuvant therapy

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning

BACKGROUND: Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS: Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS: On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS: Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides. METHODS: In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0·5. FINDINGS: Across the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0·597 (95% CI 0·522–0·737) to 0·836 (0·795–0·880) and EBV status in five of eight cohorts, with AUROCs ranging from 0·819 (0·752–0·841) to 0·897 (0·513–0·966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0·723 (95% CI 0·676–0·794) to 0·863 (0·747–0·969) for detection of microsatellite instability and from 0·672 (0·403–0·989) to 0·859 (0·823–0·919) for detection of EBV status. INTERPRETATION: Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. FUNDING: German Cancer Aid and German Federal Ministry of Health