Endoscopic mucosal biopsies of primary gastric cancers (GCs) are used to guide diagnosis, biomarker testing and treatment. Spatial intratumoural heterogeneity (ITH) may influence biopsy-derived ...information. We aimed to study ITH of primary GCs and matched lymph node metastasis (LN
).
GC resection samples were annotated to identify primary tumour superficial (PT
), primary tumour deep (PT
) and LN
subregions. For each subregion, we determined (1) transcriptomic profiles (NanoString 'PanCancer Progression Panel', 770 genes); (2) next-generation sequencing (NGS, 225 gastrointestinal cancer-related genes); (3) DNA copy number profiles by multiplex ligation-dependent probe amplification (MLPA, 16 genes); and (4) histomorphological phenotypes.
NanoString profiling of 64 GCs revealed no differences between PT
and PT
, while 43% of genes were differentially expressed between PT
versus PT
and 38% in PT
versus LN
. Only 16% of genes were differently expressed between PT
and LN
. Several genes with therapeutic potential (eg
,
and
) were overexpressed in LN
and PT
compared with PT
. NGS data revealed orthogonal support of NanoString results with 40% mutations present in PT
and/or LN
, but not in PT
. Conversely, only 6% of mutations were present in PT
and were absent in PT
and LN
. MLPA demonstrated significant ITH between subregions and progressive genomic changes from PT
to PT
/LN
.
In GC, regional lymph node metastases are likely to originate from deeper subregions of the primary tumour. Future clinical trials of novel targeted therapies must consider assessment of deeper subregions of the primary tumour and/or metastases as several therapeutically relevant genes are only mutated, overexpressed or amplified in these regions.
Familial clustering is seen in 10 % of gastric cancer cases and approximately 1-3 % of gastric cancer arises in the setting of hereditary diffuse gastric cancer (HDGC). In families with HDGC, gastric ...cancer presents at young age. HDGC is predominantly caused by germline mutations in CDH1 and in a minority by mutations in other genes, including CTNNA1. Early stage HDGC is characterized by a few, up to dozens of intramucosal foci of signet ring cell carcinoma and its precursor lesions. These include in situ signet ring cell carcinoma and pagetoid spread of signet ring cells. Advanced HDGC presents as poorly cohesive/diffuse type carcinoma, normally with very few typical signet ring cells, and has a poor prognosis. Currently, it is unknown which factors drive the progression towards aggressive disease, but it is clear that most intramucosal lesions will not have such progression.Immunohistochemical profile of early and advanced HDGC is often characterized by abnormal E-cadherin immunoexpression, including absent or reduced membranous expression, as well as "dotted" or cytoplasmic expression. However, membranous expression of E-cadherin does not exclude HDGC. Intramucosal HDGC (pT1a) presents with an "indolent" phenotype, characterized by typical signet ring cells without immunoexpression of Ki-67 and p53, while advanced carcinomas (pT > 1) display an "aggressive" phenotype with pleomorphic cells, that are immunoreactive for Ki-67 and p53. These features show that the IHC profile is different between intramucosal and more advanced HDGC, providing evidence of phenotypic heterogeneity, and may help to define predictive biomarkers of progression from indolent to aggressive, widely invasive carcinomas.
Patients with Epstein-Barr virus-positive gastric cancers or those with microsatellite instability appear to have a favourable prognosis. However, the prognostic value of the chromosomal status ...(chromosome-stable (CS) versus chromosomal instable (CIN)) remains unclear in gastric cancer.
Gene copy number aberrations (CNAs) were determined in 16 CIN-associated genes in a retrospective study including test and validation cohorts of patients with gastric cancer. Patients were stratified into CS (no CNA), CINlow (1-2 CNAs) or CINhigh (3 or more CNAs). The relationship between chromosomal status, clinicopathological variables, and overall survival (OS) was analysed. The relationship between chromosomal status, p53 expression, and tumour infiltrating immune cells was also assessed and validated externally.
The test and validation cohorts included 206 and 748 patients, respectively. CINlow and CINhigh were seen in 35.0 and 15.0 per cent of patients, respectively, in the test cohort, and 48.5 and 20.7 per cent in the validation cohort. Patients with CINhigh gastric cancer had the poorest OS in the test and validation cohorts. In multivariable analysis, CINlow, CINhigh and pTNM stage III-IV (P < 0.001) were independently associated with poor OS. CIN was associated with high p53 expression and low immune cell infiltration.
CIN may be a potential new prognostic biomarker independent of pTNM stage in gastric cancer. Patients with gastric cancer demonstrating CIN appear to be immunosuppressed, which might represent one of the underlying mechanisms explaining the poor survival and may help guide future therapeutic decisions.
Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation ...will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.
To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.
A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.
A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.
Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.
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•Structural changes in metastasis-free lymph nodes show clinical importance.•Artificial intelligence could facilitate the analysis of lymph node images.•Multi-scale deep learning models are frequently chosen for pathology tasks.•Further research is warranted for clinically interpretable lymph node characterization.
Although radical surgery remains the cornerstone of cure in resectable gastric cancer, survival remains poor. Current evidence-based (neo)adjuvant strategies have shown to improve outcome, including ...perioperative chemotherapy, postoperative chemoradiotherapy and postoperative chemotherapy. However, these regimens suffer from poor patient compliance, particularly in the postoperative phase of treatment. The CRITICS-II trial aims to optimize preoperative treatment by comparing three treatment regimens: (1) chemotherapy, (2) chemotherapy followed by chemoradiotherapy and (3) chemoradiotherapy.
In this multicentre phase II non-comparative study, patients with clinical stage IB-IIIC (TNM 8th edition) resectable gastric adenocarcinoma are randomised between: (1) 4 cycles of docetaxel+oxaliplatin+capecitabine (DOC), (2) 2 cycles of DOC followed by chemoradiotherapy (45Gy in combination with weekly paclitaxel and carboplatin) or (3) chemoradiotherapy. Primary endpoint is event-free survival, 1 year after randomisation (events are local and/or regional recurrence or progression, distant recurrence, or death from any cause). Secondary endpoints include: toxicity, surgical outcomes, percentage radical (R0) resections, pathological tumour response, disease recurrence, overall survival, and health related quality of life. Exploratory endpoints include translational studies on predictive and prognostic biomarkers.
The aim of this study is to select the most promising among three preoperative treatment arms in patients with resectable gastric adenocarcinoma. This treatment regimen will subsequently be compared with the standard therapy in a phase III trial.
clinicaltrials.gov NCT02931890 ; registered 13 October 2016. Date of first enrolment: 21 December 2017.
In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies ...have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.
...in 2011 the International Collaboration on Cancer Reporting (ICCR) was formed to harmonize the data sets, protocols, and checklists for pathologic reports of various cancers globally. ...Neuroendocrine tumors, nonepithelial malignancies, and secondary tumors are excluded from this data set. Because there are significant differences in the core and noncore elements, responses and commentaries between gastrectomy and endoscopic resection for gastric carcinomas, the chair and DAC in consultation with the ICCR DSC decided to separate the data sets for these 2 pathologic specimen types to maximize clarity and usability. Core Elements Neoadjuvant Therapy.-Assessment of treatment response is required for gastrectomy from patients with preoperative chemotherapy/chemoradiation. ...history of neoadjuvant therapy should be documented. ...there are also studies demonstrating no additional benefit from postoperative chemoradiation in patients after D2 and D1+ nodal dissection.10 Downstaging of lymph node metastases and/or reduction of tumor size by preoperative chemotherapy/chemoradiation have been reported by multiple clinical trials.6,11 Downstaging of the tumor may lead to a higher rate of R0 resection and increased survival.
Introduction
The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains ...controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC.
Objective
We aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility.
Methods
We trained a binary classifier on intestinal and diffuse type GC whole slide images for a subset of the TCGA cohort (
N
= 166) using attention-based multiple instance learning. The ground truth of 166 GC was obtained by two expert pathologists. We deployed the model on two external GC patient cohorts, one from Europe (
N
= 322) and one from Japan (
N
= 243). We assessed classification performance using the Area Under the Receiver Operating Characteristic Curve (AUROC) and prognostic value (overall, cancer specific and disease free survival) of the DL-based classifier with uni- and multivariate Cox proportional hazard models and Kaplan–Meier curves with log-rank test statistics.
Results
Internal validation using the TCGA GC cohort using five-fold cross-validation achieved a mean AUROC of 0.93 ± 0.07. External validation showed that the DL-based classifier can better stratify GC patients' 5-year survival compared to pathologist-based Laurén classification for all survival endpoints, despite frequently divergent model-pathologist classifications. Univariate overall survival Hazard Ratios (HRs) of pathologist-based Laurén classification (diffuse type versus intestinal type) were 1.14 (95% Confidence Interval (CI) 0.66–1.44,
p
-value = 0.51) and 1.23 (95% CI 0.96–1.43,
p
-value = 0.09) in the Japanese and European cohorts, respectively. DL-based histology classification resulted in HR of 1.46 (95% CI 1.18–1.65,
p
-value < 0.005) and 1.41 (95% CI 1.20–1.57,
p
-value < 0.005), in the Japanese and European cohorts, respectively. In diffuse type GC (as defined by the pathologist), classifying patients using the DL diffuse and intestinal classifications provided a superior survival stratification, and demonstrated statistically significant survival stratification when combined with pathologist classification for both the Asian (overall survival log-rank test
p
-value < 0.005, HR 1.43 (95% CI 1.05–1.66,
p
-value = 0.03) and European cohorts (overall survival log-rank test
p
-value < 0.005, HR 1.56 (95% CI 1.16–1.76,
p
-value < 0.005)).
Conclusion
Our study shows that gastric adenocarcinoma subtyping using pathologist’s Laurén classification as ground truth can be performed using current state of the art DL techniques. Patient survival stratification seems to be better by DL-based histology typing compared with expert pathologist histology typing. DL-based GC histology typing has potential as an aid in subtyping. Further investigations are warranted to fully understand the underlying biological mechanisms for the improved survival stratification despite apparent imperfect classification by the DL algorithm.
Colon cancer is currently staged with CT. However, MRI is superior in the detection of colorectal liver metastasis, and MRI is standard in local staging of rectal cancer. Optimal (local) staging of ...colon cancer could become crucial in selecting patients for neoadjuvant treatment in the near future (Fluoropyrimidine Oxaliplatin and Targeted Receptor Preoperative Therapy trial).
The purpose of this study was to evaluate the diagnostic performance of MRI for local staging of colon cancer.
This was a retrospective study.
The study was conducted at the Maastricht University Medical Centre.
In total, 55 patients with biopsy-proven colon carcinoma were included.
All of the patients underwent an MRI (1.5-tesla; T2 and diffusion-weighted imaging) of the abdomen and were retrospectively analyzed by 2 blinded, independent readers. Histopathology after resection was the reference standard. Both readers evaluated tumor characteristics, including invasion through bowel wall (T3/T4 tumors), invasion beyond bowel wall of ≥5 mm and/or invasion of surrounding organs (T3cd/T4), serosal involvement, extramural vascular invasion, and malignant lymph nodes (N+). Interobserver agreement was compared using κ statistics.
MRI had a high sensitivity (72%-91%) and specificity (84%-89%) in detecting T3/T4 tumors (35/55) and a low sensitivity (43%-67%) and high specificity (75%-88%) in detecting T3cd/T4 tumors (15/55). For detecting serosal involvement and extramural vascular invasion, MRI had a high sensitivity and moderate specificity, as well as a moderate sensitivity and specificity in the detection of nodal involvement. Interobserver agreements were predominantly good; the more experienced reader achieved better results in the majority of these categories.
The study was limited by its retrospective nature and moderate number of inclusions.
MRI has a good sensitivity for tumor invasion through the bowel wall, extramural vascular invasion, and serosal involvement. In addition, together with its superior liver imaging, MRI might become the optimal staging modality for colon cancer. However, more research is needed to confirm this. See Video Abstract at http://links.lww.com/DCR/A309.
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