Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Background

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.

Objective

To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.

Methods

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.

Results

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.

Conclusions

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.

Stroma AReactive Invasion Front Areas (SARIFA): a novel histopathologic biomarker in colorectal cancer patients and its association with the luminal tumour proportion

BACKGROUND

Stroma AReactive Invasion Front Areas (SARIFA) is a novel prognostic histopathologic biomarker measured at the invasive front in haematoxylin & eosin (H&E) stained colon and gastric cancer resection specimens. The aim of the current study was to validate the prognostic relevance of SARIFA-status in colorectal cancer (CRC) patients and investigate its association with the luminal proportion of tumour (PoT).

METHODS

We established the SARIFA-status in 164 CRC resection specimens. The relationship between SARIFA-status, clinicopathological characteristics, recurrence-free survival (RFS), cancer-specific survival (CSS), and PoT was investigated.

RESULTS

SARIFA-status was positive in 22.6% of all CRCs. SARIFA-positivity was related to higher pT, pN, pTNM stage and high grade of differentiation. SARIFA-positivity was associated with shorter RFS independent of known prognostic factors analysing all CRCs (RFS: hazard ratio (HR) 2.6, p = 0.032, CSS: HR 2.4, p = 0.05) and shorter RFS and CSS analysing only rectal cancers. SARIFA-positivity, which was measured at the invasive front, was associated with PoT-low (p = 0.009), e.g., higher stroma content, and lower vessel density (p = 0.0059) measured at the luminal tumour surface.

CONCLUSION

Here, we validated the relationship between SARIFA-status and prognosis in CRC patients and provided first evidence for a potential prognostic relevance in the subgroup of rectal cancer patients. Interestingly, CRCs with different SARIFA-status also showed histological differences measurable at the luminal tumour surface. Further studies to better understand the relationship between high luminal intratumoural stroma content and absence of a stroma reaction at the invasive front (SARIFA-positivity) are warranted and may inform future treatment decisions in CRC patients.

Automated curation of large-scale cancer histopathology image datasets using deep learning

BACKGROUND

Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient.

METHODS

We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide.

RESULTS

In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort.

CONCLUSION

Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

An investigation of the clinical impact and therapeutic relevance of a DNA damage immune response (DDIR) signature in patients with advanced gastroesophageal adenocarcinoma

BACKGROUND

An improved understanding of which gastroesophageal adenocarcinoma (GOA) patients respond to both chemotherapy and immune checkpoint inhibitors (ICI) is needed. We investigated the predictive role and underlying biology of a 44-gene DNA damage immune response (DDIR) signature in patients with advanced GOA.

MATERIALS AND METHODS

Transcriptional profiling was carried out on pretreatment tissue from 252 GOA patients treated with platinum-based chemotherapy (three dose levels) within the randomized phase III GO2 trial. Cross-validation was carried out in two independent GOA cohorts with transcriptional profiling, immune cell immunohistochemistry and epidermal growth factor receptor (EGFR) fluorescent in situ hybridization (FISH) (n = 430).

RESULTS

In the GO2 trial, DDIR-positive tumours had a greater radiological response (51.7% versus 28.5%, P = 0.022) and improved overall survival in a dose-dependent manner (P = 0.028). DDIR positivity was associated with a pretreatment inflamed tumour microenvironment (TME) and increased expression of biomarkers associated with ICI response such as CD274 (programmed death-ligand 1, PD-L1) and a microsatellite instability RNA signature. Consensus pathway analysis identified EGFR as a potential key determinant of the DDIR signature. EGFR amplification was associated with DDIR negativity and an immune cold TME.

CONCLUSIONS

Our results indicate the importance of the GOA TME in chemotherapy response, its relationship to DNA damage repair and EGFR as a targetable driver of an immune cold TME. Chemotherapy-sensitive inflamed GOAs could benefit from ICI delivered in combination with standard chemotherapy. Combining EGFR inhibitors and ICIs warrants further investigation in patients with EGFR-amplified tumours.

Prognostic and predictive impact of sex in locally advanced microsatellite instability high gastric or gastroesophageal junction cancer: An individual patient data pooled analysis of randomized clinical trials

BACKGROUND

Surgery plus peri-operative/adjuvant chemotherapy is the standard of care for locally advanced GC/GEJC, though with unsatisfactory results. dMMR/MSI-high tumors have better prognosis and scant benefit from chemotherapy as compared to pMMR/MSS ones. The differential outcome of therapies in terms of safety and efficacy according to sex is still debated in GC/GEJC patients.

METHODS

We previously performed an individual patient data pooled analysis of MAGIC, CLASSIC, ITACA-S, and ARTIST trials including GC/GEJC patients treated with surgery alone or surgery plus peri-operative/adjuvant chemotherapy to assess the value of MSI status. We performed a secondary analysis investigating the prognostic and predictive role of sex (female versus male) in the pooled analysis dataset in the overall population and patients stratified for MSI status (MSI-high versus MSS/MSI-low). Disease-free (DFS) and overall survival (OS) were calculated.

RESULTS

Patients with MSI-high tumors had improved survival as compared to MSS/MSI-low ones irrespective of sex, whereas in those with MSS/MSI-low tumors, females had numerically longer OS and DFS (5-year OS was 63.2% versus 57.6%, HR 0.842; p = 0.058, and 5-year DFS was 55.8% versus 50.8%, HR 0.850; p = 0.0504 in female versus male patients). The numerical difference for the detrimental effect of chemotherapy in MSI-high GC was higher in females than males, while the significant benefit of chemotherapy over surgery alone was confirmed in MSS/MSI-low GC irrespective of sex.

CONCLUSIONS

This pooled analysis including four randomized trials highlights a relevant impact of sex in the prognosis and treatment efficacy of MSI-high and MSS/MSI-low non-metastatic GC/GEJC.

Stroma AReactive Invasion Front Areas (SARIFA) improves prognostic risk stratification of perioperative chemotherapy treated oesophagogastric cancer patients from the MAGIC and the ST03 trial

BACKGROUND

Tumour-associated fat cells without desmoplastic stroma reaction at the invasion front (Stroma AReactive Invasion Front Areas (SARIFA)) is a prognostic biomarker in gastric and colon cancer. The clinical utility of the SARIFA status in oesophagogastric cancer patients treated with perioperative chemotherapy is currently unknown.

METHODS

The SARIFA status was determined in tissue sections from patients recruited into the MAGIC (n = 292) or ST03 (n = 693) trials treated with surgery alone (S, MAGIC) or perioperative chemotherapy (MAGIC, ST03). The relationship between SARIFA status, clinicopathological factors, overall survival (OS) and treatment was analysed.

RESULTS

The SARIFA status was positive in 42% MAGIC trial S patients, 28% MAGIC and 48% ST03 patients after pre-operative chemotherapy. SARIFA status was related to OS in MAGIC trial S patients and was an independent prognostic biomarker in ST03 trial patients (HR 1.974, 95% CI 1.555-2.507, p < 0.001). ST03 patients with lymph node metastasis (ypN + ) and SARIFA-positive tumours had poorer OS than patients with ypN+ and SARIFA-negative tumours (plogrank < 0.001).

CONCLUSIONS

The SARIFA status has clinical utility as prognostic biomarker in oesophagogastric cancer patients irrespective of treatment modality. Whilst underlying biological mechanisms warrant further investigation, the SARIFA status might be used to identify new drug targets, potentially enabling repurposing of existing drugs targeting lipid metabolism.

Germinal centres within tumour positive sentinel lymph nodes are positively associated with tumour infiltrating lymphocytes and tertiary lymphoid structures in breast cancer

BACKGROUND

Stromal tumour infiltrating lymphocytes (sTILs) and presence of tertiary lymphoid structures have been proposed as indicators of tumour-related immune response in breast cancer. An increased number of germinal centres (GCs) in lymph nodes is considered a sign of humoral immune reactivity.

AIMS

It is unclear whether a relationship exists between number and size of GCs within tumour positive sentinel lymph nodes (SLNpos), sTILs and tertiary lymphoid structures within matched primary breast cancer and breast cancer subtype.

METHODS

Axillary SLNpos from 175 patients with breast cancer were manually contoured in digitized haematoxylin and eosin stained sections. Total SLN area, GC number and GC area were measured in SLNpos with the largest metastatic area. To correct for SLN size, GC number and GC area were divided by SLN area. sTILs and presence of tertiary lymphoid structures were assessed in the primary breast cancer.

RESULTS

A higher GC number and larger GC area were found in patients with high sTILs (≥2%) (both P < 0.001) and in patients with presence of tertiary lymphoid structures (PGC number = 0.034 and PGC area = 0.016). Triple negative and HER2-positive (N = 45) breast cancer subtypes had a higher GC number and higher sTILs compared to hormone receptor positive, HER2-negative breast cancer (N = 130) (PGC number < 0.001 and PsTILs= 0.001).

CONCLUSION

This study suggests GCs measured within SLNpos might be useful indicators of the humoral anti-tumour immune response in breast cancer. Future studies are needed investigating underlying biological mechanisms and prognostic value of GCs in SLNs.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

BACKGROUND

Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

METHODS

In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables.

FINDINGS

We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors.

INTERPRETATION

Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work.

FUNDING

The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

The Role of Nonmetastatic Lymph Nodes in the Survival of Colorectal Cancer: A Systematic Review

Objective

In this review, we aim to provide an overview of literature on lymph node (LN) histomorphological features and their relationship with the prognosis in colorectal cancer (CRC).

Background

Lymph nodes play a crucial role in the treatment and prognosis of CRC. The presence of LN metastases considerably worsens the prognosis in CRC patients. Literature has shown that the total number of LNs and the number negative LNs (LNnegs) has prognostic value in CRC patients. In esophageal carcinoma, LN size seems to be surrogate of the host antitumor response and a potentially clinically useful new prognostic biomarker for (y)pN0 esophageal carcinoma.

Methods

A comprehensive search was performed in Pubmed, Embase, Medline, CINAHL, and the Cochrane library in March 2021. The PRISMA guidelines were followed. Only studies focusing on histomorphological features and LN size and their relation to overall survival were selected.

Results

A total of 9 unique articles met all inclusion criteria and were therefore included in this systematic review. Six of these studies investigated HMF (eg, paracortical hyperplasia, germinal center predominance, and sinus histiocytosis) and 4 studies LNneg size and their relationship with overall survival. The presence of paracortical hyperplasia and an increased number of large LNnegs is related to a more favorable prognosis in CRC.

Conclusion

The results of this systematic review seem to support the hypothesis that there is a relationship between the host antitumor response reflected in different histomorphological reaction patterns visible in LNnegs and LNneg size related to survival in CRC patients.

Programmed death-ligand 1 (PD-L1) expression in primary gastric adenocarcinoma and matched metastases

BACKGROUND

Combination of immunotherapy and chemotherapy is recommended for first line treatment of gastric adenocarcinoma (GC) patients with locally advanced unresectable disease or metastatic disease. However, data regarding the concordance rate between PD-L1 combined positive score (CPS) in primary GC and matched regional lymph node metastasis (LNmet) or matched distant metastasis (Dmet) is limited.

METHODS

Tissue microarray sections from primary resected GC, LNmet and Dmet were immunohistochemically stained with anti-PD-L1 (clone SP263). PD-L1 expression was scored separately in tumour cells and immune cells and compared between matched primary GC, LNmet and/or Dmet. CPS was calculated and results for CPS cut-offs 1 and 5 were compared between matched samples.

RESULTS

275 PD-L1 stained GC were analysed. 189 primary GC had matched LNmet. CPS cut-off 1 concordance rate between primary GC and LNmet was 77%. 23 primary GC had matched Dmet but no matched LNmet, CPS cut-off 1 concordance rate was 70%. 63 primary GC had both matched LNmet and matched Dmet, CPS cut-off 1 concordance rate of 67%. CPS cut-off 5 results were similar. The proportion of PD-L1 positive tumour cells increased from primary GC (26%) to LNmet (42%) and was highest in Dmet (75%).

CONCLUSION

Our study showed up to 33% discordance of PD-L1 CPS between primary GC and LNmet and/or Dmet suggesting that multiple biopsies of primary GC and metastatic sites might need to be tested before considering treatment options. Moreover, this is the first study that seems to suggest that tumour cells acquire PD-L1 expression during disease progression.

The clinical importance of the host anti-tumour reaction patterns in regional tumour draining lymph nodes in patients with locally advanced resectable gastric cancer: a systematic review and meta-analysis

BACKGROUND

The status of regional tumour draining lymph nodes (LN) is crucial for prognostic evaluation in gastric cancer (GaC) patients. Changes in lymph node microarchitecture, such as follicular hyperplasia (FH), sinus histiocytosis (SH), or paracortical hyperplasia (PH), may be triggered by the anti-tumour immune response. However, the prognostic value of these changes in GaC patients is unclear.

METHODS

A systematic search in multiple databases was conducted to identify studies on the prognostic value of microarchitecture changes in regional tumour-negative and tumour-positive LNs measured on histopathological slides. Since the number of GaC publications was very limited, the search was subsequently expanded to include junctional and oesophageal cancer (OeC).

RESULTS

A total of 28 articles (17 gastric cancer, 11 oesophageal cancer) met the inclusion criteria, analyzing 26,503 lymph nodes from 3711 GaC and 1912 OeC patients. The studies described eight different types of lymph node microarchitecture changes, categorized into three patterns: hyperplasia (SH, FH, PH), cell-specific infiltration (dendritic cells, T cells, neutrophils, macrophages), and differential gene expression. Meta-analysis of five GaC studies showed a positive association between SH in tumour-negative lymph nodes and better 5-year overall survival. Pooled risk ratios for all LNs showed increased 5-year overall survival for the presence of SH and PH.

CONCLUSIONS

This systematic review suggests that sinus histiocytosis and paracortical hyperplasia in regional tumour-negative lymph nodes may provide additional prognostic information for gastric and oesophageal cancer patients. Further studies are needed to better understand the lymph node reaction patterns and explore their impact of chemotherapy treatment and immunotherapy efficacy.

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

AIM

Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL).

METHODS

Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status.

RESULTS

The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests.

CONCLUSION

GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.

The prognostic value of the histological shape of tumor negative sentinel nodes in breast cancer

Introduction

Sentinel lymph node (SLN) metastasis is an important predictor of prognosis in breast cancer (BC) patients, guiding treatment decisions. However, patients with the same BC subtype and tumor negative SLN (SLNneg) can have different survival outcomes. We hypothesized that the host anti-tumor immune reaction in SLNneg is important and results in morphometrically measurable changes in SLN size or shape which are related to patient prognosis.

Methods

Surface area, circumference, long axis and short axis were histologically measured in 694 SLNneg from 356 cases of invasive BC and 67 ductal carcinoma in situ cases. The area occupied by fat was categorized as less or more than 50%. The long to short axis (L/S) ratio was calculated. The relationship between SLNneg morphometries and clinicopathological variables like tumor-infiltrating lymphocytes (TILs) within the primary tumor, as well as prognosis at 10 years follow up were analyzed.

Results

The mean SLNneg surface area was 78.7mm2, circumference 40.3mm, long axis 13.1mm, short axis 8.2mm and L/S ratio 1.7. Larger surface area, long axis and short axis, including age >55 years were associated with higher body mass index (BMI) and SLN fat over 50% (p<0.003). In invasive BC, a high SLNneg L/S ratio (≥1.9) was related to poorer disease-free (HR=1.805, 95%CI 1.182-2.755, p=0.006) and overall (HR=2.389, 95%CI 1.481-3.851, p<0.001) survival. A low SLNneg L/S ratio (<1.9) was associated with high TILs in the primary BC (≥10%) (p=0.005). However a high TIL count was not of prognostic relevance.

Conclusions

This is the first study to suggest that morphometric characteristics of axillary SLNneg, like L/S ratio, could be used to predict prognosis in patients with SLNneg invasive BC of all subtypes. The association between low L/S ratio and high TILs suggest that SLN shape is related to immunological functioning of the SLN and could be used in addition to TIL evaluation. Regarding the dubious role of TILs in hormone receptor positive breast cancer, SLNneg morphometry to gain information about host immune status could especially be of benefit in this subtype. Further studies are warranted to better understand the underlying biological mechanisms.

Clinical Features of Gastric Signet Ring Cell Cancer: Results from a Systematic Review and Meta-Analysis

BACKGROUND

Conflicting results about the prognostic relevance of signet ring cell histology in gastric cancer have been reported. We aimed to perform a meta-analysis focusing on the clinicopathological features and prognosis of this subgroup of cancer compared with other histologies.

METHODS

A systematic literature search in the PubMed database was conducted, including all publications up to 1 October 2021. A meta-analysis comparing the results of the studies was performed.

RESULTS

A total of 2062 studies referring to gastric cancer with signet ring cell histology were identified, of which 262 studies reported on its relationship with clinical information. Of these, 74 were suitable to be included in the meta-analysis. A slightly lower risk of developing nodal metastases in signet ring cell tumours compared to other histotypes was found (especially to undifferentiated/poorly differentiated/mucinous and mixed histotypes); the lower risk was more evident in early and slightly increased in advanced gastric cancer. Survival tended to be better in early stage signet ring cell cancer compared to other histotypes; no differences were shown in advanced stages, and survival was poorer in metastatic patients. In the subgroup analysis, survival in signet ring cell cancer was slightly worse compared to non-signet ring cell cancer and differentiated/well-to-moderately differentiated adenocarcinoma.

CONCLUSIONS

Most of the conflicting results in signet ring cell gastric cancer literature could be derived from the lack of standardisation in their classification and the comparison with the different subtypes of gastric cancer. There is a critical need to strive for a standardised classification system for gastric cancer, fostering clarity and coherence in the forthcoming research and clinical applications.

Pathologic Lymph Node Regression After Neoadjuvant Chemotherapy Predicts Recurrence and Survival in Esophageal Adenocarcinoma: A Multicenter Study in the United Kingdom

PURPOSE

There is limited evidence regarding the prognostic effects of pathologic lymph node (LN) regression after neoadjuvant chemotherapy for esophageal adenocarcinoma, and a definition of LN response is lacking. This study aimed to evaluate how LN regression influences survival after surgery for esophageal adenocarcinoma.

METHODS

Multicenter cohort study of patients with esophageal adenocarcinoma treated with neoadjuvant chemotherapy followed by surgical resection at five high-volume centers in the United Kingdom. LNs retrieved at esophagectomy were examined for chemotherapy response and given a LN regression score (LNRS)-LNRS 1, complete response; 2, <10% residual tumor; 3, 10%-50% residual tumor; 4, >50% residual tumor; and 5, no response. Survival analysis was performed using Cox regression adjusting for confounders including primary tumor regression. The discriminatory ability of different LN response classifications to predict survival was evaluated using Akaike information criterion and Harrell C-index.

RESULTS

In total, 17,930 LNs from 763 patients were examined. LN response classified as complete LN response (LNRS 1 ≥1 LN, no residual tumor in any LN; n = 62, 8.1%), partial LN response (LNRS 1-3 ≥1 LN, residual tumor ≥1 LN; n = 155, 20.3%), poor/no LN response (LNRS 4-5; n = 303, 39.7%), or LN negative (no tumor/regression; n = 243, 31.8%) demonstrated superior discriminatory ability. Mortality was reduced in patients with complete LN response (hazard ratio [HR], 0.35; 95% CI, 0.22 to 0.56), partial LN response (HR, 0.72; 95% CI, 0.57 to 0.93) or negative LNs (HR, 0.32; 95% CI, 0.25 to 0.42) compared with those with poor/no LN response. Primary tumor regression and LN regression were discordant in 165 patients (21.9%).

CONCLUSION

Pathologic LN regression after neoadjuvant chemotherapy was a strong prognostic factor and provides important information beyond pathologic TNM staging and primary tumor regression grading. LN regression should be included as standard in the pathologic reporting of esophagectomy specimens.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study

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.

Association between adjuvant therapy and survival in colorectal cancer patients according to metabolic Warburg-subtypes

PURPOSE

Tumor location and tumor node metastasis (TNM) stage guide treatment decisions in colorectal cancer (CRC) patients. However, patients with the same disease stage do not benefit equally from adjuvant therapy. Hence, there remains an urgent clinical need to identify prognostic and/or predictive biomarker(s) to personalize treatment decisions. In this exploratory study, we investigated whether our previously defined metabolic Warburg-subtypes can predict which CRC patients might derive survival benefit from adjuvant therapy.

METHODS

Information regarding treatment (surgery only: n = 1451; adjuvant radiotherapy: n = 82; or adjuvant chemotherapy: n = 260) and Warburg-subtype (Warburg-low: n = 485, -moderate: n = 641, or -high: n = 667) was available for 1793 CRC patients from the Netherlands Cohort Study (NLCS). Kaplan-Meier curves and Cox regression models were used to investigate survival benefit from adjuvant therapy compared to surgery-only for the different Warburg-subtypes.

RESULTS

Patients with Warburg-moderate CRC (HRCRC-specific 0.64; 95% CI 0.47-0.86, HRoverall 0.61; 95% CI 0.47-0.80), and possibly Warburg-high CRC (HRCRC-specific 0.86; 95% CI 0.65-1.14, HRoverall 0.82; 95% CI 0.64-1.05), had survival benefit from adjuvant therapy. No survival benefit was observed for patients with Warburg-low CRC (HRCRC-specific 1.07; 95% CI 0.76-1.52, HRoverall 0.95; 95% CI 0.70-1.30). There was a significant interaction between Warburg-subtype and adjuvant therapy for CRC-specific survival (p = 0.049) and overall survival (p = 0.035).

CONCLUSION

Our results suggest that Warburg-subtypes may predict survival benefit from adjuvant therapy in CRC patients. A survival benefit from adjuvant therapy was observed for patients with Warburg-moderate and possibly Warburg-high CRC, but not for patients with Warburg-low CRC. Future prospective studies are necessary to validate our findings.

Pathological regression of primary tumour and metastatic lymph nodes following chemotherapy in resectable OG cancer: pooled analysis of two trials

BACKGROUND

No definitive largescale data exist evaluating the role of pathologically defined regression changes within the primary tumour and lymph nodes (LN) of resected oesophagogastric (OG) adenocarcinoma following neoadjuvant chemotherapy and the impact on survival.

METHODS

Data and samples from two large prospective randomised trials (UK MRC OE05 and ST03) were pooled. Stained slides were available for central pathology review from 1619 patients. Mandard tumour regression grade (TRG) and regression of tumour within LNs (LNR: scored as present/absent) were assessed and correlated with overall survival (OS) using a Cox regression model. An exploratory analysis to define subgroups with distinct prognoses was conducted using a classification and regression tree (CART) analysis.

RESULTS

Neither trial demonstrated a relationship between TRG score and the presence or absence of LNR. In univariable analysis, lower TRG, lower ypN stage, lower ypT stage, presence of LNR, presence of well/moderate tumour differentiation, and absence of tumour at resection margin were all associated with better OS. However, the multivariable analysis demonstrated that only ypN, ypT, grade of differentiation and resection margin (R0) were independent indicators of prognosis. Exploratory CART analysis identified six subgroups with 3-year OS ranging from 83% to 22%; with ypN stage being the most important single prognostic variable.

CONCLUSIONS

Pathological LN stage within the resection specimen was the single most important determiner of survival. Our results suggest that the assessment of regression changes within the primary tumour or LNs may not be necessary to define the prognosis further.

Tumour infiltrating lymphocytes and survival after adjuvant chemotherapy in patients with gastric cancer: post-hoc analysis of the CLASSIC trial

BACKGROUND

Only a subset of gastric cancer (GC) patients with stage II-III benefits from chemotherapy after surgery. Tumour infiltrating lymphocytes per area (TIL density) has been suggested as a potential predictive biomarker of chemotherapy benefit.

METHODS

We quantified TIL density in digital images of haematoxylin-eosin (HE) stained tissue using deep learning in 307 GC patients of the Yonsei Cancer Center (YCC) (193 surgery+adjuvant chemotherapy [S + C], 114 surgery alone [S]) and 629 CLASSIC trial GC patients (325 S + C and 304 S). The relationship between TIL density, disease-free survival (DFS) and clinicopathological variables was analysed.

RESULTS

YCC S patients and CLASSIC S patients with high TIL density had longer DFS than S patients with low TIL density (P = 0.007 and P = 0.013, respectively). Furthermore, CLASSIC patients with low TIL density had longer DFS if treated with S + C compared to S (P = 0.003). No significant relationship of TIL density with other clinicopathological variables was found.

CONCLUSION

This is the first study to suggest TIL density automatically quantified in routine HE stained tissue sections as a novel, clinically useful biomarker to identify stage II-III GC patients deriving benefit from adjuvant chemotherapy. Validation of our results in a prospective study is warranted.

Clinical Significance of Pre-treatment Circumferential Tumor Location in Patients With cStage IB-III Esophageal Squamous Cell Cancer

BACKGROUND/AIM

To determine the clinical significance of pre-treatment circumferential tumor location within the esophageal wall in patients with locally advanced esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy.

PATIENTS AND METHODS

Patients (n=96) with cStage IB-III esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy were categorized into two groups based on the circumferential tumor location within the esophageal wall on diagnostic endoscopy: left or anterior wall group (L/A patients, n=49); right or posterior wall group (R/P patients, n=47). Analyses were conducted to examine the relationship between circumferential tumor location, clinicopathological characteristics, 3-year overall survival (3Y-OS), and 3-year recurrence-free survival (3Y-RFS).

RESULTS

The lymph node status and recurrence rates of mediastinal lymph node metastases were significantly higher in patients with L/A than in patients with R/P. Furthermore, patients with L/A had significantly poorer 3Y-OS and 3Y-RFS than those with R/P. Tumor location within the esophageal wall was identified in multivariate analysis as an independent risk factor for 3Y-RFS (hazard ratio=2.92, 95% confidence interval=1.35-6.32, p=0.0064).

CONCLUSION

Pre-treatment of circumferential tumor located within the esophageal wall may be a useful prognostic factor in patients with cStageIB-III esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy.

Dissecting the genetic heterogeneity of gastric cancer

BACKGROUND

Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture.

METHODS

We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO.

FINDINGS

Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level.

INTERPRETATION

Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO.

FUNDING

German Research Foundation (DFG).

Relationship between the Warburg effect in tumour cells and the tumour microenvironment in colorectal cancer patients: Results from a large multicentre study

Colorectal cancer (CRC) remains one of the most prevalent and deadly cancers worldwide. The tumour-node-metastasis stage (TNM) is currently the most clinically important tool to predict prognosis for CRC patients. However, patients with the same TNM stage can have different prognoses. The metabolic status of tumour cells (Warburg-subtype) has been proposed as potential prognostic factor in CRC. However, potential biological mechanisms underlying the relationship between Warburg-subtype and prognosis have not been investigated in detail. One potential mechanism could be that the metabolic status of tumour cells affects the tumour microenvironment (TME). Our objective was to investigate the relationship between Warburg-subtypes and the TME. Haematoxylin/Eosin stained tumour tissue microarray cores from 2171 CRC patients from the Netherlands Cohort Study were semi quantitatively assessed for tumour infiltrating lymphocytes (TILs) and relative tumour stroma content. 5745 cores were assessed by putting each core in one of four categories for both TILs and stroma. The relationship between Warburg-subtype, TILs, and tumour stroma content was investigated. The frequency of CRC in the different TIL categories was (n, %): very low (2538, 44.2), low (2463, 42.9), high (722, 12.6), and very high (22, 0.4). The frequency of CRC in the different tumour stroma content categories was: ≤ 25% (2755, 47.9), > 25% ≤ 50% (1553, 27) > 50% ≤ 75% (905, 15.8), and > 75% (532, 9.3). There was neither an association between Warburg-subtype and tumour stroma content (p = 0.229) nor between Warburg-subtype and TILs (p = 0.429). This is the first study to investigate the relationship between Warburg-subtypes and the TME in a large population-based series of CRC patients. Our data suggest that the prognostic value of Warburg-subtypes cannot be directly attributed to differences in TILs or tumour stroma content. Our results require confirmation in an independent series.

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.

Prognostic Significance of Negative Lymph Node Long Axis in Esophageal Cancer: Results From the Randomized Controlled UK MRC OE02 Trial

OBJECTIVE

To analyze the relationship between negative lymph node (LNneg) size as a possible surrogate marker of the host antitumor immune response and overall survival (OS) in esophageal cancer (EC) patients.

BACKGROUND

Lymph node (LN) status is a well-established prognostic factor in EC patients. An increased number of LNnegs is related to better survival in EC. Follicular hyperplasia in LNneg is associated with better survival in cancer-bearing mice and might explain increased LN size.

METHODS

The long axis of 304 LNnegs was measured in hematoxylin-eosin stained sections from resection specimens of 367 OE02 trial patients (188 treated with surgery alone (S), 179 with neoadjuvant chemotherapy plus surgery (C+S)) as a surrogate of LN size. The relationship between LNneg size, LNneg microarchitecture, clinicopathological variables, and OS was analyzed.

RESULTS

Large LNneg size was related to lower pN category ( P = 0.01) and lower frequency of lymphatic invasion ( P = 0.02) in S patients only. Irrespective of treatment, (y)pN0 patients with large LNneg had the best OS. (y)pN1 patients had the poorest OS irrespective of LNneg size ( P < 0.001). Large LNneg contained less lymphocytes ( P = 0.02) and had a higher germinal centers/lymphocyte ratio ( P = 0.05).

CONCLUSIONS

This is the first study to investigate LNneg size in EC patients randomized to neoadjuvant chemotherapy followed by surgery or surgery alone. Our pilot study suggests that LNneg size is a surrogate marker of the host antitumor immune response and a potentially clinically useful new prognostic biomarker for (y)pN0 EC patients. Future studies need to confirm our results and explore underlying biological mechanisms.

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an "uncertain" category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.

Energy balance-related factors in childhood and adolescence and risk of colorectal cancer based on KRAS, PIK3CA, and BRAF mutations and MMR status

KRAS mutations (KRAS_mut ), PIK3CA_mut , BRAF_mut , and deficient DNA mismatch repair (dMMR) have been associated with the Warburg effect. We previously reported differential associations between early-life energy balance-related factors (height, energy restriction, body mass index [BMI]) and colorectal cancer (CRC) subtypes based on the Warburg effect. We now investigated associations of early-life energy balance-related factors and the risk of CRC subgroups based on mutation and MMR status. Data from the Netherlands Cohort Study was used. KRAS_mut , PIK3CA_mut, BRAF_mut, and MMR status were available for 2349 CRC cases, and complete covariate data for 1934 cases and 3911 subcohort members. Multivariable-adjusted Cox regression was used to estimate associations of height, energy restriction proxies (exposure to Dutch Hunger Winter, Second World War, Economic Depression), and early adult BMI (age 20 years) with risk of CRC based on individual molecular features and combinations thereof (all-wild-type+MMR-proficient [pMMR]; any-mutation/dMMR). Height was positively associated with any-mutation/dMMR CRC but not all-wild-type+pMMR CRC, with the exception of rectal cancer in men, and with heterogeneity in associations observed for colon cancer in men (p-heterogeneity = 0.049) and rectal cancer in women (p-heterogeneity = 0.014). Results on early-life energy restriction proxies in relation to the risk of CRC subgroups did not show clear patterns. Early adult BMI was positively, but not significantly, associated with KRAS_mut colon cancer in men and with BRAF_mut and dMMR colon cancer in women. Our results suggest a role of KRAS_mut , PIK3CA_mut , BRAF_mut , and dMMR in the etiological pathway between height and CRC risk. KRAS_mut might potentially play a role in associations of early adult BMI with colon cancer risk in men, and BRAF_mut and dMMR in women.

BioXmark® liquid fiducials to enable radiotherapy tumor boosting in rectal cancer, a feasibility trial

•

BioXmark® is a novel liquid fiducial marker for image-guided radiotherapy.

•

The marker remained stable during chemoradiotherapy in 96% of rectal cancer cases.

•

The fiducial allows for image tracking on CT-based imaging modalities.

•

Marker visibility was good using CT-based imaging without any relevant artifacts.

•

The marker is easy to inject without marker related adverse events.

Background and purpose

Dose-escalation in rectal cancer (RCa) may result in an increased complete response rate and thereby enable omission of surgery and organ preservation. In order to implement dose-escalation, it is crucial to develop a technique that allows for accurate image-guided radiotherapy. The aim of the current study was to determine the performance of a novel liquid fiducial marker (BioXmark®) in RCa patients during the radiotherapy course by assessing its positional stability on daily cone-beam CT (CBCT), technical feasibility, visibility on different imaging modalities and safety.

Materials and methods

Prospective, non-randomized, single-arm feasibility trial with inclusion of twenty patients referred for neoadjuvant chemoradiotherapy for locally advanced RCa. Primary study endpoint was positional stability on CBCT. Furthermore, technical aspects, safety and clinical performance of the marker, such as visibility on different imaging modalities, were evaluated.

Results

Seventy-four markers from twenty patients were available for analysis. The marker was stable in 96% of the cases. One marker showed clinically relevant migration, one marker was lost before start of treatment and one marker was lost during treatment. Marker visibility was good on computed tomography (CT) and CBCT, and moderate on electronic portal imaging (EPI). Marker visibility on magnetic resonance imaging (MRI) was poor during response evaluation.

Conclusion

The novel liquid fiducial marker demonstrated positional stability. We provide evidence of the feasibility of the novel fiducial marker for image-guided radiotherapy on daily cone beam CT for RCa patients.

Chromatin Rewiring by Mismatch Repair Protein MSH2 Alters Cell Adhesion Pathways and Sensitivity to BET Inhibition in Gastric Cancer

Mutations in the DNA mismatch repair gene MSH2 are causative of microsatellite instability (MSI) in multiple cancers. Here, we discovered that besides its well-established role in DNA repair, MSH2 exerts a novel epigenomic function in gastric cancer. Unbiased CRISPR-based mass spectrometry combined with genome-wide CRISPR functional screening revealed that in early-stage gastric cancer MSH2 genomic binding is not randomly distributed but rather is associated specifically with tumor-associated super-enhancers controlling the expression of cell adhesion genes. At these loci, MSH2 genomic binding was required for chromatin rewiring, de novo enhancer-promoter interactions, maintenance of histone acetylation levels, and regulation of cell adhesion pathway expression. The chromatin function of MSH2 was independent of its DNA repair catalytic activity but required MSH6, another DNA repair gene, and recruitment to gene loci by the SWI/SNF chromatin remodeler SMARCA4/BRG1. Loss of MSH2 in advanced gastric cancers was accompanied by deficient cell adhesion pathway expression, epithelial-mesenchymal transition, and enhanced tumorigenesis in vitro and in vivo. However, MSH2-deficient gastric cancers also displayed addiction to BAZ1B, a bromodomain-containing family member, and consequent synthetic lethality to bromodomain and extraterminal motif (BET) inhibition. Our results reveal a role for MSH2 in gastric cancer epigenomic regulation and identify BET inhibition as a potential therapy in MSH2-deficient gastric malignancies.

SIGNIFICANCE

DNA repair protein MSH2 binds and regulates cell adhesion genes by enabling enhancer-promoter interactions, and loss of MSH2 causes deficient cell adhesion and bromodomain and extraterminal motif inhibitor synthetic lethality in gastric cancer.

Epidemiological, clinical and endoscopic characteristics of colorectal neuroendocrine neoplasms: a population-based study in the Netherlands

Background and study aims  Neuroendocrine neoplasms (NEN) account for a small number of colorectal neoplasms. Endoscopic detection is essential for diagnosis, treatment and follow-up. Little is known about incidence of NENs in colonoscopy populations or the relationship between clinical, endoscopic and histopathologic features. We evaluated epidemiology, endoscopic and clinical characteristics of colorectal NENs in a population-based cohort. Patients and methods  Medical records of NEN cases were cross-linked with the national pathology database from January 2001 to December 2015, in South Limburg County, the Netherlands, covering four endoscopy units. Senior pathologists reviewed and classified NENs using World Health Organization 5th edition (2019) guidelines. Results  The number of colorectal NEN diagnoses was stable over time with 0.6 NEN per 1,000 patients. A total of NENs were detected in 85 patients: 65 neuroendocrine tumors (NETs) and 20 poorly differentiated neuroendocrine carcinomas (NECs). Rectal NETs were usually small sessile/submucosal lesions with yellowish (lipoma-like) color. Colonic NETs were larger sessile/submucosal lesions with darker color compared to background. Colorectal NECs presented as large, dark-colored lesions with ulcerated/necrotizing areas. Conclusions  Our population-based data point to a stable and low incidence of 0.6 NEN per 1,000 patients in the Netherlands. Rectal NETs mainly present as small sessile yellowish lesions. Colonic NETs present as larger and darker lesions than background mucosa and NECs as darker lesions than background with ulceration/necrosis. Standardized endoscopic characterization of colorectal NENs is necessary to improve recognition of these lesions and provide a basis for evidence-based treatment and surveillance recommendations.

Association between mutational subgroups, Warburg-subtypes, and survival in patients with colorectal cancer

BACKGROUND

Previous research suggests that Warburg-subtypes are related to potentially important survival differences in colorectal cancer (CRC) patients. In the present study, we investigated whether mutational subgroups based on somatic mutations in RAS, BRAF, PIK3CA, and MET, which are known to promote the Warburg-effect, as well as mismatch repair (MMR) status, hold prognostic value in CRC. In addition, we investigated whether Warburg-subtypes provide additional prognostic information, independent of known prognostic factors like TNM stage.

METHODS

CRC patients (n = 2344) from the prospective Netherlands Cohort Study (NLCS) were classified into eight mutually exclusive mutational subgroups, based on observed mutations in RAS, BRAF, PIK3CA, and MET, and MMR status: All-wild-type + MMR_proficient , KRAS_mut  + MMR_proficient , KRAS_mut  + PIK3CA_mut  + MMR_proficient , PIK3CA_mut  + MMR_proficient , BRAF_mut  + MMR_proficient , BRAF_mut  + MMR_deficient , other + MMR_proficient , and other + MMR_deficient . Kaplan-Meier curves and Cox regression models were used to investigate associations between mutational subgroups and survival, as well as associations between our previously established Warburg-subtypes and survival within these mutational subgroups.

RESULTS

Compared to patients with all-wild-type + MMR_proficient CRC, patients with KRAS_mut  + MMR_proficient , KRAS_mut  + PIK3CA_mut  + MMR_proficient , BRAF_mut  + MMR_proficient , or other + MMR_proficient CRC had a statistically significant worse survival (HR_CRC-specific ranged from 1.29 to 1.88). In contrast, patients with other + MMR_deficient CRC had the most favorable survival (HR_CRC-specific 0.48). No statistically significant survival differences were observed for the Warburg-subtypes within mutational subgroups.

CONCLUSION

Our results highlight the prognostic potential of mutational subgroups in CRC. Warburg-subtypes did not provide additional prognostic information within these mutational subgroups. Future larger-scale prospective studies are necessary to validate our findings and to examine the potential clinical utility of CRC subtyping based on mutational subgroups.

Energy balance-related factors and risk of colorectal cancer based on KRAS, PIK3CA, and BRAF mutations and MMR status

Introduction

KRAS mutations (KRAS_mut), PIK3CA_mut, BRAF_mut, and mismatch repair deficiency (dMMR) have been associated with the Warburg-effect. We previously observed differential associations between energy balance-related factors (BMI, clothing-size, physical activity) and colorectal cancer (CRC) subtypes based on the Warburg-effect. We now investigated whether associations between energy balance-related factors and risk of CRC differ between subgroups based on mutation and MMR status.

Methods

Information on molecular features was available for 2349 incident CRC cases within the Netherlands Cohort Study (NLCS), with complete covariate data available for 1934 cases and 3911 subcohort members. Multivariable-adjusted Cox-regression was used to estimate associations of energy balance-related factors with risk of CRC based on individual molecular features (KRAS_mut; PIK3CA_mut; BRAF_mut; dMMR) and combinations thereof (all-wild-type + MMR-proficient (pMMR); any-mutation/dMMR).

Results

In men, BMI and clothing-size were positively associated with risk of colon, but not rectal cancer, regardless of molecular features subgroups; the strongest associations were observed for PIK3CA_mut colon cancer. In women, however, BMI and clothing-size were only associated with risk of KRAS_mut colon cancer (p-heterogeneity_{KRASmut versus all-wild-type+pMMR} = 0.008). Inverse associations of non-occupational physical activity with risk of colon cancer were strongest for any-mutation/dMMR tumors in men and women, and specifically for PIK3CA_mut tumors in women. Occupational physical activity was inversely associated with both combination subgroups of colon cancer in men.

Conclusion

In men, associations did not vary according to molecular features. In women, a role of KRAS mutations in the etiological pathway between adiposity and colon cancer is suggested, and of PIK3CA mutations between physical activity and colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04019-9.

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

OBJECTIVE

To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.

DESIGN

The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.

RESULTS

From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022).

CONCLUSION

Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit.

TRIAL REGISTRATION NUMBER

UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial).

Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application

BACKGROUND

Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds.

METHOD

We trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities.

RESULTS

Classifiers achieved clinical-grade performance, yielding an area under the receiver operating curve (AUROC) of up to 0.96 without using any manual annotations. Subsequently, we show that the AI system can be applied as a rule-out test: by using cohort-specific thresholds, on average 52.73% of tumors in each surgical cohort [total number of MSI/dMMR = 1020, microsatellite stable (MSS)/ proficient mismatch repair (pMMR) = 7323 patients] could be identified as MSS/pMMR with a fixed sensitivity at 95%. In an additional cohort of N = 1530 (MSI/dMMR = 211, MSS/pMMR = 1319) endoscopy biopsy samples, the system achieved an AUROC of 0.89, and the cohort-specific threshold ruled out 44.12% of tumors with a fixed sensitivity at 95%. As a more robust alternative to cohort-specific thresholds, we showed that with a fixed threshold of 0.25 for all the cohorts, we can rule-out 25.51% in surgical specimens and 6.10% in biopsies.

INTERPRETATION

When applied in a clinical setting, this means that the AI system can rule out MSI/dMMR in a quarter (with global thresholds) or half of all CRC patients (with local fine-tuning), thereby reducing cost and turnaround time for molecular profiling.

Increasing frequency of gene copy number aberrations is associated with immunosuppression and predicts poor prognosis in gastric adenocarcinoma

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

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.

Energy balance‐related factors in childhood and adolescence and risk of colorectal cancer expressing different levels of proteins involved in the Warburg‐effect

Early‐life (childhood to adolescence) energy balance‐related factors (height, energy restriction, BMI) have been associated with adult colorectal cancer (CRC) risk. Warburg‐effect activation via PI3K/Akt‐signaling might explain this link. We investigated whether early‐life energy balance‐related factors were associated with risk of Warburg‐subtypes in CRC. We used immunohistochemistry for six proteins involved in the Warburg‐effect (LDHA, GLUT1, MCT4, PKM2, P53, and PTEN) on tissue microarrays of 2399 incident CRC cases from the prospective Netherlands Cohort Study (NLCS). Expression levels of all proteins were combined into a pathway‐based sum score and categorized into three Warburg‐subtypes (Warburg‐low/‐moderate/‐high). Multivariable Cox‐regression analyses were used to estimate associations of height, energy restriction proxies (exposure to Dutch Hunger Winter; Second World War [WWII]; Economic Depression) and adolescent BMI with Warburg‐subtypes in CRC. Height was positively associated with colon cancer in men, regardless of Warburg‐subtypes, and with Warburg‐low colon and Warburg‐moderate rectal cancer in women. Energy restriction during the Dutch Hunger Winter was inversely associated with colon cancer in men, regardless of Warburg‐subtypes. In women, energy restriction during the Hunger Winter and WWII was inversely associated with Warburg‐low colon cancer, whereas energy restriction during the Economic Depression was positively associated with Warburg‐high colon cancer. Adolescent BMI was positively associated with Warburg‐high colon cancer in men, and Warburg‐moderate rectal cancer in women. In conclusion, the Warburg‐effect seems to be involved in associations of adolescent BMI with colon cancer in men, and of energy restriction during the Economic Depression with colon cancer in women. Further research is needed to validate these results.

Data Set for Reporting Carcinoma of the Stomach in Gastrectomy

Context.—

A standardized detailed surgical pathology report is the cornerstone of gastric cancer management.

Objective.—

To guide management and prognostication for patients with gastric carcinomas globally, the International Collaboration on Cancer Reporting aimed to produce an evidence-based international pathology reporting data set with a panel of globally recognized expert pathologists and clinicians.

Design.—

Based on published guidelines/data sets for gastric carcinomas, a working draft was developed by the chair of the expert panel of pathologists and clinicians. The draft was then circulated to the panel and discussed in a series of teleconferences and email communications until consensus was achieved. The draft data set was uploaded on the International Collaboration on Cancer Reporting Web site for public comment. The data set was reviewed in consideration of the feedback, and a final version was approved by the panel.

Results.—

This data set was developed for gastrectomy specimens for primary gastric carcinomas, including neuroendocrine carcinomas and mixed neuroendocrine-nonneuroendocrine neoplasms. Well-differentiated neuroendocrine tumors, nonepithelial malignancies, and secondary tumors were excluded from this data set. The final data set contains 15 core (required) elements and 8 noncore (recommended) elements. A commentary is provided for each element.

Conclusions.—

The International Collaboration on Cancer Reporting has published freely available, evidence-based data sets for gastric cancer reporting. Standardized reporting has been shown to improve patient care and facilitates data exchange and analysis for quality assurance, cancer epidemiology, and clinical and basic research.

Expression of proteins associated with the Warburg-effect and survival in colorectal cancer

Previous research has suggested that the expression of proteins related to the Warburg effect may have prognostic value in colorectal cancer (CRC), but results remain inconsistent. Our objective was to investigate the relationship between Warburg-subtypes and patient survival in a large population-based series of CRC patients. In the present study, we investigated the expression of six proteins related to the Warburg effect (LDHA, GLUT1, MCT4, PKM2, p53, PTEN) by immunohistochemistry on tissue microarrays (TMAs) from 2,399 incident CRC patients from the prospective Netherlands Cohort Study. Expression levels of the six proteins were combined into a pathway-based sum-score and patients were categorised into three Warburg-subtypes (low/moderate/high). The associations between Warburg-subtypes and CRC-specific and overall survival were investigated using Kaplan-Meier curves and Cox regression models. CRC patients were classified as Warburg-low (n = 695, 29.0%), Warburg-moderate (n = 858, 35.8%) or Warburg-high (n = 841, 35.1%). Patients with Warburg-high CRC had the poorest CRC-specific [hazard ratio (HR) 1.17; 95% CI 1.00-1.38] and overall survival (HR 1.19; 95% CI 1.05-1.35), independent of known prognostic factors. In stratified analyses, this was particularly true for patients with tumour-node-metastasis (TNM) stage III CRC (HR_CRC-specific 1.45; 95% CI 1.10-1.92 and HR_overall 1.47; 95% CI 1.15-1.87), and cancers located in the rectum (HR_overall 1.56; 95% CI 1.15-2.13). To our knowledge, this is the first study to identify the prognostic value of immunohistochemistry-based Warburg-subtypes in CRC. Our data suggest that Warburg-subtypes are related to potentially important differences in CRC survival. Further research is required to validate our findings and to investigate the potential clinical utility of these Warburg-subtypes in CRC.

Histopathological Features in Colonic Biopsies at Diagnosis Predict Long-term Disease Course in Patients with Crohn's Disease

BACKGROUND AND AIMS

Crohn's disease [CD] is characterised by a heterogeneous disease course. Patient stratification at diagnosis using clinical, serological, or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features to discriminate between a long-term mild and severe disease course.

METHODS

Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis were reviewed by two gastrointestinal pathologists after developing a standardised form comprising 15 histopathological features. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristic [ROC] curves. Models were internally validated using bootstrapping to obtain optimism-corrected performance estimates.

RESULTS

In total, 817 biopsies from 137 patients [64 mild, 73 severe cases] were included. Using clinical baseline characteristics, disease course could only moderately be predicted (area under receiver operating characteristic curve [AUROC]: 0.738 [optimism 0.018], 95% confidence interval [CI] 0.65-0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic biopsies a combination of [1] basal plasmacytosis, [2] severe lymphocyte infiltration in lamina propria, [3] Paneth cell metaplasia, and [4] absence of ulcers were identified and resulted in significantly better prediction of a severe course (AUROC: 0.883 [optimism 0.033], 95% CI 0.82-0.94, sensitivity 80.4%, specificity 84.2%).

CONCLUSIONS

In this first study investigating the additive predictive value of histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improving stratification and facilitating clinical decision making.

Validation of the 'Inflammatory Bowel Disease-Distribution, Chronicity, Activity [IBD-DCA] Score' for Ulcerative Colitis and Crohn´s Disease

BACKGROUND AND AIMS

Histological scoring plays a key role in the assessment of disease activity in ulcerative colitis [UC] and is also important in Crohn´s disease [CD]. Currently, there is no common scoring available for UC and CD. We aimed to validate the Inflammatory Bowel Disease [IBD]-Distribution [D], Chronicity [C], Activity [A] score [IBD-DCA score] for histological disease activity assessment in IBD.

METHODS

Inter- and intra-rater reliability were assessed by 16 observers on biopsy specimens from 59 patients with UC and 25 patients with CD. Construct validity and responsiveness to treatment were retrospectively evaluated in a second cohort of 30 patients.

RESULTS

Inter-rater reliability was moderate to good for the UC cohort (intraclass correlation coefficients [ICCs] = 0.645, 0.623, 0.767 for D, C, and A, respectively) and at best moderate for the CD cohort [ICC = 0.690, 0.303, 0.733 for D, C, and A, respectively]. Intra-rater agreement ranged from good to excellent in both cohorts. Correlation with the Nancy Histological Index [NHI] was moderate and strong with the Simplified Geboes Score [SGS] and a Visual Analogue Scale [VAS], respectively. Large effect sizes were obtained for all three parameters. External responsiveness analysis revealed correlated changes between IBD-DCA score and NHI, SGS and VAS.

CONCLUSIONS

The IBD-DCA score is a simple histological activity score for UC and CD, agreed and validated by a large group of IBD specialists. It provides reliable information on treatment response. Therefore, it has potential value for use in routine diagnostics as well as clinical studies.

Spatial profiling of gastric cancer patient-matched primary and locoregional metastases reveals principles of tumour dissemination

OBJECTIVE

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_met).

DESIGN

GC resection samples were annotated to identify primary tumour superficial (PT_sup), primary tumour deep (PT_deep) and LN_met 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.

RESULTS

NanoString profiling of 64 GCs revealed no differences between PT_sup1 and PT_sup2, while 43% of genes were differentially expressed between PT_sup versus PT_deep and 38% in PT_sup versus LN_met. Only 16% of genes were differently expressed between PT_deep and LN_met. Several genes with therapeutic potential (eg IGF1, PIK3CD and TGFB1) were overexpressed in LN_met and PT_deep compared with PT_sup. NGS data revealed orthogonal support of NanoString results with 40% mutations present in PT_deep and/or LN_met, but not in PT_sup. Conversely, only 6% of mutations were present in PT_sup and were absent in PT_deep and LN_met. MLPA demonstrated significant ITH between subregions and progressive genomic changes from PT_sup to PT_deep/LN_met.

CONCLUSION

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.

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.

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Oxaliplatin/capecitabine or carboplatin/paclitaxel-based preoperative chemoradiation for resectable oesophageal adenocarcinoma (NeoSCOPE): Long-term results of a randomised controlled trial

AIM

This is the first randomised study to evaluate toxicity and survival outcomes of two neoadjuvant chemoradiotherapy (CRT) regimens for patients with localised oesophageal adenocarcinoma (OAC) or gastro-oesophageal junction (GOJ) adenocarcinoma. The initial results showed comparable toxicity between regimens and pathological complete response (pCR) rate favouring CarPacRT. Herein, we report survival, progression patterns, and long-term toxicity after a median follow-up of 40.7 months.

METHODS

NeoSCOPE was an open-label, UK multicentre, randomised, phase II trial. Eighty-five patients with resectable OAC or GOJ adenocarcinoma, ≥cT3 and/or ≥cN1 (TNM v7), suitable for neoadjuvant CRT, were recruited between October 2013 and February 2015. Patients were randomised to OxCapRT (oxaliplatin 85 mg/m2 on Days 1, 15, and 29; capecitabine 625 mg/m2 orally twice daily on days of radiotherapy [RT]) or CarPacRT (carboplatin AUC2; paclitaxel 50 mg/m2 on Days 1, 8, 15, 22, and 29). RT dose was 45 Gy/25 fractions/5 weeks. Both arms received induction chemotherapy (two cycles oxaliplatin 130 mg/m2 on Day 1, capecitabine 625 mg/m2 orally twice daily on Days 1-21) before CRT. Surgery was performed 6-8 weeks after CRT. The primary end-point was pCR. Secondary end-points were toxicity, progression-free survival (PFS), overall survival (OS), and patterns of progression.

RESULTS

Eighty-five patients were recruited from 17 UK centres. The median OS was 41.7 months (95% confidence interval [CI] 19.6 to not reached) in the OxCapRT arm and was not reached in the CarPacRT arm (multivariable hazard ratio [HR] = 0.48, 95% CIs: 0.24-0.95, P = 0.035). The median PFS was 32.6 months (95% CIs: 17.1 to not reached) in the OxCapRT arm and was not reached in the CarPacRT arm (multivariable HR = 0.54, 95% CIs: 0.29-1.01, P = 0.053). In both arms, the distant progression was twice as common as locoregional progression.

CONCLUSIONS

OS and PFS favoured neoadjuvant CarPacRT over OxCapRT. Distant was more common than locoregional progression; therefore, priority should be given to optimising the systemic treatment component.

CLINICAL TRIAL INFORMATION

EudraCT Number: 2012-000640-10; ClinicalTrials.gov: NCT01843829.