A microsatellite based multiplex PCR method for the detection of chromosomal instability in gastric cancer

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

The reckoning of chromosomal instability: past, present, future

Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.

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

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

Molecular subtyping of gastroesophageal dysplasia heterogeneity according to TCGA/ACRG classes

Gastric adenocarcinoma has recently been classified into several subtypes on the basis of molecular profiling, which has been successfully reproduced by immunohistochemistry (IHC) and in situ hybridization (ISH). A series of 73 gastroesophageal dysplastic lesions (37 gastric dysplasia and 36 Barrett dysplasia; 44 low-grade dysplasia and 29 high-grade dysplasia) was investigated for mismatch repair proteins, E-cadherin, p53, and EBER status, to reproduce The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG) molecular clustering. Overall, the dysplastic lesions were classified as follows: according to TCGA classification, EBV, 0/73 (0%), MSI, 6/73 (8.2%), GS, 4/73 (5.5%), CIN, 63/73 (86.3%); according to ACRG molecular subtyping, MSI, 6/73 (8.2%), MSS/EMT, 4/73 (5.5%), MSS/TP53⁻, 33/73 (45.2%), MSS/TP53⁺, 30/73 (41.1%). A positive association was found between MSS/TP53⁻ and Barrett dysplasia (p = 0.0004), between MSS/TP53⁺ and LG dysplasia (p = 0.001) and between MSS/TP53⁺ and gastric dysplasia (p = 0.0018). Gastroesophageal dysplastic lesions proved to be heterogenous in terms of TCGA/ACRG classes, but with a different distribution from that of cancers, with no EBV-positive cases, an increasing presence of mismatch repair deficiency from low grade to high grade lesions, and a prevalence of p53 aberrations in Barrett dysplasia. The present study further demonstrated that gastroesophageal dysplastic lesions may be characterized by alterations in predictive/prognostic biomarkers, and this should be considered in routine diagnostic.

Deep Learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin-eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, Deep Learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. This article is protected by copyright. All rights reserved.

Anatomic Subsites and Prognosis of Gastric Signet Ring Cell Carcinoma: A SEER Population-Based 1 : 1 Propensity-Matched Study

Background. The dismal prognosis of gastric signet ring cell carcinoma (GSRC) is a global problem. The current study is conducted to comprehensively evaluate clinicopathological features and survival outcomes in GSRC patients stratified by anatomic subsites. Then, predictive nomograms are constructed and validated to improve the effectiveness of personalized management. Method. The patients diagnosed with GSRC were recruited from the online SEER database. The influence of anatomic subsites on overall survival (OS) and cancer-specific survival (CSS) was evaluated using multivariate Cox regression and Kaplan-Meier analysis. Then, we employed propensity score matching (PSM) technique to decrease selection bias and balance patients’ epidemiological factors. Predictive nomograms were constructed and validated. Sensitivity analysis was performed to validate the conclusion. Results. Multivariate Cox regression demonstrated that the patients with overlapping gastric cancer (OGC) suffered the highest mortality risk for OS (HR, 1.29; 95% CI, 1.23-1.36; $P<0.001$ ) and CSS (HR, 1.33; 95% CI, 1.28-1.37; $P<0.001$ ). Age, TNM stage, tumor localization, tumor size, surgery, and chemotherapy presented a highly significant relationship with OS and CSS. Following subgroup and PSM analysis, OGC patients were confirmed to have the worst OS and CSS. Then, nomograms predicting 6-month, 12-month, and 36-month survival were constructed. The area under the curve (AUC) value in ROC was 0.775 (95% CI, 0.761-0.793) for 6-month survival, 0.789 (95% CI, 0.776-0.801) for 12-month survival, and 0.780 (95% CI, 0.765-0.793) for 36-month survival in the OS group, while in the CSS group, it was 0.771 (95% CI, 0.758-0.790) for 6-month survival, 0.781 (95% CI, 0.770-0.799) for 12-month survival, and 0.773 (95% CI, 0.762-0.790) for 36-month survival. Conclusion. We identified anatomic subsites as a predictor of survival in those with GSRC. Patients with OGC suffered the highest mortality risk. The proposed nomograms allowed a relatively accurate survival prediction for GSRC patients.

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

BACKGROUND

The Cancer Genome Atlas (TCGA) consortium described EBV positivity(+), high microsatellite instability (MSI-H), genomic stability (GS) and chromosomal instability (CIN) as molecular subtypes in gastric carcinomas (GC). We investigated the predictive and prognostic value of these subtypes with emphasis on CIN in the context of neoadjuvant chemotherapy (CTx) in GC.

METHODS

TCGA subgroups were determined for 612 resected adenocarcinomas of the stomach and gastro-oesophageal junction (291 without, 321 with CTx) and 143 biopsies before CTx. EBV and MSI-H were analysed by standard assays. CIN was detected by multiplex PCRs analysing 22 microsatellite markers. Besides the TCGA classification, CIN was divided into four CIN-subgroups: low, moderate, substantial, high. Mutation profiling was performed for 52 tumours by next-generation sequencing.

RESULTS

EBV(+) (HR, 0.48; 95% CI, 0.23-1.02), MSI-H (HR, 0.56; 95% CI, 0.35-0.89) and GS (HR, 0.72; 95% CI, 0.45-1.13) were associated with increased survival compared to CIN in the resected tumours. Considering the extended CIN-classification, CIN-substantial was a negative prognostic factor in uni- and multivariable analysis in resected tumours with CTx (each p < 0.05). In biopsies before CTx, CIN-high predicted tumour regression (p = 0.026), but was not prognostically relevant.

CONCLUSION

A refined CIN classification reveals tumours with different biological characteristics and potential clinical implications.

Detecting Chromosome Instability in Cancer: Approaches to Resolve Cell-to-Cell Heterogeneity

Chromosome instability (CIN) is defined as an increased rate of chromosome gains and losses that manifests as cell-to-cell karyotypic heterogeneity and drives cancer initiation and evolution. Current research efforts are aimed at identifying the etiological origins of CIN, establishing its roles in cancer pathogenesis, understanding its implications for patient prognosis, and developing novel therapeutics that are capable of exploiting CIN. Thus, the ability to accurately identify and evaluate CIN is critical within both research and clinical settings. Here, we provide an overview of quantitative single cell approaches that evaluate and resolve cell-to-cell heterogeneity and CIN, and discuss considerations when selecting the most appropriate approach to suit both research and clinical contexts.