The Molecular Associations of Signet-Ring Cell Carcinoma in Colorectum: Meta-Analysis and System Review

Unraveling the complexities of colorectal cancer and its promising therapies - An updated review

Colorectal cancer (CRC) continues to be a global health concern, necessitating further research into its complex biology and innovative treatment approaches. The etiology, pathogenesis, diagnosis, and treatment of colorectal cancer are summarized in this thorough review along with recent developments. The multifactorial nature of colorectal cancer is examined, including genetic predispositions, environmental factors, and lifestyle decisions. The focus is on deciphering the complex interactions between signaling pathways such as Wnt/β-catenin, MAPK, TGF-β as well as PI3K/AKT that participate in the onset, growth, and metastasis of CRC. There is a discussion of various diagnostic modalities that span from traditional colonoscopy to sophisticated molecular techniques like liquid biopsy and radiomics, emphasizing their functions in early identification, prognostication, and treatment stratification. The potential of artificial intelligence as well as machine learning algorithms in improving accuracy as well as efficiency in colorectal cancer diagnosis and management is also explored. Regarding therapy, the review provides a thorough overview of well-known treatments like radiation, chemotherapy, and surgery as well as delves into the newly-emerging areas of targeted therapies as well as immunotherapies. Immune checkpoint inhibitors as well as other molecularly targeted treatments, such as anti-epidermal growth factor receptor (anti-EGFR) as well as anti-vascular endothelial growth factor (anti-VEGF) monoclonal antibodies, show promise in improving the prognosis of colorectal cancer patients, in particular, those suffering from metastatic disease. This review focuses on giving readers a thorough understanding of colorectal cancer by considering its complexities, the present status of treatment, and potential future paths for therapeutic interventions. Through unraveling the intricate web of this disease, we can develop a more tailored and effective approach to treating CRC.

Individualized prediction of conditional survival for colorectal signet-ring cell carcinoma patients

Background

Conditional survival (CS) considers the time already survived after surgery and may provide additional survival information. The authors sought to construct and validate novel conditional survival nomograms for the prediction of conditional overall survival (OS) and cancer-specific survival (CSS) of colorectal signet-ring cell carcinoma (SRCC) patients.

Methods

Patients diagnosed with stage I-III SRCC between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The formula calculating CS was: CS(x|y) = S(x+y)/S(x), where S(x) represents the survival at x years. CS nomograms were then constructed to predict the 5-year conditional OS and CSS, followed by internal validation.

Results

A total of 944 colorectal SRCC patients were finally identified in this study. The 5-year OS and CSS improved gradually with additional survival time. Univariate and multivariate Cox regression analysis conducted in training set revealed that age, race, T stage, LNR, and perineural invasion were independent risk factors for both OS and CSS. Two nomograms with considerable predictive ability were successfully constructed [area under the curve (AUC) for OS: 0.788; AUC for CSS: 0.847] and validated (AUC for OS: 0.773; AUC for CSS: 0.799) for the prediction of 5-year OS and CSS, based on the duration of 1-4 years post-surgery survival.

Conclusions

The probability of achieving 5-year OS and 5-year CSS in colorectal SRCC patients improved gradually with additional time. Conditional nomograms considering survival time will be more reliable and informative for risk stratification and postoperative follow-up.

Location Has Prognostic Impact on the Outcome of Colorectal Mucinous Adenocarcinomas

Mucinous (colloid) adenocarcinomas (MAs) are a rare histological subtype of tumors defined by extracellular mucin comprising more than 50% of the tumor. These tumors are on a continuum of mucin-producing malignancies with signet ring cell adenocarcinomas (SRCCs), which instead produce intracellular mucin. Mucin-containing cancers occur primarily in the stomach and colon, where for SRCCs, outcomes are relatively worse in the proximal stomach and the rectum. It is not known if MAs have similar outcomes. In this study, we use the Surveillance, Epidemiology, and End Results (SEER) database to examine the effects of tumor localization, age, sex, and stage on colorectal and gastric cancer outcomes for MAs. For right colon cancers, MAs are more common, particularly in females, and have slightly better or equivalent outcomes across all stages and ages compared to conventional adenocarcinomas, but outcomes are progressively worse compared to conventional adenocarcinomas for left colon and rectal cancers. Unlike SRCCs, MAs have similar outcomes to conventional adenocarcinomas in all stomach locations. Overall, these results suggest that MAs have an intrinsically different tumor biology in the left colon and rectum that promotes pathogenesis. Decoding this phenomenon could lead to more effectively tailored patient treatment regimens.

Effects of Tumor Localization, Age, and Stage on the Outcomes of Gastric and Colorectal Signet Ring Cell Adenocarcinomas

Signet ring cell adenocarcinomas (SRCCs) are a rare histological adenocarcinoma subtype, classically thought to have a worse prognosis than conventional adenocarcinomas. The majority of these cancers occur in the stomach, colon, and rectum. Their rarity means that most epidemiological studies into their pathology are often underpowered, and interpretations from these reports are mixed. In this study, we use the Surveillance, Epidemiology, and End Results Program (SEER) database to examine the effects of tumor localization, age, and stage on gastric and colorectal cancer outcomes. For early onset localized and regional gastric cancers, SRCCs have the same overall risk of mortality compared to conventional adenocarcinomas. Over the age of 50 years, SRCCs have worse outcomes across all stages. Gastric SRCCs are 2-3-fold more likely in younger patients, and more heavily favor the distal stomach. Like conventional adenocarcinomas, proximal gastric SRCCs have decreased survival. Across all ages, stages, and locations, colorectal SRCCs have worse outcomes. SRCCs favor the right colon, but outcomes are significantly worse for the left colon and rectal cancers. Relative to adenocarcinomas, colorectal SRCCs have the worst outcomes in younger patients. Overall, these results provide insights into SRCC disease patterns that cannot be surmised outside of population-level data.

The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers

Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.