Background/Objectives: Colorectal cancer (CRC) remains a leading cause of cancer mortality globally, with KRAS mutations occurring in 30–40% of cases, contributing to poor prognosis and resistance to anti-EGFR therapy. This review explores the biological significance, clinical implications, and therapeutic targeting of KRAS mutations in CRC. Methods: A comprehensive analysis of the existing literature and clinical trials was performed, highlighting the role of KRAS mutations in CRC pathogenesis, their impact on prognosis, and recent advancements in targeted therapies. Specific attention was given to emerging therapeutic strategies and resistance mechanisms. Results: KRAS mutations drive tumor progression through persistent activation of MAPK/ERK and PI3K/AKT signaling pathways. These mutations influence the tumor microenvironment, cancer stem cell formation, macropinocytosis, and cell competition. KRAS-mutant CRC exhibits poor responsiveness to anti-EGFR monoclonal antibodies and demonstrates primary and acquired resistance to KRAS inhibitors. Recent breakthroughs include the development of KRAS G12C inhibitors (sotorasib and adagrasib) and promising agents targeting G12D mutations. However, response rates in CRC remain suboptimal compared to other cancers, necessitating combination therapies and novel approaches, such as vaccines, nucleic acid-based therapeutics, and macropinocytosis inhibitors. Conclusions: KRAS mutations are central to CRC pathogenesis and present a significant therapeutic challenge. Advances in KRAS-targeted therapies offer hope for improved outcomes, but resistance mechanisms and organ-specific differences limit efficacy. Continued efforts in personalized treatment strategies and translational research are critical for overcoming these challenges and improving patient survival.

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Purpose: One of the main reasons for cancer treatment resistance is the existence of cancer stem-like cells (CSCs). Here, we elucidated the relationship between low proteasome activity cells (LPACs) and CSCs. Experimental Design: The human colorectal cancer cell lines HCT116, SW480, DLD1, and KM12SM were engineered to stably express a green fluorescent molecule fused to the degron of ornithine decarboxylase, resulting in an accumulation of the fluorescence in LPACs. LPACs were isolated by flow cytometry. Treatment resistance (radio- and chemotherapy) and the capacity of LPACs to act as CSCs were analyzed. Microarray analysis was performed to reveal genes related to treatment resistance. The prognostic impact of potent genes was examined in 190 patients with colorectal cancer. Results: LPACs had a significantly increased capacity for radioresistance and chemoresistance (5-fluorouracil and oxaliplatin), significantly lower reactive oxygen species activity, and significantly increased sphere formation capacity compared with non-LPACs. The number of cells in the G0–G1 phase was significantly higher among LPACs. Subcutaneous injection of as few as 20 LPACs led to tumor formation in immunologically incompetent mice. Microarray analysis revealed that the expression of EP300-interacting inhibitor of differentiation 3 (EID3) was significantly increased in LPACs. In vitro assay revealed that EID3 positively controlled cell proliferation and treatment resistance. The high expression of EID3 was an adverse prognostic indicator in patients with colorectal cancer (P = 0.0400). Conclusions: LPACs have characteristic treatment resistance and act as CSCs in colorectal cancer. In addition, EID3 is one of the potential regulators of treatment resistance in colorectal cancer and may be a potential therapeutic target. Clin Cancer Res; 22(21); 5277–86. ©2016 AACR.

The adenosine triphosphate-binding cassette transporter-homologous gene ATP11A belongs to an extended family of adenosine triphosphate-binding cassette transporters. We analysed the ATP11A gene in 7 colorectal cancer cell lines and 95 paired cases of colorectal cancer and non-cancerous regions to demonstrate the importance of ATP11A expression in colorectal cancer prognosis. ATP11A was expressed in the 7 colorectal cancer cell lines. ATP11A mRNA expression was higher in colorectal cancer tissue than in corresponding normal tissue (P<0.001). Patients with high ATP11A expression showed a poorer disease-free survival rate compared to those with low expression (P<0.001), thus indicating that increase in ATP11A expression was an independent predictor of metachronous metastasis of colorectal cancer. The present study suggests that ATP11A is a useful predictive marker of metastasis in colorectal cancer patients.

The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice.