Heterogeneity in Colorectal Cancer: A Challenge for Personalized Medicine?

High expression of RUNX1 in colorectal cancer subtype accelerates malignancy by inhibiting HMGCR

Colorectal cancer (CRC) presents a complex landscape, characterized by both inter-tumor and intra-tumor heterogeneity. RUNX1, a gene implicated in modulating tumor cell growth, survival, and differentiation, remains incompletely understood regarding its impact on CRC prognosis. In our investigation, we discerned a positive correlation between elevated RUNX1 expression and aggressive phenotypes across various CRC subtypes. Notably, knockdown of RUNX1 demonstrated efficacy in restraining CRC proliferation both in vitro and in vivo, primarily through inducing apoptosis and impeding cell proliferation. Mechanistically, we unveiled a direct regulatory link between RUNX1 and cholesterol synthesis, mediated by its control over HMGCR expression. Knockdown of RUNX1 in CRC cells triggered HMGCR transcriptional activation, culminating in elevated cholesterol levels that subsequently hindered cancer progression. Clinically, heightened RUNX1 expression emerged as a prognostic marker for adverse outcomes in CRC patients. Our findings underscore the pivotal involvement of RUNX1 in CRC advancement and its potential as a therapeutic target. The unique influence of RUNX1 on cholesterol synthesis and HMGCR transcriptional regulation uncovers a novel pathway contributing to CRC progression.

Post recurrence survival in early versus late period and its prognostic factors in rectal cancer patients

To identify factors associated with post-recurrence survival (PRS), we examined our institutional recurrence patterns following definitive resection for rectal cancer. We reviewed all patients with rectal cancer diagnosed at three hospitals in the east of Iran from 2011 to 2020. The optimal cut-off value was determined by receiver operating characteristic (ROC) analysis to determine early recurrence. The effect of recurrence time was evaluated on PRS. 326 eligible patients with a mean ± SD age of 56 ± 12.8 years were included in this study. In a median (IQR: Inter-quartile range) follow-up time of 76 (62.2) months, 106 (32.5%) patients experienced at least any recurrence (locoregional or distant metastasis) following primary resection. The median (IQR) time from initial surgery to recurrence was 29.5 (31.2) months. Based on ROC analysis, early recurrence was specified at ≤ 29 months. However, for the patients who experienced only locoregional recurrence, 33 months was the cut-off to define early recurrence. Recurrence time and recurrence management were both significant variables on PRS. Moreover, TNM staging was significantly associated with early recurrence (P = 0.003). In this research, recurrence time, recurrence management and TNM staging were found to be correlated with PRS.

Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer

As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication. Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis. Both manual and automated scores were correlated to clinicopathological and survival data. E-cadherin data generated through digital image analysis was superior to manual evaluation when investigating for clinicopathological correlations in colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = <0.001), higher T-stage (p = <0.001), higher grade (p = <0.001), N2 nodal stage (p = <0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) survival status. Manual assessment was only correlated with higher grade tumours, though other correlations become apparent only when assessed for morphological expression pattern (circumferential, basolateral, parallel) irrespective of intensity. Digital assessment of E-cadherin is effective for prognostication of colorectal cancer and may potentially offer benefits of improved objectivity, accuracy, and economy of time. Incorporating tools to assess patterns of staining may further improve such digital assessment in the future.

Association of Clinicopathological Factors With MMP13 (rs2252070) Gene Polymorphism in Swedish Patients With Colorectal Cancer

BACKGROUND/AIM

Matrix metalloproteinase 13 (MMP13) has been reported to be involved in tumor development and progression, including of colorectal cancer (CRC). This study aimed at evaluating whether the MMP13 rs2252070 gene polymorphism is associated with clinicopathological factors and its influence on long-term survival in Swedish patients with CRC.

PATIENTS AND METHODS

A total of 723 patients with CRC were genotyped using TaqMan single nucleotide polymorphism assays based on polymerase chain reaction.

RESULTS

Assessing clinicopathological factors, we demonstrated that having the G/G genotype for MMP13 rs2252070 was significantly associated with poor differentiation, higher serum level of carcinoembryonic antigen and higher lymph node status. Moreover, the presence of a G allele was significantly related to larger tumor size in rectal cancer but had a significantly protective role against mucinous cancer, perineural invasion and lymphovascular invasion. Kaplan-Meier analysis showed no difference between genotypes regarding cancer-specific survival.

CONCLUSION

Our findings highlight the potential of MMP13 rs2252070 polymorphism as a useful predictor of poor differentiation, serum level of carcinoembryonic antigen, lymph node status, tumor size, mucinous cancer, perineural invasion and lymphovascular invasion in patients with CRC.

Large-scale external validation and meta-analysis of gene methylation biomarkers in tumor tissue for colorectal cancer prognosis

BACKGROUND

DNA methylation biomarkers in colorectal cancer (CRC) tissue hold potential as prognostic indicators. However, individual studies have yielded heterogeneous results, and external validation is largely absent. We conducted a comprehensive external validation and meta-analysis of previously suggested gene methylation biomarkers for CRC prognosis.

METHODS

We performed a systematic search to identify relevant studies investigating gene methylation biomarkers for CRC prognosis until March 2024. Our external validation cohort with long-term follow-up included 2303 patients with CRC from 22 hospitals in southwest Germany. We used Cox regression analyses to assess associations between previously suggested gene methylation biomarkers and prognosis, adjusting for clinical variables. We calculated pooled hazard ratios (HRs) and their 95% confidence intervals (CIs) using random-effects models.

FINDINGS

Of 151 single gene and 29 multiple gene methylation biomarkers identified from 121 studies, 37 single gene and seven multiple gene biomarkers were significantly associated with CRC prognosis after adjustment for clinical variables. Moreover, the directions of these associations with prognosis remained consistent between the original studies and our validation analyses. Seven single biomarkers and two multi-biomarker signatures were significantly associated with CRC prognosis in the meta-analysis, with a relatively strong level of evidence for CDKN2A, WNT5A, MLH1, and EVL.

INTERPRETATION

In a comprehensive evaluation of the so far identified gene methylation biomarkers for CRC prognosis, we identified candidates with potential clinical relevance for further investigation.

FUNDING

The German Research Council, the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, the German Federal Ministry of Education and Research.

Metastatic Colorectal Cancer 2.0

Colorectal cancer (CRC) ranks third in frequency among cancers diagnosed in males and second in females [...].

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning

Objective

The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data.

Materials and methods

The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively.

Results

A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS).

Conclusions

Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.

Molecular insights into clinical trials for immune checkpoint inhibitors in colorectal cancer: Unravelling challenges and future directions

Colorectal cancer (CRC) is a complex disease with diverse etiologies and clinical outcomes. Despite considerable progress in development of CRC therapeutics, challenges remain regarding the diagnosis and management of advanced stage metastatic CRC (mCRC). In particular, the five-year survival rate is very low since mCRC is currently rarely curable. Over the past decade, cancer treatment has significantly improved with the introduction of cancer immunotherapies, specifically immune checkpoint inhibitors. Therapies aimed at blocking immune checkpoints such as PD-1, PD-L1, and CTLA-4 target inhibitory pathways of the immune system, and thereby enhance anti-tumor immunity. These therapies thus have shown promising results in many clinical trials alone or in combination. The efficacy and safety of immunotherapy, either alone or in combination with CRC, have been investigated in several clinical trials. Clinical trials, including KEYNOTE-164 and CheckMate 142, have led to Food and Drug Administration approval of the PD-1 inhibitors pembrolizumab and nivolumab, respectively, for the treatment of patients with unresectable or metastatic microsatellite instability-high or deficient mismatch repair CRC. Unfortunately, these drugs benefit only a small percentage of patients, with the benefits of immunotherapy remaining elusive for the vast majority of CRC patients. To this end, primary and secondary resistance to immunotherapy remains a significant issue, and further research is necessary to optimize the use of immunotherapy in CRC and identify biomarkers to predict the response. This review provides a comprehensive overview of the clinical trials involving immune checkpoint inhibitors in CRC. The underlying rationale, challenges faced, and potential future steps to improve the prognosis and enhance the likelihood of successful trials in this field are discussed.

Diagnostic Utility of Next-Generation Sequencing in Circulating Free DNA and a Comparison With Matched Tissue in Gallbladder Carcinoma

Mutation detection for therapy monitoring in cell-free DNA (cfDNA) is used clinically for some malignancies. Gallbladder carcinoma (GBC) presents a diagnostic challenge and has limited late-stage treatment options. To our knowledge, this novel study examines, for the first time, genomic alterations in cfDNA from GBC to assess diagnostic accuracy and therapeutic options. The concordance of somatic genomic changes in cfDNA and DNA from paired tumor tissue was analyzed. Paired serum and tissue samples from 40 histologically proven GBC, 20 cholecystitis, and 4 normal (noninflamed gallbladder) controls were included. Targeted next-generation sequencing with a 22-gene panel (Colon and Lung Cancer Research Panel v2, Thermo Scientific) in cfDNA and tumor tissue with high depth and uniform coverage on ION Personal Genome Machine (ION, PGM) was performed. A spectrum of 223 mutations in cfDNA and 225 mutations in formalin-fixed paraffin-embedded tissue DNA were identified in 22 genes. Mutations ranged from 1 to 17 per case. In cfDNA frequent alterations were in TP53 (85.0%), EGFR (52.5%), MET (35%) CTNNB1, SMAD4, BRAF (32.5%), PTEN (30%), FGFR3 and PIK3CA (27.5%), NOTCH1 (25.0%), and FBXW7 and ERBB4 (22.5%). At least one clinically actionable mutation was identified in all cfDNA samples. Paired samples shared 149 of 225 genetic abnormalities (66.2%). Individual gene mutation concordance ranged from 44.44% to 82.0% and was highest for EGFR (82.0%), BRAF and NOTCH1 (80.0%), TP53 (73.08%), MET (72.22%), and ERBB4 (71.42%) with a significant level of correlation (Spearman r = 0.91, P ≤ .0001). The sensitivity and specificity of the TP53 gene at the gene level was the highest (94.44% and 100.0%, respectively). Overall survival was higher for ERBB4 and ERBB2 mutant tumors. The adenocarcinoma subtype revealed specific genetic changes in ERBB4, SMAD4, ERBB2, PTEN, KRAS, and NRAS. NGS-based cfDNA mutation profiling can be used to diagnose GBC before surgery to guide treatment decisions. Targeted therapy identified in GBC included SMAD4, ERBB2, ERBB4, EGFR, KRAS, BRAF, PIK3CA, MET, and NRAS.

Targeted Nanoparticles: the Smart Way for the Treatment of Colorectal Cancer

Colorectal cancer (CRC) is a widespread cancer that starts in the digestive tract. It is the third most common cause of cancer deaths around the world. The World Health Organization (WHO) estimates an expected death toll of over 1 million cases annually. The limited therapeutic options as well as the drawbacks of the existing therapies necessitate the development of non-classic treatment approaches. Nanotechnology has led the evolution of valuable drug delivery systems thanks to their ability to control drug release and precisely target a wide variety of cancers. This has also been extended to the treatment of CRC. Herein, we shed light on the pertinent research that has been performed on the potential applications of nanoparticles in the treatment of CRC. The various types of nanoparticles in addition to their properties, applications, targeting approaches, merits, and demerits are discussed. Furthermore, innovative therapies for CRC, including gene therapies and immunotherapies, are also highlighted. Eventually, the research gaps, the clinical potential of such delivery systems, and a future outlook on their development are inspired.

Spatial Transcriptomic Profiling of Tetraspanins in Stage 4 Colon Cancer from Primary Tumor and Liver Metastasis

Stage 4 colon cancer (CC) presents a significant global health challenge due to its poor prognosis and limited treatment options. Tetraspanins, the transmembrane proteins involved in crucial cancer processes, have recently gained attention as diagnostic markers and therapeutic targets. However, their spatial expression and potential roles in stage 4 CC tissues remain unknown. Using the GeoMx digital spatial profiler, we profiled all 33 human tetraspanin genes in 48 areas within stage 4 CC tissues, segmented into immune, fibroblast, and tumor compartments. Our results unveiled diverse gene expression patterns across different primary tumor sub-regions. CD53 exhibited distinct overexpression in the immune compartment, hinting at a potential role in immune modulation. TSPAN9 was specifically overexpressed in the fibroblast compartment, suggesting involvement in tumor invasion and metastasis. CD9, CD151, TSPAN1, TSPAN3, TSPAN8, and TSPAN13 displayed specific overexpression in the tumor compartment, indicating potential roles in tumor growth. Furthermore, our differential analysis revealed significant spatial changes in tetraspanin expression between patient-matched stage 4 primary CC and metastatic liver tissues. These findings provide spatially resolved insights into the expression and potential roles of tetraspanins in stage 4 CC progression, proposing their utility as diagnostic markers and therapeutic targets. Understanding this landscape is beneficial for tailoring therapeutic strategies to specific sub-tumor regions in the context of stage 4 CC and liver metastasis.

Adaptive Control of Tumor Growth

Cancer treatment optimizations select the most optimum combinations of drugs, sequencing schedules, and appropriate doses that would limit toxicity and yield an improved patient quality of life. However, these optimizations often lack an adequate consideration of cancer's near-infinite potential for evolutionary adaptation to therapeutic interventions. Adapting cancer therapy based on monitored tumor burden and clonal composition is an intuitively sound approach to the treatment of cancer as an inherently complex and adaptive system. The adaptation would be driven by clinical outcome setpoints embodying the aims to thwart therapeutic resistance and maintain a long-term management of the disease or even a cure. However, given the nonlinear, stochastic dynamics of tumor response to therapeutic interventions, adaptive therapeutic strategies may at least need a one-step-ahead prediction of tumor burden to maintain their control over tumor growth dynamics. The article explores the feasibility of adaptive cancer treatment driven by tumor state feedback assuming cell adaptive fitness to be the underlying source of phenotypic plasticity and pathway entropy as a biomarker of tumor growth trajectory. The exploration is undertaken using deterministic and stochastic models of tumor growth dynamics.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer

INTRODUCTION

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments.

METHOD

IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC.

CONCLUSION

Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

Exploring extracellular matrix and prostaglandin pathway alterations across varying resection margin distances of right-sided colonic adenocarcinoma

BACKGROUND

Surgical resection followed by indicated adjuvant therapy offers potential curative treatment in colonic adenocarcinoma. Beyond the well-established seed and soil theory of colon cancer progression, the 'normal-appearing' tissues near the tumor are not genuinely normal and remain as remnants in patients following surgery. Our objective was to elucidate the alteration of gene expression and pathways across various distances of resection margins in right-sided colonic adenocarcinoma.

METHODS

Twenty-seven fresh samples of primary cancer and 56 matched non-tumor tissues adjacent to the tumor (NAT) were collected from patients with resectable right-sided colon cancer. NAT were systematically obtained at varying distances (1, 5, and 10 cm) on both proximal and distal sides. Comprehensive gene expression analysis was performed using 770-gene PanCancer Progression Panel, delineating distinctive pathways and functional predictions for each region.

RESULTS

Distinctive gene signatures and pathways exhibited by normal-appearing tissues were discovered at varying distances from cancer. Notably, SFRP2, PTGDS, COL1A1, IL1B, THBS2, PTGIS, COL1A2, NPR1, and BGN were upregulated, while ENPEP, MMP1, and NRCAM were downregulated significantly in 1-cm tissue compared to farther distances. Substantial alterations in the extracellular matrix (ECM) and prostaglandin/thromboxane synthesis were significantly evident at the 1-cm distance. Functional analysis indicated enhanced cell viability and survival, alongside reduced cellular death and apoptosis.

CONCLUSIONS

Different distances exerted a significant impact on gene alteration within the normal-looking mucosa surrounding primary cancer, influenced by various mechanisms. These findings may highlight potential therapeutic targets related to the ECM and prostaglandin/thromboxane pathways for treatment strategies.

The Importance of Feasibility Assessment in the Design of ctDNA Guided Trials - Results From the OPTIPAL II Study

INTRODUCTION

Both quantitative and molecular changes in ctDNA can hold important information when treating metastatic colorectal cancer (mCRC), but its clinical utility is yet to be established. Before conducting a large-scale randomized trial, it is essential to test feasibility. This study investigates whether ctDNA is feasible for detecting patients who will benefit from treatment with epidermal growth factor receptor inhibitors and the prognostic value of circulating tumor DNA (ctDNA) response.

MATERIALS AND METHODS

Patients with mCRC, who were considered for systemic palliative treatment and were eligible for ctDNA analysis. Mutational testing on cell-free DNA (cfDNA) was done by ddPCR. ctDNA response from baseline to the third treatment cycle was evaluated in patients with detectable ctDNA at baseline. ctDNA maximum response was defined as undetectable ctDNA at the third treatment cycle, ctDNA partial response as any decrease in the ctDNA level, and ctDNA progression as any increase in the ctDNA level.

RESULTS

Forty-nine patients were included. The time to test results for mutational testing on cfDNA was significantly shorter than on tumor tissue (p < .001). Progression-free survival were 11.2 months (reference group), 7.5 months (HR = 10.7, p= .02), and 4.6 months (HR = 11.4, p= .02) in patients with ctDNA maximum response, partial response, and progression, respectively. Overall survival was 31.2 months (reference group), 15.2 months (HR = 4.1, p= .03), and 9.0 months (HR = 2.6, p= .03) in patients with ctDNA maximum response, partial response, and progression, respectively.

CONCLUSION

Pretreatment mutational testing on cfDNA in daily clinic is feasible and can be applied in randomized clinical trials evaluating the clinical utility of ctDNA. Early dynamics in ctDNA during systemic treatment hold prognostic value.

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data

Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03\% \pm 5.37\%$, $97.42\% \pm 3.94\%$, $98.27\% \pm 1.81\%$ and $93.04\% \pm 6.88\%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Intratumor heterogeneity in colorectal cancer: Distribution of tumor suppressor gene variants with regard to patient lymph node status

Intratumor heterogeneity (ITH) results from accumulation of somatic mutations in the fractions of successive cancer cell generations. We aimed to use deep sequencing to investigate ITH in colorectal tumors with particular emphasis on variants in oncogenes (ONC) and tumor suppressor genes (TSG). Samples were collected from 16 patients with colorectal cancer and negative or positive lymph node status (n = 8 each). We deep-sequenced a panel of 56 cancer-related genes in the central and peripheral locations of T3 size primary tumors and healthy mucosa. The central region of T3 tumors has a different frequency profile and composition of genetic variants. This mutation profile is capable of independently discriminating patients with different lymph node status (p = 0.028) in the central region. We noted an increasing number of mutations outside of the central region of the tumor and a higher number of mutations in tumors from node-positive patients. Unexpectedly, in the healthy mucosa, we identified somatic mutations with variant allele frequencies, characteristic not only of heterozygotes and homozygotes but also of other discrete peaks (e.g., around 10%, 20%), suggestive of clonal expansion of certain mutant alleles. We found differences in the distribution of variant allele frequencies in TSGs when comparing node-negative and node-positive tumors (p = 0.029), as well as central and peripheral regions (p = 0.00399). TSGs may play an important role in the escape of the tumor toward metastatic colonization.

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

BACKGROUND

The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.

AIM

Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free-text medical record data and structured laboratory data to predict LM in postoperative CRC patients.

METHODS

We used a robust dataset of 1463 patients and leveraged state-of-the-art natural language processing (NLP) and machine learning techniques to construct a two-layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two-tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free-text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score-based nomogram using the top 13 valid predictors identified in our study.

RESULTS

The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs.

CONCLUSION

This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision-making.

Dynamic Monitoring of Circulating Tumor DNA in Patients With Metastatic Colorectal Cancer

PURPOSE

Plasma circulating tumor DNA (ctDNA) is a valuable resource for tumor characterization and for monitoring of residual disease during treatment; however, it is not yet introduced in metastatic colorectal cancer (mCRC) routine clinical practice. In this retrospective exploratory study, we evaluated the role of ctDNA in patients with mCRC treated with chemotherapy plus bevacizumab.

MATERIALS AND METHODS

Fifty-three patients were characterized for RAS and BRAF status on tumor tissue before the start of treatment. Plasma was collected at baseline, at first clinical evaluation, and at disease progression. ctDNA analysis was performed using Oncomine Colon cfDNA Assay on the Ion S5 XL instrument.

RESULTS

At baseline, from a plasma sample, RAS, BRAF, or PIK3CA mutations were detected in 44 patients. A high correspondence was observed between ctDNA and tumor tissue mutations (KRAS 100%, NRAS 97.9%, BRAF 97.9%, PIK3CA 90%). Low baseline variant allele frequency (VAF) was found to be associated with longer median progression-free survival (PFS) compared with those with high VAF (15.9 v 12.2 months, P = .02). A higher PFS {12.29 months (95% CI, 9.03 to 17.9) v 8.15 months (95% CI, 2.76 to not available [NA]), P = .04} and overall survival (34.1 months [95% CI, 21.68 to NA] v 11.1 months [95% CI, 3.71 to NA], P = .003) were observed in patients with large decline in VAF at first evaluation.

CONCLUSION

ctDNA analysis is useful for molecular characterization and tumor response monitoring in patients with mCRC. Quantitative variations of released ctDNA are associated with clinical outcomes.

Ibero-American Consensus Review and Incorporation of New Biomarkers for Clinical Practice in Colorectal Cancer

Advances in genomic technologies have significantly improved the management of colorectal cancer (CRC). Several biomarkers have been identified in CRC that enable personalization in the use of biologic agents that have shown to enhance the clinical outcomes of patients. However, technologies used for their determination generate massive amounts of information that can be difficult for the clinician to interpret and use adequately. Through several discussion meetings, a group of oncology experts from Spain and several Latin American countries reviewed the latest literature to provide practical recommendations on the determination of biomarkers in CRC based on their clinical experience. The article also describes the importance of looking for additional prognostic biomarkers and the use of histopathology to establish an adequate molecular classification. Present and future of immunotherapy biomarkers in CRC patients are also discussed, together with several techniques for marker determination, including liquid biopsy, next-generation sequencing (NGS), polymerase chain reaction (PCR), and fecal immunohistochemical tests. Finally, the role of Molecular Tumor Boards in the diagnosis and treatment of CRC is described. All of this information will allow us to highlight the importance of biomarker determination in CRC.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Senescence-based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies

Cellular senescence has been listed as a hallmark of cancer, but its role in colorectal cancer (CRC) remains unclear. We comprehensively evaluated the transcriptome, genome, digital pathology, and clinical data from multiple datasets of CRC patients and proposed a novel senescence subtype for CRC. Multi-omics data was used to analyze the biological features, tumor microenvironment, and mutation landscape of senescence subtypes, as well as drug sensitivity and immunotherapy response. The senescence score was constructed to better quantify senescence in each patient for clinical use. Unsupervised learning revealed three transcriptome-based senescence subtypes. Cluster 1, characterized by low senescence and activated proliferative pathways, was sensitive to chemotherapeutic drugs. Cluster 2, characterized by intermediate senescence and high immune infiltration, exhibited significant immunotherapeutic advantages. Cluster 3, characterized by high senescence, high immune, and stroma infiltration, had a worse prognosis and maybe benefit from targeted therapy. We further constructed a senescence scoring system based on seven senescent genes through machine learning. Lower senescence scores were highly predictive of longer disease-free survival, and patients with low senescence scores may benefit from immunotherapy. We proposed the senescence subtypes of CRC and our findings provide potential treatment interventions for each CRC senescence subtype to promote precision treatment.

Advances in Mapping Tumor Progression from Precancer Atlases

Tissue profiling technologies present opportunities for understanding transition from precancerous lesions to malignancy, which may impact risk stratification, prevention, and even cancer treatment. A human precancer atlas building effort is ongoing to tackle the significant challenge of decoding the heterogeneity among cells, specimens, and patients. Here, we discuss the findings resulting from atlases built across precancer types, including those found in colon, breast, lung, stomach, cervix, and skin, using bulk, single-cell, and spatial profiling strategies. We highlight two main themes that emerge across precancer types: the ordering of molecular events that occur during tumor progression and the fluctuation of microenvironmental response during precancer progression. We further highlight the key challenges of data integration across large cohorts of patients, and the need for computational tools to reliably annotate and quality control high-volume, high-dimensional data.

Colorectal cancer and therapy response: a focus on the main mechanisms involved

Introduction

The latest GLOBOCAN 2021 reports that colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Most CRC cases are sporadic and associated with several risk factors, including lifestyle habits, gut dysbiosis, chronic inflammation, and oxidative stress.

Aim

To summarize the biology of CRC and discuss current therapeutic interventions designed to counteract CRC development and to overcome chemoresistance.

Methods

Literature searches were conducted using PubMed and focusing the attention on the keywords such as "Current treatment of CRC" or "chemoresistance and CRC" or "oxidative stress and CRC" or "novel drug delivery approaches in cancer" or "immunotherapy in CRC" or "gut microbiota in CRC" or "systematic review and meta-analysis of randomized controlled trials" or "CSCs and CRC". The citations included in the search ranged from September 1988 to December 2022. An additional search was carried out using the clinical trial database.

Results

Rounds of adjuvant therapies, including radiotherapy, chemotherapy, and immunotherapy are commonly planned to reduce cancer recurrence after surgery (stage II and stage III CRC patients) and to improve overall survival (stage IV). 5-fluorouracil-based chemotherapy in combination with other cytotoxic drugs, is the mainstay to treat CRC. However, the onset of the inherent or acquired resistance and the presence of chemoresistant cancer stem cells drastically reduce the efficacy. On the other hand, the genetic-molecular heterogeneity of CRC often precludes also the efficacy of new therapeutic approaches such as immunotherapies. Therefore, the CRC complexity made of natural or acquired multidrug resistance has made it necessary the search for new druggable targets and new delivery systems.

Conclusion

Further knowledge of the underlying CRC mechanisms and a comprehensive overview of current therapeutic opportunities can provide the basis for identifying pharmacological and biological barriers that render therapies ineffective and for identifying new potential biomarkers and therapeutic targets for advanced and aggressive CRC.

Single-cell sequencing technology in colorectal cancer: a new technology to disclose the tumor heterogeneity and target precise treatment

Colorectal Cancer (CRC) is one of the most common gastrointestinal tumors, and its high tumor heterogeneity makes traditional sequencing methods incapable of obtaining information about the heterogeneity of individual cancer cells in CRC. Therefore, single-cell sequencing technology can be applied to better analyze the differences in genetic and protein information between cells, to obtain genomic sequence information of single cells, and to more thoroughly analyze the cellular characteristics and interactions in the CRC microenvironment. This will provide a more comprehensive understanding of colorectal cancer development and metastasis and indicate the treatment plan and prognosis. In this study, we review the application of single-cell sequencing to analyze the tumor microenvironment of CRC, explore the mechanisms involved in CRC metastasis and progression, and provide a reference for potential treatment options.

HAMLET effect on cell death and mitochondrial respiration in colorectal cancer cell lines with KRAS/BRAF mutations

PURPOSE

Treatment of advanced colorectal cancer (CRC) depends on the correct selection of personalized strategies. HAMLET (Human Alpha-lactalbumin Made LEthal to Tumor cells) is a natural proteolipid milk compound that might serve as a novel cancer prevention and therapy candidate. Our purpose was to investigate HAMLET effect on viability, death pathway and mitochondrial bioenergetics of CRC cells with different KRAS/BRAF mutational status in vitro.

METHODS

We treated three cell lines (Caco-2, LoVo, WiDr) with HAMLET to evaluate cell metabolic activity and viability, flow cytometry of apoptotic and necrotic cells, pro- and anti-apoptotic genes, and protein expressions. Mitochondrial respiration (oxygen consumption) rate was recorded by high-resolution respirometry system Oxygraph-2 k.

RESULTS

The HAMLET complex was cytotoxic to all investigated CRC cell lines and this effect is irreversible. Flow cytometry revealed that HAMLET induces necrotic cell death with a slight increase in an apoptotic cell population. WiDr cell metabolism, clonogenicity, necrosis/apoptosis level, and mitochondrial respiration were affected significantly less than other cells.

CONCLUSION

HAMLET exhibits irreversible cytotoxicity on human CRC cells in a dose-dependent manner, leading to necrotic cell death and inhibiting the extrinsic apoptosis pathway. BRAF-mutant cell line is more resistant than other type lines. HAMLET decreased mitochondrial respiration and ATP synthesis in CaCo-2 and LoVo cell lines but did not affect WiDr cells' respiration. Pretreatment of cancer cells with HAMLET has no impact on mitochondrial outer and inner membrane permeability.

A glimpse of research cores and frontiers on the relationship between long noncoding RNAs (lncRNAs) and colorectal cancer (CRC) using the VOSviewer tool

As lncRNAs are essential participants in colorectal carcinogenesis. This study aimed to use the VOSviewer tool to access the research cores and frontiers on the relationship between lncRNAs and CRC. Our findings showed that the mechanism of lncRNA in the occurrence and development of CRC was the core theme of the field. (1) Immunotherapy and immune microenvironment of CRC and lncRNAs, (2) CRC and lncRNAs in exosomes and (3) CRC and lncRNA-targeted therapy might represent three research frontiers. A comprehensive understanding of their existing mechanisms and the search for new regulatory paradigms are the core topics of future research. This knowledge will also help us select appropriate targeting methods and select appropriate preclinical models to promote clinical translation and ultimately achieve precise treatment of CRC.

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .

Angiogenesis regulators S100A4, SPARC and SPP1 correlate with macrophage infiltration and are prognostic biomarkers in colon and rectal cancers

Introduction

Increasing evidence suggests that it is necessary to find effective and robust clinically validated prognostic biomarkers that can identify "high-risk" colorectal cancer (CRC) patients. Currently, available prognostic factors largely include clinical-pathological parameters and focus on the cancer stage at the time of diagnosis. Among cells of tumor microenvironment (TME) only Immunoscore classifier based on T lymphocytes showed high predictive value.

Methods

In the present study, we performed the complex analysis of mRNA and protein expression of crucial regulators of tumor angiogenesis and tumor progression, expressed by tumor-associated macrophages (TAMs): S100A4, SPP1 and SPARC. Colon and rectal cancer patients were investigated independently and in a combined cohort (CRC). For mRNA expression, we analyzed RNA sequencing data obtained from TCGA (N=417) and GEO (N=92) cohorts of colorectal cancer patients. For protein expression, we performed IHC digital quantification of tumor tissues obtained from 197 patients with CRC treated in the Department of abdominal oncology in Clinics of Tomsk NRMC.

Results

High S100A4 mRNA expression accurately predicted poor survival for patients with CRC independently of cancer type. SPARC mRNA level was independent prognostic factors for survival in colon but not in rectal cancer. SPP1 mRNA level had significant predictive value for survival in both rectal and colon cancers. Analysis of human CRC tissues revealed that S100A4, SPP1 and SPARC are expressed by stromal compartments, in particular by TAMs, and have a strong correlation with macrophage infiltration. Finally, our results indicate that chemotherapy-based treatment can change the predictive direction of S100A4 for rectal cancer patients. We found that S100A4 stromal levels were higher in patients with better response to neoadjuvant chemotherapy/chemoradiotherapy, and S100A4 mRNA levels predicted better DFS among non-responders.

Discussion

These findings can help improve the prognosis of patients with CRC based on S100A4, SPP1 and SPARC expression levels.

Clonal diversity in KRAS mutant colorectal adenocarcinoma under treatment: Monitoring of cfDNA using reverse hybridization and DNA sequencing platforms

Biological heterogeneity is a key feature of malignancies that significantly contributes to disease progression and therapy resistance. Residual/relapsed tumor foci may represent genetically divergent subclones, which remain uncovered as repeated and multiple tumor sampling is usually limited. The analysis of circulating free DNA (cfDNA) from the peripheral blood plasma (also called a liquid biopsy, LB) is a new achievement that provides an effective tool for follow-up monitoring of cancer-related genetic status. The present study highlights the phenomenon of mutational variability observed in patients with metastatic KRAS mutant colorectal cancer (mCRC) during treatment with bevacizumab in combination in a longitudinal fashion. The prospective study included 490 mCRC patients evaluated between 2020 and 2022 in our institution. Out of the 211 KRAS mutant cases (43.06%) 12 tumors were identified with multiple KRAS gene variants (5.68%). Detailed follow-up investigations were possible in 3 of these patients including the genotyping of the primary and available metastatic tumors, and the peripheral blood cfDNA. cfDNA was collected from three different time points before and between cycles of combined treatment with bevacizumab chemotherapy. KRAS gene variants were identified using reverse-hybridization strips, and next-generation sequencing (NGS), and confirmed by conventional Sanger sequencing. Interestingly, surgery and multiple treatment cycles reorganized the mutational profiles in the selected cases. The effect of the treatments resulted either in the overrepresentation of one of the pre-existing gene variants or in the appearance of new KRAS variants absent in the primary sample, according to the plasma cfDNA findings. Besides the KRAS variants demonstrated by targeted analysis, NGS mutational profiling identified some additional pathogenic variants from the cfDNA samples (including NRAS and MET alterations). In conclusion, plasma cfDNA sampling enables the monitoring of mutational heterogeneity and subclonal dynamics of the actual metastatic tumor mass in mCRC. The pattern of molecular profile potentially reflects a differential drug response determining further progression.

PFKFB3 overexpression in monocytes of patients with colon but not rectal cancer programs pro-tumor macrophages and is indicative for higher risk of tumor relapse

Introduction

Circulating monocytes are main source for tumor-associated macrophages (TAMs) that control tumor growth, angiogenesis, metastasis and therapy resistance. We raised the questions how monocyte programming is affected by growing tumors localized in colon and rectal sections, and how treatment onsets affect monocyte programming in the circulation.

Methods

Patients with rectal cancer and colon cancer were enrolled in the study. Peripheral blood monocytes were characterized by phenotypic analysis using flow cytometry, by transcriptomic analysis using RNA sequencing and by gene expression analysis using real-time RT-PCR. Phenotypic analysis was performed with IF/confocal microscopy. Spatial transcriptomic analysis was applied using GeoMX DSP-NGS.

Results

In patients with rectal cancer, increased amount of CCR2+ monocytes was indicative for the absence of both lymphatic and hematogenous metastasis. In contrast, in patients with colon cancer CD163+ monocytes were indicative for LN metastasis. NGS analysis identified tumor-specific transcriptional programming of monocytes in all CRC patients compared to healthy individuals. The key transcriptional difference between monocytes of patients with colon and rectal cancer was increased expression of PFKFB3, activator of glycolysis that is currently considered as therapy target for major solid cancers. PFKFB3-expressing monocyte-derived macrophages massively infiltrated tumor in colon. Nanostring technology identified correlation of PFKFB3 with amount and tumor-promoting properties of TAMs in colon but not in rectal cancer. PFKFB3 was indicative for tumor relapse specifically in colon cancer.

Discussion

Our findings provide essential argument towards CRC definition to cover two clinically distinct cancers - colon cancer and rectal cancer, that differentially interact with innate immunity.

New developments in targeted therapy for metastatic colorectal cancer

Colorectal cancer (CRC) is the second most lethal cancer worldwide and the prognosis of metastatic CRC (mCRC) remains poor. Recent advancements in translational research have led to the identification of several new therapeutic targets and improved the treatment outcome of patients with tumours harbouring BRAF V600E mutation, (HER2) ErBB2 alterations, NTRK gene fusions and KRAS(G12C) mutation. Improved understanding towards the mechanism of resistance to targeted therapy such as anti-epidermal growth factor receptor antibodies and the evolving role of therapeutic monitoring with circulating tumour DNA (ctDNA) has enabled the longitudinal tracking of clonal evolution during treatment and the individualization of subsequent treatments. To broaden the community-based implementation of precision oncology in directing targeted therapies for patients with gastrointestinal cancers including mCRC, the feasibility of 'Master Protocols' that utilizes ctDNA-based genotyping platforms is currently being evaluated. Such protocols encompass both observational and interventional clinical trials of novel targeted therapies conducted within a large clinical trial network. In this review, we will discuss the latest developments in targeted therapies, and therapeutic strategies for overcoming acquired drug resistance in patients with mCRC.

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Synergistic cytotoxicity of perifosine and ABT-737 to colon cancer cells

An acidic environment and hypoxia within the tumour are hallmarks of cancer that contribute to cell resistance to therapy. Deregulation of the PI3K/Akt pathway is common in colon cancer. Numerous Akt-targeted therapies are being developed, the activity of Akt-inhibitors is, however, strongly pH-dependent. Combination therapy thus represents an opportunity to increase their efficacy. In this study, the cytotoxicity of the Akt inhibitor perifosine and the Bcl-2/Bcl-xL inhibitor ABT-737 was tested in colon cancer HT-29 and HCT-116 cells cultured in monolayer or in the form of spheroids. The efficacy of single drugs and their combination was analysed in different tumour-specific environments including acidosis and hypoxia using a series of viability assays. Changes in protein content and distribution were determined by immunoblotting and a "peeling analysis" of immunohistochemical signals. While the cytotoxicity of single agents was influenced by the tumour-specific microenvironment, perifosine and ABT-737 in combination synergistically induced apoptosis in cells cultured in both 2D and 3D independently on pH and oxygen level. Thus, the combined therapy of perifosine and ABT-737 could be considered as a potential treatment strategy for colon cancer.

Comprehensive microRNA analysis across genome-edited colorectal cancer organoid models reveals miR-24 as a candidate regulator of cell survival

Somatic mutations drive colorectal cancer (CRC) by disrupting gene regulatory mechanisms. Distinct combinations of mutations can result in unique changes to regulatory mechanisms leading to variability in the efficacy of therapeutics. MicroRNAs are important regulators of gene expression, and their activity can be altered by oncogenic mutations. However, it is unknown how distinct combinations of CRC-risk mutations differentially affect microRNAs. Here, using genetically-modified mouse intestinal organoid (enteroid) models, we identify 12 different modules of microRNA expression patterns across different combinations of mutations common in CRC. We also show that miR-24-3p is aberrantly upregulated in genetically-modified mouse enteroids irrespective of mutational context. Furthermore, we identify an enrichment of miR-24-3p predicted targets in downregulated gene lists from various mutational contexts compared to WT. In follow-up experiments, we demonstrate that miR-24-3p promotes CRC cell survival in multiple cell contexts. Our novel characterization of genotype-specific patterns of miRNA expression offer insight into the mechanisms that drive inter-tumor heterogeneity and highlight candidate microRNA therapeutic targets for the advancement of precision medicine for CRC.

Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology

Introduction

We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making.

Methods

We studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed ex vivo to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM).

Results

Stereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account.

Conclusion and discussion

This simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist’s classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care.

Comprehensive landscape and future perspectives of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC): Based on a bibliometric analysis

This review aimed to use bibliometric analysis to sort out, analyze and summarize the knowledge foundation and hot topics in the field of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC), and point out future trends to inspire related research and innovation. We used CiteSpace to analyze publication outputs, countries, institutions, authors, journals, references, and keywords. Knowledge foundations, hotspots, and future trends were then depicted. The overall research showed the trend of biomedical-oriented multidisciplinary. Much evidence indicates that lncRNA plays the role of oncogene or tumor suppressor in the occurrence and development of CRC. Besides, many lncRNAs have multiple mechanisms. lncRNAs and metastasis of CRC, lncRNAs and drug resistance of CRC, and the clinical application of lncRNAs in CRC are current research hotspots. Through insight into the development trend of lncRNAs in CRC, this study will help researchers extract hidden valuable information for further research.

Defining A Liquid Biopsy Profile of Circulating Tumor Cells and Oncosomes in Metastatic Colorectal Cancer for Clinical Utility

Simple Summary

Metastatic colorectal cancer (mCRC) is typified by its tumor heterogeneity and changing disease states, suggesting that personalized medicine approaches could be vital to improving clinical practice. As a minimally invasive approach, the liquid biopsy has the potential to be a powerful longitudinal prognostic tool. We investigated mCRC patients’ peripheral blood samples using an enrichment-free single-cell approach to capture the broader rare-event population beyond the conventionally detected epithelial-derived circulating tumor cell (CTC). Our analysis reveals a heterogenous profile of CTCs and oncosomes not commonly found in normal donor samples. We identified select rare cell types based on their distinct immunofluorescence expression and morphology across multiple assays. Lastly, we highlight correlations between enumerations of the blood-based analytes and progression-free survival. This study clinically validates an unbiased rare-event approach in the liquid biopsy, motivating future studies to further investigate these analytes for their prognostic potential.

Abstract

Metastatic colorectal cancer (mCRC) is characterized by its extensive disease heterogeneity, suggesting that individualized analysis could be vital to improving patient outcomes. As a minimally invasive approach, the liquid biopsy has the potential to longitudinally monitor heterogeneous analytes. Current platforms primarily utilize enrichment-based approaches for epithelial-derived circulating tumor cells (CTC), but this subtype is infrequent in the peripheral blood (PB) of mCRC patients, leading to the liquid biopsy’s relative disuse in this cancer type. In this study, we evaluated 18 PB samples from 10 mCRC patients using the unbiased high-definition single-cell assay (HDSCA). We first employed a rare-event (Landscape) immunofluorescence (IF) protocol, which captured a heterogenous CTC and oncosome population, the likes of which was not observed across 50 normal donor (ND) samples. Subsequent analysis was conducted using a colorectal-targeted IF protocol to assess the frequency of CDX2-expressing CTCs and oncosomes. A multi-assay clustering analysis isolated morphologically distinct subtypes across the two IF stains, demonstrating the value of applying an unbiased single-cell approach to multiple assays in tandem. Rare-event enumerations at a single timepoint and the variation of these events over time correlated with progression-free survival. This study supports the clinical utility of an unbiased approach to interrogating the liquid biopsy in mCRC, representing the heterogeneity within the CTC classification and warranting the further molecular characterization of the rare-event analytes with clinical promise.

Identification of stage-associated exosome miRNAs in colorectal cancer by improved robust and corroborative approach embedded miRNA-target network

Background

Colorectal cancer (CRC) is a common gastrointestinal tumor with high morbidity and mortality. At the molecular level, patients at different stages present considerable heterogeneity. Although the miRNA in exosome is an effective biomarker to reveal tumor progression, studies based on stage-associated exosome miRNA regulatory network analysis still lacking. This study aims to identify CRC stage-associated exosome miRNAs and reveal their potential function in tumor progression.

Methods

In this study, serum and cellular exosome miRNA expression microarrays associated with CRC were downloaded from GEO database. Stage-common (SC) and stage-specific (SS) differentially expressed miRNAs were extracted and their targets were identified based on 11 databases. Furthermore, miRNA SC and SS regulatory function networks were built based on the CRC phenotypic relevance of miRNA targets, and the corresponding transcription factors were identified. Concurrently, the potential stage-associated miRNAs were identified by receiver-operating characteristic (ROC) curve analysis, survival analysis, drug response analysis, ceRNA analysis, pathway analysis and a comprehensive investigation of 159 publications.

Results

Ten candidate stage-associated miRNAs were identified, with three SC (miR-146a-5p, miR-22-3p, miR-23b-3p) and seven SS (I: miR-301a-3p, miR-548i; IIIA: miR-23a-3p; IV: miR-194-3p, miR-33a-3p, miR-485-3p, miR-194-5p) miRNAs. Additionally, their targets were enriched in several vital cancer-associated pathways such as TGF-beta, p53, and hippo signaling pathways. Moreover, five key hotspot target genes (CCNA2, MAPK1, PTPRD, MET, and CDKN1A) were demonstrated to associated with better overall survival in CRC patients. Finally, miR-23b-3p, miR-301a-3p and miR-194-3p were validated being the most stably expressed stage-associated miRNAs in CRC serum exosomes, cell exosomes and tissues.

Conclusions

These CRC stage-associated exosome miRNAs aid to further mechanism research of tumor progression and provide support for better clinical management in patients with different stages.

A prognostic six-gene expression risk-score derived from proteomic profiling of the metastatic colorectal cancer secretome

The necessity to accurately predict recurrence and clinical outcome in early stage colorectal cancer (CRC) is critical to identify those patients who may benefit from adjuvant chemotherapy. Here, we developed and validated a gene-based risk-score algorithm for patient stratification and personalised treatment in early stage disease based on alterations in the secretion of metastasis-related proteins. A quantitative label-free proteomic analysis of the secretome of highly and poorly metastatic CRC cell lines with different genetic backgrounds revealed 153 differentially secreted proteins (fold-change >5). These changes in the secretome were validated at the transcriptomic level. Starting from 119 up-regulated proteins, a six-gene/protein-based prognostic signature composed of IGFBP3, CD109, LTBP1, PSAP, BMP1, and NPC2 was identified after sequential discovery, training, and validation in four different cohorts. This signature was used to develop a risk-score algorithm, named SEC6, for patient stratification. SEC6 risk-score components showed higher expression in the poor prognosis CRC subtypes: consensus molecular subtype 4 (CMS4), CRIS-B, and stem-like. High expression of the signature was also associated with patients showing dMMR, CIMP⁺ status, and BRAF mutations. In addition, the SEC6 signature was associated with lower overall survival, progression-free interval, and disease-specific survival in stage II and III patients. SEC6-based risk stratification indicated that 5-FU treatment was beneficial for low-risk patients, whereas only aggressive treatments (FOLFOX and FOLFIRI) provided benefits to high-risk patients in stages II and III. In summary, this novel risk-score demonstrates the value of the secretome compartment as a reliable source for the retrieval of biomarkers with high prognostic and chemotherapy-predictive capacity, providing a potential new tool for tailoring decision-making in patient care.

Identification of genes with high heterogeneity of expression as a predictor of different prognosis and therapeutic responses in colorectal cancer: a challenge and a strategy

Background

Molecular heterogeneity is one of the most important concerns in colorectal cancer (CRC), which results in a wide range of therapy responses and patient prognosis. We aimed to identify the genes with high heterogeneity of expression (HHE) and their relation with prognosis and drug resistance.

Methods

Two cohort studies, the cancer genome atlas (TCGA) and the GSE39582, were used to discover oncogenes genes with HHE. The relationship between identified genes with clinical and genomic characteristics was evaluated based on TCGA data. Also, the GDSC and CCLE data were used for drug resistance and sensitivity. Sixty CRC samples were used to validate the obtained data by RT-qPCR.

Results

Findings revealed that 132 genes with HHE were found to be up-regulated in both cohorts and were enriched in pathways such as hypoxia, angiogenesis, and metastasis. Forty-nine of selected genes related to clinical and genomic variables, including stage, common mutations, the tumor site, and microsatellite state that were ignored. The expression level of CXCL1, SFTA2, SELE, and SACS as genes with HHE were predicted survival patients, and RT-qPCR results demonstrated that levels of SELE and SACS had HHE in CRC samples. The expression of many identified genes like BGN, MMP7, COL11A1, FAP, KLK10, and TNFRSE11B was associated with resistance to chemotherapy drugs.

Conclusions

Some genes expression, including SELE, SACS, BGN, KLK10, COL11A1, and TNFRSE11B have an oncogenic function with HHE, and their expression can be used as indicators for differing treatment responses and survival rates in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02694-9.

The neuronal protein Neuroligin 1 promotes colorectal cancer progression by modulating the APC/β-catenin pathway

Background

Colorectal cancer (CRC) remains largely incurable when diagnosed at the metastatic stage. Despite some advances in precision medicine for this disease in recent years, new molecular targets, as well as prognostic/predictive markers, are highly needed. Neuroligin 1 (NLGN1) is a transmembrane protein that interacts at the synapse with the tumor suppressor adenomatous polyposis Coli (APC), which is heavily involved in the pathogenesis of CRC and is a key player in the WNT/β-catenin pathway.

Methods

After performing expression studies of NLGN1 on human CRC samples, in this paper we used in vitro and in vivo approaches to study CRC cells extravasation and metastasis formation capabilities. At the molecular level, the functional link between APC and NLGN1 in the cancer context was studied.

Results

Here we show that NLGN1 is expressed in human colorectal tumors, including clusters of aggressive migrating (budding) single tumor cells and vascular emboli. We found that NLGN1 promotes CRC cells crossing of an endothelial monolayer (i.e. Trans-Endothelial Migration or TEM) in vitro, as well as cell extravasation/lung invasion and differential organ metastatization in two mouse models. Mechanistically, NLGN1 promotes APC localization to the cell membrane and co-immunoprecipitates with some isoforms of this protein stimulates β-catenin translocation to the nucleus, upregulates mesenchymal markers and WNT target genes and induces an “EMT phenotype” in CRC cell lines

Conclusions

In conclusion, we have uncovered a novel modulator of CRC aggressiveness which impacts on a critical pathogenetic pathway of this disease, and may represent a novel therapeutic target, with the added benefit of carrying over substantial knowledge from the neurobiology field.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02465-4.

CISD2 Promotes Proliferation of Colorectal Cancer Cells by Inhibiting Autophagy in a Wnt/β-Catenin-Signaling-Dependent Pathway

The aim of this study is to investigate the role of CDGSH iron-sulfur domain 2 (CISD2) in colorectal cancer (CRC). The purpose of this study was to investigate the role of CDGSH iron-sulfur domain 2 (CISD2) in colorectal cancer (CRC) progression. The expression of CISD2 in CRC cell lines was measured by western blotting. Functional assays including MTT assays and colony formation assays were performed to explore the role of CISD2 in regulating tumor growth. Flow cytometry analysis was used to examine the percentage of apoptotic CRC cells. Expression of apoptosis-related gene, autophagy-related markers, and the protein included in Wnt/β-Catenin signaling was also determined by western blotting. The in vivo role of CISD2 was also examined in a xenograft model. CISD2 expression was significantly increased in CRC cells. CISD2 promoted the CRC cell proliferation and inhibited the apoptosis and autophagy of CRC cells. Moreover, knockdown of CISD2 inhibited the activation of Wnt/β-Catenin-signaling pathway. Knockdown of CISD2 inhibited the tumor growth in nude mice. CISD2 promoted colorectal cancer development by inhibiting CRC cell apoptosis and autophagy depending on activating Wnt/β-Catenin-signaling pathway.

AD80, a multikinase inhibitor, as a potential drug candidate for colorectal cancer therapy

AIMS

Colorectal cancer (CRC) is a very heterogeneous disease. One of its hallmarks is the dysregulation of protein kinases, which leads to molecular events related to carcinogenesis. Hence, kinase inhibitors have been developed and are a new strategy with promising potential for CRC therapy. This study aims to explore AD80, a multikinase inhibitor, as a drug option for CRC, with evaluation of the PI3K/AKT/mTOR and MAPK (ERK1/2) status of CRC cells' panel and the cytotoxicity of AD80 in those cells, as well as in normal colon cells.

MAIN METHODS

Cellular and molecular mechanisms, such as clonogenicity, cell cycle, morphology, protein, and mRNA expression, were investigated in CRC cells after AD80 exposure.

KEY FINDINGS

Results show that PI3K/AKT/mTOR and MAPK signaling pathways are upregulated in CRC cellular models, with increased phosphorylation of mTOR, P70S6K, S6RP, 4EBP1, and ERK1/2. Hence, AD80 selectively reduces cell viability of CRC cells. Therefore, the antitumor mechanisms of AD80, such as clonogenicity inhibition (reduction of colony number and size), G₂/M arrest (increased G₂/M population, and CDKN1B mRNA expression), DNA damage (increased H2AX and ERK1/2 phosphorylation, and CDKN1A, GADD45A mRNA expression), apoptosis (increased PARP1 cleavage, and BAX, PMAIP1, BBC3 mRNA expression) and inhibition of S6RP phosphorylation were validated in CRC model.

SIGNIFICANCE

Our findings reinforce kinases as promising cancer therapeutic targets for the treatment of colorectal cancer, suggesting AD80 as a drug candidate.

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.

Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study

PURPOSE

To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.

METHODS

Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test.

RESULTS

Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively).

CONCLUSION

We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.