Prognostic genome and transcriptome signatures in colorectal cancers

Primary tumour location, molecular alterations, treatments, and outcome in a population-based metastatic colorectal cancer cohort

BACKGROUND

Metastatic colorectal cancer (mCRC) patients in trials are selected. The aim was to study mCRC features population-based.

METHODS

All 765 mCRC patients in the Uppsala region, Sweden, 2010-2020 were identified and analysed for RAS (n = 356/708) and BRAF-V600E (n = 123/708) mutations (mt) and deficient mismatch repair (dMMR, n = 58/643).

RESULTS

Right colon primary tumours were associated with BRAF-V600Emt and dMMR and had worse median overall survival (mOS) than left colon or rectal mCRC. RAS&BRAF wildtype (wt) and proficient MMR were seen in 22%, 45%, and 31% of right colon, left colon, and rectum, respectively. Patients with right colon primaries received best supportive care only more often (34% vs 25% vs 24%) and metastasectomy less often (21% vs 31% vs 33%) than left colon and rectal primaries. In molecularly homogeneous subgroups (RAS&BRAFwt/RASmt/BRAF-V600Emt/dMMR) no difference in mOS were seen between right and left colon primaries, whereas rectal primaries had better mOS (26/15/8/9 vs 24/21/8/8 vs 32/23/6/NA months, respectively). This was also the case in homogenous treatment groups. Primary tumour location turned non-significant in multivariable OS analyses.

CONCLUSIONS

The high variation of BRAF-V600Emt, RASmt, dMMR, and treatment allocation population-based per primary tumour location explain the poor outcome in right-sided cancers.

CDX1 and CDX2 suppress colon cancer stemness by inhibiting β-catenin-facilitated formation of Pol II-DSIF-PAF1C complex

Homeobox transcription factors CDX1 and CDX2 (hereafter, CDX1/2) play key roles in determining the identity of intestinal epithelial cells and regulating their stem cell functions. However, the role of CDX1/2 in regulating colon cancer stemness and the underlying mechanisms are unclear. Here, we show that complete loss of Cdx1 or concurrent loss of Cdx1/2 increased the stemness and malignancy of intestinal tumors. Consistently, CDX1/2 reduced the expression of cancer stemness-related genes, including LGR5. CDX1/2 bound to the downstream region of the LGR5 transcription start site (TSS), a region where β-catenin also binds. Despite increased H3 acetylation and an open chromatin structure, CDX1/2 reduced the occupancy of DRB sensitivity-inducing factor (DSIF), RNA polymerase II-associated factor 1 (PAF1), and RNA polymerase II (Pol II) complexes around the LGR5 TSS. Through their homeodomains, CDX1/2 inhibited the β-catenin-facilitated formation of active Pol II complexes containing DSIF and PAF1 complexes by preventing the interaction between β-catenin and these complexes, in an additive manner. Our findings suggest that CDX1/2 cooperatively suppressed colonic tumorigenesis and cancer stemness by antagonizing β-catenin via the DSIF and PAF1 complexes. Additionally, DSIF and PAF1 complexes acted as transcriptional platforms that integrated and funneled both tumor-suppressive and oncogenic signals into the expression of genes that control colon cancer stemness.

Early immune evasion in colorectal cancer: interplay between stem cells and the tumor microenvironment

Most colorectal cancers (CRCs) are characterized by a low mutational burden and an immune-cold microenvironment, limiting the efficacy of immune checkpoint blockade (ICB) therapies. While advanced tumors exhibit diverse immune evasion mechanisms, emerging evidence suggests that aspects of immune escape arise much earlier, within precancerous lesions. In this review, we discuss how early driver mutations and epigenetic alterations contribute to the establishment of an immunosuppressive microenvironment in CRC. We also highlight the dynamic crosstalk between cancer cells, stromal niche cells, and immune cells driving immune evasion and liver metastasis. A deeper understanding of these early events may guide the development of more effective preventive and therapeutic strategies for CRC.

Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

Clonal dispersal, resulting from the intermingling of tumor cell subpopulations, is thought to be a key driver of tumor heterogeneity. Despite advances in spatial modeling of cancer biology, quantification of clonal dispersal has been challenging. This study introduces a straightforward method, relying on fluorescent cell barcoding, to quantify clonal dispersal in various in vitro and in vivo models of colorectal cancer (CRC). Our approach allows for precise localization of clones and uncovering the degree of clonal mixing across different CRC models. Our findings suggest that clonal dispersal is correlated with the expression of genes involved in epithelial-mesenchymal transition and CMS4-related signaling pathways. We further identify a dispersal gene signature, associated with intratumor heterogeneity, which is a robust clinical predictor of poor prognosis and recurrence in CRC, highlighting its potential as a prognostic marker and a putative direction for therapeutic targeting.

Analysis of Colorectal Cancer Gene Mutations and Application of Long Blocker Displacement Amplification Technology for High-Throughput Mutation Detection

Genetic mutation detection for colorectal cancer (CRC) is crucial for precision diagnosis and treatment, yet current methods often suffer from challenges such as low sensitivity, time consumption, and high costs. In our preliminary bioinformatic analysis of 751 CRC cases from The Cancer Genome Atlas and 131 Chinese patient samples, APC, TP53, and KRAS were identified as the most frequently mutated genes. Among them, KRAS missense mutations emerged as key diagnostic biomarkers. In this study, we applied a fluorescence-based long block displacement amplification (LBDA) sensing method for the rapid, high-throughput, and cost-effective detection of KRAS genetic mutations. In the LBDA system, SYBR Green dye binds to the amplified double-stranded DNA, generating a fluorescence signal that directly reflects the abundance of mutant types (MTs). This real-time signal output enables the enrichment and sensitive detection of MTs, establishing LBDA as an efficient biosensing platform for KRAS genotyping. Using this technique, a detection limit of 0.08% variant allele frequency was achieved with 20 ng of synthetic DNA input. To evaluate clinical performance, the LBDA method was applied to 118 tissue samples from 59 CRC patients, including tumor and matched peritumoral tissues. For 59 CRC tumor samples, LBDA successfully identified KRAS mutations in 37.29% of cases, closely matching results (42.37%) obtained by next-generation sequencing and achieving 88% sensitivity and 100% specificity. In conclusion, this study presents a rapid and cost-effective mutation detection method based on optical biosensing, offering strong potential for advancing personalized CRC diagnosis and treatment.

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response

BACKGROUND

Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability.

METHODS

Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis.

RESULTS

Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets.

CONCLUSIONS

Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Multi-omics sequencing of gastroesophageal junction adenocarcinoma reveals prognosis-relevant key factors and a novel immunogenomic classification

BACKGROUND

Gastroesophageal junction adenocarcinoma (GEJAC) exhibits distinct molecular characteristics due to its unique anatomical location. We sought to investigate effective and reliable molecular classification of GEJAC to guide personalized treatment.

METHODS

We analyzed the whole genomic, transcriptomic, T-cell receptor repertoires, and immunohistochemical data in 92 GEJAC patients and delineated the landscape of genetic and immune alterations. In addition to COSMIC nomenclature, the de novo nomenclature was also utilized to define signatures and investigate their correlation with survival. A novel molecular subtype was developed and validated in other cohorts.

RESULTS

We found 30 mutated driver genes, 7 novel genomic signatures, 3 copy-number variations, and 2 V-J gene usages related to prognosis that were not identified in previous study. A high frequency of COSMIC-SBS-384-1 and De novo-SV-32-A was associated with more neoantigen generation and a better survival. Using 19 molecular features, we identified three immune-related subtypes (immune inflamed, intermediate, and deserted) with discrete profiles of genomic signatures, immune status, and clinical outcome. The immune deserted subtype (27.2%) was characterized by an earlier KRAS mutation, worse immune reaction, and prognosis than the other two subtypes. The immune inflamed subtypes exhibited the highest levels of neoantigens, TCR/pMHC-binding strength, CD8 + T-cell infiltration, IFN-α/γ response pathways, and survival rate.

CONCLUSIONS

These results emphasize the immune reaction and prognostic value of novel molecular classifications based on multi-omics data and provide a solid basis for better management of GEJAC.

Rational Design of Bioinspired Lipoprotein System to Improve Penetration in Colorectal Peritoneal Metastases

Chemotherapy of lethal colorectal peritoneal metastases (PM) is notoriously challenged by poor drug delivery efficiency in PM tumors. Inspired by the histopathological examinations of PM tumors from colon cancer patients, a C[RGDfK] peptide-modified bioinspired lipoprotein (R-BLP) system was optimized from 8 formulations with profound penetrating ability in PM tumors of colorectal cancers. Then, a chemotherapeutic 7-ethyl-10-hydroxy-camptothecin (SN38)-loaded R-BLP (termed SR-BLP) was designed to promote their penetration in PM tumors and improve the chemotherapeutic efficacy. In CT26-induced PM models, SR-BLP exhibited better penetration profiles in PM tumors over a counterpart liposomal formulation. SR-BLP treatment produced an 80.03% suppression of PM incidence with obvious DNA damage and topoisomerase I (TOP I) downregulation and caused a 1.78-fold prolongation of survival time. Therefore, the histopathological features-inspired R-BLP provides an encouraging tumor-penetrating delivery platform for the effective chemotherapy of colorectal PM.

The YTHDF3-DT/miR-301a-3p /INHBA axis attenuates autophagy-dependent ferroptosis in lung adenocarcinoma

YTHDF3-DT, a long non-coding RNA (lncRNA) significantly upregulated in lung adenocarcinoma (LUAD), is associated with poor patient prognosis and plays critical roles in LUAD progression. Clinical data and in vitro analyses revealed that YTHDF3-DT expression correlates with worse overall survival and increased lymph node metastasis in LUAD patients. Functional studies demonstrated that YTHDF3-DT activates the TGF-β and PI3K/Akt/mTOR signaling pathways via INHBA, a key target influenced by YTHDF3-DT. Mechanistically, YTHDF3-DT stabilizes INHBA mRNA by acting as a competing endogenous RNA (ceRNA) for miR-301a-3p, forming a YTHDF3-DT/miR-301a-3p/INHBA axis. This axis regulates ferroptosis in an autophagy-dependent manner in LUAD cells, with YTHDF3-DT promoting cell survival by altering autophagic activity and mitigating ferroptosis-induced cell death. In vivo experiments further validated the role of YTHDF3-DT in tumor growth and ferroptosis regulation, highlighting its potential as a therapeutic target in LUAD. Our data contribute toward a significant mechanistic understanding of the molecules involved in the crosstalk between ferroptosis and autophagy, providing potential therapeutic targets to complement the existing therapies for overcoming the developed resistance in patients with LUAD.

Construction and Value Analysis of a Prognostic Assessment Model Based on Radiomics and Genetic Data for Colorectal Cancer

Aims/Background Colorectal cancer (CRC) is one of the major global health problems, with high morbidity and mortality, underscoring the need for new diagnostic and prognostic tools. Therefore, this study aims to evaluate the significance of integrating radiomics with genetic data in CRC prognostic assessment and improve the accuracy of prognosis prediction. Methods This study included computed tomography (CT) images from 225 CRC patients and RNA-seq information from 654 patients, obtained from the TICA database. Key radiomics features and genes were identified through radiomics feature extraction, least absolute shrinkage and selection operator (LASSO) regression analysis, and Kaplan-Meier survival analysis. Furthermore, a CRC prognostic model was constructed using these key genes and radiomics features. Results This study identified 170 key radiomics features. Out of them, five were significantly associated with CRC prognosis. Transcriptome data analysis identified 8 key genes, among which the high expressions of Inhibin Subunit Beta B (INHBB), Potassium Voltage-Gated Channel Subfamily Q Member 2 (KCNQ2), and Ubiquilin Like (UBQLNL) were significantly correlated with poor prognosis. Age, tumor stage, pathological T stage, and pathological N stage were determined as independent prognostic factors. Moreover, immune infiltration analysis demonstrated that the immune score of the low-risk group was higher than that of the high-risk group, with significant differences in some immune cells, and key genes were correlated with immune cells. Additionally, the constructed CRC prognostic model incorporating three genes, INHBB, KCNQ2, and UBQLNL, exhibited high prediction accuracy in the validation set, with area under the curve (AUC) values of 0.80, 0.87, and 0.84 at 1-year, 3-year, and 5-year, respectively, indicating good prediction performance and reliability of the model. Conclusion The multimodal data combining radiomics features and gene expression data can improve the accuracy of CRC prognostic assessment, providing a valuable prognostic prediction tool for clinical practice and helping to guide the selection and optimization of treatment regimens.

Comprehensive genomic dependency landscape of a human colon cancer organoid

Identifying genetic dependencies in human colon cancer could help identify effective treatment strategies. Genome-wide CRISPR-Cas9 dropout screens have the potential to reveal genetic dependencies, some of which could be exploited as therapeutic targets using existing drugs. In this study, we comprehensively characterized genetic dependencies present in a colon cancer organoid avatar, and validated tumor-specific selectivity of select pharmacologic agents. We conducted a genome-wide CRISPR dropout screen to elucidate the genetic dependencies that interacted with select driver somatic mutations. We found distinct genetic dependencies that interacted with WNT, MAPK, PI3K, TP53, and mismatch repair pathways and validated targets that could be exploited as treatments for this specific subtype of colon cancer. These findings demonstrate the utility of functional genomic screening in the context of personalized medicine.

Single-cell and spatial transcriptomic analysis reveals tumor cell heterogeneity and underlying molecular program in colorectal cancer

Background

Colorectal cancer (CRC) is a highly heterogeneous tumor, with significant variation in malignant cells, posing challenges for treatment and prognosis. However, this heterogeneity offers opportunities for personalized therapy.

Methods

The consensus non-negative matrix factorization algorithm was employed to analyze single-cell transcriptomic data from CRC, which helped identify malignant cell expression programs (MCEPs). Subsequently, a crosstalk network linking MCEPs with immune/stromal cell trajectory development was constructed using Monocle3 and NicheNet. Additionally, bulk RNA-seq data were utilized to systematically explore the relationships between MCEPs, clinical features, and genetic mutations. A prognostic model was then established through Lasso and Cox regression analyses, integrating clinical data into a nomogram for personalized risk prediction. Furthermore, key genes associated with MCEPs and their potential therapeutic targets were identified using protein-protein interaction networks, followed by molecular docking to predict drug-binding affinity.

Results

We classified CRC malignant cell transcriptional states into eight distinct MCEPs and successfully constructed crosstalk networks between these MCEPs and immune or stromal cells. A prognostic model containing 15 genes was developed, demonstrating an AUC greater than 0.8 for prognostic evaluation over 1 to 10 years when combined with clinical features. A key drug target gene TIMP1 was identified, and several potential targeted drugs were discovered.

Conclusion

This study demonstrated that characterization of the malignant cell transcriptional programs could effectively reveal the biological features of highly heterogeneous tumors like CRC and exhibit significant potential in tumor prognosis assessment. Our research provides new theoretical and practical directions for CRC prognosis and targeted therapy.

Hallmarks of artificial intelligence contributions to precision oncology

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.

Development of a prognostic risk model for colorectal cancer and association of the prognostic model with cancer stem cell and immune cell infiltration

Background

The development of a prognostic model for patients with colorectal cancer (CRC) can facilitate the assessment of patient survival and the effectiveness of clinical treatments. A reasonable prognostic model can provide a basis for individualized treatment, prognostic risk stratification, and subsequent therapy for CRC patients. The aim of our study was to construct a prognostic model for patients with CRC using sequencing data derived from The Cancer Genome Atlas (TCGA) database.

Methods

Sequencing data of paracancerous tissues (n=51) and CRC samples (n=647) were downloaded from the TCGA database. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify prognostic factors. A restricted cubic spline (RCS) model was used to assess the nonlinear relationship between risk score and poor overall survival (OS). The Genomics of Drug Sensitivity in Cancer (GDSC) database was accessed to evaluate the correlation between the prognostic model's risk score and drug sensitivity. The single-sample gene set enrichment analysis (ssGSEA), estimate, and CIBERSORT algorithms were applied to quantify the association between prognostic genes and immune cell infiltration in CRC.

Results

Our findings revealed that six genes, including Niemann-Pick C1-like 1 (NPC1L1) [hazard ratio (HR) =1.53; 95% confidence interval (CI): 1.08-2.17; P=0.02], glucagon-like peptide 2 receptor (GLP2R) (HR =0.68; 95% CI: 0.48-0.97; P=0.04), solute carrier family 8 member A3 (SLC8A3) (HR =0.67; 95% CI: 0.47-0.96; P=0.03), alpha-1-microglobulin/bikunin precursor (AMBP) (HR =0.64; 95% CI: 0.45-0.91; P=0.01), single-pass membrane protein with coiled-coil domains 2 (SMCO2) (HR =0.68; 95% CI: 0.48-0.97; P=0.03), and tetratricopeptide repeat domain 16 (TTC16) (HR =1.55; 95% CI: 1.09-2.20; P=0.02) function as independent prognostic factors for CRC. Based on these six genes, the developed prognostic assessment model identified a strong association between high risk score and poor OS (HR =2.43; 95% CI: 1.67-3.53; P<0.001) in patients with CRC. Furthermore, the analysis revealed a nonlinear relationship (P<0.001) between continuous variation in risk score and the risk of poor OS. Additionally, specific genes included in the prognostic model were found to be strongly associated with cancer stem cell and immune cell infiltration in CRC.

Conclusions

We developed a prognostic risk model incorporating a six-gene panel for patients with CRC. Our analysis revealed a nonlinear relationship between this prognostic model and OS in patients with CRC. A high risk score was associated with poor prognosis, indicating that the adverse outcomes observed in patients with CRC may be influenced by cancer stem cell and immune cell infiltration. Our model provides a promising predictive method for the prognosis of CRC patients, but it still needs to be validated in a larger sample size.

Geographic and age-related variations in mutational processes in colorectal cancer

Colorectal cancer incidence rates vary geographically and have changed over time. Notably, in the past two decades, the incidence of early-onset colorectal cancer, affecting individuals under the age of 50 years, has doubled in many countries. The reasons for this increase are unknown. Here, we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown etiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia, and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals diagnosed before age 40 than in those over 70, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that early-life mutagenic exposure to colibactin-producing bacteria may contribute to the rising incidence of early-onset colorectal cancer.

Multiple regulatory events contribute to a widespread circular RNA downregulation in precancer and early stage of colorectal cancer development

BACKGROUND

Early detection of colorectal cancer (CRC) significantly improves its management and patients' survival. Circular RNAs (circRNAs) are peculiar covalently closed transcripts involved in gene expression modulation whose dysregulation has been extensively reported in CRC cells. However, little is known about their alterations in the early phases of colorectal carcinogenesis.

METHODS

In this study, we performed an integrative analysis of circRNA profiles in RNA-sequencing (RNA-Seq) data of 96 colorectal cancers, 27 adenomas, and matched adjacent mucosa tissues. We also investigated the levels of cognate linear transcripts and those of regulating RNA-binding proteins (RBPs). Levels of circRNA-interacting microRNAs (miRNAs) were explored by integrating data of small RNA-Seq performed on the same samples.

RESULTS

Our results revealed a significant dysregulation of 34 circRNAs (paired adj. p < 0.05), almost exclusively downregulated in tumor tissues and, prevalently, in early disease stages. This downregulation was associated with decreased expression of circRNA host genes and those encoding for RBPs involved in circRNA biogenesis, including NOVA1, RBMS3, and MBNL1. Guilt-by-association analysis showed that dysregulated circRNAs correlated with increased predicted activity of cell proliferation, DNA repair, and c-Myc signaling pathways. Functional analysis showed interactions among dysregulated circRNAs, RBPs, and miRNAs, which were supported by significant correlations among their expression levels. Findings were validated in independent cohorts and public datasets, and the downregulation of circLPAR1(2,3) and circLINC00632(5) was validated by ddPCR.

CONCLUSIONS

These results support that multiple altered regulatory mechanisms may contribute to the reduction of circRNA levels that characterize early colorectal carcinogenesis.

A compendium of Amplification-Related Gain Of Sensitivity genes in human cancer

While the effect of amplification-induced oncogene expression in cancer is known, the impact of copy-number gains on "bystander" genes is less understood. We create a comprehensive map of dosage compensation in cancer by integrating expression and copy number profiles from over 8000 tumors in The Cancer Genome Atlas and cell lines from the Cancer Cell Line Encyclopedia. Additionally, we analyze 17 cancer open reading frame screens to identify genes toxic to cancer cells when overexpressed. Combining these approaches, we propose a class of 'Amplification-Related Gain Of Sensitivity' (ARGOS) genes located in commonly amplified regions, yet expressed at lower levels than expected by their copy number, and toxic when overexpressed. We validate RBM14 as an ARGOS gene in lung and breast cancer cells, and suggest a toxicity mechanism involving altered DNA damage response and STING signaling. We additionally observe increased patient survival in a radiation-treated cancer cohort with RBM14 amplification.

SMARCA4 regulates the NK-mediated killing of senescent cells

Induction of senescence by chemotherapeutic agents arrests cancer cells and activates immune surveillance responses to contribute to therapy outcomes. In this investigation, we searched for ways to enhance the NK-mediated elimination of senescent cells. We used a staggered screen approach, first identifying siRNAs potentiating the secretion of immunomodulatory cytokines to later test for their ability to enhance NK-mediated killing of senescent cells. We identified that genetic or pharmacological inhibition of SMARCA4 enhanced senescent cell elimination by NK cells. SMARCA4 expression is elevated during senescence and its inhibition derepresses repetitive elements, inducing the SASP via activation of cGAS/STING and MAVS/MDA5 pathways. Moreover, a PROTAC targeting SMARCA4 synergized with cisplatin to increase the infiltration of CD8 T cells and mature, activated NK cells in an immunocompetent model of ovarian cancer. Our results indicate that SMARCA4 inhibitors enhance NK-mediated surveillance of senescent cells and may represent senotherapeutic interventions for ovarian cancer.

Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines

Introduction

Colorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the treatment of metastatic colorectal cancer are 5-fluorouracil, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct a predictive gene expression-based classifier to separate responders and non-responders.

Methods

In this study, we analyzed gene expression profiles of seven patient-derived CRC organoids and performed correlation analyses between gene expression and IC50 values for the three standard-of-care chemotherapeutic drugs. We also included in the study publicly available datasets of colorectal cancer cell lines, thus combining two different in vitro models relevant to cancer research. Logistic regression was used to build gene expression-based classifiers for metastatic Stage IV and non-metastatic Stage II/III CRC patients. Prognostic performance was evaluated through Kaplan-Meier survival analysis and log-rank tests, while independent prognostic significance was assessed using multivariate Cox proportional hazards modeling.

Results

A small set of genes showed consistent correlation with resistance to chemotherapy across different datasets. While some genes were previously implicated in cancer prognosis and drug response, several were linked to drug resistance for the first time. The resulting gene expression signatures successfully stratified Stage II/III and Stage IV CRC patients, with potential clinical utility for improving treatment outcomes after further validation.

Discussion

This study highlights the advantages of integrating diverse experimental models, such as organoids and cell lines, to identify novel prognostic biomarkers and enhance the understanding of chemotherapy resistance in CRC.

Establishment and characterization of a sigmoid colon cancer organoid with spinal metastasis

Background

Sigmoid colon cancer with spinal metastases is rare in distant metastasis. In addition, the prognosis of colon cancer patients with spinal metastases is extremely poor. In order to find effective therapeutic agents, we need to know the biological characteristics of such patients from related models.

Methods

We collected sigmoid colon cancer tissue from a young female subject who was diagnosed with sigmoid colon cancer with multiple spinal metastases. We successfully established a sigmoid colon cancer organoid using this tissue and investigated drug screening in the patient. HE staining, immunohistochemistry, and DNA sequencing were utilized to compare the biological characteristics between the original tumor and the organoid. Furthermore, we investigated the drug screening of the sigmoid colon cancer organoid in vitro.

Results

A colon cancer organoid from sigmoid colon cancer with spinal metastases was successfully established. The organoid culture maintained the morphological features, histological features, and genomic landscape of the corresponding sigmoid colon cancer cells. Moreover, we performed drug screening tests to evaluate the effects of chemotherapeutic drugs and targeted drugs.

Conclusion

The sigmoid colon cancer organoid with spinal metastases was a favorable preclinical model to explore the clinicopathologic characteristics of colon cancer patients with spinal metastases.

Self-assembled PROTACs enable protein degradation to reprogram the tumor microenvironment for synergistically enhanced colorectal cancer immunotherapy

Both β-catenin and STAT3 drive colorectal cancer (CRC) growth, progression, and immune evasion, and their co-overexpression is strongly associated with a poor prognosis. However, current small molecule inhibitors have limited efficacy due to the reciprocal feedback activation between STAT3 and β-catenin. Inspired by the PROteolysis TArgeting Chimera (PROTAC), a promising pharmacological modality for the selective degradation of proteins, we developed a strategy of nanoengineered peptide PROTACs (NP-PROTACs) to degrade both β-catenin and STAT3 effectively. The NP-PROTACs were engineered by coupling the peptide PROTACs with DSPE-PEG via disulfide bonds and self-assembled into nanoparticles. Notably, the dual degradation of β-catenin and STAT3 mediated by NP-PROTACs led to a synergistic antitumor effect compared to single-target treatment. Moreover, NP-PROTACs treatment enhanced CD103+ dendritic cell infiltration and T-cell cytotoxicity, alleviating the immunosuppressive microenvironment induced by β-catenin/STAT3 in CRC. These results highlight the potential of NP-PROTACs in facilitating the simultaneous degradation of two pathogenic proteins, thereby providing a novel avenue for cancer therapy.

Prognostic value of combined systemic inflammation response index and prognostic nutritional index in colorectal cancer patients

BACKGROUND

The prognosis of colorectal cancer (CRC) patients is notably influenced by both inflammation and nutritional status. The prognostic nutritional index (PNI) and systemic inflammatory response index (SIRI) have been reported in prognostic studies of various tumors. However, the efficacy of the combination of the two in predicting the prognosis of CRC patients has not been studied.

AIM

To evaluate the effectiveness of PNI and SIRI in predicting the prognosis of patients with CRC.

METHODS

We retrospectively gathered data from 470 CRC patients who underwent feasible radical surgery at Xinjiang Cancer Hospital. The optimal cut-off values for SIRI and PNI, along with their predictive power for survival, were determined through area under the receiver operating characteristic curve using time-dependent receiver operating characteristic analysis. The Kaplan-Meier method and log-rank test were applied to assess prognostic impact, and a multifactorial Cox proportional hazards model was employed for analysis. Additionally, a new model, PSIRI, was developed and assessed for its survival prediction capability.

RESULTS

The optimal cutoff values for PNI and SIRI were determined to be 47.80 and 1.38, respectively. Based on these values, patients were categorized into high PNI and low PNI groups, as well as high SIRI and low SIRI groups. Significant differences in age, T stage, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) subgroups were observed between the PNI groups in the baseline profile. In the SIRI group, notable differences were found in gender, T stage, nerve invasion, intravascular tumor emboli, NLR, MLR, and PLR subgroups. Both low PNI and high SIRI were identified as independent risk factors for poor prognosis in CRC patients. When combined into the PSIRI model, it was shown that patients with a PSIRI ≤ 1 had a higher risk of death compared to those with a PSIRI of 2.

CONCLUSION

We assessed the impact of PNI and SIRI on the prognostic survival of CRC patients and developed a new model, PSIRI. This model demonstrated superior predictive accuracy, with a concordance index of 0.767.

Co-occurring mutations identify prognostic subgroups of microsatellite stable colorectal cancer

BACKGROUND

Co-occurring mutations in pairs of genes can pinpoint clinically relevant subgroups of cancer. Most colorectal cancers (CRCs) are microsatellite stable (MSS) and have few frequent mutations. Large patient cohorts and broad genomic coverage are needed for comprehensive co-mutation profiling.

METHODS

Co-mutations were identified in a population-based Swedish cohort analyzed by whole-genome sequencing (n=819 stage I-IV MSS CRCs). Prognostic value was further evaluated in a publicly available dataset of clinically sequenced metastatic CRCs (MSK-IMPACT; n=934 MSS). Multivariable Cox proportional hazards analyses with clinicopathological parameters were performed for locoregional (stage I-III) and metastatic (stage IV and recurrent) cancers separately.

RESULTS

Prevalent co-mutations were detected in 23 unique gene pairs, 20 of which included APC, TP53, KRAS and/or PIK3CA. Several co-mutations involving APC were associated with good overall survival in locoregional CRC, including APC-TCF7L2 (multivariable HR: 0.49, 95% CI 0.27-0.89). This co-mutation was prognostic also in metastatic cancers (multivariable HR: 0.49 and 0.37, 95% CI: 0.24-0.98 and 0.17-0.82 in the Swedish and MSK cohorts, respectively). APC-SOX9 co-mutations were mutually exclusive with APC-TCF7L2, and the co-mutations combined had stronger prognostic associations than APC alone in both metastatic cohorts. BRAF p.V600E-RNF43 co-mutations were associated with poor overall and recurrence-free survival in locoregional CRC (multivariable HR: 4.13 and 3.2, 95% CI: 1.78-9.54 and 1.53-8.04, respectively).

CONCLUSIONS

We report a genome-wide evaluation of co-occurring mutations in MSS CRCs, and suggest that co-mutations can improve the prognostic stratification compared to single mutations alone.

Estimating the Number of Polygenic Diseases Among Six Mutually Exclusive Entities of Non-Tumors and Cancer

In the era of precision medicine with increasing amounts of sequenced cancer and non-cancer genomes of different ancestries, we here enumerate the resulting polygenic disease entities. Based on the cell number status, we first identified six fundamental types of polygenic illnesses, five of which are non-cancerous. Like complex, non-tumor disorders, neoplasms normally carry alterations in multiple genes, including in 'Drivers' and 'Passengers'. However, tumors also lack certain genetic alterations/epigenetic changes, recently named 'Goners', which are toxic for the neoplasm and potentially constitute therapeutic targets. Drivers are considered essential for malignant transformation, whereas environmental influences vary considerably among both types of polygenic diseases. For each form, hyper-rare disorders, defined as affecting <1/108 individuals, likely represent the largest number of disease entities. Loss of redundant tumor-suppressor genes exemplifies such a profoundly rare mutational event. For non-tumor, polygenic diseases, pathway-centered taxonomies seem preferable. This classification is not readily feasible in cancer, but the inclusion of Drivers and possibly also of epigenetic changes to the existing nomenclature might serve as initial steps in this direction. Based on the detailed genetic alterations, the number of polygenic diseases is essentially countless, but different forms of nosologies may be used to restrict the number.

Characterization of cell states in biliary tract cancers identifies mechanisms of therapeutic resistance in a phase II trial of DKN-01/nivolumab

Biliary tract cancers demonstrate profound therapeutic resistance, and broadly effective therapies for refractory disease are lacking. We conducted a single-arm, second-line phase II trial combining DKN-01, a humanized monoclonal antibody targeting Dickkopf-1 (DKK-1), and nivolumab to treat patients with advanced biliary tract cancer (NCT04057365). No objective responses were seen. To identify mechanisms of treatment failure, we analyzed paired pre-treatment and on-treatment biopsies using scRNA-seq and constructed a detailed molecular classification of malignant and immune cells. We annotated five biliary tract cancer malignant cell states: classical, basal, mesenchymal, neural-like, and endothelial-like. Neural-like and endothelial-like states, which drive therapeutic resistance in other cancers, have not previously been described in BTC. Malignant cell states co-varied with distinct immune cell states, revealing diverse mechanisms of myeloid and T-cell mediated immune suppression, including M2 myeloid and terminally exhausted T cell programs that were induced by DKN-01/nivolumab. Here, we provide the first systematic classification of functionally annotated cell states in biliary tract cancer and provide new insight into resistance mechanisms to an immunotherapy combination that can inform the next generation of trials.

Two Decades of Progress in Personalized Medicine of Colorectal Cancer in Serbia-Insights from the Institute for Oncology and Radiology of Serbia

BACKGROUND

It is projected that, by 2040, the number of new cases of colorectal cancer (CRC) will increase to 3.2 million, and the number of deaths to 1.6 million, highlighting the need for prevention strategies, early detection and adequate follow-up. In this study, we aimed to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future.

METHODS

Epidemiological data were derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits.

RESULTS

Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) showed a mutation in MMR genes with pathogenic or likely pathogenic significance and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene.

CONCLUSIONS

Although significant improvements in CRC management have occurred globally in recent years, a strategic approach leading to population-based systemic solutions is required. The high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy will be especially important factors for countries with limited healthcare resources like Serbia.