Search Results (9)

Background: Colorectal cancer (CRC) continues to represent one of the most common and lethal malignant tumors globally. Notably, only patients diagnosed with microsatellite instability-high (MSI-H) colorectal cancer derive substantial clinical benefits from immune checkpoint inhibitor therapy. As critical immune cells that infiltrate tumors, γδT cells are tightly linked to the therapeutic response in colorectal cancer patients with microsatellite instability (MSI) colorectal cancer. However, the heterogeneous characteristics of γδT cells in colorectal cancer with different microsatellite statuses and their specific roles in regulating immunotherapy responses remain unclear. Methods: We performed dimensionality reduction and clustering analysis on γδT cells from a single-cell RNA sequencing dataset to explore diversity and functional characteristics of distinct γδT cell subsets. Meanwhile, bulk transcriptome data were applied to further investigate the immune infiltration, clinical characteristics, and immune checkpoint molecule expression in CRC patients stratified by distinct γδT cell subpopulations. Results: We identified five γδT cell subsets, among which the C4_CXCL13 γδT cell subsets was enriched in MSI CRC and exhibited an exhausted-like T cell phenotype while retaining robust cytotoxic function. A signature score based on these 17 marker genes was associated with survival, immune infiltration, and therapeutic response, thus representing a potentially valuable independent prognostic factor. Conclusions: The C4_CXCL13 γδT cell subset represents a characteristic subset in MSI CRC and is closely associated with clinical prognosis and benefit from immunotherapy. It represents a potential clinical marker for classifying patients and estimating the response to immunotherapy, offering a novel target for personalized immunotherapy in CRC. Full article

Machine learning (ML) algorithms hold the potential to outperform the selection of patients for immunotherapy (ICIs) compared to previous biomarker studies. We analyzed the predictive performance of ML models and compared them to traditional clinical biomarkers (TCBs) in the field of gastrointestinal (GI) cancers. The study has been registered in PROSPERO (number: CRD42023465917). A systematic search of PubMed was conducted to identify studies applying different ML algorithms to GI cancer patients treated with ICIs using tumor RNA gene expression profiles. The outcomes included were response to immunotherapy (ITR) or survival. Additionally, we compared the ML methodology details and predictive power inherent in the published gene sets using 5-fold cross-validation and logistic regression (LR), on an available well-defined ICI-treated metastatic gastric cancer (GC) cohort (n = 45). A set of standard clinical ICI biomarkers (MLH, MSH, and CD8 genes, plus PMS2 and PD-L1)) and de-novo calculated principal components (PCs) of the original datasets were also included as additional points of comparison. Nine articles were identified as eligible to meet the inclusion criteria. Three were pan-cancer studies, five assessed GC, and one studied colorectal cancer (CRC). Classification and regression models were used to predict ICI efficacy. Next, using LR, we validated the predictive power of applied ML algorithms on RNA signatures, using their reported receiver operating characteristics (ROC) analysis area under the curve (AUC) values on a well-defined ICI-treated gastric cancer (GC) dataset (n = 45). In two cases our method has outperformed the published results (reported/LR comparison: 0.74/0.831, 0.67/0.735). Besides the published studies, we have included two benchmarks: a set of TCBs and using principal components based on the whole dataset (PCA, 99% explained variance, 40 components). Interestingly, a study using a selected gene set (immuno-oncology panel) with AUC = 0.83 was the only one that outperformed the TCB (AUC = 0.8) and the PCA (AUC =0.81) results. Cross-validation of the predictive performance of these genes on the same GC dataset and an investigation of their prognostic role on a collated multi-cohort GC dataset of n = 375 resected, or chemotherapy-treated patients revealed that genes mannose-6-phosphate receptor (M6PR), Indoleamine 2,3-Dioxygenase 1 (IDO1), Neuropilin-1 (NRP1), and MAGEA3 performed similarly, or better than established biomarkers like PD-L1 and MSI. We found an immuno-oncology panel with an AUC = 0.83 that outperformed the clinical benchmark or the PC results. We recommend further investigation and experimental validation in the case of M6PR, IDO1, NRP1, and MAGEA3 expressions based on their strong predictive power in GC ITR. Well-designed studies with larger sample sizes and nonlinear ML models might help improve biomarker selections. Full article

Background: Defective DNA repair systems result in the accumulation of mutations, loss of genomic integrity, and eventually cancer. Following initial malignant transformation due to specific DNA damage and defective DNA repair, cancer cells become reliant upon other DNA repair pathways for their survival. The co-occurrence of specific repair deficiencies brings catastrophic outcomes such as cell death for cancer cells and thus holds promise as a potential therapeutic strategy. Exploring the co-occurrence and mutual exclusivity of mutational signatures provides valuable knowledge regarding combinations of defective repair pathways that are cooperative and confer selective advantage to cancer cells and those that are detrimental and cannot be tolerated by them. Methods: Taking advantage of mutational signature profiling, we analyzed whole-genome sequences of 1014 breast cancers to reveal the underlying mutational processes and their interrelationships. Results: We found an inverse relationship between deficiencies of homologous recombination (HRd) and non-homologous end joining (NHEJd) with reactive oxygen species (ROS). Moreover, HRd and NHEJd co-occurred with APOBEC but were mutually exclusive with mismatch repair deficiency (MMRd) and ROS. Our analysis revealed that SBS8 and SBS39 signatures of currently unknown etiology correlate with NHEJd. ID1 and ID2 signatures co-occur with ROS and have mutual exclusivity with HRd, SBS8, SBS39 and NHEJd. The ID4 signature, with currently unknown etiology, has mutual exclusivity with HRd and NHEJd and co-occurred with ROS. On the other hand, the ID15 signature, with currently unknown etiology, co-occurred with SBS8, SBS39, HRd, NHEJd and DBS2, while having an inverse relationship with MMRd and ROS. Comparing the mutational signatures of HRd and non-HRd TNBC genomes reveals the unique presence of ROS signatures in non-HRd tumors and the lack of ROS signature in HRd tumors. Conclusion: Taken together, these analyses indicate the possible application of mutation signatures and their interactions in advancing patient stratification and suggest appropriate therapies targeting the make-up of individual tumors’ mutational processes. Ultimately, this information provides the opportunity to discover promising synthetic lethal candidates targeting DNA repair systems. Full article

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision. Full article

TGFβ signaling in the tumor microenvironment (TME) drives immune evasion and is a negative predictor of immune checkpoint inhibitor (ICI) efficacy in colorectal cancer (CRC). TIM-3, an inhibitory receptor implicated in anti-tumor immune responses and ICI resistance, has emerged as an immunotherapeutic target. This study investigated TIM-3, M2 macrophages and the TGFβ-activated TME, in association with microsatellite instability (MSI) status and consensus molecular subtypes (CMSs). Transcriptomic cohorts of CRC tissues, organoids and xenografts were examined (n = 2240). TIM-3 and a TGFβ-inducible stromal protein, VCAN, were evaluated in CRC specimens using immunohistochemistry (n = 45). TIM-3 expression on monocytes and generated M2 macrophages was examined by flow cytometry. We found that the expression of HAVCR2 (TIM-3) significantly correlated with the transcriptional signatures of TGFβ, TGFβ-dependent stromal activation and M2 macrophage, each of which were co-upregulated in CMS4, CMS1 and MSI CRCs across all datasets. Tumor-infiltrating TIM-3⁺ immune cells accumulated in TGFβ-responsive cancer stroma. TIM-3 was increased on M2-polarized macrophages, and on monocytes in response to TGFβ treatment. In conclusion, we identified a close association between TIM-3 and M2-like polarization of macrophages in the TGFβ-rich TME. Our findings provide new insights into personalized immunotherapeutic strategies based on the TME for CRCs. Full article

Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are low-risk thyroid lesions most often characterised by RAS-type mutations. The histological diagnosis may be challenging, and even immunohistochemistry and molecular approaches have not yet provided conclusive solutions. This study characterises a set of NIFTPs by Matrix-Assisted Laser Desorption/Ionisation (MALDI)–Mass Spectrometry Imaging (MSI) to highlight the proteomic signatures capable of overcoming histological challenges. Archived formalin-fixed paraffin-embedded samples from 10 NIFTPs (n = 6 RAS-mutated and n = 4 RAS-wild type) were trypsin-digested and analysed by MALDI–MSI, comparing their profiles to normal tissue and synchronous benign nodules. This allowed the definition of a four-peptide signature able to distinguish RAS-mutant from wild-type cases, the latter showing proteomic similarities to hyperplastic nodules. Moreover, among the differentially expressed signals, Peptidylprolyl Isomerase A (PPIA, 1505.8 m/z), which has already demonstrated a role in the development of cancer, was found overexpressed in NIFTP RAS-mutated nodules compared to wild-type lesions. These results underlined that high-throughput proteomic approaches may add a further level of biological comprehension for NIFTPs. In the future, thanks to the powerful single-cell detail achieved by new instruments, the complementary NGS–MALDI imaging sequence might be the correct methodological approach to confirm that the current NIFTP definition encompasses heterogeneous lesions that must be further characterised. Full article

Predicting the survival outcomes of patients with colorectal cancer (CRC) remains challenging. We investigated the prognostic significance of the transcriptome and tumour-infiltrating lymphocyte T-cell receptor (TIL/Tc-TCR) repertoire and analysed TIL/Tc-TCR sequences of The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) CRC cohorts. Using a multivariate Cox regression, we tested whether TIL/Tc-TCR repertoire, patient and tumour characteristics (stage, sidedness, total non-synonymous mutations, microsatellite instability (MSI) and transcriptional signatures) correlated with patient overall survival (OS) and designed a prognostic nomogram. A multivariate analysis (C-index = 0.75) showed that only patient age, disease stage, TIL/Tc degree of infiltration and clonality were independent prognostic factors for OS. The cut-offs for patients’ allocation to TIL/Tc abundance subgroups were determined using a strategy of maximally selected rank statistics with the OptimalCutpoints R package. These were “high”, “low” and “very high” (90 th percentile) TIL/Tc infiltration-stratified OS (median not reached, 67 and 44.3 months; p < 0.001); the results were validated in the CPTAC cohort. TIL/Tc clonality was prognostic (median OS in “high” vs. “low” clonality not reached and 67.3 months; p = 0.041) and independent of TIL/Tc infiltration. Whilst tumour sidedness was not prognostic, the “very highly” infiltrated tumours were prevalent among right-sided CRCs (p = 0.039) and showed distinct immunological features, with lower Th1 signature (p = 0.004), higher PD-L1 expression (p < 0.001) and likely enrichment in highly suppressory IL1R1⁺ Tregs (FoxP3 and IL1R1 overexpression, p < 0.001). TIL/Tc abundance and clonality are independent prognosticators in CRC and, combined with clinical variables, refine risk stratification. We identified a subset of CRCs with “very high” TIL/Tc infiltration, poor prognosis and distinct genetic and immunologic features, which may benefit from alternative therapeutic approaches. These results need validation in prospective patient cohorts. Full article

Lynch-like syndrome (LLS) is an increasingly common clinical challenge with an underlying molecular basis mostly unknown. To shed light onto it, we focused on a very young LLS early-onset colorectal cancer (CRC) cohort (diagnosis ≤ 40 y.o.), performing germline and tumor whole-exome sequencing (WES) of 15 patients, and additionally analyzing their corresponding tumor mutational burden (TMB) and mutational signatures. We identified four cases (27%) with double somatic putative variants in mismatch repair (MMR) core genes, as well as three additional cases (20%) with double MSH3 somatic alterations in tumors with unexplained MSH2/MSH6 loss of expression, and two cases (13%) with POLD1 potential biallelic alterations. Average TMB was significantly higher for LLS cases with double somatic alterations. Lastly, nine predicted deleterious variants in genes involved in the DNA repair functions and/or previously associated with CRC were found in nine probands, four of which also showed MMR biallelic somatic inactivation. In conclusion, we contribute new insights into LLS CRC, postulating MSH3 and POLD1 double somatic alterations as an underlying cause of a microsatellite instability (MSI) phenotype, proposing intrinsic biological differences between LLS with and without somatic alterations, and suggesting new predisposing candidate genes in this scenario. Full article

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis. Full article