Germline MLH1 and MSH6 mutations from two Lynch syndrome families identified in a patient with early-onset of endometrial cancer: A case report.

Introduction: Lynch syndrome is caused by a germline mutation in mismatch repair (MMR) genes, leading to the loss of expression of MMR heterodimers, either MLH1/PMS2 or MSH2/MSH6, or isolated loss of PMS2 or MSH6. Concurrent loss of both heterodimers is uncommon, and patients carrying pathogenic variants affecting different MMR genes are rare, leading to the lack of cancer screening recommendation for these patients.Case presentation:Here, we reported a female with a family history of Lynch syndrome with MLH1 c.676C > T mutation. She developed endometrial cancer at 37 years old, with loss of MLH1/PMS2 expression. Immunohistochemical staining on tumor samples incidentally detected the additional loss of MSH6 expression. Whole exome sequencing on genomic DNA from peripheral blood revealed MSH6 c.2731C > T mutation, which was confirmed to be inherited from her mother, who had an early-onset ascending colon cancer without cancer family history. Conclusion: This is a rare case of the Lynch syndrome harboring germline mutations simultaneously in two different MMR genes inherited from two families with Lynch syndrome. The diagnosis of endometrial cancer at the age less than 40 years is uncommon for Lynch syndrome-related endometrial cancer. This suggests an earlier cancer screening for patients carrying two MMR mutations.

Tight junction protein cingulin variant is associated with cancer susceptibility by overexpressed IQGAP1 and Rac1-dependent epithelial-mesenchymal transition.

BACKGROUND: Cingulin (CGN) is a pivotal cytoskeletal adaptor protein located at tight junctions. This study investigates the link between CGN mutation and increased cancer susceptibility through genetic and mechanistic analyses and proposes a potential targeted therapeutic approach. METHODS: In a high-cancer-density family without known pathogenic variants, we performed tumor-targeted and germline whole-genome sequencing to identify novel cancer-associated variants. Subsequently, these variants were validated in a 222 cancer patient cohort, and CGN c.3560C > T was identified as a potential cancer-risk allele. Both wild-type (WT) (c.3560C > C) and variant (c.3560C > T) were transfected into cancer cell lines and incorporated into orthotopic xenograft mice model for evaluating their effects on cancer progression. Western blot, immunofluorescence analysis, migration and invasion assays, two-dimensional gel electrophoresis with mass spectrometry, immunoprecipitation assays, and siRNA applications were used to explore the biological consequence of CGN c.3560C > T. RESULTS: In cancer cell lines and orthotopic animal models, CGN c.3560C > T enhanced tumor progression with reduced sensitivity to oxaliplatin compared to the CGN WT. The variant induced downregulation of epithelial marker, upregulation of mesenchymal marker and transcription factor, which converged to initiate epithelial-mesenchymal transition (EMT). Proteomic analysis was conducted to investigate the elements driving EMT in CGN c.3560C > T. This exploration unveiled overexpression of IQGAP1 induced by the variant, contrasting the levels observed in CGN WT. Immunoprecipitation assay confirmed a direct interaction between CGN and IQGAP1. IQGAP1 functions as a regulator of multiple GTPases, particularly the Rho family. This overexpressed IQGAP1 was consistently associated with the activation of Rac1, as evidenced by the analysis of the cancer cell line and clinical sample harboring CGN c.3560C > T. Notably, activated Rac1 was suppressed following the downregulation of IQGAP1 by siRNA. Treatment with NSC23766, a selective inhibitor for Rac1-GEF interaction, resulted in the inactivation of Rac1. This intervention mitigated the EMT program in cancer cells carrying CGN c.3560C > T. Consistently, xenograft tumors with WT CGN showed no sensitivity to NSC23766 treatment, but NSC23766 demonstrated the capacity to attenuate tumor growth harboring c.3560C > T. CONCLUSIONS: CGN c.3560C > T leads to IQGAP1 overexpression, subsequently triggering Rac1-dependent EMT. Targeting activated Rac1 is a strategy to impede the advancement of cancers carrying this specific variant.

Distinct Features of Plasma Ultrashort Single-Stranded Cell-Free DNA as Biomarkers for Lung Cancer Detection.

BACKGROUND: Using broad range cell-free DNA sequencing (BRcfDNA-Seq), a nontargeted next-generation sequencing (NGS) methodology, we previously identified a novel class of approximately 50 nt ultrashort single-stranded cell-free DNA (uscfDNA) in plasma that is distinctly different from 167 bp mononucleosomal cell-free DNA (mncfDNA). We hypothesize that uscfDNA possesses characteristics that are useful for disease detection. METHODS: Using BRcfDNA-Seq, we examined both cfDNA populations in the plasma of 18 noncancer controls and 14 patients with late-stage nonsmall cell lung carcinoma (NSCLC). In comparison to mncfDNA, we assessed whether functional element (FE) peaks, fragmentomics, end-motifs, and G-Quadruplex (G-Quad) signatures could be useful features of uscfDNA for NSCLC determination. RESULTS: In noncancer participants, compared to mncfDNA, uscfDNA fragments showed a 45.2-fold increased tendency to form FE peaks (enriched in promoter, intronic, and exonic regions), demonstrated a distinct end-motif-frequency profile, and presented with a 4.9-fold increase in G-Quad signatures. Within NSCLC participants, only the uscfDNA population had discoverable FE peak candidates. Additionally, uscfDNA showcased different end-motif-frequency candidates distinct from mncfDNA. Although both cfDNA populations showed increased fragmentation in NSCLC, the G-Quad signatures were more discriminatory in uscfDNA. Compilation of cfDNA features using principal component analysis revealed that the first 5 principal components of both cfDNA subtypes had a cumulative explained variance of >80%. CONCLUSIONS: These observations indicate that the distinct biological processes of uscfDNA and that FE peaks, fragmentomics, end-motifs, and G-Quad signatures are uscfDNA features with promising biomarker potential. These findings further justify its exploration as a distinct class of biomarker to augment pre-existing liquid biopsy approaches.

Clinical Characteristics and Responses to Immune Checkpoint Inhibitors in RET-Aberrant Digestive Tract Tumours.

BACKGROUND: RET plays an oncogenic role, and its aberrations are potentially actionable. However, they have seldom been reported in tumours other than lung or thyroid cancers. The correlation of RET aberrations with clinical characteristics, co-occurring aberrations, and responses to immune checkpoint inhibitors (ICPi) have not been explored in digestive tract tumours. OBJECTIVES: The aim of the study was to elucidate the clinical characteristics, frequently co-altered genes, and treatment responses in RET-aberrant digestive tract tumours. PATIENTS AND METHODS: We retrospectively evaluated patients with digestive tract cancers for RET-aberrant tumours via FoundationOne CDx tumour-based selected genome sequencing from Jan 2016 to Jan 2021. RESULTS: In a median follow-up time of 51 months, a total of 453 patients were analysed. RET-aberrant tumours accounted for 4.4% in the studied population (n = 20), and 1.1% had an oncogenic fusion (n = 5). APC, KRAS, TP53, MSH6 and STK11 were the differentially co-altered genes (all false discovery rates <0.05). The presence of RET aberrations alone was not a significant prognostic factor. Eleven patients with RET-aberrant tumours received ICPi-based treatment and none achieved an objective response. In contrast, 47 patients with non-aberrant tumours received ICPi treatment and had an objective response rate of 27.7% and a significantly longer treatment duration (6.2 vs 2.8 months, p = 0.0008). CONCLUSIONS: Albeit rarely, RET aberrations can be found in digestive tract tumours. Patients with RET-aberrant tumours have a blunted response to ICPi and a comparable prognosis as compared with RET-wild type tumours. Together, these results provide insights into this rare but potentially actionable target in digestive tract tumours.

Dual role of sprouty2 as an inhibitor of RAS/ERK-driven proliferation and a promoter of cancer invasion in KRAS wild-type colorectal cancer.

Sprouty2 (SPRY2) is known to inhibit the RAS/MAPK/ERK pathway, and is a potential study target for cancer. The effect of SPRY2 in colorectal cancer (CRC) and whether it is influenced by KRAS mutation are not known. We manipulated SPRY2 gene expression and used an activating KRAS-mutant plasmid to determine its effect on CRC cell function in vitro and/or in vivo. We performed SPRY2 immunohistochemical staining in 143 CRC specimens and analyzed the staining results with various clinicopathological characteristics in relation to KRAS mutation status. SPRY2 knockdown in Caco-2 cells carrying the wild-type (WT) KRAS gene upregulated phosphorylated ERK (p-ERK) levels and increased cell proliferation in vitro, but inhibited cell invasion. However, SPRY2 knockdown in SW480 cells (activating KRAS mutant) or Caco-2 cells transfected with KRAS-mutant plasmid did not significantly alter p-ERK levels, cell proliferation, or invasion. The xenografts of SPRY2-knockdown Caco-2 cells were larger with less deep muscle invasion than those of control cells. The clinical cohort study revealed a positive association of SPRY2 protein expression with pT status, lymphovascular invasion, and perineural invasion in KRAS-WT CRCs. However, the associations were not observed in KRAS-mutant CRCs. Interestingly, high SPRY2 expression was related to shorter cancer-specific survival in both KRAS-WT and KRAS-mutant CRC patients. Our study demonstrated the dual role of SPRY2 as an inhibitor of RAS/ERK-driven proliferation and as a promoter of cancer invasion in KRAS-WT CRC. SPRY2 may promote the invasion and progression of KRAS-WT CRC, and might also enhance KRAS-mutant CRC progression through pathways other than invasion.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

HER2 amplification in colorectal cancer with brain metastasis: A propensity score matching study.

BACKGROUND: The association between human epidermal growth factor receptor-2 (HER2) amplification and brain metastasis (BM) in patients having colorectal cancer (CRC) has been suggested but not yet established. This study investigated the expression patterns of HER2, its association with BM, and its prognostic value in patients having CRC. METHODS: We retrospectively identified 99 patients having metastatic CRC (mCRC) and BM (the BM cohort) and compared them with a cohort of 249 patients having mCRC and without BM (the stage IV cohort) by propensity score matching. Immunohistochemical studies of HER2 on all available paraffin-embedded tumour samples, either from the primary tumour, the metastasis (brain and/or extracranial sites) or both, were performed and analysed. HER2 fluorescent in situ hybridisation was applied when necessary. The expression of HER2 was compared and correlated with survival. RESULTS: HER2 amplifications were detected in 16 (18.4%) of 87 and 9 (3.6%) of 249 patients who had specimens available in the BM and stage IV cohorts, respectively (P < .001). After propensity score matching, HER2 amplification was significantly associated with BM (odds ratio: 5.38, P = .003). HER2 heterogeneity was frequently observed not only at the single tumour level but also in paired tumour samples. A marginally significant longer survival since BM was found in patients having HER2-amplified mCRC than in those without (P = .07). CONCLUSIONS: HER2 amplification was significantly associated with BM in patients having mCRC and might have prognostic value for survival since BM. Given the heterogeneity of HER2 expression, the testing of HER2 status on available tissues from both primary and metastatic tumours should be encouraged.

PD-L1 Expression in High-Risk Early-Stage Colorectal Cancer-Its Clinical and Biological Significance in Immune Microenvironment.

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule that can regulate immune responses in the tumor microenvironment (TME); however, the clinical applications of PD-L1 in early-stage colorectal cancer (CRC) remain unclear. In this study, we aimed to investigate the relationship between PD-L1 expression and survival outcome and explore its relevant immune responses in CRC. PD-L1 expression was evaluated by immunohistochemical staining to determine the tumor proportion score and combined positive score (CPS) in a Taiwanese CRC cohort. The oncomine immune response research assay was conducted for immune gene expression analyses. CRC datasets from the TCGA database were reappraised for PD-L1-associated gene enrichment analyses using GSEA. The high expression of PD-L1 (CPS ≥ 5) was associated with longer recurrence-free survival (p = 0.031) and was an independent prognostic factor as revealed by multivariate analysis. High PD-L1 expression was related to six immune-related gene signatures, and CXCL9 is the most significant overexpressed gene in differential analyses. High CXCL9 expression correlated with increased infiltration levels of immune cells in the TME, including CD8+ T lymphocytes and M1 macrophages. These findings suggest that high PD-L1 expression is a prognostic factor of early-stage CRC, and CXCL9 may play a key role in regulating PD-L1 expression.

CHK2 activation contributes to the development of oxaliplatin resistance in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), the most common cancer type, causes high morbidity and mortality. Patients who develop drug resistance to oxaliplatin-based regimens have short overall survival. Thus, identifying molecules involved in the development of oxaliplatin resistance is critical for designing therapeutic strategies. METHODS: A proteomic screen was performed to reveal altered protein kinase phosphorylation in oxaliplatin-resistant (OR) CRC tumour spheroids. The function of CHK2 was characterised using several biochemical techniques and evident using in vitro cell and in vivo tumour models. RESULTS: We revealed that the level of phospho-CHK2(Thr68) was elevated in OR CRC cells and in ~30% of tumour samples from patients with OR CRC. We demonstrated that oxaliplatin activated several phosphatidylinositol 3-kinase-related kinases (PIKKs) and CHK2 downstream effectors and enhanced CHK2/PARP1 interaction to facilitate DNA repair. A phosphorylation mimicking CHK2 mutant, CHK2T68D, but not a kinase-dead CHK2 mutant, CHK2D347A, promoted DNA repair, the CHK2/PARP1 interaction, and cell growth in the presence of oxaliplatin. Finally, we showed that a CHK2 inhibitor, BML-277, reduced protein poly(ADP-ribosyl)ation (PARylation), FANCD2 monoubiquitination, homologous recombination and OR CRC cell growth in vitro and in vivo. CONCLUSION: Our findings suggest that CHK2 activity is critical for modulating oxaliplatin response and that CHK2 is a potential therapeutic target for OR CRC.

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care.

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer.

To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.

A Prediction Model for Tumor Recurrence in Stage II-III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling.

BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. METHODS: Pertinent clinicopathological features were compiled from medical records and standardized pathology reports of patients with stage II and III CRC. Four ML models based on logistic regression (LR), random forest (RF), classification and regression decision trees (CARTs), and support vector machine (SVM) were applied for the development of the prediction algorithm. The area under the curve (AUC) of the ML models was determined in order to compare the prediction accuracy. Genomic studies were performed using a panel-targeted next-generation sequencing approach. RESULTS: A total of 1073 patients who received curative intent surgery at the National Cheng Kung University Hospital between January 2004 and January 2019 were included. Based on conventional statistical methods, chemotherapy (p = 0.003), endophytic tumor configuration (p = 0.008), TNM stage III disease (p < 0.001), pT4 (p < 0.001), pN2 (p < 0.001), increased numbers of lymph node metastases (p < 0.001), higher lymph node ratios (LNR) (p < 0.001), lymphovascular invasion (p < 0.001), perineural invasion (p < 0.001), tumor budding (p = 0.004), and neoadjuvant chemoradiotherapy (p = 0.025) were found to be correlated with the tumor recurrence of patients with stage II-III CRC. While comparing the performance of different ML models for predicting cancer recurrence, the AUCs for LR, RF, CART, and SVM were found to be 0.678, 0.639, 0.593, and 0.581, respectively. The LR model had a better accuracy value of 0.87 and a specificity value of 1 in the testing set. Two prognostic factors, age and LNR, were selected by multivariable analysis and the four ML models. In terms of age, older patients received fewer cycles of chemotherapy and radiotherapy (p < 0.001). Right-sided colon tumors (p = 0.002), larger tumor sizes (p = 0.008) and tumor volumes (p = 0.049), TNM stage II disease (p < 0.001), and advanced pT3-4 stage diseases (p = 0.04) were found to be correlated with the older age of patients. However, pN2 diseases (p = 0.005), lymph node metastasis number (p = 0.001), LNR (p = 0.004), perineural invasion (p = 0.018), and overall survival rate (p < 0.001) were found to be decreased in older patients. Furthermore, PIK3CA and DNMT3A mutations (p = 0.032 and 0.039, respectively) were more frequently found in older patients with stage II-III CRC compared to their younger counterparts. CONCLUSIONS: This study demonstrated that ML models have a comparable predictive power for determining cancer recurrence in patients with stage II-III CRC after surgery. Advanced age and high LNR were significant risk factors for cancer recurrence, as determined by ML algorithms and multivariable analyses. Distinctive genomic profiles may contribute to discrete clinical behaviors and survival outcomes between patients of different age groups. Studies incorporating complete molecular and genomic profiles in cancer prediction models are beneficial for patients with stage II-III CRC.

The Expression Quantitative Trait Loci in Immune Response Genes Impact the Characteristics and Survival of Colorectal Cancer.

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.

Association between Altered Oncogenic Signaling Pathways and Overall Survival of Patients with Metastatic Colorectal Cancer.

Systemic characterization of genomic alterations into signaling pathways helps to understand the molecular pathogenies of colorectal cancer; however, their clinical implications remain unclear. Here, 128 patients with metastatic colorectal cancer (mCRC) receiving targeted next generation sequencing were retrospectively enrolled to analyze the impact of altered oncogenic pathways on clinical outcome. The datasets from Memorial Sloan Kettering Cancer Center were used for validation. In 123 patients with non-MSI-high tumor, the most common mutated gene was TP53 (84.6%), followed by APC (78.0%), KRAS (49.6%), and SMAD4 (22.8%). When mutated genes were allocated into signaling pathways defined as The Cancer Genome Atlas Pan-Cancer Analysis Project, alterations of cell cycle, Wnt, p53, RTK-RAS, PI3K, TGF-ß, Notch, and Myc pathways were identified in 88%, 87%, 85%, 75%, 28%, 26%, 17%, and 10% of mCRC tissues, respectively. The survival analyses revealed that Myc and TGF-ß pathway alterations were associated with a shorter overall survival (OS) (hazard ratio [HR]: 2.412; 95% confidence interval [CI]: 1.139-5.109; p = 0.018 and HR: 2.754; 95% CI: 1.044-7.265; p = 0.033, respectively). The negative prognostic impact of altered TGF-ß pathway was maintained in patients receiving an anti-EGFR antibody. The OS of patients with mCRC carrying MYC and BRAF mutation was shorter than those with either MYC or BRAF mutation (HR: 4.981, 95% CI: 0.296-83.92; p = 0.02). These findings have clinical implications, such as prognosis prediction, treatment guidance, and molecular-targeted therapy development.

CD40 Pathway and IL-2 Expression Mediate the Differential Outcome of Colorectal Cancer Patients with Different CSF1R c.1085 Genotypes.

Colony-stimulating factor 1 receptor (CSF-1R) acts as the receptor for colony stimulating factor 1, a cytokine that controls the production, differentiation, and function of macrophages. Prior studies showed cancer patients harboring germline CSF1R c.1085A>G genetic variant had better survival. Here, primary tumor samples from a stage III colorectal cancer (CRC) cohort were analyzed by a targeted gene expression assay containing 395 immune-related genes to study the immune mechanism underlying the different outcomes. CRC patients with CSF1R c.1085 genotype A_G had a better disease-free and overall survival than those with CSF1R genotype A_A. Compared to the group of patients without CSF1R variant, higher CD40LG expression, a surface marker of T cells, was found in the tumor tissues of patients with CSF1R c.1085 variant. In parallel with the higher CD40LG gene expression, immunofluorescent staining also showed more CD3+CD40L+ T cell infiltrates in tumors with CSF1R c.1085 genotype A_G. Moreover, higher IL-2 expression, known to be regulated by CD40 pathway, was also observed in tumors with CSF1R c.1085 genotype A_G than genotype A_A. Higher IL-2 expression generated by the interaction of CD40 ligand and CD40 between T cells and macrophages with CSF1R c.1085A>G variant is the potential mechanism explaining the different outcomes.

Comprehensively Exploring the Mutational Landscape and Patterns of Genomic Evolution in Hypermutated Cancers.

Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients with six different cancer types using deep targeted sequencing and gene expression data from 78 colorectal cancer patients, whereby driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution were investigated. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Most hypermutated endometrial and pancreatic cancer patients carry genetic mutations in EGFR, FBXW7, and PIK3CA that are linked to immunotherapy resistance, while hypermutated head and neck cancer patients carry genetic mutations associated with better treatment responses, such as ATM and BRRCA2 mutations. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and DNA repair defects are mutational drivers that are signatures for hypermutated cancer. Cancer driver mutations and other mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational prediction, we identified NF1 p.T700I and NOTCH1 p.V2153M as tumor-associated neoantigens, representing potential therapeutic targets for immunotherapy. Sequential mutations were used to predict hypermutated cancers based on genomic evolution. Using a logistic model, we achieved an area under the curve (AUC) = 0.93, accuracy = 0.93, and sensitivity = 0.81 in the testing set. The sequential patterns were distinct among the six cancer types, and the sequential mutation order of MSH2 and the coexisting BRAF genetic mutations influenced the hypermutated phenotype. The TP53~MLH1 and NOTCH1~TET2 sequential mutations impacted colorectal cancer survival (p-value = 0.027 and 0.0001, respectively) by reducing the expression of PTPRCAP (p-value = 1.06 × 10-6) and NOS2 (p-value = 7.57 × 10-7) in immunity. Sequential mutations are significant for hypermutated cancers, which are characterized by mutational heterogeneity. In addition to driver mutations and mutational signatures, sequential mutations in cancer evolution can impact hypermutated cancers. They characterize potential responses or predictive markers for hypermutated cancers. These data can also be used to develop hypermutation-associated drug targets and elucidate the evolutionary biology of cancer survival. In this study, we conducted a comprehensive analysis of mutational patterns, including sequential mutations, and identified useful markers and therapeutic targets in hypermutated cancer patients.

MET Mutation Is a Potential Therapeutic Target for Advanced Endometrial Cancer.

An optimal therapeutic regimen for endometrial cancer with extra-uterine metastasis is unavailable. This study aims to improve our understanding of the genomic landscape of advanced endometrial cancer and identify potential therapeutic targets. The clinical and genomic profiles of 81 patients with stage III or IV endometrial cancer were integrated. To identify genomic aberrations associated with clinical outcomes, Cox proportional hazard regression was used. The impacts of the genomic aberrations were validated in vitro and in vivo. The mutation status of MET, U2AF1, BCL9, PPP2R1A, IDH2, CBL, BTK, and CHEK2 were positively correlated with poor clinical outcomes. MET mutations occurred in 30% of the patients who presented with poor overall survival (hazard ratio, 2.606; 95% confidence interval, 1.167~5.819; adjusted p-value, 0.067). Concurrent MET and KRAS mutations presented with the worst outcomes. MET mutations in hepatocyte growth factor (HGF)-binding (58.1%) or kinase (16.2%) domains resulted in differential HGF-induced c-MET phosphorylation. Different types of MET mutations differentially affected tumor growth and displayed different sensitivities to cisplatin and tyrosine kinase inhibitors. MET N375S mutation is a germline variant that causes chemoresistance to cisplatin, with a high incidence in Eastern Asia. This study highlights the ethnic differences in the biology of the disease, which can influence treatment recommendations and the genome-guided clinical trials of advanced endometrial cancer.

Phase 1b study of pegylated arginine deiminase (ADI-PEG 20) plus Pembrolizumab in advanced solid cancers.

Background: Pegylated arginine deiminase (ADI-PEG 20) is a metabolism-based strategy that depletes arginine, resulting in tumoral stress and cytotoxicity. Preclinically, ADI-PEG 20 modulates T-cell activity and enhances the therapeutic efficacy of programmed death-1 (PD-1) inhibition. Methods: A phase 1b study, including a dose-escalation cohort and an expansion cohort, was undertaken to explore the effects of ADI-PEG 20 in combination with pembrolizumab, an anti-PD-1 antibody, for safety, pharmacodynamics, and response. CD3 levels and programmed death-ligand 1 (PD-L1) expression were assessed in paired biopsies collected prior to and after ADI-PEG 20 treatment but before pembrolizumab. Results: Twenty-five patients, nine in the dose-escalation cohort and sixteen in the expansion cohort, were recruited. Treatment was feasible with adverse events consistent with those known for each agent, except for Grade 3/4 neutropenia which was higher than expected, occurring in 10/25 (40%) patients. Mean arginine levels were suppressed for 1-3 weeks, but increased gradually. CD3+ T cells increased in 10/12 (83.3%) subjects following ADI-PEG 20 treatment, including in three partial responders (p = .02). PD-L1 expression was low and increased in 3/10 (30%) of subjects. Partial responses occurred in 6/25 (24%) heavily pretreated patients, in both argininosuccinate synthetase 1 proficient and deficient subjects. Conclusions: The immunometabolic combination was safe with the caveat that the incidence of neutropenia might be increased compared with either agent alone. ADI-PEG 20 treatment increased T cell infiltration in the low PD-L1 tumor microenvironment. The recommended phase 2 doses are 36 mg/m2 weekly for ADI-PEG 20 and 200 mg every 3 weeks for pembrolizumab.

Clinical Utility of a Cell-Free DNA Assay in Patients With Colorectal Cancer.

The analysis of cell-free DNA (cfDNA) is rapidly emerging as a powerful approach to guide the clinical care of cancer patients. Several comprehensive cfDNA assays designed to detect mutations across several genes are now available. Here, we analyzed the use of a cfDNA panel in colorectal cancer (CRC) patients. Twenty-eight CRC patients with relapse or metastatic disease and 31 patients with no evidence of disease (NED) were enrolled. Genomic alterations in cfDNA were analyzed by the Oncomine™ Pan-Cancer Cell-Free Assay that detects hotspot mutations, small indels, copy number changes, and gene fusions across 52 genes. In the NED group, genomic alterations in cfDNA were detected in 12/31 patients (38.7%). The detection of alterations was more common in patients who were ≥60 years old, and the most common genomic alteration was a TP53 mutation. Fifty percent of the TP53 mutations were frequently or very frequently found in human cancers. Among 28 patients with relapse or metastatic disease, 22 (78.6%) had genomic alterations in cfDNA. The alterations were detected most frequently in TP53 (n = 10), followed by KRAS (n = 9). Actionable targets for CRC, including ERBB2 amplification and BRAF mutations, could be identified by this cfDNA assay. Compared with mutational profiling routinely analyzed using tumor samples, several additional targets with currently available therapies, including IDH1, IDH2, and PDGFRA mutations, were discovered. The cfDNA assay could identify potentially actionable targets for CRC. Identifying how to filter out cancer-like genomic alterations not derived from tumors remains a challenge.