Abstract
Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived ...cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.
Locally advanced rectal cancer (LARC) presents a challenge in identifying molecular markers linked to the response to neoadjuvant chemoradiotherapy (nCRT). This study aimed to utilize a sensitive ...proteomic method, data-independent mass spectrometry (DIA-MS), to extensively analyze the LARC proteome, seeking individuals with favorable initial responses suitable for a watch-and-wait approach. This research addresses the unmet need to understand the response to treatment, potentially guiding personalized strategies for LARC patients. Post-treatment assessment included MRI scans and proctoscopy. This research involved 97 LARC patients treated with intense chemoradiotherapy, comprising radiation and chemotherapy. Out of 97 LARC included in this study, we selected 20 samples with the most different responses to nCRT for proteome profiling (responders vs. non-responders). This proteomic approach shows extensive proteome coverage in LARC samples. The analysis identified a significant number of proteins compared to a prior study. A total of 915 proteins exhibited differential expression between the two groups, with certain signaling pathways associated with response mechanisms, while top candidates had good predictive potential. Proteins encoded by genes SMPDL3A, PCTP, LGMN, SYNJ2, NHLRC3, GLB1, and RAB43 showed high predictive potential of unfavorable treatment outcome, while RPA2, SARNP, PCBP2, SF3B2, HNRNPF, RBBP4, MAGOHB, DUT, ERG28, and BUB3 were good predictive biomarkers of favorable treatment outcome. The identified proteins and related biological processes provide promising insights that could enhance the management and care of LARC patients.
Identification of non-metastatic colorectal cancer (CRC) patients with a high risk of recurrence after tumor resection is important to select patients who might benefit from adjuvant treatment. ...Cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) analyses after surgery are promising biomarkers to predict recurrence in these patients. However, these analyses face several challenges and do not allow guidance of neoadjuvant treatment, which might become a novel standard option in colon cancer treatment. The prognostic value of cfDNA/ctDNA before surgery is unclear. This systematic review aims to provide an overview of publications in which the prognostic value of presurgery cfDNA/ctDNA in non-metastatic CRC patients was studied and is performed according to PRISMA guidelines. A total of 29 out of 1233 articles were included and categorized into three groups that reflect the type of approach: measurement of cfDNA, ctDNA somatic alterations, and ctDNA methylation. Overall, a clear association between presurgery cfDNA/ctDNA and the outcome was not observed, but large studies that primarily focus on the prognostic value of presurgery cfDNA/ctDNA are lacking. Designing and performing studies that focus on the value of presurgery cfDNA/ctDNA is needed, in addition to standardization in the reporting of cfDNA/ctDNA results according to existing guidelines to improve comparability and interpretation among studies.
Hyperactivation of Wnt and Ras-MAPK signalling are common events in development of colorectal adenomas. Further progression from adenoma-to-carcinoma is frequently associated with 20q gain and ...overexpression of Aurora kinase A (AURKA). Interestingly, AURKA has been shown to further enhance Wnt and Ras-MAPK signalling. However, the molecular details of these interactions in driving colorectal carcinogenesis remain poorly understood. Here we first performed differential expression analysis (DEA) of AURKA knockdown in two colorectal cancer (CRC) cell lines with 20q gain and AURKA overexpression. Next, using an exact algorithm, Heinz, we computed the largest connected protein-protein interaction (PPI) network module of significantly deregulated genes in the two CRC cell lines. The DEA and the Heinz analyses suggest 20 Wnt and Ras-MAPK signalling genes being deregulated by AURKA, whereof β-catenin and KRAS occurred in both cell lines. Finally, shortest path analysis over the PPI network revealed eight 'connecting genes' between AURKA and these Wnt and Ras-MAPK signalling genes, of which UBE2D1, DICER1, CDK6 and RACGAP1 occurred in both cell lines. This study, first, confirms that AURKA influences deregulation of Wnt and Ras-MAPK signalling genes, and second, suggests mechanisms in CRC cell lines describing these interactions.
The core spliceosomal Sm proteins were recently proposed as cancer-selective lethal targets in non-small cell lung cancer (NSCLC). In contrast, the loss of the commonly mutated cancer target SF3B1 ...appeared to be toxic to non-malignant cells as well. In the current study, the transcriptomes of A549 NSCLC cells, in which SF3B1 or SNRPD3 was silenced, were compared using RNA sequencing. The skipping of exon 4 of the proteasomal subunit beta type-3 (PSMB3) mRNA, resulting in a shorter PSMB3-S variant, occurred only after silencing SNRPD3. This observation was extended to the other six Sm genes. Remarkably, the alternative splicing of PSMB3 mRNA upon Sm gene silencing was not observed in non-malignant IMR-90 lung fibroblasts. Furthermore, PSMB3 was found to be overexpressed in NSCLC clinical samples and PSMB3 expression correlated with Sm gene expression. Moreover, a high PSMB3 expression corresponds to worse survival in patients with lung adenocarcinomas. Finally, silencing the canonical full-length PSMB3-L, but not the shorter PSMB3-S variant, was cytotoxic and was accompanied by a decrease in proteasomal activity. Together, silencing Sm genes, but not SF3B1, causes a cytotoxic alternative splicing switch in the PSMB3 mRNA in NSCLC cells only.
(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical ...parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma.
Abstract
Secretory leukocyte protease inhibitor (SLPI) is a pleiotropic protein produced by healthy intestinal epithelial cells. SLPI regulates NF-κB activation, inhibits neutrophil proteases and has ...broad antimicrobial activity. Recently, increased SLPI expression was found in various types of carcinomas and was suggested to increase their metastatic potential. Indeed, we demonstrated that SLPI protein expression in colorectal cancer (CRC) liver metastases and matched primary tumors is associated with worse outcome, suggesting that SLPI promotes metastasis in human CRC. However, whether SLPI plays a role in CRC before distant metastases have formed is unclear. Therefore, we examined whether SLPI expression is associated with prognosis in CRC patients with localized disease. Using a cohort of 226 stage II and 160 stage III CRC patients we demonstrate that high SLPI protein expression is associated with reduced disease recurrence in patients with stage III micro-satellite stable tumors treated with adjuvant chemotherapy, independently of established clinical risk factors (hazard rate ratio 0.54,
P
-value 0.03). SLPI protein expression was not associated with disease-free survival in stage II CRC patients. Our data suggest that the role of SLPI in CRC may be different depending on the stage of disease. In stage III CRC, SLPI expression may be unfavorable for tumors, whereas SLPI expression may be beneficial for tumors once distant metastases have established.
The risk of recurrence after resection of a stage II or III colon cancer, and therefore qualification for adjuvant chemotherapy (ACT), is traditionally based on clinicopathological parameters. ...However, the parameters used in clinical practice are not able to accurately identify all patients with or without minimal residual disease. Some patients considered 'low-risk' do develop recurrence (undertreatment), whilst other patients receiving ACT might not have developed recurrence at all (overtreatment). We previously analysed tumour tissue expression of 28 protein biomarkers that might improve identification of patients at risk of recurrence. In the present study we aimed to build a prognostic classifier based on these 28 biomarkers and clinicopathological parameters.
Classification and regression tree (CART) analysis was used to build a prognostic classifier based on a well described cohort of 386 patients with stage II and III colon cancer. Separate classifiers were built for patients who were or were not treated with ACT. Routine clinicopathological parameters and tumour tissue immunohistochemistry data were included, available for 28 proteins previously published. Classification trees were pruned until lowest misclassification error was obtained. Survival of the identified subgroups was analysed, and robustness of the selected CART variables was assessed by random forest analysis (1000 trees).
In patients not treated with ACT, prognosis was estimated best based on expression of KCNQ1. Poor disease-free survival (DFS) was observed in those with loss of expression of KCNQ1 (HR = 3.38 (95% CI 2.12 - 5.40); p < 0.001). In patients treated with ACT, key prognostic factors were lymphovascular invasion (LVI) and expression of KCNQ1. Patients with LVI showed poorest DFS, whilst patients without LVI and high expression of KCNQ1 showed most favourable survival (HR = 7.50 (95% CI 3.57-15.74); p < 0.001). Patients without LVI and loss of expression of KCNQ1 had intermediate survival (HR = 3.91 (95% CI 1.76 - 8.72); p = 0.001).
KCNQ1 and LVI were identified as key features in prognostic classifiers for disease-free survival in stage II and III colon cancer patients.
Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer
. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. ...Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer.
The fecal immunochemical test (FIT) for detecting hemoglobin is used widely for noninvasive colorectal cancer (CRC) screening, but its sensitivity leaves room for improvement.
To identify novel ...protein biomarkers in stool that outperform or complement hemoglobin in detecting CRC and advanced adenomas.
Case-control study.
Colonoscopy-controlled referral population from several centers.
315 stool samples from one series of 12 patients with CRC and 10 persons without colorectal neoplasia (control samples) and a second series of 81 patients with CRC, 40 with advanced adenomas, and 43 with nonadvanced adenomas, as well as 129 persons without colorectal neoplasia (control samples); 72 FIT samples from a third independent series of 14 patients with CRC, 16 with advanced adenomas, and 18 with nonadvanced adenomas, as well as 24 persons without colorectal neoplasia (control samples).
Stool samples were analyzed by mass spectrometry. Classification and regression tree (CART) analysis and logistic regression analyses were performed to identify protein combinations that differentiated CRC or advanced adenoma from control samples. Antibody-based assays for 4 selected proteins were done on FIT samples.
In total, 834 human proteins were identified, 29 of which were statistically significantly enriched in CRC versus control stool samples in both series. Combinations of 4 proteins reached sensitivities of 80% and 45% for detecting CRC and advanced adenomas, respectively, at 95% specificity, which was higher than that of hemoglobin alone (P < 0.001 and P = 0.003, respectively). Selected proteins could be measured in small sample volumes used in FIT-based screening programs and discriminated between CRC and control samples (P < 0.001).
Lack of availability of antibodies prohibited validation of the top protein combinations in FIT samples.
Mass spectrometry of stool samples identified novel candidate protein biomarkers for CRC screening. Several protein combinations outperformed hemoglobin in discriminating CRC or advanced adenoma from control samples. Proof of concept that such proteins can be detected with antibody-based assays in small sample volumes indicates the potential of these biomarkers to be applied in population screening.
Center for Translational Molecular Medicine, International Translational Cancer Research Dream Team, Stand Up to Cancer (American Association for Cancer Research and the Dutch Cancer Society), Dutch Digestive Foundation, and VU University Medical Center.
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