Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors

High expression of PAX8-AS1 correlates with poor prognosis and response to fluorouracil-based chemotherapy in stage II colon cancer

Decisions regarding adjuvant therapy in patients with stage II colon cancer remains controversial and challenging. We aimed to determine novel biomarkers that help to predict relapse free survival (RFS) and identify a subset of patients with stage II colon cancer who could gain survival benefits from adjuvant chemotherapy. Public microarray datasets of stage II colon cancer samples were extracted from Gene Expression Omnibus database. Global gene expression changes were then analyzed between the paired early relapse and long-term survival group to identify the differentially expressed mRNAs and lncRNAs. Based on Lasso Cox regression modeling analysis, a total of 30 mRNAs and 2 lncRNAs were finally identified. With specific formula, stage II patients in training and validation sets were divided into low and risk groups with significantly different RFS. PAX8-AS1 is the novel lncRNA which showed the highest upregulation in early relapse group. Patients with high PAX8-AS1 expression level showed notably poorer RFS in both meta GEO cohort (P = 0.04, Figure 4B) and FUSCC cohort (P < 0.001, Figure 4C). Among the stage II patients with high PAX8-AS1 level, administration of fluorouracil-based adjuvant chemotherapy provided a substantial improvement in RFS (P = 0.002, Figure 3C). Further mechanistic study unveiled that PAX8-AS1 increases the response of CRC cells to chemotherapy in vitro and in vivo by maintaining the mRNA stability of PAX8. In conclusion, PAX8-AS1 as a novel and reliable biomarker for predicting prognosis and identification of patients with stage II disease who could gain survival benefit from fluorouracil-based adjuvant chemotherapy.

The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine

The SCRUM-Japan MONSTAR-SCREEN consortium is a nationwide molecular profiling project employing artificial intelligence-driven multiomics analyses for patients with advanced malignancies, aiming to develop novel therapeutics and diagnostics and deliver effective drugs to patients. Concurrently, studies assessing molecular residual disease-based precision medicine for resectable solid tumors, including CIRCULATE-Japan, are ongoing. The substantial data generated by these platforms are stored within a state-of-the-art supercomputing infrastructure, VAPOR CONE. Since 2015, our project has registered over 24,000 patients as of December 2023. Among 16,144 patients with advanced solid tumors enrolled in MONSTAR-SCREEN projects, 5.0% have participated in matched clinical trials, demonstrating a 29.2% objective response rate and 14.8-month median survival (95% CI, 13.4-16.3) for patients treated in the matched clinical trials. Notably, patients who received matched therapy demonstrated significantly prolonged overall survival compared with those who did not (hazard ratio 0.77; 95% confidence interval, 0.71-0.83). Significance: Our nationwide molecular profiling initiative played pivotal roles in facilitating the enrollment of patients with advanced solid tumors into matched clinical trials and highlighted the substantial survival benefits of patients treated with matched therapy. We aim to facilitate an industry-academia data-sharing infrastructure ecosystem, fostering new drug discovery paradigms and precision medicine.

Prognostic models for colorectal cancer recurrence using carcinoembryonic antigen measurements

Objective

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide. A considerable percentage of patients who undergo surgery with curative intent will experience cancer recurrence. Early identification of individuals with a higher risk of recurrence is crucial for healthcare professionals to intervene promptly and devise appropriate treatment strategies. In this study, we developed prognostic models for CRC recurrence using machine learning models on a limited number of CEA measurements.

Method

A dataset of 1927 patients diagnosed with Stage I-III CRC and referred to Zuyderland Hospital for surgery between 2008 and 2016 was utilized. Machine learning models were trained using this comprehensive dataset, which included demographic details, clinicopathological factors, and serial measurements of Carcinoembryonic Antigen (CEA). In this study, the predictive performance of these models was assessed, and the key prognostic factors influencing colorectal cancer (CRC) recurrence were pinpointed.

Result

Among the evaluated models, the gradient boosting classifier demonstrated superior performance, achieving an Area Under the Curve (AUC) score of 0.81 and a balanced accuracy rate of 0.73. Recurrence prediction was shown to be feasible with an AUC of 0.71 when using only five post-operative CEA measurements. Furthermore, key factors influencing recurrence were identified and elucidated.

Conclusion

This study shows the transformative role of machine learning in recurrence prediction for CRC, particularly by investigating the minimum number of CEA measurements required for effective recurrence prediction. This approach not only contributes to the optimization of clinical workflows but also facilitates the development of more effective, individualized treatment plans, thereby laying the groundwork for future advancements in this area. Future directions involve validating these models in larger and more diverse cohorts. Building on these efforts, our ultimate goal is to develop a risk-based follow-up strategy that can improve patient outcomes and enhance healthcare efficiency.

Molecular Signatures for Microbe-Associated Colorectal Cancers

Background

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics.

Methods

A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS).

Results

The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs.

Conclusions

Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

An invasion front gene expression signature for higher-risk patient selection in stage IIA MSS colon cancer

Stage II colon cancer (CC) encompasses a heterogeneous group of patients with diverse survival experiences: 87% to 58% 5-year relative survival rates for stages IIA and IIC, respectively. While stage IIA patients are usually spared the adjuvant chemotherapy, some of them relapse and may benefit from it; thus, their timely identification is crucial. Current gene expression signatures did not specifically target this group nor did they find their place in clinical practice. Since processes at invasion front have also been linked to tumor progression, we hypothesize that aside from bulk tumor features, focusing on the invasion front may provide additional clues for this stratification. A retrospective matched case-control collection of 39 stage IIA microsatellite-stable (MSS) untreated CCs was analyzed to identify prognostic gene expression-based signatures. The endpoint was defined as relapse within 5 years vs. no relapse for at least 6 years. From the same tumors, three different classifiers (bulk tumor, invasion front, and constrained baseline on bulk tumor) were developed and their performance estimated. The baseline classifier, while the weakest, was validated in two independent data sets. The best performing signature was based on invasion front profiles [area under the receiver operating curve (AUC) = 0.931 (0.815-1.0)] and contained genes associated with KRAS pathway activation, apical junction complex, and heme metabolism. Its combination with bulk tumor classifier further improved the accuracy of the predictions.

Unraveling the role of tumor sidedness in prognosis of stage II colon cancer

Background

Stage II colon cancer has varying risks for metastasis, and treatment strategies depend on molecular and clinicopathological features. While tumor-sidedness is a well-accepted prognostic factor for stage III/IV colon cancer, its role in stage II is controversial. Understanding its effect in stage II is crucial for improving treatment strategies.

Methods

We analyzed clinical and follow-up data of colon cancer from the Surveillance, Epidemiology, and End Results database (2004-2017). Patients were divided into a primary study cohort (2010-2017) and a validation cohort (2004-2009). The baseline characteristics between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) groups were compared. Moreover, the effect of tumor size on cancer-specific survival (CSS) was evaluated using Kaplan-Meier analysis.

Results

The study involved 87,355 patients in the study cohort and 65,858 in the validation cohort. Of the study cohort, 52.3% were diagnosed with RCC. The median age was 64 years old, with 48.5% females and 76.8% of white people. In addition, stage II RCC showed better CSS compared with LCC (5-year CSS 88.0% vs 85.5%, P < 0.001), while stage III/IV RCC demonstrated poorer outcomes. Multivariate Cox regression analysis identified that the right-sidedness was a positive prognostic factor in stages I/II but negative in stages III (HR 1.10, P < 0.001) and IV (HR 1.26, P < 0.001). Chemotherapy rates decreased in RCC, particularly in stage II (RCC vs LCC: 16.2% vs 28.5%, P < 0.001). Subgroup analysis, stratified by T3/T4 stages and chemotherapy status, further highlighted better survival outcomes in RCC.

Conclusions

RCC is associated with a significantly better prognosis in stage II. The importance of considering tumor-sidedness in clinical decision-making and the design of future clinical trials should be emphasized.

Precision Oncology in Gastrointestinal and Colorectal Cancer Surgery

Precision medicine is used to treat gastrointestinal malignancies including esophageal, gastric, small bowel, colorectal, and pancreatic cancers. Cutting-edge assays to detect and treat these cancers are active areas of research and will soon become standard of care. Colorectal cancer is a prime example of precision oncology as disease site is no longer the final determinate of treatment. Here, the authors describe how leveraging an understanding of tumor biology translates to individualized patient care using evidence-based practices.

Integrating bulk and single-cell data to predict the prognosis and identify the immune landscape in HNSCC

The complex interplay between tumour cells and the tumour microenvironment (TME) underscores the necessity for gaining comprehensive insights into disease progression. This study centres on elucidating the elusive the elusive role of endothelial cells within the TME of head and neck squamous cell carcinoma (HNSCC). Despite their crucial involvement in angiogenesis and vascular function, the mechanistic diversity of endothelial cells among HNSCC patients remains largely uncharted. Leveraging advanced single-cell RNA sequencing (scRNA-Seq) technology and the Scissor algorithm, we aimed to bridge this knowledge gap and illuminate the intricate interplay between endothelial cells and patient prognosis within the context of HNSCC. Here, endothelial cells were categorized into Scissorhigh and Scissorlow subtypes. We identified Scissor+ endothelial cells exhibiting pro-tumorigenic profiles and constructed a prognostic risk model for HNSCC. Additionally, four biomarkers also were identified by analysing the gene expression profiles of patients with HNSCC and a prognostic risk prediction model was constructed based on these genes. Furthermore, the correlations between endothelial cells and prognosis of patients with HNSCC were analysed by integrating bulk and single-cell sequencing data, revealing a close association between SHSS and the overall survival (OS) of HNSCC patients with malignant endothelial cells. Finally, we validated the prognostic model by RT-qPCR and IHC analysis. These findings enhance our comprehension of TME heterogeneity at the single-cell level and provide a prognostic model for HNSCC.

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse

BACKGROUND

Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers.

METHODS

The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed.

RESULTS

We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer.

CONCLUSIONS

In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management

Colorectal cancer (CRC) is the second cause of cancer-related deaths in both sexes globally and presents different clinical outcomes that are described by a range of genomic and epigenomic alterations. Despite the advancements in CRC screening plans and treatment strategies, the prognosis of CRC is dismal. In the last two decades, molecular biomarkers predictive of prognosis have been identified in CRC, although biomarkers predictive of treatment response are only available for specific biological drugs used in stage IV CRC. Translational clinical trials mainly based on "omic" strategies allowed a better understanding of the biological heterogeneity of CRCs. These studies were able to classify CRCs into subtypes mainly related to prognosis, recurrence risk, and, to some extent, also to treatment response. Accordingly, the comprehensive molecular characterizations of CRCs, including The Cancer Genome Atlas (TCGA) and consensus molecular subtype (CMS) classifications, were presented to improve the comprehension of the genomic and epigenomic landscapes of CRCs for a better patient management. The CMS classification obtained by the CRC subtyping consortium categorizes CRC into four consensus molecular subtypes (CMS1-4) characterized by different prognoses. In this review, we discussed the CMS classification in different settings with a focus on its relationships with precursor lesions, tumor immunophenotype, and gut microbiota, as well as on its role in predicting prognosis and/or response to pharmacological treatments, as a crucial step towards precision medicine.

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BACKGROUND

The low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.

METHODS

Saliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.

RESULTS

There were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.

CONCLUSIONS

The results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.

Development of novel DNAJB6-KIAA1522-p-mTOR three-protein prognostic prediction models for CRC

BACKGROUND

To evaluate the prognostic value of DNAJB6, KIAA1522, and p-mTOR expression for colorectal cancer (CRC) and to develop effective prognostic models for CRC patients.

METHODS

The expression of DNAJB6, KIAA1522, and p-mTOR (Ser2448) was detected using immunohistochemistry in 329 CRC specimens. The prognostic values of the three proteins in the training cohort were assessed using Kaplan-Meier curves and univariate and multivariate Cox proportional hazards models. Prediction nomogram models integrating the three proteins and TNM stage were constructed. Subsequently, calibration curves, receiver operating characteristic (ROC) curves, the concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of the nomograms in the training and validation cohorts.

RESULTS

The three proteins DNAJB6, KIAA1522, and p-mTOR were significantly overexpressed in CRC tissues (each P < 0.01), and their expression was an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) (each P < 0.05). The area under the ROC curves (AUC) and C-index values were approximately 0.7. Additionally, the calibration curves showed that the predicted values and the actual values fit well. Furthermore, DCA curves indicated that the clinical value of the nomogram models was higher than that of TNM stage. Overall, the novel prediction models have good discriminability, sensitivity, specificity and clinical utility.

CONCLUSION

The nomograms containing DNAJB6, KIAA1522, and p-mTOR may be promising models for predicting postoperative survival in CRC.

Development and validation of postoperative circulating tumor DNA combined with clinicopathological risk factors for recurrence prediction in patients with stage I-III colorectal cancer

BACKGROUND

Circulating tumor DNA (ctDNA) detection following curative-intent surgery could directly reflect the presence of minimal residual disease, the ultimate cause of clinical recurrence. However, ctDNA is not postoperatively detected in ≥ 50% of patients with stage I-III colorectal cancer (CRC) who ultimately recur. Herein we sought to improve recurrence risk prediction by combining ctDNA with clinicopathological risk factors in stage I-III CRC.

METHODS

Two independent cohorts, both consisting of early-stage CRC patients who underwent curative surgery, were included: (i) the discovery cohort (N = 124) with tumor tissues and postoperative plasmas for ctDNA determination; and (ii) the external validation cohort (N = 125) with available ctDNA results. In the discovery cohort, somatic variations in tumor tissues and plasmas were determined via a 733-gene and 127-gene next-generation sequencing panel, respectively.

RESULTS

In the discovery cohort, 17 of 108 (15.7%) patients had detectable ctDNA. ctDNA-positive patients had a significantly high recurrence rate (76.5% vs. 16.5%, P < 0.001) and short recurrence-free survival (RFS; P < 0.001) versus ctDNA-negative patients. In addition to ctDNA status, the univariate Cox model identified pathologic stage, lymphovascular invasion, nerve invasion, and preoperative carcinoembryonic antigen level associated with RFS. We combined the ctDNA and clinicopathological risk factors (CTCP) to construct a model for recurrence prediction. A significantly higher recurrence rate (64.7% vs. 8.1%, P < 0.001) and worse RFS (P < 0.001) were seen in the high-risk patients classified by the CTCP model versus those in the low-risk patients. Receiver operating characteristic analysis demonstrated that the CTCP model outperformed ctDNA alone at recurrence prediction, which increased the sensitivity of 2 year RFS from 49.6% by ctDNA alone to 87.5%. Harrell's concordance index, calibration curve, and decision curve analysis also suggested that the CTCP model had good discrimination, consistency, and clinical utility. These results were reproduced in the validation cohort.

CONCLUSION

Combining postoperative ctDNA and clinical risk may better predict recurrence than ctDNA alone for developing a personalized postoperative management strategy for CRC.

Cost-effectiveness of precision molecular diagnostic tests for stage II colorectal cancer

Background

In colorectal cancer, inappropriate use of adjuvant chemotherapies may lead to significant increases in healthcare costs and harms to patients. Genome-based interventions are being increasingly used in the stratification of patients according to their risk profiles. However, earlier cost-effectiveness analyses of precision molecular diagnostics have indicated a paucity of data on comparative health economic outcomes. Our aim was to compare the cost-effectiveness of marketed genomic tests used in the prognosis of stage II colorectal cancer patients.

Methods

A Markov model was developed to compare the cost-effectiveness of treatment guided by any one of the following genomic tests: 12-gene assay or the 18-gene expression assay or the 482-gene signature or the Immunoscore assay in a hypothetical cohort of patients (n=1,000) with stage II colorectal cancer. Our study investigated outcomes in three health states: no recurrence, recurrence and death. This study was conducted from a societal perspective, and a 3% discount was applied to the costs and health outcomes. Sensitivity analyses were performed to assess the uncertainty of model parameters on the results.

Results

The cost of the Immunoscore assay strategy in stage II colorectal cancer patients was estimated to be US $23,564 with a gain of 3.903 quality-adjusted life years (QALYs) as compared with the 12-gene assay strategy at US $24,545 and 3.903 QALYs; the 18-gene assay strategy at US $28,374 and 3.623 QALYs; and the 482-gene signature treatment strategy at US $33,315 with 3.704 QALYs. Sensitivity analyses indicated that incremental cost-effectiveness ratio (ICER) values were sensitive to costs of genomic tests and adjuvant chemotherapies; and utilities related to patients in the no-recurrence health state.

Conclusions

Overall, the Immunoscore assay seems to be a dominant strategy at a threshold willingness-to-pay of $50,000 per QALY, but in the US other tests have been used for longer. Thus, the 12-gene assay may generate cost savings compared to the 18-gene expression assay. The findings of our study may provide useful information to policymakers regarding selection of the most appropriate genomic test, and resource allocation decisions.

Identification and verification of a 4-gene signature predicting the overall survival of cervical cancer

Cervical cancer (CC) is one of the most common gynecological malignancies, ranking fourth in both incidence and mortality in women worldwide. Early screening and treatment are of great significance in reducing the incidence and mortality of CC. Due to the complex molecular mechanisms of tumor progression, the predictive power of traditional clinical information is limited. In this study, an effective molecular model is established to assess prognosis of patients with CC and guide clinical treatment so as to improve their survival rate. Three high quality datasets (GSE138080, GSE52904, GSE67522) of expression profiling were obtained from gene expression omnibus (GEO) database. Another mRNA expression and clinicopathological data of CC were obtained from The Cancer Genome Atlas (TCGA) dataset. The bioinformatic analyses such as univariate analysis, multivariate Cox proportional-hazards model (Cox) analysis and lasso regression analysis were conducted to select survival-related differentially expressed genes (DEGs) and further establish a prognostic gene signature. Moreover, the performance of prognostic gene signature was evaluated based on Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) and tumor immunity analysis were carried out to elucidate the molecular mechanisms and immune relevance. A 4-gene signature comprising procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 (PLOD2), spondin1 (SPON1), secreted phosphoprotein 1 (SPP1), ribonuclease H2 subunit A (RNASEH2A) was established to predict overall survival (OS) of CC. The ROC curve indicated good performance of the 4-gene signature in predicting OS of CC based on the TCGA dataset. The 4-gene signature classified the patients into high-risk and low-risk groups with distinct OS rates of CC. Univariate analysis and multivariate Cox regression analysis revealed that the 4-gene signature was an independent factor affecting the prognosis of patients with CC. Our study developed a 4-gene signature capable of predicting the OS of CC. The findings may be beneficial to individualized clinical treatment and timely follow-up for patients with CC.

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

BACKGROUND

Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications.

METHODS

We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures.

RESULTS

Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively.

CONCLUSION

Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy

PURPOSE

Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a "watch and wait" strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.

EXPERIMENTAL DESIGN

We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.

RESULTS

A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).

CONCLUSION

The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a "watch and wait" strategy.

TRANSLATIONAL RELEVANCE

Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for "watch and wait".

Dysregulated Expression of Three Genes in Colorectal Cancer Stratifies Patients into Three Risk Groups

Despite advances in recent years in the study of the molecular profile of sporadic colorectal cancer (sCRC), the specific genetic events that lead to increased aggressiveness or the development of the metastatic process of tumours are not yet clear. In previous studies of the gene expression profile (GEP) using a high-density array (50,000 genes and 6000 miRNAs in a single assay) in sCRC tumours, we identified a 28-gene signature that was found to be associated with an adverse prognostic value for predicting patient survival. Here, we analyse the differential expression of these 28 genes for their possible association with tumour local aggressiveness and metastatic processes in 66 consecutive sCRC patients, followed for >5 years, using the NanoString nCounter platform. The global transcription profile (expression levels of the 28 genes studied simultaneously) allowed us to discriminate between sCRC tumours and nontumoral colonic tissues. Analysis of the biological and functional significance of the dysregulated GEPs observed in our sCRC tumours revealed 31 significantly altered canonical pathways. Among the most commonly altered pathways, we observed the increased expression of genes involved in signalling pathways and cellular processes, such as the PI3K-Akt pathway, the interaction with the extracellular matrix (ECM), and other functions related to cell signalling processes (SRPX2). From a prognostic viewpoint, the altered expression of BST2 and SRPX2 genes were the only independent variables predicting for disease-free survival (DFS). In addition to the pT stage at diagnosis, dysregulated transcripts of ADH1B, BST2, and FER1L4 genes showed a prognostic impact on OS in the multivariate analysis. Based on the altered expression of these three genes, a scoring system was built to stratify patients into low-, intermediate-, and high-risk groups with significantly different 5-year OS rates: 91%, 83%, and 52%, respectively. The prognostic impact was validated in two independent series of sCRC patients from the public GEO database (n = 562 patients). In summary, we show a strong association between the altered expression of three genes and the clinical outcome of sCRC patients, making them potential markers of suitability for adjuvant therapy after complete tumour resection. Additional prospective studies in larger series of patients are required to confirm the clinical utility of the newly identified biomarkers because the number of patients analysed remains small.

SAMHD1 as a prognostic and predictive biomarker in stage II colorectal cancer: A multicenter cohort study

Background

The identification of high-risk population patients is key to the personalized treatment options for the stage II colorectal cancers. The use of proteomics in the prognosis of patients with stage II colorectal cancer remains unclear.

Methods

Using quantitative proteomics, we analyzed proteins that are differentially expressed in the tumor and adjacent normal tissues of 11 paired colorectal cancer patients with and without recurrence selected by a nested case-control design. Of the 21 identified proteins, we selected one candidate protein. The association of the corresponding gene of the selected protein with overall survival (OS) and adjuvant chemotherapy was analyzed using two independent cohorts of patients with stages II colorectal cancer.

Results

Sterile α motif and histidine-aspartate domain-containing protein 1 (SAMHD1) was selected as the candidate biomarker. A group of 124 patients (12.5%) were stratified into SAMHD1-high subgroup. The 5-year OS rate of SAMHD1-high patients was lower than that of SAMHD1-low patients with stage II colorectal cancer (discovery cohort: hazard ratio [HR] = 2.89, 95% confidence interval [CI], 1.17-7.18, P = 0.016; validation cohort: HR = 2.25, 95% CI, 1.17-4.34, P = 0.013). The Cox multivariate analysis yielded similar results. In a pooled database, the 5-year OS rate was significantly different between patients with and without adjuvant chemotherapy among stage II SAMHD1-low tumors than in patients with stage II SAMHD1-high tumors (88% vs. 77%, P = 0.032).

Conclusions

SAMHD1-high expression could help in identifying patients with stage II colorectal cancer with poor prognosis and less benefit from adjuvant chemotherapy.

Tumor Biomarker Testing for Metastatic Colorectal Cancer: a Canadian Consensus Practice Guideline

The systemic therapy management of metastatic colorectal cancer (mCRC) has evolved from primarily cytotoxic chemotherapies to now include targeted agents given alone or in combination with chemotherapy, and immune checkpoint inhibitors. A better understanding of the pathogenesis and molecular drivers of colorectal cancer not only aided the development of novel targeted therapies but led to the discovery of tumor mutations which act as predictive biomarkers for therapeutic response. Mutational status of the KRAS gene became the first genomic biomarker to be established as part of standard of care molecular testing, where KRAS mutations within exons 2, 3, and 4 predict a lack of response to anti- epidermal growth factor receptor therapies. Since then, several other biomarkers have become relevant to inform mCRC treatment; however, there are no published Canadian guidelines which reflect the current standards for biomarker testing. This guideline was developed by a pan-Canadian advisory group to provide contemporary, evidence-based recommendations on the minimum acceptable standards for biomarker testing in mCRC, and to describe additional biomarkers for consideration.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma

Background

Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis.

Methods

Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations.

Results

We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM.

Conclusion

We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.

Evaluating the quality of the economic evidence in colorectal cancer genomics studies

The increase in the use of genome-based screening and diagnostic tests adds to the overall costs of oncologic care for colorectal cancer. This, in turn, has resulted in an increase in published economic analyses. Aim: To perform a systematic literature review of the available economic evidence evaluating the value of genomic testing for colorectal cancer and appraise the quality of the economic studies conducted to date. Methods: A systematic review of the literature for economic studies of colorectal cancer genomics from January 2006 through October 2020, and evaluation of study quality using the Quality of Health Economic Studies (QHES) instrument was conducted. The validated QHES was then applied to a final set of articles that met eligibility criteria. Results: Our search of the literature initially yielded 12,859 records. A final set of 49 articles met our inclusion criteria. The QHES score ranged from 24 to 100, with an average score of 82. Most of the studies (n = 40, 82%) scored above 75 and were considered of good quality. Conclusion: Our analysis revealed that most of the economic analyses of colorectal cancer genomic molecular diagnostics in the literature may be of good quality. There is, however, some variation in methodological rigor between the articles.

Circulating Tumor DNA Analysis Guiding Adjuvant Therapy in Stage II Colon Cancer

BACKGROUND

The role of adjuvant chemotherapy in stage II colon cancer continues to be debated. The presence of circulating tumor DNA (ctDNA) after surgery predicts very poor recurrence-free survival, whereas its absence predicts a low risk of recurrence. The benefit of adjuvant chemotherapy for ctDNA-positive patients is not well understood.

METHODS

We conducted a trial to assess whether a ctDNA-guided approach could reduce the use of adjuvant chemotherapy without compromising recurrence risk. Patients with stage II colon cancer were randomly assigned in a 2:1 ratio to have treatment decisions guided by either ctDNA results or standard clinicopathological features. For ctDNA-guided management, a ctDNA-positive result at 4 or 7 weeks after surgery prompted oxaliplatin-based or fluoropyrimidine chemotherapy. Patients who were ctDNA-negative were not treated. The primary efficacy end point was recurrence-free survival at 2 years. A key secondary end point was adjuvant chemotherapy use.

RESULTS

Of the 455 patients who underwent randomization, 302 were assigned to ctDNA-guided management and 153 to standard management. The median follow-up was 37 months. A lower percentage of patients in the ctDNA-guided group than in the standard-management group received adjuvant chemotherapy (15% vs. 28%; relative risk, 1.82; 95% confidence interval [CI], 1.25 to 2.65). In the evaluation of 2-year recurrence-free survival, ctDNA-guided management was noninferior to standard management (93.5% and 92.4%, respectively; absolute difference, 1.1 percentage points; 95% CI, -4.1 to 6.2 [noninferiority margin, -8.5 percentage points]). Three-year recurrence-free survival was 86.4% among ctDNA-positive patients who received adjuvant chemotherapy and 92.5% among ctDNA-negative patients who did not.

CONCLUSIONS

A ctDNA-guided approach to the treatment of stage II colon cancer reduced adjuvant chemotherapy use without compromising recurrence-free survival. (Supported by the Australian National Health and Medical Research Council and others; DYNAMIC Australian New Zealand Clinical Trials Registry number, ACTRN12615000381583.).

Consensus molecular subtype differences linking colon adenocarcinoma and obesity revealed by a cohort transcriptomic analysis

Colorectal cancer (CRC) is the third-leading cause of cancer-related deaths in the United States and worldwide. Obesity—a worldwide public health concern—is a known risk factor for cancer including CRC. However, the mechanisms underlying the link between CRC and obesity have yet to be fully elucidated in part because of the molecular heterogeneity of CRC. We hypothesized that obesity modulates CRC in a consensus molecular subtype (CMS)-dependent manner. RNA-seq data and associated tumor and patient characteristics including body weight and height data for 232 patients were obtained from The Cancer Genomic Atlas–Colon Adenocarcinoma (TCGA-COAD) database. Tumor samples were classified into the four CMSs with the CMScaller R package; body mass index (BMI) was calculated and categorized as normal, overweight, and obese. We observed a significant difference in CMS categorization between BMI categories. Differentially expressed genes (DEGs) between obese and overweight samples and normal samples differed across the CMSs, and associated prognostic analyses indicated that the DEGs had differing associations on survival. Using Gene Set Enrichment Analysis, we found differences in Hallmark gene set enrichment between obese and overweight samples and normal samples across the CMSs. We constructed Protein-Protein Interaction networks and observed differences in obesity-regulated hub genes for each CMS. Finally, we analyzed and found differences in predicted drug sensitivity between obese and overweight samples and normal samples across the CMSs. Our findings support that obesity impacts the CRC tumor transcriptome in a CMS-specific manner. The possible associations reported here are preliminary and will require validation using in vitro and animal models to examine the CMS-dependence of the genes and pathways. Once validated the obesity-linked genes and pathways may represent new therapeutic targets to treat colon cancer in a CMS-dependent manner.

Identification of a Four-Gene Signature for Determining the Prognosis of Papillary Thyroid Carcinoma by Integrated Bioinformatics Analysis

Purpose

Although well-differentiated papillary thyroid carcinoma (PTC) has an indolent nature and usually an excellent prognosis, some patients experience disease recurrence or death. The aim of this study was to identify prognostic markers to stratify PTC patients.

Patients and Methods

Eight gene-expression profiles (GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE53157, GSE60542, and GSE104005) were obtained from the Gene Expression Omnibus and used to analyze differentially expressed genes (DEGs) between PTC tissues and non-tumor tissues. Univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed to identify prognostic genes and establish a risk-score model based on the integrated DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were used to validate the prognostic performance of the risk score. A nomogram was constructed based on The Cancer Genome Atlas dataset and Multivariable Cox regression analysis.

Results

A total of 165 upregulated and 207 downregulated DEGs were screened. A four-gene signature including PAPSS2, PCOLCE2, PTX3, and TGFBR3 was identified. The risk-score model showed a strong diagnosis performance for identifying patients with a poor prognosis. KM analysis showed that patients with low risk scores had a significantly more favorable overall survival (OS) than those with high risk scores (p = 0.0002). ROC curves based on the four-gene signature showed better performances in predicting 1-, 3-, and 5-year survival than did the American Joint Committee on Cancer staging system (area under the curve: 0.86 vs 0.84, 0.80 vs 0.63, and 0.79 vs 0.73, respectively). Furthermore, when combined with age and tumor status from the nomogram, the four-gene signature achieved a good performance in guiding postoperative follow-up surveillance of patients with PTC.

Conclusion

The four-gene signature was found to be a novel and reliable biomarker with great potential for clinical application in risk stratification and OS prediction in patients with PTC.

Sustainable Clinical Development of Adjuvant Chemotherapy for Colon Cancer

Numerous clinical studies in an adjuvant setting have been conducted and the combination therapy of 5-fluorouracil and oxaliplatin has been established as the standard treatment for Stage III and as an option for high-risk Stage II patients. Biologics such as bevacizumab and antiepidermal growth factor receptor antibodies have failed to show additional survival benefits. The indication of adjuvant chemotherapy has been determined according to the pathological stage. Nevertheless, a pathological diagnosis does not necessarily result in selection of the optimal treatment. To improve treatment decisions, many trials have aimed to stratify patients into treatment groups using genomic testing. Recently, gene signature, Immunoscore, and circulating tumor DNA (ctDNA) assays have been reported and among them, ctDNA was shown to be a promising accurate predictive marker for recurrence. Treatment of ctDNA-positive patients with aggressive chemotherapy may reduce recurrence rates. The ultimate goal is to accurately predict the risk of recurrence and to prevent recurrence in colon cancer patients. In this review we focus on the clinical development of adjuvant chemotherapy and stratification of patients according to risk of recurrence and the future direction of adjuvant chemotherapy.

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis

Background

The mechanism of cancer occurrence and development could be understood with multi‐omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).

Methods

Clinical follow‐up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA‐Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic‐related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene‐based prediction model for LUAD was validated in validation datasets.

Results

The study filtered 2071 prognostic‐related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis‐related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression‐related. A 7‐gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5‐year AUC of survival in the validation set and training set was all ˃ 0.67. Similar results were obtained from multi‐omics validation datasets.

Conclusions

The study builds a novel 7‐gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

OBJECTIVE

To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.

DESIGN

Scoping review.

METHODS

We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.

RESULTS

Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.

CONCLUSIONS

While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

Prognostic Value of N6-Methyladenosine-Related lncRNAs in Early-Stage Colorectal Cancer: Association With Immune Cell Infiltration and Chemotherapeutic Drug Sensitivity

Purpose: Accumulating evidence indicates that N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) play a crucial role in the occurrence and development of several cancers. We aimed to explore the potential role of m6A-related lncRNA signatures in predicting prognosis for early-stage (stages I and II) colorectal cancer (CRC).

Methods: m6A-related lncRNA data were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen for prognostic m6A-related lncRNAs. Immune characteristics were analyzed in different subgroups created via unsupervised clustering analysis. Next, patients were randomly divided into training and test cohorts. In the training cohort, least absolute shrinkage and selection operator (LASSO) regression was performed to establish a prognostic model. The predictive value of the signature was evaluated in the training and test cohorts. Drug sensitivity was also examined.

Results: A total of 1,478 m6A-related lncRNAs were identified. Two subgroups were created based on the expression of seven prognostic m6A-related lncRNAs. Prognosis was worse for cluster 1 than for cluster 2, and cluster 1 was characterized by increased numbers of M2 macrophages, decreased numbers of memory B cells, and higher expression of checkpoint genes when compared with cluster 2. Five m6A-related lncRNAs were selected to establish a risk prediction signature via LASSO regression. The 3 years overall survival (OS) was higher in the low-risk group than in the high-risk group. The area under the curve at 1, 2, and 3 years was 0.929, 0.954, and 0.841 in the training cohort and 0.664, 0.760, and 0.754 in the test cohort, respectively. Multivariate Cox regression analysis suggests that the risk score was an independent predictor of OS in both the training and test cohorts. A prognostic nomogram based on the five m6A-related lncRNAs and their clinical features was built and verified. The high-risk group was more sensitive to chemotherapeutic drugs (camptothecin and cisplatin) than the low-risk group.

Conclusion: We identified two molecular subgroups of early-stage CRC with unique immune features based on seven prognostic m6A-related lncRNAs. Subsequent analyses demonstrated the usefulness of a five m6A-related lncRNA signature as a potential indicator of prognosis in patients with early-stage CRC.

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background

Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence.

Methods

The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database.

Results

Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group.

Conclusions

Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08827-z.

Circulating Tumour DNA as a Potential Cost-Effective Biomarker to Reduce Adjuvant Chemotherapy Overtreatment in Stage II Colorectal Cancer

BACKGROUND AND OBJECTIVE

Substantial adjuvant chemotherapy (AC) overtreatment for stage II colorectal cancer results in a health and financial burden. Circulating tumour DNA (ctDNA) can improve patient selection for AC by detecting micro-metastatic disease. We estimated the health economic potential of ctDNA-guided AC for stage II colorectal cancer.

METHODS

A cost-utility analysis was performed to compare ctDNA-guided AC to standard of care, where 22.6% of standard of care patients and all ctDNA-positive patients (8.7% of tested patients) received AC and all ctDNA-negative patients (91.3%) did not. A third preference-sensitive ctDNA strategy was included where 6.8% of ctDNA-negative patients would receive AC. A state-transition model was populated using data from a prospective cohort study and clinical registries. Health and economic outcomes were discounted at 5% over a lifetime horizon from a 2019 Australian payer perspective. Extensive scenario and probabilistic analyses quantified model uncertainty.

RESULTS

Compared to standard of care, the ctDNA and preference-sensitive ctDNA strategies increased quality-adjusted life-years by 0.20 (95% confidence interval - 0.40 to 0.81) and 0.19 (- 0.40 to 0.78), and resulted in incremental costs of AUD - 4055 (- 16,853 to 8472) and AUD - 2284 (- 14,685 to 10,116), respectively. Circulating tumour DNA remained cost effective at a willingness to pay of AUD 20,000 per quality-adjusted life-year gained throughout most scenario analyses in which the proportion of ctDNA-positive patients cured by AC and compliance to a ctDNA-negative test results were decreased.

CONCLUSIONS

Circulating tumour-guided AC is a potentially cost-effective strategy towards reducing overtreatment in stage II colorectal cancer. Results from ongoing randomised clinical studies will be important to reduce uncertainty in the estimates.

Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

How I treat stage II colon cancer patients

Stage II colon cancer (CC) is probably one of the best prognosis gastrointestinal tumors seen in our consultations, but often takes a lot of time for physicians to determine appropriate treatment because of the limited benefit of adjuvant chemotherapy (CT) in these patients, together with the limited evidence in this situation. How to choose the best treatment for each individual patient is thus dependent on molecular (microsatellite instability/microsatellite stability status) and clinico-pathological features relevant enough to classify these tumors into low-, intermediate- and high-risk stage II disease and to choose an appropriate attitude for each of these subgroups. In practice, the first step in treatment decision making must be to assess the patient's status and comorbidities to see if the patient is eligible for an adjuvant treatment. Then, as fluoropyrimidines (FPs) are the corner stone of CC adjuvant treatment, screening for dihydropyrimidine dehydrogenase deficiency is mandatory in western countries. Finally, depending on the patient's characteristics and tumor risk stage, the strategy may be surveillance, adjuvant FP alone or oxaliplatin-based adjuvant CT. In the near future, new tools such as Immunoscore® (HalioDx; Luminy Biotech Enterprises, Marseille Cedex, France) and circulating tumor DNA may help to identify more precisely patients with minimal residual disease for more personalized adjuvant treatment approaches.

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Stage II CC is a heterogeneous disease with a complex management due to the limited data and benefit of adjuvant CT.

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Risk stratification through prognostic parameters is crucial to aid clinicians in determining the appropriate therapy.

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Lymph node sampling <12 and pT4 are currently recognized as the major prognostic features associated with worse survival.

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Adjuvant CT should be considered by incorporating prognostic features and balanced against patient's age and comorbidities.

ELF3 Overexpression as Prognostic Biomarker for Recurrence of Stage II Colorectal Cancer

BACKGROUND/AIM

Adjuvant chemotherapy for high-risk Stage II colorectal cancer (CRC) is weakly recommended; however, no consensus exists on "high-risk" definition. Prognostic biomarker identification is important for selecting patients with poor prognosis who may benefit from adjuvant chemotherapy.

MATERIALS AND METHODS

Using Microarray data analyses, ELF3 was identified as a candidate gene highly expressed in Stage II CRC with distant recurrences. ELF3 mRNA expression in 168 Stage II CRC patients was subjected to quantitative RT-PCR analysis and ELF3 protein expression in 185 patients was quantified by immunohistochemical analysis. The relationship between mRNA and protein expression levels and patient characteristics were also investigated.

RESULTS

The overall recurrence rate and relapse-free survival were significantly poorer in the ELF3 high-expression than the low-expression group at the mRNA and protein levels. High ELF3 mRNA and protein expression levels were independent poor prognostic factors.

CONCLUSION

High ELF3 expression was associated with recurrence of Stage II.

Genome-Wide Analysis Reveals Hypoxic Microenvironment Is Associated With Immunosuppression in Poor Survival of Stage II/III Colorectal Cancer Patients

Background: Hypoxia is associated with a poorer clinical outcome and resistance to chemotherapy in solid tumors; identifying hypoxic-related colorectal cancer (CRC) and revealing its mechanism are important. The aim of this study was to assess hypoxia signature for predicting prognosis and analyze relevant mechanism.

Methods: Patients without chemotherapy were selected for the identification of hypoxia-related genes (HRGs). A total of six independent datasets that included 1,877 CRC patients were divided into a training cohort and two validation cohorts. Functional annotation and analysis were performed to reveal relevant mechanism.

Results: A 12-gene signature was derived, which was prognostic for stage II/III CRC patients in two validation cohorts [TCGA, n = 509, hazard ratio (HR) = 2.14, 95% confidence interval (CI) = 1.18 – 3.89, P = 0.01; metavalidation, n = 590, HR = 2.46, 95% CI = 1.59 – 3.81, P < 0.001]. High hypoxic risk was correlated with worse prognosis in CRC patients without adjuvant chemotherapy (HR = 5.1, 95% CI = 2.51 – 10.35, P < 0.001). After integration with clinical characteristics, hypoxia-related gene signature (HRGS) remained as an independent prognostic factor in multivariate analysis. Furthermore, enrichment analysis found that antitumor immune response was suppressed in the high hypoxic group.

Conclusions: HRGS is a promising system for estimating disease-free survival of stage II/III CRC patients. Hypoxia tumor microenvironment may be via inhibiting immune response to promote chemoresistance in stage II/III CRC patients.

Integrated approaches for precision oncology in colorectal cancer: The more you know, the better

Colorectal cancer (CRC) is one of the most common human malignancies accounting for approximately 10 % of worldwide cancer incidence and mortality. While early-stage CRC is mainly a preventable and curable disease, metastatic colorectal cancer (mCRC) remains an unmet clinical need. Moreover, about 25 % of CRC cases are diagnosed only at the metastatic stage. Despite the extensive molecular and functional knowledge on this disease, systemic therapy for mCRC still relies on traditional 5-fluorouracil (5-FU)-based chemotherapy regimens. On the other hand, targeted therapies and immunotherapy have shown effectiveness only in a limited subset of patients. For these reasons, there is a growing need to define the molecular and biological landscape of individual patients to implement novel, rationally driven, tailored therapies. In this review, we explore current and emerging approaches for CRC management such as genomic, transcriptomic and metabolomic analysis, the use of liquid biopsies and the implementation of patients' preclinical avatars. In particular, we discuss the contribution of each of these tools in elucidating patient specific features, with the aim of improving our ability in advancing the diagnosis and treatment of colorectal tumors.

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes (P < 0.01), 336 copies of amplified genes (P < 0.05), and 36 copies of deleted genes (P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases' surviving state.

Use of Circulating Cell-Free DNA to Guide Precision Medicine in Patients with Colorectal Cancer

Patient-specific biomarkers form the foundation of precision medicine strategies. To realize the promise of precision medicine in patients with colorectal cancer (CRC), access to cost-effective, convenient, and safe assays is critical. Improvements in diagnostic technology have enabled ultrasensitive and specific assays to identify cell-free DNA (cfDNA) from a routine blood draw. Clinicians are already employing these minimally invasive assays to identify drivers of therapeutic resistance and measure genomic heterogeneity, particularly when tumor tissue is difficult to access or serial sampling is necessary. As cfDNA diagnostic technology continues to improve, more innovative applications are anticipated. In this review, we focus on four clinical applications for cfDNA analysis in the management of CRC: detecting minimal residual disease, monitoring treatment response in the metastatic setting, identifying drivers of treatment sensitivity and resistance, and guiding therapeutic strategies to overcome resistance.

The EMA assessment of encorafenib in combination with cetuximab for the treatment of adult patients with metastatic colorectal carcinoma harbouring the BRAFV600E mutation who have received prior therapy

On 2 June 2020, a marketing authorisation valid through the European Union (EU) was issued for encorafenib in combination with cetuximab in adult patients with metastatic colorectal carcinoma (mCRC) with the BRAFV600E mutation who had received prior systemic therapy. Encorafenib plus cetuximab was evaluated in a randomised phase III trial of encorafenib plus binimetinib plus cetuximab versus encorafenib plus cetuximab versus cetuximab plus irinotecan or FOLFIRI (control arm) to adult patients with BRAFV600E mCRC who had received prior therapy for metastatic disease. The median overall survival was 9.3 months [95% confidence interval (CI): 8.05-11.30] versus 5.88 months (95% CI: 5.09-7.10) for encorafenib plus cetuximab (doublet) versus the control arm, respectively [hazard ratio (HR) 0.61, 95% CI: 0.48-0.77]. Progression-free survival (PFS) was 4.27 months (95% CI: 4.07-5.45) versus 1.54 months (95% CI: 1.48-1.91) (HR 0.44; 95% CI: 0.35-0.55). The most frequent adverse events in patients receiving encorafenib plus cetuximab were fatigue, nausea, diarrhoea, acneiform dermatitis, abdominal pain, arthralgia, decreased appetite, vomiting and rash. The aim of this manuscript is to summarise the scientific review of the application leading to regulatory approval in the EU.

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Encorafenib was approved in combination with cetuximab for patients with previously treated BRAF plus colorectal carcinoma.

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The original submission also included binimetinib, which was withdrawn during the procedure.

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The benefit–risk balance was considered positive due to a large benefit on PFS and strong biologic rationale.

Precision medicine for adjuvant chemotherapy of resected colorectal cancer

Colorectal cancer (CRC) is the most common cancer and the second leading cause of cancer death in Japan. Surgical resection is the only curative option for localized disease. However, undetectable micrometastases remaining after curative surgery may cause disease recurrence. Adjuvant chemotherapy aims to eradicate these micrometastases to improve the cure rate. Unfortunately, few reliable prognostic and predictive markers are available that identify patients at high risk for CRC during early-stage disease. However, promising biomarkers may become available in the near future. Such biomarkers provide information for stratifying a patient's risk and for selecting the optimal treatment. Here, we provide an overview of current relevant prognostic and predictive biomarkers applicable to adjuvant treatment of early-stage CRC and focus on the future of this field.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD

PURPOSE

This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD).

MATERIALS AND METHODS

Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens.

RESULTS

A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues.

CONCLUSION

In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

KRT17 as a prognostic biomarker for stage II colorectal cancer

Adjuvant chemotherapy is considered for patients with stage II colorectal cancer (CRC) characterized by poor prognostic clinicopathological features; however, current stratification algorithms remain inadequate for identifying high-risk patients. To develop prognostic assays, we conducted a step-wise screening and validation strategy using nine cohorts of stage II patients based on multiple platforms, including microarray, RNA-sequencing (RNA-seq) and immunohistochemistry (IHC) on formalin-fixed paraffin-embedded (FFPE) tissues. Four microarray datasets (total n = 458) were used as the discovery set to screen for single genes associated with postoperative recurrence. Prognostic values of candidate genes were evaluated in three independent microarray/RNA-seq validation cohorts (n = 89, n = 93 and n = 183, respectively), and then IHC for KRT17 was conducted in two independent FFPE series (n = 110 and n = 44, respectively). We found that high levels of KRT17 transcript expression were significantly associated with poor relapse-free survival (RFS) not only in the discovery set, but also in three validation cohorts, and its prognostic impact was independent of conventional factors by multivariate analyses. Positive staining of KRT17 protein was significantly associated with poor RFS in two independent FFPE cohorts. KRT17 protein expression had independent prognostic impact on RFS in a multivariate model adjusted for conventional variables, including high-risk clinicopathological features. In conclusion, using nine independent cohorts consisting of 997 stage II patients, we identified and validated the expression of KRT17 transcript and KRT17 protein as a robust prognostic biomarker that can discriminate postoperative stage II patients who are at high probability of disease recurrence, providing additional prognostic stratification beyond the currently available high-risk factors.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

PRPS-ST: A protocol-agnostic self-training method for gene expression-based classification of blood cancers

Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing data sets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression data sets. In comparison to gold standards, we demonstrate favorable performance of PRPS-ST in gene expression-based classification of DLBCL and B-ALL using a diverse variety of gene expression data types and pre-processing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.

Circulating tumor DNA guided adjuvant chemotherapy in stage II colon cancer (MEDOCC-CrEATE): study protocol for a trial within a cohort study

BACKGROUND

Accurate detection of patients with minimal residual disease (MRD) after surgery for stage II colon cancer (CC) remains an urgent unmet clinical need to improve selection of patients who might benefit form adjuvant chemotherapy (ACT). Presence of circulating tumor DNA (ctDNA) is indicative for MRD and has high predictive value for recurrent disease. The MEDOCC-CrEATE trial investigates how many stage II CC patients with detectable ctDNA after surgery will accept ACT and whether ACT reduces the risk of recurrence in these patients.

METHODS/DESIGN

MEDOCC-CrEATE follows the 'trial within cohorts' (TwiCs) design. Patients with colorectal cancer (CRC) are included in the Prospective Dutch ColoRectal Cancer cohort (PLCRC) and give informed consent for collection of clinical data, tissue and blood samples, and consent for future randomization. MEDOCC-CrEATE is a subcohort within PLCRC consisting of 1320 stage II CC patients without indication for ACT according to current guidelines, who are randomized 1:1 into an experimental and a control arm. In the experimental arm, post-surgery blood samples and tissue are analyzed for tissue-informed detection of plasma ctDNA, using the PGDx elio™ platform. Patients with detectable ctDNA will be offered ACT consisting of 8 cycles of capecitabine plus oxaliplatin while patients without detectable ctDNA and patients in the control group will standard follow-up according to guideline. The primary endpoint is the proportion of patients receiving ACT when ctDNA is detectable after resection. The main secondary outcome is 2-year recurrence rate (RR), but also includes 5-year RR, disease free survival, overall survival, time to recurrence, quality of life and cost-effectiveness. Data will be analyzed by intention to treat.

DISCUSSION

The MEDOCC-CrEATE trial will provide insight into the willingness of stage II CC patients to be treated with ACT guided by ctDNA biomarker testing and whether ACT will prevent recurrences in a high-risk population. Use of the TwiCs design provides the opportunity to randomize patients before ctDNA measurement, avoiding ethical dilemmas of ctDNA status disclosure in the control group.

TRIAL REGISTRATION

Netherlands Trial Register: NL6281/NTR6455 . Registered 18 May 2017, https://www.trialregister.nl/trial/6281.