Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders

Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer

PURPOSE

A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects.

METHODS

We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect.

RESULTS

Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65).

CONCLUSION

The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Non-metastatic colon cancer: French Intergroup Clinical Practice Guidelines for diagnosis, treatments, and follow-up (TNCD, SNFGE, FFCD, GERCOR, UNICANCER, SFCD, SFED, SFRO, ACHBT, SFP, AFEF, and SFR)

INTRODUCTION

This article is a summary of the French intergroup guidelines regarding the management of non-metastatic colon cancer (CC), revised in November 2022.

METHODS

These guidelines represent collaborative work of all French medical and surgical societies involved in the management of CC. Recommendations were graded in three categories (A, B, and C) according to the level of evidence found in the literature published up to November 2022.

RESULTS

Initial evaluation of CC is based on clinical examination, colonoscopy, chest-abdomen-pelvis computed tomography (CT) scan, and carcinoembryonic antigen (CEA) assay. CC is usually managed by surgery and adjuvant treatment depending on the pathological findings. The use of adjuvant therapy remains a challenging question in stage II disease. For high-risk stage II CC, adjuvant chemotherapy must be discussed and fluoropyrimidine monotherapy or oxaliplatin-based chemotherapy proposed according to the type and number of poor prognostic features. Oxaliplatin-based chemotherapy (FOLFOX or CAPOX) is the current standard for adjuvant therapy of patients with stage III CC. However, these regimens are associated with significant oxaliplatin-induced neurotoxicity. The results of the recent IDEA study provide evidence that 3 months of treatment with CAPOX is as effective as 6 months of oxaliplatin-based therapy in patients with low-risk stage III CC (T1-3 and N1). A 6-month oxaliplatin-based therapy remains the standard of care for high-risk stage III CC (T4 and/or N2). For patients unfit for oxaliplatin, fluoropyrimidine monotherapy is recommended.

CONCLUSION

French guidelines for non-metastatic CC management help to offer the best personalized therapeutic strategy in daily clinical practice. Each individual case must be discussed within a multidisciplinary tumor board and then the treatment option decided with the patient.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Principles of risk assessment in colon cancer: immunity is key

In clinical practice, the administration of adjuvant chemotherapy (ACT) following tumor surgical resection raises a critical dilemma for stage II colon cancer (CC) patients. The prognostic features used to identify high-risk CC patients rely on the pathological assessment of tumor cells. Currently, these factors are considered for stratifying patients who may benefit from ACT at early CC stages. However, the extent to which these factors predict clinical outcomes (i.e. recurrence, survival) remains highly controversial, also uncertainty persists regarding patients' response to treatment, necessitating further investigation. Therefore, an imperious need is to explore novel biomarkers that can reliably stratify patients at risk, to optimize adjuvant treatment decisions. Recently, we evaluated the prognostic and predictive value of Immunoscore (IS), an immune digital-pathology assay, in stage II CC patients. IS emerged as the sole significant parameter for predicting disease-free survival (DFS) in high-risk patients. Moreover, IS effectively stratified patients who would benefit most from ACT based on their risk of recurrence, thus predicting their outcomes. Notably, our findings revealed that digital IS outperformed the visual quantitative assessment of the immune response conducted by expert pathologists. The latest edition of the WHO classification for digestive tumor has introduced the evaluation of the immune response, as assessed by IS, as desirable and essential diagnostic criterion. This supports the revision of current cancer guidelines and strongly recommends the implementation of IS into clinical practice as a patient stratification tool, to guide CC treatment decisions. This approach may provide appropriate personalized therapeutic decisions that could critically impact early-stage CC patient care.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer

BACKGROUND

TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS).

METHODS

A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS.

RESULTS

The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction.

CONCLUSION

The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

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.

Personalizing adjuvant therapy for patients with colorectal cancer

The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.

Enhanced Clinical Utility of Molecular Budding Signature as a Recurrence Risk Determinant in Stage II and III Colon Cancer Patients

BACKGROUND

A molecular budding signature (MBS), which consists of seven tumor budding-related genes, was recently presented as a prominent prognostic indicator in colon cancer (CC) using microarray data acquired from frozen specimens. This study aimed to confirm the predictive power of MBS for recurrence risk based on formalin-fixed, paraffin-embedded (FFPE) materials.

METHODS

This research utilized the same microarray data from a prior multicenter study using FFPE whole tissue sections, which retrospectively reviewed 232 stage II CC patients without adjuvant chemotherapy and 302 stage III CC patients with adjuvant chemotherapy. All patients underwent upfront curative surgery without neoadjuvant therapy between 2009 and 2012. An MBS score was calculated using the mean of log2 [each signal] of seven genes (MSLN, SLC4A11, WNT11, SCEL, RUNX2, MGAT3, and FOXC1) as described before.

RESULTS

The MBS-low group exhibited a better relapse-free survival (RFS) than the MBS-high group in stage II (P = 0.0077) and in stage III CC patients (P = 0.0003). Multivariate analyses revealed that the MBS score was an independent prognostic factor in both stage II (P = 0.0257) and stage III patients (P = 0.0022). Especially among T4, N2, or both (high-risk) stage III patients, the MBS-low group demonstrated markedly better RFS compared with the MBS-high group (P = 0.0013).

CONCLUSIONS

This study confirmed the predictive power of the MBS for recurrence risk by employing FFPE materials in stage II/III CC patients.

Development and Validation of a 15-gene Expression Signature with Superior Prognostic Ability in Stage II Colorectal Cancer

Currently, there is no consensus about the use of adjuvant chemotherapy for patients with stage II colorectal cancer. Here, we aimed to identify and validate a prognostic mRNA expression signature for the stratification of patients with stage II colorectal cancer according to their risk for relapse. First, publicly available mRNA expression profiling datasets from 792 primary, stage II colorectal cancers from six different training cohorts were analyzed to identify genes that are consistently associated with patient relapse-free survival (RFS). Second, the identified gene expression signature was experimentally validated using NanoString technology and computationally refined on primary colorectal cancer samples from 205 patients with stage II colorectal cancer. Third, the refined signature was validated in two independent publicly available cohorts of 166 patients with stage II colorectal cancer. Bioinformatics analysis of training cohorts identified a 61-gene signature that was highly significantly associated with RFS (HR = 37.08, P = 2.68*10-106, sensitivity = 89.29%, specificity = 89.61%, and AUC = 0.937). The experimental validation and refinement revealed a 15-gene signature that robustly predicted relapse in three independent cohorts: an in-house cohort (HR = 20.4, P = 8.73*10-23, sensitivity = 90.32%, specificity = 80.99%, AUC = 0.812), GSE161158 (HR = 5.81, P = 3.57*10-4, sensitivity = 64.29%, specificity = 81.67%, AUC = 0.796), and GSE26906 (HR = 7.698, P = 7.26*10-8, sensitivity = 61.54%, specificity = 78.33%, AUC = 0.752). In the pooled training cohort, the 15-gene signature (HR = 4.72, P = 7.76*10-25, sensitivity = 75%, specificity = 67.44%, AUC = 0.784) was superior to the Oncotype DX colon 7-gene signature (HR = 2.698, P = 6.3*10-8, sensitivity = 62.16%, specificity = 55.5%, AUC = 0.633). We report the identification and validation of a novel mRNA expression signature for robust prognostication and stratification of patients with stage II colorectal cancer, with superior performance in the analyzed validation cohorts when compared with clinicopathologic biomarkers and signatures currently used for stage II colorectal cancer prognostication.

Significance

We identified and validated a 15-gene expression signature for robust prognostication and stratification of patients with stage II colorectal cancer, with superior performance when compared with currently used biomarkers. Therefore, the 15-gene expression signature has the potential to improve the prognostication and treatment decisions for patients with stage II colorectal cancer.

A Multi-Omics Overview of Colorectal Cancer to Address Mechanisms of Disease, Metastasis, Patient Disparities and Outcomes

Human colorectal cancer (CRC) is one of the most common malignancies in men and women across the globe, albeit CRC incidence and mortality shows a substantial racial and ethnic disparity, with the highest burden in African American patients. Even with effective screening tools such as colonoscopy and diagnostic detection assays, CRC remains a substantial health burden. In addition, primary tumors located in the proximal (right) or distal (left) sides of the colorectum have been shown to be unique tumor types that require unique treatment schema. Distal metastases in the liver and other organ systems are the major causes of mortality in CRC patients. Characterizing genomic, epigenomic, transcriptomic and proteomic (multi-omics) alterations has led to a better understanding of primary tumor biology, resulting in targeted therapeutic advancements. In this regard, molecular-based CRC subgroups have been developed that show correlations with patient outcomes. Molecular characterization of CRC metastases has highlighted similarities and differences between metastases and primary tumors; however, our understanding as to how to improve patient outcomes based on metastasis biology is lagging and remains a major obstacle to improving CRC patient outcomes. In this review, we will summarize the multi-omics features of primary CRC tumors and their metastases across racial and ethnic groups, the differences in proximal and distal tumor biology, molecular-based CRC subgroups, treatment strategies and challenges for improving patient outcomes.

Impact of the 12-gene recurrence score in influencing adjuvant chemotherapy prescription in mismatch repair proficient stage II/III colonic carcinoma-a systematic review and meta-analysis

INTRODUCTION

The 12-gene recurrence score (RS) is a clinically validated assay which predicts recurrence risk in patients with stage II/III colon cancer. Decisions regarding adjuvant chemotherapy may be guided using this assay or based on the judgement of tumour board.

AIMS

To assess the concordance between the RS and MDT decisions regarding adjuvant chemotherapy in colon cancer.

METHODS

A systematic review was performed in accordance with PRISMA guidelines. Meta-analyses were performed using the Mantel-Haenszel method using the Review Manager version 5.4 software.

RESULTS

Four studies including 855 patients with a mean age of 68 years (range: 25-90 years) met inclusion criteria. Overall, 79.2% had stage II disease (677/855) and 20.8% had stage III disease (178/855). For the entire cohort, concordant results between the 12-gene assay and MDT were more likely than discordant (odds ratio (OR): 0.38, 95% confidence interval (CI): 0.25-0.56, P < 0.001). Patients were more likely to have chemotherapy omitted than escalated when using the RS (OR: 9.76, 95% CI: 6.72-14.18, P < 0.001). For those with stage II disease, concordant results between the 12-gene assay and MDT were more likely than discordant (OR: 0.30, 95% CI: 0.17-0.53, P < 0.001). In stage II disease, patients were more likely to have chemotherapy omitted than escalated when using the RS (OR: 7.39, 95% CI: 4.85-11.26, P < 0.001).

CONCLUSIONS

The use of the 12-gene signature refutes the decision of tumour board in 25% of cases, with 75% of discordant decisions resulting in omission of adjuvant chemotherapy. Therefore, it is possible that a proportion of such patients are being overtreated when relying on tumour board decisions alone.

Opportunities on the horizon for the management of early colon cancer

There is a clear unmet need to improve early colon cancer management. This review encompasses the current systemic treatment landscape and summarises novel and pivotal trials. The Immunoscore and circulating tumour DNA (ctDNA) are studied to evaluate which patients should receive no, 3, or 6 months of adjuvant treatment. Several trials also test escalating treatment strategies for non-cleared ctDNA following standard adjuvant chemotherapy. Advances made in treating patients with metastatic colon cancer are now being translated to the early colon cancer setting. Two ongoing RCTs study immune checkpoint inhibitors (ICI) in patients with microsatellite instable high (MSI-H) early colon cancer as adjuvant treatment. Neo-adjuvant treatment is being studied in several ongoing RCTs as well. The complete response rate in patients with MSI-H tumours following ICI in neoadjuvant trials has potential organ-sparing implications.

Health economic evidence for adjuvant chemotherapy in stage II and III colon cancer: a systematic review

OBJECTIVE

The aims of this study was to appraise the health economic evidence for adjuvant chemotherapy (AC) strategies in stage II and III colon cancer (CC) and identify gaps in the available evidence that might inform further research.

METHOD

A systematic review of published economic evaluations was undertaken. Four databases were searched and full-text publications in English were screened for inclusion. A narrative synthesis was performed to summarise the evidence.

RESULTS

Thirty-eight studies were identified and stratified by cancer stage and AC strategy. The majority (89%) were full economic evaluations considering both health outcomes, usually measured as quality-adjusted life years (QALYs), and costs. AC was found to be cost-effective compared to no AC for both stage II and III CC. Oral and oxaliplatin-based AC was cost-effective for stage III. Three months of CAPOX was cost-effective compared to 6-month in high-risk stage II and stage III CC. Preliminary evidence suggests that biomarker approaches to AC selection in stage II can reduce costs and improve health outcomes. Notably, assessment of QALYs were predominantly reliant on a small number of non-contemporary health-utility studies. Only 32% of studies considered societal costs such as travel and time off work.

CONCLUSIONS

Published economic evaluations consistently supported the use of AC in stage II and III colon cancer. Biomarker-driven approaches to patient selection have great potential to be cost-effective, but more robust clinical and economic evidence is warranted. Patient surveys embedded into clinical trials may address critical knowledge gaps regarding accurate assessment of QALYs and societal costs in the modern era.

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.

DNA methylation profile in CpG-depleted regions uncovers a high-risk subtype of early-stage colorectal cancer

BACKGROUND

The current risk stratification system defined by clinicopathological features does not identify the risk of recurrence in early-stage (stage I-II) colorectal cancer (CRC) with sufficient accuracy. We aimed to investigate whether DNA methylation could serve as a novel biomarker for predicting prognosis in early-stage CRC patients.

METHODS

We analyzed the genome-wide methylation status of CpG loci using Infinium MethylationEPIC array run on primary tumor tissues and normal mucosa of early-stage CRC patients to identify potential methylation markers for prognosis. The machine-learning approach was applied to construct a DNA methylation-based prognostic classifier for early-stage CRC (MePEC) using the 4 gene methylation markers FAT3, KAZN, TLE4, and DUSP3. The prognostic value of the classifier was evaluated in 2 independent cohorts (n = 438 and 359, respectively).

RESULTS

The comprehensive analysis identified an epigenetic subtype with high risk of recurrence based on a group of CpG loci in the CpG-depleted region. In multivariable analysis, the MePEC classifier was independently and statistically significantly associated with time to recurrence in validation cohort 1 (hazard ratio = 2.35, 95% confidence interval = 1.47 to 3.76, P < .001) and cohort 2 (hazard ratio = 3.20, 95% confidence interval = 1.92 to 5.33, P < .001). All results were further confirmed after each cohort was stratified by clinicopathological variables and molecular subtypes.

CONCLUSIONS

We demonstrated the prognostic statistical significance of a DNA methylation profile in the CpG-depleted region, which may serve as a valuable source for tumor biomarkers. MePEC could identify an epigenetic subtype with high risk of recurrence and improve the prognostic accuracy of current clinical variables in early-stage CRC.

Comprehensive analysis of the prognosis, tumor microenvironment, and immunotherapy response of SDHs in colon adenocarcinoma

Background

Succinate dehydrogenase (SDH), one of the key enzymes in the tricarboxylic acid cycle, is mainly found in the mitochondria. SDH consists of four subunits encoding SDHA, SDHB, SDHC, and SDHD. The biological function of SDH is significantly related to cancer progression. Colorectal cancer (CRC) is one of the most common malignant tumors globally, whose most common histological subtype is colon adenocarcinoma (COAD). However, the correlation between SDH factors and COAD remains unclear.

Methods

The data on pan-cancer was obtained from The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis showed the prognostic ability of SDHs. The cBioPortal database reflected genetic variations of SDHs. The correlation analysis was conducted between SDHs and mitochondrial energy metabolism genes (MMGs) and the protein-protein interaction (PPI) network was built. Consequently, Univariate and Multivariate Cox Regression Analysis on SDHs and other clinical characteristics were conducted. A nomogram was established. The ssGSEA analysis visualized the association between SDHs and immune infiltration. Immunophenoscore (IPS) explored the correlation between SDHs and immunotherapy, and the correlation between SDHs and targeted therapy was investigated through Genomics of Drug Sensitivity in Cancer. Finally, qPCR and immunohistochemistry detected SDHs' expression.

Results

After assessing SDHs differential expression in pan-cancer, we found that SDHB, SDHC, and SDHD benefit COAD patients. The cBioPortal database demonstrated that SDHA was the top gene in mutation frequency rank. Correlation analysis mirrored a strong link between SDHs and MMGs. We formulated a nomogram and found that SDHB, SDHC, SDHD, and clinical characteristics correlated with COAD patients' survival. For T helper cells, Th2 cells, and Tem, SDHA, SDHB, SDHC, and SDHD were significantly enriched in the high expression group. Moreover, COAD patients with high SDHA expression were more suitable for immunotherapy. And COAD patients with different SDHs' expression have different sensitivity to targeted drugs. Further verifying the gene and protein expression levels of SDHs, we found that the tissues were consistent with the bioinformatics analysis.

Conclusions

Our study analyzed the expression and prognostic value of SDHs in COAD, explored the pathway mechanisms involved, and the immune cell correlations, indicating that SDHs might be biomarkers for COAD patients.

Gene Expression Profiles in Cancers and Their Therapeutic Implications

ABSTRACT

The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.

Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection

Current histopathological staging procedures in colorectal cancer (CRC) depend on midline division of the lymph nodes (LNs) with one section of hematoxylin and eosin staining. Cancer cells outside this transection line may be missed, which could lead to understaging of Union for International Cancer Control Stage II high-risk patients. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid molecular diagnostic tool for LN metastases detection. It is a molecular technique that can analyze the entire LN tissue using a reverse-transcriptase loop-mediated isothermal amplification reaction to detect tumor-specific cytokeratin 19 mRNA. Our findings suggest that the OSNA assay has a high diagnostic accuracy in detecting metastatic LNs in CRC and a high negative predictive value. OSNA is a standardized, observer-independent technique, which may lead to more accurate staging. It has been suggested that in stage II CRC, the upstaging can reach 25% and these patients can access postoperative adjuvant chemotherapy. Moreover, intraoperative OSNA sentinel node evaluation may allow early CRC to be treated with organ-preserving surgery, while in more advanced-stage disease, a tailored lymphadenectomy can be performed considering the presence of aberrant lymphatic drainage and skip metastases.

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.

Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer

BACKGROUND AND OBJECTIVE

Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop a prognostic signature to stratify outcomes and benefit from different chemotherapy regimens by analyzing whole slide images (WSI) using deep learning.

METHODS

We proposed an unsupervised deep learning network (variational autoencoder and generative adversarial network) in 180,819 image tiles from the training set (147 patients) to develop a WSI signature for predicting the disease-free survival (DFS) and overall survival (OS) of patients, and tested in validation set of 63 patients. An integrated nomogram was constructed to investigate the incremental value of deep learning signature (DLS) to TNM stage for individualized outcomes prediction.

RESULTS

The DLS was associated with DFS and OS in both training and validation sets and proved to be an independent prognostic factor. Integrating the DLS and clinicopathologic factors showed better performance (C-index: DFS, 0.748; OS, 0.794; in the validation set) than TNM stage. In patients whose DLS and clinical risk levels were inconsistent, their risk of relapse was reclassified. In the subgroup of patients treated with 3 months, high-DLS was associated with worse DFS (hazard ratio: 3.622-7.728).

CONCLUSIONS

The proposed based-WSI DLS improved risk stratification and could help identify patients with stage III CRC who may benefit from the prolonged duration of chemotherapy.

Low G9a expression is a tumor progression factor of colorectal cancer via IL-8 promotion

The histone methyltransferase G9a is expressed in various types of cancer cells, including colorectal cancer (CRC) cells. Interleukin (IL)-8, also known as C-X-C motif chemokine ligand 8 (CXCL8), is a chemokine that plays a pleiotropic function in the regulation of inflammatory responses and cancer development. Here, we examined the relationship between G9a and IL-8 and the clinical relevance of this association. We immunohistochemically analyzed 235 resected CRC samples to correlate clinical features. Samples with high G9a expression had better overall survival and relapse-free survival than those with low G9a expression. Univariate and multivariate analyses demonstrated that low G9a expression remained a significant independent prognostic factor for increased disease recurrence and decreased survival (P<0.05). G9a was expressed at high levels in commercially available CRC cell lines HCT116 and HT29. Knockdown of G9a by siRNA, shRNA, or the G9a-specific inhibitor BIX01294 upregulated IL-8 expression. The number of spheroids was significantly increased in HCT116 cells with stably suppressed G9a expression, and the number of spheroids was significantly decreased in HCT116 cells with stably suppressed IL-8 expression. Thus, the suppression of IL-8 by G9a may result in a better prognosis in CRC cases with high G9a expression. Furthermore, G9a may suppress cancer stemness and increase chemosensitivity by controlling IL-8. Therefore, G9a is a potential novel marker for predicting CRC prognosis, and therapeutic targeting of G9a in CRC should be contraversial.

Colorectal cancer concurrent gene signature based on coherent patterns between genomic and transcriptional alterations

Background

The aim of the study was to enhance colorectal cancer prognostication by integrating single nucleotide polymorphism (SNP) and gene expression (GE) microarrays for genomic and transcriptional alteration detection; genes with concurrent gains and losses were used to develop a prognostic signature.

Methods

The discovery dataset comprised 32 Taiwanese colorectal cancer patients, of which 31 were assayed for GE and copy number variations (CNVs) with Illumina Human HT-12 BeadChip v4.0 and Omni 25 BeadChip v1.1. Concurrent gains and losses were declared if coherent manners were observed between GE and SNP arrays. Concurrent genes were also identified in The Cancer Genome Atlas Project (TCGA) as the secondary discovery dataset (n = 345).

Results

The “universal” concurrent genes, which were the combination of z-transformed correlation coefficients, contained 4022 genes. Candidate genes were evaluated within each of the 10 public domain microarray datasets, and 1655 (2000 probe sets) were prognostic in at least one study. Consensus across all datasets was used to build a risk predictive model, while distinct relapse-free/overall survival patterns between defined risk groups were observed among four out of five training datasets. The predictive accuracy of recurrence, metastasis, or death was between 61 and 86% (cross-validation area under the receiver operating characteristic (ROC) curve: 0.548-0.833) from five independent validation studies.

Conclusion

The colorectal cancer concurrent gene signature is prognostic in terms of recurrence, metastasis, or mortality among 1746 patients. Genes with coherent patterns between genomic and transcriptional contexts are more likely to provide prognostication for colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09627-9.

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”.

SEOM-GETTHI clinical guideline for the practical management of molecular platforms (2021)

The improvement of molecular alterations in cancer as well as the development of technology has allowed us to bring closer to clinical practice the determination of molecular alterations in the diagnosis and treatment of cancer. The use of multidetermination platforms is spreading in most Spanish hospitals. The objective of these clinical practice guides is to review their usefulness, and establish usage guidelines that guide their incorporation into clinical practice.

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer

PURPOSE

Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has been postulated as a potential predictive biomarker for benefit from adjuvant chemotherapy. However, very limited success has been achieved in using biomarkers, including deep-learning-based markers, to facilitate the decision for adjuvant chemotherapy despite recent advances of artificial intelligence.

METHODS

We trained and internally validated CRCNet using 780 Stage II/III CRC patients from Molecular and Cellular Oncology. Independent external validation of the model was performed using 337 Stage II/III CRC patients from The Cancer Genome Atlas (TCGA).

RESULTS

CRCNet stratified the patients into high, medium, and low-risk subgroups. Multivariate Cox regression analyses confirmed that CRCNet risk groups are statistically significant after adjusting for existing risk factors. The high-risk subgroup significantly benefits from adjuvant chemotherapy. A hazard ratio (chemo-treated vs untreated) of 0.2 (95% Confidence Interval (CI), 0.05-0.65; P = 0.009) and 0.6 (95% CI 0.42-0.98; P = 0.038) are observed in the TCGA and MCO Fluorouracil-treated patients, respectively. Conversely, no significant benefit from chemotherapy is observed in the low- and medium-risk groups (P = 0.2-1).

CONCLUSION

The retrospective analysis provides further evidence that H&E image-based biomarkers may potentially be of great use in delivering treatments following surgery for Stage II/III CRC, improving patient survival, and avoiding unnecessary treatment and associated toxicity, and warrants further validation on other datasets and prospective confirmation in clinical trials.

Oncotype DX testing does not affect clinical practice in stage IIa colon cancer

Although studies have demonstrated the 12-gene Oncotype DX Colon Recurrence Score's (RS) validity in predicting recurrence and influence on physician-patient decision-making, its discriminatory power and inability to predict response to treatment make its clinical impact uncertain. We sought to evaluate the influence of RS in the decision to offer adjuvant chemotherapy after resection of stage IIa colon cancer. A review of patients with stage IIa colon cancer who obtained the RS at a tertiary academic medical center was conducted. The main study outcome was decision to start adjuvant chemotherapy. The association between RS and the decision to obtain adjuvant chemotherapy was evaluated utilizing the Wilcoxon rank-sum test and area under the receiver operating characteristic curve. 52 of 105 patients with stage IIa colon cancer underwent RS testing. Overall, seven of 52 patients (13%) received adjuvant chemotherapy. 34 (65%) patients obtained the RS test despite having multiple other recurrence risk factors. There were no significant associations between any patient/tumor characteristic and RS score (all p > 0.08) or starting adjuvant chemotherapy (all p > 0.15). On multivariable analysis, there was no significant effect of RS on the odds of undergoing chemotherapy (OR 1.07, 95% CI 0.98-1.19; p = 0.14). There was no clear association between RS and starting adjuvant chemotherapy (AUC 0.64, 95% CI 0.36-0.91; p = 0.25). RS was not associated with the decision to start adjuvant chemotherapy. Given its lack of association with clinical decision-making and inability to predict clinical outcome, our data suggest the RS should not be obtained in patients with stage IIa colon cancer.

Patient-specific meta-analysis of 12-gene colon cancer recurrence score validation studies for recurrence risk assessment after surgery with or without 5FU and oxaliplatin

BACKGROUND

Individualized estimates of the risk of recurrence in colon cancer patients are needed that reflect current medical practice and available treatment options.

METHODS

Three validation studies of the 12-gene colon recurrence score assay were used with pre-specified patient-specific meta-analysis (PSMA) methods to integrate the 12-gene Oncotype DX Colon Recurrence Score result (RS) with the clinical and pathology risk factors stage, T-stage, mis-match repair (MMR) status, and number of nodes examined to calculate individualized recurrence risk estimates. Baseline risk estimation used the most recent studies, so the risk estimates reflect current medical practice. The effect of fluorouracil (5FU) was estimated with a meta-analysis of two studies. The effect of oxaliplatin was estimated using one of the RS assay validation studies, in which patients were randomized to 5FU with or without oxaliplatin.

RESULTS

The RS result and each of the clinical-pathologic factors provided independent prognostic information for recurrence. Among stage II, T3, MMR-proficient patients with ≥12 nodes examined (the most common scenario), patients with RS ≤30 (approximately 48%) have estimated 5-year recurrence risk ≤10% with surgery alone. Among stage IIIA/B, T3, MMR-deficient patients with ≥12 nodes examined, patients with RS ≤19 (approximately 14%) have an estimated 5-year recurrence risk ≤10% with surgery alone. Among stage IIIA/B, T3, MMR-proficient patients with ≥12 nodes examined, those with RS ≤14 (approximately 6%) have estimated 5-year recurrence risk ≤10% with 5FU alone.

DISCUSSION

The PSMA integrates the 12-gene colon RS result with clinical and pathology factors to provide individualized recurrence risk estimates that reflect current medical practice. The risk estimates are in a range that may help inform treatment decisions for a substantial number of stage II and stage III patients.

Early Postoperative Serum Carcinoembryonic Antigen Is a Stronger Independent Prognostic Factor for Stage II Colorectal Cancer Patients Than T4 Stage and Preoperative CEA

BACKGROUND

Serum carcinoembryonic antigen (CEA) is an important biomarker for diagnosis, prognosis, recurrence, metastasis monitoring, and the evaluation of the effect of chemotherapy in colorectal cancer (CRC). However, few studies have focused on the role of early postoperative CEA in the prognosis of stage II CRC.

METHODS

Patients with stage II CRC diagnosed between January 2007 and December 2015 were included. Receiver operating characteristic (ROC) curves were used to obtain the cutoff value of early postoperative CEA, CEA ratio and CEA absolute value. The areas under curves (AUCs) were used to estimate the predictive abilities of the CEA and T stage. The stepwise regression method was used to screen the factors included in the Cox regression analysis. Before and after propensity score (PS) - adjusted Cox regression and sensitivity analysis were used to identify the relationship between early postoperative CEA and prognosis. Meta-analysis was performed to verify the results. Kaplan-Meier survival curves were used to estimate the effects of CEA on prognosis.

RESULTS

We included 1081 eligible patients. ROC curves suggested that the cutoff value of early postoperative CEA was 3.66 ng/ml (P <0.001) and the AUC showed early postoperative CEA was the most significant prognostic marker in stage II CRC (P = 0.0189). The Cox regression and sensitivity analysis before and after adjusting for PS both revealed elevated early postoperative CEA was the strongest independent prognostic factor of OS, DFS, and CSS (P < 0.001). Survival analysis revealed that patients with elevated early postoperative CEA had lower OS (53.62% VS 84.16%), DFS (50.03% VS 86.75%), and CSS (61.77% VS 90.30%) than patients with normal early postoperative CEA (P < 0.001). When the postoperative CEA was positive, the preoperative CEA level showed no significant effect on the patient's prognosis (all P-values were > 0.05). Patients with a CEA ratio ≤0.55 or CEA absolute value ≤-0.98 had a worse prognosis (all P-values were < 0.001). Survival analysis suggested that adjuvant chemotherapy for stage II CRC patients with elevated early postoperative CEA may improve the CSS (P = 0.040).

CONCLUSIONS

Early postoperative CEA was a better biomarker for prognosis of stage II CRC patients than T stage and preoperative CEA, and has the potential to become a high-risk factor to guide the prognosis and treatment of stage II CRC patients.

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.

Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis treatment and follow-up of patients with localised colon cancer

The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, treatment and follow-up of localised colon cancer was published in 2020. It was decided by both the ESMO and the Japanese Society of Medical Oncology (JSMO) to convene a special virtual guidelines meeting in March 2021 to adapt the ESMO 2020 guidelines to take into account the ethnic differences associated with the treatment of localised colon cancer in Asian patients. These guidelines represent the consensus opinions reached by experts in the treatment of patients with localised colon cancer representing the oncological societies of Japan (JSMO), China (CSCO), India (ISMPO), Korea (KSMO), Malaysia (MOS), Singapore (SSO) and Taiwan (TOS). The voting was based on scientific evidence and was independent of the current treatment practices and drug availability and reimbursement situations in the different Asian countries.

Use of Molecular Assays and Circulating Tumor DNA in Early-Stage Colorectal Cancer: A Roundtable Discussion of the Gastrointestinal Cancer Therapy Expert Group

The use of genomic testing is rapidly emerging as an important clinical tool both for cancer diagnosis and for guiding treatment decisions in a wide range of malignancies, including gastrointestinal (GI) cancers such as colorectal cancer (CRC). Advances in technologies such as polymerase chain reaction and next-generation sequencing methods have made it possible to noninvasively screen for CRC through, for example, the use of blood- or stool-based testing, with high specificity. Tests are also available that can provide prognostic information beyond traditional clinicopathologic factors such as tumor size, grade, and nodal status, which can enable clinicians to more accurately risk stratify patients for recurrence. Lastly, in the setting of resected CRC, tests are now available that can detect circulating tumor DNA as a means for noninvasive minimal/molecular residual disease monitoring, thereby potentially guiding the use of adjuvant chemotherapy and/or escalating or de-escalating therapy. The Gastrointestinal Cancer Therapy Expert Group (GICTEG) recently convened a virtual meeting to discuss current issues related to genomic testing in GI cancer, with the goal of providing guidance on the use of these tests for the practicing community oncologist, for whom GI cancer may be only one of many tumor types encountered. This article provides a summary of the discussion and highlights the key opinions of the GICTEG on this topic. IMPLICATIONS FOR PRACTICE: The Gastrointestinal Cancer Therapy Expert Group seeks to provide practical guidance and opinion on the treatment of gastrointestinal malignancies, including colorectal cancer (CRC), for the practicing community oncologist in situations for which guidelines from established bodies, such as the National Comprehensive Cancer Network and the American Society of Clinical Oncology, may be less clear. In the present report, clinical guidance on the use of molecular assays for a range of clinical indications in CRC is presented, including the use of circulating tumor DNA to detect minimal/molecular residual disease in patients with successfully resected early-stage CRC.

Adjuvant Therapies in Colon Cancer

Most of the patients with localized colon cancer undergo curative resection. However, significant number of patients will recur with metastatic disease, especially those with node positive cancer. Adjuvant chemotherapy has shown to improve cure rate and survival by eradicating micrometastases. The benefit of adjuvant therapy is well established in node-positive cancers, while their role in stage II cancer is not well defined. A number of molecular markers have been identified that are prognostic and/or predictive in colon cancer. Such molecular markers, and other clinicopathological features play an important role in selection of appropriate therapy and duration of treatment. Emerging evidence for the utility of genomic profiling or detection of circulating tumor DNA (ctDNA) are promising which may further facilitate decision making in the future. This chapter reviews the evolution of adjuvant therapy for resected colon cancer, the current evidence and the factors influence the choice of therapy.

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.

•

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.

Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer

Risk stratification of men with clinically localized prostate cancer has historically relied on basic clinicopathologic parameters such as prostate specific antigen level, grade group, and clinical stage. However, prostate cancer often behaves in ways that cannot be accurately predicted by these parameters. Thus, recent efforts have focused on developing tissue-based genomic tests that provide greater insights into the risk of a given patient's disease. Multiple tests are now commercially available and provide additional prognostic information at various stages of the care pathway for prostate cancer. Indeed, early evidence suggests that these assays may have a significant impact on patient and physician decision-making. However, the impact of these tests on oncologic outcomes remains less clear. In this review, we highlight recent advances in the use of tissue-based biomarkers in the treatment of prostate cancer and identify the existing evidence supporting their clinical use.

Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

OBJECTIVE

Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis.

METHODS

We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence.

RESULTS

By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein-protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%.

CONCLUSION

These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

Prognostic and Predictive Value of Tumor Budding in Colorectal Cancer

BACKGROUND

Tumor budding (TB) is an adverse prognostic factor in colorectal cancer (CRC). International consensus on a standardized assessment method has led to its wider reporting. However, uncertainty regarding its clinical value persists. This study aimed to (1) confirm the prognostic significance of TB, particularly in stage II CRC; (2) to determine optimum thresholds for TB risk grouping; and (3) to determine whether TB influences responsiveness to chemotherapy.

METHODS

TB was assessed in CRC sections from 1575 QUASAR trial patients randomized between adjuvant chemotherapy and observation. Optimal risk group cutoffs were determined by maximum likelihood methods, with their influence on recurrence and mortality investigated in stratified log-rank analyses on exploratory (n = 504), hypothesis-testing (n = 478), and final (n = 593) data sets.

RESULTS

The optimal threshold for high-grade TB (HGTB) was ≥ 10 buds per 1.23 mm². High-grade TB tumors had significantly worse outcomes than those with lower TB: 10-year recurrence 36% versus 22% (risk ratio, 2.00 [95% CI, 1.62-2.45]; 2P < .0001) and 10-year mortality 50% vs. 37% (risk ratio, 1.53 [95% CI, 1.34-1.76]; 2P < .0001). The prognostic significance remained equally strong after allowance for other pathological risk factors, including stage, grade, lymphovascular invasion, and mismatch repair status. There was a nonsignificant trend toward increasing chemotherapy efficacy with increasing bud counts.

CONCLUSIONS

TB is a strong independent predictor of recurrence. Chemotherapy efficacy is comparable in patients with higher and lower TB; hence, absolute reductions in recurrence and death with chemotherapy should be about twice as large in patients with ≥ 10 than < 10 TB counts.

Impact of the 12-gene recurrence score assay on deciding adjuvant chemotherapy for stage II and IIIA/B colon cancer: the SUNRISE-DI study

BACKGROUND

Recent advances in adjuvant chemotherapy for early colon cancer have widened physicians' recommendations on the regimen and duration (3 or 6 months) of the treatment. We conducted this prospective study to evaluate whether the 12-gene recurrence score (12-RS) assay affected physicians' recommendations on adjuvant treatment selection.

PATIENTS AND METHODS

Patients with stage IIIA/IIIB or stage II colon cancer were enrolled. After the patients discussed adjuvant treatment with their treating physicians, the physicians filled in the questionnaire before assay indicating the treatment recommendation. When the 12-RS assay results were available, the physicians again filled in the questionnaire after assay. The primary endpoint was the rate of change in treatment recommendations from before to after the assay, with a threshold rate of change being 20%. Patients with stage IIIA/B to II were enrolled in a ratio of 2 : 1.

RESULTS

Overall, the treatment recommendations changed in 40% of cases after obtaining 12-RS assay results. Recommendations were changed in 45% (80/178; 95% confidence interval, 37% to 53%; P < 0.001) and 30% (29/97; 95% confidence interval, 21% to 40%; P < 0.001) of patients with stage IIIA/B and II colon cancer, respectively. Patients with stage IIIA/B cancer had significantly more change than those with stage II cancer (P = 0.0148). From before to after the 12-RS assay, the percentage of patients whose physicians reported being confident in their treatment recommendations significantly increased from 54% to 81% in stage IIIA/B (P < 0.001) and from 65% to 83% in stage II (P < 0.001).

CONCLUSION

Our study confirmed the usefulness of the 12-RS assay in aiding the physician-patient decision-making process for tailoring adjuvant chemotherapy for stage IIIA/B colon cancer.

Young-Onset Colon Cancer and Recurrence Risk by Gene Expression

The incidence and mortality from colorectal cancer in younger adults (younger than 55 years) is increasing. We reviewed the complete database of a gene-expression test, Oncotype DX Colon Recurrence Score test, to determine age-related differences in recurrence score (RS) and single-gene results (7 cancer-related of the 12-gene assay). We included 20 478 stage II and III A and B colon cancer patients submitted to Genomic Health. RS results were grouped by low-, intermediate-, and high-risk groups. Single-gene scores were described using median and interquartile range. Of all patients 72.5% and 72.6% of those younger than 40 years had low-risk RS. Comparing older with younger patients, RS or single-gene expression did not differ by age group or stage. Young-onset colon cancer does not differ by expression of the RS component genes. Most patients with stage II and III colon cancer have low-risk disease as measured by the 12-gene assay, regardless of age.

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 value of desmoplastic reaction characterisation in stage II colon cancer: prospective validation in a Phase 3 study (SACURA Trial)

BACKGROUND

The characterisation of desmoplastic reaction (DR) has emerged as a new, independent prognostic determinant in colorectal cancer. Herein, we report the validation of its prognostic value in a randomised controlled study (SACURA trial).

METHODS

The study included 991 stage II colon cancer patients. DR was classified by the central review as Mature, Intermediate or Immature based on the presence of hyalinised collagen bundles and myxoid stroma at the desmoplastic front. All clinical and pathological data, including DR characterisations, were prospectively recorded and analysed 5 years after the completion of the registration.

RESULTS

The five-year relapse-free survival (RFS) rate was the highest in the Mature group (N = 638), followed by the Intermediate (N = 294) and Immature groups (N = 59). Multivariate analysis revealed that DR classification was an independent prognostic factor, and based on Harrell's C-index, the Cox model for predicting RFS was significantly improved by including DR. In the conditional inference tree analysis, DR categorisation was the first split factor for predicting RFS, followed by T-stage, microsatellite instability status and budding.

CONCLUSIONS

Histological categorisation of DR provides important prognostic information that could contribute to the efficient selection of stage II colon cancer patients who would benefit from postoperative adjuvant therapy.

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.

A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients

Studies have shown that tumor microenvironment (TME) might affect drug sensitivity and the classification of colorectal cancer (CRC). Using TME-specific gene signature to identify CRC subtypes with distinctive clinical relevance has not yet been tested. A total of 18 "bulk" RNA-seq datasets (total n = 2269) and four single-cell RNA-seq datasets were included in this study. We constructed a "Signature associated with FOLFIRI resistant and Microenvironment" (SFM) that could discriminate both TME and drug sensitivity. Further, SFM subtypes were identified using K-means clustering and verified in three independent cohorts. Nearest template prediction algorithm was used to predict drug response. TME estimation was performed by CIBERSORT and microenvironment cell populations-counter (MCP-counter) methods. We identified six SFM subtypes based on SFM signature that discriminated both TME and drug sensitivity. The SFM subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics, specific enrichments of gene signatures, signaling pathways, prognosis, gut microbiome patterns, and tumor lymphocytes infiltration. Among them, SFM-C and -F were immune suppressive. SFM-F had higher stromal fraction with epithelial-to-mesenchymal transition phenotype, while SFM-C was characterized as microsatellite instability phenotype which was responsive to immunotherapy. SFM-D, -E, and -F were sensitive to FOLFIRI and FOLFOX, while SFM-A, -B, and -C were responsive to EGFR inhibitors. Finally, SFM subtypes had strong prognostic value in which SFM-E and -F had worse survival than other subtypes. SFM subtypes enable the stratification of CRC with potential chemotherapy response thereby providing more precise therapeutic options for these patients.

A universal molecular prognostic score for gastrointestinal tumors

Colorectal and gastric cancers are a leading cause of cancer deaths in developed countries. Precise estimation of prognosis is important with regard to clinical decision making for individuals with such cancers. We here comprehensively compiled a complete atlas of prognostic genes based on an integrated meta-analysis of one of the largest assembled colorectal cancer cohorts. A simple yet robust machine learning approach was then applied to establish a universal molecular prognostic score (mPS_colon) that relies on the expression status of only 16 genes and which was validated with independent data sets. This score was found to be an independent prognostic indicator in multivariate models including cancer stage, to be valid independent of tumor characteristics or patient ethnicity, and to be also applicable to gastric cancer. We conclude that mPS_colon is a universal prognostic classifier for patients with gastrointestinal cancers and that it should prove informative for optimization of personalized therapy for such patients.