Clinical Calculator Based on Molecular and Clinicopathologic Characteristics Predicts Recurrence Following Resection of Stage I-III Colon Cancer

Clinical Calculator for Predicting Freedom From Recurrence After Resection of Stage I-III Colon Cancer in Patients With Microsatellite Instability

PURPOSE

Outcome for patients with nonmetastatic, microsatellite instability (MSI) colon cancer is favorable: however, high-risk cohorts exist. This study was aimed at developing and validating a nomogram model to predict freedom from recurrence (FFR) for patients with resected MSI colon cancer.

PATIENTS AND METHODS

Data from patients who underwent curative resection of stage I, II, or III MSI colon cancer in 2014-2021 (model training cohort, 384 patients, 33 events; median follow-up, 38.8 months) were retrospectively collected from institutional databases. Variables associated with recurrence in multivariable analysis were selected for inclusion in the clinical calculator. The calculator's predictive accuracy was measured with the concordance index and validated using data from patients who underwent treatment for MSI colon cancer in 2007-2013 (validation cohort, 164 patients, eight events; median follow-up, 84.8 months).

RESULTS

T category and number of positive lymph nodes were significantly associated with recurrence in multivariable analysis and were selected for inclusion in the clinical calculator. The calculator's concordance index for FFR in the model training cohort was 0.812 (95% CI, 0.742 to 0.873), compared with 0.759 (95% CI, 0.683 to 0.840) for the staging schema of the eighth edition of the American Joint Committee on Cancer Staging Manual. The concordance index for the validation cohort was 0.744 (95% CI, 0.666 to 0.822), confirming robust predictive accuracy.

CONCLUSION

Although in general patients with nonmetastatic MSI colon cancer had favorable outcome, patients with advanced T category and multiple metastatic lymph nodes had higher risk of recurrence. The clinical calculator identified patients with MSI colon cancer at high risk for recurrence, and this could inform surveillance strategies. In addition, the model could be used in trial design to identify patients suitable for novel adjuvant therapy.

Prognostic scoring system based on indicators reflecting the tumor glandular differentiation and microenvironment for patients with colorectal cancer

Prognostic stratification is an urgent concern for patients with colorectal cancer (CRC). The desmoplastic reaction (DR) is speculated to mirror the tumor microenvironment. DR types are considered independent prognostic indicators in CRC, but have not been incorporated in previous prognostic nomograms. We aimed to assess the prognostic significance of a novel approach incorporating histopathological indicators reflecting tumor glandular differentiation and microenvironment. We evaluated 329 consecutive patients with CRC who underwent surgical resection at Kansai Medical University. Histological glandular differentiation was scored as 2 (0 point), 3 (1 point), or 4 (2 points). Tumor buddings (TBs) were classified as TB1 (0 point), TB2 (1 point), or TB3 (2 points). pT1 or 2 was considered as 0 point, pT3 or 4 + DR non-immature type as 1 point, and pT3 or 4 + DR immature type as 2 points. Lymph node metastasis was classified as pN0 (0 point), pN1 (1 point), or pN2 (2 points). The preoperative carcinoembryonic antigen levels were categorized as < 5.0 ng/mL (0 point) and ≧5.0 (1 point). Considering these factors, the following D&M (tumor differentiation and microenvironment) scoring system was applied: I (0-2 points), II (3-4 points), III (5-6 points), and IV (7-9 points). Kaplan-Meier curves showed significant differences in disease-specific survival and recurrence-free survival among the assigned scores, highlighting their enhanced utility compared with the American Joint Committee on Cancer 8th edition staging system. The D&M scoring system was valuable as the initial prognostic nomogram, including DR.

Functional proteomics of colon cancer Consensus Molecular Subtypes

BACKGROUND

The Colorectal Cancer Subtyping Consortium established four Consensus Molecular Subtypes (CMS) in colorectal cancer: CMS1 (microsatellite-instability [MSI], Immune), CMS2 (Canonical, epithelial), CMS3 (Metabolic), and CMS4 (Mesenchymal). However, only MSI tumour patients have seen a change in their disease management in clinical practice. This study aims to characterise the proteome of colon cancer CMS and broaden CMS's clinical utility.

METHODS

One-hundred fifty-eight paraffin samples from stage II-III colon cancer patients treated with adjuvant chemotherapy were analysed through DIA-based mass-spectrometry proteomics.

RESULTS

CMS1 exhibited overexpression of immune-related proteins, specifically related to neutrophils, phagocytosis, antimicrobial response, and a glycolytic profile. These findings suggested potential therapeutic strategies involving immunotherapy and glycolytic inhibitors. CMS3 showed overexpression of metabolic proteins. CMS2 displayed a heterogeneous protein profile. Notably, two proteomics subtypes within CMS2, with different protein characteristics and prognoses, were identified. CMS4 emerged as the most distinct group, featuring overexpression of proteins related to angiogenesis, extracellular matrix, focal adhesion, and complement activation. CMS4 showed a high metastatic profile and suggested possible chemoresistance that may explain its worse prognosis.

CONCLUSIONS

DIA proteomics revealed new features for each colon cancer CMS subtype. These findings provide valuable insights into potential therapeutic targets for colorectal cancer subtypes in the future.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study

BACKGROUND

Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC).

MATERIALS AND METHODS

In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy.

RESULTS

The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not.

CONCLUSION

The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

Association of survival with adjuvant chemotherapy in patients with stage IB gastric cancer: a multicentre, observational, cohort study

Background

Recurrence following radical resection in patients with stage IB gastric cancer (GC) is not uncommon. However, whether postoperative adjuvant chemotherapy could reduce the risk of recurrence in stage IB GC remains contentious.

Methods

We collected data on 2110 consecutive patients with pathologic stage IB (T1N1M0 or T2N0M0) GC who were admitted to 8 hospitals in China from 2009 to 2018. The survival of patients who received adjuvant chemotherapy was compared with that of postoperative observation patients using propensity score matching (PSM). Two survival prediction models were constructed to estimate the predicted net survival gain attributable to adjuvant chemotherapy.

Findings

Of the 2110 patients, 1344 received adjuvant chemotherapy and 766 received postoperative observation. Following the 1-to-1 matching, PSM yielded 637 matched pairs. Among matched pairs, adjuvant chemotherapy was not associated with improved survival compared with postoperative observation (OS: hazard ratio [HR], 0.72; 95% CI, 0.52-1.00; DFS: HR, 0.91; 95% CI, 0.64-1.29). Interestingly, in the subgroup analysis, reduced mortality after adjuvant chemotherapy was observed in the subgroups with elevated serum CA19-9 (HR, 0.22; 95% CI, 0.08-0.57; P = 0.001 for multiplicative interaction), positive lymphovascular invasion (HR, 0.32; 95% CI, 0.17-0.62; P < 0.001 for multiplicative interaction), or positive lymph nodes (HR, 0.17; 95% CI, 0.07-0.38; P < 0.001 for multiplicative interaction). The survival prediction models mainly based on variables associated with chemotherapy benefits in the subgroup analysis demonstrated good calibration and discrimination, with relatively high C-indexes. The C-indexes for OS were 0.74 for patients treated with adjuvant chemotherapy and 0.70 for patients treated with postoperative observation. Two nomograms were built from the models that can calculate individualized estimates of expected net survival gain attributable to adjuvant chemotherapy.

Interpretation

In this cohort study, pathologic stage IB alone was not associated with survival benefits from adjuvant chemotherapy compared with postoperative observation in patients with early-stage GC. High-risk clinicopathologic features should be considered simultaneously when evaluating patients with stage IB GC for adjuvant chemotherapy.

Funding

National Natural Science Foundation of China; the National Key R&D Program of China.

Dickkopf-1 drives perineural invasion via PI3K-AKT signaling pathway in head and neck squamous cancer

Perineural invasion (PNI) leads to the poor prognosis of head and neck squamous cancer (HNSCC) patients, but the mechanism of PNI remains unclear. Dickkopf-1 (DKK1), a secretory protein in the Wnt signaling pathway, was found indeed upregulated in HNSCC cells and tissues. Higher expression of DKK1 was statistically relevant to T stage, N stage, PNI, and poor prognosis of HNSCC. DKK1 overexpression enhanced the migration abilities of cancer cells. Moreover, DKK1-overexpressing cancer cells promoted cancer cells invasion of peripheral nerves in vitro and in vivo. Mechanistically, DKK1 could promote the PI3K-AKT signaling pathway. The migration abilities of neuroblastoma cells, which were enhanced by DKK1-overexpressing HNSCC cell lines, could be reversed by an inhibitor of Akt (MK2206). The association of DKK1 with PNI was also confirmed in HNSCC samples. Variables, including T stage, N stage, DKK1 expression, and PNI, were used to establish a nomogram to predict the survival probability and disease-free probability at 3 and 5 years. In summary, DKK1 can promote the PI3K-AKT signaling pathway in tumor cells and then could induce neuritogenesis and facilitate PNI. MK2206 may be a potential therapeutic target drug for HNSCC patients with PNI.

Tumour deposits are independently associated with recurrence in colon cancer

AIM

Tumour deposits are focal aggregates of cancer cells in pericolic fat and mesentery, distinct from vessels, nerves and lymphatics. Their presence upstages lymph node negative patients but is ignored in lymph node positive patients. We investigated the clinicopathological factors associated with tumour deposits and their impact on recurrence in lymph node positive and negative patients.

METHOD

Clinicopathological variables were collected from the medical records of patients with Stage I-III colon cancer who underwent resection in 2017-2019. Pathology was reviewed by a gastrointestinal pathologist. Patients with rectal cancer, metastasis, and concurrent malignancy were excluded.

RESULTS

Tumour deposits were noted in 69 (9%) of 770 patients. They were associated with the presence of lymph node metastasis, advanced T category, poorly differentiated tumours, microsatellite stable subtype and lymphovascular and perineural invasion (p < 0.05). The presence of tumour deposits (hazard ratio 2.48, 95% CI 1.49-4.10) and of lymph node metastasis (hazard ratio 3.04, 95% CI 1.72-5.37) were independently associated with decreased time to recurrence. There was a weak correlation (0.27) between the number of tumour deposits and the number of positive lymph nodes.

CONCLUSION

Tumour deposits are associated with more advanced disease and high-risk pathological features. The presence of tumour deposits and lymph node metastasis were found to be independent risk factors for decreased time to recurrence. A patient with both lymph node metastasis and tumour deposits is more than twice as likely to have recurrence compared with a patient with only lymph node metastasis. Tumour deposits independently predict recurrence and should not be ignored in lymph node positive patients.

Location matters: spatial dynamics of tumor-infiltrating T cell subsets is prognostic in colon cancer

Background

Colon cancer is a heterogeneous disease and consists of various molecular subtypes. Despite advances in high-throughput expression profiling, limitations remain in predicting clinical outcome and assigning specific treatment to individual cases. Tumor-immune interactions play a critical role, with tumors that activate the immune system having better outcome for the patient. The localization of T cells within tumor epithelium, to enable direct contact, is essential for antitumor function, but bulk DNA/RNA sequencing data lacks spatial distribution information. In this study, we provide spatial T cell tumor distribution and connect these data with previously determined genomic data in the AC-ICAM colon cancer patient cohort.

Methods

Colon cancer patients (n=90) with transcriptome data available were selected. We used a custom multiplex immunofluorescence assay on colon tumor tissue sections for quantifying T cell subsets spatial distribution in the tumor microenvironment, in terms of cell number, location, mutual distance, and distance to tumor cells. Statistical analyses included the previously determined Immunologic Constant of Rejection (ICR) transcriptome correlation and patient survival, revealing potential prognostic value in T cell spatial distribution.

Results

T cell phenotypes were characterized and CD3+CD8-FoxP3- T cells were found to be the predominant tumor-infiltrating subtype while CD3+FoxP3+ T cells and CD3+CD8+ T cells showed similar densities. Spatial distribution analysis elucidated that proliferative T cells, characterized by Ki67 expression, and Granzyme B-expressing T cells were predominantly located within the tumor epithelium. We demonstrated an increase in immune cell density and a decrease in the distance of CD3+CD8+ T cells to the nearest tumor cell, in the immune active, ICR High, immune subtypes. Higher densities of stromal CD3+FoxP3+ T cells showed enhanced survival outcomes, and patients exhibited superior clinical benefits when greater spatial distances were observed between CD3+CD8-FoxP3- or CD3+CD8+ T cells and CD3+FoxP3+ T cells.

Conclusion

Our study's in-depth analysis of the spatial distribution and densities of major T cell subtypes within the tumor microenvironment has provided valuable information that paves the way for further research into the intricate relationships between immune cells and colon cancer development.

Validation of a Clinical Calculator Predicting Freedom From Colon Cancer Recurrence After Surgery on the Basis of Molecular and Clinical Variables

BACKGROUND

The Memorial Sloan Kettering clinical calculator for estimating the likelihood of freedom from colon cancer recurrence on the basis of clinical and molecular variables was developed at a time when testing for microsatellite instability was performed selectively, based on patient age, family history, and histologic features. Microsatellite stability was assumed if no testing was done.

OBJECTIVE

This study aimed to validate the calculator in a cohort of patients who had all been tested for microsatellite instability.

DESIGN

Retrospective cohort analysis.

SETTINGS

Comprehensive cancer center.

PATIENTS

This study included consecutive patients who underwent curative resection for stage I, II, or III colon cancer between 2017 and 2019.

INTERVENTION

Universal testing of mircrosatellite phenotype in all cases.

MAIN OUTCOME MEASURES

The calculator's predictive accuracy was assessed using the concordance index and a calibration plot of predicted versus actual freedom from recurrence at 3 years after surgery. For a secondary sensitivity analysis, the presence of a tumor deposit(s) (disease category N1c) was considered equivalent to one positive lymph node (category N1a).

RESULTS

With a median follow-up of 32 months among survivors, the concordance index for the 745 patients in the cohort was 0.748 (95% CI, 0.693-0.801), and a plot of predicted versus observed recurrences approached the 45° diagonal, indicating good discrimination and calibration. In the secondary sensitivity analysis for tumor deposits, the concordance index was 0.755 (95% CI, 0.700-0.806).

LIMITATIONS

This study was limited by its retrospective, single-institution design.

CONCLUSIONS

These results, based on inclusion of actual rather than imputed microsatellite stability status and presence of tumor deposits, confirm the predictive accuracy and reliability of the calculator. See Video Abstract .

VALIDACIN DE UNA CALCULADORA CLNICA QUE PREDICE LA AUSENCIA DE RECURRENCIA POSTQUIRURGICA DEL CNCER DE COLON SOBRE LA BASE DE VARIABLES MOLECULARES Y CLNICAS

ANTECEDENTES:La calculadora clínica del Memorial Sloan Kettering para la estimación de la probabilidad de ausencia de recurrencia del cáncer de colon sobre la base de variables clínicas y moleculares, se desarrolló en un momento en que las pruebas para la inestabilidad de microsatélites se realizaban de forma selectiva, basadas en la edad del paciente, los antecedentes familiares y las características histológicas. Se asumía la estabilidad micro satelital si no se realizaba ninguna prueba.OBJETIVO:El objetivo de este estudio fue validar la calculadora en una cohorte de pacientes a los que se les había realizado la prueba de inestabilidad de microsatélites.DISEÑO:Análisis de cohorte retrospectivo.AJUSTE:Centro integral de cáncer.PACIENTES:Pacientes consecutivos con cáncer de colon que fueron sometidos a resección curativa por cáncer de colon en estadios I, II o III entre los años 2017 y 2019.PRINCIPALES MEDIDAS DE RESULTADO:La precisión predictiva de la calculadora fue evaluada mediante el índice de concordancia y un gráfico de calibración de la ausencia de recurrencia predecida versus la real a los 3 años tras la cirugía. A los efectos de un análisis secundario de sensibilidad, la presencia de depósito(s) tumoral(es) (categoría de enfermedad N1c) se consideró equivalente a un ganglio linfático positivo (categoría N1a).RESULTADOS:Con una mediana de seguimiento de 32 meses entre los supervivientes, el índice de concordancia para los 745 pacientes de la cohorte fue de 0,748 (intervalo de confianza del 95 %, 0,693 a 0,801), y una gráfica de recurrencias previstas versus observadas se acercó a la diagonal de 45°, indicando una buena discriminación y calibración. En el análisis secundario de sensibilidad para depósitos tumorales, el índice de concordancia fue de 0,755 (intervalo de confianza del 95 %, 0,700 a 0,806).LIMITACIONES:Diseño retrospectivo, institución única.CONCLUSIONES:Estos resultados, basados en la inclusión real del estado de estabilidad de microsatélites en lugar de imputado y la presencia de depósitos tumorales, confirman la precisión predictiva y la confiabilidad de la calculadora. (Traducción-Dr Osvaldo Gauto ).

Lymph Node Metastases and Associated Recurrence-Free Survival in Microsatellite Stable and Unstable Colon Cancer

BACKGROUND

In contrast to microsatellite stable (MSS) colon cancer, predictors of lymph node metastases and their association with recurrence are not well-defined in microsatellite instability (MSI) colon cancer.

METHODS

A cohort of nonmetastatic colon cancer patients undergoing surgery between 2015 and 2021 were evaluated for predictors of lymph node metastases (LNMs) and their association with recurrence-free survival (RFS).

RESULTS

Of 1466 patients included in the analyses, 361 (25 %) had MSI. Compared with MSS, MSI was associated with earlier stage, fewer LNMs in the patients with N1 or N2 disease, and fewer high-risk features. Compared with the T3-T4 MSS patients, the odds ratios for LNM were 0.52 (95% confidence interval [CI], 0.38-0.71) for the T3-T4 MSI patients, 0.27 (95% CI, 0.38-0.71) for the T1-T2 MSS patients, and 0.15 (95 % CI, 0.08-0.26) for the T1-T2 MSI patients. In both groups, LNMs were associated with T category, patient age, and venous, lymphatic, or perineural invasion. In the MSS patients, LNMs were additionally associated with patient sex and histologic grade. Compared with the MSS patients, the MSI patients with N0 and N1 disease had a better 3-year RFS. However, the MSI patients with N2 disease had a lower rate of 3-year RFS than the MSS patients (hazard ratio, 19.75 vs 4.49).

CONCLUSIONS

In MSI colon cancer, LNMs are 50 % less prevalent, but the factors associated with LNM are like those in MSS colon cancer. The improved prognosis traditionally associated with early-stage MSI colon cancers dissipates with four or more LNMs. These findings should be taken into consideration by clinicians selecting the most appropriate course of treatment for MSI colon cancer.

Identification and initial validation of maximal tumor area as a novel prognostic factor for overall and disease-free survival in patients with resectable colon cancer: a retrospective study

BACKGROUND

The tumor area may be a potential prognostic indicator. The present study aimed to determine and validate the prognostic value of tumor area in curable colon cancer.

METHODS

This retrospective study included a training and validation cohorts of patients who underwent radical surgery for colon cancer. Independent prognostic factors for overall survival (OS) and disease-free survival (DFS) were identified using Cox proportional hazards regression models. The prognostic discrimination was evaluated using the integrated area under the receiver operating characteristic curves (iAUCs) for prognostic factors and models. The prognostic discrimination between tumor area and other individual factors was compared, along with the prognostic discrimination between the tumor-node-metastasis (TNM) staging system and other prognostic models. Two-sample Wilcoxon tests were carried out to identify significant differences between the two iAUCs. A two-sided P <0.05 was considered statistically significant.

RESULTS

A total of 3051 colon cancer patients were included in the training cohort and 872 patients in the validation cohort. Tumor area, age, differentiation, T stage, and N stage were independent prognostic factors for both OS and DFS in the training cohort. Tumor area had a better OS and DFS prognostic discrimination characteristics than T stage, maximal tumor diameter, differentiation, tumor location, and number of retrieved lymph nodes. The novel prognostic model of T stage + N stage + tumor area (iAUC for OS, 0.714, P <0.001; iAUC for DFS, 0.694, P <0.001) showed a better prognostic discrimination than the TNM staging system (T stage + N stage; iAUC for OS, 0.664; iAUC for DFS, 0.658). Similar results were observed in an independent validation cohort.

CONCLUSIONS

Tumor area was identified as an independent prognostic factor for both OS and DFS in curable colon cancer patients, and in cases with an adequate number of retrieved lymph nodes. The novel prognostic model of combining T stage, N stage, and tumor area may be an alternative to the current TNM staging system.

Effect of visceral fat area on the accuracy of preoperative CT-N staging of colorectal cancer

OBJECTIVE

To investigate the effect of visceral fat area (VFA) on the accuracy of preoperative CT-N staging of colorectal cancer.

METHODS

We retrospectively reviewed the clinical and imaging data of 385 CRC patients who underwent surgical resection for colorectal cancer between January 2018 and July 2021. Preoperative CT-N staging and imaging features were determined independently by two radiologists. Using postoperative pathology as the gold standard, patients were divided into accurately and incorrectly staged groups, and clinical and imaging characteristics were compared between the two groups. VFA and subcutaneous fat area (SFA) at the L3 vertebral level, sex, age, BMI, tumor location, size, and tumor circumference ratio (TCR) were included. Logistic regression analysis was used to evaluate the independent factors influencing the accuracy of preoperative N staging of colorectal cancer.

RESULTS

Of the 385 patients enrolled, 259 (67.27%) were in the preoperative N-stage accurate staging group, and 126 (32.73%) were in the incorrectly staged group. Univariate analysis showed that there were significant differences in BMI, tumor location, VFA, SFA, size and TCR between the two groups (P<0.05). Logistic regression analysis showed that VFA (95% CI: 1.277, 3.813; P=0.005) and TCR (95% CI: 1.649, 17.545; P=0.005) were independent factors affecting the accuracy of N staging. The optimal cutoff points for VFA and TCR in predicting incorrect staging were 110 cm2 and 0.675, respectively.

CONCLUSIONS

Colorectal cancer patients with lower VFA and higher TCR and preoperative CT-N staging had an increased risk for diagnostic errors.

Integrated Analysis Identifies DPP7 as a Prognostic Biomarker in Colorectal Cancer

Colorectal cancer has a poor prognosis and is prone to recurrence and metastasis. DPP7, a prolyl peptidase, is reported to regulate lymphocyte quiescence. However, the correlation of DPP7 with prognosis in CRC remains unclear. With publicly available cohorts, the Wilcoxon rank-sum test and logistic regression were employed to analyze the relationship between DPP7 expression and the clinicopathological features of CRC patients. Specific pathways of differentially expressed genes were determined through biofunctional analysis and gene set enrichment analysis (GSEA). qPCR and immunohistochemical staining were used to determine DPP7 expression levels in surgical specimens. The public dataset and analysis of the biospecimens of CRC patients revealed that DPP7, in the CRC samples, was expressed significantly higher than in non-tumor tissues. Moreover, increased DPP7 was significantly associated with a higher N stage, lymphatic invasion, and shorter overall survival. Functionally, DPP7 is involved in neuroactive ligand-receptor interaction and olfactory transduction signaling. We identified a series of targeted drugs and small-molecule drugs with responses to DPP7. To conclude, DPP7 is a valuable diagnostic and prognostic biomarker for CRC and considered as a new therapeutic target.

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data

Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II-III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19-0.45, vs. higher risk; P < 0.0001) and could be externally validated using The Cancer Genome Atlas (TCGA) data (P = 0.0004). BART demonstrated model flexibility, interpretability, and comparable or superior performance to other machine-learning models. Integrated bioinformatic analyses using BART with tumor-specific factors can robustly stratify colorectal cancer patients into prognostic groups and be readily applied to clinical oncology practice.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma

Introduction

Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear.

Methods

Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes.

Results

Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system.

Discussion

Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

An N6-methyladenosine regulation- and mRNAsi-related prognostic index reveals the distinct immune microenvironment and immunotherapy responses in lower-grade glioma

BACKGROUND

N6-methyladenosine (m6A) modification is involved in tumorigenesis and progression as well as closely correlated with stem cell differentiation and pluripotency. Moreover, tumor progression includes the acquisition of stemness characteristics and accumulating loss of differentiation phenotype. Therefore, we integrated m6A modification and stemness indicator mRNAsi to classify patients and predict prognosis for LGG.

METHODS

We performed consensus clustering, weighted gene co-expression network analysis, and least absolute shrinkage and selection operator Cox regression analysis to identify an m6A regulation- and mRNAsi-related prognostic index (MRMRPI). Based on this prognostic index, we also explored the differences in immune microenvironments between high- and low-risk populations. Next, immunotherapy responses were also predicted. Moreover, single-cell RNA sequencing data was further used to verify the expression of these genes in MRMRPI. At last, the tumor-promoting and tumor-associated macrophage polarization roles of TIMP1 in LGG were validated by in vitro experiments.

RESULTS

Ten genes (DGCR10, CYP2E1, CSMD3, HOXB3, CABP4, AVIL, PTCRA, TIMP1, CLEC18A, and SAMD9) were identified to construct the MRMRPI, which was able to successfully classify patients into high- and low-risk group. Significant differences in prognosis, immune microenvironment, and immunotherapy responses were found between distinct groups. A nomogram integrating the MRMRPI and other prognostic factors were also developed to accurately predict prognosis. Moreover, in vitro experiments illustrated that inhibition of TIMP1 could inhibit the proliferation, migration, and invasion of LGG cells and also inhibit the polarization of tumor-associated macrophages.

CONCLUSION

These findings provide novel insights into understanding the interactions of m6A methylation regulation and tumor stemness on LGG development and contribute to guiding more precise immunotherapy strategies.

Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer

The prognosis of patients with colorectal cancer (CRC) mostly relies on the classic tumor node metastasis (TNM) staging classification. A more accurate and convenient prediction model would provide a better prognosis and assist in treatment. From May 2014 to December 2017, patients who underwent an operation for CRC were enrolled. The proposed feature ensemble vision transformer (FEViT) used ensemble classifiers to benefit the combinations of relevant colonoscopy features from the pretrained vision transformer and clinical features, including sex, age, family history of CRC, and tumor location, to establish the prognostic model. A total of 1729 colonoscopy images were enrolled in the current retrospective study. For the prediction of patient survival, FEViT achieved an accuracy of 94 % with an area under the receiver operating characteristic curve of 0.93, which was better than the TNM staging classification (90 %, 0.83) in the experiment. FEViT reduced the limited receptive field and gradient disappearance in the conventional convolutional neural network and was a relatively effective and efficient procedure. The promising accuracy of FEViT in modeling survival makes the prognosis of CRC patients more predictable and practical.

Clinical Score to Predict Recurrence in Patients with Stage II and Stage III Colon Cancer

Background: The prognosis of patients with stage II and stage III colon cancer is heterogeneous. Clinical and pathological characteristics, such as tumor budding, may help to further refine the recurrence risk. Methods: We included all the patients with localized colon cancer at Hospital Universitario La Paz from October 2016 to October 2021. We built a prognostic score for recurrence in the training cohort based on multivariate cox regression analysis and categorized the patients into two risk groups. Results: A total of 440 patients were included in the training cohort. After a median follow-up of 45 months, 81 (18%) patients had a first tumor recurrence. T4, N2, and high tumor budding remained with a p value <0.05 at the last step of the multivariate cox regression model for time to recurrence (TTR). We assigned 2 points to T4 and 1 point to N2 and high tumor budding. Forty-five percent of the patients were assigned to the low-risk group (score = 0). Compared to the high-risk group (score 1−4), patients in the low-risk group had a significantly longer TTR (hazard ratio for disease recurrence of 0.14 (95%CI: 0.00 to 0.90; p < 0.045)). The results were confirmed in the validation cohort. Conclusions: In our study, we built a simple score to predict tumor recurrence based on T4, N2, and high tumor budding. Patients in the low-risk group, that comprised 44% of the cohort, had an excellent prognosis.

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Background

To construct prognostic model of colorectal cancer (CRC) recurrence and metastasis (R&M) with traditional Chinese medicine (TCM) factors based on different machine learning (ML) methods. Aiming to offset the defects in the existing model lacking TCM factors.

Methods

Patients with stage I-III CRC after radical resection were included as the model data set. The training set and the internal verification set were randomly divided at a ratio of 7: 3 by the "set aside method". The average performance index and 95% confidence interval of the model were calculated by repeating 100 tests. Eight factors were used as predictors of Western medicine. Two types of models were constructed by taking "whether to accept TCM intervention" and "different TCM syndrome types" as TCM predictors. The model was constructed by four ML methods: logistic regression, random forest, Extreme Gradient Boosting (XGBoost) and support vector machine (SVM). The predicted target was whether R&M would occur within 3 years and 5 years after radical surgery. The area under curve (AUC) value and decision curve analysis (DCA) curve were used to evaluate accuracy and utility of the model.

Results

The model data set consisted of 558 patients, of which 317 received TCM intervention after radical resection. The model based on the four ML methods with the TCM factor of "whether to accept TCM intervention" showed good ability in predicting R&M within 3 years and 5 years (AUC value > 0.75), and XGBoost was the best method. The DCA indicated that when the R&M probability in patients was at a certain threshold, the models provided additional clinical benefits. When predicting the R&M probability within 3 years and 5 years in the model with TCM factors of "different TCM syndrome types", the four methods all showed certain predictive ability (AUC value > 0.70). With the exception of the model constructed by SVM, the other methods provided additional clinical benefits within a certain probability threshold.

Conclusion

The prognostic model based on ML methods shows good accuracy and clinical utility. It can quantify the influence degree of TCM factors on R&M, and provide certain values for clinical decision-making.

Peritumoral tertiary lymphoid structure and tumor stroma percentage predict the prognosis of patients with non-metastatic colorectal cancer

Background

Tertiary lymphoid structures (TLSs) are crucial in promoting and maintaining positive anti-tumor immune responses. The tumor stroma has a powerful immunosuppressive function that could exclude tumor-infiltrating lymphocytes from the tumor beds and lead to a “cold” phenotype. TLSs and tumor stroma percentage (TSP) are significantly associated with the prognosis of patients with certain cancers. However, the exact roles of TLSs and TSP and their intrinsic relationship are still largely unknown in colorectal cancer (CRC).

Methods

TLSs and TSP were assessed using hematoxylin-eosin (H&amp;E) and/or immunohistochemistry (IHC) staining from 114 CRC patients in the training set and 60 CRC patients in the external validation set. The correlation between TILs, TLS and clinicopathological characteristics and their prognostic values were assessed. Finally, we plotted a Nomogram including the TLS, TSP and tumor-node-metastasis (TNM) stage to predict the probability of recurrence-free survival (RFS) at 2- and 5-years in non-metastatic colorectal cancer (nmCRC) patients.

Results

Peritumoral TLS (P-TLS), intratumoral TLS (In-TLS) and high TSP (H-TSP, &gt;50%) were present in 99.1%, 26.3% and 41.2% patients, respectively. H-TSP tumor tends to be associated with lower P-TLS density (P =0.0205). The low P-TLS density (&lt; 0.098/mm2) was significantly associated with reduced RFS (HR=6.597 95% CI: 2.882-15.103, P &lt;0.001) and reduced overall survival (OS) (HR=6.628 95% CI: 2.893-15.183, P &lt; 0.001) of nmCRC patients. In-TLS was not of significance in evaluating the clinical outcomes of nmCRC patients. H-TSP was significantly associated with reduced RFS (HR=0.126 95% CI: 0.048-0.333, P &lt;0.001) and reduced OS (HR=0.125 95% CI: 0.047-0.332, P &lt;0.001) of nmCRC patients. The 5-year RFS of the high P-TLS, low-TLS, H-TSP, and L-TSP groups were 89.7%, 47.2%, 53.2%, and 92.5%, respectively. The P-TLS density, TSP and TNM stage were independent prognosis factors of nmCRC patients. The Nomogram, including the P-TLS density, TSP and TNM stage, outperformed the TNM stage.

Conclusions

High P-TLS density and low TSP (L-TSP) were independent and favorable prognostic factors of nmCRC patients, which might provide new directions for targeted therapy in the CRC tumor microenvironment, especially the tumor immune microenvironment.

Real-world survival of colon cancer after radical surgery: A single-institutional retrospective analysis

The survival rate for colon cancer after radical surgery has been the focus of extensive debate. To assess the postoperative survival and prognostic factors for overall survival (OS), we collected clinicopathological information for 2,655 patients. The survival time and potential risk factors for OS were analyzed by using Kaplan–Meier curves, Cox proportional hazards models, best subset regression (BSR), and least absolute shrinkage and selection operator (LASSO). The 5-year survival rates of stage I–IV colon cancer were 96.6%, 88.7%, 69.9%, and 34.3%, respectively. Adjuvant chemotherapy improved the survival rate (90.4% vs. 82.4%, with versus without adjuvant chemotherapy, respectively) in stage II patients with high-risk factors. Elevated preoperative carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were significantly associated with worse OS compared with patients without these elevations. Less than 12 versus more than 12 harvested lymph nodes (LNs) affected prognosis (84.6% vs. 89.7%, respectively). Regarding the lymph node ratio (LNR), the 5-year OS rate was 89.2%, 71.5%, 55.8%, and 34.5% in patients with LNR values of 0, 0.3, 0.3–0.7, and &gt;0.7, respectively. We constructed a nomogram comprising the independent factors associated with survival to better predict prognosis. On the basis of these findings, we propose that stage II colon cancer patients without high-risk factors and with both elevated preoperative CEA and CA199 should receive adjuvant therapy. Furthermore, the LNR could complement TNM staging in patients with &lt;12 harvested LNs. Our nomogram might be useful as a new prognosis prediction system for colon cancer patients.

A Dynamic Clinical Calculator for Estimating Conditional Recurrence-Free Survival After Total Neoadjuvant Therapy for Rectal Cancer and Either Surgery or Watch-and-Wait Management

IMPORTANCE

The risk of recurrence in patients with locally advanced rectal cancer has historically been determined after surgery, relying on pathologic variables. A growing number of patients are being treated without surgery, and their risk of recurrence needs to be calculated differently.

OBJECTIVE

To develop a dynamic calculator for estimating the probability of recurrence-free survival (RFS) in patients with rectal cancer who undergo total neoadjuvant therapy (TNT) (induction systemic chemotherapy and chemoradiotherapy) and either surgery or watch-and-wait management.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included patients who presented with stage II or III rectal cancer between June 1, 2009, and March 1, 2015, at a comprehensive cancer center. Conditional modeling was incorporated into a previously validated clinical calculator to allow the probability of RFS to be updated based on whether the patient remained in watch-and-wait management or underwent delayed surgery. Data were analyzed from November 2021 to March 2022.

EXPOSURE

TNT followed by immediate surgery or watch-and-wait management with the possibility of delayed surgery.

MAIN OUTCOMES AND MEASURES

RFS, concordance index, calibration curves.

RESULTS

Of the 302 patients in the cohort, 204 (68%) underwent surgery within 3 months from TNT completion (median [range] age, 51 [22-82] years; 78 [38%] women), 54 (18%) underwent surgery more than 3 months from TNT completion (ie, delayed surgery; median [range] age, 62 [31-87] years; 30 [56%] female), and 44 (14%) remained in watch-and-wait management as of April 21, 2021 (median [range] age, 58 [32-89] years; 16 [36%] women). Among patients who initially opted for watch-and-wait management, migration to surgery due to regrowth or patient choice occurred mostly within the first year following completion of TNT, and RFS did not differ significantly whether surgery was performed 3.0 to 5.9 months (73%; 95% CI, 52%-92%) vs 6.0 to 11.9 months (71%; 95% CI, 51%-99%) vs more than 12.0 months (70%; 95% CI, 49%-100%) from TNT completion (P = .70). RFS for patients in the watch-and-wait cohort at 12 months from completion of TNT more closely resembled patients who had undergone surgery and had a pathologic complete response than the watch-and-wait cohort at 3 months from completion of TNT. Accordingly, model performance improved over time, and the concordance index increased from 0.62 (95% CI, 0.53-0.71) at 3 months after TNT to 0.66 (95% CI, 0-0.75) at 12 months.

CONCLUSIONS AND RELEVANCE

In this cohort study of patients with rectal cancer, the clinical calculator reliably estimated the likelihood of RFS for patients who underwent surgery immediately after TNT, patients who underwent delayed surgery after entering watch-and-wait management, and patients who remained in watch-and-wait management. Delayed surgery following attempted watch-and-wait did not appear to compromise oncologic outcomes. The risk calculator provided conditional survival estimates at any time during surveillance and could help physicians counsel patients with rectal cancer about the consequences of alternative treatment pathways and thereby support informed decisions that incorporate patients' preferences.

Development and validation of nomograms for predicting survival in patients with de novo metastatic triple-negative breast cancer

Metastatic triple-negative breast cancer (mTNBC) is a heterogeneous disease with a poor prognosis. Individualized survival prediction tool is useful for this population. We constructed the predicted nomograms for breast cancer-specific survival (BCSS) and overall survival (OS) using the data identified from the Surveillance, Epidemiology, and End Results database. The Concordance index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC) and the calibration curves were used for the discrimination and calibration of the nomograms in the training and validation cohorts, respectively. 1962 mTNBC patients with a median follow-up was 13 months (interquartile range, 6–22 months), 1639 (83.54%) cases died of any cause, and 1469 (74.87%) died of breast cancer. Nine and ten independent prognostic factors for BCSS and OS were identified and integrated to construct the nomograms, respectively. The C-indexes of the nomogram for BCSS and OS were 0.694 (95% CI 0.676–0.712) and 0.699 (95% CI 0.679–0.715) in the training cohort, and 0.699 (95% CI 0.686–0.712) and 0.697 (95% CI 0.679–0.715) in the validation cohort, respectively. The AUC values of the nomograms to predict 1-, 2-, and 3-year BCSS and OS indicated good specificity and sensitivity in internal and external validation. The calibration curves showed a favorable consistency between the actual and the predicted survival in the training and validation cohorts. These nomograms based on clinicopathological factors and treatment could reliably predict the survival of mTNBC patient. This may be a useful tool for individualized healthcare decision-making.

Prediction of Metachronous Peritoneal Metastases After Radical Surgery for Colon Cancer: A Scoring System Obtained from an International Multicenter Cohort

Background

Since novel strategies for prevention and treatment of metachronous peritoneal metastases (mPM) are under study, it appears crucial to identify their risk factors. Our aim is to establish the incidence of mPM after surgery for colon cancer (CC) and to build a statistical model to predict the risk of recurrence.

Patients and Methods

Retrospective analysis of consecutive pT3–4 CC operated at five referral centers (2014–2018). Patients who developed mPM were compared with patients who were PM-free at follow-up. A scoring system was built on the basis of a logistic regression model.

Results

Of the 1423 included patients, 74 (5.2%) developed mPM. Patients in the PM group presented higher preoperative carcinoembryonic antigen (CEA) [median (IQR): 4.5 (2.5–13.0) vs. 2.7 (1.5–5.9), P = 0.001] and CA 19-9 [median (IQR): 17.7 (12.0–37.0) vs. 10.8 (5.0–21.0), P = 0.001], advanced disease (pT4a 42.6% vs. 13.5%; pT4b 16.2% vs. 3.2%; P < 0.001), and negative pathological characteristics. Multivariate logistic regression identified CA 19-9, pT stage, pN stage, extent of lymphadenectomy, and lymphovascular invasion as significant predictors, and individual risk scores were calculated for each patient. The risk of recurrence increased remarkably with score values, and the model demonstrated a high negative predictive value (98.8%) and accuracy (83.9%) for scores below five.

Conclusions

Besides confirming incidence and risk factors for mPM, our study developed a useful clinical tool for prediction of mPM risk. After external validation, this scoring system may guide personalized decision-making for patients with locally advanced CC.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-022-12097-9.

Analysis of KRAS, NRAS, and BRAF Mutations, Microsatellite Instability, and Relevant Prognosis Effects in Patients With Early Colorectal Cancer: A Cohort Study in East Asia

Background

Early colorectal cancer (ECRC) refers to any size of colorectal cancer (CRC) whose depth of invasion is limited to the mucosa and submucosa. About 10% of patients with ECRC die from cancer after surgery. KRAS, NRAS, and BRAF mutations and microsatellite instability (MSI) are considered diagnostic and prognostic markers in CRC. However, their characteristics in ECRC and whether postoperative chemotherapy based on them will benefit ECRC patients or not remain unknown.

Patients and Methods

Patients with ECRC and 298 patients with advanced colorectal cancer (ACRC) were collected in our hospital from January 2013 to December 2015. The Amplification Refractory Mutation System (ARMS)-PCR was used to perform the KRAS, NRAS, and BRAF mutant tests.

Results

In ECRC patients, 43 cases of KRAS mutation were found, accounting for 69.35%. Interestingly, among KRAS mutations, there were 10 KRAS multi-site mutation patients (16.13% in 62 ECRC patients). Moreover, the NRAS mutation rate was 3.23% but no BRAF mutation was found and only 1 case of MSI-High was detected. KRAS mutation was only related to the depth of tumor invasion whereas KRAS multi-site mutations were related to mucus components and tumor size. As far as NRAS is concerned, mutations were associated with elevated CEA, mucus components, and the depth of tumor invasion. Notably, compared with 2.35% KRAS multi-site mutation in ACRC, the rate of KRAS multi-site mutation in ECRC was much higher. Furthermore, Cox regression analysis revealed that KRAS mutation could be an independent prognostic factor of ECRC in patients who have undergone endoscopic resection or surgery.

Conclusion

Patients with ECRC might benefit from KRAS mutation testing but not from postoperative chemotherapy.

Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery

Objective

This study aimed to develop prognostic prediction models for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) who received neoadjuvant therapy (neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy) and radical surgery. A baseline nomogram and a post-operative nomogram were constructed before neoadjuvant therapy and after surgery. The predictive performance of the constructed nomograms was internally validated and compared to the TNM staging system.

Materials and Methods

A total of 245 patients diagnosed with Siewert type II/III AEG and treated with neoadjuvant therapy followed by radical surgery at The Fourth Hospital of Hebei Medical University between January 2011 and December 2017 were enrolled. The variables before neoadjuvant therapy were defined as baseline factors, while the variables of baseline factors along with the variables of treatment and postoperative pathology were defined as post-operative factors. To construct the corresponding nomograms, independent predictors of baseline and post-operative factors were identified. The C-index and a time-dependent receiver operating characteristic curve were used to evaluate the model’s discrimination ability. The calibration ability of the model was determined by comparing the probability of predicted free-recurrence to the actual free-recurrence. Decision curve analysis (DCA) was used to determine the clinical usefulness of the nomogram.

Results

Among the baseline factors, age, cT stage, cN stage, Borrmann type, and staging laparoscopy were independent prognostic predictors. In contrast, among the post-operative factors, age, cN stage, staging laparoscopy, ypT stage, clinical response, number of positive lymph nodes, number of negative lymph nodes, laurén classification, and lymphatic, or perineural invasion (VELPI) were independent prognostic predictors. The two nomograms were constructed using the independent predictors of prognosis. The C-indexes for the baseline and post-operative nomograms were 0.690 (95% CI, 0.644-0.736) and 0.817 (95% CI, 0.782-0.853), respectively. The AUCs of the baseline nomogram at 3 and 5 years were both greater than cTNM (73.1 vs 58.8, 76.1 vs 55.7). Similarly, the AUCs of the post-operative nomogram were both greater than ypTNM (85.2 vs 69.1, 88.2 vs 71.3) at 3 and 5 years. The calibration curves indicated that both models had a high degree of calibration ability. By comparing the DCA at 3 and 5 years, we determined that the two nomograms constructed had better clinical utility than the TNM staging system.

Conclusions

The constructed nomograms have a more accurate predictive ability than the eighth edition TNM staging system, which can be useful for treatment selection and follow-up monitoring of patients.

Intraoperative opioids are associated with decreased recurrence rates in colon adenocarcinoma: a retrospective observational cohort study

BACKGROUND

Opioid-induced immunomodulation may be important in colon adenocarcinoma, where tumour DNA mismatch repair (MMR) can determine the level of immune activation with consequences for therapeutic response and prognosis. We evaluated the relationship between intraoperative opioid exposure, MMR subtype, and oncological outcomes after surgery for colon adenocarcinoma.

METHODS

Intraoperative opioid use (standardised by calculating morphine milligram equivalents) during stage I-III colon adenocarcinoma resection was reviewed retrospectively. Tumours were classified as DNA mismatch repair deficient (dMMR) or proficient (pMMR) by immunohistochemistry. The primary outcome was local tumour recurrence, distant tumour recurrence, or both (multivariable analysis). The exposures of interest were intraoperative analgesia and tumour subtype. Opioid-related gene expression was analysed using The Cancer Genome Atlas Colon Adenocarcinoma transcriptomic data.

RESULTS

Clinical and pathological data were analysed from 1157 subjects (median age, 60 [51-70] yr; 49% female) who underwent curative resection for stage I-III colon adenocarcinoma. Higher intraoperative opioid doses were associated with reduced risk of tumour recurrence (hazard ratio=0.92 per 10 morphine milligram equivalents; 95% confidence interval [95% CI], 0.87-0.98; P=0.007), but not with overall survival. In tumours deficient in DNA MMR, tumour recurrence was less likely (HR=0.38; 95% CI, 0.21-0.68; P=0.001), with higher opioid dose associated with eightfold lower recurrence rates. Gene expression related to opioid signalling was different between dMMR and pMMR tumours.

CONCLUSIONS

Higher intraoperative opioid dose was associated with a lower risk of tumour recurrence after surgery for stage I-III colon adenocarcinoma, but particularly so in tumours in which DNA MMR was deficient.

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.

Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort

Purpose

The present study aimed to develop prognostic prediction models based on machine learning (ML) for non-metastatic colon cancer (CRC), which can provide a precise quantitative risk assessment and serve as an assistive method for treatment strategy development. The possibility of improving prediction accuracy using nonlinear methods compared to linear methods was investigated.

Patients and Methods

A cancer-specific survival (CSS) model constructed using logistic regression, extreme gradient boosting (XGBoost), and random forest algorithms was trained on the Surveillance, Epidemiology, and End Results datasets for 15,254 patients with non-metastatic CRC (split into training [70%] and internal validation [30%] datasets) and externally validated with an outpatient cohort of 311 cases from Xiyuan Hospital in China. A Chinese cohort was also used to develop recurrence and metastasis (R&M) models for CRC patients. The experiments for each model were performed 100 times to obtain average scores and 95% confidence intervals. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values.

Results

The XGBoost approach showed the highest AUC values of 0.86 (0.84-0.88), 0.82 (0.81-0.83), and 0.81 (0.79-0.82) for one-, three-, and five-year CSS cohorts, respectively, along with a relatively high generalization ability. The XGBoost approach also performed best for the R&M model, with the AUC values of 0.71 (0.64-0.79), 0.79 (0.74-0.86), and 0.89 (0.82-0.95) for one-, three-, and five-year R&M cohorts, respectively. The rankings of predictor importance for the CSS and R&M models were different, and the higher model accuracy was associated with more prognostic predictors.

Conclusion

Three different ML algorithms for developing prognostic prediction models for non-metastatic CRC were compared. The predictive performance results showed that the nonlinear XGBoost approach performed best, suggesting that it can be used for quantifying the prognostic risk. It was also demonstrated that the model performance can be improved when more prognostic predictors are considered.

Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy

Purpose

This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT).

Methods

We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool.

Results

In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005-1.172; p = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115-0.486; p < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028-1.155; p = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860-0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did "treat all" or "treat none" strategies.

Conclusion

The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT.

Development and Assessment of a Clinical Calculator for Estimating the Likelihood of Recurrence and Survival Among Patients With Locally Advanced Rectal Cancer Treated With Chemotherapy, Radiotherapy, and Surgery

IMPORTANCE

Predicting outcomes in patients receiving neoadjuvant therapy for rectal cancer is challenging because of tumor downstaging. Validated clinical calculators that can estimate recurrence-free survival (RFS) and overall survival (OS) among patients with rectal cancer who have received multimodal therapy are needed.

OBJECTIVE

To develop and validate clinical calculators providing estimates of rectal cancer recurrence and survival that are better for individualized decision-making than the American Joint Committee on Cancer (AJCC) staging system or the neoadjuvant rectal (NAR) score.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study developed risk models, graphically represented as nomograms, for patients with incomplete pathological response using Cox proportional hazards and multivariable regression analyses with restricted cubic splines. Because patients with complete pathological response to neoadjuvant therapy had uniformly favorable outcomes, their predictions were obtained separately. The study included 1400 patients with stage II or III rectal cancer who received treatment with chemotherapy, radiotherapy, and surgery at 2 comprehensive cancer centers (Memorial Sloan Kettering [MSK] Cancer Center and Siteman Cancer Center [SCC]) between January 1, 1998, and December 31, 2017. Patients from the MSK cohort received chemoradiation, surgery, and adjuvant chemotherapy from January 1, 1998, to December 31, 2014; these patients were randomly assigned to either a model training group or an internal validation group. Models were externally validated using data from the SCC cohort, who received either chemoradiation, surgery, and adjuvant chemotherapy (chemoradiotherapy group) or short-course radiotherapy, consolidation chemotherapy, and surgery (total neoadjuvant therapy with short-course radiotherapy group) from January 1, 2009, to December 31, 2017. Data were analyzed from March 1, 2020, to January 10, 2021.

EXPOSURES

Chemotherapy, radiotherapy, chemoradiotherapy, and surgery.

MAIN OUTCOMES AND MEASURES

Recurrence-free survival and OS were the outcome measures, and the discriminatory performance of the clinical calculators was measured with concordance index and calibration plots. The ability of the clinical calculators to predict RFS and OS was compared with that of the AJCC staging system and the NAR score. The models for RFS and OS among patients with incomplete pathological response included postoperative pathological tumor category, number of positive lymph nodes, tumor distance from anal verge, and large- and small-vessel venous and perineural invasion; age was included in the risk model for OS. The final clinical calculators provided RFS and OS estimates derived from Kaplan-Meier curves for patients with complete pathological response and from risk models for patients with incomplete pathological response.

RESULTS

Among 1400 total patients with locally advanced rectal cancer, the median age was 57.8 years (range, 18.0-91.9 years), and 863 patients (61.6%) were male, with tumors at a median distance of 6.7 cm (range, 0-15.0 cm) from the anal verge. The MSK cohort comprised 1069 patients; of those, 710 were assigned to the model training group and 359 were assigned to the internal validation group. The SCC cohort comprised 331 patients; of those, 200 were assigned to the chemoradiotherapy group and 131 were assigned to the total neoadjuvant therapy with short-course radiotherapy group. The concordance indices in the MSK validation data set were 0.70 (95% CI, 0.65-0.76) for RFS and 0.73 (95% CI, 0.65-0.80) for OS. In the external SCC data set, the concordance indices in the chemoradiotherapy group were 0.71 (95% CI, 0.62-0.81) for RFS and 0.72 (95% CI, 0.59-0.85) for OS; the concordance indices in the total neoadjuvant therapy with short-course radiotherapy group were 0.62 (95% CI, 0.49-0.75) for RFS and 0.67 (95% CI, 0.46-0.84) for OS. Calibration plots confirmed good agreement between predicted and observed events. These results compared favorably with predictions based on the AJCC staging system (concordance indices for MSK validation: RFS = 0.69 [95% CI, 0.64-0.74]; OS = 0.67 [95% CI, 0.58-0.75]) and the NAR score (concordance indices for MSK validation: RFS = 0.56 [95% CI, 0.50-0.63]; OS = 0.56 [95% CI, 0.46-0.66]). Furthermore, the clinical calculators provided more individualized outcome estimates compared with the categorical schemas (eg, estimated RFS for patients with AJCC stage IIIB disease ranged from 7% to 68%).

CONCLUSIONS AND RELEVANCE

In this prognostic study, clinical calculators were developed and validated; these calculators provided more individualized estimates of the likelihood of RFS and OS than the AJCC staging system or the NAR score among patients with rectal cancer who received multimodal treatment. The calculators were easy to use and applicable to both short- and long-course radiotherapy regimens, and they may be used to inform surveillance strategies and facilitate future clinical trials and statistical power calculations.

Lymph node status and its impact on the prognosis of left-sided and right-sided colon cancer: A SEER population-based study

Background

Some significant differences exist between the outcomes of left‐ and right‐sided colon cancer patients. The presence of nodal metastases is a critical prognostic factor, especially in the absence of distant metastasis. Our research studied the lymph nodes status of left‐ and right‐sided colon cancer patients to determine the influence of this factor on prognosis.

Methods

Our data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. We used the chi‐square test to analyze the clinicopathological characteristics. The X‐tile program was adopted to acquire optimal cutoff points of lymph node index. Kaplan–Meier curves were used to analyze prognosis and multivariate Cox regression models were performed to identify the independent factors associated with survival. Nomograms were built to predict the overall survival of patients, Harrell's C‐index and calibration plots were used to validate the nomograms.

Results

The study included 189,941 patients with colon cancer without metastasis (left 69,885, right 120,056) between 2004 and 2015. There are more patients with adequate examined lymph nodes in right‐sided. Lymph node status in patients with right colon cancer has a more significant impact on the risk of death. LODDS (C‐index: 0.583; AIC: 6875.4) was used to assess lymph node status. The nomograms showed that lymph node status was the main factor to predict the outcome in right‐sided colon patients.

Conclusions

The influence of lymph node status on predicting prognosis is significantly different between patients with left and right colon cancer without metastasis. The tumor site needs to be considered when lymph node status is used to assess the outcome of patients.

Survival impact of concurrent chemoradiotherapy for elderly patients with synchronous oligometastatic esophageal squamous cell carcinoma: A propensity score matching and landmark analyses

PURPOSE

To evaluate the potential benefits of concurrent chemoradiotherapy (CCRT), and to establish a nomogram for predicting survival outcomes of elderly patients with synchronous oligometastatic esophageal squamous cell carcinoma (SOEC).

MATERIALS AND METHODS

This study eventually enrolled 314 elderly patients who initially diagnosed with SOEC from two centers. Treatment responses and outcomes of 151 patients receiving CCRT and 163 patients undergoing chemotherapy alone (CT) were compared. Propensity score matching and landmark analyses were performed to control potential confounding factors. A nomogram was established on the basis of the Cox regression model.

RESULTS

After a median follow-up of 42.3 months, CCRT was superior to CT alone in objective response rate (ORR, 59.6% vs. 39.9%, P < 0.001), median progression-free survival (PFS, 10.0 vs. 7.2 months, P < 0.001), and median overall survival (OS, 18.5 vs. 15.6 months, P < 0.001). The propensity score matching (PSM) and landmark analyses redemonstrated the same trend (P < 0.01). On hierarchical analysis, patients with 1-3 metastatic lesions involving one organ displayed longer median PFS (9.0 vs. 7.8 months, P = 0.008) and OS (17.8 vs. 15.2 months, P < 0.001) than those with 4-5 metastatic lesions involving 2-3 organs. The major toxicities of grade III or higher for CCRT included leukocytopenia (23.2%), radiation esophagitis (7.3%), and radiation pneumonitis (8.6%). Cox multivariate analysis showed that the number of metastatic lesions (P = 0.012) and tumor response (P < 0.001) were independent prognostic factors associated with OS. A nomogram was established by incorporating the number of metastatic lesions and tumor response, with a concordance index of 0.743 after internal cross-validation. Calibration curves and decision curve analysis confirmed that nomogram had a favorable predictive value for individualized survival.

CONCLUSIONS

Compared with CT alone, CCRT exhibited superior efficacy and acceptable toxicity in the first-line treatment for elderly patients with SOEC. The current study supports the oligometastatic definition of ≤3 metastatic lesions involving one organ for esophageal cancer patients. The constructed nomogram can effectively predict the individualized survival.

Accurate population-based model for individual prediction of colon cancer recurrence

BACKGROUND

Prediction models are useful tools in the clinical management of colon cancer patients, particularly when estimating the recurrence rate and, thus, the need for adjuvant treatment. However, the most used models (MSKCC, ACCENT) are based on several decades-old patient series from clinical trials, likely overestimating the current risk of recurrence, especially in low-risk groups, as outcomes have improved over time. The aim was to develop and validate an updated model for the prediction of recurrence within 5 years after surgery using routinely collected clinicopathologic variables.

MATERIAL AND METHODS

A population-based cohort from the Swedish Colorectal Cancer Registry of 16,134 stage I-III colon cancer cases was used. A multivariable model was constructed using Cox proportional hazards regression. Three-quarters of the cases were used for model development and one quarter for internal validation. External validation was performed using 12,769 stage II-III patients from the Norwegian Colorectal Cancer Registry. The model was compared to previous nomograms.

RESULTS

The nomogram consisted of eight variables: sex, sidedness, pT-substages, number of positive and found lymph nodes, emergency surgery, lymphovascular and perineural invasion. The area under the curve (AUC) was 0.78 in the model, 0.76 in internal validation, and 0.70 in external validation. The model calibrated well, especially in low-risk patients, and performed better than existing nomograms in the Swedish registry data. The new nomogram's AUC was equal to that of the MSKCC but the calibration was better.

CONCLUSION

The nomogram based on recently operated patients from a population registry predicts recurrence risk more accurately than previous nomograms. It performs best in the low-risk groups where the risk-benefit ratio of adjuvant treatment is debatable and the need for an accurate prediction model is the largest.

Predicting survival in colorectal carcinoma after curative resection: a new prognostic nomogram

Several prognostic nomograms designed to predict survival after curative resection for colorectal cancer (CRC) have been proposed. Recently, routine pathological assessment has evolved with subtle changes to the AJCC staging system, and routine screening for mismatch repair deficiency (MMRd). Therefore we sought to develop and validate a new prognostic nomogram. All cause survival data from 4517 consecutive patients with primary CRC were used as independent training and validation cohorts to develop a final model including only: age, sex, tumour stage, nodal status, number of lymph nodes resected, apical node status, distant metastases, thin-walled vascular invasion, and MMR status. Patients were stratified into four risk groups to assess model discrimination and calibration. To assess discrimination, the area-under-the-curve (AUC) of a receiver-operator-curve (ROC), concordance-index (C-index), and D-index were calculated. The model was compared to the Memorial Sloan Kettering Cancer Center (MSKCC) CRC nomogram and the AJCC TNM staging. Based on the 5-year ROC analysis, the AUC for our model was 0.81 (0.79 and 0.74 for MSKCC and AJCC, respectively). Moreover, our model demonstrated a concordance index of 0.77 (95% CI 0.70-0.82) compared to 0.75 (95% CI 0.68-0.81) for MSKCC and 0.73 (95% CI 0.65-0.79) for AJCC. In conclusion, our new prognostic nomogram incorporates a larger number of clinically relevant prognostic markers, including MMR status, and therefore demonstrates improved predictive capability. As these factors are routinely assessed, it is hoped that this model will inform prognostication and difficult management decisions, such as patient selection for adjuvant therapy.