Nomograms integrating the collagen signature and systemic immune-inflammation index for predicting prognosis in rectal cancer patients.

BACKGROUND: This study aimed to develop and validate a model based on the collagen signature and systemic immune-inflammation index to predict prognosis in rectal cancer patients who underwent neoadjuvant treatment. METHODS: Patients with rectal cancer who had residual disease after neoadjuvant treatment at two Chinese institutions between 2010 and 2018 were selected, one used as a training cohort and the other as a validation cohort. In total, 142 fully quantitative collagen features were extracted using multiphoton imaging, and a collagen signature was generated by least absolute shrinkage and selection operator Cox regression. Nomograms were developed by multivariable Cox regression. The performance of the nomograms was assessed via calibration, discrimination and clinical usefulness. The outcomes of interest were overall survival and disease-free survival calculated at 1, 2 and 3 years. RESULTS: Of 559 eligible patients, 421 were selected (238 for the training cohort and 183 for the validation cohort). The eight-collagen-features collagen signature was built and multivariable Cox analysis demonstrated that it was an independent prognostic factor of prognosis along with the systemic immune-inflammation index, lymph node status after neoadjuvant treatment stage and tumour regression grade. Then, two nomograms that included the four predictors were computed for disease-free survival and overall survival. The nomograms showed satisfactory discrimination and calibration with a C-index of 0.792 for disease-free survival and 0.788 for overall survival in the training cohort and 0.793 for disease-free survival and 0.802 for overall survival in the validation cohort. Decision curve analysis revealed that the nomograms could add more net benefit than the traditional clinical-pathological variables. CONCLUSIONS: The study found that the collagen signature, systemic immune-inflammation index and nomograms were significantly associated with prognosis.

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer.

Importance: The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. Objective: To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. Design, Setting, and Participants: This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. Main Outcomes and Measures: The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. Results: This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). Conclusions and Relevance: These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.

BACKGROUND: Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. METHODS: Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. RESULTS: By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations. CONCLUSIONS: This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Use of patient-derived tumor organoid platform to predict the benefit of postoperative adjuvant chemotherapy for poor responders to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Postoperative adjuvant chemotherapy (AC) for poor responders to neoadjuvant chemoradiotherapy (nCRT) remains debatable among patients with locally advanced rectal cancer (LARC), necessitating biomarkers to accurately predict the benefits of AC. This study aimed to develop a patient-derived tumor organoid (PDTO) platform to predict the benefit of AC in LARC patients showing poor nCRT response. PDTOs were established using irradiated rectal cancer specimens with poor nCRT responses, and their sensitivity to chemotherapy regimens was tested. The half-maximal inhibitory concentration (IC50) value for the PDTO drug test was defined based on the clinical outcomes, and the accuracy of the PDTO prognostic predictions was calculated. Predictive models were developed and validated using the PDTO drug test results. Between October 2018 and December 2021, 86 PDTOs were successfully constructed from 138 specimens (success rate 62.3%). The optimal IC50 cut-off value for the organoid drug test was 39.31 µmol/L, with a sensitivity of 84.75%, a specificity of 85.19%, and an accuracy of 84.88%. Multivariate Cox regression analysis revealed that the PDTO drug test was an independent predictor of prognosis. A nomogram based on the PDTO drug test was developed, showing good prognostic ability in predicting the 2-year and 3-year disease-free survivals (AUC of 0.826 [95% CI, 0.721-0.931] and 0.902 [95% CI, 0.823-0.982], respectively) and overall survivals (AUC of 0.859 [95% CI, 0.745-0.973] and 0.885 [95% CI, 0.792-0.978], respectively). The PDTO drug test can predict the benefit of postoperative AC in poor responders with LARC to nCRT.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer.

Objectives: The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore. Methods: A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method. Results: The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients. Conclusions: The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Cutoff value of IC50 for drug sensitivity in patient-derived tumor organoids in colorectal cancer.

Patient-derived tumor organoids (PDTOs) have the potential to be used to predict the patient response to chemotherapy. However, the cutoff value of the half-maximal inhibition concentration (IC50) for PDTO drug sensitivity has not been validated with clinical cohort data. We established PDTOs and performed a drug test in 277 samples from 242 CRC patients who received FOLFOX or XELOX chemotherapy. After follow-up and comparison of the PDTO drug test and final clinical outcome results, the optimal IC50 cutoff value for PDTO drug sensitivity was 43.26 µmol/L. This PDTO drug test-defined cutoff value could predict patient response with 75.36% sensitivity, 74.68% specificity, and 75% accuracy. Moreover, this value distinguished groups of patients with significant differences in survival benefit. Our study is the first to define the IC50 cutoff value for the PDTO drug test to effectively distinguish CRC patients with chemosensitivity or nonsensitivity and predict survival benefits.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer.

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagenDL classifier, rather than low-collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Development and validation of a collagen signature to predict the prognosis of patients with stage II/III colorectal cancer.

The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CSTME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of "TNM stage + CSTME" with that of TNM stage alone. Results indicated that the CSTME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180-3.962, p < 0.0001), and the integration of the TNM stage and CSTME had a better prognostic value than that of the TNM stage alone (AUC(TNM+CSTME) = 0.772, AUC TNM = 0.687, p < 0.0001). This study provided an application of "seed and soil" strategy for prognosis prediction and individualized therapy.

Patient-Derived Tumor Organoids Can Predict the Progression-Free Survival of Patients With Stage IV Colorectal Cancer After Surgery.

BACKGROUND: Recent studies have shown patient-derived tumor organoids can predict the drug response of patients with cancer. However, the prognostic value of patient-derived tumor organoid-based drug tests in predicting the progression-free survival of patients with stage IV colorectal cancer after surgery remains unknown. OBJECTIVE: This study aimed to explore the prognostic value of patient-derived tumor organoid-based drug tests in patients with stage IV colorectal cancer after surgery. DESIGN: Retrospective cohort study. SETTINGS: Surgical samples were obtained from patients with stage IV colorectal cancer at the Nanfang Hospital. PATIENTS: A total of 108 patients who underwent surgery with successful patient-derived tumor organoid culture and drug testing were recruited between June 2018 and June 2019. INTERVENTIONS: Patient-derived tumor organoid culture and chemotherapeutic drug testing. MAIN OUTCOMES MEASURES: Progression-free survival. RESULTS: According to the patient-derived tumor organoid-based drug test, 38 patients were drug sensitive and 76 patients were drug resistant. The median progression-free survival was 16.0 months in the drug-sensitive group and 9.0 months in the drug resistant group ( p < 0.001). Multivariate analyses showed that drug resistance (HR, 3.38; 95% CI, 1.84-6.21; p < 0.001), right-sided colon (HR, 3.50; 95% CI, 1.71-7.15; p < 0.001), mucinous adenocarcinoma (HR, 2.47; 95% CI, 1.34-4.55; p = 0.004), and non-R0 resection (HR, 2.70; 95% CI, 1.61-4.54; p < 0.001) were independent predictors of progression-free survival. The new patient-derived tumor organoid-based drug test model, which includes the patient-derived tumor organoid-based drug test, primary tumor location, histological type, and R0 resection, was more accurate than the traditional clinicopathological model in predicting progression-free survival ( p = 0.001). LIMITATIONS: A single-center cohort study. CONCLUSIONS: Patient-derived tumor organoids can predict progression-free survival in patients with stage IV colorectal cancer after surgery. Patient-derived tumor organoid drug resistance is associated with shorter progression-free survival, and the addition of patient-derived tumor organoid drug tests to existing clinicopathological models improves the ability to predict progression-free survival.

Indocyanine green for radical lymph node dissection in patients with sigmoid and rectal cancer: randomized clinical trial.

BACKGROUND: D3 lymph node dissection is recommended for patients with advanced sigmoid and rectal cancer in Japan. This trial aimed to investigate the feasibility of indocyanine green (ICG) as a tracer to increase the nodal harvest during D3 lymph node dissection in patients with sigmoid and rectal cancer. METHODS: This prospective randomized clinical trial was performed between May 2021 and April 2022. The inclusion criteria were patients with stage I-III sigmoid or rectal cancer eligible for laparoscopic resection. Patients were 1: 1 randomized to either the ICG group (endoscopic ICG injection at the tumour site and intraoperative imaging to guide dissection) or the control group (routine laparoscopic white-light imaging). All patients were treated with D3 dissection, and the primary outcome was the number of harvested lymph nodes at the D3 level. RESULTS: Out of 210 patients screened, a total of 66 patients were enrolled and randomized. Patients in the two groups presented similar ages and clinical stages (ICG group versus control group, median age of 58.0 versus 58.5 years; stage III 36.4 per cent versus 36.4 per cent, whereas the rate of rectal cancer was 27.3 per cent versus 48.5 per cent respectively). ICG imaging was helpful for completely dissecting D3 lymph nodes and could identify a median of more than 2 (range 1-6) D3 lymph nodes neglected by routine laparoscopic white-light imaging during surgery. The median number of D3 lymph nodes harvested in the ICG group was significantly higher than that in the control group (7.0 versus 5.0, P = 0.003); however, there was no significant difference in the median numbers of positive D1, D2, and D3 lymph nodes between the two groups. CONCLUSION: ICG is safe and feasible to guide D3 lymph node dissection and can increase the number of harvested D3 lymph nodes in patients with sigmoid and rectal cancer. Registration number: NCT04848311 (http://www.clinicaltrials.gov).

Preoperative carbon nanoparticle injection improves inferior mesenteric artery lymph node retrieval in patients with rectal cancer.

BACKGROUND: Inferior mesenteric artery lymph node (station 253 node) metastasis occurs in approximately 0.3% to 13.9% of rectal cancer patients. This study examined whether carbon nanoparticles could aid in harvesting more station 253 nodes and evaluated the relationship between the number of station 253 nodes retrieved and station 253 node metastasis. METHOD: A total of 480 consecutive rectal cancer patients were enrolled in this prospective cohort study between August 2014 and October 2018. Ninety-one patients (18.96%) received a preoperative submucosal injection of carbon nanoparticles (CN+ group), and 389 patients did not receive an injection (CN- group). The number of lymph node retrievals was analyzed, and the relevant risk factors for station 253 node metastasis were identified using univariate and multivariate analyses. RESULTS: The mean number of station 251, 252, and 253 lymph nodes and total lymph nodes retrieved in the CN+ group were higher than that retrieved in the CN- group. The percentage of patients with ≥4 station 253 nodes retrieved (54.0% vs 28.3%, P = .004) were higher in the CN+ group than in the CN- group. Retrieval of ≥4 station 253 nodes was an independent risk factor for station 253 node metastasis (OR: 2.40, 95% CI: 1.22-4.74, P = .012). CONCLUSION: The preoperative submucosal injection of carbon nanoparticles was helpful for increasing the number of station 253 nodes harvested, and a minimum of 4 examined station 253 nodes was necessary for standard D3 lymph node dissection in rectal cancer.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.

BACKGROUND: The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. METHODS: In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. RESULTS: The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. CONCLUSIONS: The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.