Joint Modelling of Colorectal Cancer Recurrence and Death after resection using Multi-State Model with Cured Fraction

Alafchi, Behnaz; Roshanaei, Ghodratollah; Tapak, Leili; Abasi, Mohammad

doi:10.21203/rs.2.15882/v1

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…[26], some studies reported similar results [27][28]. Also, in some studies, age was signi cantly associated with local recurrence and distance [29][30] [31]. Also, according to the results of several studies, age is signi cantly associated with the survival of patients with colorectal cancer.…”

Section: Discussionmentioning

confidence: 83%

A Bayesian Approach to Model the Underlying Predictors of Early Recurrence and Postoperative Death in Patients with Colorectal Cancer

Mahmoudi

Fallah

Roshanaei

et al. 2022

Preprint

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Objective: This study, aimed at using a Bayesian approach semi-competing risks technique to model the underlying predictors of early recurrence and postoperative death in patients with colorectal cancer (CRC).Methods: This study was a retrospective cohort of 284 patients with colorectal cancer who underwent surgery referred to Imam Khomeini clinic in Hamadan from 2001 to 2017. The primary outcomes were the probability of recurrence, the probability of death without recurrence, and the probability of death after recurrence. The patients 'recurrence status was determined from patients' records. The Bayesian survival modeling was carried out by semi-competing risks illness-death models in R 4.1 softwareResults: The results showed that gender(TR = 0.764 , 95% CI: 0.456 - 0.855), age at diagnosis (TR =0.764 , 95 % CI: 0.538 - 0.935 ), T 3 stage (TR = 0601 , 95 % CI : 0.530 - 0.713) , N2 stage ( TR = 0.714 , 95 % CI : 0.577 - 0.935 ) , tumor size (TR = 0.709 , 95 % CI : 0.610 - 0.929), Grade (differentiation level) (TR = 0.856 , 95 % CI: 0.733 - 0.988 for poor level , TR = 0.648 , 95 % CI : 0.503 - 0.955 for moderate level) and number of chemotherapy (TR =1.583 , 95 % CI: 1.367 - 1.863) was significantly related with recurrence . Also, age at diagnosis (TR= 0.396 , 95% CI : 0.313 - 0.532) , metastasis to other sites (TR = 0.566 , 95% CI : 0.490 - 0.835) , T3 stage (TR = 0.363 , 95 % CI : 0.592 - 0.301), T4 stage (TR = 0.434 , 95 % CI : 0.347 - 0.545) , Grade (differentiation level) at moderate level (TR = 0.527 , 0.95 %CI : 0.387 - 0.674), tumor size (TR = 0.595 95 % CI : 0.500 - 0.679) and number of chemotherapy (TR= 1.541 , 95% CI : 1.332 - 2.243) was the significantly predicted the death. Also, age at diagnosis (TR= 0.659, 95% CI: 0.559 - 0.803) and number of chemotherapy (TR= 2.029, 95% CI: 1.792 - 2.191) was significantly related to death after recurrence.Conclusion: According to the specific results obtained for terminal and nonterminal events, appropriate screening strategies and the earlier detection of CRC leads to substantial improvements in the survival of patients.

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Section: Discussionmentioning

confidence: 83%

A Bayesian Approach to Model the Underlying Predictors of Early Recurrence and Postoperative Death in Patients with Colorectal Cancer

Mahmoudi

Fallah

Roshanaei

et al. 2022

Preprint

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“…Conversely, evaluating RFS aims to estimate the probability of relapse by considering death as censoring. However, those separate analyses do not provide completely satisfying results due to a failure to reveal associations between relapse and death [12,13]. In contrast, the IDM simultaneously deals with endpoints focusing on transitioning from one state to another.…”

Section: Korean Cancer Associationmentioning

confidence: 99%

“…Several medical articles have estimated event-related predictive probabilities via the IDM in patients with breast cancer [12,14,15], ovarian cancer [16], or colon cancer [13]. However, to our knowledge, the IDM has not been implemented in patients with lung cancer.…”

Section: Korean Cancer Associationmentioning

confidence: 99%

The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer

et al. 2022

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Purpose The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model.Materials and Methods We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model.Results The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model.Conclusion Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.

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“…Although early diagnosis and advancing therapies have greatly improved clinical outcomes, more than half of CRC patients demonstrate recurrence after initial treatments. The 5-year survival rate is only 14% for patients with distant metastasis [1]. Cancer-associated fibroblasts (CAFs) are the major components in the tumor microenvironment (TME), secreting active cytokines and growth factors to remodel the extracellular matrix and accelerate tumor progression [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Cancer‐associated fibroblasts potentiate colorectal cancer progression by crosstalk of the IGF2–IGF1R and Hippo–YAP1 signaling pathways

Zhang

Chen

et al. 2022

The Journal of Pathology

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Colorectal cancer (CRC) is one of the most common cancers worldwide. The tumor microenvironment exerts crucial effects in driving CRC progression. Cancer-associated fibroblasts (CAFs) serve as one of the most important tumor microenvironment components promoting CRC progression. This study aimed to elucidate the novel molecular mechanisms of CAF-secreted insulin-like growth factor (IGF) 2 in colorectal carcinogenesis. Our results indicated that IGF2 was a prominent factor upregulated in CAFs compared with normal fibroblasts. CAF-derived conditioned media (CM) promoted tumor growth, migration, and invasion of HCT 116 and DLD-1 cells. IGF1R expression is significantly increased in CRC, serving as a potent receptor in response to IGF2 stimulation and predicting unfavorable outcomes for CRC patients. Apart from the PI3K-AKT pathway, RNA-seq analysis revealed that the YAP1-target signature serves as a prominent downstream effector to mediate the oncogenic signaling of IGF2-IGF1R. By single-cell RNA sequencing (scRNA-seq) and immunohistochemical validation, IGF2 was found to be predominantly secreted by CAFs, whereas IGF1R was expressed mainly by cancer cells. IGF2 triggers the nuclear accumulation of YAP1 and upregulates YAP1 target signatures; however, these effects were abolished by either IGF1R knockdown or inhibition with picropodophyllin (PPP), an IGF1R inhibitor. Using CRC organoid and in vivo studies, we found that cotargeting IGF1R and YAP1 with PPP and verteporfin (VP), a YAP1 inhibitor, enhanced antitumor effects compared with PPP treatment alone. In conclusion, this study revealed a novel molecular mechanism by which CAFs promote CRC progression. The findings highlight the translational potential of the IGF2-IGF1R-YAP1 axis as a prognostic biomarker and therapeutic target for CRC.

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Joint Modelling of Colorectal Cancer Recurrence and Death after resection using Multi-State Model with Cured Fraction

Cited by 4 publications

References 33 publications

A Bayesian Approach to Model the Underlying Predictors of Early Recurrence and Postoperative Death in Patients with Colorectal Cancer

A Bayesian Approach to Model the Underlying Predictors of Early Recurrence and Postoperative Death in Patients with Colorectal Cancer

The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer

Cancer‐associated fibroblasts potentiate colorectal cancer progression by crosstalk of the IGF2–IGF1R and Hippo–YAP1 signaling pathways

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