Search

We aimed to investigate the effectiveness of oncological treatments in metastatic CRC related to comorbidities and age. This retrospective study included 1105 patients from three oncological centers. aaCCI and CCI was available from 577 patients. An aaCCI &gt, 3 was of the highest predictive value compared to other aaCCI-levels, CCI or age (p &lt, 0.001 for all). Treatment (best supportive care (BSC), systemic treatment only (STO) and resection of metastases (ROM)) significantly prolonged survival in patients with aaCCI &gt, 3 (STO: HR 0.39, CI 0.29–0.51, ROM: HR 0.16, CI 0.10–0.24) and patients older than 70 years (STO: HR 0.56, CI 0.47–0.66, ROM: HR 0.23, 0.18–0.30). Median overall survival was shorter in patients with aaCCI or age &gt, 70 years and interaction for treatment type not significant for aaCCI, but significant for age older or younger than 70 years (STO: p = 0.01, ROM p = 0.02). BSC is more often considered as optimal care for patients with an aaCCI &gt, 3 (37.6% vs. 12.4%, p &lt, 0.001) or age &gt, 70 years (35.7% vs. 11.2%, 0.001). Older patients or patients with comorbidities benefit from cancer-specific therapy independently of their age and comorbidities.

Background Metastatic colorectal cancer (mCRC) remains a lethal disease. Survival, however, is increasing due to a growing number of treatment options. Yet due to the number of prognostic factors and their interactions, prediction of mortality is difficult. The aim of this study is to provide a clinical model supporting prognostication of mCRC mortality in daily practice. Methods Data from 1104 patients with mCRC in three prospective cancer datasets were used to construct and validate Cox models. Input factors for stepwise backward method variable selection were sex, RAS/BRAF-status, microsatellite status, treatment type (no treatment, systemic treatment with or without resection of metastasis), tumor load, location of primary tumor, metastatic patterns and synchronous or metachronous disease. The final prognostic model for prediction of survival at two and 3 years was validated via bootstrapping to obtain calibration and discrimination C-indices and dynamic time dependent AUC. Results Age, sidedness, number of organs with metastases, lung as only site of metastasis, BRAF mutation status and treatment type were selected for the model. Treatment type had the most prominent influence on survival (resection of metastasis HR 0.26, CI 0.21–0.32; any treatment vs no treatment HR 0.31, CI 0.21–0.32), followed by BRAF mutational status (HR 2.58, CI 1.19–1.59). Validation showed high accuracy with C-indices of 72.2 and 71.4%, and dynamic time dependent AUC’s of 76.7 ± 1.53% (both at 2 or 3 years), respectively. Conclusion The mCRC mortality prediction model is well calibrated and internally valid. It has the potential to support both, clinical prognostication for treatment decisions and patient communication.

Activin A is involved in the regulation of a surprisingly broad number of processes that are relevant for cancer development and treatment; it is implicated in cell autonomous functions and multiple regulatory functions in the tumor microenvironment.This article summarizes the current knowledge about activin A in cell growth and death, migration and metastasis, angiogenesis, stemness and drug resistance, regulation of antitumor immunity, and cancer cachexia. We explore the role of activin A as a biomarker and discuss strategies for using it as target for cancer therapy. Literature retrieved from Medline until 25 June 2020 was considered.While many functions of activin A were investigated in preclinical models, there is currently limited experience from clinical trials. Activin A has growth- and migration-promoting effects, contributes to immune evasion and cachexia and is associated with shorter survival in several cancer types. Targeting activin A could offer the chance to simultaneously limit tumor growth and spreading, improve drug response, boost antitumor immune responses and improve cancer-associated or treatment-associated cachexia, bone loss, and anemia. Nevertheless, defining which patients have the highest likelihood of benefiting from these effects is challenging and will require further work.