Activated signaling pathways and targeted therapies in desmoid-type fibromatosis: A literature review

Desmoid-type fibromatosis (DTF) is a rare, soft tissue tumor of mesenchymal origin which is characterized by local infiltrative growth behavior. Besides "wait and see," surgery and radiotherapy, several systemic treatments are available for symptomatic patients. Recently, targeted therapies are being explored in DTF. Unfortunately, effective treatment is still hampered by the limited knowledge of the molecular mechanisms that prompt DTF tumorigenesis. Many studies focus on Wnt/b-catenin signaling, since the vast majority of DTF tumors harbor a mutation in the CTNNB1 gene or the APC gene. The established role of the Wnt/b-catenin pathway in DTF forms an attractive therapeutic target, however, drugs targeting this pathway are still in an experimental stage and not yet available in the clinic. Only few studies address other signaling pathways which can drive uncontrolled growth in DTF such as: JAK/STAT, Notch, PI3 kinase/AKT, mTOR, Hedgehog, and the estrogen growth regulatory pathways. Evidence for involvement of these pathways in DTF tumorigenesis is limited and predominantly based on the expression levels of key pathway genes, or on observed clinical responses after targeted treatment. No clear driver role for these pathways in DTF has been identified, and a rationale for clinical studies is often lacking. In this review, we highlight common signaling pathways active in DTF and provide an up-to-date overview of their therapeutic potential

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types. Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1- weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset. Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74. Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

Background: Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI. Methods: Patients with an MDM2-negative lipoma or MDM2-positive WDLPS and a pretreatment T1-weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random-split cross-validation. The performance of the models wa

Active surveillance in desmoid-type fibromatosis: A systematic literature review

Background: This study evaluates the results of the active surveillance (AS) approach in adult patients with desmoid-type fibromatosis (DTF) because AS is advocated as a front-line approach for DTF in the European consensus guidelines. Methods: A systematic literature search was conducted (December 19th, 2019, updated on April 14th, 2020). Studies describing the outcomes of the AS approach were included. The PRISMA guidelines were used. Results: Twenty-five articles were included for data retrieval. Forty-two percent of reported patients (1480 of 3527 patients) received AS, the majority were women and the majority had a primary tumour. The median age at diagnosis ranged from 28 to 59 years. Common tumour sites were the extremities/girdles (n = 273), the abdominal wall (n = 253) and the trunk (n = 153). The median reported percentage of progressive disease, stable disease and partial response was 20% (interquartile range [IQR]: 13–35%), 59% (IQR: 37–69%) and 19% (IQR 3–23%), respectively. In 640 patients, the outcome was not specified. The median reported percentage of shifting to an active form of treatment was 29%, most commonly to systemic treatment (n = 195) and surgery (n = 107). The reported median follow-up time ranged between 8 and 73 months. The reported median time to progression and/or initiation of the subgroup shifting from AS to ‘active’ therapy ranged from 6.3 months to 19.7 months. Conclusion: The majority of patients undergoing AS have either stable disease or a partial response, and about one-third of patients shift to an active form of treatment. Selecting patients who will benefit from active surveillance upfront should be the priority of future studies