EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3)

Introduction: There is no standard treatment after resection of colorectal liver metastases and the role of systemic therapy remains controversial. To avoid over- or undertreatment, proper risk stratification with regard to postoperative treatment strategy is highly needed. We recently demonstrated the prognostic relevance of EMT-related (epithelial-mesenchymal transition) genes in stage II/III CRC. As EMT is a major step in CRC progression, we now aimed to analyse the prognostic relevance of EMT-related genes in stage IV CRC using the study cohort of the FIRE-3 trial, an open-label multi-centre randomised controlled phase III trial of patients with metastatic CRC. Methods: Overall and progression free survival were considered as endpoints (n = 350). To investigate the prognostic relevance of EMT-related genes on either endpoint, we compared predictive performance of different models using clinical data only to models using gene data in addition to clinical data, expecting better predictive performance if EMT-related genes have prognostic value. In addition to baseline models (Kaplan Meier (KM), (regularised) Cox), Random Survival Forest (RSF), and gradient boosted trees (GBT) were fit to the data. Repeated, nested five-fold cross-validation was used for hyperparameter optimisation and performance evaluation. Predictive performance was measured by the integrated Brier score (IBS). Results: The baseline KM model showed the best performance (OS: 0.250, PFS: 0.251). None of the other models were able to outperform the KM when using clinical data only according to the IBS scores (OS: 0.253 (Cox), 0.256 (RSF), 0.284 (GBT); PFS: 0.254 (Cox), 0.256 (RSF), 0.276 (GBT)). When adding gene data, performance of GBT improved slightly (OS: 0.262 vs. 0.284; PFS: 0.268 vs. 0.276), however, none of the models performed better than the KM baseline. Conclusion: Overall, the results suggest that the prognostic relevance of EMT-related genes may be stage-dependent and that EMT-related genes have no prognostic relevance in stage IV CRC

Mutational profiles of metastatic colorectal cancer treated with FOLFIRI plus cetuximab or bevacizumab before and after secondary resection (AIO KRK 0306; FIRE-3)

Secondary resection of metastases is recommended in metastatic colorectal cancer (mCRC). Data describing changes in mutational profiles of corresponding primary tumor and metastatic tissue after conversion treatment are limited. Next generation sequencing was performed in formalin-fixed mCRC samples from patients of the FIRE-3 trial (FOLFIRI plus cetuximab or bevacizumab) before treatment start (baseline) and after secondary resection of metastases (post baseline). Changes of mutational profiles and tumor mutational burden (TMB) were assessed within a post-hoc analysis. Median overall survival (OS), progression-free survival (PFS) and objective response rate (ORR) were compared between treatment arms. Paired tumor samples were obtained from 25 patients (19 RAS wild-type, 6 RAS mutant by pyrosequencing). ORR (92.0% vs 58.0%) and OS (60.8 vs 35.4 months, hazard ratio = 0.39 [95% CI 0.14-1.12], P = .08) were higher for patients receiving cetuximab. After conversion therapy, 56 alterations (42 in the cetuximab and 14 in the bevacizumab arm) were newly observed in 18 patients (9 each treated with cetuximab or bevacizumab). Gains (n = 21) and losses (n = 21) of alterations occurred during cetuximab-based treatment, while mainly gains of alterations occurred during bevacizumab (n = 10). Three of nine patients treated with cetuximab that presented a change of mutational profiles, developed resistance to cetuximab. Mutational profiles were largely comparable before and after treatment with anti-VEGF or anti-EGFR directed monoclonal antibodies after secondary resection. Mutations associated with resistance to anti-EGFR antibodies were observed in only one-third of patients

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Proteomics uncover EPHA2 as a potential novel therapeutic target in colorectal cancer cell lines with acquired cetuximab resistance

Background In metastatic colorectal cancer (mCRC), acquired resistance against anti-EGFR targeted monoclonal antibodies, such as cetuximab (CET), was shown to be frequently caused by activating alterations in the RAS genes KRAS or NRAS. To this day, no efficient follow-up treatment option has emerged to treat mCRC in such a setting of resistance. Methods To uncover potential targets for second-line targeted therapies, we used mass-spectrometric proteomics to shed light on kinome reprogramming in an established cellular model of acquired, KRAS-associated CET resistance. Results This CET resistance was reflected by significant changes in the kinome, most of them individual to each cell line. Interestingly, all investigated resistant cell lines displayed upregulation of the Ephrin type-A receptor 2 (EPHA2), a well-known driver of traits of progression. Expectedly resistant cell lines displayed increased migration (p < 0.01) that was significantly reduced by targeting the EPHA2 signalling axis using RNA interference (RNAi) (p < 0.001), ephrin-A1 stimulation (p < 0.001), dasatinib (p < 0.01), or anti-EPHA2 antibody treatment (p < 0.001), identifying it as an actionable target in mCRC with acquired CET resistance. Conclusion These results highlight EPHA2 and its role in mCRC with KRAS-gene mutated acquired CET resistance and support its use as a potential actionable target for the development of future precision medicine therapies

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.ISSN:1361-8415ISSN:1361-842