Dabrafenib plus trametinib in BRAFV600E-mutated rare cancers: the phase 2 ROAR trial

BRAFV600E alterations are prevalent across multiple tumors. Here we present final efficacy and safety results of a phase 2 basket trial of dabrafenib (BRAF kinase inhibitor) plus trametinib (MEK inhibitor) in eight cohorts of patients with BRAFV600E-mutated advanced rare cancers: anaplastic thyroid carcinoma (n = 36), biliary tract cancer (n = 43), gastrointestinal stromal tumor (n = 1), adenocarcinoma of the small intestine (n = 3), low-grade glioma (n = 13), high-grade glioma (n = 45), hairy cell leukemia (n = 55) and multiple myeloma (n = 19). The primary endpoint of investigator-assessed overall response rate in these cohorts was 56%, 53%, 0%, 67%, 54%, 33%, 89% and 50%, respectively. Secondary endpoints were median duration of response (DoR), progression-free survival (PFS), overall survival (OS) and safety. Median DoR was 14.4 months, 8.9 months, not reached, 7.7 months, not reached, 31.2 months, not reached and 11.1 months, respectively. Median PFS was 6.7 months, 9.0 months, not reached, not evaluable, 9.5 months, 5.5 months, not evaluable and 6.3 months, respectively. Median OS was 14.5 months, 13.5 months, not reached, 21.8 months, not evaluable, 17.6 months, not evaluable and 33.9 months, respectively. The most frequent (≥20% of patients) treatment-related adverse events were pyrexia (40.8%), fatigue (25.7%), chills (25.7%), nausea (23.8%) and rash (20.4%). The encouraging tumor-agnostic activity of dabrafenib plus trametinib suggests that this could be a promising treatment approach for some patients with BRAFV600E-mutated advanced rare cancers. ClinicalTrials.gov registration: NCT02034110 .

Abstract PO-021: Humans cannot accurately detect mucinous colorectal carcinoma from CT images, can AI help?

Background: Mucinous colorectal carcinoma (CRC) is found in 10-20% of patients and is associated with worse prognosis and treatment resistance. The early identification of mucinous tumor component at baseline and monitoring resistant clones at follow-up is challenging in clinical practice, which hinders appropriate and timely treatment selection. At CT, being routinely acquired in clinical practice, mucinous tumors can be characterized by semantic features, such as hypoattenuation and more heterogeneous enhancement than the non-mucinous tumors (Wnorowski et al 2019). However, the diagnostic accuracy of such CT findings reaches at most 62% (Young et al 2007). This can be substantially improved by utilizing robust feature quantification using state-of-art machine learning and neural network techniques. Materials and Methods: 7 mucinous and 7 non-mucinous CRC CTs were included in the model development (80% training and 20% validation) and 2 mucinous and 2 non-mucinous independent patients were used to test the model performance. Multiple lesions (primary and metastatic) were semi-automatically segmented in 3D Slicer (N=32 development and N=12 test). Three different classification models were generated using CT images: (1) a logistic regression model based on a newly developed hypodense tissue connectivity (HTC) metric, (2) a logistic regression model using a set of automatically selected radiomics (RAD) features (shape, 1st order and 2nd order) and (3) a convolutional neural network model (CNN) based on the ResNet architecture and automatically selected features. HTC was computed as a ratio between the volume of the connected hypodense tissue (0<HU<30) and total tumor volume. CT images were converted to 2D axial sections to increase the input size for CNN (resulting in 952 images). Class weights were used to mitigate the effect of in-balance between the number of mucinous and non-mucinous images. Performance of the model was quantified with Accuracy (Acc), Sensitivity (Sen), Specificity (Spec) and AUC. Results: The classification performance of all three models was first assessed using 10-fold cross validation, where the RAD and CNN models performed better than HTC model. (Mean±std) RAD: Acc 0.9±0.15, Sen 0.92±0.15, Spec 0.9±0.2, AUC 0.95±0.13; CNN: Acc 0.93±0.05, Sen 0.93±0.03, Spec 0.93±0.19, AUC 0.95±0.01; HTC: Acc 0.72±0.23, Sen 0.7±0.4, Spec 0.75±0.33, AUC 0.84±0.32. However, the situation changed in test set, where HTC and CNN models provide the best results. RAD: Acc 0.67, Sen 1.0, Spec 0, AUC 0.5; CNN: Acc 0.97, Sen 0.98, Spec 0.96, AUC 0.97; HTC: Acc 0.92, Sen 1.0, Spec 0.75, AUC 1.0. Conclusions: All studied models improved the accuracy of mucinous lesion identification compared to the literature reported value. Further method development and validation on larger multi-center cohorts would allow gaining confidence in models applicability in the clinical setting.

Citation Format: Kinga Bernatowicz, Raquel Perez Lopez, Hector Garcia Palmer, Elena Elez Fernandez, Jose Fernandez Navarro, Marta Ligero Hernandez, Alonso Garcia Ruiz, Manuel Escobar Amores. Humans cannot accurately detect mucinous colorectal carcinoma from CT images, can AI help? [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-021.

A phase 2 randomised study of veliparib plus FOLFIRI±bevacizumab versus placebo plus FOLFIRI±bevacizumab in metastatic colorectal cancer

Background

Metastatic colorectal cancer (mCRC) has low survival rates. We assessed if addition of veliparib, concurrent to FOLFIRI, improves survival in patients with previously untreated mCRC.

Methods

This study compared veliparib (200 mg BID for 7 days of each 14-day cycle) to placebo, each with FOLFIRI. Bevacizumab was allowed in both arms. The primary endpoint was progression-free survival (PFS).

Results

Patients were randomised to receive veliparib (n = 65) or placebo (n = 65) in combination with FOLFIRI. Median PFS was 12 vs 11 months (veliparib vs placebo) [HR = 0.94 (95% CI: 0.60, 1.48)]. Median OS was 25 vs 27 months [HR = 1.26 (95% CI: 0.74, 2.16)]. Response rate was 57% vs 62%. Median DOR was 11 vs 9 months [HR = 0.73 (95% CI: 0.38, 1.40)]. AEs with significantly higher frequency (p < 0.05) in the veliparib group were anaemia (39% vs 19%, p = 0.019) and neutropenia (66% vs 37%, p = 0.001) for common AEs (≥20%); neutropenia (59% vs 22%, p < 0.001) for common Grade 3/4 AEs (≥5%); none in serious AEs. Haematopoietic cytopenias were more common with veliparib (79% vs 52%, p = 0.003). Fourteen percent of patients on veliparib and 15% on placebo discontinued treatment due to AEs.

Conclusion

Veliparib added to FOLFIRI ± bevacizumab demonstrated similar efficacy as FOLFIRI ± bevacizumab in frontline mCRC patients. No unexpected safety concerns occurred.

Implications of oncogenomics for cancer research and clinical oncology

The explosion of information generated by large-scale functional genomics technologies has resulted in an exponential increase in the number of potential genes and proteins available for pharmaceutical and diagnostic research development. In order to tap this potential, the primary challenge is to develop a strategy to effectively integrate and extract meaning from the human genomic sequence information that has been generated since the start of the Human Genome Project. This article deals with the strategies being applied by academics and by the biotechnology sector to sort and triage this information with the ultimate goal of identifying future therapeutic targets for cancer and other diseases.