ATR Inhibitor AZD6738 (Ceralasertib) Exerts Antitumor Activity as a Monotherapy and in Combination with Chemotherapy and the PARP Inhibitor Olaparib.

AZD6738 (ceralasertib) is a potent and selective orally bioavailable inhibitor of ataxia telangiectasia and Rad3-related (ATR) kinase. ATR is activated in response to stalled DNA replication forks to promote G2-M cell-cycle checkpoints and fork restart. Here, we found AZD6738 modulated CHK1 phosphorylation and induced ATM-dependent signaling (pRAD50) and the DNA damage marker γH2AX. AZD6738 inhibited break-induced replication and homologous recombination repair. In vitro sensitivity to AZD6738 was elevated in, but not exclusive to, cells with defects in the ATM pathway or that harbor putative drivers of replication stress such as CCNE1 amplification. This translated to in vivo antitumor activity, with tumor control requiring continuous dosing and free plasma exposures, which correlated with induction of pCHK1, pRAD50, and γH2AX. AZD6738 showed combinatorial efficacy with agents associated with replication fork stalling and collapse such as carboplatin and irinotecan and the PARP inhibitor olaparib. These combinations required optimization of dose and schedules in vivo and showed superior antitumor activity at lower doses compared with that required for monotherapy. Tumor regressions required at least 2 days of daily dosing of AZD6738 concurrent with carboplatin, while twice daily dosing was required following irinotecan. In a BRCA2-mutant patient-derived triple-negative breast cancer (TNBC) xenograft model, complete tumor regression was achieved with 3 to5 days of daily AZD6738 per week concurrent with olaparib. Increasing olaparib dosage or AZD6738 dosing to twice daily allowed complete tumor regression even in a BRCA wild-type TNBC xenograft model. These preclinical data provide rationale for clinical evaluation of AZD6738 as a monotherapy or combinatorial agent. SIGNIFICANCE: This detailed preclinical investigation, including pharmacokinetics/pharmacodynamics and dose-schedule optimizations, of AZD6738/ceralasertib alone and in combination with chemotherapy or PARP inhibitors can inform ongoing clinical efforts to treat cancer with ATR inhibitors.

Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images.

Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.