In a recent article in Science, Tomasetti and colleagues present an expanded model for cancer risk, which they claim demonstrates the relative contribution of mutations caused by replication errors, environment, and heredity. The foundation of this model is the theory that the overwhelming driver of cancer risk is mutations. This perspective will present experimental evidence and evolutionary theory to challenge the basis of this underlying theory. An argument will be presented that the mutation-centric model of cancer suggests unrealistic solutions to cancer and distracts the research community from more promising approaches that consider tissue context. Cancer Res; 77(22); 1–4. ©2017 AACR.
Long Noncoding RNA and Cancer: A New Paradigm
In addition to mutations or aberrant expression in the protein-coding genes, mutations and misregulation of noncoding RNAs, in particular long noncoding RNAs (lncRNA), appear to play major roles in cancer. Genome-wide association studies of tumor samples have identified a large number of lncRNAs associated with various types of cancer. Alterations in lncRNA expression and their mutations promote tumorigenesis and metastasis. LncRNAs may exhibit tumor-suppressive and -promoting (oncogenic) functions. Because of their genome-wide expression patterns in a variety of tissues and their tissue-specific expression characteristics, lncRNAs hold strong promise as novel biomarkers and therapeutic targets for cancer. In this article, we have reviewed the emerging functions and association of lncRNAs in different types of cancer and discussed their potential implications in cancer diagnosis and therapy. Cancer Res; 77(15); 1–17. ©2017 AACR.
APOBEC3A and 3B activities render cancer cells susceptible to ATR inhibition
The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like APOBEC3A and 3B have emerged as key mutation drivers in cancer. Here we show that APOBEC3A and 3B activities impose a unique type of replication stress by inducing abasic sites at replication forks. In contrast to cells under other types of replication stress, APOBEC3A-expressing cells were selectively sensitive to ATR inhibitors (ATRi), but not to a variety of DNA replication inhibitors and DNA-damaging drugs. In proliferating cells, APOBEC3A modestly elicited ATR but not ATM. ATR inhibition in APOBEC3A-expressing cells resulted in a surge of abasic sites at replication forks, revealing an ATR-mediated negative feedback loop during replication. The surge of abasic sites upon ATR inhibition associated with increased accumulation of single-stranded DNA, a substrate of APOBEC3A, triggering an APOBEC3A-driven feedforward loop that ultimately drove cells into replication catastrophe. In a panel of cancer cell lines, ATRi selectively induced replication catastrophe in those harboring high APOBEC3A and/or 3B activities, showing that APOBEC3A and 3B activities conferred susceptibility to ATRi. Our results define an APOBEC-driven replication stress in cancer cells that may offer an opportunity for ATR-targeted therapy.
Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution
The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 1–14. ©2017 AACR.
