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Genomic features are often examined at extremes to determine the impact of mutations. These genomic regions span from the trinucleotide context to megabases that underlie chromatin and chromosomal features. Examining mutational dynamics at the mesoscale, the intermediate span of the genome, Buisson et al. characterized the mutational dynamics of cancer (see the Perspective by Carter). They found that mutations caused by the APOBEC enzyme in DNA stem-loops, a mesoscale feature of the genome, could drive recurrent mutations. Many of these types of mutations have been identified as likely drivers of cancer. However, APOBEC-generated mutations outside of stem-loops were more likely to be cancer driver mutations, providing a genomic context for separating cancer driver from passenger mutations. Science , this issue p. eaaw2872 ; see also p. 1228 An analysis of more than 2500 human tumors reveals that cancer driver and passenger mutations can be identified from mesoscale genomic context. Cancer drivers require statistical modeling to distinguish them from passenger events, which accumulate during tumorigenesis but provide no fitness advantage to cancer cells. The discovery of driver genes and mutations relies on the assumption that exact positional recurrence is unlikely by chance; thus, the precise sharing of mutations across patients identifies drivers. Examining the mutation landscape in cancer genomes, we found that many recurrent cancer mutations previously designated as drivers are likely passengers. Our integrated bioinformatic and biochemical analyses revealed that these passenger hotspot mutations arise from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops. Conversely, recurrent APOBEC-signature mutations not in stem-loops are enriched in well-characterized driver genes and may predict new drivers. This demonstrates that mesoscale genomic features need to be integrated into computational models aimed at identifying mutations linked to diseases.

APOBEC mutagenesis, a major driver of cancer evolution, is known for targeting TpC sites in DNA. Recently, we showed that APOBEC3A (A3A) targets DNA hairpin loops. Here, we show that DNA secondary structure is in fact an orthogonal influence on A3A substrate optimality and, surprisingly, can override the TpC sequence preference. VpC (non-TpC) sites in optimal hairpins can outperform TpC sites as mutational hotspots. This expanded understanding of APOBEC mutagenesis illuminates the genomic Twin Paradox, a puzzling pattern of closely spaced mutation hotspots in cancer genomes, in which one is a canonical TpC site but the other is a VpC site, and double mutants are seen only in trans , suggesting a two-hit driver event. Our results clarify this paradox, revealing that both hotspots in these twins are optimal A3A substrates. Our findings reshape the notion of a mutation signature, highlighting the additive roles played by DNA sequence and DNA structure. The APOBEC mutation signature contributes to a significant percentage of human cancers. Here the authors via biochemical and computational analyses shed light on how DNA primary sequence and secondary structure jointly influence A3A substrate optimality.

APOBEC3A is a cytidine deaminase driving mutagenesis, DNA replication stress and DNA damage in cancer cells. While the APOBEC3A-induced vulnerability of cancers offers an opportunity for therapy, APOBEC3A protein and mRNA are difficult to quantify in tumors due to their low abundance. Here, we describe a quantitative and sensitive assay to measure the ongoing activity of APOBEC3A in tumors. Using hotspot RNA mutations identified from APOBEC3A-positive tumors and droplet digital PCR, we develop an assay to quantify the RNA-editing activity of APOBEC3A. This assay is superior to APOBEC3A protein- and mRNA-based assays in predicting the activity of APOBEC3A on DNA. Importantly, we demonstrate that the RNA mutation-based APOBEC3A assay is applicable to clinical samples from cancer patients. Our study presents a strategy to follow the dysregulation of APOBEC3A in tumors, providing opportunities to investigate the role of APOBEC3A in tumor evolution and to target the APOBEC3A-induced vulnerability in therapy. The DNA cytosine deaminases APOBEC3A and APOBEC3B have emerged from cancer genomics studies as drivers of mutation in cancers and tumor heterogeneity. Here the authors present a computational approach to identify the RNA mutations specifically driven by APOBEC3A, and developed an RNA mutation-based assay to quantify ongoing APOBEC3A activity in tumor cells.

APOBEC is a mutagenic source in human papillomavirus (HPV)-mediated malignancies, including HPV+ oropharyngeal squamous cell carcinoma (HPV + OPSCC), and in HPV genomes. It is unknown why APOBEC mutations predominate in HPV + OPSCC, or if the APOBEC-induced mutations observed in both human cancers and HPV genomes are directly linked. We performed sequencing of host somatic exomes, transcriptomes, and HPV16 genomes from 79 HPV + OPSCC samples, quantifying APOBEC mutational burden and activity in both host and virus. APOBEC was the dominant mutational signature in somatic exomes. In viral genomes, there was a mean of five (range 0–29) mutations per genome. The mean of APOBEC mutations in viral genomes was one (range 0–5). Viral APOBEC mutations, compared to non-APOBEC mutations, were more likely to be low-variant allele fraction mutations, suggesting that APOBEC mutagenesis actively occurrs in viral genomes during infection. HPV16 APOBEC-induced mutation patterns in OPSCC were similar to those previously observed in cervical samples. Paired host and viral analyses revealed that APOBEC-enriched tumor samples had higher viral APOBEC mutation rates (p = 0.028), and APOBEC-associated RNA editing (p = 0.008), supporting the concept that APOBEC mutagenesis in host and viral genomes is directly linked and occurrs during infection. Using paired sequencing of host somatic exomes, transcriptomes, and viral genomes, we demonstrated for the first-time definitive evidence of concordance between tumor and viral APOBEC mutagenesis. This finding provides a missing link connecting APOBEC mutagenesis in host and virus and supports a common mechanism driving APOBEC dysregulation.

Background Quiescence (G0) is a transient, cell cycle-arrested state. By entering G0, cancer cells survive unfavorable conditions such as chemotherapy and cause relapse. While G0 cells have been studied at the transcriptome level, how post-transcriptional regulation contributes to their chemoresistance remains unknown. Results We induce chemoresistant and G0 leukemic cells by serum starvation or chemotherapy treatment. To study post-transcriptional regulation in G0 leukemic cells, we systematically analyzed their transcriptome, translatome, and proteome. We find that our resistant G0 cells recapitulate gene expression profiles of in vivo chemoresistant leukemic and G0 models. In G0 cells, canonical translation initiation is inhibited; yet we find that inflammatory genes are highly translated, indicating alternative post-transcriptional regulation. Importantly, AU-rich elements (AREs) are significantly enriched in the upregulated G0 translatome and transcriptome. Mechanistically, we find the stress-responsive p38 MAPK-MK2 signaling pathway stabilizes ARE mRNAs by phosphorylation and inactivation of mRNA decay factor, Tristetraprolin (TTP) in G0. This permits expression of ARE mRNAs that promote chemoresistance. Conversely, inhibition of TTP phosphorylation by p38 MAPK inhibitors and non-phosphorylatable TTP mutant decreases ARE-bearing TNFα and DUSP1 mRNAs and sensitizes leukemic cells to chemotherapy. Furthermore, co-inhibiting p38 MAPK and TNFα prior to or along with chemotherapy substantially reduces chemoresistance in primary leukemic cells ex vivo and in vivo. Conclusions These studies uncover post-transcriptional regulation underlying chemoresistance in leukemia. Our data reveal the p38 MAPK-MK2-TTP axis as a key regulator of expression of ARE-bearing mRNAs that promote chemoresistance. By disrupting this pathway, we develop an effective combination therapy against chemosurvival.

Histologic transformation from non-small cell to small cell lung cancer has been reported as a resistance mechanism to targeted therapy in EGFR -mutant and ALK fusion-positive lung cancers. Whether small cell transformation occurs in other oncogene-driven lung cancers remains unknown. Here we analyzed the genomic landscape of two pre-mortem and 11 post-mortem metastatic tumors collected from an advanced, ROS1 fusion-positive lung cancer patient, who had received sequential ROS1 inhibitors. Evidence of small cell transformation was observed in all metastatic sites at autopsy, with inactivation of RB1 and TP53 , and loss of ROS1 fusion expression. Whole-exome sequencing revealed minimal mutational and copy number heterogeneity, suggestive of “hard” clonal sweep. Patient-derived models generated from autopsy retained features consistent with small cell lung cancer and demonstrated resistance to ROS1 inhibitors. This case supports small cell transformation as a recurring resistance mechanism, and underscores the importance of elucidating its biology to expand therapeutic opportunities.

Leukemias and their bone marrow microenvironments undergo dynamic changes over the course of disease. However, little is known about the circulation kinetics of leukemia cells, nor the impact of specific factors on the clearance of circulating leukemia cells (CLCs) from the blood. To gain a basic understanding of CLC dynamics over the course of disease progression and therapeutic response, we apply a blood exchange method to mouse models of acute leukemia. We find that CLCs circulate in the blood for 1–2 orders of magnitude longer than solid tumor circulating tumor cells. We further observe that: (i) leukemia presence in the marrow can limit the clearance of CLCs in a model of acute lymphocytic leukemia (ALL), and (ii) CLCs in a model of relapsed acute myeloid leukemia (AML) can clear faster than their untreated counterparts. Our approach can also directly quantify the impact of microenvironmental factors on CLC clearance properties. For example, data from two leukemia models suggest that E-selectin, a vascular adhesion molecule, alters CLC clearance. Our research highlights that clearance rates of CLCs can vary in response to tumor and treatment status and provides a strategy for identifying basic processes and factors that govern the kinetics of circulating cells. A blood exchange system reveals that leukemia cells clear faster in the blood than circulating tumor cells from solid tumors and that clearance rates can vary in response to tumor and treatment status.

Acquired drug resistance to anticancer targeted therapies remains an unsolved clinical problem. Although many drivers of acquired drug resistance have been identified 1 – 4 , the underlying molecular mechanisms shaping tumour evolution during treatment are incompletely understood. Genomic profiling of patient tumours has implicated apolipoprotein B messenger RNA editing catalytic polypeptide-like (APOBEC) cytidine deaminases in tumour evolution; however, their role during therapy and the development of acquired drug resistance is undefined. Here we report that lung cancer targeted therapies commonly used in the clinic can induce cytidine deaminase APOBEC3A (A3A), leading to sustained mutagenesis in drug-tolerant cancer cells persisting during therapy. Therapy-induced A3A promotes the formation of double-strand DNA breaks, increasing genomic instability in drug-tolerant persisters. Deletion of A3A reduces APOBEC mutations and structural variations in persister cells and delays the development of drug resistance. APOBEC mutational signatures are enriched in tumours from patients with lung cancer who progressed after extended responses to targeted therapies. This study shows that induction of A3A in response to targeted therapies drives evolution of drug-tolerant persister cells, suggesting that suppression of A3A expression or activity may represent a potential therapeutic strategy in the prevention or delay of acquired resistance to lung cancer targeted therapy. Induction of APOBEC3A in response to targeted therapies drives evolution of drug-tolerant persister cells, suggesting that its suppression may represent a potential therapeutic strategy in the prevention of acquired resistance to lung cancer targeted therapy.

Genomic and drug screens identify SHP2 blockade as a therapeutic approach for ALK-rearranged non-small cell lung cancers developing resistance through ALK-mutant-independent mechanisms. Most anaplastic lymphoma kinase ( ALK )-rearranged non-small-cell lung tumors initially respond to small-molecule ALK inhibitors, but drug resistance often develops 1 , 2 , 3 , 4 . Of tumors that develop resistance to highly potent second-generation ALK inhibitors, approximately half harbor resistance mutations in ALK , while the other half have other mechanisms underlying resistance. Members of the latter group often have activation of at least one of several different tyrosine kinases driving resistance 5 . Such tumors are not expected to respond to lorlatinib—a third-generation inhibitor targeting ALK that is able to overcome all clinically identified resistant mutations in ALK 5 , 6 —and further therapeutic options are limited 5 . Herein, we deployed a shRNA screen of 1,000 genes in multiple ALK-inhibitor-resistant patient-derived cells (PDCs) to discover those that confer sensitivity to ALK inhibition. This approach identified SHP2, a nonreceptor protein tyrosine phosphatase, as a common targetable resistance node in multiple PDCs. SHP2 provides a parallel survival input downstream of multiple tyrosine kinases that promote resistance to ALK inhibitors. Treatment with SHP099, the recently discovered small-molecule inhibitor of SHP2, in combination with the ALK tyrosine kinase inhibitor (TKI) ceritinib halted the growth of resistant PDCs through preventing compensatory RAS and ERK1 and ERK2 (ERK1/2) reactivation. These findings suggest that combined ALK and SHP2 inhibition may be a promising therapeutic strategy for resistant cancers driven by several different ALK-independent mechanisms underlying resistance.