INTRODUCTION: Extensive tumor sequencing efforts have transformed the way in which cancer driver genes are identified. Appropriate statistical modeling is crucial for distinguishing true drivers from passenger events that accumulate during tumorigenesis but provide no fitness advantage to cancer cells. A central assumption used in discovering driver genes and specific driver mutations is that exact positional recurrence is unlikely by chance: Seeing exactly the same DNA base pair mutated recurrently across patients is taken as proof that the mutation must be under functional selection for contributing to tumor fitness. The assumption is that mutational processes, being essentially random, are unlikely to hit the exact same base pair over and over again. However, although functional selection is clearly a key cause of recurrent mutations in cancers, whether it is the only prominent cause is not known. RATIONALE: To distinguish driver mutations from passengers, it is critical to understand the landscape of background mutations in cancer genomes. Recent pan-cancer mutation analyses have revealed rules of mutation distribution at the smallest (one to three base pairs) and largest (megabase) scales. At the small scale, mutational processes such as those attributable to sunlight, cigarette smoke, or random DNA copying errors generate patterns known as mutational signatures at the trinucleotide level. At the opposite extreme, the cell’snucleus is organized into two large compartments known as A and B, each consisting of multi-megabase chromatin domains. Compartment A contains gene-rich, open, active, early-replicating euchromatin. Compartment B contains gene-poor, closed, inactive, and late-replicating heterochromatin. Mutation frequency is generally higher in compartment B. Cancer genomes have been studied in detail at these two opposite scales, but less attention has been paid so far to the intervening “mesoscale.” RESULTS: We investigated the influence of mesoscale genomic features on mutational recurrence. We found that mutagenesis by the cytidine deaminase APOBEC3A is uniquely sensitive to mesoscale features, specifically the ability of DNA to adopt particular “hairpin” (stem-loop) structures while transiently single-stranded. Identifying DNA loci that can form hairpins requires sequence analysis at the mesoscale (~30–base pair) level. Combining biochemistry and bioinformatics, we deduced the features of APOBEC3A’s optimal DNA substrates, revealing that cytosine bases presented in a short loop at the end of a strongly paired stem can be mutated up to 200 times as frequently as nonhairpin sites. Analyzing the most frequent APOBEC mutations in protein-coding regions of cancer genomes, we identified numerous recurrent mutations at optimal hairpins in genes unconnected to cancer. Conversely, we found that mutational hotspots at nonoptimal sites are enriched in known cancer driver genes. CONCLUSION: Our results indicate that there are multiple possible routes to mutational hotspots in cancer. Functional mutations...

Highlights d Cancer-associated fibroblasts contribute to pancreatic cancer heterogeneity d Cancer cells can have a double-positive phenotype: proliferation and invasion d High CAF abundance linked with DP cells enriched for MAPK and STAT3 co-signaling d Intra-tumoral gland types provide tissue heterogeneity linked with clinical outcome

The cornerstone of treatment for advanced ALK-positive lung cancer is sequential therapy with increasingly potent and selective ALK inhibitors. The third-generation ALK inhibitor lorlatinib has demonstrated clinical activity in patients who failed previous ALK inhibitors. To define the spectrum of mutations that confer lorlatinib resistance, we performed accelerated mutagenesis screening of Ba/F3 cells expressing EML4-ALK. Under comparable conditions,-ethyl--nitrosourea (ENU) mutagenesis generated numerous crizotinib-resistant but no lorlatinib-resistant clones harboring single mutations. In similar screens with EML4-ALK containing single resistance mutations, numerous lorlatinib-resistant clones emerged harboring compound mutations. To determine the clinical relevance of these mutations, we analyzed repeat biopsies from lorlatinib-resistant patients. Seven of 20 samples (35%) harbored compound mutations, including two identified in the ENU screen. Whole-exome sequencing in three cases confirmed the stepwise accumulation of mutations during sequential treatment. These results suggest that sequential ALK inhibitors can foster the emergence of compound mutations, identification of which is critical to informing drug design and developing effective therapeutic strategies. Treatment with sequential first-, second-, and third-generation ALK inhibitors can select for compound mutations that confer high-level resistance to ALK-targeted therapies. A more efficacious long-term strategy may be up-front treatment with a third-generation ALK inhibitor to prevent the emergence of on-target resistance..

Most anaplastic lymphoma kinase (ALK)-rearranged non-small-cell lung tumors initially respond to small-molecule ALK inhibitors, but drug resistance often develops. 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. 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-and further therapeutic options are limited. 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.

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.

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.

Background: Most ALK-positive lung cancers will develop ALK-independent resistance after treatment with next-generation ALK inhibitors. MET amplification has been described in patients progressing on ALK inhibitors, but frequency of this event has not been comprehensively assessed. Methods: We performed fluorescence in-situ hybridization and/or next-generation sequencing on 207 post-treatment tissue (n=101) or plasma (n=106) specimens from patients with ALK-positive lung cancer to detect MET genetic alterations. We evaluated ALK inhibitor sensitivity in cell lines with MET alterations and assessed antitumor activity of ALK/MET blockade in ALK-positive cell lines and two patients with MET-driven resistance. Results: MET amplification was detected in 15% of tumor biopsies from patients relapsing on next-generation ALK inhibitors, including 12% and 22% of biopsies from patients progressing on second-generation inhibitors or lorlatinib, respectively. Patients treated with a second-generation ALK inhibitor in the first-line setting were more likely to develop MET amplification than those who had received next-generation ALK inhibitors after crizotinib (p=0.019). Two tumor specimens harbored an identical ST7-MET rearrangement, one of which had concurrent MET amplification. Expressing ST7-MET in the sensitive H3122 ALK-positive cell line induced resistance to ALK inhibitors that was reversed with dual ALK/MET inhibition. MET inhibition re-sensitized a patient-derived cell line harboring both ST7-MET and MET amplification to ALK inhibitors. Two patients with ALK-positive lung cancer and acquired MET alterations achieved rapid responses to ALK/MET combination therapy. Conclusions: Treatment with next-generation ALK inhibitors, particularly in the first-line setting, may select for MET-driven resistance. Patients with acquired MET alterations may derive clinical benefit from therapies that target both ALK and MET.

Purpose: Current standard initial therapy for advanced, ROS proto-oncogene 1, receptor tyrosine kinase fusion (ROS1)-positive (ROS1+) non–small cell lung cancer (NSCLC) is crizotinib or entrectinib. Lorlatinib, a next-generation anaplastic lymphoma kinase/ROS1 inhibitor, recently demonstrated efficacy in ROS1+ NSCLC, including in crizotinib-pretreated patients. However, mechanisms of lorlatinib resistance in ROS1+ disease remain poorly understood. Here, we assessed mechanisms of resistance to crizotinib and lorlatinib. Experimental Design: Biopsies from patients with ROS1+ NSCLC progressing on crizotinib or lorlatinib were profiled by genetic sequencing. Results: From 55 patients, 47 post-crizotinib and 32 post-lorlatinib biopsies were assessed. Among 42 post-crizotinib and 28 post-lorlatinib biopsies analyzed at distinct timepoints, ROS1 mutations were identified in 38% and 46%, respectively. ROS1 G2032R was the most commonly occurring mutation in approximately one third of cases. Additional ROS1 mutations included D2033N (2.4%) and S1986F (2.4%) post-crizotinib and L2086F (3.6%), G2032R/L2086F (3.6%), G2032R/S1986F/L2086F (3.6%), and S1986F/L2000V (3.6%) post-lorlatinib. Structural modeling predicted ROS1L2086F causes steric interference to lorlatinib, crizotinib, and entrectinib, while it may accommodate cabozantinib. In Ba/F3 models, ROS1L2086F, ROS1G2032R/L2086F, and ROS1S1986F/G2032R/L2086F were refractory to lorlatinib but sensitive to cabozantinib. A patient with disease progression on crizotinib and lorlatinib and ROS1 L2086F received cabozantinib for nearly 11 months with disease control. Among lorlatinib-resistant biopsies, we also identified MET amplification (4%), KRAS G12C (4%), KRAS amplification (4%), NRAS mutation (4%), and MAP2K1 mutation (4%). Conclusions: ROS1 mutations mediate resistance to crizotinib and lorlatinib in more than one third of cases, underscoring the importance of developing next-generation ROS1 inhibitors with potency against these mutations, including G2032R and L2086F. Continued efforts are needed to elucidate ROS1-independent resistance mechanisms.