Changes in Circulating Tumor DNA Reflect Clinical Benefit Across Multiple Studies of Patients With Non-Small-Cell Lung Cancer Treated With Immune Checkpoint Inhibitors.

PURPOSE: As immune checkpoint inhibitors (ICI) become increasingly used in frontline settings, identifying early indicators of response is needed. Recent studies suggest a role for circulating tumor DNA (ctDNA) in monitoring response to ICI, but uncertainty exists in the generalizability of these studies. Here, the role of ctDNA for monitoring response to ICI is assessed through a standardized approach by assessing clinical trial data from five independent studies. PATIENTS AND METHODS: Patient-level clinical and ctDNA data were pooled and harmonized from 200 patients across five independent clinical trials investigating the treatment of patients with non-small-cell lung cancer with programmed cell death-1 (PD-1)/programmed death ligand-1 (PD-L1)-directed monotherapy or in combination with chemotherapy. CtDNA levels were measured using different ctDNA assays across the studies. Maximum variant allele frequencies were calculated using all somatic tumor-derived variants in each unique patient sample to correlate ctDNA changes with overall survival (OS) and progression-free survival (PFS). RESULTS: We observed strong associations between reductions in ctDNA levels from on-treatment liquid biopsies with improved OS (OS; hazard ratio, 2.28; 95% CI, 1.62 to 3.20; P < .001) and PFS (PFS; hazard ratio 1.76; 95% CI, 1.31 to 2.36; P < .001). Changes in the maximum variant allele frequencies ctDNA values showed strong association across different outcomes. CONCLUSION: In this pooled analysis of five independent clinical trials, consistent and robust associations between reductions in ctDNA and outcomes were found across multiple end points assessed in patients with non-small-cell lung cancer treated with an ICI. Additional tumor types, stages, and drug classes should be included in future analyses to further validate this. CtDNA may serve as an important tool in clinical development and an early indicator of treatment benefit.

Quantitative PCR-Based Method to Assess Cell-Free DNA Quality, Adjust Input Mass, and Improve Next-Generation Sequencing Assay Performance.

Cell-free (cf)DNA-based testing has undergone increasingly wide adoption, including assays for the detection of circulating tumor DNA. Due to nucleosome protection, cfDNA has a distinctive fragment size of 160 to 180 bp. However, cfDNA can be contaminated with high molecular weight genomic DNA from blood cells released in plasma during sample collection. Such contamination can lead to decreased sensitivity or inconsistent results in cfDNA next-generation sequencing assays. This article describes a technical advancement in which a quantitative PCR method is used for high molecular weight contamination assessment and input mass adjustment, and has been demonstrated to improve consistency of performance in a circulating tumor DNA next-generation sequencing workflow.

ALK Mutation Status Before and After Alectinib Treatment in Locally Advanced or Metastatic ALK-Positive NSCLC: Pooled Analysis of Two Prospective Trials.

INTRODUCTION: The effectiveness of ALK receptor tyrosine kinase (ALK) inhibitors can be limited by the development of ALK resistance mutations. This exploratory analysis assessed the efficacy of alectinib in patients with NSCLC and ALK point mutations using pooled data from two single-arm phase II studies. METHODS: Studies NP28673 and NP28761 enrolled adults with locally advanced/metastatic ALK-positive NSCLC who had progressed on crizotinib. ALK mutation analysis was conducted on cell-free DNA from 187 patients post-crizotinib/pre-alectinib, and from 49 of these patients who subsequently progressed on alectinib. RESULTS: Baseline characteristics were generally balanced across patient subgroups. At baseline, 34 distinct ALK mutations were identified in 48 of 187 patients (25.7%). Median investigator-assessed progression-free survival was longer in patients without ALK single-nucleotide variants (n = 138) versus those with (n = 48): 10.2 months (95% confidence interval [CI]: 8.1-14.3) versus 5.6 months (95% CI: 4.5-10.9), respectively. Sixteen of 32 patients (50%) with ALK resistance mutations to crizotinib achieved an investigator-assessed response to alectinib (all partial responses); most of these ALK mutations were known to be sensitive to alectinib. Analysis of plasma samples obtained post-progression on alectinib revealed that 26 of 49 (53%) samples harbored 16 distinct ALK mutations, with known alectinib-resistance mutations, I1171 T/N/S, G1202R, and V1180L, observed in 15 of 49 (31%) tumors. CONCLUSIONS: Alectinib appears clinically active against ALK rearrangements and mutations, as well as several ALK variants that can cause resistance to crizotinib. The use of cell-free DNA in plasma samples may be an alternative noninvasive method for monitoring resistance mutations during therapy.

Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA.

Patients with diffuse large B cell lymphoma (DLBCL) exhibit marked diversity in tumor behavior and outcomes, yet the identification of poor-risk groups remains challenging. In addition, the biology underlying these differences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and genomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverse outcomes. To address this hypothesis, we applied cancer personalized profiling by deep sequencing (CAPP-Seq) analysis to tumor biopsies and cell-free DNA samples from 92 lymphoma patients and 24 healthy subjects. At diagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independently predictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally defined tumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple somatic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imaging for the detection of minimal residual disease and facilitated noninvasive identification of emergent resistance mutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishing indolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive prediction of histological transformation. Collectively, our results demonstrate that ctDNA analysis reveals biological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.

Integrated digital error suppression for improved detection of circulating tumor DNA.

High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by about threefold, and synergize when combined to yield ∼15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to non-small cell lung cancer (NSCLC) patients, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and >99.99% specificity at the variant level, and with 90% sensitivity and 96% specificity at the patient level. In addition, our approach allowed monitoring of NSCLC ctDNA down to 4 in 10(5) cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings.

Quantitative proteomic analysis reveals concurrent RNA-protein interactions and identifies new RNA-binding proteins in Saccharomyces cerevisiae.

A growing body of evidence supports the existence of an extensive network of RNA-binding proteins (RBPs) whose combinatorial binding affects the post-transcriptional fate of every mRNA in the cell-yet we still do not have a complete understanding of which proteins bind to mRNA, which of these bind concurrently, and when and where in the cell they bind. We describe here a method to identify the proteins that bind to RNA concurrently with an RBP of interest, using quantitative mass spectrometry combined with RNase treatment of affinity-purified RNA-protein complexes. We applied this method to the known RBPs Pab1, Nab2, and Puf3. Our method significantly enriched for known RBPs and is a clear improvement upon previous approaches in yeast. Our data reveal that some reported protein-protein interactions may instead reflect simultaneous binding to shared RNA targets. We also discovered more than 100 candidate RBPs, and we independently confirmed that 77% (23/30) bind directly to RNA. The previously recognized functions of the confirmed novel RBPs were remarkably diverse, and we mapped the RNA-binding region of one of these proteins, the transcriptional coactivator Mbf1, to a region distinct from its DNA-binding domain. Our results also provided new insights into the roles of Nab2 and Puf3 in post-transcriptional regulation by identifying other RBPs that bind simultaneously to the same mRNAs. While existing methods can identify sets of RBPs that interact with common RNA targets, our approach can determine which of those interactions are concurrent-a crucial distinction for understanding post-transcriptional regulation.

Improved discovery of molecular interactions in genome-scale data with adaptive model-based normalization.

BACKGROUND: High throughput molecular-interaction studies using immunoprecipitations (IP) or affinity purifications are powerful and widely used in biology research. One of many important applications of this method is to identify the set of RNAs that interact with a particular RNA-binding protein (RBP). Here, the unique statistical challenge presented is to delineate a specific set of RNAs that are enriched in one sample relative to another, typically a specific IP compared to a non-specific control to model background. The choice of normalization procedure critically impacts the number of RNAs that will be identified as interacting with an RBP at a given significance threshold - yet existing normalization methods make assumptions that are often fundamentally inaccurate when applied to IP enrichment data. METHODS: In this paper, we present a new normalization methodology that is specifically designed for identifying enriched RNA or DNA sequences in an IP. The normalization (called adaptive or AD normalization) uses a basic model of the IP experiment and is not a variant of mean, quantile, or other methodology previously proposed. The approach is evaluated statistically and tested with simulated and empirical data. RESULTS AND CONCLUSIONS: The adaptive (AD) normalization method results in a greatly increased range in the number of enriched RNAs identified, fewer false positives, and overall better concordance with independent biological evidence, for the RBPs we analyzed, compared to median normalization. The approach is also applicable to the study of pairwise RNA, DNA and protein interactions such as the analysis of transcription factors via chromatin immunoprecipitation (ChIP) or any other experiments where samples from two conditions, one of which contains an enriched subset of the other, are studied.

Whi3, an S. cerevisiae RNA-binding protein, is a component of stress granules that regulates levels of its target mRNAs.

RNA binding proteins (RBPs) are vital to the regulation of mRNA transcripts, and can alter mRNA localization, degradation, translation, and storage. Whi3 was originally identified in a screen for small cell size mutants, and has since been characterized as an RBP. The identification of Whi3-interacting mRNAs involved in mediating cellular responses to stress suggested that Whi3 might be involved in stress-responsive RNA processing. We show that Whi3 localizes to stress granules in response to glucose deprivation or heat shock. The kinetics and pattern of Whi3 localization in response to a range of temperatures were subtly but distinctly different from those of known components of RNA processing granules. Deletion of Whi3 resulted in an increase in the relative abundance of Whi3 target RNAs, either in the presence or absence of heat shock. Increased levels of the CLN3 mRNA in whi3Δ cells may explain their decreased cell size. Another mRNA target of Whi3 encodes the zinc-responsive transcription factor Zap1, suggesting a role for Whi3 in response to zinc stress. Indeed, we found that whi3Δ cells have enhanced sensitivity to zinc toxicity. Together our results suggest an expanded model for Whi3 function: in addition to its role as a regulator of the cell cycle, Whi3 may have a role in stress-dependent RNA processing and responses to a variety of stress conditions.

Proteome-wide search reveals unexpected RNA-binding proteins in Saccharomyces cerevisiae.

The vast landscape of RNA-protein interactions at the heart of post-transcriptional regulation remains largely unexplored. Indeed it is likely that, even in yeast, a substantial fraction of the regulatory RNA-binding proteins (RBPs) remain to be discovered. Systematic experimental methods can play a key role in discovering these RBPs--most of the known yeast RBPs lack RNA-binding domains that might enable this activity to be predicted. We describe here a proteome-wide approach to identify RNA-protein interactions based on in vitro binding of RNA samples to yeast protein microarrays that represent over 80% of the yeast proteome. We used this procedure to screen for novel RBPs and RNA-protein interactions. A complementary mass spectrometry technique also identified proteins that associate with yeast mRNAs. Both the protein microarray and mass spectrometry methods successfully identify previously annotated RBPs, suggesting that other proteins identified in these assays might be novel RBPs. Of 35 putative novel RBPs identified by either or both of these methods, 12, including 75% of the eight most highly-ranked candidates, reproducibly associated with specific cellular RNAs. Surprisingly, most of the 12 newly discovered RBPs were enzymes. Functional characteristics of the RNA targets of some of the novel RBPs suggest coordinated post-transcriptional regulation of subunits of protein complexes and a possible link between mRNA trafficking and vesicle transport. Our results suggest that many more RBPs still remain to be identified and provide a set of candidates for further investigation.

Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus.

We previously mapped the type 2 diabetes mellitus-2 locus (T2dm2), which affects fasting insulin levels, to distal chromosome 19 in a leptin-deficient obese F2 intercross derived from C57BL/6 (B6) and BTBR T+ tf/J (BTBR) mice. Introgression of a 7-Mb segment of the B6 chromosome 19 into the BTBR background (strain 1339A) replicated the reduced insulin linked to T2dm2. The 1339A mice have markedly impaired insulin secretion in vivo and disrupted islet morphology. We used subcongenic strains derived from 1339A to localize the T2dm2 quantitative trait locus (QTL) to a 242-kb segment comprising the promoter, first exon and most of the first intron of the Sorcs1 gene. This was the only gene in the 1339A strain for which we detected amino acid substitutions and expression level differences between mice carrying B6 and BTBR alleles of this insert, thereby identifying variation within the Sorcs1 gene as underlying the phenotype associated with the T2dm2 locus. SorCS1 binds platelet-derived growth factor, a growth factor crucial for pericyte recruitment to the microvasculature, and may thus have a role in expanding or maintaining the islet vasculature. Our identification of the Sorcs1 gene provides insight into the pathway underlying the pathophysiology of obesity-induced type 2 diabetes mellitus.