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Acute myeloid leukemias (AML) are characterized by mutations of tumor suppressor and oncogenes, involving distinct genes in adults and children. While certain mutations have been associated with the increased risk of AML relapse, the genomic landscape of primary chemotherapy-resistant AML is not well defined. As part of the TARGET initiative, we performed whole-genome DNA and transcriptome RNA and miRNA sequencing analysis of pediatric AML with failure of induction chemotherapy. We identified at least three genetic groups of patients with induction failure, including those with NUP98 rearrangements, somatic mutations of WT1 in the absence of apparent NUP98 mutations, and additional recurrent variants including those in KMT2C and MLLT10 . Comparison of specimens before and after chemotherapy revealed distinct and invariant gene expression programs. While exhibiting overt therapy resistance, these leukemias nonetheless showed diverse forms of clonal evolution upon chemotherapy exposure. This included selection for mutant alleles of FRMD8 , DHX32 , PIK3R1 , SHANK3 , MKLN1 , as well as persistence of WT1 and TP53 mutant clones, and elimination of FLT3 , PTPN11 , and NRAS mutant clones. These findings delineate genetic mechanisms of primary chemotherapy resistance in pediatric AML, which should inform improved approaches for its diagnosis and therapy.

While the genomes of normal tissues undergo dynamic changes over time, little is understood about the temporal-spatial dynamics of genomes in premalignant tissues that progress to cancer compared to those that remain cancer-free. Here we use whole genome sequencing to contrast genomic alterations in 427 longitudinal samples from 40 patients with stable Barrett’s esophagus compared to 40 Barrett’s patients who progressed to esophageal adenocarcinoma (ESAD). We show the same somatic mutational processes are active in Barrett’s tissue regardless of outcome, with high levels of mutation, ESAD gene and focal chromosomal alterations, and similar mutational signatures. The critical distinction between stable Barrett’s versus those who progress to cancer is acquisition and expansion of TP53 −/− cell populations having complex structural variants and high-level amplifications, which are detectable up to six years prior to a cancer diagnosis. These findings reveal the timing of common somatic genome dynamics in stable Barrett’s esophagus and define key genomic features specific to progression to esophageal adenocarcinoma, both of which are critical for cancer prevention and early detection strategies. Barrett’s esophagus is a pre-malignant condition that can progress to esophageal cancer. Here, the authors carry out whole genome sequencing of samples from patients who did or did not progress to cancer and find that mutations in many genes occur regardless of progression status, but also find features associated with progressive disease.

Reliable detection of somatic variations is of critical importance in cancer research. Here we present Lancet, an accurate and sensitive somatic variant caller, which detects SNVs and indels by jointly analyzing reads from tumor and matched normal samples using colored de Bruijn graphs. We demonstrate, through extensive experimental comparison on synthetic and real whole-genome sequencing datasets, that Lancet has better accuracy, especially for indel detection, than widely used somatic callers, such as MuTect, MuTect2, LoFreq, Strelka, and Strelka2. Lancet features a reliable variant scoring system, which is essential for variant prioritization, and detects low-frequency mutations without sacrificing the sensitivity to call longer insertions and deletions empowered by the local-assembly engine. In addition to genome-wide analysis, Lancet allows inspection of somatic variants in graph space, which augments the traditional read alignment visualization to help confirm a variant of interest. Lancet is available as an open-source program at https://github.com/nygenome/lancet . Giuseppe Narzisi et al. present Lancet, a genome-wide somatic variant caller using localized colored de Bruijn graphs. Comparisons using real and simulated data show that Lancet has improved accuracy for single nucleotide variants and indels compared to widely used methods MuTect2, LoFreq and Strelka2.

To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278x coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. Somatic calling was generally consistent between the two platforms despite minor differences at the read level. We designed and implemented a novel pipeline for the analysis of tumor-normal samples, using multiple variant callers. We show that coupled with a high-confidence filtering strategy, the use of combination of tools improves the accuracy of somatic variant calling. We also demonstrate the utility of the dataset by creating an artificial purity ladder to evaluate the somatic pipeline and benchmark methods for estimating purity and ploidy from tumor-normal pairs. The data and results of the pipeline are made accessible to the cancer genomics community.

Intracranial metastases in prostate cancer are uncommon but clinically aggressive. A detailed molecular characterization of prostate cancer intracranial metastases would improve our understanding of their pathogenesis and the search for new treatment strategies. We evaluated the clinical and molecular characteristics of 36 patients with metastatic prostate cancer to either the dura or brain parenchyma. We performed whole genome sequencing (WGS) of 10 intracranial prostate cancer metastases, as well as WGS of primary prostate tumors from men who later developed metastatic disease ( n  = 6) and nonbrain prostate cancer metastases ( n  = 36). This first study focused on WGS of prostate intracranial metastases led to several new insights. First, there was a higher diversity of complex structural alterations in prostate cancer intracranial metastases compared to primary tumor tissues. Chromothripsis and chromoplexy events seemed to dominate, yet there were few enrichments of specific categories of structural variants compared with non-brain metastases. Second, aberrations involving the AR gene, including AR enhancer gain were observed in 7/10 (70%) of intracranial metastases, as well as recurrent loss of function aberrations involving TP53 in 8/10 (80%), RB1 in 2/10 (20%), BRCA2 in 2/10 (20%), and activation of the PI3K/AKT/PTEN pathway in 8/10 (80%). These alterations were frequently present in tumor tissues from other sites of disease obtained concurrently or sequentially from the same individuals. Third, clonality analysis points to genomic factors and evolutionary bottlenecks that contribute to metastatic spread in patients with prostate cancer. These results describe the aggressive molecular features underlying intracranial metastasis that may inform future diagnostic and treatment approaches.

Cystic fibrosis is a life-threatening genetic disorder that has been associated with mutations in the CFTR [cystic fibrosis transmembrane conductance regulator (ATP-binding cassette sub-family C, member 7)] gene. Hundreds of CFTR mutations have been detected to date. Current CFTR genotyping assays target a subset of these mutations, particularly a mutation panel recommended by the American College of Medical Genetics for carrier screening of the general population. Fast sequencing of the entire coding sequence in a scalable manner could expand the detection of CFTR mutations and facilitate management of costs and turnaround times in the clinical laboratory. We describe a proof-of-concept CFTR assay that uses PCR target enrichment and next-generation sequencing on the Ion Torrent Personal Genome Machine™ (PGM™) platform. The scalability of the assay was demonstrated, with an average mean depth of coverage ranging from 500× to 3500×, depending on the number of multiplexed patient samples and the Ion Torrent chip used. In a blinded study of 79 previously genotyped patient DNA samples and cell lines, our assay detected most of the mutations, including single-nucleotide variants, small insertions and deletions, and large copy-number variants. The reproducibility was 100% for detecting mutations in independent runs. Our assay demonstrated high specificity, with only 2 false-positive calls (at 2184delA) found in 2 samples caused by a sequencing error in a homopolymer stretch of sequence. The detection rate for variants of unknown significance was very low in the targeted region. With continued optimization and system refinements, PGM sequencing promises to be a powerful, rapid, and scalable means of clinical diagnostic sequencing.

Current efforts to understand antibiotic resistance on the whole genome scale tend to focus on known genes even as high throughput sequencing strategies uncover novel mechanisms. To identify genomic variations associated with antibiotic resistance, we employed a modified genome-wide association study; we sequenced genomic DNA from pools of E. coli clinical isolates with similar antibiotic resistance phenotypes using SOLiD technology to uncover single nucleotide polymorphisms (SNPs) unanimously conserved in each pool. The multidrug-resistant pools were genotypically similar to SMS-3-5, a previously sequenced multidrug-resistant isolate from a polluted environment. The similarity was evenly spread across the entire genome and not limited to plasmid or pathogenicity island loci. Among the pools of clinical isolates, genomic variation was concentrated adjacent to previously reported inversion and duplication differences between the SMS-3-5 isolate and the drug-susceptible laboratory strain, DH10B. SNPs that result in non-synonymous changes in gyrA (encoding the well-known S83L allele associated with fluoroquinolone resistance), mutM, ligB, and recG were unanimously conserved in every fluoroquinolone-resistant pool. Alleles of the latter three genes are tightly linked among most sequenced E. coli genomes, and had not been implicated in antibiotic resistance previously. The changes in these genes map to amino acid positions in alpha helices that are involved in DNA binding. Plasmid-encoded complementation of null strains with either allelic variant of mutM or ligB resulted in variable responses to ultraviolet light or hydrogen peroxide treatment as markers of induced DNA damage, indicating their importance in DNA metabolism and revealing a potential mechanism for fluoroquinolone resistance. Our approach uncovered evidence that additional DNA binding enzymes may contribute to fluoroquinolone resistance and further implicate environmental bacteria as a reservoir for antibiotic resistance.

Aggressive variant and androgen receptor (AR)-independent castration resistant prostate cancers (CRPC) represent the most significant diagnostic and therapeutic challenges in prostate cancer. This study examined a case of simultaneous progression of both adenocarcinoma and squamous tumors from the same common origin. Using whole-genome and transcriptome sequencing from 17 samples collected over >6 years, we established the clonal relationship of all samples, defined shared complex structural variants, and demonstrated both divergent and convergent evolution at AR . Squamous CRPC-associated circulating tumor DNA was identified at clinical progression prior to biopsy detection of any squamous differentiation. Dynamic changes in the detection rate of histology-specific clones in circulation reflected histology-specific sensitivity to treatment. This dataset serves as an illustration of non-neuroendocrine transdifferentiation and highlights the importance of serial sampling at progression in CRPC for the detection of emergent non-adenocarcinoma histologies with implications for the treatment of lineage plasticity and transdifferentiation in metastatic CRPC.

BackgroundClearance of circulating tumor DNA (ctDNA) following checkpoint blockade (CB) can precede radiographic response,1 2 though current state of the art ctDNA detection via targeted panels faces limited sensitivity in low burden disease (figure 1). We previously showed that whole genome sequencing (WGS) of plasma can overcome low input of ctDNA to dynamically track low volume malignancy using matched tumor tissue.3 We therefore sought to evaluate ctDNA for tracking early response to checkpoint blockade (CB) in melanoma, and developed a novel classifier that allows us to track disease without matched tumor tissue for expanded applicability in immunotherapy.MethodsTo identify ctDNA sparsely diluted in noncancerous plasma cell free DNA (cfDNA), we developed Phoenix, a deep-learning classifier that uses genomic and epigenomic features to distinguish single nucleotide variants (SNVs) in melanoma from sequencing noise. We evaluated Phoenix on a retrospective cohort of serially sampled plasma from patients with advanced cutaneous melanoma on CB (nivolumab alone or with ipilimumab). Plasma was collected at 0, 3, 6 and 12 weeks after first dose of immunotherapy. ctDNA dynamics were compared to radiographic imaging results at 12 weeks.ResultsWe trained Phoenix on tumor-confirmed SNVs in plasma from a single patient with high tumor mutational burden (TMB) melanoma and cfDNA from age-matched patients without known cancer. Overall ctDNA signal-to-noise enrichment ranged from 100 - 260x in validation patients (n=2) with bulky disease. Phoenix learned key features of melanoma ctDNA including the UV mutational signature and short fragment size (figure 2), and sensitively tracked persistent low burden disease seen on imaging (figure 3). To validate these findings, we expanded our cohort (n= 15) of serially tracked tumors. In our preliminary analysis of 12 patients, Phoenix detected pretreatment ctDNA in 92% of patients at a specificity of 97% (figure 4), compared with only 17% with the benchmark in the field (iChorCNA, a plasma-based WGS liquid biopsy tool; table 1). Phoenix detected a decrease in ctDNA 3 weeks after initiation of CB in 80% of patients (figure 5) with an objective response on imaging. No change in ctDNA was seen in patients who did not respond to treatment.ConclusionsPhoenix successfully identified pretreatment melanoma ctDNA without matched tumor tissue and identified response to CB as early as 3 weeks after treatment. Our ongoing studies aim to optimize this technology for early identification of CB response in clinical practice.Abstract 33 Figure 1WGS of plasma increases sensitivity in low-burden diseaseLikelihood of ctDNA SNV detection is a function of tumor fraction, depth, and breadth (number of candidate sites). Because the limited number of genomic equivalents exhausts depth in targeted sequencing, detection sensitivity is limited by the relatively small number of sites in a clinical panel. In contrast, WGS at modest depth (35x) can detect low tumor fraction by integrating signal from thousands of SNVs across the genome.Abstract 33 Figure 2Phoenix learns key covariates for melanoma ctDNAPhoenix was trained on tumor-confirmed SNVs in plasma from patients with high burden melanoma and cfDNA from age-matched patients without known cancer. We aggregated Phoenix positive (ctDNA, blue) and negative (cfDNA, red) predictions on SNVs from a held out validation melanoma plasma sample. Phoenix ctDNA predictions correctly reflect important melanoma SNV attributes including UV-signature (C>T trinucleotide context, a), low DNase accessibility (b), late replication timing (c), and short fragment length (d).Abstract 33 Figure 3Phoenix sensitively tracks response to nivolumabPlasma samples were collected to monitor treatment response to nivolumab. Treatment monitoring by computed tomography (CT) shows response to therapy but residual disease after 3 months of therapy (a). Phoenix quantifies tumor response, matching radiographic changes, in higher temporal resolution than what is feasible with imaging (b). IchorCNA sensitivity captures initial treatment response dynamics but does not detect residual disease after 3 months of treatment (c). Log z score is calculated from a single plasma sample for each timepoint compared to a panel of control samples (n = 37).Abstract 33 Table 1Characteristics of patients at baseline and ctDNA dynamicsAbstract 33 Figure 4Phoenix detects pre- and intratreatment melanoma ctDNAWe evaluated Phoenix post-filter sample-level detection rate. Phoenix detects ctDNA in 92% of pretreatment melanoma plasma samples (green, n=12) at a specificity of 97% relative to held-out noncancerous controls (blue, n=38). Phoenix detected ctDNA in 84% of postreatment plasma samples (n=38, yellow), indicating full ctDNA clearance in 7/38 samples.Abstract 33 Figure 5ctDNA response to checkpoint blockade after 3 weeksSerial plasma samples were taken from patients on checkpoint blockade (nivolumab alone or with ipilimumab). ctDNA burden was measured as detection rate among post-filter candidate SNVs and compared to a 97% specificity boundary among a panel of healthy controls. Phoenix detects a response to checkpoint blockade, measured as a decrease in ctDNA detection rate, as early as 3 weeks as shown in 3 patients (MSK-38, MSK-40, MSK 42).AcknowledgementsThanks to support from the Conquer Cancer FoundationEthics ApprovalUse of human data in this study was approved by Memorial Sloan Kettering’s IRB, Assurance Number FWA0000499ReferencesZhang Q, Luo J, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov 2020 pp: CD-20-0047. doi:10.1158/2159-8290.CD-20-0047Bratman SV, Yang SYC., Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-0096-53.Zviran A, Schulman RC, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med 2020;26(7):1114–1124. doi:10.1038/s41591-020-0915-3Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun2017;8(1):1324. Published 2017 Nov 6. doi:10.1038/s41467-017-00965-y