Clinical validation of a cell-free DNA fragmentome assay for augmentation of lung cancer early detection.

Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Genome-wide repeat landscapes in cancer and cell-free DNA.

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Evaluation of simulation methods for tumor subclonal reconstruction.

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Elucidating the Heterogeneity of Immunotherapy Response and Immune-Related Toxicities by Longitudinal ctDNA and Immune Cell Compartment Tracking in Lung Cancer.

PURPOSE: Although immunotherapy is the mainstay of therapy for advanced non-small cell lung cancer (NSCLC), robust biomarkers of clinical response are lacking. The heterogeneity of clinical responses together with the limited value of radiographic response assessments to timely and accurately predict therapeutic effect-especially in the setting of stable disease-calls for the development of molecularly informed real-time minimally invasive approaches. In addition to capturing tumor regression, liquid biopsies may be informative in capturing immune-related adverse events (irAE). EXPERIMENTAL DESIGN: We investigated longitudinal changes in circulating tumor DNA (ctDNA) in patients with metastatic NSCLC who received immunotherapy-based regimens. Using ctDNA targeted error-correction sequencing together with matched sequencing of white blood cells and tumor tissue, we tracked serial changes in cell-free tumor load (cfTL) and determined molecular response. Peripheral T-cell repertoire dynamics were serially assessed and evaluated together with plasma protein expression profiles. RESULTS: Molecular response, defined as complete clearance of cfTL, was significantly associated with progression-free (log-rank P = 0.0003) and overall survival (log-rank P = 0.01) and was particularly informative in capturing differential survival outcomes among patients with radiographically stable disease. For patients who developed irAEs, on-treatment peripheral blood T-cell repertoire reshaping, assessed by significant T-cell receptor (TCR) clonotypic expansions and regressions, was identified on average 5 months prior to clinical diagnosis of an irAE. CONCLUSIONS: Molecular responses assist with the interpretation of heterogeneous clinical responses, especially for patients with stable disease. Our complementary assessment of the peripheral tumor and immune compartments provides an approach for monitoring of clinical benefits and irAEs during immunotherapy.

Cell-free DNA approaches for cancer early detection and interception.

Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.

Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer.

Purpose: Although immunotherapy is the mainstay of therapy for advanced non-small cell lung cancer (NSCLC), robust biomarkers of clinical response are lacking. The heterogeneity of clinical responses together with the limited value of radiographic response assessments to timely and accurately predict therapeutic effect -especially in the setting of stable disease-call for the development of molecularly-informed real-time minimally invasive predictive biomarkers. In addition to capturing tumor regression, liquid biopsies may be informative in evaluating immune-related adverse events (irAEs). Experimental design: We investigated longitudinal changes in circulating tumor DNA (ctDNA) in patients with metastatic NSCLC who received immunotherapy-based regimens. Using ctDNA targeted error-correction sequencing together with matched sequencing of white blood cells and tumor tissue, we tracked serial changes in cell-free tumor load (cfTL) and determined molecular response for each patient. Peripheral T-cell repertoire dynamics were serially assessed and evaluated together with plasma protein expression profiles. Results: Molecular response, defined as complete clearance of cfTL, was significantly associated with progression-free (log-rank p=0.0003) and overall survival (log-rank p=0.01) and was particularly informative in capturing differential survival outcomes among patients with radiographically stable disease. For patients who developed irAEs, peripheral blood T-cell repertoire reshaping, assessed by significant TCR clonotypic expansions and regressions were noted on-treatment. Conclusions: Molecular responses assist with interpretation of heterogeneous clinical responses especially for patients with stable disease. Our complementary assessment of the tumor and immune compartments by liquid biopsies provides an approach for monitoring of clinical benefit and immune-related toxicities for patients with NSCLC receiving immunotherapy. Statement of translational relevance: Longitudinal dynamic changes in cell-free tumor load and reshaping of the peripheral T-cell repertoire capture clinical outcomes and immune-related toxicities during immunotherapy for patients with non-small cell lung cancer.

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer.

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

H2Bub1 loss is an early contributor to clear cell ovarian cancer progression.

Epigenetic aberrations, including posttranslational modifications of core histones, are major contributors to cancer. Here, we define the status of histone H2B monoubiquitylation (H2Bub1) in clear cell ovarian carcinoma (CCOC), low-grade serous carcinoma, and endometrioid carcinomas. We report that clear cell carcinomas exhibited profound loss, with nearly all cases showing low or negative H2Bub1 expression. Moreover, we found that H2Bub1 loss occurred in endometriosis and atypical endometriosis, which are established precursors to CCOCs. To examine whether dysregulation of a specific E3 ligase contributes to the loss of H2Bub1, we explored expression of ring finger protein 40 (RNF40), ARID1A, and UBR7 in the same case cohort. Loss of RNF40 was significantly and profoundly correlated with loss of H2Bub1. Using genome-wide DNA methylation profiles of 230 patients with CCOC, we identified hypermethylation of RNF40 in CCOC as a likely mechanism underlying the loss of H2Bub1. Finally, we demonstrated that H2Bub1 depletion promoted cell proliferation and clonogenicity in an endometriosis cell line. Collectively, our results indicate that H2Bub1 plays a tumor-suppressive role in CCOCs and that its loss contributes to disease progression.

CRISPR-Cas9 for selective targeting of somatic mutations in pancreatic cancers.

Somatic mutations are desirable targets for selective elimination of cancer, yet most are found within the noncoding regions. We propose a novel, cancer-specific killing approach using CRISPR-Cas9 which exploits the requirement of a protospacer adjacent motif (PAM) for Cas9 activity. Through whole genome sequencing (WGS) of paired tumor minus normal (T-N) samples from three pancreatic cancer patients (Panc480, Panc504, and Panc1002), we identified an average of 417 somatic PAMs per tumor produced from single base substitutions. We analyzed 591 paired T-N samples from The International Cancer Genome Consortium and discovered medians of ~455 somatic PAMs per tumor in pancreatic, ~2800 in lung, and ~3200 in esophageal cancer cohorts. Finally, we demonstrated >80% selective cell death of two targeted pancreatic cancer cell lines in co-cultures using 4-9 sgRNAs, targeting noncoding regions, designed from the somatic PAM discovery approach. We also showed no off-target activity from these tumor-specific sgRNAs through WGS.

Mechanism of delayed cell death following simultaneous CRISPR-Cas9 targeting in pancreatic cancers.

When we transduced pancreatic cancers with sgRNAs that targeted 2-16 target sites in the human genome, we found that increasing the number of CRISPR-Cas9 target sites produced greater cytotoxicity, with >99% growth inhibition observed by targeting only 12 sites. However, cell death was delayed by 2-3 weeks after sgRNA transduction, in contrast to the repair of double strand DNA breaks (DSBs) that happened within 3 days after transduction. To explain this discrepancy, we used both cytogenetics and whole genome sequencing to interrogate the genome. We first detected chromatid and chromosome breaks, followed by radial formations, dicentric, ring chromosomes, and other chromosomal aberrations that peaked at 14 days after transduction. Structural variants (SVs) were detected at sites that were directly targeted by CRISPR-Cas9, including SVs generated from two sites that were targeted, but the vast majority of SVs (89.4%) were detected elsewhere in the genome that arose later than those directly targeted. Cells also underwent polyploidization that peaked at day 10 as detected by XY FISH assay, and ultimately died via apoptosis. Overall, we found that the simultaneous DSBs induced by CRISPR-Cas9 in pancreatic cancers caused chromosomal instability and polyploidization that ultimately led to delayed cell death.

Cell-Free DNA Fragmentomes in the Diagnostic Evaluation of Patients With Symptoms Suggestive of Lung Cancer.

BACKGROUND: The diagnostic workup of individuals suspected of having lung cancer can be complex and protracted because conventional symptoms of lung cancer have low specificity and sensitivity. RESEARCH QUESTION: Among individuals with symptoms of lung cancer, can a blood-based approach to analyze cell-free DNA (cfDNA) fragmentation (the DNA evaluation of fragments for early interception [DELFI] score) enhance evaluation for the possible presence of lung cancer? STUDY DESIGN AND METHODS: Adults were referred to Bispebjerg Hospital (Copenhagen, Denmark) for diagnostic evaluation of initial imaging anomalies and symptoms consistent with lung cancer. Numbers and types of symptoms were extracted from medical records. cfDNA from plasma samples obtained at the prediagnostic visit was isolated, sequenced, and analyzed for genome-wide cfDNA fragmentation patterns. The relationships among clinical presentation, cancer status, and DELFI score were examined. RESULTS: A total of 296 individuals were analyzed. Median DELFI scores were higher for those with lung cancer (n = 98) than those without cancer (n = 198; 0.94 vs 0.19; P < .001). In a multivariate model adjusted for age, smoking history, and presenting symptoms, the addition of the DELFI score improved the prediction of lung cancer for those who demonstrated symptoms (area under the receiver operating characteristic curve, 0.74-0.94). INTERPRETATION: The DELFI score distinguishes individuals with lung cancer from those without cancer better than suspicious symptoms do. These results represent proof-of-concept support that fragmentation-based biomarker approaches may facilitate diagnostic resolution for patients with concerning symptoms of lung cancer.

Potential utility of risk stratification for multicancer screening with liquid biopsy tests.

Our proof-of-concept study reveals the potential of risk stratification by the combined effects of age, polygenic risk scores (PRS), and non-genetic risk factors in increasing the risk-benefit balance of rapidly emerging non-invasive multicancer early detection (MCED) liquid biopsy tests. We develop and validate sex-specific pan-cancer risk scores (PCRSs), defined by the combination of body mass index, smoking, family history of cancers, and cancer-specific polygenic risk scores (PRSs), to predict the absolute risk of developing at least one of the many common cancer types. We demonstrate the added value of PRSs in improving the predictive performance of the risk factors only model and project the positive and negative predictive values for two promising multicancer screening tests across risk strata defined by age and PCRS.

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions.

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

Metastatic Colorectal Cancer Treatment Response Evaluation by Ultra-Deep Sequencing of Cell-Free DNA and Matched White Blood Cells.

PURPOSE: Circulating tumor DNA (ctDNA) has the potential to guide therapy selection and monitor treatment response in patients with metastatic cancer. However, germline and clonal hematopoiesis-associated alterations can confound identification of tumor-specific mutations in cell-free DNA (cfDNA), often requiring additional sequencing of tumor tissue. The current study assessed whether ctDNA-based treatment response monitoring could be performed in a tumor tissue-independent manner by combining ultra-deep targeted sequencing analyses of cfDNA with patient-matched white blood cell (WBC)-derived DNA. EXPERIMENTAL DESIGN: In total, 183 cfDNA and 49 WBC samples, along with 28 tissue samples, from 52 patients with metastatic colorectal cancer participating in the prospective phase III CAIRO5 clinical trial were analyzed using an ultra-deep targeted sequencing liquid biopsy assay. RESULTS: The combined cfDNA and WBC analysis prevented false-positives due to germline or hematopoietic variants in 40% of patients. Patient-matched tumor tissue sequencing did not provide additional information. Longitudinal analyses of ctDNA were more predictive of overall survival than standard-of-care radiological response evaluation. ctDNA mutations related to primary or acquired resistance to panitumumab were identified in 42% of patients. CONCLUSIONS: Accurate calling of ctDNA mutations for treatment response monitoring is feasible in a tumor tissue-independent manner by combined cfDNA and patient-matched WBC genomic DNA analysis. This tissue biopsy-independent approach simplifies sample logistics and facilitates the application of liquid biopsy ctDNA testing for evaluation of emerging therapy resistance, opening new avenues for early adaptation of treatment regimens.

Detecting Liver Cancer Using Cell-Free DNA Fragmentomes.

Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome analyses to evaluate 724 individuals from the United States, the European Union, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multifeature fragmentome data, the sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for noninvasive cancer detection. SIGNIFICANCE: There is a great need for accessible and sensitive screening approaches for HCC worldwide. We have developed an approach for examining genome-wide cfDNA fragmentation features to provide a high-performing and cost-effective approach for liver cancer detection. See related commentary Rolfo and Russo, p. 532. This article is highlighted in the In This Issue feature, p. 517.

Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data.

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR (Robinson et al., Bioinformatics 26(1):139-140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers.

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. SIGNIFICANCE: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Detecting and quantifying antibody reactivity in PhIP-Seq data with BEER.

SUMMARY: Because of their high abundance, easy accessibility in peripheral blood, and relative stability ex vivo, antibodies serve as excellent records of environmental exposures and immune responses. Phage Immuno-Precipitation Sequencing (PhIP-Seq) is the most efficient technique available for assessing antibody binding to hundreds of thousands of peptides at a cohort scale. PhIP-Seq is a high-throughput approach for assessing antibody reactivity to hundreds of thousands of candidate epitopes. Accurate detection of weakly reactive peptides is particularly important for characterizing the development and decline of antibody responses. Here, we present BEER (Bayesian Enrichment Estimation in R), a software package specifically developed for the quantification of peptide reactivity from PhIP-Seq experiments. BEER implements a hierarchical model and produces posterior probabilities for peptide reactivity and a fold change estimate to quantify the magnitude. BEER also offers functionality to infer peptide reactivity based on the edgeR package, though the improvement in speed is offset by slightly lower sensitivity compared to the Bayesian approach, specifically for weakly reactive peptides. AVAILABILITY AND IMPLEMENTATION: BEER is implemented in R and freely available from the Bioconductor repository at https://bioconductor.org/packages/release/bioc/html/beer.html.