Search

Patient characteristics

Correlation heatmap of Pearson correlation coefficients of hierarchically clustered individual cells across stromal populations identified by originating lesion type and tSNE cluster suggests multiple unique subtypes.

Figures S1, S2, S3 and S4. APC and KRAS genotyping of crypts and bulk biopsies in adenomas from MAP, UPF, LS and cMMRD cases. Figures S5, S6 and S7. APC and KRAS genotyping of crypts and bulk biopsies in adenomas from SPO cases. Figure S8, S9 and S10. Hyerarchical clustering analysis of crypts based on KRAS genotyping results. Figure S11. Comparative analysis of mutant allele discrimination by (A) Light Cycler 480 (Roche) and (B) nanofluidic PCR using Dynamic array (Fluidigm). Figure S12. A. Comparative analysis of KRAS genotyping using whole genome amplified (WGA) DNA or pre-amplified DNA using 15 cycles of a nested PCR. Figure S13. Genotyping results including water controls of random KRAS hotspot mutation analysis after performing a pre-amplification step using a nested PCR apporach. Figure S14. Independent validation of KRAS hotspot mutation that have been detected by Nanofluidic Dynamic array using the Nanofluidic Digital PCR genotyping as an alternative approach. Figure S15. Independent validation of KRAS hotspot mutation that have been detected by Nanofluidic Dynamic array using the Nanofluidic Digital PCR genotyping as an alternative approach.

Mathematical model simulation of low delta tumor growth (lambda = 0.2)

Table S1: Samples analyzed by DNA and RNA sequencing; Table S2: DNA (n=67) and RNA (n=48) sequencing quality metrics for each sample; Table S3: Exonic mutations in known lung adenocarcinoma drivers and other cancer associated genes; Table S4: Genes mutated in atypical adenomatous hyperplasias and lung adenocarcinomas; Table S5: Validation of specific BRAF, KRAS and EGFR mutations using digital PCR; Table S6: Genes differentially expressed among normal lung tissues, atypical adenomatous hyperplasias and lung adenocarcinomas; Table S7: Genes differentially expressed between BRAF-mutant, KRAS-mutant and BRAF/KRAS wild type atypical adenomatous hyperplasias; Table S8: Markers of immune signaling that are aberrantly expressed between normal lung tissues, atypical adenomatous hyperplasias and lung adenocarcinomas.

Purpose:Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology.Experimental Design:We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology.Results:In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth.Conclusions:At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

Purpose:Early detection of pancreatic ductal adenocarcinoma (PDAC) remains elusive. Precursor lesions of PDAC, specifically intraductal papillary mucinous neoplasms (IPMNs), represent a bona fide pathway to invasive neoplasia, although the molecular correlates of progression remain to be fully elucidated. Single-cell transcriptomics provides a unique avenue for dissecting both the epithelial and microenvironmental heterogeneities that accompany multistep progression from noninvasive IPMNs to PDAC.Experimental Design:Single-cell RNA sequencing was performed through droplet-based sequencing on 5,403 cells from 2 low-grade IPMNs (LGD-IPMNs), 2 high-grade IPMNs (HGD-IPMN), and 2 PDACs (all surgically resected).Results:Analysis of single-cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of noninvasive dysplasia to invasive cancer. Although HGD-IPMNs expressed many core signaling pathways described in PDAC, LGD-IPMNs harbored subsets of single cells with a transcriptomic profile that overlapped with invasive cancer. Notably, a proinflammatory immune component was readily seen in low-grade IPMNs, composed of cytotoxic T cells, activated T-helper cells, and dendritic cells, which was progressively depleted during neoplastic progression, accompanied by infiltration of myeloid-derived suppressor cells. Finally, stromal myofibroblast populations were heterogeneous and acquired a previously described tumor-promoting and immune-evading phenotype during invasive carcinogenesis.Conclusions:This study demonstrates the ability to perform high-resolution profiling of the transcriptomic changes that occur during multistep progression of cystic PDAC precursors to cancer. Notably, single-cell analysis provides an unparalleled insight into both the epithelial and microenvironmental heterogeneities that accompany early cancer pathogenesis and might be a useful substrate to identify targets for cancer interception.See related commentary by Hernandez-Barco et al., p. 2027

Purpose: The majority of genomic alterations causing intratumoral heterogeneity (ITH) in colorectal cancer are thought to arise during early stages of carcinogenesis as a burst but only after truncal mutations in APC have expanded a single founder clone. We have investigated if the initial source of ITH is consequent to multiple independent lineages derived from different crypts harboring distinct truncal APC and driver KRAS mutations, thus challenging the prevailing monoclonal monocryptal model.Experimental Design: High-depth next-generation sequencing and SNP arrays were performed in whole-lesion extracts of 37 familial adenomatous polyposis colorectal adenomas. Also, ultrasensitive genotyping of hotspot mutations of APC and KRAS was performed using nanofluidic PCRs in matched bulk biopsies (n = 59) and crypts (n = 591) from 18 adenomas and seven carcinomas and adjacent normal tissues.Results: Multiple co-occurring truncal APC and driver KRAS alterations were uncovered in whole-lesion extracts from adenomas and subsequently confirmed to belong to multiple clones. Ultrasensitive genotyping of bulk biopsies and crypts revealed novel undetected APC mutations that were prominent among carcinomas, whereas abundant wild-type APC crypts were detected in adenomas. KRAS mutational heterogeneity within crypts was evident in both adenomas and carcinomas with a higher degree of concordance between biopsy and crypt genotyping in carcinomas. Nonrandom heterogeneity among crypts was also observed.Conclusions: The striking degree of nonrandom intercrypt heterogeneity in truncal and driver gene mutations observed in adenomas and carcinomas is consistent with a polycryptal model derived from multiple independent initiation linages as the source of early ITH in colorectal carcinogenesis. Clin Cancer Res; 23(19); 5936–47. ©2017 AACR.

Purpose:Little is known about the genetic alterations characteristic of small bowel adenocarcinoma (SBA). Our purpose was to identify targetable alterations and develop experimental models of this disease.Experimental Design: Whole-exome sequencing (WES) was completed on 17 SBA patient samples and targeted-exome sequencing (TES) on 27 samples to confirm relevant driver mutations. Two SBA models with ERBB2 kinase activating mutations were tested for sensitivity to anti-ERBB2 agents in vivo and in vitro. Biochemical changes were measured by reverse-phase protein arrays.Results:WES identified somatic mutations in 4 canonical pathways (WNT, ERBB2, STAT3, and chromatin remodeling), which were validated in the TES cohort. Although APC mutations were present in only 23% of samples, additional WNT-related alterations were seen in 12%. ERBB2 mutations and amplifications were present in 23% of samples. Patients with alterations in the ERBB2 signaling cascade (64%) demonstrated worse clinical outcomes (median survival 70.3 months vs. 109 months; log-rank HR = 2.4, P = 0.03). Two ERBB2-mutated (V842I and Y803H) cell lines were generated from SBA patient samples. Both demonstrated high sensitivity to ERBB2 inhibitor dacomitinib (IC50 < 2.5 nmol/L). In xenografts derived from these samples, treatment with dacomitinib reduced tumor growth by 39% and 59%, respectively, whereas it had no effect in an SBA wild-type ERBB2 model.Conclusions:The in vitro and in vivo models of SBA developed here provide a valuable resource for understanding targetable mutations in this disease. Our findings support clinical efforts to target activating ERBB2 mutations in patients with SBA that harbor these alterations.

There is a dearth of knowledge about the pathogenesis of premalignant lung lesions, especially for atypical adenomatous hyperplasia (AAH), the only known precursor for the major lung cancer subtype adenocarcinoma (LUAD). In this study, we performed deep DNA and RNA sequencing analyses of a set of AAH, LUAD, and normal tissues. Somatic BRAF variants were found in AAHs from 5 of 22 (23%) patients, 4 of 5 of whom had matched LUAD with driver EGFR mutations. KRAS mutations were present in AAHs from 4 of 22 (18%) of patients. KRAS mutations in AAH were only found in ever-smokers and were exclusive to BRAF-mutant cases. Integrative analysis revealed profiles expressed in KRAS-mutant cases (UBE2C, REL) and BRAF-mutant cases (MAX) of AAH, or common to both sets of cases (suppressed AXL). Gene sets associated with suppressed antitumor (Th1; IL12A, GZMB) and elevated protumor (CCR2, CTLA-4) immune signaling were enriched in AAH development and progression. Our results reveal potentially divergent BRAF or KRAS pathways in AAH as well as immune dysregulation in the pathogenesis of this premalignant lung lesion. Cancer Res; 77(22); 6119–30. ©2017 AACR.

Additional methods

Figure S1: Mutation burden in atypical adenomatous hyperplasia and lung adenocarcinoma; Figure S2: Mutation burden in atypical adenomatous hyperplasia and lung adenocarcinoma based on tobacco history; Figure S3: Mutations in cancer driver genes in atypical adenomatous hyperplasia and lung adenocarcinoma; Figure S4: Representative validation of somatic mutations by digital PCR.

Table S1. Summary of adenomas with multiple APC and KRAS somatic hits identified by next-generation sequencing. Table S2. Clinical and pathologic characteristics of patients analyzed in this manuscript. Table S3. Analysis of APC, KRAS and 5q loss using AmpliSeq and SNP array TableS4. Number of normal mucosa, adenomas, tumors and crypts analyzed per case. NM; normal mucosa. Table S5. Analytical sensibility of Light Cycler® 480 (Roche) and dynamic array (Fluidigm) to detect KRAS mutations. Table S6. In silico of the APC somatic hits observed by next-generaiton sequencing that were labelled as VUS. B, benign; N, neutral; D, damaging; P, Probably damaging. Table S7. APC analysis of adenomas and carcinomas in bulk biopsies and isolated crypts. The analysis of adjancent normal mucosa was not performed. Table S8. KRAS genotyping results of adenomas and carcinomas by digital and dynamic array (Fluidigm) in bulk biopsies and isolated crypts Table S9. Primers and probes used in this manuscript.

Supplementary Materials and Methods (Includes reference to Table S9 and Figures S11-15)

Supplementary methods including detailed methods on specimen collection and evaluation; DNA and RNA isolation; deep DNA targeted sequencing and transcriptome sequencing; identification and validation of somatic mutations; and expression analysis.

Supplemental legend

Mathematical model simulation of high delta tumor growth (lambda = 1.5)

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non–small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown. Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC. Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis. Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared “two-hit” alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1). Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities (“B allele” frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the “B allele” frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

Purpose: Little is known about the genetic alterations characteristic of small bowel adenocarcinoma (SBA). Our purpose was to identify targetable alterations and develop experimental models of this disease. Experimental Design: Whole-exome sequencing (WES) was completed on 17 SBA patient samples and targeted-exome sequencing (TES) on 27 samples to confirm relevant driver mutations. Two SBA models with ERBB2 kinase activating mutations were tested for sensitivity to anti-ERBB2 agents in vivo and in vitro. Biochemical changes were measured by reverse-phase protein arrays. Results: WES identified somatic mutations in 4 canonical pathways (WNT, ERBB2, STAT3, and chromatin remodeling), which were validated in the TES cohort. Although APC mutations were present in only 23% of samples, additional WNT-related alterations were seen in 12%. ERBB2 mutations and amplifications were present in 23% of samples. Patients with alterations in the ERBB2 signaling cascade (64%) demonstrated worse clinical outcomes (median survival 70.3 months vs. 109 months; log-rank HR = 2.4, P = 0.03). Two ERBB2-mutated (V842I and Y803H) cell lines were generated from SBA patient samples. Both demonstrated high sensitivity to ERBB2 inhibitor dacomitinib (IC50 < 2.5 nmol/L). In xenografts derived from these samples, treatment with dacomitinib reduced tumor growth by 39% and 59%, respectively, whereas it had no effect in an SBA wild-type ERBB2 model. Conclusions: The in vitro and in vivo models of SBA developed here provide a valuable resource for understanding targetable mutations in this disease. Our findings support clinical efforts to target activating ERBB2 mutations in patients with SBA that harbor these alterations.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. Experimental Design: We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. Results: In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. Conclusions: At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

Objective Uterine carcinosarcoma (UCS) is a rare and aggressive form of uterine cancer. It is bi-phasic, exhibiting histological features of both malignant epithelial (carcinoma) and mesenchymal (sarcoma) elements, reflected in ambiguity in accepted treatment guidelines. We sought to study the genomic and transcriptomic profiles of these elements individually to gain further insights into the development of these tumors. Methods We macro-dissected carcinomatous, sarcomatous, and normal tissues from formalin fixed paraffin embedded uterine samples of 10 UCS patients. Single nucleotide polymorphism microarrays, targeted DNA sequencing and whole-transcriptome RNA-sequencing were performed. Somatic chromosomal alterations (SCAs), point mutation and gene expression profiles were compared between carcinomatous and sarcomatous components. Results In addition to TP53, other recurrently mutated genes harboring putative driver or loss-of-function mutations included PTEN, FBXW7, FGFR2, KRAS, PIK3CA and CTNNB1, genes known to be involved in UCS. Intra-patient somatic mutation and SCA profiles were highly similar between paired carcinoma and sarcoma samples. An epithelial-mesenchymal transition (EMT) signature tended to differentiate components, with EMT-like status more common in advanced-stage patients exhibiting higher inter-component SCA heterogeneity. Conclusions From DNA analysis, our results indicate a monoclonal disease origin for this cohort. Yet expression-derived EMT statuses of the carcinomatous and sarcomatous components were often discrepant, and advanced cases displayed greater genomic heterogeneity. Therefore, separately-profiled components of UCS tumors may better inform disease progression or potential.

Purpose: The majority of genomic alterations causing intratumoral heterogeneity (ITH) in colorectal cancer are thought to arise during early stages of carcinogenesis as a burst but only after truncal mutations in APC have expanded a single founder clone. We have investigated if the initial source of ITH is consequent to multiple independent lineages derived from different crypts harboring distinct truncal APC and driver KRAS mutations, thus challenging the prevailing monoclonal monocryptal model. Experimental Design: High-depth next-generation sequencing and SNP arrays were performed in whole-lesion extracts of 37 familial adenomatous polyposis colorectal adenomas. Also, ultrasensitive genotyping of hotspot mutations of APC and KRAS was performed using nanofluidic PCRs in matched bulk biopsies (n = 59) and crypts (n = 591) from 18 adenomas and seven carcinomas and adjacent normal tissues. Results: Multiple co-occurring truncal APC and driver KRAS alterations were uncovered in whole-lesion extracts from adenomas and subsequently confirmed to belong to multiple clones. Ultrasensitive genotyping of bulk biopsies and crypts revealed novel undetected APC mutations that were prominent among carcinomas, whereas abundant wild-type APC crypts were detected in adenomas. KRAS mutational heterogeneity within crypts was evident in both adenomas and carcinomas with a higher degree of concordance between biopsy and crypt genotyping in carcinomas. Nonrandom heterogeneity among crypts was also observed. Conclusions: The striking degree of nonrandom intercrypt heterogeneity in truncal and driver gene mutations observed in adenomas and carcinomas is consistent with a polycryptal model derived from multiple independent initiation linages as the source of early ITH in colorectal carcinogenesis. Clin Cancer Res; 23(19); 5936–47. ©2017 AACR.

Pancreatic ductal adenocarcinoma (PDAC) requires mitochondrial oxidative phosphorylation (OXPHOS) to fuel its growth; however, broadly inhibiting this pathway might also disrupt essential mitochondrial functions in normal tissues. PDAC cells exhibit abnormally fragmented mitochondria that are essential to the oncogenicity of PDAC, but it was unclear if this mitochondrial feature was a valid therapeutic target. Here, we present evidence that normalizing the fragmented mitochondria of pancreatic cancer via the process of mitochondrial fusion reduces OXPHOS, which correlates with suppressed tumor growth and improved survival in preclinical models. Mitochondrial fusion was achieved by genetic or pharmacologic inhibition of dynamin-related protein-1 (Drp1) or through overexpression of mitofusin-2 (Mfn2). Notably, we found that oral leflunomide, an FDA-approved arthritis drug, promoted a 2-fold increase in Mfn2 expression in tumors and was repurposed as a chemotherapeutic agent, improving the median survival of mice with spontaneous tumors by 50% compared with vehicle. We found that the chief tumor-suppressive mechanism of mitochondrial fusion was enhanced mitophagy, which proportionally reduced mitochondrial mass and ATP production. These data suggest that mitochondrial fusion is a specific and druggable regulator of pancreatic cancer growth that could be rapidly translated to the clinic.

Somatic copy number alterations (SCNAs), including deletions and duplications, serve as hallmarks of tumorigenesis. SCNAs may span entire chromosomes and typically result in deviations from an expected one-to-one ratio of alleles at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation (CBS) algorithm, providing segment mean copy number estimates from Affymetrix single-nucleotide polymorphism DNA microarray total (log R ratio) intensities, but not allele-specific (“B allele”) intensities that inform of AI. Here we seek to provide a TCGA-wide description of AI in tumor genomes, including AI induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH), using a powerful haplotype-based method applied to allele-specific intensities. We present AI summaries for all 33 tumor sites and propose an automated adjustment procedure to improve calibration of existing SCNA calls in TCGA for tumors with high levels of aneuploidy where baseline intensities were difficult to establish without annotation of AI. Overall, 94% of tumor samples exhibited AI. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p as well as mixed event types on 8p and 13q. The AI-based approach identified frequent cnLOH on 17p across multiple tumor sites, with additional site-specific cnLOH patterns. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

Background Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury. Results In this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information. Conclusions The systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

Motivation Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer. Results The REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and ‘B allele’ frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or ‘mirrored subclonal allelic imbalance’, is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. Availability and implementation RECUR is available as an R application. Source code and documentation are available at scheet.org. Supplementary information Supplementary data are available at Bioinformatics online.

Background & Aims We aimed to investigate the clinical utility of circulating tumor cell DNA (ctDNA) and exosome DNA (exoDNA) in pancreatic cancer. Methods We collected liquid biopsy samples from 194 patients undergoing treatment for localized or metastatic pancreatic adenocarcinoma from April 7, 2015, through October 13, 2017 (425 blood samples collected before [baseline] and during therapy). Additional liquid biopsy samples were collected from 37 disease control individuals. Droplet digital polymerase chain reaction was used to determine KRAS mutant allele fraction (MAF) from ctDNA and exoDNA purified from plasma. For the longitudinal analysis, we analyzed exoDNA and ctDNA in 123 serial blood samples from 34 patients. We performed analysis including Cox regression, Fisher exact test, and Bayesian inference to associate KRAS MAFs in exoDNA and ctDNA with prognostic and predictive outcomes. Results In the 34 patients with potentially resectable tumors, an increase in exoDNA level after neoadjuvant therapy was significantly associated with disease progression (P = .003), whereas ctDNA did not show correlations with outcomes. Concordance rates of KRAS mutations present in surgically resected tissue and detected in liquid biopsy samples were greater than 95%. On univariate analysis, patients with metastases and detectable ctDNA at baseline status had significantly shorter times of progression-free survival (PFS) (hazard ratio [HR] for death, 1.8; 95% CI, 1.1–3.0; P = .019), and overall survival (OS) (HR, 2.8; 95% CI, 1.4–5.7; P = .0045) compared with patients without detectable ctDNA. On multivariate analysis, MAFs ≥5% in exoDNA were a significant predictor of PFS (HR, 2.28; 95% CI, 1.18–4.40; P = .014) and OS (HR, 3.46; 95% CI, 1.40–8.50; P = .007). A multianalyte approach showed detection of both ctDNA and exoDNA MAFs ≥5% at baseline status to be a significant predictor of OS (HR, 7.73, 95% CI, 2.61–22.91, P = .00002) on multivariate analysis. In the longitudinal analysis, an MAF peak above 1% in exoDNA was significantly associated with radiologic progression (P = .0003). Conclusions In a prospective cohort of pancreatic cancer patients, we show how longitudinal monitoring using liquid biopsy samples through exoDNA and ctDNA provides both predictive and prognostic information relevant to therapeutic stratification.

There is a dearth of knowledge about the pathogenesis of premalignant lung lesions, especially for atypical adenomatous hyperplasia (AAH), the only known precursor for the major lung cancer subtype adenocarcinoma (LUAD). In this study, we performed deep DNA and RNA sequencing analyses of a set of AAH, LUAD, and normal tissues. Somatic BRAF variants were found in AAHs from 5 of 22 (23%) patients, 4 of 5 of whom had matched LUAD with driver EGFR mutations. KRAS mutations were present in AAHs from 4 of 22 (18%) of patients. KRAS mutations in AAH were only found in ever-smokers and were exclusive to BRAF-mutant cases. Integrative analysis revealed profiles expressed in KRAS-mutant cases (UBE2C, REL) and BRAF-mutant cases (MAX) of AAH, or common to both sets of cases (suppressed AXL). Gene sets associated with suppressed antitumor (Th1; IL12A, GZMB) and elevated protumor (CCR2, CTLA-4) immune signaling were enriched in AAH development and progression. Our results reveal potentially divergent BRAF or KRAS pathways in AAH as well as immune dysregulation in the pathogenesis of this premalignant lung lesion. Cancer Res; 77(22); 6119–30. ©2017 AACR.

Multiple endocrine neoplasia type 1 (MEN1) is a rare, autosomal-dominant disorder caused by alterations in the MEN1 gene on chromosome 11.1 Ninety percent of patients with MEN1 initially present with hyperparathyroidism (HPT), affecting men and women equally (unlike sporadic HPT) and at a younger age of onset (compared with sporadic HPT).2 Other commonly affected endocrine glands include the pancreas and pituitary; >20 different types of tumors have been described in MEN1 patients. Of particular concern is the development of pancreatic neuroendocrine tumors (PNET), some of which produce gastrin, insulin, or other secretory hormones; metastatic PNETs are the leading cause of disease-specific death in MEN1 patients.3,4 Identifying a cellular mechanism behind the multiple neoplasms occurring in MEN1 patients could eventually enable clinicians to predict and properly screen for the development of PNET at the time of HPT presentation has the important potential to prevent downstream morbidity and mortality. A number of genes are known to be involved in neuroendocrine tumorigenesis, including MEN1, RET, VHL, TSC1, and TSC2,5 with mutations in MEN1 remaining the most common form of genetic predisposition to neuroendocrine tumors. More than 1,300 mutations6 in the MEN1 gene have been reported in families with MEN1 syndrome, yet the exact mechanisms by which these mutations cause the MEN1-related pathologies are not known.3,7,8 The heterozygous germline-inactivating mutation in the MEN1 may be followed by loss of the normal copy of this gene or a somatic inactivating mutation (second hit), leading to complete loss of function of the encoded protein menin. However, genotype–phenotype analysis has not revealed a clear pattern of disease penetrance in MEN1 patients.9,10 Additionally, it is reported that 5–30% of MEN1 patients do not have an identifiable mutation by standard testing.7,11 Loss of heterozygosity (LOH) has been described in parathyroid tissue of sporadic HPT patients, particularly in chromosome 11 in association with MEN1 gene alterations.8,12 No relationship has been found between MEN1-related HPT and tumor suppressor genes, such as TP53,13 although mutations of this gene have been associated with other neuroendocrine tumors. In contrast, whole-exome sequencing of parathyroid tissue from sporadic HPT patients has not revealed clear mutation patterns. Somatic mutations in the MEN1 gene have been described in 15– 35% of non-MEN1 parathyroid adenomas and are implicated in the pathology of sporadic HPT.8,12,14 However, few other somatic variants have been found to be harbored with significant frequency in sporadic HPT adenomas.12 Whole-exome sequencing has previously been used to identify somatic mutations in samples from sporadic HPT adenomas, parathyroid carcinomas, and other sites (ie, PNET), but not MEN1-related HPT.8,12,15 Because of the known germline mutation in MEN1 patients, we hypothesized that whole-exome sequencing on blood and tissue samples of HPT patients could aid in the following goals: (1) Identify acquired somatic mutations involved in functional pathways and tumorigenic networks, and (2) elucidate additional germline risk factors associated with outcomes.

By 2030, pancreatic ductal adenocarcinoma (PDAC) will likely become the second leading cause of cancer-related death. A lack of actionable mutations continues to be a significant challenge in addressing the disease in a precision medicine setting. As a result, current treatment options are often limited to genotype-independent cytotoxic agents. Patient derived tumor organoid (PDOs) models have gained traction as potential tools for therapeutic stratification. We characterized single cell gene expression profiles of PDOs and applied an in silico therapeutic drug prediction model to identify and validate combinatorial strategies targeting heterogeneous subpopulations. Four established PDOs from PDAC were profiled through single cell RNA-Seq analysis in order to identify subpopulations of cancer cells with differing therapeutic response. The PDOs were then subjected to a high-throughput drug screen of 764 agency approved candidates. Top candidates, including combinatorial drug selections, were orthogonally validated through single cell gene expression analysis and evaluated for efficacy and synergistic interactions. Single-cell transcriptomic profiling of PDOs revealed unique subpopulations of cancer cells with differential expression of DNA Damage response, protein and fatty acid metabolism, and inflammatory related pathways. Effective combinatorial therapies were projected based on pharmacogenomic predictions targeting opposite gene expression patterns of each subpopulation and validated through drug library screens. In a PDO, common sensitivity nodes utilizing epigenetic modifiers were detected among all tumor derived subpopulations. A synergy matrix seeded with PDOs and evaluated using Bliss independence revealed that combinatorial treatment with HDAC-inhibitors and PARP1-inhibitors produced the most profound synergistic result. Mechanistically, HDAC inhibitors limit the activity of DNA repair proteins which in conjunction with PARP-inhibition leads to synergistic outcomes. We have demonstrated the utility of single cell transcriptomic profiling from PDOs in identifying viable treatment options using a pharmacogenomic strategy. The sophisticated heterogeneity of PDOs, revealed the necessity of using combinatorial strategies to target all relevant subpopulations. Furthermore, this strategy revealed an exciting synergistic interplay between HDAC and PARP1 inhibitors, an interaction that has never before been shown in a PDAC case. Looking forward, our model not only may be a vehicle used to discover new drug candidates and elucidate novel mechanisms, but may also be the first step towards the establishment of a true precision medicine paradigm for PDAC. Citation Format: Vincent Bernard, F. Anthony San Lucas, Jonathan Huang, Reid T. Powell, Paola A. Guerrero, Alexander Semaan, Clifford C. Stephan, Peter Davies, Gauri R. Varadhachary, Matthew H. Katz, Cullen M. Taniguchi, Hector A. Alvarez, Senthil Muthuswamy, Anirban Maitra. Single cell transcriptomic profiling of ex vivo tumoroid models reveal therapeutic vulnerabilities of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1402.

Somatic chromosomal alterations play an important role in the development and progression of cancer. Allelic imbalance (AI) resulting from such changes (gain, loss, or copy-neutral loss-of-heterozygosity; cnLOH) is defined as a deviation from the 1:1 ratio of inherited parental haplotypes. Observed “B allele” frequencies (BAFs) at germline heterozygous loci, from SNP array or next-generation sequencing data, are used to detect AI. In addition, log R ratio or read-depth data, can be analyzed alone or jointly with BAFs to detect gains and losses. When AI is detected in multiple intra-individual samples, spanning similar loci, it is natural to assess whether these signals reflect the same underlying mutation. One way to do this would be to note if the observed AI differs in mutation type (gain, loss), an approach that falls short for events of the same type and subtle AI events that cannot be classified as gains, losses, or cnLOH. In such a case, or with two mutations of the same type, samples may differ in their maternal/paternal haplotype balance and consequently their alleles will shift in opposite directions. This phenomenon is indicative of a recurrent or independent mutation, or an error in chromosome segregation that generates two “mirrored” clones, one with a gain and the other with a loss. Recent studies have used such a directional AI analysis to yield important insights into cancer initiation and progression (Jakubek et al. Cancer Research 2016, Jamal-Hanjani et al. New England Journal of Medicine 2017, Turajlic et al. Cell 2018). We developed REpeat Chromosomal changes Uncovered by Reflection (RECUR) to explicitly test directionality of AI from multiple samples with overlapping AI segments through the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and BAFs from at least 2 samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. It can help identify genomic regions under selective pressure for example recurrent deletions/gains in the same genomic loci and/or regions with generalized genomic instability. Integration of directional AI, copy number, and somatic mutation data can help build more accurate phylogenetic trees and further illuminate the timing and distribution of somatic chromosomal aberrations to offer insights into cancer initiation and progression. RECUR is available at scheet.org Citation Format: Yasminka A. Jakubek, F. Anthony San Lucas, Paul Scheet. RECUR: Algorithm for directional allelic imbalance profiling and visualization from multi-sample data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2476.

RNA-seq is commonly used to profile tumors through transcript quantification and detection of alternative transcripts and gene fusions. Recent advancements enabled assessment of allele-specific gene expression. In this study, we characterize chromosomal allelic-imbalances (AI) through the identification of haplotype-specific patterns in RNA-seq data. We processed SNP-array, whole-exome sequencing (WES), and RNA-seq data for two cancer sites from The Cancer Genome Atlas (TCGA): colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD). For the WES and RNA-seq data, we phased variants in the 1000 Genomes Project database and then used the hapLOHseq algorithm to detect AI. AI events detected in the Affymetrix SNP array 6.0 data were used as a gold standard, as it queries a much larger proportion of heterozygous sites relative to WES and RNA-seq. A comparison of AI calls derived from the TCGA COAD RNA-seq and WES data to the gold standard events inferred from SNP-array yielded similar specificity estimates of 92% and 96% in WES and RNA-seq, respectively. However, with a sensitivity of 43%, RNA-seq derived AI calls had decreased overlap with gold standard events, compared to WES derived AI calls with 86% sensitivity. The TCGA LUAD analysis resulted in specificity estimates of 88% and 95% in WES and RNA-seq, respectively, and sensitivity estimates of 84% and 35%, respectively, for identifying gold standard events. Events detected exclusively in RNA-seq are classified as false positives in our sensitivity/specificity calculations. Possible validity of those events was assessed by investigating clinical correlations between RNA-seq only events and clinical phenotypes such as cancer stage, overall survival, and mutations in 21 bona fide tumor suppressor genes (TSG) involved in chromatin modification. We found a marginally significant correlation between the number of RNA-seq only events and the number of mutations in this set of TSG genes (p value = 0.07, corr. = 0.19) for the COAD study; however, we did not observe such a correlation in the LUAD study. This may be explained by a potential larger influence of extrinsic environmental factors, smoking, leading to epigenetic changes in the LUAD. In the LUAD, we observed a potential clinical association between the existence of RNA-specific events and worse overall survival (p-value = 0.15). Although at lower sensitivity levels, our results suggest that RNA-seq may be used to detect AI in the absence of available DNA profiles. Clinically, our results may indicate that higher RNA-specific AI load may have a worse overall prognosis and we are exploring this further in additional TCGA datasets. The AI we detect in the RNA-seq samples may reflect long-range epigenetic dysregulation. RNA-exclusive AI may be caused by mutations in cancer driver and lineage genes may lead to selective cis-epigenetic chromatin alterations affecting large genomic regions. Citation Format: Zuhal Ozcan, F. Anthony San Lucas, Yasminka Yakubek, Yihua Liu, Richard G. Fowler, Paul A. Scheet. The University of Texas M.D. Detection of chromosomal allelic imbalances from RNAseq data using hapLOHseq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1662.

Patterns of linkage disequilibrium are often depicted pictorially by using tools that rely on visualizations of raw data or pairwise correlations among individual markers. Such approaches can fail to highlight some of the more interesting and complex features of haplotype structure. To enable natural visual comparisons of haplotype structure across subgroups of a population (e.g. isolated subpopulations or cases and controls), we propose an alternative visualization that provides a novel graphical representation of haplotype frequencies. We introduce Haploscope, a tool for visualizing the haplotype cluster frequencies that are produced by statistical models for population haplotype variation. We demonstrate the utility of our technique by examining haplotypes around the LCT gene, an example of recent positive selection, in samples from the Human Genome Diversity Panel. Haploscope, which has flexible options for annotation and inspection of haplotypes, is available for download at http://scheet.org/software.

IMPORTANCE: Colorectal carcinomas in patients with Lynch syndrome (LS) arise in a background of mismatch repair (MMR) deficiency, display a unique immune profile with upregulation of immune checkpoints, and response to immunotherapy. However, there is still a gap in understanding the pathogenesis of MMR-deficient colorectal premalignant lesions, which is essential for the development of novel preventive strategies for LS. OBJECTIVE: To characterize the immune profile of premalignant lesions from a cohort of patients with LS. DESIGN, SETTING, AND PARTICIPANTS: Whole-genome transcriptomic analysis using next-generation sequencing was performed in colorectal polyps and carcinomas of patients with LS. As comparator and model of MMR-proficient colorectal carcinogenesis, we used samples from patients with familial adenomatous polyposis (FAP). In addition, a total of 47 colorectal carcinomas (6 hypermutants and 41 nonhypermutants) were obtained from The Cancer Genome Atlas (TCGA) for comparisons. Samples were obtained from the University of Texas MD Anderson Cancer Center and "Regina Elena" National Cancer Institute, Rome, Italy. All diagnoses were confirmed by genetic testing. Polyps were collected at the time of endoscopic surveillance and tumors were collected at the time of surgical resection. The data were analyzed from October 2016 to November 2017. MAIN OUTCOMES AND MEASURES: Assessment of the immune profile, mutational signature, mutational and neoantigen rate, and pathway enrichment analysis of neoantigens in LS premalignant lesions and their comparison with FAP premalignant lesions, LS carcinoma, and sporadic colorectal cancers from TCGA. RESULTS: The analysis was performed in a total of 28 polyps (26 tubular adenomas and 2 hyperplastic polyps) and 3 early-stage LS colorectal tumors from 24 patients (15 [62%] female; mean [SD] age, 48.12 [15.38] years) diagnosed with FAP (n = 10) and LS (n = 14). Overall, LS polyps presented with low mutational and neoantigen rates but displayed a striking immune activation profile characterized by CD4 T cells, proinflammatory (tumor necrosis factor, interleukin 12) and checkpoint molecules (LAG3 [lymphocyte activation gene 3] and PD-L1 [programmed cell death 1 ligand 1]). This immune profile was independent of mutational rate, neoantigen formation, and MMR status. In addition, we identified a small subset of LS polyps with high mutational and neoantigen rates that were comparable to hypermutant tumors and displayed additional checkpoint (CTLA4 [cytotoxic T-lymphocyte-associated protein 4]) and neoantigens involved in DNA damage response (ATM and BRCA1 signaling). CONCLUSIONS AND RELEVANCE: These findings challenge the canonical model, based on the observations made in carcinomas, that emphasizes a dependency of immune activation on the acquisition of high levels of mutations and neoantigens, thus opening the door to the implementation of immune checkpoint inhibitors and vaccines for cancer prevention in LS.

Background: Prognostic and therapeutic stratification of pancreatic ductal adenocarcinoma (PDAC) patients remains elusive due to a lack of effective biomarkers, and a predilection towards metastatic disease. Peripheral blood-based liquid biopsies for tumor markers has emerged as a potential minimally invasive strategy for tumor monitoring. We have implemented a liquid biopsy assay utilizing DNA derived from vesicles known as exosomes (exoDNA) and circulating tumor DNA (ctDNA) in the metastatic setting to determine the potential utility of these liquid biopsy compartments in tumor management. Methods: A total of 318 plasma samples from 123 metastatic pancreatic patients were prospectively collected. ExoDNA and ctDNA were then extracted from matched plasma samples. Digital PCR, was used to identify codon 12/13 KRAS gene mutations. We assessed clinical endpoints in relation to progression free survival (PFS) and overall survival (OS) using univariate and multivariate analyses. In a metastatic PDAC patient, six serial liquid biopsies and seven tissue biopsies taken throughout disease progression underwent whole genome sequencing for detection of copy number events. Results: Detection rates of KRAS mutations in exoDNA and ctDNA at baseline treatment naïve staus were 61.0% and 52.9%, respectively. On multivariate COX regression analysis, exoDNA KRAS mutant allelic fraction (MAF) ≥ 5% was a significant predictor of poorer PFS (HR 2.28, 95% CI 1.18-4.40, P=0.014) and OS (HR 3.46, 95% CI 1.40-8.50, P=0.007) in metastatic patients. Among 34 patients, liquid biopsy tumor monitoring was performed across 127 serial blood draws during a median followup time of 11.1 months. The presence of an exoDNA MAF peak ≥ 1% during tumor monitoring was significantly correlated to radiological progression (p=0.0003). Specifically, detection of an exoDNA MAF peak ≥ 1% preceded radiological progression by a median of 50 days compared to a median of 0 days for CA19-9 (p=0.03). CtDNA did not emerge as a significant predictor of survival outcomes in our cohort. In a patient with multiple longitudinal liquid biopsies, comprehensive genomic profiling of exoDNA further demonstrated our ability to capture additional mutational events as they emerged during therapy and correlated to progression including amplifications in ERBB2 and MYC and deletions in CDKN2A and SMAD4. Conclusions: Liquid biopsies in PDAC provide direct evidence of those patients likely to experience poorer outcomes allowing for more effective therapeutic stratification. Liquid biopsies also demonstrate utility in characterization of putative emerging driver events during disease progression. Citation Format: Vincent Bernard, Dong U. Kim, F. Anthony San Lucas, Jonathan Castillo, Kelvin Allenson, Feven C. Mulu, Bret M. Stephens, Jonathan Huang, Eugene Koay, Cullen M. Taniguchi, Milind Javle, Robert A. Wolff, Matthew H. Katz, Gauri R. Varadhachary, Hector A. Alvarez, Anirban Maitra. Circulating nucleic acids as biomarkers of prognosis and chemorefractory status in metastatic pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2599.

BACKGROUND: There are very few strategies to treat non-small cell lung cancer (NSCLC) at its primitive stages largely due to our poor understanding of molecular aberrations in development of the malignancy and that would be ideal targets for early treatment. Work from our group and others revealed that visually "normal" airway cells carry alterations (e.g. expression and copy number changes) that are characteristic of the nearby lung tumor, signifying a "field of cancerization" that is pertinent to the pathobiology of the tumor. Yet, the landscape of driver alterations in the cancerization field of the normal-appearing airway remains largely unexplored. METHODS: 409 cancer-associated genes were surveyed by deep targeted sequencing in tumor adjacent (small airways) and distant (mainstem bronchi/large airways, nasal epithelia, normal lung parenchyma) normal-appearing tissues as well as in multi-region tissues from paired tumors contrasting events in blood, for a total of 500 samples from 48 patients with early-stage NSCLC (11 squamous cell carcinomas and 37 adenocarcinomas). Somatic point mutations were interrogated by aggregating multiple mutation callers. Mutation were then paired with our recent study of genome-wide copy number alterations (CNAs) inferred from high-density whole-genome SNP microarrays. RESULTS: We identified somatic point mutations in 76 normal-appearing field samples (75% of patients) which were overall concordant with profiles in corresponding NSCLCs. Somatic mutation signatures associated with smoking, APOBEC activity and DNA mismatch repair were common to both airway field samples and NSCLCs from smoker cases. Airway field mutation burdens, while varied, increased significantly with proximity to the tumor. Most field mutations were observed in tumor-adjacent small airways (from 31 patients) some of which were in known drivers such as TP53, KEAP1, STK11 and KRAS. Somatic mutations were also identified in more distant normal samples including large airways (TP53, SETD2, CDKN2A), normal lung parenchyma (RB1, RET) and nasal epithelium (AKT1). We then integrated point mutation and CNA data and found "two-hit" gene alteration patterns in airway field samples that were consistent with their matched NSCLCs, including point mutated gene/CNA pairs such as KEAP1 and STK11/19p loss, TP53/17p loss and KRAS/12p gain. Some additional cases exhibited single hits (e.g., TP53) in their airway field progressing to "two-hits" (e.g., TP53/17p loss) in the matched NSCLC. CONCLUSIONS: Our integrative high-throughput sequence and genome-wide SNP analyses implicate early mutational processes and putative drivers in the progression of the mutagenized "normal" airway field of cancerization to NSCLC thus offering insights into strategies for interception via early detection and personalized prevention. Citation Format: Smruthy Sivakumar, Yasminka Jakubek, F Anthony San Lucas, Wenhua Lang, Christina McDowell, Zachary Weber, Carmen Behrens, Neda Kalhor, Cesar Moran, Randa El-Zein, Gareth Davies, Junya Fujimoto, Reza Mehran, Stephen G. Swisher, Jing Wang, Jerry Fowler, Steven Dubinett, Avrum E. Spira, Erik Ehli, Ignacio I. Wistuba, Paul Scheet, Humam Kadara. Somatic mutational processes in the cancerization field of the normal-appearing airway reveal early drivers in the development of non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3997.

Osteonecrosis is a dose-limiting toxicity in the treatment of pediatric acute lymphoblastic leukemia (ALL). Prior studies on the genetics of osteonecrosis have focused on patients ≥10 years of age, leaving the genetic risk factors for the larger group of children

Cancer-associated fibroblasts (CAFs) are a major cellular component of tumor microenvironment in most solid cancers. Altered cellular metabolism is a hallmark of cancer, and much of the published literature has focused on neoplastic cell-autonomous processes for these adaptations. We demonstrate that exosomes secreted by patient-derived CAFs can strikingly reprogram the metabolic machinery following their uptake by cancer cells. We find that CAF-derived exosomes (CDEs) inhibit mitochondrial oxidative phosphorylation, thereby increasing glycolysis and glutamine-dependent reductive carboxylation in cancer cells. Through 13C-labeled isotope labeling experiments we elucidate that exosomes supply amino acids to nutrient-deprived cancer cells in a mechanism similar to macropinocytosis, albeit without the previously described dependence on oncogenic-Kras signaling. Using intra-exosomal metabolomics, we provide compelling evidence that CDEs contain intact metabolites, including amino acids, lipids, and TCA-cycle intermediates that are avidly utilized by cancer cells for central carbon metabolism and promoting tumor growth under nutrient deprivation or nutrient stressed conditions. DOI: http://dx.doi.org/10.7554/eLife.10250.001, eLife digest Cancer cells behave differently from healthy cells in many ways. Healthy cells rely on structures called mitochondria to provide them with energy via a process that requires oxygen. However cancer cells don’t rely on this process, and instead release energy by breaking down sugars outside of the mitochondria. This may explain why cancer cells are able to thrive even when little oxygen is available. Cancer cells also interact with neighboring cells called fibroblasts, which are a major part of a tumor’s microenvironment, and recruit them into the tumors. The fibroblasts communicate with cancer cells, in part, by releasing chemical messengers packaged into tiny bubble-like structures called exosomes. Recent studies have suggested that these exosomes may help cancer cells to thrive, but there are many questions remaining about how they might do this. Now, Zhao et al. show that the fibroblasts smuggle essential nutrients to cancer cells via the exosomes and disable oxygen-based energy production in cancer cells. First, exosomes released by cancer-associated fibroblasts from people with prostate cancer were collected and marked with a green dye. Next, the green-labeled exosomes were mixed with prostate cancer cells, and shown to be absorbed by the cells. Oxygen-based energy release was dramatically reduced in the exosome-absorbing cells, and sugar-based energy release increased. Next, Zhao et al examined the contents of the exosomes, and found that they contain the building blocks of proteins, fats, and other important molecules. Next, the experiments revealed that both prostate cancer and pancreatic cancer cells deprived of nutrients can use these smuggled resources to continue to grow. Importantly, this process did not involve the protein Kras, which previous studies had show helps cancer cells absorb nutrients. These findings suggest that preventing exosomes from smuggling resources to starving cancer cells might be an effective strategy to treat cancers. DOI: http://dx.doi.org/10.7554/eLife.10250.002