Background: Immunocompromised patients have higher risk for virally-driven lymphomas associated with pathogens including Epstein-Barr Virus (EBV), Kaposi Sarcoma Herpesvirus (KSHV), HTLV-1, and ...HIV-1. Within this high-risk patient population, early cancer detection remains a critically unmet clinical need. Recently, analysis of viral cell-free DNA (cfDNA) has shown promise as a predictive biomarker for both immune status and cancer risk. In solid organ transplant recipients, immunosuppression is associated with expansion of a circulating family of viruses called Anelloviridae (De Vlaminck I, Cell, 2013). In screening for EBV-driven nasopharyngeal carcinomas (NPC), EBV cfDNA fragment length distributions distinguish normal adults with transient viremia from those with high risk of NPC (Lam WK, PNAS, 2018). We developed VirCAPP-Seq (Viral Cancer Personalized Profiling by Deep Sequencing) as a clinical virome capture approach for enriching and deeply sequencing circulating viral nucleic acids to elucidate virome composition, fragment length distribution, host genome integration sites, and viral polymorphisms. These features of viral cfDNA may clarify patient risk for malignancy and immunosuppression.
Method: 180 viral species from 15 families were selected on the basis of 3 criteria: (1) Known oncoviruses (eg, EBV, Human Papillomavirus (HPV), Hepatitis B Virus (HBV)); (2) Commensal viruses associated with immune state (eg, Anelloviridae); (3) Common clinically relevant viruses (Adenovirus, CMV, BK, etc.). Viral reference genomes were segmented into continuous sliding 50mer windows, and compared to hg19 and a pan-viral reference database for homology using BLASTN. Non-unique regions were excluded. Remaining regions were clustered using CD-HIT at 85% homology to minimize redundant probes (figure 1A). We applied VirCAPP-Seq to plasma samples of patients with known levels of EBV viremia. To evaluate possible interactions of VirCAPP-Seq with human probes, we applied the viral panel alone and in combination with a CAPP-Seq lymphoma panel (Kurtz DM, JCO, 2018). Reads were aligned to a composite reference genome consisting of 180 viral reference genomes and hg19. For each targeted virus, plasma (GE) were estimated by computing the ratio of unique viral depth to human depth, and multiplying by the measured plasma DNA concentration and plasma volume. We correlated the sequencing-based GE estimate to viral titers determined by a clinical RT-PCR assay measured in a CLIA laboratory. Viral integration sites were evaluated with Virus-Clip (Ho DW, Oncotarget, 2015).
Results: The final VirCAPP-Seq design consists of 2349 target regions across 180 species, spanning 2.097 Mb. A median of 95.9% of each targeted virus was covered by the panel (IQR 85.1% - 99.0%). Sequence-based phylogenetic analysis demonstrated clear relationships between members of each viral family. However, following in silico masking of non-unique regions, these phylogenetic relationships were no longer discernible, demonstrating that our design avoids targeting regions with interspecies ambiguity (figure 1B). Surprisingly, addition of VirCAPP-Seq to a human capture panel did not significantly impact the human on-target rate compared to human-only capture of lymphoma CAPP-Seq targets (Kurtz et al 2018 JCO), suggesting minimal non-specific interaction between viral probes and host DNA (figure 1C). Across EBV-positive samples, VirCAPP-Seq enriched EBV cfDNA a median of 214,000x compared to off-target EBV abundance in human-only capture (IQR 55,100x - 277,600x) (figure 1D). EBV-positive samples achieved a median EBV depth of 1015x (IQR 210x - 2802x) after enrichment. Additionally, viral GE estimates from read counts were highly correlated with viral titer estimates by clinical RT-PCR (r = 0.92, p < 0.001, n=9) (figure 1E). We observed several significant correlations between virome measurements across a range of immunosuppression states and lymphoma subtypes, with details to be presented at the meeting.
Conclusions: VirCAPP-Seq enables simultaneous enrichment of clinically relevant viral and host cfDNA from plasma over several orders of magnitude. This framework holds promise for monitoring diverse features of viral cfDNA that capture risks for malignancy and immunosuppression, facilitating personalized diagnostic and therapeutic interventions.
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Kurtz:Roche: Consultancy. Advani:Janssen: Research Funding; Kura: Research Funding; Merck: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cell Medica, Ltd: Consultancy; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Forty-Seven: Research Funding; Agensys: Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Consultancy, Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharma: Research Funding; Millennium: Research Funding; Regeneron: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Stanford University: Employment, Equity Ownership; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees. Diehn:AstraZeneca: Consultancy; BioNTech: Consultancy; Quanticell: Consultancy; Novartis: Consultancy; Roche: Consultancy.
Background
Diffuse large B-cell lymphoma (DLBCL) is a genetically and clinically heterogeneous disease. The cell-of-origin (COO) classification subdivides DLBCL into the transcriptionally defined ...activated B-cell (ABC) and germinal center B-cell (GCB) subtypes. Recently, 2 novel classifiers based on genetic features were independently proposed further unraveling the diversity of DLBCL Schmitz et al, NEJM2018; Chapuy et al, Nat Med2018. The concordance between the 2 novel classification systems has not yet been systematically studied. However, both classifiers are largely complementary to COO subtypes, and describe overlapping genotypes.
We previously demonstrated the feasibility of COO classification by noninvasive plasma genotyping in a limited gene panel using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) Scherer et al, STM2016, and this approach has now been replicated by others. In this study, we take first steps toward a comprehensive non-invasive classification of novel DLBCL genetic subtypes using a limited gene panel.
Methods
We analyzed genetic profiling of 476 DLBCL patients reported by Schmitz et al (NEJM 2018) as a training set to build 2 classifiers in a limited gene panel applicable to plasma genotyping from CAPP-Seq: (1) A COO classifier (i.e. ABC, GCB and Unclassified); (2) A comprehensive genetic classifier (i.e. EZB, BN2, MCD, N1 and Other as defined in Schmitz et al, NEJM 2018). Features were limited to genetic alterations captured by our plasma genotyping panel, and those with population frequency of at least 10% in at least one genetic subtype. Our final model comprised 100 features: 64 recurrently mutated genes, 26 amplifications, 7 deletions and 3 translocations (BCL2, BCL6 and MYC). After cross-validation in the training set, we applied the 2 classifiers to the dataset from Chapuy et al (Nat Med 2018) as well as pretreatment plasma genotyping data from patients previously reported by our group Kurtz et al, JCO 2018.
Results
We first evaluated our 2 classifiers in a 10-fold cross-validation (CV) framework in the NEJM 2018 dataset of Schmitz et al. Despite modest performance of our GCB/ABC classification, COO labels had the expected significant prognostic associations (Fig. 1A). Overall accuracy of our second classifier to determine novel genetic subtypes was 82% (Fig. 1B). Consistent with the original study, inferred MCD and N1 subtypes had adverse prognosis compared to EZB and BN2 (Fig. 1C).
We next applied our classifiers to the Chapuy et al (Nat Med 2018) dataset. Again, consistent with findings by Schmitz et al (NEJM 2018), the EZB subset of GCB cases had inferior outcome compared to non-EZB cases (Fig. 1D). We next examined the cross-correlation between the two classifiers and observed the expected enrichment patterns of ABCs in the MCD subset and enrichment of GCBs in the EZB subset (Fig. 1E).
Finally, we applied our classifiers to plasma genotyping data previously reported by our group Kurtz et al., JCO 2018. We restricted the analysis to cases with a mean variant allele fraction ≥0.5% (n=68). Similar to the original study, 59% of cases (40/68) were labeled unclassifiable (i.e. Other). We compared the distribution of COO subtypes within the Schmitz genetic clusters. Representation of ABC and GCB within the clusters inferred from Plasma genotyping (Fig. 1F) was similar to the distribution from Tumor genotyping (Fig. 1E).
Conclusions
We describe 2 new classifiers applicable to noninvasive plasma genotyping data that recapitulate transcriptionally and genetically defined DLBCL subtypes. Using independent datasets, we show the feasibility of classification with a limited feature set with good prediction accuracy and prognostic stratification of defined subtypes. Genotyping of pretreatment plasma samples suggest that comprehensive non-invasive classification of genetic subtypes of DLBCL is achievable.
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Kurtz:Roche: Consultancy. Diehn:BioNTech: Consultancy; Quanticell: Consultancy; Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy. Alizadeh:Roche: Consultancy; Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Pfizer: Research Funding.
•A FNSF is needed to reduce the knowledge gaps to a fusion DEMO and accelerate progress toward fusion energy.•FNSF will test and qualify first-wall/blanket components and materials in a DEMO-relevant ...fusion environment.•The Advanced Tokamak approach enables reduced size and risks, and is on a direct path to an attractive target power plant.•Near term research focus on specific tasks can enable starting FNSF construction within the next ten years.
An accelerated fusion energy development program, a “fast-track” approach, requires proceeding with a nuclear and materials testing program in parallel with research on burning plasmas, ITER. A Fusion Nuclear Science Facility (FNSF) would address many of the key issues that need to be addressed prior to DEMO, including breeding tritium and completing the fuel cycle, qualifying nuclear materials for high fluence, developing suitable materials for the plasma-boundary interface, and demonstrating power extraction. The Advanced Tokamak (AT) is a strong candidate for an FNSF as a consequence of its mature physics base, capability to address the key issues, and the direct relevance to an attractive target power plant. The standard aspect ratio provides space for a solenoid, assuring robust plasma current initiation, and for an inboard blanket, assuring robust tritium breeding ratio (TBR) >1 for FNSF tritium self-sufficiency and building of inventory needed to start up DEMO. An example design point gives a moderate sized Cu-coil device with R/a=2.7m/0.77m, κ=2.3, BT=5.4T, IP=6.6 MA, βN=2.75, Pfus=127MW. The modest bootstrap fraction of ƒBS=0.55 provides an opportunity to develop steady state with sufficient current drive for adequate control. Proceeding with a FNSF in parallel with ITER provides a strong basis to begin construction of DEMO upon the achievement of Q∼10 in ITER.
Mastering nuclear fusion, which is an abundant, safe, and environmentally competitive energy, is a great challenge for humanity. Tokamak represents one of the most promising paths toward controlled ...fusion. Obtaining a high-performance, steady-state, and long-pulse plasma regime remains a critical issue. Recently, a big breakthrough in steady-state operation was made on the Experimental Advanced Superconducting Tokamak (EAST). A steady-state plasma with a world-record pulse length of 1056 s was obtained, where the density and the divertor peak heat flux were well controlled, with no core impurity accumulation, and a new high-confinement and self-organizing regime (Super I-mode = I-mode + e-ITB) was discovered and demonstrated. These achievements contribute to the integration of fusion plasma technology and physics, which is essential to operate next-step devices.
Background: Anti-CD19 chimeric antigen receptor (CAR19) T-cells have significant activity in patients with relapsed/refractory DLBCL (rrDLBCL). While the majority of rrDLBCL patients receiving ...axicabtagene ciloleucel (Axi-cel)achieve complete responses, a significant subset of patients experience disease progression (Locke FL, et al. Lancet Oncol. 2019). Circulating tumor DNA (ctDNA) analysis has demonstrated utility for predicting therapeutic benefit in DLBCL, as well as for detecting emergent resistance mechanisms to targeted therapies. Here we apply cell-free DNA (cfDNA) analysis to patients receiving Axi-cel, to characterize molecular responses, resistance mechanisms, and to track CAR19 cells.
Methods: We performed Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) on DNA from germline and plasma samples collected prior to CAR T-cell infusion, multiple time-points post infusion, and, where available, at the time of relapse from 30 patients receiving Axi-cel for rrDLBCL at Stanford University. We designed a novel hybrid-capture panel and analysis pipeline designed to detect both tumor variants, as well as Axi-cel specific recombinant retroviral sequences to quantify CAR19 levels in cfDNA. Tumor variants were identified prior to and following Axi-cel therapy to assess for emergent variants, and Axi-cel specific sequences were quantified.
Results: The median follow-up for the 30 patients after Axi-cel infusion was 10 months, with 47% (14/30) of patients experiencing disease progression after Axi-cel therapy. We identified an average of 164.3 SNVs per case (range:1-685) before Axi-cel therapy; the most common coding variants identified at baseline were in MLL2 (29.2%), BCL2 (22.5%), and TP53 (19.3%). When treated as a continuous variable, pretreatment ctDNA levels were prognostic of PFS (HR 2.16, 95% CI 1.11-4.21, P=0.02). Using a previously established ctDNA threshold to stratify disease burden (2.5 log10(hGE/mL); Kurtz et al. JCO 2018), we observed significantly superior PFS in patients with low pretreatment ctDNA levels treated with Axi-cel (Fig. 1A). In the majority of Axi-cel treated patients (62.9%), ctDNA was detectable at day 28, and PFS was significantly longer in patients with undetectable ctDNA at this time-point (Fig. 1B). Multiple putative resistance mechanisms were identified at relapse after Axi-cel, including emergent variants in CD19, HVEM, and TP53, as well as copy number gains in PD-L1 (Fig. 1C). For example, in one patient, a CD19 stop-gain mutation, which was not detected prior to treatment or at the time of the first interim PET scan, emerged at the time of relapse (Fig. 1D). Finally, we found cfDNA evidence for Axi-cel DNA in 74% of patients 28 days after therapy, including in patients without evidence of circulating CAR T-cells in PBMCs. Axi-cel levels in cfDNA as measured by CAPP-Seq were significantly correlated with CAR19 flow cytometry (Pearson r=0.55, P=.015; Fig. 1E).
Conclusions: Baseline and interim ctDNA measurements have prognostic significance in DLBCL patients being treated with CAR19 T-cells, and potential emergent resistance mutations, including in CD19, can be identified in patients via cfDNA analysis. Quantification of CAR19 T-cells using cfDNA is significantly correlated with flow cytometric quantification, indicating that these cells can be quantified via cfDNA. Taken together, these data indicate that cfDNA analysis is a powerful tool for predicting response to CAR19 therapy, identifying genomic determinants of resistance and quantifying CAR19 cells, which may in turn inform the next therapeutic steps.
Figure 1: A) Kaplan Meier analysis of PFS, with patients stratified based on pre-Axi-cel therapy ctDNA level, above and below a previously established threshold (2.5 log10haploid Genome Equivalents/mL). B) A Kaplan Meier plot depicting PFS stratification for patients with detectable versus undetectable ctDNA at day 28 after Axi-cel infusion. C) Oncoprint depicting selected emergent and baseline tumor variants in progressors and non-progressors after Axi-cel therapy. D) Change in mean ctDNA variant allele frequency (VAF) and emergence of a CD19 stop-gain mutation (CD19 pTrpX) at the time of relapse in a patient who initially achieved a CR at day 28 after CAR19 infusion. E) Relationship between CAR19 T-cell quantification by cfDNA and flow cytometry. (ND: Not detected)
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Kurtz:Roche: Consultancy. Chabon:Lexent Bio Inc: Consultancy. Khodadoust:Corvus Pharmaceuticals: Research Funding. Majzner:Xyphos Inc.: Consultancy; Lyell Immunopharma: Consultancy. Mackall:Obsidian: Research Funding; Lyell: Consultancy, Equity Ownership, Other: Founder, Research Funding; Nektar: Other: Scientific Advisory Board; PACT: Other: Scientific Advisory Board; Bryologyx: Other: Scientific Advisory Board; Vor: Other: Scientific Advisory Board; Roche: Other: Scientific Advisory Board; Adaptimmune LLC: Other: Scientific Advisory Board; Glaxo-Smith-Kline: Other: Scientific Advisory Board; Allogene: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Apricity Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Diehn:Roche: Consultancy; Quanticell: Consultancy; Novartis: Consultancy; AstraZeneca: Consultancy; BioNTech: Consultancy. Miklos:Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Juno: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees. Alizadeh:Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Roche: Consultancy; Pfizer: Research Funding.
Renal oncocytomas are benign tumors characterized by a marked accumulation of mitochondria. We report a combined exome, transcriptome, and metabolome analysis of these tumors. Joint analysis of the ...nuclear and mitochondrial (mtDNA) genomes reveals loss-of-function mtDNA mutations occurring at high variant allele fractions, consistent with positive selection, in genes encoding complex I as the most frequent genetic events. A subset of these tumors also exhibits chromosome 1 loss and/or cyclin D1 overexpression, suggesting they follow complex I loss. Transcriptome data revealed that many pathways previously reported to be altered in renal oncocytoma were simply differentially expressed in the tumor’s cell of origin, the distal nephron, compared with other nephron segments. Using a heuristic approach to account for cell-of-origin bias we uncovered strong expression alterations in the gamma-glutamyl cycle, including glutathione synthesis (increased GCLC) and glutathione degradation. Moreover, the most striking changes in metabolite profiling were elevations in oxidized and reduced glutathione as well as γ-glutamyl-cysteine and cysteinyl-glycine, dipeptide intermediates in glutathione biosynthesis, and recycling, respectively. Biosynthesis of glutathione appears adaptive as blockade of GCLC impairs viability in cells cultured with a complex I inhibitor. Our data suggest that loss-of-function mutations in complex I are a candidate driver event in renal oncocytoma that is followed by frequent loss of chromosome 1, cyclin D1 overexpression, and adaptive up-regulation of glutathione biosynthesis.
Implementing cancer precision medicine in the clinic requires assessing the therapeutic relevance of genomic alterations. A main challenge is the systematic interpretation of whole-exome sequencing ...(WES) data for clinical care.
One hundred sixty-five adults with metastatic colorectal and lung adenocarcinomas were prospectively enrolled in the CanSeq study. WES was performed on DNA extracted from formalin-fixed paraffin-embedded tumor biopsy samples and matched blood samples. Somatic and germ-line alterations were ranked according to therapeutic or clinical relevance. Results were interpreted using an integrated somatic and germ-line framework and returned in accordance with patient preferences.
At the time of this analysis, WES had been performed and results returned to the clinical team for 165 participants. Of 768 curated somatic alterations, only 31% were associated with clinical evidence and 69% with preclinical or inferential evidence. Of 806 curated germ-line variants, 5% were clinically relevant and 56% were classified as variants of unknown significance. The variant review and decision-making processes were effective when the process was changed from that of a Molecular Tumor Board to a protocol-based approach.
The development of novel interpretive and decision-support tools that draw from scientific and clinical evidence will be crucial for the success of cancer precision medicine in WES studies.Genet Med advance online publication 26 January 2017.
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