PURPOSE:Large B-cell lymphomas (LBCLs) are curable, but patients with residual disease after therapy invariably experience progression. Ultrasensitive methods to detect circulating tumor DNA (ctDNA) as minimal residual disease (MRD) may improve the determination of remission. METHODS:We integrated data from five prospective studies of frontline anthracycline-based chemotherapy in patients with LBCL. Tumor-specific phased variants were identified from pretreatment samples and monitored at landmark time points. Serial plasma specimens were blindly analyzed for detectable ctDNA as MRD. MRD status was compared with conventional response criteria for prognosis of progression-free survival (PFS). RESULTS:We studied ctDNA-MRD in 137 patients by monitoring 409 plasma specimens over time. Detectable ctDNA rates decreased during therapy with 55% and 78% of patients achieving undetectable ctDNA after two cycles and at the end of therapy, respectively. After a median follow-up of 37 months, the 2-year PFS for patients with detectable versus undetectable ctDNA after two cycles was 67% versus 96% (P = .0025; hazard ratio [HR], 6.9) and after therapy was 29% versus 97% (P < .0001; HR, 28.7), respectively. Ninety-two (94%) patients with undetectable ctDNA at the end of therapy remained alive without progression, while 19 (68%) patients with detectable ctDNA progressed or died. MRD status at the end of therapy had greater prognostic utility than conventional lymphoma response criteria using positron emission tomography (PET) scans (HR, 3.6 for positive PET and 28.3 for detectable ctDNA). CONCLUSION:Ultrasensitive ctDNA detection after frontline LBCL therapy is more prognostic than conventional radiographic response criteria. A refined definition of remission with ctDNA-MRD may improve clinical and psychological outcomes for patients with LBCL.

Introduction: Circulating tumor DNA (ctDNA) has emerged as a tool to characterize tumors and track minimal residual disease (MRD) in many malignancies, including diffuse large B-cell lymphoma (DLBCL). ctDNA levels have been shown as prognostic after first-line treatment and CD19-targeted chimeric antigen receptor (CAR19) T cell therapy in DLBCL. Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]), an antibody-drug conjugate comprising an antibody targeting CD19 and a pyrrolobenzodiazepine dimer cytotoxin, is approved for relapsed/refractory (R/R) DLBCL. The ultrasensitive ctDNA-MRD detection method, phased variant enrichment and detection sequencing (PhasED-Seq) was applied to evaluate molecular response and mutational genotypes in patients (pts) undergoing Lonca treatment. Methods: LOTIS-2 study (NCT03589469) evaluated the efficacy of Lonca in R/R DLBCL after ≥ 2 lines of prior systemic therapy. Samples from 33 LOTIS-2 pts were profiled by PhasED-Seq (Foresight Diagnostics), representing a range of best responses to therapy, including 8 complete response (CR), 14 partial response (PR), and 11 progressive disease (PD). Pts received a median of 4 treatment cycles with Lonca (range 2-22). Baseline plasma and peripheral blood mononuclear cells were used to identify tumor-specific phased variants (PVs), which were used to monitor ctDNA-MRD after 1 cycle of treatment (cycle 2, day 1 [C2D1]) and at the end of treatment (EOT). Samples were reported quantitatively with levels of ctDNA-MRD and qualitatively as ctDNA-MRD positive or negative. Absolute ctDNA levels and log-fold change (LFC) in ctDNA were compared with outcomes including best overall response, progression-free survival (PFS), and overall survival (OS), as determined by the independent review committee. The ctDNA mutational profile of each pt was evaluated at baseline and EOT to assess emerging clonal alterations with emphasis on CD19. Results: PVs were successfully genotyped from pretreatment plasma in 31/33 pts (94%) with sufficient material for analysis. The median pretreatment ctDNA level was 141 haploid genome equivalents /mL (hGE/mL) (range 0.4-3608), similar to previous DLBCL studies in first-line and relapsed settings. Interestingly, pretreatment ctDNA levels were not predictive of outcomes to treatment (hazard ratio [HR] 0.99, P=0.96). The prognostic value of ctDNA levels was assessed at C2D1. Absolute ctDNA levels and the change from baseline levels were significantly prognostic for the response to treatment. Pts achieving either a CR or a PR had significantly greater reduction in their ctDNA levels than those failing to respond (CR vs PD, median LFC -1.5 vs 0.1, P=0.001; PR vs PD, median LFC -1.1 vs 0.1, P=0.006) (Fig 1A). Interestingly, pts achieving a CR or a PR did not have significantly different changes in ctDNA at C2D1 ( P=0.15). When dividing pts at the median into those with high vs low ctDNA levels at C2D1, lower levels of ctDNA were significantly associated with superior PFS and OS (PFS: HR 4.4, P=0.009; OS: HR 3.2, P=0.01) (Fig 1B). At the EOT, 5 of 6 CR pts had cleared their ctDNA-MRD to undetectable levels vs 1 of 6 at C2D1. The CR pt who did not show deepening of molecular response was the only CR pt who ended Lonca treatment for radiographic disease progression; all other CR pts ended treatment for other reasons (e.g., toxicity, transplant, or persistent complete remission). To assess for emergent clonal mutations, the mutational profiles of pts were evaluated before Lonca treatment and at EOT. Prior studies in DLBCL after CAR19 T-cell therapy have revealed recurrent, although infrequent, mutations in CD19 as a mechanism of resistance. In this cohort, no emergent alterations in CD19 at the EOT (0/31 pts) were observed. One pt was identified with a baseline mutation in CD19 (R363C); this pt achieved a PR followed by PD after 3 cycles of treatment with persistence of the mutation in CD19. Conclusions: ctDNA molecular response assessment using PhasED-Seq is prognostic for outcomes in pts receiving Lonca monotherapy. ctDNA levels as early as C2D1 can predict outcomes and are indicative of a fast response to Lonca. Furthermore, molecular responses can deepen with additional cycles. CD19 alterations do not appear to be a common emergent mechanism of resistance to Lonca. In this exploratory study, ctDNA-MRD predicts Lonca efficacy and outcomes and should be further considered as a universal biomarker in DLBCL.

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.

SUMMARY Here we present a browser based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. AVAILABILITY AND IMPLEMENTATION SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.

Abstract The TGF-β pathway is a multifunctional signaling cascade with context-dependent roles in diverse biologic processes, including tumor promotion or suppression, metastasis, stem cell homeostasis, and immune suppression. Due to its highly context-dependent nature, decoding functional outcomes of the TGF-β pathway in specific tissues is highly challenging. Here, we present comprehensive genomic, transcriptomic and epigenomic analyses of the TGF-β pathway identified by 44 core pathway genes across 33 TCGA tumor types and 9125 samples. The core pathway genes involve TGF-β like ligands, receptors, intracellular SMAD molecules and adaptors. Although individual core pathway genes were rarely mutated or copy number altered in different cancer types, 41% of all samples have at least one genomic alteration in the TGF-β pathway, predominantly in the form of mutations. We identified a highly conserved TGF-β downstream gene expression signature associated with alterations in core pathway genes, suggesting that the alterations in the pathway have shared functional consequences. We observed a significant enrichment of the genomic alterations in gastrointestinal cancers (GI) with a distinct gene expression signature. The newly identified gene expression signature (over- or downregulation of key TGF-β downstream genes) in pan-cancer cohort was associated with significantly poor prognosis, particularly when it co-occurred with genomic alterations in the core pathway. Analysis of mutational hotspot sites revealed 6 genes with hotspots recurring in at least 9 (up to 78) mutational incidences. The hotspot mutations were also highly enriched in GI cancers. We identified previously characterized cancer mutation sites on SMAD4 and SMAD2 as hotspots mainly in GI cancers. We hypothesized novel functions to two of the newly identified hotpot sites through structural and trancriptomic analyses, and two other novel hotspot sites in the pathway await functional characterization. miRNA and epigenomic analyses revealed that TGF-β pathway activity is limited by epigenetic silencing or miRNA expression, especially in cancers with very low pathway gene expression levels. This multidimensional study provides the multifacefed landscape of TGF-β signaling in both individual disease and pan-cancer settings to guide future functional and therapeutic studies of this key cancer pathway. Citation Format: Anil Korkut, Sobia Zaidi, Rupa Kanchi, Ashton C. Berger, Gordon Robertson, Lawrence N. Kwong, Mike Datto, Jason Roszik, Shiyun Ling, Andre Schultz, Visweswaran Ravikumar, Ganiraju Manyam, Arvind Rao, Simon Shelley, Yuexin Liu, Zhenlin Ju, Donna Hansel, Guillermo de Velasco, Arjun Pennathur, Jesper B. Andersen, Colm J. O'Rourke, Kazufumi Ohshiro, Wilma Jogunoori, Nancy Gough, Shulin Li, Hatice Osmanbeyoglu, Andres Houseman, Shuyun Rao, Maciej Wiznerowicz, Jian Chen, Shoujun Gu, Wencai Ma, Jiexin Zhang, Pan Tong, Andrew D. Cherniack, Chuxia Deng, Linda Resar-Smith, Jaffer Ajani, The Cancer Genome Atlas Research Network, John N. Weinstein, Lopa Mishra, Rehan Akbani. A pan-cancer atlas of genomic, epigenomic and transcriptomic alterations in the TGF-β pathway [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 3413.

The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets, and to link basic mechanisms to behavioral data. Several modeling frameworks have been proposed for bipolar disorder, which represent competing hypothesis about the basic framework of the disorder. Here, we test these hypotheses with self-report assessments of mania and depression symptoms from 178 bipolar patients followed prospectively for 4 or more years. Statistical analysis of the data did not support the hypotheses that mood arises from a rhythmic process or multiple stable states (e.g., mania or depression) or that manic and depressive symptoms are highly anti-correlated. Alternatively, it is shown that bipolar disorder could arise from an inability for mood to quickly return to normal when perturbed. This latter concept is embodied by an affective instability model that can be personalized to the clinical course of any individual with chronic disorders that have an affective component.

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.

We are far from a comprehensive understanding of the dynamics of mood in bipolar disorder. However, a number of models of mood have emerged to describe the pathological fluctuation in mood that is characteristic of this disorder. These models are surprisingly diverse in their dynamical principles, e.g. whether mood is periodic or whether mania and depression are stable points when ignoring external influences. This chapters presents a selective summary of existing models of mood in bipolar disorder and introduces two new models. We focus on a key question: how to differentiate between models when only time courses of mood are available. For each model we consider, time courses are evaluated through data transformations and statistical techniques, including estimating survival functions and spectral density. We then provide guidelines on how to decide whether a certain modeling assumption, e.g. periodicity, is appropriate.

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.

BACKGROUND:Flux Balance Analysis (FBA) is a widely used tool to model metabolic behavior and cellular function. Applications of FBA span a breadth of research from synthetic engineering of biofuels to understanding evolutionary adaptations. FBA predicts metabolic reaction fluxes that optimize a given objective. This objective is generally defined for unicellular organisms by a theoretical reaction which simulates biomass production. FBA has been extremely successful at predicting in E. coli growth rates under different media and gene essentiality, amongst other things. In order to improve predictions, additional constraints are coupled with optimization of the biomass function. Studies have suggested, however, that unicellular organisms - like multicellular organisms - do not grow at optimal rates. To further improve FBA predictions, particularly of internal cell fluxes, new techniques to explore the sub-optimal solution space need to be developed.RESULTS:We present an innovative FBA method called corsoFBA based on the optimization of protein cost at sub-optimal objective levels. Our method shows good agreement with experimental data of E. coli grown at different dilution rates. Maintaining the objective function close to its maximum value predicts metabolic states that closely resemble low dilution rates; while higher dilution rates can be mirrored by lowering the biomass production value. By using a modified version of Extreme Pathways, we are also able to quantify the energy production and overall protein cost for all possible pathways in the central carbon metabolism.CONCLUSION:Metabolic flux distributions at the optimal objective can be substantially different from the near-optimal distributions. Importantly, the behavior of E. coli central carbon metabolism can be better predicted by exploring the sub-optimal FBA solution space. The corsoFBA method presented here is able to predict the behavior of PEP Carboxylase, the glyoxylate shunt and the Entner-Doudoroff pathway at different glucose levels, a behavior not predicted by the minimization of metabolic steps and FBA alone. This technique can be used to better predict internal cell fluxes under different conditions, and corsoFBA will be of great help for the study of cells from multicellular organisms using Flux Balance Analysis.