Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

Diversity of intratumoral regulatory T cells in B-cell non-Hodgkin lymphoma

Tumor-infiltrating regulatory T cells (Tregs) contribute to an immunosuppressive tumor microenvironment. Despite extensive studies, the prognostic impact of tumor-infiltrating Tregs in B-cell non-Hodgkin lymphomas (B-NHLs) remains unclear. Emerging studies suggest substantial heterogeneity in the phenotypes and suppressive capacities of Tregs, emphasizing the importance of understanding Treg diversity and the need for additional markers to identify highly suppressive Tregs. Here, we applied single-cell RNA sequencing and T-cell receptor sequencing combined with high-dimensional cytometry to decipher the heterogeneity of intratumoral Tregs in diffuse large B-cell lymphoma and follicular lymphoma (FL), compared with that in nonmalignant tonsillar tissue. We identified 3 distinct transcriptional states of Tregs: resting, activated, and unconventional LAG3+FOXP3- Tregs. Activated Tregs were enriched in B-NHL tumors, coexpressed several checkpoint receptors, and had stronger immunosuppressive activity compared with resting Tregs. In FL, activated Tregs were found in closer proximity to CD4+ and CD8+ T cells than other cell types. Furthermore, we used a computational approach to develop unique gene signature matrices, which were used to enumerate each Treg subset in cohorts with bulk gene expression data. In 2 independent FL cohorts, activated Tregs was the major subset, and high abundance was associated with adverse outcome. This study demonstrates that Tregs infiltrating B-NHL tumors are transcriptionally and functionally diverse. Highly immunosuppressive activated Tregs were enriched in tumor tissue but absent in the peripheral blood. Our data suggest that a deeper understanding of Treg heterogeneity in B-NHL could open new paths for rational drug design, facilitating selective targeting to improve antitumor immunity.

Single-cell mapping identifies MSI+ cells as a common origin for diverse subtypes of pancreatic cancer

Identifying the cells from which cancers arise is critical for understanding the molecular underpinnings of tumor evolution. To determine whether stem/progenitor cells can serve as cells of origin, we created a Msi2-CreERT2 knock-in mouse. When crossed to CAG-LSL-MycT58A mice, Msi2-CreERT2 mice developed multiple pancreatic cancer subtypes: ductal, acinar, adenosquamous, and rare anaplastic tumors. Combining single-cell genomics with computational analysis of developmental states and lineage trajectories, we demonstrate that MYC preferentially triggers transformation of the most immature MSI2+ pancreas cells into multi-lineage pre-cancer cells. These pre-cancer cells subsequently diverge to establish pancreatic cancer subtypes by activating distinct transcriptional programs and large-scale genomic changes, and enforced expression of specific signals like Ras can redirect subtype specification. This study shows that multiple pancreatic cancer subtypes can arise from a common pool of MSI2+ cells and provides a powerful model to understand and control the programs that shape divergent fates in pancreatic cancer.

High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.

CD4 T cells and toxicity from immune checkpoint blockade

Immune-related toxicities, otherwise known as immune-related adverse events (irAEs), occur in a substantial fraction of cancer patients treated with immune checkpoint inhibitors (ICIs). Ranging from asymptomatic to life-threatening, ICI-induced irAEs can result in hospital admission, high-dose corticosteroid treatment, ICI discontinuation, and in some cases, death. A deeper understanding of the factors underpinning severe irAE development will be essential for improved irAE prediction and prevention, toward maximizing the benefits and safety profiles of ICIs. In recent work, we applied mass cytometry, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing, and bulk T-cell receptor (TCR) sequencing to identify pretreatment determinants of severe irAE development in patients with advanced melanoma. Across 71 patients separated into three cohorts, we found that two baseline features in circulation-elevated activated CD4 effector memory T-cell abundance and TCR diversity-are associated with severe irAE development, independent of the affected organ system within 3 months of ICI treatment initiation. Here, we provide an extended perspective on this work, synthesize and discuss related literature, and summarize practical considerations for clinical translation. Collectively, these findings lay a foundation for data-driven and mechanistic insights into irAE development, with the potential to reduce ICI morbidity and mortality in the future.

p53 governs an AT1 differentiation programme in lung cancer suppression

Lung cancer is the leading cause of cancer deaths worldwide¹. Mutations in the tumour suppressor gene TP53 occur in 50% of lung adenocarcinomas (LUADs) and are linked to poor prognosis^1-4, but how p53 suppresses LUAD development remains enigmatic. We show here that p53 suppresses LUAD by governing cell state, specifically by promoting alveolar type 1 (AT1) differentiation. Using mice that express oncogenic Kras and null, wild-type or hypermorphic Trp53 alleles in alveolar type 2 (AT2) cells, we observed graded effects of p53 on LUAD initiation and progression. RNA sequencing and ATAC sequencing of LUAD cells uncovered a p53-induced AT1 differentiation programme during tumour suppression in vivo through direct DNA binding, chromatin remodelling and induction of genes characteristic of AT1 cells. Single-cell transcriptomics analyses revealed that during LUAD evolution, p53 promotes AT1 differentiation through action in a transitional cell state analogous to a transient intermediary seen during AT2-to-AT1 cell differentiation in alveolar injury repair. Notably, p53 inactivation results in the inappropriate persistence of these transitional cancer cells accompanied by upregulated growth signalling and divergence from lung lineage identity, characteristics associated with LUAD progression. Analysis of Trp53 wild-type and Trp53-null mice showed that p53 also directs alveolar regeneration after injury by regulating AT2 cell self-renewal and promoting transitional cell differentiation into AT1 cells. Collectively, these findings illuminate mechanisms of p53-mediated LUAD suppression, in which p53 governs alveolar differentiation, and suggest that tumour suppression reflects a fundamental role of p53 in orchestrating tissue repair after injury.

Abstract 6670: Association between circulating CD4 memory T cell levels and severe immune-related adverse events in melanoma patients treated with immune checkpoint blockade

Introduction: Severe immune-related adverse events (irAEs) occur in up to 60% of melanoma patients treated with immune checkpoint inhibitors (ICIs), causing substantial treatment-related morbidity and in the most severe cases, death. There is no clinical assay to predict who will develop severe ICI-induced irAEs and who will not. Using single-cell profiling assays applied to a retrospective melanoma cohort treated with ICIs, we recently showed that higher baseline levels of circulating CD4 effector memory T (TEM) cells were associated with severe irAE development, independent of the affected organ system (Lozano et al. Nature Medicine, 2022). As part of a prospective validation study of 100 melanoma patients, here we report our initial findings on the first 24 patients accrued to date.

Methods: We prospectively collected pre-ICI blood from 24 metastatic melanoma patients from two academic medical centers from February 2021 onward. Peripheral blood was collected pre-treatment on the day of immunotherapy (cycle 1 day 1). We then isolated peripheral blood mononuclear cells (PBMCs) and applied mass cytometry by time of flight (CyTOF) to profile 38 leukocyte markers including markers specific to CD4 TEM cells. Cellular subpopulations were quantified using Cytobank v9. Patients underwent routine medical oncology follow-up during and after ICI treatment including grading of irAEs using the Common Terminology Criteria for Adverse Events v5.

Results: Median follow-up time after pre-ICI blood collection was 9.1 months (range 2.6-18.5). Fifteen (63%) patients received combination (anti-PD1/anti-CTLA4) ICIs while the remainder received anti-PD1 monotherapy. Eight patients developed severe (grade 3+) irAEs at a median of 8.5 weeks (range 4-21) after treatment initiation, including 4 who developed life-threatening (grade 4) irAEs. Severe and life-threatening irAE development spanned 9 separate organ systems, most commonly gastrointestinal, dermatological, and hepatic. Using CyTOF, we found that circulating CD4 TEM cells were more abundant in patients who developed severe irAEs compared to those who did not (P = 0.01; AUC = 0.81), irrespective of the involved organ systems. Moreover, median-splitting the patient cohort into two groups based on pretreatment CD4 TEM levels revealed that patients with low CD4 TEMs had significantly longer freedom from severe irAE than those with high CD4 TEMs (not reached vs. 4.6 months; P = 0.013; HR = 6.7). There was no significant difference in pretreatment CD4 TEM levels between patients with and without durable clinical benefit to ICIs (P = 0.75).

Conclusion: Circulating CD4 TEM levels measured by CyTOF were associated with severe irAE development, independent of response status, in patients with advanced melanoma. These findings could form the basis for pretreatment ICI risk stratification in the future.

Citation Format: Abul Usmani, Noah Earland, Wubing Zhang, Peter K. Harris, Antonietta Bacchiocchi, Aishwarya Nene, David Y. Chen, Mario Sznol, Ruth Halaban, Aaron M. Newman, Aadel A. Chaudhuri. Association between circulating CD4 memory T cell levels and severe immune-related adverse events in melanoma patients treated with immune checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6670.

A spatial map of human macrophage niches reveals context-dependent macrophage functions in colon and breast cancer

Tumor-associated macrophages (TAMs) display heterogeneous phenotypes. Yet the exact tissue cues that shape macrophage functional diversity are incompletely understood. Here we discriminate, spatially resolve and reveal the function of five distinct macrophage niches within malignant and benign breast and colon tissue. We found that SPP1 TAMs reside in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 tissue resident macrophages (TRMs) supports the plasma cell tissue niche. We discover that IL4I1 macrophages populate niches with high cell turnover where they phagocytose dying cells. Significantly, IL4I1 TAMs abundance correlates with anti-PD1 treatment response in breast cancer. Furthermore, NLRP3 inflammasome activation in NLRP3 TAMs correlates with neutrophil infiltration in the tumors and is associated with poor outcome in breast cancer patients. This suggests the NLRP3 inflammasome as a novel cancer immunetherapy target. Our work uncovers context-dependent roles of macrophage subsets, and suggests novel predictive markers and macrophage subset-specific therapy targets.

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

Multiomic analysis for optimization of combined focal and immunotherapy protocols in murine pancreatic cancer

Background: Although combination immunotherapies incorporating local and systemic components have shown promising results in treating solid tumors, varied tumor microenvironments (TMEs) can impact immunotherapeutic efficacy. Method: We designed and evaluated treatment strategies for breast and pancreatic cancer combining magnetic resonance-guided focused ultrasound (MRgFUS) ablation and antibody therapies. With a combination of single-cell sequencing, spectral flow cytometry, and histological analyses, we profiled an immune-suppressed KPC (Kras^+/LSL-G12D; Trp53^+/LSL-R172H; Pdx1-Cre) pancreatic adenocarcinoma (MT4) model and a dense epithelial neu deletion (NDL) HER2⁺ mammary adenocarcinoma model with a greater fraction of lymphocytes, natural killer cells and activated dendritic cells. We then performed gene ontology analysis, spectral and digital cytometry to assess the immune response to combination immunotherapies and correlation with survival studies. Result: Based on gene ontology analysis, adding ablation to immunotherapy enriched immune cell migration pathways in the pancreatic cancer model and extensively enriched wound healing pathways in the breast cancer model. With CIBERSORTx digital cytometry, aCD40 + aPD-1 immunotherapy combinations enhanced dendritic cell activation in both models. In the MT4 TME, adding the combination of aCD40 antibody and checkpoint inhibitors (aPD-1 and aCTLA-4) with ablation was synergistic, increasing activated natural killer cells and T cells in distant tumors. Furthermore, ablation with immunotherapy upregulated critical Ly6c myeloid remodeling phenotypes that enhance T-cell effector function and increased granzyme and protease encoding genes by as much as 100-fold. Ablation combined with immunotherapy then extended survival in the MT4 model to a greater extent than immunotherapy alone. Conclusion: In summary, TME profiling informed a successful multicomponent treatment protocol incorporating ablation and facilitated differentiation of TMEs in which ablation is most effective.

Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis

Metastasis is responsible for most breast cancer-related deaths; however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by tumor necrosis factor-α and interleukin-6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.

Inferring gene expression from cell-free DNA fragmentation profiles

Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.

Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis

Acral melanoma, the most common melanoma subtype among non-White individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we perform integrative genomic and clinical profiling of acral melanomas from 104 patients treated in North America (n = 37) or China (n = 67). We find that recurrent, late-arising focal amplifications of cytoband 22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 - a known tumor suppressor in other cancers - is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines causes apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiates processes associated with metastasis, including anchorage-independent growth, formation of spheroids, and an increase in MAPK and SRC activities. Our results provide insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target.

T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma

Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs. By analyzing 93 pre- and early on-ICI blood samples and 3 patient cohorts (n = 27, 26 and 18), we found that 2 pretreatment factors in circulation-activated CD4 memory T cell abundance and TCR diversity-are associated with severe irAE development regardless of organ system involvement. We also explored on-treatment changes in TCR clonality among patients receiving combination therapy and linked our findings to the severity and timing of irAE onset. These results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.

Atlas of clinically distinct cell states and ecosystems across human solid tumors

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Integrated spatial multiomics reveals fibroblast fate during tissue repair

In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.

The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).

Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Molecular and Immunologic Signatures are Related to Clinical Benefit from Treatment with Vocimagene Amiretrorepvec (Toca 511) and 5-Fluorocytosine (Toca FC) in Patients with Glioma

PURPOSE

High-grade gliomas (HGGs) are central nervous system tumors with poor prognoses and limited treatment options. Vocimagene amiretrorepvec (Toca 511) is a retroviral replicating vector encoding cytosine deaminase, which converts extended release 5-fluorocytosine (Toca FC) into the anticancer agent, 5-fluorouracil. According to preclinical studies, this therapy kills cancer cells and immunosuppressive myeloid cells in the tumor microenvironment, leading to T-cell-mediated antitumor immune activity. Therefore, we sought to elucidate this immune-related mechanism of action in humans, and to investigate potential molecular and immunologic indicators of clinical benefit from therapy.

PATIENTS AND METHODS

In a phase I clinical trial (NCT01470794), patients with recurrent HGG treated with Toca 511 and Toca FC showed improved survival relative to historical controls, and some had durable complete responses to therapy. As a part of this trial, we performed whole-exome DNA sequencing, RNA-sequencing, and multiplex digital ELISA measurements on tumor and blood samples.

RESULTS

Genetic analyses suggest mutations, copy-number variations, and neoantigens are linked to survival. Quantities of tumor immune infiltrates estimated by transcript abundance may potentially predict clinical outcomes. Peak values of cytokines in peripheral blood samples collected during and after therapy could indicate response.

CONCLUSIONS

These results support an immune-related mechanism of action for Toca 511 and Toca FC, and suggest that molecular and immunologic signatures are related to clinical benefit from treatment.

Abstract 3443: Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors

Tumors are complex ecosystems consisting of malignant, immune, and stromal elements whose dynamic interactions drive patient survival and response to therapy. A comprehensive understanding of the diversity of cellular states within the tumor microenvironment (TME), and their patterns of co-occurrence, could provide new diagnostic tools for improved disease management and novel targets for therapeutic intervention. To address this challenge, we developed EcoTyper, a novel machine learning framework for large-scale identification of TME cell states and their co-association patterns from bulk, single-cell, and spatially resolved tumor expression data. EcoTyper starts by “purifying” cell type-specific gene expression profiles of epithelial cells, immune, and stromal cell types from bulk tissue transcriptomes using CIBERSORTx (Newman et al., Nat Biotechnol 2019). It then identifies transcriptional states for each cell type, validates them in scRNA-seq data, and uncovers co-occurrence patterns between cell states in order to define tumor cellular ecosystems. Applied to 6,475 tumor and adjacent normal samples from solid tumor types profiled by The Cancer Genome Atlas (TCGA), EcoTyper identified robust transcriptional states across 12 major cell types, including epithelial, fibroblast, endothelial, and 9 immune subsets. These states included both known and novel cellular phenotypes, nearly all of which could be validated in a compendium of scRNA-seq tumor atlases spanning ~140,000 cells. Most cell states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly associated with overall survival, both in TCGA and in 9,062 held-out tumor specimens (Gentles/Newman et al., Nat Medicine 2015). We found that specific cell states co-occur in distinct cellular communities with characteristic patterns of ligand-receptor interactions, genomic features, clinical outcomes, and spatial organization. One such ecosystem defined a normal-like state that was strongly enriched in non-malignant samples. Others delineated novel pro- and anti-tumor inflammatory environments involving specific fibroblast, endothelial, and immune cell transcriptional programs. In summary, large-scale deconvolution of cell type-specific transcriptomes across thousands of solid tumors revealed a comprehensive atlas of TME cell states and cellular ecosystems. Our results provide a high-resolution portrait of cellular heterogeneity in the TME across multiple solid tumor types, with implications for novel diagnostics and immunotherapeutic targets.

References:

1. Newman, A.M., et al., Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol, 2019. 37(7): p. 773-782.

2. Gentles, A.J., et al., The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature medicine, 2015. 21(8): p. 938.

Citation Format: Bogdan A. Luca, Chloé B. Steen, Armon Azizi, Magdalena Matusiak, Joanna Przybyl, Nastaran Neishaboori, Almudena Espín Pérez, Maximilian Diehn, Ash A. Alizadeh, Matt van de Rijn, Andrew J. Gentles, Aaron M. Newman. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3443.

Abstract 1557: Landscape of tumor cell states and cellular ecosystems in diffuse large B cell lymphoma

The tumor microenvironment (TME) plays critical roles in cancer development, tumor progression, and susceptibility to therapy. However, the phenotypic states and interaction patterns of its underlying cell types remain poorly understood. To address this challenge, we developed a new computational framework, EcoTyper, for large-scale dissection of cell states and cellular communities (i.e., ecosystems) from tumor genomic profiles. EcoTyper integrates single-cell RNA sequencing (scRNA-seq) with CIBERSORTx, an algorithm for bulk RNA-seq deconvolution (Newman et al., Nat Biotechnol, 2019), to identify and validate cellular states and ecosystems that reflect fundamental distinctions in TME biology. Here we applied EcoTyper to diffuse large B cell lymphoma (DLBCL), an aggressive B cell malignancy with clinically distinct molecular subtypes and several immunologically-active therapies. We sought to determine whether EcoTyper could reveal novel biological variation in the DLBCL TME linked to tumor subtypes and genotypes, therapeutic responses, and clinical outcomes. We applied our approach to define transcriptional states from 13 cell types, including malignant, immune, and stromal cells, in over 1,500 DLBCL tumors. Remarkably, nearly all cell states identified by EcoTyper were validated in independent scRNA-seq and bulk RNA-seq datasets. Moreover, many cells states reflected novel phenotypic groupings, and the majority showed strong associations with overall survival, specific mutational profiles, and tumor molecular subtypes. Additionally, by identifying DLBCL tumors with similar communities of cellular states, we defined novel cellular ecosystems, or “ecotypes”, with distinct biological characteristics and clinical outcomes. Several ecotypes showed significant enrichments in canonical or novel tumor genotypes, suggesting an evolutionary interplay between the tumor and host TME. In summary, we developed a novel computational framework to dissect the TME at scale and present the most comprehensive atlas to date of cell states and cellular communities in DLBCL. Our approach is extensible to nearly any cancer type and may lead to the development of novel diagnostics and individualized immunotherapies.

Citation Format: Chloe B. Steen, Bogdan Luca, Mohammad S. Esfahani, Barzin Y. Nabet, Brian Sworder, Farshad Farshidfar, David Kurtz, Chih Long Liu, Ranjana H. Advani, Yasodha Natkunam, June H. Myklebust, Maximilian Diehn, Andrew Gentles, Ash Alizadeh, Aaron M. Newman. Landscape of tumor cell states and cellular ecosystems in diffuse large B cell lymphoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1557.

Abstract 5666: A noninvasive approach for early prediction of therapeutic benefit from immune checkpoint inhibition for lung cancer

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICI) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we analyze 211 samples from 99 patients and demonstrate that pre-treatment circulating tumor DNA (ctDNA) and circulating immune profiles are independently associated with DCB. We further show that ctDNA dynamics after a single ICI infusion can identify the majority of patients who will achieve DCB. Integrating these determinants, we describe an entirely noninvasive multi-analyte assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA- On-treatment) that robustly predicted DCB, and that was validated in two independent cohorts (AUC = 0.89-0.93, PPV = 92-100%, HR = 0.04-0.11). Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICI.

Citation Format: Barzin Y. Nabet, Mohammad S. Esfahani, Emily G. Hamilton, Jacob J. Chabon, Everett J. Moding, Hira Rizvi, Chloe B. Steen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Henning Stehr, Linda Goljenola, Michael C. Jin, Young-Jun Jeon, Diane Tseng, Taha Merghoub, Joel W. Neal, Heather A. Wakelee, Sukhmani K. Padda, Kavitha J. Ramchandran, Millie Das, Rene F. Bonilla, Christopher Yoo, Emily L. Chen, Ryan B. Ko, Aaron M. Newman, Matthew D. Hellmann, Ash A. Alizadeh, Maximilian Diehn. A noninvasive approach for early prediction of therapeutic benefit from immune checkpoint inhibition for lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5666.

Analytical validation of digital cytometry (iSort) for leukocyte enumeration using stored blood

3542

Background: Blood leukocyte enumeration is a cornerstone for clinical diagnosis and immune monitoring of diverse cancers and immunotherapies. Existing methods rely on intact/living cells and can thus be limiting due to handling time constraints and need for predefined antibody panels. While indirect cytometry methods including digital cytometry can overcome this limitation on archival specimens, their clinical performance has not been extensively characterized. We developed iSort, a novel transcriptome deconvolution method based on CIBERSORTx. Here, we comprehensively evaluated iSort and validated it against established diagnostic standards. Methods: We recruited 36 healthy adult blood donors and characterized their blood leukocyte profiles. We used several established clinical cytometry methods requiring intact cells in a CLIA laboratory including Complete Blood Count [CBC] and 6-color TBNK [TBNK]. We also immunophenotyped leukocytes by a research flow cytometry panel (FACS) and by mass cytometry (CyTOF). We then used these techniques as standards for validating leukocyte populations enumerated by iSort. iSort was performed on whole blood through deconvolution of 22 subsets from RNA-Seq. We assessed iSort’s analytical detection performance by spiking purified lymphocyte subsets into lymphodepleted human blood and by simulating blood mixtures using defined leukocyte mixtures within latin square designs. We assessed iSort concordance with an FDA approved IVD assay (TBNK) comparing distinct RNA-Seq library preparation chemistries. Results: iSort was highly correlated with TBNK/CBC across CD4 T, CD8 T, B cells, NK cells, monocytes, and neutrophils (r≥0.95). When comparing correlations to TBNK/CBC, we found no significant differences between iSort, CyTOF, and FACS, nor between RNA-Seq library chemistries. iSort demonstrated high linearity at low abundance levels (0.1 - 1%, r = 0.99, B-cells spiked into lymphodepleted blood samples after Rituximab) and at higher abundance levels (0.5 - 90%, r > 0.99) across lymphoid and myeloid subsets. iSort also showed high reproducibility among triplicate blood tubes for each population (median CV = 11%). Conclusions: iSort digital cytometry achieves highly accurate and robust leukocyte enumeration for diverse hematopoietic subsets. Given its favorable performance against existing clinical standards that require intact/living cells, iSort is a promising approach for the development of immunotherapy biomarkers.

Analytical validation of iSort digital cytometry for leukocyte enumeration in clinical tumor specimens

e15243

Background: Tumor infiltrating leukocytes (TILs) are major determinants of anti-tumor immune responses and mediate effector functions for diverse immunotherapies. Techniques relying on intact cells such as flow cytometry (FACS), immunohistochemistry (IHC), and scRNA-Seq can be powerful for measuring TIL heterogeneity. However, such methods are often impractical on large patient cohorts, can be poorly quantitative, and some cannot be applied to formalin-fixed paraffin embedded (FFPE) specimens. We developed iSort, a transcriptome deconvolution method based on CIBERSORTx for TIL enumeration. We evaluated iSort by comparing its performance in FFPE and fresh frozen (FF) human tumor tissues to established methods. Methods: We profiled human tumors for diverse TIL subsets, as quantified by iSort, FACS, and IHC. iSort was performed on RNA-Seq libraries derived from bulk human tumors, including FF tumor tissue samples, tumor cell suspensions obtained after dissociation, and FFPE specimens. We assessed the influence of tissue fixation on common TIL subsets enumerated by iSort, by comparing matched pairs of FF and FFPE lung tumor specimens (NSCLC). Finally, we compared iSort with automated microscopic enumeration of CD8 TILs by IHC on primary human lung tumors (FFPE/NSCLC). Results: iSort TIL enumeration was highly correlated with FACS across primary human tumors (CRC = 3, HNSCC = 1) for common TIL subsets (B cells, CD8 T, CD4 T, monocytes/macrophages, dendritic cells, PMNs/MDSCs) and total immune content (median r = 0.91 [0.71 - 0.99]). Tissue dissociation caused significant depletion of monocytes/macrophages (p < 0.01), when comparing bulk tumor specimens with cell suspensions after sample digestion. iSort showed high correlation across 12 TIL subsets when comparing 9 pairs of FF and FFPE specimens (r = 0.90). Finally, we observed high linearity between iSort and microscopic enumeration of CD8 TILs from IHC (r = 0.87, n = 5). Conclusions: iSort digital cytometry achieves accurate and robust TIL enumeration as compared with FACS and IHC, showing comparable performance in fixed and frozen primary human tumors. Importantly, iSort is directly applicable to bulk tumors and is unaffected by tissue dissociation artifacts, unlike methods requiring cell suspensions for TIL enumeration (FACS, scRNA-seq). Therefore, iSort is a promising approach for development and measurement of immunotherapy biomarkers.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Circulating Tumor DNA Analysis for Detection of Minimal Residual Disease After Chemoradiotherapy for Localized Esophageal Cancer

BACKGROUND & AIMS

Biomarkers are needed to risk stratify after chemoradiotherapy for localized esophageal cancer. These could improve identification of patients at risk for cancer progression and selection of additional therapy.

METHODS

We performed deep sequencing (CAncer Personalized Profiling by deep Sequencing, [CAPP-Seq]) analyses of plasma cell-free DNA collected from 45 patients before and after chemoradiotherapy for esophageal cancer, as well as DNA from leukocytes and fixed esophageal tumor biopsy samples collected during esophagogastroduodenoscopy. Patients were treated from May 2010 through October 2015; 23 patients subsequently underwent esophagectomy, and 22 did not undergo surgery. We also sequenced DNA from blood samples from 40 healthy control individuals. We analyzed 802 regions of 607 genes for single-nucleotide variants previously associated with esophageal adenocarcinoma or squamous cell carcinoma. Patients underwent imaging analyses 6-8 weeks after chemoradiotherapy and were followed for 5 years. Our primary aim was to determine whether detection of circulating tumor DNA (ctDNA) after chemoradiotherapy is associated with risk of tumor progression (growth of local, regional, or distant tumors, detected by imaging or biopsy).

RESULTS

The median proportion of tumor-derived DNA in total cell-free DNA before treatment was 0.07%, indicating that ultrasensitive assays are needed for quantification and analysis of ctDNA from localized esophageal tumors. Detection of ctDNA after chemoradiotherapy was associated with tumor progression (hazard ratio, 18.7; P < .0001), formation of distant metastases (hazard ratio, 32.1; P < .0001), and shorter disease-specific survival times (hazard ratio, 23.1; P < .0001). A higher proportion of patients with tumor progression had new mutations detected in plasma samples collected after chemoradiotherapy than patients without progression (P = .03). Detection of ctDNA after chemoradiotherapy preceded radiographic evidence of tumor progression by an average of 2.8 months. Among patients who received chemoradiotherapy without surgery, combined ctDNA and metabolic imaging analysis predicted progression in 100% of patients with tumor progression, compared with 71% for only ctDNA detection and 57% for only metabolic imaging analysis (P < .001 for comparison of either technique to combined analysis).

CONCLUSIONS

In an analysis of cell-free DNA in blood samples from patients who underwent chemoradiotherapy for esophageal cancer, detection of ctDNA was associated with tumor progression, metastasis, and disease-specific survival. Analysis of ctDNA might be used to identify patients at highest risk for tumor progression.

Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With this framework, single-cell or bulk-sorted RNA sequencing data can be used to learn molecular signatures of distinct cell types from a small collection of biospecimens. These signatures can then be repeatedly applied to characterize cellular heterogeneity from bulk tissue transcriptomes without physical cell isolation. In this chapter, we provide a detailed primer on CIBERSORTx and demonstrate its capabilities for high-throughput profiling of cell types and cellular states in normal and neoplastic tissues.

Functional significance of U2AF1 S34F mutations in lung adenocarcinomas

The functional role of U2AF1 mutations in lung adenocarcinomas (LUADs) remains incompletely understood. Here, we report a significant co-occurrence of U2AF1 S34F mutations with ROS1 translocations in LUADs. To characterize this interaction, we profiled effects of S34F on the transcriptome-wide distribution of RNA binding and alternative splicing in cells harboring the ROS1 translocation. Compared to its wild-type counterpart, U2AF1 S34F preferentially binds and modulates splicing of introns containing CAG trinucleotides at their 3' splice junctions. The presence of S34F caused a shift in cross-linking at 3' splice sites, which was significantly associated with alternative splicing of skipped exons. U2AF1 S34F induced expression of genes involved in the epithelial-mesenchymal transition (EMT) and increased tumor cell invasion. Finally, S34F increased splicing of the long over the short SLC34A2-ROS1 isoform, which was also associated with enhanced invasiveness. Taken together, our results suggest a mechanistic interaction between mutant U2AF1 and ROS1 in LUAD.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction

Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.

Determining cell type abundance and expression from bulk tissues with digital cytometry

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

A functional subset of CD8+ T cells during chronic exhaustion is defined by SIRPα expression

Prolonged exposure of CD8+ T cells to antigenic stimulation, as in chronic viral infections, leads to a state of diminished function termed exhaustion. We now demonstrate that even during exhaustion there is a subset of functional CD8+ T cells defined by surface expression of SIRPα, a protein not previously reported on lymphocytes. On SIRPα+ CD8+ T cells, expression of co-inhibitory receptors is counterbalanced by expression of co-stimulatory receptors and it is only SIRPα+ cells that actively proliferate, transcribe IFNγ and show cytolytic activity. Furthermore, target cells that express the ligand for SIRPα, CD47, are more susceptible to CD8+ T cell-killing in vivo. SIRPα+ CD8+ T cells are evident in mice infected with Friend retrovirus, LCMV Clone 13, and in patients with chronic HCV infections. Furthermore, therapeutic blockade of PD-L1 to reinvigorate CD8+ T cells during chronic infection expands the cytotoxic subset of SIRPα+ CD8+ T cells.

Complex mammalian-like haematopoietic system found in a colonial chordate

Haematopoiesis is an essential process that evolved in multicellular animals. At the heart of this process are haematopoietic stem cells (HSCs), which are multipotent and self-renewing, and generate the entire repertoire of blood and immune cells throughout an animal's life1. Although there have been comprehensive studies on self-renewal, differentiation, physiological regulation and niche occupation in vertebrate HSCs, relatively little is known about the evolutionary origin and niches of these cells. Here we describe the haematopoietic system of Botryllus schlosseri, a colonial tunicate that has a vasculature and circulating blood cells, and interesting stem-cell biology and immunity characteristics2-8. Self-recognition between genetically compatible B. schlosseri colonies leads to the formation of natural parabionts with shared circulation, whereas incompatible colonies reject each other3,4,7. Using flow cytometry, whole-transcriptome sequencing of defined cell populations and diverse functional assays, we identify HSCs, progenitors, immune effector cells and an HSC niche, and demonstrate that self-recognition inhibits allospecific cytotoxic reactions. Our results show that HSC and myeloid lineage immune cells emerged in a common ancestor of tunicates and vertebrates, and also suggest that haematopoietic bone marrow and the B. schlosseri endostyle niche evolved from a common origin.

Genomic Feature Selection by Coverage Design Optimization

We introduce a novel data reduction technique whereby we select a subset of tiles to "cover" maximally events of interest in large-scale biological datasets (e.g., genetic mutations), while minimizing the number of tiles. A tile is a genomic unit capturing one or more biological events, such as a sequence of base pairs that can be sequenced and observed simultaneously. The goal is to reduce significantly the number of tiles considered to those with areas of dense events in a cohort, thus saving on cost and enhancing interpretability. However, the reduction should not come at the cost of too much information, allowing for sensible statistical analysis after its application. We envisage application of our methods to a variety of high throughput data types, particularly those produced by next generation sequencing (NGS) experiments. The procedure is cast as a convex optimization problem, which is presented, along with methods of its solution. The method is demonstrated on a large dataset of somatic mutations spanning 5000+ patients, each having one of 29 cancer types. Applied to these data, our method dramatically reduces the number of gene locations required for broad coverage of patients and their mutations, giving subject specialists a more easily interpretable snapshot of recurrent mutational profiles in these cancers. The locations identified coincide with previously identified cancer genes. Finally, despite considerable data reduction, we show that our covering designs preserve the cancer discrimination ability of multinomial logistic regression models trained on all of the locations (> 1M).

Circulating Tumor DNA Measurements As Early Outcome Predictors in Diffuse Large B-Cell Lymphoma

PURPOSE

Outcomes for patients with diffuse large B-cell lymphoma remain heterogeneous, with existing methods failing to consistently predict treatment failure. We examined the additional prognostic value of circulating tumor DNA (ctDNA) before and during therapy for predicting patient outcomes.

PATIENTS AND METHODS

We studied the dynamics of ctDNA from 217 patients treated at six centers, using a training and validation framework. We densely characterized early ctDNA dynamics during therapy using cancer personalized profiling by deep sequencing to define response-associated thresholds within a discovery set. These thresholds were assessed in two independent validation sets. Finally, we assessed the prognostic value of ctDNA in the context of established risk factors, including the International Prognostic Index and interim positron emission tomography/computed tomography scans.

RESULTS

Before therapy, ctDNA was detectable in 98% of patients; pretreatment levels were prognostic in both front-line and salvage settings. In the discovery set, ctDNA levels changed rapidly, with a 2-log decrease after one cycle (early molecular response [EMR]) and a 2.5-log decrease after two cycles (major molecular response [MMR]) stratifying outcomes. In the first validation set, patients receiving front-line therapy achieving EMR or MMR had superior outcomes at 24 months (EMR: EFS, 83% v 50%; P = .0015; MMR: EFS, 82% v 46%; P < .001). EMR also predicted superior 24-month outcomes in patients receiving salvage therapy in the first validation set (EFS, 100% v 13%; P = .011). The prognostic value of EMR and MMR was further confirmed in the second validation set. In multivariable analyses including International Prognostic Index and interim positron emission tomography/computed tomography scans across both cohorts, molecular response was independently prognostic of outcomes, including event-free and overall survival.

CONCLUSION

Pretreatment ctDNA levels and molecular responses are independently prognostic of outcomes in aggressive lymphomas. These risk factors could potentially guide future personalized risk-directed approaches.

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Combination Approach for Detecting Different Types of Alterations in Circulating Tumor DNA in Leiomyosarcoma

Purpose: The clinical utility of circulating tumor DNA (ctDNA) monitoring has been shown in tumors that harbor highly recurrent mutations. Leiomyosarcoma represents a type of tumor with a wide spectrum of heterogeneous genomic abnormalities; thus, targeting hotspot mutations or a narrow genomic region for ctDNA detection may not be practical. Here, we demonstrate a combinatorial approach that integrates different sequencing protocols for the orthogonal detection of single-nucleotide variants (SNV), small indels, and copy-number alterations (CNA) in ctDNA.Experimental Design: We employed Cancer Personalized Profiling by deep Sequencing (CAPP-Seq) for the analysis of SNVs and indels, together with a genome-wide interrogation of CNAs by Genome Representation Profiling (GRP). We profiled 28 longitudinal plasma samples and 25 tumor specimens from 7 patients with leiomyosarcoma.Results: We detected ctDNA in 6 of 7 of these patients with >98% specificity for mutant allele fractions down to a level of 0.01%. We show that results from CAPP-Seq and GRP are highly concordant, and the combination of these methods allows for more comprehensive monitoring of ctDNA by profiling a wide spectrum of tumor-specific markers. By analyzing multiple tumor specimens in individual patients obtained from different sites and at different times during treatment, we observed clonal evolution of these tumors that was reflected by ctDNA profiles.Conclusions: Our strategy allows for the comprehensive monitoring of a broad spectrum of tumor-specific markers in plasma. Our approach may be clinically useful not only in leiomyosarcoma but also in other tumor types that lack recurrent genomic alterations. Clin Cancer Res; 24(11); 2688-99. ©2018 AACR.

Abstract A05: Circulating tumor DNA levels correlate with response to treatment in LMS patients

Circulating tumor DNA (ctDNA) has significant potential for several clinical applications, including assessment of treatment response and monitoring of recurrent/residual disease. We performed a pilot study to explore the feasibility of ctDNA monitoring in patients with leiomyosarcoma (LMS).

We profiled matching plasma and FFPE tumor specimens from 9 LMS patients. We analyzed between 2 to 6 longitudinal plasma samples (median of 5) and between 1 to 7 tumor specimens (median of 2) per patient. ctDNA analysis was performed on plasma samples collected pre-/post-surgery, throughout chemo-/radiotherapy and during follow-up. We used two separate approaches in our study: 1) targeted deep sequencing of ctDNA, tumor DNA and germline DNA to detect single nucleotide variants and indels using Cancer Personalized Profiling by deep Sequencing with integrated digital error suppression (CAPP-Seq; with a median deduplicated depth of sequencing of 2,136x); 2) copy number variant analysis in ctDNA by genome representation profiling (GRP; median coverage across the whole genome 0.23x) and in the matched tumors by SNP arrays. One patient was excluded from the analysis due to inadequate sequencing coverage in tumor specimen.

For CAPP-Seq analysis, we designed a custom 184kb capture panel targeting 89 genes that are recurrently mutated in LMS. Using strict variant calling criteria (requiring that variants be present on each strand of the original DNA “duplex” molecule) our panel identified a median of one nonsynonymous coding/splicing variant per tumor. We detected the same variants in TP53, RB1 and ATRX genes in ctDNA of 6/8 patients (with a baseline sensitivity of 87.5% and overall specificity of 98.96% calculated using plasma from 24 healthy donors). These six patients presented with advanced disease at the time of the first blood collection and were progressing throughout multiple lines of therapy. Two patients who did not have any variants detectable by CAPP-Seq in plasma had localized disease at the time of the first blood collection and/or responded well to the therapy. We found that changes in ctDNA levels appear to correspond with the extent of disease and response to treatment. Specifically, ctDNA levels decreased in a subset of patients after surgery or at the time of temporary response to chemo- and/or radiotherapy. Congruently, increases in ctDNA levels correlated with progression in most of the patients. There was a high correlation between ctDNA levels detected by CAPP-Seq (quantified as mutant molecules/mL plasma) and GRP (quantified as percent of genome showing copy number aberrations) across all plasma samples (Pearson's r= 0.88, p &lt; 0.0001), but in a few samples ctDNA was detected by only one of the two assays.

Our results suggest that serial analysis of ctDNA is a promising approach for evaluation of treatment response in LMS patients. Validation of these findings in a prospective study on a larger group of patients will be required to determine the use of this approach in a clinical setting.

References:

CAPP-Seq: PMIDs 24705333, 27018799

GRP: PMIDs 25585704, 26687610

Citation Format: Joanna Przybyl, Jacob J. Chabon, Lien Spans, Kristen Ganjoo, Sujay Vennam, Aaron M. Newman, Erna Forgó, Sushama Varma, Shirley Zhu, Maria Debiec-Rychter, Ash Alizadeh, Maximilian Diehn, Matt van de Rijn. Circulating tumor DNA levels correlate with response to treatment in LMS patients [abstract]. In: Proceedings of the AACR Conference on Advances in Sarcomas: From Basic Science to Clinical Translation; May 16-19, 2017; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(2_Suppl):Abstract nr A05.

Early B cell changes predict autoimmunity following combination immune checkpoint blockade

Combination checkpoint blockade (CCB) targeting inhibitory CTLA4 and PD1 receptors holds promise for cancer therapy. Immune-related adverse events (IRAEs) remain a major obstacle for the optimal application of CCB in cancer. Here, we analyzed B cell changes in patients with melanoma following treatment with either anti-CTLA4 or anti-PD1, or in combination. CCB therapy led to changes in circulating B cells that were detectable after the first cycle of therapy and characterized by a decline in circulating B cells and an increase in CD21lo B cells and plasmablasts. PD1 expression was higher in the CD21lo B cells, and B cell receptor sequencing of these cells demonstrated greater clonality and a higher frequency of clones compared with CD21hi cells. CCB induced proliferation in the CD21lo compartment, and single-cell RNA sequencing identified B cell activation in cells with genomic profiles of CD21lo B cells in vivo. Increased clonality of circulating B cells following CCB occurred in some patients. Treatment-induced changes in B cells preceded and correlated with both the frequency and timing of IRAEs. Patients with early B cell changes experienced higher rates of grade 3 or higher IRAEs 6 months after CCB. Thus, early changes in B cells following CCB may identify patients who are at increased risk of IRAEs, and preemptive strategies targeting B cells may reduce toxicities in these patients.

Profiling Tumor Infiltrating Immune Cells with CIBERSORT

Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.

Targeted chromatin ligation, a robust epigenetic profiling technique for small cell numbers

The complexity and inefficiency of chromatin immunoprecipitation strategies restrict their sensitivity and application when examining rare cell populations. We developed a new technique that replaces immunoprecipitation with a simplified chromatin fragmentation and proximity ligation step that eliminates bead purification and washing steps. We present a simple single tube proximity ligation technique, targeted chromatin ligation, that captures histone modification patterns with only 200 cells. Our technique eliminates loss of material and sensitivity due to multiple inefficient steps, while simplifying the workflow to enhance sensitivity and create the potential for novel applications.

Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling

Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here, we apply cancer personalized profiling by deep sequencing (CAPP-seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I-III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the first posttreatment blood sample, indicating reliable identification of MRD. Posttreatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months, and 53% of patients harbored ctDNA mutation profiles associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in patients with lung cancer can be accurately detected using CAPP-seq and may allow personalized adjuvant treatment while disease burden is lowest.Significance: This study shows that ctDNA analysis can robustly identify posttreatment MRD in patients with localized lung cancer, identifying residual/recurrent disease earlier than standard-of-care radiologic imaging, and thus could facilitate personalized adjuvant treatment at early time points when disease burden is lowest. Cancer Discov; 7(12); 1394-403. ©2017 AACR.See related commentary by Comino-Mendez and Turner, p. 1368This article is highlighted in the In This Issue feature, p. 1355.

Data normalization considerations for digital tumor dissection

In a recently published article in Genome Biology, Li and colleagues introduced TIMER, a gene expression deconvolution approach for studying tumor-infiltrating leukocytes (TILs) in 23 cancer types profiled by The Cancer Genome Atlas. Methods to characterize TIL biology are increasingly important, and the authors offer several arguments in favor of their strategy. Several of these claims warrant further discussion and highlight the critical importance of data normalization in gene expression deconvolution applications.Please see related Li et al correspondence: www.dx.doi.org/10.1186/s13059-017-1256-5 and Zheng correspondence: www.dx.doi.org/10.1186/s13059-017-1258-3.

Macrophage infiltration and genetic landscape of undifferentiated uterine sarcomas

Endometrial stromal tumors include translocation-associated low- and high-grade endometrial stromal sarcomas (ESS) and highly malignant undifferentiated uterine sarcomas (UUS). UUS is considered a poorly defined group of aggressive tumors and is often seen as a diagnosis of exclusion after ESS and leiomyosarcoma (LMS) have been ruled out. We performed a comprehensive analysis of gene expression, copy number variation, point mutations, and immune cell infiltrates in the largest series to date of all major types of uterine sarcomas to shed light on the biology of UUS and to identify potential novel therapeutic targets. We show that UUS tumors have a distinct molecular profile from LMS and ESS. Gene expression and immunohistochemical analyses revealed the presence of high numbers of tumor-associated macrophages (TAMs) in UUS, which makes UUS patients suitable candidates for therapies targeting TAMs. Our results show a high genomic instability of UUS and downregulation of several TP53-mediated tumor suppressor genes, such as NDN, CDH11, and NDRG4. Moreover, we demonstrate that UUS carry somatic mutations in several oncogenes and tumor suppressor genes implicated in RAS/PI3K/AKT/mTOR, ERBB3, and Hedgehog signaling.

Analysis of circulating tumor DNA in localized lung cancer for detection of molecular residual disease and personalization of adjuvant strategies

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Background: Identifying localized non-small cell lung cancer (NSCLC) patients with residual disease following curative intent therapy is difficult due to normal tissue changes caused by surgery or radiation and an inability to detect microscopic disease. Analysis of circulating tumor DNA (ctDNA) might enable identification of molecular residual disease (MRD) and personalization of adjuvant treatment approaches but has not been explored in lung cancer. Methods: We applied CAPP-Seq, an ultra-sensitive next-generation sequencing based ctDNA quantitation method, to pre- and post-treatment blood samples from a cohort of 41 patients treated with chemoradiation, radiotherapy or surgery for stage I-III primary lung cancer. Detection of ctDNA at a single MRD time-point within 4 months of treatment completion was compared with surveillance by cross-sectional imaging. Furthermore, we developed an approach for identification of tumor mutation burden based on mutations detected in plasma, leveraging whole exome sequencing data from 1,177 NSCLCs sequenced by TCGA. Results: Median follow-up time was 35 months. Pre-treatment ctDNA was detected in 38 (93%) patients and 19 (46%) had detectable post-treatment ctDNA MRD. MRD+ patients displayed significantly inferior 3-year freedom from progression (0% vs. 92%; HR 38; P < 0.0001) and 3-year overall survival (8% vs. 75%; HR 12; P < 0.0001) than MRD- patients. Detection of ctDNA MRD had positive and negative predictive values for disease progression of 100% and 93%, respectively. Furthermore, we non-invasively identified activating EGFR mutations or high mutational burden (≥5 CAPP-Seq non-synonymous mutations, corresponding to > 200 non-synonymous mutations per exome or > 4 single nucleotide variants per megabase of exome) in 47% of patients with detectable ctDNA MRD, suggesting potentially favorable responses to TKIs and immune checkpoint inhibitors, respectively. Conclusions: Our results indicate that ctDNA analysis accurately detects MRD in localized lung cancer patients and could facilitate personalized adjuvant treatment at early time-points when disease burden is minimal.