Immune-restoring CAR-T cells display antitumor activity and reverse immunosuppressive TME in a humanized ccRCC mouse model

One of the major barriers that have restricted successful use of chimeric antigen receptor (CAR) T cells in the treatment of solid tumors is an unfavorable tumor microenvironment (TME). We engineered CAR-T cells targeting carbonic anhydrase IX (CAIX) to secrete anti-PD-L1 monoclonal antibody (mAb), termed immune-restoring (IR) CAR G36-PDL1. We tested CAR-T cells in a humanized clear cell renal cell carcinoma (ccRCC) orthotopic mouse model with reconstituted human leukocyte antigen (HLA) partially matched human leukocytes derived from fetal CD34+ hematopoietic stem cells (HSCs) and bearing human ccRCC skrc-59 cells under the kidney capsule. G36-PDL1 CAR-T cells, haploidentical to the tumor cells, had a potent antitumor effect compared to those without immune-restoring effect. Analysis of the TME revealed that G36-PDL1 CAR-T cells restored active antitumor immunity by promoting tumor-killing cytotoxicity, reducing immunosuppressive cell components such as M2 macrophages and exhausted CD8+ T cells, and enhancing T follicular helper (Tfh)-B cell crosstalk.

Curated single cell multimodal landmark datasets for R/Bioconductor

BACKGROUND

The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.

RESULTS

We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data.

CONCLUSIONS

We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Genome-scale functional genomics identify genes preferentially essential for multiple myeloma cells compared to other neoplasias

Clinical progress in multiple myeloma (MM), an incurable plasma cell (PC) neoplasia, has been driven by therapies that have limited applications beyond MM/PC neoplasias and do not target specific oncogenic mutations in MM. Instead, these agents target pathways critical for PC biology yet largely dispensable for malignant or normal cells of most other lineages. Here we systematically characterized the lineage-preferential molecular dependencies of MM through genome-scale clustered regularly interspaced short palindromic repeats (CRISPR) studies in 19 MM versus hundreds of non-MM lines and identified 116 genes whose disruption more significantly affects MM cell fitness compared with other malignancies. These genes, some known, others not previously linked to MM, encode transcription factors, chromatin modifiers, endoplasmic reticulum components, metabolic regulators or signaling molecules. Most of these genes are not among the top amplified, overexpressed or mutated in MM. Functional genomics approaches thus define new therapeutic targets in MM not readily identifiable by standard genomic, transcriptional or epigenetic profiling analyses.

Abstract 886: Fine-tuned CAIX targeted CAR-T cells exhibit superior efficacy and mitigate on-target off-tumor side effects

Chimeric Antigen Receptor (CAR) T cell therapy is a new type of “living drug” that has proven to be a powerful immunotherapy for hematologic malignancies. To date, there are six CAR-T products approved by the FDA for hematologic malignancies, four targeting CD19, and two targeting B-cell maturation antigen (BCMA). However, this success has not yet been transferred to solid tumors. A major hurdle is the on-target off-tumor toxicities due to the shared expression of target antigen on normal tissues. Carbonic anhydrase IX (CAIX) is highly expressed in clear cell renal cell carcinoma (ccRCC); however, it is also expressed on bile duct at a lower physiological level leading to off-tumor toxicity of CAIX targeted therapies. The first anti-CAIX CAR-T studies, using the 1st generation G250 CAR-T cells plus IL-2 to treat patients with metastatic ccRCC, caused severe liver enzyme abnormalities in the treated patients after CAR-T cell infusions. To understand CAIX expression on tumor and normal tissues, we quantified CAIX expression on ccRCC patient samples and healthy bile duct tissues using direct stochastic optical reconstruction microscopy (dSTORM) which provides single-molecule resolution. Tet-On inducible CAIX expressing cell lines were established to mimic various CAIX densities on normal tissue and tumor samples. Using biolayer interferometry (BLI) and avidity analyzer, we identified a low-affinity, high-avidity anti-CAIX CAR G9. G9 CAR-T cells only kill CAIX high ccRCC tumor cells but not CAIX low normal cholangiocytes, and exhibited a CAIX density dependent response to Tet-On inducible CAIX expressing cell lines. Compared to high-affinity G250 CAR-T cells, G9 showed a better safety profile and a wider therapeutic window. G9 demonstrated a superior ex vivo efficacy on ccRCC patient derived organotypic tumor spheroids (PDOTS) 3D cultures which recapitulate ccRCC patient tumor microenvironment (TME), as well as low toxicity on cholangiocyte derived organotypic spheroids (CDOS). In summary, affinity/avidity fine-tuned CAIX targeted CAR-T cell therapy holds promise to achieve cures of ccRCC by efficaciously killing tumor cells and mitigating on-target off-tumor toxicity on normal tissues.

Citation Format: Yufei Wang, Alicia Buck, Gabriella Kastrunes, Rabia Abbas, Michael Lynch, Zhou Zhong, Song-My Hoang, Andras Miklosi, Kun Huang, Jae-Won Cho, Marion Grimaud, Cecile Razimbaud, Matthew Chang, Atef Fayed, Audrey Apollon, Nithyassree Murugan, Ze-Hua Li, Tran Thai, Luann Zerefa, Brandon Piel, Elena Ivanova, Amy Cameron, Quang-De Nguyen, Zhu Zhu, Kevin Wei, Yasmin Nabil Laimon, Aseman Bagheri Sheshdeh, Sabina Signoretti, David A. Braun, Catherine J. Wu, Toni K. Choueiri, Jon Wee, Cloud P. Paweletz, Martin Hemberg, Aedin C. Culhane, David A. Barbie, Gordon J. Freeman, Wayne A. Marasco. Fine-tuned CAIX targeted CAR-T cells exhibit superior efficacy and mitigate on-target off-tumor side effects [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 886.

Anti-CAIX BBζ CAR4/8 T cells exhibit superior efficacy in a ccRCC mouse model

Improving CAR-T cell therapy for solid tumors requires a better understanding of CAR design and cellular composition. Here, we compared second-generation (BBζ and 28ζ) with third-generation (28BBζ) carbonic anhydrase IX (CAIX)-targeted CAR constructs and investigated the antitumor effect of CAR-T cells with different CD4/CD8 proportions in vitro and in vivo. The results demonstrated that BBζ exhibited superior efficacy compared with 28ζ and 28BBζ CAR-T cells in a clear-cell renal cell carcinoma (ccRCC) skrc-59 cell bearing NSG-SGM3 mouse model. The mice treated with a single dose of BBζ CD4/CD8 mixture (CAR4/8) showed complete tumor remission and remained tumor-free 72 days after CAR-T cells infusion. In the other CAR-T and control groups, tumor-infiltrating T cells were recovered and profiled. We found that BBζ CAR8 cells upregulated expression of major histocompatibility complex (MHC) class II and cytotoxicity-associated genes, while downregulating inhibitory immune checkpoint receptor genes and diminishing differentiation of regulatory T cells (Treg cells), leading to excellent therapeutic efficacy in vivo. Increased memory phenotype, elevated tumor infiltration, and decreased exhaustion genes were observed in the CD4/8 untransduced T (UNT) cells compared with CD8 alone, indicating that CD4/8 would be the favored cellular composition for CAR-T cell therapy with long-term persistence. In summary, these findings support that BBζ CAR4/8 cells are a highly potent, clinically translatable cell therapy for ccRCC.

Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain

BACKGROUND

We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.

METHODS

We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.

RESULTS

We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.

CONCLUSIONS

Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.

IMPACT

This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

Genome-scale screens identify factors regulating tumor cell responses to natural killer cells

To systematically define molecular features in human tumor cells that determine their degree of sensitivity to human allogeneic natural killer (NK) cells, we quantified the NK cell responsiveness of hundreds of molecularly annotated 'DNA-barcoded' solid tumor cell lines in multiplexed format and applied genome-scale CRISPR-based gene-editing screens in several solid tumor cell lines, to functionally interrogate which genes in tumor cells regulate the response to NK cells. In these orthogonal studies, NK cell-sensitive tumor cells tend to exhibit 'mesenchymal-like' transcriptional programs; high transcriptional signature for chromatin remodeling complexes; high levels of B7-H6 (NCR3LG1); and low levels of HLA-E/antigen presentation genes. Importantly, transcriptional signatures of NK cell-sensitive tumor cells correlate with immune checkpoint inhibitor (ICI) resistance in clinical samples. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.

An Embryonic Diapause-like Adaptation with Suppressed Myc Activity Enables Tumor Treatment Persistence

Treatment-persistent residual tumors impede curative cancer therapy. To understand this cancer cell state we generated models of treatment persistence that simulate the residual tumors. We observe that treatment-persistent tumor cells in organoids, xenografts, and cancer patients adopt a distinct and reversible transcriptional program resembling that of embryonic diapause, a dormant stage of suspended development triggered by stress and associated with suppressed Myc activity and overall biosynthesis. In cancer cells, depleting Myc or inhibiting Brd4, a Myc transcriptional co-activator, attenuates drug cytotoxicity through a dormant diapause-like adaptation with reduced apoptotic priming. Conversely, inducible Myc upregulation enhances acute chemotherapeutic activity. Maintaining residual cells in dormancy after chemotherapy by inhibiting Myc activity or interfering with the diapause-like adaptation by inhibiting cyclin-dependent kinase 9 represent potential therapeutic strategies against chemotherapy-persistent tumor cells. Our study demonstrates that cancer co-opts a mechanism similar to diapause with adaptive inactivation of Myc to persist during treatment.

Abstract PO041: Landscape of molecular events regulating tumor cell responses to natural killer cells

Natural killer (NK) cells exhibit potent activity in pre-clinical models of diverse hematologic malignancies and solid tumors and infusion of high numbers of NK cells, either autologous or allogeneic, after their ex vivo expansion and activation, has been feasible and safe in clinical studies. To systematically define molecular features in human tumor cells which determine their degree of sensitivity to human allogeneic NK cells, we quantified the NK cell responsiveness of hundreds of molecularly-annotated “DNA-barcoded” solid tumor cell lines in multiplexed format (PRISM; Profiling Relative Inhibition Simultaneously in Mixtures approach), correlating cytotoxicity scores for each cell line with the CCLE transcriptional data (RNA-seq), to reveal genes that are associated with resistance or sensitivity to NK cells. In addition, we applied genome-scale CRISPR-based gene editing screens in several solid tumor cell lines to interrogate, at a functional level, which genes regulate tumor cell response to NK cells. Based on these orthogonal studies, NK sensitive tumor cells tend to exhibit high levels of the NK cell-activating ligand B7-H6 (NCR3LG1); low levels of the inhibitory ligand HLA-E; microsatellite instability (MSI) status; high transcriptional signature for chromatin remodeling complexes and low antigen presentation machinery genes. Treatment with an HDAC inhibitor reduced the sensitivity of SW620 colon cancer cells, increased antigen presentation machinery, including HLA-E, and reduced B7-H6. Importantly, we observe that transcriptional signatures of NK cell-sensitive tumor cells correlate with immune checkpoint inhibitor resistance in clinical samples. Strikingly, comprehensive analysis of the CCLE transcriptional signatures revealed that cell lines with mesenchymal-like program tend to be more sensitive to NK cells treatment, compared with cell lines of epithelial-like program. Indeed, mesenchymal tumors tend to have lower expression of antigen presentation machinery in both CCLE and TCGA, suggesting a link between these two machieneries. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.

Citation Format: Michal Sheffer, Emily Lowry, Nicky Beelen, Minasri Borah, Suha Naffar-Abu Amara, Chris C. Mader, Jennifer Roth, Aviad Tsherniak, Olga Dashevsky, Sara Gandolfi, Samantha Bender, Jordan Bryan, Cong Zhu, Li Wang, Ricardo De-Matos Simoes, Channing Yu, Yiguo Hu, Olli Dufva, Marios Giannakis, Todd Golub, Rizwan Romee, Satu Mustjoki, Aedin C. Culhane, Lotte Wieten, Constantine S. Mitsiades. Landscape of molecular events regulating tumor cell responses to natural killer cells [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO041.

Functional Genomics Identify Distinct and Overlapping Genes Mediating Resistance to Different Classes of Heterobifunctional Degraders of Oncoproteins

Heterobifunctional proteolysis-targeting chimeric compounds leverage the activity of E3 ligases to induce degradation of target oncoproteins and exhibit potent preclinical antitumor activity. To dissect the mechanisms regulating tumor cell sensitivity to different classes of pharmacological "degraders" of oncoproteins, we performed genome-scale CRISPR-Cas9-based gene editing studies. We observed that myeloma cell resistance to degraders of different targets (BET bromodomain proteins, CDK9) and operating through CRBN (degronimids) or VHL is primarily mediated by prevention of, rather than adaptation to, breakdown of the target oncoprotein; and this involves loss of function of the cognate E3 ligase or interactors/regulators of the respective cullin-RING ligase (CRL) complex. The substantial gene-level differences for resistance mechanisms to CRBN- versus VHL-based degraders explains mechanistically the lack of cross-resistance with sequential administration of these two degrader classes. Development of degraders leveraging more diverse E3 ligases/CRLs may facilitate sequential/alternating versus combined uses of these agents toward potentially delaying or preventing resistance.

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study

Background

Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods

In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I ² value was less than 0·4.

Findings

The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation

Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding

National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.

Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study

Background In this study we phenotyped individuals hospitalised with coronavirus disease 2019 (COVID-19) in depth, summarising entire medical histories, including medications, as captured in routinely collected data drawn from databases across three continents. We then compared individuals hospitalised with COVID-19 to those previously hospitalised with influenza. Methods We report demographics, previously recorded conditions and medication use of patients hospitalised with COVID-19 in the US (Columbia University Irving Medical Center [CUIMC], Premier Healthcare Database [PHD], UCHealth System Health Data Compass Database [UC HDC], and the Department of Veterans Affairs [VA OMOP]), in South Korea (Health Insurance Review & Assessment [HIRA]), and Spain (The Information System for Research in Primary Care [SIDIAP] and HM Hospitales [HM]). These patients were then compared with patients hospitalised with influenza in 2014-19. Results 34,128 (US: 8,362, South Korea: 7,341, Spain: 18,425) individuals hospitalised with COVID-19 were included. Between 4,811 (HM) and 11,643 (CUIMC) unique aggregate characteristics were extracted per patient, with all summarised in an accompanying interactive website (http://evidence.ohdsi.org/Covid19CharacterizationHospitalization/). Patients were majority male in the US (CUIMC: 52%, PHD: 52%, UC HDC: 54%, VA OMOP: 94%,) and Spain (SIDIAP: 54%, HM: 60%), but were predominantly female in South Korea (HIRA: 60%). Age profiles varied across data sources. Prevalence of asthma ranged from 4% to 15%, diabetes from 13% to 43%, and hypertensive disorder from 24% to 70% across data sources. Between 14% and 33% were taking drugs acting on the renin-angiotensin system in the 30 days prior to hospitalisation. Compared to 81,596 individuals hospitalised with influenza in 2014-19, patients admitted with COVID-19 were more typically male, younger, and healthier, with fewer comorbidities and lower medication use. Conclusions We provide a detailed characterisation of patients hospitalised with COVID-19. Protecting groups known to be vulnerable to influenza is a useful starting point to minimize the number of hospital admissions needed for COVID-19. However, such strategies will also likely need to be broadened so as to reflect the particular characteristics of individuals hospitalised with COVID-19.

Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data

Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges. Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix factorization method that is relatively fast, and can easily scale to large datasets when used with sparse-matrix representations. In this review, we provide a guide to PCA and related methods. We describe the relationship between PCA and singular value decomposition, the difference between PCA of a correlation and covariance matrix, the impact of scaling, log-transforming, and standardization, and how to recognize a horseshoe or arch effect in a PCA. We describe canonical correlation analysis (CCA), a popular matrix factorization approach for the integration of single-cell data from different platforms or studies. We discuss alternatives to CCA and why additional preprocessing or weighting datasets within the joint decomposition should be considered.

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

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.

Software for the Integration of Multiomics Experiments in Bioconductor

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.

MECP2 Is a Frequently Amplified Oncogene with a Novel Epigenetic Mechanism That Mimics the Role of Activated RAS in Malignancy

An unbiased genome-scale screen for unmutated genes that drive cancer growth when overexpressed identified methyl cytosine-guanine dinucleotide (CpG) binding protein 2 (MECP2) as a novel oncogene. MECP2 resides in a region of the X-chromosome that is significantly amplified across 18% of cancers, and many cancer cell lines have amplified, overexpressed MECP2 and are dependent on MECP2 expression for growth. MECP2 copy-number gain and RAS family member alterations are mutually exclusive in several cancer types. The MECP2 splicing isoforms activate the major growth factor pathways targeted by activated RAS, the MAPK and PI3K pathways. MECP2 rescued the growth of a KRAS(G12C)-addicted cell line after KRAS downregulation, and activated KRAS rescues the growth of an MECP2-addicted cell line after MECP2 downregulation. MECP2 binding to the epigenetic modification 5-hydroxymethylcytosine is required for efficient transformation. These observations suggest that MECP2 is a commonly amplified oncogene with an unusual epigenetic mode of action.

SIGNIFICANCE

MECP2 is a commonly amplified oncogene in human malignancies with a unique epigenetic mechanism of action. Cancer Discov; 6(1); 45-58. ©2015 AACR.This article is highlighted in the In This Issue feature, p. 1.

RelA-Induced Interferon Response Negatively Regulates Proliferation

Both oncogenic and tumor-suppressor activities are attributed to the Nuclear Factor kappa B (NF-kB) pathway. Moreover, NF-kB may positively or negatively regulate proliferation. The molecular determinants of these opposing roles of NF-kB are unclear. Using primary human mammary epithelial cells (HMEC) as a model, we show that increased RelA levels and consequent increase in basal transcriptional activity of RelA induces IRF1, a target gene. Induced IRF1 upregulates STAT1 and IRF7, and in consort, these factors induce the expression of interferon response genes. Activation of the interferon pathway down-regulates CDK4 and up-regulates p27 resulting in Rb hypo-phosphorylation and cell cycle arrest. Stimulation of HMEC with IFN-γ elicits similar phenotypic and molecular changes suggesting that basal activity of RelA and IFN-γ converge on IRF1 to regulate proliferation. The anti-proliferative RelA-IRF1-CDK4 signaling axis is retained in ER+/HER2- breast tumors analyzed by The Cancer Genome Atlas (TCGA). Using immuno-histochemical analysis of breast tumors, we confirm the negative correlation between RelA levels and proliferation rate in ER+/HER2- breast tumors. These findings attribute an anti-proliferative tumor-suppressor role to basal RelA activity. Inactivation of Rb, down-regulation of RelA or IRF1, or upregulation of CDK4 or IRF2 rescues the RelA-IRF1-CDK4 induced proliferation arrest in HMEC and are points of disruption in aggressive tumors. Activity of the RelA-IRF1-CDK4 axis may explain favorable response to CDK4/6 inhibition observed in patients with ER+ Rb competent tumors.

Abstract 2355: Risk prediction for late-stage ovarian cancer by meta-analysis of 1,525 patient samples

Background: Ovarian cancer causes over 15,000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients. However, existing ovarian cancer gene signatures did not make it to the clinic so far. Expression data of thousands of patients from a large number of different cohorts is now available in the public domain. Harvesting all this information for building signatures is a challenging “big data” problem, but has the potential to yield more robust and accurate signatures.

Methods: We developed and validated two gene expression signatures of potential clinical relevance in advanced stage serous ovarian cancer, the first for predicting survival and the second for predicting the outcome of initial debulking surgery. We integrated 13 publicly available datasets totaling 1,525 subjects. We trained prediction models using a meta-analysis variation on the Compound Covariate method, tested models via a “leave-one-dataset-out” procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and qRT-PCR in two further independent cohorts of 179 and 78 patients, respectively.

Results: The survival signature stratified patients into high- and low-risk groups (HR=2.19; 95% CI, 1.84 to 2.61) significantly better than the best previously published overall survival signature (P = 0.039). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2) were validated by qRT-PCR (P < 0.05) and POSTN, CXCL14 and phosphorylated Smad2/3 by immunohistochemistry (P < 0.001) as independent predictors of debulking status. The sum of IHC intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (AUC 0.89; 95% CI 0.84 to 0.93). We present the strongest evidence to date for the existence of a biologic basis of suboptimal outcome of debulking surgery.

Conclusions: Our survival signature provides the most accurate and validated prognostic model for early and advanced stage high-grade serous ovarian cancer. The debulking signature accurately (92.8%) predicts the outcome of cytoreductive surgery and will have clinical utility if the accuracy of the immunohistochemistry tool observed in our initial 179-patient validation cohort is confirmed in prospective validation. This tool will potentially allow for the identification of those patients who will not benefit from primary debulking surgery, sparing them the toxicity of extensive surgery and the delay until the initiation of chemotherapy. These patients can be triaged to neoadjuvant chemotherapy with interval debulking.

Citation Format: Markus Riester, Wei Wei, Aedin C. Culhane, Lorenzo Trippa, Franziska Michor, Curtis Huttenhower, Giovanni Parmigiani, Michael Birrer. Risk prediction for late-stage ovarian cancer by meta-analysis of 1,525 patient samples. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2355. doi:10.1158/1538-7445.AM2014-2355

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0-HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors.

Abstract B067: Taxonomy of breast cancer based on normal cell phenotype and ontology

The classification of lymphoma and leukemia is successfully done by using normal cell types as a reference point which is one of the many reasons that better therapies are available for their treatment. In case of breast cancer, clinically it is classified as Estrogen Receptor positive, Human Epidermal Growth Factor Receptor 2 (Her2) positive and triple negative breast cancer while in the research settings, it has been classified by mRNA expression profiling but these classification systems are still evolving. Our laboratory attempted to classify breast cancer by examining the expression of different keratin isoforms and Hormone Receptors (HR) studied through immunostaining in 36 normal breast samples and later in 1731 breast tumors from the Nurses' Health Study (NHS) cohort with a patient follow up extending beyond 25 years. Among the various receptors tested, three of these receptors: Androgen Receptor, Estrogen Receptor and Vitamin D Receptor were capable of identifying four major subgroups being designated as HR0-3, depending on the number of hormone receptors present. More importantly, radically different survival outcomes were observed between these HR sub-groups through Kaplan Meier curve analysis; which highlights their usefulness as vital biological markers as wells as targets for breast cancer treatment. Furthermore, the characterization of these HR was carried out in 20 different breast cancer cell lines and the combination of different receptor modulators alone or in combination was attempted with better anti-proliferative effects being observed in the combinatorial group than either treatment alone. Thus, our new classification not only re-classifies breast cancer but also gives insight on how patients could be treated through targeting hormone receptors.

Citation Format: Ankita Thakkar, Sandro Santagata, Ayse Ergonul, Bin Wang, Terri Woo, Rong Hu, J. Chuck Harrell, George McNamara, Matthew Schwede, Aedin C. Culhane, David Kindelberger, Scott Rodig, Andrea Richardson, Stuart J. Schnitt, Rulla M. Tamimi, Tan A. Ince. Taxonomy of breast cancer based on normal cell phenotype and ontology. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B067.

A three-gene model to robustly identify breast cancer molecular subtypes

BACKGROUND

Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.

METHODS

Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.

RESULTS

SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.

CONCLUSION

Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

The cocaine- and amphetamine-regulated transcript mediates ligand-independent activation of ERα, and is an independent prognostic factor in node-negative breast cancer

Personalized medicine requires the identification of unambiguous prognostic and predictive biomarkers to inform therapeutic decisions. Within this context, the management of lymph node-negative breast cancer is the subject of much debate with particular emphasis on the requirement for adjuvant chemotherapy. The identification of prognostic and predictive biomarkers in this group of patients is crucial. Here, we demonstrate by tissue microarray and automated image analysis that the cocaine- and amphetamine-regulated transcript (CART) is expressed in primary and metastatic breast cancer and is an independent poor prognostic factor in estrogen receptor (ER)-positive, lymph node-negative tumors in two separate breast cancer cohorts (n=690; P=0.002, 0.013). We also show that CART increases the transcriptional activity of ERα in a ligand-independent manner via the mitogen-activated protein kinase pathway and that CART stimulates an autocrine/paracrine loop within tumor cells to amplify the CART signal. Additionally, we demonstrate that CART expression in ER-positive breast cancer cell lines protects against tamoxifen-mediated cell death and that high CART expression predicts disease outcome in tamoxifen-treated patients in vivo in three independent breast cancer cohorts. We believe that CART profiling will help facilitate stratification of lymph node-negative breast cancer patients into high- and low-risk categories and allow for the personalization of therapy.

Analysis of differential gene expression in colorectal cancer and stroma using fluorescence-activated cell sorting purification

Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) for generating cell populations for gene expression analysis and to compare the gene expression of FACS-purified tumour parenchyma to that of whole tumour biopsies. Single cell suspensions were generated from colorectal tumour biopsies and tumour parenchyma was separated using FACS. Fluorescence-activated cell sorting allowed reliable estimation and purification of cell populations, generating parenchymal purity above 90%. RNA from FACS-purified and corresponding whole tumour biopsies was hybridised to Affymetrix oligonucleotide microarrays. Whole tumour and parenchymal samples demonstrated differential gene expression, with 289 genes significantly overexpressed in the whole tumour, many of which were consistent with stromal gene expression (e.g., COL6A3, COL1A2, POSTN, TIMP2). Genes characteristic of colorectal carcinoma were overexpressed in the FACS-purified cells (e.g., HOX2D and RHOB). We found FACS to be a robust method for generating samples for gene expression analysis, allowing simultaneous assessment of parenchymal and stromal compartments. Gross stromal contamination may affect the interpretation of cancer gene expression microarray experiments, with implications for hypotheses generation and the stability of expression signatures used for predicting clinical outcomes.

Multiple markers for melanoma progression regulated by DNA methylation: insights from transcriptomic studies

The incidence of melanoma is increasing rapidly, with advanced lesions generally failing to respond to conventional chemotherapy. Here, we utilized DNA microarray-based gene expression profiling techniques to identify molecular determinants of melanoma progression within a unique panel of isogenic human melanoma cell lines. When a poorly tumorigenic cell line, derived from an early melanoma, was compared with two increasingly aggressive derivative cell lines, the expression of 66 genes was significantly changed. A similar pattern of differential gene expression was found with an independently derived metastatic cell line. We further examined these melanoma progression-associated genes via use of a tailored TaqMan Low Density Array (LDA), representing the majority of genes within our cohort of interest. Considerable concordance was seen between the transcriptomic profiles determined by DNA microarray and TaqMan LDA approaches. A range of novel markers were identified that correlated here with melanoma progression. Most notable was TSPY, a Y chromosome-specific gene that displayed extensive down-regulation in expression between the parental and derivative cell lines. Examination of a putative CpG island within the TSPY gene demonstrated that this region was hypermethylated in the derivative cell lines, as well as metastatic melanomas from male patients. Moreover, treatment of the derivative cell lines with the DNA methyltransferase inhibitor, 2'-deoxy-5-azacytidine (DAC), restored expression of the TSPY gene to levels comparable with that found in the parental cells. Additional DNA microarray studies uncovered a subset of 13 genes from the above-mentioned 66 gene cohort that displayed re-activation of expression following DAC treatment, including TSPY, CYBA and MT2A. DAC suppressed tumor cell growth in vitro. Moreover, systemic treatment of mice with DAC attenuated growth of melanoma xenografts, with consequent re-expression of TSPY mRNA. Overall, our data support the hypothesis that multiple genes are targeted, either directly or indirectly, by DNA hypermethylation during melanoma progression.

Oligonucleotide microarray analysis of gene expression in neuroblastoma displaying loss of chromosome 11q

A number of distinct subtypes of neuroblastoma exist with different genetic abnormalities that are predicative of outcome. Whole chromosome gains are usually associated with low stage disease and favourable outcome, whereas loss of 1p, 3p and 11q, unbalanced gain of 17q and MYCN amplification (MNA) are indicative of high stage disease and unfavourable prognosis. Although MNA and loss of 11q appear to represent two distinct genetic subtypes of advanced stage neuroblastoma, a detailed understanding of how these subtypes differ in terms of global gene expression is still lacking. We have used metaphase comparative genomic hybridization (CGH) analysis in combination with oligonucleotide technology to identify patterns of gene expression that correlate with specific genomic imbalances found in primary neuroblastic tumours and cell lines. The tumours analysed in this manner included a ganglioneuroma, along with various ganglioneuroblastoma and neuroblastoma of different stages and histopathological classifications. Oligonucleotide microarray-based gene expression profile analysis was performed with Affymetrix HU133A arrays representing approximately 14 500 unique genes. The oligonucleotide microarray results were subsequently validated by quantitative real-time PCR, immunohistochemical staining, and by comparison of specific gene expression patterns with published results. Hierarchical clustering of gene expression data distinguished tumours on the basis of stage, differentiation and genetic abnormalities. A number of genes were identified whose patterns of expression were highly correlated with 11q loss; supporting the concept that loss of 11q represents a distinct genetic subtype of neuroblastoma. The implications of these results in the process of neuroblastoma development and progression are discussed.

Detection of the interleukin 18 family in rat brain by RT-PCR

Interleukin 18, an inflammatory cytokine, mediates its effects by interaction with its receptor complex, consisting of the IL-18 receptor (IL-18R) and receptor accessory protein (AcPL). A functional inhibitor of IL-18, the IL-18 binding protein (IL-18BP), has been identified recently. This study reports the detection of IL-18, IL-18R, AcPL and IL-18BP mRNA expression in the brain of normal adult rats using RT-PCR.

Cloning of rat brain interleukin-18 cDNA

The interleukin-1 (IL-1) family comprises IL-1 alpha and IL-1 beta and an endogenous IL-1 receptor antagonist (IL-1ra). IL-1 has diverse actions in the brain and has been implicated in both acute and chronic neurodegeneration. However, neither IL-1 alpha nor IL-1 beta are neurotoxic per se in vivo, so other IL-1 related ligands may be important in neurodegeneration. The cytokine interleukin-18 (also called interferon gamma inducing factor, IGIF) was first isolated from the liver of mice during toxic shock. It was later proposed as a member of the IL-1 family, based on protein sequence homology with IL-1 beta and IL-1ra, and has tentatively been called IL-1 gamma. We cloned IL-18 from adult rat brain and demonstrated, by RT-PCR, that it is expressed constitutively in cerebellum, hippocampus, hypothalamus, cortex and striatum. Rat brain IL-18 shows close homology to mouse and human IL-18, and to the recently published sequence from the rat adrenal gland. Mouse pro-IL-18 and pro-IL-1 beta are processed by caspase-1. We demonstrate that caspase-1 also cleaves rat IL-18 in vitro and that the caspase inhibitor, zVAD-DCB inhibits this cleavage.

Apoptosis: molecular regulation of cell death

The field of apoptosis is unusual in several respects. Firstly, its general importance has been widely recognised only in the past few years and its surprising significance is still being evaluated in a number of areas of biology. Secondly, although apoptosis is now accepted as a critical element in the repertoire of potential cellular responses, the picture of the intra-cellular processes involved is probably still incomplete, not just in its details, but also in the basic outline of the process as a whole. It is therefore a very interesting and active area at present and is likely to progress rapidly in the next two or three years. This review emphasises recent work on the molecular mechanisms of apoptosis and, in particular, on the intracellular interactions which control this process. This latter area is of crucial importance since dysfunction of the normal control machinery is likely to have serious pathological consequences, probably including oncogenesis, autoimmunity and degenerative disease. The genetic analysis of programmed cell death during the development of the nematode Caenorhabditis elegans has proved very useful in identifying important events in the cell death programme. Recently defined genetic connections between C. elegans cell death and mammalian apoptosis have emphasised the value of this system as a model for cell death in mammalian cells, which, inevitably, is more complex. The signals inducing apoptosis are very varied and the same signals can induce differentiation and proliferation in other situations. However, some pathways appear to be of particular significance in the control of cell death; recent analysis of the apoptosis induced through the cell-surface Fas receptor has been especially important for immunology. Two gene families are dealt with in particular detail because of their likely importance in apoptosis control. These are, first, the genes encoding the interleukin-1 beta-converting enzyme family of cysteine proteases and, second, those related to the proto-oncogene bcl-2. Both of these families are homologous to cell death genes in C. elegans. In mammalian cells the number of members of both families which have been identified is growing rapidly and considerable effort is being directed towards establishing the roles played by each member and the ways in which they interact to regulate apoptosis. Other genes with established roles in the regulation of proliferation and differentiation are also important in controlling apoptosis. Several of these are known proto-oncogenes, e.g. c-myc, or tumour suppressors, e.g. p53, an observation which is consistent with the importance of defective apoptosis in the development of cancer. Viral manipulation of the apoptosis of host cells frequently involves interactions with these cellular proteins. Finally, the biochemistry of the closely controlled cellular self-destruction which ensues when the apoptosis programme has been engaged is also very important. The biochemical changes involved in inducing phagocytosis of the apoptotic cell, for example, allow the process to be neatly integrated within the tissues, under physiological conditions. Molecular defects in this area too may have important pathological consequences.