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The molecular characterization of cancer through genomics, data from multiomics technologies, molecular-driven clinical trials, and internet-enabled devices capturing patient context and real-world data are creating an unprecedented big data revolution across the cancer research-care continuum. While big data has translated to benefit for some patients, it has also created new problems. Our intent in this brief communication is to explore some examples of progress and key challenges that remain. The problems are not intractable, but success will require rethinking and rebuilding an information and evidence-based learning system that moves beyond paralysis to shape a better future for patients with cancer.

Introduction: Deconvolution of multi-nodal perturbations in cancer network architecture demands highly multiplexed profiling assays. We demonstrate the value of polyligand profiling of tumor systems states using libraries of single stranded oligodeoxynucleotides (ssODN) to distinguish between tumor tissue from breast cancer patients who did or did not derive benefit from treatment regimens containing trastuzumab. Methods: This study included cases from women with invasive breast cancer who received chemotherapy+ trastuzumab (C+T) or trastuzumab monotherapy with available retrospective data on the time to next treatment (TTNT). A library of 2x1012 unique ssODN was exposed to FFPE tissues from patients who benefited (B) or not (NB) from trastuzumab-based regimens in several rounds of positive and negative selection. Two enriched libraries were screened on independent set of 42 B and 19 NB cases using a modified IHC protocol for detection of bound ssODNs. Poly-Ligand Profiles (PLP) were scored by a blinded pathologist. Two libraries, EL-NB and EL-B, showed significant p-values between groups of responders and non-responders. A Cox-PH model was fitted using either tumors' HER2 status or PLP test results as the independent variable. Median survival time was calculated from the Kaplan-Meier estimate. A separate group of 63 cases with TTNT data from chemotherapy without trastuzumab was used as a control to distinguish prognostic from predictive performance. Results: The PLP scores of EL-NB and EL-B were assessed by receiver operating characteristic (ROC) curves and resulted in a combined AUC value of 0.81. EL-NB and EL-B were able to effectively classify B and NB patients with either HER2-negative/equivocal (AUC = 0.73) or HER2-positive cancers (AUC = 0.84). In contrast, HER2 status alone yielded an AUC value of 0.47. The combined PLP scores for the independent set of 63 patients treated with C excluding trastuzumab resulted in an AUC value of 0.53, indicating that the assay was predictive and not simply prognostic. Kaplan-Meier curves analysis shows that PLP+ cases have 429 days median TTNT, while PLP- cases have 129 days (HR = 0.38, log-rank p = 0.001). Analysis based on HER2 status showed no significant difference in TTNT between patients that were HER2+ (280 days) or HER2-negative/equivocal (336 days, HR = 1.27, log-rank p =0.45). Summary: Performance of the PLP assay in differentiating patients who did or did not benefit from trastuzumab therapy outperforms the standard IHC assay for HER2 status. These results represent a promising step towards the development of a CDx to identify the 50-70% of HER2+ patients who will not benefit from trastuzumab. In addition, PLP also has the potential to identify the HER2-negative/equivocal patients who may benefit from trastuzumab-containing regimens. Citation Format: Domenyuk V, Gatalica Z, Santhanam R, Wei X, Stark A, Kennedy P, Toussaint B, Levenberg S, Wang R, Xiao N, Greil R, Rinnerthaler G, Gampenrieder S, Heimberger AB, Berry DJ, Barker A, Demetri GD, Quackenbush J, Marshall JL, Poste G, Vacirca JL, Vidal GA, Schwartzberg LS, Halbert DD, Voss A, Miglarese MR, Famulok M, Mayer G, Spetzler D. Polyligand profiling differentiates cancer patients according to their benefit of treatment [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-09.

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan–Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development., Patients’ selection is particularly important in cancer treatment. Here the authors present a proof-of-principle methodology that could be potentially important in assisting therapeutic decisions in the treatment of breast cancer patients.

We are in the midst of a technological revolution that is providing new insights into human biology and cancer. In this era of big data, we are amassing large amounts of information that is transforming how we approach cancer treatment and prevention. Enactment of the Cancer Moonshot within the 21st Century Cures Act in the USA arrived at a propitious moment in the advancement of knowledge, providing nearly US$2 billion of funding for cancer research and precision medicine. In 2016, the Blue Ribbon Panel (BRP) set out a roadmap of recommendations designed to exploit new advances in cancer diagnosis, prevention, and treatment. Those recommendations provided a high-level view of how to accelerate the conversion of new scientific discoveries into effective treatments and prevention for cancer. The US National Cancer Institute is already implementing some of those recommendations. As experts in the priority areas identified by the BRP, we bolster those recommendations to implement this important scientific roadmap. In this Commission, we examine the BRP recommendations in greater detail and expand the discussion to include additional priority areas, including surgical oncology, radiation oncology, imaging, health systems and health disparities, regulation and financing, population science, and oncopolicy. We prioritise areas of research in the USA that we believe would accelerate efforts to benefit patients with cancer. Finally, we hope the recommendations in this report will facilitate new international collaborations to further enhance global efforts in cancer control.

Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737–43. ©2017 AACR.

An ever-expanding understanding of the molecular basis of the more than 200 unique diseases collectively called cancer, combined with efforts to apply these insights to clinical care, is forming the foundation of an era of personalized medicine that promises to improve cancer treatment. At the same time, these extraordinary opportunities are occurring in an environment of intense pressure to contain rising healthcare costs. This environment presents a challenge to oncology research and clinical care, because both are becoming progressively more complex and expensive, and because the current tools to measure the cost and value of advances in care (e.g., comparative effectiveness research, cost-effectiveness analysis, and health technology assessments) are not optimized for an ecosystem moving toward personalized, patient-centered care. Reconciling this tension will be essential to maintaining progress in a cost-constrained environment, especially because emerging innovations in science (e.g., increasing identification of molecular biomarkers) and in clinical process (implementation of a learning healthcare system) hold potential to dramatically improve patient care, and may ultimately help address the burden of rising costs. For example, the rapid pace of innovation taking place within oncology calls for increased capability to integrate clinical research and care to enable continuous learning, so that lessons learned from each patient treated can inform clinical decision making for the next patient. Recognizing the need to define the policies required for sustained innovation in cancer research and care in an era of cost containment, the stakeholder community must engage in an ongoing dialogue and identify areas for collaboration. This article reflects and seeks to amplify the ongoing robust discussion and diverse perspectives brought to this issue by multiple stakeholders within the cancer community, and to consider how to frame the research and regulatory policies necessary to sustain progress against cancer in an environment of constrained resources. Clin Cancer Res; 20(5); 1081–6. ©2014 AACR.

Complex Adaptive Systems (CAS) are ubiquitous and composed of many interacting “agents” that exhibit independent properties and behaviors that function together with their environment to produce emergent properties. Emergence and emergent properties cannot be predicted by isolated understanding of these interacting agents/components; but can be demonstrated by observing the outcomes associated with dynamically changing interacting components of a CAS. Obviously, evolution also plays a key role in driving emergence as a defining feature of biological CAS. Biological CAS are highly heterogeneous and complex, both within and across broad scales of time and space. Biological CAS are also non-linear which means that predicting outcomes is difficult. Moreover, it is impossible to “fix” a CAS, rather the identification of leverage points that can alter the trajectory of the system to achieve desired outcomes is a more logical approach. Until recently, systems of such high dimensionality were not sufficiently tractable to understand and apply CAS principles to a disease as complex as cancer. However, recent progress in the development of advanced technologies such as computation, machine learning, artificial intelligence, and modeling portend a day when cancer will be viewed and managed as a CAS. These “big data” tools offer new and innovative opportunities to mine, manage, manipulate, model and simulate cancer to derive the information needed to manage it as a CAS. In terms of applying these principles at least one approach to treating cancer, immunotherapy, suggests that achieving a future state where cancer is viewed through the lens of CAS is well underway. Immunotherapy represents a paradigm shift in cancer therapy in that it targets the immune system, which is a quintessential CAS. When immunotherapy is successful the outcome is a “homeostatic reset” of what is an extraordinarily complex interaction between cancer and the immune system. Together these two complex systems comprise a CAS that promises to re-define how we treat and prevent cancer. A variety of immunotherapeutics (dominated by checkpoint inhibitors) have produced durable responses (possible cures) in a few patients against some cancers. These agents essentially block signals that the tumor employs to keep the immune system from recognizing and killing the cancer. However, the interaction of cancer and the immune system is a dynamic CAS that will ultimately require a detailed understanding of the cellular and microenvironmental changes that occur in patients in response to specific immunotherapeutic interventions. The challenges we face are significant including: identifying responders/non-responders; determining doses; predicting and controlling toxicities; developing rational combinations; and creating new targeted systems-based therapies. Fortunately, many of these challenges can be met by defining the “states” produced by some of the defining alterations observed in responsive and non-responsive patients including “omics” alterations, types of immune cells, temporal relationships, immune activation, humoral factors, etc. Although early, models and platforms to describe, annotate, model and simulate these systems alterations are emerging. In the past several years, we have developed a modeling platform that permits the study of the immune system and its interaction with cancer. “Cell Studio” is an immune-modeling engine that seeks to examine cancer and the immune response as a dynamic CAS by using real world data on the immune system and cancer to develop and inform computational models. Cell Studio permits the user to conduct in silico experiments of defined time and complexity. It combines agent-based and mathematical modeling approaches to capture multiscale dynamics within the immune system. The engine permits user creation of multiple different types of immune cells each with different classes of properties including different collections of cell surface receptors at different concentrations and affinities as well as the capacity to release and respond to cytokines. Multiple compartments corresponding to different body niches (e.g. lymph node, tumor environment) can be created. Mathematical models govern phenomena such as diffusion and cell tracking of cytokine gradients. As a CAS, based on a finite number of “rules” the system is self-organizing and can display emergent properties. User defined therapeutic interventions such as drug administration can be incorporated to assess the system’s response. Cell Studio is implemented using a gaming platform so that the in silico experiments can be visualized in 3D - in real time if desired. This permits researchers to perform experiments similar to those done using biologic model systems and visualizing the results. Like most video gaming platforms, different user views, overviews, individual cell movement, etc. are available and real-time as well as cumulative statistical outputs are captured and displayed. Unlike biologic model systems the simulations can be time reversed to identify, visualize, and manipulate key events. Experimentation using the Cell Studio modeling engine shows that it can recapitulate the longitudinal events in biologic model systems. Additionally, it can recover “immunophenotypes” observed in human studies of immunotherapy in cancer. It is anticipated that the in silico modeling can augment current biologic modeling strategies - especially since it can be run with numbers of replicates of virtual experiments that are not practical with biologic model systems. Additionally, it promises to assist in the rationale to develop combinatorial interventions hitting multiple immune targets and in understanding factors that modulate successful outcomes. In summary, the implications of viewing, studying and developing strategic approaches to fundamentally understand the cancer - immune system CAS are profound. Cell Studio is a next generation novel and powerful approach to analyze and model specific components of this dynamic and integrated CAS for the benefit of patients. Citation Format: Anna D. Barker, Kenneth Buetow. Viewing cancer as a complex adaptive system and managing immunotherapy as “homeostatic reset” [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY36-03.

AACR Staff ![Figure][1] On April 8, 2013, in an unprecedented effort to highlight the critical importance of biomedical research, the American Association for Cancer Research (AACR) joined with more than 200 organizations representing a broad spectrum of research interests and diseases

12067Background: The MAESTRO trial randomized 693 locally advanced or metastatic pancreatic cancer patients to gemcitabine (G) + placebo vs G + evofosfamide (GE). OS hazard ratio (HR) was 0.84; p =...

Nanotechnology will have great impact on how cancer is diagnosed and treated in the future. New technologies to detect and image cancerous changes and materials that enable new methods of cancer treatment will radically alter patient outcomes. The National Cancer Institute (NCI) Alliance for Nanotechnology in Cancer sponsors research in cancer prevention, diagnosis, and therapy and promotes translation of basic science discoveries into clinical practice. The Fourth Annual NCI Alliance Principal Investigator Meeting was held in Manhattan Beach, California October 20-22, 2009. Presented here are highlights from the research presentations at the meeting, in the areas of in vitro diagnostics, targeted delivery of anticancer and contrast enhancement agents, and nanotherapeutics and therapeutic monitoring.

I-SPY 2 (investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2) is a process targeting the rapid, focused clinical development of paired oncologic therapies and biomarkers. The framework is an adaptive phase II clinical trial design in the neoadjuvant setting for women with locally advanced breast cancer. I-SPY 2 is a collaborative effort among academic investigators, the National Cancer Institute, the US Food and Drug Administration, and the pharmaceutical and biotechnology industries under the auspices of the Foundation for the National Institutes of Health Biomarkers Consortium.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

The proceedings of a workshop focusing on a project to evaluate the use of fluorodeoxyglucose-positron emission tomography (FDG-PET) as a tool to measure treatment response in non-Hodgkin lymphoma (NHL) are described. Sponsored by the Leukemia & Lymphoma Society, the Foundation of the National Institutes of Health, and the National Cancer Institute, and attended by representatives of the Food and Drug Administration, the Centers for Medicare and Medicaid Services, and scientists and clinical researchers from academia and the pharmaceutical and medical imaging industries, the workshop reviewed the etiology and current standards of care for NHL and proposed the development of a clinical trial to validate FDG-PET imaging techniques as a predictive biomarker for cancer therapy response. As organized under the auspices of the Oncology Biomarker Qualification Initiative, the three federal health agencies and their private sector and nonprofit/advocacy group partners believe that FDG-PET not only demonstrates the potential to be used for the diagnosis and staging of many cancers but in particular can provide an early indication of therapeutic response that is well correlated with clinical outcomes for chemotherapy for this common form of lymphoma. The development of standardized criteria for FDG-PET imaging and establishment of procedures for transmission, storage, quality assurance, and analysis of PET images afforded by this demonstration project could streamline clinical trials of new treatments for more intractable forms of lymphoma and other cancers and, hence, accelerate new drug approvals.

Traditional phase 3 clinical trials compare an experimental arm with control. They inefficiently use patients, time, and finances. Dramatic and rapid changes in biology makes such trials untenable. We describe an alternative drug development strategy that we are using in a particular setting, the trial GBM AGILE (Glioblastoma Multiforme Adaptive Global Innovative Learning Environment). The trial’s design employs many innovations. Some aspects are similar to those of I-SPY 2 (see 4 articles in July 7, 2016 NEJM) but GBM AGILE extends I-SPY 2 in many ways. (1) It is a Bayesian platform trial that simultaneously evaluates many treatment arms (including combinations) from many companies. (2) Arms are added to the trial at any time and leave when they have been evaluated, whether positively or negatively. (3) An arm’s sample size is adaptive and based on frequent analyses of the trial results. (4) Every arm has an initial stage in which it is randomized adaptively: arms performing better in disease subtypes are assigned with higher probability to such patients. (5) An arm that performs sufficiently well in a disease subset moves seamlessly into a small (50-patient) confirmatory, registration stage in the same subset, with equal randomization against control. (6) All experimental arms are compared against a common control arm that is assigned to 20% of patients in every subtype; a bridging model takes advantage of having many arms in the trial and many comparisons among arms, and enables indirect randomization comparisons of all arms with all controls. (7) Patient subtypes are defined by line of therapy, MGMT methylation status for newly diagnosed patients, and biomarkers associated with targeted therapies, although adaptive randomization enables us to draw conclusions about off-target effects. The many possible subtypes means that there are many possible drug indications. So there are many possible “error types” and no single definition of statistical power. For example, the trial may conclude that a drug’s indication is “recurrent, biomarker-positive” disease when in truth it is “all recurrent” disease. We show how the design addresses this issue and we define “pure type I error.” GBM AGILE’s primary endpoint is overall survival (OS). To make the design more efficient we incorporate evaluations of patients’ statuses over time using a longitudinal model based on periodic MRI assessments and performance status. The longitudinal model and its components are not end points but rather provide auxiliary information that enables multiply imputing OS for surviving patients. We represent the trial’s coordinating committees that are made up of more than 150 enthusiastic and devoted disease experts and advocates from around the globe, including from Australia and China. The U.S. FDA has been enormously helpful in designing GBM AGILE, especially as regards its potential for drug and biomarker registration. Our approach provides a model for other diseases, including those outside of cancer. Citation Format: Donald A. Berry, Todd Graves, Jason Connor, Brian Alexander, Timothy Cloughesy, Anna Barker, Scott M. Berry, for the GBM AGILE Global Alliance. Adaptively randomized seamless-phase multiarm platform trial: Glioblastoma Multiforme Adaptive Global Innovative Learning Environment (GBM AGILE) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3594. doi:10.1158/1538-7445.AM2017-3594

Making precision (personalized) medicine a routine clinical reality will require a comprehensive inventory of validated biomarkers and molecular diagnostic tests to stratify disease subtypes and improve accuracy in diagnosis and treatment selection. Realization of this promise has been hindered by the poor productivity of biomarker identification and validation. This situation reflects deficiencies that are pervasive across the entire spectrum of biomarker R&D, from discovery to clinical validation and in the failure of regulatory and reimbursement policies to accommodate new classes of biomarkers. The launch of the National Biomarker Development Alliance is the culmination of a 2-year review and consultation process involving diverse stakeholders to advance standards, best practices and guidelines to enhance biomarker discovery and validation by adoption of systems-based approaches and trans-sector collaboration between academia, clinical medicine and relevant private and public sector stakeholders.

Biomarker development is rarely successful Realizing the much vaunted promise of precision (personalized) medicine is linked inextricably to the availability of robust and clinically relevant biomarkers. Given their importance in supporting more informed treatment decisions, enabling earlier diagnosis and driving the development of molecular based therapies, biomarkers arguably represent the greatest potential value in biomedicine today. In short, biomarkers are the ‘holy grail’ of precision medicine. The BCR–ABL and HER-2 proteins are prototypic examples of the clinical utility of biomarkers in cancer that have transformed the classification of lymphocytic leukemia and HER-2 positive breast cancer, respectively, and driven more rational treatment decisions. Unfortunately, these remain rare examples. The potential of biomarkers to revolutionize the detection, diagnosis and clinical management of cancer remains largely unrealized, with fewer than 100 biomarkers approved by regulatory agencies as companion diagnostics for molecular profiling to guide treatment selection [1]. There is a staggering asymmetry between the several 100,000 publications describing candidate biomarkers and the mere fraction of 1% of these that ever progress to clinical practice [2]. The systematic failure in biomarker R&D is further illustrated by the fact that the US FDA has approved less than one protein biomarker per year since the mid 1990s [3]. The burden of these failures, which come at incalculable cost in wasted investment, intellectual resources and patient samples, are incompatible with charting a reliable intellectual foundation for molecular medicine and improved patient care.

In order to enhance biomedical research and development efficiency and innovation, nontraditional research collaborations have emerged that feature the sharing of information, resources, and capabilities. Although many of these so-called precompetitive collaborations are in the field of oncology, the lessons they offer are broadly applicable to other subfields of translational medicine.

Nanotechnology is a ‘disruptive technology’, which can lead to a generation of new diagnostic and therapeutic products, resulting in dramatically improved cancer outcomes. The National Cancer Institute (NCI) of National Institutes of Health explores innovative approaches to multidisciplinary research allowing for a convergence of molecular biology, oncology, physics, chemistry, and engineering and leading to the development of clinically worthy technological approaches. These initiatives include programmatic efforts to enable nanotechnology as a driver of advances in clinical oncology and cancer research, known collectively as the NCI Alliance for Nanotechnology in Cancer (ANC). Over the last 5 years, ANC has demonstrated that multidisciplinary approach catalyzes scientific developments and advances clinical translation in cancer nanotechnology. The research conducted by ANC members has improved diagnostic assays and imaging agents, leading to the development of point-of-care diagnostics, identification and validation of numerous biomarkers for novel diagnostic assays, and the development of multifunctional agents for imaging and therapy. Numerous nanotechnology-based technologies developed by ANC researchers are entering clinical trials. NCI has re-issued ANC program for next 5 years signaling that it continues to have high expectations for cancer nanotechnology's impact on clinical practice. The goals of the next phase will be to broaden access to cancer nanotechnology research through greater clinical translation and outreach to the patient and clinical communities and to support development of entirely new models of cancer care. WIREs Nanomed Nanobiotechnol 2010 2 450–460 For further resources related to this article, please visit the WIREs website

The cytotoxic potential and ability to produce endogenous ecotropic virus(es) of thymic-, splenic-, and lymph node-derived lymphocytes were significantly (and differentially) altered following thymosin treatment of young (nonleukemic) and aged (preleukemic) AKR mice. Thymosin administration markedly enhanced the cytotoxic potential of both splenic and lymph node lymphocytes from aged animals, but effected a moderate increase only in the spleens of young mice. Thymosin enhanced the production of endogenous virus(es) by thymocytes from aged mice, with the opposite effect noted in young animals. Splenectomy generally reduced the cytotoxic activity and virus titers of lymphocytes from both groups, and thymosin administration further reduced the cytotoxic response. These findings probably reflect overall effects on both regulatory lymphocyte subpopulations and different endogenous viruses in young and aged mice.

The effects of transplanting thymic (LTC), splenic (LSC), and lymph node (LLNC) lymphocytes derived from overtly leukemic AKR mice into preleukemic syngeneic animals were studied. Each of these thymus-derived (T cell) populations produced a different and distinct pathology in recipient mice. Animals receiving LTC exhibited thymoma and enlargement of peripheral lymphoid tissues. Gross organomegaly was also noted in mice given LSC, but thymic atrophy was uniformly observed. The thymus appeared normal in mice receiving LLNC, but marked enlargement of peripheral lymphoid tissues again were observed. The differences noted in disease pathologies correlated with the “homing” patterns of the subpopulations investigated. These findings suggest that subpopulations of T cells exist in mice with a thymus-derived neoplastic disorder.

Rabbit anti-C3H mouse brain-associated antiserum (BA-θ) was tested for its effect on the immunologic responsiveness of preleukemic and leukemic AKR mice to sheep erythrocytes. This BA-θ antiserum was cytotoxic in vitro for both C3H and for AKR thymocytes, and was immunosuppressive in vivo . Greater immunosuppression was effected by the antiserum in preleukemic AKR mice than in leukemic animals. Control rabbit serum also was cytotoxic in vitro and immunosuppressive in vivo , but this activity was removed by absorption with homologous erythrocytes and liver tissue, and with agarose. Conversely, absorption of the rabbit anti-BA-θ serum had no effect on either the in vitro cytotoxic activity, or on the in vivo immunosuppression.

SummaryLymphocytes recovered from normal donors and from patients with CLL were examined for interferon-producing capacity, following treatment with tilorone hydrochloride in vitro. The interferon response observed in normal lymphocyte cultures appeared to be correlated with the toxic effect of the inducer. The effect observed in the leukemic lymphocyte cultures was delayed, but of a similar magnitude. The data indicate that leukemic cells are either more resistant to the toxic effects of this agent, or their metabolic activity is less than observed in normal lymphocytes.

The effect of the sulfhydryl inhibitor iodoacetate and of iodoacetate-treated thymocytes from leukemic AKR mice (ITC) on the humoral immune response of preleukemic AKR mice (8 or 12 weeks old) to sheep erythrocytes (SRBC) was assessed. Six days after a single injection of iodoacetate or ITC (viable or nonviable), mice were administered SRBC, and their spleens and thymuses assayed for total number of plaque-forming cells (PFC) four days later. Histopathologic studies also were performed on samplings of mice from all test and control groups. Significant increases in total numbers of splenic PFC were noted in mice given either iodoacetate or viable ITC. Histopathologic examination of the spleens of AKR mice from these two groups revealed large numbers of mature lymphoid cells in the red pulp areas. The IgG response was absent in mice administered ITC (viable or nonviable).

A des souris AKR leucemiques on administre un total de 5 injections de sulfhydryle inhibiteur, iodoacetate, a 10 jours d'intervalle. Ce regime produit une augmentation de survivance moyenne de 15 jours par rapport au groupe temoin. 40% des souris traitees vivent dela de la periode d'experimentation. Chez certaines souris traitees, on constate l'augemtation statistiquement assez importante des nombres totaux de cellules spleniques formatrices de plaquettes a tous les stades d'experimentation.

International audience; The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumors from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of over 25,000 cancer genomes at the genomic, epigenomic, and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically-relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies.