Daniel Gallahan

The mouse mammary tumor associated gene INT3 is a unique member of the NOTCH gene family (NOTCH4)

The INT3 gene is frequently rearranged in mouse mammary tumor virus (MMTV)-induced mammary tumors of the CzechII mouse strain. We have completed the nucleotide sequence of the normal 6.5 Kb INT3 RNA and defined the intron/exon boundaries of the gene. The open reading frame of INT3 RNA should encode a 200 kd protein which shares 60% homology with the mouse homologue of Drosophila NOTCH. INT3 is unique among other members of the NOTCH family by containing 29 instead of 36 EGF-like repeats in the extracellular domain of the gene product. Five novel EGF-like repeats have been created as consequence of apparent small deletions which have occurred within the coding region for the extracellular domain during evolution. Nucleotide sequence analysis of host-viral junction fragments from nine independent MMTV-induced mammary tumors containing a rearranged INT3 gene reveals that all of the integration events occur within a 174 bp region 3′ of the sequences encoding the LIN12 repeats in the INT3 extracellular domain and 5′ of the sequences encoding the transmembrane domain. Therefore, the only tumorigenic INT3 mutations resulting from MMTV proviral insertions are those which results in the expression of the intracellular domain. This strongly suggests that MMTV-induced activation of INT3 is manifest in the absence of the regulatory action of the extracellular domain, including the LIN12 repeat sequences, leaving the expressed intracellular domain constitutively free to function in its role in mammary tumorigenesis.

A community computational challenge to predict the activity of pairs of compounds

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Systems biologists seek fuller integration of systems biology approaches in new cancer research programs

Systems biology takes an interdisciplinary approach to the systematic study of complex interactions in biological systems. This approach seeks to decipher the emergent behaviors of complex systems rather than focusing only on their constituent properties. As an increasing number of examples illustrate the value of systems biology approaches to understand the initiation, progression, and treatment of cancer, systems biologists from across Europe and the United States hope for changes in the way their field is currently perceived among cancer researchers. In a recent EU-US workshop, supported by the European Commission, the German Federal Ministry for Education and Research, and the National Cancer Institute of the NIH, the participants discussed the strengths, weaknesses, hurdles, and opportunities in cancer systems biology.

Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008).

The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer‐related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5–20years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine). Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ‐line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer‐relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression. A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer‐relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data. Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high‐quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects. Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas. A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.

Understanding mammary gland development through the imbalanced expression of growth regulators

Functional differentiation of mammary tissue progresses in distinct phases spanning puberty and pregnancy. Here we have analyzed and compared the effects of transforming growth factor β1 (TGFβ1), TGFα, and whey acidic protein (WAP), the Notch‐related cell fate protein Int3, and p53 and pRb on mammary development. We chose transgene expression from the WAP gene promoter which is only active in mammary alveolar cells. The imbalanced expression of these molecules specifically altered development and differentiation of the gland. While TGFα did not disturb alveolar outgrowth, little or no alveolar structures developed in the presence of Int3. TGFβ1, WAP, and the expression of SV40 T‐antigen—which inactivates p53 and pRb—reduced overall alveolar development. The expression of individual milk protein genes was affected differentially by the transgenes. A WAP‐lacZ transgene served as an additional indicator of terminal differentiation of alveolar cells. Homogeneous expression of lacZ was seen in mice transgenic for lacZ, or for TGFα and lacZ. In contrast, only a few differentiated cells were observed in the presence of TGFβ1 and Tag. Thus, the expression of growth regulators in the same defined subset of mammary cells results in distinct developmental changes and a specific pattern of alveolar differentiation.

Report on EU-USA workshop: how systems biology can advance cancer research.

The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer‐related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5–20years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine). Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ‐line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer‐relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression. A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer‐relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data. Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high‐quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects. Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas. A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.

Report on EU-USA Workshop

The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer‐related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5–20years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine). Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ‐line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer‐relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression. A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer‐relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data. Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high‐quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects. Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas. A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.

T-cell receptor b-V repertoire expression in the absence of an endogenous mouse mammary tumor provirus

A class of superantigens has recently been described for which T-cell recognition is dependent predominantly on the V region of the T-cell receptor 13 (Tcr) chain, with much less influence from other components of the Tcrc~ and ~ chains (Abe et al. 1988, 1991; Bill et al. 1988; Fry and Matis 1988; Kappler et al. 1988; MacDonald et al. 1988; Pullen et al. 1988; Vacchio and Hodes 1989). When expressed as endogenously encoded self antigens in the mouse, superantigens mediate Tcrb-V-specific deletion during T-cell development. Each of the endogenous superantigens analyzed to date is determined by one or more mouse mammary tumor virus (MMTV) proviral genes (Dyson et al. 1991; Frankel et al. 1991; Woodland et al. 1991). In order to assess the ability of self ligands other than MMTV to mediate Tcrb-V-specific negative selection, the Tcrb-V repertoire was characterized in Czech II mice, a unique strain of feral mice which are free of endogenous MMTV proviruses (Gallahan et al. 1982). Mus musculus musculus feral mice Czech II were established from a single breeding pair (Gallahan and Callahan 1987) and maintained by the National Cancer Institute. Other mice were obtained from the Jackson Laboratory (Bar Harbor, ME) or the Frederick Cancer Research Center (Frederick, MD). T cells were analyzed by two-color indirect immunofluorescence as previously described (Vacchio et al. 1990). The results are expressed as the mean +/SEM for 3 -12 determinations. The profile of Tcrb-V expression was characterized in Czech II mice, an inbred strain of feral mice with no genomic MMTV proviruses (Callahan et al. 1982).