Dr Sarah-Jane Schramm

Post Doctoral Research Associate
Cancer Systems Biology and Translational Bioinformatics
Centre for Cancer Research, The Westmead Institute for Medical Research

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Biographical details

G'day. I'm an early career post doc specialising in Cancer Systems Biology and Translational Bioinformatics. I have a special focus in melanoma but i do extend my research to other tumor types, such as breast and colon, in order to better understand the molecular behaviour of cancers generally.

Put simply, most of the work i do involves sifting through genomic information derived directly from tumor cells. The aims are to generate new biomarkers of melanoma prognosis that can help the clinician in his or her management of the disease, and to identify much needed therapeutic strategies particularly in cases where the tumor has spread.

One of the most enjoyable aspects of my research is that the analysis of entire genomes (so-called 'big data') is necessarily a multi-disciplinary field incorporating the expertise of a variety of clinicians, biologists, statisticians, bioinformaticians and more. This means that much of my time is spent learning from, and collaborating across, different but increasingly related disciplines. An exciting recent development arising from this team-based approach has been IMAGO: a web-based software application that supports multidisciplinary oncology teams in their clinical decision-making. IMAGO was created at The University of Sydney by combing expertise from the Sydney Medical School, The School of Mathematics and Statistics, and the School of Information Technologies. Its inaugural version has been designed for use at the Melanoma Institute Australia. However, subsequent editions will be adapted for use by other multidisciplinary teams elsewhere. Notably, this project was generously supported by a grant from The University of Sydney Cancer Research Fund: HMR+ Implementation Fund (MRI) scheme, meaning that your donations to that fund have had a clear and clinically-relevant impact in managing melanoma.

Understanding the molecular basis of melanoma is a complex and ongoing challenge but genomic information will continue to both change the spectrum of patients eligible for various treatments and open up new therapeutic options. The field is moving very rapidly and i am extremely optimistic about the impacts of our research and the discoveries ahead.

*Since July 2012, I have been employed as a Post Doctoral Research Associate in the Sydney Medical School at The University of Sydney.

*Between January 2009 and July 2012 I held a University of Sydney Postgraduate Award (UPA). I studied under the supervision of Professor Graham Mann in the Sydney Medical School (The University of Sydney) and the co-supervision of Professor Mark Wilkins at the School of Biotechnology and Biomolecular Sciences and the Systems Biology Initiative (University of New South Wales). My PhD thesis, ‘Molecular determinants of melanoma progression and prognosis’, was awarded in March 2014.

Research interests

* Molecular biomarkers of melanoma progression and prognosis

* Cancer systems biology

* Translational bioinformatics

* Network biology

* Integrative -omics

* Network visualisation

Awards and honours


	

Software applications:

IMAGO (Project lead) – a web-based application to support multidisciplinary oncology teams in their clinical decision-making by combining and visualising patient information from a variety of sources. IMAGO is aimed at aiding end-user cognition by integrating various data in a way that points to plausible actionability. The inaugural version of IMAGO has been designed for use at the Melanoma Institute Australia. However, subsequent editions can be adapted for use by other multidisciplinary teams elsewhere. See my bio above for more details.

	VAN (Co-author) – an R package for identifying biologically perturbed networks via differential variability analysis.

Software applications:

IMAGO (2015-16; project lead) – a web-based application to support multidisciplinary oncology teams in their clinical decision-making by combining and visualising patient information from a variety of sources. IMAGO is aimed at aiding end-user cognition by integrating various data in a way that points to plausible actionability. The inaugural version of IMAGO has been designed for use at the Melanoma Institute Australia. However, subsequent editions can be adapted for use by other multidisciplinary teams elsewhere. See my bio above for more details.

VAN (2013; co-author) – an R package for identifying biologically perturbed networks via differential variability analysis.

Selected grants

2014

  • Enhancing Multi-Disciplinary Team (MDT) decision making through knowledge transfer $130,000; Schramm S; DVC Research/Cancer Research Fund.

Selected publications

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Journals

  • Jayawardana, K., Schramm, S., Tembe, V., Mueller, S., Thompson, J., Scolyer, R., Mann, G., Yang, J. (2016). Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma. Journal of Investigative Dermatology, 136(1), 245-254. [More Information]
  • Jayawardana, K., Schramm, S., Haydu, L., Thompson, J., Scolyer, R., Mann, G., Muller, S., Yang, J. (2015). Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. International Journal of Cancer, 136(4), 863-874. [More Information]
  • Tembe, V., Schramm, S., Stark, M., Patrick, E., Jayaswal, V., Tang, Y., Barbour, A., Hayward, N., Thompson, J., Scolyer, R., Yang, J., Mann, G. (2015). microRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis. Pigment Cell & Melanoma Research, 28(3), 254-226. [More Information]
  • Mann, G., Pupo, G., Campain, A., Carter, C., Schramm, S., Pianova, S., Gerega, S., Desilva, C., Lai, K., Wilmott, J., Hersey, P., Kefford, R., Thompson, J., Yang, J., Scolyer, R., et al (2013). BRAF Mutation, NRAS Mutation, and the Absence of an Immune-Related Expressed Gene Profile Predict Poor Outcome in Patients with Stage III Melanoma. Journal of Investigative Dermatology, 133(2), 509-517. [More Information]
  • Schramm, S., Li, S., Jayaswal, V., Fung, D., Campain, A., Pang, C., Scolyer, R., Yang, J., Mann, G., Wilkins, M. (2013). Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden. Pigment Cell & Melanoma Research, 26(5), 708-722. [More Information]
  • Schramm, S., Menzies, A., Mann, G. (2013). Molecular biomarkers of prognosis in melanoma: how far are we from the clinic? Melanoma Research, 23(6), 423-425. [More Information]
  • Schramm, S., Jayaswal, V., Goel, A., Li, S., Yang, J., Mann, G., Wilkins, M. (2013). Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations. Proteomics, 13(23-24), 3393-3405. [More Information]
  • Jayaswal, V., Schramm, S., Mann, G., Wilkins, M., Yang, J. (2013). VAN: an R package for identifying biologically perturbed networks via differential variability analysis. BMC Research Notes, 6(1), 1-9. [More Information]
  • Schramm, S., Campain, A., Scolyer, R., Yang, J., Mann, G. (2012). Review and Cross-Validation of Gene Expression Signatures and Melanoma Prognosis. Journal of Investigative Dermatology, 132(2), 274-283. [More Information]
  • Schramm, S., Mann, G. (2011). Melanoma Prognosis: A REMARK-based systematic review and bioinformatic analysis of immunohistochemical and gene microarray studies. Molecular Cancer Therapeutics, 10(8), 1520-1528. [More Information]

Conferences

  • Barter, R., Schramm, S., Mann, G., Yang, J. (2014). Network-based biomarkers enhance classical approaches to prognostic gene expression signatures. International Conference on Bioinformatics 2014 (InCoB2014), Australia: BMC Public Health. [More Information]

2016

  • Jayawardana, K., Schramm, S., Tembe, V., Mueller, S., Thompson, J., Scolyer, R., Mann, G., Yang, J. (2016). Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma. Journal of Investigative Dermatology, 136(1), 245-254. [More Information]

2015

  • Jayawardana, K., Schramm, S., Haydu, L., Thompson, J., Scolyer, R., Mann, G., Muller, S., Yang, J. (2015). Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. International Journal of Cancer, 136(4), 863-874. [More Information]
  • Tembe, V., Schramm, S., Stark, M., Patrick, E., Jayaswal, V., Tang, Y., Barbour, A., Hayward, N., Thompson, J., Scolyer, R., Yang, J., Mann, G. (2015). microRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis. Pigment Cell & Melanoma Research, 28(3), 254-226. [More Information]

2014

  • Barter, R., Schramm, S., Mann, G., Yang, J. (2014). Network-based biomarkers enhance classical approaches to prognostic gene expression signatures. International Conference on Bioinformatics 2014 (InCoB2014), Australia: BMC Public Health. [More Information]

2013

  • Mann, G., Pupo, G., Campain, A., Carter, C., Schramm, S., Pianova, S., Gerega, S., Desilva, C., Lai, K., Wilmott, J., Hersey, P., Kefford, R., Thompson, J., Yang, J., Scolyer, R., et al (2013). BRAF Mutation, NRAS Mutation, and the Absence of an Immune-Related Expressed Gene Profile Predict Poor Outcome in Patients with Stage III Melanoma. Journal of Investigative Dermatology, 133(2), 509-517. [More Information]
  • Schramm, S., Li, S., Jayaswal, V., Fung, D., Campain, A., Pang, C., Scolyer, R., Yang, J., Mann, G., Wilkins, M. (2013). Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden. Pigment Cell & Melanoma Research, 26(5), 708-722. [More Information]
  • Schramm, S., Menzies, A., Mann, G. (2013). Molecular biomarkers of prognosis in melanoma: how far are we from the clinic? Melanoma Research, 23(6), 423-425. [More Information]
  • Schramm, S., Jayaswal, V., Goel, A., Li, S., Yang, J., Mann, G., Wilkins, M. (2013). Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations. Proteomics, 13(23-24), 3393-3405. [More Information]
  • Jayaswal, V., Schramm, S., Mann, G., Wilkins, M., Yang, J. (2013). VAN: an R package for identifying biologically perturbed networks via differential variability analysis. BMC Research Notes, 6(1), 1-9. [More Information]

2012

  • Schramm, S., Campain, A., Scolyer, R., Yang, J., Mann, G. (2012). Review and Cross-Validation of Gene Expression Signatures and Melanoma Prognosis. Journal of Investigative Dermatology, 132(2), 274-283. [More Information]

2011

  • Schramm, S., Mann, G. (2011). Melanoma Prognosis: A REMARK-based systematic review and bioinformatic analysis of immunohistochemical and gene microarray studies. Molecular Cancer Therapeutics, 10(8), 1520-1528. [More Information]

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