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Cancer Math and Rhabdomyosarcoma: Using engineering principles to develop personalized therapies for patients with rhabdomyosarcoma

Last year Dr. Noah Berlow, a Postdoctoral Fellow at the Children’s Cancer Therapy Development Institute (c-TDI) presented a webinar on the work he is doing in cancer math. With the reductions in federal research funding – and much greater cuts coming – cc-TDI is reaching out to crowdfund this important project.

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Here is a summary of why this research is so important.


No matter their age or disease, patients undergo first- and second-line therapy. When these approaches fail, clinically or pre-clinically validated options are often limited or nonexistent. These patients without treatment options deserve something new. We believe the most promising treatments for these patients are well beyond the one drug, one target approaches commonplace among precision medicine or clinical trial approaches. Rather, the answer will be found in combination drug therapies personalized to the patient. We believe personalized approaches represent the future of cancer treatment for high-risk patients. Oncologists treating patients with rare and high-risk diseases agree.

From our research, we believe that life-extending, personalized drug combinations can be developed by integrating experimental, genomics and computational approaches to understand the biological mechanisms underlying tumor cell survival. We have developed an integrated computational approach termed Probabilistic Target Inhibitor Maps (PTIMs), or “Cancer Math”, and we have validated our approach of creating personalized drug combinations in silico, in vitro and in vivo to create these personalized drug combinations. However, to take this computational approach from the lab to the clinic, we need more preclinical evidence.

Our goal with this project is to take the first key step towards building the evidence we need to help children battling rhabdomyosarcoma and their families  get personalized drug combinations.  The most exciting aspect of this research is that while the initial proving ground of “Cancer Math” is sarcoma, once validated we could use apply “Cancer Math” to treat patients with any cancer type.

Why is this important?

Identifying a drug combination to treat rhabdomyosarcoma, a disease that when resistant to chemotherapy is often fatal, is critical for the patients and families managing this disease.  Additionally, the Cancer Math method used to identify the two-drug combination could be used to identify personalized therapies for any person with any type of cancer.

Who will benefit?

Initially this project will benefit sarcoma patients and their families as well as treating physicians.  Ultimately, this innovative engineering forward, cancer math approach could be used to identify personalized therapies for any type of cancer.


We are seeking $25,000 to validate promising initial findings for a two-drug combination therapy for treating rhabdomyosarcoma AND an engineering forward Cancer Math approach to identifying personalized therapies for all types of cancer.