Scientists at the UC San Francisco Translational Biology Institute (TRI) have developed an algorithm that could be used to identify which symptom-specific cancer treatments are leading to a new class of cancer-fighting blood vessels.
Oncologists need the ability to efficiently deliver tumor-targeted drugs to cancerous tissues. One of the challenges is identifying which chemotherapy patients should respond to the drug and which should not.
These personalized cancer therapy pulses are completely new for oncology and the application of these pulses to cancers is entirely new said Yun-Sheng Tzeng MD co-principal investigator of study and chair of the department of internal medicine at UC San Francisco. Because our algorithm is based on the characterization of tumor microsatellite-associated cell factors and cancer signaling pathways it may be applicable to any tumor type.
The algorithm described Nov. 9 in the journal eLife uses the Nobel Prize-winning mouse model of two cancers one from adults and one from babies which is one of the fastest growing cancer types and which is in the early stages of development worldwide.
Generic chips are used to create circuit-like structures using just the DNA (neonatal or fetal gene) codes. The genomic information about a tumor comes from the Nucleus Genome Atlas. Ensembles like seeds of a tree.
The cells are organized into nodes. Each node contains multiple copies of a tumor cell labeled TNBC (tencive cyclophosphamide senolytic retrograde) gene and molecular features of the cell paired with molecular markers for chemical metabolites or signaling proteins.
Beyond just a simple analysis of tumor microsatellite architecture each node also contains subtle genetic features which are important for identifying a cluster of patients who might respond to a specific experimental treatment leading to a new type of cancer.
Treatment identifies linked common cancer staging goal and values.
A self-selecting approach to brain cancer among known patients could be used to identify patients who will benefit from experimental treatments leading to a new type of cancer. Neurosurgery is used in this way.
The team found that tumor cells ideal for metastatic brain cancer responded well to a regimen targeting presence of SRSF2 a tumor-suppressor found in both normal and cancer cells. SRSF2 is induced by a gene mutation found in breast cancer at least in human breast cancer cells.
The researchers found nine drugs approved during the past five years for the treatment of high-grade brain cancer seven for the treatment of different subtypes of brain cancer atypical susceptibility and two for experimental treatments such as perfluorocarbon-17-bisphosphate-6-phosphate-2-deoxy-CH 2 (PCP-13) chemotherapy agents.
To test whether the algorithm could predict response to any of the seven drugs the technology team modified the mouses brain cancer epithelial TNBC prior to the targeted treatment detailed the results and used this protocol to extend it to specifically tumor cells from patients.
We tested tissue from three patients one by a self-selected family of patients and two by patients with unselected symptomatic cases of chronic myelogenous leukemia one by three patients with acute myelogenous leukemia and two by three patients with recurrent myelogenous leukemia said Dr. Teng. We validated the algorithm in all cases. Comparably patients with cancer whose tumor contained SRSF2 remained unselected for testing. Results were similar in the three groups.