Volume 2
Journal of Molecular Cancer
Cancer & Primary Healthcare 2019
May 20-21, 2019
Page 11
Cancer Research & Oncology
Primary Healthcare and Medicare Summit
May 20-21, 2019 | Rome, Italy
25
th
Global Meet on
World Congress on
&
J Mol Cancer, Volume 2
Cancer treatment in the era of precision medicine
Traditional approach to cancer treatment generally involves “one-size-fits-all” treatments and procedures
(e.g., chemotherapy, radiation therapy, and surgery), which is focused largely at fighting a particular type
of cancer (e.g., liver, lung, colorectal). However, this approach ignores the unique nature of an individual
patient’s cancer, despite the fact that the complex genotypic and phenotypic heterogeneity of an individual
patient’s cancer/tumor has a profound influence on the clinical responses to targeted anticancer therapies.
Genetic sequencing of tumors is conducted for only a small number of patients (~2%), and the large
number (>4.5 M) of options and potential for drug-drug interactions have precluded widespread adoption
of combination therapies. Current approach to treatment response planning and assessment also lacks an
efficient method to consolidate biomarker changes into a holistic understanding of treatment response.
Major goals of successful combination therapy include the ability to: (a) cover most of the patient’s
aberrations with a minimal number of drugs, (b) achieve enhanced effectiveness through drug
synergy, (c) reduce the frequency and severity of adverse events (AEs) and (d) minimize the potential
to develop drug resistance. While the majority of research on chemotherapies focus on cellular
and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific
measures that contribute to treatment response. Development of effective combination therapy
is also challenging because many cancer drugs act on intersecting signaling pathways and thus can
potentially interfere or antagonize each other. One approach to identify effective combinations is
by precise targeting of synergistic combinations, which exhibit enhanced therapeutic efficacy when
combined at lower doses. However, identification of synergistic drug combinations is often a labor-
and resource-intensive process. We developed a precise, multimodal computational model that can
leverage clinically-available measurements to optimize treatment selection and schedules for patients.
Biography
IgorFTsigelny isanexpert instructuralbiology,molecularmodeling,bioinformatics,structure-baseddrugdesignandpersonalizedcancermedi-
cine.Hepublished>200articles,4scientificbooksandaround15patents.Thebook‘ProteinStructurePrediction:BioinformaticApproach’thathe
edited has been called ‘TheBible of all current prediction techniques’byBioPlanet Bioinformatics Forums. His computational study of molecular
mechanisms of Parkinson’s disease was included in the US Department of Energy publication ‘Decade of Discovery’where the best computa-
tional studies of the decade 1999–2009 have been described. He is a Professor in the UCSan Diego and CSO of CureMatch Inc. (San Diego).
itsigeln@ucsd.eduIgor F Tsigelny
CureMatch Inc, USA