cancer-primary-healthcare-2019-keynote

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

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).

Igor F Tsigelny

CureMatch Inc, USA