Leading Educational Consultants to Watch In 2020

EXPERT’S VIEWPOINT
The creation of the “perfectly healthy” individual without
any genetic condition is coming closer to reality. In the
meantime, the development of revolutionary interventions
to treat progressive blindness due to a mutated gene
(RPE65) and hemophilia represent some examples of such
novel and impactful opportunities. At a more practical level,
today pharmacogenomic testing is increasingly offered to
patients to predict the efficacy and toxicity profile of
commonly used drugs, allowing physicians to practice a
safer and more precise therapeutic intervention.
Perhaps the area with the greatest potential impact for
precision medicine is oncology, by definition a genetic
disease. Many alterations in gene-controlling essential
functions in cancer cells have already been discovered, but
genetically-directed therapeutic interventions have been
successful in only a minority of cases. Generally, these
interventions are beneficial when the genetic change in the
cancer cells is predominantly in one gene location in all the
cells.
The most successful example of gene-directed therapy is
represented by imatinib (Gleevec), FDA-approved for the
treatment of chronic myeloid leukemia (CML) and
gastrointestinal stromal tumors (GISTs). The clinical use of
this agent also provided the opportunity to learn about
resistance mechanisms to targeted therapies and the
importance of monitoring the complex biology of cancer
cells. Thus, we need to use multigene testing to be able to
assess a more complex pathway analysis and to use the
same testing in a longitudinal fashion.
The availability of sophisticated genomic analysis allowing
investigators to evaluate large panels of cancer genes
(somatic DNA alterations), sometimes associated with
mRNA expression profiling using a small amount of tissue,
is becoming an affordable reality for every tumor and
available to every patient. Most of these tests perform
parallel testing of germline (individual patient) DNA,
giving a more complete understanding of the cancerassociated
risk and potential therapies. Moreover, the use of
blood-based genomic testing or liquid biopsy allows for a
continuous monitoring of somatic (tumor) alterations over
time, with the assumption that we can potentially predict
benefit or response and use imaging studies more
judiciously. The availability of these novel tests created a
paradoxical situation in clinical practice. Many times
oncologists are not prepared to deal with the complexity of
the information provided, or do not have access to the
identified therapeutic interventions. There is a new and
pressing need to provide every physician with the tools and
capability to rapidly access this expanded knowledge and
translate it into more effective and individualized
therapeutic options.
Large organizations have created infrastructures such as
molecular tumor boards (MTBs) and have expanded their
portfolio of clinical trials to include more targeted agents.
Even in such a scenario, if we were to rapidly escalate the
use of molecular diagnostics in every patient with advanced
cancer and incorporate the use of liquid biopsy into
everyday practice, the time and resources needed to analyze
individual data over time would be overwhelming. This
challenging and complex task requires the incorporation of
artificial intelligence (AI) capabilities in the practice of
oncology allowing rapid bioinformatics analysis of complex
genetic codes and provides the list of cancer-related
genomic abnormalities likely to drive individual tumors.
The use of AI can drastically reduce the time to analyze the
‘current’ scientific and clinical evidence available in the
literature (sometimes thousands of manuscripts) to predict
the pathway- or gene-direct agent likely to produce the best
therapeutic benefit. In the near future, it will also
incorporate structured clinical data from thousands of real
cases, improving the predictive value of the structured
reports.
The evaluation and treatment of a cancer patient is moving
closer to the reality seen in science-fiction literature and
movies. For every medical consultation, a treatment and
prediction of benefit can only be accurately prepared after
analysis of both tissue and blood samples for somatic and
germline mutations. The simplified report of this complex
analysis is performed with the support of both sophisticated
laboratories supported by AI in a short time, and repeated
over time in the blood to monitor response to treatment and
potentially change it. Imaging studies will become less
useful and the overwhelming data will furnish evidence for
new models to develop drugs, ultimately reducing cost. T R
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