NcXHF

PERSONALIZED TREATMENT OF HEAD AND NECK CANCER
negative counterparts. 11 Underlying these differences in clinical
phenotypes are distinct molecular characteristics of
HPV-positive and HPV-negative tumors. 12 Gene expression
studies have pointed to subtypes of the disease with different
clinical and molecular characteristics. A recent analysis
based on four discovery cohorts and two independent validation
datasets classifıed HNSCC into three super groups
called inflamed/mesenchymal (IMS), basal (BA), and classical
(CL) based on previously used nomenclature. 12 Interestingly,
HPV-positive tumors fell into only two of these groups
(IMS and CL), whereas the BA group consisted exclusively of
HPV-negative cancers. Furthermore, the classifıcation appears
to be prognostic for overall survival even within the
HPV-positive tumors where the IMS HPV-positive group,
enriched for immune-related genes, had the best outcome.
TP53 mutations were shown to be exclusive to HPV-negative
tumors, presumably as a result of the phenocopying effect of E6
viral protein-mediated TP53 degradation. Similarly, whereas
CDKN2A has been shown to be affected by copy number losses
or mutations to the coding sequences of the gene, this was not
seen in HPV-positive tumors as a result of its inactivation by the
E7 viral protein. Other notable differences are seen in kinases
that could be potential targets for therapy including more frequent
mutation of PIK3CA and FGFR 2/3 in HPV-positive tumors
contrasted with amplifıcation of EGFR, FGFR1, and
CCDN1 seen exclusively in HPV-negative tumors. 12-13
In tumors lacking HPV infection, long-term studies of
TP53 mutation have shown that its presence is associated
with poor outcomes, an effect that seems to be accentuated by
the functional effect of the mutation. Recent work by Gross et
al looking across multiple data-layers has refıned the interpretation
of TP53 mutations by showing that they usually cooccur
with loss of the 3p chromosomal region and that the
adverse prognostic effect coincides with the combinations of
the two events. 14 The putative gene(s) on 3p have yet to be
fully elucidated but one candidate is FHIT, which functions
as a tumor suppressor and has been linked with radiosensitivity,
perhaps explaining its key role in HNSCC. 15
Larger cohorts such as The Cancer Genome Atlas (TCGA)
have facilitated attention to increasingly rare genomic drivers
of the disease. One example is Caspase 8 mutation, which
only compromises approximately 8% of the TCGA patient
cohort. Despite its relatively low frequency, this event has
proven interesting as it occurs in mutual exclusion of the
TP53 mutation/3p deletion event, and has been shown to accelerate
tumor growth through inhibition of death receptor
mediated apoptosis pathways. 14,16 These tumors seem to
have developed from a very different mechanism of oncogenesis
than either their TP53 mutated, or HPV-positive counterparts,
and patients with the alteration would likely benefıt
from different treatment strategies.
Even as we begin to understand the drivers of HNSCC progression,
new unsolved problems arise such as understanding
the heterogeneity of the disease and the interplay of clonal
populations within a single tumor. Current studies rely on
molecular measurements taken from a single tumor sample
at a single time-point, usually before intervention. The observation
that patients with a high degree of intratumoral heterogeneity
tend to have worse outcomes proposes new research
directions to quantify the tumor not as a single unit but as a
collection of diverse cellular populations. 17 Profıling at multiple
sites within a tumor as well as multiple time-points
along the treatment and progression of the tumor will add a
new dimension to our understanding of HNSCC. However,
to understand this more complex data, bigger and more phenotypically
rich cohorts will be required.
THE RISE OF BIG DATA IN CANCER
In the age of the $1,000 genome, a new market for consumer
genomics has emerged that promises both immediately actionable
results to the patient as well as a treasure trove of
data for the institutions overseeing its collection and maintenance.
At its current state, clinical sequencing remains an optimistic
enterprise with few truly actionable events detected by
such assays. Although current prospects for such personalized
FIGURE 1. Schematic of the Precision Medicine Paradigm on the (A) Population and (B) Individual Patient Levels
A
Population Level
B
Individual Patient Level
Diagnostics
- gene mutation panels
- whole-exome sequencing
- gene/mirna expression panels
Data Warehouses
- Human Longevity Inc.
- 23 & Me
- government-funded cohorts
Host Factors
- risk behaviors
- genetic susceptibility
- alcohol/tobacco use
- HPV vaccination status
Tumor Factors
- tumor grade/stage
- tumor location
- HPV status/strain
- molecular heterogeneity
Synthesizing Platforms
- Flatiron Health
- NexBio
- IBM Watson Oncology
Disease Understanding
Clinical
Phenotype
Outcome
+ treatment
Re-evaluate at disease persistance
or recurrence
asco.org/edbook | 2015 ASCO EDUCATIONAL BOOK 29