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<h5 class="section-title" id="d1224779e543">Question</h5>
<p id="d1224779e545">Can clinical and genomic data obtained in routine clinical care
be linked in a Health
Insurance Portability and Accountability Act–compliant manner to yield clinically
relevant insights?
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<h5 class="section-title" id="d1224779e548">Findings</h5>
<p id="d1224779e550">A deidentified database of 28 998 patients with cancer, approximately
85% of whom
were treated in a community setting, was generated by linking electronic health record–derived
longitudinal clinical data with comprehensive tumor genomic profiling. Analysis of
4064 patients with non–small cell lung cancer revealed clinical, genomic, and therapeutic
associations that were consistent with prior reports and extended previous observations
on evolving community practice patterns.
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<h5 class="section-title" id="d1224779e553">Meaning</h5>
<p id="d1224779e555">Using data obtained from routine clinical care to generate a
validated, multi-institution
clinicogenomic database is feasible and can yield novel, clinically meaningful insights.
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<h5 class="section-title" id="d1224779e559">Importance</h5>
<p id="d1224779e561">Data sets linking comprehensive genomic profiling (CGP) to clinical
outcomes may accelerate
precision medicine.
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<h5 class="section-title" id="d1224779e564">Objective</h5>
<p id="d1224779e566">To assess whether a database that combines EHR-derived clinical
data with CGP can
identify and extend associations in non–small cell lung cancer (NSCLC).
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<h5 class="section-title" id="d1224779e569">Design, Setting, and Participants</h5>
<p id="d1224779e571">Clinical data from EHRs were linked with CGP results for 28 998
patients from 275
US oncology practices. Among 4064 patients with NSCLC, exploratory associations between
tumor genomics and patient characteristics with clinical outcomes were conducted,
with data obtained between January 1, 2011, and January 1, 2018.
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<h5 class="section-title" id="d1224779e574">Exposures</h5>
<p id="d1224779e576">Tumor CGP, including presence of a driver alteration (a pathogenic
or likely pathogenic
alteration in a gene shown to drive tumor growth); tumor mutation burden (TMB), defined
as the number of mutations per megabase; and clinical characteristics gathered from
EHRs.
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<h5 class="section-title" id="d1224779e579">Main Outcomes and Measures</h5>
<p id="d1224779e581">Overall survival (OS), time receiving therapy, maximal therapy
response (as documented
by the treating physician in the EHR), and clinical benefit rate (fraction of patients
with stable disease, partial response, or complete response) to therapy.
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<h5 class="section-title" id="d1224779e584">Results</h5>
<p id="d1224779e586">Among 4064 patients with NSCLC (median age, 66.0 years; 51.9%
female), 3183 (78.3%)
had a history of smoking, 3153 (77.6%) had nonsquamous cancer, and 871 (21.4%) had
an alteration in
<i>EGFR</i>,
<i>ALK</i>, or
<i>ROS1</i> (701 [17.2%] with
<i>EGFR</i>, 128 [3.1%] with
<i>ALK</i>, and 42 [1.0%] with
<i>ROS1 </i>alterations). There were 1946 deaths in 7 years. For patients with a driver
alteration,
improved OS was observed among those treated with (n = 575) vs not treated with (n = 560)
targeted therapies (median, 18.6 months [95% CI, 15.2-21.7] vs 11.4 months [95% CI,
9.7-12.5] from advanced diagnosis;
<i>P</i> < .001). TMB (in mutations/Mb) was significantly higher among smokers
vs nonsmokers
(8.7 [IQR, 4.4-14.8] vs 2.6 [IQR, 1.7-5.2];
<i>P</i> < .001) and significantly lower among patients with vs without an alteration
in
<i>EGFR</i> (3.5 [IQR, 1.76-6.1] vs 7.8 [IQR, 3.5-13.9];
<i>P</i> < .001),
<i>ALK</i> (2.1 [IQR, 0.9-4.0] vs 7.0 [IQR, 3.5-13.0];
<i>P</i> < .001),
<i>RET</i> (4.6 [IQR, 1.7-8.7] vs 7.0 [IQR, 2.6-13.0];
<i>P</i> = .004), or
<i>ROS1</i> (4.0 [IQR, 1.2-9.6] vs 7.0 [IQR, 2.6-13.0];
<i>P</i> = .03). In patients treated with anti–PD-1/PD-L1 therapies (n = 1290, 31.7%),
TMB
of 20 or more was significantly associated with improved OS from therapy initiation
(16.8 months [95% CI, 11.6-24.9] vs 8.5 months [95% CI, 7.6-9.7];
<i>P</i> < .001), longer time receiving therapy (7.8 months [95% CI, 5.5-11.1]
vs 3.3 months
[95% CI, 2.8-3.7];
<i>P</i> < .001), and increased clinical benefit rate (80.7% vs 56.7%;
<i>P</i> < .001) vs TMB less than 20.
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<h5 class="section-title" id="d1224779e649">Conclusions and Relevance</h5>
<p id="d1224779e651">Among patients with NSCLC included in a longitudinal database
of clinical data linked
to CGP results from routine care, exploratory analyses replicated previously described
associations between clinical and genomic characteristics, between driver mutations
and response to targeted therapy, and between TMB and response to immunotherapy. These
findings demonstrate the feasibility of creating a clinicogenomic database derived
from routine clinical experience and provide support for further research and discovery
evaluating this approach in oncology.
</p>
</div><p class="first" id="d1224779e654">This cancer epidemiology study links electronic
health record (EHR) data with comprehensive
genomic profiling to identify patient characteristics and gene alterations associated
with overall survival, response to targeted and immune therapies, and other clinical
outcomes in patients non–small cell lung cancer (NSCLC).
</p>