Mutational Analysis of Selected High-
Grade Malignancies in a
Premenopausal Gynecologic Cancer
Population: A Potential for Targeted
Therapies?
Lauren J. Pinckney, MD
OB/GYN Faculty Mentor:
Larry E Puls, MD
Background
Incidence (2017)
% Cases < 45 yo(2009-2013)
5 yr survival (2006-2012)
Ovarian ~22K 12.3 46.2
Endometrial ~61K 7 81.7
Cervical ~13 38.3 67.5
Gynecologic Cancer
Background
• Identified mutations in these cancers may
identify novel therapeutics for targetable
mutations to improve outcomes
Objectives
• Identify genetic abnormalities in aggressive
gynecologic cancers in young women (<40
years of age)
• Evaluate mutational patterns that may be
predictive of outcomes
• Identify potential novel treatment options.
Study Design
21 patients
6 poor outcomes 14 good outcomes
20 met inclusion criteria
1 inadequate tissue
Retrospective Cohort
Materials and Methods
Clinicopathologic Data Collection
Gynecologic Pathologist Review
Genomic Sequencing
Mutational Function Evaluation
Targeted Therapy Review
Analysis
• Cohorts compared across clinicopathologic,
demographic, and genetic variables using 1- and 2-tailed
t-tests and ANOVA
• Classification and Regression Tree (CART) analysis
produced a predictive model for outcome (Salford
Systems’ SPM7)
Results
Demographic Data
• Survival Median 469 days
– Mean (Range): 563.2 (15-2333)
• ECOG Scores
– 0 (n=19)
– 2 (n=1)
Results
Primary Tumor Site
n=20 P-value
Ovary/Peritoneal 12 (60%)
0.495Endometrial 5 (25%)
Cervical 3 (15%)
Tumor Data
HistologicGrade
n=20 P-value
Low Grade (Sarcoma)
1 (5%)
n/aII 1 (5%)
III 17 (85%)
IV 1 (5%)Histologic Type n=20 P-value
Epithelial (Serous/Clear Cell/Mucinous)
10 (50%)
0.159
Neuroendocrine 4 (20%)
Sarcoma 3 (15%)
High Grade Sex Cord Stromal
2 (10%)
Endometrioid(Cervix)
1 (5%)
Stage (FIGO/Sarcoma Scale)
n=20 P-value
I 6 (30%)
0.848II 3 (15%)
III 9 (45%)
IV 2 (10%)
Results
400 genes examined
230 code for protein dysfunction
170 no associated dysfunction
218 no significant assoc with outcomes
12 dysfunction-coding genes associated with outcomes
6 with mutation targeted therapy identified
6 no targeted therapy
Mutations for Dysfunction and Therapeutic Targets
ALKASXL
ASXL1EGFR
NTRK1ROS1
Epithelial Profile
Location Chemo Novel Targets (n) Profiles
Ovary 7 (64%)
Carbo/Taxol (7) AKAP9 (3)CMPK (1)MBD1 (1)ASXL1 (1)ERBB3 (2)KMT2D (1)
ERBB3 only (1)*AKAP9 only (3)
CMPK, ASXL1, ERBB3 (1)MBD1, KMT2D (1)
Cervix 2 (18%)
CarboTaxol (1)Plat/Etop (1)
None n/a
Endometrial 2 (18%)
Carbo/Taxol (2) CMPK (1)MBD1 (1)ROS1 (1)
GPR124 (1)
CMPK, MBD1 (1)ROS1, GPR124 (1)
Neuroendocrine Profile
Location Chemo Novel Targets (n) Profiles
Ovary 2(50%)
Plat/Etop (2) AKAP9 (1)ASXL1 (1)MBD1 (1)
ALK (1)GPR124 (1)KMT2D (1)
AKAP9, ASXL1, MBD1 (1)ALK, GPR134, KMT2D (1)*
Cervix 1 (25%)
Plat/Etop (1) AKAP9 (1)ASXL1 (1)
CMPK1 (1)
AKAP9, ASXL1, CMPK1 (1)
Endometrial 1 (25%)
Carbo/Taxol(1)
ROS1 (1)ALK (1)
ERBB3 (1)GPR124 (1)KMT2D (1) NTRK1 (1)
ROS1, ALK, ERBB3, GPR134,KMT2D, NTRK1 (1) *
Sarcoma/Other Profile
Location Chemo Novel Targets (n) Profiles
Ovary 3(60%)
Plat/Etop (1)None (1)Unknown (1)
MBD1 (1)ROS1 (3)EGFR (1)
ERBB3 (1)KMT2D (1)NTRK1 (1)
MBD1, ROS1 (1)ROS1, EGFR (1) *
ROS1, ERBB3, KMT2D, NTRK1 (1)*
Endometrial 2 (20%)
Gemzar/Taxotere(1)None (1)
CMPK1 (1)MBD1 (1)
GPR124 (1)
CMPK, MBD1 (1)GPR124 only (1)*
Mutation Profiles
Cancer Type Location Chemo Mutation ProfileMutation
Load
Epithelial Ovary Carbo/Taxol ERBB3 only (1) 22
NE Ovary Plat/Etoposide ALK, GPR124, KMT2D (1) 27
Endometrial Carbo/TaxolROS1, ALK, ERBB3, GPR124,
KMT2D, NTRK1 (1)111
Unknown ROS1, EGFR (1) 27
Sarcoma/Other Ovary NoneROS1, ERBB3, KMT2D,
NTRK1 (1)29
Endometrial None GPR124 (1) 21
Mutation profiles associated with poor
outcome in our cohort
Genes mutated in our cohort
found to be significant: Targeted
therapiesGene N=20 P-value (1-
tailed)P-value (2-tailed)
Targeted Therapy
ASXL1 3 (15%) 0.120 0.022 Sorafenib
ALK 2 (10%) 0.010 0.022 Crizotinib, Ceritinib
EGFR 1 (5%) 0.065 0.130 Erlotinib, Afatinib, Gefitinib
ERBB3 4 (20%) 0.014 0.028 MM-121, MM-111, U3-1287/AMG-888 (clinical trials)
GPR124 4 (20%) 0.014 0.028 miR-138-5p (cell lines)
KMT2D 4 (20%) 0.014 0.130 None currently
NTRK1 2 (10%) 0.109 0.022 Imatinib
AKAP9 5 (25%) 0.050 0.100 None currently
ASXL 2 (10%) 0.011 0.241 Sorafenib
CMPK1 5 (25%) 0.050 0.100 Deactivation of gemcitabine
MBD1 5 (25%) 0.050 0.100 Drug resistance
ROS1 5 (25%) 0.050 0.100 Crizotinib
ResultsCART Profile for Poor Outcome
n=20
Outcomes: 6 bad, 14 good
AKAP9 (+) n=5 (100% good) AKAP9 (-)
MBD1 (+) n=4 (100% good)
MBD1 (-)
Mutational Load <20.5 n=2 (100% good)
Mutational Load > 20.5
APC (+) n=2
(100% good)APC (-)
APBL2 (+) n=1 (100% good)
APBL2 (-) n=6 (100% bad)
Conclusions
• Go with standard therapy until you find a reason to do otherwise
• Potential value in novel targeted therapies
• Synergy between genotyping and phenotyping
• Established framework (pilot study)
• Could patient who did well with standard therapy have done better with targeted therapy?
• Strengths
– Large number of genes
– Looking at multiple variables at once
– CART analysis (unique and novel in this field)
• Weaknesses
– Small cohort
– Variability in patient population
Discussion
Acknowledgements
Thanks to:
• Justin Collins
• Matt Gevaert
• Dr. Christine Schammel
• Dr. David Schammel
• Catherine Davis
• Katie Floyd
• Dr. Jeff Elder
• Dr. Jeff Edenfield
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