Mutational Analysis of Selected High- Grade Malignancies ... · Mutational Analysis of Selected High-Grade Malignancies in a Premenopausal Gynecologic Cancer Population: A Potential

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

Disclosures

I have no meaningful conflicts of interest to

disclose

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?

Future Studies

• Larger cohorts

• Prospective research protocol

• Development of genetic platform

• 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

References

• Siegel, R., Miller, K.D., Jemal, A. (2017) Cancer Statistics, 2017. CA Cancer J. Clin., 67: 7-30. 7-30.

• SEER Cancer Stat Fact Sheets. National Cancer Institute. Seercancer.gov/statfacts

• Genetics.bwh.harvard.edu/pph2/dokuwiki/about

• www.drugbank.ca

• Manchana, T., Ittiwut, C., Kavanagh, J. Targeted Therapies for Rare Gynecological Cancers. Lancet

Oncol., 11 (2010), pp. 685-693.

• Jelinic, P., Mueller, J.J., Olvera, N., Dao, F., Scott, S.N., Shah, R., Gao, J., Schultz, N., Gonen, M.,

Soslow, R.A., Berger, M.F., Levine, D.A. Recurrent SMARCA4 Mutations in Small Cell Carcinoma of

the Ovary. Nat. Genet., 46 (2014), pp. 424-428.

• Gerlinger, M., Rowan, A.J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P.,

Matthews, N., Stewart, A., Tarpey, P., Varela, I., Phillimore, B., Begum, S., McDonald, N.Q., Butler,

A., Jones, D., Raine, K., Latimer, C., Santos, C.R., Nohadani, M., Eklund, A.C., Spencer-Dene, B.,

Clark, G., Pickering, L., Stamp, G., Gore, M., Szallasi, Z., Downward, J., Futreal, P.A., Swanton, C.

Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. N. Engl. J.

Med., 366 (2012), pp. 883-892.

• Roychowdhury, S., Iyer, M.K., Robinson, D.R., Lonigro, R.J., Wu, Y.M., Cao, X., Kalyana-Sundaram,

S., Sam, L., Balbin, O.A., Quist, M.J., Barrette, T., Everett, J., Siddiqui, J., Kunju, L.P., Navone, N.,

Araujo, J.C., Troncoso, P., Logothetis, C.J., Innis, J.W., Smith, D.C., Lao, C.D., Kim, S.Y., Roberts,

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To come..

• Discussion slides for

– CART

– SIFT/Polyphen

• Genes associated with phenotypes

Demographics (1 of 2)

Demographics (2 of 2)

Genes associated in the literature

Epithelial Neuroendocrine Sarcoma

APCARID1A

BRAFBRCA1BRCA2

CDKN2ACTNNB1 DNMT3A

ERBB2FBXW7FGFR2KRASMLH1MSH6MYCNF1

NRASPIK3CA

PPP2R1APTENRNF43

SMARCA4TP53

APCFHIT

FGFR1KRASMCC

MEN-2MYCLP16

PTENR8

SOXTP53

BRCA2CTNNB1

EGFRFOXL2c-KITP16

TP53