Gene Expression Profiling in Malignancies: New Insights into Cancer Care

Gene Expression Profiling in Malignancies: New Insights into Cancer Care Surgical Grand Rounds February 8, 2017 Martin Fleming, M.D. Chief, Division of Surgical Oncology West Cancer Center University of Tennessee Health Science Center

Malignant Melanoma

Cutaneous Melanoma Facts In 2017, 87,110 new cases of melanoma are predicted in the United States 52,170 men 34,940 women In 2017, 9,730 deaths from melanoma are predicted in the United States 6,380 men -3,350 women American Cancer Society Key Statistics for Melanoma Skin Cancer

Clark s Levels and Breslow Depth (mm) of Melanoma

Overview of AJCC Melanoma Staging Stage 0 ptis N0 M0 Stage I pt1 <1mm N0 M0 pt2 1-2mm N0 M0 Stage II pt3 2-4mm N0 M0 Stage III pt4 >4mm N0 M0 Any pt N1 M0 Any pt N2 M0 Stage IV Any pt Any N M1

Details of AJCC Melanoma Staging pt4b Satellite lesions within 2cm of the primary tumor. M1 Distant metastases M1a Metastases in skin or subcutaneous tissue or lymph nodes beyond regional lymph nodes M1b Pulmonary metastases M1c Visceral metastases other than lung

Melanoma Survival by AJCC Stage 100 Melanoma in situ 80 Stage I (n=58,000) % Surviving 60 40 Stage II (n=9,400) Stage III (n=5,200) 20 0 Stage IV (n=3,100) Source: SEER (Surveillance, Epidemiology, and End Results) In situ = limited to epidermal layer only AJCC, 2010; n = annual incidence estimates from SEER, 2012 0 2 4 6 8 10 12 14 Survival (Years)

World Wide Melanoma Mortality

Caris Molecular Intelligence A comprehensive molecular testing on the DNA, RNA and Proteins to identify the biomarkers driving a patient s tumor. This information may be a powerful tool to aid oncologists in personalizing cancer therapies for their patients.

Caris Molecular Intelligence Ovarian Cancer Study A study conducted on 450 patients, showed that treatments consistent with the predictions of CMI tumor profiling significantly improved the OS in patients with ovarian, primary peritoneal and fallopian tube carcinomas

Caris Molecular Intelligence Ovarian Cancer Study

Caris Molecular Intelligence Caris Molecular Intelligence allows physicians to: Navigate among therapies with potential benefit Identify therapies that may not have been considered Determine drugs with potential lack of benefit (avoiding unnecessary toxicities and costs) Match patients to clinical trials

Caris Molecular Intelligence Ovarian Cancer Study With Caris Molecular Intelligence, over 6,000 doctors from 58 countries were able to solve the problem of cancer treatment selection for more than 70,000 patients suffering from cancers of 150 different histologic subtypes.

Gene Expression Profiling in Uveal Melanoma Test launched in 2010 15-gene expression profile test Prospective clinical validation study 1 Strong separation of metastatic risk by class % metastasis free p<0.0001 Months Class 1 (low risk) Class 2 (high risk) Adopted by >95% of ocular oncologists as standard of care Achieved a market penetration of 70% Transformed care by enabling personalized treatment planning 2 1 Onken, 2012, Ophthalmology 2 Aaberg, 2014, Clinical Ophthalmology

MSLT-1: Twice as many node-negative patients died compared to node-positive Initial Report - 2006 Final Report - 2014 Sentinel-node negative (83 events) Sentinel-node positive (41 events) Morton, N Engl J Med, 2006; Morton, N Engl J Med, 2014

Somewhere between 80-65% of patients that die of melanoma have thin or nodenegative tumors AJCC Analysis (Jan 2010) MSLT-1 (NEJM, 2006 / NEJM, 2014) AJCC Survival by Stage # Deceased at 5 years / Total Incidence Melanoma Specific Survival by Stage Stage 5-year Stage 5-year # Deceased at 5 years Stage I (T1a/b, T2a) N0, 91-97% 1,740 5,220 / 58,000 M0 Stage II T2b-T4b, N0, M0 Stage III Any T, N1-3, M0 53-82% 1,692 4,418 / 9,400 II 90% 62 / 83 40-78% 1,144 3,120 / 5,200 III 72% 32 / 41 AJCC v7, 2010. Morton, N Engl J Med, 2006; Morton, N Engl J Med, 2014

Cellular functions represented in GEP signature Migration/chemotaxis/ metastasis Chemokine/secreted molecules Gap junction/cellular adhesion CXCL14 SPP1 CLCA2 S100A9 S100A8 CCL14 MGP SPP1 GJA1 DSC1 PPL Differentiation/ proliferation Cell surface receptors Structural proteins Angiogenesis regulator CRABP2 SPRRIB BTG1 TACST D2 CLCA2 ROBO1 MGP SPP1 CST6 CXCL14 Lymphocytic invasion Transcription factor LTA4H TRIM29 Extracellular functions KRT6B KRT14 Gerami, Clin Cancer Res 2015

Cellular Pathways Represented in GEP Signature Discriminant genes SPP1 CXCL14 CLCA2 MGP S100A8 BTG1 SAP130 ARG1 KRT6B GJA1 ID2 BAP1 EIF1B S100A9 CRABP2 KRT14 ROBO1 RBM23 TACSTD2 DSC1 SPRR1B TRIM29 AQP3 TYRP1 PPL LTA4H CST6 Also in UM signature Biological process Molecular function Cellular component Pathways represented (# of genes) Tissue/organ development (16) Response to metal ion (8) Epithelium development (10) Epithelial cell differentiation (8) Calcium ion binding (4) Receptor binding (5) Cell-cell/anchoring junction (4) Non-membrane-bound organelle (12) Cytoskeleton (7) Gerami, Clin Cancer Res 2015

Gene Expression Profiling Workflow CM tumor tissue RNA Isolation cdna generation and amplification (14X) Microfluidics PCR gene card 28 discriminant gene targets and 3 control genes Analysis of GEP with a proprietary algorithm to determine class and metastatic risk Class 1 low metastatic risk Class 2 high metastatic risk

Gene Expression Profiling for Cutaneous Melanoma Available for clinical use today for Stage I and II melanoma Accurately identifies metastatic risk in early stage melanoma tumors Independent of AJCC stage factors and SLNB status Performed on FFPE primary tumor tissue from either biopsy or excision N=104 p<0.0001 Class 1 Class 2

GEP GEP Prognostic Prognostic Data Data Improves Improves Prediction Prediction Over Over SLNB Negative SLNB Status Negative for Overall Status for Survival Overall Survival SLNB GEP in SLNB- Patients OS % surviving 100% 75% 50% 25% 0% 0 n=217 p=0.006 2 4 6 Time (years) SLNB- SLNB+ SLNB- (n=159) SLNB+ (n=58) Events 44 18 5-yr OS 71% 62% 8 10 % survival 100% 75% 50% 25% n=159 p<0.0001 0% 0 2 4 6 Time (years) Class 1/ SLNB- Class 2/ SLNB- Class 1/SLNB- (n=67) Class 2/SLNB- (n=92) Events 7 37 5-yr OS 91% 55% 8 10 Gerami, JAAD in press 2015 Censor date: May 2013

Disease-Free, Distant Metastasis-free and Overall Survival SLNB AND GEP Prognostic Prediction DFS Class 1/SLN- Class 1/SLN+ Class 2/SLN- Class 2/SLN+ DMFS Class 1/SLN- Class 1/SLN+ Class 2/SLN- Class 2/SLN+ OS Class 1/SLN- Class 1/SLN+ Class 2/SLN- Class 2/SLN+ DFS = Disease Free Survival; DMFS = Distant Metastasis-Free Survival; OS = Overall Survival Gerami, JAAD in press 2015 Censor date: May 2013

AJCC stage I and II subgroup vs. GEP Class 2 AJCC Stage Total Patients # Patients with Metastasis # Metastasized Patients called Class 2 (%) I / IA / IB 119 9 5 (56%) IIA 45 21 19 (90%) IIB 42 28 27 (96%) IIC 14 11 11 (100%) Metastasis = distant or LN metastasis All patients with available subgroup information Gerami, Clin Cancer Res 2015 Censor date: May 2013

GEP Probability Score: Confidence of Prediction (n=268) 0 Class 1 Normal 0.5 0.41 Reduced Reduced 0.59 Class 2 Normal 1

Performance of a 31-gene expression profile in an independent cohort of 601 cutaneous melanoma patients J. S. Zager, MD 1 B. R. Gastman, MD 2 S. Leachman, MD, PhD 3 R. C. Gonzalez, MD 4 M. D. Fleming, MD 5 L. K. Ferris, MD, PhD 6 J. Ho, MD 7 A. Miller, MD 8 R. W. Cook, PhD 9 K. R. Covington, PhD 9 K. Meldi-Plasseraud, PhD 9 B. Middlebrook, BS 9 L. H. Kaminester, MD 10 A. Greisinger, PhD 11 S. I. Estrada, MD 12 D. M. Pariser, MD 13 L. D. Cranmer, MD, PhD 14 J. L. Messina, MD 15 J. D. Wayne, MD 16-18 K. A. Delman, MD 19 D. H. Lawson, MD 20 P. Gerami, MD 17,18,21 1 Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center, Tampa, US 2 Department of Plastic Surgery, Cleveland Clinic Lerner Research Institute, Cleveland, US 3 Department of Dermatology, Knight Cancer Institute, Oregon Health & Science University, Portland, US 4 Department of Medical Oncology, University of Colorado School of Medicine, Denver, US 5 Department of Surgical Oncology, The University of Tennessee Health Science Center, Memphis, US 6 Department of Dermatology, University of Pittsburgh Medical Center, Pittsburgh, US 7 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, US 8 Miller Dermatology and Dermatologic Surgery, Yorba Linda, US 9 Research & Development, Castle Biosciences, Inc., Friendswood, US 10 Palm Beach Dermatology, North Palm Beach, US 11 Research & Development, Kelsey Research Foundation, Houston, US 12 Affiliated Dermatology, Scottsdale, US 13 Pariser Dermatology Specialists, Norfolk, US 14 Department of Sarcoma Medical Oncology, Seattle Cancer Care Alliance, Seattle, US 15 Department of Anatomic Pathology, H. Lee Moffitt Cancer Center, Tampa, US 16 Department of Surgical Oncology, Northwestern University Feinberg School of Medicine, Chicago, US 17 Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, US 18 Skin Cancer Institute, Northwestern University, Lurie Comprehensive Cancer Center, Chicago, US 19 Department of Surgery, Emory University Winship Cancer Institute, Atlanta, US 20 Department of Hematology and Medical Oncology, Emory University Winship Cancer Institute, Atlanta, US 21 Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, US

Performance of a 31-gene expression profile in an independent cohort of 601 cutaneous melanoma patients Table 4. RFS, DMFS, and MSS rates in the population of patients receiving a sentinel lymph node biopsy from the 601-patient cohort CI, confidence interval; DMFS, distant metastasis-free survival; GEP, gene expression profile; RFS, recurrence-free survival; SLN, sentinel lymph node; MSS, melanoma-specific survival RFS (# events/95% CI) DMFS (# events/95% CI) MSS (# events/95% CI) Class 1 (N = 181) 80% (37/75-87%) 88% (24/84-93%) 97% (7/94-100%) Class 2 (N = 195) 54% (89/47-62%) 61% (75/54-69%) 79% (35/73-86%) SLN- (N = 219) 81% (44/76-86%) 87% (33/82-91%) 96% (9/93-99%) SLN+ (N = 157) 46% (82/39-56%) 55% (66/47-65%) 75% (33/68-84%) Class 1/SLN- (N = 125) 89% (15/83-95%) 94% (9/90-98%) 98% (2/96-100%) Class 1/SLN+ (N = 56) 61% (22/49-76%) 74% (15/63-88%) 93% (5/86-100%) Class 2/SLN- (N = 94) 70% (29/61-80%) 77% (24/68-86%) 93% (7/88-96%) Class 2/SLN+ (N = 101) 37% (60/28-49%) 44% (51/34-56%) 63% (28/52-76%) Submitted for Publication Zager, et al.

Performance of a 31-gene expression profile in an independent cohort of 601 cutaneous melanoma patients Submitted for Publication Zager, et al.

Performance of a prognostic 31-gene expression profile test in Stage III cutaneous melanoma subjects. Table 1: DMFS Stage III (n=207) Stage IIIA (n=76) Class Class 1 (n=63) Class 2 (n=144) Class 1 (n=34) Class 2 (n=42) Percent Recurrencefree (95%CI) p-value 72% (61-85%) <0.0001 42% (33-52%) 75% (61-92%) <0.05 53% (39-74%) Abstract Submitted to ASCO

Performance of a prognostic 31-gene expression profile test in Stage III cutaneous melanoma subjects. Table 2: MSS Stage III (n=207) Stage IIIA (n=76) Class Class 1 (n=63) Class 2 (n=144) Class 1 (n=34) Class 2 (n=42) Percent Recurrence-free (95%CI) p-value 92% (85-100%) <0.0001 66% (56-76%) 97% (91-100%) <0.02 Abstract Submitted to ASCO

Application of Gene Expression Profiling in the Management of Cutaneous Melanoma This is a prospective study of 190 patients with invasive CM at a single academic tertiary-care center. Primary tumor specimens were submitted for GEP categorizing the risk of the tumor as Class I (low) or Class II (high). The GEP classification was then compared to the pathologic features and clinical outcome. Huang, Xin; Hewgley, William P.; Guerrero, Whitney; Fleming,Martin

Application of Gene Expression Profiling in the Management of Cutaneous Melanoma Performance of GEP GEP Outcome N (%) Class 1 137 72.1 Class 2 38 20.0 Cellularity not achieved 10 5.3 Failure of multiple genes to amplify 5 2.6 Total 190 100 Huang, Xin; Hewgley, William P.; Guerrero, Whitney; Fleming,Martin

Application of Gene Expression Profiling in the Management of Cutaneous Melanoma Insurance Coverage Coverage N (%) No letter required 71 37.5 Letter required 119 62.5 Huang, Xin; Hewgley, William P.; Guerrero, Whitney; Fleming,Martin

Application of Gene Expression Profiling in the Management of Cutaneous Melanoma Predictors of Regional Nodal Metastasis Univariate Multivariate OR 2.50% 97.50% OR 2.50% 97.50% Class 2 8.1* 2.6 26.6 2.6 0.6 12.2 Breslow Thickness 1.00 -- -- -- -- -- -- 1.00-1.99 6.7* 1.5 47.6 2.5 0.4 19.2 2 18.5* 4 133.3 3.3 0.4 34.9 Ulceration 2.7 0.9 8 0.5 0.1 1.8 Mitotic Rate 1.3* 1.1 1.5 1.1 1 1.3 Huang, Xin; Hewgley, William P.; Guerrero, Whitney; Fleming,Martin

Application of Gene Expression Profiling in the Management of Cutaneous Melanoma Conclusion: CM s pathologic features correlate well with the results of GEP. However, high-risk GEP classification increases the odds of positive SLNB much more than traditional high-risk pathologic features. Obtaining GEP prior to definitive surgical planning may be beneficial, especially when SLNB is not indicated by pathologic features. In addition, patients with negative SLNB but high-risk GEP may be undertreated by current standard of care. Long-term follow up of these patients may require frequent exams and regular imaging. Huang, Xin; Hewgley, William P.; Guerrero, Whitney; Fleming,Martin

Gene Expression Profiling: A non-invasive assay to predict the metastatic potential of primary melanoma

Gene Expression Profiling in Soft Tissue Sarcoma Grading TABLE 1: Liposarcoma v. All Soft Tissue Sarcoma LS STS [a] Demographics N(%) N(%) p Age 57.2 59.3 0.55 Sex (male) Male 17 (47) 58 (44) 0.40 Female 20 (53) 75 (56) 0.80 Race African American 16 (43) 62 (42.2) 0.72 Caucasian 21 (57) 83 (56.5) 0.54 Other 0 2 (1.5) BMI 30.76 28.22 0.18 Mean follow up (months) 35.2 30.46 0.45 [b] Treatment Surgery 37 (100) 116 (87) 0.02 Neoadjuvant 2 (5) 13 (10) 0.41 Adjuvant 3 (8) 20 (15) 0.28 Radiation 10 (27) 56 (42) 0.10 [c] Complication profile Any complications 10 (27) 37 (28) 0.92 Clavien-Dindo Grade 1 1 (10) 12 (32) 0.16 Grade 2 7 (70) 18 (49) 0.23 Grade 3a 0 0 Grade 3b 2 (20) 7 (19) 0.94 Grade 4a 0 0 Grade 4b 0 0 Grade 5 0 0

Gene Expression Profiling in Soft Tissue Sarcoma Grading There is a clear relationship between the molecular genetics of sarcomatous tumors and their observed clinical behavior. Genetic changes drive presentation and grade of liposarcomas. Amplification of certain genetic loci affects dedifferentiation and progression. In the absence of molecular testing, variability in sarcoma grading can lead to an unpredictable clinical course, demanding careful long term follow up care. Guerrero, Pfeffer, and Fleming

Gene Expression Profiling in Soft Tissue Sarcoma Grading We intend first to determine the extent of variability in sarcoma diagnosis. A panel of pathologists will examine formalin-fixed paraffinembedded (FFPE) samples of patients seen in the Methodist hospital system. The formalin-fixed paraffin-embedded (FFPE) patient samples will then be used for RNA extraction using for gene expression profile analysis on the Nanostring platform with the pancancer cartridge. We hope to identify candidate biomarkers that will correlate with patterns of observed clinical behavior. This will be the foundation for future grants to be submitted upon completion of these tests Guerrero, Pfeffer, and Fleming

Gene Expression Profiling in Merkel Cell Carcinoma Merkel Cell Carcinoma: New Directions in the Pathogenesis of a Rare Malignancy Whitney Guerrero MD, Amy Wise BS. Elizabeth Lee BS, Lorraine Albritton PhD, Martin Fleming MD

Gene Expression Profiling in Merkel Cell Carcinoma Merkel Cell Carcinoma: New Directions in the Pathogenesis of a Rare Malignancy Whitney Guerrero MD, Amy Wise BS Elizabeth Lee BS, Lorraine Albritton PhD, Martin Fleming MD

Gene Expression Profiling in Merkel Cell Carcinoma Identify differences in the gene expression profile of aggressive compared to low risk MCC. We hypothesize that comparison of the gene expression profiles of tumors from patients with more aggressive disease and lower overall survival to the expression profile from the subset with higher overall survival rates will identify differences in expression that correlate with disease progression and overall survival. We expect to identify a gene expression signature that is prognostic of more aggressive disease. Merkel Cell Carcinoma: New Directions in the Pathogenesis of a Rare Malignancy Whitney Guerrero MD, Amy Wise BS. Elizabeth Lee BS, Lorraine Albritton PhD, Martin Fleming MD

Gene Expression Profiling in Malignancies: New Insights into Cancer Care Surgical Grand Rounds February 8, 2017 Martin Fleming, M.D. Chief, Division of Surgical Oncology West Cancer Center University of Tennessee Health Science Center