Genomic and functional fidelity of PDX models of small cell lung - - PowerPoint PPT Presentation

Genomic and functional fidelity of PDX models of small cell lung cancer

Anna Farago, MD, PhD October 30, 2017

Disclosures

Consulting for Pharmamar SA, Merrimack Pharmaceuticals, Takeda, Abbvie Honorarium from Foundation Medicine Travel expenses, food or lodging from Pharmamar SA, Abbvie Research funding (to institution) from AstraZeneca, Pharmamar SA, Abbvie, Loxo Oncology, Ignyta Inc., Bristol-Myers Squibb, Merck I will discuss off-label use of olaparib and temozolomide.

Molecular Classification of NSCLC

KRAS 25% No Known Genotype EGFR 13% ALK 4% NTRK1 FGFR BRAF HER2 MET Exon 14 PIK3CA ROS1 MET amp RET

SCLC Non-squamous (adeno) Squamous

Molecular Classification of SCLC

SCLC

Molecular Classification of SCLC

SCLC SCLC

Molecular Classification of SCLC

SCLC

Small cell lung cancer: Summary

• High-grade neuroendocrine tumor with high metastatic

potential.

• 15-20% of the estimated 1.6 million new lung cancer cases

annually world-wide.

• Metastatic SCLC has a median survival of 9-10 months and

a 5-year overall survival of < 2%.

• Most common genetic alterations: p53 & Rb1 inactivation. No

clear targetable genetic drivers.

• First-line treatment for metastatic disease is combination

platinum plus etoposide or irinotecan.

• Topotecan is the only FDA-approved second-line therapy,

with response rates generally 10-30%.

Noda et al., 2002; Hanna et al., 2006; Rossi et al., 2012; Seto et al., 2014; Slotman et al., 2015; Ardizzoni et al., 1997; O’Brien et al., 2006; Eckardt et al., 2007; Shepard et al., 2007; Pietanza et al., 2012; Owanikoko et al., 2012; NCCN Guidelines

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SCLC in the clinic

Initial Presentation Response to Therapy Relapse

Why is SCLC so sensitive to chemotherapy initially? What is the best choice of therapy for relapsed SCLC?

Approaches to studying SCLC

Tissue samples from patients Cell lines Genetically engineered mouse models

Thunnissen et al., J Thor Oncol 2017, 12:334 Calbo et al., Cancer Cell 2011; 19:244 Gazdar et al., 1980; Carney et al., 1985; Gazdar et al., 2010; Meuwissen et al., 2003; Schaffer et al., 2010; McFadden et al., 2014

Approaches to studying SCLC

Tissue samples from patients Cell lines Genetically engineered mouse models

Thunnissen et al., J Thor Oncol 2017, 12:334 Calbo et al., Cancer Cell 2011; 19:244 Gazdar et al., 1980; Carney et al., 1985; Gazdar et al., 2010; Meuwissen et al., 2003; Schaffer et al., 2010; McFadden et al., 2014

Biopsy PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 (if available) Patient-derived xenografts (PDX)

SCLC PDX development

• Patient-derived xenografts (PDXs) can

model disease behavior in vivo without an in vitro intermediate.

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Rx Repeat as above Biopsy PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 (if available) Relapse

SCLC PDX development

• Patient-derived xenografts (PDXs) can

model disease behavior in vivo without an in vitro intermediate.

• Dive and colleagues pioneered

development of SCLC PDXs from circulating tumor cells (CTC-derived PDXs; CDXs).1

• 1. Hodgkinson et al., Nat Med 2014; 20: 897-905.

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Rx Repeat as above Biopsy PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 (if available) Relapse

SCLC PDX development

• Patient-derived xenografts (PDXs) can

model disease behavior in vivo without an in vitro intermediate.

• Dive and colleagues pioneered

development of SCLC PDXs from circulating tumor cells (CTC-derived PDXs; CDXs).1

• The CTC iChip is a microfluidic device

that can enrich for CTCs by depletion

• f CD45+ leukocytes.2,3
• We utilized the CTC iChip to collect

CTCs from SCLC patients and implanted the isolated cell pool into NSG mice.

• 1. Hodgkinson et al., Nat Med 2014; 20: 897-905.
• 2. Karabacak et al., Nat Protoc. 2014; 9: 694-710.
• 3. Ozkumur et al., Sci Transl Med. 2013; 5:179ra47.

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Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

In collaboration with Haber/Maheswaran labs

SCLC PDX development

• 2. Karabacak et al., Nat Protoc. 2014; 9: 694-710.
• 3. Ozkumur et al., Sci Transl Med. 2013; 5:179ra47.

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Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

In collaboration with Haber/Maheswaran labs

SCLC PDX development

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PDX summary statistics

31 SCLC models from 27 patients

PDX Take Rate (P0 / Attempts) CTC (iChip) 15 / 43 (35%) Biopsy 14 / 17 (82%) CTC (RosetteSep/Ficoll) 1 / 4 Effusions 1 / 3 P0 latency (median ± st. dev.) CTC 112 ± 65 days Biopsy / Effusion 75 ± 31 days Model Establishment Passage success 32 / 32 models Cryopreservation 21 / 21 models

PDX initiation cutoff: June 30, 2016 Data analysis cutoff: February 17, 2017

Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

In collaboration with Haber/Maheswaran labs

SCLC PDX development

PDX initiation cutoff: June 30, 2016 Data analysis cutoff: February 17, 2017

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PDX summary statistics

31 SCLC models from 27 patients

PDX Take Rate (P0 / Attempts) CTC (iChip) 15 / 43 (35%) Biopsy 14 / 17 (82%) CTC (RosetteSep/Ficoll) 1 / 4 Effusions 1 / 3 P0 latency (median ± st. dev.) CTC 112 ± 65 days Biopsy / Effusion 75 ± 31 days Model Establishment Passage success 31 / 31 models Cryopreservation 21 / 21 models Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

In collaboration with Haber/Maheswaran labs

SCLC PDX development

Patient course Model #

CTC-derived Biopsy-derived Platinum/etoposide treatment Other treatment

PDX initiation cutoff: June 30, 2016 Data analysis cutoff: February 17, 2017

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PDX summary statistics

31 SCLC models from 27 patients

PDX Take Rate (P0 / Attempts) CTC (iChip) 15 / 43 (35%) Biopsy 14 / 17 (82%) CTC (RosetteSep/Ficoll) 1 / 4 Effusions 1 / 3 P0 latency (median ± st. dev.) CTC 112 ± 65 days Biopsy / Effusion 75 ± 31 days Model Establishment Passage success 31 / 31 models Cryopreservation 21 / 21 models

In collaboration with Haber/Maheswaran labs

Assessing fidelity of models: Comparison back to patient tumor How well do PDXs model the patient tumor?

 Histology  Genomics  Function

Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

Histology

CTC MGH1504 H&E SYP Chrg RB Biopsy MGH1512 H&E SYP Chrg RB

Pathologic Confirmation Model H&E + IHC: SCLC features 27 / 29 models*

All cases reviewed by Dr. Mari Mino-Kenudson

* 2 models had H&E consistent with SCLC but no NE marker expression

Histology

CTC MGH1504 H&E SYP Chrg RB Biopsy MGH1512 H&E SYP Chrg RB

Pathologic Confirmation Model H&E + IHC: SCLC features 27 / 29 models* Model H&E ≈ Patient 14 / 14 models Model IHC ≈ Patient 12 / 13 models

All cases reviewed by Dr. Mari Mino-Kenudson

* 2 models had H&E consistent with SCLC but no NE marker expression

Assessing fidelity of models: Comparison back to patient tumor How well do PDXs model the patient tumor?

 Histology  Genomics  Function

Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

Cases selected for whole exome sequencing

Model number PDX type Time difference between biopsy and model initiation (days) P0 latency (days) Patient clinical stage Patient prior therapies MGH1504-1 CTC 3 160 LS none MGH1514-1 CTC 4 130 ES none MGH1515-1 CTC 8 138 ES none MGH1518-1 biopsy 81 ES none MGH1525-1 CTC 1 45 ES none

Copy Number Analysis

Julie George Martin Peifer Roman Thomas

Copy Number Analysis

Julie George Martin Peifer Roman Thomas

Mutation overview

Julie George Martin Peifer Roman Thomas

Mutation overview

Julie George Martin Peifer Roman Thomas

TP53 and RB1 alterations identified and consistent in FFPE biopsy, P0, P1/P2

Julie George Martin Peifer Roman Thomas

MGH1504-1 MGH1512-1 MGH1514-1 MGH1515-1 MGH1518-1 MGH1525-1 MGH1528-1

MGH1504: CTC-derived model

Patient Biopsy FFPE:

• Tumor content: 97%
• TP53 mutation p.S94*

Model number PDX type Time difference between biopsy and model initiation (days) P0 latency (days) Patient clinical stage Patient prior therapies

MGH1504-1 CTC 3 160 LS none Biopsy 281 mutations

Patient biopsy PDX P0 PDX P2

Julie George Martin Peifer Roman Thomas

MGH1504: CTC-derived model

Patient Biopsy FFPE:

• Tumor content: 97%
• TP53 mutation p.S94*

P0 PDX:

• Tumor content: 99%
• TP53 mutation p.S94*

Model number PDX type Time difference between biopsy and model initiation (days) P0 latency (days) Patient clinical stage Patient prior therapies

MGH1504-1 CTC 3 160 LS none Biopsy PDX P0

Patient biopsy PDX P0 PDX P2 10 14

Julie George Martin Peifer Roman Thomas

MGH1504: CTC-derived model

Patient Biopsy FFPE:

• Tumor content: 97%
• TP53 mutation p.S94*

P0 PDX:

• Tumor content: 99%
• TP53 mutation p.S94*

P2 PDX:

• Tumor content: 99%
• TP53 mutation p.S94*

Model number PDX type Time difference between biopsy and model initiation (days) P0 latency (days) Patient clinical stage Patient prior therapies

MGH1504-1 CTC 3 160 LS none Biopsy  PDX P0  PDX P2

Patient biopsy PDX P0 PDX P2 10

Julie George Martin Peifer Roman Thomas

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Mutational profiles of patient biopsy and PDX models closely overlap

MGH1504

CTC

MGH1512

biopsy

MGH1514

CTC

MGH1518

biopsy

MGH1525

CTC

MGH1528

CTC Patient biopsy PDX P0 PDX P2

Julie George Martin Peifer Roman Thomas

Assessing fidelity of models: Comparison back to patient tumor How well do PDXs model the patient tumor?

 Histology  Genomics  Function

Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

SCLC in the clinic

Initial Presentation Response to Therapy Relapse

Molecular Classification of SCLC

SCLC

PARP inhibitor + temozolomide

PARP inhibition in SCLC

• PARP1 regulates base excision repair, homologous recombination,

and non-homologous end joining. Inhibition of PARP enzymatic activity blocks PARP-mediated DNA repair.1

• PARP1 is highly expressed in SCLC compared to other cancers.2, 3
• SCLC cell lines are sensitive to PARP inhibitors. PARP sensitivity

is not associated with BRCA1/2 mutations or HR defects.4,5

• “Trapping” of PARP complexes to sites of DNA single stranded

breaks by PARP inhibitors can cause failure of repair and induction

• f double strand breaks.6
• PARP inhibitors synergize with agents that increase prevalence of

single stranded breaks in tumor models, including SCLC models.7,8

1. Sonnenblick et al., 2015 2. Byers et al., 2012 3. Cardnell et al., 2013 4. Stewart et al., 2017 5. George et al., 2015 6. Hopkins et al., 2015 7. Murai et al., 2014 8. Lok et al., 2016

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Rationale for olaparib + temozolomide in relapsed SCLC

• Catalytic inhibitor of PARP1 and

PARP2

• Moderate PARP-trapping activity1
• FDA-approved as monotherapy for

patients with BRCA-mutated advanced ovarian cancer who have been treated with 3 or more lines of chemotherapy

Olaparib Temozolomide

• Alkylating agent that induces

single strand DNA breaks

• FDA-approved in newly

diagnosed glioblastoma multiforme and refractory anaplastic astrocytoma

• Single agent activity in SCLC2
• STOMP SCLC trial: Maintenance olaparib vs placebo following first-line chemotherapy.

No significant PFS or OS benefit.3

• SCLC second-line phase 2 study: temozolomide + veliparib vs temozolomide + placebo.

Improved response rate with combo, but no significant difference in 4-month PFS.4

1. Hopkins et al., 2015 2. Pietanza et al., 2012 3. Woll et al., WCLC, 2016 4. Pietanza et al., ASCO Abstract # 8512, 2016

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Phase 1 study schema: olaparib tablets and temozolomide in SCLC

• Histologically confirmed SCLC, not a

candidate for potentially curative therapy

• Radiographic progression after one

platinum based chemotherapy regimen (with any additional number

• f subsequent therapies allowed)
• ECOG PS 0-2
• Treated and stable brain metastases

are allowed. Asymptomatic brain mets < 1 cm are allowed.

• Primary objective: Determine RP2D of combination olaparib and temozolomide
• Secondary objectives: Safety and tolerability, exploratory biomarker analyses
• Dose limiting toxicities (DLTs) monitored during cycle 1
• Disease assessment Q6 weeks, RECIST 1.1

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NCT02446704

Dose level 1:

• O 100 mg PO BID
• T 50 mg/m2 PO QPM

Dose level 2:

• O 100 mg PO BID
• T 75 mg/m2 PO QPM

Dose level 3:

• O 200 mg PO BID
• T 75 mg/m2 PO QPM

Dose level 4:

• O 200 mg PO BID
• T 100 mg/m2 PO QPM

Olaparib tablets + Temozolomide dosed days 1-7 of each 21-day cycle

Phase 1 Patient characteristics

N=13 Age, years, median (range) 66.3 (39.2 – 85.2) Sex, male/female (%) 4 (31) / 9 (69) ECOG performance status, n (%) 1 12 (92) 2 1 (8) Prior cancer therapies, n (%) 1 3 (23) 2 4 (31) 3 1 (8) >3 5 (38) Prior response to platinum-based therapy* Sensitive (%) 8 (62) Resistant (%) 5 (38)

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* Based on time from completion of first-line therapy to start of second-line therapy. Sensitive: ≥ 90 days Resistant: < 90 days

Phase 1 Treatment emergent adverse events related to study drugs

Adverse Event Term, n (%) Grade 1 Grade 2 Grade 3 All Grades Anemia 4 (31) 1 (8) 3 (23) 8 (62) Nausea 5 (38) 3 (23) 8 (62) Thrombocytopenia 2 (15) 4 (31) 2 (15) 8 (62) Neutropenia 5 (38) 5 (38) Fatigue 2 (15) 2 (15) 4 (31) WBC count decreased 1 (8) 3 (23) 4 (31) Vomiting 2 (15) 1 (8) 3 (23) Diarrhea 1 (8) 1 (8) 2 (15) AST elevation 2 (15) 2 (15)

Listed are adverse events that were reported in at least 2 of the patients and that were deemed by the investigators to be possibly, probably, or definitely related to study drug(s). For each patient, only the highest grade of each AE is included. There were no DLTs, SAEs or grade 4 or 5 AEs. Data cut off: Feb 6, 2017

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Phase 1 dose reductions

Final dose level Starting dose level

1 2 3 4 1 2 3 4

Dose reductions in DLT-evaluable patients in the phase 1 portion. Dose reductions were required for patients who started in dose levels 3 and 4. One patient stopped treatment after 4 days due to disease-related symptoms and was not evaluable for DLT.

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Phase 1 Efficacy

Best response Dose level 1 Dose level 2 Dose level 3 Dose level 4 All dose levels (%) Partial response 2 1 2 1 6 (46) Stable disease 1 1 1 2 5 (38) Progressive disease 1 1 2 (15)

• 80
• 70
• 60
• 50
• 40
• 30
• 20
• 10

10 20 30 40

Best Objective Response, RECIST 1.1

Objective response rate all confirmed responses 46% Median progression free survival months (range) (N=13) 5.6 (2.1 – N/A) Median duration of treatment months (range) (N=13) 5.0 (0.1 – 11.6)

Shown are responses for all patient treated in the phase 1 portion (N=13)

 Patient with 0% response, progressive disease (new lesion) * Patients still on treatment as of data cutoff, Feb 6 2017

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

* *

Phase 1 Efficacy by platinum sensitivity

Best response Dose level 1 Dose level 2 Dose level 3 Dose level 4 All dose levels (%) Partial response 2 1 2 1 6 (46) Stable disease 1 1 1 2 5 (38) Progressive disease 1 1 2 (15)

• 80
• 70
• 60
• 50
• 40
• 30
• 20
• 10

10 20 30 40

Best Objective Response, RECIST 1.1

Objective response rate all confirmed responses 46% Median progression free survival months (range) (N=13) 5.6 (2.1 – N/A) Median duration of treatment months (range) (N=13) 5.0 (0.1 – 11.6)

Shown are responses for all patient treated in the phase 1 portion (N=13)

 Patient with 0% response, progressive disease (new lesion) * Patients still on treatment as of data cutoff, Feb 6 2017

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 Platinum sensitive Platinum resistant

* *

PDX MGH1528 models behavior of patient tumor over time

Day 89: Nadir Baseline Day 158: Progression Duration of response: 6.5 months Depth of response: -47%

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post-treatment initiation

CTC-derived model: MGH1528-1

Vehicle Olaparib TMZ O/T

EP OT Other tx

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PDX MGH1528 models behavior of patient tumor over time

Day 89: Nadir Baseline Day 158: Progression Duration of response: 6.5 months Depth of response: -47%

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post-treatment initiation

CTC-derived model: MGH1528-1

Vehicle Olaparib TMZ O/T

EP OT Other tx

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post-treatment initiation

CTC-derived model: MGH1528-2

Vehicle Olaparib TMZ O/T

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PDX MGH1518 models behavior of patient tumor over time

Day 186: Nadir Baseline Day 228: Progression

EP OT

Duration of response: 7.5 months Depth of response: -50%

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation

Biopsy-derived model: MGH1518-1

Vehicle Olaparib TMZ O/T

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation

Biopsy-derived model: MGH1518-3

Vehicle Olaparib TMZ O/T

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation 0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation

TTP: Time to progression ITV: Initial tumor volume

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation 0% 50% 100% 150% 200% 20 40 60 Tumor volume Days post treatment initiation

TTP: Time to progression ITV: Initial tumor volume

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

Proposed Biomarkers*

SLFN11 Sensitivity to PARP inhibitors

* Stewart et al., Oncotarget 2017; Lok et al., Clin Cancer Res 2017; Gardner et al., Cancer Cell 2017; Murai et al., Oncotarget 2016

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

SLFN11 Tubulin

Proposed Biomarkers*

SLFN11 Sensitivity to PARP inhibitors

* Stewart et al., Oncotarget 2017; Lok et al., Clin Cancer Res 2017; Gardner et al., Cancer Cell 2017; Murai et al., Oncotarget 2016

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

SLFN11 Tubulin MGMT

Proposed Biomarkers*

MGMT Reverses alkylation by TMZ SLFN11 Sensitivity to PARP inhibitors

* Stewart et al., Oncotarget 2017; Lok et al., Clin Cancer Res 2017; Gardner et al., Cancer Cell 2017; Murai et al., Oncotarget 2016

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Response TTP Best response (%ITV) TTP (200% ITV)

• 100%
• 50%
• 0%

≥100 d 50 d 0 d

“Co-clinical trial” of olaparib/temozolomide in SCLC PDX models

SLFN11 Tubulin MGMT

Proposed Biomarkers*

MGMT Reverses alkylation by TMZ SLFN11 Sensitivity to PARP inhibitors

• SLFN11 and MGMT are incomplete biomarkers for sensitivity
• Co-clinical trial in PDX models enables hypothesis testing of

proposed biomarkers and further mechanistic studies

* Stewart et al., Oncotarget 2017; Lok et al., Clin Cancer Res 2017; Gardner et al., Cancer Cell 2017; Murai et al., Oncotarget 2016

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Molecular Classification of SCLC

SCLC

PARP inhibitor + temozolomide

Molecular Classification of SCLC

SCLC

DLL3-targeted antibody-drug conjugate PARP inhibitor + temozolomide

DLL3 in SCLC

Saunders et al., Sci Transl Med 2015; 7:302; Rudin et al., Lancet Oncology 2017

• DLL3 is a negative regulator of Notch signaling.
• May be up-regulated by ASCL1, a neuroendocrine transcription

factor that is expressed in SCLCs.

• Normally localizes to Golgi, but is expressed on the surface of

the majority of SCLCs.

Rovalpitzumab Tesirine (Rova-T, SC16LD6.5)

Saunders et al., Sci Transl Med 2015; 7:302

SCRX16-001: First-in-Human Study Schema

Presented By Charles Rudin at 2016 ASCO Annual Meeting

Rova-T

Rudin et al., Lancet Oncology 2017

Application of PDX models to study Rova-T sensitivity and resistance

• How does DLL3 expression change
• ver time in treated SCLCs?
• How does sensitivity to Rova-T

change over time, and how do tumors acquire resistance?

Application of PDX models to study Rova-T sensitivity and resistance

Patient course Model #

CTC-derived Biopsy-derived Platinum/etoposide treatment Other treatment

• How does DLL3 expression change
• ver time in treated SCLCs?
• How does sensitivity to Rova-T

change over time, and how do tumors acquire resistance?

Molecular Classification of SCLC

SCLC

DLL3-targeted antibody-drug conjugate PARP inhibitor + temozolomide

Molecular Classification of SCLC

SCLC

DLL3-targeted antibody-drug conjugate PARP inhibitor + temozolomide Immune checkpoint inhibitors

Summary

• Although SCLC has classically been viewed as a uniform disease, new studies

indicate that there may be subgroups with select sensitivity to particular strategies.

• To study the diverse and evolving spectrum of SCLC tumors, we have developed a

platform for generating PDX models from CTCs and biopsies with high efficiency.

• Our PDX models faithfully recapitulate the histology, genomics and drug sensitivities
• f donor patient tumors.
• A phase 1 clinical trial of olaparib tablets and temozolomide is tolerable and shows

promising clinical activity, with an objective response rate of 46% among 13 pre- treated SCLC.

• A co-clinical trial in mice shows a range of sensitivities to olaparib/temozolomide.

These models serve as a platform for discovery of biomarkers and mechanisms of drug sensitivity and resistance with correlative patient clinical data.

Assessing fidelity of models: Comparison back to patient tumor How well do PDXs model the patient tumor?

 Histology  Genomics  Function

Rx Repeat as above iChipNeg PDX Samples:

• FFPE
• Snap freeze
• Cryopreserve

P0 P1 P2 Relapse Biopsy (if available)

Our Team

Anna Farago Nicholas Dyson Benjamin Drapkin Peter Igo Sarah Phat Roxana Azimi Bailey Grinnell Daniel Haber Shyamala Maheswaran Aaron Hata Mari Mino-Kenudson Jeffrey Engelman Roman Thomas Julie George Martin Peifer Nima Abedpour Alice Shaw Lecia Sequist Jerry Azzoli Rebecca Heist Justin Gainor Zosia Piotrowska Jennifer Temel Inga Lennes Funding V Foundation Harvard Catalyst