Defining Vulnerabilities of T-cell Lymphomas: we are close to the finalization…of the beginning David Weinstock dweinstock@partners.org http://weinstock.dfci.harvard.edu
Disclosures Company Research Speakers Advisory Employee Consultant Stockholder Other name support bureau board Travera X X Founder Novartis X X Dragonfly X Aileron X Abbvie X Astra Zeneca X Surface X Oncology Monsanto Expert Witness Genentech Expert Witness Verastem X Daiichi X DxTerity X
Translational Discovery in Peripheral T-Cell Lymphomas In vitro drug response Targeted agents T-cell differentiation 3D 2D Lymphomagenesis PDX clinical trials Patient-Derived Xenografts (PDX) Transgenic models Primary lymphomas & PDX cell lines MSKCC Stanford GATA-3 Surgery Weill-Cornell Nebraska Clinical Core Pathology Core Model Generation Core Phase I and II Characterization, Banking, PDX development NGS, Spectral Imaging Cell line generation Dana-Farber/BWH Industrial and Academic Partners
3 years of progress Wu et al. Cancer Cell 2015 Crescenzo et al. Cancer Cell 2015 Townsend et al. Cancer Cell 2016 Yoda et al. Nature Medicine 2016 Dunford et al. Nature Genetics 2017 Horwitz et al. Blood 2018 Murakami and Weinstock. Nature 2018 Buchner et al. Cell 2018 Ng et al. Nature Communications 2018 (in press) Ng et al. Blood 2018 (in press) Intlekofer et al. Nature 2018 (in press)
Cutting edge in genomics-based drug selection Letai. Nature Med 2017
Personalized Medicine for Cancer: Personalized Medicine for Infectious Genetic Testing Disease: Antibiotic Susceptibility Testing Highly successful for some cancers Highly successful across a wide but unavailable for most range of organisms and drugs Letai et al. Nat. Med. (2017); Proliferation is a Vivek Prasad, Nature. (2016); Friedman et al. Nat. Rev. Cancer (2015) functional biomarker
Vulnerability screening to define targets Ng et al. Nat Comm 2018 (in press)
Targeting both MDM2 and MDMX Aileron Therapeutics
Patient-derived xenografts to model human cancer Murakami and Weinstock, Nature 2017
Patient Mouse tumor Cutaneous NK/T CD3 CD8 CD3 CD8 CD56 EBER CD56 EBER
Defining biomarkers, toxicity and resistance 100 vs. p=0.0002 vs. p=0.0036 Survival(%) 50 0 0 20 40 60 80 100 120 140 160 Randomization Days Survival upon engraftment Pharmacodynamics Biomarkers Townsend et al. Cancer Cell 2016
Enabling Research on Human Cancer T-ALL Alveolar Soft Part Sarcoma HSTL AML Inflammatory myofibroblastic tumor Primary cutaneous CD30+ TCL B-ALL Neurofibroma T-PLL AUL Osteosarcoma AITL BPDCN Rhabdoid tumor ALK+ ALCL Mantle cell lymphoma Solid pseudopapillary tumor ALK- ALCL Double-hit lymphoma Wilms Tumor Mycosis Fungoides Marginal zone lymphoma Merkel cell carcinoma Sezary Syndrome Follicular lymphoma 400 Solid Tumors from Novartis Cutaneous NK/TCL Transformed follicular lymphoma Extranodal NK/TCL Diffuse large B-cell lymphoma PTCL, NOS High-grade with MYC rearr ATLL Many, many people and especially Giorgio Inghirami
Public Repository of Xenografts (www.PRoXe.org)
Public Repository of Xenografts (www.PRoXe.org)
CBTL-81777; Disseminated hepatosplenic T-cell lymphoma Raphael Koch, Noriaki Yoshida, Amanda Christie, Kay Shigemori
WCTL-81162; Subcutaneous Alk+ anaplastic large cell lymphoma Note: 1 ALRN-treated mouse was found dead on day 3, no obvious toxicity, cause of death unknown Raphael Koch, Noriaki Yoshida, Amanda Christie, Kay Shigemori
DFTL-78024; Disseminated angioimmunoblastic T-cell lymphoma Raphael Koch, Noriaki Yoshida, Amanda Christie, Kay Shigemori
DFTL-22685; Cutaneous T-cell lymphoma/Sezary Raphael Koch, Noriaki Yoshida, Amanda Christie, Kay Shigemori
DFTL-28776; Disseminated T-cell prolymphocytic leukemia Raphael Koch, Noriaki Yoshida, Amanda Christie, Kay Shigemori
Stapled peptide against MDM2/MDM4 – patients #1 and #2 Oct 22, 2015: Pre-Dose, 2.1 mg/kg May 2, 2016: Cycle 6
Start with low-hanging fruit: highly targetable WCTL-81162 WCTL-91953 DFTL-28776 DFTL-78024 DFTL-94393 Classification Karpas299 Alk+ ALCL SUDHL1 DERL-2 SUPM2 MAC2A SR786 SMZ1 FEPD Alk- ALCL MyLa DL40 KIJK MTA L82 HH PTCL-NOS AITL Classification Sample type HS-TCL NKT NPM1:ALK CTCL TBL1XR1:TP63 T-PLL FOXK2:TP63 MKLN1-AS1:DUSP22 Sample type PCM1:JAK2 PDX BCR:JAK2 Cell Line NPM1:TYK2 Alteration FYN:BACH2 no fusion VAV1:SF1 CD53:PDGFRB fusion Ng et al. Nature Commun 2018 (in press)
Start with low-hanging fruit: JAK2 fusions spleen/body spleen weight infiltration Ruxolitinib Wu et al. Cancer Cell 2015; Ng et al. Nature Commun 2018 (in press)
CTLA4-CD28 and ICOS-CD28 fusions co-opt checkpoint signaling APC Jurkat CD80 CD86 Anti-CTLA4 CTLA4-CD28 TCL cell Nat Genet. 2015 47(9):1056-60
Ipilimumab blocks CTLA4-CD28-mediated transformation CTLA4-Ab (ug/mL) 0 day3 1 4 5 10 Ratio to day 0 20 2 0 EV-Av WT-Av Mut-Av Mutated CTLA4-CD28 Empty Vector CTLA4-CD28
Raphael Koch, Liz Brem, Tony Letai
Defining therapeutic vulnerabilities using functional approaches Requirements 1. Rapid and precise 2. Small sample size from blood or fine needle aspirate 3. Single cell resolution Scott Manalis, PhD Koch Institute/MIT
Change in mass indicates drug effect
Suspended mic icrochannel resonator (SMR) frequency time
Suspended microchannel resonator (SMR) in array Cermak et al. Nat Biotech 2016
Serial SMR
Testing multiple drugs in leukemia samples Mark Murakami, MD Mark Stevens, PhD Cermak et al. Nat Biotech 2016, Stevens et al. Nat Biotech 2016
Linking Mass and MAR to scRNA-Seq for each cell Alex Shalek, Doug Lauffenberger, MIT
Si Single-box design Ten systems are up up and run unning
Stage II: LifeScaleAST for Rapid Antibiotic Susceptibility Testing
fNIH funded testing in humans – 2018
Testing in humans – projected 2019 Examples of available agents IDH2 inhibitors BCL2 inhibitors Failure PI3K inhibitors MCL1 inhibitors Empiric trial CDK9 inhibitors selection XPO1 inhibitors Patients with Bromodomain inhibitors relapsed SYK inhibitors hematologic JAK inhibitors malignancies SMR-driven MDM2 inhibitors trial selection HSP90 inhibitors Spliceosome inhibitors Failure Demethylating agents Anti-metabolites Antibody-drug conjugates Novel chemotherapies
• MSKCC Philippe Armand, M.D., Ph.D. Weinstock laboratory • • • Andy Intlekoffer, M.D., Ph.D. Richard Stone, M.D. Nicolas Cordero • • • Martha Wadleigh, M.D. Steve Horwitz, M.D. Tovah Day, Ph.D. • • • David Fisher, M.D. Allison Moskowitz, M.D. Hailey Fuchs • • • Eric Jacobsen, M.D. Natasha Galasso Saliva Jain, M.D. • • • Craig Thompson, M.D. Caron Jacobson, M.D. Kristen Jones • • • Ahmet Dogan, M.D., Ph.D. Ann LaCasce, M.D. Jacob Layer, M.S. • • • Ross Levine, M.D. Marlise Luskin, M.D. Catharine Leahy • • Ore Odejide, M.D. Loretta Li, M.D. • Koch Institute-MIT Huiyun Liu • • Scott Manalis, Ph.D. DF/HCC Chen Lossos, M.S. • • • Alex Shalek, Ph.D. Jon Aster, M.D., Ph.D. Abner Louissaint, M.D., Ph.D. • • David Dorfman, M.D., Ph.D. Sara Morrow • • Alejandra Gutierrez, M.D., Ph.D. Cornell Mark Murakami, M.D. • • • Tim Graubert, M.D. Giorgio Inghirami, M.D., Ph.D Sam Ng, M.D., Ph.D. • • • Danilo Fiore, Ph.D. Marian Harris, M.D. Foster Powers • • • Jia Ruan, M.D. Tom Kupper, M.D., Ph.D. Kay Shigemori • • Tom Look, M.D. Tony Tran • • Marcela Maus, M.D., Ph.D. Stanford University Alex van Scoyk • • • Elizabeth Morgan, M.D. Youn Kim, M.D. Amanda Christie (former) • • • Michael Khodadoust, M.D. Stu Orkin, M.D. Mark Stevens, Ph.D. (former) • • Hidde Plough, Ph.D. Noriaki Yoshida, M.D. (former) • University of Gottingen Jerry Ritz, M.D. • • Scott Rodig, M.D., Ph.D. Raphael Koch, M.D. DFCI Hematologic Oncology • • Scott Armstrong, M.D., Ph.D. Andrew Lane, M.D., Ph.D. • • David Williams, M.D., Ph.D. Aileron Therapeutics Dan DeAngelo, M.D., Ph.D. • • • Manuel Aivado, M.D. Henry Long, Ph.D. Arnie Freedman, M.D. • • Myles Brown, M.D., Ph.D. Ilene Galinsky, N.P. • Travera, inc. Jim Griffin, M.D. • • Mark Stevens, Ph.D. Margaret Shipp, M.D. • Rob Kimmerling, Ph.D.
Linked scRNA-seq: Workflow From SMR Trimming for Genes x cells Prioritize normalization quality cells Analyze complete dataset matrix schemes ( scone *) ( scone *) Cell1 Cell2 . . . Gene1 scRNA-seq library Gene2 . prep (smart-seq2) . and sequencing . *SMR step is a “viability filter” so we enrich for at least somewhat healthy cells *Cole, M. et al. Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq (bioRxiv, 2017) 1 2
scRNA-seq: Treatment 633 cells; 6 individual mice B Cell development / HSC treated and untreated; spleen and bone marrow Translation / tRNA charging Heat shock / UPR response mTOR signaling down Proliferation / cell cycle progression Pretreatment Treated
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