Drug combinations identified by high-throughput screening promote cell cycle transition and upregulate Smad pathways in myeloma

Tyler J Peat; Snehal M Gaikwad; Wendy Dubois; Nana Gyabaah-Kessie; Shuling Zhang; Sayeh Gorjifard; Zaw Phyo; Megan Andres; V Keith Hughitt; R Mark Simpson; Margaret A Miller; Andrew T Girvin; Andrew Taylor; Daniel Williams; Nelson D'Antonio; Yong Zhang; Adhithi Rajagopalan; Evan Flietner; Kelli Wilson; Xiaohu Zhang; Paul Shinn; Carleen Klumpp-Thomas; Crystal McKnight; Zina Itkin; Lu Chen; Dickran Kazandijian; Jing Zhang; Aleksandra M Michalowski; John K Simmons; Jonathan Keats; Craig J Thomas; Beverly A Mock

doi:10.1016/j.canlet.2023.216284

Drug combinations identified by high-throughput screening promote cell cycle transition and upregulate Smad pathways in myeloma

Cancer Lett. 2023 Aug 1:568:216284. doi: 10.1016/j.canlet.2023.216284. Epub 2023 Jun 24.

Authors

Tyler J Peat¹, Snehal M Gaikwad², Wendy Dubois², Nana Gyabaah-Kessie², Shuling Zhang², Sayeh Gorjifard³, Zaw Phyo⁴, Megan Andres⁴, V Keith Hughitt², R Mark Simpson², Margaret A Miller⁵, Andrew T Girvin⁶, Andrew Taylor⁶, Daniel Williams⁶, Nelson D'Antonio⁶, Yong Zhang⁷, Adhithi Rajagopalan⁸, Evan Flietner⁸, Kelli Wilson⁹, Xiaohu Zhang⁹, Paul Shinn⁹, Carleen Klumpp-Thomas⁹, Crystal McKnight⁹, Zina Itkin⁹, Lu Chen⁹, Dickran Kazandijian¹⁰, Jing Zhang⁸, Aleksandra M Michalowski², John K Simmons¹¹, Jonathan Keats¹², Craig J Thomas¹³, Beverly A Mock¹⁴

Affiliations

¹ Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, USA. Electronic address: tpeat@purdue.edu.
² Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
³ Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; University of Washington, Seattle, WA, USA.
⁴ Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; Johns Hopkins University, Baltimore, MD, USA.
⁵ Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, USA.
⁶ Palantir Technologies, Palo Alto, CA, USA.
⁷ Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; Office of Oncologic Diseases, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
⁸ McArdle Research Labs, University of Wisconsin, Madison, WI, USA.
⁹ Chemical Genomics Center, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, USA.
¹⁰ Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA.
¹¹ Natera, San Carlos, CA, USA.
¹² Translational Genomics Research Institute, Phoenix, AZ, USA.
¹³ Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA; Chemical Genomics Center, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, USA.
¹⁴ Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. Electronic address: mockb@mail.nih.gov.

PMID: 37356470
PMCID: PMC10408729 (available on 2024-08-01)
DOI: 10.1016/j.canlet.2023.216284

Abstract

Drug resistance and disease progression are common in multiple myeloma (MM) patients, underscoring the need for new therapeutic combinations. A high-throughput drug screen in 47 MM cell lines and in silico Huber robust regression analysis of drug responses revealed 43 potentially synergistic combinations. We hypothesized that effective combinations would reduce MYC expression and enhance p16 activity. Six combinations cooperatively reduced MYC protein, frequently over-expressed in MM and also cooperatively increased p16 expression, frequently downregulated in MM. Synergistic reductions in viability were observed with top combinations in proteasome inhibitor-resistant and sensitive MM cell lines, while sparing fibroblasts. Three combinations significantly prolonged survival in a transplantable Ras-driven allograft model of advanced MM closely recapitulating high-risk/refractory myeloma in humans and reduced viability of ex vivo treated patient cells. Common genetic pathways similarly downregulated by these combinations promoted cell cycle transition, whereas pathways most upregulated were involved in TGFβ/SMAD signaling. These preclinical data identify potentially useful drug combinations for evaluation in drug-resistant MM and reveal potential mechanisms of combined drug sensitivity.

Keywords: Dinaciclib; Entinostat; MYC; Myeloma; p16.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Cell Cycle
Cell Line, Tumor
Drug Combinations
Drug Resistance, Neoplasm
Drug Synergism
High-Throughput Screening Assays
Humans
Multiple Myeloma* / drug therapy
Multiple Myeloma* / genetics

Substances

Drug Combinations

Abstract

Publication types

MeSH terms

Substances

Grants and funding