PARP14 inhibition restores PD-1 immune checkpoint inhibitor response following IFNγ-driven acquired resistance in preclinical cancer models

Resistance mechanisms to immune checkpoint blockade therapy (ICBT) limit its response duration and magnitude. Paradoxically, Interferon γ (IFNγ), a key cytokine for cellular immunity, can promote ICBT resistance. Using syngeneic mouse tumour models, we confirm that chronic IFNγ exposure confers resistance to immunotherapy targeting PD-1 (α-PD-1) in immunocompetent female mice. We observe upregulation of poly-ADP ribosyl polymerase 14 (PARP14) in chronic IFNγ-treated cancer cell models, in patient melanoma with elevated IFNG expression, and in melanoma cell cultures from ICBT-progressing lesions characterised by elevated IFNγ signalling. Effector T cell infiltration is enhanced in tumours derived from cells pre-treated with IFNγ in immunocompetent female mice when PARP14 is pharmacologically inhibited or knocked down, while the presence of regulatory T cells is decreased, leading to restoration of α-PD-1 sensitivity. Finally, we determine that tumours which spontaneously relapse in immunocompetent female mice following α-PD-1 therapy upregulate IFNγ signalling and can also be re-sensitised upon receiving PARP14 inhibitor treatment, establishing PARP14 as an actionable target to reverse IFNγ-driven ICBT resistance.

ADP-ribosyltransferases, an update on function and nomenclature

ADP-ribosylation, a modification of proteins, nucleic acids, and metabolites, confers broad functions, including roles in stress responses elicited, for example, by DNA damage and viral infection and is involved in intra- and extracellular signaling, chromatin and transcriptional regulation, protein biosynthesis, and cell death. ADP-ribosylation is catalyzed by ADP-ribosyltransferases (ARTs), which transfer ADP-ribose from NAD+ onto substrates. The modification, which occurs as mono- or poly-ADP-ribosylation, is reversible due to the action of different ADP-ribosylhydrolases. Importantly, inhibitors of ARTs are approved or are being developed for clinical use. Moreover, ADP-ribosylhydrolases are being assessed as therapeutic targets, foremost as antiviral drugs and for oncological indications. Due to the development of novel reagents and major technological advances that allow the study of ADP-ribosylation in unprecedented detail, an increasing number of cellular processes and pathways are being identified that are regulated by ADP-ribosylation. In addition, characterization of biochemical and structural aspects of the ARTs and their catalytic activities have expanded our understanding of this protein family. This increased knowledge requires that a common nomenclature be used to describe the relevant enzymes. Therefore, in this viewpoint, we propose an updated and broadly supported nomenclature for mammalian ARTs that will facilitate future discussions when addressing the biochemistry and biology of ADP-ribosylation. This is combined with a brief description of the main functions of mammalian ARTs to illustrate the increasing diversity of mono- and poly-ADP-ribose mediated cellular processes.

Selective Pharmaceutical Inhibition of PARP14 Mitigates Allergen-Induced IgE and Mucus Overproduction in a Mouse Model of Pulmonary Allergic Response

The type 2 cytokines IL-4 and IL-13, which share use of an IL-4 receptor α-chain and its nuclear induction of the transcription factor STAT6, are crucial in elicitation and maintenance of allergic conditions including asthma. STAT6 binds poly(ADP-ribose) polymerase (PARP)14, an ADP-ribosyl monotransferase. Elimination of PARP14 by gene targeting led to attenuation of OVA-specific allergic lung inflammation. However, PARP14 has multiple functional domains apart from the portion that catalyzes ADP-ribosylation, and it is not clear whether inhibition of the catalytic function has any biological consequence. Using BALB/c mice sensitized to the allergen Alternaria alternata, we show that peroral administration of RBN012759, a highly selective inhibitor of ADP-ribosylation by PARP14 with negligible impact on other members of the PARP gene family, achieved biologically active plasma concentrations and altered several responses to the Ag. Specifically, the pharmaceutical compound decreased mucus after allergen challenge, blunted the induced increases in circulating IgE, and prevented suppression of IgG2a. We conclude that PARP14 catalytic activity can contribute to pathogenesis in allergic or atopic processes and propose that other biological endpoints dependent on ADP-ribosylation by PARP14 can be targeted using selective inhibition.

Abstract 2154: PARP7 inhibitor RBN-2397 increases tumoral IFN signaling leading to various tumor cell intrinsic effects and tumor regressions in mouse models

Targeting cytosolic nucleic acid sensing pathways to activate the Type I interferon (IFN) response is an emerging therapeutic strategy being explored in oncology. The PARP family consists of seventeen enzymes that regulate fundamental biological processes including response to cellular stress. PARP7 (TIPARP) is a stress-induced mono-ART that catalyzes the transfer of a single unit of ADP-ribose onto substrates (MARylation) to regulate their function and plays a role in suppressing the Type I IFN response in tumor cells (Gozgit 2021 Cancer Cell). RBN-2397 is the first potent and selective small molecule inhibitor of PARP7 catalytic function. To investigate the cell autonomous effects of PARP7 inhibition, we performed a cell line screen to identify PARP7 dependent cancer cell lines. We found that treatment of a subset of lines across several cancers led to a robust decrease in cell viability. Additionally, dosing of tumor bearing mice led to complete regressions in NCI-H1373 lung cancer xenografts. To investigate the mechanism of action (MOA) leading to decreased cell viability, we treated NCI-H1373 cells with RBN-2397 and found accumulation of cells in the G0/G1 phase of the cell cycle indicative of a cell cycle arrest. This arrest in NCI-H1373 cells was associated with the induction of senescence and increased mRNA expression of senescence associated secretory phenotype (SASP) genes. To evaluate the in vivo MOA, we performed an NCI-H1373 xenograft study and collected tumors after 7 days of RBN-2397 treatment. PARP7 inhibition led to decreased expression of Ki67, and increased expression of P21 and cleaved caspase-3, suggesting decreased proliferation and increased apoptosis. Increased expression of SASP genes was also observed in RBN-2397 treated tumors. Finally, we investigated transcriptional changes after RBN-2397 treatment by RNA sequencing. In addition to the effects observed in Type I IFN signaling, we also observed differential expression of genes associated with other pathways including autophagy and energy metabolism. Further evaluation of key autophagy proteins revealed that RBN-2397 affects autophagy flux and leads to a decrease in the oxygen consumption rate of cells and reduced ATP production from the mitochondria, suggesting that a change in energy metabolism may be related to the tumor intrinsic effect of RBN-2397. In summary, we show treatment of cancer cells with RBN-2397 not only leads to activation of tumor cell IFN signaling, but also causes G1 arrest and senescence, and changes in cancer cell autophagy and energy metabolism. In vivo, RBN-2397 treatment leads to complete tumor regressions in xenografts accompanied by decreased proliferation and increased apoptosis of tumor cells. RBN-2397 is currently being evaluated in the clinic as single agent in selected cancer types (NCT04053673) and in combination with anti-PD-1 therapies.

Citation Format: Jennifer R. Molina, Joseph M. Gozgit, Melissa M. Vasbinder, Ryan P. Abo, Kaiko kunii, Kristy G. Kuplast-Barr, Bin Gui, Sunaina P. Nayak, Elena Minissale, Kerren K. Swinger, Tim J. Wigle, Alvin Z. Lu, Danielle J. Blackwell, Christina R. Majer, Yue Ren, Ellen Bamberg, Mario Niepel, Jan-Rung Mo, William D. Church, Ahmed S. Mady, Jeff Song, Zacharenia A. Varsamis, Luke Utley, Patricia E. Rao, Timoty J. Mitchison, Kevin W. Kuntz, Victoria M. Richon, Kristen McEachern, Heike Keilhack. PARP7 inhibitor RBN-2397 increases tumoral IFN signaling leading to various tumor cell intrinsic effects and tumor regressions in mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2154.

PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition triggers antitumor immunity

PARP7 is a monoPARP that catalyzes the transfer of single units of ADP-ribose onto substrates to change their function. Here, we identify PARP7 as a negative regulator of nucleic acid sensing in tumor cells. Inhibition of PARP7 restores type I interferon (IFN) signaling responses to nucleic acids in tumor models. Restored signaling can directly inhibit cell proliferation and activate the immune system, both of which contribute to tumor regression. Oral dosing of the PARP7 small-molecule inhibitor, RBN-2397, results in complete tumor regression in a lung cancer xenograft and induces tumor-specific adaptive immune memory in an immunocompetent mouse cancer model, dependent on inducing type I IFN signaling in tumor cells. PARP7 is a therapeutic target whose inhibition induces both cancer cell-autonomous and immune stimulatory effects via enhanced IFN signaling. These data support the targeting of a monoPARP in cancer and introduce a potent and selective PARP7 inhibitor to enter clinical development.

Abstract 1021: Investigating the mechanism of PARP7 inhibition in type I interferon signaling by arrayed CRISPR screening

Genomic instability in cancer cells leads to cellular stress through the accumulation of aberrant nucleic acid species in the cytosol. We have shown that PARP7, a monoPARP, is a negative regulator of cytosolic nucleic acid sensing in cancer cells. RBN-2397 is a potent and selective PARP7 inhibitor that induces antitumor immunity in preclinical models and is currently being evaluated in a Phase I clinical trial. In our preclinical investigations, we found that in a subset of cancer cell lines, such as NCI-H1373, inhibition of PARP7 triggers Type I IFN release, STAT1 phosphorylation, and growth arrest. In contrast, other cell lines, for example, HARA, do not mount an IFN-response upon PARP7 inhibition, even though they are responsive to transfection of exogenous nucleic acids and PARP7 is expressed and enzymatically active. To investigate the underlying mechanism of PARP7 inhibition and to determine the drivers of the differential sensitivity across cell lines we performed arrayed CRISPR knockout screens, targeting approximately 240 genes in the nucleic acid sensing and IFN signaling pathways, in the presence and absence of PARP7 inhibition. Our arrayed screens confirmed multiple hits from a previous genome-wide pooled synthetic/lethal CRISPR dropout screen. For example, targeting genes in the cGAS/STING pathway conferred resistance to PARP7 inhibition in the NCI-H1373 responder cells, suggesting a critical dependence on this sensing pathway. In the PARP7 inhibitor-resistant HARA cells, deletion of components of innate immune-signaling (such as AIM2 and ADAR1), the NF-κB pathway, and genes involved in autophagy sensitized the cells to PARP7 inhibition. We further delineated the function of PARP7 by comparing the effects of the CRISPR perturbation across different cellular readouts such as STAT1 phosphorylation, IFN release, and proliferation. With our work, we shed light on the mechanism by which PARP7 acts as a critical suppressor of the innate immune response. Our findings demonstrate both redundancy and crosstalk between different nucleic acid-sensing pathways and may explain why some cell lines are resistant to PARP7 inhibition.

Citation Format: Bin Gui, Ryan Abo, Patrick Flynn, Alvin Z. Lu, Jan-Rung Mo, Joseph M. Gozgit, Melissa M. Vasbinder, Zacharenia A. Varsamis, Andrew Santospago, Victoria M. Richon, Kevin W. Kuntz, Heike Keilhack, Timothy J. Mitchison, Mario Niepel. Investigating the mechanism of PARP7 inhibition in type I interferon signaling by arrayed CRISPR screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1021.

In Vitro and Cellular Probes to Study PARP Enzyme Target Engagement

Poly(ADP-ribose) polymerase (PARP) enzymes use nicotinamide adenine dinucleotide (NAD⁺) to modify up to seven different amino acids with a single mono(ADP-ribose) unit (MARylation deposited by PARP monoenzymes) or branched poly(ADP-ribose) polymers (PARylation deposited by PARP polyenzymes). To enable the development of tool compounds for PARP monoenzymes and polyenzymes, we have developed active site probes for use in in vitro and cellular biophysical assays to characterize active site-directed inhibitors that compete for NAD⁺ binding. These assays are agnostic of the protein substrate for each PARP, overcoming a general lack of knowledge around the substrates for these enzymes. The in vitro assays use less enzyme than previously described activity assays, enabling discrimination of inhibitor potencies in the single-digit nanomolar range, and the cell-based assays can differentiate compounds with sub-nanomolar potencies and measure inhibitor residence time in live cells.

Abstract 1344: Small molecule inhibitor of CD38 modulates its intra- and extracellular functions leading to antitumor activity

CD38 is an ADP-ribosyl cyclase that converts NAD+ to ADP-ribose (ADPR) or cyclic ADPR (cADPR) and nicotinamide. The enzyme can exist in either an ecto- or endo-catalytic orientation with different sub-cellular localization, and therefore can regulate internal and external NAD+ pools. Both NAD+ and cADPR can impact T cell fitness and effector function, and CD38 has been shown to be increased in settings of chronic T cell activation. CD38 can mediate the non-canonical generation of the immune suppressive adenosine by catabolizing extracellular NAD+ resulting in immunosuppression in the microenvironment. Upon immune checkpoint inhibitor (ICI) therapy, CD38 is upregulated on cancer cells to drive ICI resistance. Therefore CD38, through its catalytic activity, has been implicated in tumor immune suppression and ICI resistance. Genetic knockout of CD38 has been shown to prevent tumor growth and improve T cell fitness. Here, we describe the effects of CD38 inhibition using a small molecule inhibitor on these key metabolites in various cellular and tumor models.

RBN013209 is a potent and selective small molecule inhibitor of CD38 catalytic function. We demonstrate that inhibition of CD38 with RBN013209 prevents conversion of extracellular NAD+ to ADPR or cADPR in cancer cell lines and PBMCs. Similarly, RBN013209 inhibited intracellular CD38 activity and elevated intracellular NAD+ levels in cultured human primary T cells. Oral administration of RBN013209 to naïve mice resulted in dose-dependent elevation of NAD+ and reduction of ADPR in various tissues such as spleen and liver. We next assessed the expression of CD38 protein by immunohistochemistry following ICI treatment in various syngeneic cancer models to select a model for efficacy studies. We observed increases in CD38 expression on tumor cells and infiltrating immune cells in MC38 colon cancer and B16-F10 and Cloudman S91 melanoma models. In the MC38 tumor model, we observed single agent antitumor activity with RBN013209 that was associated with changes in NAD+ and ADPR. In B16-F10 tumor-bearing mice, we observed antitumor activity with RBN013209 in combination with anti-PD-L1 therapy. To evaluate CD38 as a biomarker in clinical samples, we assessed and confirmed the tumor expression of CD38 protein from lung, prostate and kidney cancer patients.

Here, we show that inhibition of CD38 with a small molecule affects both intra- and extra-cellular CD38 activity and modulates key metabolites playing an important role in immunomodulation. Further, our data indicate that CD38 is increased by ICI treatment and that inhibition of CD38 can lead to antitumor activity.

Citation Format: Prashant Shambharkar, Danielle J. Blackwell, Melissa M. Vasbinder, Laurie B. Schenkel, Kaiko Kunii, Jenkins L. Lemera, Kristy G. Kuplast-Barr, Yue Ren, Ellen Bamberg, W. David Church, Christina R. Majer, Luke Utley, Kristen McEachern, Mario Niepel, Tim J. Wigle, Kevin W. Kuntz, Victoria M. Richon, Heike Keilhack, Joseph M. Gozgit. Small molecule inhibitor of CD38 modulates its intra- and extracellular functions leading to antitumor activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1344.

Abstract 1348: Targeted degradation of PARP14 Using a heterobifunctional small molecule

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types and has been shown to promote the pro-tumor M2 polarization of macrophages and support Th2/Th17 signaling in models of allergic airway disease. PARP14 is a large 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates, three macrodomains that bind mono-ADP-ribose, a WWE domain that serves as a binding module for poly-ADP-ribose, and an RNA recognition motif. We have previously shown that the potent and reversible enzymatic inhibitor, RBN012759 (IC50 < 0.003 μM, 300-fold selective over monoPARPs, > 1,000-fold selective over polyPARPs), links PARP14 catalytic inhibition with suppression of the antitumor immune response in human primary macrophages and human kidney cancer explants. While this catalytic inhibitor of PARP14 was able to suppress IL-4-driven pro-tumor gene expression in macrophages, it is unknown what roles the non-enzymatic biomolecular recognition motifs play in the biological function of PARP14. To further understand this, we describe a heterobifunctional small molecule, RBN012811, based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD+-binding site and recruits the E3 ligase cereblon to ubiquitinate PARP14 and selectively target it for degradation. RBN012811 has a IC50 of 0.01 μM against PARP14 in a biophysical assay and is at least 200-fold selective over all other PARPs. In KYSE-270 cancer cells, RBN012811 has a half-maximal degradation concentration (DC50) of 0.005 μM and it does not cause degradation of other PARP enzymes. In human primary macrophages PARP14 degradation by RBN012811 led to a dose-dependent decrease of IL-10 release induced by IL-4 stimulation. Our data demonstrates that RBN012811 is a useful tool to enable further exploration of the role of PARP14 in inflammation and cancer.

Citation Format: Tim Wigle, Yue Ren, Jennifer Molina, Danielle Blackwell, Laurie Schenkel, Kerren Swinger, Anne Cheug, Ryan Abo, Elena Minissale, Alvin Lu, Christina Majer, William Church, Bryan Dorsey, Mario Niepel, Nicholas Perl, Kristy Kuplast-Barr, Kristen McEachern, Melissa Vasbinder, Heike Keilhack, Kevin Kuntz. Targeted degradation of PARP14 Using a heterobifunctional small molecule [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1348.

Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types, influencing pro-tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN-γ and IL-4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono-ADP-ribose and poly-ADP-ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL-4 driven pro-tumor gene expression in macrophages, however it is not clear what roles the non-enzymatic biomolecular recognition motifs play in PARP14-driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD⁺ -binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.

A potent and selective PARP14 inhibitor decreases protumor macrophage gene expression and elicits inflammatory responses in tumor explants

PARP14 has been implicated by genetic knockout studies to promote protumor macrophage polarization and suppress the antitumor inflammatory response due to its role in modulating interleukin-4 (IL-4) and interferon-γ signaling pathways. Here, we describe structure-based design efforts leading to the discovery of a potent and highly selective PARP14 chemical probe. RBN012759 inhibits PARP14 with a biochemical half-maximal inhibitory concentration of 0.003 μM, exhibits >300-fold selectivity over all PARP family members, and its profile enables further study of PARP14 biology and disease association both in vitro and in vivo. Inhibition of PARP14 with RBN012759 reverses IL-4-driven protumor gene expression in macrophages and induces an inflammatory mRNA signature similar to that induced by immune checkpoint inhibitor therapy in primary human tumor explants. These data support an immune suppressive role of PARP14 in tumors and suggest potential utility of PARP14 inhibitors in the treatment of cancer.

Abstract 3405: PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition leads to tumor regression

Targeting cytosolic nucleic acid sensing pathways and the Type I interferon (IFN) response is an emerging therapeutic strategy being explored in oncology. PARP7 is a member of the monoPARP class of enzymes, which catalyze the transfer of single units of ADP-ribose onto substrates to change their function. PARP7 expression is increased by cellular stress and aromatic hydrocarbons, and the PARP7 gene is amplified in cancers, especially in those of the upper aerodigestive tract. PARP7 has also been reported to negatively regulate the Type I IFN response by interacting with TBK1 during viral infection. Herein, we identify PARP7 as a novel negative regulator of cytosolic nucleic acid sensing in tumor cells.

RBN-2397, is a potent and selective small molecule inhibitor of PARP7 catalytic function. We identified a subset of cancers exhibiting dependency on PARP7 for proliferation and found that cell lines with higher baseline expression of interferon stimulated genes were more sensitive. We further show that inhibition of PARP7 by RBN-2397 restores Type I IFN signaling as demonstrated by the induction of STAT1 phosphorylation and up-regulation of genes enriched for Type I IFN signaling in NCI-H1373 lung cancer cells. We examined the antitumor effects of once daily orally administered RBN-2397 in SCID mice with subcutaneous NCIH1373 xenograft tumors and observed a dose-dependent effect of RBN-2397 on tumor growth, with regressions at dose levels ≥30 mg/kg. To evaluate the antitumor immune response in vivo, we administered RBN-2397 to CT26 tumor-bearing, immunocompetent BALB/c mice, and observed significant tumor growth inhibition at all dose levels with complete and durable regressions in a subset of mice. All of these tumor-free mice rejected a challenge of injected CT26 cells, but were able to develop 4T1 tumors, demonstrating induction of tumor-specific adaptive immune memory. The antitumor effects of RBN-2397 were further enhanced when combined with an immune checkpoint inhibitor, anti-PD1. Using CRISPR-Cas9 to knockout either TBK1 or IFNAR1 in CT26 cells, we demonstrated that RBN-2397 antitumor immunity is dependent on the effects of tumor-derived Type I interferon on immune cells.

Here, we show for the first time that cancer cells use PARP7 to suppress the Type I IFN response to cytosolic nucleic acids. We have discovered and developed RBN-2397, a first-in-class, potent and selective inhibitor of PARP7. We show RBN-2397 restores Type I IFN signaling in the tumor, causes complete tumor regressions and adaptive immunity in murine models. RBN-2397 is the first agent to enter clinical trials that targets this tumor-intrinsic vulnerability.

Citation Format: Joseph M. Gozgit, Melissa M. Vasbinder, Ryan P. Abo, Kaiko Kunii, Kristy G. Kuplast-Barr, Bin Gui, Alvin Z. Lu, Kerren K. Swinger, Tim J. Wigle, Danielle J. Blackwell, Christina R. Majer, Yue Ren, Mario Niepel, Zacharenia A. Varsamis, Sunaina P. Nayak, Ellen Bamberg, Jan-Rung Mo, William Church, Jeff Song, Luke Utley, Patricia E. Rao, Timothy J. Mitchison, Kevin W. Kuntz, Victoria M. Richon, Heike Keilhack. PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition leads to tumor regression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3405.

Abstract DDT02-01: RBN-2397: A first-in-class PARP7 inhibitor targeting a newly discovered cancer vulnerability in stress-signaling pathways

RBN-2397: A first-in-class PARP7 inhibitor targeting a newly discovered cancer vulnerability in stress-signaling pathways PARP7 is a monoPARP that catalyzes the transfer of single units of ADP-ribose onto substrates to change their function (MARylation). PARP7 expression is increased by cellular stresses, including aromatic hydrocarbons and the PARP7 gene is amplified in cancers, especially in those of the upper aerodigestive tract. PARP7 has also been reported to negatively regulate the Type I interferon (IFN) response by interacting with TBK1 during viral infection. As part of our drug discovery efforts to identify inhibitors of PARP7, we utilized structure-based drug design to optimize an unselective monoPARP inhibitor identified by screening Ribon's internal compound collection of PARP inhibitors. Further optimization of potency and physicochemical properties led to the discovery of RBN-2397, a potent and selective small molecule inhibitor of PARP7 catalytic function. A co-crystal structure of RBN-2397 demonstrated binding of the compound in the NAD+-binding pocket. Binding to cellular PARP7 is demonstrated by the ability of RBN-2397 to displace an active site probe in a NanoBRET assay. Functionally, RBN-2397 leads to the inhibition of MARylation of multiple intracellular proteins in PARP7-overexpressing SK-MES-1 cells. We identified a subset of cancers exhibiting dependency on PARP7 for proliferation. Cell lines with higher baseline expression of interferon stimulated genes are more sensitive to RBN-2397 in proliferation assays. We further show that inhibition of PARP7 by RBN-2397 restores Type I IFN signaling as demonstrated by the induction of STAT1 phosphorylation and upregulation of genes enriched for Type I IFN signaling in NCI-H1373 lung cancer cells. Oral dosing of RBN-2397 results in durable, complete tumor regression in a NCI-H1373 lung cancer xenograft and induces tumor-specific adaptive immune memory in an immunocompetent mouse cancer model that is dependent on tumor-derived Type I IFN signaling. Herein, we describe the discovery of the small molecule PARP7 inhibitor RBN-2397, the first therapeutic agent targeting PARP7 to enter clinical trials, and the first disclosure of the inhibitor. We demonstrate PARP7 is a novel therapeutic target and inhibition of PARP7 by RBN-2397 induces both cancer cell autonomous and immune stimulatory effects via enhanced IFN signaling.

Citation Format: Melissa M. Vasbinder, Joseph M. Gozgit, Ryan P. Abo, Kaiko Kunii, Kristy G. Kuplast-Barr, Bin Gui, Alvin Z. Lu, Kerren K. Swinger, Tim J. Wigle, Danielle J. Blackwell, Christina R. Majer, Yue Ren, Mario Niepel, Zacharenia A. Varsamis, Sunaina P. Nayak, Ellen Bamberg, Jan-Rung Mo, W David Church, Jeff Song, Luke Utley, Patricia E. Rao, Timothy J. Mitchison, Kevin W. Kuntz, Victoria M. Richon, Heike Keilhack. RBN-2397: A first-in-class PARP7 inhibitor targeting a newly discovered cancer vulnerability in stress-signaling pathways [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr DDT02-01.

Abstract 506: A bespoke screening platform to study mono(ADP-ribosylation)

Mono(ADP-ribosylation) (MARylation) and poly(ADP-ribosylation) (PARylation) are post-translational modifications deposited on multiple amino acids by PARP enzymes using nicotinamide adenine dinucleotide (NAD+) as the ADP-ribose donating substrate. While there are approved drugs and clinical trials on-going for inhibitors of the polyPARP enzymes that deposit poly(ADP-ribose) (specifically PARP1 and PARP2 inhibitors), monoPARP enzymes that deposit mono(ADP-ribose) are only recently gaining recognition for their role in cellular stress signaling, inflammation and cancer. However, there is a lack of chemical probes to study their function in cells and in vivo. An important first step to generating chemical probes for monoPARPs is to develop screening assays to enable determination of potency and selectivity of inhibitors during the hit finding and lead optimization phases. The development of enzyme assays is complicated by the fact that the substrates for the majority of the monoPARPs are unknown, and even for those with identified substrates, it is uncertain how they engage their substrates. Here we describe the development of robust high-throughput biochemical and cellular monoPARP assays that overcome the lack of knowledge around the substrates and construction of a family-wide screening panel. We highlight derivatized microplates that activate the enzymes to self-MARylate in dissociation enhanced lanthanide fluorescence assays (DELFIA), antibodies that recognize MARylation in in-cell western (ICW) and immunofluorescence (IF) assays, and NAD+-competitive molecular probes that are used to develop in vitro time-resolved fluorescence resonance energy transfer (TR-FRET) and cellular NanoLuc bioluminescence resonance energy transfer (NanoBRET) probe displacement assays. Additionally, we employ several methods to characterize inhibitor binding kinetics. These assays have been used in high-throughput screening campaigns of up to 500,000 compounds, as well as in the development of potent and selective inhibitors of multiple monoPARP enzymes including RBN012759, a tool compound for PARP14 that inhibits in vitro with an IC50 of 3 nM and in cells using with an IC50 of 9 nM, and is 300-fold selective over all other PARP enzymes.

Citation Format: Tim J. Wigle, Danielle J. Blackwell, Laurie B. Schenkel, Yue Ren, William D. Church, Hetvi J. Desai, Kerren K. Swinger, Andrew G. Santospago, Christina R. Majer, Alvin Z. Lu, Mario Niepel, Nicholas R. Perl, Melissa M. Vasbinder, Heike Keilhack, Kevin W. Kuntz. A bespoke screening platform to study mono(ADP-ribosylation) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 506.

Abstract 4381: A multi-omic characterization of PARP enzymes in cancer to identify novel monoPARP drug targets

The poly-ADP-ribose polymerases (PARPs) are a family of 17 enzymes with conserved catalytic domains. They regulate a wide variety of important cellular processes including cellular stress signaling pathways implicated in inflammation and cancer. Much of the PARP research has been dedicated to the four polyPARPs (PARP1, 2, 5a, and 5b) which transfer poly-ADP-ribose chains on their target proteins. In particular, the critical role of PARP1/2 in DNA damage response and repair has been studied extensively, leading to effective cancer therapy. However, the majority of PARPs are monoPARPs, which transfer a single ADP-ribose to their target proteins. Recently, several of these family members have emerged in the literature as playing cancer-specific roles. While focused studies of individual monoPARPs are ongoing, a broad integrated in silico survey of the complete PARP family has yet to be done. Thus, we set out to characterize the molecular features of PARPs and their role in human cancer by mining the deep collection of publicly available molecular data from primary cancer, normal tissue samples and cancer cell lines.

We designed and executed in silico analyses with the data available in the largest cancer public datasets, The Cancer Genome Atlas (TCGA) and the Cancer Dependency Map (DepMap). We explored standard oncogene hypotheses for all the PARPs, including mutational hotspots, copy-number variations, tumor mRNA overexpression, survival associations to genomic or expression variation, and cancer cell line dependency. Notably, two of the monoPARPs, PARP7 and PARP10, were found to be frequently amplified in multiple cancer types. Further analyses were aimed to identify significant bivariate relationships between PARP molecular features (e.g., expression and methylation) and other cancer-related biomarkers, such as tumor mutation burden or microsatellite instability. For gene expression data, we specifically determined associations between PARP mRNA levels and inferred tumor immune cell types from bulk RNA-sequencing, which implicated five monoPARPs having expression variation strongly associated with the tumor immune cell contexture in ten or more cancers. We also explored patterns of gene co-expression among the PARPs themselves and against the full genome.

Our results provide the first pan-cancer in silico characterization of the PARP family, revealing a broad molecular and potential mechanistic diversity among the PARPs across cancer. Notwithstanding the lack of traditional oncogenic features, such as mutational hotspots, in the PARPs, our analyses highlight several monoPARPs with potential oncogenic roles and further support our focus of targeting these in the clinic.

Citation Format: Ryan P. Abo, Mario Niepel, Heike Keilhack. A multi-omic characterization of PARP enzymes in cancer to identify novel monoPARP drug targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4381.

Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.

Receptor-based mechanism of relative sensing and cell memory in mammalian signaling networks

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.

Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer

We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line–ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and respecting the multi-indexed structure of the data, the analysis of clusters can be optimized for biological interpretation and therapeutic understanding. We then perform a systematic, large-scale exploration of mechanistic models of MAPK–AKT crosstalk for each cluster. This analysis allows us to quantify the heterogeneity of breast cancer cell subtypes, and leads to hypotheses about the signalling mechanisms that mediate the response of the cell lines to ligands.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

GRcalculator: an online tool for calculating and mining dose-response data

BACKGROUND

Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017).

RESULTS

We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics .

CONCLUSIONS

GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.

Abstract 142: Exploiting replicative vulnerabilities to counter incomplete responses to PI3K/Akt/mTOR inhibition

While a broad range of cancers harbor mutations that dysregulate PI3K-Akt-mTOR signaling, most small molecule drugs targeting this pathway have failed to demonstrate efficacy for the treatment of solid tumors. In this study, we investigated the mechanisms that account for drug efficacy and failure in PI3K-pathway dysregulated triple negative breast cancer (TNBC) cell lines using a diverse collection of 25 PI3K-Akt-mTOR inhibitors. Drug response phenotypes and changes in signaling were quantified using time-lapse imaging and quantitative single cell immunofluorescence microscopy, respectively. We identified GSK2126458 and Torin2 as having superior potency and efficacy in PI3K-pathway dysregulated TNBC. Unlike other PI3K pathway inhibitors, whose ineffectiveness arises from insufficient induction of apoptosis and variable inhibition of cell cycle progression at G1/S, GSK2126458 and Torin2 each rapidly induce caspase 3/7 activity and durably inhibit the proliferation of surviving cells. While the effectiveness of GSK2126458 in vitro appears to arise from near-complete suppression of PI3K-Akt-mTOR signaling, this approach has already proved unachievable in clinical trials. An alternative therapeutic strategy was identified by characterizing the mechanism of action of Torin2, a tool compound that inhibits both mTOR kinase and the DNA damage response kinases ATR, ATM, and DNA-PK. Unlike all other PI3K-Akt-mTOR inhibitors studied, Torin2 counters incomplete drug block at G1/S by concomitant induction of intolerable replication stress in S phase cells. The unique cell cycle pharmacology of Torin2 is recreated by combining inhibitors of mTOR and ATR/Chk1 kinases presently undergoing evaluation in clinical trials. In the context of combination therapy, where cytotoxicity arises from targeting S phase vulnerabilities rather than from PI3K-Akt-mTOR inhibition in G1, submaximal doses of mTOR kinase inhibitors are sufficient and confer benefit by preventing the outgrowth of cells that survive fractional killing from ATR/Chk1 inhibition. These findings suggest a novel strategy for mitigating the failure of precision monotherapy and have implications for the treatment of tumors where genetic lesions in the PI3K pathway co-occur with replicative vulnerabilities.

Citation Format: Sameer Chopra, Mario Niepel, Anne Jenney, Marc Hafner, Peter Sorger. Exploiting replicative vulnerabilities to counter incomplete responses to PI3K/Akt/mTOR inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 142. doi:10.1158/1538-7445.AM2017-142

Abstract 972: Improving pre-clinical cancer pharmacogenomics with novel drug sensitivity metrics based on growth rate inhibition

Drug sensitivity in growing cells is conventionally quantified by IC50, AUC, or Emax values, but these metrics suffer from a fundamental flaw: they highly depend on the division rate of cell lines. This dependency creates artefactual correlations between genetic alterations and drug sensitivity which impede biomarker discovery. To address this issue, we recently developed novel drug response metrics insensitive to the number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) under treatment using fixed assays.

Here, we illustrate the flaws of using IC50 values for pharmacogenomic studies by reanalyzing a recently published large dataset of drug sensitivity and showing cases in which differences in division rates drive associations between IC50 values and tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known associations between drug resistance and genomic markers such as PTEN loss driving lapatinib resistance in breast cancer cell lines. We also show how use of GRmax values, a measure of drug efficacy, allows quantifying differences in the phenotypes and distinguishing cytostatic from cytotoxic response. For drugs that have a narrow range of GR50 values like taxanes, efficacy is the most relevant metric: in many ovarian BCL2-deleted cell lines, docetaxel induces a cytotoxic response (negative GRmax values), whereas wild-type lines elicit a cytostatic response (positive GRmax values). Because efficacy (GRmax) varies independently of potency (GR50), we conclude that both metrics are complementary for pharmacogenomics and should be studied jointly.

Adopting GR metrics requires only modest changes in experimental protocols and analysis is facilitated by our interactive website: GRcalculator.org. We expect GR metrics to improve the identification of reliable drug response biomarkers and enhance the reproducibility of large-scale sensitivity studies.

Citation Format: Marc Hafner, Mario Niepel, Peter K. Sorger. Improving pre-clinical cancer pharmacogenomics with novel drug sensitivity metrics based on growth rate inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 972. doi:10.1158/1538-7445.AM2017-972

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells

Measuring the potencies of small-molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high-throughput experiments in multi-well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well-suited to large-scale studies and the second is a time-dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate-based experiments. © 2017 by John Wiley & Sons, Inc.

Designing Drug-Response Experiments and Quantifying their Results

We developed a Python package to help in performing drug-response experiments at medium and high throughput and evaluating sensitivity metrics from the resulting data. In this article, we describe the steps involved in (1) generating files necessary for treating cells with the HP D300 drug dispenser, by pin transfer or by manual pipetting; (2) merging the data generated by high-throughput slide scanners, such as the Perkin Elmer Operetta, with treatment annotations; and (3) analyzing the results to obtain data normalized to untreated controls and sensitivity metrics such as IC₅₀ or GR₅₀ . These modules are available on GitHub and provide an automated pipeline for the design and analysis of high-throughput drug response experiments, that helps to prevent errors that can arise from manually processing large data files. © 2017 by John Wiley & Sons, Inc.

Abstract P6-07-33: Metrics of drug sensitivity based on growth rate inhibition correct for the confounding effects of variable division rates

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance.

Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity.

Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells.Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance.

Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity.

Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells.

Citation Format: Hafner M, Niepel M, Sorger PK. Metrics of drug sensitivity based on growth rate inhibition correct for the confounding effects of variable division rates [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-07-33.

Abstract IA05: New methods and better theory for pre-clinical cancer pharmacogenomics

Drug dose-response measurements are the cornerstone of pre-clinical assessments of new and existing therapeutics. As a means to identify biomarkers of drug response, high-throughput studies have attempted to relate drug sensitivity across hundreds of cell lines and hundreds of drugs to inhibition of mitogensis and induction of apoptosis. And, by analogy with antibiotic susceptibility testing, it is increasingly possible to screen primary human tumor cells as a means to personalize therapy for individual patients. I will describe new ways of performing such studies that promise to avoid common confounders.

The metrics usually used to parameterize drug response (IC50, Emax, or AUC) are based on assessing the cell count of a treated condition relative to an untreated control. All of these metrics suffer from a fundamental flaw: cell lines divide at very different rates and those that undergo more divisions over the course of an assay are scored as more sensitive than cell lines with fewer divisions, even if their inherent drug sensitivities are identical. We developed a new method to parameterize drug response, the growth rate inhibition (GR) metrics, which is based on the ratio of growth rates under treatment conditions in relation to an untreated control. GR metrics are independent of cell growth over the course of the experiment and thus enable us to accurately compare cell lines with varying growth rates or experimental conditions that can alter growth rates.

I will describe the use of GR metrics to analyze six breast cancer cell lines treated with ~100 small molecule inhibitors at six doses. We focused on inhibitors targeting key signaling nodes such as PI3K, AKT, or MAPK, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs), and many of the drugs studied are currently in clinical trials. We measured the cell's transcriptional response at 3 hours and 24 hours using the L1000 transcriptional profiling assay and scored phenotypes by imaging at 3 days. We find that some drugs, particularly those targeting cell cycle kinases and chaperones, elicit near identical responses in all cell lines both transcriptionally and phenotypically. Kinase inhibitors, on the other hand, predominantly elicit cell type-specific responses at the molecular and phenotypic levels. Interestingly, a subset of signaling kinase inhibitors induces significant change at the transcriptional level without affecting cell growth. Analysis of the transcriptional profiles under these conditions allows us to identify synergistic drug combinations, which act by blocking adaptive drug resistance. Thus, inexpensive, high-throughput transcript profiling can uncover subtle mechanisms of drug sensitivity and resistance and guide the design of effective drug combinations.

Our work shows the value of using drug response metrics that are independent of growth rate. This applies to high-throughput profiling of cell lines, but also to patient-derived tumor and normal cells. GR metrics are also useful whenever cells are manipulated in ways that can affect division times (e.g. by varying the microenvironment or introducing genetic changes). We expect GR metrics to improve pre-clinical pharmacology and precision therapeutic assays employing patient-derived tumor cells.

Citation Format: Mario Niepel, Marc Hafner, Qiaonan Duan, Aravind Subramanian, Avi Ma'ayan, Peter K. Sorger. New methods and better theory for pre-clinical cancer pharmacogenomics. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr IA05.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS². The L1000CDS² search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS² search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS² to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS², we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS² we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS² tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Abstract 788: Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations

Transcriptional profiling of drug-treated cells yields high dimensional response signatures that allow drugs to be compared with each other. For example, the Connectivity Map collects signatures that are aggregated across multiple cell types. However, most therapeutic drugs are effective only against a subset of disease genotypes, particularly in the case of anti-cancer drugs. Here we ask how transcriptional signatures vary across cell lines and dose and correlate these signatures to the phenotypic response (growth inhibition). Using these cell line specific signatures, we inferred which signaling pathways are perturbed by specific kinase inhibitors and identified synergistic drug combinations.

We treated 6 breast cancer cell lines with more than 100 targeted inhibitors at 6 doses and measured their transcriptional response at 2 time points. We focused on inhibitors targeting key the PI3K and MAPK signaling pathways, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs); many of them are currently studied in clinical trials. We identified that ∼40% of the perturbations induce a significant difference in their gene expression profile. Clustering revealed the signatures are time point specific. Some clusters contain perturbations from multiple cell lines, like CDK inhibitors that down regulate genes related to the cell cycle in all six lines. In contrast, clusters comprising inhibitors of the PI3K and MAPK pathways are specific to each cell line and pathway. The perturbations induced by RTK and non-RTK inhibitors cluster with either the PI3K or the MAPK inhibitors depending on the cell line. Thus, the transcriptional response allows us to identify differences in pathway usage between cell lines, in particular to which pathway RTKs signal predominantly.

We found that the significance of the transcriptional signature is not necessarily related to growth inhibition. In particular, some inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT20 that induces strong transcriptional and biochemical responses but inhibits growth by only ∼20%. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated by MEK or EGFR inhibition. This suggests that EGFR and PI3K inhibitors act synergistically in BT20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We validated the most promising drug pair by treating xenografts.

We have shown how we can use measurements of expression signatures and cellular phenotypes following single drug perturbations to identify drug combinations that are synergistic in individual cell lines. This approach is a step toward the rational design of co-drugging strategies with differential effect and larger therapeutic windows.

Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Evan Paull, Josh Stuart, Aravind Subramanian, Avi Ma’ayan, Peter K. Sorger. Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 788.

Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

Various genetic mutations associated with cancer are known to alter cell signaling, but it is not clear whether they dysregulate signaling pathways by altering the abundance of pathway proteins. Using a combination of RNA sequencing and ultrasensitive targeted proteomics, we defined the primary components-16 core proteins and 10 feedback regulators-of the epidermal growth factor receptor (EGFR)-mitogen-activated protein kinase (MAPK) pathway in normal human mammary epithelial cells and then quantified their absolute abundance across a panel of normal and breast cancer cell lines as well as fibroblasts. We found that core pathway proteins were present at very similar concentrations across all cell types, with a variance similar to that of proteins previously shown to display conserved abundances across species. In contrast, EGFR and transcriptionally controlled feedback regulators were present at highly variable concentrations. The absolute abundance of most core proteins was between 50,000 and 70,000 copies per cell, but the adaptors SOS1, SOS2, and GAB1 were found at far lower amounts (2000 to 5000 copies per cell). MAPK signaling showed saturation in all cells between 3000 and 10,000 occupied EGFRs, consistent with the idea that adaptors limit signaling. Our results suggest that the relative stoichiometry of core MAPK pathway proteins is very similar across different cell types, with cell-specific differences mostly restricted to variable amounts of feedback regulators and receptors. The low abundance of adaptors relative to EGFR could be responsible for previous observations that only a fraction of total cell surface EGFR is capable of rapid endocytosis, high-affinity binding, and mitogenic signaling.

Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.

Abstract B39: A novel analytical approach to accurately assess in vitro drug responses for breast cancer therapy

The measurement of drug dose-response is the cornerstone of pre-clinical assessments of novel therapeutics. High-throughput studies have attempted to identify intrinsic drivers of drug sensitivity by measuring the response of hundreds of cell lines to hundreds of drugs and it has been proposed to use the drug response of primary human tumor cells as a way to personalize therapy for individual patients.

The commonly used metrics to parameterize drug response (IC50, Emax, or AUC) are based on assessing the cell count of a treated condition relative to an untreated control. Yet all of these metrics suffer from a fundamental flaw: Cell lines undergoing more divisions over the course of an assay—be it due to the length of the experiment or speed of division—are scored as more sensitive than cell lines with less divisions, even if their inherent drug sensitivities are identical. We developed a new method to parameterize drug response, the growth rate inhibition (GR) metrics, which is based on the ratio of growth rates under treatment conditions in relation to an untreated control. The GR metrics are independent of cell growth over the course of the experiment and thus enable us to accurately compare cell lines with varying growth rates or experimental conditions, like genetic or micro-environment perturbations, that can alter growth rates.

We use the GR metric to reanalyze a high throughput dataset of approximately 50 breast cancer cell lines that were treated with therapeutic inhibitors. We find that the GR50 clearly identifies ErbB2amp cells as being highly sensitive to lapatinib, an ErbB2 inhibitor, a fact that is obscured by the classic IC50 metric due to the slow growth of ErbB2amp cell. For paclitaxel, the IC50 suggests that the sensitivity of the cell lines spans a range of more than 100-fold. Surprisingly, the GR50 shows that all breast cancer cell lines are about equally sensitive to paclitaxel, at a value consistent with the drug's binding affinity to stabilized microtubules in vitro. It is rather the maximal response (GRmax) that distinguishes the response of the different lines.

We also apply the GR to study how drug responses are affected by plating density, an often poorly controlled variable in drug response measurements. Previous reports suggest that at high density cells generally become more resistant to therapy, however, we find that this is often an artifact of the IC50 metric due to slowed growth at high confluence. The emerging picture is more complex, with trends that are rarely uniform across cell lines or drugs. It appears that drug- or cell line-specific biological mechanisms drive density-dependent drug responses, for example through media conditioning effects or metabolic changes.

Our work shows the importance of using a metric based on growth rates for any systematic comparison of drug sensitivity where the speed of growth is not uniform for all compared cells. This includes high-throughput profiling of cell lines, but also the comparisons of patient-derived tumor and normal cells, or any system where cells are manipulated in ways that can affect their growth rates, like changing the microenvironment or genetically altering the cells.

Citation Format: Mario Niepel, Marc Hafner, Peter K. Sorger. A novel analytical approach to accurately assess in vitro drug responses for breast cancer therapy. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr B39.

Abstract B184: A novel analytical approach to accurately assess in vitro responses to targeted therapeutics

The measurement of drug dose-response is the cornerstone of pre-clinical assessments of novel therapeutics. High-throughput studies have attempted to identify intrinsic drivers of drug sensitivity by measuring the response of hundreds of cell lines to hundreds of drugs and it has been proposed to use the drug response of primary human tumor cells as a way to personalize therapy for individual patients.

The commonly used metrics to parameterize drug response (IC50, Emax, or AUC) are based on assessing the cell count of a treated condition relative to an untreated control. Yet all of these metrics suffer from a fundamental flaw: Cell lines undergoing more divisions over the course of an assay—due to the length of the experiment or the division rate —are scored as more sensitive than cell lines with less divisions, even if their inherent drug sensitivities are identical. We developed and experimentally validated a new method to parameterize drug response, the growth rate inhibition (GR) metrics, which is based on the ratio of growth rates under treatment and untreated control conditions. The GR metrics are independent of cell growth over the course of the experiment and thus enable us to accurately compare cell lines with varying growth rates or experimental conditions, like genetic or micro-environment perturbations, which can alter growth rates.

We use the GR metric to reanalyze a high throughput dataset of approximately 50 breast cancer cell lines that were treated with therapeutic inhibitors. We find that the GR50 clearly identifies ErbB2amp cells as being highly sensitive to ErbB2 inhibitors, a fact that is obscured by the classic IC50 metric due to the generally slower growth of ErbB2amp cell lines. For paclitaxel, the IC50suggests that the sensitivity of the cell lines spans a range of more than 100-fold and its value is correlated with the line division rate. Surprisingly, the GR50 shows that all breast cancer cell lines are about equally sensitive to paclitaxel, at a value consistent with the drug's binding affinity to stabilized microtubules in vitro. It is rather the maximal response (GRmax) that distinguishes the response of the different lines.

We also apply the GR metrics to study how drug sensitivities are affected by plating density, an often poorly controlled variable in drug response measurements. Previous reports suggest that at high density cells generally become more resistant to therapy, however, we find that this is often an artifact of the IC50 metric due to slowed growth at high confluence. Using the GR50, the emerging picture is more complex, with trends that are rarely uniform across cell lines or drugs. Correcting for differences in growth rates reveals that drug- or cell line-specific biological mechanisms drive density-dependent drug responses, for example through media conditioning effects or metabolic changes.

Our work shows the importance of using a metric based on growth rates for any systematic comparison of drug sensitivity where speed of growth are not uniform for all compared cells. This includes high-throughput profiling of cell lines, but also the comparisons of patient-derived tumor and normal cells, or any system where cells are manipulated in ways that can affect their growth rates, like changing the microenvironment or genetically altering the cells.

Citation Format: Mario Niepel, Marc Hafner, Peter K. Sorger. A novel analytical approach to accurately assess in vitro responses to targeted therapeutics. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B184.

Abstract B20: Transcriptional landscape of drug response guides the design of specific and potent drug combinations

Understanding responses to targeted agents is a key step toward the design of new therapeutic strategies that improve clinical cancer care. Here, we profiled the effects of a collection of kinase inhibitors using the L1000 transcriptional assay and combined these results with both phenotypic and biochemical response measurements to gain a more complete understanding of drug response. Using algorithms that reconstruct which signaling pathways are perturbed by specific kinase inhibitors, we identified potentially synergistic drug combinations and validated them experimentally.

We treated six breast cancer cell lines with more than 100 targeted inhibitors at six doses and measured their transcriptional response at two time points. We focused on inhibitors targeting key the PI3K and MAPK signaling pathways, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs); many of them are currently studied in clinical trials. We identified that 37% of the perturbations induce a significant difference in their gene expression profile based on the characteristic direction of the response. Clustering of signatures revealed they are time point specific: 3 hour signatures differ from the 24 hour ones. Some clusters contain perturbations from multiple cell lines, like CDK inhibitors that down regulate genes related to the cell cycle in all six lines. In contrast, clusters comprising inhibitors of the PI3K/AKT and MAPK pathways are specific to each cell line and pathway. The perturbations induced by RTK and non-RTK inhibitors cluster with either the PI3K or the MAPK inhibitors depending on the cell line. Thus, the transcriptional response allow us to identify differences in pathway usage between cell lines, in particular which RTK signals predominantly to the PI3K or the MAPK pathway.

When we related transcriptional response to the growth inhibition after three days, we found that the strength of the transcriptional signature is not necessarily related to growth inhibition. In particular, we identified cases where inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT-20 that induces strong transcriptional and biochemical responses but only 20-30% of growth inhibition. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated following MEK or EGFR inhibition. This suggests that EGFR inhibitors and PI3K inhibitors act synergistically in BT-20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We are currently verifying the most promising drug pair in xenografts.

We have shown how we can use measurements of expression signatures and cellular phenotypes following single drug perturbations to identify drug combinations that are potent and specific to individual cell lines. This approach is a step toward the rational design of co-drugging strategies with differential effect and larger therapeutic windows.

Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Evan Paull, Josh Stuart, Aravind Subramanian, Avi Ma'ayan, Peter Sorger. Transcriptional landscape of drug response guides the design of specific and potent drug combinations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B20.

Abstract 3744: Single-cell analysis of adaptive resistance and fractional responses of melanoma cells to RAF/MEK inhibition

Treatment of BRAFV600E melanomas with drugs, such as vemurafenib, that inhibit RAF/MEK signaling is effective in the short term, but remission is not durable. Acquired drug resistance is thought to involve short-term adaptive responses that compensate for RAF/MEK inhibition via up-regulation of other pro-growth mechanisms. Thus, understanding and ultimately preventing adaptive responses is a key to durable therapy. Systematic data comparing BRAFV600E tumor cells is generally lacking and it is not known whether adaptation is fundamentally similar across cell types or among individual cells within a cell population.

We apply a systematic approach to studying the responses of human melanoma cell lines to five drugs, RAF and MEK inhibitors, with the overall goal of (i) characterizing variability in adaptation with time, dose, cell type and across individual cells, (ii) discovering new or poorly characterized adaptive mechanisms, and (iii) demonstrating the effectiveness of a high-throughput approach involving multiplex measurement, single-cell analysis and computational modeling. The data involves time-course measurement of total level and activity of signaling proteins and cell state markers using array-based methods and single-cell immunofluorescence assays as well as measurement of apoptosis and cell viability under the same conditions. Statistical modeling using partial least squares regression (PLSR) revealed which of the changes in the ∼200,000 point dataset were phenotypically consequential.

We found that responses to RAF inhibitors are remarkably diverse and involve multiple pathways that can be up or down-regulated over time, with significant variability across cell types and individual cells. We identified a role for JNK/c-Jun signaling in altering the cell-cycle distribution of melanoma cells, causing apoptosis-resistant cells to accumulate and drug maximal effect (Emax) to fall; co-drugging with RAF and JNK inhibitors or JUN knockdown reverse this effect. The primary effect of JNK inhibitors is to minimize the cell-to-cell variability in pS6 suppression, promoting the induction of apoptosis.

Our study shows that a systems-level approach (combining high density time-dependent measurements, quantitative modeling and single-cell analysis) may provide a general framework for evaluating new drugs with adaptive and paradoxical response, and identifying potentially useful combination therapies.

Citation Format: Mohammad Fallahi-Sichani, Nathan J. Moerke, Mario Niepel, Tnghu Zhang, Nathanael S. Gray, Peter K. Sorger. Single-cell analysis of adaptive resistance and fractional responses of melanoma cells to RAF/MEK inhibition. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3744. doi:10.1158/1538-7445.AM2015-3744

Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis

Drugs that inhibit RAF/MEK signaling, such as vemurafenib, elicit profound but often temporary anti-tumor responses in patients with BRAF^V600E melanoma. Adaptive responses to RAF/MEK inhibition occur on a timescale of hours to days, involve homeostatic responses that reactivate MAP kinase signaling and compensatory mitogenic pathways, and attenuate the anti-tumor effects of RAF/MEK inhibitors. We profile adaptive responses across a panel of melanoma cell lines using multiplex biochemical measurement, single-cell assays, and statistical modeling and show that adaptation involves at least six signaling cascades that act to reduce drug potency (IC₅₀) and maximal effect (i.e., E_max ≪ 1). Among these cascades, we identify a role for JNK/c-Jun signaling in vemurafenib adaptation and show that RAF and JNK inhibitors synergize in cell killing. This arises because JNK inhibition prevents a subset of cells in a cycling population from becoming quiescent upon vemurafenib treatment, thereby reducing drug E_max. Our findings demonstrate the breadth and diversity of adaptive responses to RAF/MEK inhibition and a means to identify which steps in a signaling cascade are most predictive of phenotypic response.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Abstract P6-05-01: Basal and induced receptor profiles cluster cell lines into subtypes and predict drug response in a panel of breast cancer lines

Molecular subtype is a critical determinant of therapeutic approaches in breast cancer. The subtype is based on the expression of three receptors: The Her2/ErbB2 receptor tyrosine kinase (RTK), whose over-expression defines the HER2amp subtype, and the estrogen or progesterone nuclear hormone receptors, whose over-expression defines the HR+ subtype. Triple negative breast cancers (TNBCs) express low levels of all three receptors. HER2amp status serves as a biomarker for therapy with anti-Her2/ErbB2 antibodies such as trastuzumab or pertuzumab and HR+ status is a biomarker for therapy with hormone receptor antagonists such as tamoxifen. TNBCs are usually treated with cytotoxic chemotherapy. However, breast cancer subtypes are heterogeneous, clinical and mRNA subtypes are not identical, and even the best available biomarker, HER2amp status, correctly predicts response to trastuzumab in only a subset of patients.

Projects such as the Cancer Cell Line Encyclopedia aim to identify drug response determinants by relating dose-response profiles for multiple compounds to genomic features across cancer cell collections. Recent studies have suggested that measures of target activity such as phosphorylation are more correlated with drug response than mutation status or gene expression, and steady-state protein data is becoming available for tumors and cell lines. However, few studies have tried to relate the biochemistry of signal transduction to drug response on a large scale, mainly because the measurements are demanding and the utility of the approach is unproven.

In this work, we ask whether measurement of the basal and perturbed states of immediate-to-early signaling proteins in serum-starved cells, a robust measurement that can be collected with relatively little biological variability, is predictive of tumor subtype as well as of drug response. We measured the abundance and basal phosphorylation state of nuclear and cell surface receptors and of downstream signaling kinases in the standardized NCI-ICBP43 cell line collection. Because perturbation reveals features of signal transduction that are not apparent by steady state profiling, and because biological ligands present in the microenvironment can alter drug sensitivity, we also collected response profiles by exposing cells to a diverse set of growth factors and cytokines and measuring the activities of downstream signaling kinases before and after ligand addition. We present how the resulting set of ∼3×105 receptor and cell response measurements segregated with clinical subtype and how these proteomic measurements predict the sensitivity of cells to a range of targeted anti-cancer drugs.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-05-01.

The nuclear basket proteins Mlp1p and Mlp2p are part of a dynamic interactome including Esc1p and the proteasome

Mlp1p and Mlp2p form the basket of the yeast nuclear pore complex (NPC) and contribute to NPC positioning, nuclear stability, and nuclear envelope morphology. The Mlps also embed the NPC within an extended interactome, which includes protein complexes involved in mRNP biogenesis, silencing, spindle organization, and protein degradation.

The basket of the nuclear pore complex (NPC) is generally depicted as a discrete structure of eight protein filaments that protrude into the nucleoplasm and converge in a ring distal to the NPC. We show that the yeast proteins Mlp1p and Mlp2p are necessary components of the nuclear basket and that they also embed the NPC within a dynamic protein network, whose extended interactome includes the spindle organizer, silencing factors, the proteasome, and key components of messenger ribonucleoproteins (mRNPs). Ultrastructural observations indicate that the basket reduces chromatin crowding around the central transporter of the NPC and might function as a docking site for mRNP during nuclear export. In addition, we show that the Mlps contribute to NPC positioning, nuclear stability, and nuclear envelope morphology. Our results suggest that the Mlps are multifunctional proteins linking the nuclear transport channel to multiple macromolecular complexes involved in the regulation of gene expression and chromatin maintenance.

Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines

Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least-squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt, whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers of drug sensitivity and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)

MOTIVATION

Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations.

RESULTS

Application of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.

AVAILABILITY

Software is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie.

CONTACT

jstuart@ucsc.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mass spectrometry based method to increase throughput for kinome analyses using ATP probes

Protein kinases play critical roles in many biological and pathological processes, making them important targets for therapeutic drugs. Here, we desired to increase the throughput for kinome-wide profiling. A new workflow coupling ActivX ATP probe (AAP) affinity reagents with isotopic labeling to quantify the relative levels and modification states of kinases in cell lysates is described. We compared the new workflow to a classical proteomics approach in which fractionation was used to identify low-abundance kinases. We find that AAPs enriched approximately 90 kinases in a single analysis involving six cell lines or states in a single run, an 8-fold improvement in throughput relative to the classical approach. In general, AAPs cross-linked to both the active and inactive states of kinases but performing phosphopeptide enrichment made it possible to measure the phospho sites of regulatory residues lying in the kinase activation loops, providing information on activation state. When we compared the kinome across the six cell lines, representative of different breast cancer clinical subtypes, we observed that many kinases, particularly receptor tyrosine kinases, varied widely in abundance, perhaps explaining the differential sensitivities to kinase inhibitor drugs. The improved kinome profiling methods described here represent an effective means to perform systematic analysis of kinases involved in cell signaling and oncogenic transformation and for analyzing the effect of different inhibitory drugs.

Abstract 3031: The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs

Currently, clinicians use the levels of the hormone receptors for estrogen (ER) and progesterone (PR) and amplifications of the ErbB2 receptor tyrosine kinase (RTK) to distinguish three groups of breast cancers and guide the choice of therapeutic treatments. In this work we show that a limited number of measurements characterizing breast cancer cell lines at the level of RTKs can be used to predict cell line sensitivity to therapeutic inhibitors.

We chose a panel of approximately 40 breast cancer cell lines, treated them with 22 different growth factors and cytokines at a saturating and subsaturating dose for 10, 30, and 90 min and measured the phosphorylation of key kinases downstream of the receptors. In addition, we measured the expression and phosphorylation levels of approximately 20 RTKs under serum starvation conditions. We used the measurements to predict the GI50 values of the cell lines in response to over 45 therapeutic inhibitors by partial least square regression. We found that our measurements are sufficient to predict the response to about half of the drugs studied. The majority of drugs we are able to predict target RTKs directly or kinases immediately downstream of the receptors. In some cases we are able to predict the response to drugs that target processes seemingly unrelated to our measurements. We are currently exploring the data to uncover what drives sensitivity and resistance to drugs, gain mechanistic insight in the action of cancer therapeutics, and find predictors of drug response that may be useful in a clinical setting.

Our results suggest that a carefully selected set of measurements might be sufficient to classify responsiveness of breast cancers to a number of cancer drugs currently used in the clinic. This is particularly relevant in the case of cancers like triple negative breast cancers for which choosing effective treatment has proven difficult.

Citation Format: Mario Niepel, Marc A. Hafner, Emily A. Pace, Birgit Schoeberl, Peter K. Sorger. The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3031. doi:10.1158/1538-7445.AM2013-3031

Characterization of Torin2, an ATP-competitive inhibitor of mTOR, ATM, and ATR

mTOR is a highly conserved serine/threonine protein kinase that serves as a central regulator of cell growth, survival, and autophagy. Deregulation of the PI3K/Akt/mTOR signaling pathway occurs commonly in cancer and numerous inhibitors targeting the ATP-binding site of these kinases are currently undergoing clinical evaluation. Here, we report the characterization of Torin2, a second-generation ATP-competitive inhibitor that is potent and selective for mTOR with a superior pharmacokinetic profile to previous inhibitors. Torin2 inhibited mTORC1-dependent T389 phosphorylation on S6K (RPS6KB1) with an EC(50) of 250 pmol/L with approximately 800-fold selectivity for cellular mTOR versus phosphoinositide 3-kinase (PI3K). Torin2 also exhibited potent biochemical and cellular activity against phosphatidylinositol-3 kinase-like kinase (PIKK) family kinases including ATM (EC(50), 28 nmol/L), ATR (EC(50), 35 nmol/L), and DNA-PK (EC(50), 118 nmol/L; PRKDC), the inhibition of which sensitized cells to Irradiation. Similar to the earlier generation compound Torin1 and in contrast to other reported mTOR inhibitors, Torin2 inhibited mTOR kinase and mTORC1 signaling activities in a sustained manner suggestive of a slow dissociation from the kinase. Cancer cell treatment with Torin2 for 24 hours resulted in a prolonged block in negative feedback and consequent T308 phosphorylation on Akt. These effects were associated with strong growth inhibition in vitro. Single-agent treatment with Torin2 in vivo did not yield significant efficacy against KRAS-driven lung tumors, but the combination of Torin2 with mitogen-activated protein/extracellular signal-regulated kinase (MEK) inhibitor AZD6244 yielded a significant growth inhibition. Taken together, our findings establish Torin2 as a strong candidate for clinical evaluation in a broad number of oncologic settings where mTOR signaling has a pathogenic role.

Abstract PR5: The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs

Currently, clinicians use the levels of the hormone receptors for estrogen (ER) and progesterone (PR) and amplifications of the ErbB2 receptor tyrosine kinase (RTK) to distinguish three groups of breast cancers and guide the choice of therapeutic treatments. In this work we show that a limited number of measurements characterizing breast cancer cell lines at the level of RTKs can be used to predict cell line sensitivity to therapeutic inhibitors.

We chose a panel of approximately 40 breast cancer cell lines, treated them with 22 different growth factors and cytokines at a saturating and subsaturating dose for 10, 30, and 90 min and measured the phosphorylation of key kinases downstream of the receptors. In addition, we measured the expression and phosphorylation levels of approximately 20 RTKs under serum starvation conditions. We used the measurements to predict the GI50 values of the cell lines in response to over 70 therapeutic inhibitors by partial least square regression. We found that our measurements are sufficient to predict the response to about half of the drugs studied. The majority of drugs we are able to predict target RTKs directly or kinases immediately downstream of the receptors. In some cases we are able to predict the response to drugs that target processes seemingly unrelated to our measurements. We are currently exploring the data to uncover what drives sensitivity and resistance to drugs, gain mechanistic insight in the action of cancer therapeutics, and find predictors of drug response that may be useful in a clinical setting.

Our results suggest that a carefully selected set of measurements might be sufficient to classify responsiveness of breast cancers to a number of cancer drugs currently used in the clinic. This is particularly relevant in the case of cancers like triple-negative breast cancers for which choosing effective treatment has proven difficult.

This proffered talk is also presented as Poster A17.This proffered talk is also presented as Poster A17.

Citation Format: Mario Niepel, Marc Hafner, Emily Pace, Birgit Schoeberl, Peter K. Sorger. The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR5.

Discovery of potent and selective covalent inhibitors of JNK

The mitogen-activated kinases JNK1/2/3 are key enzymes in signaling modules that transduce and integrate extracellular stimuli into coordinated cellular response. Here, we report the discovery of irreversible inhibitors of JNK1/2/3. We describe two JNK3 cocrystal structures at 2.60 and 2.97 Å resolution that show the compounds form covalent bonds with a conserved cysteine residue. JNK-IN-8 is a selective JNK inhibitor that inhibits phosphorylation of c-Jun, a direct substrate of JNK, in cells exposed to submicromolar drug in a manner that depends on covalent modification of the conserved cysteine residue. Extensive biochemical, cellular, and pathway-based profiling establish the selectivity of JNK-IN-8 for JNK and suggests that the compound will be broadly useful as a pharmacological probe of JNK-dependent signal transduction. Potential lead compounds have also been identified for kinases, including IRAK1, PIK3C3, PIP4K2C, and PIP5K3.

Kinome-wide selectivity profiling of ATP-competitive mammalian target of rapamycin (mTOR) inhibitors and characterization of their binding kinetics

An intensive recent effort to develop ATP-competitive mTOR inhibitors has resulted in several potent and selective molecules such as Torin1, PP242, KU63794, and WYE354. These inhibitors are being widely used as pharmacological probes of mTOR-dependent biology. To determine the potency and specificity of these agents, we have undertaken a systematic kinome-wide effort to profile their selectivity and potency using chemical proteomics and assays for enzymatic activity, protein binding, and disruption of cellular signaling. Enzymatic and cellular assays revealed that all four compounds are potent inhibitors of mTORC1 and mTORC2, with Torin1 exhibiting ∼20-fold greater potency for inhibition of Thr-389 phosphorylation on S6 kinases (EC(50) = 2 nM) relative to other inhibitors. In vitro biochemical profiling at 10 μM revealed binding of PP242 to numerous kinases, although WYE354 and KU63794 bound only to p38 kinases and PI3K isoforms and Torin1 to ataxia telangiectasia mutated, ATM and Rad3-related protein, and DNA-PK. Analysis of these protein targets in cellular assays did not reveal any off-target activities for Torin1, WYE354, and KU63794 at concentrations below 1 μM but did show that PP242 efficiently inhibited the RET receptor (EC(50), 42 nM) and JAK1/2/3 kinases (EC(50), 780 nM). In addition, Torin1 displayed unusually slow kinetics for inhibition of the mTORC1/2 complex, a property likely to contribute to the pharmacology of this inhibitor. Our results demonstrated that, with the exception of PP242, available ATP-competitive compounds are highly selective mTOR inhibitors when applied to cells at concentrations below 1 μM and that the compounds may represent a starting point for medicinal chemistry efforts aimed at developing inhibitors of other PI3K kinase-related kinases.

A cell cycle phosphoproteome of the yeast centrosome

Centrosomes organize the bipolar mitotic spindle, and centrosomal defects cause chromosome instability. Protein phosphorylation modulates centrosome function, and we provide a comprehensive map of phosphorylation on intact yeast centrosomes (18 proteins). Mass spectrometry was used to identify 297 phosphorylation sites on centrosomes from different cell cycle stages. We observed different modes of phosphoregulation via specific protein kinases, phosphorylation site clustering, and conserved phosphorylated residues. Mutating all eight cyclin-dependent kinase (Cdk)-directed sites within the core component, Spc42, resulted in lethality and reduced centrosomal assembly. Alternatively, mutation of one conserved Cdk site within γ-tubulin (Tub4-S360D) caused mitotic delay and aberrant anaphase spindle elongation. Our work establishes the extent and complexity of this prominent posttranslational modification in centrosome biology and provides specific examples of phosphorylation control in centrosome function.