Enhancing anticancer activity of macrophages through rational drug combinations.

Targeting tumor-associated macrophages (TAMs) is an emerging approach being tested in multiple clinical trials. TAMs, depending on their differentiation state, can exhibit pro- or antitumorigenic functions. For example, the M2-like phenotype represents a protumoral state that can stimulate tumor growth, angiogenesis, metastasis, therapy resistance, and immune evasion by expressing immune checkpoint proteins. In this issue of the JCI, Vaccaro and colleagues utilized an innovative drug screen approach to demonstrate that targeting driver oncogenic signaling pathways concurrently with anti-CD47 sensitizes tumor cells, causing them to undergo macrophage-induced phagocytosis. The combination treatment altered expression of molecules on the tumor cells that typically limit phagocytosis. It also reprogrammed macrophages to an M1-like antitumor state. Moreover, the approach was generalizable to tumor cells with different oncogenic pathways, opening the door to precision oncology-based rationale combination therapies that have the potential to improve outcomes for patients with oncogene-driven lung cancers and likely other cancer types.

The Breast Cancer Proteome and Precision Oncology.

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci.

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

Single-Cell Proteomics Analysis of Recurrent Low-Grade Serous Ovarian Carcinoma and Associated Brain Metastases.

Between 2% and 6% of epithelial ovarian cancer (EOC) patients develop brain metastases (brain mets), which are incurable and invariably result in death. This poor outcome is associated with a lack of established guidelines for the detection and treatment of brain mets in EOC patients. In this study, we characterize an unusual case of low-grade serous ovarian carcinoma (LGSOC) that metastasized to the brain. Using a spatially oriented single-cell proteomics platform, we compared sequential biopsies of a primary tumor with a peritoneal recurrence and brain mets. We identified several targetable oncogenic pathways and immunosuppressive mechanisms that are amplified in the brain mets and could be involved in the progression of LGSOC to the brain. Furthermore, we were able to identify cell populations that are shared between the primary tumor and the brain mets, suggesting that cells that have a propensity for metastasis to the brain could be identified early during the course of disease. Taken together, our findings further a path for personalized therapeutic decisions in LGSOC.

Identification of biomarkers of response to preoperative talazoparib monotherapy in treatment naïve gBRCA+ breast cancers.

Germline mutations in BRCA1 or BRCA2 exist in ~2-7% of breast cancer patients, which has led to the approval of PARP inhibitors in the advanced setting. We have previously reported a phase II neoadjuvant trial of single agent talazoparib for patients with germline BRCA pathogenic variants with a pathologic complete response (pCR) rate of 53%. As nearly half of the patients treated did not have pCR, better strategies are needed to overcome treatment resistance. To this end, we conducted multi-omic analysis of 13 treatment naïve breast cancer tumors from patients that went on to receive single-agent neoadjuvant talazoparib. We looked for biomarkers that were predictive of response (assessed by residual cancer burden) after 6 months of therapy. We found that all resistant tumors exhibited either the loss of SHLD2, expression of a hypoxia signature, or expression of a stem cell signature. These results indicate that the deep analysis of pre-treatment tumors can identify biomarkers that are predictive of response to talazoparib and potentially other PARP inhibitors, and provides a framework that will allow for better selection of patients for treatment, as well as a roadmap for the development of novel combination therapies to prevent emergence of resistance.

Characterization of anticancer drug resistance by reverse-phase protein array: new targets and strategies.

INTRODUCTION: Drug resistance is the main barrier to achieving cancer cures with medical therapy. Cancer drug resistance occurs, in part, due to adaptation of the tumor and microenvironment to therapeutic stress at a proteomic level. Reverse-phase protein arrays (RPPA) are well suited to proteomic analysis of drug resistance due to high sample throughput, sensitive detection of phosphoproteins, and validation for a large number of critical cellular pathways. AREAS COVERED: This review summarizes contributions of RPPA to understanding and combating drug resistance. In particular, contributions of RPPA to understanding resistance to PARP inhibitors, BRAF inhibitors, immune checkpoint inhibitors, and breast cancer investigational therapies are discussed. Articles reviewed were identified by MEDLINE, Scopus, and Cochrane search for keywords 'proteomics,' 'reverse-phase protein array,' 'drug resistance,' 'PARP inhibitor,' 'BRAF inhibitor,' 'immune checkpoint inhibitor,' and 'I-SPY' spanning October 1, 1960 - October 1, 2021. EXPERT OPINION: Precision oncology has thus far failed to convert the armament of targeted therapies into durable responses for most patients, highlighting that genetic sequencing alone is insufficient to guide therapy selection and overcome drug resistance. Combined genomic and proteomic analyses paired with creative drug combinations and dosing strategies hold promise for maturing precision oncology into an era of improved patient outcomes.

A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis.

Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenged by the lack of robust methods to segment cells, identify subcellular compartments, and extract relevant features. Variational autoencoder (VAE) approaches produce encouraging results by mapping an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to uninformative technical variation. Here we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features, including emergent features not obvious from prior knowledge. We show that the proposed architecture improves analysis by making distinct cell populations more separable compared to traditional and recent extensions of VAE architectures and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other analytic modalities. Better feature extraction and image analysis methods enabled by the ME-VAE will advance our understanding of complex cell biology and enable discoveries previously hidden behind image complexity ultimately improving medical outcomes and drug discovery.

Antitumor Activity of a Mitochondrial-Targeted HSP90 Inhibitor in Gliomas.

PURPOSE: To investigate the antitumor activity of a mitochondrial-localized HSP90 inhibitor, Gamitrinib, in multiple glioma models, and to elucidate the antitumor mechanisms of Gamitrinib in gliomas. EXPERIMENTAL DESIGN: A broad panel of primary and temozolomide (TMZ)-resistant human glioma cell lines were screened by cell viability assays, flow cytometry, and crystal violet assays to investigate the therapeutic efficacy of Gamitrinib. Seahorse assays were used to measure the mitochondrial respiration of glioma cells. Integrated analyses of RNA sequencing (RNAseq) and reverse phase protein array (RPPA) data were performed to reveal the potential antitumor mechanisms of Gamitrinib. Neurospheres, patient-derived organoids (PDO), cell line-derived xenografts (CDX), and patient-derived xenografts (PDX) models were generated to further evaluate the therapeutic efficacy of Gamitrinib. RESULTS: Gamitrinib inhibited cell proliferation and induced cell apoptosis and death in 17 primary glioma cell lines, 6 TMZ-resistant glioma cell lines, 4 neurospheres, and 3 PDOs. Importantly, Gamitrinib significantly delayed the tumor growth and improved survival of mice in both CDX and PDX models in which tumors were either subcutaneously or intracranially implanted. Integrated computational analyses of RNAseq and RPPA data revealed that Gamitrinib exhibited its antitumor activity via (i) suppressing mitochondrial biogenesis, OXPHOS, and cell-cycle progression and (ii) activating the energy-sensing AMP-activated kinase, DNA damage, and stress response. CONCLUSIONS: These preclinical findings established the therapeutic role of Gamitrinib in gliomas and revealed the inhibition of mitochondrial biogenesis and tumor bioenergetics as the primary antitumor mechanisms in gliomas.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer.

Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

A Modified Nucleoside 6-Thio-2'-Deoxyguanosine Exhibits Antitumor Activity in Gliomas.

PURPOSE: To investigate the therapeutic role of a novel telomere-directed inhibitor, 6-thio-2'-deoxyguanosine (THIO) in gliomas both in vitro and in vivo. EXPERIMENTAL DESIGN: A panel of human and mouse glioma cell lines was used to test therapeutic efficacy of THIO using cell viability assays, flow cytometric analyses, and immunofluorescence. Integrated analyses of RNA sequencing and reverse-phase protein array data revealed the potential antitumor mechanisms of THIO. Four patient-derived xenografts (PDX), two patient-derived organoids (PDO), and two xenografts of human glioma cell lines were used to further investigate the therapeutic efficacy of THIO. RESULTS: THIO was effective in the majority of human and mouse glioma cell lines with no obvious toxicity against normal astrocytes. THIO as a monotherapy demonstrated efficacy in three glioma cell lines that had acquired resistance to temozolomide. In addition, THIO showed efficacy in four human glioma cell lines grown as neurospheres by inducing apoptotic cell death. Mechanistically, THIO induced telomeric DNA damage not only in glioma cell lines but also in PDX tumor specimens. Integrated computational analyses of transcriptomic and proteomic data indicated that THIO significantly inhibited cell invasion, stem cell, and proliferation pathways while triggering DNA damage and apoptosis. Importantly, THIO significantly decreased tumor proliferation in two PDO models and reduced the tumor size of a glioblastoma xenograft and a PDX model. CONCLUSIONS: The current study established the therapeutic role of THIO in primary and recurrent gliomas and revealed the acute induction of telomeric DNA damage as a primary antitumor mechanism of THIO in gliomas.

Phase Ib Dose Expansion and Translational Analyses of Olaparib in Combination with Capivasertib in Recurrent Endometrial, Triple-Negative Breast, and Ovarian Cancer.

PURPOSE: On the basis of strong preclinical rationale, we sought to confirm recommended phase II dose (RP2D) for olaparib, a PARP inhibitor, combined with the AKT inhibitor capivasertib and assess molecular markers of response and resistance. PATIENTS AND METHODS: We performed a safety lead-in followed by expansion in endometrial, triple-negative breast, ovarian, fallopian tube, or peritoneal cancer. Olaparib 300 mg orally twice daily and capivasertib orally twice daily on a 4-day on 3-day off schedule was evaluated. Two dose levels (DL) of capivasertib were planned: 400 mg (DL1) and 320 mg (DL-1). Patients underwent biopsies at baseline and 28 days. RESULTS: A total of 38 patients were enrolled. Seven (18%) had germline BRCA1/2 mutations. The first 2 patients on DL1 experienced dose-limiting toxicities (DLT) of diarrhea and vomiting. No DLTs were observed on DL-1 (n = 6); therefore, DL1 was reexplored (n = 6) with no DLTs, confirming DL1 as RP2D. Most common treatment-related grade 3/4 adverse events were anemia (23.7%) and leukopenia (10.5%). Of 32 evaluable subjects, 6 (19%) had partial response (PR); PR rate was 44.4% in endometrial cancer. Seven (22%) additional patients had stable disease greater than 4 months. Tumor analysis demonstrated strong correlations between response and immune activity, cell-cycle alterations, and DNA damage response. Therapy resistance was associated with receptor tyrosine kinase and RAS-MAPK pathway activity, metabolism, and epigenetics. CONCLUSIONS: The combination of olaparib and capivasertib is associated to no serious adverse events and demonstrates durable activity in ovarian, endometrial, and breast cancers, with promising responses in endometrial cancer. Importantly, tumor samples acquired pre- and on-therapy can help predict patient benefit.

Targeting mTOR signaling overcomes acquired resistance to combined BRAF and MEK inhibition in BRAF-mutant melanoma.

Targeting MAPK pathway using a combination of BRAF and MEK inhibitors is an efficient strategy to treat melanoma harboring BRAF-mutation. The development of acquired resistance is inevitable due to the signaling pathway rewiring. Combining western blotting, immunohistochemistry, and reverse phase protein array (RPPA), we aim to understanding the role of the mTORC1 signaling pathway, a center node of intracellular signaling network, in mediating drug resistance of BRAF-mutant melanoma to the combination of BRAF inhibitor (BRAFi) and MEK inhibitor (MEKi) therapy. The mTORC1 signaling pathway is initially suppressed by BRAFi and MEKi combination in melanoma but rebounds overtime after tumors acquire resistance to the combination therapy (CR) as assayed in cultured cells and PDX models. In vitro experiments showed that a subset of CR melanoma cells was sensitive to mTORC1 inhibition. The mTOR inhibitors, rapamycin and NVP-BEZ235, induced cell cycle arrest and apoptosis in CR cell lines. As a proof-of-principle, we demonstrated that rapamycin and NVP-BEZ235 treatment reduced tumor growth in CR xenograft models. Mechanistically, AKT or ERK contributes to the activation of mTORC1 in CR cells, depending on PTEN status of these cells. Our study reveals that mTOR activation is essential for drug resistance of melanoma to MAPK inhibitors, and provides insight into the rewiring of the signaling networks in CR melanoma.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

Systems approach to rational combination therapy: PARP inhibitors.

Poly (ADP-ribose) polymerase inhibitors (PARPi) have demonstrated activity across a broad spectrum of molecular backgrounds and tumor types, with the greatest activity observed in patients with aberrations in the homologous recombination DNA damage repair pathway. Despite remarkable responses in a subset of patients, the response is usually modest and transient due to the almost inevitable emergence of resistance. Tumors develop resistance through rapid adaptation to the effects of PARPi as well as by generation or selection of genomic aberration. Although adaptive responses results in drug resistance, it also induces therapeutic vulnerabilities that could be exploited with rational combination therapies. To fulfill this role, we established the combinatorial adaptive response therapy (CART) platform by performing reverse-phase protein arrays to characterize adaptive responses, and develop rational combination therapies. Our series of studies strongly support the efficacy of this strategy, wherein targeting the emerging adaptive responses to PARPi with MEK/ERK inhibitors, WEE1/ATR inhibition (inhibitors of S-phase and G2 DNA damage checkpoint), and PI3K/AKT/mTOR inhibition, and showed promising anti-tumor activity in various preclinical models. Importantly, this approach has been proven highly efficient, and several combinational therapies developed from the CART platform are being evaluated in ongoing clinical trials (NCT03801369, NCT03586661, NCT03162627, NCT03544125, NCT02659241, NCT02208375, NCT02316834, and NCT03637491).

Using Reverse Phase Protein Array (RPPA) to Identify and Target Adaptive Resistance.

Tumor cells and the tumor ecosystem rapidly evolve in response to therapy. This tumor evolution results in the rapid emergence of drug resistance that limits the magnitude and duration of response to therapy including chemotherapy, targeted therapy, and immunotherapy. Thus, there is an urgent need to understand and interdict tumor evolution to improve patient benefit to therapy. Reverse phase protein array (RPPA) provides a powerful tool to evaluate and develop approaches to target the processes underlying one form of tumor evolution: adaptive evolution. Tumor cells and the tumor microenvironment rapidly evolve through rewiring of protein networks to bypass the effects of therapy. In this review, we present the concepts underlying adaptive resistance and use of RPPA in understanding resistance mechanisms and identification of effective drug combinations. We further demonstrate that this novel information is resulting in biomarker-driven trials aimed at targeting adaptive resistance and improving patient outcomes.

Correction to: FAK-ERK activation in cell/matrix adhesion induced by the loss of apolipoprotein E stimulates the malignant progression of ovarian cancer.

In the original publication of this manuscript [1], Fig. 5E lower panel was incorrect due to an error in the preparation of these figures for publication. It was noticed that in the lower panel of Fig. 5E, one mouse image of ApoE-/- + PBS group (upper) was a photograph coming from ApoE-/- + BAPN pre-treatment group (lower). The corrected figure appears below. We apologize for any confusion this may have caused.

Proteomics advances for precision therapy in ovarian cancer.

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior. Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with 'ovarian cancer' including 'proteomics', 'proteogenomic', 'reverse-phase protein array', 'mass spectrometry', and 'adaptive response', were used to search PubMed. Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.