The Use of Oncology Electronic Health Record Databases to Assess the Effectiveness of Breast Cancer Treatment

Merola, David Harvard University ProQuest Dissertations & Theses 2022 해외박사(DDOD)

Background: Non-experimental studies using large healthcare databases may be well-suited for addressing relevant questions in clinical oncology that pertain to the safety and effectiveness of medications. They complement randomized trials by including frail and complex patients seen in routine care that reflect real-world practice patterns and treatment adherence. Historically, pharmacoepidemiology research in the oncology setting has been limited, mainly due to poor capture of important confounding factors in real-world data sources (e.g., tumor grade, histology, and location, laboratory values, biomarkers, and performance status). However, more recently, quality and availability of secondary data in oncology have been emerging in specialized electronic health record (EHR) systems. These longitudinal databases are derived from several major sources of clinical information: 1) Physician medication ordering systems, 2) Physician notes from outpatient oncology encounters, 3) Molecular diagnostics, 4) Structured fields within the health record. Collectively, such data sources permit ascertainment of patients’ demographics, cancer types, treatment history, and an array of confounders and health outcomes necessary for comparative effectiveness studies of oncology drugs. Despite these advancements, the use of oncology EHR databases still poses many challenges that stem from a lack of linkage to alternative data sources, such as claims or high-quality tumor registries. This results in poor capture of out-of-network encounters, medical procedures, or inpatient encounters, as well as missing data. Consequently, it is unknown whether these challenges can be overcome with currently available epidemiological and statistical methods, and ultimately if these data are suitable for clinical investigations.The objectives of this body of work are to: 1) explore the utility of specialty oncology EHR databases in comparative effectiveness research; 2) build a framework that will support drawing causal conclusions from EHR-based studies in the oncology setting in light of the limitations of EHRs; and 3) identify and implement markers for data quality and study validity that can be used to assess confidence in findings. To achieve these objectives, two comparative effectiveness studies of first-line treatments for advanced breast cancer were conducted and calibrated against randomized clinical trials—the PALOMA-2 trial and the PARSIFAL trial. Additionally, an algorithm was constructed to predict completeness in an EHR-based oncology cohort, which was subsequently implemented in the two comparative effectiveness studies as a sensitivity analysis. In particular, effect estimates in the non-randomized studies were calculated among subjects with increasingly higher levels of predicted data completeness to see if the estimates converged to the randomized trial estimates. In this way, predicted completeness was assessed as a potential tool to improve study validity.Methods: To construct the prediction algorithm for data completeness, a Medicare-linked EHR database derived from two academic medical centers in Massachusetts was used. This linked database was constructed from many sources of clinical information; namely, healthcare claims (inpatient, outpatient, and pharmacy), physician drug orders, unstructured notes, and billing codes from medical procedures and inpatient or outpatient provider encounters. This permitted ascertainment of patient demographics, vitals, height and weight, medical procedures, medications, timing of provider encounters, and diagnoses, which were used to create candidate predictors of data completeness. The study population consisted of subjects that had a year of continuous enrollment in Medicare, were at least 65 years old, and had one or more outpatient oncology encounter in the EHR system. Data completeness was quantified by the “continuity ratio,” defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. Least absolute shrinkage and selection operator (LASSO) regression was used to select candidate predictors, which were regressed on continuity ratio. The performance of the final model was assessed using the coefficient of determination and Spearman’s correlation of predicted vs. observed EHR-continuity. We quantified misclassification of several comorbidities and medications within deciles of continuity ratio by calculating the ratio and standardized difference of the proportion of subjects classified as having each covariate when using outpatient EHR data alone vs. outpatient EHR data and claims.For the first comparative effectiveness study, an oncology EHR database derived from outpatient oncology practices within the US Oncology Network was used to estimate the rate of time-to-next-treatment (TTNT) in palbociclib-letrozole users versus letrozole-only users. TTNT was chosen as an endpoint because it was well-observed in the EHR database and appeared to serve as a meaningful surrogate for treatment effectiveness in the PALOMA-2 trial. All eligibility criteria, treatments, and outcome variables were defined to mimic the trial as closely as possible. Patients with evidence of a breast cancer subtype inconsistent with the PALOMA-2 study population (i.e., hormone-negative, HER-2 positive) were excluded. To address missing data, 50 complete datasets were constructed using multiple imputation by chained equations. In each of the imputed datasets, a Cox proportional hazards model was fit to estimate the hazard ratio of TTNT in an intention-to-treat analysis analogous to the trial. All 50 estimates were subsequently pooled.In the second comparative effectiveness study, a similar approach was undertaken. We used the same longitudinal EHR data from outpatient oncology practices across the US to emulate the PARSIFAL trial in its treatments and selection criteria as closely as possible. Multiple imputation was employed to account for missing data in patient characteristics. Baseline characteristics were compared and hazard ratios with 95% confidence intervals for overall survival were estimated fitting a multivariable proportional hazards model. Findings in both comparative effectiveness studies were compared to their respective RCTs result with qualitative assessment and standardized difference estimates.Results: In the PALOMA-2 emulation study, there were 3,836 study-eligible advanced breast cancer subjects. The hazard ratio for TTNT in the observational study (HR: 0.62; 95% CI: 0.56-0.68) was closely aligned with that of the randomized trial (HR: 0.64; 95% CI: 0.52-0.78) (Standardized Difference = -0.05). In the PARSIFAL trial emulation, 1,886 subjects were selected into the study cohort following application of all eligibility criteria. Although the 3-year survival was meaningfully lower in clinical practice (59%) compared to the RCT (78%), the relative effect size was HR=1.07 (95% CI: 0.86 – 1.35), similar to the RCT (HR=1.00; 0.68 – 1.48, Standardized Difference = 0.04). Restriction of the study cohort by increasing levels of continuity ratio did not appreciably influence effect estimates in the PALOMA-2 trial emulation, but shifted the effect estimate of the PARSIFAL trial emulation away from the RCT estimate with wider confidence intervals.Conclusion: This body of work calls for more emulations using a principled approach and methods for addressing the various threats to validity that can arise from the use of oncology EHR databases. Likewise, agreed-upon reporting standards can facilitate summarization of global efforts in advancing the use of RWD in clinical oncology. In the context of comparative effectiveness studies of oncology drugs, confounding may not be the most critical issue given the current data density in oncology EHR systems. Rather, it may be that more complete data will be needed for specific outcomes and possibly biomarkers. Overall, the field of real-world evidence in oncology is developing in a very positive direction as we are applying causal inference methods and as data sources continue to evolve and become richer in data granularity and continuity.

Bioengineering targeted nanodrugs for hematologic malignancies: An innovation in pediatric oncology

Krishnan, Vinu University of Delaware 2015 해외박사(DDOD)

Chemotherapy for pediatric cancers employs combinations of highly toxic drugs. This has achieved 5-year survival rates exceeding 90% in children treated for leukemia -- the most prominent form of pediatric cancer. However, delayed onset of harmful side effects in more than 60% of survivors result in death or low quality of life post therapy. This is primarily due to the non-specific effect of drugs on healthy dividing cells in a growing child. Nanomedicine has advanced tremendously to improve adult cancer therapy, but as yet has had minimal impact in pediatric oncology. There is a pressing need for innovative therapeutic strategies that can reduce life-threatening side effects caused by conventional chemotherapy in the clinic. Targeting chemotherapeutic agents specifically to leukemia cells may alleviate treatment-related toxicity in children. The research objective of this dissertation is to bioengineer and advance preclinically a novel nanotherapeutic approach that can specifically target and deliver drugs into leukemic cells. Dexamethasone (Dex) is one of the most commonly used chemotherapeutic drugs in treating pediatric leukemia. For the first part in this study, we encapsulated Dex in polymeric NPs and validated its anti-leukemic potential in vitro and in vivo. NPs with an average diameter of 110 nm were assembled from an amphiphilic block copolymer of poly(ethylene glycol) (PEG) and poly-caprolactone (PCL) bearing pendant cyclic ketals (ECT2). The blank NPs were nontoxic to cultured cells in vitro and to mice in vivo. Encapsulation of Dex into the NPs (Dex-NP) did not compromise the bioactivity of the drug. Dex-NPs induced glucocorticoid phosphorylation and showed cytotoxicity similar to free drug when treated with leukemic cells. Studies using NPs labeled with fluorescent dyes revealed leukemic cell surface binding and internalization. In vivo biodistribution studies showed NP accumulation in the liver and spleen with subsequent clearance of particles with time. In a preclinical model of leukemia, Dex-NPs significantly improved the quality of life and survival of mice compared to the group treated with free Dex. In the second section, we demonstrate, that doxorubicin (DOX, an anthracycline commonly used in pediatric leukemia therapy) when encapsulated within 80 nm sized NPs and modified with targeting ligands against CD19 (a B-lymbhoblast antigen, CD19-DOX-NPs) can be delivered in a CD19-specific manner to leukemic cells. The CD19-DOX-NPs were internalized via receptor-mediated endocytosis and imparted cytotoxicity in a CD19-dependent manner in CD19 positive (CD19+) leukemic cells. Leukemic mice treated with CD19-DOX-NPs survived significantly longer and manifested a higher degree of agility indicating reduced apparent systemic toxicity during treatment compared to mice treated with free DOX. This study for the first time shows the efficacy of polymeric NPs to target and deliver chemotherapeutic drugs in pediatric oncology and suggests that targeted nanotherapy can potentially improve the therapeutic efficacy of conventional chemotherapy and reduce treatment-related side effects in children.

Alternative medicine in childhood cancer: Challenges of vernacular health perspectives to biomedicine

Lambrinidou, Yanna University of Pennsylvania 2006 해외박사(DDOD)

The conventional treatment of pediatric malignancies is hailed as one of oncology's greatest successes. Yet surveys report that a significant percentage---even the majority---of families who seek pediatric cancer care utilize alternative therapies as well. This phenomenon has perplexed many members of the medical community. Some have attributed it to parents' psychological vulnerability or diminished reasoning capacity and have advocated public education about the dangers of "unproven" treatments. Others have called for cautious tolerance toward those alternative therapies that seem harmless. Common among all responses is a commitment to child protection. Encoded in bioethics and the law, this commitment presumes the superiority of conventional cancer care over all other forms of healing and requires the reining in of families who stray from prescribed treatments. This dissertation employs experience-centered ethnography to highlight vernacular health perspectives on childhood cancer. It demonstrates that at the center of parental treatment decisions lie views on concepts such as "health," "safety," "effectiveness," and "acceptable risk" that depart from biomedical definitions of the same terms. Situating those views in historical contexts that reveal medicocentric ideologies in oncology, health law, and bioethics, it demonstrates that the differences between biomedical and vernacular worldviews stem more from divergent ontologies and epistemologies than from "expert" and "lay" differences in the comprehension of scientific facts. In light of the fact that conventional cancer care guarantees neither a cure nor a life free of serious side effects, this dissertation suggests that in its effort to protect children with cancer, the medical establishment may at times compromise their health and even harm them. At the center of this paradox lies a paternalistic health policy that amplifies the voice of medicine while drowning out vernacular perspectives. In pediatric cancer, this policy can deprive children of treatments that are seemingly less toxic than standard interventions and potentially of significant therapeutic value. The dissertation concludes with a call for a trust-building health policy that inserts vernacular health perspectives into biomedical, legal, and bioethical discourses. It argues that such a policy would generate improved health outcomes for children with cancer and increased satisfaction for families and physicians alike.

Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy

Braman, Nathaniel Case Western Reserve University ProQuest Dissertat 2020 해외박사(DDOD)

As the arsenal of therapeutic strategies in the fight against cancer grows, so too does the need for predictive biomarkers that can precisely guide their use in order to match patients with their optimal personalized treatment plan. Currently, clinicians often have little recourse but to initiate treatment and monitor a tumor for signs of response or progression, which exposes non-responsive patients to overtreatment, harmful side effects, and windows of ineffective therapy that increase a patient’s risk of progression or metastasis. Thus, there is an urgent need for new sources of predictive biomarkers to help more effectively plan personalized treatment strategies. Radiological images acquired before treatment may contain previously untapped predictive information that can be quantified in the form of computational imaging biomarkers. The vast majority of existing computational imaging biomarkers provides analysis limited to the tumor region itself. However, the tumor environment contains critical biological information pertinent to tumor progression and treatment outcome, such as tumor-associated vascularization and immune response. This dissertation focuses on the development of new, biologically-inspired computational imaging biomarkers targeting the tumor environment for the prediction of response to a wide range of chemotherapeutic and targeted treatment strategies in oncology. First, we explore measurements of textural heterogeneity within the tumor and surrounding peritumoral environment, and demonstrate the ability to predict therapeutic response and tumor biology to neoadjuvant chemotherapy in primary and targeted therapy in primary and metastatic breast cancer. Second, we introduce morphologic techniques for the quantification of the twistedness and organization of the tumor-associated vasculature, and demonstrate their association with response and survival following four different therapeutic strategies in breast cancer MRI and non-small cell lung cancer CT. Third, we present novel deep learning strategies involving the training of specialized convolutional neural networks tailored to the prediction of response to chemotherapy and targeted therapy from MRI. Finally, we present a novel methodology to combine the computational imaging biomarkers across both the tumor and peritumoral environment.

High Energy Gamma Detection for Minimally Invasive Surgery

Chapman, Gregg J The Ohio State University ProQuest Dissertations & 2017 해외박사(DDOD)

Intraoperative detection of radio-labeled cancer has become a standard of care for some forms of cancer surgery. Most commercially available gamma detection probes are designed for use with low energy radioisotopes. Many new radiotracers exhibit positron emission which ultimately decays into two 511 kilo-electron volts (KeV) high energy gamma emissions. Gamma detection probes capable of capturing this energy require heavy side shielding to block off-axis radiation, making them both large and cumbersome for intraoperative use. Moreover, minimally invasive surgical procedures, performed either laparoscopically or robotically, are rapidly replacing open procedures in many areas of surgical oncology. To detect high energy radioisotopes with a gamma detection probe capable of being introduced into the surgical field laparoscopically, a significant change to the approach of intraoperative gamma detection is required. Gamma detection probes must be re-designed with both increased sensitivity at high energy, and an alternative to the heavy metal shielding. The necessity for side shielding can be eliminated by using two detectors in combination with software to limit the field of view. To achieve increased sensitivity, the detection system can be configured to detect a broader energy range that includes gamma counts from Compton scattered radiation. Compton scattered radiation is the result of incomplete photoelectric absorption within the detection crystal. In currently marketed designs, it is excluded from the accumulation of gamma counts because it reduces the spatial resolution of the probe. A third methodology is required to recover this loss of spatial resolution associated with the expanded energy range. A statistical basis for probe positivity can be used to improve the spatial accuracy of the radiation source measurement.This research investigates the viability of applying these three methodologies to reduce the diameter of gamma radiation probes while simultaneously increasing the sensitivity at an energy of 511 KeV. A positive outcome defines the parameters for a subsequent implementation of laparoscopic and robotic probes to be used for the detection of positron emitting radionuclides. It is evident from the study that a detector pair can limit the field of view without the use of side shielding. When the energy range is expanded to include Compton scattered radiation, probe sensitivity is increased by two orders of magnitude. A statistical criterion for probe positivity recovers the loss of spatial resolution associated with the use of a wider energy acceptance range. The statistical criterion is also capable of differentiating a radiation source from background at tumor-to-background ratios as low as 1.1-to-1 if the gamma counts are sufficiently high. The data also suggests that the depth of the radiation source may be calculated using the count rates from the detector pair, under limited conditions. However, further investigation is required.Surface mapping of the radioactivity emitted from phantom models demonstrates that the ratio of two detector counts is a more sensitive indicator of spatial differences in radioactivity compared to count rates from a single detector. This finding suggests that a new criterion for probe positivity based on count rate ratios may improve localization of radio-labeled tumors.

Induction of T cell immunity overcomes resistance to PD-1 and CTLA-4 blockade and improves survival in pancreatic cancer

Winograd, Rafael University of Pennsylvania 2015 해외박사(DDOD)

Disabling the function of immune checkpoint molecules can unlock T cell immunity against cancer, yet despite remarkable clinical success with monoclonal antibodies (mAb) that block PD-1 or CTLA-4 resistance remains common and essentially unexplained. Certain tumors, especially pancreatic carcinoma, are fully refractory to these antibodies. As reported in this thesis, I used a genetically engineered mouse model of pancreatic carcinoma in which spontaneous immunity is minimal, and found that PD-L1 is prominent in the tumor microenvironment, a phenotype confirmed in patients. Tumor infiltrating T cells express PD-1 even more prominently than T cells in a classical model of chronic infection, in which alphaPD-1 mAb mediates clinical benefit. Despite this striking expression of PD-1 and PD-L1 in the pancreatic tumor microenvironment, treatment with anti-PD-1 mAb, with or without anti-CTLA-4 mAb, fails in well-established tumors, recapitulating clinical results. Agonist anti-CD40 mAb with chemotherapy, deployed as a vaccine, induces T cell immunity and reverses the complete resistance of pancreatic tumors to alphaPD-1 and alphaCTLA-4. The combination of alphaCD40/chemotherapy plus alphaPD-1 and/or alphaCTLA-4 induces regression of subcutaneous tumors, improves overall survival, and confers curative protection from multiple rechallenges, consistent with immune memory not otherwise achievable. Combinatorial treatment nearly doubles survival of mice with spontaneous pancreatic cancers, revealing a clinical opportunity. These findings suggest that in non immunogenic tumors, epitomized by pancreatic carcinoma, baseline refractoriness to checkpoint inhibitors may be rescued by the priming of a T cell response with an antitumor vaccine. These studies indicate that understanding the immunobiology of differing tumor types may improve the ability to rationally design combinatorial immunotherapies in oncology.

Dissecting cancer using computational pathway-analysis

MacNeil, Shelley M The University of Utah ProQuest Dissertations & Th 2017 해외박사(DDOD)

Cancer is extremely challenging to treat as every patient responds differently to treatments, depending on the specific molecular aberrations and deregulated signaling pathways driving their tumors. To address this heterogeneity and improve patient outcomes, therapies targeting specific pathways have been developed. The use of computational pathway analysis tools and genomic data can help guide the use of targeted therapies by assessing which pathways are deregulated in patient subpopulations and individual tumors. However, most pathway analysis tools do not account for complex interactions inherent to signaling pathways, and are not capable of integrating different types of genomic data (multiomic data). To address these limitations, this dissertation focuses on developing user-friendly multiomic gene set analysis tools, and utilizing bioinformatics tools to measure pathway activation for multiple pathways simultaneously in cancer. Chapter 2 first describes the need for genomics and pathway-based analyses in cancer using the commonly aberrant RAS pathway as an example. Chapter 3 utilizes pathway-based gene expression signatures and the pathway analysis toolkit ASSIGN to interrogate pathways from the growth factor receptor network (GFRN) in breast cancer. Two discrete phenotypes, which correlated with mechanisms of apoptosis and drug response, were characterized from GFRN activity. These phenotypes have the potential to pinpoint more effective breast cancer treatments. Chapter 4 describes the development of Gene Set Omic Analysis (GSOA), a novel gene set analysis tool which uses machine learning to identify pathway differences between two given biologicalconditions from multiomic data. GSOA demonstrated its capacity to identify pathways known to play a role in various cancers, and improves upon other methods because of its ability to decipher complex multigene and multiomic patterns. Chapter 5 describes GSOA-shiny, a novel web application for GSOA, which provides biologists with lack of bioinformatics experience access to multiomic gene set analysis from an easy-to-use interface. Overall, this dissertation presents novel breast cancer phenotypes with clinical implications, provides the research community with gene expression signatures for GFRN components, and presents an innovative method and web application for gene set analysis--all contributing to furthering the field of personalized oncology.

Investigations into the Structure and Function of Type I Polyketide Synthases

Koch, Aaron A ProQuest Dissertations & Theses University of Mich 2017 해외박사(DDOD)

The polyketide (PK) class of natural products constitutes an abundant array of secondary metabolites produced in microorganisms, many of which possess potential medicinal value, especially in the area of oncology. Polyketides are assembled biosynthetically via the megaenzymes polyketide synthases (PKSs) through an assembly line process of stepwise condensations of simple malonic acid building blocks derived from primary metabolism. Despite the usefulness of natural products in medicine, the development of polyketide natural products into new drugs is often hindered by their suboptimal pharmacological properties, highlighting the need for their modification by medicinal chemistry. However, low natural abundance and high structural complexity often necessitates lengthy and expensive synthetic routes to natural product analogs, thus impeding their clinical development. A promising method for expanding the chemical diversity within polyketide natural products is PKS bioengineering, whereby natural product analogs are generated by engineering new functionality into the enzymes responsible for their production instead of through synthetic derivatization. While notable successes in PKS engineering have been achieved, many attempts result in decreased product yields or fail to produce the predicted molecules entirely. The studies in this thesis focus on investigating the structural and mechanistic parameters that govern PKS catalysis in order to increase the potential of harnessing these enzymes as biocatalysts for the production of new polyketide analogs. First, a series of engineered PKS modules was generated by combining modules from the pikromycin, erythromycin, and juvenimycin biosynthetic pathways with non-native TE domains and analyzed for substrate flexibility in vitro. The results from this study implicated the TE domain as the dominant catalytic bottleneck in the full-module processing of unnatural substrates. We next focused our investigations on probing the TE directly as an excised domain, subsequently confirming the previously observed catalytic bottleneck. Mutational analysis of the Pik TE domain resulted in an engineered variant (S148C) with improved substrate flexibility and catalytic efficiency, which eliminated the aforementioned bottleneck and allowed for the production of diastereomeric macrolactone analogs. Finally, we performed molecular dynamics (MD) simulations coupled with quantum mechanical (QM) calculations of the native and engineered TE domains to provide a mechanistic rational for our experimental observations. Taken together, the results herein provide further insight into the catalytic and mechanistic parameters that govern the productive functioning of engineered PKSs. Our identification of the thioesterase domain as a key catalytic bottleneck in the processing of unnatural substrates builds the groundwork for future engineering of PKS TE domains in order to generate more flexible catalysts for the production of novel natural product analogs.

Mitigating Myeloid-Driven Pathways of Immune Suppression to Enhance Cancer Immunotherapy Efficacy

Colligan, Sean Henry State University of New York at Buffalo ProQuest D 2022 해외박사(DDOD)

The discovery and application of immune checkpoint inhibitors (ICIs) has revolutionized the therapeutic landscape in oncology. This discovery culminated from the elegant body-of-work of Drs. James Allison and Tasuku Honjo, who earned the Nobel Prize in Physiology or Medicine in 2018. Their work underscored the importance of the inhibitory signals, CTLA-4 and PD-1, in the mechanism of T cell activation and effector function and how such regulatory interactions can be exploited by cancer to suppress antitumor T cell-immunity. Based on such a fundamental understanding of T cell-tumor biology, ICIs were developed for clinical use to block those interactions and have led to remarkable improvements in clinical outcomes across diverse cancer types. However, such clinical trials further revealed that these therapies are only effective in subsets of patients. It is now thought that ICI efficacy can be hampered by innate and acquired mechanisms of resistance, highlighting a critical need for the development of novel combination approaches to improve their activity. One prominent mechanism is tumor-driven immune suppression, and one key cell type capable of mediating such potent immune suppression is the myeloid-derived suppressor cell (MDSC). MDSCs comprise heterogenous populations of immature myeloid cells that are largely granulocytic, are induced in response to diverse stromal- or tumor-derived factors and originate from bone marrow myeloid progenitors. A fundamental gap in MDSC biology is the lack of clinically translatable therapeutic strategies that target their production (‘biogenesis’). Therefore, we tested the overarching hypothesis that targeting the biogenesis of MDSCs to facilitate their differentiation and maturation effectively reduces MDSC burden, reverses immune suppression, and enhances the efficacy of ICIs.In this dissertation, we present a novel approach for suppressing MDSC biogenesis in the bone marrow. This approach is based on recent advances in our understanding of how an unexpected class of compounds, known as dihydroorotate dehydrogenase (DHODH) inhibitors, restores terminal differentiation/maturation of leukemic myeloid progenitors, which share a similar developmental origin with MDSCs. Altogether, we propose that this strategy is conceptually and therapeutically innovative in that: (i) it targets MDSC biogenesis in the bone marrow to mitigate MDSC burden and bolsters immunotherapy efficacy; (ii) it repurposes drugs currently in clinical trials of acute myeloid leukemia (AML), such as the DHODH inhibitor brequinar (BRQ); and (iii) it may inform the design of novel combination immunotherapy platforms in patients wherein MDSCs play important roles in the neoplastic process. We tested our concept using preclinical models of triple-negative breast cancer (TNBC), a neoplastic disease associated with a robust granulocytic MDSC response. Here, report the following major new findings: 1) BRQ plus the ICI agent, anti-PD-1 antibody, elicited robust antitumor activity greater than single-agent treatment in two tested TNBC models; 2) BRQ plus anti-PD-1 antibody reduced the extent of spontaneous lung metastases, a common site for metastatic TNBC; 3) BRQ dampened the development of immune suppressive MDSCs from bone marrow myeloid progenitors accompanied by myeloid maturation, indicating that BRQ targeted MDSC points-of-origin to aid differentiation; 4) BRQ enhanced CD8+ T cell activation, as measured by changes in markers of activation, and that CD8+ T cells were required for therapeutic efficacy of the combination regimen based on in vivo CD8+ T cell depletion experiments; and 5) BRQ acted in the bone marrow to impair MDSC generation, based on comprehensive immunophenotyping and single-cell RNA sequencing analyses. We also validated key in vitro results using a second DHODH inhibitor and showed that BRQ drove myeloid differentiation/maturation in a human bone marrow culture system.Overall, our new findings advance the field by exploiting the concept of differentiation therapy in MDSC biology to improve the efficacy of immunotherapy, such as ICIs. Given the recent FDA approval for the use of ICIs in early or advanced TNBC, we believe our work is a significant advance in the field by demonstrating a novel and clinically feasible approach for potentially improving ICI efficacy or other forms of immunotherapy in cancer patients. Furthermore, since MDSCs accumulate in multiple types of malignancies, we believe our work is more broadly applicable to the field of tumor immunology and immunotherapy.

Hypermethylation of ANK2 on both Canine Mammary Tumors and Human Breast Cancer

요하네스 요세프스 스커보트 서울대학교 대학원 2021 국내석사

개의 유선암 (canine mammary gland tumor)은 사람 여성의 유방암과 같이 암컷 개에서 가장 흔히 발견되는 암이다. 개의 유선암과 사람의 유방암에 존재하는 여러 유사성 때문에 개의 유선암을 연구하는 것은 수의학에 국한된 것이 아니라 사람의 유방암을 이해하기 위한 비교의학적 측면에서 매우 중요하다. DNA 메틸화를 조직 및 액체생검에서 생체 표지자로 사용하는 시도는 많이 진행되고 있지만, 개의 유선암에 대한 연구는 매우 제한적으로 이루어지고 있다. 이 연구에서, 우리는 개의 유선암 조직의 메틸롬(Methylome) 분석을 통해 ANK2 및 EPAS 유전자의 인트론 영역에서 정상 조직과 다른 차등 메틸화 영역(Differentially methylated regions, DMGs)을 발견하였다. 또한, 이 두 지역의 차등 메틸화를 조직뿐만 아니라 환자유래 혈장에 존재하는 순환 유리 DNA (Cell free DNA, cfDNA)로부터 정량 할 수 있는 정량적 메틸화 특이적 PCR(quantitative methylation specific PCR, qMSP) 방법을 확립하였다. 이 방법을 통해 우리는 두 영역의 유선암 특이 과 메틸화를 추가된 조직 생검 시료를 통해 확인하였다. 나아가, ANK2인트론 영역은 조직 생검 시료뿐만 아니라 유선암 환자 유래 혈장에 존재하는 cfDNA에서도 유의한 메틸화를 보였다. 이 결과는 ANK2 및 EPAS의 인트론 영역의 메틸화가 유선암의 조직은 물론 액체생검을 위한 생체표지자로 사용될 수 있음을 의미한다. 흥미롭게도, 우리는 이러한 ANK2의 개유선암 특이 과메틸화는 비교 의학적인 측면에서 사람의 유방암에서도 과메틸화 되어있는 경향을 확인할 수 있었다. 이 연구 결과는 비교의학적 접근이 가지는 장점을 잘 보여주며, 이를 이용한 실제 임상적용을 위한 활용가능성을 높여준다. Canine Mammary Tumors (CMT) constitute the most common tumor types found in female dogs. Understanding this cancer through extensive research is important not only for clinical veterinary applications, but also in the scope of comparative oncology. The use of DNA methylation as a biomarker has been noted for numerous cancers in the form of both tissue and liquid biopsies, yet the study of methylation in CMT has been limited. By analyzing our canine Methyl-binding domain sequencing (MBD-seq) data, we identified intron regions of canine ANK2 and EPAS1 as differentially methylated regions (DMGs) in CMT. Subsequently, we established quantitative Methylation Specific PCR (qMSP) of ANK2 and EPAS1 to validate the target hypermethylation in CMT tissue, as well as cell free DNA (cfDNA) from CMT plasma. Both ANK2 and EPAS1 were hypermethylated in CMT and highlighted as potential tissue biomarkers in CMT. ANK2 additionally showed significant hypermethylation in the plasma cfDNA of CMT, indicating that it could be a potential liquid biopsy biomarker as well. A similar trend towards hypermethylation was indicated in HBC at a specific CpG of the ANK2 target on the orthologous human region, which validates the comparative approach using aberrant methylation in CMT.