Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC. / 2020-01-25

Development of tyrosine kinase inhibitors (TKIs) against oncogenic drivers has significantly improved survival of patients with oncogene-mutated non-small cell lung cancer (NSCLC). However, acquired resistance to TKIs emerges over time in essentially all patients who initially respond. Recent evidence suggests that drug-tolerant persister (DTP) cells, which survive and adapt to targeted therapies during an early phase of treatment, play an important role in the emergence of drug resistance. A previous study reported that cluster of differentiation 74 (CD74) expression is upregulated in epidermal growth factor receptor (EGFR)-mutated lung cancer after treatment with EGFR-TKIs and that CD74 can be one of the DTP cell markers. However, both the mechanism underlying CD74 expression and the role of CD74 in DTP cells remain unclear. In the current study, an attempt was made to identify the mechanism using cell culture systems and transgenic mouse models. The results confirmed CD74 upregulation at the messenger RNA (mRNA) level after treatments with TKIs in various oncogene-mutated cell lines, including those with EGFR mutations, ROS1 fusions, and ALK fusions. The class II transactivator (CIITA), upstream of CD74, and tumor necrosis factor (TNF)-α expression were induced by treatments with TKIs in tumor cells, leading to an increase in CD74 expression. In addition, the results showed that treatments with TKIs enhance the autocrine secretion of macrophage migration inhibitory factor (MIF), a ligand of CD74, from tumor cells. This implied that autocrine stimulation of CD74 signaling blocks apoptosis and causes emergence of DTP cells. To examine whether CD74 plays an important role in the emergence of resistance to TKIs in vivo, experiments were completed in which lung-specific EGFR-L858R-T790M transgenic mice were crossed with Cd74 knockout mice. The results showed that complete deletion of CD74 overcomes or delays resistance to TKIs. Taken together, the results of this study suggest that the MIF-CD74 axis can be a novel target to overcome resistance in driver-mutated NSCLC. / 2026-02-28T00:00:00Z

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24186号 / 医博第4880号 / 新制||医||1060(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 中山 健夫, 教授 平井 豊博, 教授 中本 裕士 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Robotic-assisted segmentectomy is a pulmonary resection procedure that is emerging as an alternative to lobectomy for the treatment of early-stage lung cancer tumours less than 2 cm in maximal diameter. Segmentectomy offers better lung function after surgery by only removing a few segments of the lobe that contain the tumour, and sparing remaining healthy lung tissue. As tumours are being more frequently detected in their early-stages, segmentectomy has gained considerable attention for its potential as a primary treatment option for suspected nodules less than 3 cm in maximal diameter. However, there is a reluctance in adopting segmentectomy due to technical challenges while performing the operation, and the lack of high-quality prospective data compared to lobectomy, which is the current standard of care. From a technical standpoint, segmentectomy is difficult to perform because the pulmonary lines that separate segments, or intersegmental planes, are invisible. This poses a challenge for the operating surgeon in determining where to resect the lung tissue to obtain adequate margin distance from the tumour. Near-infrared mapping (NIF) with indocyanine green dye (ICG) is a recent advancement in robotic-assisted segmentectomy that provides a complete delineation of the intersegmental plane. Previous work at our center has also shown that this technique was associated with an increase in the oncological margin distance compared to the surgeons’ initially estimated resection line. Given that segmentectomy is associated with a learning curve, we evaluated whether this was observed due to our early experience in robotic-assisted segmentectomy, and hypothesized that the added benefit of ICG would diminish as more cases were performed. In Chapter 2, we used a temporal analysis to monitor surgeon experience over time, and found that the clinical utility of NIF mapping diminished after approximately 42 cases with ICG, and the surgeon began to identify the location of the intersegmental plane more accurately and consistently without ICG injection since. The second barrier in the adoption of segmentectomy is the lack of high quality-prospective data. Current evidence pertaining to the effectiveness of segmentectomy in terms of cancer-related outcomes is inconclusive and difficult to generalize to the current lung cancer population. In Chapter 3, we performed a secondary analysis of a prospectively collected database of participants who underwent robotic-assisted segmentectomy or lobectomy for tumours less than 3 cm. The oncological efficacy of segmentectomy can be evaluated by the measuring the number of lymph node stations sampled intraoperatively and rates of nodal upstaging, and comparing these outcomes to pulmonary lobectomy. These are important surrogate outcomes that can be readily evaluated, and have been shown to predict overall survival after lung resection. We observed that these outcomes, including overall survival, were similar between patients who underwent segmentectomy and lobectomy for tumours less than 3 cm. While these findings were consistent for patients that underwent segmentectomy for tumours between 2 and 3 cm, recurrence-free survival was found to be significantly lower after segmentectomy compared to lobectomy. In conclusion, the clinical utility of near-infrared mapping diminishes over time, which is indicative of an improved ability to perform robotic-assisted segmentectomy as more cases were attempted. Second, adequate lymph node evaluation can be expected after segmentectomy, reducing the likelihood of missing positive lymph nodes. Although patients who underwent segmentectomy for tumours greater than 2 cm may be at a greater risk of experiencing recurrence compared to lobectomy, this population did not experience any reductions in overall survival. / Thesis / Master of Health Sciences (MSc)

<p> Using maps, correlation and multiple regression, an ecologic analysis was performed to examine the geographic distribution of cancer incidence in ontario with respect to selected ethnic, socio-economic and environmental characteristics for the 10 year period, 1976-1985. Two of the most common causes of cancer deaths, stomach and lung, were studied for each sex separately. The unit of analysis consisted of census divisions. The information used for the cancer were standardized incidence rates from the Ontario cancer Registry. The data for the ecologic variables was obtained from the 1981 Census of Canada. Two of the ecologic variables, education and income (low and median) were used to account for the effects of smoking. </p> <p> Correlation co-efficients were significant for both sites of cancer for males and females for % urban and population density revealing the possibility of a positive relationship with cancer incidence and environmental characteristics. Ethnicity was strongly related to male and female stomach cancer. </p> <p> Significant regression models were obtained for each of the cancer sites using a stepwise procedure with backward elimination. For each of the "best fit" equations, median income and education were included to control for smoking effects. Population density was significant in all equations at the 0.05 level. The percentage urban was significant for all except female stomach cancer. Manufacturing had a negative significant relationship for all cancer sites (male and female). </p> <p> Also included in the study were descriptive statistics and cancer maps to determine the strongest cancer distributions in Ontario. For each site, northern Ontario contained the highest rates. In southern Ontario, urban areas such as Hamilton-Wentworth, and Toronto-York had high rates for all cancers (except Hamilton-Wentworth for male lung cancer). </p> / Thesis / Bachelor of Arts (BA)

Image-guided radiation therapy (IGRT) provides accurate and precise tumour targeting. To ensure adequate coverage in IGRT, a planning target volume (PTV) margin is added around the target to account for treatment uncertainties. Treatment plans are designed to deliver a high percentage of the prescription dose to the PTV; thus, portions of healthy tissue are also subjected to high radiation dose. IGRT employs dedicated devices that enable visual assessment of some treatment uncertainties, such as variations in patient set-up. Safe and effective IGRT delivery requires adherence to disease site-specific protocols that describe process details such as imaging technique, alignment method, and corrective action levels. Protocol design is challenging since its effect on treatment accuracy is currently unknown. This thesis aims to understand the interplay between lung IGRT protocol parameters by developing a framework that quantifies geometrical accuracy. Deformable image registration was used to account for changes in target shape and size throughout treatment. Sufficient accuracy was considered when at least 99% of the target surface fell within the PTV. This analysis revealed that the clinical 10 mm PTV margin can be safely reduced by at least 2 mm in each direction. Evaluation of IGRT accuracy was extended to spinal cord alignment. Simulations were carried out with various matching strategies to correct for set-up error, including rotational off-sets. Inappropriate combinations of matching strategies and safety margins resulted in sub-optimal geometrical coverage. Various lung IGRT protocol options were recommended to optimize accuracy and workflow efficiency. For example, an 8 mm PTV margin can be used with spinal cord alignment, a 4 mm cord margin, and up to 5° of rotational error. A more aggressive protocol involved a 6 mm PTV margin with direct target alignment, a 5 mm cord margin, and a 4° rotational tolerance. / Thesis / Master of Science (MSc)

Background and aim: Lung cancer is the leading cause of cancer incidence and mortality in Canada. Population-based screening programs using low dose computed tomography are being more widely used. Screening reduces lung cancer mortality. It also introduces potential ethical issues that need to be elucidated to inform the ethical, equitable, and effective implementation of screening programs. This aim of this research was to begin developing an understanding of what the ethical issues are and how they are being, and should be, approached in health policy. Methods: Using empirical ethics inquiry, this research produced descriptive evidence via three independent studies: a systematic literature review and mixed methods integrative synthesis of public perspectives on screening benefits and harms, and two qualitative description studies about public and key informants’ ethical and social values on ethical issues in screening. Results: The major finding of this research was the preponderance of ethical issues located within health and social systems and structures, including equity of screening access, stigma against people who currently smoke commercial tobacco, commercialization of tobacco, and the need for increased investment in primary prevention of lung cancer. These ethical issues reflect the social, economic, and political determinants of lung cancer and the means available to reduce the burden of lung cancer in Canada, including but not limited to screening. In health policy, there was a lack of ethical frameworks or principles currently being used to address these ethical issues and the sometimes-conflicting perspectives found between the public and key informants. Discussion: Future empirical and normative research is needed to understand ethical and social values related to screening by populations with high lung cancer incidence and mortality, and to integrate empirical evidence with appropriate ethical theories to make recommendations for ethical, equitable, and effective population-based LDCT lung cancer screening policy in Canada. / Thesis / Candidate in Philosophy / Lung cancer is the top cause of cancer in Canada. An estimated 30,000 people were diagnosed with lung cancer and 20,700 people died from lung cancer in 2022. Screening is being more widely used to find and treat lung cancer in earlier stages. There are some ethical questions to consider, like how to ensure that screening programs are fair and effective. This research focused on understanding what the ethical issues are and how they could be solved in health policy. Perspectives on ethical issues were collected and analyzed from the public and lung cancer screening leaders. The two major ethical issues were fair access to screening and stigma against people who currently smoke commercial tobacco. There was a lack of ethical guidance to address these issues in health policy. Ethical concepts about justice and individual choice, and ethics research with key communities, may help navigate ethical issues in health policy.

Squamous cell carcinoma of the bronchus is the second most common and fatal subtype of lung cancer. In the process of squamous carcinogenesis, the normal bronchial epithelium undergoes a series of histologic transformations known as the metaplasia-dysplasia-carcinoma sequence. These intermediate histologic patterns are called premalignant lesions, and occur prior to the development of cancer. Compared to early stage cancer, survival following resection of premalignant lesions approaches 100%, highlighting the promise of lung cancer interception. However, because of our lack of understanding of the molecular events during squamous carcinogenesis, we are currently unable to predict which lesions will progress to cancer, and we do not have molecular targets for noninvasive treatment. The work in this thesis seeks to improve our understanding of the changes associated with grades of premalignant histology and progression at the level of single cells. I analyzed single cell RNA sequencing data from a cohort of 41 lesions from 26 patients, encompassing the normal-appearing bronchus, premalignant lesions, and early stage carcinoma. I described histology-associated changes in basal cells. Basal cells from low grade lesions expressed genes related to the maintenance of the normal epithelium, while basal cells from high grade lesions expressed genes related to the cell cycle and detoxification of the airway from smoking toxicants. Secondly, I identified a high grade lesion undergoing the epithelial-to-mesenchymal transition. These cells transitioned from a high grade basal cell state, lost their expression of basal cell markers, and expressed canonical EMT genes, including SPARC, FN1, and MMP2. Finally, I identified shifts in T cell subtypes and widespread expression of exhaustion markers PD-1, CTLA4, LAG3, and TIGIT co-occurring with high grade basal cells. Secondly, I leveraged our single cell data to identify gene modules associated with histology and progression in bulk RNA sequencing data. I identified a module of genes expressed in B and dendritic cells involved in antigen presentation through the MHC II pathway whose expression was decreased in progressive lesions. I also identified a module of stromal-expressed genes that were less expressed in progressive lesions, which had previously been unidentified. Associations between module expression and progression were validated in a second data set. This work improves our understanding of the signaling and interactions between cell types associated with histology and progression of premalignant lesions. These findings may be used to improve our prognostication and treatment of premalignant lesions.

A well-established method for treating lung cancer is curative-intent radiation therapy (RT). The most significant challenge for RT is to accurately target the lesion volume while avoiding the irradiation of surrounding healthy tissue. Currently at the Juravinski Cancer Centre (JCC), treatment plans for lung cancer patients are completed using fluorodeoxyglucose positron emission tomography (FDG-PET) and four-dimensional computed tomography (4DCT) images. There is no clear protocol, however, to compensate for respiratory motion in PET images and it is not known how lesion volumes generated from PET reflect the true volume. This project evaluated methods to optimize the use of PET images in the radiation treatment planning workflow and quantify the effects of respiratory motion. First, a 4D XCAT digital phantom was used to quantify respiratory motion and its effects on lesion displacement. A CTN physical phantom and 3D-printed irregularly shaped lesion were imaged to determine the accuracy of the PET EDGE automated segmentation algorithm (ASA). Lastly, rigid and deformable image registration techniques were used to propagate the diagnostic PET scan of the irregular lesion to the 4D planning CT. PET EDGE was used to generate target volumes which were then compared to internal target volumes (ITVs) generated from manual contouring of the 4DCT image alone. We found that lesion displacement due to respiratory motion can be adequately modeled using a moving platform set to oscillate 1 cm and 2 cm for normal and deep breathing, respectively. Optimal target delineation was found when diagnostic PET was propagated to the planning CT using rigid image registration for lesions that experienced 1 cm of oscillatory motion during imaging. In contrast, PET EDGE would overestimate volumes in static cases and underestimate volumes in instances of 2 cm dynamic motion meant to simulate deep breathing. / Thesis / Master of Science (MSc)

Background: Before making any treatment decisions for patients with non-small cell lung cancer (NSCLC), it is crucial to determine whether the cancer has spread to the mediastinal lymph nodes (LNs). The preferred method for mediastinal staging is Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA). However, EBUS-TBNA has been reported to generate inconclusive results as much as 40% of the time. Since this jeopardizes good patient care, there is near-universal consensus on the need to develop and study a novel method for LN staging. Recent research has shown that AI and deep learning are used to accurately interpret images with comparisons to clinicians in radiology, pathology, and cardiology. Additionally, EBUS-Elastography is a novel modality which could be used as an adjunct to EBUS-TBNA for LN staging. This technology uses impedance ultrasonography to measure tissue stiffness. Methods: There are three parts to this thesis. The first part involved the training, validating, and testing NeuralSeg, a deep neural network, to predict LN malignancy based on B-mode EBUS-TBNA images. The second part of this thesis involves EBUS-Elastography, defining the blue colour threshold and the optimal SAR cut-off value based on the blue threshold that most accurately distinguished benign and malignant LN. Finally, this thesis's third part involves validating part II's findings. Results: Part I resulted in an overall accuracy of 80.63% (76.93% to 83.97%), a sensitivity of 43.23% (35.30% to 51.41%), a specificity of 96.91% (94.54% to 98.45%), a positive predictive value of 85.90% (76.81% to 91.80%), a negative predictive value of 79.68% (77.34% to 81.83%), and an AUC of 0.701 (0.646 to 0.755). Part II Level 60 was chosen as the blue threshold with an AUC of 0.89 (95% CI: 0.77-1.00), and the optimal SAR cut off was found to be 0.4959 with a sensitivity of 92.30% (95% CI: 62.10% to 99.60%) and a specificity of 76.50% (95% CI: 49.80% to 92.20%). Using the blue threshold and SAR cut-off, the results of part III resulted in an overall accuracy of 70.59% (95% (CI) 63.50% to 77.01%), the sensitivity of 43.04% (CI: 31.94% to 54.67%), and a specificity of 90.74% (CI: 83.63% to 95.47%). Conclusion: It was observed that both AI and AI-powered EBUS-Elastography achieved high specificities on larger sample sizes, indicative that these methods may be helpful in identifying LN malignancy. However, due to the novelty of these technologies, more extensive multi-centre studies must be conducted before these processes can be standardized. / Thesis / Master of Health Sciences (MSc) / Non-Small Cell Lung Cancer (NSCLC) treatment decisions are made using vital information by performing biopsies to collect tissue from the lymph nodes near the lungs. The current method is called Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA), which involves a scope with a fine needle attached to it. This scope is led down the airway and guided by ultrasound to obtain the tissue needed to determine whether the lymph nodes have cancerous tissue. If the lymph nodes contain cancerous tissue, the patient may need chemotherapy; however, lung surgery may be the best treatment option if they do not. Many factors impact how successfully these tissue samples can be obtained, such as the skill and experience of the surgeon. These factors often lead to inconclusive results, making it difficult to make correct treatment decisions. Novel technologies such as Artificial Intelligence and Elastography are being used to diagnose lung cancer by interpreting images and providing information on tissue stiffness. We trained an Artificial Intelligence program to predict malignancy based on EBUS-TBNA images. Additionally, we trained the AI program to analyze Elastography images to aid us in understanding the relationship between the colour pattern of the elastography images and cancerous tissue. This thesis assesses how these novel technologies contribute to lung cancer diagnosis.