Multiple Cancer Subtypes Research Articles

P2 purinergic receptor expression and function in tumor-related immune cells.

P2 purinergic receptor expression is dysregulated in multiple cancer subtypes and is associated with worse outcomes. Studies identify roles for P2 purinergic receptors in tumor cells that drive disease aggressiveness. There is also sufficient evidence that P2 purinergic receptor expression within the tumor microenvironment (TME) is critical for disease initiation and progression. Immune cells constitute a significant component of the TME and display both tumorigenic and anti-tumorigenic potential. Studies pre-dating the investigation of P2 purinergic receptors in cancer identify P2 receptor expression on multiple immune cells including macrophages, neutrophils, T-cells, and dendritic cells; all of which are implicated in tumor initiation, tumor promotion, or response to treatment. Herein, we discuss P2 purinergic receptor expression and function in tumor-related immune cells. We provide a rationale for further investigations of P2 purinergic receptors within the TME to better define the mechanistic pathways of inflammation-mediate tumorigenesis and explore P2 purinergic receptors as potential targets for novel immunotherapeutic approaches.

Abstract C020: Nuclear encoded mitochondrial protein MAGMAS as a potential predictor of taxane sensitivity in advanced prostate cancer patients and differential diagnostic marker of tumor aggressiveness in men of African ancestry

Abstract Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death among men in the United States (U.S.). Strikingly, men of African ancestry (AA) are 1.7 times more likely to be diagnosed with PCa and more than twice as likely to die of PCa than men of European origin (EU). These disparities are driven by socioeconomic, lifestyle, environmental, and biological/genetic factors. Prostate-specific antigen screening is limited in identifying men with aggressive PCa and exposes men to increased harm with minimal reduction in mortality from PCa. Specifically, several studies report that men of AA are considered to have low-risk prostate cancer by clinical parameters, and those who enrolled in active surveillance programs progress to a higher Gleason-grade cancer and high-volume status than men of EA. Also, men of AA in low to moderate-risk groups appear to be at a higher risk for biochemical recurrence after curative-intent treatment. Therefore, there is a need to understand molecular mechanisms underlying PCa aggressiveness in men of AA that are translatable to the clinic as reliable biomarkers and for the development of novel therapeutic targets. Evidence supports that the mitochondria have a fundamental influence on all steps of oncogenesis, including malignant transformation, tumor progression, and treatment resistance. Regarding cancer health disparities, genes encoding mitochondrial complexes involved in oxidative phosphorylation are upregulated to a greater extent in tumor specimens from AA patients than those from EA patients, supporting the rationale for investigating the role of mitochondria in cancer health disparities. This study investigates Magmas, a nuclear-encoded mitochondrial protein, as a potential mitochondrial marker for PCa tumor aggressiveness and a therapeutic target for advanced PCa. Several observations support the rationale for this study: 1) Magmas overexpression in multiple aggressive cancer subtypes, including high-grade prostate, ovarian cancer, and malignant gliomas. 2) AA PCa cell line overexpresses Magmas similar to chemo-resistant PCa cells, whereas the EA cell line is shown to exhibit lower expression. 3) In human cells, overexpression of Magmas suppresses oxidative stress by regulating the reactive oxygen species (ROS) levels. 4) In chemo-resistant PCa cell lines, pharmacological inhibition of Magmas re-sensitized PCa cells to taxane treatment; and 5) non-cytotoxic pharmacological inhibition of Magmas in chemo-resistant PCa cells dramatically reduced clonogenicity potential. We will evaluate the hypothesis that AA PCa tissue will overexpress Magmas and that overexpression will predict tumor aggressiveness, and 2) that pharmacological inhibition of Magmas will overcome treatment resistance in advanced PCa models. The proposed exploratory study is highly relevant as it will reveal the biological basis for race-related differential expression of Magmas, providing critical insights into mitochondrial determinants that contribute to PCa mortality disparities. Citation Format: Alfonso Duran, Kristen Whitley, Krystal Santiago, Bhaskar Das, Carlos Casiano, Frankis Almaguel. Nuclear encoded mitochondrial protein MAGMAS as a potential predictor of taxane sensitivity in advanced prostate cancer patients and differential diagnostic marker of tumor aggressiveness in men of African ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C020.

Cardiovascular health and cancer mortality: evidence from US NHANES and UK Biobank cohort studies

BackgroundThe American Heart Association recently introduced a novel cardiovascular health (CVH) metric, Life's Essential 8 (LE8), for health promotion. However, the relationship between LE8 and cancer mortality risk remains uncertain.MethodsWe investigated 17,076 participants from US National Health and Nutrition Examination Survey (US NHANES) and 272,727 participants from UK Biobank, all free of cancer at baseline. The CVH score, based on LE8 metrics, incorporates four health behaviors (diet, physical activity, smoking, and sleep) and four health factors (body mass index, lipid, blood glucose, and blood pressure). Self-reported questionnaires assessed health behaviors. Primary outcomes were mortality rates for total cancer and its subtypes. The association between CVH score (continuous and categorical variable) and outcomes was examined using Cox model with adjustments. Cancer subtypes-related polygenic risk score (PRS) was constructed to evaluate its interactions with CVH on cancer death risk.ResultsOver 141,526 person-years in US NHANES, 424 cancer-related deaths occurred, and in UK Biobank, 8,872 cancer deaths were documented during 3,690,893 person-years. High CVH was associated with reduced overall cancer mortality compared to low CVH (HR 0.58, 95% CI 0.37–0.91 in US NHANES; 0.51, 0.46–0.57 in UK Biobank). Each one-standard deviation increase in CVH score was linked to a 19% decrease in cancer mortality (HR: 0.81; 95% CI: 0.73–0.91) in US NHANES and a 19% decrease (HR: 0.81; 95% CI: 0.79–0.83) in UK Biobank. Adhering to ideal CVH was linearly associated with decreased risks of death from lung, bladder, liver, kidney, esophageal, breast, colorectal, pancreatic, and gastric cancers in UK Biobank. Furthermore, integrating genetic data revealed individuals with low PRS and high CVH exhibited the lowest mortality from eight cancers (HRs ranged from 0.36 to 0.57) compared to those with high PRS and low CVH. No significant modification of the association between CVH and mortality risk for eight cancers by genetic predisposition was observed. Subgroup analyses showed a more pronounced protective association for overall cancer mortality among younger participants and those with lower socio-economic status.ConclusionsMaintaining optimal CVH is associated with a substantial reduction in the risk of overall cancer mortality. Adherence to ideal CVH correlates linearly with decreased mortality risk across multiple cancer subtypes. Individuals with both ideal CVH and high genetic predisposition demonstrated significant health benefits. These findings support adopting ideal CVH as an intervention strategy to mitigate cancer mortality risk and promote healthy aging.

MICIL: Multiple-Instance Class-Incremental Learning for skin cancer whole slide images

Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm’s effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.

Abstract 6351: MUC1*-ADCs are effective against heterogeneous solid tumor cancers

Abstract Purpose: To develop a MUC1*-ADC for the treatment of over 75% of solid tumor cancers. Methods: MUC1* (muk 1 star) is a growth factor receptor created after cleavage and release of the N-terminal portion of MUC1. The targeting antibody, huMNC2, has already demonstrated safety and cancer selectivity in a 1st-in-human CAR-T clinical trial for treatment of metastatic breast cancers. MNC2 is an IgG1 antibody that only recognizes the conformational epitope created when MUC1 is cleaved by specific tumor-associated enzymes. Confocal microscopy and pHlourin2 were employed to measure antibody internalization as a function of time. We conjugated MNC2 to MMAE, MMAF, Dxd and exatecan via several different linkers. MUC1*-ADCs with DARs ranging from 4-8 were tested in vitro and in vivo for efficacy against a panel of solid tumor cancers. Heterogenous MUC1* tumors were made by mixing different ratios of mCherry wild-type cancer cells with GFP MUC1* overexpressed cancer cells. Resulting H scores ranged from 10-280 (max 300). Results: Cancer cells internalized the MUC1* MNC2 antibody within 2 hours. In vitro, MUC1*-ADC IC50s ranged from 1.3nM to 20.0nM, depending on the linker-payload and cancer subtype, wherein the majority had an IC50 of about 10nM. In general, efficacy in animals was seen across multiple cancer sub-types treated with multiple MUC1*-ADCs. Efficacy in animals xenografted with heterogeneous MUC1* tumors showed a dependence on antigen density. Greatest efficacy was observed for breast cancer xenografts with an H Score >10 treated with MNC2-Deruxtecan. However, only pancreatic tumors with higher H Scores were completely eliminated by MNC2-MMAE, MNC2-Deruxtecan and MNC2-exatecan. MNC2-ADC and MNC2-CAR share the same antibody. No off-tumor toxicity was observed in animals treated with MNC2-ADC, which mimicked lack of off-target toxicities for MNC2-CAR T cells in humans. Conclusions: These data, combined with MNC2-CAR T data from our 1st-in-human trial for metastatic breast cancer, supports a conclusion that the MUC1* antibody MNC2 is safe and could have high therapeutic value as a MUC1*-ADC for multiple solid tumors with both high and low antigen density. Citation Format: Cynthia Carol Bamdad, Benoit S. Smagghe, Scott T. Moe, Mark G. Carter, Trevor J. Grant, Kevin R. Yi, Michael J. Nash, Jacy P. Marquez, Natalie K. Miller, Jac-Leen S. Nash, Dan S. Miller, Danica M. Walkley, Andrew K. Stewart. MUC1*-ADCs are effective against heterogeneous solid tumor cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6351.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints.

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operatingcharacteristics.

Abstract A021: Evolutionary immunotherapy in NSCLC: An integrated approach

Abstract Purpose: Our study examines tumor growth dynamics and immunosuppression under the presence of immune attack to identify optimal immune pulsing treatment strategies. Motivation: Checkpoint inhibitors disrupting the PD-1/PD-L1 axis are used in multiple cancer subtypes. However, not all patients respond, and predictive biomarkers are needed to better stratify patients. Therapy for PD-L1, the most studied biomarker, has given mixed results in non-small cell lung cancer (NSCLC). The complexity of the immune response and plastic nature of PD-L1/PD-1 expression on tumor and immune cells contributes to the heterogeneity of success. IFN-γ, a cytokine released by activated immune cells, induces PD-L1 expression in some tumor cells. Chronic IFN-γ results in long-term PD-L1 expression. However, relaxation dynamics following removal of IFN-γ remains unexplored. In this study, we investigate these complex PD-L1 dynamics through a unique combination of in vitro studies and mathematical modeling. METHODS: In vitro experimental data: We have collected in vitro and temporal confocal microscopy images of PD-L1 expression on NSCLC cells, which were either untreated or treated for 48 hours with high dose IFN-γ followed by chronic low dose IFN-γ for 21 days.Agent-based model: To reinforce our in vitro experiments and findings, we developed a hybrid agent-based model (ABM) to study PD-L1 dynamics. The agents in the ABM are tumor cells having variable PD-L1 expression, and immune cells that secrete IFN-γ. Tumor cells can be randomly seeded or seeded as a cluster of cells. IFN-γ is modeled as either a binary well-mixed pulse (to simulate the in vitro experimental setup), or as a diffusible via a reaction-diffusion process (to simulate the tumor-immune cell interactions in an in-vivo spatial manner). This allows us to investigate the effects of various pulsing strategies of immunotherapy on tumor evolution where each pulse can be a variable dose of immune cells, given at regular or irregular intervals. RESULTS: Our in vitro experimental data and microscopy images confirm that PD-L1 expression continues to increase within the cells after a 48-hour IFN-γ exposure followed by low levels of IFN-γ. Our ABM has been calibrated to capture in vitro data dynamics for different conditions. Our initial results indicate that pulsing immunotherapy may lead to better overall immune efficacy than continuous immunotherapy, by preventing sustained suppression that increases immune cell exhaustion, despite the potential for tumor regrowth between pulses. We also hypothesize that pulsing immunotherapy will have different immunosuppression effects when tumors are clustered or randomly distributed. CONCLUSION: We have quantified the temporal dynamics of PD-L1 expression in NSCLC cells under different conditions of IFN-γ, and built an ABM to study tumor-immune evolution under various pulsing strategies. The results of this work could lead to improved therapy schedules in patients responsive to checkpoint inhibitors that exploit the complex dynamics of immune response and suppression. Citation Format: Sandhya Prabhakaran, Taylor M. Bursell, Kimberly Luddy, Julian Pineiro, Rafael Bravo, Jeffrey West, Megan Johnson, Mark Robertson-Tessi, Amer A. Beg, Jhanelle E. Gray, Scott Antonia, Robert A. Gatenby, Alexander R. A. Anderson. Evolutionary immunotherapy in NSCLC: An integrated approach [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A021.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

Outcomes in patients with pancreatic and ampullary cancer whose tumors harbor pathogenic APC mutations.

693 Background: APC (adenomatous polyposis coli) is master regulator of the WNT signaling and beta-catenin pathway. Pathogenic (path) mutations in APC are well known for their connection to familial adenomatous polyposis, but germline (G) mutations in APC can give rise to other cancers, and somatic (S) mutations in APC can be found in multiple cancer subtypes. The clinical relevance for tumors that harbor path APC mutations is unknown. Methods: An established database of patients whose tumors have undergone genetic testing (Next-generation DNA sequencing, NGS) was queried for the presence of an APC mutation. Each APC mutation identified was assessed using the NCI Clinvar data base for determination of a path/likely path APC mutation. Patient/disease characteristics and treatment (tx) information were captured from electronic medical record review. Median overall survival (OS) was defined from the time of diagnosis of advanced/metastatic disease until death. Median time to progression (TTP) for each tx regimen was defined from the initiation of tx until documented disease progression. Results: A database of over 13000 patients entered from 2018 to present included 133 patients (1%) with G- or S-APC mutations, of which 41 were identified as path/likely path. Of the 41 patients, 56% were male, 73% were Caucasian and 24% African Americans, with a median age of 67 (range 34-90). Only 32% of patients had known G-APC mutations, but 34% had a second primary cancer, and 37% had a family history of known G-APC mutation-associated cancers. Only one patient had clinical manifestations of a polyposis syndrome. Of the 13 patients with known G-APC mutations, clinical characteristics were similar to the larger group, except that 100% of the G-APC-mutated patients were Caucasian, and the rate of family history of APC-associated cancers was 54%. Fifty-four percent of the overall group of 41 patients had a pathological diagnosis of pancreatic cancer, while the remaining 46% were ampullary or duodenal cancers. (Only 2 patients [5%] had tail of the pancreas cancers). Of the 37 patients who had somatic tumor NGS, only 70% harbored a KRAS gene mutation. For the OS analysis (n=24), the mOS was 11.7 months (range 5.2 – 56.9). Of the 22 patients evaluable for TTP, the mTTP for 5FU-based tx was 5.8 months, and 6.4 months for gemcitabine-based tx. Conclusions: APC-mutated pancreatic and ampullary cancers are uncommon, but exhibit survival rates, as well as TTP on both 5FU and gemcitabine-based txs that are similar to that seen with most pancreatic adenocarcinomas. However, 30% of APC-mutated cancers are KRAS wild type, and may harbor other driving mutations. As the loss of APC function triggers an accumulation of beta-catenin, targeted therapies that inhibit beta-catenin-regulated transcription and downregulate WNT-related signaling could be a promising tx approach for patients whose tumors harbor path/likely path APC mutations.

Abstract PR13: Single-cell mapping reveals a common origin for diverse subtypes of pancreatic cancer

Abstract The detailed understanding of the molecular basis of tumor initiation and progression is essential for the effective management and treatment of any cancer, including pancreatic cancer. To determine if the stem and progenitor cells can serve as cells of origin for pancreatic cancer, we have created a new mouse strain in which tamoxifen inducible Cre (CreERT2) is knocked into the endogenous locus encoding the stem cell regulator Musashi2 (Msi2). When crossed to a Cre-regulatable Myc transgenic (CAG-LSL-MycT58A) strain, Msi2-CreERT2 mice develop multiple pancreatic cancer subtypes: pancreatic ductal adenocarcinoma (PDAC), adenosquamous carcinoma of the pancreas (ASCP), and acinar cell carcinoma (ACC) following Myc activation in Msi2+ cells in the adult stage. A combination of single-cell genomics with computational analysis of developmental states and lineage trajectories of tumors in our model suggests that oncogenic Myc preferentially triggers the transformation of the most immature subset of Msi2+ cells, leading to the rise of a common pool of pre-cancer cells with multi-lineage potential. These pre-cancer cells subsequently take distinct fates within the same mice by activation of distinct transcriptional programs and large-scale genomic changes. Additionally, combining transcriptomic and functional genomic approaches, we have identified hyaluronan-mediated motility receptor (HMMR) as a potential novel dependency in mouse and human ASCP cells, providing a framework to develop interception strategies for this lethal disease. In summary, our study shows that multiple pancreatic cancer subtypes can arise from a common pool of Msi2+ cells and provides a powerful framework to understand and control the programs that shape divergent fates in pancreatic cancer. Citation Format: Nirakar Rajbhandari, Michael Hamilton, Tannishtha Reya. Single-cell mapping reveals a common origin for diverse subtypes of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr PR13.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview.

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

A Combinatorial Regulatory Platform Determines Expression of RNA Polymerase III Subunit RPC7α (POLR3G) in Cancer.

RNA polymerase III (Pol III) subunit RPC7α, which is encoded by POLR3G in humans, has been linked to both tumor growth and metastasis. Accordantly, high POLR3G expression is a negative prognostic factor in multiple cancer subtypes. To date, the mechanisms underlying POLR3G upregulation have remained poorly defined. We performed a large-scale genomic survey of mRNA and chromatin signatures to predict drivers of POLR3G expression in cancer. Our survey uncovers positive determinants of POLR3G expression, including a gene-internal super-enhancer bound with multiple transcription factors (TFs) that promote POLR3G expression, as well as negative determinants that include gene-internal DNA methylation, retinoic-acid induced differentiation, and MXD4-mediated disruption of POLR3G expression. We show that novel TFs identified in our survey, including ZNF131 and ZNF207, functionally enhance POLR3G expression, whereas MXD4 likely obstructs MYC-driven expression of POLR3G and other growth-related genes. Integration of chromatin architecture and gene regulatory signatures identifies additional factors, including histone demethylase KDM5B, as likely influencers of POLR3G gene activity. Taken together, our findings support a model in which POLR3G expression is determined with multiple factors and dynamic regulatory programs, expanding our understanding of the circuitry underlying POLR3G upregulation and downstream consequences in cancer.

Single-cell mapping identifies MSI+ cells as a common origin for diverse subtypes of pancreatic cancer

Identifying the cells from which cancers arise is critical for understanding the molecular underpinnings of tumor evolution. To determine whether stem/progenitor cells can serve as cells of origin, we created a Msi2-CreERT2 knock-in mouse. When crossed to CAG-LSL-MycT58A mice, Msi2-CreERT2 mice developed multiple pancreatic cancer subtypes: ductal, acinar, adenosquamous, and rare anaplastic tumors. Combining single-cell genomics with computational analysis of developmental states and lineage trajectories, we demonstrate that MYC preferentially triggers transformation of the most immature MSI2+ pancreas cells into multi-lineage pre-cancer cells. These pre-cancer cells subsequently diverge to establish pancreatic cancer subtypes by activating distinct transcriptional programs and large-scale genomic changes, and enforced expression of specific signals like Ras can redirect subtype specification. This study shows that multiple pancreatic cancer subtypes can arise from a common pool of MSI2+ cells and provides a powerful model to understand and control the programs that shape divergent fates in pancreatic cancer.

DeepGene Transformer: Transformer for the gene expression-based classification of cancer subtypes

Cancer and its subtypes constitute approximately 30% of all causes of death globally and display a wide range of heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled the use of precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision-making. Over the past decade, conventional machine learning (ML) and deep learning (DL) algorithms have been widely espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward the identification of cancer biomarkers. Hence, an end-to-end deep learning approach, DeepGene Transformer, is proposed which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a pre-requisite for the current classification algorithms. Comparative analysis reveals that the proposed DeepGene Transformer outperformed the commonly used traditional and state-of-the-art classification algorithms and can be considered an efficient approach for classifying cancer and its subtypes, indicating that any improvement in deep learning models in computational biologists can be reflected well in this domain as well.

A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer.

Image-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated. We performed a case-cohort study of 8110 women aged 40-74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer-Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up. The AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70-0.80) to 0.68 (95%CI: 0.66-0.69) 1-10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66-0.78) to 0.65 (95%CI: 0.63-0.66) for the imaging-only model and 0.62 (95%CI: 0.55-0.68) to 0.60 (95%CI: 0.58-0.61) for Tyrer-Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model, p < 0.01, and 4.6% for Tyrer-Cuzick, p < 0.01. The lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer-Cuzick models.

Distribution of non-Hodgkin lymphomas across US: Are Hispanics any different from non-Hispanics?

e18619 Background: Non-Hodgkin lymphoma (NHL) encompasses various subtypes that differ in incidence, etiology, clinical features, and survival. NHL subtypes can be divided into B-cell and T-cell lymphomas, when classifying them based on cell of origin. The distribution of NHL subtypes has been known to variate between geographic locations, and always results are reported without classifying them among ethnicities. Hispanics are by now, the largest ethnic minority in US, according to the latest 2020 US Census, and it is of utmost importance to understand variations related to ethnicity. This is to our knowledge, the largest retrospective study comparing NHL frequency of subtypes in Hispanics vs Non-Hispanics. Methods: Data were analyzed on NHL patients in the US and reported to the Surveillance, Epidemiology, and End Results (SEER) 18 database between 2000 and 2018. SEER 18 contains the most comprehensive population-based cancer information in the US, covering approximately 27% of the US population and up to 36% of HI alone. Racial groups analyzed included NH whites and HI whites. Results: There were 366516 patients diagnosed with NHL over the 8-year study period included in our study. Most of these patients belong to the B-cell NHL type, comprising 91% of the group. The distribution of the major subtypes of NHL is shown in the pie charts. We noted differences in the distribution of major NHL subtypes among NH and H. Among the B-Cell NHL subtypes, Hispanics had higher proportions of DLBCL (42% vs. 31%), FL (19% vs. 17%), and Burkitt lymphoma (4% vs. 2%) than NH. On the contrary, a significant difference was found, with a higher presence of CLL/SLL in NH, which was seen in 31% vs. 15% in H. Within the T-Cell NHL subtypes, compared with non-Hispanics, Hispanics had higher proportions of NK/T cell lymphoma (11% vs. 3%) and anaplastic large cell lymphoma (15 vs. 12%) but a lower proportion of mycosis (26% vs. 30) in NH and cutaneous T-cell lymphoma (9% vs. 13%). The rest of the subtypes showed to have similar proportion rates. Conclusions: In conclusion, our study shows evidence of a significant difference in subtype distribution between NH and H patients. The involvement of a complex process in developing NHL and the etiologic heterogeneity among subtypes has been previously described. These differences in subtype distribution across races may be due to geographic location, genetic variants, occupational exposures, infectious conditions, family history, and socioeconomic status. Analyzing and comparing multiple cancer subtypes can provide critical clues for future epidemiological investigations to improve care among our minorities. Due to the growing Hispanic population in the US, it is significantly vital to represent the differences in subtype presentation graphically.

Abstract 124: Autocrine signaling extends PD-L1 expression in NSCLC following an active immune response

Abstract Checkpoint inhibitors disrupting the PD-1/PD-L1 axis are used in multiple cancer subtypes. However, not all patients respond, and predictive biomarkers are needed to better stratify patients. PD-L1, the most studied biomarker, has given mixed results. The complexity of the immune response and plastic nature of PD-L1 expression on tumor and immune cells contributes to the heterogeneity of success. Not all malignant epithelial cells express PD-L1: some express it only when the correct signal is present, while others have constitutive expression. Additionally, the PD-L1 protein has multiple functions outside immune regulation, making its dynamics difficult to predict. In this study, we investigate these complex PD-L1 dynamics through a unique combination of in vitro studies and mathematical modeling simulations. IFN-γ, a pleotropic cytokine released by activated immune cells, induces PD-L1 expression in some tumor cells. Chronic IFN-γ results in long-term PD-L1 expression. However, relaxation dynamics following removal of IFN-γ remains unexplored. We show that PD-L1 relaxation depends on autocrine signaling, demonstrated by multiple in vitro assays seeded with varying population densities. Following 48h IFN-γ treatment, sub-confluent tumor cell populations demonstrated increased duration of PD-L1 expression, linking cell density to PD-L1 expression. Conditioned-media from A549 cells previously treated with IFN-γ induced PD-L1 on IFN-γ naïve tumor cell populations, indicative of autocrine signaling not previously identified. To reinforce our in vitro findings, we develop a hybrid agent-based model (ABM) to study PD-L1 dynamics. Tumor cells have variable PD-L1 expression and IFN-γ is modeled via reaction-diffusion. IFN-γ triggers tumor cells to secrete a diffusible factor that promotes PD-L1 expression. Initial fits of the ABM capture in vitro data dynamics for different conditions. In this ongoing project, we plan to identify the underlying mechanism responsible for extended PD-L1 expression in NSCLC cells, including investigation into specific molecular pathways. Identifying the cause for this phenomenon could help clinicians improve targeted therapy and introduce a potential avenue to enhance treatment in patients responsive to checkpoint inhibitors. Citation Format: Taylor M. Bursell, Sandhya Prabhakaran, Kimberly Luddy, Julian Pineriro, Rafael Bravo, Jeffrey West, Mark Robertson-Tessi, Aner Beg, Jhanelle Gray, Scott Antonia, Robert Gatenby, Alexander Anderson. Autocrine signaling extends PD-L1 expression in NSCLC following an active immune response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 124.

Triple-negative breast cancer cells invade adipocyte/preadipocyte-encapsulating geometrically inverted mammary organoids.

This paper describes the manufacture of geometrically inverted mammary organoids encapsulating primary mammary preadipocytes and adipocytes. Material manipulation in an array of 192 hanging drops induces cells to self-assemble into inside-out organoids where an adipose tissue core is enveloped by a cell-produced basement membrane, indicated by laminin V staining and then a continuous layer of mammary epithelial cells. This inverted tissue structure enables investigation of multiple mammary cancer subtypes, with a significantly higher extent of invasion by triple-negative MDA-MB-231 breast cancer cells compared to MCF7 cells. By seeding cancer cells into co-culture around pre-formed organoids with encapsulated preadipocytes/adipocytes, invasion through the epithelium, then into the adipose core is observable through acquisition of confocal image stacks of whole mount specimens. Furthermore, in regions of the connective tissue core where invasion occurs, there is an accumulation of collagen in the microenvironment. Suggesting that this collagen may be conducive to increased invasiveness, the anti-fibrotic drug pirfenidone shows efficacy in this model by slowing invasion. Comparison of adipose tissue derived from three different donors shows method consistency as well as the potential to evaluate donor cell-based biological variability. Insight box Geometrically inverted mammary organoids encapsulating primary preadipocytes/adipocytes (P/As) are bioengineered using a minimal amount of Matrigel scaffolding. Use of this eversion-free method is key to production of adipose mammary organoids (AMOs) where not only the epithelial polarity but also the entire self-organizing arrangement, including adipose position, is inside-out. While an epithelial-only structure can analyze cancer cell invasion, P/As are required for invasion-associated collagen deposition and efficacy of pirfenidone to counteract collagen deposition and associated invasion. The methods described strike a balance between repeatability and preservation of biological variability: AMOs form consistently across multiple adipose cell donors while revealing cancer cell invasion differences.

Inferring cancer common and specific gene networks via multi-layer joint graphical model

Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which is a crucial step towards precision cancer diagnosis and treatment. In this study, we propose a new sparse regularized multi-layer decomposition graphical model to jointly estimate the gene networks of multiple cancer types. Our model can handle various types of gene expression data and decomposes each cancer-type-specific network into three components, i.e., globally shared, partially shared and cancer-type-unique components. By identifying the globally and partially shared gene network components, our model can explore the heterogeneous similarities between different cancer types, and our identified cancer-type-unique components can help to reveal the regulatory mechanisms unique to each cancer type. Extensive experiments on synthetic data illustrate the effectiveness of our model in joint estimation of multiple gene networks. We also apply our model to two real data sets to infer the gene networks of multiple cancer subtypes or cell lines. By analyzing our estimated globally shared, partially shared, and cancer-type-unique components, we identified a number of important genes associated with common and specific regulatory mechanisms across different cancer types.

Lyt-200, a Humanized Anti-Galectin-9 Antibody, Exhibits Preclinical Efficacy in Models of Hematological Malignancies

Patients with relapse and refractory (R/R) hematological malignancies typically have 5-year overall survival rates of less than 50% when receiving chimeric antigen receptor (CAR) T-cell therapy and less than 30% after receiving hematopoietic stem cell transplantation (HSCT). Human leukemia cells utilize a variety of biochemical mechanisms, which allow them to escape host immune surveillance. These molecular pathways cause impairment of the anti-cancer activities of immune cells, which could attack and kill leukemia cells. These dismal outcomes for patients with R/R disease highlight the need for novel treatment regimens when current therapeutic options are exhausted. Galectins are s-type lectins that promote diverse biological processes including adhesion, signaling, and immunosuppression. In acute myeloid leukemia (AML), galectin-9 (GAL-9) maintains the leukemia initiating cell (LIC) population and plays an important role in treatment resistance. We recently tested LYT-200, a fully humanized IgG4 αGAL-9 monoclonal antibody, developed by PureTech Health, which has so far been well tolerated with no dose limiting toxicities in a Phase I clinical trial for solid tumors (NCT04666688). We tested LYT-200 in vitro and in vivo across multiple hematological malignancies. Flow cytometry was used to assess the surface expression of GAL-9 on human B-ALL, AML, T-cell acute lymphoblastic leukemia (T-ALL), and diffuse large B-cell lymphoma (DLBCL) cell lines relative to receptor T-cell immunoglobulin and mucin domain-containing protein-3 (TIM-3) and Programmed cell death protein-1 (PD-1) expression. Compared to healthy human peripheral blood mononuclear cells (PBMCs), where GAL-9 surface expression was low or absent depending on the immune cell, GAL-9 was highly expressed on the surface of all human blood cancer cell lines tested (>100-fold increase). Notably, GAL-9 expression was comparable to PD-1 surface expression and higher than that of TIM-3 on AML, B-ALL, T-ALL, and DLBCL cells. Treating all cell lines with LYT-200 relative to control immunoglobulin resulted in extensive cell death, which occurred after 48 and 72 hours of treatment. Furthermore, a side-by-side comparison with αTIM-3 antibody treatment at equivalent concentrations, revealed that LYT-200 treatment was significantly more effective at killing blood cancer cells at low doses of 10-100 ng/mL in vitro. The in vitro efficacy of LYT-200 against multiple blood cancer subtypes extended to in vivo protection and survival benefit in murine models of T-ALL and AML, both in immunocompromised and immunocompetent mice. When immunocompromised mice were transplanted with human T-ALL cell lines or patient-derived samples, single-agent treatment with LYT-200 (1.5 mg/kg/weekly) was comparable to survival results obtained with methotrexate (0.75 mg/kg/weekly) administration. Notably, the combination of LYT-200 and methotrexate treatments led to the best survival outcomes in both contexts. Similar results were obtained in xenograft studies of AML, where single-agent LYT-200 (1.5 mg/kg/weekly) treatment was more effective at protecting mice than cytarabine (100 mg/kg/weekly). However, combining both treatments resulted in the best survival outcomes with greater than 80% of mice surviving over 2 months after disease appearance. In all, our results demonstrate that GAL-9 is an important driver of multiple blood cancer subtypes and that targeting GAL-9 with LYT-200 represents a potential novel treatment strategy which warrants additional pre-clinical and clinical testing.