Citations

Chemotherapy-induced cognitive impairment (CICI) has emerged as a significant medical problem without therapeutic options. Using the platinum-based chemotherapy cisplatin to model CICI, we revealed robust elevations in the adenosine A 2A receptor (A 2A R) and its downstream effectors, cAMP and CREB, by cisplatin in the adult mouse hippocampus, a critical brain structure for learning and memory. Notably, A 2A R inhibition by the Food and Drug Administration–approved A 2A R antagonist KW-6002 prevented cisplatin-induced impairments in neural progenitor proliferation and dendrite morphogenesis of adult-born neurons, while improving memory and anxiety-like behavior, without affecting tumor growth or cisplatin’s antitumor activity. Collectively, our study identifies A 2A R signaling as a key pathway that can be therapeutically targeted to prevent cisplatin-induced cognitive impairments.

Abstract Introduction: Individuals with cancer may be at higher risk of atrial fibrillation (AF). However, sufficient data are lacking in AF risk among breast cancer survivors, especially for the mid-to long-term risk of AF and the impact of cancer treatment on this association. We aimed to investigate the risk of incident AF in women diagnosed with breast cancer and evaluate the association by length of follow-up and breast cancer treatment. Methods: Using the Korean Health Insurance Service database, we studied 96,476 women who were diagnosed with breast cancer (aged≥20 years) without prior history of AF and breast cancer and underwent breast cancer surgery between January 2007 and December 2013. They were matched 1:3 to a cancer-free population (n=289,428) based on birth year and sex and were followed to the end of 2018. Using ICD-10 codes (I48.0 to I48.4, I.48.9), AF was defined when it was a discharge diagnosis or was verified more than twice in an outpatient clinic. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of AF associated with breast cancer relative to the cancer-free population, adjusting for age, income, and comorbidities including diabetes, hypertension and hyperlipidemia. Results: During a median follow-up of 6.8 years, AF developed in 2,105 women with breast cancer (3.2 per 1,000 person-years). Mean age at baseline among participants was 49.6 years. Having breast cancer was associated with increased risk of AF (HR, 2.04; 95% CI, 1.93-2.15). This association was stronger for women aged <50 years (HR, 3.98; 95% CI, 3.59-4.40). When we repeated analyses with lag periods of two years to explore the possibility of reverse causation and mid-to long-term risk of AF (n=91,370), the association was largely attenuated but remained significant (HR, 1.18; 95% CI, 1.09-1.28 for total women and HR, 1.60; 95% CI, 1.38-1.86 for women aged <50 years). However, no increased risk of AF was observed in women with breast cancer aged >65 years. Compared with no use of anthracyclines and taxanes, respectively, ever use of anthracyclines and taxanes was associated with increased risk of AF after further adjusting for other cancer treatment modalities including use of trastuzumab, radiotherapy, and endocrine treatment (HR, 1.25; 95% CI, 1.12-1.40 and HR, 1.37; 95% CI, 1.23-1.53, respectively). This association persisted only among women with breast cancer ever treated with anthracyclines when analysis was limited to those with two year-lag periods (HR, 1.23; 95% CI, 1.04-1.45). Conclusions: In this large-scale nationwide cohort study in South Korea, women with breast cancer had an elevated risk of incident AF, especially for women with breast cancer younger than 50 years, regardless of the length of follow-up. Our findings also suggest that cardiotoxic side effects of cancer treatment such as use of anthracyclines might increase both short-term and mid-to long-term risk of AF among women with breast cancer. Citation Format: Yong-Moon ("Mark") Park, Wonyoung Jung, Yohwan Yeo, Sang Hyun Park, Ronda Henry-Tillman, Hong Seok Lee, Tasneem Z. Naqvi, Kyungdo Han, Dong Wook Shin. Risk of atrial fibrillation among women diagnosed with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3214.

Abstract Pediatric Acute Myeloid Leukemia (AML) with a FLT3 internal tandem duplication (FLT3-ITD) is a challenging disease due to poor outcomes in many patients. The 4-year progression-free survival is still only 31%. Current biomarkers are insufficient to predict why certain patients with FLT3-ITD AML relapse and others do not. The development of prognostic biomarkers in FLT3-ITD pediatric AML may help improve the outcomes and management of these patients. We acquired a panel of 37 diagnostic samples with 18 samples from FLT3-ITD pediatric AML patients with poor outcomes (relapse within 3 years) and 19 from favorable outcome patients that did not exhibit relapse for at least 5 years. Next, we performed FACS to isolate CD34+CD38dim and CD34+CD38+ cells. These two populations were then sequenced using 10x Genomics 3’ end single-cell RNA sequencing (scRNAseq). Using this scRNAseq dataset comprising over 250k single cells, we first investigated if the frequency of specific RNA clusters of AML cells may predict patient outcome. We found 5 clusters had significantly different frequencies between patients with favorable and poor outcomes (p < 0.05). Using these clusters, we performed CIBERSORTx deconvolution on publicly available FLT3-ITD AML bulk RNAseq samples from TCGA (n = 36), BeatAML (n = 47), and the TARGET project (ntrain = 72; ntest = 26) to identify the inferred cluster composition present in each AML patient from these datasets. Next we built LASSO prediction models using the inferred scRNAseq clusters by CIBERSORTx and incorporating clinical features including leukemic blast percentage, bone marrow leukemic blast percentage, and WBC at diagnosis. We found these prediction models to be highly specific as a prognostic biomarker for pediatric AML (p << 0.01). We were unable to train a model directly using genes from the publicly available bulk RNAseq data for pediatric FLT3-ITD AML, highlighting the relevance of using our deconvolution based biomarker. Between the high (> median) and low (< median) LASSO score groups, there was ~50% difference in the number of patients relapsing after 4 years. When we included this LASSO score in a multivariate cox regression (likelihood ratio p value = 0.007), the LASSO score (HR = 6.8, p = 0.026, CI = 1.26 - 37.28) showed additional predictive ability when including FLT3-ITD allelic ratio (HR = 1.83, p = 0.38, CI = 0.47 - 7.15) and MRD (HR = 3.95, p = 0.021 CI = 0.051 - 0.79). These results demonstrate that incorporating rare AML cell populations into a prognostic biomarker could add substantial clinical information to existing biomarkers. This indicates single-cell derived prognostic models can and should be explored further for pediatric FLT3-ITD AML and demonstrates a platform that could be used for other malignancies, including non-hematologic tumors, to create cell-type biomarkers. Citation Format: Robert Schauner, Zachary Jackson, Nethrie Idippily, Grace Lee, Sheela Karunanithi, Shivaprasaad Manjappa, Tae Hyun Hwang, David Wald. New platform for novel biomarker discovery using single-cell RNA sequencing on pediatric acute myeloid leukemia samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4080.

Abstract Histological distribution and density of tumor infiltrating lymphocytes (TILs) are known to be informative to cancer patients’ outcome. However, due to the heterogeneity and huge size of digitized pathology slides, it has been a challenging problem to quantify TILs-related features in whole slide images (WSI). This study aimed to explore the impact of spatial distribution of TILs quantified by deep learning (DL) approaches based on digitalized WSI stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The cancer and TILs regions are first separately identified by deep learning models in the WSI. The TILs densities at tumor invasive margins are then quantified by image analysis algorithms and used for prognosis of progression-free survival (PFS) in CRC patients. The prognostic impact of spatial distributions of TILs in CRC patients was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Mäkinen (KM) grading method and compared to DL approaches. On multivariate analysis, TILs densities within 200µm of the invasive margin (f_im200) was remained as the most significant prognostic factor for PFS (HR 0.004 [95% CI, 0.0001-0.1502], p=.002) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031, [95% CI 0.001-0.645], p=.024). Inter-observer agreement of manual KM grading was insignificant in both cohorts. The survival analysis by pathologists’ manual KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort. In summary, automated quantification of TILs at the invasive margin showed a prognostic utility to predict PFS, and could provide reproducible TILs density measurement in CRC patients. Citation Format: Hongming Xu, Yoon Jin Cha, Jean Clemenceau, Jinhwan Choi, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Spatial analysis of tumor infiltrating lymphocytes in histological images using deep learning predicts progression-free survival in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 642.

Abstract Background: Microsatellite Instability (MSI) is a prognostic and predictive biomarker which can guide treatments including immunotherapy for patients with gastrointestinal cancer. Universal MSI testing would benefit to patients to receive a better therapy, but many patients remain untested. To deliver broadly accessible MSI testing, deep learning models have demonstrated their feasibility for MSI prediction using H&E-stained whole-slide images (WSIs). However, to be deployed into clinical routine care, prediction models should be validated in datasets from multicenter and multiethnic groups. In addition, these models should provide uncertainty of prediction to help clinicians to make informed decisions. Method: We develop a MSI prediction model using WSIs based on Deep Gaussian process (DGP), a Bayesian model being able to model the uncertainty in prediction. We implement a DGP model in the transfer learning, where a WPI is decomposed into multiple non-overlap image patches which are converted to feature vectors by a pretrained convolutional neural network (CNN). Then, the DGP model makes a prediction for an image by averaging score function values at all the feature vectors in the image in weakly supervised learning (WSL). To test our method, we collected H&E stained colorectal/stomach cancer slides (n=1,619) from multi-institutions and multiethnic groups, including TCGA Colorectal (CRC; n=351) and Stomach Adenocarcinoma (STAD; n=174) and six datasets from tertiary hospitals in Korea (i.e., Yonsei-1 (n=174), Yonsei-1-remade (n=146), Yonsei-2 (n=95), St. Mary-1 (n=48), St. Mary-2 (n=50), and Yonsei-CLASSIC trial (n=581)). We also compare our method to multiple deep learning methods, including Densenet, Resnet, Shufflenet, Googlenet, Squeezenet and two WSL models, MIL (Campanell et al., 2019) and CLAM (Lu et al., 2021). Result: In each cancer type, we train each model using TCGA dataset as discovery cohort and tested on the rest of datasets as validation cohort. We use the area under of roc curve (AUC) for the evaluation metric. For the CRC cohorts, our DGP method achieves overall best performance with 0.81 (Yonsei-1), 0.82 (Yonsei-1-remade), 0.89 (Yonsei-2), 0.85 (St. Mary-1) 0.75 (St. Mary-2) AUCs compared to the state-of-the art methods. For the STAD cohorts, our method also achieves overall best performance with 0.74 AUC (Yonsei-classic). In addition, incorporating the uncertainty in prediction measured by our method improves the model’s performance to predict MSI. Specifically, removing cases with prediction results with high uncertainty, which could lead false positive prediction, significantly improves MSI prediction performance. Taken together, these results demonstrate the robustness and generalisability of the DGP model for MSI prediction across multicenter and multiethnic datasets. Citation Format: Sunho Park, Hongming Xu, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Deep Gaussian process with uncertainty estimation Improves microsatellite instability prediction based on whole slide image: A retrospective multicenter and multiethnic cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5012.

Abstract Background: Gastric carcinogenesis has been found to be associated with intragastric microbiome, gastric mucosal gene expression, and immune cells comprising the tumor microenvironment separately in independent studies. However, to date, there has been no multi-omic investigations analyzing these biological traits simultaneously. Thus, there is a paucity in our understanding of the intricate relationship between intragastric microbes and the host in gastric disease. To address this gap, we aimed to comprehensively investigate the microbiome, host gene expression, and immune cells in the stomach across a range of different states along the gastric carcinogenesis pathway. Methods: Gastric biopsy samples were obtained from healthy individuals or patients with gastritis or gastric cancer (biopsy cohort, n=30). Surgical samples (of cancer and adjacent severe gastritis tissues) were obtained from gastric cancer patients (surgery cohort, n=40). Using 16S rRNA gene sequencing, RNA-seq, and cell-type enrichment analysis of each participant’s biospecimens, we identified associations between the microbiome and host transcriptome or inferred immune cell types. Results: Microbiome composition and gene expression patterns showed significant differences between disease states (healthy, gastritis, or cancer). Expression of ‘Cell cycle’ pathway genes was enriched in gastric cancer in both biopsy and surgery cohorts. In the biopsy cohort, Helicobacteraceae, which was abundant in gastritis, was highly correlated with FAM3D, which was previously found to be involved in gastrointestinal inflammation. In the surgery cohort, Lachnospiraceae, which was abundant in gastric cancer, was highly correlated with UBD, which is known to regulate mitosis and reduce cell cycle time. Cell-type enrichment analysis revealed lower B cell infiltration in gastric cancer than in gastritis (both cohorts). Tissue-infiltrating B cells were associated with Helicobacteraceae. Conclusions: Associations between the intragastric microbiome, human gene expression, and tissue-infiltrating immune cells are unique to gastric disease state. The findings herein may aid the discovery of novel biomarkers or therapeutic targets of gastric cancer. Citation Format: Chan Hyuk Park, Changjin Hong, A-reum Lee, Jaeyun Sung, Tae Hyun Hwang. Integrated analysis of intragastric microbiome and human gene expression uncovers genetic, microbial, and immunological associations in gastritis and gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5013.

Abstract Background: The tumor microenvironment is a biological environment comprising of heterogeneous cellular components, such as tumor, immune, and stromal cells. Stromal cells in the tumor microenvironment have been shown that they play an important role in cancer development, but how they are linked to patient prognosis, chemotherapy response, and immunotherapy resistance is not fully investigated yet. The present study sought to investigate the stroma composition in the tumor microenvironment by using spatial analysis and deep learning. Method: We analyzed stroma composition in Whole Slide Images (WSIs) of hematoxylin and eosin-stained formalin-fixed paraffin-embedded (FFPE) sections from 4 patient cohorts: St. Mary (37 patients), Yonsei-1 (622 patients), Yonsei-2 (66 patients), and TCGA-STAD (359 patients). St. Mary was collected from St. Mary Hospital, Seoul, Republic of Korea. Yonsei-1 and Yonsei-2 were collected from Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. Each WSI was tessellated into 256x256 um at 40X magnification and normalized using Macenko and Reinhard normalization methods to reduce the color variability of the stained tissue images. Two deep learning models (ResNet and U-Net) were used to create prediction models using Kather’s tumor dataset and a crowdsourcing stroma dataset. Models were trained over 100 epochs, using the Adam optimizer, 1e-4 learning rate, a binary loss function, and L2 regularization. Trained models were used to predict WSIs including tumor, stroma, tumor-infiltrating lymphocytes (TILs), Microsatellite instability. Spatial analysis was conducted by using Getis-Ord-Gi*, a local statistic that identifies a statistically significant hot-spot region by providing relatedness with its neighbors. Tumor hot-spot regions were computed based on z-scores in the predicted WSIs and used for understanding the spatial variation of cancer cells, recognizing statistically significant regions and their spatial relatedness. Results: In Yonsei-1, stroma composition was significantly higher in the chemotherapy high-risk group (p-value 2.9e-09). The proportion of stroma composition is significantly different with the risk groups (p-value 0.074). In TCGA-STAD, the proportion of stroma composition is significantly different with ACTA2 and Microsatellite instability (p-value 0.028). In St. Mary, Yonsei-1, and Yonsei-2, stroma composition was significantly higher in the immunotherapy treatment non-response group (p-value 0.001). Conclusion:These findings suggest that stroma composition in the tumor microenvironment is strongly associated with patient prognosis, chemotherapy response, and immunotherapy resistance on stomach cancer. Citation Format: Sanghoon Lee, Isaiah Pressman, Hongming Xu, Tae Hyun Hwang. Spatial image analysis on stroma composition linking to patient prognosis, chemotherapy response, and immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5044.

Purpose: In RE-MIND2 (NCT04697160), patient-level outcomes from the L-MIND study (NCT02399085) of tafasitamab plus lenalidomide were retrospectively compared with patient-level matched observational cohorts treated with National Cancer Care Network (NCCN)/European Society for Medical Oncology (ESMO)-listed systemic therapies for relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Patients and Methods: Data were collected from health records of eligible patients aged ≥18 years with histologically confirmed DLBCL who had received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy). Patients from L-MIND were matched with patients from the RE-MIND2 observational cohort using estimated propensity score-based 1:1 nearest-neighbor matching, balanced for nine covariates. The primary analysis compared tafasitamab plus lenalidomide with patients who received any systemic therapy for R/R DLBCL (pooled in one cohort) or bendamustine plus rituximab (BR) or rituximab plus gemcitabine and oxaliplatin (R-GemOx; as two distinct cohorts). The primary endpoint was overall survival (OS). Secondary endpoints included treatment response and time-to-event outcomes. Results: In RE-MIND2, 3,454 patients were enrolled from 200 sites in North America, Europe, and Asia-Pacific. Strictly matched pairs of patients consisted of tafasitamab plus lenalidomide versus systemic therapies pooled (n = 76 pairs), versus BR (n = 75 pairs), and versus R­GemOx (n = 74 pairs). Significantly prolonged OS was reported with tafasitamab plus lenalidomide versus systemic pooled therapies [hazard ratios (HR): 0.55; P = 0.0068], BR (HR: 0.42; P < 0.0001), and R-GemOx (HR: 0.47; P = 0.0003). Conclusions: RE-MIND2, a retrospective observational study, met its primary endpoint, demonstrating prolonged OS with tafasitamab plus lenalidomide versus BR and R-GemOx. See related commentary by Cherng and Westin, p. 3908

7560 Background: Tafasitamab (tafa) + lenalidomide (LEN) demonstrated efficacy in adult patients (pts) with R/R DLBCL ineligible for autologous stem-cell transplant in the pivotal Phase II study L-MIND (NCT02399085) and received accelerated approval in the United States in 2020 and conditional marketing authorization in Europe and Canada in 2021 in this setting. RE-MIND2 (NCT04697160), an observational, retrospective cohort study, compared pt outcomes from L-MIND with matched pt cohorts treated with other NCCN/ESMO recommended therapies. Methods: Methodology for RE-MIND2 has been presented previously. Hypothesis-generating analyses were conducted for pt subgroups of number of extranodal sites (ENS) (0–1 vs ≥2) and elevated lactate dehydrogenase (LDH) (yes vs no) in matched cohorts of pts receiving tafa + LEN vs systemic therapies pooled (STP), polatuzumab vedotin + bendamustine + rituximab (pola-BR), rituximab + LEN (R2), and CD19 CAR-T therapies (CAR-T). The primary endpoint was overall survival (OS). Results: Of 3,454 pts enrolled, 961, 106, 106, and 149 were treated with STP, pola-BR, R2, and CAR-T, resulting in 76, 24, 33, and 37 matched pairs for pts receiving tafa + LEN, respectively. Hazard ratios (HR) for OS show a trend toward favoring tafa + LEN in most pt subgroups (Table). Conclusion: These analyses suggest that tafa + LEN may be associated with improved OS vs selected systemic therapies for certain pts with high-risk disease and may further inform physicians’ treatment choices for pts with R/R DLBCL. These analyses are not powered for statistical comparison; small sample sizes in some subgroups result in wide confidence intervals (CI) and so results must be interpreted with caution. Data for other treatment cohorts, pt subgroups, and endpoints will be presented. Funding: MorphoSys AG. Clinical trial information: NCT04697160. [Table: see text]