United States

Background: Historically, central nervous system (CNS) involvement in the setting of acute myeloid leukemia (AML) was identified on the basis of symptoms, including headaches, cranial nerve deficits, and mental status changes. In recent years, screening recommendations have been developed to include FLT3 gene mutations, monocytic features, hyperleukocytosis, and CNS symptoms. However, the incidence, prognosis, and optimal treatment strategy are not well described.

Methods: This study was conducted through a retrospective electronic medical record review at the University of Kansas Medical Center from years 2015-2020. The inclusion criteria were AML patients who received a diagnostic lumbar puncture. Data included the overall incidence of CNS involvement among adult AML patients, AML characteristics associated with CNS involvement, and number of intrathecal treatment doses required for CNS clearance. The protocol was approved by the University of Kansas Medical Center Human Subjects Committee. 

Results: Forty-eight patients met our inclusion criteria; 25 were CNS-positive and 23 were CNS-negative (overall screening detection rate of 52%). The incidence of CNS involvement among only newly diagnosed AML patients was 3.24%. Indications for lumbar puncture included 26 patients with CNS symptoms, 24 with a monocytic phenotype, 16 with the FLT3 mutation, and 14 with hyperleukocytosis, with many having more than one characteristic concerning for CNS involvement. Screening detection rates were 58% for patients with 3 characteristics suggestive of CNS involvement, 60% for 2 characteristics, 45% for CNS symptoms, and 30% for monocytic phenotype. The CNS-positive group consisted of 28% with 3 characteristics, 24% with 2 characteristics, 20% with CNS symptoms, 12% with monocytic phenotype, 4% with FLT3 mutation, and 4% with hyperleukocytosis. Seventy-two percent of the CNS-positive group achieved confirmed CNS clearance with 72% of those patients only requiring 1 or 2 doses of intrathecal chemotherapy.

Conclusion: This data indicates that current screening guidelines appear to properly identify a patient population at risk for CNS involvement. Due to the diverse patient presentations in this analysis, these results also emphasize the need for ongoing research to further delineate risk factors for CNS involvement in AML. Improved identification of risk factors will likely lead to earlier detection and improved patient outcomes.

AMA Research Challenge 2022

Central Nervous System Involvement in Adult Acute Myeloid Leukemia Patients

Welcome to the largest national, multi-specialty research event **featuring research from 750 medical students, residents and fellows, and international medical graduates.**


#### Event Details
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At the conclusion of this event, the AMA will announce the top five finalists who will advance to the AMA Research Challenge finals, present their research to an elite panel of judges and compete to win a $10,000 grand prize sponsored by Laurel Road.
 

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 **Speakers**
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Ricardo Correa, MD, EdD
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Stuart M. Greenstein, MD
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Stephanie Strohbeen, MD
 
 **Oct. 22, 11 a.m. — Noon Central**
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Jessica Li, MD/PhD Candidate
![](https://assets.underline.io/uploads/markdown_image/1/image/9950ef2e0237f54cb3d8280334d42491.jpg)
Charles Lopresto, DO

### Sponsor
AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in the judging or scoring process to select the winner. The winner will be announced on Dec. 7 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

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Register to explore research in a variety of topics. AMA members get exclusive access to score posters in the semifinals.

**Please use the link below to register for free if you have not already done so:**

https://www.ama-assn.org/about/events/ama-research-challenge

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Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


poster

Abstract Title: Characterization of Natural Killer and Cytotoxic T Cell Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a poor prognosis and resistance to systemic therapies including immunotherapy. Although PDAC has been linked to low T cell infiltrate, the contribution of natural killer (NK) cells has received less attention. Our objective was to evaluate the immune parameters PDAC, including markers of NK cells, to determine if NK cells associate with patient outcomes. 

Methods: We analyzed tumors from 93 PDAC patients treated from 2012 – 2020. Predictor variables included tumor infiltrating lymphocytes (TILs), T cell markers (CD3+, CD8+, CD45RO+), NK markers (NKp46) and NK inhibitory markers (TIGIT and MHC-I). TILs were scored from 0-3, and immune markers were scored from 0-300. We also evaluated neutrophil and lymphocyte levels in the blood. Primary outcome variables were metastasis-free survival (MFS) and overall survival (OS), analyzed by the Kaplan-Meier method. 


Results: Mean age was 70, 55% were female, and the mean tumor size was 3.1±1.1cm. 89% involved the pancreatic head, and 63% were lymph node positive. The majority of patients received adjuvant therapy. With a median follow up of 24 months, median survival was 35 months. Blood lymphocytes levels prior to surgery were positively correlated with TILs (p=0.008 r=0.3). Median TILs infiltration was 1.1, and 50% were 0 or 1. Mean CD3+ score was 20.6±1, and mean NKp46 was 3.1±3.9. Although there was slight positive correlation between T cell and NK cell scores (CD3+/NKp46 p=0.005, r=0.3; CD8+/NKp46 p=0.05, r=0.2), neither T nor NK cell infiltration was associated with MFS or OS. Similarly, there was a tight positive correlation between MHC-I expression and all T cell markers (CD3+, CD8+ and CD45RO+), but not with NKp46 nor with survival outcomes (MFS/OS, P>0.05). TIGIT expression was also low (mean 30.8±21.9).

Conclusion: NK and T cell infiltrates are overall low in PDAC and do not associate with oncologic outcomes. Further characterization of the immune infiltrate in PDAC, including inhibitory signals and suppressive cell types, may yield better biomarkers of prognosis and immune targeting in this refractory disease.


Characterization of Natural Killer and Cytotoxic T Cell Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

Introduction: BTEX (benzene, toluene, ethylbenzene, and xylene) is well known for its toxicity via environmental, occupational, and recreational exposures. We aim to study the association of socioeconomic disparity and cancer amongst individuals with exposure of BTEX.
Methods: A retrospective cross-sectional study was performed between 2013 and 2018 utilizing the NHANES database. Adult individuals having data on socio-demographic questionnaires and lab findings on exposure to BTEX were included. Prevalence of cancer was identified amongst exposure to BTEX (vs no-BTEX). Univariate (chi-square test and Mann-Whitney U test) and Multivariate (survey logistic regression) analysis was performed to evaluate the epidemiologic characteristics of individuals exposed to BTEX and association of cancer with BTEX exposure in comparison to no BTEX exposure. The p value of 0.05 considered statistically significant.
Results: 124,162 participants were identified with BTEX exposure. Individuals with BTEX exposure were young (49 vs 51-year-old), male (91% vs female: 87%), and Mexican American (92% vs Non-Hispanic Black: 89% vs Non-Hispanic White: 89% vs other Hispanic: 87%). (p<0.0001) Univariate analysis showed higher total prevalence of cancer in BTEX (9.3% vs. 1.3%; p<0.0001) compared to no BTEX. Individuals with BTEX exposure had higher prevalence of blood cancer (0.47% vs 0.00; p<0.0001), leukemia (0.56% vs 0.00; p<0.0001), and lymphoma (1.72% vs 0.39%; p<0.0001) in comparison with no exposure. Multivariate analysis showed participants with BTEX exposure had 10% higher risk of cancer (aOR: 1.10, 95%CI: 1.10-1.10; p<0.0001) compared to no BTEX exposure. Additionally, age (1.07, 1.07-1.07- with increment in 10 years; p<0.0001), Non-Hispanic White (4.79, 4.79-4.80; p<0.0001), Hispanics (2.49, 2.49-2.50; p<0.0001), and Non-Hispanic Black (2.21, 2.21-2.22; p<0.0001) were associated with higher odds of BTEX exposure-associated cancer. (c=0.82) 
Conclusion: Age, race, gender, and income were highly associated with BTEX exposure. Future studies are warranted to evaluate the association of the risk of BTEX-related cancer with sociodemographic disparities.

Demographic disparities and risk of cancer amongst the United States population with exposure to BTEX (benzene, toluene, ethylbenzene, and xylene) - a cross-sectional study

Background
Numerous studies have attempted to construct cell classifiers based on machine learning in recent years. The following project examines the feasibility of building deep learning models to accurately distinguish cells that are nearly morphologically indistinguishable. The dataset used in the project is the International Symposium on Biomedical Imaging (ISBI) 2019 C-NMC dataset. The image repository was released to the public as part of a global machine learning competition that culminated in a symposium where the best performing models were presented. The cells were collected through bone marrow biopsies from 69 patients suffering from ALL and 49 healthy patients . Lymphoblasts within this dataset were digitally isolated and displayed on a black background.

Methods
Three different neural network models were created: a simple Logistic Regression model, a Convolutional Neural Network (CNN), and a ResNet50V2 neural network. To yield better results, simple adjustments to standard neural network parameters were implemented. The original dataset was imbalanced, so weights were applied to balance the dataset. Transformations, such as mirroring and rotation, were incorporated to increase the size of the dataset, which was necessary to address the limited dataset size. 

Results
The results of these models were promising with the highest performing model, CNN, accomplishing an accuracy score of 75%.

Conclusion
Ultimately, this branch of research is worth exploring further. Tuning parameters during model construction directly resulted in accuracy improvements. This has been proven in other studies used on the same dataset, which were able to accomplish higher accuracy scores based on different tuning methodologies.

Diagnosing Acute Lymphoblastic Leukemia with Machine Learning


Introduction: Lung adenocarcinoma is the most prevalent non-small cell lung cancer. Up to 15% of lung adenocarcinomas are characterized by mutations in the epidermal growth factor receptor (EGFR) gene. Osimertinib, an EGFR tyrosine kinase inhibitor, is an effective method of treatment in EGFR-mutant lung cancer. However, osimertinib resistance is frequent. The mechanisms of Osimertinib are heterogenous but here we describe a new case of osimertinib resistant carcinosarcoma. 
Case history: A 63-year-old woman presented with headaches, right arm weakness, and gait instability. Radiographic evaluation showed a left parietal lobe mass, enlarged hilar lymph nodes, and numerous lung nodules. CT-guided biopsy of one lung nodule revealed an invasive adenocarcinoma which expressed TTF-1 but was negative for p-40 immunostaining. Tumor next generation sequencing revealed a rare exon 19 EGFR (E747_A750del) deletion. The patient was treated with stereotactic radiosurgery for brain metastasis followed by osimertinib immunotherapy. More than a year later, lead to a recurrence of the patient’s symptoms. Repeat radiographic evaluation showed new mediastinal lymphadenopathy. Fine needle aspiration of the enlarged mediastinal lymph nodes revealed carcinosarcoma with minimal adenocarcinoma component. A repeat tumor next generation sequencing confirmed the same EGFR exon 19 deletion (E746_A750del) in addition to TP53/Rb mutation and a high tumor mutational burden. 
Conclusion: This case demonstrates another association of osmertinib resistance in pulmonary adenocarcinoma with a specific EGFR mutation. This case is unique as it also documents histologic transformation of EGFR-mutated lung adenocarcinoma into a carcinosarcoma as a possible underlying mechanism of osimertinib resistance. 


EGFR-mutated lung adenocarcinoma transformation to carcinosarcoma as an osimertinib resistance mechanism

Abstract Title: Characterizing Outcomes of Immune Checkpoint Inhibition Treatment in Patients with advanced Renal Cell Carcinoma

Michael Pannell1, Maishara Muquith1, David Hsieh2, MD
1. University of Texas Southwestern Medical School
2. University of Texas Southwestern, Internal Medicine, Division of Hematology-Oncology

Background: Immune checkpoint inhibitors (ICIs) have become a standard of care treatment for patients with metastatic renal cell carcinoma (mRCC), with previous retrospective studies demonstrating potential benefits with increased tumor sensitivity to sequential treatment after ICI failure. An increasingly important area of investigation is characterizing the outcomes of ICI treatment and subsequent therapies in patients with advanced renal cell carcinoma. We examine characteristics of ICI treatment outcomes in a real-world cohort.

Methods: This IRB approved retrospective cohort study included patients with advanced renal cell carcinoma treated with palliative-intent PD-1/PD-L1 inhibitors at a single institution between 2016 and 2019. Clinical data including age, sex, cancer type, and treatment lines were obtained from the electronic medical record. Radiographic responses were determined from available CT/MRI studies and categorized using Response Evaluation Criteria In Solid Tumors 1.1 criteria. Response categories include complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD).

Results: Of 110 patients with mRCC that initiated ICI therapy between 2016 and 2019, 95 were treated with palliative intent and had subsequent imaging to evaluate their response. The overall objective response rate was 18.8% (n=18), with 18 patients demonstrating a partial response to treatment, 26 demonstrating stable disease, and 52 showing disease progression. ICI therapy as 1st line palliative treatment demonstrated the highest objective response rate of 27.5% (n=14), decreasing for subsequent treatment lines (13% and 0% for 2nd and 3rd+). Progression Free Survival (PFS) and Overall Survival (OS) were measured across ICI type and treatment line, with overall median values being 82 and 926 days, respectively. Additionally, patients were treated with either nivolumab monotherapy, nivolumab/ipilimumab combination, or pembrolizumab concurrent axitinib. Those treated with nivolumab monotherapy had a median PFS=79.5 days (n=32) and a median OS=1068.5 days (15 cases ongoing at the time of data analysis), compared to nivolumab/ipilimumab median PFS=83 days (n=55) and median OS=706 days (22 ongoing), and pembrolizumab median PFS=169 days (n=9) and median OS=559 (4 ongoing).

Conclusion: This study suggests that specific ICI therapies and treatment line may be associated with objective response and survival in patients with mRCC. Further identifying these associations could result in improved clinical outcomes.

Immune Checkpoint Inhibition Treatment Outcomes in Advanced Renal Cell Carcinoma

Background: Impact of race in advanced stage non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) is conflicting. Few studies have significant representation of Black patients, and these studies lack detail on access to care, such as time to treatment initiation (TTI). Our study sought to examine racial disparities in TTI, overall survival (OS), and progression-free survival (PFS) using a population that was almost equally Black and White. 

Methods: This was an IRB approved retrospective cohort study of stage IV NSCLC patients > 18 years receiving immunotherapy at our center between 2014 and 2021. Associations between demographic characteristics and median values were examined using Chi-square tests. Kaplan-Meier curves and multivariate Cox proportional hazards model determined predictors of OS and PFS. Analyses were done using IBM PSAW (SPSS v.28) with p-value < 0.05 indicating significance.

Results: Of 194 patients who met the eligibility criteria, 42.3% were Black (n=82). On Kaplan-Meier analysis, no difference was observed between White and Black patients with respect to median PFS (6.30 versus 7.90 months; log-rank p=0.977) and OS (9.50 versus 11.77 months; log-rank p=0.457). On multivariate analysis, there was no difference in PFS (HR: 0.98; 95% CI: 0.68,1.43; p=0.928) or OS (HR: 1.01; 95% CI: 0.67,1.51; p=0.969) when controlling for race, sex, age at diagnosis, pack years, marital status, insurance type, histology, and Charlson Comorbidity Index (CCI). There was no difference in treatment selection between White and Black patients (p=0.184; chi-square), nor was there a difference in median time to overall treatment initiation (28.00 days versus 22.00 days; p=0.159). A difference was evident for immunotherapy initiation (34.00 versus 27.00 days; p=0.042) between White and Black patients respectively.

Conclusion: Although race was expected to permeate patient outcomes, OS and PFS did not differ between Black and White patients. The significantly shorter time to immunotherapy treatment for Black patients is an unexpected finding. In our study, we highlighted that equitable access reduces disparities, bridges gaps in care, and improves patient outcomes, regardless of race. Further studies with equitable representation of diverse racial groups are warranted to better understand how race impacts cancer outcomes.

Impact of race on outcomes of advanced stage non-small cell lung cancer in an equal access setting

MSIDx: Pan-cancer detection of microsatellite instability through targeted microsatellite sequencing enables immunotherapy

Abstract Title: Non-responder prediction in patients with advanced urothelial carcinoma using machine learning.

Background: Post FDA approval immunotherapy for cancer becomes widely used for locally advanced or metastatic urothelial carcinomas (1) with 16.5% of patients suffering Grade 3-5 side effects (2) from treatment. The current investigation wanted to classify patients that will not respond to immune checkpoint inhibitors (ICI) and potentially prevent side effects for these patients.

Methods: Pretreatment normalized ribonucleic acid Sequencing (RNASeq), clinical and other data for patients with advanced UC receiving PD-1 axis inhibitors were downloaded from gene expression omnibus (GEO) datasets project PRJNA735749 (3). This data also included responses to treatment data. Responders were categorized as patients with either complete response (CR), partial response (PR), or stable disease (SD) while non-responders comprised patients with progressive disease (PD). With a 70/30 training/test data split we developed four distinct algorithms were developed using a complete or targeted gene expression profile (high vs low expression with non-RNASeq data; total mutational burden (TMB), clinical data, and FGFR mutational status. The performance of each of the algorithms was assessed by comparing a matric of test set accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under the receiver-operating curves.

Results: A total of 89 patients (from a total of 103) had complete RNASeq, DNA, and clinical data. The complete RNASeq + non-RNASeq data showed test set; accuracy (18.5%), sensitivity (100%), specificity (0.0%), PPV (25%), NPV (UD*). The targeted RNASeq + non-RNA Seq data showed test set; accuracy (70.4%), sensitivity (0.0%), specificity (100%), PPV (undefined), NPV (70.4%). The targeted RNASeq (Hi/Low cutoff) + non- RNASeq data using a Multilayer Perceptron (MLP) classifier showed test set; accuracy (81.5%), sensitivity (20.0%), specificity (95.5%), PPV (50.0%), NPV (84.0%). The targeted RNASeq (Hi/Low cutoff) + non-RNASeq data using a TensorFlow (TF) classifier showed test set; accuracy (77.8%), sensitivity (20.0%), specificity (90.9%), PPV (33.3%), NPV (83.3%).

Conclusion: The MLP/deep-learning classifier for targeted normalized high vs low RNA expression with TMB, clinical characteristics, and FGFR mutational status shows better overall results compared to other algorithms. However, these results need external validation and with a larger dataset and we may also be able to predict responders in these patients.


Non-responder prediction in patients with advanced urothelial carcinoma using machine learning

Background 
In patients diagnosed with cancer, pain can interfere with daily functioning, affecting overall quality of life. The predictors of pain in patients with cancer are not well-delineated. Further, pain has been associated with inflammation, yet previous research attempting to understand this link has been limited secondary to the lack of inclusion of other covariates associated with inflammation. The aims of this study were to describe pain intensity and interference in patients with cancer, investigate the predictors and consequences of pain, and examine the associations between pain and cytokines while covarying factors associated with inflammation. 

Methods
This study was a secondary analysis of baseline data from three prospective studies conducted between 2008-2019 at the University of Pittsburgh Medical Center Cancer Center. Patients completed questionnaires assessing sociodemographic characteristics, pain, depression, sleep, and fatigue. Serum levels of cytokines IL-1alpha, IL-1beta, TNF-alpha, IL-10, IL-2, and IFN-gamma were assessed. Analyses of Variance, Chi-square analyses, and linear regression were performed to test the hypotheses. 


Results
Of the 779 participants, the mean age was 63.5 years (S.D.=11.02), 57.8% male, 90.6% Caucasian. 67.3% of patients reported pain, of which 50.4% of patients reported was cancer-related. Significant predictors of pain intensity and interference included lower education level (beta=-2.800, 95% CI=[-4.374,-1.227], p=0.01) and a diagnosis of hepatocellular carcinoma or cholangiocarcinoma (beta=-0.947, 95% CI=[-1.551,-0.343], p=0.002). After adjusting for sociodemographic, disease-specific, and treatment-related factors, pain significantly predicted higher levels of depression (beta=0.587, 95% CI=[0.425,0.749], p<0.001), fatigue (beta=-0.858, 95% CI=[-1.031,-0.684], p<0.001), and fewer hours of sleep per night (beta=-0.055, 95% CI=[-0.088,-0.023], p=0.001). After adjusting for sociodemographic, disease-specific, and treatment-related factors, pain was not significantly related to serum levels of IL-1alpha, IL-1beta, TNF-alpha, IL-10, IL-2, or IFN-gamma.

Conclusion
Nearly one-third of patients reported cancer-related pain, which was linked to poorer quality of life. Pain, however, was not found to be associated with inflammatory biomarkers, warranting further research into underlying biological mechanisms of pain and related symptoms of depression, fatigue, and poor sleep. Given that pain predictors included lower education level and diagnosis of liver cancer, interventions should be designed and implemented for these patients with the greatest need for pain management.

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