Citations

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]

1552 Background: Immune checkpoint inhibitor therapy-related (ICI) pneumonitis poses a significant challenge in patients with cancer. There is a need for predictive modeling to identify those at high risk of developing serious adverse events of ICI pneumonitis. Methods: A newly created database of cancer patients diagnosed with ICI pneumonitis seen at Mayo Clinic from 2014-2022 was utilized for exploratory data analysis. Modeling was used to determine if three clinical outcomes could be predicted, namely risk of death from: pneumonitis prior to starting immunotherapy, pneumonitis at time of diagnosis of ICI pneumonitis, and any cause at time of ICI pneumonitis diagnosis. The data consisted of 170 patients with annotation for 71 clinical features. Data was divided into features available at time of cancer diagnosis and at time of ICI pneumonitis diagnosis. Exploratory modeling was performed using the gradient boosting technique Xgboost (Chen, 2015) and conducted using k-fold balanced cross validation best practices as well as a train/test/validate schema with 70%/20%/10% data proportions, respectively. Model reverse engineering was done with Shapley statistics (Lundberg, 2017) to determine which features had the largest contribution per model. Once identified, only those highly weighted features were used for logistic regression analysis providing more reproducible predictions by decreasing model variance. Results: Risk of death from pneumonitis prior to starting immunotherapy was modeled with an area under the curve of the receiver operator characteristic (AUC-ROC) of 0.79 with the most contributory features including lymphocyte count, oxygen dependence, PFT values, and PD-L1 expression. Logistic regression produced an AUC-ROC of 0.87 (95% CI 0.72-1.0, p < 0.0013). Risk of death from pneumonitis at the time of ICI pneumonitis diagnosis was modeled with an AUC-ROC of 0.85 and the most contributory features were similar to the list described in the previous endpoint. Logistic regression produced an AUC-ROC of 0.89 (95% CI 0.81-0.96, p < 0.0001). Risk of death from any cause at the time of ICI pneumonitis diagnosis until follow up concluded by 12/2022 produced a model with an AUC-ROC of 0.75 with the most contributory features including supplemental oxygen, PFT values, basic laboratory values, and PD-L1 expression, among others. Logistic regression produced an AUC-ROC of 0.85 (95% CI 0.76-0.93, p < 0.0001). Conclusions: We demonstrate that commonly available clinical data can be used to identify patients at high risk of death from ICI pneumonitis. This study identified clinical features that were predictive in each scenario from which further concerted effort could produce a new clinical model to provide clinician decision support when considering immunotherapy. Further studies should be done to further elucidate feature interdependence and generalizability.

TPS1115 Background: The current first-line (1L) standard of care (SOC) for human epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC) is trastuzumab (T) plus pertuzumab (P) and a taxane. Despite advances in 1L SOC, most patients progress during maintenance therapy with T + P. Tucatinib is a tyrosine kinase inhibitor (TKI) approved in combination with T and capecitabine for adults with HER2+ MBC, with and without brain metastases (BM). In HER2CLIMB, the addition of tucatinib significantly prolonged progression-free survival (PFS) and overall survival (OS) in patients with HER2+ MBC and was well tolerated. Adding tucatinib also reduced the risk of disease progression or death in patients with untreated and/or active BM (Murthy et al. 2020, Curigliano et al. 2021). HER2CLIMB-05 investigates whether adding tucatinib to 1L SOC as maintenance therapy will extend PFS while maintaining quality of life (QOL). Methods: HER2CLIMB-05 (NCT05132582) is a phase 3, randomized, double-blind study evaluating tucatinib plus T + P as maintenance therapy for HER2+ MBC. Approximately 650 patients will be enrolled. Eligible patients will have advanced HER2+ disease, no progression on 4–8 cycles of prior 1L SOC, Eastern Cooperative Oncology Group Performance Status of 0 or 1, and no or asymptomatic BM. Exclusion criteria include prior treatment with anti-HER2 and/or anti-epidermal growth factor receptor TKI (prior SOC for early BC is permitted) or inability to undergo contrast magnetic resonance imaging of the brain. Patients will be randomized 1:1 to receive either tucatinib or placebo twice daily, with T + P once every 21 days. Patients with hormone receptor-positive disease may receive endocrine therapy. The primary endpoint is investigator-assessed PFS. Secondary endpoints include OS (key endpoint), PFS by Blinded Independent Central Review (BICR), time to deterioration of health-related QOL, central nervous system PFS, safety, and pharmacokinetic (PK) parameters. PFS and OS will be compared using a 2-sided stratified log-rank test between treatment groups. Time-to-event endpoints will be summarized using the Kaplan–Meier method. PK and safety data will be summarized using descriptive statistics. Enrollment is ongoing in the US, Canada, Brazil, APAC and EU countries with additional sites planned. Clinical trial information: NCT05132582 .

Abstract Two inherited autosomal dominant cancer predisposition conditions - BRCA related hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) - are termed Centers for Disease Control and Prevention Tier 1 (CDCT1) genetic conditions, for which early identification and intervention have a positive impact on public health. Selection of individuals for genetic testing for these conditions is based on personal and family history. The goal of this study was to evaluate whether screening in a tertiary medical center using exome sequencing could efficiently identify carriers of HBOC and LS and to determine the frequency of incremental carriers identified outside of traditional clinical practice guidelines. Participants from three geographically diverse Mayo Clinic practices in the USA (Rochester MN, Phoenix AZ, Jacksonville FL) consented to clinical “Exome+” sequencing (Helix, San Mateo, CA; Mayo GeneGuide, Rochester, MN) in the TAPESTRY study (NCT05212428), which links sequencing data with electronic health records and the return of CDCT1 genetic findings. For this study we focused on two inherited cancer predisposition conditions: BRCA-related HBOC (BRCA1 and BRCA2) and LS (MLH1, MSH2, MSH6, PMS2 and EPCAM). Detailed chart review to collect demographic information, personal and family history, and assessment of clinical practice guidelines for genetic evaluation (National Cancer Control Network 2021). To date, 44,306 patients have enrolled and sequenced in TAPESTRY. Annotation and interpretation of all variants in 7 genes for HBOC and LS resulted in identification of 550 carriers (prevalence 1.24%) which included 387 with HBOC (27.2% BRCA1, 42.8% BRCA2) and 163 with LS (12.3% MSH6, 8.8% PMS2, 4.5% MLH1, 3.8% MSH2 and 0.2% EPCAM). Demographics of the cohort included: 62.7% female, mean age 55.2 years, non-white race 9.6% and 3.8% Hispanic/Latino ethnicity. A personal history of cancer was present in 46.4% of test positive patients, including 22.5% of HBOC patients with breast or ovarian cancer and 20.9% of LS patients with colorectal or uterine cancer. More than half of the patients (52.1%) were newly diagnosed with HBOC and LS based on the results of this study. Overall, 39.2% of HBOC/LS carriers identified through Exome+ sequencing did not satisfy NCCN criteria for genetic evaluation, this was higher for LS (56.2%) compared to HBOC (32%). Amongst those newly diagnosed with HBOC/LS (n=286), NCCN criteria were not satisfied in 60% of cases (78% for LS and 51% for HBOC). Of the pathogenic germline variant carriers who met NCCN guidelines for testing, 34.2% were not aware of their diagnosis prior to participation in this study. Our results emphasize the need for wide genomic screening for CDCT1 cancer predisposition syndromes. Such screening could identify at-risk carriers of HBOC and LS, who would not otherwise be identified through clinical practice guidelines. Citation Format: Emily Gay, Niloy Jewel Samadder, Michelle L. Bublitz, Melanie M. Peterson, Tammy A. Wilson, Lorelei A. Bandel, Sebastian M. Armasu, Robert A. Vierkant, Matthew J. Ferber, Eric W. Klee, Nicholas B. Larson, Teresa M. Kruisselbrink, Timothy B. Curry, Jan B. Egan, Jennifer L. Kemppainen, Jessa S. Bidwell, Jennifer L. Anderson, Tammy M. McAllister, T'Nita S. Walker, Katie L. Kunze, Vanda Lindpere, Michael A. Golafshar, Margaret Klint, Richard J. Presutti, William V. Bobo, Aleksander Sekulic, Jolene M. Summer Bolster, Cheryl L. Willman, Konstantinos N. Lazaridis. Genetic screening in a tertiary medical center identifies carriers of cancer predisposition diseases that would be missed by clinical guidelines [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 5768.

Abstract Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. Significance: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

Abstract Poorly differentiated extrapulmonary neuroendocrine carcinomas (EP NECs) are aggressive cancers characterized by a high Ki‐67 index, rapid tumor growth and poor survival, and are subdivided into small and large cell carcinoma. For small cell carcinoma of the lung, a pulmonary NEC, the combination of cytotoxic chemotherapy (CTX) and a checkpoint inhibitor (CPI) is considered standard therapy and superior to CTX alone. EP NECs are typically treated with platinum‐based regimens, some clinicians have adopted the addition of a CPI to CTX based on data from trials in patients with small cell carcinoma of the lung. In this retrospective study of EP NECs, we report 38 patients treated with standard first‐line CTX and 19 patients treated with CTX plus CPI. We did not observe any additional benefit of adding CPI to CTX in this cohort.

Abstract Background: The current first-line (1L) standard of care (SOC) for human epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC) is trastuzumab (T) plus pertuzumab (P) and a taxane. Despite advances in 1L SOC, most patients (pts) progress during maintenance therapy with T+P. Tucatinib is a tyrosine kinase inhibitor (TKI) approved in combination with T and capecitabine for adults with HER2+ MBC, with and without brain metastases (BM). In HER2CLIMB, the addition of tucatinib significantly prolonged progression-free survival (PFS) and overall survival (OS) in pts with HER2+ MBC and was well tolerated. Adding tucatinib also reduced the risk of disease progression or death in pts with untreated and/or active BM (Murthy et al. 2020, Curigliano et al. 2021). HER2CLIMB-05 investigates whether adding tucatinib to 1L SOC as maintenance therapy will extend PFS while maintaining quality of life (QOL). Methods: HER2CLIMB-05 (NCT05132582) is a phase 3, randomized, double-blind study evaluating tucatinib plus T+P as maintenance therapy for HER2+ MBC. Approximately 650 pts will be enrolled. Eligible pts will have advanced HER2+ disease, no progression on 4-8 cycles of prior 1L SOC, ECOG Performance Status of 0 or 1, and no or asymptomatic BM. Exclusion criteria include prior treatment with anti-HER2 and/or anti-epidermal growth factor receptor TKI (prior SOC for early BC is permitted) or inability to undergo contrast magnetic resonance imaging of the brain. Pts will be randomized 1:1 to receive either tucatinib or placebo twice daily, with T+P once every 21 days. Pts with HR+ disease may receive endocrine therapy. The primary endpoint is investigator-assessed PFS. Secondary endpoints include OS (key endpoint), PFS by blinded independent central review (BICR), time to deterioration of health-related QOL, central nervous system PFS, safety, and pharmacokinetic (PK) parameters. PFS and OS will be compared using a 2-sided stratified log-rank test between treatment groups. Time-to-event endpoints will be summarized using the Kaplan-Meier method. PK and safety data will be summarized using descriptive statistics. Enrollment is ongoing in the US, Canada, and several EU and APAC countries, with additional sites planned. This abstract was previously presented at ESMO-BC 2022, FPN (Final Publication Number): 415, by Veronique Dieras (reused with permission). Citation Format: Erika Hamilton, Junji Tsurutani, Joohyuk Sohn, Giuseppe Curigliano, Ciara C. O’Sullivan, Miguel Martín, Konstantinos Tryfonidis, Libero Santarpia, Shan Yang, Veronique Dieras. Phase 3 study of tucatinib or placebo in combination with trastuzumab and pertuzumab as maintenance therapy for HER2+ metastatic breast cancer (HER2CLIMB-05, trial in progress) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT065.

Abstract Hereditary factors play a role in the development of several cancers. Identification of germline predisposition can have implications for treatment and cancer prevention. While previous studies have found that universal multigene panel testing in patients with cancer yields increased detection of actionable heritable variants compared to targeted testing, patients belonging to racial and ethnic minority groups have been underrepresented in these studies. We undertook a prospective multi-site study of germline alternations among solid tumor cancer patients from under-represented minority (URM) populations receiving care at Mayo Clinic Comprehensive Cancer Centers between January 2020 and November 2022. Patients with a new or active cancer diagnosis (all stages) not selected for family history of cancer were tested with a &gt;80-gene next generation sequencing panel. Of 532 patients (mean [SD] age, 57.2 [12.7] years; 320 (60.2%) female), 247 (46.4%) were Hispanic/Latino, 130 (24.4%) Black/African American, 80 (15.0%) Asian, 66 (12.4%) American Indian/Alaskan Native, 2 (0.4%) Native Hawaiian/Pacific Islander, and 7 (1.3%) other. PGV were found in 43 patients (8.1%), including 29 moderate- or high- penetrance cancer susceptibility genes. Variants of uncertain significance (VUS) were found in 272 patients (51.1%). Of those with a PGV, 29 (67.4%) had incremental clinically actionable findings that would not have been detected by established testing criteria, of which 16 were moderate- or high-penetrance variants. Younger age of diagnosis, gender, race, ethnicity, and stage of disease were not predictive of PGV. In this study of under-represented minority cancer patients, universal genetic testing found that 1 in 12 patients harbor a PGV, more than two-thirds of whom would remain undetected using current guidelines. The paucity of genomic data in URM populations also results in a large proportion of VUS which may lead to under-estimation of disease associated variants. Table 1. Clinical and Demographic Characteristics of Included Patients Positive Negative VUS Total N (%) 43 217 272 532 (8.1%) (40.8%) (51.1%) Age - mean (SD) 53.7 57.0 57.8 57.2 (12.7) (12.6) (12.8) (12.7) Gender - Female 26 132 162 320 (60.5%) (60.8%) (59.6%) (60.2%) Race/Ethnicity Hispanic/Latino 24 110 113 247 (55.8%) (50.7%) (41.5%) (46.4%) Black/African 5 56 69 130 American (11.6%) (25.8%) (25.4%) (24.4%) Asian 10 (23.3%) 23 (10.6%) 47 (17.3%) 80 (15.0%) American Indian/Alaskan Native 4 (9.3%) 26 (12.0%) 36 (13.2%) 66 (12.4%) Native Hawaiian/Pacific Islander 0 (0.0%) 0 (0.0%) 2 (0.7%) 2 (0.4%) Other 0 (0.0%) 2 (0.9%) 5 (1.8%) 7 (1.3%) Tumor Types Breast 19 (44.2%) 80 (36.9%) 110 (40.4%) 209 (39.3%) CNS 1 (2.3%) 2 (0.9%) 1 (0.4%) 4 (0.8%) GI 7 (16.3%) 46 (21.2%) 58 (21.3%) 111 (20.9%) GU 8 (18.6%) 57 (26.3%) 62 (22.8%) 127 (23.9%) GYN 0 (0.0%) 8 (3.7%) 10 (3.7%) 18 (3.4%) Head/Neck 5 (11.6%) 8 (3.7%) 6 (2.2%) 19 (3.6%) Lung 0 (0.0%) 6 (2.8%) 11 (4.0%) 17 (3.2%) Melanoma 0 (0.0%) 2 (0.9%) 1 (0.4%) 3 (0.6%) Sarcoma 2 (4.7%) 4 (1.8%) 4 (1.5%) 10 (1.9%) Other 1 (2.3%) 4 (1.8%) 9 (3.3%) 14 (2.6%) Citation Format: Kevork A. Khadarian, Sailaja Pisipati, Jeremy C. Jones, Gerardo Colon-Otero, Donald W. Northfelt, Lindsey Gary, Idara U. Ekpoh, Giovanna G. Moreno, Edward D. Esplin, Robert Nussbaum, Brandie Heald, Margaret A. Klint, Kathleen A. Barrus, Tanios S. Bekaii-Saab, Aleksandar Sekulic, Michael A. Golafshar, Katie L. Kunze, Cheryl L. Willman, Konstantinos N. Lazaridis, Niloy Jewel Samadder. Universal genetic testing for hereditary cancer syndromes in an under-represented minority population [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 1921.