bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical utility of AlphaMissense in predicting pathogenicity of DNA damage repair genes in cancer.
Authors: Yazaki Shu , Pei Xin , Powell Simon N. , Khan Atif J. , Setton Jeremy , Riaz Nadeem 
Published: June 2024

10581 Background: Deficiencies in DNA damage repair (DDR) impact cancer predisposition and various treatment responses.Functional implications of missense variants in DDR genes remain elusive.AlphaMissense, a new AI-based method, accurately predicts the pathogenicity of missense variants, but its clinical utility requires validation. We evaluated the accuracy of AlphaMissense predictions by analyzing known mutational signatures, genomic characteristics, and therapeutic vulnerabilities of DDR-deficient tumors. Methods: We analyzed data from TCGA (n=8,178) and MSK-IMPACT targeted panel sequencing (n=56,695) to evaluate the performance of AlphaMissense in predicting clinically actionable DDR genes, including core homologous recombination (HR) genes ( BRCA1/2, PALB2, RAD51C), POLE, and ATM. Missense mutations were classified as known pathogenic by OncoKB or previous literature, newly pathogenic identified with AlphaMisense, or benign. Mutational signatures were computed using SigMA algorithm, classifying each signature as positive at >20%. The irradiated tumor control was evaluated in patients with ATM missense mutations who received radiotherapy. Results: Among 6,743 unique DDR missense mutations from MSK-IMPACT, 372 (3.5%) were known pathogenic, and 1,182 (17.5%) were new pathogenic mutations. In 205 tumors with bi-allelic core-HR missense mutations in breast, ovarian, pancreatic, and prostate cancers, 20 (9.8%) had new pathogenic mutations. Tumors with new pathogenic mutations more frequently exhibited HR-deficient signatures (65%) than wild-type (31.8%, p < .001), similar to tumors with known pathogenic mutations (62.8%, p = 1.0). In TCGA, HR-deficient signatures tended to be more frequent in tumors with new pathogenic missense mutations than in wild-type tumors (41.7% vs. 21.0%, p = .16). Of 1,010 tumors with POLE missense mutations from MSK-IMPACT, 300 (30%) had new pathogenic mutations. The presence of POLE signatures in tumors with new pathogenic mutations (11.9%) was not significantly different from those with benign mutations (4.3%, p = .19) and less frequent than in tumors with known pathogenic mutations (91.0%, p < .001). In MSK-IMPACT, tumors with new pathogenic ATM missense mutations were less likely to have TP53 mutations than benign missense mutations (32.7% vs. 56.6%, p < .001), consistent with bona-fide pathogenicity. Further, patients with new pathogenic ATM missense mutations showed increased radiosensitivity, characteristic of ATM deficiency, and improved irradiated tumor control compared to those with benign missense mutations (Hazard ratio 0.58, 95% Cl 0.35-0.95, p = .03). Conclusions: AlphaMissense can identify previously unknown pathogenic DDR missense mutations, but its accuracy is gene-dependent. AlphaMissense pathogenicity predictions for DDR genes can utilized to help classify mutations, but may not be sufficient on their own.
Correlation of patient HLA class I genotypes with severe immune-related adverse events: A retrospective study in Chinese population.
Authors: Ai Luoyan , Xu Zhenghang , Zhu Mengxuan , Yu Yiyi , Liu Tianshu 
Published: June 2024

10586 Background: Immune checkpoint inhibitors (ICIs) are effective in multiple tumors. However, about 10% of immune-related adverse events (irAEs) are severe and can have serious or even life-threatening consequences for patients. We investigated whether Human leukocyte antigen (HLA) genes predispose to and thus hold a predictive role of developing of severe irAEs in Chinese population. Methods: In a case/control study, we collected data from patients with metastatic cancer treated with ICIs and developed severe irAEs (Grade ≥3 and required hospitalization). Patients treated with ICIs without any irAEs were used as controls. Data was collected from15th Nov, 2019 until 10th Oct, 2023. HLA typing was performed via next generation sequencing. Results: We enrolled 83 patients with severe irAEs and 286 patients treated with ICIs without any irAEs. We found HLA-B*15:27 (OR=9.1, X21,95 = 7.2, P = 0.008) and HLA-C*04:01 (OR=2.5, X21,95 = 7.0, P =0.008) were significantly associated with severe overall irAEs risk. When assessing irAE individually, a significant association between HLA-B*13:02 and serious myocarditis (OR = 3.4, X21,95 = 6.2, P =0.01) as well as striking enrichment for HLA-A*02:03 haplotypes in severe adrenal insufficiency (OR = 10.0, X21,95 = 19.1, P<0.0001) were observed. Conclusions: Specific HLA alleles were associated with severe irAEs. Consideration of pre-prescription HLA typing and vigilance for serious reactions to ICIs are warranted.
Genetic counseling and subsequent cascade genetic testing for women with a family history of pathogenic BRCA1 mutation: A cost-effectiveness model.
Authors: Walker Allison , Marmelstein Briana , Fillman Corrine , Dillon Monica , Graul Ashley 
Published: June 2024

10621 Background: BRCA1 pathogenic mutations significantly increase the lifetime risk for breast and ovarian cancer, but risk reducing interventions such as bilateral salpingo-oophorectomy (RRBSO) and mastectomy (RRM) can improve outcomes. For women with a known family history of pathogenetic mutation, genetic counseling and testing is recommended. We sought to determine the cost-effectiveness (CE) of referral to genetic counseling with subsequent cascade testing. Methods: A decision analytic model was created using TreeAgePro 2023 software to evaluate the strategies of routine genetics consultation with Ambry panel testing versus no referral. It was assumed that known BRCA1 carriers would undergo annual breast cancer screening with mammogram and MRI at 25yo, RRBSO at 35yo, and RRM at 40yo. Non-carriers underwent routine annual mammogram at 40yo. Additionally, women who did not undergo genetic testing initially do so at the time of diagnosis of first cancer (breast of ovary) and subsequently underwent all recommended risk reducing strategies if found to have a pathogenic BRCA1 mutation. Outcomes included cost, quality adjusted life years (QALYs), ovarian and breast cancer cases and deaths. Univariate and multi-variate analyses were used to investigate the impact of the strategies. Results: In a Monte Carlo Microsimulation cohort of 10,000 women with a first degree relative with a pathogenic BRCA1 mutation, referral to genetic counseling with testing was the dominate strategy. This strategy resulted in a similar mean cost ($185,543 vs $185,171), but with 2,322 additional QALYs and incremental CE ratio (ICER) of 1,605 per QALY. This dominant strategy also resulted in 73 fewer ovarian cancer cases and 545 fewer breast cancer cases. Sensitivity analysis for the age of RRM was remarkable for an annual reduction in breast cancer cases by 97 cases and an annual cost savings of 375,658 for year prior to age 40 that mastectomy was performed. Conclusions: Routine genetic counseling and screening for all patients with a known family history of pathogenic BRCA1 mutation reduces breast and ovarian cancer diagnoses by prompting risk-reducing procedures without significant increase in cost when compared to diagnosis-based testing. Routine access to genetic counseling in a high-risk population would help inform earlier counseling and further decrease cancer risk, particularly as earlier RRM may improve outcomes. This is further supported by the low cost to implement. Improvement in identification and early referral could be targeted. [Table: see text]
Shareable artificial intelligence to extract cancer outcomes from electronic health records.
Authors: Kehl Kenneth L. , Jee Justin , Pichotta Karl , Trukhanov Pavel , Fong Christopher , Waters Michele , Nichols Chelsea , Cerami Ethan , Schrag Deb , Schultz Nikolaus 
Published: June 2024

11000 Background: Clinical outcomes such as response, progression, and metastasis represent crucial data for observational cancer research, but outside of clinical trials, such outcomes are usually recorded only in unstructured notes in electronic health records (EHRs). Manual EHR annotation is too resource-intensive to scale to large datasets. Individual cancer centers have trained artificial intelligence natural language processing (AI/NLP) models to extract outcomes from their EHRs. However, due to concerns that models trained on protected health information (PHI) might encode private data, such models usually cannot be exported to other centers. Methods: EHR data from Dana-Farber Cancer Institute (DFCI) and Memorial-Sloan Kettering (MSK) collected through the AACR Project GENIE Biopharma Collaborative were used to train and evaluate Bidirectional Encoder Representations from Transformers (BERT)-based NLP models to extract outcomes from imaging reports and oncologist notes annotated with the Pathology, Radiology/Imaging, Signs/Symptoms, Medical oncologist, and bioMarkers (PRISSMM) framework. Document-level outcomes included the presence of active cancer; response; progression; and metastatic sites. ‘Teacher’ models trained on DFCI EHR data were used to label imaging reports and discharge summaries from the public MIMIC-IV dataset. ‘Student’ models trained to use MIMIC documents to predict teacher labels were transferred to MSK for evaluation. Results: Teacher models were trained at DFCI on 30,332 imaging reports for 2609 patients and 32,173 oncologist notes for 2917 patients with non-small cell lung, colorectal, breast, prostate, pancreatic, or urothelial cancer. The models were used to label 217,642 imaging reports and 141,377 discharge summaries from MIMIC for student model training. These DFCI-trained student models were evaluated at MSK, demonstrating high discrimination (AUROC > 0.90) across outcomes. Conclusions: This privacy-preserving “teacher-student” AI/NLP framework could expedite linkage of genomic data to clinical outcomes across institutions, accelerating precision cancer research. [Table: see text]
Efficacy of eSyM: Acute care utilization among patients with cancer who do versus do not report ePROs.
Authors: Hassett Michael J. , Uno Hajime , Tramontano Angela , Cronin Christine , Bian Jessica J , Dizon Don S. , Hazard-Jenkins Hannah W. , Osarogiagbon Raymond U. , Wong Sandra L. , Schrag Deb 
Published: June 2024

11001 Background: Patients (pts) receiving cancer treatment frequently experience burdensome symptoms that compromise outcomes and necessitate acute care. Prior clinical trials have demonstrated that electronic patient-reported outcome (ePRO)-based symptom management programs improve outcomes in controlled settings. Deploying these programs in routine care settings remains challenging. With funding from the Cancer Moonshot IMPACT Consortium, we created eSyM – an ePRO-based, EHR-integrated symptom management program – to facilitate the widespread adoption of active symptom management efforts. Methods: eSyM was deployed across six health systems from September 2019-August 2022 via a modified stepped-wedge cluster randomized pragmatic trial. Pts starting chemotherapy (CHEM) or undergoing surgery (SURG) for a suspected or confirmed thoracic, gastrointestinal, or gynecologic cancer were prompted to complete symptom questionnaires regularly; those reporting symptoms were offered additional supports. To assess eSyM efficacy, we studied pts who were eligible to use the program – comparing those who completed at least one symptom questionnaire to those who did not. Outcomes included emergency department (ED) visits and inpatient encounters (INPT) at 30 and 90-days. Odds ratios with 95% CIs were derived after adjusting to account for the propensity to report ePROs as a function of age, sex, race/ethnicity, employment, marital status, poverty, rurality, insurance, comorbidity, cancer, treatment goal, institution, and calendar time. Results: Among eSyM-eligible pts, 51% (N = 10,454/20,471) completed at least one symptom questionnaire (median 4 reports/patient) – 47% (3815/8187) for CHEM and 54% (6639/12,293) for SURG. Comparing symptom reporters to non-reporters, the proportion of CHEM+SURG pts experiencing an ED event was 5.3% vs. 7.1% at 30 days and 10.0% vs. 12.9% at 90 days; and the proportion experiencing an INPT event was 6.7% vs. 11.3% at 30 days and 14.0% vs. 19.5% at 90 days ( p< 0.001 for all). Adjusted ORs appear in the. Conclusions: After accounting for propensity to report symptoms, completing at least one symptom questionnaire was associated with lower odds of experiencing an ED or INPT encounter among CHEM and SURG pts across six diverse health systems. eSyM engagement reduced acute care utilization. This EHR-integrated symptom management solution is broadly available to health systems that use Epic. Clinical trial information: NCT03850912 . [Table: see text]
Data-driven strategies for enhancing diversity in oncology clinical trials: Optimization of eligibility criteria.
Authors: Griffith Sandra , Benedum Corey M. , Bozkurt Selen , Bhagat Ruma , Richie Nicole , Lavery Bea , Sarkar Somnath 
Published: June 2024

11020 Background: Advancing inclusive clinical research is essential to benefit all patients (pts) and is an expectation, and in some cases a requirement, by regulators. Enabling the inclusion of representative populations should begin at protocol design. However, it is not always clear which changes in eligibility criteria (EC) will have the most impact on representativeness and whether the impact will have similar effect across historically underrepresented populations (HUPs; i.e., pts ≥75 yrs, females, non-Latinx[NL] Black, or Latinx pts). We have defined a novel application of scoring to assess implications of EC on inclusion of HUPs in trials. Methods: Using real-world data from the US nationwide Flatiron Health electronic health record-derived de-identified database for aNSCLC as a benchmark (N=50,263), we selected pts who met trial EC and therapy indication criteria for 9 Phase III aNSCLC trials (2012–2018). We quantified representativeness of the eligible cohort via log disparity [LD], a machine learning fairness metric for the odds of selecting a subgroup vs. the rest of the population; 100% represents parity and 80% is sometimes considered a lower bound for representativeness, but higher benchmarks (e.g., 95%) may be more appropriate. We then identified EC drivers of representativeness by removing or modifying EC. Results: Across HUP groups, the majority of trials (≥55%) were below parity. Removing creatinine clearance (CrCl) had a consistently positive impact for older pts (median % Δ [range]: +2 [1, 32]) and NL Black pts (+2 [1, 5]), but the direction of impact differed across trials for female and Latinx pts (increase in representativeness for some trials, decrease for others). The impact of removing ECOG Performance Status (PS) varied across HUPs (positive for older pts, neutral or positive for female pts, trial-dependent for Latinx and NL Black pts); e.g., in Keynote010, removing ECOG PS increased LD for older pts (93% to 99%), but decreased LD for Latinx pts (97% to 93%). Across trials, removing hemoglobin, HIV, and Hepatitis B/C had a consistently positive or neutral impact on LD for NH Black pts (e.g., hemoglobin median % Δ [range]: +3 [2, 5]), however, the impact was negative or trial-dependent for Latinx and older pts. LD scores can also guide modification of cutpoints to optimize representativeness; e.g., for Keynote189, lowering the CrCL cutpoint from 50 to to 37 mL/min could be expected to improve representativeness for older patients (LD: 66% to 80%, the lower bound for representativeness). Conclusions: It is important to consider the complex nature of diversity when designing trials to ensure that optimizing inclusion of one group of pts does not inadvertently reduce representativeness for another group. These findings support a data-driven approach to modifying EC during protocol design to maximize inclusion of HUPs in concert with a robust operational plan.
Underrepresentation of Asian Americans, Native Hawaiians, and other Pacific Islanders (AA & NHPI) in cancer clinical trials that led to FDA approvals in 2010-2022.
Authors: Leong Ruby , Yang Felice , Lee Jennifer J. , Srivastava Geetika , Wedam Suparna B. , Dilawari Asma Ali , Fan Chaohong , Tran Grace , Lee Sso , Andrada Frances , Lee Clara , Fashoyin-Aje Lola A. , Amiri-Kordestani Laleh , Singh Harpreet , Lemery Steven , Pazdur Richard , Rivera Donna , Gao Jennifer , Kim Tamy 
Published: June 2024

11026 Background: About 6% and 0.3% of the total United States (US) population identify as Asian or NHPI alone, respectively. The most commonly diagnosed cancers among AA & NHPI in the US are breast, prostate, lung, thyroid, and colorectal. Liver, gastric, and head and neck cancers occur in higher rates in AA & NHPI vs. non-Hispanic White. Enrollment of Asian & NHPI patients in multiregional cancer clinical trials may provide additional data regarding intrinsic and extrinsic factors (e.g., diet, infections, environmental exposures) impacting the etiology of cancer. We investigated enrollment trends of NHPI and Asian (within and outside of US) patients in cancer clinical trials that led to an FDA approval from 2010-2022. Methods: We analyzed data from ~98,000 patients in 171 cancer therapeutic clinical trials that led to FDA approvals for breast, prostate, lung, thyroid, colorectal, liver, gastric, and head and neck cancer indications from 2010-2022. Separate race categories of Asian and Native Hawaiian and other Pacific Islander were used. Results: Descriptive statistics of NHPI and Asians within and outside of US enrollment in breast, prostate, lung, thyroid, colorectal, liver, gastric, and head and neck cancer trials that led to an FDA approval from 2010-2022 are summarized in the table. Enrollment of Asian patients in the US was <1% (except for liver cancer) and NHPI patients was <0.2%. Conclusions: Although cancer is the leading cause of death for AA & NHPI, AA & NHPI are under-represented in cancer clinical trials, especially when data are further disaggregated into enrollment of NHPI and Asians within and outside of the US. Cancer etiology may vary in Asians in the US vs. Asia due to different intrinsic/extrinsic risk factors, underscoring the importance of enrolling more AA & NHPI into clinical trials to expand the evidence supporting drug approvals in the US and to advance health equity through clinical trial diversity. [Table: see text]
Historic redlining, modern neighborhood structures, and mortality in children, adolescents, and young adults with cancer.
Authors: Karvonen Kristine , Vu Annie , Lin Katherine , Gibbons Joseph , Mendoza Jason , Chow Eric Jessen , Winestone Lena E. , Gomez Scarlett L. 
Published: June 2024

11037 Background: Historic redlining, referring to the Home Owners Loan Corporation (HOLC) program’s racially biased risk monitoring maps in the 1930’s, has indelible impacts on modern day neighborhood characteristics and health outcomes. The impact of redlining, a form of structural racism, on children, adolescents, and young adults with cancer (CAYACs) is unknown. This retrospective cohort study evaluates the association between redlining and mortality in CAYACs. Methods: Using the California Cancer Registry, we identified cases <25 years old diagnosed with first primary malignant cancer between 2000-2019. Using merged HOLC maps and U.S. Census data we determined case redlining status (A “Best”, B “Still Desirable”, C “Declining”, D “Hazardous”, and N “Not graded”) by residence at diagnosis. We defined residents of D neighborhoods redlined-exposed and residents of A or B neighborhoods unexposed or referent. Kaplan Meier methods and multivariable Cox proportional hazard models were applied to calculate overall survival. Additional models accounted for census tract-level neighborhood SES (nSES, a composite measure incorporating seven SES domains), neighborhood racial and ethnic composition (or typology) and racialized economic segregation (via index of concentration at the extremes). Results: In total 8,210 cases were HOLC-graded, the median age was 16 years, 46% were female, and 48% had solid tumors. Overall survival at 5 years was inferior for D cases vs. A or B cases (OS 80.2%, 95% CI: 78.5-81.7 vs. OS 88.2, 95% CI: 84.0-91.3). Adjusting for individual clinical characteristics (age, sex, year of diagnosis, and cancer stage), D cases experienced greater mortality (HR 1.32, 95% CI: 1.12-1.55) compared to A or B cases. Additional adjustment for insurance and nSES attenuated the relationship (HR 1.17, 95%CI: 1.00-1.36 and HR 1.07, 95% CI: 0.92-1.24 respectively). Similar patterns were observed in models adjusting for racialized economic segregation (HR 1.07, 95% CI: 0.92-1.24) and typology (HR 1.10, 95% CI: 0.94-1.28) in place of nSES. Conclusions: In this population-based study, redlined-exposed CAYACs experienced greater mortality compared to unexposed CAYACs. Adjusting for insurance and contemporary nSES attenuated the association between redlining and mortality. Findings suggest current neighborhood attributes may be downstream manifestations of structural racism with survival implications for CAYACs.
Characteristics and pregnancy outcomes of pregnant women by cancer diagnosis status.
Authors: Periyanayagam Usha , Popadic Lyuba , Devine Francesca , Wei Yuqin 
Published: June 2024

11038 Background: Given the broad exclusion of pregnant patients from clinical trials, limited evidence exists on the impact of cancer diagnoses on pregnancy outcomes. This study aimed to describe patient characteristics and end of pregnancy events among women with and without a cancer diagnosis before pregnancy. Methods: This retrospective claims study used data from the Komodo Research Dataset between 01/01/2016–08/31/2023 to identify adult women with a known gestation of pregnancy record and end of pregnancy outcome. Eligible members needed to be continuously enrolled in medical and prescription drug plans for both 365 days before (baseline) and after the derived pregnancy start (index date). This pregnancy cohort was then stratified according to cancer status, evidenced by any cancer diagnosis during the baseline period, to allow assessment of pregnancy outcomes. Valid pregnancy outcomes included full or preterm live birth and pregnancy loss such as miscarriage, stillbirth, and elective termination. Only the earliest pregnancy observed for each eligible member was analyzed. Results: Among the 1,921,866 pregnant women included in the analyses, only 0.7% (N=12,723) of patients had any cancer diagnosis in the year prior to pregnancy start. Cancer patients were older than non-cancer patients (mean age: 33.8 and 29.2 years, respectively). Commercial medical insurance was more common (67.7% vs. 58.9%) among cancer vs. non-cancer patients and Medicaid enrollment was less common (30.3% vs. 40.6%) upon pregnancy start. The proportion of patients with a live birth was lower among cancer vs. non-cancer patients (59.7% vs. 67.1%), while the proportion with pregnancy loss was higher (31.6% vs. 25.8%). Medicaid enrollment was more common among patients that had a pregnancy loss relative to live birth for both cancer and non-cancer patients (35.5% vs. 27.5% and 45.7% vs. 38.7%, respectively). The proportion of black patients was higher among patients with a pregnancy loss relative to those with live birth in both cohorts (cancer: 11.1% vs. 8.3%; non-cancer: 16.9% vs. 13.7%), while the proportion of white patients was similar for cancer patients with a pregnancy loss vs. live birth (37.4% vs. 37.1%) and the proportion was lower for non-cancer patients (30.4% vs. 34.4%). Conclusions: Preliminary findings suggest pregnancy loss was more common among cancer patients than non-cancer patients. Regardless of cancer status, differences in insurance coverage and race were observed for patients with a live birth relative to patients with pregnancy loss. Additional research should be conducted to identify factors driving differences in pregnancy outcomes, such as in care management by race and ethnicity, particularly among the vulnerable population of cancer patients.