Multiomic analysis of uterine leiomyomas in self-described Black and White women: molecular insights into health disparities.

BACKGROUND: Black women are at an increased risk of developing uterine leiomyomas and experiencing worse disease prognosis than White women. Epidemiologic and molecular factors have been identified as underlying these disparities, but there remains a paucity of deep, multiomic analysis investigating molecular differences in uterine leiomyomas from Black and White patients. OBJECTIVE: To identify molecular alterations within uterine leiomyoma tissues correlating with patient race by multiomic analyses of uterine leiomyomas collected from cohorts of Black and White women. STUDY DESIGN: We performed multiomic analysis of uterine leiomyomas from Black (42) and White (47) women undergoing hysterectomy for symptomatic uterine leiomyomata. In addition, our analysis included the application of orthogonal methods to evaluate fibroid biomechanical properties, such as second harmonic generation microscopy, uniaxial compression testing, and shear-wave ultrasonography analyses. RESULTS: We found a greater proportion of MED12 mutant uterine leiomyomas from Black women (>35% increase; Mann-Whitney U, P<.001). MED12 mutant tumors exhibited an elevated abundance of extracellular matrix proteins, including several collagen isoforms, involved in the regulation of the core matrisome. Histologic analysis of tissue fibrosis using trichrome staining and secondary harmonic generation microscopy confirmed that MED12 mutant tumors are more fibrotic than MED12 wild-type tumors. Using shear-wave ultrasonography in a prospectively collected cohort, Black patients had fibroids that were firmer than White patients, even when similar in size. In addition, these analyses uncovered ancestry-linked expression quantitative trait loci with altered allele frequencies in African and European populations correlating with differential abundance of several proteins in uterine leiomyomas independently of MED12 mutation status, including tetracoidpeptide repeat protein 38. CONCLUSION: Our study shows that Black women have a higher prevalence of uterine leiomyomas harboring mutations in MED12 and that this mutational status correlates with increased tissue fibrosis compared with wild-type uterine leiomyomas. Our study provides insights into molecular alterations correlating with racial disparities in uterine leiomyomas and improves our understanding of the molecular etiology underlying uterine leiomyoma development within these populations.

Disease progression, survival, and molecular disparities in Black and White patients with endometrioid endometrial carcinoma in real-world registries and GOG/NRG oncology randomized phase III clinical trials.

OBJECTIVE: Investigate racial disparities in outcomes and molecular features in Black and White patients with endometrioid endometrial carcinoma (EEC). METHODS: Black and White patients diagnosed with EEC who underwent hysterectomy ± adjuvant treatment in SEER, National Cancer Database (NCDB), the Genomics Evidence Neoplasia Information Exchange (GENIE) project (v.13.0), and eight NCI-sponsored randomized phase III clinical trials (RCTs) were studied. Hazard ratio (HR) and 95% confidence interval (CI) were estimated for cancer-related death (CRD), non-cancer death (NCD), and all-cause death. RESULTS: Black (n = 4397) vs. White (n = 47,959) patients in SEER had a HR (95% CI) of 2.04 (1.87-2.23) for CRD and 1.22 (1.09-1.36) for NCD. In NCDB, the HR (95% CI) for death in Black (n = 13,468) vs. White (n = 155,706) patients was 1.52 (1.46-1.58) dropping to 1.29 (1.23-1.36) after propensity-score matching for age, comorbidity, income, insurance, grade, stage, LVSI, and treatment. In GENIE, Black (n = 109) vs. White (n = 1780) patients had fewer PTEN, PIK3R1, FBXW7, NF1, mTOR, CCND1, and PI3K-pathway-related gene mutations. In contrast, TP53 and DNA-repair-related gene mutation frequency as well as tumor mutational burden-high status were similar in Black and White patients. In RCTs, Black (n = 187) vs. White (n = 2877) patients were more likely to have advanced or recurrent disease, higher grade, worse performance status and progressive disease. Risk of death in Black vs. White patients in RCTs was 2.19 (1.77-2.71) persisting to 1.32 (1.09-1.61) after matching for grade, stage, and treatment arm while balancing age and performance status. CONCLUSIONS: Differences exist in clinical presentation, outcomes, and molecular features in Black vs. White patients with EEC in real-world registries and RCTs. Targeted-drug development, strategies to modify social determinants, and diverse inclusion in RCTs are approaches to reduce disparities.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Immuno-Molecular Targeted Therapy Use and Survival Benefit in Patients with Stage IVB Cervical Carcinoma in Commission on Cancer®-Accredited Facilities in the United States.

PURPOSE: To investigate IMT use and survival in real-world stage IVB cervical cancer patients outside randomized clinical trials. METHODS: Patients diagnosed with stage IVB cervical cancer during 2013-2019 in the National Cancer Database and treated with chemotherapy (CT) ± external beam radiation (EBRT) ± intracavitary brachytherapy (ICBT) ± IMT were studied. The adjusted hazard ratio (AHR) and 95% confidence interval (CI) for risk of death were estimated in patients treated with vs. without IMT after applying propensity score analysis to balance the clinical covariates. RESULTS: There were 3164 evaluable patients, including 969 (31%) who were treated with IMT. The use of IMT increased from 11% in 2013 to 46% in 2019. Age, insurance, facility type, sites of distant metastasis, and type of first-line treatment were independently associated with using IMT. In propensity-score-balanced patients, the median survival was 18.6 vs. 13.1 months for with vs. without IMT (p < 0.001). The AHR was 0.72 (95% CI = 0.64-0.80) for adding IMT overall, 0.72 for IMT + CT, 0.66 for IMT + CT + EBRT, and 0.69 for IMT + CT + EBRT + ICBT. IMT-associated survival improvements were suggested in all subgroups by age, race/ethnicity, comorbidity score, facility type, tumor grade, tumor size, and site of metastasis. CONCLUSIONS: IMT was associated with a consistent survival benefit in real-world patients with stage IVB cervical cancer.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data.

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Quantitative proteomic analysis of HER2 protein expression in PDAC tumors.

Metastatic pancreatic adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, with a 5-year survival rate of only 11%, necessitating identification of novel treatment paradigms. Tumor tissue specimens from patients with PDAC, breast cancer, and other solid tumor malignancies were collected and tumor cells were enriched using laser microdissection (LMD). Reverse phase protein array (RPPA) analysis was performed on enriched tumor cell lysates to quantify a 32-protein/phosphoprotein biomarker panel comprising known anticancer drug targets and/or cancer-related total and phosphorylated proteins, including HER2Total, HER2Y1248, and HER3Y1289. RPPA analysis revealed significant levels of HER2Total in PDAC patients at abundances comparable to HER2-positive (IHC 3+) and HER2-low (IHC 1+ /2+ , FISH-) breast cancer tissues, for which HER2 screening is routinely performed. These data support a critical unmet need for routine clinical evaluation of HER2 expression in PDAC patients and examination of the utility of HER2-directed antibody-drug conjugates in these patients.

Survival disparities in non-Hispanic Black and White cervical cancer patients vary by histology and are largely explained by modifiable factors.

PURPOSE: We investigated racial disparities in survival by histology in cervical cancer and examined the factors contributing to these disparities. METHODS: Non-Hispanic Black and non-Hispanic White (hereafter known as Black and White) patients with stage I-IV cervical carcinoma diagnosed between 2004 and 2017 in the National Cancer Database were studied. Survival differences were compared using Cox modeling to estimate hazard ratio (HR) or adjusted HR (AHR) and 95% confidence interval (CI). The contribution of demographic, socioeconomic and clinical factors to the Black vs White differences in survival was estimated after applying propensity score weighting in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC). RESULTS: This study included 10,111 Black and 43,252 White patients with cervical cancer. Black patients had worse survival than White cervical cancer patients (HR = 1.40, 95% CI = 1.35-1.45). Survival disparities between Black and White patients varied significantly by histology (HR = 1.20, 95% CI = 1.15-1.24 for SCC; HR = 2.32, 95% CI = 2.12-2.54 for AC, interaction p < 0.0001). After balancing the selected demographic, socioeconomic and clinical factors, survival in Black vs. White patients was no longer different in those with SCC (AHR = 1.01, 95% CI 0.97-1.06) or AC (AHR = 1.09, 95% CI = 0.96-1.24). In SCC, the largest contributors to survival disparities were neighborhood income and insurance. In AC, age was the most significant contributor followed by neighborhood income, insurance, and stage. Diagnosis of AC (but not SCC) at ≥65 years old was more common in Black vs. White patients (26% vs. 13%, respectively). CONCLUSIONS: Histology matters in survival disparities and diagnosis at ≥65 years old between Black and White cervical cancer patients. These disparities were largely explained by modifiable factors.

Racial, ethnic and country of origin disparities in aggressive endometrial cancer histologic subtypes.

OBJECTIVE: This study investigated the risk of an aggressive endometrial cancer (EC) diagnosis by race, ethnicity, and country of origin to further elucidate histologic disparities in non-Hispanic Black (NHB), Hispanic, Asian/Pacific Islander (API), American Indian/Alaskan Native (AIAN) vs. non-Hispanic White (NHW) patients, particularly in Hispanic or API subgroups. METHODS: Patient diagnosed between 2004 and 2020 with low grade (LG)-endometrioid endometrial cancer (ECC) or an aggressive EC including grade 3 EEC, serous carcinoma, clear cell carcinoma, mixed epithelial carcinoma, or carcinosarcoma in the National Cancer Database were studied. The odds ratio (OR) and 95% confidence interval (CI) for diagnosis of an aggressive EC histology was estimated using logistic modeling. RESULTS: There were 343,868 NHW, 48,897 NHB, 30,013 Hispanic, 15,015 API and 1646 AIAN patients. The OR (95% CI) for an aggressive EC diagnosis was 3.07 (3.01-3.13) for NHB, 1.08 (1.06-1.11) for Hispanic, 1.17 (1.13-1.21) for API and 1.07 (0.96-1.19) for AIAN, relative to NHW patients. Subset analyses by country of origin illustrated the diversity in the OR for an aggressive EC diagnosis among Hispanic (1.18 for Mexican to 1.87 for Dominican), Asian (1.14 Asian Indian-Pakistani to 1.48 Korean) and Pacific Islander (1.00 for Hawaiian to 1.33 for Samoan) descendants. Hispanic, API and AIAN patients were diagnosed 5-years younger that NHW patients, and the risk for an aggressive EC histology were all significantly higher than NHW patients after correcting for age. Insurance status was another independent risk factor for aggressive histology. CONCLUSIONS: Risk of an aggressive EC diagnosis varied by race, ethnicity, and country of origin. NHB patients had the highest risk, followed by Dominican, South/Central American, Cuban, Korean, Thai, Vietnamese, and Filipino descendants.

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.

BACKGROUND: Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics. METHODS: Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor. RESULTS: LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ). CONCLUSIONS: Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

Deep learning-based segmentation of multisite disease in ovarian cancer.

PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

The murine metastatic microenvironment of experimental brain metastases of breast cancer differs by host age in vivo: a proteomic study.

Breast cancer in young patients is known to exhibit more aggressive biological behavior and is associated with a less favorable prognosis than the same disease in older patients, owing in part to an increased incidence of brain metastases. The mechanistic explanations behind these findings remain poorly understood. We recently reported that young mice, in comparison to older mice, developed significantly greater brain metastases in four mouse models of triple-negative and luminal B breast cancer. Here we have performed a quantitative mass spectrometry-based proteomic analysis to identify proteins potentially contributing to age-related disparities in the development of breast cancer brain metastases. Using a mouse hematogenous model of brain-tropic triple-negative breast cancer (MDA-MB-231BR), we harvested subpopulations of tumor metastases, the tumor-adjacent metastatic microenvironment, and uninvolved brain tissues via laser microdissection followed by quantitative proteomic analysis using high resolution mass spectrometry to characterize differentially abundant proteins potentially contributing to age-dependent rates of brain metastasis. Pathway analysis revealed significant alterations in signaling pathways, particularly in the metastatic microenvironment, modulating tumorigenesis, metabolic processes, inflammation, and neuronal signaling. Tenascin C (TNC) was significantly elevated in all laser microdissection (LMD) enriched compartments harvested from young mice relative to older hosts, which was validated and confirmed by immunoblot analysis of whole brain lysates. Additional in vitro studies including migration and wound-healing assays demonstrated TNC as a positive regulator of tumor cell migration. These results provide important new insights regarding microenvironmental factors, including TNC, as mechanisms contributing to the increased brain cancer metastatic phenotype observed in young breast cancer patients.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy.

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Automated imaging and identification of proteoforms directly from ovarian cancer tissue.

The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS2) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS2 identification of 73 proteoforms up to 54 kDa at a rate of <1 min per proteoform. AutoPiMS is directly interfaced with multifaceted proteoform imaging MS data modalities for the identification of proteoform signatures in tumor and stromal regions in ovarian cancer biopsies. From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discover 303 differential proteoforms in stroma versus tumor from the same patient. 14 of the top proteoform signatures are corroborated by MSI at 20 micron resolution including the differential localization of methylated forms of CRIP1, indicating the importance of proteoform-enabled spatial biology in ovarian cancer.

Brain proteomic atlas of alcohol use disorder in adult males.

Alcohol use disorder (AUD) affects transcriptomic, epigenetic and proteomic expression in several organs, including the brain. There has not been a comprehensive analysis of altered protein abundance focusing on the multiple brain regions that undergo neuroadaptations occurring in AUD. We performed a quantitative proteomic analysis using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of human postmortem tissue from brain regions that play key roles in the development and maintenance of AUD, the amygdala (AMG), hippocampus (HIPP), hypothalamus (HYP), nucleus accumbens (NAc), prefrontal cortex (PFC) and ventral tegmental area (VTA). Brain tissues were from adult males with AUD (n = 11) and matched controls (n = 16). Across the two groups, there were >6000 proteins quantified with differential protein abundance in AUD compared to controls in each of the six brain regions. The region with the greatest number of differentially expressed proteins was the AMG, followed by the HYP. Pathways associated with differentially expressed proteins between groups (fold change > 1.5 and LIMMA p < 0.01) were analyzed by Ingenuity Pathway Analysis (IPA). In the AMG, adrenergic, opioid, oxytocin, GABA receptor and cytokine pathways were among the most enriched. In the HYP, dopaminergic signaling pathways were the most enriched. Proteins with differential abundance in AUD highlight potential therapeutic targets such as oxytocin, CSNK1D (PF-670462), GABAB receptor and opioid receptors and may lead to the identification of other potential targets. These results improve our understanding of the molecular alterations of AUD across brain regions that are associated with the development and maintenance of AUD. Proteomic data from this study is publicly available at www.lmdomics.org/AUDBrainProteomeAtlas/ .

Metronomic dosing of ovarian cancer cells with the ATR inhibitor AZD6738 leads to loss of CDC25A expression and resistance to ATRi treatment.

OBJECTIVE: ATR kinase inhibitors promote cell killing by inducing replication stress and through potentiation of genotoxic agents in gynecologic cancer cells. To explore mechanisms of acquired resistance to ATRi in ovarian cancer, we characterized ATRi-resistant ovarian cancer cells generated by metronomic dosing with the clinical ATR inhibitor AZD6738. METHODS: ATRi-resistant ovarian cancer cells (OVCAR3 and OV90) were generated by dosing with AZD6738 and assessed for sensitivity to Chk1i (LY2603618), PARPi (Olaparib) and combination with cisplatin or a CDK4/6 inhibitor (Palbociclib). Models were characterized by diverse methods including silencing CDC25A in OV90 cells and assessing impact on ATRi response. Serum proteomic analysis of ATRi-resistant OV90 xenografts was performed to identify circulating biomarker candidates of ATRi-resistance. RESULTS: AZD6738-resistant cell lines are refractory to LY2603618, but not to Olaparib or combinations with cisplatin. Cell cycle analyses showed ATRi-resistant cells exhibit G1/S arrest following AZD6738 treatment. Accordingly, combination with Palbociclib confers resistance to AZD6738. AZD6738-resistant cells exhibit altered abundances of G1/S phase regulatory proteins, including loss of CDC25A in AZD6738-resistant OV90 cells. Silencing of CDC25A in OV90 cells confers resistance to AZD6738. Serum proteomics from AZD6738-resistant OV90 xenografts identified Vitamin D-Binding Protein (GC), Apolipoprotein E (APOE) and A1 (APOA1) as significantly elevated in AZD6738-resistant backgrounds. CONCLUSIONS: We show that metronomic dosing of ovarian cancer cells with AZD6738 results in resistance to ATR/ Chk1 inhibitors, that loss of CDC25A expression represents a mechanism of resistance to ATRi treatment in ovarian cancer cells and identify several circulating biomarker candidates of CDC25A low, AZD6738-resistant ovarian cancer cells.

Integration of Multi-omic Data in a Molecular Tumor Board Reveals EGFR-Associated ALK-Inhibitor Resistance in a Patient With Inflammatory Myofibroblastic Cancer.

Inflammatory myofibroblastic tumors (IMTs) are intermediate-grade mesenchymal neoplasms commonly characterized by chromosomal rearrangements causing constitutive activation of anaplastic lymphoma kinase (ALK) and/or ALK mutations causing reduced sensitivity to ALK tyrosine kinase inhibitors (TKI). We present a patient with an IMT who initially responded to first-line alectinib, but who later suffered disease relapse and presently survives with moderate residual disease after receiving second-line lorlatinib. Biopsy specimens were analyzed using next generation sequencing (DNA-seq and RNA-seq) and reverse phase protein microarray (RPPA) as part of an institutional Molecular Tumor Board (MTB) study. An EML4-ALK rearrangement and EGFR activation (pEGFRY1068) were present in both the primary and recurrent tumors, while a secondary ALK I1171N mutation was exclusive to the latter. EGFR signaling in the background of a secondary ALK mutation is correlated with reduced ALK TKI sensitivity in vitro, implicating an important mechanism of drug resistance development in this patient. The RPPA results also critically demonstrate that ALK signaling (ALKY1604) was not activated in the recurrent tumor, thereby indicating that standard-of-care use of third- or fourth-line ALK TKI would not likely be efficacious or durable. These results underscore the importance of real-time clinical integration of functional protein drug target activation data with NGS in the MTB setting for improving selection of patient-tailored therapy.

Factors Associated With Survival Disparities Between Non-Hispanic Black and White Patients With Uterine Cancer.

Importance: Disparities in survival exist between non-Hispanic Black (hereafter, Black) and non-Hispanic White (hereafter, White) patients with uterine cancer. Objective: To investigate factors associated with racial disparities in survival between Black and White patients with uterine cancer. Design, Setting, and Patients: This cohort study used data from the National Cancer Database on 274 838 Black and White patients who received a diagnosis of uterine cancer from January 1, 2004, to December 31, 2017, with follow-up through December 2020. Statistical analysis was performed in July 2022. Main Outcomes and Measures: Overall survival by self-reported race and evaluation of explanatory study factors associated with hazard ratio (HR) reduction for Black vs White patients. A propensity scoring approach was applied sequentially to balance racial differences in demographic characteristics, comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, and treatment. Results: The study included 32 230 Black female patients (mean [SD] age at diagnosis, 63.8 [10.0] years) and 242 608 White female patients (mean [SD] age at diagnosis, 63.5 [10.5] years) and had a median follow-up of 74.0 months (range, 43.5-113.8 months). Black patients were more likely than White patients to have low income (44.1% vs 14.0%), be uninsured (5.7% vs 2.6%), present with nonendometrioid histologic characteristics (46.1% vs 21.6%), have an advanced disease stage (34.1% vs 19.8%), receive first-line chemotherapy (33.8% vs 18.2%), and have worse 5-year survival (58.6% vs 78.5%). Among patients who received a diagnosis at younger than 65 years of age, the HR for death for Black vs White patients was 2.43 (95% CI, 2.34-2.52) in a baseline demographic-adjusted model and 1.29 (95% CI, 1.23-1.35) after balancing other factors. Comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, treatment, and unexplained factors accounted for 0.8%, 7.2%, 11.5%, 53.1%, 5.8%, 1.2%, and 20.4%, respectively, of the excess relative risk (ERR) among the younger Black vs White patients. Among patients 65 years or older, the HR for death for Black vs White patients was 1.87 (95% CI, 1.81-1.93) in the baseline model and 1.14 (95% CI, 1.09-1.19) after balancing other factors. Comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, treatment, and unexplained factors accounted for 3.0%, 7.5%, 0.0%, 56.2%, 10.6%, 6.9%, and 15.8%, respectively, of the ERR among Black vs White patients aged 65 years or older. Conclusions and Relevance: This study suggests that histologic subtype was the dominant factor associated with racial survival disparity among patients with uterine cancer, while insurance status represented the main modifiable factor for women younger than 65 years. Additional studies of interactions between biology and social determinants of health are merited.

Fear of recurrence, emotional well-being and quality of life among long-term advanced ovarian cancer survivors.

OBJECTIVE: Although advanced stage epithelial ovarian cancer is widely considered life-threatening, 17% of women with advanced disease will survive long-term. Little is known about the health-related quality of life (QOL) of long-term ovarian cancer survivors, or how fear of recurrence might affect QOL. METHODS: 58 long-term survivors with advanced disease participated in the study. Participants completed standardized questionnaires to capture cancer history, QOL, and fear of recurrent disease (FOR). Statistical analyses included multivariable linear models. RESULTS: Participants averaged 52.8 years at diagnosis and had survived >8 years (mean:13.5); 64% had recurrent disease. Mean FACT-G, FACT-O, and FACT-O-TOI (TOI) scores were 90.7 (SD:11.6), 128.6 (SD:14.8), and 85.9 (SD:10.2) respectively. Compared to the U.S. population using T-scores, QOL for participants exceeded that of healthy adults (T-score (FACT-G) = 55.9). Overall QOL was lower in women with recurrent vs. non-recurrent disease though differences did not reach statistical significance (FACT-O = 126.1 vs. 133.3, p = 0.082). Despite good QOL, high FOR was reported in 27%. FOR was inversely associated with emotional well-being (EWB) (p < 0.001), but not associated with other QOL subdomains. In multivariable analysis, FOR was a significant predictor of EWB after adjusting for QOL (TOI). A significant interaction was observed between recurrence and FOR (p = 0.034), supporting a larger impact of FOR in recurrent disease. CONCLUSION: QOL in long-term ovarian cancer survivors was better than the average for healthy U.S. women. Despite good QOL, high FOR contributed significantly to increased emotional distress, most notably for those with recurrence. Attention to FOR may be warranted in this survivor population.