Heinzen, Ethan P.

Context: Early palliative care can improve end-of-life outcomes, but referrals to palliative care specialists can be delayed in the primary care setting. Objective: This pilot study assessed the effect of a machine-learning algorithm on time to palliative care in a primary care population. Methods: Patients (aged ≥18 years) were eligible if they were empaneled with a primary care provider (PCP) from July 20, 2020, through May 30, 2021. The algorithm evaluated their health records and presented patients who were predicted to have the greatest need for palliative care. Records were then reviewed by palliative care specialists, and patients were randomized in a stepped-wedge fashion to have a referral notification sent to their PCPs if unmet palliative care needs were verified. Time-to-event outcomes were evaluated with Poisson regression models. Results: Of the 127,080 patients evaluated, 934 had their health records presented for review. Some patients were repeatedly presented by the algorithm (total presentations: 1592). In the intervention arm, PCPs were prompted to order a palliative care consultation for 142 patients. The time to 0.1% of the population receiving a palliative care consultation was 60.9 days for the intervention arm versus 71.8 days for the control arm (probability of a shorter time with the intervention, 0.88). Conclusion: A machine-learning algorithm to identify palliative care needs was successfully integrated into a primary care practice. More work is needed to improve the workflow.

Abstract Despite long-standing interest in combining topoisomerase I (TOP1) poisons with PARP inhibitors (PARPis), the mechanistic basis for the variable and often unpredictable response to this strategy remains unclear. Early preclinical studies suggested that PARP inhibition amplifies TOP1 poison-induced DNA damage, implying broad therapeutic potential; however, emerging translational and clinical data—particularly in ovarian cancer—indicate that synergy is context-dependent and governed by tumor-intrinsic features that have not been fully defined.Here we assessed determinants of response in five HR proficient ovarian cancer (OC) patient-derived xenografts (PDXs) previously treated with the SN-38 prodrug etirinotecan pegol (NKTR-102) in combination with the PARPi rucaparib (C. Nitschmann, Clin. Cancer Res. 22(2) Suppl. B50, 2016). Although all models were refractory to single-agent rucaparib, 50% exhibited enhancement of NKTR-102-induced tumor regression with the addition of rucaparib. Neither PARP1 abundance nor extent of rucaparib-mediated poly(ADP-ribose) suppression correlated with therapeutic benefit. Instead, sensitization was restricted to PDXs intrinsically responsive to NKTR-102 and was strongly associated with high levels of NKTR-102-induced TOP1-DNA covalent complexes. In further analysis, 2 of 3 resistant PDXs exhibited high levels of the ABC transporter ABCG2 despite lack of previous therapy, revealing a previously underappreciated mechanism of de novo resistance that limits effective TOP1 inhibitor activity independent of homologous recombination status. Notably, NKTR-102 response did not track with platinum/taxane sensitivity, highlighting the distinct molecular circuitry governing TOP1 poison susceptibility.Collectively, these findings demonstrate that rucaparib can potentiate NKTR-102-induced regression in tumors capable of forming abundant TOP1-DNA covalent complexes. This work identifies TOP1-DNA complex trapping and lack of ABCG2 expression as mechanistic predictors of benefit, providing an updated framework for selecting patients most likely to respond to TOP1 poison/PARPi combinations and challenging the long-standing assumption that PARP inhibition can universally resensitize tumors to TOP1 poisons. Citation Format: Annapoorna Venkatachalam, Caroline C. Nitschmann, Karen S. Flatten, Rachel Hurley, Xiaonan Hou, Cristina Correia, Paula A. Schenider, Krista Goergen, Matthew Maurer, Ethan P. Heinzen, Ann L. Oberg, S John Weroha, Paul Haluska, Scott H. Kaufmann, Elizabeth M. Swisher. TOP1-DNA protein complex as a predictive biomarker for NKTR-102/Rucaparib combination therapy in PARP-inhibitor resistant ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB364.

PURPOSE To compare mortality risk among long-term survivors of high-grade serous ovarian cancer (HGSOC) with that of the general US population and to examine how mortality and recurrence trends have evolved under changing treatment approaches. MATERIALS AND METHODS This is a retrospective cohort study including patients with histologically confirmed HGSOC treated at a single referral cancer center from 1991 to 2022. Mortality risk, expressed as standardized mortality ratios (SMRs, ratio of observed deaths in the study population to expected deaths in an age-matched US population), was assessed at diagnosis and yearly on patients without events (disease persistence, recurrence, or death from any cause). RESULTS A total of 2,074 consecutive patients were included. The median follow-up was 12.5 years (IQR, 11.7-13.8). Most patients were stage III (1,396, 69.2%), 1,299 (62.8%) underwent primary cytoreductive surgery, and 1,688 (87.4%) received platinum-taxane as first-line therapy. At diagnosis, the mortality rate was 7.40 times higher than that in the general population (95% CI, 7.04 to 7.77). By 7 event-free years, the 95% CI for the SMR included 1 for the first time, and at 10 years, the SMR was 1.05 (95% CI, 0.72 to 1.49), indicating no statistically significant excess mortality compared with the general population. In those event-free at 10 years, the 5-year cumulative incidence of ovarian cancer–related deaths (8.2% [95% CI, 4.2 to 16.0]) was comparable with that of non–ovarian cancer–related deaths (6.3% [95% CI, 2.9 to 13.8]). CONCLUSION In this cohort, mortality risk steadily declined for patients who remained event-free, approaching that of the general population by 7 years after diagnosis. These results emphasize the importance of individualized, long-term follow-up to address both recurrence risk and survivorship needs.

Abstract Background: Ovarian cancer ranks fifth in cancer deaths among women. The gold standard of care for advanced disease includes cytoreductive surgery along with adjuvant or neoadjuvant platinum/taxane-based chemotherapy. Although response to this treatment is high, chemotherapy-induced nausea and vomiting due to platinum agents and allergic reactions due to both agents are common. For nearly half a century, the glucocorticoid (GC) dexamethasone (DEX) has been administered as part of supportive care. However, there have periodically been questions about the possible deleterious effects of DEX on chemotherapy response, especially in solid tumors. Methods: Platinum-sensitive, glucocorticoid receptor (GRa) expressing ovarian cancer cells were exposed to DEX prior to chemotherapy and clonogenic survival was assessed. The effects of DEX on cell cycle and apoptosis were assessed by flow cytometry. Changes in the tumor cell transcriptome after acute DEX treatment were determined by RNA sequencing. Mice bearing orthotopic ovarian cancer patient-derived xenografts (PDXs) were treated with chemotherapy ± DEX to assess changes in chemotherapy response in vivo. Findings: DEX pre-treatment decreased platinum cytotoxicity in ovarian cancer cell lines in vitro and diminished platinum response in some PDXs analyzed for chemotherapy response ex vivo. RNA-seq analysis revealed changes in transcripts encoding proteins involved in platinum detoxification and TNFa pro-survival signaling. In ovarian cancer PDXs, DEX decreased platinum response in one model and had limited impact on both platinum and platinum/taxane chemotherapy in three distinct studies. Interpretation: DEX appears to modulate chemotherapy response in some settings in vitro and ex-vivo. The impact of DEX on chemotherapy response in vivo appears to be limited. However, further investigation of this question in vivo in non-immunosuppressed murine models appears warranted. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Cordelia Mcgehee, Sarah Finstuen-Magro, Paula A. Schneider, Rachel Hurley, Kevin L. Peterson, Ethan Heinzen, Melissa C. Larson, Xiaonon Hou, Ann L. Oberg, Saravut Weroha, Scott H. Kaufmann. Impact of dexamethasone on chemotherapy response in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B098.

Abstract Background: Ovarian cancer ranks fifth in cancer deaths among women. The gold standard of care for advanced disease includes cytoreductive surgery along with adjuvant or neoadjuvant platinum/taxane-based chemotherapy. Although response to this treatment is high, chemotherapy-induced nausea and vomiting due to platinum agents and allergic reactions due to both agents are common. For nearly half a century, the glucocorticoid (GC) dexamethasone (DEX) has been administered as part of supportive care. However, there have periodically been questions about the possible deleterious effects of DEX on chemotherapy response, especially in solid tumors. Methods: Platinum-sensitive, glucocorticoid receptor (GRα) expressing ovarian cancer cells were exposed to DEX prior to chemotherapy and clonogenic survival was assessed. The effects of DEX on cell cycle and apoptosis were assessed by flow cytometry. Changes in the tumor cell transcriptome after acute DEX treatment were determined by RNA sequencing. Mice bearing orthotopic ovarian cancer patient-derived xenografts (PDXs) were treated with chemotherapy ± DEX to assess changes in chemotherapy response in vivo. Findings: DEX pre-treatment decreased platinum cytotoxicity in ovarian cancer cell lines in vitro and diminished platinum response in some PDXs analyzed for chemotherapy response ex vivo. RNA-seq analysis revealed changes in transcripts encoding proteins involved in platinum detoxification and TNFα pro-survival signaling. In ovarian cancer PDXs, DEX decreased platinum response in one model and had limited impact on both platinum and platinum/taxane chemotherapy in three distinct studies. Interpretation: DEX appears to modulate chemotherapy response in some settings in vitro and ex-vivo. The impact of DEX on chemotherapy response in vivo appears to be limited. However, further investigation of this question in vivo in non-immunosuppressed murine models appears warranted. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Cordelia D. Mcgehee, Sarah Finstuen-Magro, Jamison VanBlaricom, Paula A. Schneider, Kevin L. Peterson, Ethan P. Heinzen, Melissa C. Larson, Xiaonon Hou, Ann L. Oberg, Saravut John Weroha, Scott H. Kaufmann. Impact of dexamethasone on chemotherapy response in ovarian cancer. [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 5058.

Abstract BackgroundFormalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data.MethodsWe optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.ResultsSamples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with CPM > 2). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.7 ng/ul and 1.68 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.58 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.803 in predicting QC status (pass/fail) of FFPE samples. ConclusionsWe provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.8 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

Abstract Benign breast disease (BBD) is an established risk factor for development of breast cancer (BC). However, little is known about the prevalence of somatic genetic alterations at the time of BBD diagnosis and relationship to subsequent BC. To characterize DNA variants in BBD and investigate association with BC risk, we isolated DNA from formalin-fixed paraffin-embedded (FFPE) BBD biopsies and assessed mutations using a panel of 93 breast cancer predisposition genes (Qiagen GeneRead). The study set consisted of 120 BBD patients from three age-matched groups defined based on 16 years of BC follow-up: BBD-controls (cancer-free after 16 years), BBD-ER+, or BBD-ER- breast cancer (within 16 years). Bioinformatics quality checks included comparisons of paired fresh-frozen and FFPE replicates to reduce false variant calls induced by FFPE artifacts. Gene-level variant burden differences among study groups were assessed using 12 statistical approaches, adjusted for patient age, year of biopsy, and histology. To investigate possible immune implications of increased variant burden, extent of leukocytes in the BBD biopsies was assessed using CD45 immunohistochemistry. To assess variant profile changes during the progression from BBD to BC, we also performed targeted sequencing of 12 BBD-matched subsequent tumor samples (six ER+ and six ER-) developed within 5 years of BBD diagnosis. Gene-level association results showed consistently skewed distributions suggesting a higher variant burden in BBD controls: among the top 10 genes associated with case-status (p&lt;0.05), all had BC-risk odds ratio estimates less than 1.0. Immune infiltrates, assessed by CD45 H-scores, were positively associated with overall variant burden (r=0.42, p=0.0031), and noticeably higher in cancer-free controls (r=0.50, p=0.005). Comparison of mutation variants in 12 BBD cases that developed cancer within 5 years of diagnosis and their subsequent tumors revealed that more than half of the non-SNP variants were shared between the BBD and the subsequent tumor (54.6%), whereas less than one quarter were unique to the tumor (20.6%) or unique to the BBD (24.9%). Through BBD-BC paired variant analysis, three subjects were found with newly emergent clones uniquely appearing in the tumors and four subjects had loss of clones in the tumor that were present in the BBD. These results show that somatic variant burden is lower in patients with BBD that progress to BC, mutation profiles of BC progression are heterogeneous, and BBD tissues with reduced variant burden also show reduced immune infiltrate. This implicates a potential protective role for immune activation in BBD. Future directions will define the characteristics of the potentially protective immune response and its role in variant heterogeneity in a larger sample set, integrated with paired RNA sequencing to better understand the pathways involved and the relationship to immune infiltration. Citation Format: Stacey J. Winham, Julie M. Cunningham, Yuanhang Liu, Aditya V. Bhagwate, Ethan P. Heinzen, Samantha J. McDonough, Melody L. Stallings-Mann, Robert A. Vierkant, Jodi M. Carter, Mark E. Sherman, Derek C. Radisky, Amy C. Degnim, Chen Wang. Somatic variant burdenin benign breast disease and association with risk of subsequent breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2274.

Abstract Background: Benign breast disease (BBD) is a risk factor for breast cancer (BC); however, little is known about the genetic alterations present at the time of BBD diagnosis, and how these relate to risk of incident BC. Methods: A subset of a long-term BBD cohort was selected to examine DNA variation across three BBD groups (42 future estrogen receptor-positive (ER+) BC, 36 future estrogen receptor-negative (ER-) BC and 42 controls cancer-free for at least 16 years post-BBD). DNA extracted from archival formalin fixed, paraffin-embedded (FFPE) tissue blocks was tested and analyzed for presence of DNA alterations using a targeted panel of 93 BC-associated genes. To address artifacts frequently observed in FFPE tissues (e.g., C>T changes), we applied three filtering strategies based on alternative allele frequencies and nucleotide substitution context. Gene-level associations were performed using two burden tests, SKAT-O and logistic regression, and adjusted for clinical and technical covariates including epithelial percentage, histologic impression, patient age and year of BBD biopsy. Results: After filtering, the variant frequency of SNPs in our samples was highly consistent with population allele frequencies reported in 1KG/ExAC (0.986, p <1e-16). Ten genes were found to be associated with later cancer status by four of 12 association methods, with nominal p-values <0.05: MED12, MSH2, BRIP1, PMS1, GATA3, MUC16, FAM175A, EXT2, MLH1 and TGFB1. Additionally, analysis revealed 11 and 19 gene-level associations with p-values <0.05 and OR<1 for all cancer cases and ER- cases, respectively. Comparing between the three case groups, BBD ER+ cases were closer to controls in mutation profile, while BBD ER- cases were distinct. Notably, the variant burden was significantly higher in controls than in either ER+ or ER- cases. CD45 expression was associated with mutational burden (p=0.003).Conclusions: Somatic mutations were more frequent in benign breast tissue from women who did not develop cancer, opening questions of clonal diversity or immune-mediated restraint on future cancer development. CD45 expression was positively associated with mutational burden, most strongly in controls. Further studies in both normal and premalignant tissues are needed to better understand the role of somatic gene mutations and their contribution to future cancer development.

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r &lt; 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.