BACKGROUND:Research studies have demonstrated that cardiotoxicity due to cancer treatments may be more prevalent in Black and other racial minority breast cancer (BC) patients. Our study examined racial disparities in receipt of BC treatments, with a focus on therapies associated with cardiotoxicity. METHODS:The study population included 12,350 female BC patients who were diagnosed with early-stage BC between January 1, 2011, and January 18, 2023, from an electronic health record-derived database. Odds ratios (ORs) and 95% confidence intervals (CI) were estimated using multivariable logistic regressions for the associations between race (Asian, Black/African American (AA), Other, White) and receipt of radiation and chemotherapy. A sensitivity analysis was conducted among 4,318 women who received chemotherapy to assess racial disparities in anthracycline use (chemotherapeutic agents known to be associated with cardiotoxicity). RESULTS:While most women received radiation (61.9%), only 35% received chemotherapy in the early-stage setting (of which 41.5% were anthracycline-based regimens). Black/AA women were more likely prescribed anthracycline-based chemotherapy than not (50.4%) compared to all other racial groups (White: 39.2%, Asian: 38.3%, Other: 44.7%). No significant associations were observed between race and radiation. We identified a racial disparity among other race women, who had a 21% (95% CI: 1.05, 1.40) higher odds of receiving chemotherapy than White women. Among women who received chemotherapy, no racial disparities were observed for receiving anthracyclines after adjusting for sociodemographic and tumor characteristics. CONCLUSIONS:Our findings provide detailed real-world evidence on treatments among diverse BC patients. Future research should elucidate if treatment inequities drive cardiotoxic disparities.

Supplementary Table S7. Confounding analyses for the association between the interaction of the TGF-B-Polygenic Risk Score and red meat intake in relation to colorectal cancer risk.

Importance:Screening by low-dose computed tomography can reduce lung cancer mortality among high-risk individuals, but many lung cancers occur among individuals with a smoking history who are not eligible for screening. Objective:To develop and validate the protein-based Integrative Analysis of Lung Cancer Risk and Etiology (INTEGRAL)-Risk model in individuals with a smoking history from the general population. Design, Setting, and Participants:Cohorts in the Lung Cancer Cohort Consortium recruited research participants in the US, Europe, Asia, and Australia between 1985 and 2009, who were followed up for lung cancer and other health outcomes until 2021. Fourteen case cohorts of 3695 participants with a smoking history within the Lung Cancer Cohort Consortium, including 2305 randomly sampled participants and 1390 patients diagnosed with lung cancer within 3 years after blood sample collection, were designed. Plasma or serum samples from each participant were assayed using the INTEGRAL protein panel in 2022. The INTEGRAL-Risk model was trained using 7 predefined case cohorts (training set; n = 1951) to estimate absolute risk of being diagnosed with lung cancer based on age, smoking history, and 13 proteins. The validity of the INTEGRAL-Risk model was assessed in 7 independent case cohorts (testing set; n = 1744) at 1, 2, and 3 years after blood collection. Exposure:Absolute risk estimates from the protein-based INTEGRAL-Risk model. Main Outcomes and Measures:The primary outcome was the validity of the INTEGRAL-Risk model in the testing set with respect to discrimination (area under the curve [AUC]) and calibration (ratio of expected-to-observed cases [E/O]). Results:A total of 3695 participants were included, with 1951 participants (including 807 with lung cancer) in the training set and 1744 participants (including 583 with lung cancer) in the testing set. In the combined 14 training and testing sets, after application of statistical weights, 323 570 participants were represented (185 016 [57%] female; median [IQR] age, 60 [51-67] years). In the independent testing set, discrimination of the INTEGRAL-Risk model was highest at 1 year of follow-up and exceeded that of the questionnaire-based PLCOm2012 (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) model (INTEGRAL-Risk AUC of 0.88 [95% CI, 0.85-0.91] vs PLCOm2012 AUC of 0.79 [95% CI, 0.75-0.83]; P value for difference <.001). Using a risk threshold to achieve the same specificity as US Preventive Services Task Force (USPSTF) 2021 criteria, the INTEGRAL-Risk model captured 85% of lung cancer cases compared with 63% by USPSTF 2021 and 70% by PLCOm2012. Discrimination of the INTEGRAL-Risk model decreased with longer prediction horizons, with a 2-year AUC of 0.84 (95% CI, 0.81-0.86) and 3-year AUC of 0.81 (95% CI, 0.79-0.83). The model was well calibrated (E/O over 3 years, 0.87 [95% CI, 0.69-1.14]). Conclusions and Relevance:Compared with questionnaire-based approaches, the protein-based INTEGRAL-Risk model improved short-term prediction of lung cancer in people with a smoking history. This model has potential to improve selection of high-risk individuals who are most likely to benefit from lung cancer screening.

Supplementary Figure S6: Natural logarithm hazard ratios of breast cancer for P-spline term scaled 313-SNP PRS with 4 degrees of freedom estimated by fully-adjusted Cox proportional hazards model stratified by alcohol consumption category among Black women in ARIC (1990-2015)

Supplementary Figure S1, LocusZoom plots.

OBJECTIVE:Reconciling cutoff thresholds for short-term (5-year) and long-term (lifetime) breast cancer risk could support tailored and evidence-based approaches to supplemental screening and risk management most relevant to short-term clinical actions. This study aims to consistently classify women at increased risk and provide 5-year risk cutoff that corresponds to a 20% lifetime risk. METHODS:Using U.S. Surveillance, Epidemiology and End Results (SEER) program population incidence data for women 40 to 74 years of age, this study reports both lifetime and 5-year population-based risk estimates controlling for competing risk and age varying breast cancer incidence. A cut point for 5-year risk equivalent to lifetime risk of 20% which triggers increased screening is generated. This computation is a weighted average incorporating age, remaining life expectancy, and population risk distribution. The primary outcome is breast cancer incidence (in situ and invasive). RESULTS:A lifetime risk threshold of 20% corresponded to markedly age-dependent 5-year risk cut points, increasing from ∼1.3% at ages 40-44 to ∼10.9% at ages 70-74. For women 40-74, 20% lifetime risk corresponds to a 5-year risk cut-off of 3.16%. CONCLUSIONS:Aligning lifetime risk of ≥20% and the 5-year breast cancer risk cutoff enhances consistency of classification of women at increased risk and clinical decision-making. Women with a ≥3.16% 5-year risk of breast cancer have risk equivalent to a lifetime risk of ≥20% on average. This can facilitate rational and evidence-based approaches to short-term and long-term risk assessment results for both risk reduction and tailored screening.

BACKGROUND:The influence of poverty on cancer outcomes beyond a woman's county of residence remains understudied, despite individuals frequently interacting within a larger ecosocial system. METHODS:A retrospective cohort study was conducted to investigate whether high poverty in residential and surrounding counties is associated with increased mortality. Women from the National Cancer Institute Surveillance, Epidemiology, and End Results database ≥20 years of age, diagnosed with primary breast cancer between 2005 and 2014, and who survived at least 1 year (N = 36,711) were included. Participants were aggregated by their county of residence and linked to their American Community Survey 5-year poverty estimates. Local Moran's I was used to categorize residential and surrounding county poverty environments as high or low using a mean cutoff. Multivariable-adjusted negative binomial regression was used to evaluate mortality relative risk (MRR) and 95% CIs for the association between residential and surrounding county poverty on all-cause mortality. RESULTS:Women in high-poverty residential (Hr) counties had a 6% increase in death (MRR = 1.06; 95% CI, 1.01-1.10) compared to women in low-poverty residential counties (Lr). Women surrounded by high-poverty (Hs) counties had a 22% increase in death (MRR = 1.22; 95% CI, 1.13-1.31) compared to women surrounded by low-poverty (Ls) counties. The combined MRR for women in Hr-Hs, Lr-Hs, and Hr-Ls counties was 1.23 (95% CI, 1.13-1.33), 1.25 (95% CI, 1.02-1.51), and 1.08 (95% CI, 0.95-1.21), respectively, when compared to Lr-Ls counties. CONCLUSION:Poverty in the surrounding counties has a greater impact on mortality among survivors than residential county poverty alone. Incorporating poverty levels from both residential and surrounding counties can improve definitions of high- and low-risk regions after a breast cancer diagnosis.

Supplementary Table S3. Weights used for polygenic risk score estimation and coordinates from single nucleotide polymorphisms considered in the study.

Abstract Polygenic risk scores (PRSs) may enhance risk stratification for pancreatic ductal adenocarcinoma (PDAC), but existing models vary widely in design, predictive performance, and cross-ancestry transferability. We developed genome-wide PRSs using Bayesian methods (LDpred2 and PRS-CS) and p value thresholding (PRSice-2) and systematically evaluated these alongside 13 published PRSs to identify models with robust predictive performance across ancestries. Using GWAS summary statistics from 7531 cases and 10,631 controls, we derived the PRSs and tested associations in an independent sample of 4508 PDAC cases and 46,189 controls, with adjustment for well-established PDAC risk factors. Among all models, the genome-wide LDpred2-based PRS showed the strongest association with PDAC (OR = 1.57 per standard deviation increase; 95% CI: 1.51–1.62) and significantly improved discrimination beyond established risk factors alone (AUC = 0.74–0.76; p < 0.0001). Importantly, the genome-wide LDpred2 PRS demonstrated consistent associations across African, Admixed American, and European ancestry groups, whereas the best-performing published PRS was associated with PDAC risk only in individuals of European ancestry. These findings support genome-wide PRSs as a promising framework for multi-ancestry risk stratification for PDAC and to inform targeted early detection strategies.

Supplementary Figure S5: Natural logarithm hazard ratios of breast cancer for P-spline term scaled 313-SNP PRS with 4 degrees of freedom estimated by fully-adjusted Cox proportional hazards model stratified by alcohol consumption category among White women in ARIC (1990-2015)

Abstract Background: Genomic profiling via liquid biopsies (LB) has advanced precision oncology decision-making; however, a major challenge is critically interpreting LB data to improve the selection and order of genotype-specific therapies. Methods: We present updated results from the first planned analysis of an observational biomarker trial, aimed at assessing the clinical utility of serial LB in patients with advanced or metastatic solid tumors (NCT05585684). Primary endpoints assessed feasibility, prevalence of actionable alterations, and fraction of patients receiving genotype-matched therapies. Secondary endpoints encompassed progression-free survival (PFS), overall survival (OS), and concordance between LB and tumor next-generation sequencing (NGS). Serial LBs at baseline, early (1-3 weeks) on therapy, and at progression, employed a clinically validated 33-gene panel NGS assay (Labcorp Plasma Focus). Matched white blood cell (WBC) NGS was used to identify clonal hematopoiesis (CH)-derived variants. Mutation actionability was evaluated using a multi-resource programmatic approach, and levels of evidence (1-4) were assigned, followed by review at the Johns Hopkins Molecular Tumor Board (JH MTB). Results: Among 50 patients, 45 baseline and 12 progression LBs were reviewed at MTB. JH MTB classified 72.5% (n=50) of baseline alterations as tumor-derived. In 19 patients with archival tissue NGS, 32 variants were detected in the baseline LB, of which 78.1% were also detected in tissue NGS. Furthermore, 23.2% (n=16) of LB alterations at baseline, 20.0% (n=6) early on-therapy, and 13.0% (n=3) at progression were CH derived. Upon MTB review, 29.2% variants at baseline, 21.9% at early on-therapy and 26.1% at progression were classified as actionable (58.8% level 1, 11.8% level 2, 11.8% level 3 and 17.6% level 4 evidence). Of 45 patients reviewed, 38 received therapeutic recommendations and 57.9% initiated recommended therapy. Patients treated with MTB recommended therapies had significantly longer median PFS and OS compared to those who received standard of care (p=0.027 and p=0.00062 respectively). MTB-recommended therapy selection was independently associated with PFS and OS, in multivariate analyses adjusting for clinical covariates including sex, smoking status, age, and prior lines of systemic therapy (p=0.043 and p=0.006, respectively). Subset of patients treated with genotype-matched MTB recommendations had significantly longer median PFS and OS compared to those who received standard of care (p=0.026 and p=0.0074 respectively). Conclusion: Our findings emphasize the importance of precision oncology interventions driven by programmatic workflows within a multidisciplinary MTB, supported by comprehensive LB molecular information to guide therapy selection and improve patient outcomes. Citation Format: Amna Jamali, Jaime Wehr, Jenna VanLiere Canzoniero, Maria Fatteh, Katerina Karaindrou, Michael Conroy, Ilias Ziakas, Mohamed Sherief, Timsy Wanchoo, Faith Too, Lily Scharpf, Ruth Moges, Dana Petry, Kala Visvanathan, Ellen Verner, Amy Greer, Kory Kreimeyer, Jonathan Spiker, Rachel Karchin, Christine L. Hann, Vincent K. Lam, Joseph Christopher Murray, Josephine Feliciano, Kristen Marrone, Julie R. Brahmer, Ming-Tseh Lin, Taxiarchis Botsis, Hao Wang, Mark Sausen, Christopher D. Gocke, Rena Xian, Jessica Tao, Valsamo (Elsa) K. Anagnostou. Liquid biopsy-informed precision oncology clinical trial to evaluate the utility of ctDNA genomic profiling for therapy optimization in patients with advanced or metastatic solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3908.

Supplementary Table S5. Results from multinomial logistic regression models to evaluate pathway-based polygenic risk score interactions with red meat and processed meat intake by topological tumor location.

Supplementary Table S1: Baseline characteristics of women in ARIC at Visit 2 (1990-1992)

Supplementary Table S3: The association between current ethanol intake and breast cancer incidence excluding extreme ethanol intake values, stratified by race in ARIC (1990-2015)

Importance Screening by low-dose computed tomography can reduce lung cancer mortality among high-risk individuals, but many lung cancers occur among individuals with a smoking history who are not eligible for screening. Objective To develop and validate the protein-based Integrative Analysis of Lung Cancer Risk and Etiology (INTEGRAL)–Risk model in individuals with a smoking history from the general population. Design, Setting, and Participants Cohorts in the Lung Cancer Cohort Consortium recruited research participants in the US, Europe, Asia, and Australia between 1985 and 2009, who were followed up for lung cancer and other health outcomes until 2021. Fourteen case cohorts of 3695 participants with a smoking history within the Lung Cancer Cohort Consortium, including 2305 randomly sampled participants and 1390 patients diagnosed with lung cancer within 3 years after blood sample collection, were designed. Plasma or serum samples from each participant were assayed using the INTEGRAL protein panel in 2022. The INTEGRAL-Risk model was trained using 7 predefined case cohorts (training set; n = 1951) to estimate absolute risk of being diagnosed with lung cancer based on age, smoking history, and 13 proteins. The validity of the INTEGRAL-Risk model was assessed in 7 independent case cohorts (testing set; n = 1744) at 1, 2, and 3 years after blood collection. Exposure Absolute risk estimates from the protein-based INTEGRAL-Risk model. Main Outcomes and Measures The primary outcome was the validity of the INTEGRAL-Risk model in the testing set with respect to discrimination (area under the curve [AUC]) and calibration (ratio of expected-to-observed cases [E/O]). Results A total of 3695 participants were included, with 1951 participants (including 807 with lung cancer) in the training set and 1744 participants (including 583 with lung cancer) in the testing set. In the combined 14 training and testing sets, after application of statistical weights, 323 570 participants were represented (185 016 [57%] female; median [IQR] age, 60 [51-67] years). In the independent testing set, discrimination of the INTEGRAL-Risk model was highest at 1 year of follow-up and exceeded that of the questionnaire-based PLCOm2012 (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) model (INTEGRAL-Risk AUC of 0.88 [95% CI, 0.85-0.91] vs PLCOm2012 AUC of 0.79 [95% CI, 0.75-0.83]; P value for difference <.001). Using a risk threshold to achieve the same specificity as US Preventive Services Task Force (USPSTF) 2021 criteria, the INTEGRAL-Risk model captured 85% of lung cancer cases compared with 63% by USPSTF 2021 and 70% by PLCOm2012. Discrimination of the INTEGRAL-Risk model decreased with longer prediction horizons, with a 2-year AUC of 0.84 (95% CI, 0.81-0.86) and 3-year AUC of 0.81 (95% CI, 0.79-0.83). The model was well calibrated (E/O over 3 years, 0.87 [95% CI, 0.69-1.14]). Conclusions and Relevance Compared with questionnaire-based approaches, the protein-based INTEGRAL-Risk model improved short-term prediction of lung cancer in people with a smoking history. This model has potential to improve selection of high-risk individuals who are most likely to benefit from lung cancer screening.

PURPOSE:Next-generation sequencing (NGS) is recommended for patients with metastatic prostate cancer (PC). Nationwide, testing rates are low. Whether PC disease characteristics and courses differ between those with and without NGS testing is unknown. We identified predictors of testing, explored likely reasons for lack of testing, and compared survival between those with and without testing. METHODS:We retrospectively reviewed patients with metastatic PC initially seen between 2020 and 2022 at Johns Hopkins. Clinical data and reasons for nontesting were abstracted from the electronic medical record. We conducted a logistic regression assessing predictors of NGS testing, adjusting for age, Gleason grade, marital status, and metastatic diagnosis year. We used Cox regression to compare overall survival, defined from the time patients had both a metastatic diagnosis and a visit at our institution until death/last follow-up, between those tested and not tested. We adjusted for age, Gleason grade, initial metastasis (M) stage, comorbidities, and time from metastatic diagnosis to first visit. RESULTS:Of the 435 patients, 257 (59%) had NGS testing. Older patients were less likely to have testing (adjusted odds ratio [aOR], 0.96 [95% CI, 0.94 to 0.98]). Unmarried patients were less likely to have testing (aOR, 0.62 [95% CI, 0.38 to 1.01]). Patients with Gleason Grade Group 5 were more likely to undergo testing than patients with Groups 1-3 (aOR, 1.86 [95% CI, 1.14 to 3.04]). Among those without testing, 139 (78%) had at least one potential reason for lack of testing in the medical record. The most common reason for nontesting was patient/disease factors (37%). CONCLUSION:Older and unmarried men with metastatic PC were less likely to obtain NGS testing, whereas those with high Gleason grade were more likely. Interventions are needed to improve testing rates.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colo n, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

BACKGROUND:Women with major depressive disorder (MDD) who develop breast cancer have higher breast cancer recurrence compared with women without MDD. The reason for the higher recurrence is hypothesized to be multifactorial. We sought to determine whether nonadherence to antidepressants is associated with increased recurrence among women with MDD and breast cancer. METHODS:We established a retrospective cohort of 6051 women (aged ≥ 18 years), with and without MDD, who were diagnosed with early-stage invasive breast cancer between 2010 and 2019 with follow-up through 2022 using medical record data from the United States Veterans Affairs Healthcare System. We assessed antidepressant adherence in women with MDD more than 2 years before breast cancer diagnosis. We evaluated multiple adherence thresholds (proportion of medication days covered), ranging from ≥20% to 100%. We used multivariable competing-risks regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the statistical interaction between MDD and antidepressant adherence on recurrence, adjusting for sociodemographic, clinical, and prognostic factors. RESULTS:Among women with MDD and breast cancer (N = 1754), 94% initiated an antidepressant. A threshold of 60% was the minimum adherence level for there to be a statistically meaningful difference in recurrence between nonadherent and adherent women with MDD. Thirty-nine percent were nonadherent at this threshold. The association between MDD and recurrence was highest among women who did not use antidepressants (HR = 2.20, 95% CI = 1.54 to 3.15), followed by women who were nonadherent (HR = 1.52, 95% CI = 1.24 to 1.86), and lowest among women who were adherent (HR = 1.19, 95% CI = 0.99 to 1.42). CONCLUSION:Adherence to antidepressants could potentially reduce recurrence in patients with breast cancer and MDD.

Supplementary Table S6. Associations between red meat/processed meat intake with colorectal cancer risk stratified by quartiles of pathway-based polygenic risk scores.