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BackgroundThe objective of this randomized trial was to evaluate the short‐term effect of bilingual written and interpersonal education regarding mammographic breast density (MBD).MethodsLatinas aged 40 to 74 years who were presenting for screening mammography were recruited and randomized 1:1:1 to receive a letter with their mammogram and MBD results (usual care [UC]), a letter plus a brochure (enhanced care [ENH]), or a letter plus a brochure and telephonic promotora education (interpersonal care [INT]). Surveys were administered at enrollment (T0) and 2 weeks to 6 months after intervention delivery (T1). Differences were assessed with χ2, Kruskal‐Wallis, and McNemar tests and pairwise comparisons as appropriate. INT metrics and audio recordings were analyzed with descriptive statistics and qualitative content analysis.ResultsBetween October 2016 and October 2019, 943 of 1108 Latina participants (85%) completed both surveys. At T1, INT participants were more likely (P < .001) to report seeing their MBD results in the letter (70.2%) than UC (53.1%) or ENH participants (55.1%). The percentage of INT women who reported speaking with a provider about MBD (29.0%) was significantly greater (P < .001) than the percentage of UC (14.7%) or ENH participants (15.6%). All groups saw significant (P < .001) but nondifferential improvements in their knowledge of MBD as a masking and risk factor. In the INT group, the promotora delivered education to 77.1% of the 446 participants randomized to INT and answered questions at 28.3% of the encounters for an average of $4.70 per participant.ConclusionsAmong Latinas in a low‐resource setting, MBD knowledge may increase with written or interpersonal education, but with modest investment, interpersonal education may better improve MBD awareness and prompt patient‐provider discussions.

Abstract Monoclonal B-cell lymphocytosis (MBL) is one of the most common pre-malignant conditions and is characterized by a circulating population of clonal B-cells with an absolute clonal B-cell count &lt; 5x10 9/L and no evidence of lymphadenopathy, organomegaly, or cytopenias. MBL can be classified by the immunophenotype: CLL-like MBL (CD5+, CD20dim), atypical MBL (CD5+, CD20+), or non-CLL-like MBL (CD5-, CD20+), as well as by the size of the clone (low-count or high-count) with low-count MBL (LC-MBL) defined as clonal B-cell &lt;0.5x10 9/L or percent clonal B-cell count &lt;85% out of total B-cell count. The prevalence of MBL varies depending on detection methods and population tested. In a Spanish screening study of 608 individuals, they identified 73 MBLs (12%) with CLL-like MBL; moreover, these authors also evaluated overall survival (OS) compared to 290 individuals without MBL and found a significantly shorter OS among these predominantly LC-MBLs. Here, we evaluate the prevalence and OS of LC-MBL in a large screening cohort of 8,297 individuals. Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, who had no prior history of hematologic malignancy, provided blood samples between 7/2009 to 4/2021. Stored peripheral blood mononuclear cells were screened for MBL with an eight-color flow cytometry assay capable of detecting clonal B-cell event to the 0.005% level. We classified each MBL by immunophenotype as CLL-like MBL, atypical MBL, and non-CLL-like MBL. Individuals with more than one immunophenotype were classified into one immunophenotype based on the following hierarchy: CLL-like MBL, then atypical MBL, then non-CLL like MBL. Based on previously published evidence, individuals were also classified by clonal counts using the percent clonal B-cell count &lt;85% out of the total B-cell count to define LC-MBL. Individuals were followed from sample date to the earliest of death, loss to follow-up, or 5/31/2021. OS for LC-MBL and controls (individuals who screened negative for MBL) was assessed by Kaplan-Meier method. Age- and sex-adjusted OS was analyzed using inverse weights methods. Cox regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CIs), adjusted for age and sex. We screened 8,297 individuals 40 years or older (median age 67 years, 39% male) and identified 1,326 (16%) with LC-MBL and 6,651 (80%) controls. Those individuals detected with high-count MBL (N=90, 1%) or individuals who had insufficient cells for flow cytometry interpretation (N=230, 3%) were excluded from subsequent analyses. The prevalence of LC-MBL was higher in males (21%) than females (14%; p&lt;0.01) and significantly increased with age (p&lt;0.01) ranging from 3% among those 40-49 years, 10% among those 50-59 years, 15% among those 60-69 years, 23% among those 70-79 years, and 29% for those 80+ years. Sixty-one individuals had clones with more than one type of immunophenotype. Based on our hierarchy, CLL-like MBL was the most common (N=1,155), followed by non-CLL-like (N=168) and atypical MBL (N=67). The median follow-up time in our cohort was 26 months (range 0-142), and 432 individuals had subsequently died. The 5-year and 10-year OS adjusted for age and sex among LC-MBLs was 95% and 87%, respectively; control estimates were 95% and 84%, respectively. We did not observe a significant difference in OS between those individuals with LC-MBL versus controls (HR=0.98, CI=0.77-1.24, P=0.84, Fig 1a), nor when we stratified individuals by age &lt;65 (P=0.77) or age &gt;= 65 (P=0.68). Among females, OS was longer among LC-MBL compared to controls (HR=0.61, CI=0.38-0.97, P=0.04); but no evidence of a difference in OS among males was observed (HR=1.22, CI=0.92-1.62, P=0.17). When we evaluated OS by MBL immunophenotype, we observed no statistically significant difference (Fig 1b, log rank P=0.44). When we stratified the MBLs by immunophenotype versus controls, we also did not observe a difference: CLL-like (HR=0.91, CI=0.71-1.18, P=0.5), atypical (HR=1.73, CI=0.92-3.27, P=0.09), non-CLL like (HR=0.98, CI=0.58-1.66, P=0.95). In the largest MBL screening cohort to date, LC-MBL was a common condition among adults 40 years or older reaching a prevalence as high as 29% among individuals 80 years of age or older. OS among those with and without LC-MBL was similar, regardless of immunophenotype and age. The longer survival in females with MBL versus controls requires further evaluation. Figure 1 Figure 1. Disclosures Parikh: Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding. Kay: Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Sunesis: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; MEI Pharma: Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: Research Funding.

Abstract Introduction: TP53 aberrations, including mutations and deletion of 17p (del17p), are important adverse prognostic markers in chronic lymphocytic leukemia (CLL). Prevalence of TP53 aberrations ranges from 7-11% in untreated CLL and increases with disease progression and treatment. Among CLL patients with TP53 aberrations, co-occurrence of TP53 mutations with del(17p) is common. CLL patients with TP53 mutations or del(17p) have significantly worse outcomes when compared to wild-type patients. Previous studies, focusing on CLL patients at time of treatment, are mixed as to whether a single or more than one TP53 aberration impacts outcomes. TP53 is less well studied in monoclonal B-cell lymphocytosis (MBL), an asymptomatic pre-malignant state of CLL. Del(17p) occurs in 3-4% of MBL individuals, and a study of 54 MBL individuals reported a 2% mutation frequency in TP53. Here we estimated prevalence and evaluated the impact of TP53 aberrations in a large cohort MBL or untreated CLL individuals. Methods : Patients with CLL or MBL diagnosed between 2000 and 2019 from the Mayo Clinic CLL Resource with pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis were considered. DNA was extracted from PBMCs with purity &gt;80% or with sorted CD5+/CD19+ clonal cells. The entire coding regions of 59 CLL driver genes were paired end sequenced. Median coverage depth was &gt;1000x per nucleotide, allowing for detection of mutations with variant allelic fraction (VAF) as low as 1%. Somatic mutations were called using MuTect2 in tumor-only mode, and high impact mutations (frameshift, nonsense, and splicing variants) and missense mutations in CLL hot spots were selected. Somatic mutations and FISH del(17p) were used to define TP53 state for each patient: 1) wild-type [no TP53 mutations and normal del(17p)], 2) single-hit [one TP53 mutation or del(17p)], or 3) multi-hit [multiple TP53 mutations or TP53 mutation and del(17p)]. Time to first treatment (TTFT) and overall survival (OS) were analyzed by TP53 state. TTFT and OS were defined as time from sample collection to first treatment or death, respectively, or last follow-up date. Median TTFT and OS was estimated by the Kaplan-Meier method. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for TTFT and OS associations. The models were adjusted for known adverse prognostic factors including clinical diagnosis (CLL or MBL), age at diagnosis, Rai stage, b2 microglobulin, and IGHV mutation status. Results: Individuals with CLL (N=597) or MBL (N=285) were analyzed for prevalence of TP53 mutations and del(17p). We found 58 CLL patients (9.7%) and 15 MBL individuals (5.3%) had TP53 mutations. The median VAF in CLL was 30.9% (&lt;10% VAF, n=21) and in MBL was 13.5% (&lt;10% VAF, n=7). Del(17p) was present in 37 patients with CLL (6.2%) and 7 individuals with MBL (2.7%). In total, 86 CLL/MBL patients had a TP53 mutation and/or del(17p): 56.8% (48 patients) were single-hit and 44.2% (38 patients) were multi-hit (Fig. 1a). Patients with any TP53 aberration had shorter TTFT than wild-type patients (median 2.3 vs 9.4 years). Among patients with TP53 aberrations, median TTFT was shorter in multi-hit patients (20 events, 1.8 years) compared to single-hit patients (24 events, 3.2 years) (Fig. 1b). In Cox regression, single-hit (HR = 1.7 [1.1-2.6]) and multi-hit (HR = 1.8 [1.1-2.9]) patients had shorter TTFT compared to wild-type patients after adjusting for covariates (Fig. 1c). Multi-hit patients also had shorter OS compared to wild-type patients, while OS in single-hit patients did not significantly differ from wild-type patients (Fig. 1d). Median OS was 5.5 years in multi-hit patients (22 deaths) compared to 15.1 years in wild-type patients (196 deaths) and 14.3 years in single-hit patients (15 deaths). In the OS model adjusted for covariates, multi-hit patients had a significant increased risk (HR = 2.6 [1.6-4.1]), but single-hit patients did not (HR = 1.4 [0.8-2.5]) compared with wild-type patients (Fig. 1e). OS HRs remained stable after censoring at time of treatment. Both TTFT and OS HRs were consistent when mutations with VAF &lt; 10% were excluded. Conclusions: This study suggests single versus multi-hit TP53 aberrations may be important for prognostic outcomes in untreated CLL and MBL patients. Prognostic metrics may need to consider single versus multi-hit TP53 aberrations and include TP53 mutations with low VAF. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: MEI Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction Monoclonal B-cell lymphocytosis (MBL), monoclonal gammopathy of undetermined significance (MGUS), and clonal hematopoiesis (CH) are three common premalignant hematological conditions. MBL is a precursor to chronic lymphocytic leukemia (CLL), MGUS is a precursor to both multiple myeloma (MM) and Waldenström macroglobulinemia (WM), and CH is a possible precursor to myeloid disease. All three conditions are independently associated with aging. Furthermore, there is evidence that all three conditions are associated with increased risk of infection. The relationship among these conditions, the prevalence with which they co-exist, and the association between the number of premalignant conditions and risk of infection is unknown. Methods Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, provided a peripheral blood sample between 7/14/2009 to 06/18/2015. Individuals were screened for MBL using eight-color flow cytometry capable of detecting clonal B-cells to the 0.005% level. MGUS was screened using a matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) assay. CH was screened using a targeted sequencing panel of 42 CH-associated genes with at least 1000x read depth and the ability to detect variant allele fractions down to 1%. All study participants resided in Olmsted County, Minnesota, the county that includes Mayo Clinic. Serious infection, defined as hospitalization due to infection, was abstracted from medical records from the time of sample collection through 12/31/2020 or date of death. The medical record abstractor collecting data was blinded to condition. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between each of the conditions. Cox regression was used to estimate hazard ratios (HR) and 95% CI to assess the association between number of premalignant conditions with risk of infection. All analyses were adjusted for age at sample collection and sex. Results A total of 407 individuals (55% male, median age 68 years) were screened for all three conditions. The prevalence was 19.9% (n=81) for MBL, 14.5% (n=59) for MGUS, and 25.6% (n=104) for CH. When we evaluated the association between each of the conditions, we found no evidence of an association between the prevalence of CH and risk of MBL (OR=0.79, 95% CI: 0.43-1.42), CH and risk of MGUS (OR=1.24, 95% CI: 0.66-2.27), or MGUS and risk of MBL (OR=1.32, 95% CI:0.66-2.52). The prevalence of co-existence of these conditions was assessed by grouping individuals based on their condition or conditions (Figure 1A). We found that 197 (48.4%) study participants had at least one premalignant condition and 10.6% had two or more. Individuals with 2 or more conditions were the oldest (median age 74 years), followed by 1 condition (median 69 years) and 0 conditions (median 67 years, P-trend &lt;0.001). Sex was not significantly different across the groups, with 55%, 58%, and 47% male for 0, 1, and 2 conditions, respectively. Medical record abstraction identified 97 serious infection events in 80 individuals. Medical records were not available on 7 individuals. The most common infections were urinary tract (20 events), pneumonia (14 events), and cellulitis (14 events). Median follow-up time was 10.6 years (range 0.1-11.5 years). Compared to individuals with no condition, MBL (HR=1.75, 95% CI: 1.06-2.88) and MGUS (HR=1.75, 95% CI: 1.04-2.93) were associated with increased risk of serious infection. We found no evidence of an association with CH (HR=1.15, 95% CI: 0.71-1.87). When grouped by the number of conditions, we observed a significant trend (P-trend &lt;0.01, Figure 1B) in risk of serious infections by the number of conditions: individuals with 1 condition had a 35% increased risk of infections (HR=1.35, 95% CI:0.83-2.20) and individuals with 2 or more conditions had a 2.6-fold increased risk (HR=2.58, 95% CI: 1.37-4.84). Discussion At least one premalignant condition was present in nearly 50% of the study population, with CH being the most prevalent. No evidence of a relationship between any of the three conditions was observed. However, we found increased susceptibility to serious infections as the number of conditions increased with individuals with at least two conditions had 2.6-fold increased risk of infections. Figure 1 Figure 1. Disclosures Parikh: Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Kumar: Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Kay: TG Therapeutics: Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Research Funding; Genentech: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: Research Funding.

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns—a woman’s left and right breasts. From 341 features, we identified “robust” features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

PURPOSETo determine the contribution of germline pathogenic variants (PVs) in hereditary cancer testing panel genes to invasive lobular carcinoma (ILC) of the breast.MATERIALS AND METHODSThe study included 2,999 women with ILC from a population-based cohort and 3,796 women with ILC undergoing clinical multigene panel testing (clinical cohort). Frequencies of germline PVs in breast cancer predisposition genes ( ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, PALB2, PTEN, RAD51C, RAD51D, and TP53) were compared between women with ILC and unaffected female controls and between women with ILC and infiltrating ductal carcinoma (IDC).RESULTSThe frequency of PVs in breast cancer predisposition genes among women with ILC was 6.5% in the clinical cohort and 5.2% in the population-based cohort. In case-control analysis, CDH1 and BRCA2 PVs were associated with high risks of ILC (odds ratio [OR] > 4) and CHEK2, ATM, and PALB2 PVs were associated with moderate (OR = 2-4) risks. BRCA1 PVs and CHEK2 p.Ile157Thr were not associated with clinically relevant risks (OR < 2) of ILC. Compared with IDC, CDH1 PVs were > 10-fold enriched, whereas PVs in BRCA1 were substantially reduced in ILC.CONCLUSIONThe study establishes that PVs in ATM, BRCA2, CDH1, CHEK2, and PALB2 are associated with an increased risk of ILC, whereas BRCA1 PVs are not. The similar overall PV frequencies for ILC and IDC suggest that cancer histology should not influence the decision to proceed with genetic testing. Similar to IDC, multigene panel testing may be appropriate for women with ILC, but CDH1 should be specifically discussed because of low prevalence and gastric cancer risk.

PURPOSE The prevalence of germline pathogenic variants (PVs) in established breast cancer predisposition genes in women in the general population over age 65 years is not well-defined. However, testing guidelines suggest that women diagnosed with breast cancer over age 65 years might have < 2.5% likelihood of a PV in a high-penetrance gene. This study aimed to establish the frequency of PVs and remaining risks of breast cancer for each gene in women over age 65 years. METHODS A total of 26,707 women over age 65 years from population-based studies (51.5% with breast cancer and 48.5% unaffected) were tested for PVs in germline predisposition gene. Frequencies of PVs and associations between PVs in each gene and breast cancer were assessed, and remaining lifetime breast cancer risks were estimated for non-Hispanic White women with PVs. RESULTS The frequency of PVs in predisposition genes was 3.18% for women with breast cancer and 1.48% for unaffected women over age 65 years. PVs in BRCA1, BRCA2, and PALB2 were found in 3.42% of women diagnosed with estrogen receptor (ER)–negative, 1.0% with ER-positive, and 3.01% with triple-negative breast cancer. Frequencies of PVs were lower among women with no first-degree relatives with breast cancer. PVs in CHEK2, PALB2, BRCA2, and BRCA1 were associated with increased risks (odds ratio = 2.9-4.0) of breast cancer. Remaining lifetime risks of breast cancer were ≥ 15% for those with PVs in BRCA1, BRCA2, and PALB2. CONCLUSION This study suggests that all women diagnosed with triple-negative breast cancer or ER-negative breast cancer should receive genetic testing and that women over age 65 years with BRCA1 and BRCA2 PVs and perhaps with PALB2 and CHEK2 PVs should be considered for magnetic resonance imaging screening.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract Clinical practice guidelines recommend that individuals with HC MBL are evaluated annually for evidence of progression as it has been shown that they progress to develop CLL requiring therapy at a rate of 1-5%/year. Next-generation sequencing identified 59 genes recurrently mutated in CLL, and we previously showed that the tumor mutational load (TML), i.e., the total number of these genes mutated, is associated with a shorter time to first treatment (TTT) among 112 HC MBLs independent of the CLL international prognostic index (CLL-IPI), an established prognostic score. Herein, we continue to support our finding in additional HC MBLs and report the pooled results across the two cohorts. From the Mayo Clinic CLL Resource, we sequenced a total 167 HC MBL (112 from the initial round and 55 from additional sequencing). We extracted DNA from CD5+/CD19+ B-cells and sequenced the coding regions of 59 driver genes. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies &gt;0.01% and by those identified in public databases, unless present in known CLL mutation hotspots. After filtering, we computed the TML based on the number of genes with high impact (nonsense, frameshift and splicing) and hotspot mutations. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI), adjusted for sex and CLL-IPI. Stratified analyses were conducted by low/intermediate risk and high/very high CLL-IPI. Of the 167 HC MBL, low risk CLL-IPI comprised 60%, intermediate risk 28%, high risk 12%; and none were very-high risk. The most commonly mutated genes were SF3B1 (11%), NOTCH1 (9%), TP53 (7%), BIRC3 (7%), and MYD88 (6%); and 52 (31%) individuals had mutations in at least one gene. The median follow-up was 4.4 years, and 26 of the 167 individuals progressed to CLL requiring therapy. HC MBL with ≥2 mutated genes had a 28% 5-year risk of needing therapy compared to 6% among those with no mutated genes. Similar to our initial finding (HR=1.53, CI:1.12-2.07; P=0.007), we found a shorter TTT in the additional HC MBLs (HR=4.63, CI:1.57-13.7, P=0.006), and in the pooled cohort (HR=1.72; CI:1.29-2.28, P=0.0002). When we stratified by CLL-IPI risk, TML remained associated with TTT among individuals with low/intermediate CLL-IPI risk in the additional HC MBLs (HR=3.52, CI:1.11-11.2, P=0.03) and in the pooled cohort (HR=2.25, CI:1.34-3.77, P=0.002). A non-significant 2.8 fold association was observed with high risk CLL-IPI in the pooled cohort. In conclusion, the TML along with the CLL-IPI provide clinicians with a more accurate prognostic information regarding disease progression requiring therapy, especially for those MBLs with low to intermediate risk based on CLL-IPI. If validated, these findings may modify current practice guidelines for individuals with HC MBL. Citation Format: Geffen Kleinstern, Cecilia Bonolo de Campos, Nicholas J. Boddicker, Daniel R. O'Brien, Shulan Tian, Chantel Barney, Kari G. Rabe, Cristine Allmer, Laura Bruins, Aaron D. Norman, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, Huihuang Yan, James R. Cerhan, Neil E. Kay, Celine Vachon, Esteban Braggio, Susan L. Slager. Tumor mutational load derived from recurrently mutated genes in chronic lymphocytic leukemia (CLL) predicts time-to-first treatment in high-count monoclonal B-cell lymphocytosis (HC MBL) [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 884.