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Background Declines in the prevalence of cigarette smoking, advances in targeted therapies, and implementation of lung cancer screening have changed the clinical landscape for lung cancer. The proportion of lung cancer deaths is increasing in those who have never smoked cigarettes. To better understand contemporary patterns in survival among patients with lung cancer, a comprehensive evaluation of factors associated with survival, including differential associations by smoking status, is needed. Methods Patients diagnosed with lung cancer between January 1, 2010, and September 30, 2019, were identified. We estimated all-cause and lung cancer-specific median, 5-year, and multivariable restricted mean survival time (RMST) to identify demographic, socioeconomic, and clinical factors associated with survival, overall and stratified by smoking status (never, former, and current). Results Analyses included 6813 patients with lung cancer: 13.9% never smoked, 54.2% formerly smoked, and 31.9% currently smoked. All-cause RMST through 5 years for those who never, formerly, and currently smoked was 32.1, 25.9, and 23.3 months, respectively. Lung cancer–specific RMST was 36.3 months, 30.3 months, and 26.0 months, respectively. Across most models, female sex, younger age, higher socioeconomic measures, first-course surgery, histology, and body mass index were positively associated, and higher stage was inversely associated with survival. Relative to White patients, Black patients had increased survival among those who formerly smoked. Conclusions We identify actionable factors associated with survival between those who never, formerly, and currently smoked cigarettes. These findings illuminate opportunities to address underlying mechanisms driving lung cancer progression, including use of first-course treatment, and enhanced implementation of tailored smoking cessation interventions for individuals diagnosed with cancer. [ABSTRACT FROM AUTHOR]

Background In the I CARE study, colon cancer patients were randomly assigned to receive follow-up care from either a general practitioner (GP) or a surgeon. Here, we address a secondary outcome, namely, detection of recurrences and effect on time to detection of transferring care from surgeon to GP. Methods Pattern, stage, and treatment of recurrences were described after 3 years. Time to event was defined as date of surgery, until date of recurrence or last follow-up, with death as competing event. Effects on time to recurrence and death were estimated as hazard ratios (HRs) using Cox regression. Restricted mean survival times were estimated. Results Of 303 patients, 141 were randomly assigned to the GP and 162 to the surgeon. Patients were male (67%) with a mean age of 68.0 (8.4) years. During follow-up, 46 recurrences were detected; 18 (13%) in the GP vs 28 (17%) in the surgeon group. Most recurrences were detected via abnormal follow-up tests (74%) and treated with curative intent (59%). Hazard ratio for recurrence was 0.75 (95% confidence interval [CI] = 0.41 to 1.36) in GP vs surgeon group. Patients in the GP group remained in the disease-free state slightly longer (2.76 vs 2.71 years). Of the patients, 38 died during follow-up; 15 (11%) in the GP vs 23 (14%) in the surgeon group. Of these, 21 (55%) deaths were related to colon cancer. There were no differences in overall deaths between the groups (HR = 0.76, 95% CI = 0.39 to 1.46). Conclusion Follow-up provided by GPs vs surgeons leads to similar detection of recurrences. Also, no differences in mortality were found. [ABSTRACT FROM AUTHOR]

To grade the long-term benefit of anticancer agents, the American Society of Clinical Oncology Value Framework (ASCO-VF) awards tail-of-the-curve bonus points by using milestone survival at twice the median control survival. Here, we propose an alternative, late-life expectancy that we defined as the area under the Kaplan-Meier curve from median control survival to the end of follow-up. We analyzed all indications of immune checkpoint inhibitors with survival data and found that 9 indications out of 13 (69.2%) qualified for ASCO-VF tail-of-the-curve bonus points either in progression-free or overall survival. Our proposed score recognized a long-term benefit not captured by the ASCO-VF, for example, for CHECKMATE-66 where twice the median overall survival was not reached. We found that nivolumab was associated with an increase of 65.3% (95% CI = 38.9 to 89.5) in overall survival late-life expectancy, which highlights its important long-term benefit. In conclusion, the ASCO-VF could be improved with the use of late-life expectancy. [ABSTRACT FROM AUTHOR]

One of the first studies to investigate this important question prospectively followed LS carriers getting colonoscopies every 3 years compared with those getting no screening and found that the 3-year screening interval decreased CRC risk and improved overall survival, establishing routine colonoscopy screening as the standard of care ([6]). At present, however, colonoscopy screening remains critical to LS surveillance, CRC risk reduction, and improved outcomes for all LS carriers. Lynch syndrome (LS) is one of the most common hereditary colorectal cancer (CRC) predisposition syndromes and is present in 1 out of 279 individuals in the general population ([1]) and in approximately 3% of unselected cases of CRC ([2]). [Extracted from the article]

Background The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic's impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. Methods We used an electronic health record–derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. Results The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = −2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = −2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age. Conclusions Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers. [ABSTRACT FROM AUTHOR]

Background: Financial toxicity is a growing problem in oncology, but no prior studies have prospectively measured the financial impact of cancer treatment in a diverse national cohort of newly diagnosed cancer patients. S1417CD was the first cooperative group-led multicenter prospective cohort study to evaluate financial hardship in metastatic colorectal cancer (mCRC) patients.Methods: Patients aged 18 years or older within 120 days of mCRC diagnosis completed quarterly questionnaires for 12 months. We estimated the cumulative incidence of major financial hardship (MFH), defined as 1 or more of increased debt, new loans from family and/or friends, selling or refinancing home, or 20% or more income decline. We evaluated the association between patient characteristics and MFH using multivariate cox regression and the association between MFH and quality of life using linear regression.Results: A total of 380 patients (median age = 59.9 years) were enrolled; 77.7% were White, 98.0% insured, and 56.5% had annual income of $50 000 or less. Cumulative incidence of MFH at 12 months was 71.3% (95% confidence interval = 65.7% to 76.1%). Age, race, marital status, and income (split at $50 000 per year) were not statistically significantly associated with MFH. However, income less than $100 000 and total assets less than $100 000 were both associated with greater MFH. MFH at 3 months was associated with decreased social functioning and quality of life at 6 months.Conclusions: Nearly 3 out of 4 mCRC patients experienced MFH despite access to health insurance. These findings underscore the need for clinic and policy solutions that protect cancer patients from financial harm. [ABSTRACT FROM AUTHOR]

Background: Tumor-based classification of human glioma portends patient prognosis, but considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.Methods: We evaluated immune cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients without glioma.Results: IDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+ T, CD8+ T, natural killer, and B cells) and higher neutrophil fractions than people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.Conclusions: The delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic. [ABSTRACT FROM AUTHOR]

Background: Immune cell transcriptome signatures have been widely used to study the lung tumor microenvironment (TME). However, it is unclear to what extent the immune cell composition in the lung TME varies across histological and molecular subtypes (intertumor heterogeneity [inter-TH]) and within tumors (intratumor heterogeneity [ITH]) and whether ITH has any prognostic relevance.Methods: Using RNA sequencing in 269 tumor samples from 160 lung cancer patients, we quantified the inter-TH of immune gene expression and immune cell abundance and evaluated the association of median immune cell abundance with clinical and pathological features and overall survival. In 39 tumors with 132 multiregion samples, we also analyzed the ITH of immune cell abundance in relation to overall survival using a variance-weighted multivariate Cox model not biased by the number of samples per tumor.Results: Substantial inter-TH of 14 immune cell types was observed even within the same histological and molecular subtypes, but early stage tumors had higher lymphocyte infiltration across all tumor types. In multiregion samples, an unbiased estimate of low ITH of overall immune cell composition (hazard ratio [HR] = 0.40, 95% confidence interval [CI] = 0.21 to 0.78; P = .007), dendritic cells (HR = 0.24, 95% CI = 0.096 to 0.58; P = .002), and macrophages (HR = 0.50, 95% CI = 0.30 to 0.84; P = .009) was strongly associated with poor survival. The ITH of 3 markers, including CD163 and CD68 (macrophages) and CCL13 (dendritic cells), was enough to characterize the ITH of the corresponding immune cell abundances and its association with overall survival.Conclusion: This study suggests that lack of immune cell diversity may facilitate tumor evasion and progression. ITH inferred from CCL13, CD163, and CD68 could be used as a prognostic tool in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.Methods: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.Results: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).Conclusions: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future. [ABSTRACT FROM AUTHOR]

Background: Recent efforts of gene expression profiling analyses recognized at least four different triple-negative breast cancer (TNBC) molecular subtypes. However, little is known regarding their tumor microenvironment (TME) heterogeneity.Methods: Here, we investigated TME heterogeneity within each TNBC molecular subtype, including immune infiltrate localization and composition together with expression of targetable immune pathways, using publicly available transcriptomic and genomic datasets from a large TNBC series totaling 1512 samples. Associations between molecular subtypes and specific features were assessed using logistic regression models. All statistical tests were two-sided.Results: We demonstrated that each TNBC molecular subtype exhibits distinct TME profiles associated with specific immune, vascularization, stroma, and metabolism biological processes together with specific immune composition and localization. The immunomodulatory subtype was associated with the highest expression of adaptive immune-related gene signatures and a fully inflamed spatial pattern appearing to be the optimal candidate for immune check point inhibitors. In contrast, most mesenchymal stem-like and luminal androgen receptor tumors showed an immunosuppressive phenotype as witnessed by high expression levels of stromal signatures. Basal-like, luminal androgen receptor, and mesenchymal subtypes exhibited an immune cold phenotype associated with stromal and metabolism TME signatures and enriched in margin-restricted spatial pattern. Tumors with high chromosomal instability and copy number loss in the chromosome 5q and 15q regions, including genomic loss of major histocompatibility complex related genes, showed reduced cytotoxic activity as a plausible immune escape mechanism.Conclusions: Our results demonstrate that each TNBC subtype is associated with specific TME profiles, setting the ground for a rationale tailoring of immunotherapy in TNBC patients. [ABSTRACT FROM AUTHOR]

Cancer treatment exposures and outcomes can often be identified in administrative claims and electronic health record data ([[6], [8]]). Limited availability of data to inform the generation of robust treatment-specific risk estimates for perinatal and maternal outcomes during cancer therapy has been a long-standing barrier to providing accurate risk counseling to inform decision making. [Extracted from the article]

Background: Risk stratification can improve the efficacy and cost-efficiency of screening programs for early detection of cancer. We sought to derive a risk stratification tool for melanoma that was suitable for the general population using only self-reported information.Methods: We used melanoma risk factor information collected at baseline from QSKIN, a prospective cohort study of Queensland adults age 40 to 69 years at recruitment (n = 41 954). We examined two separate outcomes: 1) invasive melanomas and 2) all melanomas (invasive + in situ) obtained through data linkage to the cancer registry. We used stepwise Cox proportional hazards modeling to derive the risk models in a randomly selected two-thirds sample of the data set and assessed model performance in the remaining one-third validation sample.Results: After a median follow-up of 3.4 years, 655 (1.6%) participants developed melanoma (257 invasive, 398 in situ). The prediction model for invasive melanoma included seven terms. At baseline, the strongest predictors of invasive melanoma were age, sex, tanning ability, number of moles at age 21 years, and number of skin lesions treated destructively. The model for "all melanomas" (ie, invasive and in situ) included five additional terms. Discrimination in the validation data set was high for both models (C-index = 0.69, 95% confidence interval [CI] = 0.62 to 0.76, and C-index = 0.72, 95% CI = 0.69 to 0.75, respectively), and calibration was acceptable.Conclusions: Such a tool could be used to target surveillance activities to those at highest predicted risk of developing melanoma over a median duration of 3.4 years. [ABSTRACT FROM AUTHOR]

Background: Recent progress in sequencing technologies allows us to explore comprehensive genomic and transcriptomic information to improve the current European LeukemiaNet (ELN) system of acute myeloid leukemia (AML).Methods: We compared the prognostic value of traditional demographic and cytogenetic risk factors, genomic data in the form of somatic aberrations of 25 AML-relevant genes, and whole-transcriptome expression profiling (RNA sequencing) in 267 intensively treated AML patients (Clinseq-AML). Multivariable penalized Cox models (overall survival [OS]) were developed for each data modality (clinical, genomic, transcriptomic), together with an associated prognostic risk score.Results: Of the three data modalities, transcriptomic data provided the best prognostic value, with an integrated area under the curve (iAUC) of a time-dependent receiver operating characteristic (ROC) curve of 0.73. We developed a prognostic risk score (Clinseq-G) from transcriptomic data, which was validated in the independent The Cancer Genome Atlas AML cohort (RNA sequencing, n = 142, iAUC = 0.73, comparing the high-risk group with the low-risk group, hazard ratio [HR]OS = 2.42, 95% confidence interval [CI] = 1.51 to 3.88). Comparison between Clinseq-G and ELN score iAUC estimates indicated strong evidence in favor of the Clinseq-G model (Bayes factor = 26.78). The proposed model remained statistically significant in multivariable analysis including the ELN and other well-known risk factors (HRos = 2.34, 95% CI = 1.30 to 4.22). We further validated the Clinseq-G model in a second independent data set (n = 458, iAUC = 0.66, adjusted HROS = 2.02, 95% CI = 1.33 to 3.08; adjusted HREFS = 2.10, 95% CI = 1.42 to 3.12).Conclusions: Our results indicate that the Clinseq-G prediction model, based on transcriptomic data from RNA sequencing, outperforms traditional clinical parameters and previously reported models based on genomic biomarkers. [ABSTRACT FROM AUTHOR]

Most studies on disparities in cancer have focused on differences in stage of diagnosis and access to treatment. However, the increased cardiac dose among Asian and Black patients with right-sided breast cancers was largely unchanged with the additional variables, suggesting that a different paradigm to explain cardiac dose for right-sided patients may be more appropriate. [Extracted from the article]

Poor clinical responses to checkpoint blockade with anti-CTLA-4 and anti-PD-1 antibodies in melanoma have recently been associated with acquired IFNγ resistance that protects tumor cells from the antiproliferative and pro-apoptotic cytokine activity. IFNγ-resistant melanoma cells very often lack functional expression of the IFNγ signaling pathway gene JAK2 due to gene deletions or inactivating gene mutations. Analyzing melanoma cell lines (n = 46, applying next-generation targeted sequencing and single nucleotide polymorphism arrays) as well as available genomic data sets from The Cancer Genome Atlas (TCGA) tumor tissue samples (cutaneous melanoma n = 367, lung squamous cell carcinoma n = 501, bladder urothelial carcinoma n = 408, breast invasive carcinoma n = 768, colorectal adenocarcinoma n = 257), we demonstrate that the frequent chromosomal losses of the tumor suppressor CDKN2A in melanoma and other tumor entities enhance the susceptibility to IFNγ resistance by concomitant deletion of the JAK2 gene (odds ratio = 223.17, 95% confidence interval = 66.91 to 1487.38, two-sided P = 7.6×10-46). Tumors with JAK2 mutations or homozygous JAK2 deletions demonstrate allelic losses covering both CDKN2A and JAK2. This suggests that patients with tumor chromosomal CDKN2A losses are susceptible to developing immunotherapy resistance and should be screened for JAK2 deficiency prior to and under immune checkpoint blocking therapy. [ABSTRACT FROM AUTHOR]

Background: This study developed, validated, and disseminated a generalizable informatics algorithm for detecting breast cancer recurrence and timing using a gold standard measure of recurrence coupled with data derived from a readily available common data model that pools health insurance claims and electronic health records data.Methods: The algorithm has two parts: to detect the presence of recurrence and to estimate the timing of recurrence. The primary data source was the Cancer Research Network Virtual Data Warehouse (VDW). Sixteen potential indicators of recurrence were considered for model development. The final recurrence detection and timing models were determined, respectively, by maximizing the area under the ROC curve (AUROC) and minimizing average absolute error. Detection and timing algorithms were validated using VDW data in comparison with a gold standard recurrence capture from a third site in which recurrences were validated through chart review. Performance of this algorithm, stratified by stage at diagnosis, was compared with other published algorithms. All statistical tests were two-sided.Results: Detection model AUROCs were 0.939 (95% confidence interval [CI] = 0.917 to 0.955) in the training data set (n = 3370) and 0.956 (95% CI = 0.944 to 0.971) and 0.900 (95% CI = 0.872 to 0.928), respectively, in the two validation data sets (n = 3370 and 3961, respectively). Timing models yielded average absolute prediction errors of 12.6% (95% CI = 10.5% to 14.5%) in the training data and 11.7% (95% CI = 9.9% to 13.5%) and 10.8% (95% CI = 9.6% to 12.2%) in the validation data sets, respectively, and were statistically significantly lower by 12.6% (95% CI = 8.8% to 16.5%, P < .001) than those estimated using previously reported timing algorithms. Similar covariates were included in both detection and timing algorithms but differed substantially from previous studies.Conclusions: Valid and reliable detection of recurrence using data derived from electronic medical records and insurance claims is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for breast cancer patients and those who develop recurrence. [ABSTRACT FROM AUTHOR]

Time-to-event end points are the most frequent primary end points in phase III oncology trials, both in the adjuvant and advanced settings. The evaluation of these end points is important to inform clinical practice. However, although different measures can be used to describe the effect of treatment on these end points, we believe that any treatment benefit in a given trial is best reported using various absolute and relative measures. Our goal is to help clinicians understand the strengths and limitations of the traditional and novel measures used to denote the effect of treatment in randomized trials. Although none of these measures can reliably predict the outcome of individual patients, some measures could be added to the commonly used hazard ratio to provide a more patient-oriented assessment of treatment benefit. In particular, the difference of mean survival times quantifies the average survival benefit for a patient receiving a new treatment compared with a patient treated with standard of care, whereas the net benefit quantifies the probability of a patient receiving the new treatment to live longer by at least m months (for any number of months m of interest) than a patient receiving the standard treatment. We encourage statisticians and clinical scientists to include various measures of treatment benefit in the reports of phase III trials, acknowledging that different clinical situations may call for different measures of treatment effect. By using the various available measures, we may better inform ourselves and communicate results to our patients. [ABSTRACT FROM AUTHOR]

Background: Little is known about factors that influence hospice use for patients with blood cancers. We aimed to characterize hospice enrollment in a large population of patients with B-cell non-Hodgkin lymphoma (NHL) and assess the impact of disease characteristics such as aggressiveness and curability.Methods: Using the Surveillance, Epidemiology, and End Results-Medicare database, we identified patients age 65 years and older who were diagnosed with indolent NHL, aggressive NHL, or mantle cell lymphoma (MCL, which is aggressive and incurable) and died between 1999 and 2009. We determined the prevalence of hospice use and predictors thereof, using multivariable logistic regression. All statistical tests were two-sided.Results: Of 18 777 patients, 9645 had indolent NHL, 8226 had aggressive NHL, and 906 had MCL. Of the total cohort, 41.6% enrolled in hospice, and 34.3% enrolled three or more days before death. Compared with patients with indolent NHL, those with MCL were more likely to enroll (adjusted odds ratio [AOR] = 1.72, 95% confidence interval [CI] = 1.49 to 1.98), followed by patients with aggressive NHL (AOR = 1.41, 95% CI = 1.32 to 1.50). Other factors statistically significantly associated with hospice use included older age, female sex, white race, high socioeconomic status, and later year of death.Conclusions: In this large cohort of patients with lymphoma, hospice use was substantially lower than the national average for all cancers, suggesting either the need for improvement in enrollment or that the current hospice model is not meeting this population's end-of-life needs. Moreover, the fact that patients with MCL were most likely to enroll suggests that the end-of-life phase may be more easily determined in the context of cancers that are both aggressive and incurable. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet "high-risk" criteria for MRI screening. Methods: We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided. Results: The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA. Conclusion: RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years). [ABSTRACT FROM AUTHOR]

The authors comment on their paper "Expression of a Multidrug Resistance Gene in Human Cancers" that was published in the 1989 issue of the "Journal of the National Cancer Institute." They said that every drug resistance mechanism has a fitness cost for the cells that are deploying such mechanisms. They added that knowledge about the expression of P-glycoprotein (P-gp) in cancers has resulted in significant changes in the drug development process.

The author reflects on the topic of regional nodal irradiation (RNI) in women with node-positive and high-risk node-negative breast cancer. Also cited are the benefits of systemic therapies like hormonal therapy and chemotherapy on the risk of locoregional recurrence (LRR), the effectiveness of human epidermal growth factor receptor 2 (HER2)-directed treatment, and the effectiveness of the therapies in disease-free survival (DFS).

Background: Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet "high-risk" criteria for MRI screening.Methods: We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided.Results: The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA.Conclusion: RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years). [ABSTRACT FROM AUTHOR]