Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

The population-level effects of omitting chemotherapy guided by a 21-gene expression assay in node-positive breast cancer: a simulation modeling study.

BACKGROUND: A recent trial showed that postmenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor-2 (HER2)-negative, lymph node-positive (1-3 nodes) breast cancer with a 21-gene recurrence score of ≤ 25 could safely omit chemotherapy. However, there are limited data on population-level long-term outcomes associated with omitting chemotherapy among diverse women seen in real-world practice. METHODS: We adapted an established, validated simulation model to generate the joint distributions of population-level characteristics of women diagnosed with early-stage breast cancer in the U.S. Input parameters were derived from cancer registry, meta-analyses, and clinical trial data. The effects of omitting chemotherapy on 10-year distant recurrence-free survival, life-years, and quality adjusted life-years (QALYs) were modeled for premenopausal and postmenopausal women. QALYs were discounted at 3%. Results were evaluated for subgroups stratified by race and ethnicity. Sensitivity analyses included testing results across a range of inputs. The model was validated using the published RxPONDER trial data. RESULTS: In premenopausal women, the 10-year distant recurrence-free survival rates were 85.3% with chemo-endocrine and 80.1% with endocrine therapy. The estimated life-years and QALYs gained with chemotherapy in premenopausal women were 2.1 and 0.6, respectively. There was no chemotherapy benefit in postmenopausal women. There was no variation in the absolute benefit of chemotherapy across racial or ethnic subgroups. However, there were differences in distant recurrence-free survival rates, life-years, and QALYs across groups. Sensitivity analysis showed similar results. The model closely replicated the RxPONDER trial. CONCLUSIONS: Modeled population-level outcomes show a small chemotherapy benefit in premenopausal women, but no benefit among postmenopausal women. Simulation modeling provides a useful tool to extend trial data and evaluate population-level outcomes.

Analysis of Breast Cancer Mortality in the US-1975 to 2019.

Importance: Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective: To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants: Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures: Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures: Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results: The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance: According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.

Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force.

Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective: To estimate outcomes of various mammography screening strategies. Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Breast cancer diagnosis and treatment during the COVID-19 pandemic in a nationwide, insured population.

PURPOSE: The early months of the COVID-19 pandemic led to reduced cancer screenings and delayed cancer surgeries. We used insurance claims data to understand how breast cancer incidence and treatment after diagnosis changed nationwide over the course of the pandemic. METHODS: Using the Optum Research Database from January 2017 to March 2021, including approximately 19 million US adults with commercial health insurance, we identified new breast cancer diagnoses and first treatment after diagnosis. We compared breast cancer incidence and proportion of newly diagnosed patients receiving pre-operative systemic therapy pre-COVID, in the first 2 months of the COVID pandemic and in the later part of the COVID pandemic. RESULTS: Average monthly breast cancer incidence was 19.3 (95% CI 19.1-19.5) cases per 100,000 women and men pre-COVID, 11.6 (95% CI 10.8-12.4) per 100,000 in April-May 2020, and 19.7 (95% CI 19.3-20.1) per 100,000 in June 2020-February 2021. Use of pre-operative systemic therapy was 12.0% (11.7-12.4) pre-COVID, 37.7% (34.9-40.7) for patients diagnosed March-April 2020, and 14.8% (14.0-15.7) for patients diagnosed May 2020-January 2021. The changes in breast cancer incidence across the pandemic did not vary by demographic factors. Use of pre-operative systemic therapy across the pandemic varied by geographic region, but not by area socioeconomic deprivation or race/ethnicity. CONCLUSION: In this US-insured population, the dramatic changes in breast cancer incidence and the use of pre-operative systemic therapy experienced in the first 2 months of the pandemic did not persist, although a modest change in the initial management of breast cancer continued.

Patient perspectives on window of opportunity clinical trials in early-stage breast cancer.

PURPOSE: Window of opportunity trials (WOT) are increasingly common in oncology research. In WOT participants receive a drug between diagnosis and anti-cancer treatment, usually for the purpose of investigating that drugs effect on cancer biology. This qualitative study aimed to understand patient perspectives on WOT. METHODS: We recruited adults diagnosed with early-stage breast cancer awaiting definitive therapy at a single-academic medical center to participate in semi-structured interviews. Thematic and content analyses were performed to identify attitudes and factors that would influence decisions about WOT participation. RESULTS: We interviewed 25 women diagnosed with early-stage breast cancer. The most common positive attitudes toward trial participation were a desire to contribute to research and a hope for personal benefit, while the most common concerns were the potential for side effects and how they might impact fitness for planned treatment. Participants indicated family would be an important normative factor in decision-making and, during the COVID-19 pandemic, deemed the absence of family members during clinic visits a barrier to enrollment. Factors that could hinder participation included delay in standard treatment and the requirement for additional visits or procedures. Ultimately, most interviewees stated they would participate in a WOT if offered (N = 17/25). CONCLUSION: In this qualitative study, interviewees weighed altruism and hypothetical personal benefit against the possibility of side effect from a WOT. In-person family presence during trial discussion, challenging during COVID-19, was important for many. Our results may inform trial design and communication approaches in future window of opportunity efforts.

Genetic and immunohistochemical profiling of small cell and large cell neuroendocrine carcinomas of the breast.

Neuroendocrine carcinomas (NEC) of the breast are exceedingly rare tumors, which are classified in the WHO system as small cell (SCNEC) and large cell (LCNEC) carcinoma based on indistinguishable features from their lung counterparts. In contrast to lung and enteropancreatic NEC, the genomics of breast NEC have not been well-characterized. In this study, we examined the clinicopathologic, immunohistochemical, and genetic features of 13 breast NEC (7 SCNEC, 4 LCNEC, 2 NEC with ambiguous small versus large cell morphology [ANEC]). Co-alterations of TP53 and RB1 were identified in 86% (6/7) SCNEC, 100% (2/2) ANEC, and 50% (2/4) LCNEC. The one SCNEC without TP53/RB1 alteration had other p53 pathway aberrations (MDM2 and MDM4 amplification) and was immunohistochemically RB negative. PIK3CA/PTEN pathway alterations and ZNF703 amplifications were each identified in 46% (6/13) NEC. Two tumors (1 SCNEC, 1 LCNEC) were CDH1 mutated. By immunohistochemistry, 100% SCNEC (6/6) and ANEC (2/2) and 50% (2/4) LCNEC (83% NEC) showed RB loss, compared to 0% (0/8) grade 3 neuroendocrine tumors (NET) (p < 0.001) and 38% (36/95) grade 3 invasive ductal carcinomas of no special type (IDC-NST) (p = 0.004). NEC were also more often p53 aberrant (60% vs 0%, p = 0.013), ER negative (69% vs 0%, p = 0.005), and GATA3 negative (67% vs 0%, p = 0.013) than grade 3 NET. Two mixed NEC had IDC-NST components, and 69% (9/13) of tumors were associated with carcinoma in situ (6 neuroendocrine DCIS, 2 non-neuroendocrine DCIS, 1 non-neuroendocrine LCIS). NEC and IDC-NST components of mixed tumors were clonally related and immunophenotypically distinct, lacking ER and GATA3 expression in NEC relative to IDC-NST, with RB loss only in NEC of one ANEC. The findings provide insight into the pathogenesis of breast NEC, underscore their classification as a distinct tumor type, and highlight genetic similarities to extramammary NEC, including highly prevalent p53/RB pathway aberrations in SCNEC.

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

Performance of the IBIS/Tyrer-Cuzick model of breast cancer risk by race and ethnicity in the Women's Health Initiative.

BACKGROUND: The IBIS/Tyrer-Cuzick model is used clinically to guide breast cancer screening and prevention, but was developed primarily in non-Hispanic White women. Little is known about its long-term performance in a racially/ethnically diverse population. METHODS: The Women's Health Initiative study enrolled postmenopausal women from 1993-1998. Women were included who were aged <80 years at enrollment with no prior breast cancer or mastectomy and with data required for IBIS/Tyrer-Cuzick calculation (weight; height; ages at menarche, first birth, and menopause; menopausal hormone therapy use; and family history of breast or ovarian cancer). Calibration was assessed by the ratio of observed breast cancer cases to the number expected by the IBIS/Tyrer-Cuzick model (O/E; calculated as the sum of cumulative hazards). Differential discrimination was tested for by self-reported race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian or Pacific Islander, and American Indian or Alaskan Native) using Cox regression. Exploratory analyses, including simulation of a protective single-nucleotide polymorphism (SNP), rs140068132 at 6q25, were performed. RESULTS: During follow-up (median 18.9 years, maximum 23.4 years), 6783 breast cancer cases occurred among 90,967 women. IBIS/Tyrer-Cuzick was well calibrated overall (O/E ratio = 0.95; 95% CI, 0.93-0.97) and in most racial/ethnic groups, but overestimated risk for Hispanic women (O/E ratio = 0.75; 95% CI, 0.62-0.90). Discrimination did not differ by race/ethnicity. Exploratory simulation of the protective SNP suggested improved IBIS/Tyrer-Cuzick calibration for Hispanic women (O/E ratio = 0.80; 95% CI, 0.66-0.96). CONCLUSIONS: The IBIS/Tyrer-Cuzick model is well calibrated for several racial/ethnic groups over 2 decades of follow-up. Studies that incorporate genetic and other risk factors, particularly among Hispanic women, are essential to improve breast cancer-risk prediction.

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.

BACKGROUND: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. METHODS: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. RESULTS: The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 - CRBP1, RARA - CASP3 - CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. CONCLUSIONS: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk.

PurposeWe examined racial/ethnic differences in the usage and results of germ-line multiple-gene sequencing (MGS) panels to evaluate hereditary cancer risk.MethodsWe collected genetic testing results and clinical information from 1,483 patients who underwent MGS at Stanford University between 1 January 2013 and 31 December 2015.ResultsAsians and Hispanics presented for MGS at younger ages than whites (48 and 47 vs. 55; P = 5E-16 and 5E-14). Across all panels, the rate of pathogenic variants (15%) did not differ significantly between racial groups. Rates by gene did differ: in particular, a higher percentage of whites than nonwhites carried pathogenic CHEK2 variants (3.8% vs. 1.0%; P = 0.002). The rate of a variant of uncertain significance (VUS) result was higher in nonwhites than whites (36% vs. 27%; P = 2E-4). The probability of a VUS increased with increasing number of genes tested; this effect was more pronounced for nonwhites than for whites (1.1% absolute difference in VUS rates testing BRCA1/2 vs. 8% testing 13 genes vs. 14% testing 28 genes), worsening the disparity.ConclusionIn this diverse cohort undergoing MGS testing, pathogenic variant rates were similar between racial/ethnic groups. By contrast, VUS results were more frequent among nonwhites, with potential significance for the impact of MGS testing by race/ethnicity.

Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction.

BACKGROUND: Nearly 6 million deaths and over a half trillion dollars in healthcare costs worldwide are attributed to tobacco smoking each year. Extensive research efforts have been pursued to elucidate the molecular underpinnings of smoking addiction and facilitate cessation. In this study, we genotyped and obtained both resting state and task-based functional magnetic resonance imaging from 64 non-smokers and 42 smokers. Smokers were imaged after having smoked normally ("sated") and after having not smoked for at least 12 h ("abstinent"). RESULTS: While abstinent smokers did not differ from non-smokers with respect to pairwise resting state functional connectivities (RSFCs) between 12 brain regions of interest, RSFCs involving the caudate and putamen of sated smokers significantly differed from those of non-smokers (P < 0.01). Further analyses of caudate and putamen activity during elicited experiences of reward and disappointment show that caudate activity during reward (CR) correlated with smoking status (P = 0.015). Moreover, abstinent smokers with lower CR experienced greater withdrawal symptoms (P = 0.024), which suggests CR may be related to smoking urges. Associations between genetic variants and CR, adjusted for smoking status, were identified by genome-wide association study (GWAS). Genes containing or exhibiting caudate-specific expression regulation by these variants were enriched within Gene Ontology terms that describe cytoskeleton functions, synaptic organization, and injury response (P < 0.001, FDR < 0.05). CONCLUSIONS: By integrating genomic and imaging data, novel insights into potential mechanisms of caudate activation and homeostasis are revealed that may guide new directions of research toward improving our understanding of addiction pathology.

Piwi-Interacting RNAs (piRNAs) Are Dysregulated in Renal Cell Carcinoma and Associated with Tumor Metastasis and Cancer-Specific Survival.

Piwi-interacting RNAs (piRNAs) are a distinct group of small noncoding RNAs (sncRNAs) that silence transposable genetic elements to protect genome integrity. Because of their limited expression in gonads and sequence diversity, piRNAs remain the most mysterious class of small RNAs. Studies have shown piRNAs are present in somatic cells and dysregulated in gastric, breast and liver cancers. By deep sequencing 24 frozen benign kidney and clear cell renal cell carcinoma (ccRCC) specimens and using the publically available piRNA database, we found 26,991 piRNAs present in human kidney tissue. Among 920 piRNAs that had at least two copies in one specimen, 19 were differentially expressed in benign kidney and ccRCC tissues, and 46 were associated with metastasis. Among the metastasis-related piRNAs, we found three piRNAs (piR-32051, piR-39894 and piR-43607) to be derived from the same piRNA cluster at chromosome 17. We confirmed the three selected piRNAs not to be miRNAs or miRNA-like sncRNAs. We further validated the aberrant expression of the three piRNAs in a 68-case formalin-fixed and paraffin-embedded (FFPE) ccRCC tissue cohort and showed the up-regulation of the three piRNAs to be highly associated with ccRCC metastasis, late clinical stage and poor cancer-specific survival.

SNP characteristics predict replication success in association studies.

Successful independent replication is the most direct approach for distinguishing real genotype-disease associations from false discoveries in genome-wide association studies (GWAS). Selecting SNPs for replication has been primarily based on P values from the discovery stage, although additional characteristics of SNPs may be used to improve replication success. We used disease-associated SNPs from more than 2,000 published GWASs to identify predictors of SNP reproducibility. SNP reproducibility was defined as a proportion of successful replications among all replication attempts. The study reporting association for the first time was considered to be discovery and all consequent studies targeting the same phenotype replications. We found that -Log(P), where P is a P value from the discovery study, is the strongest predictor of the SNP reproducibility. Other significant predictors include type of the SNP (e.g., missense vs intronic SNPs) and minor allele frequency. Features of the genes linked to the disease-associated SNP also predict SNP reproducibility. Based on empirically defined rules, we developed a reproducibility score (RS) to predict SNP reproducibility independently of -Log(P). We used data from two lung cancer GWAS studies as well as recently reported disease-associated SNPs to validate RS. Minus Log(P) outperforms RS when the very top SNPs are selected, while RS works better with relaxed selection criteria. In conclusion, we propose an empirical model to predict SNP reproducibility, which can be used to select SNPs for validation and prioritization.

How to interpret and apply the results of indirect calorimetry studies: a case-based tutorial.

Evidence is growing that the individual adjustment of energy targets guided by indirect calorimetry (IC) can improve outcome. With the development of a new generation of devices that are easier to use and rapid, it appears important to share knowledge and expertise that may be used to individualize nutrition care. Despite the focus of this tutorial being on one contemporary device, the principles of IC apply across existing devices and can assist tailoring the nutrition prescription and in assessing response to nutrition therapy. The present tutorial addresses its clinical application in intubated mechanically ventilated and spontaneously breathing adult patients (canopy), i.e. it covers the range from critical illness to outpatients. The cases that are presented show how the measured energy expenditure (mEE), and the respiratory quotient (RQ), i.e. the ratio of expired CO2 to consumed O2, should be applied in different cases, to adapt and individualize nutrition prescription, as it is a good marker of over- or underfeeding at the different stages of disease. The RQ also informs about the patient's body's capacity to use different substrates: the variations of RQ indicating the metabolic changes revealing insufficient or excessive feeding. The different cases reflect the use of a new generation device as a metabolic monitor that should be combined with other clinical observations and laboratory biomarkers. The tutorial also points to some shortcomings of the method, proposing alternatives.

Optimal Adjuvant Chemotherapy and Targeted Therapy for Early Breast Cancer-Cyclin-Dependent Kinase 4 and 6 Inhibitors: ASCO Guideline Rapid Recommendation Update.

ASCO Rapid Recommendation Updates highlight revisions to select ASCO guideline recommendations as a response to the emergence of new and practice-changing data. The rapid updates are supported by an evidence review and follow the guideline development processes outlined in the ASCO Guideline Methodology Manual. The goal of these articles is to disseminate updated recommendations, in a timely manner, to better inform health practitioners and the public on the best available cancer care options. Guidelines and updates are not intended to substitute for independent professional judgment of the treating provider and do not account for individual variation among patients. See appendix for disclaimers and other important information (Appendix 1 and Appendix 2, online only).