Cost-Effectiveness Analyses of the 21-Gene Assay in Breast Cancer: Systematic Review and Critical Appraisal

Molecule subtypes play important roles for second primary malignancies development based on 324,661 breast cancer survivors

The incidence trend of breast molecule subtypes was unclear. There was not quantified risk by subtype with the second primary malignancies (SPMs) and limited evidence about the risk factors for developing SPMs in first primary breast cancer(FPBC). Data from 18 SEER registries were used to identify FPBC, which were randomly selected for training and validation sets. The SPMs information of breast cancer survivors in Hebei were also collected to compare the distribution with SEER. Univariate and multivariate analysis were performed to explore the risk factors and integrated to the establishment of nomogram and risk stratification model. There was a decreased trend for TNBC, but an increased trend for Luminal A. The median survival months were 46, 46, 46 and 44 for Luminal A, Luminal B, HER2 enriched and TNBC, with the median latency time were 39, 39, 40 and 41.0 months, respecitvely, The cumulative incidence rates(CIR) of SPMs were 2.61%, 2.30%, 2.21% and 2.84%. Age at diagnosis, clinical lymph node status, radiotherapy and subtypes were independent risk factors for SPMs. A predict nomogram was established with the AUC of 0.682 and 0.679 for three- and five- year incidence risk in training set. Patients were divided into the low-risk (31.94%), intermediate-risk (51.83%) and high-risk (16.23%) groups by risk stratification model. The first common SPMs was second breast cancer in both SEER and Hebei cohort, the second and third rank SPMs were lung and gynecological cancer in SEER, but presented the opposite result in Hebei. The incidence rates and SPMs of subtypes were difference. The high risk individuals could be identified by risk stratification model, who need more closely followed up by Clinicians.

Cost-Utility Analysis of Multigene Assays to Guide Treatment Decisions for Node-Negative Early Breast Cancer

INTRODUCTION

Clinicopathologic and patient factors, such as tumor grade, size, age, and menopausal status, provide limited prognostic and predictive information in hormone receptor positive (HR +), human epidermal growth receptor 2 negative (HER2-), node-negative early-stage breast cancer, leading to potential over- or under-treatment. Multigene expression profile tests used in clinical practice in the USA, including the 21-gene assay, 70-gene assay, 12-gene assay, and 50-gene assay, offer prognostic information beyond traditional clinicopathologic features to improve treatment decisions. This study aimed to estimate the cost-effectiveness of these four multigene assays compared with clinicopathologic risk assessment alone.

METHODS

A decision tree categorized hypothetical patients with HR + /HER2- early-stage invasive breast cancer according to clinical and genomic risk, and integrated clinical expert insights for chemotherapy allocation with literature inputs. A Markov model simulated lifetime costs and outcomes of chemotherapy decisions over a patient's lifetime. The probability of distant breast cancer recurrence was derived from TAILORx (21-gene assay), MINDACT (70-gene assay), and TransATAC (12-gene assay, 50-gene assay) studies. Costs were calculated from a US societal perspective in 2021 US dollars, considering healthcare costs, lost productivity, and patient out-of-pocket expenses.

RESULTS

The 21-gene assay and 50-gene assay were less costly ( -$12,189 and  -$2410, respectively) and more effective [0.23 and 0.07 quality-adjusted life years (QALYs), respectively] compared with clinicopathologic risk alone. Similarly, the 70-gene assay and 12-gene assay are also cost-effective alternatives [incremental cost-effectiveness ratio (ICER): 27,760 and 7942, respectively].

CONCLUSIONS

All four multigene assays were cost-effective from a societal perspective, offering low net lifetime costs or savings with improved outcomes compared with clinicopathologic risk assessment alone. These assays can help refine treatment decisions by providing prognostic risk estimates. In the case of the 21-gene assay, it can also predict chemotherapy benefit leading to the highest lifetime cost savings and greatest QALY gain.

Development of a nomogram to predict recurrence scores obtained using Oncotype DX in Japanese patients with breast cancer

BACKGROUND

Chemotherapy is crucial for hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, and its survival benefits may outweigh adverse events. Oncotype DX (ODX) assesses this balance; however, it is expensive. Using nomograms to identify cases requiring ODX may be economically beneficial. We aimed to identify clinicopathological variables that correlated with the recurrence score (RS) and develop a nomogram that predicted the RS.

METHODS

We included 457 patients with estrogen receptor-positive, HER2-negative breast cancer with metastases in fewer than four axillary lymph nodes who underwent surgery and ODX at our hospital between 2007 and 2023. We developed nomograms and internally validated them in 310 patients who underwent surgery between 2007 and 2021 and validated the model's performance in 147 patients who underwent surgery between 2022 and 2023.

RESULTS

Logistic regression analysis revealed that progesterone receptor (PgR) level, histological grade (HG), and Ki67 index independently predicted the RS. A nomogram was developed using these variables to predict the RS (area under the curve [AUC], 0.870; 95% confidence interval [CI], 0.82-0.92). The nomogram was applied to the model validation group (AUC, 0.877; 95% CI, 0.80-0.95). When the sensitivity of the nomogram was 90%, the model was able to identify 52.3% low-RS and 41.2% high-RS cases not requiring ODX.

CONCLUSIONS

This was the first nomogram model developed based on data from a cohort of Japanese women. It may help determine the indications for ODX and the use of nomogram to identify cases requiring ODX may be economically beneficial.

Cost-Effectiveness Analysis of the Oncotype DX Breast Recurrence Score® Test from a US Societal Perspective

Background and Objectives

The 21-gene assay (the Oncotype DX Breast Recurrence Score® test) estimates the 10-year risk of distant recurrence in hormone receptor positive (HR+) and human epidermal growth factor receptor 2 negative (HER2-) early-stage breast cancer to inform adjuvant chemotherapy decisions. The cost-effectiveness of the 21-gene assay compared against standard clinical-pathological risk tools alone for HR+/HER2- early-stage breast cancer was assessed using an economic model informed by evidence from randomized controlled trials.

Materials and Methods

A cost-effectiveness model consisted of a decision-tree to stratify patients according to their Recurrence Score (RS) results and the use of adjuvant chemotherapy, followed by a Markov component to estimate the long-term costs and outcomes of the chosen treatment. Distributions of patients and distant recurrence probabilities were derived from the TAILORx (N0) and RxPONDER (N1) trials. The model was evaluated from a healthcare payer and societal perspective. Endocrine therapy and chemotherapy use were informed using clinical expert opinion to reflect US clinical practice and were combined with Medicare drug costs (2021) to estimate the cost of treatment. Societal costs included lost productivity and patient out-of-pocket costs obtained from literature.

Results

The Oncotype DX test generated more quality-adjusted life-years (QALYs) (N0: 0.25; N1: 0.08) at a lower cost (N0: -$13,395; N1: -$2526) compared to clinical-pathological risk alone from a societal cost perspective. The overall conclusions from the model did not change when considering a payer perspective. The main cost drivers were avoidance of distant recurrence for N0 (-$12,578), and the cost of adjuvant chemotherapy for N1 (-$2133). Lost productivity had a major impact in the societal perspective analysis (N0: -$4607; N1: -$1586).

Conclusion

Adjuvant chemotherapy decisions based on the RS result led to more life year gains and lower healthcare costs (dominant) compared to using clinical-pathological risk factors alone among patients with HR+/HER2- N0 and N1 early-stage breast cancer.

Disparities in OncotypeDx Testing and Subsequent Chemotherapy Receipt by Geography and Socioeconomic Status

BACKGROUND

OncotypeDx is a prognostic and predictive genomic assay used in early-stage hormone receptor-positive, HER2- (HR+/HER2-) breast cancer. It is used to inform adjuvant chemotherapy decisions, but not all eligible women receive testing. We aimed to assess variation in testing by demographics and geography, and to determine whether testing was associated with chemotherapy.

METHODS

For 1,615 women in the Carolina Breast Cancer Study with HR+/HER2-, Stage I-II tumors, we estimated prevalence differences (PD) and 95% confidence intervals (CI) for receipt of OncotypeDx genomic testing in association with and sociodemographic characteristics. We assessed associations between testing and chemotherapy receipt overall and by race. Finally, we calculated the proportion of eligible women receiving OncotypeDx by county-level rurality, census tract-level socioeconomic status, and Area Health Education Center regions.

RESULTS

38% (N = 609) of potentially eligible women were tested, with lower testing prevalences in Black (31%; PD, -11%; 95% CI, -16%-6%) and low-income women (24%; PD, -20%; 95% CI, -29% to -11%) relative to non-Black and higher income women. Urban participants were less likely to be tested than rural participants, though this association varied by region. Among women with low genomic risk tumors, tested participants were 29% less likely to receive chemotherapy than untested participants (95% CI, -40% to -17%). Racial differences in chemotherapy were restricted to untested women.

CONCLUSIONS

Both individual and area-level socioeconomics predict likelihood of OncotypeDx testing.

IMPACT

Variable adoption of OncotypeDx by socioeconomics and across geographic settings may contribute to excess chemotherapy among patients with HR+/HER2- cancers. See related In the Spotlight, p. 635.

Real-world use of multigene signatures in early breast cancer: differences to clinical trials

PURPOSE

In Italy, Lombardy was the first region to reimburse multigene assays (MGAs) for patients otherwise candidates for chemotherapy. This is a real-world experience of MGAs usage in six referral cancer centers in Lombardy.

METHODS

Among MGAs, Oncotype DX (RS) was used in 97% of cases. Consecutive patients tested with Oncotype DX from July 2020 to July 2022 were selected. The distribution of clinicopathologic features by RS groups (low RS: 0-25, high RS: 26-100) was assessed using chi-square and compared with those of the TAILORx and RxPONDER trials.

RESULTS

Out of 1,098 patients identified, 73% had low RS. Grade and Ki67 were associated with RS (p < 0.001). In patients with both G3 and Ki67 > 30%, 39% had low RS, while in patients with both G1 and Ki67 < 20%, 7% had high RS. The proportion of low RS in node-positive patients was similar to that in RxPONDER (82% vs 83%), while node-negative patients with low RS were significantly less than in TAILORx (66% vs 86%, p < 0.001). The distribution of Grade was different from registration trials, with more G3 and fewer G1 (38% and 3%) than in TAILORx (18% and 27%) and RxPONDER (10% and 24%) (p < 0.001). Patients ≤ 50 years were overrepresented in this series (41%) than in TAILORx and RxPONDER (31% and 24%, respectively) (p < 0.001) and, among them, 42% were node positive.

CONCLUSIONS

In this real-world series, Oncotype DX was the test almost exclusively used. Despite reimbursement being linked to pre-test chemotherapy recommendation, almost 3/4 patients resulted in the low-RS group. The significant proportion of node-positive patients ≤ 50 years tested indicates that oncologists considered Oncotype DX informative also in this population.

Single cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification

Head and neck squamous cell carcinoma (HNSCC) is characterized by high recurrence or distant metastases rate and the prognosis is challenging. There is mounting evidence that tumor-infiltrating B cells (TIL-Bs) have a crucial, synergistic role in tumor control. However, little is known about the role TIL-Bs play in immune microenvironment and the way TIL-Bs affect the outcome of immune checkpoint blockade. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, the study identified distinct gene expression patterns in TIL-Bs. HNSCC samples were categorized into TIL-Bs inhibition and TIL-Bs activation groups using unsupervised clustering. This classification was further validated with TCGA HNSCC data, correlating with patient prognosis, immune cell infiltration, and response to immunotherapy. We found that the B cells activation group exhibited a better prognosis, higher immune cell infiltration, and distinct immune checkpoint levels, including elevated PD-L1. A prognostic model was also developed and validated, highlighting four genes as potential biomarkers for predicting survival outcomes in HNSCC patients. Overall, this study provides a foundational approach for B cells-based tumor classification in HNSCC, offering insights into targeted treatment and immunotherapy strategies.

Association of radiotherapy for stage I-III breast cancer survivors and second primary malignant cancers: a population-based study

PURPOSE

With life span extending, breast cancer survivors may face the possibility of developing second primary cancers (SPCs). The objective of this research is to investigate the risk factors, risk attribute to radiotherapy and the survivalship for SPCs.

METHODS

A total of 445 523 breast cancer patients were enrolled from Surveillance, Epidemiology, and End Results database in 2000-2018. The risk factors for SPCs development were confirmed by competing risk model, and then were integrated to the nomogram establishment. The cumulative incidence of SPCs including SBC (second breast cancer), SGC (second gynecological cancer), and SLC (second lung cancer) were estimated. The radiotherapy-associated risk for SPCs were evaluated by Poisson regression in radiotherapy and no-radiotherapy. Propensity score matching was used to reduce possible bias for survival comparison.

RESULTS

There were 57.63% patients in radiotherapy. The risk factors for developing SPCs were age, year, race, tumor size, stage, radiotherapy, grade, surgery, and histology. The cumulative incidence of SPCs was 7.75% in no-radiotherapy and 10.33% in radiotherapy. SLC, SBC, and SGC also appeared the similar results. The increased risk of developing SPCs were associated with radiotherapy in majority subgroups. The dynamic radiotherapy-associated risk for SPCs by age slightly increased risk was observed. Regardless radiotherapy or no-radiotherapy, the 10-year overall survival for SBC (radiotherapy: 59.41%; no-radiotherapy: 55.53%) and SGC (radiotherapy: 48.61%; no-radiotherapy: 35.53%) were worse than that among matched patients with only primary cancers.

CONCLUSIONS

Breast cancer survivors remained a high radiotherapy-associated risk for developing SPCs. The prognosis in radiotherapy was better than in no-radiotherapy for some specific SPCs. Largely attention should be paid to these patients.

Integrating bulk and single-cell data to predict the prognosis and identify the immune landscape in HNSCC

The complex interplay between tumour cells and the tumour microenvironment (TME) underscores the necessity for gaining comprehensive insights into disease progression. This study centres on elucidating the elusive the elusive role of endothelial cells within the TME of head and neck squamous cell carcinoma (HNSCC). Despite their crucial involvement in angiogenesis and vascular function, the mechanistic diversity of endothelial cells among HNSCC patients remains largely uncharted. Leveraging advanced single-cell RNA sequencing (scRNA-Seq) technology and the Scissor algorithm, we aimed to bridge this knowledge gap and illuminate the intricate interplay between endothelial cells and patient prognosis within the context of HNSCC. Here, endothelial cells were categorized into Scissorhigh and Scissorlow subtypes. We identified Scissor+ endothelial cells exhibiting pro-tumorigenic profiles and constructed a prognostic risk model for HNSCC. Additionally, four biomarkers also were identified by analysing the gene expression profiles of patients with HNSCC and a prognostic risk prediction model was constructed based on these genes. Furthermore, the correlations between endothelial cells and prognosis of patients with HNSCC were analysed by integrating bulk and single-cell sequencing data, revealing a close association between SHSS and the overall survival (OS) of HNSCC patients with malignant endothelial cells. Finally, we validated the prognostic model by RT-qPCR and IHC analysis. These findings enhance our comprehension of TME heterogeneity at the single-cell level and provide a prognostic model for HNSCC.

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Identifying Patterns and Barriers in OncotypeDX Recurrence Score Testing in Older Patients With Early-Stage, Estrogen Receptor-Positive Breast Cancer: Implications for Guidance and Reimbursement

PURPOSE

To evaluate the clinical patterns of utilization of OncotypeDX Recurrence Score (RS) in early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer (BC) at an academic center with previously established internal reflex testing guidelines.

METHODS

RS testing in accordance with preexisting reflex criteria and predictors of utilization outside of reflex criteria were retrospectively analyzed for the years 2019-2021 in a quality improvement evaluation. Patients were grouped according to OncotypeDX testing within (cohort A) or outside (cohort B) of predefined criteria which included a cap at age older than 65 years.

RESULTS

Of 1,687 patients whose tumors had RS testing, 1,087 were in cohort A and 600 in cohort B. In cohort B, nearly half of patients were older than 65 years (n = 279; IQR, 67-72 years). For patients older than 65 years, those with RS testing were younger (median age: 69 v 73 years), with higher grade cancers (G2-3: 84.9% v 54.7%) and were more likely to be treated with chemotherapy (15.4% v 4.1%). Issues for implementation of RS testing in older patients were identified, including potential structural barriers related to the current policy on the reimbursements of genomic tests.

CONCLUSION

Internal guidelines may facilitate standardized utilization of the RS in early-BC. Our data suggest that clinicians preferred broader utilization of RS across the age spectrum, with therapeutically important consequences. Modifying the current policy for reimbursement of RS testing and in internal reflexive testing criteria for those older than 65 years is warranted.

Cost-utility analysis of genomic profiling in early breast cancer in Colombia

BACKGROUND

In Colombia, the best strategy to establish indication for adjuvant chemotherapy in early breast cancer (EBC) remains unknown. This study aimed to identify the cost-utility of Oncotype DX™ (ODX) or Mammaprint™ (MMP) tests to establish the necessity of adjuvant chemotherapy.

METHODS

This study used an adapted decision-analytic model to compare cost and outcomes of care between ODX or MMP tests and routine care without ODX or MMP tests (adjuvant chemotherapy for all patients) over a 5-year time horizon from the perspective of the Colombian National Health System (NHS; payer). Inputs were obtained from national unit cost tariffs, published literature, and clinical trial database. The study population comprised women with hormone-receptor-positive (HR +), HER2-negative, lymph-node-negative (LN0) EBC with high-risk clinical criteria for recurrence. The outcome measures were discounted incremental cost-utility ratio (ICUR; 2021 United States dollar per quality-adjusted life-year [QALY] gained) and net monetary benefit (NMB). Probabilistic (PSA) and deterministic sensitivity analysis (DSA) were performed.

RESULTS

ODX increases QALYs by 0.05 and MMP by 0.03 with savings of $2374 and $554 compared with the standard strategy, respectively, and were cost-saving in cost-utility plane. NMB for ODX was $2203 and for MMP was $416. Both tests dominate the standard strategy. Sensitivity analysis revealed that with a threshold of 1 gross domestic product per capita, ODX will be cost-effective in 95.5% of the cases compared with 70.2% cases involving MMP.DSA showed that the variable with significant influence was the monthly cost of adjuvant chemotherapy. PSA revealed that ODX was a consistently superior strategy.

CONCLUSIONS

Genomic profiling using ODX or MMP tests to define the need of adjuvant chemotherapy treatment in patients with HR + and HER2 -EBC is a cost-effective strategy that allows Colombian NHS to maintain budget.

Oncotype DX results increase concordance in adjuvant chemotherapy recommendations for early-stage breast cancer

Adjuvant chemotherapy recommendations for ER+/HER2- early-stage breast cancers (eBC) involve integrating prognostic and predictive information which rely on physician judgment; this can lead to discordant recommendations. In this study we aim to evaluate whether Oncotype DX improves confidence and agreement among oncologists in adjuvant chemotherapy recommendations. We randomly select 30 patients with ER+/HER2- eBC and recurrence score (RS) available from an institutional database. We ask 16 breast oncologists with varying years of clinical practice in Italy and the US to provide recommendation for the addition of chemotherapy to endocrine therapy and their degree of confidence in the recommendation twice; first, based on clinicopathologic features only (pre-RS), and then with RS result (post-RS). Pre-RS, the average rate of chemotherapy recommendation is 50.8% and is higher among junior (62% vs 44%; p < 0.001), but similar by country. Oncologists are uncertain in 39% of cases and recommendations are discordant in 27% of cases (interobserver agreement K 0.47). Post-RS, 30% of physicians change recommendation, uncertainty in recommendation decreases to 5.6%, and discordance decreases to 7% (interobserver agreement K 0.85). Interpretation of clinicopathologic features alone to recommend adjuvant chemotherapy results in 1 out of 4 discordant recommendations and relatively high physician uncertainty. Oncotype DX results decrease discordancy to 1 out of 15, and reduce physician uncertainty. Genomic assay results reduce subjectivity in adjuvant chemotherapy recommendations for ER +/HER2- eBC.

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BACKGROUND

The low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.

METHODS

Saliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.

RESULTS

There were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.

CONCLUSIONS

The results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.

Prognostic significance of pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography in patients with T2N1 hormone receptor-positive, ERBB2-negative breast cancer who underwent adjuvant chemotherapy

PURPOSE

To determine whether tumor uptake of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) is associated with invasive disease-free survival (IDFS) in patients with hormone receptor (HR)-positive ERBB2-negative early-stage breast cancer treated with adjuvant chemotherapy.

METHODS

This is a single-center cohort study of women with breast cancer who underwent surgery between 2008 and 2015 at Asan Medical Center, Seoul, Korea. Patients were enrolled if they were diagnosed with HR-positive ERBB2-negative breast cancer with histology of invasive ductal carcinoma, had an American Joint Committee on Cancer pathologic tumor stage of T2N1 with 1-3 positive axillary nodes, underwent preoperative ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT), and underwent breast cancer surgery followed by anthracycline- or taxane-based adjuvant chemotherapy. The primary outcome measure was IDFS. The maximum standardized uptake value (SUVmax) was dichotomized using a predefined cut-off of 4.14.

RESULTS

A total of 129 patients were included. The median follow-up period for IDFS in those without recurrence was 82 months (interquartile range, 65-106). Multivariable Cox analysis showed that SUVmax was independently associated with IDFS [adjusted hazard ratio 2.49; 95% confidence interval (CI), 1.06-5.84]. Ten-year IDFS estimates via the Kaplan-Meier method were 0.60 (95% CI, 0.42-0.74) and 0.82 (95% CI, 0.65-0.91) for high and low SUVmax groups, respectively. The overall association between SUVmax and IDFS appeared to be consistent across subgroups divided according to age, progesterone receptor status, histologic grade, or presence of lymphovascular invasion.

CONCLUSION

High SUVmax on preoperative ¹⁸F-FDG PET/CT was independently associated with reduced long-term IDFS in T2N1 HR-positive ERBB2-negative breast cancer patients who underwent adjuvant chemotherapy.

The Rochester Modified Magee Algorithm (RoMMa): An Outcomes Based Strategy for Clinical Risk-Assessment and Risk-Stratification in ER Positive, HER2 Negative Breast Cancer Patients Being Considered for Oncotype DX® Testing

INTRODUCTION

Multigene genomic profiling has become the standard of care in the clinical risk-assessment and risk-stratification of ER⁺, HER2^- breast cancer (BC) patients, with Oncotype DX^® (ODX) emerging as the genomic profile test with the most support from the international community. The current state of the health care economy demands that cost-efficiency and access to testing must be considered when evaluating the clinical utility of multigene profile tests such as ODX. Several studies have suggested that certain lower risk patients can be identified more cost-efficiently than simply reflexing all ER⁺, HER2^- BC patients to ODX testing. The Magee equations^TM use standard histopathologic data in a set of multivariable models to estimate the ODX recurrence score. Our group published the first outcome data in 2019 on the Magee equations^TM, using a modification of the Magee equations^TM combined with an algorithmic approach-the Rochester Modified Magee algorithm (RoMMa). There has since been limited published outcome data on the Magee equations^TM. We present additional outcome data, with considerations of the TAILORx risk-stratification recommendations.

METHODS

355 patients with an ODX recurrence score, and at least five years of follow-up or a BC recurrence were included in the study. All patients received either Tamoxifen or an aromatase inhibitor. None of the patients received adjuvant systemic chemotherapy.

RESULTS

There was no significant difference in the risk of recurrence in similar risk categories (very low risk, low risk, and high risk) between the average Modified Magee score and ODX recurrence score with the chi-square test of independence (p > 0.05) or log-rank test (p > 0.05). Using the RoMMa, we estimate that at least 17% of individuals can safely avoid ODX testing.

CONCLUSION

Our study further reinforces that BC patients can be confidently stratified into lower and higher-risk recurrence groups using the Magee equations^TM. The RoMMa can be helpful in the initial clinical risk-assessment and risk-stratification of BC patients, providing increased opportunities for cost savings in the health care system, and for clinical risk-assessment and risk-stratification in less-developed geographies where multigene testing might not be available.

Cost-effectiveness analysis of Oncotype DX from a Brazilian private medicine perspective: a GBECAM multicenter retrospective study

Background

Oncotype DX (ODX) is a validated assay for the prediction of risk of recurrence and benefit of chemotherapy (CT) in both node negative (N0) and 1-3 positive nodes (N1), hormone receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (eBC). Due to limited access to genomic assays in Brazil, treatment decisions remain largely driven by traditional clinicopathologic risk factors. ODX has been reported to be cost-effective in different health system, but limited data are available considering the reality of middle-income countries such as Brazil. We aim to evaluate the cost-effectiveness of ODX across strata of clinical risk groups using data from a dataset of patients from Brazilian institutions.

Methods

Clinicopathologic and ODX information were analyzed for patients with T1-T3, N0-N1, HR+/HER2- eBC who had an ODX performed between 2005 and 2020. Projections of CT indication by clinicopathologic criteria were based on binary clinical risk categorization based on the Adjuvant! Algorithm. The ODX score was correlated with the indication of CT according to TAILORx and RxPONDER data. Two decision-tree models were developed. In the first model, low and high clinical risk patients were included while in the second, only high clinical risk patients were included. The cost for ODX and CT was based on the Brazilian private medicine perspective.

Results

In all, 645 patients were analyzed; 411 patients (63.7%) had low clinical risk and 234 patients (36.3%) had high clinical risk disease. The ODX indicated low (<11), intermediate (11-25), and high (>25) risk in 119 (18.4%), 415 (64.3%), and 111 (17.2%) patients, respectively. Among 645 patients analyzed in the first model, ODX was effective (5.6% reduction in CT indication) though with an incremental cost of United States Dollar (US$) 2288.87 per patient. Among 234 patients analyzed in the second model (high clinical risk only), ODX led to a 57.7% reduction in CT indication and reduced costs by US$ 4350.66 per patient.

Conclusions

Our study suggests that ODX is cost-saving for patients with high clinical risk HR+/HER2- eBC and cost-attractive for the overall population in the Brazilian private medicine perspective. Its incorporation into routine practice should be strongly considered by healthcare providers.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases

PURPOSE

Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model.

METHODS

The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database. We performed a comprehensive bioinformatics analysis to establish and validate an immune-related gene model and subsequently identified hub genes as biomarkers associated with the molecular mechanisms of BA. To assess the model's performance for separating BA from other cholestasis diseases, we used receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the ROC. Independent datasets GSE69948 and GSE122340 were used for the validation process.

RESULTS

The model was built using eight immune-related genes, including EDN1, HAMP, SAA1, SPP1, ANKRD1, MMP7, TACSTD2, and UCA1. In the GSE46960 and validation group, it presented excellent results, and the prediction accuracy of BA in comparison to other cholestasis diseases was good. Functional enrichment analysis revealed significant immunological differences between BA and other cholestatic diseases. Finally, we found that the TNFα-NF-κB pathway is associated with EDN1 gene expression and may explain fibrosis progression, which may become a new therapeutic target.

CONCLUSION

In summary, we have successfully constructed an immune-related gene model that can distinguish BA from other cholestatic diseases, while identifying the hub gene. Our exploration of immune genes provides new clues for the early diagnosis, molecular mechanism, and clinical treatment of biliary atresia.

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Impact of the 21-Gene Recurrence Score Assay on Treatment Decisions and Cost in Patients with Node-Positive Breast Cancer: A Multicenter Study in Quebec

BACKGROUND

The 21-gene Breast Recurrence Score (RS) assay, "the assay", has led to a paradigm shift for patients with hormone receptor-positive, node-negative early breast cancer and is emerging as an important tool to assist physician-patient decisions in foregoing chemotherapy in node-positive patients. We wanted to better understand the impact of the RS assay in node-positive patients upon physician treatment decisions and treatment cost in Quebec, Canada.

PATIENTS AND METHODS

We conducted a multicenter, prospective observational trial for Estrogen/Progesterone Receptor (ER/PR)- positive, Human Epidermal Growth Factor Receptor 2 (HER2)-negative breast cancer patients with 1-3 positive lymph nodes. Physicians completed a questionnaire indicating treatment choice prior to and post availability of RS results. The primary endpoint was change in the physician's recommendation for chemotherapy prior to and post assay results. Secondary endpoints included change in physician's expressed level of confidence, and changes in estimated cost of recommended treatments prior to and post assay results.

RESULTS

For the entire cohort, physician recommendation for chemotherapy was reduced by an absolute 67.1% by knowledge of the RS assay result (P < .0001). Physician recommendation of chemotherapy was decreased by 75.9% for patients RS result <14 (P < .0001); and 67.5% for patients with RS result 14-25 (P < .0001). Changes in treatment recommendations were associated with an overall reduction in cost by 73.7% per patient, and after incorporating the cost of the RS test, a cost benefit of $823 CAN at 6-month follow-up.

CONCLUSION

Altogether, we established that the assay led to a two-third reduction in the use of chemotherapy, and was a cost-effective approach for hormone receptor-positive, node-positive breast cancer.

Cost-Effectiveness Analysis of the Oncotype DX Breast Recurrence Score® Test in Node-Negative Early Breast Cancer

Background

The 21-gene assay (the Oncotype DX Breast Recurrence Score® test) is a validated multigene assay which produces the Recurrence Score® result (RS) to inform decisions on the use of adjuvant chemotherapy in human epidermal growth factor receptor 2-negative (HER2-), hormone receptor positive (HR+) early invasive breast cancer. A model-based economic evaluation estimated the cost-effectiveness of the 21-gene assay against the use of clinical risk tools alone based on the latest evidence from prospective studies.

Methods

The proportion of patients assigned to chemotherapy conditional on their RS result was obtained from retrospective data from the Clalit registry. The probability of distant recurrence with endocrine and chemo-endocrine therapy conditional on RS result was obtained from TAILORx and NSABP B-20 trials. The cost-effectiveness of the 21-gene assay compared to using clinical risk tools alone was estimated in terms of cost per quality-adjusted life-year (QALY) over a lifetime horizon.

Results

The 21-gene assay was more effective (0.17 more quality-adjusted life years) at a lower cost (-£519) over a lifetime compared to clinical risk alone. The model results were sensitive to assumptions around the magnitude of benefit of chemotherapy in the high RS result subgroup. Other assumptions underpinning the model, such as the proportion of patients assigned to chemotherapy in the low and mid-range RS result subgroups and long-term distant recurrence probabilities, had a smaller impact on the results.

Conclusion

The analysis showed that the cost-effectiveness of the 21-gene assay is sensitive to assumptions for chemotherapy sparing for patients with RS 0-25 whose outcomes with endocrine therapy are no worse compared to chemotherapy-assigned patients, and a chemotherapy benefit in the RS 26-100 group. Future studies need to incorporate a wider set of tumour profiling tests other than the 21-gene assay to allow a direct comparison of their cost-effectiveness.

Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer

TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.

Protein expression profiling identifies a prognostic model for ovarian cancer

BACKGROUND

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

METHODS

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

RESULTS

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

CONCLUSIONS

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score©

Background: OncotypeDX Recurrence Score© (RS) is a commercially available 21-gene expression assay which estimates prognosis and guides chemoendocrine prescription in early-stage estrogen-receptor positive, human epidermal growth factor receptor-2-negative (ER+/HER2−) breast cancer. Limitations of RS testing include the cost and turnaround time of several weeks. Aim: Our aim is to develop a user-friendly surrogate nomogram capable of predicting RS. Methods: Multivariable linear regression analyses were performed to determine predictors of RS and RS > 25. Receiver operating characteristic analysis produced an area under the curve (AUC) for each model, with training and test sets were composed of 70.3% (n = 315) and 29.7% (n = 133). A dynamic, user-friendly nomogram was built to predict RS using R (version 4.0.3). Results: 448 consecutive patients who underwent RS testing were included (median age: 58 years). Using multivariable regression analyses, postmenopausal status (β-Coefficient: 0.25, 95% confidence intervals (CIs): 0.03−0.48, p = 0.028), grade 3 disease (β-Coefficient: 0.28, 95% CIs: 0.03−0.52, p = 0.026), and estrogen receptor (ER) score (β-Coefficient: −0.14, 95% CIs: −0.22−−0.06, p = 0.001) all independently predicted RS, with AUC of 0.719. Using multivariable regression analyses, grade 3 disease (odds ratio (OR): 5.67, 95% CIs: 1.32−40.00, p = 0.037), decreased ER score (OR: 1.33, 95% CIs: 1.02−1.66, p = 0.050) and decreased progesterone receptor score (OR: 1.16, 95% CIs: 1.06−1.25, p = 0.002) all independently predicted RS > 25, with AUC of 0.740 for the static and dynamic online nomogram model. Conclusions: This study designed and validated an online user-friendly nomogram from routinely available clinicopathological parameters capable of predicting outcomes of the 21-gene RS expression assay.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma

Background

Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis.

Methods

Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations.

Results

We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM.

Conclusion

We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.

Research on the Economics of Cancer-Related Health Care: An Overview of the Review Literature

We reviewed current literature reviews regarding economics of cancer-related health care to identify focus areas and gaps. We searched PubMed for systematic and other reviews with the Medical Subject Headings "neoplasms" and "economics" published between January 1, 2010, and April 1, 2020, identifying 164 reviews. Review characteristics were abstracted and described. The majority (70.7%) of reviews focused on cost-effectiveness or cost-utility analyses. Few reviews addressed other types of cancer health economic studies. More than two-thirds of the reviews examined cancer treatments, followed by screening (15.9%) and survivorship or end-of-life (13.4%). The plurality of reviews (28.7%) cut across cancer site, followed by breast (20.7%), colorectal (11.6%), and gynecologic (8.5%) cancers. Specific topics addressed cancer screening modalities, novel therapies, pain management, or exercise interventions during survivorship. The results indicate that reviews do not regularly cover other phases of care or topics including financial hardship, policy, and measurement and methods.

Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update

PURPOSE

To update recommendations on appropriate use of breast cancer biomarker assay results to guide adjuvant endocrine and chemotherapy decisions in early-stage breast cancer.

METHODS

An updated literature search identified randomized clinical trials and prospective-retrospective studies published from January 2016 to October 2021. Outcomes of interest included overall survival and disease-free or recurrence-free survival. Expert Panel members used informal consensus to develop evidence-based recommendations.

RESULTS

The search identified 24 studies informing the evidence base.

RECOMMENDATIONS

Clinicians may use Oncotype DX, MammaPrint, Breast Cancer Index (BCI), and EndoPredict to guide adjuvant endocrine and chemotherapy in patients who are postmenopausal or age > 50 years with early-stage estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+ and HER2-) breast cancer that is node-negative or with 1-3 positive nodes. Prosigna and BCI may be used in postmenopausal patients with node-negative ER+ and HER2- breast cancer. In premenopausal patients, clinicians may use Oncotype in patients with node-negative ER+ and HER2- breast cancer. Current data suggest that premenopausal patients with 1-3 positive nodes benefit from chemotherapy regardless of genomic assay result. There are no data on use of genomic tests to guide adjuvant chemotherapy in patients with ≥ 4 positive nodes. Ki67 combined with other parameters or immunohistochemistry 4 score may be used in postmenopausal patients without access to genomic tests to guide adjuvant therapy decisions. BCI may be offered to patients with 0-3 positive nodes who received 5 years of endocrine therapy without evidence of recurrence to guide decisions about extended endocrine therapy. None of the assays are recommended for treatment guidance in individuals with HER2-positive or triple-negative breast cancer. Treatment decisions should also consider disease stage, comorbidities, and patient preferences.Additional information is available at www.asco.org/breast-cancer-guidelines.

Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment.

Methods

The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset.

Results

A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4.

Conclusion

The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.

Incremental Spending Associated with Low-Value Treatments in Older Women with Breast Cancer

BACKGROUND

In most women ≥ 70 years old with hormone-receptor-positive breast cancer, axillary staging and adjuvant radiotherapy provide no survival advantage over surgery and hormone therapy alone. Despite recommendations for their omission, sentinel lymph node biopsy (SLNB) and adjuvant radiotherapy rates remain high. While treatment side effects are well documented, less is known about the incremental spending associated with SLNB and adjuvant radiotherapy.

METHODS

Using a statewide multipayer claims registry, we examined spending associated with breast cancer treatment in a retrospective cohort of women ≥ 70 years old undergoing surgery.

RESULTS

9074 women ≥70 years old underwent breast cancer resection between 2012 and 2019, with 78% (n = 7122) receiving SLNB and/or adjuvant radiotherapy within 90 days of surgery. Women undergoing SLNB were more likely to receive radiation (51% vs. 28%; p < 0.001 and OR = 2.68). Average 90-day spending varied substantially based upon treatment received, ranging from US$10,367 (breast-conserving surgery alone) to US$27,370 (mastectomy with SLNB and adjuvant radiotherapy). The relative increases in 90-day treatment spending in the breast-conserving surgery cohort was 65% for SLNB, 82% for adjuvant radiotherapy, and 120% for both treatments.

CONCLUSIONS

SLNB and adjuvant radiotherapy have significant spending implications in older women with breast cancer, even though they are unlikely to improve survival.

Cost-Effectiveness Analysis from a Societal Perspective of Recurrence Index for Distant Recurrence (RecurIndex) in Women with Hormone Receptor-Positive and HER2-Negative Early-Stage Breast Cancer

Purpose

A clinical-genomic prognostic multigene panel (RI-DR assay, RecurIndex^®), predicting the risk level of distant recurrence (DR) in early-stage breast cancer (EBC) patients with an Asian background, has been validated as a valuable tool for identifying high-risk patients to develop distant recurrence (metastasis). Although the clinical benefit of adjuvant chemotherapy from the assay’s prediction is already proved, its affordability remains uncertain. This study is the first time in which the long-term cost-effectiveness of the RI-DR assay is evaluated.

Patients and Methods

A lifetime Markov decision-analytic model was developed from a societal perspective to estimate the life-years gained (LYGs), quality-adjusted life-years (QALYs), medical costs, and incremental cost-effectiveness ratios (ICERs), comparing EBC women with and without RI-DR genomic testing. A decision tree was used to classify patients in one of the fifteen end nodes (by order, each arm was stratified by a patient being tested or not with the RI-DR assay, being treated or not with adjuvant chemotherapy and had no, minor, major, or fatal toxicity after adjuvant chemotherapy). Health utilities, costs, transition probabilities, and survival data were extracted from the scientific literature. Deterministic sensitivity analysis (DSA) and probabilistic sensitivity analysis (PSA) were performed on variables to assess the robustness of the model. A willingness-to-pay (WTP) threshold of 790,000 NT$ per QALY gained was considered as a cost-effectiveness criterion.

Results

The incremental cost per QALY gained under base-case assumptions of the model was 173,842 NT$. Findings on the variation in model input parameters were robust and confirmed that every key variable was cost-effective for the benefit of RI-DR testing.

Conclusion

The clinical-genomic RI-DR assay is cost-effective in guiding adjuvant chemotherapy decisions compared to current clinical practice guidelines.

Cost-effectiveness analysis of the Oncotype DX Breast Recurrence Score test in node-positive early breast cancer

AIMS

Given the high rate of adverse events and high cost of adjuvant chemotherapy, it is optimal to avoid its use when endocrine therapy is equally effective at preventing distant recurrence of early breast cancer. The Oncotype DX test is a predictive and prognostic multigene assay used to guide adjuvant chemotherapy decisions in early breast cancer based on a Recurrence Score (RS) result. A model-based cost-effectiveness analysis compared the Oncotype DX test to clinical risk tools alone for HR+/HER2- node-positive (1-3 axillary lymph nodes) early breast cancer patients based on results from the RxPONDER trial.

MATERIALS AND METHODS

A decision-tree and Markov model was developed in Microsoft Excel. Distributions of patients and distant recurrence probabilities with endocrine and chemo-endocrine therapy were derived from the RxPONDER trial, TransATAC and SWOG-8814. Chemotherapy assignment data were obtained from the Clalit registry. The cost of adjuvant chemotherapy was based on the distribution of treatments used in the UK combined with published drug unit costs in the UK. The cost of distant recurrence and health state utility values were obtained from literature.

RESULTS

The Oncotype DX test was found to be more effective (with an estimated 0.02 additional QALYs) at a lower estimated cost (-£989) compared to clinical risk tools alone. The results did not substantially change with more conservative clinical and cost scenarios. The RxPONDER trial was restricted to RS 0-25, and data synthesis with other studies was required to inform the analysis, which increased uncertainty.

CONCLUSIONS

The Oncotype DX test is highly likely to be cost-effective in node-positive early breast cancer. The results were driven by reduction in the use of chemotherapy with consequence avoidance of the costs and harmful effects of chemotherapy. Targeted treatment of a minority (11%) of women with RS 26-100 who benefit from chemotherapy reduced cost and improved survival.

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis

Background

The mechanism of cancer occurrence and development could be understood with multi‐omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).

Methods

Clinical follow‐up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA‐Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic‐related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene‐based prediction model for LUAD was validated in validation datasets.

Results

The study filtered 2071 prognostic‐related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis‐related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression‐related. A 7‐gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5‐year AUC of survival in the validation set and training set was all ˃ 0.67. Similar results were obtained from multi‐omics validation datasets.

Conclusions

The study builds a novel 7‐gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.

Impact of TAILORx on chemotherapy prescribing and 21-gene recurrence score-guided treatment costs in a population-based cohort of patients with breast cancer

BACKGROUND

The trial assigning individualized options for treatment (Rx) (TAILORx) confirmed the predictive value of the 21-gene recurrence score (RS) assay in hormone receptor (HR)-positive, HER2-negative, node-negative breast cancer and established thresholds for chemotherapy benefit in younger and older patients. Real-world chemotherapy use and RS-guided treatment costs in British Columbia post-TAILORx were examined.

METHODS

The authors assembled 3 cohorts of HR-positive, HER2-negative, node-negative patients with breast cancer defined by diagnosis: before RS funding (cohort 1 [C1]: January 2013-December 2013), after introduction of public RS funding (cohort 2 [C2]: July 2015-June 2016), and after TAILORx results (cohort 3 [C3]: July 2018-June 2019). Chemotherapy use was compared between cohorts by age and RS. Budgetary impacts of RS testing on chemotherapy costs were evaluated pre- and post-TAILORx.

RESULTS

Among the 2066 patients included, chemotherapy use declined by 19% after RS funding was introduced and by an additional 23% after TAILORx publication (P = .001). Reduction in chemotherapy use was significant for RS 11-20 tumors (C3 vs C2, P = .004). There was no significant change in chemotherapy use in patients >50 years old (C2:12% vs C3:10%, P = .22). RS testing was associated with higher cost savings post-TAILORx, except in patients 70 to 80 years old, where testing led to excess costs when adjusting for the low rate of RS-concordant chemotherapy prescribed.

CONCLUSIONS

TAILORx has had population-based impacts on chemotherapy prescribing in intermediate RS tumors and patients ≤50 years old. The lower clinical use of RS and increased spending in patients 70-80 years old highlights the importance of careful selection of older candidates for high-cost genomic testing.

LAY SUMMARY

The 21-gene recurrence score (RS) test helps predict whether patients with hormone-positive, HER2-negative, lymph node-negative breast cancer are likely to benefit from chemotherapy. The recent trial assigning individualized options for treatment (Rx) (TAILORx) found that patients with intermediate RS tumors did not benefit from chemotherapy. The authors assessed whether TAILORx results translated to real-world changes in chemotherapy prescribing patterns. In this study, chemotherapy use decreased by 23% after TAILORx, with the greatest reductions seen among intermediate RS tumors and younger patients. In contrast, RS testing had lower clinical value and increased treatment costs in elderly patients, which requires further study to ensure optimal care for this age group.

Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients

Background: Tumor-infiltrating lymphocytes (TILs) play important roles in the prediction of prognosis and neoadjuvant therapy (NAT) efficacy in breast cancer (BRCA) patients, in this study, we identified clinicopathological factors related to BRCA TILs, then to construct and validate nomogram to predict high density of TILs. Methods: A total of 826 patients diagnosed with BRCA in Sun Yat-Sen University cancer center were enrolled in nomogram cohort. TILs were assessed using hematoxylin-eosin (H&E) staining by two pathologists. Complete clinical data were collected for analysis. Then the enrolled patients were split into a training set and validation set at a ratio of 8:2. and the backward multivariate binary logistic regression model was used to establish nomogram for predicting BRCA TILs, which were further evaluated and validated using the C-index, receiver operating characteristic (ROC) curves and calibration curves. Then another independent NAT cohort of 106 patients was established for verifying this nomogram in NAT efficacy prediction. Results: TILs were significantly correlated with body mass index (BMI), tumor differentiation, ER, PR, HER2 expression, Ki67, blood biochemical indicators including total bilirubin (TBIL), indirect bilirubin (IBIL), total protein (TP), Globulin (GLOB), inorganic phosphorus (IP), calcium (Ca). In which ER expression level [OR = 0.987, 95%CI (0.982-0.992), p < 0.001], IP [OR = 4.462, 95%CI (1.171∼17.289), p = 0.029], IBIL [OR = 0.906, 95%CI (0.845-0.966), p = 0.004] and TP [OR = 1.053, 95%CI (1.010-1.098, p = 0.016)] were independent predictors of TILs. Then nomogram was established, for which calibration curves (C-index = 0.759) and ROC curve (AUC = 0.759, 95%CI 0.717-0.801) in training sets, calibration curves (C-index = 0.708) and ROC curve (AUC = 0.708, 95%CI 0.617-0.800) in validation sets demonstrated great evaluation efficiency. Besides, independent NAT cohort verified this nomogram can distinguish patients with greater NAT efficacy (p = 0.041). Conclusion: The finds of clinicopathological factors associated with TILs could help clinicians to understand the tumor immunity of BRCA and improve treatment system for patients, and the established nomogram with high evaluation efficiency may be used as a complement tool for distinguishing patients with better NAT efficacy.

Nonmetastatic breast cancer patients subsequently developing second primary malignancy: A population-based study

Background

With life span extending, breast cancer (BC) survivors may face the possibility of developing second primary cancer (SPC) and considerably shorten survivorship. However, little is known about multiple primary cancer (MPC) patients with nonmetastatic breast cancer as a first primary malignancy (BCFPM).

Methods

Here, we retrospectively analyzed data on cancer survivors with BCFPM diagnosed between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors for breast cancer‐specific survival (BCSS) were ascertained by the stepwise regression analysis and a competing risk model, and were integrated to the establishment of prognostic nomogram, of which the accuracy was measured by the calibration curve and the concordance index (C‐index).

Results

In total, 8616 patients were identified with 4.6% of 3‐year breast cancer‐ specific death (BCSD) and 8.6% of 5‐year BCSD. The most common SPC among BCFPM patients were female BC and lung cancer. Besides, the median latency time between BC and SPC was 22 months. At a ratio of 7:3, all patients were randomly categorized into a training cohort (n = 6032) and a validation cohort (n = 2584). By a proportional subdistribution hazards regression analysis, the following factors were considered to own independent prognostic abilities of BCSS: subtypes, grade, T classification, N classification, radiation, and sites of SPC. The nomogram could accurately predict 3‐year and 5‐year breast cancer‐associated survival of BCFPM patients with high internal and external validated C‐index, 0.715 (95% CI, 0.691–0.739), and 0.683 (95% CI, 0.642–0.724), respectively.

Conclusions

BC survivors remained a high risk of developing SPC and considerably shortened survival time. In this study, a favorable nomogram was constructed to as a prediction model for 3‐year and 5‐year BCSS of BCFPM patients, largely intending to prolong the life of these patients by assisting clinicians to make individualized follow‐up plans.

Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Background

Ovarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).

Results

We identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.

Conclusions

In this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.

Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

Prognostic and predictive markers for adjuvant therapy

PURPOSE OF REVIEW

To avoid both overtreatment and undertreatment accurate risk assessment is mandatory. The present review gives an overview of recently published articles covering prognostic and predictive factors for adjuvant therapy in early breast cancer.

RECENT FINDINGS

Gene expression signatures enhance prognostic accuracy with a high level of evidence. These signatures can be further improved by incorporating traditional pathological factors like tumor size. Newer genomic techniques like next-generation sequencing lead to a deeper understanding of the relationship between somatic mutations and prognosis or prediction of therapeutic efficacy. Furthermore, circulating tumor cells, and circulating cell-free or tumor DNA can lead to a better estimation of the risk of recurrence in early breast cancer. In addition, recent results underscore the prognostic and predictive importance of tumor-infiltrating lymphocytes and subtyping of immune cell infiltrates especially in triple-negative breast cancer.

SUMMARY

The current review highlights recent studies improving prognostication and prediction of therapeutic efficacy in early breast cancer. These advances should lead to a better risk stratification and thereby to an improved tailoring of therapies.

Clinicopathological correlates, oncological impact, and validation of Oncotype DX™ in a European Tertiary Referral Centre

Oncotype DX™ (ODX) score estimates prognosis and predicts breast cancer recurrence. It also individualizes patient adjuvant chemotherapy prescription in breast cancer. This assay relies on genetic and molecular markers; the clinicopathological phenotype of which are tested routinely. The aim of this study was determine whether clinicopathological and immunohistochemical information predicts ODX recurrence score (RS). Secondly, to assess the impact on adjuvant chemotherapy (AC) and oncological outcome of ODX testing in patients in a European tertiary referral center. Estrogen receptor positive (ER+), human epidermal growth factor receptor-2 negative (HER2-), lymph node negative (LN-), and female breast cancer patients with ODX testing performed between 2007 and 2015 were categorized into low- (<11), intermediate- (11-25), and high-risk (>25) groups. Clinicopathological and immunohistochemical correlates of RS were determined. Predictors of RS were assessed using binary logistic regression. Oncological outcome was assessed using Kaplan-Meier and Cox regression analyses. ODX was performed in 400 consecutive ER+LN- patients. Median follow-up was 74.1 months (3.0-144.4). Low grade (odds ratio [OR]:2.39; 95% confidence interval [CI]:1.04-5.51, p = 0.041) independently predicted low ODX, while high grade (OR:2.04; 95% CI: 1.19-3.49, p = 0.009) and reduced progesterone receptor (PgR) expression (OR: 2.57, 95% CI: 1.42-4.65, p = 0.002) independently predicted high ODX. Omission of AC in intermediate- (p = 0.159) and high-risk (p = 0.702) groups did not negatively impact survival. In conclusion, tumor grade independently predicts low and high RS, while PgR negativity predicts high RS. ODX reduced AC prescription without compromising oncological outcome.

Recommendations from the European Commission Initiative on Breast Cancer for multigene testing to guide the use of adjuvant chemotherapy in patients with early breast cancer, hormone receptor positive, HER-2 negative

Background

Predicting the risk of recurrence and response to chemotherapy in women with early breast cancer is crucial to optimise adjuvant treatment. Despite the common practice of using multigene tests to predict recurrence, existing recommendations are inconsistent. Our aim was to formulate healthcare recommendations for the question “Should multigene tests be used in women who have early invasive breast cancer, hormone receptor-positive, HER2-negative, to guide the use of adjuvant chemotherapy?”

Methods

The European Commission Initiative on Breast Cancer (ECIBC) Guidelines Development Group (GDG), a multidisciplinary guideline panel including experts and three patients, developed recommendations informed by systematic reviews of the evidence. Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision frameworks were used. Four multigene tests were evaluated: the 21-gene recurrence score (21-RS), the 70-gene signature (70-GS), the PAM50 risk of recurrence score (PAM50-RORS), and the 12-gene molecular score (12-MS).

Results

Five studies (2 marker-based design RCTs, two treatment interaction design RCTs and 1 pooled individual data analysis from observational studies) were included; no eligible studies on PAM50-RORS or 12-MS were identified and the GDG did not formulate recommendations for these tests.

Conclusions

The ECIBC GDG suggests the use of the 21-RS for lymph node-negative women (conditional recommendation, very low certainty of evidence), recognising that benefits are probably larger in women at high risk of recurrence based on clinical characteristics. The ECIBC GDG suggests the use of the 70-GS for women at high clinical risk (conditional recommendation, low certainty of evidence), and recommends not using 70-GS in women at low clinical risk (strong recommendation, low certainty of evidence).

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes (P < 0.01), 336 copies of amplified genes (P < 0.05), and 36 copies of deleted genes (P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases' surviving state.

Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables

Use of genomic assays to determine distant recurrence risk in patients with early stage breast cancer has expanded and is now included in the American Joint Committee on Cancer staging manual. Algorithmic alternatives using standard clinical and pathology information may provide equivalent benefit in settings where genomic tests, such as OncotypeDx, are unavailable. We developed an artificial neural network (ANN) model to nonlinearly estimate risk of distant cancer recurrence. In addition to clinical and pathological variables, we enhanced our model using intraoperatively determined global mammographic breast density (MBD) and local breast density (LBD). LBD was measured with optical spectral imaging capable of sensing regional concentrations of tissue constituents. A cohort of 56 ER+ patients with an OncotypeDx score was evaluated. We demonstrated that combining MBD/LBD measurements with clinical and pathological variables improves distant recurrence risk prediction accuracy, with high correlation (r = 0.98) to the OncotypeDx recurrence score.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

Chemotherapy Costs and 21-Gene Recurrence Score Genomic Testing Among Medicare Beneficiaries With Early-Stage Breast Cancer, 2005 to 2011

BACKGROUND

This study examined whether associations between 21-gene recurrence score (RS) genomic testing and lower costs among patients with early-stage, estrogen receptor-positive breast cancer are observable in real-world data from the Medicare population.

METHODS

A retrospective cohort study was conducted using SEER-Medicare data for a nationally representative sample of Medicare beneficiaries diagnosed from 2005 through 2011. The main outcomes were associations between RS testing and overall and chemotherapy-specific costs. The primary analysis was restricted to patients aged 66 to 75 years.

RESULTS

The primary analysis comprised 30,058 patients. Mean costs 1 year after diagnosis were $35,940 [SD, $28,894] overall, $51,127 [33,386] for clinically high-risk disease, $33,225 [$27,711] for intermediate-risk disease, and $26,695 [$19,532] for low-risk disease. Chemotherapy-specific costs followed similar trends. In multivariable analyses, RS testing was associated with significantly lower costs among high-risk patients in terms of both relative costs (cost ratio, 0.88; 99% CI, 0.82-0.94) and absolute costs ($6,606; 99% CI, $39,223-$9,290). Higher costs among low-risk and intermediate-risk patients were mainly caused by higher noncancer costs. In sensitivity analyses that included all patients aged ≥66 years (N=64,996), associations between RS testing and costs among high-risk patients were similar but less pronounced because of lower overall use of chemotherapy.

CONCLUSIONS

RS testing was associated with lower overall and chemotherapy-related costs in patients with high-risk disease, consistent with lower chemotherapy use among these patients. Higher overall costs for patients with intermediate-risk and low-risk disease were driven largely by non-treatment-related costs.

Association of 21-Gene Assay (OncotypeDX) Testing and Receipt of Chemotherapy in the Medicare Breast Cancer Patient Population Following Initial Adoption

BACKGROUND

Our objective was to investigate why early studies regarding adoption of the 21-gene recurrence score (RS) assay did not show an initial reduction in the number of patients with breast cancer receiving real-world chemotherapy.

MATERIALS AND METHODS

We addressed 2 sources of confounding suspected in previous studies: (1) the early time frame during the initial adoption phase of the RS assay, and (2) suspected selective, increased administration to patients more likely to have been chemotherapy candidates. To address selective use during initial adoption, we used updated SEER-Medicare data from 2004 and 2011. To address individual selection bias, we examined whether RS test utilization was negatively associated with rates of local chemotherapy use assessed at the hospital referral region level using conventional ordinary least squares and instrumental variable approaches to adjust for selection bias.

RESULTS

A total of 26,009 patients met inclusion criteria. Assay use was associated with a decrease in absolute percentage use of chemotherapy of 4.5% (95% confidence interval [CI], 3.2%-5.7%), which was even more pronounced in sensitivity analyses limited to later study years (2008-2011), with a decrease of 6.8% (95% CI, 5.3%-8.3%). Instrumental variable models yielded similar point estimates but were insufficiently powered to draw conclusions.

CONCLUSION

Receipt of the 21-gene assay was associated with decreased utilization of chemotherapy by 2008.

A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Background

The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes.

Methods

Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed.

Results

A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P < 0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes.

Conclusions

We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

The concordance of treatment decision guided by OncotypeDX and the PREDICT tool in real-world early-stage breast cancer

Background

Decision‐making regarding adjuvant chemotherapy for early‐stage breast cancer can be guided by genomic assays such as OncotypeDX. The concordance of expected clinical decisions guided by OncotypeDX and prognostication online tools such as PREDICT is unknown.

Methods

We performed a retrospective single‐center cohort study comprising all women with estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (HER2) negative, node negative disease, whose tumors were sent for OncotypeDX analysis. Expected decision on adjuvant chemotherapy was evaluated using OncotypeDX and using PREDICT. The concordance between these two tools was calculated. The impact on concordance of prespecified features was assessed, including age, tumor size, intensity of ER and progesterone receptor (PR), grade, Ki67 and perineural and lymphovascular invasion.

Results

A total of 445 women were included. Overall concordance was 75% (K = 0.284). The concordance was significantly higher for grade 1 disease compared to grade 2‐3 (93% vs 72%, P < .001), tumor ≤ 1 cm compared to >1 cm (85% vs 72%, P = .009), PR positive compared to PR negative (78% vs 58%, P < .001) and ki67 < 10% compared to ≥10% (92% vs 63%, P < .001). The intensity of ER and the presence of perineural or lymphovascular invasion had no significant impact on concordance.

Conclusions

Compared to PREDICT, using OncotypeDx in node negative, ER positive disease is expected to change the clinical decision in a quarter of patients. The concordance between OncotypeDx and PREDICT is influenced by pathological features. In patients with very low risk, treatment decisions may be made based solely on clinical risk assessment.