Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer.

Importance: The association of tumor-infiltrating lymphocyte (TIL) abundance in breast cancer tissue with cancer recurrence and death in patients with early-stage triple-negative breast cancer (TNBC) who are not treated with adjuvant or neoadjuvant chemotherapy is unclear. Objective: To study the association of TIL abundance in breast cancer tissue with survival among patients with early-stage TNBC who were treated with locoregional therapy but no chemotherapy. Design, Setting, and Participants: Retrospective pooled analysis of individual patient-level data from 13 participating centers in North America (Rochester, Minnesota; Vancouver, British Columbia, Canada), Europe (Paris, Lyon, and Villejuif, France; Amsterdam and Rotterdam, the Netherlands; Milan, Padova, and Genova, Italy; Gothenburg, Sweden), and Asia (Tokyo, Japan; Seoul, Korea), including 1966 participants diagnosed with TNBC between 1979 and 2017 (with follow-up until September 27, 2021) who received treatment with surgery with or without radiotherapy but no adjuvant or neoadjuvant chemotherapy. Exposure: TIL abundance in breast tissue from resected primary tumors. Main Outcomes and Measures: The primary outcome was invasive disease-free survival [iDFS]. Secondary outcomes were recurrence-free survival [RFS], survival free of distant recurrence [distant RFS, DRFS], and overall survival. Associations were assessed using a multivariable Cox model stratified by participating center. Results: This study included 1966 patients with TNBC (median age, 56 years [IQR, 39-71]; 55% had stage I TNBC). The median TIL level was 15% (IQR, 5%-40%). Four-hundred seventeen (21%) had a TIL level of 50% or more (median age, 41 years [IQR, 36-63]), and 1300 (66%) had a TIL level of less than 30% (median age, 59 years [IQR, 41-72]). Five-year DRFS for stage I TNBC was 94% (95% CI, 91%-96%) for patients with a TIL level of 50% or more, compared with 78% (95% CI, 75%-80%) for those with a TIL level of less than 30%; 5-year overall survival was 95% (95% CI, 92%-97%) for patients with a TIL level of 50% or more, compared with 82% (95% CI, 79%-84%) for those with a TIL level of less than 30%. At a median follow-up of 18 years, and after adjusting for age, tumor size, nodal status, histological grade, and receipt of radiotherapy, each 10% higher TIL increment was associated independently with improved iDFS (hazard ratio [HR], 0.92 [0.89-0.94]), RFS (HR, 0.90 [0.87-0.92]), DRFS (HR, 0.87 [0.84-0.90]), and overall survival (0.88 [0.85-0.91]) (likelihood ratio test, P < 10e-6). Conclusions and Relevance: In patients with early-stage TNBC who did not undergo adjuvant or neoadjuvant chemotherapy, breast cancer tissue with a higher abundance of TIL levels was associated with significantly better survival. These results suggest that breast tissue TIL abundance is a prognostic factor for patients with early-stage TNBC.

Patient-Reported Quality of Life 6 Years After Breast Cancer.

This cohort study assesses quality-of-life trajectories up to 6 years after breast cancer diagnosis among individuals in France.

Genomic landscape of liquid biopsy mutations in TP53 and DNA damage genes in cancer patients.

Next-generation sequencing (NGS) assays based on plasma cell-free DNA (cfDNA) are increasingly used for clinical trials inclusion. Their optimized limit of detection applied to a large number of genes leads to the identification of mutations not confirmed in tissue. It becomes essential to describe the characteristics and consequences of these liquid biopsy-only mutations. In the STING protocol (Gustave Roussy, NCT04932525), 542 patients with advanced solid cancer had cfDNA-based and tissue-based NGS analysis (performed by FoundationOne® Liquid CDx and FoundationOne CDx™, respectively). Mutations identified in the liquid biopsy but not in the paired tissue were considered as liquid biopsy-only mutations irrespective of their variant allelic frequency (VAF). Out of 542 patients, 281 (51.8%) harbored at least one liquid biopsy-only mutation. These patients were significantly older, and more heavily pretreated. Liquid biopsy-only mutations occurring in TP53, and in DDR genes (ATM, CHEK2, ATR, BRCA2, and BRCA1) accounted for 90.8% of all the mutations. The median VAF of these mutations was generally low (0.37% and 0.40% for TP53 and DDR genes respectively). The variant type repartition depended on the gene. Liquid biopsy-only mutations affected hotspot in TP53 codon 273, 125, 195, 176, 237 or 280 and ATM codon 2891 and 3008. In a subset of 37 patients, 75.0%, 53.5% and 83.3% of the liquid biopsy-only mutations occurring respectively in ATM, TP53, and CHEK2 were confirmed in the matching whole blood sample. Although liquid biopsy-only mutations makes the interpretation of liquid biopsy results more complex, they have distinct characteristics making them more easily identifiable.

Clinical trial inclusion in patients with relapsed/refractory neuroblastoma following the European Precision Cancer Medicine trial MAPPYACTS.

INTRODUCTION: Despite poor survival for patients with relapsed or refractory neuroblastoma, only 10-16% of patients are reported to be included in early phase trials. This study aimed to explore the impact of molecular profiling within the prospective precision cancer medicine trial MAPPYACTS (NCT02613962) on subsequent early phase trial recruitment and treatment by matched targeted therapies in this population. METHODS AND MATERIALS: Clinical data from all French patients with relapsed/refractory neuroblastoma enrolled in MAPPYACTS were analyzed for subsequent matched/non-matched targeted treatment based on clinical tumor board (CMTB) recommendations. RESULTS: From 93 patients with neuroblastoma included in French centers, 78 (84%) underwent whole exome and RNA sequencing and were discussed in the CMTB. Higher rate of successful sequencing analysis was observed in patients with relapsed disease compared to those with refractory disease (p = 0.0002). Among the 50 patients that presented with a new disease relapse/progression after the CMTB recommendations, 35 patients (70%) had at least one actionable alteration identified on the tumor at the time of relapse. Eighteen patients (36%) were included in an early phase clinical trial, 11 of these with a matched agent, 7 with a non-matched treatment; 13 patients were included in the AcSé ESMART trial. Five patients (10%) received a matched targeted therapy outside a clinical trial. CONCLUSION: Patients with neuroblastoma in the European MAPPYACTS trial were more likely to be included in early phase trials compared to previous reports. Early deep sequencing at first treatment failure, comprehensive therapeutic discussions in molecular tumor boards and innovative trials like AcSé -ESMART improve access to innovative therapies for patients with relapsed/refractory neuroblastoma. CLINICAL TRIAL REGISTRATION: NCT02613962.

A Case-Only Genome-Wide Interaction Study of Smoking and Bladder Cancer Risk: Results from the COBLAnCE Cohort.

Bladder cancer (BC) is the 6th most common cancer worldwide, with tobacco smoking considered as its main risk factor. Accumulating evidence has found associations between genetic variants and the risk of BC. Candidate gene-environment interaction studies have suggested interactions between cigarette smoking and NAT2/GSTM1 gene variants. Our objective was to perform a genome-wide association case-only study using the French national prospective COBLAnCE cohort (COhort to study BLAdder CancEr), focusing on smoking behavior. The COBLAnCE cohort comprises 1800 BC patients enrolled between 2012 and 2018. Peripheral blood samples collected at enrolment were genotyped using the Illumina Global Screening Array with a Multi-Disease drop-in panel. Genotyping data (9,719,614 single nucleotide polymorphisms (SNP)) of 1674, 1283, and 1342 patients were analyzed for smoking status, average tobacco consumption, and age at smoking initiation, respectively. A genome-wide association study (GWAS) was conducted adjusting for gender, age, and genetic principal components. The results suggest new candidate loci (4q22.1, 12p13.1, 16p13.3) interacting with smoking behavior for the risk of BC. Our results need to be validated in other case-control or cohort studies.

Insights adjusting for non-adherence in randomized clinical trials: a reanalysis of an adjuvant trial of tamoxifen duration in early breast cancer.

BACKGROUND: Several randomized clinical trials provide evidence of the survival benefit of extended adjuvant tamoxifen in women with estrogen receptor (ER)-positive early breast cancer (BC). However, non-adherence may lead to underestimate treatment effects using intention to treat (ITT) methods. We reanalyzed a randomized trial using contemporary statistical methods adjusting for non-adherence. METHODS: The TAM01 study was a phase 3 trial including women with early BC, who had completed 2-3 years of adjuvant tamoxifen between 1986 and 1995. Participants were randomly assigned to continue tamoxifen up to 10 years or to discontinue the treatment at randomization. Invasive disease-free survival (iDFS) and overall survival (OS) were estimated using marginal structural models (MSM) and rank preserving structural failure time model (RPSFTM). RESULTS: Of 3830 patients enrolled, 2485 were randomized to extended tamoxifen, and 1345 to treatment discontinuation. The 10-year non-adherence rate in the extended group was 27.2%. Among women with ER-positive BC (n = 2402), extended tamoxifen was associated with a 45% and 21% relative improvement in iDFS by MSM and RPSFTM, respectively (Hazard Ratio (HR), 0.55; 95% Confidence Interval (CI), 0.48-0.64 and HR, 0.79; 95%CI, 0.67-0.95, respectively), a considerable greater benefit than in the ITT analysis (HR, 0.90; 95%CI, 0.81-0.99). The OS reanalysis revealed a substantial benefit of extended tamoxifen (MSM: HR, 0.70; 95%CI, 0.59-0.83; RPSFTM: HR, 0.85; 95%CI, 0.67-1.04), compared to the ITT analyses (HR, 0.94; 95%CI, 0.84-1.07). CONCLUSION: This analysis emphasizes both the importance of adherence to hormonotherapy in hormone-receptor positive early BC and the usefulness of more complex statistical analyses.

Association of Molecular Profiles and Mutational Status With Distinct Histological Lung Adenocarcinoma Subtypes. An Analysis of the LACE-Bio Data.

BACKGROUND: Adjuvant chemotherapy (AC) is indicated for stage II and stage III lung adenocarcinomas (ADC). Using the LACE Bio II database, we analyzed the distribution of various mutations across the subtypes of ADCs and studied the prognostic and predictive roles of PD-L1, TMB, and Tumor Infiltrating Lymphocytes (TILs). MATERIALS AND METHODS: Clinical and genomic data from the LACE Bio II data were extracted. Patients were divided into ADC subtypes, in which the grouping was done based on their known clinical behavior (Lepidic [LEP], Acinar/Papillary [ACI or PAP], Micropapillary/Solid [MIP or SOL], Mucinous [MUC] and Others). Kaplan-Meier (KM) and log-rank test were used to compare survival based on PD-L1, TMB, TILs and combinations of TMB with PD-L1 and TILs. Adjusted Hazard Ratios (HR) were analyzed with Overall Survival (OS), Disease-Free Survival (DFS) and Lung Cancer-Specific Survival (LCSS) as endpoints. RESULTS: A total of 375 ADC patients were identified. MIP/SOL was the subtype most commonly positive for various biomarkers. PD-L1 Negative/high TMB was associated with better outcomes in terms of OS (HR = 0.46 [0.23-0.89], P = .021) and DFS (HR = 0.52 [0.30-0.90], P = .02), relative to PD-L1 Negative/low TMB. High TMB predicted worse outcome with AC use in terms of OS (ratio of hazard ratio rHR = 2.75 [1.07-7.04], P = .035). Marked TILs had better outcome with AC for DFS (rHR = 0.22 [0.06-0.87], P = .031 and LCSS (rHR = 0.08 [0.01-0.66], P = .019) respectively. There was also a beneficial effect of AC among patients with Marked TILs/low TMB in terms of DFS (rHR = 0.06 [0.01-0.53], P = .011). CONCLUSION: High TMB has a prognostic role in resectable lung ADC. The high TMB group had a poor outcome with AC, suggesting that this group may be better served with immune checkpoint therapy.

Individual Participant Data Network Meta-Analysis of Neoadjuvant Chemotherapy or Chemoradiotherapy in Esophageal or Gastroesophageal Junction Carcinoma.

PURPOSE: The optimal neoadjuvant treatment for resectable carcinoma of the thoracic esophagus (TE) or gastroesophageal junction (GEJ) remains a matter of debate. We performed an individual participant data (IPD) network meta-analysis (NMA) of randomized controlled trials (RCTs) to study the effect of chemotherapy or chemoradiotherapy, with a focus on tumor location and histology subgroups. PATIENTS AND METHODS: All, published or unpublished, RCTs closed to accrual before December 31, 2015 and having compared at least two of the following strategies were eligible: upfront surgery (S), chemotherapy followed by surgery (CS), and chemoradiotherapy followed by surgery (CRS). All analyses were conducted on IPD obtained from investigators. The primary end point was overall survival (OS). The IPD-NMA was analyzed by a one-step mixed-effect Cox model adjusted for age, sex, tumor location, and histology. The NMA was registered in PROSPERO (CRD42018107158). RESULTS: IPD were obtained for 26 of 35 RCTs (4,985 of 5,807 patients) corresponding to 12 comparisons for CS-S, 12 for CRS-S, and four for CRS-CS. CS and CRS led to increased OS when compared with S with hazard ratio (HR) = 0.86 (0.75 to 0.99), P = .03 and HR = 0.77 (0.68 to 0.87), P < .001 respectively. The NMA comparison of CRS versus CS for OS gave a HR of 0.90 (0.74 to 1.09), P = .27 (consistency P = .26, heterogeneity P = .0038). For CS versus S, a larger effect on OS was observed for GEJ versus TE tumors (P = .036). For the CRS versus S and CRS versus CS, a larger effect on OS was observed for women (P = .003, .012, respectively). CONCLUSION: Neoadjuvant chemotherapy and chemoradiotherapy were consistently better than S alone across histology, but with some variation in the magnitude of treatment effect by sex for CRS and tumor location for CS. A strong OS difference between CS and CRS was not identified.

Genomic Profiling of Metastatic Castration-Resistant Prostate Cancer Samples Resistant to Androgen Receptor Pathway Inhibitors.

PURPOSE: The androgen receptor axis inhibitors (ARPI; e.g., enzalutamide, abiraterone acetate) are administered in daily practice for men with metastatic castration-resistant prostate cancer (mCRPC). However, not all patients respond, and mechanisms of both primary and acquired resistance remain largely unknown. EXPERIMENTAL DESIGN: In the prospective trial MATCH-R (NCT02517892), 59 patients with mCRPC underwent whole-exome sequencing (WES) and/or RNA sequencing (RNA-seq) of samples collected before starting ARPI. Also, 18 patients with mCRPC underwent biopsy at time of resistance. The objectives were to identify genomic alterations associated with resistance to ARPIs as well as to describe clonal evolution. Associations of genomic and transcriptomic alterations with primary resistance were determined using Wilcoxon and Fisher exact tests. RESULTS: WES analysis indicated that no single-gene genomic alterations were strongly associated with primary resistance. RNA-seq analysis showed that androgen receptor (AR) gene alterations and expression levels were similar between responders and nonresponders. RNA-based pathway analysis found that patients with primary resistance had a higher Hedgehog pathway score, a lower AR pathway score and a lower NOTCH pathway score than patients with a response. Subclonal evolution and acquisition of new alterations in AR-related genes or neuroendocrine differentiation are associated with acquired resistance. ARPIs do not induce significant changes in the tumor transcriptome of most patients; however, programs associated with cell proliferation are enriched in resistant samples. CONCLUSIONS: Low AR activity, activation of stemness programs, and Hedgehog pathway were associated with primary ARPIs' resistance, whereas most acquired resistance was associated with subclonal evolution, AR-related events, and neuroendocrine differentiation. See related commentary by Slovin, p. 4323.

Resistance to Selective FGFR Inhibitors in FGFR-Driven Urothelial Cancer.

Several fibroblast growth factor receptor (FGFR) inhibitors are approved or in clinical development for the treatment of FGFR-driven urothelial cancer, and molecular mechanisms of resistance leading to patient relapses have not been fully explored. We identified 21 patients with FGFR-driven urothelial cancer treated with selective FGFR inhibitors and analyzed postprogression tissue and/or circulating tumor DNA (ctDNA). We detected single mutations in the FGFR tyrosine kinase domain in seven (33%) patients (FGFR3 N540K, V553L/M, V555L/M, E587Q; FGFR2 L551F) and multiple mutations in one (5%) case (FGFR3 N540K, V555L, and L608V). Using Ba/F3 cells, we defined their spectrum of resistance/sensitivity to multiple selective FGFR inhibitors. Eleven (52%) patients harbored alterations in the PI3K-mTOR pathway (n = 4 TSC1/2, n = 4 PIK3CA, n = 1 TSC1 and PIK3CA, n = 1 NF2, n = 1 PTEN). In patient-derived models, erdafitinib was synergistic with pictilisib in the presence of PIK3CA E545K, whereas erdafitinib-gefitinib combination was able to overcome bypass resistance mediated by EGFR activation. SIGNIFICANCE: In the largest study on the topic thus far, we detected a high frequency of FGFR kinase domain mutations responsible for resistance to FGFR inhibitors in urothelial cancer. Off-target resistance mechanisms involved primarily the PI3K-mTOR pathway. Our findings provide preclinical evidence sustaining combinatorial treatment strategies to overcome bypass resistance. See related commentary by Tripathi et al., p. 1964. This article is featured in Selected Articles from This Issue, p. 1949.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.

BACKGROUND: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. METHODS: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. RESULTS: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. CONCLUSIONS: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

CustOmics: A versatile deep-learning based strategy for multi-omics integration.

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

Integrative Pan-Cancer Genomic and Transcriptomic Analyses of Refractory Metastatic Cancer.

Metastatic relapse after treatment is the leading cause of cancer mortality, and known resistance mechanisms are missing for most treatments administered to patients. To bridge this gap, we analyze a pan-cancer cohort (META-PRISM) of 1,031 refractory metastatic tumors profiled via whole-exome and transcriptome sequencing. META-PRISM tumors, particularly prostate, bladder, and pancreatic types, displayed the most transformed genomes compared with primary untreated tumors. Standard-of-care resistance biomarkers were identified only in lung and colon cancers-9.6% of META-PRISM tumors, indicating that too few resistance mechanisms have received clinical validation. In contrast, we verified the enrichment of multiple investigational and hypothetical resistance mechanisms in treated compared with nontreated patients, thereby confirming their putative role in treatment resistance. Additionally, we demonstrated that molecular markers improve 6-month survival prediction, particularly in patients with advanced breast cancer. Our analysis establishes the utility of the META-PRISM cohort for investigating resistance mechanisms and performing predictive analyses in cancer. SIGNIFICANCE: This study highlights the paucity of standard-of-care markers that explain treatment resistance and the promise of investigational and hypothetical markers awaiting further validation. It also demonstrates the utility of molecular profiling in advanced-stage cancers, particularly breast cancer, to improve the survival prediction and assess eligibility to phase I clinical trials. This article is highlighted in the In This Issue feature, p. 1027.

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine.

BACKGROUND: The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint. RESULTS: To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect. CONCLUSIONS: Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

Molecular analysis for refractory rare cancers: Sequencing battle continues - learnings for the MOSCATO-01 study.

BACKGROUND: For patients with metastatic rare cancers, treatments are limited. How systematic tumor sequencing can improve therapeutic possibilities in this population? PATIENTS AND METHODS: Patients with rare cancer were identified in the MOSCATO-01 trial. Patients' outcome was measured by progression-free survival (PFS) and overall survival (OS). RESULTS: The most frequently identified histologic subypes were ovarian adenocarcinoma (N = 13), carcinoma of unknown primary (N = 11), and leiomyosarcoma (N = 10). Ninety-nine (39%) of them had at least one targetable cancer molecular alteration Forty-nine patients (50%) received the therapy proposed by the molecular tumor board, and 13 patients (26%, 95%CI 15-41%) achieved a PFS2/PFS1 > 1.3. The median PFS2 on matched treatment subgroup was 2.3 months (95% CI 1.8-3.6) and the median OS was 11.4 months (95% CI 9-15.5). CONCLUSIONS: The molecular screening of patients with refractory, metastatic rare cancers might increase the therapeutic options. Facilitating access strategy to molecular-driven clinical trials or agnostic-approved treatment is crucial.

Cost-Effectiveness Analysis of Sequential Treatment Strategies for Advanced Melanoma in Real Life in France.

Nine drugs have been marketed for 10 years for the treatment of advanced melanoma (AM). With half of patients reaching a second line, the optimal sequence of treatments remains unclear. To inform policy-makers about their efficiency, we performed a cost-effectiveness analysis of sequential strategies in clinical practice in France, for BRAF-mutated and wild-type patients. A multistate model was developed to describe treatment sequences, associated costs, and health outcomes over 10 years. Sequences, clinical outcomes, utility scores, and economic data were extracted from the prospective Melbase cohort, collecting individual data in 1518 patients since 2013, from their AM diagnosis until their death. To adjust the differences in patients' characteristics among sequences, weighting by inverse probability was used. In the BRAF-mutated population, the MONO-targeted therapies (TT)-anti-PD1 sequence was the less expensive, whereas the anti-PD1-BI-TT sequence had an incremental cost-effectiveness ratio (ICER) of 180,441 EUR/QALY. Regarding the BRAF wild-type population, the three sequences constituted the cost-effective frontier, with ICERs ranging from 116 to 806,000 EUR/QALY. For BRAF-mutated patients, the sequence anti-PD1-BI-TT appeared to be the most efficient one in BRAF-mutated AM patients until 2018. Regarding the BRAF wild-type population until 2018, the sequence starting with IPI+NIVO appeared inefficient compared to anti-PD1, considering the extra cost for the QALY gained.

Association of Adherence to Endocrine Therapy Among Patients With Breast Cancer and Potential Drug-Drug Interactions.

Importance: Suboptimal adherence to endocrine therapy (ET) among patients with hormone-receptor-positive breast cancer significantly affects survival outcomes and is associated with higher hospitalization rates and health care costs. Weak adherence to long-term treatments has multiple determinants, including disease characteristics, treatment adverse effects, and patients' attributes, such as age and comorbidities. Objective: To examine whether potential drug-drug interactions (PDDI) with tamoxifen or aromatase inhibitor were associated with adherence to ET in patients with early and advanced breast cancer. Design, Setting, and Participants: This cohort study used anonymized health record data of women with breast cancer who received ET in a private observational primary care database. Patients eligible for analysis included women aged 18 years or older who had a reported diagnosis of breast cancer and received ET with tamoxifen or aromatase inhibitor between 1994 and 2021. Data were analyzed 2021. Exposures: Adherence to ET during a given year was defined by a medication possession ratio of 80% or greater over 1-year prescription periods. PDDI were categorized into absent, minor (a combination to take into account), moderate (combination requiring precautions for use), major (combination not recommended), and contraindicated according to guidelines in the Claude Bernard Drug Database. Main Outcomes and Measures: We used regression models to estimate odds ratios (ORs) and 95% CIs for the associations between adherence and age, baseline comorbidities, PDDI, and adherence to ET during the previous year. Results: A total of 10 863 patients who were prescribed ET for breast cancer were eligible for the analysis (age 70 years or older, 3509 patients [32.3%]). In the tamoxifen cohort (3564 patients), PDDI were reported in 497 of 3670 patients (13.5%) at baseline (moderate, 254 patients [51.1%]; major, 227 patients [45.7%]), 2047 of 4831 patients (42.4%) at year 1, 1127 of 2751 patients (41.0%) at year 2, 761 of 1861 patients (40.9%) at year 3, 376 of 1058 patients (35.5%) at year 4, and 201 of 593 patients (33.9%) at year 5. In the aromatase inhibitor cohort (7299 patients), PDDI were reported in 592 of 7437 patients (8.0%) at baseline (moderate in 588 of 592 patients [99.3%]), which reached 2875 of 9031 patients (31.8%) at year 1 and ranged between 31.4% (1802 of 5730 patients in year 2) and 32.8% (791 of 2411 in year 4) throughout the study period. No association between adherence and PDDI was found in the tamoxifen (OR, 0.99; 95% CI, 0.91-1.08) or aromatase inhibitor (OR, 1.05; 95% CI, 0.95-1.15) cohort. Conclusions and Relevance: In this cohort of patients with hormone-receptor-positive breast cancer, PDDI with tamoxifen and aromatase inhibitors were not associated with adherence to ET.

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology.

The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.