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).

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.

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.

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.

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.

Association between physical activity and neoadjuvant chemotherapy completion and pathologic complete response in primary breast cancer: the CANTO study.

BACKGROUND: Regular physical activity is associated with improved symptom control in patients with breast cancer but its association with chemotherapy completion or response is unclear. METHODS: Using a prospective design, 1075 breast cancer patients receiving neoadjuvant chemotherapy between March 2012 and February 2017 were studied. Physical activity was assessed using the Global Physical Activity Questionnaire [GPAQ-16], quantified in standardised MET-h/wk. Chemotherapy completion was defined as the proportion of patients completing planned treatment course, requiring dose reduction, or requiring dose delay. Response was evaluated by pathologic complete response (pCR). Associations between physical activity and primary outcomes were assessed using multivariable logistic regression models. RESULTS: There was no differences between any chemotherapy completion outcome on the basis of physical activity classification. The percent of patients not completing planned treatment was 5.7% for ≦0.33 MET-h/wk, compared with 6.8% for 0.34-16.65 MET-h/wk, and 4.6% for ≥16.6 MET-h/wk (p = 0.52). No significant relationships were observed between physical activity dose classification and pCR for the overall cohort or upon stratification by clinical subtype. CONCLUSION: Future studies are required to further investigate the relationship between pre-treatment levels of physical activity and function on treatment completion and response in breast and other cancer populations. CLINICAL TRIAL REGISTRATION: NCT01993498.

Low level of Fibrillarin, a ribosome biogenesis factor, is a new independent marker of poor outcome in breast cancer.

BACKGROUND: A current critical need remains in the identification of prognostic and predictive markers in early breast cancer. It appears that a distinctive trait of cancer cells is their addiction to hyperactivation of ribosome biogenesis. Thus, ribosome biogenesis might be an innovative source of biomarkers that remains to be evaluated. METHODS: Here, fibrillarin (FBL) was used as a surrogate marker of ribosome biogenesis due to its essential role in the early steps of ribosome biogenesis and its association with poor prognosis in breast cancer when overexpressed. Using 3,275 non-metastatic primary breast tumors, we analysed FBL mRNA expression levels and protein nucleolar organisation. Usage of TCGA dataset allowed transcriptomic comparison between the different FBL expression levels-related breast tumours. RESULTS: We unexpectedly discovered that in addition to breast tumours expressing high level of FBL, about 10% of the breast tumors express low level of FBL. A correlation between low FBL mRNA level and lack of FBL detection at protein level using immunohistochemistry was observed. Interestingly, multivariate analyses revealed that these low FBL tumors displayed poor outcome compared to current clinical gold standards. Transcriptomic data revealed that FBL expression is proportionally associated with distinct amount of ribosomes, low FBL level being associated with low amount of ribosomes. Moreover, the molecular programs supported by low and high FBL expressing tumors were distinct. CONCLUSION: Altogether, we identified FBL as a powerful ribosome biogenesis-related independent marker of breast cancer outcome. Surprisingly we unveil a dual association of the ribosome biogenesis FBL factor with prognosis. These data suggest that hyper- but also hypo-activation of ribosome biogenesis are molecular traits of distinct tumors.

Uptake of Recommendations for Posttreatment Cancer-Related Fatigue Among Breast Cancer Survivors.

BACKGROUND: Physical activity (PA) and psychosocial interventions are recommended management strategies for cancer-related fatigue (CRF). Randomized trials support the use of mind-body techniques, whereas no data show benefit for homeopathy or naturopathy. METHODS: We used data from CANTO (ClinicalTrials.gov identifier: NCT01993498), a multicenter, prospective study of stage I-III breast cancer (BC). CRF, evaluated after primary treatment completion using the EORTC QLQ-C30 (global CRF) and QLQ-FA12 (physical, emotional, and cognitive dimensions), served as the independent variable (severe [score of ≥40/100] vs nonsevere). Outcomes of interest were adherence to PA recommendations (≥10 metabolic equivalent of task [MET] h/week [GPAQ-16]) and participation in consultations with a psychologist, psychiatrist, acupuncturist, or other complementary and alternative medicine (CAM) practitioner (homeopath and/or naturopath) after CRF assessment. Multivariable logistic regression examined associations between CRF and outcomes, adjusting for sociodemographic, psychologic, tumor, and treatment characteristics. RESULTS: Among 7,902 women diagnosed from 2012 through 2017, 36.4% reported severe global CRF, and 35.8%, 22.6%, and 14.1% reported severe physical, emotional, and cognitive CRF, respectively. Patients reporting severe global CRF were less likely to adhere to PA recommendations (60.4% vs 66.7%; adjusted odds ratio [aOR], 0.82; 95% CI, 0.71-0.94; P=.004), and slightly more likely to see a psychologist (13.8% vs 7.5%; aOR, 1.29; 95% CI, 1.05-1.58; P=.014), psychiatrist (10.4% vs 5.0%; aOR, 1.39; 95% CI, 1.10-1.76; P=.0064), acupuncturist (9.8% vs 6.5%; aOR, 1.46; 95% CI, 1.17-1.82; P=.0008), or CAM practitioner (12.5% vs 8.2%; aOR, 1.49; 95% CI, 1.23-1.82; P<.0001). There were differences in recommendation uptake by CRF dimension, including that severe physical CRF was associated with lower adherence to PA (aOR, 0.74; 95% CI, 0.63-0.86; P=.0001) and severe emotional CRF was associated with higher likelihood of psychologic consultations (aOR, 1.37; 95% CI, 1.06-1.79; P=.017). CONCLUSIONS: Uptake of recommendations to improve CRF, including adequate PA and use of psychosocial services, seemed suboptimal among patients with early-stage BC, whereas there was a nonnegligible interest in homeopathy and naturopathy. Findings of this large study indicate the need to implement recommendations for managing CRF in clinical practice.

Penalized Poisson model for network meta-analysis of individual patient time-to-event data.

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study.

BACKGROUND: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues. METHODS: We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability. RESULTS: Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results. CONCLUSIONS: Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications.

Individual-specific networks for prediction modelling - A scoping review of methods.

BACKGROUND: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. METHODS: We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase. RESULTS: Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. CONCLUSION: The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Informing the development of multidisciplinary interventions to help breast cancer patients return to work: a qualitative study.

PURPOSE: Return to work (RTW) after breast cancer (BC) can be a major challenge for patients. Multidisciplinary interventions seem to be effective but the role of digital solutions is under-developed and therefore not evaluated. We explored the preferences, needs, and barriers regarding RTW interventions, including opinions about the use of digital approaches to deliver such interventions. METHODS: We conducted a qualitative study based on interviews with 30 patients with BC and 18 healthcare providers in four French regions. Emergent themes were identified using thematic content analysis. RESULTS: Most providers declared that they did not proactively address RTW with patients, mainly due to having other priorities and a lack of knowledge. The following themes emerged: several development and deployment barriers regarding RTW interventions exist, multidisciplinary interventions are preferred, and there is a need to maintain contact between the patient and workplace during sick leave, including pathways and interlocutors that can facilitate RTW. Participants had mostly positive representations of using digital tools to facilitate RTW; however, fear of loss of human contact and the exacerbation of inequalities were identified as possible risks associated with the development of digital-only interventions. CONCLUSIONS: Interventions blending the needs and preferences of patients with BC and the healthcare system are warranted. A personalized multimodal approach with mixed digital and in-person features has surfaced as a possible solution to address the weaknesses of existing interventions. IMPLICATIONS FOR CANCER SURVIVORS: Since most women work at the time of diagnosis, it is of particular relevance to build interventions promoting RTW.

A two-stage drop-the-losers design for time-to-event outcome using a historical control arm.

Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.

Determinants of use of oral complementary-alternative medicine among women with early breast cancer: a focus on cancer-related fatigue.

BACKGROUND: Despite the questionable effectiveness of oral complementary and alternative medicine (OCAM) in relieving cancer-related symptoms, including fatigue (CRF), many patients use it aiming to improve their quality of life. We assessed factors associated with OCAM use, focusing on CRF. METHODS: Women with stage I-III breast cancer (BC) were included from CANTO (NCT01993498). OCAM use was defined as taking homeopathy, vitamins/minerals, or herbal/dietary supplements. Multivariable multinomial logistic regressions evaluated associations of CRF (EORTC QLQ-C30), patient, and treatment characteristics with OCAM use. RESULTS: Among 5237 women, 23.0% reported OCAM use overall (49.3% at diagnosis, 50.7% starting post-diagnosis), mostly homeopathy (65.4%). Mean (SD) CRF score was 27.6 (24.0) at diagnosis and 35.1 (25.3) at post-diagnosis. More intense CRF was consistently associated with OCAM use at diagnosis and post-diagnosis [adjusted odds ratio (aOR) for 10-point increase 1.05 (95% Confidence interval 1.01-1.09) and 1.04 (1.01-1.09) vs. never use, respectively]. Odds of using OCAM at diagnosis were higher among older [for 5-year increase, 1.09 (1.04-1.14)] and more educated patients [college vs. primary 1.80 (1.27-2.55)]. Women with income > 3000 [vs. < 1500 euros/month, 1.44 (1.02-2.03)], anxiety [vs. not, 1.25 (1.01-1.54)], and those receiving chemotherapy [vs. not, 1.32 (1.04-1.68)] had higher odds of using OCAM post-diagnosis. CONCLUSION: One-in-four patients reported use of OCAM. More severe CRF was consistently associated with its use. Moreover, older, better educated, wealthier, more anxious women, and those receiving chemotherapy seemed more prone to use OCAM. Characterizing profiles of BC patients more frequently resorting to OCAM may help deliver targeted information about its benefits and potential risks.

Prospective Multicenter Study Validate a Prediction Model for Surgery Uptake Among Women with Atypical Breast Lesions.

BACKGROUND: Diagnosis of atypical breast lesions (ABLs) leads to unnecessary surgery in 75-90% of women. We have previously developed a model including age, complete radiological target excision after biopsy, and focus size that predicts the probability of cancer at surgery. The present study aimed to validate this model in a prospective multicenter setting. - METHODS: Women with a recently diagnosed ABL on image-guided biopsy were recruited in 18 centers, before wire-guided localized excisional lumpectomy. Primary outcome was the negative predictive value (NPV) of the model. RESULTS: The NOMAT model could be used in 287 of the 300 patients included (195 with ADH). At surgery, 12 invasive (all grade 1), and 43 in situ carcinomas were identified (all ABL: 55/287, 19%; ADH only: 49/195, 25%). The area under the receiving operating characteristics curve of the model was 0.64 (95% CI 0.58-0.69) for all ABL, and 0.63 for ADH only (95% CI 0.56-0.70). For the pre-specified threshold of 20% predicted probability of cancer, NPV was 82% (77-87%) for all ABL, and 77% (95% CI 71-83%) for patients with ADH. At a 10% threshold, NPV was 89% (84-94%) for all ABL, and 85% (95% CI 78--92%) for the ADH. At this threshold, 58% of the whole ABL population (and 54% of ADH patients) could have avoided surgery with only 2 missed invasive cancers. CONCLUSION: The NOMAT model could be useful to avoid unnecessary surgery among women with ABL, including for patients with ADH. CLINICAL TRIAL REGISTRATION: NCT02523612.

The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice.

Immune checkpoint inhibitor therapies targeting PD-1/PD-L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD-L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab-paclitaxel. However, concerns regarding variability between immunohistochemical PD-L1 assay performance and inter-reader reproducibility have been raised. High tumor-infiltrating lymphocytes (TILs) have also been associated with response to PD-1/PD-L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin-stained slides and have shown reliable inter-reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD-L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD-L1 and TIL analyses as a more comprehensive immuno-oncological biomarker for patient selection for PD-1/PD-L1 inhibition-based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk-management framework that may help mitigate the risks of suboptimal patient selection for immuno-therapeutic approaches in clinical trials and daily practice based on combined TILs/PD-L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.

BACKGROUND: The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. RESULTS: We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effect; and to favor the selection of variables that, in addition to having a large effect, belong to a group with a large overall effect. We considered the case of prespecified and disjoint groups and proposed diverse weights for the adaptive lasso method. In particular we proposed the product Max Single Wald by Single Wald weighting (MSW*SW) which takes into account the information of the group to which it belongs and of this biomarker. Through simulations, we compared the selection and prediction ability of our approach with the standard lasso, the composite Minimax Concave Penalty (cMCP), the group exponential lasso (gel), the Integrative L1-Penalized Regression with Penalty Factors (IPF-Lasso), and the Sparse Group Lasso (SGL) methods. In addition, we illustrated the methods using gene expression data of 614 breast cancer patients. CONCLUSIONS: The adaptive lasso with the MSW*SW weighting method incorporates both the information in the grouping structure and the individual variable. It outperformed the competitors by reducing the false discovery rate without severely increasing the false negative rate.

Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer.

We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; ≥50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ER-positive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBC <50%). In conclusion, reactive stroma in HER2-positive/ER-negative early breast cancer tumors may predict resistance to adjuvant trastuzumab therapy.

Predictive classifier for intensive treatment of head and neck cancer.

BACKGROUND: This study was designed to test the hypothesis that the effectiveness of intensive treatment for locoregionally advanced head and neck cancer (LAHNC) depends on the proportion of patients' overall event risk attributable to cancer. METHODS: This study analyzed 22,339 patients with LAHNC treated in 81 randomized trials testing altered fractionation (AFX; Meta-Analysis of Radiotherapy in Squamous Cell Carcinomas of Head and Neck [MARCH] data set) or chemotherapy (Meta-Analysis of Chemotherapy in Head and Neck Cancer [MACH-NC] data set). Generalized competing event regression was applied to the control arms in MARCH, and patients were stratified by tertile according to the ω score, which quantified the relative hazard for cancer versus competing events. The classifier was externally validated on the MACH-NC data set. The study tested for interactions between the ω score and treatment effects on overall survival (OS). RESULTS: Factors associated with a higher ω score were a younger age, a better performance status, an oral cavity site, higher T and N categories, and a p16-negative/unknown status. The effect of AFX on OS was greater in patients with high ω scores (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.85-0.99) and medium ω scores (HR, 0.91; 95% CI, 0.84-0.98) versus low ω scores (HR, 0.97; 95% CI, 0.90-1.05; P for interaction = .086). The effect of chemotherapy on OS was significantly greater in patients with high ω scores (HR, 0.81; 95% CI, 0.75-0.88) and medium ω scores (HR, 0.86; 95% CI, 0.78-0.93) versus low ω scores (HR, 0.96; 95% CI, 0.86-1.08; P for interaction = .011). CONCLUSIONS: LAHNC patients with a higher risk of cancer progression relative to competing mortality, as reflected by a higher ω score, selectively benefit from more intensive treatment.