Biomedical subjects

Stefan Michiels

Publications and source records attributed to Stefan Michiels.

17 recordsLinked to original sources

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗

PubMed2026

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0·375-0·473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0·73 [95% CI 0·66-0·82]; q<0·0001, distant disease-free survival was 0·70 [0·61-0·79]; q<0·0001, and overall survival was 0·72 [0·63-0·82]; q<0·0001 for sTIL scores and 0·80 [0·73-0·89]; q<0·0001, 0·77 [0·69-0·86]; q<0·0001, and 0·79 [0·70-0·88]; q=0·0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans↗

PubMed2025

Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.

BACKGROUND: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms. METHOD: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas. RESULTS: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN. Conversely, no meaningful difference was found in pathogenic single-nucleotide variant frequencies, reinforcing the notion that they are early events in tumorigenesis. CDKN2A homozygous deletion was linked to poor clinical outcome in patients with early tumours (overall survival hazard ratio 2.17, 95% CI: 1.43-3.28, corrected p-value = 0.01). Furthermore, we found that KRAS mutant allele specific imbalance, i.e. focal amplification of the mutant allele, is more prevalent in locoregional or distant samples of metastatic patients than in early lesions (8.4%, 13% and 2.8% respectively). This observation was replicated in three public cohorts. Tumours with KRAS mutant allele specific imbalance show specific patterns of co-occurrence and mutual exclusion with alterations in key cancer genes like CDKN2A, TP53, STK11 and NKX2-1, often in a tumour type dependent manner. CONCLUSION: Advanced LUAD tumours exhibit higher copy-number alteration burden, with distinct alterations associated with tumour progression and metastasis. CDKN2A homozygous deletions predict poor prognosis in early disease, while KRAS mutant allele-specific imbalance is enriched in advanced tumours.

Humans↗

PubMed2025

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help understand these interactions. Despite recent advances in representing multimodal data for weakly supervised tasks in the medical domain, achieving a coherent and interpretable fusion of whole slide images and multi-omics data is still a challenge. Each modality operates at distinct biological levels, introducing substantial correlations between and within data sources. In response to these challenges, we propose a novel deep-learning-based approach designed to represent multi-omics & histopathology data for precision medicine in a readily interpretable manner. While our approach demonstrates superior performance compared to state-of-the-art methods across multiple test cases, it also deals with incomplete and missing data in a robust manner. It extracts various scores characterizing the activity of each modality and their interactions at the pathway and gene levels. The strength of our method lies in its capacity to unravel pathway activation through multimodal relationships and to extend enrichment analysis to spatial data for supervised tasks. We showcase its predictive capacity and interpretation scores by extensively exploring multiple TCGA datasets and validation cohorts. The method opens new perspectives in understanding the complex relationships between multimodal pathological genomic data in different cancer types and is publicly available on Github.

Deep Learning↗

PubMed2006

Sodium/iodide symporter (NIS) gene expression is the limiting step for the onset of thyroid function in the human fetus.

CONTEXT: Terminal differentiation of the human thyroid is characterized by the onset of follicle formation and thyroid hormone synthesis at 11 gestational weeks (GW). OBJECTIVE: This study aimed to investigate the ontogeny of thyroglobulin (Tg), thyroid peroxidase (TPO), sodium/iodide symporter (NIS), pendrin (PDS), dual oxidase 2 (DUOX2), thyroid-stimulating hormone receptor (TSHR), and thyroid transcription factor 1 (TITF1), forkhead box E1 (FOXE1), and paired box gene 8 (PAX8) in the developing human thyroid. DESIGN: Thyroid tissues from human embryos and fetuses (7-33 GW; n = 45) were analyzed by quantitative PCR to monitor mRNA expression for each gene and by immunohistochemistry to determine the cellular distribution of TITF1, TSHR, Tg, TPO, NIS, and the onset of T4 production. A broken line regression model was fitted for each gene to compare the loglinear increase in expression before and after the onset of T4 synthesis. RESULTS: TITF1, FOXE1, PAX8, TSHR, and DUOX2 were stably expressed from 7 to 33 GW. Tg, TPO, and PDS expression was detectable as early as 7 GW and was correlated with gestational age (all, P < 0.01), and the slope of the regression line was significantly different before and after the onset of T4 synthesis at 11 GW (all, P < 0.01). NIS expression appeared last and showed the highest fit by the broken line regression model of all genes (correlation age P < 0.0001, broken line regression P < 0.0001). Immunohistochemical studies detected TITF1, TSHR, and Tg in unpolarized thyrocytes before follicle formation. T(4) and NIS labeling were only found in developing follicles from 11 GW on. CONCLUSION: These results imply a key role of NIS for the onset of human thyroid function.

Female↗

PubMed2006

Expression and possible role of hPTTG1/securin in cutaneous malignant melanoma.

Human pituitary tumour-transforming gene 1 or hPTTG1 is a proto-oncogene that codes for securin, a protein involved in sister chromatid separation. Based on previous microarray data, we studied the expression of hPTTG1/securin in melanocytic lesions. In contrast to nevi and radial growth phase melanomas, securin was expressed by scattered cells in the vertical growth phase, suggesting a role in tumour progression. In a series of 29 nodular and 29 superficial spreading melanomas, matched for all histological prognostic parameters, securin expression was significantly correlated with the nodular subtype (P=0.018) and not related to thickness. In other cancers, hPTTG1 is involved in various oncogenic pathways, including induction of neovascularisation and aneuploidy, and inhibition of p53 activity. We found coexpression of securin with wild-type p53 in the same neoplastic cells in a minority of melanomas. Expression of securin was significantly correlated with the extent of aneuploidy but not with basic fibroblast growth factor immunoreactivity or microvessel density. DNA cytometry revealed that nuclei-overexpressing securin frequently showed tetraploidy or aneuploidy. Our data show that hPTTG1 is frequently overexpressed in nodular melanoma, and suggest that hPTTG1 may act as an oncogene in the vertical growth phase, either by inhibiting anaphase, thereby causing aneuploidy and genomic instability, or by modulating the function of p53, thereby impairing apoptosis.

Adult↗

PubMed2006

Application of an acceptance sampling plan for post-production quality control of chemotherapeutic batches in an hospital pharmacy.

BACKGROUND: About 26,000 chemotherapeutic batches were produced in 2003 in the Institute Gustave Roussy and 83% were qualitatively and quantitatively assessed in post-production controls via an analytical platform. The rate of non-conformity (outside the specification limits of the target concentration +/-10%) decreased from 8.9% to 2.2% between years 2001 and 2003. A cost- and time-saving acceptance sampling plan was applied to assay fewer batches whilst maintaining an accurate estimate of the quality level. METHODS: The opportunity to apply a single sampling plan by attributes with an acceptance quality level of 2.2% was evaluated. A prognostic study using a logistic regression model was performed for some drugs to identify risk factors associated with the non-conformity rate of preparations. RESULTS: Out of 26 drugs, 17 have not been sampled, since they were prepared less than 400 times per year. For six drugs, a reduction of about 50% in the number of assays was estimated. Three drugs were "at risk" of being non-conform: for these drugs, all batches were analysed. CONCLUSIONS: The sampling plan allowed a reduction of almost 8000 analyses with respect to the number of batches analysed for 6 drugs. For the 3 drugs with the higher risk to be non-conform, associated risk factors were identified to set up corrective actions.

Antineoplastic Agents↗

PubMed2006

Gene expression profiling of primary cutaneous melanoma and clinical outcome.

BACKGROUND: Gene expression profiling data for human primary cutaneous melanomas are scarce because of the lack of retrospective collections of frozen tumors. To identify differentially expressed genes that may be involved in melanoma progression and prognosis, we investigated the relationship between gene expression profiles and clinical outcome in a cohort of patients with primary melanoma. METHODS: Labeled complementary RNA (cRNA) from each tissue sample was hybridized to a pangenomic 44K 60-mer oligonucleotide microarray. Class comparison and class prediction analyses were performed to identify genes whose expression in primary melanomas was associated with 4-year distant metastasis-free survival among 58 patients with at least 4 years of follow-up, distant metastasis, or death. Results were validated immunohistochemically at the protein level in 176 independent primary melanomas from patients with a median clinical follow-up of 8.5 years. Survival was analyzed with a Cox multivariable model and stratified log-rank test. All statistical tests were two-sided. RESULTS: We identified 254 genes that were associated with distant metastasis-free survival of patients with primary melanoma. These 254 genes include genes involved in activating DNA replication origins, such as minichromosome maintenance genes and geminin. Twenty-three of these genes were studied at the protein level; expression of five (MCM4, P = .002; MCM3, P = .030; MCM6, P = .004; KPNA2, P = .021; and geminin, P = .004) was statistically significantly associated with overall survival in the validation set. In a multivariable Cox model adjusted for tumor thickness, ulceration, age, and sex, expression of MCM4 (hazard ratio [HR] of death = 4.04, 95% confidence interval [CI] = 1.39 to 11.76; P = .010) and MCM6 (HR of death = 7.42, 95% CI = 1.99 to 27.64; P = .003) proteins was still statistically significantly associated with overall survival. CONCLUSION: We identified 254 genes whose expression was associated with metastatic dissemination of cutaneous melanomas. These genes may shed light on the molecular mechanisms underlying poor prognosis in melanoma patients.

Adolescent↗

PubMed2006

HEX, PAX-8 and TTF-1 gene expression in human thyroid tissues: a comparative analysis with other genes involved in iodide metabolism.

OBJECTIVE: Benign and malignant thyroid tumours are characterized by alterations of the expression level of thyroid-specific genes involved in the iodide metabolism. Imbalance in the levels of transcription factors has been recognized as a critical molecular event in the development of neoplasm. The delineation of eventual correlations existing between the expression of transcription factors and of putative target genes in physiological and pathological conditions could be relevant to better understand tumorigenesis. PATIENTS AND METHODS: We examined the expression levels of transcription factors involved in thyroid development [thyroid transcription factor 1 (TTF-1), paired box gene 8 (PAX-8) and haematopoietically expressed homeobox (HEX)] in 101 thyroid tissues, including 14 normal thyroid tissues, 13 hyperfunctioning tissues, 27 benign adenomas and 47 follicular or papillary carcinomas. Then, we compared their expression levels with those of thyroid-specific genes involved in iodide metabolism. RESULTS: In benign tumours, PAX-8 and TTF-1 gene expression levels were not significantly different from the expression levels in normal tissues. However, a significant decrease was found in carcinomas. Interestingly, HEX gene expression was significantly decreased in both hyper- and hypofunctioning benign tissues and also in carcinomas. Expression levels of Pendred syndrome (PDS), natrium iodine symporter (NIS), thyroglobulin (Tg), thyroid peroxidase (TPO) and dual oxidase 1 or 2 (DUOX2) genes were significantly correlated with the expression of PAX-8 and with that of HEX. Expression level of TTF-1 was weakly correlated only with the expression levels of PDS and DUOX2. CONCLUSION: Our findings suggest that alterations in the transcription factors PAX-8, TTF-1 and HEX gene expression, by acting individually or together, have a role in both thyroidal tumorigenesis and in the dedifferentiation process.

Case-Control Studies↗

PubMed2005

Validation of microarray data by quantitative reverse-transcriptase polymerase chain reaction.

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Breast Neoplasms↗

PubMed2005

Bias in the estimation of false discovery rate in microarray studies.

MOTIVATION: The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion pi(0) of non-differentially expressed (non-DE) genes. In most microarray studies, many genes have small effects not easily separable from non-DE genes. As a result, current methods often overestimate pi(0) and FDR, leading to unnecessary loss of power in the overall analysis. METHODS: For the common two-sample comparison we derive a natural mixture model of the test statistic and an explicit bias formula in the standard estimation of pi(0). We suggest an improved estimation of pi(0) based on the mixture model and describe a practical likelihood-based procedure for this purpose. RESULTS: The analysis shows that a large bias occurs when pi(0) is far from 1 and when the non-centrality parameters of the distribution of the test statistic are near zero. The theoretical result also explains substantial discrepancies between non-parametric and model-based estimates of pi(0). Simulation studies indicate mixture-model estimates are less biased than standard estimates. The method is applied to breast cancer and lymphoma data examples. AVAILABILITY: An R-package OCplus containing functions to compute pi(0) based on the mixture model, the resulting FDR and other operating characteristics of microarray data, is freely available at http://www.meb.ki.se/~yudpaw CONTACT: yudi.pawitan@meb.ki.se and alexander.ploner@meb.ki.se.

Computer Simulation↗

PubMed2005

False discovery rate, sensitivity and sample size for microarray studies.

MOTIVATION: In microarray data studies most researchers are keenly aware of the potentially high rate of false positives and the need to control it. One key statistical shift is the move away from the well-known P-value to false discovery rate (FDR). Less discussion perhaps has been spent on the sensitivity or the associated false negative rate (FNR). The purpose of this paper is to explain in simple ways why the shift from P-value to FDR for statistical assessment of microarray data is necessary, to elucidate the determining factors of FDR and, for a two-sample comparative study, to discuss its control via sample size at the design stage. RESULTS: We use a mixture model, involving differentially expressed (DE) and non-DE genes, that captures the most common problem of finding DE genes. Factors determining FDR are (1) the proportion of truly differentially expressed genes, (2) the distribution of the true differences, (3) measurement variability and (4) sample size. Many current small microarray studies are plagued with large FDR, but controlling FDR alone can lead to unacceptably large FNR. In evaluating a design of a microarray study, sensitivity or FNR curves should be computed routinely together with FDR curves. Under certain assumptions, the FDR and FNR curves coincide, thus simplifying the choice of sample size for controlling the FDR and FNR jointly.

Algorithms↗

PubMed2005

Follicular thyroid tumors with the PAX8-PPARgamma1 rearrangement display characteristic genetic alterations.

Follicular thyroid carcinomas (FTC) arise through oncogenic pathways distinct from those involved in the papillary histotype. Recently, a t(2;3)(q13;p25) rearrangement, which juxtaposes the thyroid transcription factor PAX8 to the peroxisome proliferator-activated receptor (PPAR) gamma1, was described in FTCs. In this report, we describe gene expression in 11 normal tissues, 4 adenomas, and 8 FTCs, with or without the PAX8-PPARgamma1 translocation, using custom 60-mer oligonucleotide microarrays. Results were confirmed by quantitative real-time polymerase chain reaction of 65 thyroid tissues and by immunohistochemistry. Statistical analysis revealed a pattern of 93 genes discriminating FTCs, with or without the translocation, that were morphologically undistinguishable. Although the expression of thyroid-specific genes was detectable, none appeared to be differentially regulated between tumors with or without the translocation. Differentially expressed genes included genes related to lipid/glucose/amino acid metabolism, tumorigenesis, and angiogenesis. Surprisingly, several PPARgamma target genes were up-regulated in PAX8-PPARgamma-positive FTCs such as angiopoietin-like 4 and aquaporin 7. Moreover many genes involved in PAX8-PPARgamma expression profile presented a putative PPARgamma-promoter site, compatible with a direct activity of the fusion product. These data identify several differentially expressed genes, such as FGD3, that may serve as potential targets of PPARgamma and as members of novel molecular pathways involved in the development of thyroid carcinomas.

Adenocarcinoma, Follicular↗

PubMed2005

Meta-analysis when only the median survival times are known: a comparison with individual patient data results.

BACKGROUND: The hazard ratio (HR) is the most appropriate measure for time to event outcomes such as survival. In systematic reviews, HRs can be calculated either from the raw trial data obtained as part of an individual patient data (IPD) meta-analysis or from the appropriate trial-level summary statistics. However, the information required for the latter are seldom reported in sufficient detail to allow reviewers to calculate HRs. In contrast, the median survival and survival rates at specific time points are frequently presented. We aimed to evaluate retrospectively the performance of meta-analyses using median survival times and survival rates by comparing them with meta-analyses using IPD to calculate HRs. METHODS: IPD from thirteen published meta-analyses (MAs) in cancers with high mortality rates were used. Median survival and survival rates were calculated from the IPD rather than taken from publications so that the same trials, patients, and extended follow-up are used in each analysis. RESULTS AND CONCLUSIONS: We show that using median survival times or survival rates at a particular point in time are not reasonable surrogate measures for meta-analyses of survival outcomes and that, wherever possible, HRs should be calculated. Individual trial publications reporting on time to event outcomes, therefore, should provide more detailed statistical information, preferably logHRs and their variances, or their estimators.

Data Interpretation, Statistical↗

PubMed2004

Benefits of adding a drug to a single-agent or a 2-agent chemotherapy regimen in advanced non-small-cell lung cancer: a meta-analysis.

CONTEXT: Randomized trials have demonstrated that adding a drug to a single-agent or to a 2-agent regimen increased the tumor response rate in patients with advanced non-small-cell lung cancer (NSCLC), although its impact on survival remains controversial. OBJECTIVE: To evaluate the clinical benefit of adding a drug to a single-agent or 2-agent chemotherapy regimen in terms of tumor response rate, survival, and toxicity in patients with advanced NSCLC. DATA SOURCES AND STUDY SELECTION: Data from all randomized controlled trials performed between 1980 and 2001 (published between January 1980 and October 2003) comparing a doublet regimen with a single-agent regimen or comparing a triplet regimen with a doublet regimen in patients with advanced NSCLC. There were no language restrictions. Searches of MEDLINE and EMBASE were performed using the search terms non-small-cell lung carcinoma/drug therapy, adenocarcinoma, large-cell carcinoma, squamous-cell carcinoma, lung, neoplasms, clinical trial phase III, and randomized trial. Manual searches were also performed to find conference proceedings published between January 1982 and October 2003. DATA EXTRACTION: Two independent investigators reviewed the publications and extracted the data. Pooled odds ratios (ORs) for the objective tumor response rate, 1-year survival rate, and toxicity rate were calculated using the fixed-effect model. Pooled median ratios (MRs) for median survival also were calculated using the fixed-effect model. ORs and MRs lower than unity (<1.0) indicate a benefit of a doublet regimen compared with a single-agent regimen (or a triplet regimen compared with a doublet regimen). DATA SYNTHESIS: Sixty-five trials (13 601 patients) were eligible. In the trials comparing a doublet regimen with a single-agent regimen, a significant increase was observed in tumor response (OR, 0.42; 95% confidence interval [CI], 0.37-0.47; P<.001) and 1-year survival (OR, 0.80; 95% CI, 0.70-0.91; P<.001) in favor of the doublet regimen. The median survival ratio was 0.83 (95% CI, 0.79-0.89; P<.001). An increase also was observed in the tumor response rate (OR, 0.66; 95% CI, 0.58-0.75; P<.001) in favor of the triplet regimen, but not for 1-year survival (OR, 1.01; 95% CI, 0.85-1.21; P =.88). The median survival ratio was 1.00 (95% CI, 0.94-1.06; P =.97). CONCLUSION: Adding a second drug improved tumor response and survival rate. Adding a third drug had a weaker effect on tumor response and no effect on survival.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2004

Uncertain benefit from surgery in patients with lung metastases from breast carcinoma.

BACKGROUND: Isolated lung metastases have been reported to occur in 10-20% of all women with breast carcinoma. The authors described a series of patients who underwent surgery for lung metastases from breast carcinoma. METHODS: They reviewed the files of 125 consecutive patients who underwent surgery with a curative intent for lung metastases from breast carcinoma between 1972 an 1998 at a single institution. Survival curves were plotted by the Kaplan-Meier method. Prognostic factors were identified using the log-rank test and a Cox proportional hazards model for univariate and multivariate analyses, respectively. RESULTS: The median age at surgery was 53 years. There was a median of 1 resected metastasis (range, 1-16 resected metastases). The median size of the largest metastasis was 19 mm (range, 5-70 mm). The median disease-free interval (DFI) was 3 years. The median follow-up time after surgery was 8.5 years (range, 25 days to 22 years). The 3-year, 5-year, and 10-year probabilities of survival were 58% (95% confidence interval [95% CI], 49-67%), 45% (95% CI, 36-55%), and 30% (95% CI, 21-41%), respectively. The median survival time after surgery was 4.2 years. Complete resection was achieved in 96 patients. The quality of the resection (complete vs. incomplete) was not a statistically significant prognostic factor by univariate analysis and there was no significant difference between these two groups in terms of adjuvant postoperative therapy. The characteristics of the primary tumor and the number of metastases (one vs. two or more) had no detectable influence on survival. The size of the largest metastasis (> 20 mm or < or = 20 mm) and the DFI (< or = 3 years vs. > 3 years) were highly significant prognostic factors (P = 0.006 and P = 0.003, respectively). This was confirmed by multivariate analysis. Patients with a DFI < or = 3 years and/or the largest metastasis > 20 mm reportedly had a poor outcome (median survival, 2.6 years vs. 8.5 years for patients with none of these poor prognostic factors). CONCLUSIONS: Resection of lung metastases from breast carcinoma was associated with a significant 5-year survival rate of 45%. Whether these encouraging findings resulted from the surgical procedure itself or the preoperative selection of patients remained uncertain. When surgery is considered in this setting, the size of the largest metastasis and the DFI should be taken into account.

Adult↗

PubMedDate not supplied

Prediction of cancer outcome with microarrays: a multiple random validation strategy.

BACKGROUND: General studies of microarray gene-expression profiling have been undertaken to predict cancer outcome. Knowledge of this gene-expression profile or molecular signature should improve treatment of patients by allowing treatment to be tailored to the severity of the disease. We reanalysed data from the seven largest published studies that have attempted to predict prognosis of cancer patients on the basis of DNA microarray analysis. METHODS: The standard strategy is to identify a molecular signature (ie, the subset of genes most differentially expressed in patients with different outcomes) in a training set of patients and to estimate the proportion of misclassifications with this signature on an independent validation set of patients. We expanded this strategy (based on unique training and validation sets) by using multiple random sets, to study the stability of the molecular signature and the proportion of misclassifications. FINDINGS: The list of genes identified as predictors of prognosis was highly unstable; molecular signatures strongly depended on the selection of patients in the training sets. For all but one study, the proportion misclassified decreased as the number of patients in the training set increased. Because of inadequate validation, our chosen studies published overoptimistic results compared with those from our own analyses. Five of the seven studies did not classify patients better than chance. INTERPRETATION: The prognostic value of published microarray results in cancer studies should be considered with caution. We advocate the use of validation by repeated random sampling.

Gene Expression Profiling↗