Which Patients Need Chemotherapy? From Pathological Risk Factors to Gene Signatures and Evaluation of Endocrine Response

Background

Chemotherapy, used either before or after surgery, has significantly improved survival in early breast cancer. Accurate risk assessment is essential to avoid both overtreatment and undertreatment. This review provides an overview of the evolution of chemotherapy as well as risk factors for tailored systemic therapies in early breast cancer - from pathologic risk factors to gene expression signatures to endocrine response assessment.

Summary

Chemotherapy has improved dramatically in recent decades from its beginnings with conventionally dosed cyclophosphamide plus methotexate plus 5-fluorouracil to dose-dense anthracycline- and taxane-containing regimens. Similarly, risk assessment has evolved starting from traditional pathologic risk factors such as tumor size, axillary nodal status, and grading. In recent decades, gene expression signatures have improved prognostic accuracy with a high level of evidence. In turn, these signatures can be further improved by incorporating the aforementioned pathologic factors. As an important step away from this static assessment, dynamic assessment of proliferation factor Ki-67 after short-term preoperative endocrine treatment has gained interest to improve risk assessment in early hormone receptor-positive breast cancer.

Key Message

This review highlights advances in chemotherapy and risk assessment in early breast cancer, from pathologic risk factors for recurrence to gene expression signatures and endocrine response assessment. These developments are leading to better risk stratification and thus better adaptation of therapies.

Molecular determinants of clinical outcomes of pembrolizumab in recurrent ovarian cancer: Exploratory analysis of KEYNOTE-100

OBJECTIVE

This prespecified exploratory analysis evaluated the association of gene expression signatures, tumor mutational burden (TMB), and multiplex immunohistochemistry (mIHC) tumor microenvironment-associated cell phenotypes with clinical outcomes of pembrolizumab in advanced recurrent ovarian cancer (ROC) from the phase II KEYNOTE-100 study.

METHODS

Pembrolizumab-treated patients with evaluable RNA-sequencing (n = 317), whole exome sequencing (n = 293), or select mIHC (n = 125) data were evaluated. The association between outcomes (objective response rate [ORR], progression-free survival [PFS], and overall survival [OS]) and gene expression signatures (T-cell-inflamed gene expression profile [TcellinfGEP] and 10 non-TcellinfGEP signatures), TMB, and prespecified mIHC cell phenotype densities as continuous variables was evaluated using logistic (ORR) and Cox proportional hazards regression (PFS; OS). One-sided p-values were calculated at prespecified α = 0.05 for TcellinfGEP, TMB, and mIHC cell phenotypes and at α = 0.10 for non-TcellinfGEP signatures; all but TcellinfGEP and TMB were adjusted for multiplicity.

RESULTS

No evidence of associations between ORR and key axes of gene expression was observed. Negative associations were observed between outcomes and TcellinfGEP-adjusted glycolysis (PFS, adjusted-p = 0.019; OS, adjusted-p = 0.085) and hypoxia (PFS, adjusted-p = 0.064) signatures. TMB as a continuous variable was not associated with outcomes (p > 0.05). Positive associations were observed between densities of myeloid cell phenotypes CD11c+ and CD11c+/MHCII-/CD163-/CD68- in the tumor compartment and ORR (adjusted-p = 0.025 and 0.013, respectively).

CONCLUSIONS

This exploratory analysis in advanced ROC did not find evidence for associations between gene expression signatures and outcomes of pembrolizumab. mIHC analysis suggests CD11c+ and CD11c+/MHCII-/CD163-/CD68- phenotypes representing myeloid cell populations may be associated with improved outcomes with pembrolizumab in advanced ROC.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT02674061.

Tamoxifen-predictive value of gene expression signatures in premenopausal breast cancer: data from the randomized SBII:2 trial

BACKGROUND

Gene expression (GEX) signatures in breast cancer provide prognostic information, but little is known about their predictive value for tamoxifen treatment. We examined the tamoxifen-predictive value and prognostic effects of different GEX signatures in premenopausal women with early breast cancer.

METHODS

RNA from formalin-fixed paraffin-embedded tumor tissue from premenopausal women randomized between two years of tamoxifen treatment and no systemic treatment was extracted and successfully subjected to GEX profiling (n = 437, NanoString Breast Cancer 360™ panel). The median follow-up periods for a recurrence-free interval (RFi) and overall survival (OS) were 28 and 33 years, respectively. Associations between GEX signatures and tamoxifen effect were assessed in patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+ /HER2-) tumors using Kaplan-Meier estimates and Cox regression. The prognostic effects of GEX signatures were studied in the entire cohort. False discovery rate adjustments (q-values) were applied to account for multiple hypothesis testing.

RESULTS

In patients with ER+/HER2- tumors, FOXA1 expression below the median was associated with an improved effect of tamoxifen after 10 years with regard to RFi (hazard ratio [HR]FOXA1(high) = 1.04, 95% CI = 0.61-1.76, HRFOXA1(low) = 0.30, 95% CI = 0.14-0.67, qinteraction = 0.0013), and a resembling trend was observed for AR (HRAR(high) = 1.15, 95% CI = 0.60-2.20, HRAR(low) = 0.42, 95% CI = 0.24-0.75, qinteraction = 0.87). Similar patterns were observed for OS. Tamoxifen was in the same subgroup most beneficial for RFi in patients with low ESR1 expression (HRRFi ESR1(high) = 0.76, 95% CI = 0.43-1.35, HRRFi, ESR1(low) = 0.56, 95% CI = 0.29-1.06, qinteraction = 0.37). Irrespective of molecular subtype, higher levels of ESR1, Mast cells, and PGR on a continuous scale were correlated with improved 10 years RFi (HRESR1 = 0.80, 95% CI = 0.69-0.92, q = 0.005; HRMast cells = 0.74, 95% CI = 0.65-0.85, q < 0.0001; and HRPGR = 0.78, 95% CI = 0.68-0.89, q = 0.002). For BC proliferation and Hypoxia, higher scores associated with worse outcomes (HRBCproliferation = 1.54, 95% CI = 1.33-1.79, q < 0.0001; HRHypoxia = 1.38, 95% CI = 1.20-1.58, q < 0.0001). The results were similar for OS.

CONCLUSIONS

Expression of FOXA1 is a promising predictive biomarker for tamoxifen effect in ER+/HER2- premenopausal breast cancer. In addition, each of the signatures BC proliferation, Hypoxia, Mast cells, and the GEX of AR, ESR1, and PGR had prognostic value, also after adjusting for established prognostic factors. Trial registration This trial was retrospectively registered in the ISRCTN database the 6th of December 2019, trial ID: https://clinicaltrials.gov/ct2/show/ISRCTN12474687 .

Genome-wide transcriptome study using deep RNA sequencing for myocardial infarction and coronary artery calcification

Background

Coronary artery calcification (CAC) is a noninvasive measure of coronary atherosclerosis, the proximal pathophysiology underlying most cases of myocardial infarction (MI). We sought to identify expression signatures of early MI and subclinical atherosclerosis in the Framingham Heart Study (FHS). In this study, we conducted paired-end RNA sequencing on whole blood collected from 198 FHS participants (55 with a history of early MI, 72 with high CAC without prior MI, and 71 controls free of elevated CAC levels or history of MI). We applied DESeq2 to identify coding-genes and long intergenic noncoding RNAs (lincRNAs) differentially expressed in MI and high CAC, respectively, compared with the control.

Results

On average, 150 million paired-end reads were obtained for each sample. At the false discovery rate (FDR) < 0.1, we found 68 coding genes and 2 lincRNAs that were differentially expressed in early MI versus controls. Among them, 60 coding genes were detectable and thus tested in an independent RNA-Seq data of 807 individuals from the Rotterdam Study, and 8 genes were supported by p value and direction of the effect. Immune response, lipid metabolic process, and interferon regulatory factor were enriched in these 68 genes. By contrast, only 3 coding genes and 1 lincRNA were differentially expressed in high CAC versus controls. APOD, encoding a component of high-density lipoprotein, was significantly downregulated in both early MI (FDR = 0.007) and high CAC (FDR = 0.01) compared with controls.

Conclusions

We identified transcriptomic signatures of early MI that include differentially expressed protein-coding genes and lincRNAs, suggesting important roles for protein-coding genes and lincRNAs in the pathogenesis of MI.

[Mutation signatures in head and neck squamous cell carcinoma : Pathogenesis and therapeutic potential]

BACKGROUND

The pathogenesis of head and neck squamous cell carcinoma (HNSCC) is a complex and multistage process which results from the interaction of exogenous and endogenous cellular processes. Each of these processes leaves a characteristic pattern of mutations on the tumor genome, a so-called mutational signature.

STATE OF THE ART

The subject of current studies is to decipher specific signatures of mutational processes operating during HNSCC pathogenesis and to address their prognostic value. Computational analysis of genomic sequencing data by The Cancer Genome Atlas (TCGA) revealed mutational signatures 1, 2, 4, 5, 7, and 13 as the main players in HNSCC pathogenesis. Signature 16 was first discovered in human papillomavirus (HPV)-negative oral and oropharyngeal tumors. In many studies, an association of signature 16 with alcohol and tobacco consumption as well as with an unfavorable prognosis was described.

Prognostic value of transcriptomic determination of tumour-infiltrating lymphocytes in localised breast cancer

PURPOSE

Tumour-infiltrating lymphocyte (TIL) detection by histology is associated with outcomes in breast cancer; nevertheless, analysis standardisation is difficult. We determined whether transcriptomic data could generate a genomic signature that estimated TIL infiltrates and determined patient prognosis in localised breast cancer.

EXPERIMENTAL DESIGN

Using 1928 transcriptomic profiles of pure cells, we generated a genetic signature specific to lymphocyte, myeloid, stromal and cancer cells. We then computed a score based on this signature and tested the association between the score and the TILs estimated for patients in an adjuvant setting from public and private data sets. We tested the capacity of the transcriptomic RNA TIL score to predict disease-free survival (DFS) or overall survival (OS) through multivariate Cox models adjusted for classical clinical variables and PAM50 molecular classification in two public data sets (Carte d'Identité des Tumeurs [CIT], n = 530; Metabric, n = 1832).

RESULTS

A high RNA TIL score was significantly associated with the presence of a high level of TILs as assessed by histology. The score was also associated with DFS and OS in multivariate Cox models adjusted for molecular and clinical variables (CIT: OS hazard ratio [HR] = 0.15 [0.04, 0.61], p-value = 0.007; DFS: 0.27 [0.08, 0.8] p-value = 0.02; Metabric: OS HR = 0.87 [0.77, 0.97], p-value = 0.01). The association between the RNA TIL score and survival was tested by univariate analysis in each molecular subgroup; the RNA TIL score was associated with survival only in basal-like tumours.

CONCLUSIONS

Determination of the TIL rate using a transcriptomic signature is feasible and has a high prognostic value in patients with basal-like tumours in an adjuvant setting.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map.

METHODS

We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results.

RESULTS

We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub.

CONCLUSIONS

Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases.

BBS9 gene in nonsyndromic craniosynostosis: Role of the primary cilium in the aberrant ossification of the suture osteogenic niche

Nonsyndromic craniosynostosis (NCS) is the premature ossification of skull sutures, without associated clinical features. Mutations in several genes account for a small number of NCS patients; thus, the molecular etiopathogenesis of NCS remains largely unclear. Our study aimed at characterizing the molecular signaling implicated in the aberrant ossification of sutures in NCS patients. Comparative gene expression profiling of NCS patient sutures identified a fused suture-specific signature, including 17 genes involved in primary cilium signaling and assembly. Cells from fused sutures displayed a reduced potential to form primary cilia compared to cells from control patent sutures of the same patient. We identified specific upregulated splice variants of the Bardet Biedl syndrome-associated gene 9 (BBS9), which encodes a structural component of the ciliary BBSome complex. BBS9 expression increased during in vitro osteogenic differentiation of suture-derived mesenchymal cells of NCS patients. Also, Bbs9 expression increased during in vivo ossification of rat sutures. BBS9 functional knockdown affected the expression of primary cilia on patient suture cells and their osteogenic potential. Computational modeling of the upregulated protein isoforms (observed in patients) predicted that their binding affinity within the BBSome may be affected, providing a possible explanation for the aberrant suture ossification in NCS.

Gene signatures and expression of miRNAs associated with efficacy of panitumumab in a head and neck cancer phase II trial

OBJECTIVE

Platinum-based chemotherapy plus the anti-EGFR monoclonal antibody (mAb) cetuximab is used to treat recurrent/metastatic (RM) head-neck squamous cell carcinoma (HNSCC). Recently, we defined Cluster3 gene-expression signature as a potential predictor of favorable progression-free survival (PFS) in cetuximab-treated RM-HNSCC patients and predictor of partial metabolic FDG-PET response in an afatinib window-of-opportunity trial. Another anti-EGFR-mAb (panitumumab) was used as the treatment agent in RM-HNSCC patients in the phase II PANI01trial. PANI01 tumor samples were analyzed using functional genomics to explore response predictors to anti-EGFR therapy.

MATERIALS AND METHODS

Whole-gene expression and real-time PCR analyses were applied to pre-treatment samples from 25 PANI01 patients. Three gene signatures (Cluster3 score, RAS onco-signature, microenvironment score) and seven selected miRNAs were separately analyzed for association with panitumumab efficacy.

RESULTS

Cluster3 expression levels had a profile with a significant bimodal separation of samples (P =  3.08 E-13). Higher RAS activation, microenvironment score, and miRNA expression were associated with low-Cluster3 patients. The same biomarkers were separately associated with PFS. Patients with high-Cluster3 had significantly longer PFS than patients with low-Cluster3 (median PFS: 174 versus 51 days; log-rank P = 0.0021). ROC analysis demonstrated accuracy in predicting PFS (AUC = 0.877).

CONCLUSIONS

Despite differences in clinical settings and anti-EGFR inhibitors used for treatment, response prediction by the Cluster3 signature and selected miRNAs was essentially the same. Translation into a useful clinical assay requires validation in a broader setting.

Molecular Progression Risk Score for Prediction of Muscle Invasion in Primary T1 High-Grade Bladder Cancer

BACKGROUND

Pathologic T1 high-grade (pT1HG) bladder cancer (BC) is characterized by a high progression rate and constitutes an important clinical challenge; however, there is no consensus on the prediction of progression in pT1HG BC. The purpose of this study was to validate previously published molecular progression risk score (MoPRS) for predicting muscle-invasive disease in pT1HG BC.

MATERIALS AND METHODS

The expression of an 8-gene progression-related classifier identified from microarray data was analyzed by real-time PCR, and the MoPRS was calculated in 121 newly recruited patients with pT1HG BC. Progression was defined as muscle invasion or metastasis.

RESULTS

Overall, the disease of 28 patients (23.1%) progressed to muscle-invasive BC during the median follow-up of 63.7 (interquartile range, 17.6-96.4) months. The MoPRS was significantly higher in 1973 World Health Organization grade 3 than grade 2 tumors (P = .004). Early development of invasive BC was more prevalent in the highest quartile MoPRS group than in the lowest to 75th percentile MoPRS groups according to Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that the MoPRS was an independent predictor of invasive BC, either as a continuous variable (hazard ratio, 1.624; 95% confidence interval, 1.266-2.082; P < .001) or as a categorical variable (hazard ratio, 3.089; 95% confidence interval, 1.335-7.150; P = .008).

CONCLUSION

The MoPRS was an independent prognostic factor for identifying patients at high risk of invasive BC in patients with pT1HG BC. This scale may help identify patients who could benefit from more aggressive therapeutic intervention such as early cystectomy.

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

BACKGROUND

The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

METHODS

Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

RESULTS

Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

CONCLUSIONS

Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

Nomogram integrating gene expression signatures with clinicopathological features to predict survival in operable NSCLC: a pooled analysis of 2164 patients

Background

The current tumor-node-metastasis (TNM) staging system is insufficient to predict outcome of patients with operable Non-Small Cell Lung Cancer (NSCLC) owing to its phenotypic and genomic heterogeneity. Integrating genomic signatures with clinicopathological factors may provide more detailed evaluation of prognosis.

Methods

All 2164 clinically annotated NSCLC samples (1326 in the training set and 838 in the validation set) with corresponding microarray data from 17 cohorts were pooled to develop and validate a clinicopathologic-genomic nomogram based on Cox regression model. Two computational methods were applied to these samples to capture expression pattern of genomic signatures representing biological statuses. Model performance was measured by the concordance index (C-index) and calibration plot. Risk group stratification was proposed for the nomogram.

Results

Multivariable analysis of the training set identified independent factors including age, TNM stage, combined prognostic classifier, non-overlapping signature, and the ratio of neutrophil to plasma cells. The C-index of the nomogram for predicting survival was statistically superior to that of the TNM stage (training set, 0.686 vs 0.627, respectively; P < .001; validation set, 0.689 vs 0.638, respectively; P < .001). The calibration plots showed that the predicted 1-, 3- and 5-year survival probabilities agreed well with the actual observations. Stratifying patients into three risk groups detected significant differences among survival curves.

Conclusions

These findings offer preliminary evidence that genomic data provide independent and complementary prognostic information and incorporation of this information can refine prognosis in NSCLC. Prospective studies are required to further explore the value of this composite model for prognostic stratification and tailored therapeutic strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-016-0477-x) contains supplementary material, which is available to authorized users.

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.

β-PDGF receptor expressed by hepatic stellate cells regulates fibrosis in murine liver injury, but not carcinogenesis

BACKGROUND & AIMS

Rapid induction of β-PDGF receptor (β-PDGFR) is a core feature of hepatic stellate cell activation, but its cellular impact in vivo is not well characterized. We explored the contribution of β-PDGFR-mediated pathway activation to hepatic stellate cell responses in liver injury, fibrogenesis, and carcinogenesis in vivo using genetic models with divergent β-PDGFR activity, and assessed its prognostic implications in human cirrhosis.

METHODS

The impact of either loss or constitutive activation of β-PDGFR in stellate cells on fibrosis was assessed following carbon tetrachloride (CCl4) or bile duct ligation. Hepatocarcinogenesis in fibrotic liver was tracked after a single dose of diethylnitrosamine (DEN) followed by repeated injections of CCl4. Genome-wide expression profiling was performed from isolated stellate cells that expressed or lacked β-PDGFR to determine deregulated pathways and evaluate their association with prognostic gene signatures in human cirrhosis.

RESULTS

Depletion of β-PDGFR in hepatic stellate cells decreased injury and fibrosis in vivo, while its auto-activation accelerated fibrosis. However, there was no difference in development of DEN-induced pre-neoplastic foci. Genomic profiling revealed ERK, AKT, and NF-κB pathways and a subset of a previously identified 186-gene prognostic signature in hepatitis C virus (HCV)-related cirrhosis as downstream of β-PDGFR in stellate cells. In the human cohort, the β-PDGFR signature was not associated with HCC development, but was significantly associated with a poorer outcome in HCV cirrhosis.

CONCLUSIONS

β-PDGFR is a key mediator of hepatic injury and fibrogenesis in vivo and contributes to the poor prognosis of human cirrhosis, but not by increasing HCC development.

Multi-level gene expression signatures, but not binary, outperform Ki67 for the long term prognostication of breast cancer patients

Proliferation-related gene signatures have been proposed to aid breast cancer management by providing reproducible prognostic and predictive information on a patient-by-patient basis. It is unclear however, whether a less demanding assessment of cell division rate (as determined in clinical setting by expression of Ki67) can function in place of gene profiling. We investigated agreement between literature-, distribution-based, as well as signature-derived values for Ki67, relative to the genomic grade index (GGI), 70-gene signature, p53 signature, recurrence score (RS), and the molecular subtype models of Sorlie, Hu, and Parker in representative sets of 253 and 159 breast cancers with a median follow-up of 13 and 14.5 years, respectively. The relevance for breast cancer specific survival was also addressed in uni- and bivariate Cox models. Taking both cohorts into account, our broad approach identified ROC optimized Ki67 cutoffs in the range of 8-28%. With optimum signature-reproducing cutoffs, similarity in classification of individual tumors was higher for binary signatures (72-85%), than multi-level signatures (67-73%). Consistent with strong agreement, no prognostic superiority was noted for either Ki67 or the binary GGI, 70-gene and p53 signatures in the Uppsala dataset by bivariate analyses. In contrast, Ki67-independent prognostic capacity could be demonstrated for RS and molecular subtypes according to Sorlie, Hu and Parker in both datasets. Our results show that the added prognostic value of binary proliferation-related gene signatures is limited for Ki67-assessed breast cancers. More complex, multi-level descriptions have a greater potential in short- and long-term prognostication for biologically relevant breast cancer subgroups.

Challenges of incorporating gene expression data to predict HCC prognosis in the age of systems biology

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The recurrence of HCC after curative treatments is currently a major hurdle. Identification of subsets of patients with distinct prognosis provides an opportunity to tailor therapeutic approaches as well as to select the patients with specific sub-phenotypes for targeted therapy. Thus, the development of gene expression profiles to improve the prediction of HCC prognosis is important for HCC management. Although several gene signatures have been evaluated for the prediction of HCC prognosis, there is no consensus on the predictive power of these signatures. Using systematic approaches to evaluate these signatures and combine them with clinicopathologic information may provide more accurate prediction of HCC prognosis. Recently, Villanueva et al^[13] developed a composite prognostic model incorporating gene expression patterns in both tumor and adjacent tissues to predict HCC recurrence. In this commentary, we summarize the current progress in using gene signatures to predict HCC prognosis, and discuss the importance, existing issues and future research directions in this field.