Development and Validation of a New Prognostic System for Patients with Hepatocellular Carcinoma

BACKGROUND: Prognostic assessment in patients with hepatocellular carcinoma (HCC) remains controversial. Using the Italian Liver Cancer (ITA.LI.CA) database as a training set, we sought to develop and validate a new prognostic system for patients with HCC. METHODS AND FINDINGS: Prospective collected databases from Italy (training cohort, n = 3,628; internal validation cohort, n = 1,555) and Taiwan (external validation cohort, n = 2,651) were used to develop the ITA.LI.CA prognostic system. We first defined ITA.LI.CA stages (0, A, B1, B2, B3, C) using only tumor characteristics (largest tumor diameter, number of nodules, intra- and extrahepatic macroscopic vascular invasion, extrahepatic metastases). A parametric multivariable survival model was then used to calculate the relative prognostic value of ITA.LI.CA tumor stage, Eastern Cooperative Oncology Group (ECOG) performance status, Child-Pugh score (CPS), and alpha-fetoprotein (AFP) in predicting individual survival. Based on the model results, an ITA.LI.CA integrated prognostic score (from 0 to 13 points) was constructed, and its prognostic power compared with that of other integrated systems (BCLC, HKLC, MESIAH, CLIP, JIS). Median follow-up was 58 mo for Italian patients (interquartile range, 26-106 mo) and 39 mo for Taiwanese patients (interquartile range, 12-61 mo). The ITA.LI.CA integrated prognostic score showed optimal discrimination and calibration abilities in Italian patients. Observed median survival in the training and internal validation sets was 57 and 61 mo, respectively, in quartile 1 (ITA.LI.CA score 64 1), 43 and 38 mo in quartile 2 (ITA.LI.CA score 2-3), 23 and 23 mo in quartile 3 (ITA.LI.CA score 4-5), and 9 and 8 mo in quartile 4 (ITA.LI.CA score > 5). Observed and predicted median survival in the training and internal validation sets largely coincided. Although observed and predicted survival estimations were significantly lower (log-rank test, p < 0.001) in Italian than in Taiwanese patients, the ITA.LI.CA score maintained very high discrimination and calibration features also in the external validation cohort. The concordance index (C index) of the ITA.LI.CA score in the internal and external validation cohorts was 0.71 and 0.78, respectively. The ITA.LI.CA score's prognostic ability was significantly better (p < 0.001) than that of BCLC stage (respective C indexes of 0.64 and 0.73), CLIP score (0.68 and 0.75), JIS stage (0.67 and 0.70), MESIAH score (0.69 and 0.77), and HKLC stage (0.68 and 0.75). The main limitations of this study are its retrospective nature and the intrinsically significant differences between the Taiwanese and Italian groups. CONCLUSIONS: The ITA.LI.CA prognostic system includes both a tumor staging-stratifying patients with HCC into six main stages (0, A, B1, B2, B3, and C)-and a prognostic score-integrating ITA.LI.CA tumor staging, CPS, ECOG performance status, and AFP. The ITA.LI.CA prognostic system shows a strong ability to predict individual survival in European and Asian populations

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

EarlyR: A Robust Gene Expression Signature for Predicting Outcomes of Estrogen Receptor–Positive Breast Cancer

Introduction Early stage estrogen receptor (ER)-positive breast cancer may be treated with chemotherapy in addition to hormone therapy. Currently available molecular signatures assess the risk of recurrence and the benefit of chemotherapy; however, these tests may have large intermediate risk groups, limiting their usefulness. Methods The EarlyR prognostic score was developed using integrative analysis of microarray data sets and formalin-fixed, paraffin-embedded–based quantitative real-time PCR assay and validated in Affymetrix data sets and METABRIC cohort using Cox proportional hazards models and Kaplan-Meier survival analysis. Concordance index was used to measure the probability of prognostic score agreement with outcome. Results The EarlyR score and categorical risk strata (EarlyR-Low, EarlyR-Int, EarlyR-High) derived from expression of ESPL1, MKI67, SPAG5, PLK1 and PGR was prognostic of 8-year distant recurrence-free interval in Affymetrix (categorical P = 3.5 × 10−14; continuous P = 8.8 × 10−15) and METABRIC (categorical P < 2.2 × 10−16; continuous P < 10−16) data sets of ER+ breast cancer. Similar results were observed for the breast cancer–free interval end point. At most 13% of patients were intermediate risk and at least 66% patients were low risk in both ER+ cohorts. The EarlyR score was significantly prognostic (distant recurrence-free interval; P < .001) in both lymph node–negative and lymph node–positive patients and was independent from clinical factors. EarlyR and surrogates of current molecular signatures were comparable in prognostic significance by concordance index. Conclusion The 5-gene EarlyR score is a robust prognostic assay that identified significantly fewer patients as intermediate risk and more as low risk than currently available assays. Further validation of the assay in clinical trial–derived cohorts is ongoing

The RAG Model: a new paradigm for genetic risk stratification in multiple myeloma

Molecular studies have shown that multiple myeloma is a highly genetically heterogonous disease which may manifest itself as any number of diverse subtypes each with variable clinicopathological features and outcomes. Given this genetic heterogeneity, a universal approach to treatment of myeloma is unlikely to be successful for all patients and instead we should strive for the goal of personalised therapy using rationally informed targeted strategies. Current DNA sequencing technologies allow for whole genome and exome analysis of patient myeloma samples that yield vast amounts of genetic data and provide a mutational overview of the disease. However, the clinical utility of this information currently lags far behind the sequencing technology which is increasingly being incorporated into clinical practice. This paper attempts to address this shortcoming by proposing a novel genetically based “traffic-light” risk stratification system for myeloma, termed the RAG (Red, Amber, Green) model, which represents a simplified concept of how complex genetic data may be compressed into an aggregate risk score. The model aims to incorporate all known clinically important trisomies, translocations, and mutations in myeloma and utilise these to produce a score between 1.0 and 3.0 that can be incorporated into diagnostic, prognostic, and treatment algorithms for the patient

Pathway relevance ranking for tumor samples through network-based data integration

The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients: A Machine Learning Approach

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. RESULTS: Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. CONCLUSIONS: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors