Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.

BACKGROUND: Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort. METHODS: The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers. RESULTS: Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively). CONCLUSION: In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.

Promotion of the Warburg effect is associated with poor benefit from adjuvant chemotherapy in colorectal cancer.

Metabolic reprogramming, including the Warburg effect, is a hallmark of cancer. Indeed, the diversity of cancer metabolism leads to cancer heterogeneity, but accurate assessment of metabolic properties in tumors has not yet been undertaken. Here, we performed absolute quantification of the expression levels of 113 proteins related to carbohydrate metabolism and antioxidant pathways, in stage III colorectal cancer surgical specimens from 70 patients. The Warburg effect appeared in absolute protein levels between tumor and normal mucosa specimens demonstrated. Notably, the levels of proteins associated with the tricarboxylic citric acid cycle were remarkably reduced in the malignant tumors which had relapsed after surgery and treatment with 5-fluorouracil-based adjuvant therapy. In addition, the efficacy of 5-fluorouracil also decreased in the cultured cancer cell lines with promotion of the Warburg effect. We further identified nine and eight important proteins, which are closely related to the Warburg effect, for relapse risk and 5-fluorouracil benefit, respectively, using a biomarker exploration procedure. These results provide us a clue for bridging between metabolic protein expression profiles and benefit from 5-fluorouracil adjuvant chemotherapy.

Combination of lymphovascular invasion and the AJCC TNM staging system improves prediction of prognosis in N0 stage gastric cancer: results from a high-volume institution.

BACKGROUND: This study sought to explore whether lymphovascular invasion can affect the prognosis of patients with stage N0 gastric cancer and to evaluate the survival benefit of adjuvant chemotherapy for such patients. METHOD: From January 2006 to December 2011, a total of 2102 gastric cancer patients undergoing radical gastric resection were enrolled in this study. Homogeneity, discriminatory ability, and monotonicity of gradients in the combination of lymphovascular invasion and the 8th edition of the AJCC staging system and the 8th edition of the AJCC staging system alone were compared using linear trend χ2, likelihood ratio χ2 statistics, and Akaike information criterion (AIC) calculations. The Kaplan-Meier method and the log-rank test were used to analyze between-group differences in survival rate. RESULT: The median follow-up time of the whole group was 58 months, and the average age of the whole group was 63.9 years (range 21-89 years). The 3-year and 5-year overall survival rates in N0 patients with lymphovascular invasion were lower than those in N0 patients without lymphovascular invasion (3-year OS: 78.3% vs 92.5%, 5-year OS: 70.0% vs 88.3%, p < 0.001). A multivariate analysis showed that age (p < 0.001), lymphovascular invasion (p < 0.001), and pT (p < 0.001) were independent risk factors for the prognosis of N0 patients. Compared with the 8th edition of the AJCC staging system alone, the 8th AJCC staging system combined with lymphovascular invasion demonstrated a better linear trend χ2, likelihood ratio χ2 statistics, and AIC value (68.99 vs 58.58, 70.18 vs 58.36, 1473.38 vs 1485.04). In pT3N0M0 patients with lymphovascular invasion, the 3-year and 5-year overall survival rates of the adjuvant chemotherapy group were higher than those of the surgery alone group (3-year OS: 83.3% vs 68.2%, 5-year OS: 72.3% vs 50.0%, p = 0.048). CONCLUSION: Lymphovascular invasion is an independent prognostic factor in N0 patients. The 8th AJCC staging system combined with lymphovascular invasion can improve the accuracy of the AJCC staging system for N0 patients. Moreover, adjuvant chemotherapy improves the survival of pT3N0M0 patients with lymphovascular invasion.

Tumor-infiltrating CD8+ T cells combined with tumor-associated CD68+ macrophages predict postoperative prognosis and adjuvant chemotherapy benefit in resected gastric cancer.

BACKGROUND: Tumor-infiltrating immune cells are present in various malignant tumors, but their clinical significance in gastric cancer (GC) remains unclear. This study aimed to investigate the prognostic significance of tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). METHODS: Using a prospective database containing 401 cases of GC, we evaluated TIL (cluster of differentiation 8 (CD8) expression) and TAM (cluster of differentiation 68 (CD68) expression) statuses via immunohistochemical staining. RESULTS: Compared with CD8+ TIL-negative cases (n = 196, 48.6%), CD8+ TIL-positive cases (n = 205, 51.1%) showed significantly better recurrence-free survival (RFS) [log-rank p<0.001; multivariate HR: 0.372; 95% confidence interval (CI): 0.239-0.579, p<0.001]. In contrast, compared with CD68+ TAM-negative cases (n = 217, 54.1%), CD68+ TAM-positive cases (n = 184, 45.9%) had significantly poor RFS [log-rank p<0.001; multivariate HR: 2.182; 95% CI: 1.435-3.318, p<0.001]. Thus, patients with a positive CD8+ TIL and negative CD68+ TAM status exhibited significantly increased RFS. Multivariate analysis demonstrated that CD8+ TILs and CD68+ TAMs may serve as independent prognostic markers for RFS. Incorporating CD8+ TIL and CD68+ TAM statuses into the AJCC TNM system generated a predictive model with better predictive accuracy for RFS. More importantly, patients with a positive TIL and negative TAM status showed a tendency of improved RFS after postoperative adjuvant chemotherapy (PAC). Similar results were obtained by overall survival (OS) analysis. CONCLUSIONS: CD8+ TIL and CD68+ TAM statuses were identified as independent prognostic factors that may be integrated into the current TNM staging system to refine risk stratification and to better predict the survival benefit from PAC in patients with GC. TRIAL REGISTRATION: The current controlled trial was registered at ClinicalTrials.gov (ID: NCT02327481 ) on December 30, 2014.

Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition.

In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stages I-IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A-, B- and C-type). The subtypes were validated in 543 stages II and III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on three biological hallmarks of the tumor: epithelial-to-mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with microsatellite instability and cellular proliferation. A-type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C-type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype and show no benefit from adjuvant chemotherapy treatment. Both A-type and B-type tumors have a more proliferative and epithelial phenotype and B-types benefit from adjuvant chemotherapy. B-type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer.

Objectives: The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore. Methods: A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method. Results: The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients. Conclusions: The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Identifies Immune Feature Genes for Prediction of Chemotherapy Benefit in Cancer.

Chemotherapy is still the most fundamental treatment for advanced cancers so far. Previous studies have indicated that immune cell infiltration (ICI) index could serve as a biomarker to predict chemotherapy benefit in breast cancer and colorectal cancer. However, due to different responses of tumor infiltrating immune cells (TIICs) to chemotherapy, the prediction efficiency of ICI index is not fully confirmed by now. In our study, we first extended this conclusion in 7 cancers that high ICI index could certainly indicate chemotherapy benefit (P<0.05). But we also found the fraction of different TIICs and the interaction of TIICs were varies greatly from cancer to cancer. Therefore, we executed correlation and causal network analysis to identify chemotherapy associated immune feature genes, and fortunately identified six co-owned immune feature genes (CD48, GPR65, C3AR1, CD2, CD3E and ARHGAP9) in 10 cancers (BLCA, BRCA, COAD, LUAD, LUSC, OV, PAAD, SKCM, STAD and UCEC). Base on this, we developed a chemotherapy benefit prediction model within six co-owned immune feature genes through random forest classifying (AUC =0.83) in cancers mentioned above, and validated its efficiency in external datasets. In short, our work offers a novel model with a shrinking panel which has the potential to guide optimal chemotherapy in cancer.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Association of dysbindin expression with individualized postoperative prognosis and chemotherapy benefit among patients with gastric adenocarcinoma.

Background: The current model for predicting prognosis and chemotherapy response of patients with gastric adenocarcinoma is the TNM staging system, which may lack adequate accuracy and evaluations of molecular features at the individual level. We aimed to develop a prediction model to assess the individualized prognosis and responsiveness to fluorouracil-based adjuvant chemotherapy. Method: This retrospective study concluded 2 independent cohorts of patients with GAC. The expression of dysbindin was quantified and evaluated the association with the overall survival for GAC patients. A prediction model for postoperative overall survival was generated and internally and externally validated. The interaction between dysbindin expression and PACT was detected in advanced GAC patients. Results: Of the 637 patients enrolled in the study, 425 were men (66.7%) with a mean (SD) age of 59.79 (9.81) years. High levels of dysbindin expression predicted a poor prognosis in patients with GAC. Multivariate analysis demonstrated dysbindin expression was an independent prognostic predictor of overall survival in the test, validation and combined cohorts. A prognostic predictive model incorporating age, dysbindin expression, pathological differentiation, Lauren's classification and the TNM staging system was established. This model had better predictive accuracy for overall survival than the traditional TNM staging system and was internally and externally validated. More importantly, advanced GAC patients with low dysbindin expression were likely to benefit from fluorouracil-based PACT. Conclusion: The risk stratification model incorporating dysbindin expression and TNM staging system showed better predictive accuracy. Advanced GAC patients with low dysbindin expression revealed better response of fluorouracil-based adjuvant chemotherapy.

Molecular Subtype May Be More Associated With Prognosis and Chemotherapy Benefit Than Tumor Size in T1N0 Breast Cancer Patients: An Analysis of 2,168 Patients for Possible De-Escalation Treatment.

PURPOSE: Breast cancer (BC) patients with T1N0 tumors have relatively favorable clinical outcomes. However, it remains unclear whether molecular subtypes can aide in prognostic prediction for such small, nodal-negative BC cases and guide decision-making about escalating or de-escalating treatments. PATIENTS AND METHODS: T1N0 BC patients diagnosed between 2009 and 2017 were included and classified into three subgroups according to receptor status: 1) hormonal receptor (HR)+/human epidermal growth factor receptor-2 (HER2)-; 2) HER2+; and 3) triple negative (TN) (HR-/HER2-). Patients' characteristics and relapse events were reviewed. Kaplan-Meier analysis and Cox regression were used to assess the iDFS and BCSS. The effects of risk factors and adjuvant treatment benefits were evaluated by calculating hazard ratios (HRs) for invasive disease-free survival (iDFS) and breast cancer-specific survival (BCSS) with Cox proportional hazards models. RESULTS: In total, 2,168 patients (1,435 HR+/HER2-, 427 HER2+, 306 TN) were enrolled. The 5-year iDFS rates were 93.6, 92.7, and 90.6% for HR+/HER2-, HER2+, and TN patients, respectively (P = 0.039). Multivariate analysis demonstrated that molecular subtype (P = 0.043), but not tumor size (P = 0.805), was independently associated with iDFS in T1N0 BC. TN patients [HRs = 1.77, 95% confidence interval (CI) = 1.11-2.84, P = 0.018] had a higher recurrence risk than HR+/HER2- patients. Adjuvant chemotherapy benefit was not demonstrated in all T1N0 patients but interacted with molecular subtype status. TN (adjusted HRs = 2.31, 95% CI = 0.68-7.54) and HER2+ (adjusted HRs = 2.26, 95% CI = 0.95-5.63) patients receiving chemotherapy had superior iDFS rates. Regarding BCSS, molecular subtype tended to be related to outcome (P = 0.053) and associated with chemotherapy benefit (P = 0.005). CONCLUSION: Molecular subtype was more associated with disease outcome and chemotherapy benefit than tumor size in T1N0 BC patients, indicating that it may guide possible clinical de-escalating therapy in T1N0 BC.

Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients.

Gastric cancer (GC) is a typical heterogeneous malignant tumor, whose insensitivity to chemotherapy is a common cause of tumor recurrence and metastasis. There is no doubt regarding the effectiveness of adjuvant chemotherapy (ACT) for GC, but the population for whom it is indicated and the selection of specific options remain the focus of present research. The conventional pathological TNM prediction focuses on cancer cells to predict prognosis, while they do not provide sufficient prediction. Enhanced computed tomography (CT) scanning is a validated tool that assesses the involvement of careful identification of the tumor, lymph node involvement, and metastatic spread. Using the radiomics approach, we selected the least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for predicting the overall survival (OS) and disease-free survival (DFS) of patients with complete postoperative gastric cancer and further identifying candidate benefits from ACT. The radiomics trait-associated genes captured clinically relevant molecular pathways and potential chemotherapeutic drug metabolism mechanisms. Our results of precise surrogates using radiogenomics can lead to additional benefit from adjuvant chemotherapy and then survival prediction in postoperative GC patients.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer.

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

Development and External Validation of a Nomogram to Predict Recurrence-Free Survival After R0 Resection for Stage II/III Gastric Cancer: An International Multicenter Study.

Background: The benefit of adjuvant chemotherapy varies widely among patients with stage II/III gastric cancer (GC), and tools predicting outcomes for this patient subset are lacking. We aimed to develop and validate a nomogram to predict recurrence-free survival (RFS) and the benefits of adjuvant chemotherapy after radical resection in patients with stage II/III GC. Methods: Data on patients with stage II/III GC who underwent R0 resection from January 2010 to August 2014 at Fujian Medical University Union Hospital (FMUUH) (n = 1,240; training cohort) were analyzed by Cox regression to identify independent prognostic factors for RFS. A nomogram including these factors was internally and externally validated in FMUUH (n = 306) and a US cohort (n = 111), respectively. Results: The multivariable analysis identified age, differentiation, tumor size, number of examined lymph nodes, pT stage, pN stage, and adjuvant chemotherapy as associated with RFS. A nomogram including the above 7 factors was significantly more accurate in predicting RFS compared with the 8th AJCC-TNM staging system for patients in the training cohort. The risk of peritoneal metastasis was higher and survival after recurrence was significantly worse among patients calculated by the nomogram to be at high risk than those at low risk. The nomogram's predictive performance was confirmed in both the internal and external validation cohorts. Conclusion: A novel nomogram is available as a web-based tool and accurately predicts long-term RFS for GC after radical resection. The tool can also be used to determine the benefit of adjuvant chemotherapy by comparing scores with and without this intervention.

Plasma protein-based signature predicts distant metastasis and induction chemotherapy benefit in Nasopharyngeal Carcinoma.

Rationale: Currently, for locoregionally advanced nasopharyngeal carcinoma (LA-NPC), there is no effective blood-based method to predict distant metastasis. We aimed to detect plasma protein profiles to identify biomarkers that could distinguish patients with NPC who are at high risk of posttreatment distant metastasis. Methods: A high-throughput antibody array was initially applied to detect 1000 proteins in pretreatment plasma from 16 matched LA-NPC patients with or without distant metastasis after radical treatment. Differentially expressed proteins were further examined using a low-throughput array to construct a plasma protein-based signature for distant metastasis (PSDM) in a cohort of 226 patients. Results: Fifty circulating proteins were differentially expressed between metastatic and non-metastatic patients and 18 were proven to be strongly correlated with distant metastasis-free survival (DMFS) in NPC. A PSDM signature consisting of five proteins (SLAMF5, ESM-1, MMP-8, INSR, and Serpin A5) was established to assign patients with NPC into a high-risk group and a low-risk group. Patients in the high-risk group had shorter DMFS (P < 0.001), disease-free survival (DFS) (P < 0.001) and overall survival (OS) (P < 0.001). Moreover, the PSDM performed better than N stage and Epstein-Barr virus (EBV) DNA load at effectively identifying patients with NPC at high risk of metastasis. For patients in the high-risk group, induction chemotherapy significantly improved DMFS, DFS, and OS. Conclusions: The PSDM could be a useful liquid biopsy tool to effectively predict distant metastasis and the benefit of induction chemotherapy in patients with LA-NPC.

Oncotype DX Breast Recurrence Score Distribution and Chemotherapy Benefit Among Women of Different Age Groups With HR-Positive, HER2-Negative, Node-Negative Breast Cancer in the SEER Database.

Objective: To explore the distribution of Oncotype DX Breast Recurrence Score (RS), the proportion of receiving chemotherapy, and the relationship between RS and chemotherapy benefit according to detailed age groups in women with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-negative (HR+/HER2-/N0) breast cancer. Methods: This was an extensive, comprehensive, population-based retrospective study. Data on individuals with breast cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program. The cohort was divided into five groups by age (≤ 35, 36-50, 51-65, 66-80, >80 years). RS distribution and chemotherapy proportion among different age groups were analyzed, and the overall survivals between patients receiving chemotherapy and those not/unknown were compared in each age group. Results: The study cohort comprised 49,539 patients and the largest age group was 51-65 years. The percentage of patients with low-risk RS (0-10) increased with age, whereas those with intermediate-risk RS (11-25) decreased with age (except for the group of 36-50 years, which had the highest rate of intermediate-risk RS). The age group ≤35 years has the greatest rate of high-risk RS (26-100). The proportion of receiving chemotherapy decreased with age in all RS risk categories. Overall survival was benefited by chemotherapy only in the age group of 66-80 years with intermediate- and high-risk RS, and chemotherapy seemed to do more harm than good for patients older than 80 years. Conclusions: In the present study, we identified the distribution of RS, the proportion of receiving chemotherapy, and the relationship between RS and chemotherapy benefit according to a detailed age grouping for women with HR+/HER2-/N0 breast cancer, which may help in making individualized clinical decisions.