Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer

A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer

BACKGROUND

Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.

METHODS

A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.

RESULTS

Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.

CONCLUSIONS

Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

BACKGROUND

Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches.

METHODS

Here we describe SPECTRA, an approach to derive quantitative variables that capture the intrinsic variation in gene expression of a tissue type. We applied the SPECTRA approach to bulk RNA sequencing from malignant cells (CD138+) in patients from the Multiple Myeloma Research Foundation CoMMpass study.

RESULTS

A set of 39 spectra variables were derived to represent multiple myeloma cells. We used these variables in predictive modeling to determine spectra-based risk scores for overall survival, progression-free survival, and time to treatment failure. Risk scores added predictive value beyond known clinical and expression risk factors and replicated in an external dataset. Spectrum variable S5, a significant predictor for all three outcomes, showed pre-ranked gene set enrichment for the unfolded protein response, a mechanism targeted by proteasome inhibitors which are a common first line agent in multiple myeloma treatment. We further used the 39 spectra variables in descriptive modeling, with significant associations found with tumor cytogenetics, race, gender, and age at diagnosis; factors known to influence multiple myeloma incidence or progression.

CONCLUSIONS

Quantitative variables from the SPECTRA approach can predict clinical outcomes in multiple myeloma and provide a new avenue for insight into tumor differences by demographic groups.

IMPACT

The SPECTRA approach provides a set of quantitative phenotypes that deeply profile a tissue and allows for more comprehensive modeling of gene expression with other risk factors.

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Background

This study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness.

Methods

We collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model.

Results

This study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve.

Conclusion

The DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

Fibroblast growth factor receptor signalling dysregulation and targeting in breast cancer

Fibroblast Growth Factor Receptor (FGFR) signalling plays a critical role in breast embryonal development, tissue homeostasis, tumorigenesis and metastasis. FGFR, its numerous FGF ligands and signalling partners are often dysregulated in breast cancer progression and are one of the causes of resistance to treatment in breast cancer. Furthermore, FGFR signalling on epithelial cells is affected by signals from the breast microenvironment, therefore increasing the possibility of breast developmental abnormalities or cancer progression. Increasing our understanding of the multi-layered roles of the complex family of FGFRs, their ligands FGFs and their regulatory partners may offer novel treatment strategies for breast cancer patients, as a single agent or rational co-target, which will be explored in depth in this review.

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Background

A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs.

Method

We collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model.

Results

The 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen (P < 0.05). We used the training cohort to construct a deep-learning model, for which the C-index was 0.833, or about 5% higher than that for the CPH model (0.786). The C-index of the deep-learning model for the test cohort was 0.822, which was also higher than that for the CPH model (0.782). The areas under the ROC curve for the 28-day, 90-day, and 1-year survival probabilities were 0.875, 0.865, and 0.874, respectively, in the deep-learning model, respectively, and 0.830, 0.843, and 0.806 in the CPH model. These values indicate that the survival analysis model based on deep learning is better than the traditional CPH model in predicting the survival of CCU patients.

Conclusion

A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.

Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database

BACKGROUND

Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB.

METHODS

Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families.

RESULTS

Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10-8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers.

CONCLUSIONS

Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees.

IMPACT

Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.See all articles in this CEBP Focus section, "Modernizing Population Science."

Hsa_circ_0002483 inhibited the progression and enhanced the Taxol sensitivity of non-small cell lung cancer by targeting miR-182-5p

In this study, we identified a novel circRNA, circ_0002483, and further investigated its functions in the progression and Taxol resistance of NSCLC. We found that circ_0002483 was expressed at low levels in NSCLC tissues and cell lines. Functional assays indicated that circ_0002483 overexpression significantly inhibited NSCLC cell proliferation and invasion in vitro and in vivo and enhanced the sensitivity of NSCLC cells to Taxol. Mechanistically, circ_0002483 was identified to sponge multiple miRNAs including miR-182-5p (also named miR-182), miR-520q-3p, miR-582-3p, miR-587, and miR-655. In addition, circ_0002483 was also demonstrated to regulate the expression of GRB2, FOXO1, and FOXO3, three target genes of miR-182-5p, by sponging miR-182-5p. Circ_0002483 was demonstrated to inhibit NSCLC progression in vitro and in vivo and enhanced the sensitivity of NSCLC cells to Taxol by sponging miR-182-5p to release the inhibition on GRB2, FOXO1, and FOXO3 mRNAs.

Hypermethylated KLF9 Is An Independent Prognostic Factor For Favorable Outcome In Breast Cancer

Background and objective

Breast cancer (BC) is the most lethal human malignancy and is the leading cause of cancer-associated death in women worldwide. Krüppel-like factor 9 (KLF9) belongs to a family of transcriptional regulators and its role in BC has not been fully investigated.

Method

Data mining was used to analyze BC data from The Cancer Genome Atlas (TCGA) database, which was downloaded using the UCSC Xena browser. The differential expression and methylation level of KLF9 was analyzed in patients with BC and corresponding normal controls enrolled from our hospital. Besides, the correlation of KLF9 methylation and prognosis was explored, and gene set enrichment analysis (GSEA) was conducted to identify the potential signaling pathway of KLF9 involved.

Results

Both TCGA and BC tissues indicated hypermethylation of the KLF9 promoter region in patients with BC compared with normal controls, which might account for the dysregulation of KLF9 in patients with BC. Besides, hypermethylation of KLF9 was detected in patients with estrogen or progesterone receptor-positive and non-triple-negative disease. Further, hypermethylation of KLF9 was demonstrated to be a potential independent biomarker in obtaining favorable outcomes in BC. By GSEA, tumor-associated biological processes and signaling pathway were identified, which indicated that KLF9 might play a vital role in the carcinogenesis of BC.

Conclusion

KFL9 plays an important role in the carcinogenesis of BC through the multiple tumor-associated signaling pathway. The hypermethylation of KLF9 resulted in its reduced expression in BC, while the hypermethylation of KLF9 has potential in the prediction of favorable outcomes in BC.