Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms

Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.

Evaluation of Relationship between Intrinsic Radiosensitivity (Survival Fraction at 2 Gy) and Gamma-H2AX Test and Apoptosis of Lymphocytes in Breast Cancer Patients

Background

Radiotherapy is one of the routine treatment strategies for breast cancer (BC) patients. Different responses of the patient to radiation due to different intrinsic radiosensitivity (RS) were induced to the researcher try to introduce a standard assay for the prediction of RS. Clonogenic assay is recognized as a gold standard method in this subject but because of some of its disadvantages, it is needed for alternative assays. In this study, two assays were evaluated for this reason in ten BC patients with different RSs.

Methods

The peripheral blood of 10 volunteers with BC was obtained, and the peripheral blood mononuclear cells were extracted. After exposed with 2 Gy, survival fraction at 2 Gy (SF2) was calculated by clonogenic assay. γ-H2AX assay was performed for all patients, and apoptosis assay was evaluated for three represented categorized patients.

Results

RS of patients showed SF2 and categorized in three groups (high, medium, and low RS). Double-strand breaks (DSBs) were decreased in high radiosensitive patients, but the residual DSBs were clearly higher than other two groups. It is shown that the repair system in these patients is lower active than others. Apoptosis frequency in patient 4 is highly active which could induce the enhancement of her RS.

Conclusion

γ-H2AX and apoptosis assays could predict the intrinsic RS, but evaluation of them separately is not sufficient for this aim. It is necessary to consider all the parameters together and consideration of the combination of assays could fit a better prediction of intrinsic RS.

Development and validation of a radiosensitivity model to evaluate radiotherapy benefits in pan-cancer

Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.

Association of preoperative and postoperative circulating tumour DNA (ctDNA) with PIK3CA gene mutation with risk of recurrence in patients with non-metastatic breast cancer

BACKGROUND

Circulating tumour DNA (ctDNA), contains tumour-specific gene mutation in blood circulation and could aid in postoperative risk stratification of non-metastatic breast cancer. In this study, we investigated the feasibility of detecting PIK3CA gene mutations in ctDNA in the preoperative (preop) and postoperative period (postop), and its prognostic significance in patients with breast cancer.

METHODS

A cohort of patients with breast cancer undergoing curative surgery with available blood samples preoperatively and postoperatively (Post op) at either Post op time period; week 1-2, week 3-4 or weeks 5-12 were enrolled. PIK3CA gene mutations at exons 9 and 20 were detected in ctDNA with High resolution melting (HRM) PCR and Allele specific fluorescence probe-based PCR.

RESULTS

A total of 62 patients (age, median (IQR), 51.50 (45.0-65.0) years), with a median follow-up of 90 months (interquartile range (IQR),60-120 months) were enrolled. In total, 25 (40.3%) and 22 (35%) patients with breast cancer had detectable PIK3CA gene mutations in ctDNA in preoperative and postoperative period, respectively. PIK3CA gene mutations in ctDNA in postoperative period (hazard ratio (H.R: 18.05, p = 0.001) were a negative prognostic factor for recurrencefree survival (RFS) and overall survival (OS) (H.R: 11.9, p = 0.01) in patients with breast cancer. Subgroup analysis of ctDNA indicate that positive ctDNA in both preoperative/postoperative period and post op period only were found to have prognostic effect on RFS and OS (RFS; p < 0.0001, O·S; p = 0.0007). Moreover, ctDNA-based detection preceded clinical detection of recurrence in patients with an average lead time of 12 months (IQR:20-28.5 months) across all the breast cancer subtypes.

CONCLUSION

We highlighted the prognostic ability of ctDNA in patients with breast cancer in perioperative period. However, future prospective studies are needed to assess the utility of ctDNA in clinical practice.

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis

BACKGROUND

Breast cancer incidence has been on the rise significantly in the Asian population, occurring at an earlier age and a later stage. The potential predictive value of molecular subtypes, biomarkers, and genetic variations has not been deeply explored in the Asian population. This study evaluated the effect of molecular subtype classification and the presence or absence of biomarkers and genetic variations on pathological complete response (pCR) after neoadjuvant treatment in Asian breast cancer patients.

METHODS

A systematic search was conducted in MEDLINE (PubMed), Science Direct, Scopus, and Cochrane Library databases. Studies were selected if they included Asian breast cancer patients treated with neoadjuvant chemotherapy and contained data for qualitative or quantitative analyses. The quality of the included studies was assessed using the Newcastle Ottawa Scale. Following the random effects model, pooled odds ratios or hazard ratios with 95% confidence intervals for pCR were analysed using Review Manager Software. Heterogeneity between studies was assessed using Cochran's Q-test and I2 test statistics.

RESULTS

In total, 19,708 Asian breast cancer patients were pooled from 101 studies. In the neoadjuvant setting, taxane-anthracycline (TA) chemotherapy showed better pCR outcomes in triple-negative breast cancer (TNBC) (p<0.0001) and human epidermal growth factor receptor 2 enriched (HER2E) (p<0.0001) than luminal breast cancer patients. Similarly, taxane-platinum (TP) chemotherapy also showed better pCR outcomes in TNBC (p<0.0001) and HER2E (p<0.0001). Oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative, HER2-positive and high Ki-67 were significantly associated with better pCR outcomes when treated with either TA or TP. Asian breast cancer patients harbouring wildtype PIK3CA were significantly associated with better pCR outcomes when treated with TA in the neoadjuvant setting (p=0.001).

CONCLUSIONS

In the neoadjuvant setting, molecular subtypes (HER2E and TNBC), biomarkers (ER, PR, HER2, HR, Ki-67, nm23-H1, CK5/6, and Tau), and gene (PIK3CA) are associated with increased pCR rates in Asian breast cancer patients. Hence, they could be further explored for their possible role in first-line treatment response, which can be utilised to treat breast cancer more efficiently in the Asian population. However, it needs to be further validated with additional powered studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021246295.

Low-dose aspirin can inhibit exosomal release induced by radiotherapy in breast cancer and attenuate its inhibitory effect on NK cell proliferation

BACKGROUND

Breast cancer (BC) seriously threatens women's health. Aspirin plays a key role in the treatment and prognosis of BC.

OBJECTIVE

To explore the effect of low-dose aspirin on BC radiotherapy through the mechanism of exosomes and natural killer (NK) cells.

METHODS

BC cells were injected into the left chest wall to establish a BC model in nude mice. Tumor morphology and size were observed. Immunohistochemical staining for Ki-67 was used to observe the proliferation of tumor cells. TUNEL was used to detect the apoptosis of cancer cells. Protein levels of exosomal biogenesis- and secretion-related genes (Rab 11, Rab27a, Rab27b, CD63, and Alix) were detected by Western blot. Flow cytometry was used to detect apoptosis. Transwell assays were used to detect cell migration. A clonogenic assay was used to detect cell proliferation. Exosomes of BT549 and 4T1-Luc cells were extracted and observed by electron microscopy. After the coculture of exosomes and NK cells, the activity of NK cells was detected by CCK-8.

RESULTS

The protein expression of genes related to exosomal genesis and secretion (Rab 11, Rab27a, Rab27b, CD63, and Alix) in BT549 and 4T1-Luc cells was upregulated under radiotherapy treatment. Low doses of aspirin inhibited exosome release from BT549 and 4T1-Luc cells and alleviated the inhibitory effect of BC cell exosomes on NK cell proliferation. In addition, knocking down Rab27a reduced the protein levels of exosome-related and secretion-related genes in BC cells, further enhancing the promotive effect of aspirin on NK cell proliferation, while overexpressing Rab27a had the opposite effect. Aspirin was combined at a radiotherapeutic dose of 10 Gy to enhance the radiotherapy sensitivity of radiotherapy-tolerant BC cells (BT549R and 4T1-LucR). Animal experiments have also verified that aspirin can promote the killing effect of radiotherapy on cancer cells and significantly inhibit tumor growth.

CONCLUSION

Low doses of aspirin can inhibit the release of BC exosomes induced by radiotherapy and weaken their inhibition of NK cell proliferation, promoting radiotherapy resistance.

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy

Aim

The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer.

Methods

This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates.

Results

The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%.

Conclusions

The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

A Precise Approach for Radiotherapy of Breast Cancer

Radiotherapy is an integral part of the multidisciplinary management of breast cancer (BC). There have been multiple recent advances in the delivery of radiotherapy, reviewed with a critical discussion of the evidence from trials investigating adjuvant ultra-hypofractionation and partial breast irradiation for early-stage BC, and the locoregional management of lymph nodes in locally advanced BC. Multiple precision medicine-based approaches have been developed as prognostic and/or predictive for BC patients and identifying biomarkers of radioresistance could help identify patients that may benefit from dose-escalated radiotherapy or radiosensitizers. Radiotherapy after breast reconstruction is an area of current controversy in the field, and we evaluated the decision-making considerations in this situation. The oligometastatic state is an emerging field for many cancer sites based on recent trials investigating ablative radiotherapy for oligometastatic BC. This chapter is an overview of radiotherapy for BC, with a focus on recent advances in early-stage, locally advanced, and oligometastatic disease.

Circulation microRNA expression profiles in patients with complete responses to chemoradiotherapy in nasopharyngeal carcinoma

Background

Aims

Methods

Results

Conclusion

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

An Eleven-microRNA Signature Related to Tumor-Associated Macrophages Predicts Prognosis of Breast Cancer

The dysregulation of microRNAs (miRNAs) has been known to play important roles in tumor development and progression. However, the understanding of the involvement of miRNAs in regulating tumor-associated macrophages (TAMs) and how these TAM-related miRNAs (TRMs) modulate cancer progression is still in its infancy. This study aims to explore the prognostic value of TRMs in breast cancer via the construction of a novel TRM signature. Potential TRMs were identified from the literature, and their prognostic value was evaluated using 1063 cases in The Cancer Genome Atlas Breast Cancer database. The TRM signature was further validated in the external Gene Expression Omnibus GSE22220 dataset. Gene sets enrichment analyses were performed to gain insight into the biological functions of this TRM signature. An eleven-TRM signature consisting of mir-21, mir-24-2, mir-125a, mir-221, mir-22, mir-501, mir-365b, mir-660, mir-146a, let-7b and mir-31 was constructed. This signature significantly differentiated the high-risk group from the low-risk in terms of overall survival (OS)/ distant-relapse free survival (DRFS) (p value < 0.001). The prognostic value of the signature was further enhanced by incorporating other independent prognostic factors in a nomogram-based prediction model, yielding the highest AUC of 0.79 (95% CI: 0.72−0.86) at 5-year OS. Enrichment analyses confirmed that the differentially expressed genes were mainly involved in immune-related pathways such as adaptive immune response, humoral immune response and Th1 and Th2 cell differentiation. This eleven-TRM signature has great potential as a prognostic factor for breast cancer patients besides unravelling the dysregulated immune pathways in high-risk breast cancer.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

The Legend of ATP: From Origin of Life to Precision Medicine

Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life’s energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Purpose

We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.

Methods and Materials

Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.

Results

In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.

Conclusions

The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy

Purpose

To explore the association of genes in “PD-L1 expression and PD-1 check point pathway in cancer” to radiotherapy survival benefit.

Methods and materials

Gene expression data and clinical information of cancers were downloaded from TCGA. Radiotherapy survival benefit was defined based on interaction model. Fast backward multivariate Cox regression was performed using stacking multiple interpolation data to identify radio-sensitive (RS) genes.

Results

Among the 73 genes in PD-L1/PD-1 pathway, we identified 24 RS genes in BRCA data set, 25 RS genes in STAD data set and 20 RS genes in HNSC data set, with some crossover genes. Theoretically, there are two types of RS genes. The expression level of Type I RS genes did not affect patients' overall survival (OS), but when receiving radiotherapy, patients with different expression level of Type I RS genes had varied survival benefit. Oppositely, Type II RS genes affected patients' OS. And when receiving radiotherapy, those with lower OS could benefit a lot. Type II RS genes in BRCA had strong positive correlation and closely biological interactions. When performing cluster analysis using these related Type II RS genes, patients could be divided into RS group and non-RS group in BRCA and METABRIC data sets.

Conclusions

Our study explored potential radio-sensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway and revealed a strong association between this pathway and radiotherapy survival benefit. New types of RS genes could be identified based on expanded definition to RS genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01951-x.

A call to action: molecular pathology in Brazil

Background

Adoption of molecular pathology in Brazil is currently very limited. Of note, there are no programs for training new molecular pathologists in the country; thus, documents compiling nationally applicable information on molecular pathology are few.

Methods

A selected panel of Brazilian experts in fields related to molecular pathology were provided with a series of relevant questions to address prior to the multi-day conference. Within this conference, each narrative was discussed and edited by the entire group, through numerous drafts and rounds of discussion until a consensus was achieved.

Results

The panel proposes specific and realistic recommendations for implementing molecular pathology in cancer care in Brazil. In creating these recommendations, the authors strived to address all barriers to the widespread use and impediments to access mentioned previously within this manuscript.

Conclusion

This manuscript provides a review of molecular pathology principles as well as the current state of molecular pathology in Brazil. Additionally, the panel proposes practical and actionable recommendations for the implementation of molecular pathology throughout the country in order to increase awareness of the importance molecular pathology in Brazil.

Biomarkers for translational oncology - Peggy Olive's contribution

PURPOSE

Peggy Olive of the BC cancer research center (BCCRC), Vancouver, Canada, dedicated her career to improving the efficiency of radiation in the treatment of cancer. Keenly interested in the study of hypoxic cell radiosensitizers, she recognized the importance of DNA repair in improving the efficacy of radiotherapy. At the BCCRC she developed two methods for clinical practice that detect and quantitate DNA damage in mammalian cells. The alkaline comet assay and phosphorylated gamma histone H2AX (γH2AX) protein foci staining were two sensitive and attractive techniques that she attempted to apply in clinical practice.

CONCLUSION

Peggy Olive was able to establish the comet and the γH2AX assays as prospective predictive biomarkers in the application of personalized radiation treatment and improved cancer treatment outcomes. Nevertheless, several studies with a large number of samples are required before application of these biomarkers in routine radiotherapy could become a reality. The advent of 'omis' and microchip technologies envisage successful outcomes of future research in this direction.

Identification of Autophagy Related circRNA-miRNA-mRNA-Subtypes Network With Radiotherapy Responses and Tumor Immune Microenvironment in Non-small Cell Lung Cancer

Lung cancer (LC) is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide, and most LCs are non-small cell lung cancer (NSCLC). Radiotherapy is one of the most effective treatments for patients with lung cancer, either alone or in combination with other treatment methods. However, radiotherapy responses vary considerably among NSCLC patients. The efficacy of radiotherapy is influenced by several factors, among which autophagy is of importance. Autophagy is induced by radiotherapy and also influences cell responses to radiation. We explored the clinical significance of autophagy-related genes (ARGs) and gene sets (ARGSs) and the underlying mechanism in NSCLC patients treated with radiotherapy. First, differentially expressed ARGs (SNCA, SESN3, DAPL1, and ELAPOR1) and miRNAs (miR-205-5p, miR-26a-1-3p, miR-6510-3p, miR-194-3p, miR-215-5p, and miR-375-3p) were identified between radiotherapy-resistant and radiotherapy-sensitive groups. An autophagy-related radiosensitivity risk signature (ARRS) by nine ARmRNAs/miRNAs and an autophagy-related overall survival risk signature (AROS) by three ARmRNAs were then constructed with estimated AUCs of 0.8854 (95% CI: 0.8131–0.9576) and 0.7901 (95% CI: 0.7168–0.8685), respectively. The correlations between ARGSs or prognostic signatures and clinicopathological factors, short-term radiotherapy responses (radiotherapy sensitivity), long-term radiotherapy responses (overall survival), and immune characteristics were analyzed. Both ARGSs and prognostic signatures were related to immune checkpoint inhibitors (ICIs), infiltration of tumor-infiltrating immune cells (TIICs), and the activity of the cancer immune cycle. Finally, after target prediction and correlation analysis, circRNA (hsa_circ_0019709, hsa_circ_0081983, hsa_circ_0112354, hsa_circ_0040569, hsa_circ_0135500, and hsa_circ_0098966)-regulated miRNA/ARmRNA axes (miR-194-3p/SESN3, miR-205-5p/ELAPOR1, and miR-26a-1-3p/SNCA) were considered potential modulatory mechanisms by influencing the regulation of autophagy, macroautophagy, and chaperone-mediated autophagy.

Multiomics Differences in Lung Squamous Cell Carcinoma Patients with High Radiosensitivity Index Compared with Those with Low Radiosensitivity Index

Objectives

Radiosensitivity Index (RSI) can predict intrinsic radiotherapy sensitivity. We analyzed multiomics characteristics in lung squamous cell carcinoma between high and low RSI groups, which may help understand the underlying molecular mechanism of radiosensitivity and guide optional treatment for patients in the future.

Methods

The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data were used to download clinical data, mRNA, microRNA, and lncRNA expression. Differential analyses, including mRNA, miRNA, lncRNA, and G.O. and KEGG, and GSVA analyses, were performed with R. Gene set enrichment analysis was done by GSEA. miRNA-differentially expressed gene network and ceRNA network were analyzed and graphed by the Cytoscape software.

Results

In TCGA data, 542 patients were obtained, including 171 in the low RSI group (LRSI) and 371 in the high RSI group (HRSI). In RNAseq, 558 significantly differentially expressed genes (DEGs) were obtained. KRT6A was the most significantly upregulated gene and IDO1 was the most significantly downregulated gene. In miRNAseq, miR-1269a was the most significantly upregulated. In lncRNAseq, LINC01871 was the most upregulated. A 66-pair interaction between differentially expressed genes and miRNAs and an 11-pair interaction between differential lncRNAs and miRNAs consisted of a ceRNA network, of which miR-184 and miR-490-3p were located in the center. In the GEO data, there were 40 DEGs. A total of 17 genes were founded in both databases, such as ADAM23, AHNAK2, BST2, COL11A1, CXCL13, FBN2, IFI27, IFI44L, MAGEA6, and PTGR1. GSVA analysis revealed 31 significant pathways. GSEA found 87 gene sets enriched in HRSI and 91 gene sets in LRSI. G.O. and KEGG of RNA expression levels revealed that these genes were most enriched in T cell activation and cytokine-cytokine receptor interaction.

Conclusions

Patients with lung squamous cell carcinoma have different multiomics characteristics between two groups. These differences may have an essential significance with radiotherapy effect.

A Novel Approach for the Discovery of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy (RT) is an important treatment modality for the local control of breast cancer (BC). Unfortunately, not all patients that receive RT will obtain a therapeutic benefit, as cancer cells that either possess intrinsic radioresistance or develop resistance during treatment can reduce its efficacy. For RT treatment regimens to become personalised, there is a need to identify biomarkers that can predict and/or monitor a tumour's response to radiation. Here we describe a novel method to identify such biomarkers. Liquid chromatography-mass spectrometry (LC-MS) was used on conditioned media (CM) samples from a radiosensitive oestrogen receptor positive (ER⁺) BC cell line (MCF-7) to identify cancer-secreted biomarkers which reflected a response to radiation. A total of 33 radiation-induced secreted proteins that had higher (up to 12-fold) secretion levels at 24 h post-2 Gy radiation were identified. Secretomic results were combined with whole-transcriptome gene expression experiments, using both radiosensitive and radioresistant cells, to identify a signature related to intrinsic radiosensitivity. Gene expression analysis assessing the levels of the 33 proteins showed that 5 (YBX3, EIF4EBP2, DKK1, GNPNAT1 and TK1) had higher expression levels in the radiosensitive cells compared to their radioresistant derivatives; 3 of these proteins (DKK1, GNPNAT1 and TK1) underwent in-lab and initial clinical validation. Western blot analysis using CM samples from cell lines confirmed a significant increase in the release of each candidate biomarker from radiosensitive cells 24 h after treatment with a 2 Gy dose of radiation; no significant increase in secretion was observed in the radioresistant cells after radiation. Immunohistochemistry showed that higher intracellular protein levels of the biomarkers were associated with greater radiosensitivity. Intracellular levels were further assessed in pre-treatment biopsy tissues from patients diagnosed with ER⁺ BC that were subsequently treated with breast-conserving surgery and RT. High DKK1 and GNPNAT1 intracellular levels were associated with significantly increased recurrence-free survival times, indicating that these two candidate biomarkers have the potential to predict sensitivity to RT. We suggest that the methods highlighted in this study could be utilised for the identification of biomarkers that may have a potential clinical role in personalising and optimising RT dosing regimens, whilst limiting the administration of RT to patients who are unlikely to benefit.

Deciphering molecular mechanisms of metastasis: novel insights into targets and therapeutics

BACKGROUND

The transition of a primary tumour to metastatic progression is driven by dynamic molecular changes, including genetic and epigenetic alterations. The metastatic cascade involves bidirectional interactions among extracellular and intracellular components leading to disintegration of cellular junctions, cytoskeleton reorganization and epithelial to mesenchymal transition. These events promote metastasis by reprogramming the primary cancer cell's molecular framework, enabling them to cause local invasion, anchorage-independent survival, cell death and immune resistance, extravasation and colonization of distant organs. Metastasis follows a site-specific pattern that is still poorly understood at the molecular level. Although various drugs have been tested clinically across different metastatic cancer types, it has remained difficult to develop efficacious therapeutics due to complex molecular layers involved in metastasis as well as experimental limitations.

CONCLUSIONS

In this review, a systemic evaluation of the molecular mechanisms of metastasis is outlined and the potential molecular components and their status as therapeutic targets and the associated pre-clinical and clinical agents available or under investigations are discussed. Integrative methods like pan-cancer data analysis, which can provide clinical insights into both targets and treatment decisions and help in the identification of crucial components driving metastasis such as mutational profiles, gene signatures, associated pathways, site specificities and disease-gene phenotypes, are discussed. A multi-level data integration of the metastasis signatures across multiple primary and metastatic cancer types may facilitate the development of precision medicine and open up new opportunities for future therapies.

Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls. Personalized Interventions and Breast Cancer

Breast cancer, the most frequent malignancy in women worldwide, is a heterogeneous group of diseases, characterized by distinct molecular aberrations. In precision medicine, radiation oncology for breast cancer aims at tailoring treatment according to tumor biology and each patient’s clinical features and genetics. Although systemic therapies are personalized according to molecular sub-type [i.e. endocrine therapy for receptor-positive disease and anti-human epidermal growth factor receptor 2 (HER2) therapy for HER2-positive disease] and multi-gene assays, personalized radiation therapy has yet to be adopted in the clinical setting. Currently, attempts are being made to identify prognostic and/or predictive factors, biomarkers, signatures that could lead to personalized treatment in order to select appropriate patients who might, or might not, benefit from radiation therapy or whose radiation therapy might be escalated or de-escalated in dosages and volumes. This overview focuses on what has been achieved to date in personalized post-operative radiation therapy and individual patient radiosensitivity assessments by means of tumor sub-types and genetics.

Cytokines in various molecular subtypes of breast cancer

INTRODUCTION

Breast cancer is a heterogeneous disease that has multiple molecular and morphological subtypes. Nonetheless, the relation between various molecular subtypes and functional characteristics of a tumor in terms of cytokine secretion remains unknown.

METHODS

We studied spontaneous and mitogen-induced cytokine secretion by invasive breast carcinoma of no special type (IBC NST; cultured tumors and cultured peripheral blood cells), depending on a molecular tumor subtype (where "mitogens" means "polyclonal activators" (PA): phytohemagglutinin p, phytohemagglutinin M, concanavalin A, and Escherichia coli lipopolysaccharide). Enzyme-linked immunosorbent assays were used to determine concentrations of IL-6, IL-8, IL-10, IL-17, IL-18, IL-1β, IL-1Ra, TNF-α, IFN-γ, G-CSF, GM-CSF, VEGF, and MCP-1 in culture supernatants of the tumors and peripheral blood cells.

RESULTS

The luminal B HER2-positive molecular subtype of IBC NST was found to feature the highest spontaneous secretion of IL-6 and IL-8 and the highest mitogen-induced secretion of IL-6, IL-8, IL-1Ra, and TNF-α by tumors; the highest mitogen-induced secretion of IL-2, IL-6, IL-8, IL-1β, TNF-α, IFN-γ, and G-CSF by peripheral blood cells; and the highest cytokine-producing potential (the ratio of mitogen-induced to spontaneous secretion) of peripheral blood cells for the secretion of IL-6, IL-8, and IL-1Ra as compared to other molecular subtypes. The triple-negative subtype of IBC NST was characterized by the lowest cytokine-producing potential of tumors for the secretion of IL-6 and IL-8 as compared to other molecular subtypes as well as a lower "stimulation index of polyclonal activators" (calculated as (cytokine secretion after incubation with PA)/(spontaneous cytokine secretion)) for IL-18 secretion as compared to luminal subtypes. The XYZ correlated with a suppressive effect of PA on cytokine secretion by tumors of the triple-negative molecular subtype.

CONCLUSION

Therefore, our findings indicate that in IBC NST of luminal B HER2-positive and triple-negative molecular subtypes, the cytokine network has distinctive functional features.

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer

Breast cancer is the most common cancer in women worldwide. "Breast cancer" encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a "catch-all" term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.

Is It Possible to Personalize the Diagnosis and Treatment of Breast Cancer during Pregnancy?

The main goal of precision medicine in patients with breast cancer is to tailor the treatment according to the particular genetic makeup and the genetic changes in the cancer cells. Breast cancer occurring during pregnancy (BCP) is a complex and difficult clinical problem. Although it is not very common, both maternal and fetal outcome must be always considered when planning treatment. Pregnancy represents a significant barrier to the implementation of personalized treatment for breast cancer. Tailoring therapy mainly takes into account the stage of pregnancy, the subtype of cancer, the stage of cancer, and the patient’s preference. Results of the treatment of breast cancer in pregnancy are as yet not very satisfactory because of often delayed diagnosis, and it usually has an unfavorable outcome. Treatment of patients with pregnancy-associated breast cancer should be centralized. Centralization may result in increased experience in diagnosis and treatment and accumulated data may help us to optimize the treatment approaches, modify general treatment recommendations, and improve the survival and quality of life of the patients.