Prognostic and predictive biomarkers with therapeutic targets in breast cancer: A 2022 update on current developments, evidence, and recommendations.

OBJECTIVE: To evaluate and validate the recent and emerging data for prognostic and predictive biomarkers with therapeutic targets in breast cancer. DATA SOURCES: A literature search from January 2015 to March 2022 was performed using the key terms breast cancer, clinical practice guidelines, gene mutations, genomic assay, immune cancer therapy, predictive and/or prognostic biomarkers, and targeted therapies. STUDY SELECTION AND DATA EXTRACTION: Relevant clinical trials, meta-analyses, seminal articles, and published evidence- and consensus-based clinical practice guidelines in the English language were identified, reviewed and evaluated. DATA SYNTHESIS: Breast cancer is a biologically heterogeneous disease, leading to wide variability in treatment responses and survival outcomes. Biomarkers for breast cancer are evolving from traditional biomarkers in immunohistochemistry (IHC) such as estrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor type 2 (HER2) to genetic biomarkers with therapeutic implications (e.g. breast cancer susceptibility gene 1/2 [BRCA1/2], estrogen receptor α [ESR1] gene mutation, HER2 gene mutation, microsatellite instability [MSI], phosphatidylinositol 3-kinase catalytic subunit 3Cα [PIK3CA] gene mutation, neurotrophic tyrosine receptor kinase [NTRK] gene mutation). In addition, current data are most robust for biomarkers in immunotherapy (e.g. programmed cell death receptor ligand-1 [PD-L1], microsatellite instability-high [MSI-H] or deficient mismatch repair [dMMR]). Oncotype DX assay remains the best validated gene expression assay that is both predictive and prognostic whereas MammaPrint is prognostic for genomic risk. CONCLUSIONS: Biomarker-driven therapies have the potential to confer greater therapeutic advantages than standard-of-care therapies. The purported survival benefits associated with biomarker-driven therapies should be weighed against their potential harms.

The radiobiological effect of using Acuros XB vs anisotropic analytical algorithm on hepatocellular carcinoma stereotactic body radiation therapy.

The purpose of this work was to study the radiobiological effect of using Acuros XB (AXB) vs Analytic Anisotropic Algorithm (AAA) on hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT). Seventy SBRT volumetric modulated arc therapy (VMAT) plans for HCC were calculated using AAA and AXB respectively with the same treatment parameters. Published tumor control probability (TCP) and normal tissue complication probability (NTCP) models were used to quantify the effect of dosimetric difference between AAA and AXB on TCP, NTCP and uncomplicated tumor control probability (UTCP). There was an average decrease of 2.5% in 6-month TCP. Normal liver has the largest average decrease in NTCP which was 59.7%. Bowels followed with 26.6% average decrease in NTCP. Duodenum, stomach and esophagus had 10.2%, 5.1%, and 4.3% average decrease in NTCP. There was an average decrease of 1.8% and up to 7.2% in 6-month UTCP. There was an overall decrease in TCP, NTCP, and UTCP for HCC SBRT plans calculated using AXB compared to AAA which could be clinically significant.

Analysis of Hepatocellular Carcinoma Stereotactic Body Radiation Therapy Dose Prescription Method Using Uncomplicated Tumor Control Probability Model.

PURPOSE: This work was to establish an uncomplicated tumor control probability (UTCP) model using hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT) clinical data in our institution. The model was then used to analyze the current dose prescription method and to seek the opportunity for improvement. METHODS AND MATERIALS: A tumor control probability (TCP) model was generated based on local clinical data using the maximum likelihood method. A UTCP model was then formed by combining the established TCP model with the normal tissue complication probability model based on the study by Dawson et al. The authors investigated the dependence of maximum achievable UTCP on planning target volume equivalent uniform dose (EUD) at various ratio between planning target volume EUD and normal liver EUD (T/N EUD ratios). A new term uncomplicated tumor control efficiency (UTCE) was also introduced to analyze the outcome. A UTCE value of 1 implied that the theoretical maximum UTCP for the corresponding T/N EUD ratio was achieved. RESULTS: The UTCE of the HCC SBRT patients based on the current dose prescription method was found to be 0.93 ± 0.05. It was found that the UTCE could be increased to 0.99 ± 0.03 by using a new dose prescription scheme, for which the UTCP could be maximized while keeping the normal tissue complication probability value smaller than 5%. CONCLUSIONS: The dose prescription method of the current HCC SBRT in our institution was analyzed using a UTCP model established based on local clinical data. It was shown that there could be a potential to increase the prescription dose of HCC SBRT. A new dose prescription scheme was proposed to achieve better UTCP. Additional clinical trials would be required to validate the proposed dose prescription scheme in the future.