A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation

Genomically Guided Breast Radiation Therapy: A Review of the Current Data and Future Directions

Purpose

To highlight the current evidence and the limitations in data to support a personalized approach in breast oncology radiation therapy management and define steps needed for clinical implementation.

Methods and Materials

A critical review of the current literature on the use of genomics in breast radiation therapy was undertaken by a group of breast radiation oncologists to discuss current data, future directions, and challenges.

Results

A summary of the existing data, ongoing clinical trials, and future directions is provided. The authors note many groups have developed radiation-specific genomic assays, which demonstrate promise in prediction of local control and benefit from radiation therapy; however, prospective validation of their utility is needed. Limitations continue to exist in our understanding of tumor biology and how it can be integrated into clinical practice.

Conclusions

Given the relative ubiquity of breast radiation therapy, the variety of dose and fractionation approaches, and the current data to support a personalized approach, it is our belief that the delivery of breast radiation therapy is uniquely poised for a genomically personalized radiation therapy approach. Prospective clinical trials implementing genomic signatures are needed at this time to advance the field.

Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis

BACKGROUND

Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm.

METHODS

We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic.

FINDINGS

Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97-0·99]; p=0·0017) and overall survival (0·97 [0·95-0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97-1·01; p=0·53) for time to first recurrence and 1·00 (0·96-1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97-1·03; p=1·00) for time to first recurrence and 1·00 (0·98-1·02; p=0·87) for overall survival.

INTERPRETATION

The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose.

FUNDING

None. VIDEO ABSTRACT.

Screening and Validation of Molecular Targeted Radiosensitizers

The development of molecular targeted drugs with radiation and chemotherapy are critically important for improving the outcomes of patients with hard-to-treat, potentially curable cancers. However, too many preclinical studies have not translated into successful radiation oncology trials. Major contributing factors to this insufficiency include poor reproducibility of preclinical data, inadequate preclinical modeling of inter-tumoral genomic heterogeneity that influences treatment sensitivity in the clinic, and a reliance on tumor growth delay instead of local control (TCD50) endpoints. There exists an urgent need to overcome these barriers to facilitate successful clinical translation of targeted radiosensitizers. To this end, we have employed 3D cell culture assays to better model tumor behavior in vivo. Examples of successful prediction of in vivo effects with these 3D assays include radiosensitization of head and neck cancers by inhibiting epidermal growth factor receptor or focal adhesion kinase signaling, and radioresistance associated with oncogenic mutation of KRAS. To address the issue of tumor heterogeneity we leveraged institutional resources that allow high-throughput 3D screening of radiation combinations with small molecule inhibitors across genomically characterized cell lines from lung, head and neck, and pancreatic cancers. This high-throughput screen is expected to uncover genomic biomarkers that will inform the successful clinical translation of targeted agents from the NCI CTEP portfolio and other sources. Screening "hits" need to be subjected to refinement studies that include clonogenic assays, addition of disease-specific chemotherapeutics, target/biomarker validation, and integration of patient-derived tumor models. The chemoradiosensitizing activities of the most promising drugs should be confirmed in TCD50 assays in xenograft models with/without relevant biomarker and utilizing clinically relevant radiation fractionation. We predict that appropriately validated and biomarker-directed targeted therapies will have a higher likelihood than past efforts to be successfully incorporated into the standard management of hard-to-treat tumors.

A 25-year perspective on the evolution of radiation treatment of urologic cancers

Advances in radiotherapy technology and technique over the last 3 decades have revolutionized radiation treatment options for genitourinary malignancies. The development of more focused and accurate radiation treatment has facilitated safe delivery of dose-escalated treatment that improves disease control and the development of shorter-duration hypofractionated treatment regimens that are more convenient for patients and improve access to treatment. The management of oligometastatic disease is evolving with ablative treatment of oligometastasis and the primary for select patients and shorter-duration palliative treatment regimens. Work is ongoing to personalize radiation treatment regimens for genitourinary malignancies based on molecular biomarkers.

Genomic identification of sarcoma radiosensitivity and the clinical implications for radiation dose personalization

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Soft tissue sarcomas have traditionally been treated with a one-size fits all approach, despite a wide range of histologies and clinical outcomes.

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The radiosensitivity index has demonstrated that soft tissue sarcomas are in general radioresistant, however exhibit a wide range of radiosensitivity.

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These differences in radiosensitivity are associated with decreased locoregional control in patients with radioresistant histologies.

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Using the radiosensitivity index we identify specific histologies of soft tissue sarcoma that may be more radioresistant, and suggest a genomic-based radiation dosing framework.

Background

Soft-tissue sarcomas (STS) are heterogeneous with variable response to radiation therapy (RT). Utilizing the radiosensitivity index (RSI) we estimated the radiobiologic ratio of lethal to sublethal damage (α/β), genomic-adjusted radiation dose(GARD), and in-turn a biological effective radiation dose (BED).

Methods

Two independent cohorts of patients with soft-tissue sarcoma were identified. The first cohort included 217 genomically-profiled samples from our institutional prospective tissue collection protocol; RSI was calculated for these samples, which were then used to dichotomize the population as either highly radioresistant (HRR) or conventionally radioresistant (CRR). In addition, RSI was used to calculate α/β ratio and GARD, providing ideal dosing based on sarcoma genomic radiosensitivity. A second cohort comprising 399 non-metastatic-STS patients treated with neoadjuvant RT and surgery was used to validate our findings.

Results

Based on the RSI of the sample cohort, 84% would historically be considered radioresistant. We identified a HRR subset that had a significant difference in the RSI, and clinically a lower tumor response to radiation (2.4% vs. 19.4%), 5-year locoregional-control (76.5% vs. 90.8%), and lower estimated α/β (3.29 vs. 5.98), when compared to CRR sarcoma. Using GARD, the dose required to optimize outcome in the HRR subset is a BED_α/β=3.29 of 97 Gy.

Conclusions

We demonstrate that on a genomic scale, that although STS is radioresistant overall, they are heterogeneous in terms of radiosensitivity. We validated this clinically and estimated an α/β ratio and dosing that would optimize outcome, personalizing dose.

Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy

In the era of immunotherapy, there lacks of a reliable genomic predictor to identify optimal patient populations in combined radiotherapy and immunotherapy (CRI). The purpose of this study is to investigate whether genomic scores defining radiosensitivity are associated with immune response. Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, radiosensitivity index (RSI) was calculated. A total of 12832 primary tumours across 11 major cancer types were analysed for the association with DNA repair, cellular stemness, macrophage polarisation, and immune subtypes. Additional 585 metastatic tissues were extracted from MET500. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Proteomic differential analysis was used to identify significant proteins according to RSI categories. Gene Set Variance Analysis (GSVA) was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. RSI was significantly associated with homologous DNA repair, cancer stemness and immune-related molecular features. Lower RSI was associated with higher fraction of M1 macrophage. Differential proteomic analysis identified significantly higher TAP2 expression in RSI-Low colorectal tumours. In the TCGA cohort, dominant interferon-γ (IFN-γ) response was characterised by low RSI and predicted better response to programmed cell death 1 (PD-1) blockade. In conclusion, in addition to radiation response, our study identified RSI to be associated with various immune-related features and predicted response to PD-1 blockade, thus, highlighting its potential as a candidate biomarker for CRI.

Predictive value of transcriptional expression of Krüppel-like factor-6 (KLF6) in head and neck carcinoma patients treated with radiotherapy

PURPOSE

To analyse the relationship between the transcriptional expression of Krüppel-like factor-6 (KLF6) and local response to treatment with radiotherapy in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

We determined the transcriptional expression of KLF6 in tumour biopsies obtained before treatment with radiotherapy in 83 HNSCC patients. The KLF6 expression was categorized according to the local control of the disease with a recursive partitioning analysis.

RESULTS

During the follow-up period, 27 patients (32.5%) had a local recurrence of the tumour. Patients with local recurrence had significantly higher levels of KLF6 expression than patients in which radiotherapy achieved local control of the disease (P = 0.029). Five-year local recurrence-free survival for patients with a high transcriptional expression of KLF6 (n = 46) was 51.1% (95% CI 36.4-66.2%), and for patients with low expression it was 85.6% (95% CI 73.9-97.3%) (P = 0.0001). The results of a multivariate analysis showed that patients with a high KLF6 expression had a 3.8 times higher risk of local recurrence after treatment with radiotherapy (95% CI 1.4-10.5, P = 0.008).

CONCLUSION

Transcriptional expression of KLF6 was significantly related to local control in HNSCC patients treated with radiotherapy. Patients with high levels of KLF6 expression had a significantly higher risk of local recurrence after treatment.

Prognostic and Predictive Factors in Advanced Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease arising from the mucosa of the upper aerodigestive tract. Despite multimodality treatments approximately half of all patients with locally advanced disease relapse and the prognosis of patients with recurrent or metastatic HNSCC is dismal. The introduction of checkpoint inhibitors improved the treatment options for these patients and pembrolizumab alone or in combination with a platinum and fluorouracil is now the standard of care for first-line therapy. However, approximately only one third of unselected patients respond to this combination and the response rate to checkpoint inhibitors alone is even lower. This shows that there is an urgent need to improve prognostication and prediction of treatment benefits in patients with HNSCC. In this review, we summarize the most relevant risk factors in the field and discuss their roles and limitations. The human papilloma virus (HPV) status for patients with oropharyngeal cancer and the combined positive score are the only biomarkers consistently used in clinical routine. Other factors, such as the tumor mutational burden and the immune microenvironment have been highly studied and are promising but need validation in prospective trials.

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.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

Long non-coding RNAs (lncRNAs) are involved in diverse biological processes, including DNA damage repair, and are of interest as potential biomarkers of radiosensitivity. We investigated whether lncRNA radiosensitivity signatures could be derived for use in cancer patients treated with radiotherapy. Signature development involved radiosensitivity measurements for cell lines and primary tumor samples, and patient outcome after radiotherapy. A 10-lncRNA signature trained on radiosensitivity measurements in bladder cell lines showed a trend towards independent validation. In multivariable analyses, patients with tumors classified as radioresistant by the lncRNA signature had poorer local relapse-free survival (P = 0.065) in 151 patients with muscle-invasive bladder cancer who underwent radiotherapy. An mRNA-based radiosensitivity index signature performed similarly to the lncRNA bladder signature for local relapse-free survival (P = 0.055). Pathway analysis showed the lncRNA signature associated with molecular processes involved in radiation responses. Knockdown of one of the lncRNAs in the signature showed a modest increase in radiosensitivity in one cell line. An alternative approach involved training on primary cervical tumor radiosensitivity or local control after radiotherapy. Both approaches failed to generate a cervix lncRNA radiosensitivity signature, which was attributed to the age of samples in our cohorts. Our work highlights challenges in validating lncRNA signatures as biomarkers in archival tissue from radiotherapy cohorts, but supports continued investigation of lncRNAs for a role in radiosensitivity.

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.

A gene expression signature for predicting response to neoadjuvant chemoradiotherapy in pancreatic ductal adenocarcinoma

In patients with pancreatic ductal adenocarcinoma (PDAC), optimal treatment selection, including multimodality regimens such as neoadjuvant chemoradiotherapy (NACRT), can be clinically transformative. Unfortunately, currently no predictive biomarkers are available that can guide the use of NACRT in PDAC patients. Accordingly, herein we developed a novel gene signature that can preoperatively predict NACRT-sensitivity in PDAC patients. Herein, we evaluated the performance of a 10-gene panel in 749 PDAC cases, which included two public datasets (The Cancer Genome Atlas and International Cancer Genome Consortium; n = 276), and three clinical specimen cohorts (n = 417), and a pre-NACRT endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy cohort (n = 56). The potential predictive performance of this signature was evaluated and compared to CA-19-9 levels and key clinicopathological factors. We first evaluated the prognostic potential of a 10-gene panel which significantly predicted overall survival in both public datasets (P < .01, P < .01), and two in-house patient cohorts (P < .01, P = .04). In the pre-NACRT EUS-FNA cohort, we established a radio-sensitivity gene panel (RSGP) which yielded highly robust (area under the curve [AUC] = 0.91; 95% CI: 0.81-0.97) for predicting response to gemcitabine-based NACRT. Multivariate logistic regression analysis revealed that RSGP was an independent predictor for response to NACRT (OR = 2.70; 95% CI: 1.25-5.85), and this response-prediction was even more robust when CA-19-9 levels were included into the model. In conclusion, we have validated and developed a novel gene signature that is highly robust in predicting response to NACRT, even in preoperative settings, highlighting its clinical significance for optimizing and personalizing treatment strategies in PDAC patients.

Prediction of Response in Head and Neck Tumor: Focus on Main Hot Topics in Research

Radiation therapy is a cornerstone in the treatment of head and neck cancer patients; actually, their management is based on clinical and radiological staging with all patients at the same stage treated in the same way. Recently the increasing knowledge in molecular characterization of head and neck cancer opens the way for a more tailored treatment. Patient outcomes could be improved by a personalized radiotherapy beyond technological and anatomical precision. Several tumor markers are under evaluation to understand their possible prognostic or predictive value. In this paper we discuss those markers specific for evaluate response to radiation therapy in head and neck cancer for a shift toward a biological personalization of radiotherapy.

Radiotherapy for unresectable locally advanced non-small cell lung cancer: a narrative review of the current landscape and future prospects in the era of immunotherapy

Significant recent advances have occurred in the use of radiation therapy for locally advanced non-small cell lung cancer (LA-NSCLC). In fact, the past few decades have seen both therapeutic gains and setbacks in the evolution of radiotherapy for LA-NSCLC. The PACIFIC trial has heralded a new era of immunotherapy and has raised important questions for future study, such as the future directions of radiation therapy for LA-NSCLC in the era of immunotherapy. Modern radiotherapy techniques such as three-dimensional (3D) conformal radiotherapy and intensity-modulated radiotherapy (IMRT) provide opportunities for improved target conformity and reduced normal-tissue exposure. However, the low-dose radiation volume brought by IMRT and its effects on the immune system deserve particular attention when combing radiotherapy and immunotherapy. Particle radiotherapy offers dosimetric advantages and exhibits great immunoregulatory potential. With the ongoing improvement in particle radiotherapy techniques and knowledge, the combination of immunotherapy and particle radiotherapy has tremendous potential to improve treatment outcomes. Of particular importance are questions on the optimal radiation schedule in the settings of radio-immunotherapy. Strategies for the reduction of the irradiated field such as involved-field irradiation (IFI) and omission of clinical target volume (CTV) hold promise for better preservation of immune function while not compromising locoregional and distant control. In addition, different dose-fractionation regimens can have diverse effects on the immune system. Thus, prospective trials are urgently needed to establish the optimal dose fractionation regimen. Moreover, personalized radiotherapy which allows the tailoring of radiation dose to each individual's genetic background and immune state is of critical importance in maximizing the benefit of radiation to patients with LA-NSCLC.

Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project

Background

Despite advances in treatments, 30% to 50% of stage III‐IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.

Methods

Stage III‐IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively.

Results

The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow‐up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both.

Conclusions

This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role.

Gene Expression Profiling Identifies Akt as a Target for Radiosensitization in Gastric Cancer Cells

Background

Despite the important role of radiotherapy in cancer treatment, a subset of patients responds poorly to treatment majorly due to radioresistance. Particularly the role of radiotherapy has not been established in gastric cancer (GC). Herein, we aimed to identify a radiosensitivity gene signature and to discover relevant targets to enhance radiosensitivity in GC cells.

Methods

An oligonucleotide microarray (containing 22,740 probes) was performed in 12 GC cell lines prior to radiation. A clonogenic assay was performed to evaluate the survival fraction at 2 Gy (SF2) as a surrogate marker for radiosensitivity. Genes differentially expressed (fold change > 6, q-value < 0.025) were identified between radiosensitive and radioresistant cell lines, and quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was performed for validation. Gene set and pathway analyses were performed using Ingenuity Pathway Analysis (IPA).

Results

Radiosensitive (SF2 < 0.4) and radioresistant cell lines (SF2 ≥ 0.6) exhibited a marked difference in gene expression. We identified 68 genes that are differentially expressed between radiosensitive and radioresistant cell lines. The identified genes showed interactions via AKT, HIF1A, TGFB1, and TP53, and their functions were associated with the genetic networks associated with cellular growth and proliferation, cellular movement, and cell cycle. The Akt signaling pathway exhibited the highest association with radiosensitivity. Combinatorial treatment with MK-2206, an allosteric Akt inhibitor, and radiotherapy significantly increased cell death compared with radiotherapy alone in two radioresistant cell lines (YCC-2 and YCC-16).

Conclusion

We identified a GC-specific radiosensitivity gene signature and suggest that the Akt signaling pathway could serve as a therapeutic target for GC radiosensitization.

Comparative Genomics Reveals Distinct Immune-oncologic Pathways in African American Men with Prostate Cancer

PURPOSE

The role of immune-oncologic mechanisms of racial disparities in prostate cancer remains understudied. Limited research exists to evaluate the molecular underpinnings of immune differences in African American men (AAM) and European American men (EAM) prostate tumor microenvironment (TME).

EXPERIMENTAL DESIGN

A total of 1,173 radiation-naïve radical prostatectomy samples with whole transcriptome data from the Decipher GRID registry were used. Transcriptomic expressions of 1,260 immune-specific genes were selected to assess immune-oncologic differences between AAM and EAM prostate tumors. Race-specific differential expression of genes was assessed using a rank test, and intergene correlational matrix and gene set enrichment was used for pathway analysis.

RESULTS

AAM prostate tumors have significant enrichment of major immune-oncologic pathways, including proinflammatory cytokines, IFNα, IFNγ, TNFα signaling, ILs, and epithelial-mesenchymal transition. AAM TME has higher total immune content score (ICS^HIGH) compared with 0 (37.8% vs. 21.9%, P = 0.003). AAM tumors also have lower DNA damage repair and are genomically radiosensitive as compared with EAM. IFITM3 (IFN-inducible transmembrane protein 3) was one of the major proinflammatory genes overexpressed in AAM that predicted increased risk of biochemical recurrence selectively for AAM in both discovery [HR_AAM = 2.30; 95% confidence interval (CI), 1.21-4.34; P = 0.01] and validation (HR_AAM = 2.42; 95% CI, 1.52-3.86; P = 0.0001) but not in EAM.

CONCLUSIONS

Prostate tumors of AAM manifest a unique immune repertoire and have significant enrichment of proinflammatory immune pathways that are associated with poorer outcomes. Observed immune-oncologic differences can aid in a genomically adaptive approach to treating prostate cancer in AAM.

Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy

Peptide receptor radionuclide therapy (PRRT) is a type of radiotherapy that targets peptide receptors and is typically used for neuroendocrine tumours (NETs). Some of the key challenges in its use are the prediction of efficacy and toxicity, patient selection, and response optimisation. In this Review, we assess current knowledge on the molecular profile of NETs and the strategies and tools used to predict, monitor, and assess the toxicity of PRRT. The few mutations in tumour genes that can be evaluated (eg, ATM and DAXX) are limited to pancreatic NETs and are most likely not informative. Assays that are transcriptomic or based on genes are effective in the prediction of radiotherapy response in other cancers. A blood-based assay for eight genes (the PRRT prediction quotient [PPQ]) has an overall accuracy of 95% for predicting responses to PRRT in NETs. No molecular markers exist that can predict the toxicity of PRRT. Candidate molecular targets include seven single nucleotide polymorphisms (SNPs) that are susceptible to radiation. Transcriptomic evaluations of blood and a combination of gene expression and specific SNPs, assessed by machine learning with algorithms that are tumour-specific, might yield molecular tools to enhance the efficacy and safety of PRRT.

Prognostic potential of mid-treatment nodal response in oropharyngeal squamous cell carcinoma

BACKGROUND

We examine the prognostic implications of mid-course nodal response in oropharyngeal cancer (OPX) to radiation therapy.

METHODS

In 44 patients with node-positive OPX undergoing concurrent chemoradiation, nodal volumes were measured on cone beam CTs from days 1, 10, 20, and 35. Nodal decrease (ND) was based on percent shrinkage from day 1.

RESULTS

At a median follow-up of 17 months, the 2-year disease-free survival (DFS), locoregional control (LRC), distant metastasis-free survival (DMFS), and overall survival (OS) were 87%, 92%, 89%, and 92%, respectively. Patients with ND ≥43% at D20 had improved LRC (100% vs 78.4%, P = .03) compared to D20 ND <43%. On multivariate analysis, D20 ≥43% was independently prognostic for LRC (HR 1.17, P = .05).

CONCLUSION

Patients with low-risk oropharynx cancer with ND of ≥43% by treatment day 20 had significantly improved LRC. The prognostic benefit of ND may assist in identifying candidates for treatment de-escalation.

Emerging radiotherapy technologies and trends in nasopharyngeal cancer

Technology has always driven advances in radiotherapy treatment. In this review, we describe the main technological advances in radiotherapy over the past decades for the treatment of nasopharyngeal cancer (NPC) and highlight some of the pressing issues and challenges that remain. We aim to identify emerging trends in radiation medicine. These include advances in personalized medicine and advanced imaging modalities, standardization of planning and delineation, assessment of treatment response and adaptive re‐planning, impact of particle therapy, and role of artificial intelligence or automation in clinical care. In conclusion, we expect significant improvement in the therapeutic ratio of radiotherapy treatment for NPC over the next decade.

Precision radiotherapy for non-small cell lung cancer

Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information.

Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics.

This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.

Modeling precision genomic-based radiation dose response in rectal cancer

Aim: Genomic-based risk stratification to personalize radiation dose in rectal cancer. Patients & methods: We modeled genomic-based radiation dose response using the previously validated radiosensitivity index (RSI) and the clinically actionable genomic-adjusted radiation dose. Results: RSI of rectal cancer ranged from 0.19 to 0.81 in a bimodal distribution. A pathologic complete response rate of 21% was achieved in tumors with an RSI <0.31 at a minimal genomic-adjusted radiation dose of 29.76 when modeling RxRSI to the commonly prescribed physical dose of 50 Gy. RxRSI-based dose escalation to 55 Gy in tumors with an RSI of 0.31-0.34 could increase pathologic complete response by 10%. Conclusion: This study provides a theoretical platform for development of an RxRSI-based prospective trial in rectal cancer.

Radiation therapy in the management of the inguinal region in penile cancer: What's the evidence?

Due to its rarity and lack of prospective studies, clinical evidence for the management of the inguinal lymphatic nodal basin with radiation therapy in penile cancer (PeCa) has been limited. In this report, we review the current literature and further investigated the landscape of radiation sensitivity in nodal metastases of PeCa utilizing our well-established genome-based radiosensitivity index (RSI) platform. We hypothesized that optimal therapeutic gain could be achieved in PeCa stratified by the combination of clinicopathological parameters, genomic heterogeneity, and RSI-based radiation dose prescription (RxRSI). Similar to primary PeCa lesions, we found that the majority of PeCa nodal metastases are genomically radioresistant with significant heterogeneity. RxRSI should be considered to inform and optimize the radiation therapy dose prescription to the individual tumor biology.

Prediction of Outcomes with a Computational Biology Model in Newly Diagnosed Glioblastoma Patients Treated with Radiation Therapy and Temozolomide

PURPOSE

Precision medicine has been most successful in targeting single mutations, but personalized medicine using broader genomic tumor profiles for individual patients is less well developed. We evaluate a genomics-informed computational biology model (CBM) to predict outcomes from standard treatments and to suggest novel therapy recommendations in glioblastoma (GBM).

METHODS AND MATERIALS

In this retrospective study, 98 patients with newly diagnosed GBM undergoing surgery followed by radiation therapy and temozolomide at a single institution with available genomic data were identified. Incorporating mutational and copy number aberration data, a CBM was used to simulate the response of GBM tumor cells and generate efficacy predictions for radiation therapy (RTeff) and temozolomide (TMZeff). RTeff and TMZeff were evaluated for association with overall survival and progression-free survival in a Cox regression model. To demonstrate a CBM-based individualized therapy strategy, treatment recommendations were generated for each patient by testing a panel of 45 central nervous system-penetrant US Food and Drug Administration-approved agents.

RESULTS

High RTeff scores were associated with longer survival on univariable analysis (P < .001), which persisted after controlling for age, extent of resection, performance status, MGMT, and IDH status (P = .017). High RTeff patients had a longer overall survival compared with low RTeff patients (median, 27.7 vs 14.6 months). High TMZeff was also associated with longer survival on univariable analysis (P = .007) but did not hold on multivariable analysis, suggesting an interplay with MGMT status. Among predictions of the 3 most efficacious combination therapies for each patient, only 2.4% (7 of 294) of 2-drug recommendations produced by the CBM included TMZ.

CONCLUSIONS

CBM-based predictions of RT and TMZ effectiveness were associated with survival in patients with newly diagnosed GBM treated with those therapies, suggesting a possible predictive utility. Furthermore, the model was able to suggest novel individualized monotherapies and combinations. Prospective evaluation of such a personalized treatment strategy in clinical trials is needed.

Disease course of lung oligometastatic colorectal cancer treated with stereotactic body radiotherapy

PURPOSE

Stereotactic body radiotherapy (SBRT) or stereotactic ablative radiotherapy (SABR) has been shown to increase survival rates in oligometastatic disease (OMD), but local control of colorectal metastases remains poor. We aimed to explore the natural course of oligometastatic colorectal cancer and to investigate how SBRT of lung metastases can delay the progression to polymetastatic disease (PMD).

METHODS

107 lung oligometastases in 38 patients were treated with SBRT at a single institution. The median number of treated lesions was 2 (range 1-5). Time to PMD (ttPMD) was defined as the time from SBRT to the occurrence of >5 new metastases. Genetic biomarkers such as EGFR, KRAS, NRAS, BRAF, and microsatellite instability were investigated as predictive factors for response rates.

RESULTS

Median follow-up was 28 months. At median follow-up, 7 patients were free from disease and 31 had progression: 18 patients had sequential oligometastatic disease (SOMD) and 13 polymetastatic progression. All SOMD cases received a second SBRT course. Median progression-free survival (PFS) was 7 months (range 4-9 months); median ttPMD was 25.8 months (range 12-39 months) with 1‑ and 2‑year PFS rates of 62.5% and 53.4%, respectively. 1‑ and 2‑year local PFS (LPFS) rates were 91.5% and 80%, respectively. At univariate analysis, BRAF wildtype correlated with better LPFS (p = 0.003), SOMD after primary SBRT was associated with longer cancer-specific survival (p = 0.031). Median overall survival (OS) was 39.5 months (range 26-64 months) and 2‑year OS was 71.1%.

CONCLUSION

The present results support local ablative treatment of lung metastases using SBRT in oligometastatic colorectal cancer patients, as it can delay the transition to PMD. Patients who progressed as SOMD maintained a survival advantage compared to those who developed PMD.

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

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers

Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets.

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT-sensitive and chemotherapy-sensitive), RSCR (RT-sensitive and chemotherapy-resistant), RRCS (RT-resistant and chemotherapy-sensitive) and RRCR (RT-resistant and chemotherapy-resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

Mathematical oncology and it's application in non melanoma skin cancer - A primer for radiation oncology professionals

Cancers of the skin (the majority of which are basal and squamous cell skin carcinomas, but also include the rarer Merkel cell carcinoma) are overwhelmingly the most common of all types of cancer. Most of these are treated surgically, with radiation reserved for those patients with high risk features or anatomical locations less suitable for surgery. Given the high incidence of both basal and squamous cell carcinomas, as well as the relatively poor outcome for Merkel cell carcinoma, it is useful to investigate the role of other disciplines regarding their diagnosis, staging and treatment. Mathematical modelling is one such area of investigation. The use of mathematical modelling is a relatively recent addition to the armamentarium of cancer treatment. It has long been recognised that tumour growth and treatment response is a complex, non-linear biological phenomenon with many mechanisms yet to be understood. Despite decades of research, including clinical, population and basic science approaches, we continue to be challenged by the complexity, heterogeneity and adaptability of tumours, both in individual patients in the oncology clinic and across wider patient populations. Prospective clinical trials predominantly focus on average outcome, with little understanding as to why individual patients may or may not respond. The use of mathematical models may lead to a greater understanding of tumour initiation, growth dynamics and treatment response.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

A systematic review and meta-analysis of stereotactic body radiation therapy for colorectal pulmonary metastases

Background

There is growing evidence to support the hypothesis that radical treatment of pulmonary oligometastatic disease with stereotactic body radiation therapy (SBRT) can improve oncological outcomes. However, some reports suggest colorectal cancer (CRC) pulmonary metastases are associated with radioresistance. The present systematic review aimed to assess the local control (LC), overall survival (OS), and progression-free survival (PFS) of patients with CRC pulmonary metastases treated by SBRT. Secondary outcomes included assessment of peri-procedural complications and identification of prognostic factors on LC.

Methods

Electronic databases were systematically searched from their dates of inception using predefined criteria. Summative statistical analysis was performed for patients with CRC pulmonary metastases, and comparative meta-analysis was performed for patients with CRC versus non-CRC pulmonary metastases.

Results

Using predefined criteria, 18 relevant studies were identified from the existing literature. LC for CRC pulmonary metastases treated by SBRT at 1-, 2-, and 3-year were estimated to be 81%, 66%, and 60%, respectively. OS and PFS at 3-year were 52% and 13%, respectively. Patients with CRC pulmonary metastases were associated with significantly lower LC compared to non-CRC pulmonary metastases [HR, 2.93; 95% confidence interval (CI), 1.93-4.45; P<0.00001], but higher OS (HR, 0.61; 95% CI, 0.45-0.82; P=0.001). There were no reported periprocedural mortalities and low incidences of periprocedural morbidities.

Conclusions

These findings may have implications for patient and treatment selection, dose fractionation, and support the hypothesis that CRC pulmonary metastases may require higher biological effective doses while respecting normal tissue constraints when treated with SBRT.

Using the Radiosensitivity Index (RSI) to Predict Pelvic Failure in Endometrial Cancer Treated With Adjuvant Radiation Therapy

PURPOSE

Variability exists in the adjuvant treatment for endometrial cancer (EC) based on surgical pathology and institutional preference. The radiosensitivity index (RSI) is a previously validated multigene expression index that estimates tumor radiosensitivity. We evaluate RSI as a genomic predictor for pelvic failure (PF) in EC patients treated with adjuvant radiation therapy (RT).

METHODS AND MATERIALS

Using our institutional tissue biorepository, we identified EC patients treated between January 1999 and April 2011 with primarily endometrioid histology (n = 176; 86%) who received various adjuvant therapies. The RSI 10-gene signature was calculated for each sample using the previously published algorithm. Radiophenotype was determined using the previously identified cutpoint where RSI ≥ 0.375 denotes radioresistance (RR) and RSI < 0.375 describes radiosensitivity.

RESULTS

A total of 204 patients were identified, of which 83 (41%) were treated with adjuvant RT. Median follow-up was 38.5 months. All patients underwent hysterectomy with bilateral salpingo-oophorectomy with the majority undergoing lymph node dissection (n = 181; 88%). In patients treated with radiation, RR tumors were more likely to experience PF (3-year pelvic control 84% vs 100%; P = .02) with worse PF-free survival (PFFS) (3-year PFFS 65% vs 89%; P = .04). Furthermore, in the patients who did not receive RT, there was no difference in PF (P = .87) or PFFS (P = .57) between the RR/radiosensitive tumors. On multivariable analysis, factors that continued to predict for PF included the RR phenotype (hazard ratio [HR], 12.2; P = .003), lymph node involvement (HR, 4.4; P = .02), and serosal or adnexal involvement (HR, 5.3; P = .01).

CONCLUSIONS

On multivariable analysis, RSI was found to be a significant predictor of PF in patients treated with adjuvant RT. We propose using RSI to predict which patients are at higher risk for failing in the pelvis and may be candidates for treatment escalation in the adjuvant setting.

Intrinsic radiosensitivity, genomic-based radiation dose and patterns of failure of penile cancer in response to adjuvant radiation therapy

Purpose

Optimal postoperative radiation therapy (PORT) dose is unclear in penile squamous cell carcinoma (PeSCC). Herein, we characterized the radiosensitivity index (RSI) and genomic-adjusted radiation dose (GARD) profiles in a cohort of patients with PeSCC, and assessed the application of GARD to personalize PORT.

Methods

A total of 25 PeSCC samples were identified for transcriptomic profiling. The RSI score and GARD were derived for each sample. A cohort of 34 patients was reviewed for clinical correlation.

Results

The median RSI for PeSCC was 0.482 (range 0.215-0.682). The majority (n = 21; 84%) of cases were classified as radioresistant. PeSCC GARD ranged from 9.56 to 38.39 (median 18.25), suggesting variable therapeutic effects from PORT. We further determined the optimal GARD-based RT doses to improve locoregional control. We found that therapeutic benefit was only achieved in 52% of PeSCC lesions with PORT of 50 Gy, in contrast to 84% benefit from GARD-modeled PORT of 66 Gy. In the clinical cohort, the majority of patients presented with pathological N2 or N3 disease (n = 31; 91%) and was treated with adjuvant concurrent platinum-based chemoradiotherapy (CRT, n = 30; 88%). Fourteen of the 34 patients (41%) had locoregional recurrence (LRR), of which half had LRR within six months of completion of PORT.

Conclusions

The majority of PeSCC are intrinsically radioresistant with a low GARD-based therapeutic effect from PORT dose of 50 Gy, consistent with the observed high rate of LRR in the clinical cohort. A GARD-based strategy will allow personalizing PORT dose prescription to individual tumor biology and improve outcomes.

Characterization and radiosensitivity of HPV-related oropharyngeal squamous cell carcinoma patient-derived xenografts

Background: Oropharyngeal squamous cell carcinomas (OPSCC) are rising rapidly in incidence due to Human Papillomavirus (HPV) and/or tobacco smoking. Prognosis is better for patients with HPV-positive disease, but may also be influenced by tobacco smoking and other factors. There is a need to individualize treatment to minimize morbidity and improve prognosis. Patient-derived xenografts (PDX) is an emerging pre-clinical research model that may more accurately reflect the human disease, and is an attractive platform to study disease biology and develop treatments and biomarkers. In this study we describe the establishment of PDX models, compare PDX tumors to the human original, and assess the suitability of this model for radiotherapy research and biomarker development. Material and methods: Tumor biopsies from 34 patients with previously untreated OPSCC were implanted in immunodeficient mice, giving rise to 12 squamous cell carcinoma PDX models (7 HPV+, 5 HPV-). Primary and PDX tumors were characterized extensively, examining histology, immunohistochemistry, cancer gene sequencing and gene expression analysis. Radiosensitivity was assessed in vivo in a growth delay assay. Results: Established PDX models maintained histological and immunohistochemical characteristics as well as HPV-status of the primary tumor. Important cancer driver gene mutations, e.g., in TP53, PIK3CA and others, were preserved. Gene expression related to cancer stem cell markers and gene expression subtype were preserved, while gene expression related to hypoxia and immune response differed. Radiosensitivity studies showed high concordance with clinical observations. Conclusion: PDX from OPSCC preserves important molecular characteristics of the human primary tumor. Radiosensitivity were in accordance with clinically observed treatment response. The PDX model is a clinically relevant surrogate model of head and neck cancer. Perspectives include increased understanding of disease biology, which could lead to development of novel treatments and biomarkers.

Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy

Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.

Toward a New Framework for Clinical Radiation Biology

Radiation biology has entered the era of precision oncology, and this article reviews time-tested factors that determine the effects of fractionated radiation therapy in a wide variety of tumor types and normal tissues: the association of tumor control with radiation dose, the importance of fractionation and overall treatment time, and the role of tumor hypoxia. Therapeutic gain can only be achieved if the increased tumor toxicity produced by biological treatment modifications is balanced against injury to early-responding and late-responding normal tissues. Developments in precision oncology and immuno-oncology will allow an emphasis on treatment individualization and predictive biomarker development.

[Predictive assays for responses of tumors and normal tissues in radiation oncology]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the target volume. Tumor sensitivity to radiotherapy may be particularly inconstant depending on location, histology, somatic genetic parameters and the capacity of the immune system to infiltrate the tumor. In addition, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better predict the tumor response but also the radiation-induced late effects.

Robustness of Clonogenic Assays as a Biomarker for Cancer Cell Radiosensitivity

Photon radiation therapy is a major curative treatment for cancer. However, the lack of robust predictive biomarkers for radiosensitivity precludes personalized radiation therapy. Clonogenic assays are the gold standard method for measuring the radiosensitivity of cancer cells. Although a large number of publications describe the use of clonogenic assays to measure cancer cell radiosensitivity, the robustness of results from different studies is unclear. To address this, we conducted a comprehensive detailed literature search of 256 common cancer cell lines and identified the eight cell lines most-frequently examined for photon sensitivity using clonogenic assays. Survival endpoints and experimental parameters from all 620 relevant experiments were compiled and analyzed. We found that the coefficients of variation for SF₂ (surviving fraction after 2 Gy irradiation) and for D₁₀ (dose that yields a surviving fraction of 10%) were below 30% for all cell lines, indicating that SF₂ and D₁₀ have acceptable inter-assay precision. These data support further analysis of published data on clonogenic assays using SF₂ and D₁₀ as survival endpoints, which facilitates robust identification of biological profiles representative of cancer cell sensitivity to photons.

A Multigene Model for Predicting Tumor Responsiveness After Preoperative Chemoradiotherapy for Rectal Cancer

PURPOSE

Although preoperative chemoradiotherapy (PCRT) is regarded as a standard treatment for locally advanced rectal cancer, there is no reliable biomarker for predicting responsiveness to PCRT. We aimed to develop a biomarker model for predicting response to PCRT.

METHODS AND MATERIALS

We included 184 patients who received PCRT followed by surgical resection and categorized them as good responders (complete or near-complete regression) or poor responders (all other patients). Candidate gene mRNAs were isolated from formalin-fixed paraffin-embedded tumor specimens and analyzed using the NanoString nCounter gene expression assay. Stepwise logistic regression analysis was used to select genes in discovery and training phases. A quantitative radio-responsiveness prediction model was developed and validated using internal cross-validation groups, and the model's predictive value was assessed based on the area under the receiver operating characteristic curve (AUC).

RESULTS

By comparing the gene expressions between good and poor responders, we created a multigene mRNA model using FZD9, HRAS, ITGA7, MECOM, MMP3, NKD1, PIK3CD, and PRKCB. This panel showed good ability to predict treatment response (AUC: 0.846 for the whole data set). Internal cross-validation was performed to evaluate the model's predictive stability among 3 cohorts, which provided AUC values of 0.808-0.909. The satisfactory diagnostic performance of the radio-response prediction index persisted regardless of other clinicopathologic features such as clinical T or N stage, interval between radiation and surgery, and pretreatment carcinoembryonic antigen levels (P = .001, 95% CI, 0.686-0.905).

CONCLUSIONS

We developed a multigene mRNA-based biomarker model that allows prediction of rectal cancer response to PCRT, which may help identify patients who will benefit most from PCRT.

Utilizing the genomically adjusted radiation dose (GARD) to personalize adjuvant radiotherapy in triple negative breast cancer management

Background

Utilizing the linear quadratic model and the radiosensitivity index (RSI), we have derived an expression for the genomically adjusted radiation dose (GARD) to model radiation dose effect. We hypothesize GARD is associated with local recurrence and can be used to optimize individual triple negative breast cancer (TNBC) radiation dose.

Methods

TN patients from two independent datasets were assessed. The first cohort consisted of 58 patients treated at 5 European centers with breast conservation surgery followed by adjuvant radiotherapy (RT). The second dataset consisted of 55 patients treated with adjuvant radiation therapy.

Findings

In cohort 1, multivariable analysis revealed that as a dichotomous variable (HR: 2.5 95% CI 1–7.1; p = .05), GARD was associated with local control. This was confirmed in the second independent dataset where GARD was the only significant factor associated with local control (HR: 4.4 95% CI 1.1–29.5; p = .04). We utilized GARD to calculate an individualized radiation dose for each TN patient in cohort 2 by determining the physical dose required to achieve the GARD target value (GARD ≥ 21). While 7% of patients were optimized with a dose of 30 Gy, 91% of patients would be optimized with 70 Gy.

Interpretation

GARD is associated with local control following whole breast or post-mastectomy radiotherapy (RT) in TN patients. By modeling RT dose effect with GARD, we demonstrate that no single dose is optimal for all patients and propose the first dose range to optimize RT at an individual patient level in TNBC.

Mining of Self-Organizing Map Gene-Expression Portraits Reveals Prognostic Stratification of HPV-Positive Head and Neck Squamous Cell Carcinoma

Patients (pts) with head and neck squamous cell carcinoma (HNSCC) have different epidemiologic, clinical, and outcome behaviors in relation to human papillomavirus (HPV) infection status, with HPV-positive patients having a 70% reduction in their risk of death. Little is known about the molecular heterogeneity in HPV-related cases. In the present study, we aim to disclose the molecular subtypes with potential biological and clinical relevance. Through a literature review, 11 studies were retrieved with a total of 346 gene-expression data points from HPV-positive HNSCC pts. Meta-analysis and self-organizing map (SOM) approaches were used to disclose relevant meta-gene portraits. Unsupervised consensus clustering provided evidence of three biological subtypes in HPV-positive HNSCC: Cl1, immune-related; Cl2, epithelial–mesenchymal transition-related; Cl3, proliferation-related. This stratification has a prognostic relevance, with Cl1 having the best outcome, Cl2 the worst, and Cl3 an intermediate survival rate. Compared to recent literature, which identified immune and keratinocyte subtypes in HPV-related HNSCC, we confirmed the former and we separated the latter into two clusters with different biological and prognostic characteristics. At present, this paper reports the largest meta-analysis of HPV-positive HNSCC studies and offers a promising molecular subtype classification. Upon further validation, this stratification could improve patient selection and pave the way for the development of a precision medicine therapeutic approach.

Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy

In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).