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Purpose: This study aimed to develop noninvasive magnetic resonance imaging (MRI)-based radiomic models to predict immunophenotypes of breast cancer. Methods: A total of 182 breast cancer patients who underwent upfront surgery were analyzed, divided into training (n = 137) and validation (n = 45) cohorts. Dynamic contrast-enhanced (DCE)-MRI was acquired, and immunophenotype was determined on the surgical tumor sections: immune-inflamed (high degree of CD8+ T cells infiltrated), -excluded (CD8+ T cells accumulated at invasive margin but not efficiently infiltrated in tumor bed), and -desert (CD8+ T cells absent within tumor and at margins). Based on 833 radiomic features extracted after manual delineation, the least absolute shrinkage and selection operator method was used to build radiomic models. Results: Our radiomic models from the whole tumor sections showed moderate performance in predicting the immune-inflamed versus non-inflamed tumors, showing the highest AUC values, of 0.659 and 0.671 for training and validation cohorts, respectively. Combining the models, much improved predictability was observed, with AUC values of 0.973 and 0.985 for training and validation cohorts, respectively. Other radiomic features from tumor periphery data discriminated immune-excluded versus immune-desert status, with AUC values of 0.993 and 0.984 for training and validation cohorts, respectively. The combined models were also applicable to predicting immunophenotype for different molecular subtypes, with AUC values ≥ 0.867. Conclusions: By integrating the immunohistochemistry profiles, we established MRI-derived radiomic models to predict the detailed immunophenotype of breast cancer. This study suggests the feasibility of noninvasive assessment of tumor immune status in real-world clinics. Citation Format: Seung Hyuck Jeon, So-Woon Kim, Mirinae Seo, Yu Jin Lim. Radiomic models based on dynamic contrast-enhanced magnetic resonance imaging predict the immunophenotype reflecting spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-17.

Purpose: Regulatory T cells (Tregs) exert immunosuppressive functions and hamper antitumor immune responses in the tumor microenvironment. Understanding the heterogeneity of intratumoral Tregs, and how it changes with tumor progression, will provide clues regarding novel target molecules of Treg-directed therapies. Experimental Design: From 42 patients with renal cell carcinoma and 5 patients with ovarian cancer, immune cells from tumor and peripheral blood were isolated. We performed multicolor flow cytometry and RNA-sequencing to characterize the phenotypes and heterogeneity of intratumoral Tregs. In vitro functional assays were performed to evaluate suppressive capacity of Tregs and effect of carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1)-mediated depletion. The CT26 tumor model was used to evaluate the association between intratumoral Tregs and tumor growth, and examine the in vivo role of CEACAM1+ intratumoral Tregs on antitumor immunity. Results: We found that CEACAM1 was selectively expressed on intratumoral Tregs, whereas its expression on peripheral Tregs or other immune cells was low. The CEACAM1+ intratumoral Tregs accumulated with tumor progression, whereas the CEACAM1− subset did not. Notably, we found that CEACAM1 marked intratumoral Tregs that exhibited highly suppressive and activated phenotypes with substantial clonal expansion. Depletion of CEACAM1-expressing cells from tumor-infiltrating leukocytes led to increased effector functions of tumor-infiltrating T cells. Moreover, CEACAM1+ cell depletion further enhanced anti-PD-1–mediated reinvigoration of exhausted CD8+ T cells. Conclusions: CEACAM1 marks highly suppressive subset of intratumoral Tregs, and can be a target for selective depletion of intratumoral Tregs. These results may inform future studies on CEACAM1-mediated depletion in patients with cancer.

PurposeThe value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.Materials and MethodsPatients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian Network model to predict radiotherapy response.ResultsAltogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure–free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83.ConclusionDurable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

Purpose This study aimed to evaluate the role of postoperative radiotherapy (PORT) in intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC).Materials and Methods A total of 133 patients with histologically confirmed HPC were included from eight institutions. Gross total resection (GTR) and subtotal resection (STR) were performed in 86 and 47 patients, respectively. PORT was performed in 85 patients (64%). The prognostic effects of sex, age, performance, World Health Organization (WHO) grade, location, size, Ki-67, surgical extent, and PORT on local control (LC), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS) were estimated by univariate and multivariate analyses.Results The 10-year PFS, and OS rates were 45%, and 71%, respectively. The multivariate analysis suggested that PORT significantly improved LC (p < 0.001) and PFS (p < 0.001). The PFS benefit of PORT was maintained in the subgroup of GTR (p=0.001), WHO grade II (p=0.001), or STR (p < 0.001). In the favorable subgroup of GTR and WHO grade II, PORT was also significantly related to better PFS (p=0.028). WHO grade III was significantly associated with poor DMFS (p=0.029). In the PORT subgroup, the 0-0.5 cm margin of the target volume showed an inferior LC to a large margin with 1.0-2.0 cm (p=0.021). Time-dependent Cox proportion analysis showed that distant failures were significantly associated with poor OS (p=0.003).Conclusion This multicenter study supports the role of PORT in disease control of intracranial SFT/HPC, irrespective of the surgical extent and grade. For LC, PORT should enclose the tumor bed with sufficient margin.

Background and purpose Regarding the altered tumor immune status following cytotoxic treatment, this study aims to develop a radiomic signature to predict CD8+ tumor-infiltrating lymphocyte (TIL) density changes in chemoradiotherapy (CRT) of rectal cancer. Materials and methods We used the magnetic resonance imaging (MRI) and immunohistochemistry data before and after neoadjuvant CRT. The discovery datasets consisted of pre-CRT dataset A1 (n = 113), post-CRT datasets A2 (n = 32; predominance of tumor) and A3 (n = 20; pure fibrosis). The developed model was validated in dataset B (n = 28). Thirty-eight radiomic features from T2-weighted MRI scans were incorporated into the least absolute shrinkage and selection operator method. Results In pre-CRT dataset A1, the area under the receiver operating characteristic curve (AUC) values of radiomic score for predicting CD8+ TILs were 0.760 and 0.729 for training and validation subsets, respectively. A significant correlation was observed between the signature and CD8+ TIL density in the post-CRT dataset A2 (Pearson’s R = −0.372, P = 0.036), whereas no association was found in dataset A3 (Pearson’s R = −0.069, P = 0.77). The association was also observed in the validation dataset B (Pearson’s R = −0.374, P = 0.049). In dataset A2, the radiomic score difference predicted changes in CD8+ TIL density (AUC = 0.824). Conclusion We established the MRI-derived radiomic signature for predicting CRT-induced alterations in CD8+ TILs. This study suggests the clinical utility of radiomics-immunophenotype modeling to evaluate tumor immune status following neoadjuvant chemoradiation in rectal cancer.

This study aimed to define the gene signature associated with response to neoadjuvant chemoradiotherapy (nCRT), or chemoradiosensitivity (CRS) signature, in rectal cancer, and investigate the correlation between the CRS signature and characteristics of tumor.Three public microarray datasets of pre-nCRT rectal cancer were used to discover and validate the CRS signature, and the pathway analysis of the CRS signature was performed. Patients in The Cancer Genome Atlas (TCGA) dataset were stratified according to the CRS signature enrichment score, and mutational profile and proportions of infiltrated immune cells were compared.In the discovery dataset (GSE53781), 95 genes were upregulated in complete responders compared to non-complete responders and defined as the CRS signature. Pathways regarding DNA replication and repair processes as well as inflammatory response were enriched in the CRS signature. In the validation datasets (GSE35452 and GSE45404), patients with favorable response to nCRT exhibited higher enrichment score of the CRS. In TCGA-READ cohort, patients with high CRS signature harbored KRAS mutation in lower frequency than those with low CRS signature. In addition, proportions of proinflammatory immune cells were higher, but proportion of immunosuppressive M2 macrophages was lower in patients with high CRS signature than those with low CRS signature.The current integrative bioinformatic analysis suggests the CRS signature and showed that the CRS signature is associated with dissimilar mutational profile and increased immune response. The discovered CRS signature and related characteristics may serve as candidate of stratification factor in upcoming studies for rectal cancer.

Exogenous epidermal growth factor (EGF) causes apoptosis in EGF receptor (EGFR)-overexpressing cell lines. The apoptosis-inducing factors could be a therapeutic target. We aimed to determine the mechanism of EGF-induced apoptosis using a genome-wide clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout screen.Two-vector system of the human genome-scale CRISPR knockout library v2 was used to target 19,050 genes using 123,411 single guide RNAs (sgRNAs). Recombinant human EGF (100 nM) or distilled water four times was administered to the experimental and control groups, respectively. The read counts of each sgRNA obtained from next-generation sequencing were analyzed using the edgeR algorithm. We used another EGFR-overexpressing cell line (A549) and short hairpin RNAs (shRNAs) targeting five EGF-resistance genes for validation. DUSP1 expression in A431, A549, and HEK293FT cells was calculated using reverse transcription-quantitative polymerase chain reaction.We found 77 enriched and 189 depleted genes in the experimental group using the CRISPR-based knockout screen and identified the top five EGF-resistance genes: DDX20, LHFP, REPS1, DUSP1, and KRTAP10-12. Transfecting shRNAs targeting these genes into A549 cells significantly increased the surviving fractions after EGF treatment, compared with those observed in the control shRNA-transfected cells. The expression ratio of DUSP1 (inhibits ERK signaling) increased in A431 and A549 cells after EGF treatment. However, DUSP1 expression remained unchanged in HEK293FT cells after EGF treatment.The CRISPR-based knockout screen revealed 266 genes possibly responsible for EGF-induced apoptosis. DUSP1 might be a critical component of EGF-induced apoptosis and a novel target for EGFR-overexpressing cancers.

Immune checkpoint inhibitors have been successful in a wide range of tumor types but still have limited efficacy in immunologically cold tumors, such as breast cancers. We hypothesized that the combination of agonistic anti-OX40 (α-OX40) co-stimulation, PD-1 blockade, and radiotherapy would improve the therapeutic efficacy of the immune checkpoint blockade in a syngeneic murine triple-negative breast cancer model. Murine triple-negative breast cancer cells (4T1) were grown in immune-competent BALB/c mice, and tumors were irradiated with 24 Gy in three fractions. PD-1 blockade and α-OX40 were administered five times every other day. Flow cytometric analyses and immunohistochemistry were used to monitor subsequent changes in the immune cell repertoire. The combination of α-OX40, radiotherapy, and PD-1 blockade significantly improved primary tumor control, abscopal effects, and long-term survival beyond 2 months (60%). In the tumor microenvironment, the ratio of CD8+ T cells to CD4 + FOXP3+ regulatory T cells was significantly elevated and exhausted CD8+ T cells (PD-1+, CTLA-4+, TIM-3+, or LAG-3+ cells) were significantly reduced in the triple combination group. Systemically, α-OX40 co-stimulation and radiation significantly increased the CD103+ dendritic cell response in the spleen and plasma IFN-γ, respectively. Together, our results suggest that the combination of α-OX40 co-stimulation and radiation is a viable approach to overcome therapeutic resistance to PD-1 blockade in immunologically cold tumors, such as triple-negative breast cancer.

Immune checkpoint inhibitors are effective for advanced hepatocellular carcinoma (HCC), but there remains a need for peripheral blood biomarkers to predict the clinical response. Here, we analyzed the peripheral blood of 45 patients with advanced HCC who underwent nivolumab. During treatment, frequency of classical monocytes (CD14

Background To develop and compare delta-radiomics signatures from 2- (2D) and 3-dimensional (3D) features that predict treatment outcomes following preoperative chemoradiotherapy (CCRT) and surgery for locally advanced rectal cancer. Methods In total, 101 patients (training cohort, n = 67; validation cohort, n = 34) with locally advanced rectal adenocarcinoma between 2008 and 2015 were included. We extracted 55 features from T2-weighted magnetic resonance imaging (MRI) scans. Delta-radiomics feature was defined as the difference in radiomics feature before and after CCRT. Signatures were developed to predict local recurrence (LR), distant metastasis (DM), and disease-free survival (DFS) from 2D and 3D features. The least absolute shrinkage and selection operator regression was used to select features and build signatures. The delta-radiomics signatures and clinical factors were integrated into Cox regression analysis to determine if the signatures were independent prognostic factors. Results The radiomics signatures for LR, DM, and DFS were developed and validated using both 2D and 3D features. Outcomes were significantly different in the low- and high-risk patients dichotomized by optimal cutoff in both the training and validation cohorts. In multivariate analysis, the signatures were independent prognostic factors even when considering the clinical parameters. There were no significant differences in C-index from 2D vs. 3D signatures. Conclusions This is the first study to develop delta-radiomics signatures for rectal cancer. The signatures successfully predicted the outcomes and were independent prognostic factors. External validation is warranted to ensure their performance. Electronic supplementary material The online version of this article (10.1186/s13014-019-1246-8) contains supplementary material, which is available to authorized users.

Introduction To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging. Materials and methods A total of 466 patients with locally advanced rectal cancer who received preoperative chemoradiotherapy followed by surgical resection were collected from single center, among whom 113 (24.3%) were allocated to the holdout testing set. Complete response (pCR) was defined as Dworak tumor regression grade (TRG) 4, while good response (GR) was defined as TRG 3 or 4. Based on post-chemoradiotherapy T2-weighted axial MR images, two deep learning models were developed to predict pCR and GR, respectively. The prediction performance of the deep learning models was evaluated in the testing set and was compared to that of a senior radiologist and a radiation oncologist. Results The deep learning model showed an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.76, 0.30, 0.96, 0.67, 0.87, and 85.0% for predicting pCR and 0.72, 0.54, 0.81, 0.60, 0.77, and 71.7% for predicting GR, respectively. The deep learning model had a superior predictive performance than the observers. Fair agreement between the ground truth and the model was shown for pCR prediction (kappa = 0.34) and GR prediction (kappa = 0.36). Conclusions The post-chemoradiotherapy T2-weighted axial MR image-based deep learning model showed acceptable performance in predicting pCR or GR in patients with rectal cancer, compared with human observers.

Some patients with glioblastoma show a worsening presentation in imaging after concurrent chemoradiation, even when they receive gross total resection. Previously, we showed the feasibility of a machine learning model to predict pseudoprogression (PsPD) versus progressive disease (PD) in glioblastoma patients. The previous model was based on the dataset from two institutions (termed as the Seoul National University Hospital (SNUH) dataset, N = 78). To test this model in a larger dataset, we collected cases from multiple institutions that raised the problem of PsPD vs. PD diagnosis in clinics (Korean Radiation Oncology Group (KROG) dataset, N = 104). The dataset was composed of brain MR images and clinical information. We tested the previous model in the KROG dataset, however, that model showed limited performance. After hyperparameter optimization, we developed a deep learning model based on the whole dataset (N = 182). The 10-fold cross validation revealed that the micro-average area under the precision-recall curve (AUPRC) was 0.86. The calibration model was constructed to estimate the interpretable probability directly from the model output. After calibration, the final model offers clinical probability in a web-user interface.

Background/aim A noninvasive method for predicting a patient's response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer would be useful because this would help determine the subsequent treatment strategy. Two types of noninvasive biomarkers have previously been studied, based on radiomics and based on blood test parameters. We hypothesized that a combination of both types would provide a better predictive power, and this has not previously been investigated. Patients and methods Data from 135 patients with locally advanced rectal cancer who underwent nCRT were retrospectively allocated into training and validation cohorts in a 2:1 ratio. Sixty-five radiomics features were extracted from tumors segmented on T2-weighted magnetic resonance images. An elastic net was applied to generate four models for discerning the patients with good responses to nCRT based on radiomics features (model R), blood biomarkers (model B), both (model RB), and a linear combination of models R and B (model R+B). Results Among 65 radiomics features, 17 were selected as robust features for model development. The AUC values of model R, model B, model RB, and model R+B achieved 0.751, 0.627, 0.785, and 0.711 in the training cohort (n=90), and 0.705, 0.603, 0.679, and 0.705 in validation cohort (n=45), respectively. In the entire cohort, models RB and R+B demonstrated a significantly better performance than model B but not R. There was no correlation between the scores of models R and B (p=0.76). Radiomics features had a greater influence than blood biomarkers on models RB and R+B. Conclusion A non-redundancy between radiomics features and blood-based biomarkers was observed. Furthermore, radiomics features are more valuable in terms of predicting response to nCRT. The importance of combining non-invasive biomarkers in future investigations is highlighted.

Purpose To evaluate the survival outcomes based on molecular subtypes of breast cancer in patients with brain metastasis. Materials and methods We retrospectively reviewed 106 breast cancer patients treated for brain metastases, from January 2005 to May 2016. Patients were divided into four groups based on the tumor molecular subtype: luminal A (Estrogen Receptor [ER]/Progesterone Receptor [PR] positive, human epithelial growth factor receptor-2 [HER2] negative), luminal B (ER/PR positive, HER2 Positive), HER2 (HER2 positive and ER/PR negative), and Triple negative (TNBC). Results The median follow-up time for surviving patients was 22 months (range: 11.2-51.1 months). The median survival of all patients was 14 months, with a 1-year overall survival (OS) rate of 57.5% and a 2-year OS rate of 32.1%. Thirty patients (28.3%) had a solitary brain metastasis while 62 (58.5%) patients had multiple metastases. A significant difference was observed in the survival rates of the two groups. Based on the Karnofsky performance score, the performance status of the patients at the time of brain metastasis was also found to affect survival. Patients with different molecular subtypes had different survival rates; the luminal A group showed the highest median survival (luminal A: 23.1, luminal B: 15.0, HER2: 12.5 and TNBC: 6.4 months, respectively), which was statistically significant. Conclusion In breast cancer patients with brain metastasis, survival rates were different based on the molecular subtype of the tumor, despite various local and systemic treatments. Appropriate and tailored treatment approaches should, therefore, be considered for the different molecular subtypes.

In the present study, the ability of adjuvant trastuzumab to reduce locoregional recurrence in patients with human epidermal growth factor receptor 2 (HER2)-overexpressing breast cancer receiving adjuvant chemotherapy and radiotherapy (RT) was investigated. We retrospectively included 520 patients with HER2-overexpressing breast cancer who received surgery followed by adjuvant RT and cytotoxic chemotherapy from 2003 to 2011. Adjuvant trastuzumab was administered to 286 patients. Propensity score matching was conducted to compare trastuzumab-treated and non-treated cohorts. Median follow-up duration was 7.1 years (range 1.1–14.1 years). Propensity score matching yielded 171 matched pairs of patients with no significantly different clinical factors. An improved 7-year locoregional control (LRC) rate was observed in the trastuzumab-treated cohort compared with the non-treated cohort (95.6% vs. 89.9%, p = 0.014). Based on multivariate analysis, hormone receptor negativity (hazard ratio [HR] = 5.348, p = 0.007), positive lymph node ratio > 0.25 (HR = 2.549, p = 0.040), and lack of adjuvant trastuzumab (HR = 3.401, p = 0.017) were identified as significant risk factors for poor LRC. Adjuvant trastuzumab significantly reduced the locoregional recurrence rate in patients with one or two risk factors (7-year LRC = 95.0% vs. 84.2%, p = 0.007); however, the benefit of adjuvant trastuzumab was non-significant in patients with no risk factors (7-year LRC = 95.8% vs. 97.9%, p = 0.75). Adjuvant trastuzumab improved LRC in patients with HER2-overexpressing breast cancer receiving adjuvant RT and cytotoxic chemotherapy, especially in hormone receptor-negative, HER2-enriched subtype, and high positive lymph node ratio breast cancer.

PURPOSE To investigate positional uncertainty and its correlation with clinical parameters in spine stereotactic body radiotherapy (SBRT) using thermoplastic mask (TM) immobilization. Materials and. METHODS A total of 21 patients who underwent spine SBRT for cervical or upper thoracic spinal lesions were retrospectively analyzed. All patients were treated with image guidance using cone beam computed tomography (CBCT) and 4 degrees-of-freedom (DoF) positional correction. Initial, pre-treatment, and post-treatment CBCTs were analyzed. Setup error (SE), pre-treatment residual error (preRE), post-treatment residual error (postRE), intrafraction motion before treatment (IM1), and intrafraction motion during treatment (IM2) were determined from 6 DoF manual rigid registration. RESULTS The three-dimensional (3D) magnitudes of translational uncertainties (mean ± 2 standard deviation) were 3.7±3.5 mm (SE), 0.9±0.9 mm (preRE), 1.2±1.5 mm (postRE), 1.4±2.4 mm (IM1), and 0.9±1.0 mm (IM2), and average angular differences were 1.1°±1.2° (SE), 0.9°±1.1° (preRE), 0.9°±1.1° (postRE), 0.6°±0.9° (IM1), and 0.5°±0.5° (IM2). The 3D magnitude of SE, preRE, postRE, IM1, and IM2 exceeded 2 mm in 18, 0, 3, 3, and 1 patients, respectively. No association were found between all positional uncertainties and body mass index, pain score, and treatment location (p > 0.05, Mann-Whitney test). There was a tendency of intrafraction motion to increase with overall treatment time; however, the correlation was not statistically significant (p > 0.05, Spearman rank correlation test). CONCLUSION In spine SBRT using TM immobilization, CBCT and 4 DoF alignment correction, a minimum residual translational uncertainty was 2 mm. Shortening overall treatment time and 6 DoF positional correction may further reduce positional uncertainties.

We retrospectively evaluated an efficacy of adjuvant radiotherapy (RT) in the intracranial hemangiopericytoma (HPC) and analyzed prognostic factors influencing treatment outcomes. Among 49 patients diagnosed as localized intracranial HPC between 1995 and 2016, 31 patients received adjuvant RT after surgery; 26 with fractionated RT and 5 with stereotactic radiosurgery using Gamma Knife. After gross total resection (GTR) (n = 32) and subtotal resection (STR) (n = 17), histopathological grade was confirmed to be grade II (n = 9) or grade III (n = 40). The median follow-up period was 50 months (range 3–216 months). The local recurrence was defined as intracranial relapse within 15 mm and regional recurrence as beyond 15 mm from the margin of surgical bed. The 10-year overall survival (OS) and progression-free survival (PFS) were 69.9 and 34.4%, respectively. The 10-year local, regional, and distant failure-free rates were 56.6, 88.2, and 73.3%, respectively. Local tumor control was better with GTR followed by RT than GTR alone (p = 0.056), while there was no difference in OS. Local tumor control and OS after STR plus RT were equivalent to those after GTR alone. There were no differences in distant metastasis-free survival (DMFS) among GTR plus RT, GTR alone, and STR plus RT. Tumor volume > 40 cm3 was associated with poor PFS (p = 0.024). The local tumor recurrence was reduced by adjuvant RT after surgery. But OS or DMFS was not improved with adjuvant RT. PFS was better in the tumor with small volume at diagnosis.

Tissue-resident memory CD8+ T cells (TRMs), marked by expression of CD103, are considered to play a key role as anti-tumor immune response in breast cancer. However, prognostic value of TRMs and heterogeneity in CD103+ TRMs of breast cancer in terms of tumor-specificity needs further understanding to establish immuno-therapeutic implications. Here, we isolated tumor-infiltrating CD103+TRMs in breast cancer samples and matched peripheral blood mononuclear cells (PBMCs) which were prospectively collected from 127 patients who underwent surgical resection between July 2016 and May 2020 at Severance Hospital, Seoul, Republic of Korea. Multi-color flow cytometry analyses were performed.CD103+TRMs consist of both tumor-antigen specific CD8+ T cells and bystander CD8+ T cells, such as viral antigen-specific CD8+ T cells. Dissection of CD103+TRMs with CD39 expression reveals a clonally-distinct subpopulation exhibiting tumor-antigen specificity and reactivity with transcriptionally and phenotypically exhaustive features. CD39+CD103+ TRMs were enriched in HER2 enriched and triple-negative breast cancer(TNBC) subtypes, but not in Hormone receptor(HR) positive/HER2 not enriched subtypes, implying that the role of tumor-infiltrating CD8+ T cells in eliciting anti-tumor immune response differ among molecular subtypes. In line with early-exhaustive features of CD39+CD103+TRMs, inhibition of either PD-1 alone or both PD-1 and CTLA-4 reinvigorate the proliferation and the cytokine production of CD8+ TILs in HER2 and TNBC subtypes. Our findings indicate that different immuno-therapeutic strategies should be considered to different subtypes of breast cancer. Citation Format: Jee Ye Kim, Yong Joon Lee, Seung hyuck Jeon, Heejin Nam, Jae Hyung Jung, Seung Il Kim, Eui-Cheol Shin. Tumor-specificity of tumor-infiltrating CD8+ t cells determines the potential of immune checkpoint inhibitors in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1666.

To investigate the patterns of post-lumpectomy seroma volume (SV) change and related clinical factors to determine the benefits of adaptive planning in magnetic resonance imaging (MRI)-guided partial breast irradiation (PBI). MRI data obtained from 37 women with early breast cancer acquired at simulation and at the 1st, 6th, and 10th fractions were analyzed. The planning target volume (PTV) was defined as unequal margins of 10–15 mm added according to the directional surgical margin status of each seroma. Treatment was performed using a 0.35 T MRI-guided radiotherapy system. Univariate analysis was performed to assess the correlations between SV change rate and clinical factors. Seroma and PTV for adaptive planning were based on the images obtained at the 6th fraction. The average time intervals between surgery-simulation, simulation-1st, 1st-6th, and 6th-10th fractions were 23.1, 8.5, 7.2, and 5.9 days, respectively. Of the 37 patients, 33 exhibited decreased SV over the treatment period. The mean SV of these 33 patients decreased from 100% at simulation to 60, 48, and 40% at each MRI scan. In most cases (26/33), the logarithm of SV was inversely proportional to the elapsed time from surgery (R 2 > 0.90, Pearson’s correlation test). The volume of spared normal tissue from adaptive radiotherapy was proportional to the absolute change in SV (R 2 = 0.89, Pearson’s correlation test). Seromas exhibit exponential shrinkage over the course of PBI. In patients receiving PBI, frequent monitoring of SV could be helpful in decision-making regarding adaptive planning, especially those with a large seroma.

123 Background: Despite increasing use of cancer immunotherapies, no biomarker relevant to anti-tumor immunity has been developed in rectal cancer. The present study aims to develop a radiomic signature to predict the infiltration of CD8+ tumor-infiltrating lymphocytes (TILs). Methods: In total, 131 patients undergoing neoadjuvant chemoradiotherapy (CRT) from 2005 to 2012 were analyzed. The study included pre-CRT dataset A ( n = 113; n = 75 and 38 for training and validation) and post-CRT datasets B ( n = 32; predominance of tumor) and C ( n = 20; pure fibrosis). To build the radiomic signature, 38 features selected from pre-CRT T2-weighted MRI scans were incorporated into the least absolute shrinkage and selection operator method. Results: Higher CD8+ TIL infiltration was associated with better overall and progression-free survival ( P = 0.01 and 0.03, respectively). In pre-CRT dataset A, the area under the receiver operating characteristic curve values of radiomic score for predicting CD8+ TILs were 0.760 and 0.729 for training and validation subsets, respectively. A significant correlation was observed between the radiomic signature and CD8+ TIL density in the post-CRT dataset B (Pearson’s R = ‒0.372, P = 0.036), whereas no association was found within the dataset C accounting for pure fibrosis after CRT (Pearson’s R = ‒0.069, P = 0.77). Conclusions: We established the MRI-derived radiomic signature for predicting CD8+ TIL infiltration in the contemporary treatment era of rectal cancer. The noninvasive characterization of tumor immune status would provide insights into the role of radiomic-pathology modeling to predict the local immune phenotypes.

The stomach is one of the most deforming organs caused by respiratory motions and daily variation by food intake. Applying radiotherapy has been quite a challenge due to the high risk of missing the target as well as radiation exposure to large volumes of normal tissue. However, real-time magnetic resonance (MR)-guided radiotherapy with adaptive planning could focus the high dose radiation to the target area while minimizing neighboring normal tissue exposure and compensate for not only daily but real-time variation. Here is a case report of a patient with recurrent gastric cancer and multiple co-morbidities, unsuitable for both resection and chemotherapy, who underwent MR guided adaptive radiotherapy.

We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

To investigate and to prevent irradiation outside the treatment field caused by an electron stream in the air generated by the magnetic field during magnetic resonance image-guided accelerated partial breast irradiation (APBI). In all, 20 patients who received APBI with a magnetic resonance image-guided radiation therapy (MR-IGRT) system were prospectively studied. The prescription dose was 38.5 Gy in 10 fractions of 3.85 Gy and delivered with a tri-cobalt system (the ViewRay system). For each patient, primary plans were delivered for the first five fractions and modified plans with different gantry angles from those of the primary plan (in-treatment plans) were delivered for the remaining five fractions to reduce the skin dose. A 1 cm thick bolus was placed in front of the patient’s jaw, ipsilateral shoulder, and arm to shield them from the electron stream. Radiochromic EBT3 films were attached to the front (towards the breast) and back (towards the head) of the bolus during treatment. Correlations between the measured values and the tumor locations, treatment times, and tumor sizes were investigated. For a single fraction delivery, the average areas of the measured isodoses of 14% (0.54 Gy), 12% (0.46 Gy), and 10% (0.39 Gy) at the front of the boluses were as large as 3, 10.4, and 21.4 cm2, respectively, whereas no significant dose could be measured at the back of the boluses. Statistically significant but weak correlations were observed between the measured values and the treatment times. During radiotherapy for breast cancer with an MR-IGRT system, the patient must be shielded from electron streams in the air generated by the interaction of the magnetic field with the beams of the three-cobalt treatment unit to avoid unwanted irradiation of the skin outside the treatment field.

The goal of this study is to compare the risk of lower extremity lymphedema (LEL) between pelvic external beam radiation therapy (EBRT) and vaginal brachytherapy, and to identify risk factors for LEL in gynecologic cancer patients treated with adjuvant radiation therapy (RT) after radical surgery. A total of 263 stage I&ndash, III gynecologic cancer patients who underwent adjuvant RT were retrospectively reviewed. One-to-one case-matched analysis was conducted with propensity scores generated from patient, tumor, and treatment characteristics. Using the risk factors found in this study, high- and low-risk groups were identified. With a median follow-up of 36.0 months, 35 of 263 (13.3%) patients developed LEL. In multivariate analysis, laparoscopic surgery (HR 2.548, p = 0.024), harvesting more than 30 pelvic lymph nodes (HR 2.246, p = 0.028), and para-aortic lymph node dissection (PALND, HR 2.305, p = 0.014) were identified as independent risk factors for LEL. After propensity score matching, the LEL incidence of the brachytherapy group was significantly lower than the EBRT group (p = 0.025). In conclusion, high-risk patients with risk factors such as laparoscopic surgery, harvesting more than 30 pelvic lymph nodes, PALND, and adjuvant pelvic EBRT require closer observation for LEL.