First-in-human study of SBRT and adenosine pathway blockade to potentiate the benefit of immunochemotherapy in early-stage luminal B breast cancer: results of the safety run-in phase of the Neo-CheckRay trial

BACKGROUND

Luminal B breast cancer (BC) presents a worse prognosis when compared with luminal A BC and exhibits a lower sensitivity to chemotherapy and a lower immunogenicity in contrast to non-luminal BC subtypes. The Neo-CheckRay clinical trial investigates the use of stereotactic body radiation therapy (SBRT) directed to the primary tumor in combination with the adenosine pathway inhibitor oleclumab to improve the response to neo-adjuvant immuno-chemotherapy in luminal B BC. The trial consists of a safety run-in followed by a randomized phase II trial. Here, we present the results of the first-in-human safety run-in.

METHODS

The safety run-in was an open-label, single-arm trial in which six patients with early-stage luminal B BC received the following neo-adjuvant regimen: paclitaxel q1w×12 → doxorubicin/cyclophosphamide q2w×4; durvalumab (anti-programmed cell death receptor ligand 1 (PD-L1)) q4w×5; oleclumab (anti-CD73) q2w×4 → q4w×3 and 3×8 Gy SBRT to the primary tumor at week 5. Surgery must be performed 2-6 weeks after primary systemic treatment and adjuvant therapy was given per local guidelines, RT boost to the tumor bed was not allowed. Key inclusion criteria were: luminal BC, Ki67≥15% or histological grade 3, MammaPrint high risk, tumor size≥1.5 cm. Primary tumor tissue samples were collected at three timepoints: baseline, 1 week after SBRT and at surgery. Tumor-infiltrating lymphocytes, PD-L1 and CD73 were evaluated at each timepoint, and residual cancer burden (RCB) was calculated at surgery.

RESULTS

Six patients were included between November 2019 and March 2020. Median age was 53 years, range 37-69. All patients received SBRT and underwent surgery 2-4 weeks after the last treatment. After a median follow-up time of 2 years after surgery, one grade 3 adverse event (AE) was reported: pericarditis with rapid resolution under corticosteroids. No grade 4-5 AE were documented. Overall cosmetical breast evaluation after surgery was 'excellent' in four patients and 'good' in two patients. RCB results were 2/6 RCB 0; 2/6 RCB 1; 1/6 RCB 2 and 1/6 RCB 3.

CONCLUSIONS

This novel treatment combination was considered safe and is worth further investigation in a randomized phase II trial.

TRIAL REGISTRATION NUMBER

NCT03875573.

New Explainable Deep Cnn Design For Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy

Purpose:

To reduce breast tumor size before surgery, Neoadjuvant chemotherapy (NAC) is applied systematically to patients with local breast cancer. However, with the existing clinical protocols, it is not yet possible to have an early prediction on the effect of chemotherapy on a breast tumor. Predicting the response to chemotherapy could reduce toxicity and delay effective treatment. Computational analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) through Deep Convolution Neural Network (CNN) has proved a significant performance to classify responders and no responder’s patients. This study intends to present a new explainable Deep Learning (DL) model predicting the breast cancer response to chemotherapy based on multiple MRI inputs

Methods and material:

In this study, a cohort of 42 breast cancer patients who underwent chemotherapy was used to train and validate the proposed DL model. This dataset was provided by the Jules Bordet institute of radiology in Brussels, Belgium. 14 external subjects were used to validate the DL model able to classify responding or non-responding patients on temporal DCE-MRI volumes. The model performance was assessed by the Area Under the receiver operating characteristic Curve (AUC), accuracy, and features map visualization according to pathological complete response (Ground truth).

Results:

The developed deep learning architecture was able to predict the responding breast tumors to chemotherapy treatment in the external validation dataset with an AUC of 0.93 using parallel learning MRI images acquired at different moments. The visual results showed that the most important extracted features from non-responding tumors are in the peripheral and external tumor regions. The model proposed in this study is more efficient compared to those proposed in the literature.

Conclusion:

Even with a limited training dataset size, the developed multi-input CNN model using DCE-MR images acquired before and following the first chemotherapy was able to predict responding and non-responding tumors with higher accuracy. Thanks to the visualization of the extracted characteristics by the DL model on the responding and non-responding tumors, the latter could be used henceforth in clinical analysis after its evaluation based on more extra data.

Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study

Background: Although the advent of targeted therapy has substantially improved outcomes for HER2+ breast cancer patients, many will still fail to achieve pathological complete response (pCR) following neoadjuvant chemotherapy (NAC). In order to reduce overtreatment among patients resistant to standard HER2-targeted NAC and identify candidates for alternative therapeutic interventions, there is a need for validated markers of anti-HER2 agent benefit. The computational analysis of pretreatment imaging has shown recent promise in identifying responsive breast cancers. However, previous applications have explored response prediction among cohorts of mixed subtype and therapeutic approach, thus limiting its relevance in informing specific therapeutic strategy.

Methods: This study comprised retrospective contrast-enhanced MRI data from a total of 159 patients who received anti-HER2 therapy at 5 institutions. A deep learning (DL) model was trained and tuned using 100 HER2+ breast cancer patients who received neoadjuvant taxane (T), carboplatin (C), trastuzumab (H), and pertuzumab (P) at Institution A, of which 49 achieved pCR (ypT0/is). A convolutional neural network was designed to analyze pre- and post-contrast MRI images acquired before NAC and compute a patient's probability of achieving pCR. Institution A data was split randomly into a 85 patient training cohort, used to directly train the model, and a 15 patient internal validation cohort, used to monitor and improve training progress. Two external, held-out testing datasets were used to evaluate capability to predict response in HER2+: Testing Cohort 1, consisting of 28 patients (16 pCR, 12 non-pCR) treated with TCHP at Institution B, and Testing Cohort 2, consisting of 29 patients (10 pCR, 19 non-pCR) who received TCH at one of 3 other institutions as part of the BrUOG 211B trial. A multivariable clinical model (MCM) incorporating age, ER/PR status, stage, and size was evaluated separately and in combination with the neural network. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results: The neural network was able to strongly predict response to HER2-targeted NAC in the internal validation (AUC=.93) and testing cohorts (AUC=.84 and AUC=.77). This model offered superior performance compared to a MCM, which performed poorly across institutions. Strikingly, the higher accuracy of DL included correctly identifying responders within the ER+/PR+ subgroup of patients and non-responders within the ER-/PR- subgroup. Combining DL predictions with the clinical model improved performance to AUC of 0.89 in testing cohort 1, but did not improve AUC in cohort 2.

Conclusions: DL analysis of breast DCE-MRI could be used to better identify benefit of HER2-targeted therapeutic approaches prior to administration. As the first exploration of automated response prediction from imaging with respect to a targeted NAC approach, this work uniquely has the potential to help guide therapeutic decision-making. Our approach was effective in predicting response to multiple HER2-targeted NAC regimens, with better performance in the cohort who received TCHP (as in the training cohort). The strong performance of this model across 5 institutions is a promising indicator of its robustness and ability to tailor therapy even within clinically-distinct HER2+ patient subpopulations.

Deep learning (DL) and multivariable clinical model (MCM) pCR prediction by cohortCohortModelAUC (%)Sensitivity (%)Specificity (%)Accuracy (%)Validation (n=15, 53% pCR)DL93888687MCM66637173Testing 1 (n=28, 57% pCR)DL85889289MCM54388350DL+MCM89819286Testing 2 (n=29, 34% pCR)DL77708479MCM53208966DL+MCM76808483

Citation Format: Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi. Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-13.

Prognostic value of a three-scale grading system based on combining molecular imaging with 68Ga-DOTATATE and 18F-FDG PET/CT in patients with metastatic gastroenteropancreatic neuroendocrine neoplasias

We investigated on the added prognostic value of a three-scale combined molecular imaging with ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/CT, (compared to Ki-67 based histological grading), in gastroenteropancreatic neuroendocrine neoplasia patients. 85 patients with histologically proven metastatic gastroenteropancreatic neuroendocrine neoplasias, who underwent combined PET/CT imaging were retrospectively evaluated. Highest Ki-67 value available at time of ¹⁸F-FDG PET/CT was recorded. Patients were classified according to World Health Organization/European Neuroendocrine Tumor Society histological grades (G1, G2, G3) and into three distinct imaging categories (C1: all lesions are ¹⁸F-FDG negative/⁶⁸Ga-DOTATATE positive, C2: patients with one or more ¹⁸F-FDG positive lesions, all of them ⁶⁸Ga-DOTATATE positive, C3: patients with one or more ¹⁸F-FDG positive lesions, at least one of them ⁶⁸Ga-DOTATATE negative). The primary endpoint of the study was Progression-Free Survival, assessed from the date of ¹⁸F-FDG PET/CT to the date of radiological progression according to Response Evaluation Criteria In Solid Tumors version 1.1. Classification according to histological grade did not show significant statistical difference in median Progression-Free Survival between G1 and G2 but was significant between G2 and G3 patients. In contrast, median Progression-Free Survival was significantly higher in C1 compared to C2 and in C2 compared to C3 patients, revealing three distinctive imaging categories, each with highly distinctive prognosis. Our three-scale combined ⁶⁸Ga-DOTATATE/¹⁸F-FDG PET imaging classification holds high prognostic value in patients with metastatic gastroenteropancreatic neuroendocrine neoplasias.

Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI

BACKGROUND

Early prediction of nonresponse is essential in order to avoid inefficient treatments.

PURPOSE

To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.

STUDY TYPE

This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially recruited, with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for MRI examinations.

ASSESSMENT

Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T₁ subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis.

RESULTS

PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference.

DATA CONCLUSION

PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.

Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI

593

Background: HER2-targeted neoadjuvant chemotherapy (NAC) possesses heterogeneous outcomes and currently lacks clinically-accepted markers of response. A means of predicting which patients will benefit prior to the treatment could reduce toxicity and the delay to effective intervention. Computational analysis of MRI via a deep neural network has shown promise in identifying NAC responders among mixed receptor subtype and treatment regimen cohorts, but faces challenges due to reproducibility across institutions and has not yet been explored in the context of HER2-targeted therapy. Here we present a deep learning approach for predicting response to HER2-targeted NAC from pre-treatment MRI. Methods: 100 HER2+ breast cancer patients who received NAC with docetaxel, carboplatin, trastuzumab, and pertuzumab at Cleveland Clinic (CCF) and had pre-treatment contrast-enhanced MRI’s were included in this analysis. 49 patients achieved pathological complete response (pCR, ypT0/is), while 51 patients retained presence of residual disease following NAC (non-pCR). 85 patients were used to train a convolutional neural network to predict pCR based on pre- and post-contrast MRI images, and the model design was optimized based on performance within a 15 patient internal validation cohort. An external, held-out testing dataset consisting of 28 patients (16 pCR, 12 non-pCR) imaged and treated at University Hospitals (UH) Cleveland Medical Center was used to validate the performance of the model. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A multivariable model incorporating age, hormone receptor status, stage, and tumor size was developed and similarly evaluated. Results: The neural network was able to predict the response to HER2-targeted NAC in the internal validation cohort (AUC = 0.93) as well as in an independent cohort from a separate institution (AUC = 0.85). This model offered superior performance compared to a multivariate clinical model, which achieved AUC = 0.67 and AUC = 0.52, in internal validation and external held-out testing cohorts, respectively. Conclusions: Deep learning analysis of contrast-enhanced MRI could be used to better target anti-HER2 therapy by pre-treatment prediction of response.[Table: see text]

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status

BACKGROUND

Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments.

PURPOSE

To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy.

STUDY TYPE

Prospective, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially included with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for acquisition and MRS was performed using the syngo GRACE sequence.

ASSESSMENT

MRS and MRI examinations were performed at baseline (TP1), 24-72 hours after first chemotherapy (TP2), after the end of anthracycline treatment (TP3), and MRI only after the end of taxane treatment (TP4). Early (EMR) and late (LMR) morphological response were defined as %ΔDmax13 or %ΔDmax14, respectively. Responders were patients with %ΔDmax >30. Pathological complete response (pCR) patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, multiple regression, logistic regression, one-way analysis of variance (ANOVA) analysis were used for the analysis.

RESULTS

At TP1 there was a significant difference between response groups for tCho1 concerning EMR prediction (P = 0.05) and pCR (P < 0.05) and for K_ep 1 (P = 0.03) concerning LMR prediction. At TP2, no modification of tCho and other parameters could predict response. At TP3, ΔtCho, ΔDmax, and ΔVol could predict LMR (P < 0.05 for all parameters), pCR (P < 0.05 for all parameters), and ΔK^trans could predict only pCR (P = 0.04). Logistic regression at baseline showed the highest area under the curve (AUC) of 0.9 for prediction of pCR. The triple negative (TN) subgroup showed significantly higher tCho at baseline (P = 0.02) and higher ΔtCho levels at TP3 (P < 0.05).

DATA CONCLUSION

Baseline measurements of tCho in combination with clinicopathological criteria could predict non-pCR with a high AUC. Furthermore, tCho quantification for prediction of pCR was more sensitive for TN tumors.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;48:982-993.

Abstract 3891: Tackling the obstacles facing the implementation of a molecular screening program in an early drug development unit: The Jules Bordet Institute Program for Molecular Profiling of Metastatic Lesions - feasibility (Precision-f)

Introduction

The identification of pathways involved in carcinogenesis, the emergence of high-throughput technologies enabling tumor molecular analysis and the development of targeted therapies have led to the concept of precision medicine. Higher response rates and longer survivals have been achieved in recent genomic-driven clinical trials. However, genomic-driven cancer medicine is hindered by multiple obstacles. Our aim was to assess the feasibility of incorporating real-time targeted gene sequencing (TGS) of DNA derived from metastatic biopsies into daily clinical practice.

Experimental procedures

Precision-f (NCT01932489) is a pilot trial conducted at the Jules Bordet Institute. Patients with metastatic colorectal cancer (CRC), melanoma or non-small cell lung (NSCLC) cancer were enrolled. Two FFPE blocks and one fresh frozen sample embedded in OCT were collected from newly performed metastatic biopsies as well as one whole blood sample. The FFPE samples were checked for tumor cellularity and 5×5 μm sections were cut and sent to 2 laboratories. Targeted gene sequencing was performed on DNA extracted for the same sample using the Illumina TruSeq Amplicon Cancer Panel performed on a MiSeq Desktop Sequencer, and the Life Technologies Ion AmpliSeq Cancer Hotspot Panel performed on an Ion Personal Genome Machine. Results were reported to the institutional sequencing tumor board for discussion, annotation and treatment assignment. The main objectives were the evaluation of biopsy quality, turnaround time, the presence of “actionable” alterations, technology cross-validity and treatment assignments.

Results

Thirty-four patients were enrolled between December 2013 and August 2014: 13 NSCLC patients, 11 CRC patients, 10 melanoma patients. Successful molecular results were achieved from 32/34 biopsies (94%). The most frequent site of biopsy was the liver (10) followed by the lung (7), the skin (5) and lymph nodes (5). 27/34 (79%) samples had ≥ 20% tumor cells. 31/34 (91%) of samples had &gt; 10 ng of DNA for TGS. The median turnaround time for results reporting was 15 calendar days [8-22]. “Actionable” mutations were found for 66% (21/32) of patients, 76% (16/21) of which were treated with therapy according to the identified molecular alteration. Reasons for non-targeted therapy were: non-eligibility (2), unavailable drugs (2) and patient refusal (1). The results of the comparison of TGS data across the 2 platforms is ongoing and will be presented at the meeting.

Conclusion

The precision-f pilot trial has demonstrated the feasibility and clinical relevance of a molecular screening program in a clinical pharmacology unit. A Belgian national initiative will follow in order to enhance patient participation in genomic-driven clinical trials.

Citation Format: Philippe G. Aftimos, Marion Maetens, Catherine Sibille, Jean-François Laes, Sylvain Brohée, Thierry Berghmans, Joseph Kerger, Alain Hendlisz, Alexandre Irrthum, Olivier De Henau, Amélie Deleporte, Stylianos Drisis, Denis Larsimont, Jalal Vakili, Christos Sotiriou, Martine Piccart, Ahmad Awada. Tackling the obstacles facing the implementation of a molecular screening program in an early drug development unit: The Jules Bordet Institute Program for Molecular Profiling of Metastatic Lesions - feasibility (Precision-f). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3891. doi:10.1158/1538-7445.AM2015-3891

Abstract PD3-7: Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analyses in breast cancer

Background: Molecular screening programs are using next-generation sequencing (NGS) for cancer gene panels in metastatic biopsies. We interrogated whether plasma can be used as an alternative to metastatic biopsies.

Patients and methods: The Ion AmpliSeqTM Cancer Hotspot Panel v2 (Ion Torrent), covering approximately 2,800 COSMIC mutations from 50 cancer genes was used to analyze 70 primary and/or metastases and 29 plasma samples from 17 metastatic breast cancer patients. The targeted coverage for tissue DNA was 1000x and for plasma circulating DNA 25000x. Whole blood normal DNA was used to exclude germline variants. The Illumina technology was used for independent validation.

Results: Twelve patients had estrogen receptor (ER)+/ human epidermal growth factor receptor 2 (HER2)-, 1 ER+/HER2+, 2 ER-/HER2+ and 2 ER-/HER2- tumors. Evaluable NGS results were obtained for 61 primary/metastases and 29 plasma samples from 17 patients. When primary/metastases were analyzed, 12 of 17 patients had at least 1 mutation (median 1 mutation per patient, range 0-2) in either p53, PIK3CA, PTEN, AKT1 or IDH2 gene. When plasma was analyzed, 11 of 17 patients had at least 1 mutation (median 1 mutation per patient, range 0-2) in either p53, PIK3CA, PTEN, AKT1, IDH2 and SMAD4. All primary/metastases/plasma mutations were independently validated using the illumina technology. When we focused on metastases and plasma samples collected at the same time point, we observed that in 4 patients, no mutation was identified in either metastases or plasma, in 9 patients the same mutations were identified in metastases and plasma, in 2 patients a mutation was identified in metastases but not in plasma and in 2 patients a mutation was identified in plasma but not in metastases (Table 1). Thus, in 13 of 17 (76%) patients, metastases and plasma analysis provided concordant results whereas in 4 of 17 (24%) demonstated discordant results providing complementary information (Table 1). Conclusion: Plasma can be tested as an alternative tissue source in molecular screening programs.

Table Mutational status of synchronous metastatic biopsies and plasma samples analysed using the Ion AmpliSeqTM Cancer Hotspot Panel v2Patient IDGeneMutationMetastasis (MAF)Plasma (MAF)3PTENp.Q171EYES (27.5%)YES (25.9%)3SMAD4p.E394*NOYES (10.9%)4NONO6PIK3CAp.H1047RYES (20.5-47.7%)YES (4.6%)7TP53p.V274AYES (35.6-56.1%)YES (20.7%)8IDH2p.R140RYES (28.2)YES (0.5%)10PIK3CAp.H1047RYES (19.7%)NO11PIK3CAp.E453KYES (4.6-17.8%)YES (2.8%)11PIK3CAp.E453KYES (13.1-23%)YES (3.4%)14PIK3CAp.H1047RYES (24.8%)NO14PIK3CAp.H1047RYES (0-13.8%)YES (0.5%)16TP53p.Y103*YES (59.8-86.3%)YES (49%)17NONO19NONO20PIK3CAp.E545KNOYES (14.3%)30TP53p.M237KYES (27.6-50%)YES (51.8%)37TP53p.H193LYES (61.6-82.9%)YES (55.5%)38AKT1p.E17KYES (26-68.2%)YES (10%)38TP53p.R248WYES (23.6-56.8%)YES (5.9%)39TP53p.R136HYES (7.5%)NO40NONOMAF: Mutant allele frequency; For patients with multiple metastases samples at the same time point, the MAF range is provided. Patients 11 & 14 had 2 timepoints with synchronous metastases/plasma samples.

Citation Format: Michail Ignatiadis, Françoise Rothe, Jean-François Laes, Diether Lambrechts, Dominiek Smeets, Delphine Vincent, Marion Maetens, Debora Fumagalli, Stefan Michiels, Stylianos Drisis, Carine Moerman, Jean-Pol Detiffe, Denis Larsimont, Ahmad Awada, Martine Piccart, Christos Sotiriou. Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analyses in breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr PD3-7.

Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analysis in breast cancer

BACKGROUND

Molecular screening programs use next-generation sequencing (NGS) of cancer gene panels to analyze metastatic biopsies. We interrogated whether plasma could be used as an alternative to metastatic biopsies.

PATIENTS AND METHODS

The Ion AmpliSeq™ Cancer Hotspot Panel v2 (Ion Torrent), covering 2800 COSMIC mutations from 50 cancer genes was used to analyze 69 tumor (primary/metastases) and 31 plasma samples from 17 metastatic breast cancer patients. The targeted coverage for tumor DNA was ×1000 and for plasma cell-free DNA ×25 000. Whole blood normal DNA was used to exclude germline variants. The Illumina technology was used to confirm observed mutations.

RESULTS

Evaluable NGS results were obtained for 60 tumor and 31 plasma samples from 17 patients. When tumor samples were analyzed, 12 of 17 (71%, 95% confidence interval (CI) 44% to 90%) patients had ≥1 mutation (median 1 mutation per patient, range 0-2 mutations) in either p53, PIK3CA, PTEN, AKT1 or IDH2 gene. When plasma samples were analyzed, 12 of 17 (71%, 95% CI: 44-90%) patients had ≥1 mutation (median 1 mutation per patient, range 0-2 mutations) in either p53, PIK3CA, PTEN, AKT1, IDH2 and SMAD4. All mutations were confirmed. When we focused on tumor and plasma samples collected at the same time-point, we observed that, in four patients, no mutation was identified in either tumor or plasma; in nine patients, the same mutations was identified in tumor and plasma; in two patients, a mutation was identified in tumor but not in plasma; in two patients, a mutation was identified in plasma but not in tumor. Thus, in 13 of 17 (76%, 95% CI 50% to 93%) patients, tumor and plasma provided concordant results whereas in 4 of 17 (24%, 95% CI 7% to 50%) patients, the results were discordant, providing complementary information.

CONCLUSION

Plasma can be prospectively tested as an alternative to metastatic biopsies in molecular screening programs.

Removal of Plaque and Stent Artifacts in Subtraction CT Angiography Using Nonrigid Registration and a Volume Penalty

Computed tomography angiography (CTA) is an established tool for vascular imaging. However, high-intense structures in the contrast image can seriously hamper luminal visualisation. This can be solved by subtraction CTA, where a native image is subtracted from the contrast image. However, patient and organ motion limit the application of this technique. Within this paper, a fully automated intensity-based nonrigid 3D registration algorithm for subtraction CT angiography is presented, using a penalty term to avoid volume change during registration. Visual and automated validation on four clinical datasets clearly show that the algorithm strongly reduces motion artifacts in subtraction CTA. Most artifacts disappear, also artifacts caused by minimal displacement of stents or calcified plaques, allowing a 2D and 3D artifact-free visualisation of the vessel lumen. This enables a quick overview of the whole vascular structure and opens the possibility to the visualisation of smaller vessels.

Computer-aided detection of colonic polyps using low-dose CT acquisitions

RATIONALE AND OBJECTIVES

This report proposes an alternative method for the automatic detection of colonic polyps that is robust enough to be directly applicable on low-dose computed tomographic data.

MATERIALS AND METHODS

The polyp modeling process takes into account both the gray-level appearance of polyps (intensity profiles) and their geometry (extended Gaussian images). Spherical harmonic decompositions are used for comparison purposes, allowing fast estimation of the similarity between a candidate and a set of previously computed models. Starting from the original raw data (acquired at 55 mA), five patient data sets (prone and supine scans) are reconstructed at different dose levels (to 5 mA) by using different kernel filters, slice overlaps, and increments. Additionally, the efficacy of applying an edge-preserving smoothing filter before detection is assessed.

RESULTS

Although image quality decreases when decreasing acquisition milliamperes, all polyps greater than 6 mm are detected successfully, even at 15 mA. Although not important at high doses, smoothing improves detection results for ultra-low-dose (tube current<15 mA) data.

CONCLUSION

The advantage of low-dose scans is a significant decrease in effective dose from 4.93 to 1.61 mSv while retaining high detection values, particularly important when thinking of population screening.

Plaque and stent artifact reduction in subtraction CT angiography using nonrigid registration and a volume penalty

Computed tomography angiography (CTA) is an established tool for vessel imaging. Yet, high-intense structures in the contrast image can seriously hamper luminal visualisation. This can be solved by subtraction CTA, where a native image is subtracted from the contrast image. However, patient and organ motion limit the application of this technique. Within this paper, a fully automated intensity-based nonrigid 3D registration algorithm for subtraction CT angiography is presented, using a penalty term to avoid volume change during registration. Visual and automated validation on four clinical datasets clearly show that the algorithm strongly reduces motion artifacts in subtraction CTA. With our method, 39% to 99% of the artifacts disappear, also those caused by minimal displacement of stents or calcified plaques. This results in a better visualisation of the vessel lumen, also of the smaller vessels, allowing a faster and more accurate inspection of the whole vascular structure, especially in case of stenosis.

Computer Aided Detection for Low-Dose CT Colonography

The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database. The detection sensitivity and specificity values are evaluated at different dose levels. Starting from the original raw-data (acquired at 55mAs), 5 patient datasets (prone and supine scans) are reconstructed at different dose levels (down to 5mAs), using different kernel filters and slice increments. Although the image quality decreases when lowering the acquisition mAs, all polyps above 6mm are successfully detected even at 15 mAs. Accordingly the effective dose can be reduced from 4.93mSv to 1.61 mSv, without affecting detection capabilities, particularly important when thinking of population screening.