A Biophysico-Computational Perspective of Breast Cancer Pathogenesis and Treatment Response

Introduction: Inflammatory Breast Cancer (IBC) is a rare and very aggressive breast cancer with poor prognosis. The prevalence is different from a country to another. In Tunisia, it is about 5 to 7% of breast cancer. The aim of this study is to describe the epidemiological and histopathological features of patients with inflammatory breast cancer and to evaluate the treatment response according to the molecular subtypes. Methods: This retrospective review identified 31 patients with no metastatic IBC treated in our radiotherapy department between December 2019 and November 2020. IBC was confirmed using the clinical criteria. Baseline clinic-pathological and treatment information was retrieved from medical records. Statistical analysis was performed with IBM SPSS V.20. Results: Median age was 51.3 years [27-68]. 48% of tumors were grade 3. The average tumor size was 36mm [10-90]. The histological type was ductal carcinoma in 97%. Vascular invasion was noted in 24 patients (77%). Thirty patients were classified as stage IIIB and one patient was IIIC. 74% were hormone receptor positive and 45% were HER2 positive. Luminal B was the predominant subtype (52%) followed by Her2 positive (32%), Luminal A (23%), and triple negative (3%) All patients had chemotherapy: neoadjuvant for 26 patients (84%) and adjuvant for 5 patients (16%). Nine patients (29%) had tumor pathological complete response (pCR). Partial response was observed in 18 patients (58%). Lymph node pCR was noted in 16% of cases (n=5). Endocrine therapy and trastuzumab were given to 76% and 45% of patients, respectively. The influence of the molecular subtype was not statistically significant on the response to neoadjuvant treatment. The highest rate of pCR were 43% for Her2positive, then 27%, 21% and 9% for Luminal B, Luminal A and Triple negative, respectively (p=0.2). Conclusion: Our study showed a high percentage of hormone receptor and Her2+ (74% and 45% respectively) in IBC. Luminal B was the most frequent subtype. Anthracycline-based chemotherapy and trastuzumab improved the pCR rate: 44% for Her2positive. Triple negative showed poorer pCR than other breast cancer subtype without a significant difference. A larger study is warranted to confirm our findings.

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25 Line of therapy adjustment in a patient with advanced triple-negative breast cancer (TNBC) by using personalized ctDNA test for treatment response monitoring

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0025 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A24-A24

Author(s):

Georges Azzi ◽

Shifra Krinshpun ◽

Antony Tin ◽

Allyson Malashevich ◽

Meenakshi Malhotra ◽

...

Keyword(s):

Breast Cancer ◽

Ct Scan ◽

Treatment Response ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Bilateral Mastectomy ◽

First Line ◽

Pet Ct ◽

Line Of Therapy ◽

Pet Ct Scan

BackgroundTriple negative breast cancer (TNBC) is an aggressive form of breast cancer that is most difficult to treat due to the absence of hormone/growth factor receptors.1 2 Metastatic TNBC (mTNBC) is particularly challenging, given the limited efficacy and duration of response to chemotherapy.3 The repertoire of therapeutic options for mTNBC patients continues to increase with chemotherapeutic and immuno oncology based treatments and now includes sacituzumab govitecan, a novel antibody-chemotherapy conjugate.4MethodsHere we present a case study of a 40-year-old female who on biopsy of her left breast mass was diagnosed with TNBC. The patient underwent neoadjuvant chemotherapy with weekly administration of paclitaxel and carboplatin followed by dose-dense doxorubicin with cyclophosphamide. Following one-month, the patient underwent bilateral mastectomy, showing pathological staging ypT2 pN0. The patient underwent periodic radiological imaging along with the assessment of circulating tumor DNA in blood using a personalized and tumor-informed multiplex PCR, next-generation sequencing assay (Signatera bespoke, mPCR NGS assay) to identify the minimal residual disease (MRD) and treatment response.ResultsAfter surgery, MRD assessment revealed ctDNA positive status (0.41 MTM/mL) prompting PET/CT scan that revealed liver metastasis. Continued ctDNA monitoring showed continuous increase in ctDNA concentration (287.09 MTM/mL). Separate analyses indicated MSI-high and PD-L1 positive tumor status, leading to the initiation of the first line of therapy (nab-paclitaxel and Atezolizumab), which resulted in ctDNA decline (39.62 MTM/ml). Weekly ctDNA monitoring noted a rapid increase a month later (178 MTM/ml to 833.69 MTM/ml) within a 2-week interval, which corresponded to disease progression on imaging. Given non-responsiveness with the first-line therapy, the patient was initiated with sacituzumab govitecan. Following this, a rapid decline in the ctDNA level was observed within a week (364.07 MTM/mL) with a downward trend to 73.03 MTM/ml by two weeks. An interval PET/CT scan showed a mixed response. Continued monitoring of ctDNA demonstrated ctDNA levels <5MTM/mL for a period of two months before serially rising again (to 89.27 MTM/ml). PET-CT ordered in response to increasing ctDNA levels confirmed progression involving hepatic and lung lesions. A new line of therapy with nivolumab and ipilimumab was subsequently initiated.ConclusionsSerial monitoring of ctDNA enables early detection of therapy resistance and provides a rationale for treatment change/optimization/discontinuation as compared to periodic imaging that is currently the standard of care. The ease and convenience of using ctDNA-based testing as frequently as every week clearly identified earlier non-responsiveness to IO and also identified earlier acquired resistance to antibody-drug conjugate, enabling a prompt switch to alternative therapy.Ethics ApprovalN/AConsentN/AReferencesAnders C, Carey LA. Understanding and treating triple-negative breast cancer. Oncology (Williston Park). 2008;22(11):1233–1243.Mehanna J, Haddad FG, Eid R, Lambertini M, Kourie HR. Triple-negative breast cancer: current perspective on the evolving therapeutic landscape. Int J Womens Health2019;11:431–437. Published 2019 Jul 31. doi:10.2147/IJWH.S178349Treatment of Triple-negative Breast Cancer. American Cancer Society Website. Updated 2020. Accessed August 10, 2020. https://www.cancer.org/cancer/breast-cancer/treatment/treatment-of-triple-negative.htmlBardia A, Mayer IA, Vahdat LT, et al. Sacituzumab govitecan-hziy in refractory metastatic triple-negative breast cancer. N Engl J Med 2019;380(8):741–751. doi:10.1056/NEJMoa1814213

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Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

Scientific Reports ◽

10.1038/s41598-021-94004-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hamidreza Taleghamar ◽

Hadi Moghadas-Dastjerdi ◽

Gregory J. Czarnota ◽

Ali Sadeghi-Naini

Keyword(s):

Breast Cancer ◽

Treatment Response ◽

Quantitative Ultrasound ◽

Parametric Imaging ◽

Term Survival ◽

Adaptive Boosting ◽

Tumor Margin ◽

Parametric Images ◽

Response To Chemotherapy ◽

Pre Treatment

AbstractThe efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

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Osteoporosis and breast cancer: pathogenesis, diagnosis and treatment

Maturitas ◽

10.1016/j.maturitas.2019.04.029 ◽

2019 ◽

Vol 124 ◽

pp. 118-119

Author(s):

Peyman Hadji

Keyword(s):

Breast Cancer ◽

Diagnosis And Treatment ◽

Cancer Pathogenesis

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Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

Scientific Reports ◽

10.1038/s41598-020-77397-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Angela M. Jarrett ◽

David A. Hormuth ◽

Vikram Adhikarla ◽

Prativa Sahoo ◽

Daniel Abler ◽

...

Keyword(s):

Breast Cancer ◽

Targeted Therapy ◽

Treatment Response ◽

Growth Factor Receptor ◽

Patient Specific ◽

Mathematical Framework ◽

Breast Cancer Patients ◽

Data Types ◽

Her2 Breast Cancer ◽

Mri Scans

AbstractWhile targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

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The Motile Breast Cancer Phenotype Roles of Proteoglycans/Glycosaminoglycans

BioMed Research International ◽

10.1155/2014/124321 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 16

Author(s):

Dragana Nikitovic ◽

Katerina Kouvidi ◽

Kallirroi Voudouri ◽

Aikaterini Berdiaki ◽

Evgenia Karousou ◽

...

Keyword(s):

Breast Cancer ◽

Growth Factor ◽

Growth Factor Receptor ◽

Downstream Signaling ◽

Insulin Growth Factor ◽

Cancer Pathogenesis ◽

Cellular Mediators ◽

Epidermal Growth ◽

Cancer Phenotype ◽

Insulin Growth Factor Receptor

The consecutive stages of cancer growth and dissemination are obligatorily perpetrated through specific interactions of the tumor cells with their microenvironment. Importantly, cell-associated and tumor microenvironment glycosaminoglycans (GAGs)/proteoglycan (PG) content and distribution are markedly altered during tumor pathogenesis and progression. GAGs and PGs perform multiple functions in specific stages of the metastatic cascade due to their defined structure and ability to interact with both ligands and receptors regulating cancer pathogenesis. Thus, GAGs/PGs may modulate downstream signaling of key cellular mediators including insulin growth factor receptor (IGFR), epidermal growth factor receptor (EGFR), estrogen receptors (ERs), or Wnt members. In the present review we will focus on breast cancer motility in correlation with their GAG/PG content and critically discuss mechanisms involved. Furthermore, new approaches involving GAGs/PGs as potential prognostic/diagnostic markers or as therapeutic agents for cancer-related pathologies are being proposed.

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