Predict Therapy Response Research Articles

Beyond the Norm: The emerging interplay of complement system and extracellular matrix in the tumor microenvironment.

Ground-breaking awareness has been reached about the intricate and dynamic connection between developing tumors and the host immune system. Being a powerful arm of innate immunity and a functional bridge with adaptive immunity, the complement system (C) has also emerged as a pivotal player in the tumor microenvironment (TME). Its "double-edged sword" role in cancer can find an explanation in the controversial relationship between C capability to mediate tumor cell cytolysis or, conversely, to sustain chronic inflammation and tumor progression by enhancing cell invasion, angiogenesis, and metastasis to distant organs. However, comprehensive knowledge about the actual role of C in cancer progression is impaired by several limitations of the currently available studies. In the current review, we aim to bring a fresh eye to the controversial role of C in cancer by analyzing the interplay between C and extracellular matrix (ECM) components as potential orchestrators of the TME. The interaction of C components with specific ECM components can determine C activation or inhibition and promote specific non-canonical functions, which can, in the tumor context, favor or limit progression based on the cancer setting. An in-depth and tumor-specific characterization of TME composition in terms of C components and ECM proteins could be essential to determine their potential interactions and become a key element for improving drug development, prognosis, and therapy response prediction in solid tumors.

Predictors of response to bDMARDs and tsDMARDs in psoriatic arthritis: a pilot study on the role of musculoskeletal ultrasound

ObjectivesThis pilot study aimed to identify early predictors of drug retention in patients with clinically active peripheral psoriatic arthritis who initiated or switched to therapy with biologic and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs).MethodsClinical and ultrasound assessments were conducted at baseline (t0) and subsequently at 1 (t1), 3 (t3), and 6 (t6) months. Ultrasound evaluations targeted joints/entheses according to PsASon-Score13 and the most clinically involved joint/enthesis/tendon or the two most clinically involved joints/entheses/tendons (MIJET and 2MIJET). After 6 months of follow-up, patients were divided into two groups based on drug retention, determined by the clinician's assessment of treatment efficacy (cResponder vs. non-cResponder). Main endpoints were ultrasound changes in MIJET, 2MIJET, and GUIS (Global US Inflammation Subscore) derived from PsASon-13.ResultsTwenty-nine patients were enrolled, 22 cResponders and 7 non-cResponders at t6. In the comparison between cResponders and non-cResponders, GUIS variation significantly differed in Δt6-t0, while MIJET and 2MIJET variations were significant as early as Δt3-t0 and confirmed in Δt6-t0. The ultrasound response of MIJET and 2MIJET was faster in cResponders treated with JAKi vs. those treated with TNFi and IL-17/12-23i, significant in Δt1-t0.ConclusionsUltrasound imaging of clinically involved joint sites may be a valuable early predictor of therapy response for predicting drug retention at 6 months in patients with psoriatic arthritis.

Imaging of tumor-associated macrophage dynamics during immunotherapy using a CD163-specific nanobody-based immunotracer

Immunotherapies have emerged as an effective treatment option for immune-related diseases, such as cancer and inflammatory diseases. However, variations in patient responsiveness limit the broad applicability and success of these immunotherapies. Noninvasive whole-body imaging of the immune status of individual patients during immunotherapy could enable the prediction and monitoring of the patient's response, resulting in more personalized treatments. In this study, we developed a nanobody-based immunotracer targeting CD163, a receptor specifically expressed on macrophages. This anti-CD163 immunotracer bound to human and mouse CD163 with high affinity and specificity without competing for ligand binding. Furthermore, the tracer showed no unwanted immune cell activation and was nonimmunogenic. Upon radiolabeling of the anti-CD163 immunotracer, specific imaging of CD163+ macrophages using micro-single-photon emission computerized tomography/computed tomography or micro-positron emission tomography/CT was performed. The anti-CD163 immunotracer was able to stratify immunotherapy responders from nonresponders (NR) by visualizing differences in the intratumoral CD163+ TAM distribution in Lewis lung carcinoma-ovalbumin tumor-bearing mice receiving an anti-programmed cell death protein-1 (PD-1)/CSF1R combination treatment. Immunotherapy-responding mice showed a more homogeneous distribution of the PET signal in the middle of the tumor, while CD163+ TAMs were located at the tumor periphery in NR. As such, visualization of CD163+ TAM distribution in the tumor microenvironment could allow a prediction or follow-up of therapy response. Altogether, this study describes an immunotracer, specific for CD163+ macrophages, that allows same-day imaging and follow-up of these immune cells in the tumor microenvironment, providing a good basis for the prediction and follow-up of immunotherapy responses in cancer patients.

Downregulation of the splicing regulator NSRP1 confers resistance to CDK4/6 inhibitors via activation of interferon signaling in breast cancer.

The combination of CDK4/6 inhibitors (CDK4/6i) and endocrine therapy is the first-line therapy for ER+/Her2- breast cancer, however, the development of drug resistance limited the efficacy of the agents. Although activation of the IFN signaling pathway has been identified as a critical driver of intrinsic and acquired CDK4/6i resistance, it remains unknown how the IFN signaling pathway was activated in resistant cells. Here, we report that NSRP1, a regulator of alternative mRNA splicing is downregulated in CDK4/6i resistant breast cancer cells and contributes to CDK4/6i resistance by mediating alternative splicing of NSD2 mRNA and activation of the IFN signaling pathway. Knockdown of NSRP1 reduces CDK4/6i (palbociclib) sensitivity of MCF7 cells while overexpression of NSRP1 sensitizes MCF7-PalR cells towards palbociclib treatment. Mechanistically, RNA sequencing suggests that NSRP1 knockdown strongly activates the IFN signaling pathway in MCF7 cells and elevates the expression of most of the "IFN-related palbociclib-resistance Signature" (IRPS) genes. NSRP1 also regulates numerous alternative splicing (AS) events in breast cancer cells, several of which are key regulators of the IFN signaling pathway. Among them, the inclusion of NSD2 exon 2 is elevated upon NSRP1 knockdown. Our data further show that the inclusion of NSD2 exon 2 is increased in breast cancer and associated with the poor prognosis of patients. In addition, including NSD2 exon 2 elevates NSD2 protein expression to activate the IFN signaling pathway. This study unveils the critical role of NSRP1 in regulating the IFN signaling pathway and the CDK4/6i resistance, which could be a promising biomarker for predicting therapy response.

HER2(-Low) Expression on Circulating Tumor Cells and Corresponding Metastatic Tissue in Metastatic Breast Cancer

Introduction: Significant progress has been made in the targeted therapy of metastatic breast cancer (mBC) in recent years. In this context, new biomarkers enable personalized therapy management and individualized therapy monitoring. Therefore, the systemic treatment is based increasingly on the biological characteristics of the tumor disease. Given the challenges of obtaining fresh tumor tissue through biopsies, the significance of liquid biopsies for assessing circulating tumor cells (CTCs) or circulating tumor DNA is of growing importance for the detection of prognostic and predictive biomarkers. Multiple studies have shown that the number of CTCs decreases under therapy, especially under anti-HER2-targeted therapy, and that the expression of the HER2 status on CTCs could play a role in predicting therapy response and therapeutic monitoring. The aim of this study was to analyze the HER2 status of CTCs in mBC patients before and after 3 months of systemic therapy to evaluate changes in the number of HER2-positive CTCs. The study focuses on HER2-low, which plays an increasingly important role in clinical practice due to new developments of HER2 targeting antibody-drug conjugates. In this context, temporal and spatial heterogeneity of the disease represent a major diagnostic challenge. Methods: A total of 324 patients with complete immunohistochemistry of biopsied metastases were divided into five groups: HER2 negative (−)/hormone receptor (HR) negative (−), HER2 −/HR positive (+), HER2 +/HR±, HER2-low/HR+, and HER2-low/HR−. Before and after 3 months of a new therapeutic line for mBC, CTCs were enumerated and analyzed for HER2 expression using the CellSearch® system. Overall survival of all subgroups was calculated. Results: The analyses revealed a discrepancy between the HER2 status of CTCs and corresponding tumor tissues in 98 patients (30.2%). The number of CTCs in general and the number of HER2+ CTCs decreased during systemic treatment, mainly in HER2+ tumors, but also in the other subgroups. Conclusions: Discrepancy in the HER2 status of the metastases and of CTCs was observed in approximately one-third of patients. Measuring HER2 on CTCs could potentially offer a means to longitudinally monitor HER2 status during therapy and simultaneously address challenges such as tumor heterogeneity. Therefore, the predictive value of HER2 on CTCs should be further investigated in clinical trials.

Short-term treatment response assessment in non-surgical treatment of advanced non-small cell lung cancer based on radiomics of dual-energy CT

PurposeTo build and evaluate a pre-treatment dual-energy CT(DECT)-based clinical-radiomics nomogram for individualized prediction of short-term treatment response to non-surgical treatment in advanced non-small cell lung cancer (NSCLC). MethodsPre-treatment DECT images were retrospectively collected from 98 pathologically confirmed NSCLC with clinical stage III or IV. Short-term treatment response was determined with follow-up CT of 4–6 courses of treatment. Quantitative radiomics metrics of the lesion were extracted from dual-energy mixed images at venous phase. Least absolute shrinkage and selection operator and correlation analysis were used to select the most relevant radiomics features. Radiomics model, clinical model and clinical-radiomics model were established by multivariate logistic regression. The model with the best prediction performance was visualized as a nomogram, and the consistency between the probability of the actual occurrence of the outcome and the probability predicted by the model was measured by calibration curves. ResultsClinical stage, difference in electron density in arteriovenous phase, difference in slope of energy spectrum in arteriovenous phase, and slope of energy spectrum in venous phase of the tumor were significant clinical predictors of therapy response (P < 0.05). The clinical-radiomics model showed a higher predictive capability (AUC: 0.87 and 0.85 in training and validation sets, respectively) than the radiomics models and the clinical model. The clinical-radiomics nomogram integrating the DECT radiomics signature with clinical stage and spectrum parameters showed good calibration and discrimination. ConclusionThe clinical-radiomics nomogram based on pre-treatment DECT showed good performance in predicting clinical response to non-surgical therapy in NSCLC.

Predictive Value of Neutrophil Extracellular Traps in Neoadjuvant Chemotherapy for Muscle-Invasive Bladder Cancer.

Cisplatin-based chemotherapy is the recommended therapy for muscle-invasive bladder cancer (MIBC). However, the efficacy of MIBC for chemotherapy is only about 40%. Therefore, predictors of therapy response are urgently needed. Neutrophils form neutrophil extracellular traps (NETs), a network structure, and growing evidence indicated that it could be a prognostic and predictive marker in cancer. In MIBC, the predictive role of NETs in chemotherapy resistance is unclear. We used the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analyses to develop a NETs-associated signature score (NETs-score) for therapeutic response prediction in the discovery cohort (GSE169455). Then the NETs score-based risk stratification was verified in two validation cohorts (Taber et al.'s cohort, our institutional cohort). In the training cohort, high NETs-score was associated with poor chemotherapy response (AUC = 0.781) and reduced recurrence-free survival (RFS) (hazard ratio [HR] = 2.07, 95% confidence interval [CI]: [1.26-3.40], p = 0.003) in MIBC patients. The NETs-score was also demonstrated to be a predictive factor for the efficacy of neoadjuvant chemotherapy in the validation cohort (AUC = 0.731). The accuracy of the NETs-score was superior to other chemotherapy response predictors such as Ba/Sq expression subtype (AUC = 0.711), BRCA2 mutation (AUC = 0.692) and ERCC2 mutation (AUC = 0.548). Furthermore, in our center cohort, the expression level of H3Cit showed a significant difference between the response and no-response group (p = 0.01). Through immunohistochemical validation, NETs was an independent predictor of MIBC neoadjuvant chemotherapy efficacy as determined by the multivariate logistic regression analysis (OR = 5.94, 95% CI: 1.20-45.50, p = 0.045). Patients with high levels of NETs predicted poor response to neoadjuvant chemotherapy. This study was the first to reveal the correlation between the level of NETs in MIBC and the efficacy of chemotherapy, which may provide a theoretical basis regarding NETs inhibitors.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

Prognostic and Immunotherapeutic Role of TACC3 in Pancancer and Its Impact on Proliferation and Docetaxel Resistance in Lung Adenocarcinoma

Introduction: Transforming acidic coiled-coil containing protein 3 (TACC3) exerts a vital role in cancer progression by modulating cell division and facilitating tumor growth. Given the lack of comprehensive research on the pancancer implications of TACC3, our study aimed to analyze the functional role of TACC3 in pancancer and validate it through experimental investigations in lung adenocarcinoma. Methods: We first employed various bioinformatics techniques to investigate the expression and prognostic significance of TACC3 in pancancer. Subsequently, we analyzed the correlation between TACC3 and immune infiltration, immune checkpoints, drug sensitivity, as well as the prediction of immune therapy response. Finally, we validated the association between TACC3 and the proliferation of lung adenocarcinoma, as well as its resistance to docetaxel, through in vitro experiments. Results: Here, TACC3 exhibited high expression in human cancers and was associated with poor prognosis in various cancer types. It was also involved in immune infiltration and demonstrated a strong predictive ability for immune therapy response. Through drug sensitivity prediction, we further identified a potential association between TACC3 and docetaxel resistance, which was subsequently validated in lung adenocarcinoma. Conclusion: Our investigation of TACC3 revealed its potential as a promising target both for immunosuppression and docetaxel resistance in pancancer, especially in lung adenocarcinoma.

Prediction of electroconvulsive therapy response and remission in late-life depression: a review.

Electroconvulsive therapy is an effective and well-tolerated antidepressant treatment for the elderly population. The place of electroconvulsive therapy in the treatment sequence for depression in the elderly is currently not well established. This review aims to identify the factors that contribute to a positive response and remission in elderly patients with depression undergoing electroconvulsive therapy treatment. We searched five bibliographic databases (Medline ALL Ovid, Embase.com, APA PsycInfo Ovid, Cochrane Library Wiley and Web of Science Core Collection) for articles published between 1995 and June 2023. Of the 2149 articles screened, 19 were included in the review. No significant associations were found between remission and/or response and salivary cortisol, baseline hippocampal and white matter hyperintensities, total amyloid load or global cortical atrophy. The reviewed articles did not show a significant difference in remission between unilateral and bilateral electroconvulsive therapy treatment. Other interesting findings are that moderately elevated levels of CRP and S100B levels, lower retardation scores, poorer performance on the word reading task at baseline and longer post-ictal reorientation time may be associated with higher remission and/or response rates. Medial temporal atrophy can be associated with lower Montgomery-Åsberg Depression Rating Scale (MADRS) decrease after electroconvulsive therapy. Finally, elderly patients had higher rates of electroconvulsive therapy response; retardation and psychotic features may mediate this association. Incorporation of this data into clinical practice may facilitate a personalised approach to electroconvulsive therapy. However, research on this topic is scarce and there are few studies that focus specifically on older people.

Systematic Identification of Gene-Drug Interactions Using an Advanced CRISPR Screening Platform to Predict Therapy Response across Cancer Types

Systematic Identification of Gene-Drug Interactions Using an Advanced CRISPR Screening Platform to Predict Therapy Response across Cancer Types

Drug induced sleep endoscopy and simultaneous polysomnography to predict the effectiveness of mandibular advancement device in obstructive sleep apnea treatment.

To evaluate whether mandibular advancement device therapy is recommended in patients affected by obstructive sleep apnea. In order to predict oral appliances therapy response, drug induced sleep endoscopy with cardio-respiratory polygraphy and mandibular advancement device simulator was carried out. Patients in which upper airway obstruction was resolved on all levels and AHI was normalized (< 5/h), were referred for oral appliance therapy. At 5 months follow up, a cardio-respiratory polygraphy with MAD was performed. 36 patients who have evidence of resolution of UA collapse and AHI below 5 events per hour, were referred for MAD therapy. At follow up, the mean AHI decreased from 29.1 ± 13.1 to 3.3/h ± 1.9 (p < 0.001). All the patients were responders. Combining the evaluation of drug induced sleep endoscopy and cardio-respiratory polygraphy data simultaneously during mandibular protrusion, has the potential to be a useful tool for prediction of MAD therapy response.

From Cancer to Immune Organoids: Innovative Preclinical Models to Dissect the Crosstalk between Cancer Cells and the Tumor Microenvironment

Genomic-oriented oncology has improved tumor classification, treatment options, and patient outcomes. However, genetic heterogeneity, tumor cell plasticity, and the ability of cancer cells to hijack the tumor microenvironment (TME) represent a major roadblock for cancer eradication. Recent biotechnological advances in organotypic cell cultures have revolutionized biomedical research, opening new avenues to explore the use of cancer organoids in functional precision oncology, especially when genomics alone is not a determinant. Here, we outline the potential and the limitations of tumor organoids in preclinical and translational studies with a particular focus on lung cancer pathogenesis, highlighting their relevance in predicting therapy response, evaluating treatment toxicity, and designing novel anticancer strategies. Furthermore, we describe innovative organotypic coculture systems to dissect the crosstalk with the TME and to test the efficacy of different immunotherapy approaches, including adoptive cell therapy. Finally, we discuss the potential clinical relevance of microfluidic mini-organ technology, capable of reproducing tumor vasculature and the dynamics of tumor initiation and progression, as well as immunomodulatory interactions among tumor organoids, cancer-associated fibroblasts (CAFs) and immune cells, paving the way for next-generation immune precision oncology.

An updated overview of K-RAS G12C inhibitors in advanced stage non-small cell lung cancer.

The discovery of KRAS mutations, particularly the KRASG12C variant, has been a milestone in understanding the molecular underpinnings of non-small cell lung cancer (NSCLC). These mutations are associated with aggressive tumor behavior and resistance to conventional therapies, highlighting the urgent need for targeted interventions. In this comprehensive review, we analyze the advancements in KRAS G12C inhibitors for the treatment of non-small cell lung cancer. Literature search is made from PubMed, Medline ASCO and ESMO Annual Meetings abstracts by using the following search keywords: "sotorasib", "adagrasib", "divarasib" and "KRAS G12C inhibitors." The last search was on 5June 2024. This review highlights the importance of pharmacokinetics, pharmacodynamics and potential adverse effects for treating individual patients and ensuring the best outcomes. Additionally, the review discusses research identifying biomarkers that can predict therapy responses and mentions the combination strategies to overcome resistance. Results of the studies and ongoing clinical trials are also briefly summarized in this review. KRASG12C inhibitors sotorasib, adagrasib and the newer divarasib, has revolutionized treating patients harboring this mutation. Ongoing studies and future clinical trials will refine our understandings with the ultimate goal of improving survival and quality of life for patients with this challenging disease.

C1q-Fixing De Novo Donor Specific Antibodies in Therapeutic Management of Chronic Antibody-Mediated Rejection Postkidney Transplantation

Evidence for C1q-fixing donor-specific antibodies (DSA) after chronic antibody-mediated rejection (CABMR) treatment is lacking. We investigated if C1q-DSA could predict therapy response in patients with biopsy-proven CABMR. Twenty kidney transplant patients with late-onset DSA were enrolled. Patients with biopsy-proven CABMR received three plasma pheresis sessions, one dose of rituximab (375 mg/m2), and steroid pulse therapy. We monitored IgG-DSA, C1q-DSA, and renal graft function for >2 years post-CABMR treatment. Patients with C1q-DSA mean fluorescence intensity (MFI) decreased by less than 50% post-treatment were classified as C1q-nonresponders. We compared Banff classification scores (g, ptc, cg, c4d) before and 6 months after treatment. Fourteen (70%) of 20 patients were C1q-DSA positive. The MFIs of IgG-DSA and C1q-DSA before treatment were significantly higher in the C1q-DSA positive group than in the negative group, at 20,035 and 10,918 (P = .008) and 17,702 and 21 (P < .001), respectively. Fifteen patients (75%) were diagnosed with CABMR via biopsy, and 12 patients received rejection therapy. Five (41.7%) patients were C1q-responders and seven (58.3%) were C1q-nonresponders. The MFIs of C1q-DSA before treatment were not significantly different between the two groups (11,521 vs. 13,985). Renal graft function was stable after treatment in C1q-responders for 3 years. In contrast, renal graft function tended to deteriorate in C1q-nonresponders. Biopsy showed improvement in scores in 75% of C1q-responders while deterioration in scores in 42.9% of C1q-nonresponders. C1q-DSA may be a good predictor of outcomes after CABMR treatment.

286 (PB274): A comprehensive biobank of PD3D® models and technology platform for cancer research and therapy response prediction

286 (PB274): A comprehensive biobank of PD3D® models and technology platform for cancer research and therapy response prediction

Post-operative breast imaging: a management dilemma. Can mammographic artificial intelligence help?

BackgroundImaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy.MethodsThis cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes.ResultsMammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy “POM” score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%.ConclusionApplying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression.

Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

Deep Learning-Based Approaches Using Medical Imaging for Therapy Response Prediction in Breast Cancer: A Systematic Literature Review

Deep Learning-Based Approaches Using Medical Imaging for Therapy Response Prediction in Breast Cancer: A Systematic Literature Review

Castration-resistant prostate cancer monitoring by cell-free circulating biomarkers.

Prostate cancer is the second leading cause of male cancer-related deaths in Western countries, which is predominantly attributed to the metastatic castration-resistant stage of the disease (CRPC). There is an urgent need for better prognostic and predictive biomarkers, particularly for androgen receptor targeted agents and taxanes. We have searched the PubMed database for original articles and meta-analyses providing information on blood-based markers for castration-resistant prostate cancer monitoring, risk group stratification and prediction of therapy response. The molecular markers are discussed along with the standard clinical parameters, such as prostate specific antigen, lactate dehydrogenase or C-reactive protein. Androgen receptor (AR) alterations are commonly associated with progression to CRPC. These include amplification of AR and its enhancer, point mutations and splice variants. Among DNA methylations, a novel 5-hydroxymethylcytosine activation marker of TOP2A and EZH2 has been identified for the aggressive disease. miR-375 is currently the most promising candidate among non-coding RNAs and sphingolipid analysis has recently emerged as a novel approach. The promising biomarkers have the potential to improve the care of metastatic prostate cancer patients, however, they need further validation for routine implementation.