On-treatment biomarkers can improve prediction of response to neoadjuvant chemotherapy in breast cancer

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.

Biomarker-Based Nomogram to Predict Neoadjuvant Chemotherapy Response in Muscle-Invasive Bladder Cancer

Background/Objectives: The aim of this study was to identify response prediction and prognostic biomarkers in muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant chemotherapy (NAC). Methods: A retrospective multicentre study including 191 patients with MIBC who received NAC previous to radical cystectomy (RC) between 1996 and 2013. Gene expression patterns were analysed in 34 samples from transurethral resection of the bladder (TURB) using Illumina microarrays. The expression levels of 45 selected differentially expressed genes between responders and non-responders to NAC were validated by quantitative PCR in an independent cohort of 157 patients. Regression analysis was used to identify predictors of downstaging and relapse. A nomogram for predicting downstaging and relapse-including clinicopathological and gene expression variables-was developed. Results: The expression levels of 1352 transcripts differed between responders and non-responders to NAC. A nomogram based on the most predictive clinical variables (age, Tis (in situ), gender, history of NMIBC, and lymphadenopathy) and genes selected following the Akaike information criterion (AIC) (CBTB16, CHMP6, DDX54, CASP8, LOR, and PLEC) was then created. In addition, a three-gene expression prognostic model to predict tumour relapse was generated. This model was able to discriminate between two groups of patients with a significantly different probability of tumour relapse (HR: 2.11; CI: 1.16-3.83, p = 0.01). Conclusions: Our nomogram based on gene expression and clinical data is a useful tool to predict downstaging and tumour relapse after NAC in MIBC patients. Further validation is warranted.

Effect of neoadjuvant chemotherapy on CD14 + CD16 + monocytes and soluble CD163 in Egyptian breast cancer patients

Neoadjuvant chemotherapy (NACT) influences the anticancer response by favourably altering the immune microenvironment. However, the effects of NACT on peripheral monocytes and their prognostic contribution to the NACT response have not yet been clarified. We aimed to evaluate the potential therapeutic responses and possible predictive value of double-positive (CD14 + CD16 +) monocytes and soluble CD163 (sCD163) in Egyptian breast cancer patients. Blood samples were obtained before and after neoadjuvant therapy from 30 patients with invasive breast cancer, and the expression of CD14 and CD16 was assessed via flow cytometry. The patients' sCD163 levels were also determined in both the serum and culture supernatant using enzyme-linked immunosorbent assay (ELISA). The results revealed that NACT was associated with a significant decrease in double-positive monocytes and sCD163 levels. In addition, both double-positive monocytes and serum sCD163 were significantly associated with a partial clinical response. Double-positive monocytes and serum sCD163 levels may be related to therapeutic response, suggesting their possible predictive value in breast cancer patients receiving NACT.

Dual-layer detector spectral computed tomography quantitative parameters for predicting pathological complete remission after neoadjuvant treatment of breast cancer

Background

Breast cancer (BC) is a common cancer among women worldwide, and although the use of neoadjuvant therapy (NAT) for BC has become more widespread, there is no standardized prediction of the efficacy of NAT for BC. This study aimed to evaluate the value of quantitative parameters of dual-layer detector spectral computed tomography (DLCT) in predicting whether BC patients can achieve pathological complete response (pCR) after NAT.

Methods

Patients who were first diagnosed with BC in Shandong Cancer Hospital and Institute and received only NAT before surgery were selected for participation in this study. All breast computed tomography (CT) imaging examinations were performed using DLCT, within 1 week before initiating NAT. The gold standard for evaluating the effect of NAT is pathologic response established at surgery. The Miller-Payne grading system was applied to assess the response to NAT. Quantitative parameters were extracted from DLCT, including CT value, normalized CT value, iodine concentration (IC), normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit (HU) curve, effective atomic number, and the normalized effective atomic number. The Mann-Whitney U test was used to compare the distribution differences of DLCT quantitative parameters between the pCR group and the non-pCR group. The diagnostic performance of the quantitative parameters was analyzed by receiver operating characteristic curve.

Results

In the neoadjuvant chemotherapy group (n=80), compared with the non-pCR group, the slope of the spectral HU curve, IC, effective atomic number, and NIC of arterial phase in the pCR group were higher, and the difference was statistically significant (P<0.05); area under the curve (AUC): 0.768, 0.791, 0.834, and 0.770, respectively. In the neoadjuvant targeted therapy group (n=40), compared with the pCR group, the CT value, IC, effective atomic number, and NIC of the arterial phase in the non-pCR group were higher, and the difference was statistically significant (P<0.05); AUC: 0.844, 0.813, 0.802, and 0.766, respectively. There was no significant difference (P>0.05) in DLCT venous phase quantitative parameters between pCR and non-pCR in 70 patients treated with NAT.

Conclusions

The study suggested a possibility that DLCT provided a potential tool to develop a model for predicting pCR to NAT in BC.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

PURPOSE

Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete response (pCR) to NAC. We test machine learning (ML) models to predict pCR in breast cancer and explore methods of handling missing data.

METHODS

Four hundred and ninety-nine patients with breast cancer treated with NAC in two centers in Singapore (National Cancer Centre Singapore [NCCS] and KK Hospital) between January 2014 and December 2017 were included. Eleven clinical features were used to train five different ML models. Listwise deletion and imputation were evaluated on handling missing data. Model performance was evaluated by AUC and calibration (Brier score). Feature importance from the best performing model in the external testing data set was calculated using Shapley additive explanations.

RESULTS

Seventy-two (24.6%), 18 (24.7%), and 31 (24.8%) patients attained pCR in NCCS training, NCCS testing, and KK Women's and Children's Hospital (KKH) testing data sets, respectively. The random forest (RF) base and imputed models have the highest AUCs in the KKH cohort of 0.794 (95% CI, 0.709 to 0.873) and 0.795 (95% CI, 0.706 to 0.871), respectively, and were the best calibrated with the lowest Brier score. No statistically significant difference was noted between AUCs of the base and imputed models in all data sets. The imputed model had a larger positive predictive value (PPV; 98.2% v 95.1%) and negative predictive value (NPV; 96.7% v 90.0%) than the base model in the KKH data set. Estrogen receptor intensity, human epidermal growth factor 2 intensity, and age at diagnosis were the three most important predictors.

CONCLUSION

ML, particularly RF, demonstrates reasonable accuracy in pCR prediction after NAC. Imputing missing fields in the data can improve the PPV and NPV of the pCR prediction model.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer

BACKGROUND

Patients with pathologic complete response (pCR) to neoadjuvant chemotherapy for invasive breast cancer (BC) have better outcomes, potentially warranting less extensive surgical and systemic treatments. Early prediction of treatment response could aid in adapting therapies.

METHODS

On-treatment biopsies from 297 patients with invasive BC in three randomized, prospective neoadjuvant trials were assessed (GeparQuattro, GeparQuinto, GeparSixto). BC quantity, tumor-infiltrating lymphocytes (TILs), and the proliferation marker Ki-67 were compared to pre-treatment samples. The study investigated the correlation between residual cancer, changes in Ki-67 and TILs, and their impact on pathologic complete response (pCR) and disease-free survival (DFS).

RESULTS

Among the 297 samples, 138 (46%) were hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2-), 87 (29%) were triple-negative (TNBC), and 72 (24%) were HER2+. Invasive tumor cells were found in 70% of on-treatment biopsies, with varying rates across subtypes (HR+/HER2-: 84%, TNBC: 62%, HER2+: 51%; p < 0.001). Patients with residual tumor on-treatment had an 8% pCR rate post-treatment (HR+/HER2-: 3%, TNBC: 19%, HER2+: 11%), while those without any invasive tumor had a 50% pCR rate (HR+/HER2-: 27%; TNBC: 48%, HER2+: 66%). Sensitivity for predicting residual disease was 0.81, with positive and negative predictive values of 0.92 and 0.50, respectively. Increasing TILs from baseline to on-treatment biopsy (if residual tumor was present) were linked to higher pCR likelihood in the overall cohort (OR 1.034, 95% CI 1.013-1.056 per % increase; p = 0.001) and with a longer DFS in TNBC (HR 0.980, 95% CI 0.963-0.997 per % increase; p = 0.026). Persisting or increased Ki-67 was associated with with lower pCR probability in the overall cohort (OR 0.957, 95% CI 0.928-0.986; p = 0.004) and shorter DFS in TNBC (HR 1.023, 95% CI 1.001-1.047; p = 0.04).

CONCLUSION

On-treatment biopsies can predict patients unlikely to achieve pCR post-therapy. This could facilitate therapy adjustments for TNBC or HER2 + BC. They also might offer insights into therapy resistance mechanisms. Future research should explore whether standardized or expanded sampling enhances the accuracy of on-treatment biopsy procedures. Trial registration GeparQuattro (EudraCT 2005-001546-17), GeparQuinto (EudraCT 2006-005834-19) and GeparSixto (EudraCT 2011-000553-23).

Early evaluation of circulating tumor DNA as marker of therapeutic efficacy and prognosis in breast cancer patients during primary systemic therapy

BACKGROUND

We assessed the potential role of serial circulating tumor DNA (ctDNA) as a biomarker to monitor treatment response to primary systemic therapy (PST) in breast cancer and evaluated the predictive value of ctDNA to further identify patients with residual disease.

METHODS

We prospectively enrolled 208 plasma samples collected at three time points (before PST, after 2 cycles of treatment, before surgery) of 72 patients with stage Ⅱ-III breast cancer. Somatic mutations in plasma samples were identified using a customized 128-gene capture panel with next-generation sequencing. The correlation between early change in ctDNA levels and treatment response or long-term clinical outcomes was assessed.

RESULTS

37 of 72 (51.4%) patients harbored detectable ctDNA alterations at baseline. Patients with complete response showed a larger decrease in ctDNA levels during PST. The median relative change of variant allele fraction (VAF) was -97.4%, -46.7%, and +21.1% for patients who subsequently had a complete response (n = 11), partial response (n = 11), and no response (n = 15) (p = 0.0012), respectively. In addition, the relative change of VAF between the pretreatment and first on-treatment blood draw exhibited the optimal predictive value to tumor response after PST (area under the curve, AUC = 0.7448, p = 0.02). More importantly, early change of ctDNA levels during treatment have significant prognostic value for patients with BC, there was a significant correlation between early decrease of VAF and longer recurrence-free survival compared to those with an VAF increase (HR = 12.54; 95% CI, 2.084 to 75.42, p = 0.0063).

CONCLUSION

Early changes of ctDNA are strongly correlated with therapeutic efficacy to PST and clinical outcomes in BC patients. The integration of preoperative ctDNA evaluation could help improving the perioperative management for BC patients receiving PST.

A prospective study of HER3 expression pre and post neoadjuvant therapy of different breast cancer subtypes: implications for HER3 imaging therapy guidance

PURPOSE

HER3, a member of the EGFR receptor family, plays a central role in driving oncogenic cell proliferation in breast cancer. Novel HER3 therapeutics are showing promising results while recently developed HER3 PET imaging modalities aid in predicting and assessing early treatment response. However, baseline HER3 expression, as well as changes in expression while on neoadjuvant therapy, have not been well-characterized. We conducted a prospective clinical study, pre- and post-neoadjuvant/systemic therapy, in patients with newly diagnosed breast cancer to determine HER3 expression, and to identify possible resistance mechanisms maintained through the HER3 receptor.

EXPERIMENTAL DESIGN

The study was conducted between May 25, 2018 and October 12, 2019. Thirty-four patients with newly diagnosed breast cancer of any subtype (ER ± , PR ± , HER2 ±) were enrolled in the study. Two core biopsy specimens were obtained from each patient at the time of diagnosis. Four patients underwent a second research biopsy following initiation of neoadjuvant/systemic therapy or systemic therapy which we define as neoadjuvant therapy. Molecular characterization of HER3 and downstream signaling nodes of the PI3K/AKT and MAPK pathways pre- and post-initiation of therapy was performed. Transcriptional validation of finings was performed in an external dataset (GSE122630).

RESULTS

Variable baseline HER3 expression was found in newly diagnosed breast cancer and correlated positively with pAKT across subtypes (r = 0.45). In patients receiving neoadjuvant/systemic therapy, changes in HER3 expression were variable. In a hormone receptor-positive (ER +/PR +/HER2-) patient, there was a statistically significant increase in HER3 expression post neoadjuvant therapy, while there was no significant change in HER3 expression in a ER +/PR +/HER2+ patient. However, both of these patients showed increased downstream signaling in the PI3K/AKT pathway. One subject with ER +/PR -/HER2- breast cancer and another subject with ER +/PR +/HER2 + breast cancer showed decreased HER3 expression. Transcriptomic findings, revealed an immune suppressive environment in patients with decreased HER3 expression post therapy.

CONCLUSION

This study demonstrates variable HER3 expression across breast cancer subtypes. HER3 expression can be assessed early, post-neoadjuvant therapy, providing valuable insight into cancer biology and potentially serving as a prognostic biomarker. Clinical translation of neoadjuvant therapy assessment can be achieved using HER3 PET imaging, offering real-time information on tumor biology and guiding personalized treatment for breast cancer patients.

The Age-Male-Albumin-Bilirubin-Platelets (aMAP) Risk Score Predicts Liver Metastasis Following Surgery for Breast Cancer in Chinese Population: A Retrospective Study

Objective

The current study is conducted to investigate the potential prognostic value of the age-male-albumin-bilirubin-platelets (aMAP) score in breast cancer patients with liver metastasis after surgery.

Methods

This is a retrospective study of 178 breast cancer patients who developed liver metastasis after surgery. These patients were treated and followed up from 2000 to 2018 at our hospital. The aMAP risk score was estimated in accordance with the following formula: . The optimal cutoff value of the aMAP was evaluated via X-tile. Kaplan-Meier, Log-rank and Cox proportional hazards regression models were applied to determine the clinical influence of the aMAP score on the survival outcomes. The nomogram models were established by multivariate analyses. The calibration curves and decision curve analysis were applied to evaluate the estimated performance of the nomogram models.

Results

A total of 178 breast cancer patients were divided into low aMAP score group (<47.6) and high aMAP score group (≥47.6) via X-tile plots. The aMAP score was a potential prognostic factor in multivariate analysis. The median disease free survival (p=0.0013) and overall survival (p=0.0003) in low aMAP score group were longer than in high aMAP score group. The nomograms were constructed to predict the DFS with a C-index of 0.722 (95% CI, 0.673-0.771), and the OS with a C-index of 0.708 (95% CI, 0.661-0.755). The aMAP-based nomograms had good predictive performance.

Conclusion

The aMAP score is a potential prognostic factor in breast cancer with liver metastasis after surgery. The aMAP score-based nomograms were conducive to discriminate patients at high risks of liver metastasis and develop adjuvant treatment and prevention strategies.

pSTAT5 is associated with improved survival in patients with thick or ulcerated primary cutaneous melanoma

Identifying prognostic biomarkers to predict clinical outcomes in stage I and II cutaneous melanomas could guide the clinical application of adjuvant and neoadjuvant therapies. We aimed to investigate the prognostic value of phosphorylated signal transducer and activator of transcription 5 (pSTAT5) as a biomarker in early-stage melanoma. This study evaluated all initially staged Ib and II melanoma patients undergoing sentinel node biopsy at a tertiary centre in Brisbane, Australia between 1994 and 2007, with survival data collected from the Queensland Cancer Registry. Primary melanoma tissue from 189 patients was analysed for pSTAT5 level through immunohistochemistry. Cox regression modelling, with adjustment for sex, age, ulceration, anatomical location, and Breslow depth, was applied to determine the association between pSTAT5 detection and melanoma-specific survival. Median duration of follow-up was 7.4 years. High pSTAT5 detection was associated with ulceration and increased tumour thickness. However, multivariate analysis indicated that high pSTAT5 detection was associated with improved melanoma-specific survival (hazard ratio: 0.15, 95% confidence interval: 0.03-0.67) as compared to low pSTAT5 detection. This association persisted when pSTAT5 detection was limited to immune infiltrate or the vasculature, as well as when sentinel node positivity was accounted for. In this cohort, staining for high-pSTAT5 tumours identified a subset of melanoma patients with increased survival outcomes as compared to low-pSTAT5 tumours, despite the former having higher-risk clinicopathological characteristics at diagnosis. pSTAT5 is likely an indicator of local immune activation, and its detection could represent a useful tool to stratify the risk of melanoma progression.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Changing the role of pCR in breast cancer treatment - an unjustifiable interpretation of a good prognostic factor as a "factor for a good prognosis"

Pathologic complete response (pCR) after neoadjuvant systemic therapy (NAST) of early breast cancer (EBC) has been recognized as a good prognostic factor in the treatment of breast cancer because of its significant correlation with long-term disease outcome. Based on this correlation, pCR has been accepted by health authorities (FDA, EMA) as a surrogate endpoint in clinical trials for accelerated drug approval. Moreover, in recent years, we have observed a tendency to treat pCR in routine clinical practice as a primary therapeutic target rather than just one of the pieces of information obtained from clinical trials. These trends in routine clinical practice are the result of recommendations in treatment guidelines, such as the ESMO recommendation "…to deliver all planned (neoadjuvant) treatment without unnecessary breaks, i.e. without dividing it into preoperative and postoperative periods, irrespective of the magnitude of tumor response", because "…this will increase the probability of achieving pCR, which is a proven factor for a good prognosis…". We hypothesize that the above recommendations and trends in routine clinical practice are the consequences of misunderstanding regarding the concept of pCR, which has led to a shift in its importance from a prognostic factor to a desired treatment outcome. The origin of this misunderstanding could be a strong subconscious incentive to achieve pCR, as patients who achieved pCR after NAST had a better long-term outcome compared with those who did not. In this paper, we attempt to prove our hypothesis. We performed a comprehensive analysis of the therapeutic effects of NAST and adjuvant systemic therapy (AST) in EBC to determine whether pCR, as a phenomenon that can only be achieved at NAST, improves prognosis per se. We used published papers as a source of data, which had a decisive influence on the formation of the modern attitude towards EBC therapy. We were unable to find any evidence supporting the use of pCR as a desired therapeutic goal because NAST (reinforced by pCR) was never demonstrated to be superior to AST in any context.

Blocks identical by descent in the genomes of the indigenous population of Siberia demonstrate genetic links between populations

The gene pool of the indigenous population of Siberia is a unique system for studying population and evolutionary genetic processes, analyzing genetic diversity, and reconstructing the genetic history of populations. High ethnic diversity is a feature of Siberia, as one of the regions of the peripheral settlement of modern human. The vast expanses of this region and the small number of aboriginal populations contributed to the formation of significant territorial and genetic subdivision. About 40 indigenous peoples are settled on the territory of the Siberian historical and ethnographic province. Within the framework of this work, a large-scale population study of the gene pool of the indigenous peoples of Siberia was carried out for the first time at the level of high-density biochips. This makes it possible to fill in a significant gap in the genogeographic picture of the Eurasian population. For this, DNA fragments were analyzed, which had been inherited without recombination by each pair of individuals from their recent common ancestor, that is, segments (blocks) identical by descent (IBD). The distribution of IBD blocks in the populations of Siberia is in good agreement with the geographical proximity of the populations and their linguistic affiliation. Among the Siberian populations, the Chukchi, Koryaks, and Nivkhs form a separate cluster from the main Siberian group, with the Chukchi and Koryaks being more closely related. Separate subclusters of Evenks and Yakuts, Kets and Chulyms are formed within the Siberian cluster. Analysis of SNPs that fell into more IBD segments of the analyzed populations made it possible to compile a list of 5358 genes. According to the calculation results, biological processes enriched with these genes are associated with the detection of a chemical stimulus involved in the sensory perception of smell. Enriched for the genes found, molecular pathways are associated with the metabolism of linoleic, arachidonic, tyrosic acids and by olfactory transduction. At the same time, an analysis of the literature data showed that some of the selected genes, which were found in a larger number of IBD blocks in several populations at once, can play a role in genetic adaptation to environmental factors.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Design, synthesis and antitumor activity of novel thiophene- triazine derivatives bearing arylurea unit as potent PI3K/mTOR inhibitorss

In this article, we designed and synthesized a series of novel thiophene-triazine derivatives bearing arylurea unit as potent dual PI3K/mTOR inhibitors. The cytotoxicity of all the target compounds were evaluated against nine cancer cell lines (breast cancer cell line MCF-7, lung cancer cell lines A549, NCI-H460, H2228 and H1975, cervical cancer cell lines Hela and Hela-MDR, ovarian cancer cell lines Ovcar-2 and glioma U87MG) and the kinase inhibitory activity against PI3K/mTOR kinases was also tested. The results demonstrated that most of the target compounds exhibited moderate to excellent activity and high selectivity against one or more cancer cell lines. Among them, seven compounds displayed better activity than lead compound GDC-0941. The inhibitory activity of the most promising compound on nine cancer cell lines was 302.5 times better than that of GDC-0941 with the IC₅₀ values as low as 0.008 ± 0.002 μM, and the inhibitory activity against PI3Kα and mTOR kinase was excellent, with the IC₅₀ values of 177.41 and 12.24 nM, respectively, indicating that it was a potential dual PI3Kα/mTOR inhibitor. The Structure-Activity Relationships (SARs) indicated that the introduction of the arylurea group significantly improved the cellular and kinase activities of the target compounds. Moreover, the results of toxicity and hemolysis experiments demonstrated that the most promising compound had low toxicity and good safety. The results of PCR assay and molecular docking modes showed that it was a potential PI3K/mTOR inhibitor, which was worthy of further study.

Functional Gene Expression Signatures from On-Treatment Tumor Specimens Predict Anti-PD1 Blockade Response in Metastatic Melanoma

Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

Ultrasound Strain Elastography and Contrast-Enhanced Ultrasound in Predicting the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: A Nomogram Integrating Ki-67 and Ultrasound Features

OBJECTIVES

To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC.

METHODS

From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis.

RESULTS

There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P = .024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P = .029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P = .050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P = .004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P = .000).

CONCLUSIONS

The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.

A review of studies on omitting surgery after neoadjuvant chemotherapy in breast cancer

The advancement in systemic neoadjuvant therapy has significantly increased the pathological complete response (pCR) rate in breast cancer. As surgeries inevitably affect patients physically and psychologically and the accuracy of pCR prediction and diagnosis by minimal invasive biopsy is improving, the necessity of surgery in neoadjuvant chemotherapy (NAC) patients who achieve pCR is under debate. Thus, we conducted a literature review of studies on the selective omission of breast surgery after NAC for breast cancer patients. We summarized the existing predictive models and technologies to predict and diagnose pCR after NAC. Our research indicates that, for nearly half a century, the extent of surgery on both breast and axillary lymph nodes is decreasing, while more precise systematic treatments are increasing. NAC has advanced significantly and its pCR rates have improved, so surgery may be omitted in certain patients. However, accurately predicting pCR after NAC is still a challenge. We also described the design for a randomized clinical trial and the potential problems of omitting surgical treatment after NAC. In summary, the decrease in breast cancer surgery is an unavoidable trend, and more high-quality clinical trials need to be conducted.

Correlation between drug sensitivity profiles of circulating tumour cell-derived organoids and clinical treatment response in patients with pancreatic ductal adenocarcinoma

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is highly aggressive and has poor prognosis. There are few biomarkers to inform treatment decisions, and collecting tumour samples for testing is challenging.

METHODS

Circulating tumour cells (CTCs) from patients with PDAC liquid biopsies were expanded ex vivo to form CTC-derived organoid cultures, using a laboratory-developed biomimetic cell culture system. CTC-derived organoids were tested for sensitivity to a PDAC panel of nine drugs, with tests conducted in triplicate, and a weighted cytotoxicity score (CTS) was calculated from the results. Clinical response to treatment in patients was evaluated using Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1 criteria at the time of blood sampling and 3 months later. The correlation between CTS and clinical response was then assessed.

RESULTS

A total of 41 liquid biopsies (87.8% from patients with Stage 4 disease) were collected from 31 patients. The CTC-derived organoid expansion was achieved in 3 weeks, with 87.8% culture efficiency. CTC-derived organoid cultures were positive for EpCAM staining and negative for CD45 staining in the surface marker analysis. All patients had received a median of two lines of treatment prior to enrolment and prospective utility analysis indicated significant correlation of CTS with clinical treatment response. Two representative case studies are also presented to illustrate the relevant clinical contexts.

CONCLUSIONS

CTCs were expanded from patients with PDAC liquid biopsies with a high success rate. Drug sensitivity profiles from CTC-derived organoid cultures correlated meaningfully with treatment response. Further studies are warranted to validate the predictive potential for this approach.

Circulating Exosomal microRNAs as Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer

Background: Neoadjuvant chemotherapy (NACT) is an increasingly used approach for treatment of breast cancer. The pathological complete response (pCR) is considered a good predictor of disease-specific survival. This study investigated whether circulating exosomal microRNAs could predict pCR in breast cancer patients treated with NACT. Method: Plasma samples of 20 breast cancer patients treated with NACT were collected prior to and after the first cycle. RNA sequencing was used to determine microRNA profiling. The Cancer Genome Atlas (TCGA) was used to explore the expression patterns and survivability of the candidate miRNAs, and their potential targets based on the expression levels and copy number variation (CNV) data. Results: Three miRNAs before that NACT (miR-30b, miR-328 and miR-423) predicted pCR in all of the analyzed samples. Upregulation of miR-127 correlated with pCR in triple-negative breast cancer (TNBC). After the first NACT dose, pCR was predicted by exo-miR-141, while miR-34a, exo-miR182, and exo-miR-183 predicted non-pCR. A significant correlation between the candidate miRNAs and the overall survival, subtype, and metastasis in breast cancer, suggesting their potential role as predictive biomarkers of pCR. Conclusions: If the miRNAs identified in this study are validated in a large cohort of patients, they might serve as predictive non-invasive liquid biopsy biomarkers for monitoring pCR to NACT in breast cancer.

Early on-treatment transcriptional profiling as a tool for improving pathological response prediction in HER2-positive inflammatory breast cancer

Background

Inflammatory breast cancer (IBC) is a rare and understudied disease, with 40% of cases presenting with human epidermal growth factor receptor 2 (HER2)-positive subtype. The goals of this study were to (i) assess the pathologic complete response (pCR) rate of short-term neoadjuvant dual-HER2-blockade and paclitaxel, (ii) contrast baseline and on-treatment transcriptional profiles of IBC tumor biopsies associated with pCR, and (iii) identify biological pathways that may explain the effect of neoadjuvant therapy on tumor response.

Patients and Methods

A single-arm phase II trial of neoadjuvant trastuzumab (H), pertuzumab (P), and paclitaxel for 16 weeks was completed among patients with newly diagnosed HER2-positive IBC. Fresh-frozen tumor biopsies were obtained pretreatment (D1) and 8 days later (D8), following a single dose of HP, prior to adding paclitaxel. We performed RNA-sequencing on D1 and D8 tumor biopsies, identified genes associated with pCR using differential gene expression analysis, identified pathways associated with pCR using gene set enrichment and gene expression deconvolution methods, and compared the pCR predictive value of principal components derived from gene expression profiles by calculating and area under the curve for D1 and D8 subsets.

Results

Twenty-three participants were enrolled, of whom 21 completed surgery following neoadjuvant therapy. Paired longitudinal fresh-frozen tumor samples (D1 and D8) were obtained from all patients. Among the 21 patients who underwent surgery, the pCR and the 4-year disease-free survival were 48% (90% CI 0.29-0.67) and 90% (95% CI 66-97%), respectively. The transcriptional profile of D8 biopsies was found to be more predictive of pCR (AUC = 0.91, 95% CI: 0.7993-1) than the D1 biopsies (AUC = 0.79, 95% CI: 0.5905-0.9822).

Conclusions

In patients with HER2-positive IBC treated with neoadjuvant HP and paclitaxel for 16 weeks, gene expression patterns of tumor biopsies measured 1 week after treatment initiation not only offered different biological information but importantly served as a better predictor of pCR than baseline transcriptional analysis.

Trial Registration

ClinicalTrials.gov identifier: NCT01796197 (https://clinicaltrials.gov/ct2/show/NCT01796197); registered on February 21, 2013.

Factors affecting pathological complete response after neoadjuvant chemotherapy in breast cancer: a single-center experience

OBJECTIVE

The aim of this study was to examine the characteristics of patients admitted to our hospital with a diagnosis of breast cancer who reached pathological complete response after being operated following eight cycles of neoadjuvant chemotherapy.

METHODS

Between 2015-2020, patients with pathological complete response who were operated on after neoadjuvant chemotherapy and sent to our clinic for radiotherapy were evaluated.

RESULTS

The median age of the patients was 51 years. The most common histological type was invasive ductal cancer. The number of pathological complete response patients was 74 (28%), and the number of non-pathological complete response patients was 188 (72%). Patients with pathological complete response had a smaller tumor diameter than the non-pathological complete response group (p=0.001). For pathological complete response, T1 stage, N1 stage, NG 3, Ki-67 >20%, negative estrogen receptor, negative progesterone receptor, positive Cerb-B2, and adding trastuzumab to chemotherapy were statistically significant (p<0.05). Before neoadjuvant chemotherapy, stage T1-T2 (p=0.036), LN0-1 (p=0.026), Cerb-B2 positivity (p=0.025), and an initial nuclear grade of three (p=0.001) were found to be the factors affecting pathological complete response.

CONCLUSIONS

With neoadjuvant chemotherapy, the size of locally advanced tumors decreases, allowing breast conserving surgery. The neoadjuvant chemotherapy response can be used as an early indicator of the prognosis of patients with breast cancer. Today, neoadjuvant chemotherapy is also used for patients with early-stage, operable breast cancer because it has been shown in many studies that reaching pathological complete response is associated with positive long-term results. If we can identify patients who have reached pathological complete response before neoadjuvant chemotherapy, we think we can also determine a patient-specific treatment plan at the beginning of treatment.

Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma

Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Potential of combination of DCE-MRI and DWI with serum CA125 and CA199 in evaluating effectiveness of neoadjuvant chemotherapy in breast cancer

BACKGROUND

To determine the potential of the combination of DCE-MRI imaging method with DWI and serum CA125 and CA199 levels in the evaluation of the efficacy of neoadjuvant chemotherapy in breast cancer patients.

METHODS

Sixty-five breast cancer patients who received neoadjuvant chemotherapy in our hospital from April 2016 to April 2017 were selected as research subjects. The patients received 4 courses of neoadjuvant chemotherapy. Lesions were monitored using DCE-MRI and DWI, while ELISA was used to measure the serum expression levels of the tumour markers CA125 and CA199. The patients were divided into the remission group and ineffective group based on pathological diagnosis.

RESULTS

There were significant differences in K_ep, K_trans, ADC_min, ADC_mean, tumour volume, and serum levels of CA125 and CA199 in patients in the remission group, before and after neoadjuvant chemotherapy, and there were significant differences in post-chemotherapy values of these indexes between the remission group and the ineffective group (p < 0.01).

CONCLUSION

Combination of DCE-MRI diagnostic imaging with DWI can directly reflect the lesions in breast cancer patients after neoadjuvant chemotherapy. Serum levels of CA125 and CA199 levels are useful for evaluation of the impact of neoadjuvant chemotherapy on breast cancer patients, including risk of cancer cell metastasis and changes in some small lesions.

Integrative analysis of gut microbiome and host transcriptomes reveals associations between treatment outcomes and immunotherapy-induced colitis

Immune checkpoint inhibitors (ICIs) are widely used to treat various malignancies. Although the gut microbiome is known to influence the efficacy of ICIs on epithelial tumors, the functional interactions between gut taxa and colonic mucosa remain poorly understood. Here we performed transcriptomic profiling and 16S rRNA sequencing to investigate the relationships between mucosal gene expression and microbial composition with ICI responses and gastrointestinal immune-related adverse events (GI irAEs). In responders, genes related to DNA repair and cell cycle signatures were enriched in responders whereas signatures related to innate immune response, NFAT and IFN-γ signaling pathways were enriched in nonresponders. Gut microbial composition revealed an association between moderate GI irAE and favorable response to ICI therapy. Favorable therapeutic responses to ICI and GI irAE treatments were associated with taxa classified as Enterobacteriaceae and were related to ribonucleoprotein complex biogenesis, cytokine-mediated signaling pathway, tRNA metabolic process, and ribonucleoprotein complex assembly in the colon. These findings open new perspectives for improving the efficacy and safety of cancer immunotherapy.

Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer

BACKGROUND

The aim of this study was to develop a machine learning (ML) based model to accurately predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) using pretreatment clinical and pathological characteristics of electronic medical record (EMR) data in breast cancer (BC).

METHODS

The EMR data from patients diagnosed with early and locally advanced BC and who received NAC followed by curative surgery were reviewed. A total of 16 clinical and pathological characteristics was selected to develop ML model. We practiced six ML models using default settings for multivariate analysis with extracted variables.

RESULTS

In total, 2065 patients were included in this analysis. Overall, 30.6% (n = 632) of patients achieved pCR. Among six ML models, the LightGBM had the highest area under the curve (AUC) for pCR prediction. After hyper-parameter tuning with Bayesian optimization, AUC was 0.810. Performance of pCR prediction models in different histology-based subtypes was compared. The AUC was highest in HR+HER2- subgroup and lowest in HR-/HER2- subgroup (HR+/HER2- 0.841, HR+/HER2+ 0.716, HR-/HER2 0.753, HR-/HER2- 0.653).

CONCLUSIONS

A ML based pCR prediction model using pre-treatment clinical and pathological characteristics provided useful information to predict pCR during NAC. This prediction model would help to determine treatment strategy in patients with BC planned NAC.

Identification of differentially expressed genes-related prognostic risk model for survival prediction in breast carcinoma patients

Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

Current and future cancer staging after neoadjuvant treatment for solid tumors

Until recently, cancer registries have only collected cancer clinical stage at diagnosis, before any therapy, and pathological stage after surgical resection, provided no treatment has been given before the surgery, but they have not collected stage data after neoadjuvant therapy (NAT). Because NAT is increasingly being used to treat a variety of tumors, it has become important to make the distinction between both the clinical and the pathological assessment without NAT and the assessment after NAT to avoid any misunderstanding of the significance of the clinical and pathological findings. It also is important that cancer registries collect data after NAT to assess response and effectiveness of this treatment approach on a population basis. The prefix y is used to denote stage after NAT. Currently, cancer registries of the American College of Surgeons' Commission on Cancer only partially collect y stage data, and data on the clinical response to NAT (yc or posttherapy clinical information) are not collected or recorded in a standardized fashion. In addition to NAT, nonoperative management after radiation and chemotherapy is being used with increasing frequency in rectal cancer and may be expanded to other treatment sites. Using examples from breast, rectal, and esophageal cancers, the pathological and imaging changes seen after NAT are reviewed to demonstrate appropriate staging.

Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification

Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.

Eliminating the breast cancer surgery paradigm after neoadjuvant systemic therapy: current evidence and future challenges

In patients with operable early breast cancer, neoadjuvant systemic treatment (NST) is a standard approach. Indications have expanded from downstaging of locally advanced breast cancer to facilitate breast conservation, to in vivo drug-sensitivity testing. The pattern of response to NST is used to tailor systemic and locoregional treatment, that is, to escalate treatment in nonresponders and de-escalate treatment in responders. Here we discuss four questions that guide our current thinking about 'response-adjusted' surgery of the breast after NST. (i) What critical diagnostic outcome measures should be used when analyzing diagnostic tools to identify patients with pathologic complete response (pCR) after NST? (ii) How can we assess response with the least morbidity and best accuracy possible? (iii) What oncological consequences may ensue if we rely on a nonsurgical-generated diagnosis of, for example, minimally invasive biopsy proven pCR, knowing that we may miss minimal residual disease in some cases? (iv) How should we design clinical trials on de-escalation of surgical treatment after NST?

Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome

To elucidate the effects of neoadjuvant chemotherapy (NAC), we conduct whole transcriptome profiling coupled with histopathology analyses of a longitudinal breast cancer cohort of 146 patients including 110 pairs of serial tumor biopsies collected before treatment, after the first cycle of treatment and at the time of surgery. Here, we show that cytotoxic chemotherapies induce dynamic changes in the tumor immune microenvironment that vary by subtype and pathologic response. Just one cycle of treatment induces an immune stimulatory microenvironment harboring more tumor infiltrating lymphocytes (TILs) and up-regulation of inflammatory signatures predictive of response to anti-PD1 therapies while residual tumors are immune suppressed at end-of-treatment compared to the baseline. Increases in TILs and CD8+ T cell proportions in response to NAC are independently associated with pathologic complete response. Further, on-treatment immune response is more predictive of treatment outcome than immune features in paired baseline samples although these are strongly correlated.

Neoadjuvant chemotherapy is a therapeutic option for the treatment of breast cancer. Here, the authors characterize changes in the gene expression profiles and immune microenvironment in serial breast cancer biopsies taken before, during and after neoadjuvant chemotherapy.

Resistance to Neoadjuvant Treatment in Breast Cancer: Clinicopathological and Molecular Predictors

Neoadjuvant Chemotherapy (NAC) in Breast Cancer (BC) has proved useful for the reduction in tumor burden prior to surgery, allowing for a more extensive breast preservation and the eradication of subjacent micrometastases. However, the impact on prognosis is highly dependent on the establishment of Pathological Complete Response (pCR), in particular for Triple Negative (TN) and Hormonal Receptor negative/Human Epidermal growth factor Receptor 2 positive (HR-/HER2+) subtypes. Several pCR predictors, such as PAM50, Integrative Cluster (IntClust), mutations in PI3KCA, or the Trastuzumab Risk model (TRAR), are useful molecular tools for estimating response to treatment and are prognostic. Major evolution events during BC NAC that feature the Residual Disease (RD) are the loss of HR and HER2, which are prognostic of bad outcome, and stemness and immune depletion-related gene expression aberrations. This dynamic nature of the determinants of response to BC NAC, together with the extensive heterogeneity of BC, raises the need to discern the individual and subtype-specific determinants of resistance. Moreover, refining the current approaches for a comprehensive monitoring of tumor evolution during treatment, RD, and eventual recurrences is essential for identifying new actionable alterations and the integral best management of the disease.

The Diagnostic Performance of Minimally Invasive Biopsy in Predicting Breast Pathological Complete Response After Neoadjuvant Systemic Therapy in Breast Cancer: A Meta-Analysis

Background: Neoadjuvant systemic therapy (NST) is commonly used in patients with early stage breast cancer before definitive surgery. The standard diagnostic approach for pathologic complete response (pCR) of the breast is breast surgery and pathologic examination. In recent years, several trials investigated the predictive value of image-guided minimally invasive biopsy (MIB) for breast pCR after NST. This study conducted a meta-analysis to evaluate the diagnostic accuracy of MIB.

Materials and Methods: We identified relevant research reports in online databases through February 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to evaluate the quality of included trials. We extracted relevant data and constructed a 2 × 2 contingency table to analyze the predictive accuracy of MIB for breast pCR. Subgroup analyses and meta-regressions were also performed to investigate potential causes of heterogeneity.

Results: Nine trials (with 1,030 breast cancer patients) were included in this meta-analysis. The pooled sensitivity and specificity of MIB were 0.72 [95% confidence interval (CI): 0.61–0.81] and 0.99 (95% CI: 0.89–1.00), respectively. By combining relevant data, there were no significant differences in sensitivity or specificity among different molecular subtypes of breast cancer (P > 0.05). Subgroup analyses and meta-regressions implied that trials with responses not limited to clinical complete response (cCR) had a significantly higher accuracy of MIB than those with only cCR (RDOR: 7.65; 95% CI: 1.05–55.46; P = 0.046).

Conclusion: Current image-guided MIB methods are not accurate enough in terms of predicting breast pCR after NST. It is of utmost clinical importance to standardize the MIB procedure and incorporate other factors into the evaluation in order to improve the accuracy to an acceptable level.

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed.

HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer

PURPOSE

To investigate the relationship between hypoxia-inducible factor 1-alpha (HIF-1α), Twist family BHLH transcription factor 1 (TWIST-1), and β1 integrin (ITGB-1) expression and tumor stiffness, and evaluate performance of HIF-1α, TWIST-1, and ITGB-1 alone and in combination with Ki-67 for predicting pathological responses to neoadjuvant chemotherapy (NACT) in breast cancer (BC).

PATIENTS AND METHODS

This was a prospective cohort study of 104 BC patients receiving NACT. Tumor stiffness and oxygen score (OS) were evaluated before NACT by shear-wave elastography and optical imaging; HIF-1α, TWIST-1, ITGB-1, and Ki-67 expression were quantitatively assessed by immunohistochemistry of paraffin-embedded tumor samples obtained by core needle biopsy. Indexes were compared among different residual cancer burden (RCB) groups, and associations of HIF-1α, TWIST-1, ITGB-1, and Ki-67 with tumor stiffness and OS were examined. The value of HIF-1α, TWIST-1, ITGB-1, and Ki-67, and a possible new combined index (predRCB) for predicting NACT responses was assessed by receiver operating characteristic (ROC) curves.

RESULTS

HIF-1α, TWIST-1, and ITGB-1 expression were positively correlated with tumor stiffness and negatively with OS. Area under the ROC curves (AUCs) measuring the performance of HIF-1α, TWIST-1, ITGB-1, and Ki-67 for predicting responses to NACT were 0.81, 0.85, 0.79, and 0.80 for favorable responses, and 0.83, 0.86, 0.84, and 0.85 for resistant responses, respectively. PredRCB showed better prediction than the other individual indexes for favorable responses (AUC = 0.88) and resistant responses (AUC = 0.92).

CONCLUSION

HIF-1α, TWIST-1, ITGB-1, and Ki-67 performed well in predicting favorable responses and resistance to NACT, and predRCB improved the predictive power of the individual indexes. These results support individualized treatment of BC patients receiving NACT.