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18 pages, 3519 KiB

Open AccessArticle

Docetaxel-Mediated Uptake and Retention of Gold Nanoparticles in Tumor Cells and in Cancer-Associated Fibroblasts

by Abdulaziz Alhussan, Kyle Bromma, Monica Mesa Perez, Wayne Beckham, Abraham S Alexander, Perry L Howard and Devika B Chithrani

Cancers 2021, 13(13), 3157; https://doi.org/10.3390/cancers13133157 - 24 Jun 2021

Cited by 15 | Viewed by 2603

Abstract

Due to recent advances in nanotechnology, the application of nanoparticles (NPs) in cancer therapy has become a leading area in cancer research. Despite the importance of cancer-associated fibroblasts (CAFs) in creating an optimal niche for tumor cells to grow extensively, most of the [...] Read more.

Due to recent advances in nanotechnology, the application of nanoparticles (NPs) in cancer therapy has become a leading area in cancer research. Despite the importance of cancer-associated fibroblasts (CAFs) in creating an optimal niche for tumor cells to grow extensively, most of the work has been focused on tumor cells. Therefore, to effectively use NPs for therapeutic purposes, it is important to elucidate the extent of NP uptake and retention in tumor cells and CAFs. Three tumor cell lines and three CAF cell lines were studied using gold NPs (GNPs) as a model NP system. We found a seven-fold increase in NP uptake in CAFs compared to tumor cells. The retention percentage of NPs was three-fold higher in tumor cells as compared to CAFs. Furthermore, NP uptake and retention were significantly enhanced using a 50 nM concentration of docetaxel (DTX). NP uptake was improved by a factor of three in tumor cells and a factor of two in CAFs, while the retention of NPs was two-fold higher in tumor cells compared to CAFs, 72 h post-treatment with DTX. However, the quantity of NPs in CAFs was still three-fold higher compared to tumor cells. Our quantitative data were supported by qualitative imaging data. We believe that targeting of NPs in the presence of DTX is a very promising approach to accumulate a higher percentage of NPs and maintain a longer retention in both tumor cells and CAFs for achieving the full therapeutic potential of cancer nanotechnology. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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17 pages, 5690 KiB

Open AccessArticle

Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential

by Syuzo Kaneko, Toutai Mitsuyama, Kouya Shiraishi, Noriko Ikawa, Kanto Shozu, Ai Dozen, Hidenori Machino, Ken Asada, Masaaki Komatsu, Asako Kukita, Kenbun Sone, Hiroshi Yoshida, Noriko Motoi, Shinya Hayami, Yutaka Yoneoka, Tomoyasu Kato, Takashi Kohno, Toru Natsume, Gottfried von Keudell, Vassiliki Saloura, Hiroki Yamaue and Ryuji Hamamoto Show full author list Hide full author list

Cancers 2021, 13(9), 2126; https://doi.org/10.3390/cancers13092126 - 28 Apr 2021

Cited by 6 | Viewed by 3747

Abstract

Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the [...] Read more.

Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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19 pages, 3445 KiB

Open AccessArticle

Three-Dimensional Tumor Spheroids as a Tool for Reliable Investigation of Combined Gold Nanoparticle and Docetaxel Treatment

by Kyle Bromma, Abdulaziz Alhussan, Monica Mesa Perez, Perry Howard, Wayne Beckham and Devika B. Chithrani

Cancers 2021, 13(6), 1465; https://doi.org/10.3390/cancers13061465 - 23 Mar 2021

Cited by 17 | Viewed by 3671

Abstract

Radiotherapy and chemotherapy are the gold standard for treating patients with cancer in the clinic but, despite modern advances, are limited by normal tissue toxicity. The use of nanomaterials, such as gold nanoparticles (GNPs), to improve radiosensitivity and act as drug delivery systems [...] Read more.

Radiotherapy and chemotherapy are the gold standard for treating patients with cancer in the clinic but, despite modern advances, are limited by normal tissue toxicity. The use of nanomaterials, such as gold nanoparticles (GNPs), to improve radiosensitivity and act as drug delivery systems can mitigate toxicity while increasing deposited tumor dose. To expedite a quicker clinical translation, three-dimensional (3D) tumor spheroid models that can better approximate the tumor environment compared to a two-dimensional (2D) monolayer model have been used. We tested the uptake of 15 nm GNPs and 50 nm GNPs on a monolayer and on spheroids of two cancer cell lines, CAL-27 and HeLa, to evaluate the differences between a 2D and 3D model in similar conditions. The anticancer drug docetaxel (DTX) which can act as a radiosensitizer, was also utilized, informing future potential of GNP-mediated combined therapeutics. In the 2D monolayer model, the addition of DTX induced a small, non-significant increase of uptake of GNPs of between 13% and 24%, while in the 3D spheroid model, DTX increased uptake by between 47% and 186%, with CAL-27 having a much larger increase relative to HeLa. Further, the depth of penetration of 15 nm GNPs over 50 nm GNPs increased by 33% for CAL-27 spheroids and 17% for HeLa spheroids. These results highlight the necessity to optimize GNP treatment conditions in a more realistic tumor-life environment. A 3D spheroid model can capture important details, such as different packing densities from different cancer cell lines, which are absent from a simple 2D monolayer model. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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13 pages, 579 KiB

Open AccessArticle

Biophysical Analysis of Acute and Late Toxicity of Radiotherapy in Gastric Marginal Zone Lymphoma—Impact of Radiation Dose and Planning Target Volume

by Gabriele Reinartz, Andrea Baehr, Christopher Kittel, Michael Oertel, Uwe Haverkamp and Hans Th. Eich

Cancers 2021, 13(6), 1390; https://doi.org/10.3390/cancers13061390 - 19 Mar 2021

Cited by 5 | Viewed by 2179

Abstract

Successful studies on radiation therapy for gastric lymphoma led to a decrease in planning target volume (PTV) and radiation dose with low toxicities, maintaining excellent survival rates. It remains unclear as to which effects are to be expected concerning dose burden on organs [...] Read more.

Successful studies on radiation therapy for gastric lymphoma led to a decrease in planning target volume (PTV) and radiation dose with low toxicities, maintaining excellent survival rates. It remains unclear as to which effects are to be expected concerning dose burden on organs at risk (OAR) by decrease in PTV vs. dose and whether a direct impact on toxicity might be expected. We evaluated 72 radiation plans, generated prospectively for a cohort of 18 patients who were treated for indolent gastric lymphoma in our department. As a prospective work, four radiation plans with different radiation doses and target volumes (40 Gy-involved field, 40 Gy-involved site, 30 Gy-involved field, 30 Gy-involved site) were generated for each patient. Mean dose burden on adjacent organs was compared between the planning groups. Cohort toxicity data served to estimate parameters for the Lyman–Kutcher–Burman (LKB) model for normal tissue complication probability (NTCP). These were used to anticipate adverse events for OAR. Literature parameters were used to estimate high-grade toxicities of OAR. Decrease of dose and/or PTV led to median dose reductions between 0.13 and 5.2 Gy, with a significant dose reduction on neighboring organs. Estimated model parameters for liver, spleen, and bowel toxicity were feasible to predict cohort toxicities. NTCP for the endpoints elevated liver enzymes, low platelet count, and diarrhea ranged between 15.9 and 22.8%, 27.6 and 32.4%, and 21.8 and 26.4% for the respective four plan variations. Field and dose reduction highly impact dose burden and NTCP for OAR during stomach radiation. Our estimated LKB model parameters offer a good approximation for low-grade toxicities in abdominal organs with modern radiation techniques. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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20 pages, 2946 KiB

Open AccessArticle

Comparison of Structural and Short Variants Detected by Linked-Read and Whole-Exome Sequencing in Multiple Myeloma

by Ashwini Kumar, Sadiksha Adhikari, Matti Kankainen and Caroline A. Heckman

Cancers 2021, 13(6), 1212; https://doi.org/10.3390/cancers13061212 - 10 Mar 2021

Cited by 7 | Viewed by 4730

Abstract

Linked-read sequencing was developed to aid the detection of large structural variants (SVs) from short-read sequencing efforts. We performed a systematic evaluation to determine if linked-read exome sequencing provides more comprehensive and clinically relevant information than whole-exome sequencing (WES) when applied to the [...] Read more.

Linked-read sequencing was developed to aid the detection of large structural variants (SVs) from short-read sequencing efforts. We performed a systematic evaluation to determine if linked-read exome sequencing provides more comprehensive and clinically relevant information than whole-exome sequencing (WES) when applied to the same set of multiple myeloma patient samples. We report that linked-read sequencing detected a higher number of SVs (n = 18,455) than WES (n = 4065). However, linked-read predictions were dominated by inversions (92.4%), leading to poor detection of other types of SVs. In contrast, WES detected 56.3% deletions, 32.6% insertions, 6.7% translocations, 3.3% duplications and 1.2% inversions. Surprisingly, the quantitative performance assessment suggested a higher performance for WES (AUC = 0.791) compared to linked-read sequencing (AUC = 0.766) for detecting clinically validated cytogenetic alterations. We also found that linked-read sequencing detected more short variants (n = 704) compared to WES (n = 109). WES detected somatic mutations in all MM-related genes while linked-read sequencing failed to detect certain mutations. The comparison of somatic mutations detected using linked-read, WES and RNA-seq revealed that WES and RNA-seq detected more mutations than linked-read sequencing. These data indicate that WES outperforms and is more efficient than linked-read sequencing for detecting clinically relevant SVs and MM-specific short variants. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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15 pages, 3514 KiB

Open AccessArticle

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

by Jelmar Quist, Lawson Taylor, Johan Staaf and Anita Grigoriadis

Cancers 2021, 13(5), 991; https://doi.org/10.3390/cancers13050991 - 27 Feb 2021

Cited by 31 | Viewed by 4739

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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14 pages, 2985 KiB

Open AccessArticle

Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks

by Jun Wang, Qianying Liu, Haotian Xie, Zhaogang Yang and Hefeng Zhou

Cancers 2021, 13(4), 661; https://doi.org/10.3390/cancers13040661 - 7 Feb 2021

Cited by 71 | Viewed by 7155

Abstract

(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution [...] Read more.

(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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12 pages, 4653 KiB

Open AccessArticle

3′RNA Sequencing Accurately Classifies Formalin-Fixed Paraffin-Embedded Uterine Leiomyomas

by Miika Mehine, Sara Khamaiseh, Terhi Ahvenainen, Tuomas Heikkinen, Anna Äyräväinen, Päivi Pakarinen, Päivi Härkki, Annukka Pasanen, Ralf Bützow and Pia Vahteristo

Cancers 2020, 12(12), 3839; https://doi.org/10.3390/cancers12123839 - 19 Dec 2020

Cited by 9 | Viewed by 3793

Abstract

Uterine leiomyomas are benign smooth muscle tumors occurring in 70% of women of reproductive age. The majority of leiomyomas harbor one of three well-established genetic changes: a hotspot mutation in MED12, overexpression of HMGA2, or biallelic loss of FH. The [...] Read more.

Uterine leiomyomas are benign smooth muscle tumors occurring in 70% of women of reproductive age. The majority of leiomyomas harbor one of three well-established genetic changes: a hotspot mutation in MED12, overexpression of HMGA2, or biallelic loss of FH. The majority of studies have classified leiomyomas by complex and costly methods, such as whole-genome sequencing, or by combining multiple traditional methods, such as immunohistochemistry and Sanger sequencing. The type of specimens and the amount of resources available often determine the choice. A more universal, cost-effective, and scalable method for classifying leiomyomas is needed. The aim of this study was to evaluate whether RNA sequencing can accurately classify formalin-fixed paraffin-embedded (FFPE) leiomyomas. We performed 3′RNA sequencing with 44 leiomyoma and 5 myometrium FFPE samples, revealing that the samples clustered according to the mutation status of MED12, HMGA2, and FH. Furthermore, we confirmed each subtype in a publicly available fresh frozen dataset. These results indicate that a targeted 3′RNA sequencing panel could serve as a cost-effective and robust tool for stratifying both fresh frozen and FFPE leiomyomas. This study also highlights 3′RNA sequencing as a promising method for studying the abundance of unexploited tissue material that is routinely stored in hospital archives. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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14 pages, 1924 KiB

Open AccessArticle

A Machine-Learning Tool Concurrently Models Single Omics and Phenome Data for Functional Subtyping and Personalized Cancer Medicine

by Gift Nyamundanda, Katherine Eason, Justin Guinney, Christopher J. Lord and Anguraj Sadanandam

Cancers 2020, 12(10), 2811; https://doi.org/10.3390/cancers12102811 - 30 Sep 2020

Cited by 1 | Viewed by 4088

Abstract

One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique [...] Read more.

One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique machine-learning method, phenotype mapping (PhenMap), which jointly integrates single omics data with phenotypic information using three published breast cancer datasets (n = 2045). The PhenMap framework uses a modified factor analysis method that is governed by a key assumption that, features from different omics data types are correlated due to specific “hidden/mapping” variables (context-specific mapping variables (CMV)). These variables can be simultaneously modeled with phenotypic data as covariates to yield functional subtypes and their associated features (e.g., genes) and phenotypes. In one example, we demonstrate the identification and validation of six novel “functional” (discrete) subtypes with differential responses to a cyclin-dependent kinase (CDK)4/6 inhibitor and etoposide by jointly integrating transcriptome profiles with four different drug response data from 37 breast cancer cell lines. These robust subtypes are also present in patient breast tumors with different prognosis. In another example, we modeled patient gene expression profiles and clinical covariates together to identify continuous subtypes with clinical/biological implications. Overall, this genome-phenome machine-learning integration tool, PhenMap identifies functional and phenotype-integrated discrete or continuous subtypes with clinical translational potential. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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15 pages, 3273 KiB

Open AccessArticle

ERBB2 mRNA Expression and Response to Ado-Trastuzumab Emtansine (T-DM1) in HER2-Positive Breast Cancer

by Gaia Griguolo, Fara Brasó-Maristany, Blanca González-Farré, Tomás Pascual, Núria Chic, Tamara Saurí, Ronald Kates, Oleg Gluz, Débora Martínez, Laia Paré, Vassilena Tsvetkova, David Pesantez, Maria Vidal, Barbara Adamo, Montserrat Muñoz, Patricia Galván, Laura Barberá, Miriam Cuatrecasas, Mathias Christgen, Hans Kreipe, Inés Monge-Escartín, Patricia Villagrasa, Dolors Soy, Tommaso Giarratano, Maria Vittoria Dieci, Pierfranco Conte, Nadia Harbeck, Valentina Guarneri and Aleix Prat Show full author list Hide full author list

Cancers 2020, 12(7), 1902; https://doi.org/10.3390/cancers12071902 - 14 Jul 2020

Cited by 35 | Viewed by 7623

Abstract

Trastuzumab emtansine (T-DM1) is approved for the treatment of human epidermal growth factor receptor 2 (HER2)-positive (HER2+) metastatic breast cancer (BC) and for residual disease after neoadjuvant therapy; however, not all patients benefit. Here, we hypothesized that the heterogeneity in the response seen [...] Read more.

Trastuzumab emtansine (T-DM1) is approved for the treatment of human epidermal growth factor receptor 2 (HER2)-positive (HER2+) metastatic breast cancer (BC) and for residual disease after neoadjuvant therapy; however, not all patients benefit. Here, we hypothesized that the heterogeneity in the response seen in patients is partly explained by the levels of human epidermal growth factor receptor 2 gene (ERBB2) mRNA. We analyzed ERBB2 expression using a clinically applicable assay in formalin-fixed paraffin-embedded (FFPE) tumors (primary or metastatic) from a retrospective series of 77 patients with advanced HER2+ BC treated with T-DM1. The association of ERBB2 levels and response was further validated in 161 baseline tumors from the West German Study (WGS) Group ADAPT phase II trial exploring neoadjuvant T-DM1 and 9 in vitro BC cell lines. Finally, ERBB2 expression was explored in 392 BCs from an in-house dataset, 368 primary BCs from The Cancer Genome Atlas (TCGA) dataset and 10,071 tumors representing 33 cancer types from the PanCancer TCGA dataset. High ERBB2 mRNA was found associated with better response and progression-free survival in the metastatic setting and higher rates of pathological complete response in the neoadjuvant setting. ERBB2 expression also correlated with in vitro response to T-DM1. Finally, our assay identified 0.20–8.41% of tumors across 15 cancer types as ERBB2-high, including gastric and esophagus adenocarcinomas, urothelial carcinoma, cervical squamous carcinoma and pancreatic cancer. In particular, we identified high ERBB2 mRNA in a patient with HER2+ advanced gastric cancer who achieved a long-lasting partial response to T-DM1. Our study demonstrates that the heterogeneity in response to T-DM1 is partly explained by ERBB2 levels and provides a clinically applicable assay to be tested in future clinical trials of breast cancer and other cancer types. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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Other

Jump to: Research

27 pages, 2827 KiB

Open AccessGuidelines

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx^® Digital Spatial Profiler

by Helga Bergholtz, Jodi M. Carter, Alessandra Cesano, Maggie Chon U Cheang, Sarah E. Church, Prajan Divakar, Christopher A. Fuhrman, Shom Goel, Jingjing Gong, Jennifer L. Guerriero, Margaret L. Hoang, E. Shelley Hwang, Hellen Kuasne, Jinho Lee, Yan Liang, Elizabeth A. Mittendorf, Jessica Perez, Aleix Prat, Lajos Pusztai, Jason W. Reeves, Yasser Riazalhosseini, Jennifer K. Richer, Özgür Sahin, Hiromi Sato, Ilana Schlam, Therese Sørlie, Daniel G. Stover, Sandra M. Swain, Alexander Swarbrick, E. Aubrey Thompson, Sara M. Tolaney, Sarah E. Warren and on behalf of the GeoMx Breast Cancer Consortium Show full author list Hide full author list

Cancers 2021, 13(17), 4456; https://doi.org/10.3390/cancers13174456 - 4 Sep 2021

Cited by 53 | Viewed by 18873

Abstract

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of [...] Read more.

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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11 pages, 4913 KiB

Open AccessPerspective

Lights and Shadows in Immuno-Oncology Drug Development

by Milana Bergamino Sirvén, Sonia Pernas and Maggie C. U. Cheang

Cancers 2021, 13(4), 691; https://doi.org/10.3390/cancers13040691 - 9 Feb 2021

Cited by 1 | Viewed by 3406

Abstract

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from [...] Read more.

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance. Full article

(This article belongs to the Special Issue Emerging Technologies and Computation Methods for Precision Medicine in Cancer—Ready for Use in Clinics?)

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