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Cancers
Special Issues
Clinical and Translational Updates in Renal Cell Carcinoma
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Clinical and Translational Updates in Renal Cell Carcinoma

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Clinical Research of Cancer".

Deadline for manuscript submissions: 2 July 2024 | Viewed by 3961

Special Issue Editor

Dr. Irbaz Bin Riaz

E-Mail Website
Guest Editor
1. Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
2. Mayo Clinic Arizona, Alix School of Medicine, Rochester, MN, USA
Interests: renal cell carcinoma; living evidence; machine learning; computational biology

Special Issue Information

Dear Colleagues,

This special issue aims to highlight the latest clinical and translational developments in renal cell carcinoma. We welcome original manuscripts(preferred) and high-quality reviews covering updates on both early and advanced renal cell carcinoma.

We welcome manuscripts covering various aspects, including developments related to basic and translational science, molecular and computational biology, artificial intelligence, and machine learning, as well as the clinical management of nuanced issues in the perioperative/adjuvant and metastatic setting.

If you have an interesting proposal related to renal cell carcinoma that is not covered by the categories listed above, please feel free to submit your proposal for consideration. We look forward to receiving your high-quality contributions.

Dr. Irbaz Bin Riaz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • renal cell carcinoma
  • RCC
  • machine cearning
  • clinical
  • translational developments

Published Papers (2 papers)

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Research

11 pages, 7015 KiB  
Article
A Phase II Clinical Trial of Pembrolizumab Efficacy and Safety in Advanced Renal Medullary Carcinoma
by Chijioke Nze, Pavlos Msaouel, Mohamed H. Derbala, Bettzy Stephen, Abdulrahman Abonofal, Funda Meric-Bernstam, Nizar M. Tannir and Aung Naing
Cancers 2023, 15(15), 3806; https://doi.org/10.3390/cancers15153806 - 27 Jul 2023
Cited by 2 | Viewed by 2145
Abstract
Background. Renal medullary carcinoma (RMC) is one of most aggressive renal cell carcinomas and novel therapeutic strategies are therefore needed. Recent comprehensive molecular and immune profiling of RMC tissues revealed a highly inflamed phenotype, suggesting the potential therapeutic role for immune checkpoint therapies. [...] Read more.
Background. Renal medullary carcinoma (RMC) is one of most aggressive renal cell carcinomas and novel therapeutic strategies are therefore needed. Recent comprehensive molecular and immune profiling of RMC tissues revealed a highly inflamed phenotype, suggesting the potential therapeutic role for immune checkpoint therapies. We present the first prospective evaluation of an immune checkpoint inhibitor in a cohort of patients with RMC. Methods. A cohort of patients with locally advanced or metastatic RMC was treated with pembrolizumab 200 mg intravenously every 21 days in a phase II basket trial (ClinicalTrials.gov: NCT02721732). Responses were assessed by irRECIST. Tumor tissues were evaluated for PD-L1 expression and for tumor-infiltrating lymphocyte (TIL) levels. Somatic mutations were assessed by targeted next-generation sequencing. Results. A total of five patients were treated. All patients had advanced disease, with the majority of patients (60%) having metastatic disease at diagnosis. All patients had rapid disease progression despite pembrolizumab treatment, with a median time to progression of 8.7 weeks. One patient (patient 5) experienced sudden clinical progression immediately after treatment initiation and was thus taken off trial less than one week after receiving pembrolizumab. Conclusions. This prospective evaluation showed no evidence of clinical activity for pembrolizumab in patients with RMC, irrespective of PD-L1 or TIL levels. Full article
(This article belongs to the Special Issue Clinical and Translational Updates in Renal Cell Carcinoma)
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14 pages, 4296 KiB  
Article
A Weakly Supervised Deep Learning Model and Human–Machine Fusion for Accurate Grading of Renal Cell Carcinoma from Histopathology Slides
by Qingyuan Zheng, Rui Yang, Huazhen Xu, Junjie Fan, Panpan Jiao, Xinmiao Ni, Jingping Yuan, Lei Wang, Zhiyuan Chen and Xiuheng Liu
Cancers 2023, 15(12), 3198; https://doi.org/10.3390/cancers15123198 - 15 Jun 2023
Cited by 3 | Viewed by 1381
Abstract
(1) Background: The Fuhrman grading (FG) system is widely used in the management of clear cell renal cell carcinoma (ccRCC). However, it is affected by observer variability and irreproducibility in clinical practice. We aimed to use a deep learning multi-class model called SSL-CLAM [...] Read more.
(1) Background: The Fuhrman grading (FG) system is widely used in the management of clear cell renal cell carcinoma (ccRCC). However, it is affected by observer variability and irreproducibility in clinical practice. We aimed to use a deep learning multi-class model called SSL-CLAM to assist in diagnosing the FG status of ccRCC patients using digitized whole slide images (WSIs). (2) Methods: We recruited 504 eligible ccRCC patients from The Cancer Genome Atlas (TCGA) cohort and obtained 708 hematoxylin and eosin-stained WSIs for the development and internal validation of the SSL-CLAM model. Additionally, we obtained 445 WSIs from 188 ccRCC eligible patients in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort as an independent external validation set. A human–machine fusion approach was used to validate the added value of the SSL-CLAM model for pathologists. (3) Results: The SSL-CLAM model successfully diagnosed the five FG statuses (Grade-0, 1, 2, 3, and 4) of ccRCC, and achieved AUCs of 0.917 and 0.887 on the internal and external validation sets, respectively, outperforming a junior pathologist. For the normal/tumor classification (Grade-0, Grade-1/2/3/4) task, the SSL-CLAM model yielded AUCs close to 1 on both the internal and external validation sets. The SSL-CLAM model achieved a better performance for the two-tiered FG (Grade-0, Grade-1/2, and Grade-3/4) task, with AUCs of 0.936 and 0.915 on the internal and external validation sets, respectively. The human–machine diagnostic performance was superior to that of the SSL-CLAM model, showing promising prospects. In addition, the high-attention regions of the SSL-CLAM model showed that with an increasing FG status, the cell nuclei in the tumor region become larger, with irregular contours and increased cellular pleomorphism. (4) Conclusions: Our findings support the feasibility of using deep learning and human–machine fusion methods for FG classification on WSIs from ccRCC patients, which may assist pathologists in making diagnostic decisions. Full article
(This article belongs to the Special Issue Clinical and Translational Updates in Renal Cell Carcinoma)
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