Heather M. Shaw

1.5k total citations
34 papers, 725 citations indexed

About

Heather M. Shaw is a scholar working on Oncology, Immunology and Molecular Biology. According to data from OpenAlex, Heather M. Shaw has authored 34 papers receiving a total of 725 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Oncology, 12 papers in Immunology and 10 papers in Molecular Biology. Recurrent topics in Heather M. Shaw's work include Cancer Immunotherapy and Biomarkers (12 papers), CAR-T cell therapy research (10 papers) and Immunotherapy and Immune Responses (9 papers). Heather M. Shaw is often cited by papers focused on Cancer Immunotherapy and Biomarkers (12 papers), CAR-T cell therapy research (10 papers) and Immunotherapy and Immune Responses (9 papers). Heather M. Shaw collaborates with scholars based in United Kingdom, United States and Australia. Heather M. Shaw's co-authors include Paul Nathan, Johann S. de Bono, Timothy A. Yap, Antonio Gualberto, L Rhoda Molife, Donghua Yin, Paul Haluska, Michelle L. Roberts, Amarnath Sharma and Alex A. Adjei and has published in prestigious journals such as Journal of Clinical Oncology, Clinical Cancer Research and British Journal of Cancer.

In The Last Decade

Heather M. Shaw

33 papers receiving 714 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Heather M. Shaw United Kingdom 13 352 349 166 132 116 34 725
Margaret H. Veldman-Jones United Kingdom 8 200 0.6× 358 1.0× 82 0.5× 72 0.5× 146 1.3× 11 642
Bingyin Shi China 14 211 0.6× 389 1.1× 78 0.5× 66 0.5× 171 1.5× 28 661
Wen Liu China 15 525 1.5× 407 1.2× 58 0.3× 210 1.6× 182 1.6× 59 885
Federica Torricelli Italy 18 208 0.6× 471 1.3× 131 0.8× 241 1.8× 212 1.8× 80 1.0k
Greg Friberg United States 10 189 0.5× 259 0.7× 108 0.7× 116 0.9× 95 0.8× 15 488
Saradhi Mallampati United States 14 155 0.4× 275 0.8× 61 0.4× 94 0.7× 81 0.7× 34 592
Ken Ueki Japan 16 223 0.6× 375 1.1× 61 0.4× 66 0.5× 132 1.1× 33 882
Elaina Cajulis United States 11 301 0.9× 432 1.2× 135 0.8× 224 1.7× 126 1.1× 23 838
Sung‐Im Do South Korea 16 340 1.0× 399 1.1× 48 0.3× 175 1.3× 258 2.2× 84 913
Shanhong Tang China 14 509 1.4× 516 1.5× 445 2.7× 72 0.5× 177 1.5× 30 1.1k

Countries citing papers authored by Heather M. Shaw

Since Specialization
Citations

This map shows the geographic impact of Heather M. Shaw's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Heather M. Shaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heather M. Shaw more than expected).

Fields of papers citing papers by Heather M. Shaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Heather M. Shaw. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Heather M. Shaw. The network helps show where Heather M. Shaw may publish in the future.

Co-authorship network of co-authors of Heather M. Shaw

This figure shows the co-authorship network connecting the top 25 collaborators of Heather M. Shaw. A scholar is included among the top collaborators of Heather M. Shaw based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Heather M. Shaw. Heather M. Shaw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Long, Georgina V., Victoria Atkinson, Paolo A. Ascierto, et al.. (2024). A phase 3 trial of IMC-F106C (PRAME x CD3) plus nivolumab versus standard nivolumab regimens in HLA-A*02:01+ patients with previously untreated advanced melanoma (PRISM-MEL-301).. Journal of Clinical Oncology. 42(16_suppl). TPS9602–TPS9602. 3 indexed citations
2.
Hamid, Omid, Juanita Lopez, Daniel G. Olson, et al.. (2024). Phase 1 safety and efficacy of IMC-F106C, a PRAME × CD3 ImmTAC bispecific, in post-checkpoint cutaneous melanoma (CM).. Journal of Clinical Oncology. 42(16_suppl). 9507–9507. 10 indexed citations
3.
Hamid, Omid, Laura Collins, Marcus O. Butler, et al.. (2024). 66O Association of a blood T cell fitness gene signature with clinical benefit from ImmTAC bispecific T cell engagers. Annals of Oncology. 35. S239–S240. 1 indexed citations
4.
Shaw, Heather M., Miranda Payne, Satish Kumar, et al.. (2024). A DNA plasmid melanoma cancer vaccine, SCIB1, combined with nivolumab + ipilimumab in patients with advanced unresectable melanoma: Efficacy and safety results from the open-label phase 2 SCOPE trial.. Journal of Clinical Oncology. 42(16_suppl). 9535–9535. 1 indexed citations
5.
Vrohlings, Melissa, Maria Bouvy‐Liivrand, Reija Hieta, et al.. (2024). In silico tumor immune microenvironment (TiME) analysis of non-small cell lung cancer (NSCLC) to inform clinical development of CDR404: A first-of-its-kind MAGE-A4 targeted T-cell engager.. Journal of Clinical Oncology. 42(16_suppl). e20024–e20024.
6.
Carter, Thomas J., et al.. (2023). Tebentafusp: a first-in-class treatment for metastatic uveal melanoma. Therapeutic Advances in Medical Oncology. 15. 2604876–2604876. 32 indexed citations
7.
Lee, Karla A., Heather M. Shaw, Véronique Bataille, Paul Nathan, & Tim D. Spector. (2020). Role of the gut microbiome for cancer patients receiving immunotherapy: Dietary and treatment implications. European Journal of Cancer. 138. 149–155. 62 indexed citations
8.
Sng, Christopher C.T., Sarah Benafif, Neha Chopra, et al.. (2020). 1704P COVID-19 mortality in patients receiving anti-cancer therapy in a UK national cancer centre. Annals of Oncology. 31. S1004–S1004. 1 indexed citations
9.
10.
Dillon, Magnus T., Lorna Grove, Kate Newbold, et al.. (2018). Patritumab with Cetuximab plus Platinum-Containing Therapy in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck: An Open-Label, Phase Ib Study. Clinical Cancer Research. 25(2). 487–495. 14 indexed citations
11.
12.
Shaw, Heather M., et al.. (2016). Ipilimumab and Bevacizumab in Glioblastoma. Clinical Oncology. 28(10). 622–626. 63 indexed citations
13.
Shaw, Heather M.. (2015). Intramuscular injection. Nursing Standard. 30(6). 61–62. 7 indexed citations
14.
Shaw, Heather M. & Paul Nathan. (2013). Vemurafenib in melanoma. Expert Review of Anticancer Therapy. 13(5). 513–522. 13 indexed citations
15.
Hall, Marcia & Heather M. Shaw. (2013). Emerging treatment options for recurrent ovarian cancer: the potential role of olaparib. OncoTargets and Therapy. 6. 1197–1197. 13 indexed citations
16.
Boyd, Suzanah C., Branka Mijatov, Gulietta M. Pupo, et al.. (2012). Oncogenic B-RAFV600E Signaling Induces the T-Box3 Transcriptional Repressor to Repress E-Cadherin and Enhance Melanoma Cell Invasion. Journal of Investigative Dermatology. 133(5). 1269–1277. 42 indexed citations
17.
18.
Vance, Keith W., Heather M. Shaw, Mercedes Rodríguez, Sascha Ott, & Colin R. Goding. (2010). The Retinoblastoma Protein Modulates Tbx2 Functional Specificity. Molecular Biology of the Cell. 21(15). 2770–2779. 28 indexed citations
19.
Reid, Alison, Andrew Protheroe, Gerhardt Attard, et al.. (2009). A First-in-Man Phase I and Pharmacokinetic Study on CHR-2797 (Tosedostat), an Inhibitor of M1 Aminopeptidases, in Patients with Advanced Solid Tumors. Clinical Cancer Research. 15(15). 4978–4985. 29 indexed citations
20.
Yousem, Samuel A., Heather M. Shaw, & Kathleen Cieply. (2001). Involvement of 2p23 in pulmonary inflammatory pseudotumors. Human Pathology. 32(4). 428–433. 35 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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