Dr. Alexandra Boitor, EACR Scientific Officer, writes about her experience and scientific highlights from the EACR Conference on Cancer Genomics, which was held in Cambridge, UK from 5-7 July 2022.

In 2022 the EACR conference on Cancer Genomics returning as an in-person meeting after a 3-year break. More than 140 participants from 39 different countries met in Oxford in July to discuss the latest advances in immuno-genomics, single cell and spatial analysis, DNA methylation, data integration and genomic instability. Data presented at this conference spammed a broad range of cancer types including, but not limited to, breast, prostate, ovarian, colorectal, lung cancers, melanoma and childhood brain cancers. The scientific content presented at this meeting, be it in poster or talk format, was outstanding and the overall quality of the submitted abstracts impressed even the scientific committee of the conference.

This year, I was also able to attend the EACR Cancer Genomics conference for the first time, and I have to admit that I was truly impressed. Please forgive me, I forgot to introduce myself. I am Alexandra Boitor, Scientific Officer at the EACR. I have a scientific background in cancer research, but mainly from a molecular and cell biology perspective and full disclosure, I am far from being a specialist in genomics. Attending the Cancer Genomics conference was therefore a huge learning opportunity for me, and on this note, I cannot stress enough how important it is for young scientists to listen to talks from other research fields than their own in order to broaden their horizons.

It was incredibly difficult to pick one favourite talk to bring to your attention given the highly influential speakers present at this conference, many of them being pioneers in the cancer genomics field. A few highlights were:

  • Yardena Samuels’s talk about the complex landscape of melanoma-presented HLA-peptides, ranging from tumour-associated antigens and neoantigens to bacterial and viral antigens and the so-called “black matter” antigens and the implications this antigen heterogeneity possesses for immunotherapy. As discussed in the Q&A section, it is unclear at this stage whether these melanoma-associated antigens are tumour-specific or a result of inflammation (Bartok, Pataskar et al. 2021, Kalaora, Nagler et al. 2021).
  • Michael Platten’s talk about the development of an anti-cancer vaccine for a subset of glioma patients. This vaccine, which is now in phase I clinical trials, targets mutated isocitrate dehydrogenase type 1 (IDH1-R132H), a key driver mutation identified in subsets of patients with glioma (Platten, Bunse et al. 2021).
  • Nitzan Rosenfeld’s talk that touched on subjects such as the development of early cancer detection tests that use dry blood spots. A subject that has been presented into greater detail by PhD student Angela Ann at our Liquid Biopsy conference earlier this year (Sauer, Heider et al. 2022).
  • Yaara Oren’s talk that introduced the ‘watermelon system’ a novel tool that simultaneously allows for the detection of rare persister cells and the non-genetic mechanisms they develop to bypass cancer therapy (Oren, Tsabar et al. 2021).

At the end of the day, the speaker that impressed me the most was Dr. Mireia Crispin-Ortuzar. She talked about a couple of very interesting projects she works on that aim to predict response to neoadjuvant therapy in breast cancer and ovarian cancer patients respectively.

In the first part of her talk, Mireia presented a study that shows that in early and locally advanced breast cancers response to neoadjuvant chemotherapy is pre-determined by the tumour ecosystem. Whole-genome sequencing, whole-exosome sequencing, RNA sequencing and H&E staining were performed on biopsies taken prior to neoadjuvant chemotherapy treatment. Response to treatment was then assessed at surgery and revealed that pathologic complete response (pCR) and residual cancer burden (RCB) in early-stage breast cancer correlates with distinct molecular characteristics of the tumour. A complete response is associated with a high tumour mutation burden characterised by a high contribution of non-clock mutation burden, increased copy-number alteration burden and TP53 mutations and low subclonal heterogeneity. In addition, homologues recombinant deficiency and higher predicted neoantigen burden were noticed in patients with pCR.  APOBEC signature and activation of proliferation pathways (CDKN24, EGFR, MYC, CCNE1 were overexpressed whilst CCND1, ZNF703, ESR1 were underexpressed) also characterised biopsies taken from patients with pCR in addition to H&E and transcriptome characteristics indicative of an active tumour immune microenvironment encompassing increased innate and adaptive immune response. In contrast, RCB was associated with PIK3CA mutations, a higher percentage of subclonal mutations, chromosomal instability, increased epithelial-to-mesenchymal transition potential, immune dysfunction and loss of heterozygosity over the HLA I locus predicting a loss of up to 30% of tumour neoantigens (Sammut, Crispin-Ortuzar et al. 2022).

A machine learning framework was developed based on the genomic and transcriptomic data mentioned above together with digital pathology and other relevant clinical data. The model was trained using binary response variable (e.g pCR or RCB), cross-validated and then tested for validation on an independent cohort of 75 patients. This model demonstrated very good discrimination power: it accurately predicted pCR and predictor scores even correlate to different RCB classes despite the binary training used (Sammut, Crispin-Ortuzar et al. 2022).

The second project Mireia presented focused on using radiomic features from CT scans routinely performed in clinical practice to improve the prediction of neoadjuvant therapy response in high-grade serous ovarian carcinoma. In short, a combination of radiomics futures of the tumour such as the maximum 2D diameter, the least axis length, the elongation, the inverse difference, the homogeneity and the difference entropy can accurately identify non-responders before the neoadjuvant chemotherapy treatment as assessed by the chemotherapy response score, at the moment considered to be the gold standard in the clinic (Rundo, Beer et al. 2022).

Not only was Dr. Crispin-Ortuzar’s scientific talk extraordinary, but I was also impressed with her career path. During her ‘meet the expert’ talk Mireia revealed that she started out by studying physics for her undergraduate degree and then continued with a PhD in Particle physics at the University of Oxford, working as a member of the ATLAS collaboration at CERN. For her post-doc Mireia decided to transition into the bio-medical field. She made use of the data-analysis and computational skills she developed during her physics studies to improve personalized medicine by modelling the development of tumours in order to predict radiotherapy treatment outcomes.

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Donato, C., L. Kunz, F. Castro-Giner, A. Paasinen-Sohns, K. Strittmatter, B. M. Szczerba, R. Scherrer, N. Di Maggio, W. Heusermann, O. Biehlmaier, C. Beisel, M. Vetter, C. Rochlitz, W. P. Weber, A. Banfi, T. Schroeder and N. Aceto (2020). “Hypoxia Triggers the Intravasation of Clustered Circulating Tumor Cells.” Cell Rep 32(10): 108105.

Kalaora, S., A. Nagler, D. Nejman, M. Alon, C. Barbolin, E. Barnea, S. L. C. Ketelaars, K. Cheng, K. Vervier, N. Shental, Y. Bussi, R. Rotkopf, R. Levy, G. Benedek, S. Trabish, T. Dadosh, S. Levin-Zaidman, L. T. Geller, K. Wang, P. Greenberg, G. Yagel, A. Peri, G. Fuks, N. Bhardwaj, A. Reuben, L. Hermida, S. B. Johnson, J. R. Galloway-Peña, W. C. Shropshire, C. Bernatchez, C. Haymaker, R. Arora, L. Roitman, R. Eilam, A. Weinberger, M. Lotan-Pompan, M. Lotem, A. Admon, Y. Levin, T. D. Lawley, D. J. Adams, M. P. Levesque, M. J. Besser, J. Schachter, O. Golani, E. Segal, N. Geva-Zatorsky, E. Ruppin, P. Kvistborg, S. N. Peterson, J. A. Wargo, R. Straussman and Y. Samuels (2021). “Identification of bacteria-derived HLA-bound peptides in melanoma.” Nature 592(7852): 138-143.

Oren, Y., M. Tsabar, M. S. Cuoco, L. Amir-Zilberstein, H. F. Cabanos, J.-C. Hütter, B. Hu, P. I. Thakore, M. Tabaka, C. P. Fulco, W. Colgan, B. M. Cuevas, S. A. Hurvitz, D. J. Slamon, A. Deik, K. A. Pierce, C. Clish, A. N. Hata, E. Zaganjor, G. Lahav, K. Politi, J. S. Brugge and A. Regev (2021). “Cycling cancer persister cells arise from lineages with distinct programs.” Nature 596(7873): 576-582.

Platten, M., L. Bunse, A. Wick, T. Bunse, L. Le Cornet, I. Harting, F. Sahm, K. Sanghvi, C. L. Tan, I. Poschke, E. Green, S. Justesen, G. A. Behrens, M. O. Breckwoldt, A. Freitag, L. M. Rother, A. Schmitt, O. Schnell, J. Hense, M. Misch, D. Krex, S. Stevanovic, G. Tabatabai, J. P. Steinbach, M. Bendszus, A. von Deimling, M. Schmitt and W. Wick (2021). “A vaccine targeting mutant IDH1 in newly diagnosed glioma.” Nature 592(7854): 463-468.

Rundo, L., L. Beer, L. E. Sanchez, M. Crispin-Ortuzar, M. Reinius, C. McCague, H. Sahin, V. Bura, R. Pintican and M. Zerunian (2022). “Clinically interpretable radiomics-based prediction of histopathologic response to neoadjuvant chemotherapy in high-grade serous ovarian carcinoma.” Frontiers in oncology 12.

Sammut, S.-J., M. Crispin-Ortuzar, S.-F. Chin, E. Provenzano, H. A. Bardwell, W. Ma, W. Cope, A. Dariush, S.-J. Dawson, J. E. Abraham, J. Dunn, L. Hiller, J. Thomas, D. A. Cameron, J. M. S. Bartlett, L. Hayward, P. D. Pharoah, F. Markowetz, O. M. Rueda, H. M. Earl and C. Caldas (2022). “Multi-omic machine learning predictor of breast cancer therapy response.” Nature 601(7894): 623-629.

Sauer, C. M., K. Heider, J. Belic, S. E. Boyle, J. A. Hall, D.-L. Couturier, A. An, A. Vijayaraghavan, M. A. V. Reinius, K. Hosking, M. Vias, N. Rosenfeld and J. D. Brenton (2022). “Longitudinal monitoring of disease burden and response using ctDNA from dried blood spots in xenograft models.” EMBO Molecular Medicine n/a(n/a): e15729.