Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Abstract

With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology.

Key points: 

• Tumor heterogeneity can be described within one lesion (intralesional) or between several lesions (interlesional).

• The heterogeneous biology of tumor cells can lead to a mixed therapeutic response and should be addressed in diagnostics and the therapeutic regime.

• Quantitative image diagnostics can be enhanced using AI, improved histopathological methods, and liquid profiling in the future.

Zusammenfassung

Im Rahmen der personalisierten Tumortherapie wird es immer bedeutender, die Heterogenität von bösartigen Tumoren zu verstehen und zu berücksichtigen. Diese kann innerhalb einer Läsion (intralesional) und zwischen mehreren Tumorläsionen auftreten, die aus einem primären Tumor hervorgehen (interlesional). Die heterogenen Tumorzellen können aufgrund ihrer Biologie unterschiedliche Reaktionen auf verschiedene Behandlungen zeigen, was wiederum das Outcome der betroffenen Patienten und die Wahl der Therapie beeinflusst. Daher sollten sowohl intra- als auch interlesionale Heterogenität in der Diagnostik berücksichtigt werden. Während genetische und biologische Heterogenität wichtige Parameter in der molekularen Tumorcharakterisierung und in der Histopathologie sind, werden sie in der medizinischen Bildgebung noch nicht routinemäßig berücksichtigt. Dieser Artikel fasst die etablierten Marker für Tumorheterogenität in der Bildgebung sowie für heterogenes/gemischtes Therapieansprechen zusammen. Darüber hinaus wird ein Ausblick über aufkommende Marker gegeben. Ziel dieser Übersichtsarbeit ist es, ein umfassendes Verständnis der Heterogenität von Tumoren und ihrer Auswirkungen auf die Radiologie und die interdisziplinäre Kommunikation in der Onkologie zu vermitteln.

Kernaussagen: 

• Tumorheterogenität kann innerhalb einer Läsion (intralesional) oder zwischen mehreren Läsionen (interlesional) beschrieben werden.

• Die heterogene Biologie von Tumorzellen kann zu einer gemischten therapeutischen Reaktion führen und sollte sowohl bei Diagnose als auch Therapie berücksichtigt werden.

• Die quantitative Bilddiagnostik kann in Zukunft durch den Einsatz von KI, verbesserten histopathologischen Methoden und Liquid Profiling ergänzt werden.

Zitierweise

• Haag F, Hertel A, Tharmaseelan H et al. Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment. Fortschr Röntgenstr 2024; 196: 262 – 272

Key words

oncologic imaging - tumor heterogeneity - personalized oncology

Publication History

Received: 02 May 2023

Accepted: 16 August 2023

Article published online:
09 November 2023

• References

• 1 Schochter F, Werner K, Köstler C. et al. 53BP1 Accumulation in Circulating Tumor Cells Identifies Chemotherapy-Responsive Metastatic Breast Cancer Patients. Cancers (Basel) 2020;
• 2 Gerlinger M, Rowan AJ, Horswell S. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366: 883-892
• 3 Fidler IJ. Biological heterogeneity of cancer: implication to therapy. Hum Vaccin Immunother 2012; 8: 1141-1142
• 4 Lawson DA, Kessenbrock K, Davis RT. et al. Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol 2018; 20: 1349-1360
• 5 Biswas A, De S. Drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Am J Physiol Cell Physiol 2021; 320: C750-C760
• 6 Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 2018; 15: 81-94
• 7 Levy-Jurgenson A, Tekpli X, Kristensen VN. et al. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 2020; 10: 18802
• 8 Tharmaseelan H, Hertel A, Rennebaum S. et al. The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022;
• 9 MacMahon H, Naidich DP, Goo JM. et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology 2017; 284: 228-243
• 10 Elsayes KM, Kielar AZ, Chernyak V. et al. LI-RADS: a conceptual and historical review from its beginning to its recent integration into AASLD clinical practice guidance. J Hepatocell Carcinoma 2019; 6: 49-69
• 11 Turkbey B, Rosenkrantz AB, Haider MA. et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019; 76: 340-351
• 12 Le Bihan D, Breton E, Lallemand D. et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161: 401-407
• 13 Helenius J, Soinne L, Perkiö J. et al. Diffusion-Weighted MR Imaging in Normal Human Brains in Various Age Groups. AJNR Am J Neuroradiol 2002; 23: 194-199
• 14 Hilario A, Ramos A, Perez-Nuñez A. et al. The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. AJNR Am J Neuroradiol 2012; 33: 701-707
• 15 Hettler M, Kitz J, Seif Amir Hosseini A. et al. Comparing Apparent Diffusion Coefficient and FNCLCC Grading to Improve Pretreatment Grading of Soft Tissue Sarcoma-A Translational Feasibility Study on Fusion Imaging. Cancers (Basel) 2022;
• 16 Fehr D, Veeraraghavan H, Wibmer A. et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A 2015; 112: E6265-E6273
• 17 Kudou M, Nakanishi M, Kuriu Y. et al. Value of intra-tumor heterogeneity evaluated by diffusion-weighted MRI for predicting pathological stages and therapeutic responses to chemoradiotherapy in lower rectal cancer. J Cancer 2020; 11: 168-176
• 18 Boudabbous S, Hamard M, Saiji E. et al. What morphological MRI features enable differentiation of low-grade from high-grade soft tissue sarcoma?. BJR Open 2022; 4: 20210081
• 19 Yin Y, Sedlaczek O, Muller B. et al. Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer. IEEE Trans Med Imaging 2018; 37: 35-46
• 20 Girot C, Volk A, Walczak C. et al. New method for quantification of intratumoral heterogeneity: a feasibility study on Ktrans maps from preclinical DCE-MRI. MAGMA 2021; 34: 845-857
• 21 Gerwing M, Hoffmann E, Kronenberg K. et al. Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer. Front Oncol 2022; 12: 1000036
• 22 Crombé A, Saut O, Guigui J. et al. Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019; 50: 1773-1788
• 23 Hectors SJ, Wagner M, Bane O. et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep 2017; 7: 2452
• 24 Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61: R150-R166
• 25 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278: 563-577
• 26 Wang X, Xie T, Luo J. et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res 2022; 24: 20
• 27 Jiang L, You C, Xiao Y. et al. Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer. Cell Rep Med 2022; 3: 100694
• 28 Fan M, Chen H, You C. et al. Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Mol Biosci 2021; 8: 622219
• 29 Wilson GC, Cannella R, Fiorentini G. et al. Texture analysis on preoperative contrast-enhanced magnetic resonance imaging identifies microvascular invasion in hepatocellular carcinoma. HPB (Oxford) 2020; 22: 1622-1630
• 30 Gu Y, Huang H, Tong Q. et al. Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma. Cancers (Basel) 2023;
• 31 Granata V, Fusco R, Setola SV. et al. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17: 6
• 32 Almuhaideb A, Papathanasiou N, Bomanji J. 18F-FDG PET/CT imaging in oncology. Ann Saudi Med 2011; 31: 3-13
• 33 Hatt M, Cheze-le Rest C, van Baardwijk A. et al. Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med 2011; 52: 1690-1697
• 34 Mena E, Sheikhbahaei S, Taghipour M. et al. 18F-FDG PET/CT Metabolic Tumor Volume and Intratumoral Heterogeneity in Pancreatic Adenocarcinomas: Impact of Dual-Time Point and Segmentation Methods. Clin Nucl Med 2017; 42: e16-e21
• 35 Koh YW, Lee D, Lee SJ. Intratumoral heterogeneity as measured using the tumor-stroma ratio and PET texture analyses in females with lung adenocarcinomas differs from that of males with lung adenocarcinomas or squamous cell carcinomas. Medicine (Baltimore) 2019; 98: e14876
• 36 Bundschuh RA, Dinges J, Neumann L. et al. Textural Parameters of Tumor Heterogeneity in ¹⁸F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer. J Nucl Med 2014; 55: 891-897
• 37 Zhao Y, Liu C, Zhang Y. et al. Prognostic Value of Tumor Heterogeneity on 18F-FDG PET/CT in HR+HER2- Metastatic Breast Cancer Patients receiving 500 mg Fulvestrant: a retrospective study. Sci Rep 2018; 8: 14458
• 38 Xie Y, Liu C, Zhao Y. et al. Heterogeneity derived from 18 F-FDG PET/CT predicts immunotherapy outcome for metastatic triple-negative breast cancer patients. Cancer Med 2022; 11: 1948-1955
• 39 Bashir U, Weeks A, Goda JS. et al. Measurement of 18F-FDG PET tumor heterogeneity improves early assessment of response to bevacizumab compared with the standard size and uptake metrics in a colorectal cancer model. Nucl Med Commun 2019; 40: 611-617
• 40 Siravegna G, Lazzari L, Crisafulli G. et al. Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer. Cancer Cell 2018; 34: 148-162.e7
• 41 Gültekin MA, Türk HM, Beşiroğlu M. et al. Relationship between KRAS mutation and diffusion weighted imaging in colorectal liver metastases; Preliminary study. Eur J Radiol 2020; 125: 108895
• 42 Mao W, Zhou J, Zhang H. et al. Relationship between KRAS mutations and dual time point 18F-FDG PET/CT imaging in colorectal liver metastases. Abdom Radiol (NY) 2019; 44: 2059-2066
• 43 Tharmaseelan H, Hertel A, Tollens F. et al. Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity. Cancers (Basel) 2022;
• 44 Yousefi B, LaRiviere MJ, Cohen EA. et al. Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors. Sci Rep 2021; 11: 9984
• 45 Sayar E, Patel RA, Coleman IM. et al. Reversible epigenetic alterations mediate PSMA expression heterogeneity in advanced metastatic prostate cancer. JCI Insight 2023;
• 46 Assadi M, Manafi-Farid R, Jafari E. et al. Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA. Front Oncol 2022; 12: 1066926
• 47 Lee JW, Min HS, Lee SM. et al. Relations Between Pathological Markers and Radioiodine Scan and (18)F-FDG PET/CT Findings in Papillary Thyroid Cancer Patients With Recurrent Cervical Nodal Metastases. Nucl Med Mol Imaging 2015; 49: 127-134
• 48 Kim D-H, Jung J-H, Son SH. et al. Difference of clinical and radiological characteristics according to radioiodine avidity in pulmonary metastases of differentiated thyroid cancer. Nucl Med Mol Imaging 2014; 48: 55-62
• 49 Hong CM, Ahn B-C, Jeong SY. et al. Distant metastatic lesions in patients with differentiated thyroid carcinoma. Clinical implications of radioiodine and FDG uptake. Nuklearmedizin 2013; 52: 121-129
• 50 Feijtel D, Doeswijk GN, Verkaik NS. et al. Inter and intra-tumor somatostatin receptor 2 heterogeneity influences peptide receptor radionuclide therapy response. Theranostics 2021; 11: 491-505
• 51 Kumbrink J, Bohlmann L, Mamlouk S. et al. Serial Analysis of Gene Mutations and Gene Expression during First-Line Chemotherapy against Metastatic Colorectal Cancer: Identification of Potentially Actionable Targets within the Multicenter Prospective Biomarker Study REVEAL. Cancers (Basel) 2022;
• 52 Omari J, Drewes R, Matthias M. et al. Treatment of metastatic, imatinib refractory, gastrointestinal stroma tumor with image-guided high-dose-rate interstitial brachytherapy. Brachytherapy 2019; 18: 63-70
• 53 Zhang Q, Lou Y, Yang J. et al. Integrated multiomic analysis reveals comprehensive tumour heterogeneity and novel immunophenotypic classification in hepatocellular carcinomas. Gut 2019; 68: 2019-2031
• 54 Yoo J, Chong S, Lim C. et al. Assessment of spatial tumor heterogeneity using CT growth patterns estimated by tumor tracking on 3D CT volumetry of multiple pulmonary metastatic nodules. PLoS One 2019; 14: e0220550
• 55 Froelich MF, Heinemann V, Sommer WH. et al. CT attenuation of liver metastases before targeted therapy is a prognostic factor of overall survival in colorectal cancer patients. Results from the randomised, open-label FIRE-3/AIO KRK0306 trial. Eur Radiol 2018; 28: 5284-5292
• 56 Steinacker JP, Steinacker-Stanescu N, Ettrich T. et al. Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy. Visc Med 2021; 37: 77-83
• 57 Lau D, McLean MA, Priest AN. et al. Multiparametric MRI of early tumor response to immune checkpoint blockade in metastatic melanoma. J Immunother Cancer 2021;
• 58 Mathios D, Johansen JS, Cristiano S. et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun 2021; 12: 5060
• 59 Siravegna G, Marsoni S, Siena S. et al. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 2017; 14: 531-548
• 60 Parikh AR, Leshchiner I, Elagina L. et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat Med 2019; 25: 1415-1421
• 61 Conteduca V, Scarpi E, Caroli P. et al. Combining liquid biopsy and functional imaging analysis in metastatic castration-resistant prostate cancer helps predict treatment outcome. Mol Oncol 2022; 16: 538-548
• 62 Froelich MF, Capoluongo E, Kovacs Z. et al. The value proposition of integrative diagnostics for (early) detection of cancer. On behalf of the EFLM interdisciplinary Task and Finish Group “CNAPS/CTC for early detection of cancer”. Clin Chem Lab Med 2022; 60: 821-829
• 63 Haselmann V, Schoenberg SO, Neumaier M. et al. Integrierte Diagnostik. [Integrated diagnostics]. Radiologie (Heidelb) 2022; 62: 11-16