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Ex vivo organotypic cultures for synergistic therapy prioritization identify patient-specific responses to combined MEK and Src inhibition in colorectal cancer

Abstract

Translating preclinical studies to effective treatment protocols and identifying specific therapeutic responses in individuals with cancer is challenging. This may arise due to the complex genetic makeup of tumor cells and the impact of their multifaceted tumor microenvironment on drug response. To find new clinically relevant drug combinations for colorectal cancer (CRC), we prioritized the top five synergistic combinations from a large in vitro screen for ex vivo testing on 29 freshly resected human CRC tumors and found that only the combination of mitogen-activated protein kinase kinase (MEK) and proto-oncogene tyrosine-protein kinase Src (Src) inhibition was effective when tested ex vivo. Pretreatment phosphorylated Src (pSrc) was identified as a predictive biomarker for MEK and Src inhibition only in the absence of KRASG12 mutations. Overall, we demonstrate the potential of using ex vivo platforms to identify drug combinations and discover MEK and Src dual inhibition as an effective drug combination in a predefined subset of individuals with CRC.

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Fig. 1: Optimizing conditions for robust and versatile EVOC.
Fig. 2: EVOC can predict the response of PDX tumors to treatment.
Fig. 3: In vitro screens of drug combinations identify multiple synergistic pairs in CRC cell lines.
Fig. 4: Ex vivo screening identifies the dual inhibition of MEK and Src as effective in a subset of human CRCs.
Fig. 5: High pSrc and inhibition of phospho-extracellular signal-regulated kinase (pERK) by Src inhibitor treatment are both predictive biomarkers of response to MEK/Src inhibition.
Fig. 6: Feedback phosphorylation of MEK as a result of MEK inhibition is abrogated by the addition of Src inhibitor in CRC tumors that are sensitive to MEK/Src inhibition.

Data availability

Source data for the in vitro screens are provided as Supplementary Tables 3 and 4. Results of the evaluations of all the human samples for each drug and drug combination have been provided in Supplementary Table 6. Clinical data of the participants included in the study are provided in Supplementary Table 5. Mutations found in participant samples are provided in Supplementary Table 7. Drug doses used in the EVOC experiments are listed in Supplementary Table 2. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Jaiswal, R. & Sedger, L. M. Intercellular vesicular transfer by exosomes, microparticles and oncosomes—implications for cancer biology and treatments. Front. Oncol. 9, 125 (2019).

    PubMed  PubMed Central  Google Scholar 

  3. Goulet, C. R. et al. Cancer-associated fibroblasts induce epithelial–mesenchymal transition of bladder cancer cells through paracrine IL-6 signalling. BMC Cancer 19, 137 (2019).

    PubMed  PubMed Central  Google Scholar 

  4. Geller, L. T. et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357, 1156–1160 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Garattini, S. & Grignaschi, G. Animal testing is still the best way to find new treatments for patients. Eur. J. Intern. Med. 39, 32–35 (2017).

    PubMed  Google Scholar 

  6. Hutchinson, L. & Kirk, R. High drug attrition rates—where are we going wrong? Nat. Rev. Clin. Oncol. 8, 189–190 (2011).

    PubMed  Google Scholar 

  7. Wong, C. H., Siah, K. W. & Lo, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 273–286 (2019).

    PubMed  Google Scholar 

  8. Van Cutsem, E., Cervantes, A., Nordlinger, B., Arnold, D. & ESMO Guidelines Working Group Metastatic colorectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 25, iii1–iii9 (2014).

    PubMed  Google Scholar 

  9. Vogel, A., Hofheinz, R. D., Kubicka, S. & Arnold, D. Treatment decisions in metastatic colorectal cancer—beyond first and second line combination therapies. Cancer Treat. Rev. 59, 54–60 (2017).

    CAS  PubMed  Google Scholar 

  10. Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Dienstmann, R. et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17, 79–92 (2017).

    CAS  PubMed  Google Scholar 

  12. Wang, W. et al. Molecular subtyping of colorectal cancer: recent progress, new challenges and emerging opportunities. Semin. Cancer Biol. 55, 37–52 (2019).

    CAS  PubMed  Google Scholar 

  13. Hickman, J. A. et al. Three-dimensional models of cancer for pharmacology and cancer cell biology: capturing tumor complexity in vitro/ex vivo. Biotechnol. J. 9, 1115–1128 (2014).

    CAS  PubMed  Google Scholar 

  14. Majumder, B. et al. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat. Commun. 6, 6169 (2015).

    CAS  PubMed  Google Scholar 

  15. Grosso, S. H. G. et al. Breast cancer tissue slices as a model for evaluation of response to rapamycin. Cell Tissue Res. 352, 671–684 (2013).

    CAS  PubMed  Google Scholar 

  16. Kern, M. A. et al. Ex vivo analysis of antineoplastic agents in precision-cut tissue slices of human origin: effects of cyclooxygenase-2 inhibition in hepatocellular carcinoma. Liver Int. 26, 604–612 (2006).

    CAS  PubMed  Google Scholar 

  17. Larsson, P. et al. Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens. Sci. Rep. 10, 5798 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Fallahi-Sichani, M., Honarnejad, S., Heiser, L. M., Gray, J. W. & Sorger, P. K. Metrics other than potency reveal systematic variation in responses to cancer drugs. Nat. Chem. Biol. 9, 708–714 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Vaira, V. et al. Preclinical model of organotypic culture for pharmacodynamic profiling of human tumors. Proc. Natl Acad. Sci. USA 107, 8352–8356 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Place, T. L., Domann, F. E. & Case, A. J. Limitations of oxygen delivery to cells in culture: an underappreciated problem in basic and translational research. Free Radic. Biol. Med. 113, 311–322 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Pasetto, L. M. et al. FOLFOX versus FOLFIRI: a comparison of regimens in the treatment of colorectal cancer metastases. Anticancer Res. 25, 563–576 (2015).

    Google Scholar 

  22. Genther Williams, S. M. et al. Treatment with the PARP inhibitor, niraparib, sensitizes colorectal cancer cell lines to irinotecan regardless of MSI/MSS status. Cancer Cell Int. 15, 14 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. Tentori, L. et al. Inhibition of poly(ADP‐ribose) polymerase prevents irinotecan‐induced intestinal damage and enhances irinotecan/temozolomide efficacy against colon carcinoma. FASEB J. 20, 1709–1711 (2006).

    CAS  PubMed  Google Scholar 

  24. Chen, E. X. et al. A phase I study of olaparib and irinotecan in patients with colorectal cancer: Canadian Cancer Trials Group IND 187. Invest. New Drugs 34, 450–457 (2016).

    CAS  PubMed  Google Scholar 

  25. Anderson, G. R. et al. A landscape of therapeutic cooperativity in KRAS mutant cancers reveals principles for controlling tumor evolution. Cell Rep. 20, 999–1015 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Astsaturov, I. et al. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci. Signal. 3, ra67 (2010).

    PubMed  PubMed Central  Google Scholar 

  27. Wang, H. et al. Antisense anti-MDM2 mixed-backbone oligonucleotides enhance therapeutic efficacy of topoisomerase I inhibitor irinotecan in nude mice bearing human cancer xenografts: in vivo activity and mechanisms. Int. J. Oncol. 20, 745–752 (2002).

    CAS  PubMed  Google Scholar 

  28. Martínez-Pérez, J. et al. Prognostic relevance of Src activation in stage II-III colon cancer. Hum. Pathol. 67, 119–125 (2017).

    PubMed  Google Scholar 

  29. Talamonti, M. S., Roh, M. S., Curley, S. A. & Gallickt, G. E. Increase in activity and level of pp60c-src in progressive stages of human colorectal cancer. J. Clin. Invest. 1, 53–60 (1993).

    Google Scholar 

  30. Si, Y. et al. Src inhibits the hippo tumor suppressor pathway through tyrosine phosphorylation of Lats1. Cancer Res. 77, 4868–4880 (2017).

    CAS  PubMed  Google Scholar 

  31. Yeh, T. C. et al. Biological characterization of ARRY-142886 (AZD6244), a potent, highly selective mitogen-activated protein kinase kinase 1/2 inhibitor. Clin. Cancer Res. 13, 1576–1583 (2007).

    CAS  PubMed  Google Scholar 

  32. Wu, P.-K. & Park, J.-I. MEK1/2 inhibitors: molecular activity and resistance mechanisms. Semin. Oncol. 42, 849–862 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. McFall, T. et al. A systems mechanism for KRAS mutant allele-specific responses to targeted therapy. Sci. Signal. 12, eaaw8288 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. McFall, T. & Stites, E. C. A mechanism for the response of KRASG13D expressing colorectal cancers to EGFR inhibitors. Mol. Cell. Oncol. 7, 1701914 (2020).

    PubMed  PubMed Central  Google Scholar 

  35. Mao, C. et al. KRAS p.G13D mutation and codon 12 mutations are not created equal in predicting clinical outcomes of cetuximab in metastatic colorectal cancer. Cancer 119, 714–721 (2013).

    CAS  PubMed  Google Scholar 

  36. Sönnichsen, R. et al. Individual susceptibility analysis using patient-derived slice cultures of colorectal carcinoma. Clin. Colorectal Cancer 17, e189–e199 (2018).

    PubMed  Google Scholar 

  37. Donnadieu J. et al. Short-term culture of tumour slices reveals the heterogeneous sensitivity of human head and neck squamous cell carcinoma to targeted therapies. BMC Cancer 16, 273 (2016).

  38. Begley, C. G. & Ellis, L. M. Raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    CAS  PubMed  Google Scholar 

  39. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  Google Scholar 

  40. Ben-David, U. et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 49, 1567–1575 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee M. W. et al. Current methods in translational cancer research. Cancer Metastasis Rev. 40, 7–30 (2021).

  42. Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Voabil, P. et al. An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer. Nat. Med. 27, 1250–1261 (2021).

    CAS  PubMed  Google Scholar 

  44. Fahy, G. M. et al. Cryopreservation of precision-cut tissue slices. Xenobiotica 43, 113–132 (2013).

    CAS  PubMed  Google Scholar 

  45. Arav, A., Friedman, O., Natan, Y., Gur, E. & Shani, N. Rat hindlimb cryopreservation and transplantation—a step toward ‘organ banking’. Am. J. Transpl. 11, 2820–2828 (2017).

    Google Scholar 

  46. Lu, Y. et al. CXCL1–LCN2 paracrine axis promotes progression of prostate cancer via the Src activation and epithelial–mesenchymal transition. Cell Commun. Signal. 17, 118 (2019).

    PubMed  PubMed Central  Google Scholar 

  47. Shields, D. J. et al. Oncogenic Ras/Src cooperativity in pancreatic neoplasia. Oncogene 30, 2123–2134 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Morton, J. P. et al. Dasatinib inhibits the development of metastases in a mouse model of pancreatic ductal adenocarcinoma. Gastroenterology 139, 292–303 (2010).

    CAS  PubMed  Google Scholar 

  49. Trevino, J. G. et al. Inhibition of Src expression and activity inhibits tumor progression and metastasis of human pancreatic adenocarcinoma cells in an orthotopic nude mouse model. Am. J. Pathol. 168, 962–972 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Liu, P., Wang, Y. & Li, X. Targeting the untargetable KRAS in cancer therapy. Acta Pharm. Sin. B 9, 871–879 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. Irby, R. B. & Yeatman, T. J. Role of Src expression and activation in human cancer. Oncogene 19, 5636–5642 (2000).

    CAS  PubMed  Google Scholar 

  52. Lake, D., Corrêa, S. A. L. & Müller, J. Negative feedback regulation of the ERK1/2 MAPK pathway. Cell. Mol. Life Sci. 73, 4397–4413 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Salpeter, S. et al. Abstract CT209: A clinical trial of cResponse, a functional assay for cancer precision medicine. Cancer Res. 81, CT209 (2021).

    Google Scholar 

  54. Chen, J., Elfiky, A., Han, M., Chen, C. & Saif, M. W. The role of Src in colon cancer and its therapeutic implications. Clin. Colorectal Cancer 13, 5–13 (2014).

    PubMed  Google Scholar 

  55. Montero, J. C., Seoane, S., Ocana, A. & Pandiella, A. Inhibition of Src family kinases and receptor tyrosine kinases by dasatinib: possible combinations in solid tumors. Clin. Cancer Res. 17, 5546–5552 (2011).

    CAS  PubMed  Google Scholar 

  56. Parseghian, C. M. et al. Dual inhibition of EGFR and c-Src by cetuximab and dasatinib combined with FOLFOX chemotherapy in patients with metastatic colorectal cancer. Clin. Cancer Res. 23, 4146–4154 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the members of the Straussman Lab for helpful discussions. We thank A.L. Barda, T. Oren, A. Itzhaki-Alfia and the Tel Aviv Sourasky Medical Center Biobank for their help with tissue recruitment. We thank E. Pikarsky for his willingness to review numerous pathology results in the early and critical stages of this work. This work was supported by a grant from the Rising Tide Foundation and by a research grant from the Fabricant-Morse Families Research Fund for Humanity. R.S. is the incumbent of the Roel C. Buck Career Development Chair.

Author information

Authors and Affiliations

Authors

Contributions

N.G., Y.Z., O.M. and R.S. conceptualized and designed the study. N.G. and R.S. developed the methodology. N.G., Y.Z., R. Weiser, A.J.B., S.H., O.J., E.S., A.G., D.S., I.B., I.K., J.B., G.P., E.B.-A., S.G., E.R., A.N., R. Weitzen, H.N., T.G., B.B., A.N. and G.L. acquired data (providing animals acquired and managed participants, provided facilities and so on). N.G., R.S., O.S., O.M., O.G., G.M., D.R. and D.H. analyzed and interpreted the data (for example, statistical analyses, biostatistics and computational analyses). N.G., G.M., Y.Z., A.G., S.G., R. Weitzen, A.N., T.G., G.L., H.N., B.B. and R.S. wrote, reviewed and/or revised the manuscript. R.S. supervised the study.

Corresponding author

Correspondence to Ravid Straussman.

Ethics declarations

Competing interests

R.S. and N.G. are founders, stockholders and paid consultants of the company CuResponse, which currently validates EVOC as a predictive biomarker for the effect of anticancer drugs. G.M. worked as a part-time pathologist for Curesponse. E.S. works as a part-time technician at CuResponse. No other potential conflicts of interest were disclosed by other authors.

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Nature Cancer thanks John Minna, Nicola Valeri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Optimizing culture conditions for EVOC.

a, An example of H&E staining of colorectal cancer from a patient (top row), ovarian cancer tissue (middle row) and breast cancer tissue (bottom row) from PDX mouse models cultured in different media for five days. Black boxes mark the regions that were selected for magnification on the right. Representative pictures are presented from n=2 tumors for each of ovarian and breast cancer PDX models, and n=3 tumors for CRC cancer patients. Scale bar 50μM in low magnifications and 20μM in high magnifications. b, Examples of H&E staining of histological slices of ovarian PDX tumors used to determine the viability of cancer cells after five days in culture under the different conditions as labeled. A scoring system was used to categorize the percentage of viable cancer cells: 0 for less than 10% viable cancer cells, 1 for 10–30%, 2 for 31–60%, 3 for 61–90%, and 4 for more than 90%. c, Upper panel summarizes experiments performed using 5 human CRC tissues, in which H&E staining was used to determine the effect of using a grid or high oxygen partial pressure on the viability of cancer cells after five days in culture. The bottom panel shows representative histological slices from two of the tumors in the upper panel. Each experiment was performed using at least two tissue slices for each condition. Scale bar 40μM. d, H&E staining of a human non-small cell lung cancer (NSCLC) tumor that was sliced and immediately fixed or cultured on a titanium grid in 80% O2PP for 4 or 6 days. Scale bar 50μM. O2PP, oxygen partial pressure; DMSO, dimethyl sulfoxide.

Source data

Extended Data Fig. 2 EVOC can predict the response of PDX tumors to treatment.

a, Table summarizing the ex vivo (EV) vs. in vivo (IV) response to drugs across 16 models of colon, breast, and lung PDX tumors treated with different drugs. Ex vivo experiments were scored as follows: NR - no response (viability score 4); PR - partial response (viability scores 1–3); CR - complete response (viability score 0). In vivo experiments were scored by the percent reduction in fold change of the tumor growth between the treated and untreated control mice groups as follows: NR - No response (<30% reduction); PR - partial response (>=30 to <100% reduction); CR - complete or near-complete response (>=100% reduction). b,d,f, left panels, Bar plots demonstrating the viability scores of two NSCLC PDX tumors treated ex vivo for 4 days with a range of concentrations of docetaxel, cisplatin or trametinib. Bar errors represent the standard deviation of two (n=2) or three (n=3) tumors, each condition within an experiment was evaluated in at least two slices of tissue. Right panel, growth curves representing the in vivo response of tumors to docetaxel, cisplatin or trametinib, or vehicle control. In vivo data was generated by the Jackson Laboratories. Error bars represent standard error of the mean (for TM00356: n=8 for cisplatin and trametinib treatment and n=7 mice for vehicle and trametinib treatment; for TM00123: n=6 mice for vehicle and cisplatin treatment, n=4 mice for docetaxel treatment and n= 5 mice for trametinib treatment). c,e,g, Representative histological sections of tissues from (b), (d) and (f) stained with H&E. h,j, Left panel, bar plots demonstrating the viability scores of two breast cancer PDX tumors treated ex vivo for 4 days with a range of concentrations of 4-hydroxycyclophosphamide (active metabolite of the pro-drug cyclophosphamide) and docetaxel. h,j, Right panel, growth curves representing the in vivo response of tumors to cyclophosphamide, docetaxel, or vehicle control. In vivo data was generated by the Jackson Laboratories. Error bars represent standard error of the mean (for TM00096: n=9 mice for docetaxel treatment, n=8 mice for vehicle and n=7 mice for cyclophosphamide treatment; for TM00098: n=10 mice for cyclophosphamide and docetaxel treatment and n=9 mice for vehicle treatment). i,k, Representative histological sections of tissues from (h) and (j) stained with H&E. The p values were calculated using a two-sided rank test, comparing the viability scores of all drug concentrations tested across all experiments. Scale bars 100μM for all micrographs in the figure. Doc, docetaxel; Cis, cisplatin; Tra, trametinib; Cyc, cyclophosphamide, DMSO, dimethyl sulfoxide.

Source data

Extended Data Fig. 3 Graphs depicting results from in vivo screens.

a, List of the 24 drugs tested and their primary targets. b, 276 drug combinations were tested on eight colorectal cancer cell lines (figure 3a - screen 2). Numbers represent the number of cell lines for which the combination of drugs was synergistic as determined by a Bliss score < 0.6 (Supplementary Table 4). c, Table listing the seven top synergistic drug combinations based on screen 3. For each drug pair, the table shows the number of cell lines in which the pair was synergistic (Bliss score <0.6) in >2 of the nine tested combinations. The total number of occurrences in which the bliss score was < 0.6 out of the possible 72 occurrences (9 drug concentrations X 8 cell lines) is also depicted. H-89 2HCl, H-89-dihydrochloride.

Source data

Extended Data Fig. 4 Examples of calibrating drug doses for ex vivo organ culture using CRC PDX tumors.

a,b,c, Representative IHC and H&E staining of tumors from the CRC PDX model TM00170 (a,b) or TM00164 (c) cultured ex vivo with increasing concentrations of different drugs. Tissues were fixed after 24–96 hours of treatment, as indicated in the figure. Viability scores are depicted in the right lower corners of the H&E stained slides. Scale bars 50μM (a,b) 40μM (c). At least two tissue slices were evaluated for each condition. PKA, protein kinase A; MDM2, Mouse double minute 2 homolog; H-89 2HCl, H-89 dihydrochloride; h, hour; DMSO, dimethyl sulfoxide.

Extended Data Fig. 5 The response of CRC human tumors to five combinatory treatments.

All tumors were treated ex vivo for 4 days with either DMSO control, or drugs as indicated. Viability of the tissue slices as reflected in H&E staining was scored from 0 to 4, by a pathologist blinded to the treatments: 0 for less than 10% viable cancer cells, 1 for 10–30%, 2 for 31–60%, 3 for 61–90%, and 4 for more than 90%. FOLFOX, 5-fluorouracil/oxaliplatin; FOLFIRI, 5-fluorouracil/irinotecan; DMSO, dimethyl sulfoxide.

Source data

Extended Data Fig. 6 The effect of MEK/Src inhibition on the proliferation of cancer cells.

Example of CRC tumor from patient 28 - a responder to dual inhibition of MEK and Src. Drugs (or DMSO control) were added 16–20 hours after the slicing of the tumor, and tissue slices were fixed after four complete days of treatment. Bromodeoxyuridine (BrdU) was added to all slices 18 hours before the end of the experiment. In each condition, at least two tissue slices were evaluated. a, Representative histological sections stained with H&E. b, Representative histological slices from the same EVOC slices in (a), stained for anti-BrdU. Note that proliferation is evident as measured by the incorporation of BrdU when the tissue is treated with either selumetinib or bosutinib, but is markedly reduced when treated with both drugs. Scale bar 50μM for all micrographs in the figure.

Extended Data Fig. 7 The Src inhibitors dasatinib and bosutinib and the MEK inhibitors trametinib and selumetinib show similar response when combined with MEK inhibition and tested ex vivo on human CRC.

Drugs (or DMSO control) were added 16–20 hours after the slicing of the tumor, and tissue slices were fixed after four complete days of treatment. In each condition, at least two tissue slices were evaluated. a,b, The effect of dasatinib combined with selumetinib is compared to the effect of bosutinib combined with selumetinib c, The effect of trametinib combined with dasatinib is compared to the combination of selumetinib and bosutinib. Viability scores are depicted in the right lower corners. Scale bar 20μM for all micrographs in the figure. 5-FU, 5-fluorouracil; DMSO, dimethyl sulfoxide.

Extended Data Fig. 8 Additional examples of the response of human CRC tumors to several drug combinations tested in EVOC.

a,b,c, Drugs (or DMSO control) were added 16–20 hours after the slicing of the tumor, and tissue slices were fixed after four complete days of treatment. In each condition, at least two tissue slices were evaluated. Viability scores are depicted in the right lower corners. Scale bar 40μM for all micrographs in the figure. 5-FU, 5-fluorouracil; Ox, oxaliplatin; Ir, irinotecan; H-89 2HCl, H-89 dihydrochloride; DMSO, dimethyl sulfoxide.

Extended Data Fig. 9 Ex vivo response to chemotherapy correlates with the clinical course in selected patients.

From the 28 patients that entered the study to test the response of their CRC tissue to chemotherapy and targeted dual therapy, four patients were available for evaluation and comparison to ex vivo data. Clinical evaluation of disease state was determined by PET/CT and interpreted by the treating physician independently and without knowledge of the results of the ex vivo study. In patients: progressive disease (PD) refers to an increase in tumor size, number or location, and can include an increase in tumor marker values and a worsening of the patients’ overall condition; partial response (PR) refers to a reduction in tumor size or number; stable disease (SD) refers to no change in tumor size or number. No response (NR) in EVOC refers to tissue viability score after treatment of 4 (more than 90% viable - see also Supplementary Table 6). Partial response (PR) in EVOC refers to tissue viability score from 1–2 (more than 10 but less than 60% - see also Supplementary Table 6). FOLFOX, 5-fluorouracil and oxaliplatin; FOLFIRI, 5-fluorouracil and irinotecan; MMC, mitomycin C; LAR, low anterior resection; mets, metastasis; AR, anterior resection; CA, carcinoma; CRC, colorectal carcinoma; CET, cetuximab; ERB, erlotinib; Tx, treatment; PR, partial response (green arrows); SD, stable disease (grey arrows); PD, progressive disease (black arrows). Light blue arrows indicate time of diagnosis. Royal blue arrows indicate operative intervention. Yellow arrows indicate recurrence of disease. Red arrows indicate when tissue was obtained for ex vivo organ culture.

Extended Data Fig. 10 Sensitivity to MEK and Src inhibition does not correlate with phosphorylation of Src in CRC cell lines.

The eight CRC cell lines used in the in vitro screens (see Fig. 3) were seeded in 6 cm plates and treated as labeled for 24 hours and then collected for western blot evaluation. To determine the best drug dose for each cell line we utilized the extensive data from the in vitro screens (see text and Fig. 3): The IC20 was determined in the in vitro screen #1 and the most synergistic dose of the combined MEK/Src inhibition (selumetinib/bosutinib) (either 0.5xIC20, IC20 or 2XIC20) was determined in the in vitro screen #3. We used the most effective synergistic dose for each cell line. a, Western blot analysis of eight CRC cell lines as labeled. b, Phosphorylated Src signal intensity determined as a ratio of HSP90 (heat shock protein 90) signal intensity used as loading control. Lines represent average intensity for each group and p value = 0.21 as determined by two-tailed student t-test. The experiment was performed in 8 different cell lines (n=8 cell lines; n=1 biological repeat). MEK, mitogen-activated protein kinase kinase; ERK, extracellular signal-regulated kinase; Src, proto-oncogene tyrosine-protein kinase Src.

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Gavert, N., Zwang, Y., Weiser, R. et al. Ex vivo organotypic cultures for synergistic therapy prioritization identify patient-specific responses to combined MEK and Src inhibition in colorectal cancer. Nat Cancer 3, 219–231 (2022). https://doi.org/10.1038/s43018-021-00325-2

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