Center for Molecular Medicine Cologne

Martin Peifer - CAP 6

Computational cancer genomics

The development of massively parallel sequencing has fueled genome-centric cancer research. This technology has enabled us to sequence entire cancer genomes and transcriptomes in a relatively short period of time. However, the data produced by this approach is highly complex and difficult to analyze in order to extract the biologically relevant information. Within our group we are developing new computational methods and analyze sequencing data from tumor specimens.


Sequencing of entire cancer genomes provides a comprehensive portrait of the genomic alterations that have occurred during tumorigenesis. The most common changes are subtle sequence alterations (simple substitutions, small insertions and deletions) followed by structural changes such as copy number alterations and genomic rearrangements. On a genome-wide scale, the number of all these alterations is typically within the range of 104–106events and varies significantly between tumor types with pediatric tumors at lower end of the spectrum and lung cancers, melanoma is being the most mutated cancer genomes. However, most of these mutations are likely to have no functional relevance to the cancer cells. Thus, only a minority of mutations is biologically relevant and can be associated to the pathogenesis of the cancer cells. Finding these genome alterations from sequencing data of large collectives of patient samples is still a challenging task. 

Small cell lung cancer

Our computational methods played a central role in the analysis of 110 whole small cell lung cancer (SCLC) genomes together with 81 whole transcriptomes and 149 Affymetrix 6.0 SNP arrays for copy number analysis. We obtained a first comprehensive landscape of small cell lung cancer by applying our computational approaches to assess recurrent events in point mutations, copy numbers, and rearrangements5. In particular, we found an almost universal bi-allelic deactivation of TP53 and RB1 in these tumors and can therefore be considered as hallmark of small cell lung cancer. By clustering genomic rearrangements, we were able to detect an accumulation of small translocations within TP73. These translocations leaded to a skipping of several exons, which was confirmed by an integrative genome, transcriptome analysis. In fact, these variants of TP73 have be described earlier to act dominant negative on wild type p53 and p73 and could therefore be considered as oncogene in small cell lung cancer. Furthermore, we found a significant enrichment of damaging mutations in NOTCH family genes. This finding points towards a tumor suppressive function of NOTCH in small cell lung cancer. Indeed, overexpressing NOTCH2 in a Trp53;Rb1;Rbl2 conditional triple-knockout mouse model confirmed this notion as substantially less tumors and a prolonged tumor development was observed. 


By analyzing the genomes of 56 primary neuroblastomas we discovered genomic rearrangements affecting a region upstream the telomerase reverse transcriptase gene (TERT) in more than 20% of high-risk tumors4. These rearrangements occurred mutually exclusive with MYCN amplifications and ATRX mutations in most of the cases. Moreover, a more than 90-fold increase of TERT expression in the presence of the rearrangement was observed when compared to low-risk tumors. Since MYCNtranscriptionally activates TERT, high TERT expression and the presence of telomerase activity was observed in tumors bearing TERT rearrangements and MYCN amplifications. Of the remaining high-risk cases we found evidence for alternative telomere lengthening, thus bringing telomere maintenance into the focus of high-risk neuroblastomas. Five samples exhibited both, TERT rearrangements and MYCNamplifications. Initial and ongoing analyses have shown that these two events are subclonal in these samples, suggesting a parallel evolution of driver alterations.

Most recently, we extended our genomic analysis to 416 neuroblastoma cases using whole exome and targeted sequencing1. Together with the clinical data we were able to propose a new mechanism based risk classification of neuroblastoma (Figure 1). In this classification high-risk is exclusively characterized by the presence of telomere maintenance mechanisms (TERT rearrangements, MYCN amplifications, or ALT) otherwise the patient is at low risk to die from the disease. Furthermore, if RAS or p53 pathway mutations occurred on top of the telomere maintenance mechanisms patients did particularly poor. This group of patients therefore constitutes a very high-risk class. In total, we proposed a new classification of neuroblastoma based on molecular markers.

Tumor evolution

We were able to include the modeling and analysis2of tumor evolution in the whole genome pan-cancer analysis of the International Cancer Genome Consortium (ICGC). This allows us to apply our concepts to about 2,800 cancer genomes across more than 30 different tumor types. The project has currently reached the publication phase.

By applying these/our developments to reconstruct the subclonal architecture we were able to compare the subclonal diversification between lung adenocarcinoma and small cell lung cancer. Here, the most limiting factor for this analysis was the relatively low sequencing coverage of 35X on average for the small cell lung cancer genomes. This hampers the detection of subclonal populations, especially in samples exhibiting low tumor purity or a higher ploidy. By systematically carrying a power analysis only about half of the 110 sequenced samples were qualified for this analysis. After establishing a concise measure to capture the mutational heterogeneity of the cancer genomes, we obtained that lung adenocarcinomas showed a 3-fold higher subclonal diversification5. This low degree of subclonal diversification is reflected in an almost perfect genomic match patient derived xenografts (derived from circulating tumor cells) with the primary tumor of the same patient.

By analyzing pairs of pre-treatment and relapsed chronic lymphocytic leukemia (CLL) patients that were treated with the BCL2 inhibitor venetoclax, we reconstructed the clonal dynamics towards resistance3. We observed convergent as well as divergent evolution in the 8 patients analyzed and found that BRAF and BTG1mutations are likely linked to the venetoclax resistance. Furthermore, alternative some mutations (BRAF and PDL1amplifications) point to further treatment options.


Recently we embarked into the reconstruction of tumor evolution from single cell sequencing data. This data allows to precisely reconstruct the phylogenetic relationship of the tumor samples. This can, e.g., be used to study genetic changes that lead to therapy failure due to drug resistance.

1.  Ackermann S*, Cartolano M*,..., Peifer M*,Fischer M*.  (2018) A mechanistic classification of clinical phenotypes in neuroblastoma. Science 362, 1165-1170. *equal contribution

2.  Cun Y, Yang TP, Achter V, Lang U, Peifer M. (2018) Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.Nature Protocols 13, 1488-1501.

3.  Herling CD, Abedpour N,..., Peifer M. (2018) Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia. Nature Communications 9, 727.

4.  Peifer M*,…, Fischer M*. (2015) Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700-4.*corresponding authors

5.  George J, Lim JS,..., Peifer M*, Sage J*, Thomas RK*. (2015) Comprehensive genomic profiling of small cell lung cancer. Nature 524, 47-53. *corresponding authors

Frank, S., Ahuja, G., Bartsch, D., Russ, N., Yao, W., Kuo, J.C., Derks, J.P., Akhade, V.S., Kargapolova, Y., Georgomanolis, T., Messling, J.E., Gramm, M., Brant, L., Rehimi, R., Vargas, N.E., Kuroczik, A., Yang, T.P., Sahito, R.G.A., Franzen, J., Hescheler, J., Sachinidis, A., Peifer, M., Rada-Iglesias, A., Kanduri, M., Costa, I.G., Kanduri, C., Papantonis, A., and Kurian, L. (2019). yylncT Defines a Class of Divergently Transcribed lncRNAs and Safeguards the T-mediated Mesodermal Commitment of Human PSCs. Cell Stem Cell 24, 318-27 e8.

Jachimowicz, R.D., Beleggia, F., Isensee, J., Velpula, B.B., Goergens, J., Bustos, M.A., Doll, M.A., Shenoy, A., Checa-Rodriguez, C., Wiederstein, J.L., Baranes-Bachar, K., Bartenhagen, C., Hertwig, F., Teper, N., Nishi, T., Schmitt, A., Distelmaier, F., Ludecke, H.J., Albrecht, B., Kruger, M., Schumacher, B., Geiger, T., Hoon, D.S.B., Huertas, P., Fischer, M., Hucho, T., Peifer, M., Ziv, Y., Reinhardt, H.C., Wieczorek, D., and Shiloh, Y. (2019). UBQLN4 Represses Homologous Recombination and Is Overexpressed in Aggressive Tumors. Cell 176, 505-19 e22.

Nieroda, L., Maas, L., Thiebes, S., Lang, U., Sunyaev, A., Achter, V., and Peifer, M. (2019). iRODS metadata management for a cancer genome analysis workflow. BMC Bioinformatics 20, 29.

Ackermann S, Cartolano M, Hero B, Welte A, Kahlert Y, Roderwieser A, Bartenhagen C, Walter E, Gecht J, Kerschke L, Volland R, Menon R, Heuckmann JM, Gartlgruber M, Hartlieb S, Henrich KO, Okonechnikov K, Altmuller J, Nurnberg P, Lefever S, de Wilde B, Sand F, Ikram F, Rosswog C, Fischer J, Theissen J, Hertwig F, Singhi AD, Simon T, Vogel W, Perner S, Krug B, Schmidt M, Rahmann S, Achter V, Lang U, Vokuhl C, Ortmann M, Buttner R, Eggert A, Speleman F, O'Sullivan RJ, Thomas RK, Berthold F, Vandesompele J, Schramm A, Westermann F, Schulte JH, Peifer M, and Fischer M (2018). A mechanistic classification of clinical phenotypes in neuroblastoma. Science 362, 1165-1170.

Cun Y, Yang TP, Achter V, Lang U, and Peifer M (2018). Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust. Nat Protoc 13, 1488-1501.

Drapkin BJ, George J, Christensen CL, Mino-Kenudson M, Dries R, Sundaresan T, Phat S, Myers DT, Zhong J, Igo P, Hazar-Rethinam MH, Licausi JA, Gomez-Caraballo M, Kem M, Jani KN, Azimi R, Abedpour N, Menon R, Lakis S, Heist RS, Buttner R, Haas S, Sequist LV, Shaw AT, Wong KK, Hata AN, Toner M, Maheswaran S, Haber DA, Peifer M, Dyson N, Thomas RK, and Farago AF (2018). Genomic and Functional Fidelity of Small Cell Lung Cancer Patient-Derived Xenografts. Cancer Discov 8, 600-615.

Frank S, Ahuja G, Bartsch D, Russ N, Yao W, Kuo JC, Derks JP, Akhade VS, Kargapolova Y, Georgomanolis T, Messling JE, Gramm M, Brant L, Rehimi R, Vargas NE, Kuroczik A, Yang TP, Sahito RGA, Franzen J, Hescheler J, Sachinidis A, Peifer M, Rada-Iglesias A, Kanduri M, Costa IG, Kanduri C, Papantonis A, and Kurian L (2018). yylncT Defines a Class of Divergently Transcribed lncRNAs and Safeguards the T-mediated Mesodermal Commitment of Human PSCs. Cell Stem Cell10.1016/j.stem.2018.11.005.

George J, Walter V, Peifer M, Alexandrov LB, Seidel D, Leenders F, Maas L, Muller C, Dahmen I, Delhomme TM, Ardin M, Leblay N, Byrnes G, Sun R, De Reynies A, McLeer-Florin A, Bosco G, Malchers F, Menon R, Altmuller J, Becker C, Nurnberg P, Achter V, Lang U, Schneider PM, Bogus M, Soloway MG, Wilkerson MD, Cun Y, McKay JD, Moro-Sibilot D, Brambilla CG, Lantuejoul S, Lemaitre N, Soltermann A, Weder W, Tischler V, Brustugun OT, Lund-Iversen M, Helland A, Solberg S, Ansen S, Wright G, Solomon B, Roz L, Pastorino U, Petersen I, Clement JH, Sanger J, Wolf J, Vingron M, Zander T, Perner S, Travis WD, Haas SA, Olivier M, Foll M, Buttner R, Hayes DN, Brambilla E, Fernandez-Cuesta L, and Thomas RK (2018). Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors. Nat Commun 9, 1048.

Herling CD, Abedpour N, Weiss J, Schmitt A, Jachimowicz RD, Merkel O, Cartolano M, Oberbeck S, Mayer P, Berg V, Thomalla D, Kutsch N, Stiefelhagen M, Cramer P, Wendtner CM, Persigehl T, Saleh A, Altmuller J, Nurnberg P, Pallasch C, Achter V, Lang U, Eichhorst B, Castiglione R, Schafer SC, Buttner R, Kreuzer KA, Reinhardt HC, Hallek M, Frenzel LP, and Peifer M (2018). Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia. Nat Commun 9, 727.

Horn M, Kroef V, Allmeroth K, Schuller N, Miethe S, Peifer M, Penninger JM, Elling U, and Denzel MS (2018). Unbiased compound-protein interface mapping and prediction of chemoresistance loci through forward genetics in haploid stem cells. Oncotarget 9, 9838-9851.

Jachimowicz RD, Beleggia F, Isensee J, Velpula BB, Goergens J, Bustos MA, Doll MA, Shenoy A, Checa-Rodriguez C, Wiederstein JL, Baranes-Bachar K, Bartenhagen C, Hertwig F, Teper N, Nishi T, Schmitt A, Distelmaier F, Ludecke HJ, Albrecht B, Kruger M, Schumacher B, Geiger T, Hoon DSB, Huertas P, Fischer M, Hucho T, Peifer M, Ziv Y, Reinhardt HC, Wieczorek D, and Shiloh Y (2018). UBQLN4 Represses Homologous Recombination and Is Overexpressed in Aggressive Tumors. Cell10.1016/j.cell.2018.11.024.

Schrader A, Crispatzu G, Oberbeck S, Mayer P, Putzer S, von Jan J, Vasyutina E, Warner K, Weit N, Pflug N, Braun T, Andersson EI, Yadav B, Riabinska A, Maurer B, Ventura Ferreira MS, Beier F, Altmuller J, Lanasa M, Herling CD, Haferlach T, Stilgenbauer S, Hopfinger G, Peifer M, Brummendorf TH, Nurnberg P, Elenitoba-Johnson KSJ, Zha S, Hallek M, Moriggl R, Reinhardt HC, Stern MH, Mustjoki S, Newrzela S, Frommolt P, and Herling M (2018). Actionable perturbations of damage responses by TCL1/ATM and epigenetic lesions form the basis of T-PLL. Nat Commun 9, 697.

Bragelmann J, Dammert MA, Dietlein F, Heuckmann JM, Choidas A, Bohm S, Richters A, Basu D, Tischler V, Lorenz C, Habenberger P, Fang Z, Ortiz-Cuaran S, Leenders F, Eickhoff J, Koch U, Getlik M, Termathe M, Sallouh M, Greff Z, Varga Z, Balke-Want H, French CA, Peifer M, Reinhardt HC, Orfi L, Keri G, Ansen S, Heukamp LC, Buttner R, Rauh D, Klebl BM, Thomas RK, and Sos ML (2017). Systematic Kinase Inhibitor Profiling Identifies CDK9 as a Synthetic Lethal Target in NUT Midline Carcinoma. Cell Rep 20, 2833-45.

George J, Saito M, Tsuta K, Iwakawa R, Shiraishi K, Scheel AH, Uchida S, Watanabe SI, Nishikawa R, Noguchi M, Peifer M, Jang SJ, Petersen I, Buttner R, Harris CC, Yokota J, Thomas RK, and Kohno T (2017). Genomic Amplification of CD274 (PD-L1) in Small-Cell Lung Cancer. Clin Cancer Res 23, 1220-6.

Knittel G, Rehkamper T, Korovkina D, Liedgens P, Fritz C, Torgovnick A, Al-Baldawi Y, Al-Maarri M, Cun Y, Fedorchenko O, Riabinska A, Beleggia F, Nguyen PH, Wunderlich FT, Ortmann M, Montesinos-Rongen M, Tausch E, Stilgenbauer S, L PF, Herling M, Herling C, Bahlo J, Hallek M, Peifer M, Buettner R, Persigehl T, and Reinhardt HC (2017). Two mouse models reveal an actionable PARP1 dependence in aggressive chronic lymphocytic leukemia. Nat Commun 8, 153.

Layer JP, Kronmuller MT, Quast T, van den Boorn-Konijnenberg D, Effern M, Hinze D, Althoff K, Schramm A, Westermann F, Peifer M, Hartmann G, Tuting T, Kolanus W, Fischer M, Schulte J, and Holzel M (2017). Amplification of N-Myc is associated with a T-cell-poor microenvironment in metastatic neuroblastoma restraining interferon pathway activity and chemokine expression. Oncoimmunology 6, e1320626.

Malchers F, Ercanoglu M, Schutte D, Castiglione R, Tischler V, Michels S, Dahmen I, Bragelmann J, Menon R, Heuckmann JM, George J, Ansen S, Sos ML, Soltermann A, Peifer M, Wolf J, Buttner R, and Thomas RK (2017). Mechanisms of Primary Drug Resistance in FGFR1-Amplified Lung Cancer. Clin Cancer Res 23, 5527-36.

Mollaoglu G, Guthrie MR, Bohm S, Bragelmann J, Can I, Ballieu PM, Marx A, George J, Heinen C, Chalishazar MD, Cheng H, Ireland AS, Denning KE, Mukhopadhyay A, Vahrenkamp JM, Berrett KC, Mosbruger TL, Wang J, Kohan JL, Salama ME, Witt BL, Peifer M, Thomas RK, Gertz J, Johnson JE, Gazdar AF, Wechsler-Reya RJ, Sos ML, and Oliver TG (2017). MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora Kinase Inhibition. Cancer Cell 31, 270-85.

Schmitt A, Knittel G, Welcker D, Yang TP, George J, Nowak M, Leeser U, Buttner R, Perner S, Peifer M, and Reinhardt HC (2017). ATM Deficiency Is Associated with Sensitivity to PARP1- and ATR Inhibitors in Lung Adenocarcinoma. Cancer Res 77, 3040-56.

Weischenfeldt J, Dubash T, Drainas AP, Mardin BR, Chen Y, Stutz AM, Waszak SM, Bosco G, Halvorsen AR, Raeder B, Efthymiopoulos T, Erkek S, Siegl C, Brenner H, Brustugun OT, Dieter SM, Northcott PA, Petersen I, Pfister SM, Schneider M, Solberg SK, Thunissen E, Weichert W, Zichner T, Thomas R, Peifer M, Helland A, Ball CR, Jechlinger M, Sotillo R, Glimm H, and Korbel JO (2017). Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat Genet 49, 65-74.

Former Funding Period 01/2017 - 12/2019

Information from this funding period will not be updated anymore. New research related information is available here.

CMMC Funding Period 1/2020-12/2022

Martin Peifer - assoc. RG 16

Computational cancer genomics

Prof. Dr. Martin Peifer CMMC Cologne
Prof. Dr. Martin Peifer

Dept. for Translational Genomics - CMMC Research Building

CMMC - PI - assoc. 16

+49 221 478 96863

+49 221 478 97902

Dept. for Translational Genomics - CMMC Research Building

Robert-Koch-Str. 21

50931 Cologne

CMMC Profile Page

Curriculum Vitae (CV)

Publications on PubMed

Publications - Martin Peifer

Link to PubMed

Group Members

Nima Abedpour (PostDoc)
Maria Cartolano (PostDoc)
Milos Nikolic (PostDoc)
Stephani Pabel (PostDoc)
Sebastian Klein (PostDoc; Else-Kröner-Fresenius Program/from Pathology)
Lukas Maas (PostDoc; associated/from AG Thomas)
Armin Khonsari (PostDoc; associated/from AG Sos)
Tsun-Po Yang (doctoral student) 

Figure 1